Arts, Faculty of
Geography, Department of
DSpace
UBCV
Exler, Johannes
Doctor of Philosophy - PhD
University of British Columbia
Climate change and widespread drainage of peatlands are two main drivers behind shifts in peatland vegetation composition. Peatlands rely on shallow water levels that reduce peat oxidation rates to levels less than carbon accumulation. However, climatic warming and drainage enable non-native vegetation to encroach in areas of lowered groundwater levels where increased water loss due to plant activity cause groundwater levels to drop further. This research aimed at improving the understanding of how drainage and projected climatic change in the Pacific Northwest of North America may affect the hydrology of ombrotrophic raised bogs in the region through associated vegetation community shifts. Research was conducted in a raised bog in southwestern British Columbia, which has a history of drainage that enabled vegetation communities of different structure and species composition to establish. Multi-year energy and water flux and storage observations at an open site near the bog's center identified available energy and atmospheric demand, expressed as vapour pressure deficit (VPD), to be the main drivers of evapotranspiration loss (ET) at a daily time scale, and groundwater level was a secondary control. Interannual variability of growing season ET was driven by differences in VPD. The dominant water loss process shifted from ET in summer to lateral near-surface and surface flow in winter, which was controlled by two groundwater level thresholds. Vertical water exchange fluxes between the peat surface and the atmosphere were a function of vegetation type. A reduction in net precipitation was primarily associated with shrub interception loss, which was higher than interception loss from trees on a per unit ground area basis. At the tree level, transpiration loss was a function of tree size and species; groundwater level was a secondary species-specific control in the short term. Differences in root development between sites that affected stand transpiration were likely a result of historic drainage. Peat surface evaporation was primarily controlled by shading from shrubs and peat moisture content was a strong secondary control. Water loss to the atmosphere was higher at more vegetated sites and relative contribution of fluxes varied according to vegetation type.
Hydrological processes as function of vegetation type in sixvegetation communities in a maritime raised bogbyJohannes ExlerA DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THEREQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Geography)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)August 2023© Johannes Exler, 2023The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the dissertation entitled:Hydrological processes as function of vegetation type in six vegetationcommunities in a maritime raised bogsubmitted by Johannes Exler in partial fulfillment of the requirements for the degree of Doctor of Philosophyin GeographyExamining Committee:Robert Daniel Moore, Professor, Geography, UBCSupervisorSara Knox, Associate Professor, Geography, UBCSupervisory Committee MemberIan McKendry, Professor, Geography, UBCUniversity ExaminerAndrew Black, Professor, Land and Food Systems, UBCUniversity ExaminerAdditional Supervisory Committee Members:Roger Beckie, Professor, Earth, Ocean and Atmospheric Sciences, UBCSupervisory Committee MemberMark Johnson, Professor, Institute for Resources, Environment and Sustainability, UBCSupervisory Committee MemberiiAbstractClimate change and widespread drainage of peatlands are two main drivers behind shiftsin peatland vegetation composition. Peatlands rely on shallow water levels that reducepeat oxidation rates to levels less than carbon accumulation. However, climatic warmingand drainage enable non-native vegetation to encroach in areas of lowered groundwaterlevels where increased water loss due to plant activity cause groundwater levels to dropfurther. This research aimed at improving the understanding of how drainage and projectedclimatic change in the Pacific Northwest of North America may affect the hydrology ofombrotrophic raised bogs in the region through associated vegetation community shifts.Research was conducted in a raised bog in southwestern British Columbia, which has ahistory of drainage that enabled vegetation communities of different structure and speciescomposition to establish.Multi-year energy and water flux and storage observations at an open site near the bog’scenter identified available energy and atmospheric demand, expressed as vapour pressuredeficit (𝑉 𝑃𝐷), to be the main drivers of evapotranspiration loss (𝐸𝑇 ) at a daily time scale,and groundwater level was a secondary control. Interannual variability of growing season𝐸𝑇 was driven by differences in 𝑉 𝑃𝐷. The dominant water loss process shifted from 𝐸𝑇in summer to lateral near-surface and surface flow in winter, which was controlled by twogroundwater level thresholds.Vertical water exchange fluxes between the peat surface and the atmosphere were a functionof vegetation type. A reduction in net precipitation was primarily associated with shrubinterception loss, which was higher than interception loss from trees on a per unit groundarea basis. At the tree level, transpiration loss was a function of tree size and species;groundwater level was a secondary species-specific control in the short term. Differencesin root development between sites that affected stand transpiration were likely a result ofhistoric drainage. Peat surface evaporation was primarily controlled by shading from shrubsand peat moisture content was a strong secondary control. Water loss to the atmospherewas higher at more vegetated sites and relative contribution of fluxes varied according tovegetation type.iiiLay SummaryPeatlands must remain wet to stay healthy but many are drying out due to drainage byditch construction and climatic warming. When peatlands are too dry, new plant species canestablish and displace more typical peatland vegetation. These new species often use morewater than the original species, which can further contribute to drying of the peatland.This work focused on understanding the differences in water use between plant speciestypical of a healthy peatland and those that establish due to drainage. This study focusedon three water loss processes: interception and evaporation of rainfall by trees and shrubs,tree transpiration, and evaporation from the peat surface.ivPrefaceThis dissertation was completed under the supervision of Dr. R. Dan Moore, who providedguidance and advice on study design, analysis and editorial revisions. I am the lead authorwho conceptualized the research in coordination with Markus Merkens from Metro Van-couver Regional Parks, Sarah Howie from the City of Delta, and the Burns Bog ScientificAdvisory Panel, a body of scientists that advices Metro Vancouver Regional Parks – one ofthe main funding bodies – on medium and long term research and management strategiesfor Burns Bog. Eddy-covariance flux observations that are a principal component of studiesone and two in this dissertation were obtained from a research station that was installedand maintained by the research group of Dr. Andreas Christen, who is now at the Univer-sity of Freiburg. Since then it has been operated by the lab groups of Dr. Mark Johnsonand Dr. Sara Knox, and I helped maintain and operate the site for the duration of my fieldresearch. I led study design and collection of data that was used in studies three to five,and data analysis and writing of the entire dissertation was done by me with consultationof Dr. Moore.A version of Chapter 5 has been published as ’Exler, J. L. and R. D. Moore. 2022. Quantify-ing throughfall, stemflow and interception loss in five vegetation communities in a maritimeraised bog. Agricultural and Forest Meteorology 327:109202’vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.1 Hydrological characteristics of raised bogs . . . . . . . . . . . . . . . 21.2.2 Effect of drainage and climatic warming on peatland vegetation . . . 31.2.3 Past research on the hydrological effect of non-native peatland vege-tation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 Study objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3.1 Study 1: Daily, seasonal and interannual variability of energy balanceand evapotranspiration at an disturbed and rewetted white beakrush-Sphagnum site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3.2 Study 2: Water balance and runoff generating processes at an open,disturbed and rewetted white beakrush-Sphagnum site near the bogcenter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.3.3 Study 3: Quantifying throughfall, stemflow and interception loss infive vegetation communities in a maritime raised bog . . . . . . . . . 71.3.4 Study 4: Tree transpiration loss in four vegetation communities in amaritime raised bog in the Pacific Northwest . . . . . . . . . . . . . 71.3.5 Study 5: Peat surface evaporation and moisture dynamics in five veg-etation communities in a maritime raised bog in the Pacific Northwest 71.4 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Study site and field methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1 Study site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.1 General background . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.2 Native use of Burns Bog and the surrounding lands . . . . . . . . . . 92.1.3 Meteorology and regional climate . . . . . . . . . . . . . . . . . . . . 102.1.4 Vegetation communities in Burns Bog . . . . . . . . . . . . . . . . . 10vi2.1.5 Selection of monitoring sites within Burns Bog . . . . . . . . . . . . 112.2 Field observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.2.1 Eddy covariance and meteorological observations . . . . . . . . . . . 132.2.2 Groundwater wells and piezometers . . . . . . . . . . . . . . . . . . 142.2.3 Peat surface elevation . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2.4 Additional observations . . . . . . . . . . . . . . . . . . . . . . . . . 163 Daily, seasonal and interannual variability of energy balance and evapotranspi-ration at an disturbed and rewetted white beakrush-Sphagnum site . . . . . . . 193.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.2.1 Eddy-covariance data processing . . . . . . . . . . . . . . . . . . . . 223.2.2 Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.3.1 Interannual variability of meteorology and water table depth . . . . 263.3.2 Radiative exchange at the peat surface . . . . . . . . . . . . . . . . . 273.3.3 Energy balance analysis . . . . . . . . . . . . . . . . . . . . . . . . . 273.3.4 Controls on evapotranspiration . . . . . . . . . . . . . . . . . . . . . 313.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.4.1 Albedo and potential influence on evapotranspiration . . . . . . . . . 333.4.2 Energy balance closure and uncertainty of evapotranspiration estimates 333.4.3 Daily and seasonal controls on evapotranspiration . . . . . . . . . . 363.4.4 Interannual variability of evapotranspiration . . . . . . . . . . . . . . 373.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 Quantifying runoff generating processes and lateral flow connectivity at anopen, disturbed and rewetted white beakrush-Sphagnum site near the bog center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.2.1 Site description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.2.2 Field and laboratory measurements . . . . . . . . . . . . . . . . . . . 434.2.3 Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.3.1 Peat properties and relevant elevations . . . . . . . . . . . . . . . . . 474.3.2 Water table fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . 484.3.3 Peat surface elevation changes and surface inundation . . . . . . . . 484.3.4 Water balance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 504.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564.4.1 Peat surface elevation change . . . . . . . . . . . . . . . . . . . . . . 564.4.2 Peat surface inundation . . . . . . . . . . . . . . . . . . . . . . . . . 574.4.3 Lateral flow divergence . . . . . . . . . . . . . . . . . . . . . . . . . . 574.4.4 Water balance and timing of shift in hydrologic fluxes . . . . . . . . 584.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59vii5 Quantifying throughfall, stemflow and interception loss in five vegetation com-munities in a maritime raised bog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645.2.1 Study site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645.2.2 Field measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . 645.2.3 Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685.2.4 Regression analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745.2.5 Missing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765.3.1 Overview of study period . . . . . . . . . . . . . . . . . . . . . . . . 765.3.2 Vegetation characteristics . . . . . . . . . . . . . . . . . . . . . . . . 765.3.3 Plot-averaged throughfall . . . . . . . . . . . . . . . . . . . . . . . . 795.3.4 Tree-based throughfall in sparse canopy stands . . . . . . . . . . . . 825.3.5 Comparison of throughfall estimates . . . . . . . . . . . . . . . . . . 845.3.6 Stemflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855.3.7 Canopy water fluxes and net precipitation . . . . . . . . . . . . . . . 855.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 905.4.1 Canopy water fluxes . . . . . . . . . . . . . . . . . . . . . . . . . . . 905.4.2 Variation of throughfall with distance from tree trunks . . . . . . . . 925.4.3 Comparison of tree-based and plot-averaged throughfall . . . . . . . 935.4.4 Comparison of trough-based and plot-averaged throughfall . . . . . . 945.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 956 Tree transpiration loss in four vegetation communities in a maritime raisedbog in the Pacific Northwest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 966.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 966.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006.2.1 Field measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006.2.2 Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1016.2.3 Modelling sap flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1046.2.4 Effect of water table depth on sap flow . . . . . . . . . . . . . . . . . 1056.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1056.3.1 Overview of study period . . . . . . . . . . . . . . . . . . . . . . . . 1056.3.2 Sap flow observations . . . . . . . . . . . . . . . . . . . . . . . . . . 1056.3.3 Species-based modelling of sap flow from vapour pressure deficit and𝐷𝐵𝐻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1096.3.4 Prediction of plot-scale tree transpiration . . . . . . . . . . . . . . . 1106.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136.4.1 Effect of tree size and species on sap flow . . . . . . . . . . . . . . . 1136.4.2 Effect of water table depth on sap flow . . . . . . . . . . . . . . . . . 1166.4.3 Modelling sap flow using species-specific relations with 𝑉 𝑃𝐷 and 𝐷𝐵𝐻1186.4.4 Growing season transpiration loss . . . . . . . . . . . . . . . . . . . . 1186.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119viii7 Peat surface evaporation and moisture dynamics in five vegetation communi-ties in a maritime raised bog in the Pacific Northwest . . . . . . . . . . . . . . . 1217.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1217.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1227.2.1 Peat moisture content . . . . . . . . . . . . . . . . . . . . . . . . . . 1227.2.2 Lysimeter measurements . . . . . . . . . . . . . . . . . . . . . . . . . 1247.2.3 Equilibrium evaporation . . . . . . . . . . . . . . . . . . . . . . . . . 1267.2.4 Estimating site evaporation and uncertainty . . . . . . . . . . . . . . 1267.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1277.3.1 Meteorological conditions during the study period . . . . . . . . . . 1277.3.2 Peat moisture content . . . . . . . . . . . . . . . . . . . . . . . . . . 1287.3.3 Lysimeter evaporation . . . . . . . . . . . . . . . . . . . . . . . . . . 1287.3.4 Prediction of lysimeter evaporation . . . . . . . . . . . . . . . . . . . 1337.3.5 Estimated site evaporation . . . . . . . . . . . . . . . . . . . . . . . 1347.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1387.4.1 Sensor calibrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1387.4.2 Peat moisture content . . . . . . . . . . . . . . . . . . . . . . . . . . 1387.4.3 Lysimeter evaporation . . . . . . . . . . . . . . . . . . . . . . . . . . 1397.4.4 Site evaporation estimates adjusted for moisture content . . . . . . . 1407.4.5 Peat surface evaporation . . . . . . . . . . . . . . . . . . . . . . . . . 1407.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1418 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1428.1 Summary of key findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1428.2 Implications of climatic change and vegetation shifts on atmospheric waterexchange of peatlands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1448.3 Avenues for future research . . . . . . . . . . . . . . . . . . . . . . . . . . . 146148175Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Appendix A - Supplementary information for data from BB1 flux tower site in studies one and two . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .B Appendix B - Supplementary information for throughfall observations in studythree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178ix174List of Tables2.1 Overview of the seven most important vegetation communities in Burns Bog,according to Hebda et al. (2000). Bold font highlights the communities inwhich field observations were carried out. . . . . . . . . . . . . . . . . . . . 102.2 Geographic coordinates of the central well at each site and distances fromthe central well to wells or piezometers installed in peripheral locations. 𝐶𝑙𝑜𝑛and 𝐶𝑙𝑎𝑡 = longitude and latitude of central well in decimal degrees, 𝑑𝑁 , 𝑑𝐸,𝑑𝑆 and 𝑑𝑊 = distance of northern, eastern, southern and western peripherallocation from central well in m. . . . . . . . . . . . . . . . . . . . . . . . . . 152.3 Groundwater monitoring setup at BB1 Wb-S site. Summary of installationdepths of relevant piezometers. 𝑝𝑠 and 𝑝𝑑 = shallow and deep piezometer mid-screen distance from pipe top (m), 𝑝𝑠,𝑚𝑎𝑠𝑙 and 𝑝𝑑,𝑚𝑎𝑠𝑙 = shallow and deeppiezometer mid-screen elevation above sea level (m a.s.l.). Screen lengths ofall piezometers were 50.0 cm. . . . . . . . . . . . . . . . . . . . . . . . . . . 152.4 Groundwater monitoring setup at sites other than Wb-S. 𝑝𝑑 = piezometermid-screen distance from pipe top (m), and 𝑝𝑑,𝑚𝑎𝑠𝑙 = piezometer mid-screenelevation above sea level (m a.s.l.). Screen lengths were 40.8 cm at all sites,except for L-T where it was only 27.2 cm. . . . . . . . . . . . . . . . . . . . 162.5 Instrumentation at BB1 flux tower site with sensor accuracies. Sensible andlatent heat fluxes were computed from measurements made by the CO2 andH2O eddy covariance systems. CSI = Campbell Scientific Inc. . . . . . . . . 172.6 Instrumentation of weather stations at field sites other than BB1 with sensoraccuracies. CSI = Campbell Scientific Inc. . . . . . . . . . . . . . . . . . . . 183.1 Non-exhaustive summary of evapotranspiration studies in Sphagnum-dominated peatlands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.2 Dates of shifts in net water exchange with the atmosphere as calculatedfrom daily observations (Eq. 3.9). Shift𝑑𝑟𝑦 = date when net vertical waterexchange became negative in spring, and Shift𝑤𝑒𝑡 = date when net verticalwater exchange with the atsmophere became positive in fall. . . . . . . . . . 26x3.3 Summary of meteorological variables calculated over the growing season(April 1 through September 30) and non-growing season (October 1 throughMarch 31). 𝑃 , 𝐸 and 𝐸𝑒𝑞 = totals of precipitation, actual evapotranspi-ration and equilibrium evapotranspiration (mm) respectively, 𝛼𝑃𝑇 = meanPriestley-Taylor’s alpha, 𝛽 = mean Bowen ratio, 𝑇𝑎 = mean air temperature(∘C), 𝑒𝑎, 𝑒𝑠 and 𝑉 𝑃𝐷 = mean vapour pressure, saturation vapour pressureand vapour pressure deficit (kPa), and 𝑄∗ = cumulative net radiation (MJm-2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.4 Fitted model coefficients used to predict evapotranspiration (Eq. 3.7). . . . 324.1 Summary of relevant site characteristics. 𝐾𝑐,𝑠𝑙𝑢𝑔 = slug test based saturatedhydraulic conductivity of catotelm with similar von Post indices as catotelmat field site. Ground elevation metrics based on LiDAR (𝑧𝐿𝑖𝐷𝐴𝑅) flown onJune 16, 2018. 𝑧𝑝𝑠,10 and 𝑧𝑝𝑠,75 = 10th and 75th percentile of peat surfaceelevation distribution defined to represent hollow and hummock elevation,respectively, 𝑆𝑝𝑠 = peat surface water storage capacity and lateral flow di-vergence thresholds derived from recursive partitioning are shown as 𝑧1 and𝑧2. Values for hydraulic conductivity and specific storage are means of allsamples, unless specified differently. . . . . . . . . . . . . . . . . . . . . . . . 484.2 Dates of start (onset) and end (cessation) of lateral flow divergence (𝑑𝑖𝑣𝑄𝑙)between 2016 and 2020 based on daily water table elevation (𝑧𝑤𝑡). Thenumber of days when 𝑑𝑖𝑣𝑄𝑙 occurred was calculated as sum of days at which𝑧𝑤𝑡 was above 𝑧2 or 𝑧1. The number of days with 𝑑𝑖𝑣𝑄𝑙 for the wet seasonof 2016 to 2017 is a conservative estimate as 𝑧𝑤𝑡 was not available betweenDec. 10, 2016 and Jan. 20, 2017. . . . . . . . . . . . . . . . . . . . . . . . . 544.3 Calculated water balance for water years of 2017 to 2019. All quantitiesare in mm. WY = water year. Lateral flow divergence (𝑑𝑖𝑣𝑄𝑙, here equiva-lent to 𝜖𝑤𝑏 in Eq.4.8) was calculated without threshold condition (𝑑𝑖𝑣𝑄𝑙 =𝜖𝑤𝑏, 𝑖𝑓𝑧𝑤𝑡 > 𝑧1). 𝑄𝑔𝑤,𝑣 = vertical groundwater flux and 𝑄𝑔𝑤,ℎ = horizontalgroundwater flux. 𝜖 = residual of water year balance. Positive values offluxes other than 𝑃 indicate that water leaves the site. . . . . . . . . . . . . 565.1 Overview of interception loss studies in peatlands. . . . . . . . . . . . . . . 635.2 Characteristics of vegetation communities in which plots were established.Descriptions are based on Hebda et al. (2000) (see also Figure 5.1). The areal extent is provided in km2 and as fraction of the total area of BBECA(fBB). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66xi5.3 Vegetation metrics obtained from airborne LiDAR include tree count, treeheight, relative tree canopy area (𝐶𝑎𝑟𝑒𝑎), tree canopy radius (𝐶𝑟𝑎𝑑𝑖𝑢𝑠) andcanopy zone fractions at sites with sparse tree cover (e.g. 𝑓𝑖𝑛𝑛𝑒𝑟 is relativeplot area classified as inner canopy zone). Ground-based observations includeheight of tree canopy base, seasonal tree canopy openness (𝐶𝑜𝑝𝑒𝑛), basalarea (𝐴𝑏𝑎𝑠𝑎𝑙), mean (𝐷𝐵𝐻), 25th (𝐷𝐵𝐻25) and 75th (𝐷𝐵𝐻75) quantile ofthe distribution of tree trunk diameter at breast height, and shrub height.Height of canopy base is a ground-based observation that was obtained froma sub-sample of trees and it cannot be directly compared to tree height fromairborne LiDAR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795.4 Metrics of fit of weighted linear regression model fit to plot-average through-fall ( ̄𝑦𝑡,𝑝, ̄𝑦𝑠,𝑝) versus tree-based site averages ( ̄𝑥𝑡,𝑝 and ̄𝑥𝑠,𝑝). 𝑅𝑆𝐸 = residualstandard error (mm), 𝑎 = intercept, 𝑠𝑎 = standard error of the intercept, 𝑏= slope, 𝑠𝑏 = standard error of the slope, 𝑛 = number of observations (-). . 845.5 Summary of net precipitation flux totals and interception loss over the courseof the field campaign between May 1, 2018 and April 24 to 30, 2019, depend-ing on site. 𝑃𝑜𝑝𝑒𝑛 = gross precipitation measured at a nearby clearing ofeach site, 𝑇𝐹𝑡 and 𝑇𝐹𝑠 are tree and shrub throughfall calculated from plot-averaged observations (Eqs. 5.1 to 5.3), 𝑆𝐹𝑡 = tree stemflow per unit plotarea calculated using Eqs. 5.29 to 5.30, and 𝑆𝐹𝑠 = range of shrub stemflowper unit plot area between 5-10% of 𝑇𝐹𝑡, or 𝑃𝑜𝑝𝑒𝑛 at L-T, becoming stemflow(Päivänen, 1966; Zwieback et al., 2019), and 𝑃𝑛𝑒𝑡 = net precipitation. Allquantities are in mm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 885.6 Summary of net precipitation flux totals and interception loss between May 1and September 24 to 27, 2018, depending on site. 𝑃𝑜𝑝𝑒𝑛 = gross precipitationmeasured at a nearby clearing of each site, 𝑇𝐹𝑡 and 𝑇𝐹𝑠 are tree and shrubthroughfall calculated from plot-averaged observations (Eqs. 5.1 to 5.3), 𝑆𝐹𝑡= tree stemflow per unit plot area calculated using Eqs. 5.29 to 5.30, and𝑆𝐹𝑠 = range of shrub stemflow per unit plot area between 5-10% of 𝑇𝐹𝑡, or𝑃𝑜𝑝𝑒𝑛 at L-T, becoming stemflow (Päivänen, 1966; Zwieback et al., 2019),and 𝑃𝑛𝑒𝑡 = net precipitation. All quantities are in mm. . . . . . . . . . . . 885.7 Summary of net precipitation flux totals and interception loss between Octo-ber 30 to November 1, 2018 and January 23 to 25, 2019, depending on site.𝑃𝑜𝑝𝑒𝑛 = gross precipitation measured at a nearby clearing of each site, 𝑇𝐹𝑡and 𝑇𝐹𝑠 are tree and shrub throughfall calculated from plot-averaged obser-vations (Eqs. 5.1 to 5.3), 𝑆𝐹𝑡 = tree stemflow per unit plot area calculatedusing Eqs. 5.29 to 5.30, and 𝑆𝐹𝑠 = range of shrub stemflow per unit plotarea between 5-10% of 𝑇𝐹𝑡, or 𝑃𝑜𝑝𝑒𝑛 at L-T, becoming stemflow (Päivänen,1966; Zwieback et al., 2019), and 𝑃𝑛𝑒𝑡 = net precipitation. All quantities arein mm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 896.1 Overview of sap flow field studies conducted in peatlands. . . . . . . . . . . 99xii6.2 Spearman correlation between sap flow and water table depth from treeswith sap flow observations across a water table depth range of at least 0.20m. Site = field site at which tree was located, Species = tree species, Tree= tree identification, 𝐷𝐵𝐻 = trunk diameter at breast height (cm), 𝑟𝑠 =Spearman rank correlation coefficient, p-value = statistical significance basedon Spearman rank correlation test, n = number of daily observations, 𝑊𝑇𝑟𝑎= range of water table depth during observations (m), 𝑊𝑇𝑚𝑒𝑑 = medianwater table depth below summertime peat surface (m), 𝑊𝑇𝑚𝑖 and 𝑊𝑇𝑚𝑎 =minimum and maximum water table depth below summertime peat surface(m). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1086.3 Summary of vegetation characteristics at each field site. 𝐴𝑠𝑎𝑝 = total basalarea of living trees (m2 ha-1), and 𝑓𝑏, 𝑓ℎ and 𝑓𝑝 = fraction (%) of total basalarea of birch, hemlock and pine, respectively. . . . . . . . . . . . . . . . . . 1096.4 Fitted parameters of species-specific power-law relations (Eqs. 6.13 and 6.14)used to estimate 𝑆𝐹𝑚𝑎𝑥 and 𝑅0 from 𝐷𝐵𝐻, and 𝑘 for application in the sapflow-𝑉 𝑃𝐷 model (Eq. 6.12).The parameters 𝑡1 and 𝑡2 are the fitted valuesof 𝑡 used to compute weights in the generalized nonlinear squares model for𝑆𝐹𝑚𝑎𝑥 and 𝑅0, respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1096.5 Plot-scale volumetric sap flow between May 1 and September 30, 2019. 𝐴𝑠𝑖𝑡𝑒= area of field site, 𝑄𝑠𝑎𝑝 = cumulative volumetric sap flow, and 𝑃𝑜𝑝𝑒𝑛 = grossprecipitation measured at a nearby open site. . . . . . . . . . . . . . . . . . 1137.1 Relative tree and shrub canopy openness based on hemispherical photographsat each field site. 𝑇𝑟𝑒𝑒𝑚𝑒𝑎𝑛 = mean tree canopy openness, 𝑇𝑟𝑒𝑒𝑚𝑖𝑛 and𝑇𝑟𝑒𝑒𝑚𝑎𝑥 = minimum and maximum tree canopy openness, which correspondto leafed and non-leafed canopy openness at B-Sa. 𝑆ℎ𝑟𝑢𝑏𝑚𝑒𝑎𝑛 = mean shrubcanopy openness that was determined from a single survey in July 2018 atall sites except for L-T, where it was measured every other week during fieldvisits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1337.2 Summary metrics of predictive model used to gap-fill growing season andannual total evaporation loss (Eq. 7.8). 𝑠𝑒 = residual standard deviation(mm day-1), 𝑅2𝑎𝑑𝑗 = adjusted R2, 𝑉 𝐼𝐹 = variance inflation factor, and 𝑛 =number of observations used to fit model. 𝑏0 = model intercept, 𝑏𝑒𝑞 and 𝑏𝜃= model coefficients associated with predictors 𝐸𝑒𝑞,𝐵𝐵1 and 𝜃𝑙𝑦𝑠; values inbrackets indicate 𝑝-value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1337.3 Estimated site evaporation (𝐸𝑠𝑖𝑡𝑒), equilibrium evaporation (𝐸𝑒𝑞,𝐵𝐵1) andgross precipitation (𝑃𝑜𝑝𝑒𝑛) over the course of a growing season in five vegeta-tion communities (May 1 to September 28-30, 2018, depending on site). Thefraction 𝐸𝑠𝑖𝑡𝑒/𝐸𝑒𝑞,𝐵𝐵1 would be equivalent to Priestley-Taylor’s 𝛼 (Priestleyand Taylor, 1972). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1347.4 Estimated site evaporation (𝐸𝑠𝑖𝑡𝑒), equilibrium evaporation (𝐸𝑒𝑞,𝐵𝐵1) andgross precipitation (𝑃𝑜𝑝𝑒𝑛) over the course of a year in five vegetation com-munities (May 1, 2018 to April 28-30, 2019, depending on site). The fraction𝐸𝑠𝑖𝑡𝑒/𝐸𝑒𝑞,𝐵𝐵1 would be equivalent to Priestley-Taylor’s 𝛼 (Priestley and Tay-lor, 1972). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138xiii8.1 Open site precipitation and water loss fluxes to the atmosphere in the growingseason 2018 (May 1 to September 30). 𝑃𝑜𝑝𝑒𝑛 = open site precipitation (mm),𝐸𝑝 = peat surface evaporation (mm), 𝑇 = tree transpiration (mm), and𝐼𝐿 = interception loss (mm), which considers interception loss and stemflowfrom tree and shrub layers. Uncertainty of 𝐼𝐿+𝐸𝑝+𝑇 would be in the orderof ± 50% of 𝑇 , due to uncertainty associated with upscaling of first-ordertranspiration estimates. Shrub transpiration is excluded; it was not withinthe scope of this research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144A1.1 Metrics of fit of linear regression models used to gap-fill missing meteorolog-ical data at the eddy-covariance site. 𝑇𝑎 = air temperature (∘C), 𝑅𝐻 = rel-ative humidity (%), 𝑊𝑉 = wind speed (𝑚 𝑠−1), RMSE = root mean squareerror. Data from the Environment and Climate Change Canada weatherstation in Burns Bog is denoted by a subscript, for example 𝑇𝑎𝑒𝑐𝑏𝑏 = airtemperature (∘C). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175A1.2 Energy balance closure of entire study period at 30-minute resolution andaggregated to daily and monthly values. 𝑛 = number of observations. . . . 175A1.3 Raw automated water table elevation and peat surface elevation recordingswere site-specifically calibrated and geo-referenced using manual observationstaken approximately every two weeks from surveyed benchmarks. Duringthe study period pressure transducers were replaced a several times due totechnical issues and separate calibrations were established for each sensor.RMSE = root mean square error (m) . . . . . . . . . . . . . . . . . . . . . . 175A2.1 Metrics of fit of linear regression models of plot-averaged shrub throughfallobservations used to estimate mean throughfall ( ̄𝑦𝑡,𝑝) from median through-fall ( ̃𝑦𝑡,𝑝) of data containing censored values. 𝑅𝑆𝐸 = residual standard error(mm). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178xivList of Figures2.1 A map of Burns Bog with the extent of the Burns Bog Ecological ConservancyArea and locations of field sites. . . . . . . . . . . . . . . . . . . . . . . . . . 123.1 Monthly observations of climatic variables, evapotranspiration and watertable depth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.2 Time series of cumulative sums of daily anomalies of 𝑃 - 𝐸 for each year. . 293.3 Monthly mean radiative fluxes over time as calculated from 30-minute data. 303.4 Peat surface albedo as calculated from daily values. . . . . . . . . . . . . . . 303.5 Monthly energy fluxes between summer 2014 and 2021. . . . . . . . . . . . 313.6 Daily evapotranspiration adjusted for lack of energy balance closure versusequilibrium evapotranspiration. . . . . . . . . . . . . . . . . . . . . . . . . . 323.7 Observed versus predicted evapotranspiration from multiple regression modeland model residuals by month. . . . . . . . . . . . . . . . . . . . . . . . . . 343.8 Partial residual plots associated with the multiple regression model used topredict evapotranspiration. . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.1 A satellite image of Burns Bog with the extent of the Burns Bog EcologicalConservancy Area and the BB1 eddy-covariance site. . . . . . . . . . . . . . 414.2 Aerial photograph of eddy-covariance site, located in a rewetted whitebeakrush-Sphagnum vegetation community during the dry and wet season. . 424.3 Time series of daily water table elevations (blue) and wintertime peat surfaceelevation (black) between March 1 and November 30. . . . . . . . . . . . . . 494.4 Time series of peat-surface elevations at each of the five piezometer nestsbased on manual measurements approximately every two weeks. . . . . . . . 504.5 Manual peat surface elevation versus water table elevation for each of thefive piezometer nests and site mean. . . . . . . . . . . . . . . . . . . . . . . 514.6 Maps of inundated area across the range of observed water table elevation. . 524.7 Hysteretic relationship between inundated area and water table elevation. . 534.8 Residuals of water balance closure versus water table elevation and residualsof water balance closure versus net hydrologic input. . . . . . . . . . . . . . 544.9 Monthly water balance components at BB1 flux tower site. . . . . . . . . . 555.1 Vegetation communities in Burns Bog. . . . . . . . . . . . . . . . . . . . . . 655.2 Daily meteorological data during the two years of interception loss measure-ments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775.3 Photos of vegetation communities at monitoring sites for interception lossstudy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78xv5.4 Plot-average tree throughfall observations versus gross precipitation by site. 805.5 Plot-average shrub throughfall observations versus gross precipitation by site. 815.6 Tree-based tree throughfall observations by site and canopy zone. . . . . . . 825.7 Tree-based shrub throughfall observations by site and canopy zone. . . . . . 835.8 Comparsion of subplot-based average versus tree-based site average treethroughfall by site. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845.9 Comparsion of subplot-based average versus tree-based site average shrubthroughfall by site. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855.10 Manual plot-averaged tree throughfall versus continuous trough-based treethroughfall. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 865.11 Stemflow volume versus gross precipitation by site and tree. . . . . . . . . . 876.1 Daily meteorological observations at BB1 flux tower site and on-site watertable depth coinciding with sap flow observations in treed vegetation com-munities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1066.2 Volumetric sap flow versus vapour pressure deficit by tree species, diameterat breast height and groundwater level. . . . . . . . . . . . . . . . . . . . . 1076.3 Residuals of the sap flow-vapour pressure deficit relation versus water tabledepth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1116.4 Variation of asymptotic model coefficients by tree species. . . . . . . . . . . 1126.5 Predicted sap flow versus observed sap flow by site and tree species of treesused during model fitting and independent observations. . . . . . . . . . . . 1136.6 Relative model residuals versus water table depth arranged by field site andtree species. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1146.7 Time series of estimated daily sap flow between May 1 and September 30,2019. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1157.1 Daily observations of meteorological variables at BB1 flux tower site andpeat moisture content and water table depth observations at each of the fivefield sites. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1297.2 Manual versus stationary peat moisture observations by site. . . . . . . . . 1307.3 Time series of linearly interpolated manual peat moisture content observa-tions and daily values that have been made consistent with plot averages bysite. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1317.4 Moisture content in lysimeters versus manual field observations by site. . . 1327.5 Peat surface evaporation versus equilibrium evaporation at each field site. . 1357.6 Predicted evaporation based on predictive multi-linear regression model ver-sus observed lysimeter evaporation. . . . . . . . . . . . . . . . . . . . . . . . 1367.7 Time series of observed evaporation by site. . . . . . . . . . . . . . . . . . . 137A1.1 Time series of Pacific Decadal Oscillation and Oceanic Niño Index. . . . . . 177A2.1 Photograph of the weather station at P-tall. Two 2.44 m long troughs wereinstalled at 2 m and collected rain that was diverted into a tipping bucketthat recorded throughfall at 30-min intervals. . . . . . . . . . . . . . . . . . 179xviA2.2 Photograph of tree throughfall collectors used in the random approach (a);shrub throughfall collector with screen to prevent entrapment of small ro-dents (b), and shrub throughfall collector equipped with a styrofoam ball toprevent evaporation in summer (c). . . . . . . . . . . . . . . . . . . . . . . . 179A2.3 Photograph of tree-based throughfall setup at P-tall. Orange arrows showthe trunk of the tree being sampled and tree throughfall collectors samplinginner, outer and outside canopy zone. The dashed red line approximates thetransect along which the collectors were arranged. . . . . . . . . . . . . . . 180A2.4 a) Photograph showing stemflow measurement setup. It shows sparse shrubcover under a tree with low hanging canopy at P-tall, and b) dense shrubcover about 1 m tall under a tree with canopy not reaching below approxi-mately 9 m at P-Sa. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181A2.5 Hemispherical photographs of tree canopies (and shrub canopy at L-T) withcanopy openness corresponding to values shown in Table 5.3. a) B-Sa leafon period, b) B-Sa leafless period, c) P-Sa, d) P-tall, e) P-low and f) L-T. . 182xviiAcknowledgementsI want to express my deep gratitude to a number of people, who have been an important partof this endeavour over the past years. First and foremost, I want to thank my supervisor,Dan Moore, whose guidance and enthusiasm were an inspiration that spurred me throughoutthese years. Thank you Dan for venturing into peatland hydrology with me. I wouldalso like to thank my continuous and discontinuous committee members Dr. Sara Knox,Dr. Mark Johnson, Dr. Roger Beckie and Dr. Andreas Christen, who provided inputat various stages of the project. A special thanks goes to Markus Merkens at Parks andEnvironment, Metro Vancouver Regional District, who is a principal in the Burns BogEcological Conservancy project and who provided the framework and partial funding forthis research. The Burns Bog Scientific Advisory panel and Sarah Howie of the City of Deltaprovided critical guidance in the planning stage of this research, and Parks and EnvironmentStaff helped with access and field logistics along the way. I would not have been ableto succeed without the enduring field assistance of Jordyn Carss, Annie Dufficy, MarineDumont, Anna Kaverney, Virgil Laurent, Jan Siegismund, András Szeitz and members ofother lab groups, who did greenhouse gas research in Burns Bog. Rick Kettler providedcrucial technical assistance and logistical support at the UBC Geography flume lab.Funding was provided from Parks and Environment of the Metro Vancouver Regional Dis-trict, the Natural Sciences and Engineering Research Council and through a scholarship ofthe University of British Columbia.I would like to thank Mom and Dad for their sustained support and patience during my timehere and my brother has been an inspiration to me for most of my life. I am looking forwardto spending more time with everyone, especially my young niece, who has been a sunshinefrom the moment she joined us. The outlook of being able to spend more time with familywas a great motivator especially in the last few months. I want to express my gratitude tomy many friends here in Vancouver, who have made my time here very special. In particularI am grateful to Charlotte Miton; a UBC post-doc and great partner in converstation withwhom I shared most ups and downs over a beer at our favorite Darby’s pub. June Skeeterhas been a solid house mate and good friend over the past years, especially during theCOVID-19 pandemic. Finally, I want to thank my friends back home, many of whom madeit over to come visit and learn about my life here. I am looking forward to being closeragain; we have a lot to catch up on.xviii1. Introduction1.1. MotivationAccording to recent estimates, peatlands cover 3.81 ⋅ 106 km2 of the earth’s land surface(Joosten, 2012). In Canada, peatlands occupy around 11% of the land surface, which is equalto approximately 1.134 ⋅ 106 km2 (NWWG, 1988; Joosten, 2012). Peatlands store aboutone-third of the world’s terrestrial biosphere carbon pool (Turunen et al., 2002; Roulet et al.,2007; Yu et al., 2010; Joosten, 2012), and healthy peatlands hold climate cooling potentialdue to carbon fixation and accumulation (Dise, 2009; Lucchese et al., 2010). Peatlands alsoprovide important habitats for rare and endangered species or migrating birds, contributeto regional biodiversity and act as cultural or spiritual spaces in some cultures (e.g. Hebdaet al., 2000; Parish et al., 2008; Minayeva and Sirin, 2012). Many peatlands have beendisturbed by human activities such as drainage and peat harvesting (Joosten and Clarke,2002; Parish et al., 2008), and there has been growing concern about the sensitivity ofpeatland ecohydrological functions in the context of climatic change (e.g. Laiho et al.,2003; Tarnocai and Stolbovoy, 2006; Dieleman et al., 2015; Waddington et al., 2015; Helbiget al., 2020; Nelson et al., 2021).Peatlands are characterized by shallow water tables (Rydin and Jeglum, 2013). Water tableposition is a key variable in peatland ecohydrology and it influences most ecohydrologicaland biogeochemical functions either directly or through complex feedbacks (Beer and Blo-dau, 2007; Waddington et al., 2015). In many countries, extensive areas of peatland areutilized for forestry, agriculture or peat harvesting, and drainage is a common practiceto facilitate operation of heavy machinery (Paavilainen and Päivänen, 1995; Joosten andClarke, 2002; Holden et al., 2004). Extensive drainage networks have resulted in widespreadlowering of peatland water tables (e.g. Holden et al., 2011; Haapalehto et al., 2014), whichcan have negative effects on peatland ecohydrological functions and ecosystem health (e.g.Waddington and Price, 2000; Holden et al., 2004; Beer and Blodau, 2007; Landry andRochefort, 2012; Waddington et al., 2015). Shifts of hydrologic regimes (Erwin, 2009)and increased evapotranspiration loss connected to climate change (Gerdol et al., 2008;Waddington et al., 2015; Helbig et al., 2020) are expected to increase these effects on watertables. One negative aspect associated with lowered water levels is establishment of non-native vegetation such as shrubs or trees (e.g. Jukaine et al., 1995; Murphy et al., 2009;Pellerin et al., 2009; Talbot et al., 2010) and warmer temperatures will further drive vegeta-tion community shifts toward faster growing systems (Weltzin et al., 2000, 2003; Dielemanet al., 2015; Malhotra et al., 2020). Restoration of degraded peatlands and mitigation ofthe negative effects of climate change typically focus on actions that promote higher watertables, such as ditch blocking (e.g. Howie et al., 2009b; Holden et al., 2017) and in rare1cases vegetation control aimed at removing or killing-off mature vegetation (e.g. Dubé etal., 1995; Jutras et al., 2006; Leppä et al., 2020). A holistic understanding of hydrologicaland ecological interactions and feedbacks is required to guide future management interven-tions in disturbed peatlands and to better predict future trajectories of peatlands underchanging climatic conditions (Holden et al., 2004; Waddington et al., 2015; Chimner et al.,2017).Vegetation shifts in peatlands as a response to lowered water levels are well documented;however, hydrological processes and feedback of vegetation encroachment on water levelsor the effect of vegetation removal by management and the effect on hydrologic budgetsof peatland remain less well understood. While a conceptual framework of the complexfeedback mechanisms exists (Waddington et al., 2015), many remain quantitatively poorlyconstrained and are the subject of ongoing research (e.g. Kettridge et al., 2013; Nelson et al.,2021; Exler and Moore, 2022; Moore et al., 2022). The current understanding of peatlandhydrology and its complex interaction with ecology and climate change is mostly based onstudies in the boreal zone or continental settings, in which spring snowmelt is a key featureof the hydrological regime. The number of studies of peatlands from coastal temperateregions that are characterized by a rain-dominated maritime climate is generally low withthe exception of studies from maritime blanket bogs in the British Isles, where researchhas focused on runoff generation (e.g. Holden et al., 2001; Sottocornola and Kiely, 2010;Birkinshaw et al., 2014; Goudarzi et al., 2021), and a few others describing the hydrologyof blanket bogs on the east coast of Canada (Price, 1992a, 1992b).1.2. Literature review1.2.1. Hydrological characteristics of raised bogsRaised bogs exhibit hydrologically distinctive features that set them apart from drier ecosys-tems. A fundamentally important property is the rapid decrease of saturated hydraulicconductivity (𝐾𝑠) with depth (Holden, 2009). Values in the range of 10-3 m s-1 to 10-4 ms-1 are not uncommon near the surface (Letts et al., 2000; Kværner and Kløve, 2008), while𝐾𝑠 at depth can be as low as 10-9 m s-1 (Fraser et al., 2001; Holden and Burt, 2003; Emiliand Price, 2006; Kopp et al., 2013). High 𝐾𝑠 within the upper 50 cm, combined with shal-low water tables, make shallow subsurface flow an important mode of lateral transport interrain with significant topographic gradients (Holden and Burt, 2003; Kværner and Kløve,2008). In coastal domed bogs, saturation-excess overland flow is the dominant runoff pro-cess in wintertime and topographic depressions such as hollows influence connectivity (Freiet al., 2010; Van der Ploeg et al., 2012; Oosterwoud et al., 2017). Weak hydraulic gradientsmake lateral groundwater flow less significant in summer, at least in the interior regionsof a bog; however, when water levels are shallow groundwater flow can generate dischargewhere topographic gradients are less than 0.5% (Oosterwoud et al., 2017).Evapotranspiration (𝐸𝑇 ) is the dominant water loss process in summer (e.g. Lafleur et al.,2005; Brown et al., 2010), and shallow water tables may lessen the impact of soil moisture2limitation as a control on 𝐸𝑇 (Lafleur et al., 2005). Microtopography such as hollows,hummocks and lawns create small-scale vegetation and soil moisture patterns, which affect𝐸𝑇 (Kellner, 2001; Admiral et al., 2006; Brown et al., 2010). In winter, when the peat ismostly saturated, hummocks and lawns can remain moisture limited, causing actual 𝐸𝑇rates to be lower than potential 𝐸𝑇 , although overall rates are low.Precipitation is the sole significant recharge process in bogs, which by definition lack sig-nificant inputs of groundwater (Rydin and Jeglum, 2013). High 𝐾𝑠 and shallow watertables near the center facilitate efficient and direct groundwater recharge at open sites andinfiltration-excess overland flow does not occur (Holden et al., 2001). Towards the marginsor along drainage ditches, where water tables tend to be deeper, and in artificially drainedlocations, establishment of shrub and tree vegetation (Rydin et al., 1999; Freléchoux et al.,2000; Linderholm and Leine, 2004; Whitfield et al., 2006) results in interception loss thatoccurs in one or two canopy layers (Robinson, 1998; Birkinshaw et al., 2014; Exler andMoore, 2022).Shallow water tables dominate near the center of pristine domed bogs and groundwaterlevels gradually decrease towards the margins (Howie and van Meerveld, 2011), introducingincreasing lateral hydraulic gradients. Shallow water tables and a drainage network, ifpresent, produce a high degree of hydrological connectivity (Holden et al., 2004; Holden,2009; Wallage and Holden, 2011; Oosterwoud et al., 2017). The soft peat matrix is subject topronounced volume changes with changes in water table elevations (Fritz et al., 2008; Howieand Hebda, 2018; Nijp et al., 2019) due to a changing pore water pressure at depth (Priceand Schlotzhauer, 1999). This exceptional feature carries several implications. Storagechange is a function of both specific yield and specific storage (Price and Schlotzhauer, 1999;Bourgault et al., 2017). Lower water table elevations in summer do not translate directlyinto deeper water tables because the peat surface also lowers (Price, 2003). Consequently,the impact of moisture limitation on 𝐸𝑇 can be moderated (Lafleur et al., 2005), due to ashift in pore size distribution towards smaller pores that promotes capillary rise over longerdistances and increases unsaturated hydraulic conductivity (Nijp et al., 2017; Golubev andWhittington, 2018). Simultaneously, a shrinking peat matrix results in a shift in poresize distribution mostly due to collapse of macropores, increases peat bulk density andultimately lowers 𝐾𝑠 significantly (Price and Schlotzhauer, 1999; Price, 2003).1.2.2. Effect of drainage and climatic warming on peatland vegetationBryophytes, such as Sphagnum moss, tolerate oxygen stress in saturated peat and maintaintheir ecological niche by creating acidic and nutrient deficient conditions in ombrotrophicpeatlands (Rydin and Jeglum, 2013). This adaptation gives them an advantage over vas-cular plants that rely on aerated soil conditions for root growth (e.g. Heikurainen, 1955;Boggie, 1972; Finér and Laine, 1998; Iversen et al., 2018) and that experience impeded deeproot development (Steele et al., 1997) due to lack of oxygen at depth (King et al., 1986;Lieffers and Rothwell, 1987; Ray and Schweizer, 1994). In the interior of such pristine bogs,shrubs and trees are normally sparse and stunted by shallow water tables and low nutrientconcentrations (e.g. Bubier, 1991; Freléchoux et al., 2000; Howie and van Meerveld, 2011).3Most pristine bogs are surrounded by a margin region that consists of a rand, a narrowtransition zone with a relatively steeper topographic slope compared to the interior, and alagg, a zone along the edge of the bog that can be influenced by the surrounding ground-water (Howie et al., 2009a; Howie and van Meerveld, 2011; Langlois et al., 2015; Paradis etal., 2015). Both zones can support more productive marginal forest with increased heightand density of shrubs and trees (Rydin et al., 1999; Freléchoux et al., 2000; Langlois etal., 2017) due to higher nutrient availability and lower water tables than occur in the bog’sinterior (e.g. Damman and Dowhan, 1981; Rydin et al., 1999; Hebda et al., 2000; Howieand van Meerveld, 2011).Drainage enables non-native vegetation to establish (e.g. Landry and Rochefort, 2012;Préfontaine and Jutras, 2017) in areas of lowered water tables along drainage ditches thatreach far into the bog interior (Roy et al., 2000; Whitfield et al., 2006) or along the edgeof a bog (Linderholm and Leine, 2004). Drainage-induced changes in water table dynamicscause shifts in vegetation (e.g. Jeglum and He, 1995; Talbot et al., 2010) that createcomplex patterns of vegetation cover within bogs (Freléchoux et al., 2000; Hebda et al.,2000; Howie et al., 2020), which are in turn exacerbated by feedback among vegetation,peat structure and hydrology (Talbot et al., 2010). Trees, shrubs and ground vegetationsuch as graminoids or bryophytes can be affected differently by drainage (Murphy et al.,2009).Trees are usually scattered in the interior of peatlands and shallow groundwater levels resultin small and stunted trees that grow unevenly (Freléchoux et al., 2000). In areas withintermediate groundwater levels, tree density increases and multi-layered stands of unevenage grow taller compared to the interior. At the edge or in drained areas (e.g. Jukaine etal., 1995; Roy et al., 2000; Talbot et al., 2010), trees tend to be evenly aged and tall withhigher growth rates. Along a transect from a bog’s interior to its edge, tree height andstem diameter increase due to decreasing water levels (Freléchoux et al., 2000; Freléchouxet al., 2004), even for trees of the same age (Bubier, 1991). Higher tree abundance in drierareas is a result of increased fine root and above ground biomass production (Minkkinen etal., 1999; Murphy et al., 2009; Murphy and Moore, 2010).Shrub growth in peatlands is primarily a function of wetness and to a lesser extent tem-perature (Finér and Laine, 1998; Weltzin et al., 2000). Drainage can increase fine rootproduction when water levels are lowered to about 40 cm depth (Murphy and Moore,2010). Shallower drainage of groundwater levels up to a depth of 20 cm do not necessarilyincrease fine root production significantly (Murphy et al., 2009), but can trigger a shift inspecies composition (Murphy et al., 2009; Talbot et al., 2010). After drainage, a successiontowards forest vegetation including shrub establishment starts within 10 years and shrubgrowth rate can be reduced once trees established; however, the effect varies by species(Jukaine et al., 1995). Warming temperatures will increase shrub productivity (Weltzin etal., 2000) and warm and dry conditions can increase shrub cover by 50% at the expense ofvegetation cover on the ground (Weltzin et al., 2003).Raised bogs are particularly sensitive to drainage as Sphagnum moss - the natural bog-building genus - cannot survive once matric potential drops below -100 mb (Price andWhitehead, 2001). Lowered water levels cause a shift in Sphagnum species (Breeuwer et al.,42009) or a decrease in Sphagnum cover (Laiho et al., 2003; Talbot et al., 2010), in one partdue to increased shading from drainage-related tree or shrub growth (Jukaine et al., 1995),initiating a shift from Sphagnum and graminoids towards more shade and drought tolerantspecies and arboreal vegetation (Laiho et al., 2003; Pellerin et al., 2009). Drainage lowersherb fine root production (Murphy et al., 2009) that can reduce graminoid cover by 50%(Weltzin et al., 2003).Climatic warming affects peatland vegetation directly through increased temperatures andindirectly through its effect on water levels (Mäkiranta et al., 2017), for example throughincreased evapotranspiration (Gerdol et al., 2008; Waddington et al., 2015; Helbig et al.,2020). Overall, the impact of drying is more important than warming (Mäkiranta et al.,2017; Malhotra et al., 2020) and equivalent to the drying effect of drainage.Higher temperatures increase biomass production and wetness is the factor controllingallocation between above- and below-ground, whereas drier conditions would shift growthbelow ground (Weltzin et al., 2000). Increased fine root growth is a primary manifestationof higher below ground productivity (e.g. Finér and Laine, 1998; Dieleman et al., 2015),and shrubs benefit disproportionately compared to graminoids or trees (Malhotra et al.,2020). In addition to faster growth rates of roots, warmer temperatures could promotelonger growing seasons (Vapaavuori et al., 1992; Ruess et al., 2003; Iversen et al., 2018).Warmer and drier conditions are the most likely future climatic scenario for most peatlands.Such a change would result in a shift in plant functional type (Mäkiranta et al., 2017) thatpromotes vascular plant abundance and a shift from Sphagnum- to graminoid-dominatedsystems (Dieleman et al., 2015; Norby et al., 2019).1.2.3. Past research on the hydrological effect of non-native peatlandvegetationThe majority of past research on the effect of non-native vegetation on peatland hydrologyin the northern hemisphere has been conducted in boreal or arctic climates or in continentalsettings, where vegetation is mostly made up of a few key species and where seasonal snowaccumulation is an important part of the hydrologic regime.Interception loss studies in peatlands have been conducted in Canada (e.g. Dubé et al.,1995; Van Seters and Price, 2002; Farrick and Price, 2009; Thompson et al., 2014; Zwiebacket al., 2019), Finland (Päivänen, 1966), and the British Isles (Robinson, 1998), where spruceand pine were the most common species. Studies have also focused on birch and Labradortea (Päivänen, 1966; Zwieback et al., 2019). Interception loss studies were usually limitedto the snow-free season and did not specifically address the effect of complex multi-layeredcanopy structure.Most transpiration loss studies were conducted in subarctic dry continental or humid conti-nental climates (Köppen classification Dfc and Dfb, respectively) in Canada (e.g. Patankaret al., 2015; Warren et al., 2018; Depante et al., 2019), Russia (Iijima et al., 2014), theUnited States (Warren et al., 2021) and central (Kučerová et al., 2010) or northern Europe(Sarkkola et al., 2013). Transpiration loss was measured in black spruce, birch, trembling5aspen, larch or pine and only a few of studies estimated transpiration loss at the stand level(Kučerová et al., 2010; Sarkkola et al., 2013; Thompson et al., 2014). To the knowledge ofthe author there are no studies that measured shrub transpiration loss, especially in thecontext of hydrologic budgets.Peat surface evaporation studies in vegetated stands are rare and mostly limited to northernCanada (Lafleur and Schreader, 1994; Petrone et al., 2004) or Alaska in the United States(Heijmans et al., 2004).1.3. Study objectivesAs shown in the preceding section, vegetation-hydrology dynamics in peatlands are thesubject of ongoing research that is, with the exception of a few studies from the BritishIsles, mostly biased towards boreal or arctic environments and continental settings, whereecosystem diversity is limited and snow accumulation is an important component of theannual hydrologic regime. The Pacific Northwest is, in contrast, dominated by a maritimeclimate in which snow accumulation is a minor factor in the hydrologic regime and wherea broader diversity of vegetation is able to establish.The overall objective of this research is to expand the geoclimatic range of ecohydrologicalpeatland research with a focus on plant-hydrological interaction and feedback mechanisms,and to study interannual variability to better understand potential effects of climate changeon the ecohydrologic health of maritime raised bogs in the region. In addition to advancingthe understanding of ecohydrologic processes in peatlands, a secondary objective was todevelop improved methods for quantifying peatland water fluxes, including an alternativeapproach to sampling throughfall in areas with sparse canopies and the use of lysimetersto estimate peat surface evaporation.To accomplish these goals, the dissertation is structured around five studies that are basedon a multi-year data set of sub-daily energy and water fluxes and storages from an eddy-covariance flux tower at an open site used to study effects of interannual variability onpeatland ecohydrology (Chapters 3 and 4), and a set of ecohydrologic observations used tostudy effects of drainage-induced vegetation encroachment on peatland-atmosphere waterexchange (Chapters 5, 6 and 7). Outlines of each study are provided below. Detailedliterature reviews and statements of specific objectives are provided in each study-specificchapter.1.3.1. Study 1: Daily, seasonal and interannual variability of energy balanceand evapotranspiration at an disturbed and rewetted whitebeakrush-Sphagnum siteThe first study chapter focuses on energy exchange at the peatland-atmosphere interface ofan open site to study interannual variability of energy fluxes that drive evapotranspirationloss. It aims to identify controls on evapotranspiration at the daily, seasonal and interannual6level with a focus on the growing season, and quantitatively evaluates the effect of somehydrologic feedbacks on evapotranspiration.1.3.2. Study 2: Water balance and runoff generating processes at an open,disturbed and rewetted white beakrush-Sphagnum site near the bogcenterThe second study expands on findings from chapter one by focusing on the seasonal shift ofwater loss processes in raised bogs located in maritime climates, and to better understandtiming and drivers that control the shift. It uses a water balance approach to improveprocess-based understanding of runoff generation in central peatland areas in the contextof vertical peat surface elevation change, with an emphasis on shallow lateral subsurfaceand surface flow.1.3.3. Study 3: Quantifying throughfall, stemflow and interception loss in fivevegetation communities in a maritime raised bogThe goal of study chapter three is to provide an empirically sound basis to quantify the ef-fect of drainage-induced vegetation encroachment on net precipitation. The study estimatesinterception loss from trees and shrubs separately and compares observations from five veg-etation communities that established as a result of different drainage histories and that varyin species, structure and complexity. Multiple sampling schemes are compared and a newtree-based approach that combines ground-based observations with remotely sensed standstructure parameters is introduced that enables transferability of local throughfall observa-tions to areas with similar tree structure but different spatial arrangements of trees.1.3.4. Study 4: Tree transpiration loss in four vegetation communities in amaritime raised bog in the Pacific NorthwestThis study uses sap flow observations to provide a first-order estimate of increased tran-spiration loss from areas where trees established due to drainage. It models transpirationloss as a function of vapour pressure deficit, tree size and species, and estimates stand leveltranspiration loss over the course of one growing season. Effects of water table depth as acontrol on transpiration are explored.1.3.5. Study 5: Peat surface evaporation and moisture dynamics in fivevegetation communities in a maritime raised bog in the PacificNorthwestStudy chapter five uses lysimeters to quantify peat surface evaporation in five vegetationcommunities that vary in canopy openness and explores the relationship between vegetationshading and peat surface evaporation. The study included the development of a novel7approach for adjusting lysimeter evaporation to account for moisture content differencesbetween the lysimeters and the surrounding site. The approach involves development ofan empirical model that can also be used to estimate site evaporation for periods in whichlysimeter data are missing, to allow seasonal totals to be estimated.1.4. Thesis structureDetails on Burns Bog, the six field sites, and weather stations used to monitor micro-climatethat are common to all field sites are presented in Chapter 2. Additional field observationsand other details specific to each study are included in the corresponding chapter. Chapters3 to 7 present each study in detail. Chapter 8 concludes the thesis with a summary of keyfindings from each chapter and by synthesizing findings from Chapters 5 to 7 to give anoverview on how vegetation type affects hydrologic exchange between the peat body andthe atmosphere. In addition, Chapter 8 provides suggestions for future research.82. Study site and field methodology2.1. Study site2.1.1. General backgroundBurns Bog is an oval shaped raised bog with a north-south extent of 5.5 km, an east-west extent of 9 km (Balfour and Banack, 2000), and a total area of approximately 3000ha. In 2004, an area of approximately 2000 ha was acquired by a partnership comprisingthe Government of Canada, the Province of British Columbia, Metro Vancouver and theMunicipality of Delta (Hebda et al., 2000), and was designated as the Burns Bog EcologicalConservancy Area (BBECA). The bog is the largest documented raised bog affected byurbanization on the west coast of the Americas (Howie et al., 2009b). It is located nearthe southern extent of raised bogs in western North America (Rocchio et al., 2021), andwas recognized as a globally significant wetland under the Ramsar Convention in 2012.Elevation ranges from approximately 4-5 m a.s.l. in the center to 1-2 m a.s.l. at themargins (McElhanney, 1999), resulting in a topographic gradient of about 0.1% (Balfourand Banack, 2000). Around 29% of the bog was not harvested after major peat-harvestingoperations between 1940 and 1980 concluded (Hebda et al., 2000; Howie et al., 2009b).2.1.2. Native use of Burns Bog and the surrounding landsBurns Bog is located on the traditional and ancestral territory of many First Nations and in-digenous tribes including the sĲqəĲciĲyaPì təməxw (Katzie Indian Band), šxwməΘkwəĲyəmaPìtəməxw (Musqueam Indian Band), Semiahmoo First Nation, S’ólh Téméxw (Stó:ló Na-tion), sĲcəwaΘenaPì təməxw (Tsawwassen Indian Band), Kwantlen, Cayuse Umatilla andWalla Walla, Hul’qumi’num Treaty Group, Stz’uminus, Á,LEN¯ENEC LTE (W¯SÁNEĆ),and kwikwəĲ𝜆əm (Kwikwetlem First Nation) (Native Land Digital, 2023). Musqueam re-ferred to an area west of the bog as pəì𝜒əneməxw, which roughly translates to “prairie”,“meadow” or “meadow flat”. An area between the northern boundary of today’s bog andthe Fraser river they called xwmeĲcənəìp, which means “The high land from Sunbury downto Mud Bay” (Musqueam - A living culture, 2023).During a public consultation as part of the planning process and creation of BBECA,Katzie, Musqueam, Semiahmoo, Stó:ló and Tsawwassen indicated traditional use of BurnsBog and the surrounding wetlands for its waterways or as canoe portage, and for huntingof black bear, black-tailed deer and elk, and potentially ducks. It was an important source9of blackberries, blueberries, cranberries, Labrador Tea, salal, Sphagnum, and other plants,some of which were collected for medicinal use (Hebda et al., 2000).2.1.3. Meteorology and regional climateSouth coastal British Columbia experiences a typical maritime climatic regime with drysummers and wet winters. In summer, a Pacific High off the coast generates extendeddry and clear sky conditions. Wintertime is usually dominated by a low-pressure systemnear the Aleutian Islands in the North Pacific, resulting in mild temperatures, extensiverain and low solar radiation. Cold and dry conditions occasionally occur in winter, when ahigh-pressure system reaches south from the Yukon into northern British Columbia (Stahlet al., 2006; Pike et al., 2010).In the Lower Mainland, weather systems interact with the local influence of topography, inparticular the Coast Mountains north of the Fraser River. Frontal systems predominantlyapproach the Lower Mainland from the southwest. As a result of the orographic effectassociated with the North Shore mountains, precipitation generally increases across thebog from southwest to northeast, with mean annual precipitation ranging from 1000 to1200 mm (Hebda et al., 2000), with less than 10% of it falling as snow (Howie et al.,2009b). The majority of precipitation occurs during the winter months, whereas summersare usually dry. The mean annual temperature is reported to be 9.6∘C with a summer meanof 16.8∘C and a winter mean of 2.5∘C (Hebda et al., 2000).2.1.4. Vegetation communities in Burns BogBurns Bog includes twenty-four ecosystems, seven of which are forest ecosystems, nineare classified as shrub/sedge dominated ecosystems, and six are characterized as sparselyto non-vegetated (Hebda et al., 2000). Table 2.1 provides an overview of the dominantecosystems, and Figure 2.1 gives an overview of the spatial distribution. As indicated inTable 2.1, seven vegetation communities cover the majority of Burns Bog, of which six areconsequently targeted by this study. Due to the size of the Pine-Sphagnum ecosystem, itssub-categories will be investigated separately.Table 2.1.: Overview of the seven most important vegetation communities in Burns Bog,according to Hebda et al. (2000). Bold font highlights the communities in whichfield observations were carried out.Vegetation community Area (%)Pine-Sphagnum 45.5— Low shrub (P-low) 23— Tall shrub (P-tall) 14— Woodland 8White beak-rush - Sphagnum 13.7White beak-rush - Three-way-sedge 11.510Vegetation community Area (%)Pine-Salal (P-Sa) 7.9— Mixed coniferous forest 4.5— Grand fir- Foamflower 1.9— Skunk cabbage 1.6— Douglas fir - Kindbergia 1.1Birch-Salal (B-Sa) 3.5Hardhack shrub 3.2— Labrador tea (L-T) <1Total 94.72.1.5. Selection of monitoring sites within Burns BogPlant communities in which monitoring sites are located were selected based on two cri-teria: first, the spatial extent of each community, to maximize the total area of selectedplant communities; second, similarity of vegetation communities in relation to hydrologicalprocesses.The Pine-Sphagnum community is by far the most dominant within Burns Bog and displaysthree distinct stages of shrub and tree development: Pine-low shrub, which represents atypical vegetational stage toward which natural bog environments converge over time; Pine-tall shrub, which forms under somewhat wet conditions, is widespread within Burns Bog,and which could remain within the marginal region of a natural bog; and Pine-woodland,which occurs in relatively dry locations that would not occur in natural bogs.The White beak-rush-Sphagnum and White beak-rush-Three-way sedge communities arecomparable in their structure, as both environments possess no canopy. They are moresimilar to the natural state of bogs compared to the Pine-Sphagnum community. An eddycovariance system and additional instrumentation at the BB1 site have been operationalsince summer 2014, and the first two studies use data collected at this site.The Pine-Salal community covers a considerable portion of the bog and is affected byperimeter drainage. It formed under rather dry conditions, a niche in which the Pine-Sphagnum “woodland” community was able to establish as well.The Birch-Salal community is distinct in being a deciduous forest, with pronounced sea-sonality in interception loss – a crucial feature as interception loss is limited during thewet season. Wintertime tree transpiration is negligible due to lack of foliage, and the Salalcover is typically more dense than that of the Pine-Salal community.The Labrador tea community represents a single-layered ecosystem void of trees with denseand tall Labrador tea shrub covering the entire area. Peat thickness is less than 1 m.The following sites have been selected for monitoring in this study: White beak-rush-Sphagnum, Pine-Sphagnum “low shrub” and “tall shrub”, Pine-Salal, Birch-Salal and11B−SaWb−SL−TP−tallP−lowP−Sa N2 km49.09°N49.10°N49.11°N49.12°N49.13°N49.14°N49.15°N123.02°W 123.00°W 122.98°W 122.96°W 122.94°W 122.92°WVegetation Hebda (2000)B−Sa (0.03)P−Sa (0.08)P−wood (forest) (0.06)P−tall (0.15)P−low (0.31)Mixed forest (−)Hardhack (< 0.01)Wb−ts (0.12)Wb−S (0.18)Water lily (0.01)Open water (0.03)Disturbed (0.01)AG (−)Bare (0.02)Figure 2.1.: A map of Burns Bog with the extent of the Burns Bog Ecological ConservancyArea (red line) and locations of field sites (black squares). In the legend, B-Sa =Birch salal, P-Sa = Pine salal, P-wood = Pine woodland (mature forest), P-tall= Pine tall shrub, P-low = Pine low shrub, Mixed forest = Redcedar, fir andalder, Hardhack = Hardhack shrub, Wb-ts = White beakrush threeway-sedge(open), Wb-S = White beakrush Sphagnum (open), Water lily = vegetatedponds (open water), Disturbed = disturbed heathland, AG = agriculture, andBare = bare peat. In the legend, the areal fraction of BBECA covered by eachvegetation community is provided in brackets. The map is based on Hebdaet al. (2000); an updated version is currently being developed but not yetavailable. The inset shows the location of Burns Bog (blue square) in theregional context of southwestern British-Columbia, northwestern WashingtonState and Vancouver Island.12Labrador tea, as White beak-rush - Three-way-sedge and pine woodland communitieswould be hydrologically similar to white beak-rush-Sphagnum and Pine-Salal, respectively.In total, the selected vegetation communities cover about 63% of the bog.2.2. Field observations2.2.1. Eddy covariance and meteorological observationsAn eddy-covariance system (herein referred to as “BB1”) has been operating near-continuously since July 2014 in Burns Bog (Figure 2.1). At an open white beakrush-Sphagnum site (Wb-S) near the bog center, the system measured turbulent heat fluxes at1.8 m above the ground and meteorological variables. It consisted of a three-dimensionalsonic anemometer and an open-path CO2/H2O infrared analyzer until June 2015 and anenclosed-path CO2/H2O analyzer since June 2015 (Table 2.5). Data between July 2014through December 2020 have been used in research presented herein. The tower is part ofAmeriFlux (Site code: CA-DBB) and the site has been the focus of greenhouse gas fluxmeasurements by eddy covariance following the ditch blocking of a major drainage ditch85 m north of the tower with a wood-supported peat dam in 2008 as part of the ecologicalrestoration efforts (Christen et al., 2016; Lee et al., 2017; D’Acunha et al., 2019; Lee et al.,2021; Nyberg et al., 2022).Weather sensors mounted on/near the tower recorded precipitation at 1.0 m, four-component net radiation at 4.25 m, air temperature and relative humidity at 2.0 m andwind speed and direction at 5.0 m. Soil temperature was measured at one location at 5,10 and 50 cm below the peat surface. Three soil heat flux plates were installed at 5 cmbelow the surface at hummock (two plates) and hollow (one plate) microtopographic sites,and mean soil moisture was monitored for the upper 30 cm of the peat. Eddy-covariancesensors were scanned at a rate of 10 Hz; remaining sensors were scanned every 10 s andreadings from all sensors were aggregated and recorded as 30-minute values.Each of the remaining study sites was equipped with smaller weather stations to monitorair temperature, relative humidity and wind speed (Table 2.6). A single tipping bucketrain gauge was used to measure gross precipitation at L-T and throughfall at P-low at aheight of 2 m. At each of B-Sa, P-Sa and P-tall, throughfall was measured with two 2.44m long troughs that collected throughfall and funneled it into the same type of tippingbucket rain gauge at 2 m (Figure A2.1. Troughs were V-shaped (angle 47∘) with 10 cmtall sides and the open (i.e., top) side was 8.0 cm in width, providing a collection areaof 0.2 m2 per trough. Gross precipitation at B-Sa, P-Sa, P-tall and P-low was estimatedfrom tipping bucket rain gauge measurements at a height of 1 m at a nearby clearing. Airtemperature, relative humidity (10 second scan interval) and wind speed were stored as30-minute averages and gross precipitation or throughfall were stored as 30-minute totals.Volumetric peat moisture content was measured at 10, 20, 30 and 40 cm depth belowthe peat surface using a capacitance sensor that took a reading every 30 minutes in a13hollow microtopography near the micrometeorological station. Weather and peat moisturemeasurements were collected between May, 2018 and October, 2019.2.2.2. Groundwater wells and piezometersAt Wb-S, groundwater monitoring was conducted in a central well consisting of a PVCpipe (inner diameter 9.7 cm) that was slotted along the entire length with a slot size of0.254 mm. It was installed to a depth of approximately 100 cm, depending on peat surfaceelevation. Smaller wells made of PVC pipe (inner diameter 2.6 cm) were installed in fourperipheral locations, where they reached between 50 and 60 cm depth depending on locationand peat surface elevation. They were slotted along the entire length with a slot size ofapproximately 0.5 mm. At the remaining field sites, all wells were constructed of PVC pipe(inner diameter 3.5 cm) that was slotted along the entire length with a slot size of 0.254 mm.They were installed such that they reached a depth of approximately 150 cm, dependingon peat surface elevation, site, and location.At Wb-S, piezometers were constructed from PVC pipe (inner diameter 2.6 cm) that wasslotted along a length of 50 cm with a slot size of 0.254 mm. At the remaining sites,piezometers made of PVC pipe (inner diameter 3.5 cm) were slotted along a length of 40.8cm with the exception of L-T where the screen length was only 27.2 cm. The slot size was0.254 mm for all piezometers.At each field site, groundwater monitoring was conducted in five locations that were ar-ranged in a cross centered on the central location (Table 2.2). At Wb-S, each locationconsisted of a nest that combined a well with three piezometers (Table 2.4). At the re-maining sites the central location consisted of a well and one piezometer (Table 2.4); at theremaining four locations a well was installed, only. At Wb-S, each nest was anchored in theunderlying mineral sediment by driving a rebar that was connected to the deepest piezome-ter 40 cm into the underlying sediment. At the remaining sites the rebar was attached tothe piezometer at the central nest, or directly to the well at the remaining four locations.Deep piezometers were installed such that the bottom of the screen was located at least 50cm above the underlying silty-clay sediment to prevent interference. In nests, all additionalpipes were securely connected to the anchored piezometer prior to installation to preventvertical movement from peat volume changes. Tops of the anchored pipes were surveyedusing GNSS with an accuracy of at least ± 3 mm; the elevation of the remaining pipe topsin nests was corrected using a tape measure. After installation, all pipes were purged atleast three times to remove any debris that clogged the screens.Water table elevation has been monitored continuously in the central well of each site. AtWb-S, water table elevation was monitored by a pressure transducer since August 2015.Measurements were taken every 10 s and averages were stored every 30 minutes. At theremaining sites, water table elevation was measured by a capacitance water level logger be-tween July 2018 through October 2019. Measurements were taken every 10 s and averageswere stored every ten minutes. In addition to continuous observations, manual measure-ments of water levels in all wells and piezometer were taken approximately every two weeksusing a water level meter (Heron, dipper T). Manual measurements were taken between14Table 2.2.: Geographic coordinates of the central well at each site and distances from thecentral well to wells or piezometers installed in peripheral locations. 𝐶𝑙𝑜𝑛 and𝐶𝑙𝑎𝑡 = longitude and latitude of central well in decimal degrees, 𝑑𝑁 , 𝑑𝐸, 𝑑𝑆 and𝑑𝑊 = distance of northern, eastern, southern and western peripheral locationfrom central well in m.Site 𝐶𝑙𝑜𝑛 𝐶𝑙𝑎𝑡 𝑑𝑁 𝑑𝐸 𝑑𝑆 𝑑𝑊B-Sa -122.9376669 49.1185563 14.85 15.31 14.44 14.51P-Sa -123.0222790 49.1075569 14.06 15.07 14.87 15.15P-tall -122.9591918 49.1098046 14.15 14.85 14.77 16.24P-low -122.9608452 49.1094194 15.16 15.38 13.65 15.72L-T -123.0122161 49.1258199 14.60 14.52 14.84 15.23Wb-S -122.9849444 49.1293107 24.32 29.73 15.95 17.96Table 2.3.: Groundwater monitoring setup at BB1 Wb-S site. Summary of installationdepths of relevant piezometers. 𝑝𝑠 and 𝑝𝑑 = shallow and deep piezometer mid-screen distance from pipe top (m), 𝑝𝑠,𝑚𝑎𝑠𝑙 and 𝑝𝑑,𝑚𝑎𝑠𝑙 = shallow and deeppiezometer mid-screen elevation above sea level (m a.s.l.). Screen lengths of allpiezometers were 50.0 cm.Location 𝑝𝑠 𝑝𝑠,𝑚𝑎𝑠𝑙 𝑝𝑑 𝑝𝑑,𝑚𝑎𝑠𝑙C 1.45 2.67 4.65 -0.22N 1.45 2.74 3.77 0.61E 1.45 2.47 4.71 -0.58S 1.20 2.83 4.79 -0.50W 1.45 2.06 3.52 0.35February 2016 and October 2019 at Wb-S; at the remaining sites manual measurementswere taken between May 2018 through May 2019.2.2.3. Peat surface elevationPeat surface elevation was monitored manually by measuring the distance from the geo-referenced static pipe tops to the peat surface with a tape measure. At each site, observa-tions were taken for every pipe (i.e. more than one at nests) and averaged for each location,yielding a total of five observations per site and trip. Peat surface elevation was measuredevery two weeks between May 2018 through May 2019 at all sites except Wb-S where it wasmeasured every two weeks between May 2016 and October 2019. In addition to manual ob-servations at Wb-S, peat surface elevation was monitored continuously using an ultrasonicdistance sensor since December 2018. It was scanned every 10 s and averages were storedevery 30 minutes.15Table 2.4.: Groundwater monitoring setup at sites other than Wb-S. 𝑝𝑑 = piezometer mid-screen distance from pipe top (m), and 𝑝𝑑,𝑚𝑎𝑠𝑙 = piezometer mid-screen eleva-tion above sea level (m a.s.l.). Screen lengths were 40.8 cm at all sites, exceptfor L-T where it was only 27.2 cm.B-Sa P-Sa P-tall P-low L-T𝑝𝑑 3.29 3.28 3.27 3.28 1.70𝑝𝑑,𝑚𝑎𝑠𝑙 0.12 -1.30 0.82 0.95 1.022.2.4. Additional observationsIn addition to the measurements presented above a range of other ecohydrological mea-surements were conducted, including i) throughfall and stemflow observations (Chapter 5),sap flow observations (Chapter 6), and lysimeter measurements (Chapter 7), which arestudy-specific and detailed in the corresponding chapters.16Table 2.5.: Instrumentation at BB1 flux tower site with sensor accuracies. Sensible and latent heat fluxes were computed frommeasurements made by the CO2 and H2O eddy covariance systems. CSI = Campbell Scientific Inc.Instrument Variable Unit AccuracySonic anemometer-thermometer Wind direction ∘ ±0.7∘ at 1 m s -1(CSAT-3, CSI) 3-D wind speed 𝑣𝑥, 𝑣𝑦, 𝑣𝑧, m s -1±8.0 cm s-1 horiz.±4.0 cm s-1 vert.CO2/H2O open-path infrared gas analyzer(IRGA, LI-7500, LI-COR Inc.) until June 2015 vapor density 𝜌𝑣 and CO2 density 𝜌𝑐 mmol m-3 2% of readingCO2/H2O closed-path analyzer(LI-7200, LI-COR Inc.) since June 2015 vapor density 𝜌𝑣 and CO2 density 𝜌𝑐 mmol m-3 1% of readingCH4 open-path analyzer(I-7700, LI-COR Inc.) since June 2015 CH4 density 𝜌𝑚 mmol m-3 2% of readingFour-component net radiometer Incoming shortwave radiation 𝐾↓ W m-2 10%(CNR1, Kipp and Zonen) Incoming longwave radiation 𝐿↓ W m-2 10%Outgoing shortwave radiation 𝐾↑ W m-2 10%Outgoing longwave radiation 𝐿↑ W m-2 10%Quantum sensors (PAR) (LI-190, LI-COR Inc.)since June 2015 Photosyntetically active radiation W m-2 5%Tipping bucket rain gauge (TR-525M, Texas Electronics) Precipitation 𝑃 mm 1%Air temperature and humidity (HMP-35 A, Vaisala) Air temperature 𝑇𝑎Humidity 𝑅𝐻∘C%0.4 ∘C2%Wind speed cup anemometer / Wind Vane (RM Young) Wind speed 𝑣 and wind direction m s -1, ∘ ±0.5 m s-1, ±5∘Soil temperature thermocouples (Type T) Soil temperature at -5, -10 and -50 cm,𝑇𝑠,5, 𝑇𝑠,10, 𝑇𝑠,50∘C ± 1.0∘CGround heat flux plates (Middleton) Ground heat flux at -5 cm 𝑄𝐺 W m-2 ±5%Soil moisture probe (CS616, CSI) Volumetric water content 𝜃 % ±2.5%Pressure transducer (KPSI Series 169/173, CSI) Water table elevation 𝑊𝑇 mm ±0.25%Ultrasonic Distance Sensor (SR50-L, CSI) Peat surface elevation 𝑧𝑝 mm ±1 cm17Table 2.6.: Instrumentation of weather stations at field sites other than BB1 with sensoraccuracies. CSI = Campbell Scientific Inc.Instrument Variable Unit AccuracyAir temperature and humidity (HC2S3, CSI) Air temperature 𝑇𝑎Humidity 𝑅𝐻∘C%0.1 ∘C0.8%Tipping bucket rain gauge (Rainew, RainWise) Rainfall 𝑃 mm 5%Cup anenometer (014A Met One, CSI) Wind speed 𝑣 m s -1 1.5%Soil moisture sensor (PR2-4, Delta-T Devices) Moisture content 𝜃 m-3 m-3 4%Water level sensor (Odyssey, Dataflow Systems) Water table 𝑊𝑇 % 0.8 mm183. Daily, seasonal and interannual variabilityof energy balance and evapotranspirationat an disturbed and rewetted whitebeakrush-Sphagnum site3.1. IntroductionEvapotranspiration (𝐸) is the primary water loss process during the growing season inmany types of peatlands (e.g. Van Seters and Price, 2001; Lafleur et al., 2005; Brownet al., 2010). A dominant control on evapotranspiration is the atmospheric conditionsthat determine potential evapotranspiration rates, in particular net radiation and vapourpressure deficit (e.g. Lafleur et al., 2005; Sottocornola and Kiely, 2010; Peichl et al., 2013;Hirano et al., 2016). In ombrotrophic bogs, bryophytes cannot regulate water loss dueto the lack of stomatal control in Sphagnum (Williams and Flanagan, 1996; Brümmer etal., 2012). However, actual evapotranspiration in peatlands is usually less than potentialevapotranspiration (e.g. Price, 1991; Lafleur and Roulet, 1992; Kim and Verma, 1996;Admiral et al., 2006; Humphreys et al., 2006; Brown et al., 2010; Wu et al., 2010), indicatingsome level of control by surface moisture availability as the water table drops below thesurface. Although surface moisture supply can become limiting as the water table drops,relating moisture supply to water table depth is complicated by the nonlinear and hystereticnature of the soil moisture characteristic curve (Price and Schlotzhauer, 1999; Kellner andHalldin, 2002; Schouwenaars and Gosen, 2007). As a consequence, water table depth isnot always a reliable indicator of moisture availability at the peat surface (Price, 1997). Inaddition, even when the water table has been drawn down, rain events can produce short-term increases in near-surface peat moisture content and thus moisture availability.It is widely accepted that peatlands are sensitive to climate change (e.g. Roulet et al., 1992;Tarnocai and Stolbovoy, 2006), and that climatic warming will increase evapotranspirationloss from Sphagnum-dominated peatlands (Helbig et al., 2020). Increased evapotranspira-tion could drive greater and more persistent water table drawdown that could ultimatelyresult in establishment of vascular plants, which could further increase evapotranspirationand water table drawdown (Howie et al., 2020). However, by limiting surface moistureavailability, water table drawdown could produce a negative feedback on water loss by evap-otranspiration (Waddington et al., 2015) and thus enhance the resilience of a peatland’secohydrological functioning to climatic variability and change. Therefore, assessment ofthe climatic sensitivity of a peatland requires an understanding of the factors that control19peatland evapotranspiration and their interactions across daily to seasonal to interannualtime scales.Table 4.1 summarizes a selection of studies focusing on evapotranspiration and its controlsin Sphagnum-dominated peatlands with little vascular vegetation cover (leaf area index< ~1.5). As seen in the table, the existing body of literature on evapotranspiration fromSphagnum-dominated peatlands is largely biased towards summertime or snow-free observa-tions (e.g. Sonnentag et al., 2010) over one or two years in continental settings. A notableexception is Sottocornola and Kiely (2010), who reported year-round evapotranspirationfrom a relatively undisturbed blanket bog in a maritime setting in Ireland over the courseof five years. Past studies have shown that non-growing-season evapotranspiration loss canbe a significant component of the water balance in continental (Lafleur et al., 2005) andmaritime (Sottocornola and Kiely, 2010) settings; however, no studies reported interan-nual variability of year-round evapotranspiration loss from a domed ombrotrophic bog in amaritime setting.The objective of this study was to document the magnitude and variability of evapotran-spiration from a peatland in the Pacific Northwest of North America, which experiences aMediterranean climate with cool, wet winters, during which the water table remains nearor at the peat surface, and warm, dry summers, during which the water table progressivelydrops below the surface (Howie et al., 2020). Climate change is projected to result inwarmer, drier summers in the coming decades, and possibly an earlier shift from winter-wetto summer-dry conditions and thus an extended period of water table drawdown (Moteand Salathé, 2010; Wuebbles et al., 2017). There is a dearth of process-based researchon peatland ecohydrology in the region, which limits our understanding of sensitivity toclimate change and the implications for peatland restoration.The primary objective was to quantify the relative roles of available energy, atmosphericdemand, and surface moisture availability as controls on evapotranspiration on daily, sea-sonal and interannual time scales. In addition, we examined the hypothesis that an earliershift from net gain (i.e. 𝑃 > 𝐸) to net loss (i.e. 𝑃 < 𝐸) in spring is an important control ontotal seasonal evapotranspiration loss that would result in greater water table drawdown.This study focused on a disturbed and rewetted Sphagnum-dominated open site in BurnsBog, an ombrotrophic bog in southwestern British Columbia, Canada (Howie et al., 2009a),which is located near the southern extent of raised bogs in western North America (Rocchioet al., 2021). Burns Bog was recognized as a globally significant wetland under the RamsarConvention in 2012. The study draws upon over six years of year-round data collected ata flux tower site.20Table 3.1.: Non-exhaustive summary of evapotranspiration studies in Sphagnum-dominated peatlands. Peatland types are pf =poor fen, bm = boreal mire, s-p = spruce peatland, mf = moderately rich fen, sp b = Sphagnum-dominated open bog,f and b = fen and bog, ms = mixed mire system, pf = poor fen, rf = rich fen, bb = blanket bog, b = bog. Climatesare shown as Köppen classification, where Dfb = continental humid warm summer, Dfc = continental sub-arctic coldsummer, Cfb = temperate oceanic, and Cfc = subpolar oceanic. Potential evapotranspiration models are 𝐸𝑜 = Penmanopen water evaporation, 𝐸𝑒𝑞 = equilibrium evapotranspiration, and 𝐸𝑝𝑚 = Penman-Monteith evapotranspiration.Location Type Climate Vegetation ET ReferenceUSA pf Dfb Sphagnum and shrub, LAI 0.4 to 0.7 1.0 𝐸𝑜 Kim and Verna (1996)Canada pf Dfb Sphagnum 0.73 𝐸𝑜 Lafleur and Roulet (1992)Finland bm Dfc Sphagnum, scattered dwarf pine 0.58 to 0.68 𝐸𝑝𝑚 Wu et al. (2010)Canada s-p Dfc Feathermoss, 80% tree canopy 0.77 to 0.87 𝐸𝑒𝑞 Brown et al. (2010)Canada s-p Dfc Sphagnum, 35% tree canopy 0.77 to 0.87 𝐸𝑒𝑞 Brown et al. (2010)Canada s-p Dfc Sphagnum and lichen, 30% tree canopy 0.78 to 0.94 𝐸𝑒𝑞 Brown et al. (2010)Canada mf Dfc Moss, sedge, tree LAI 0.38 to 1.3, shrub LAI 1.6 0.79 to 1.04 𝐸𝑒𝑞 Sonnentag et al. (2010)Japan sp b Dfb Sphagnum, shrub LAI 1.3 Hirano et al. (2016)Russia f and b Dfc Sphagnum, sedge, shrub LAI 0 to 1.5 0.62 𝐸𝑝𝑚 Runkle et al. (2014)Sweden ms Dfc Sphagnum, sedge, heather 0.86 to 1.17 𝐸𝑒𝑞 Peichl et al. (2013)Canada pf Dfc Sphagnum, shrub, LAI 0.5 1.05 𝐸𝑒𝑞 Humphreys et al. (2006)Canada rf Dfb/Dfc Moss, shrub LAI 0.43 0.88 𝐸𝑒𝑞 Humphreys et al. (2006)Canada rf Dfc Moss, shrub LAI 0.43 0.88 𝐸𝑒𝑞 Humphreys et al. (2006)Ireland bb Cfb Bare 50%, moss 25%, grass/heather 25% 0.42 𝐸𝑜 Sottocornola and Kiely (2010)Sweden b Dfb Moss, shrub LAI 0.3 0.71 to 0.85 𝐸𝑒𝑞 Kellner (2001)Canada b Dfb 0.44 to 0.59 𝐸𝑝𝑚 Lafleur et al. (2005)213.2. Methods3.2.1. Eddy-covariance data processingBecause the BB1-tower was powered by solar panels, days of short daylight and overcastskies occasionally necessitated shut-down of eddy-covariance instruments to preserve power.During these periods, meteorological sensors were normally still operating. On four occa-sions (21 days over more than six years) the entire system was shut off. During these peri-ods, meteorological variables were gap-filled using linear regression models with hourly dataavailable from a weather station operated by Environment and Climate Change Canada onthe northwestern edge of the bog, approximately 1.3 km from the flux tower. Metrics of fitfor these gap-filling relations are provided in the appendix (Table A1.1).Turbulent fluxes were calculated by applying a double coordinate rotation, spike removal,block averaging, and time-lag removal by covariance maximization (Moncrieff et al., 1997).A correction for the effect of air density fluctuations on turbulent fluxes was applied (Webbet al., 1980). Latent heat fluxes were removed when the infrared signal strength was less than80% (LICOR, 2016) and during periods of insufficient turbulence (low friction velocity, 𝑢∗),which were identified using a moving point test in the REddyProc R package framework(Papale et al., 2006; Wutzler et al., 2018). Gap-filling was based on a look-up table method(Reichstein et al., 2005; Wutzler et al., 2018). Latent and sensible heat fluxes were correctedto achieve energy balance closure by means of the Bowen-ratio approach (Twine et al.,2000). This correction has been shown to produce evapotranspiration estimates that agreewell with independent automated lysimeter observations (Gebler et al., 2015; Hirschi et al.,2017; Mauder et al., 2018).Lee et al. (2017) and Lee et al. (2021) found that at least 80% of the cumulative fluxesduring the day (6 am to 6 pm) originated from within 200 m of the tower mainly to the south,and observations from the north where the surface was covered by different vegetation werefiltered out. Vegetation, disturbance history and microtopography (checked with LiDAR)in the footprint area were similar to those in the water balance control volume (see Chapter4), supporting the use of the eddy-covariance fluxes in the water balance calculations. Thefootprint analysis can be found in the supplementary material of Lee et al. (2017) or in themain body of Lee et al. (2021).3.2.2. CalculationsAll data processing and analysis described below were conducted using the R programminglanguage.Energy and radiation balanceThe energy balance can be expressed as follows:22𝑄∗ = 𝑄𝐻 +𝑄𝐸 +𝑄𝐺 (3.1)where 𝑄∗ = net radiation (W m-2), 𝑄𝐻 = the sensible heat flux (W m-2), 𝑄𝐸 = thelatent heat flux associated with evapotranspiration/condensation (W m-2), and 𝑄𝐺 = theconductive heat flux into the soil (W m-2). All terms on the right-hand side are positive ifenergy is being transported away from the surface. The formulation of Eq. 3.1 neglects aircolumn heat storage and photosynthesis, as well as removal/addition of heat by lateral fluxdivergence associated with groundwater and surface water flow, as well as latent energyassociated with soil freezing/thawing and snowmelt. However, these should be negligibleduring the growing season, when the water table is below the surface, considering themaritime climate and the weak hydraulic gradients driving groundwater flow. Heat storagein ponding water is also not represented in Eq. 3.1, but this term would only be importantin the shoulder and winter seasons, when all fluxes, including 𝑄𝐸, would be small relativeto the growing season.Net radiation comprises both shortwave (solar) and longwave (terrestrial) radiative ex-changes:𝑄∗ = 𝐾↓(1 − 𝛼) + 𝐿↓ − 𝐿↑ (3.2)where 𝐾↓ = shortwave irradiance (incident solar radiation in W m-2)), 𝛼 = the albedo(representing the fraction of shortwave irradiance that is reflected), 𝐿↓ = incident longwaveradiation (W m-2) emitted by the atmosphere, and 𝐿↑ = the longwave radiation (W m-2)emitted and reflected by the bog’s water and soil surface.All radiative fluxes on the right-hand side of Eq. 3.2 were measured directly using a four-component net radiation sensor and turbulent fluxes (𝑄𝐻 and𝑄𝐸) in Eq. 3.1 were measuredby the eddy-covariance system. Ground heat flux was computed using a combined heat fluxplate and calorimetry approach (Liebethal et al., 2005) as follows:𝑄𝐺 = 𝑄ℎ𝑓𝑝 +Δ𝐻𝑔Δ𝑡 (3.3)where 𝑄ℎ𝑓𝑝 = average heat flux measured by heat flux plates at a depth of 5 cm, and 𝐻𝑔= heat storage between the heat flux plates and the peat surface. Heat flux calculationswere based on 30-minute averages. The heat storage was computed as:𝐻𝑔 = (𝜃 ⋅ 𝑐𝑤 + 𝑉𝑜 ⋅ 𝑐𝑜 + (1 − 𝑉𝑜 − 𝜃) ⋅ 𝑐𝑎) ⋅ 𝑇𝑠,5 ⋅ Δ𝑧 (3.4)where 𝜃 = volumetric soil moisture (m3 m-3), 𝑐𝑤 = specific heat capacity of water (4180 MJm-3 K-1), 𝑉𝑜 = volumetric organic matter content (m3 m-3) here estimated as 𝑉𝑜 = 1− 𝜃99,where 𝜃99 = 99th percentile of observed 𝜃 (m3 m-3), 𝑐𝑜 = specific heat capacity of peat(3140 MJ m-3 K-1) (Lal, 2005), 𝑐𝑎 = specific heat capacity of air (1006 MJ m-3 K-1), 𝑇𝑠,5= soil temperature at a depth of 5 cm (∘C), and Δ𝑧 = effective soil depth, here 0.05 m.23Application of Eqs. 3.3 and 3.4 implicitly assumed that changes in temperature do not varybetween the surface and 5 cm depth.Equilibrium evapotranspirationEquilibrium evapotranspiration (𝐸𝑒𝑞, m s-1) refers to evapotranspiration from a freely evap-orating surface when the ambient air is saturated (Priestley and Taylor, 1972). It wascalculated as follows:𝐸𝑒𝑞 =1𝐿𝑣 ⋅ 𝜌𝐻2𝑂𝑠𝑠 + 𝛾 ⋅ (𝑄∗ −𝑄𝐺) (3.5)where 𝐿𝑣 = latent heat of vaporization (J kg-1), which was computed as a function of airtemperature, 𝜌𝐻2𝑂 = density of water, here 1000 kg m-3, 𝑠 = slope of saturation vapour pres-sure curve (kPa ∘C-1), 𝛾 = psychometric constant (kPa ∘C-1), 𝑄∗ = net radiation (W m-2)and 𝑄𝐺 = ground heat flux at the peat surface (W m-2). It is commonly found that poten-tial evapotranspiration is somewhat higher than 𝐸𝑒𝑞 because horizontal advection and/orvertical down-mixing typically maintain unsaturated conditions, even over extensive wetsurfaces. For example, Priestley and Taylor (1972) reported that potential evapotranspira-tion averaged 1.26 times 𝐸𝑒𝑞.A number of peatland hydrology studies have derived site-specific values of the Priestley-Taylor 𝛼𝑃𝑇 coefficient, defined as𝛼𝑃𝑇 =𝐸𝐸𝑒𝑞(3.6)where 𝐸 is actual evapotranspiration (m s-1) (e.g. Humphreys et al., 2006; Brown et al.,2010; Peichl et al., 2013). In this study, calculated values of seasonal 𝛼𝑃𝑇 were used tocompare moisture availability as a control on evapotranspiration between years.Multiple linear regression model of evapotranspirationTo explore the controls on evapotranspiration, a multiple regression model of the followingform was fit to daily data for the months of April through September:𝐸𝑜𝑏𝑠 = 𝑏0 + 𝑏1𝐸𝑒𝑞 + 𝑏2𝑉 𝑃𝐷 + 𝑏3𝑊𝑇𝐷+ 𝑏4𝐴𝑃𝐼 + 𝑒 (3.7)where 𝐸𝑜𝑏𝑠 = observed evapotranspiration (mm day-1), 𝐸𝑒𝑞 = equilibrium evapotranspira-tion (mm day-1), 𝑉 𝑃𝐷 = vapour pressure deficit (kPa), 𝑊𝑇𝐷 = water table depth belowthe wintertime peat surface, i.e. positive values indicate water table elevations below thepeat surface elevation (m), 𝑒 = residual (mm day-1), and 𝐴𝑃𝐼 is an antecedent precipitationindex that was computed as:24𝐴𝑃𝐼𝑡 = 𝑃𝑡 + 𝑘 ⋅ 𝐴𝑃𝐼𝑡−1 (3.8)where 𝐴𝑃𝐼𝑡 and 𝐴𝑃𝐼𝑡−1 = antecedent precipitation index (mm) at day 𝑡 and 𝑡 − 1, 𝑃𝑡= precipitation (mm) at day 𝑡, and 𝑘 is a parameter that lies between 0 and 1, whichdetermines the persistence of the effect of rainfall. The value of 𝑘 was determined byvarying it from 0.001 to 0.95, with an interval of 0.001, and using the value of 𝑘 thatgenerated the lowest value of Akaike’s Information Criterion when fitting Eq. 3.7.The fit of the regression model was evaluated by plotting observed versus predicted valuesand boxplots of residuals by month, and by generating partial residual plots (also calledcomponent + residual plots), which can be useful for identifying nonlinearity (Fox andWeisberg, 2018). A partial residual plot is generated by plotting 𝑒 + 𝑏𝑗𝑥𝑗 versus 𝑥𝑗, where𝑥𝑗 is the 𝑗-th predictor variable and 𝑏𝑗 is the regression coefficient for that variable.In addition, variance inflation factors were computed to assess multi-collinearity among thepredictor variables. A value greater than 10 is often taken to indicate that multi-collinearitymay have an undue influence on the estimated coefficients (Neter et al., 1996).Shift detection in net water exchange between atmosphere and peat surfaceWater exchange between atmosphere and peat surface occurs as precipitation (gain) andevapotranspiration (loss). Both fluxes varied seasonally and shifts from net gain to net losswere identified by examining cumulative sums of deviations from the mean, a commonlyused method for detecting shifts in time-series data (Regier et al., 2019). The approachinvolves three steps. First, daily deviations from the annual mean of the difference betweenprecipitation (𝑃 ) and evapotranspiration (𝐸) were calculated as follows:Δ𝑃𝐸𝑖 = 𝑝𝑒𝑖 − 𝑝𝑒 (3.9)where Δ𝑃𝐸𝑖 = daily deviation from the annual mean (mm), 𝑝𝑒𝑖 = difference betweenprecipitation and evapotranspiration on day 𝑖 (mm), and 𝑝𝑒 = annual mean of the differencebetween precipitation and evapotranspiration (mm). Second, the cumulative sum (𝑆𝑡) wascalculated as:𝑆𝑡 =𝑡∑𝑖=1Δ𝑃𝐸𝑖 (3.10)Third, dates on which maximum and minimum of ∑Δ𝑃𝐸 occurred were identified, asthese days indicate the shifts from net gain to net loss and vice versa, respectively.253.3. Results3.3.1. Interannual variability of meteorology and water table depthThe majority of annual precipitation fell between October and April each year, typicallyexceeding 100 mm per month, while summer precipitation was typically below 50 mmper month (Figure 3.1). Precipitation generally exceeded evapotranspiration in winter andduring the shoulder seasons in fall and spring (Figure 3.1), but evapotranspiration exceededprecipitation for four or more months each year.The shift in 𝑃 – 𝐸 from net loss in summer to net gain in winter typically occurred betweenearly September and early October; however, the shift from net gain to net loss in spring wasless consistent and ranged from mid-February in 2019 to mid-May in 2017 (Table 3.2). Thewater table rose to the peat surface in winter and usually dropped to 0.25 to 0.3 m belowthe wintertime peat surface elevation each summer. The most extreme water table dropoccurred in 2015, when the water table dropped 0.35 m below the peat surface. Between2015 and 2020, the maximum seasonal water table drawdown appears to correlate withinterannual variations in 𝑉 𝑃𝐷.Table 3.2.: Dates of shifts in net water exchange with the atmosphere as calculated fromdaily observations (Eq. 3.9). Shift𝑑𝑟𝑦 = date when net vertical water exchangebecame negative in spring, and Shift𝑤𝑒𝑡 = date when net vertical water exchangewith the atsmophere became positive in fall.Shift𝑑𝑟𝑦 Shift𝑤𝑒𝑡Mar 29, 2015 Oct 06, 2015Mar 13, 2016 Oct 05, 2016May 16, 2017 Oct 09, 2017Apr 16, 2018 Sep 08, 2018Feb 19, 2019 Sep 08, 2019Mar 02, 2020 Sep 22, 2020Table 3.3.: Summary of meteorological variables calculated over the growing season (April1 through September 30) and non-growing season (October 1 through March31). 𝑃 , 𝐸 and 𝐸𝑒𝑞 = totals of precipitation, actual evapotranspiration and equi-librium evapotranspiration (mm) respectively, 𝛼𝑃𝑇 = mean Priestley-Taylor’salpha, 𝛽 = mean Bowen ratio, 𝑇𝑎 = mean air temperature (∘C), 𝑒𝑎, 𝑒𝑠 and 𝑉 𝑃𝐷= mean vapour pressure, saturation vapour pressure and vapour pressure deficit(kPa), and 𝑄∗ = cumulative net radiation (MJ m-2).Period 𝑃 𝐸 𝑃 − 𝐸 𝐸𝑒𝑞 𝛼𝑃𝑇 𝛽 𝑇𝑎 𝑒𝑎 𝑒𝑠 𝑉 𝑃𝐷 𝑄∗2015 288 485 -197 572 0.85 0.85 15.03 1.41 1.81 0.48 2223.02016 388 376 12 579 0.65 1.33 14.89 1.40 1.77 0.44 2245.426Period 𝑃 𝐸 𝑃 − 𝐸 𝐸𝑒𝑞 𝛼𝑃𝑇 𝛽 𝑇𝑎 𝑒𝑎 𝑒𝑠 𝑉 𝑃𝐷 𝑄∗2017 429 441 -12 575 0.77 0.96 14.37 1.39 1.73 0.42 2242.72018 384 455 -71 600 0.76 0.96 14.44 1.42 1.75 0.42 2282.72019 453 364 89 575 0.63 1.42 14.31 1.41 1.72 0.37 2240.12020 410 356 54 574 0.62 1.45 14.39 1.42 1.72 0.35 2225.82015-2016 781 95 687 67 1.40 0.24 5.94 0.84 0.96 0.14 288.92016-2017 798 61 737 46 1.33 0.27 4.17 0.74 0.87 0.12 179.52017-2018 804 63 741 61 1.03 0.76 4.86 0.78 0.89 0.12 277.32018-2019 616 79 537 84 0.94 1.02 4.76 0.77 0.90 0.14 350.32019-2020 727 74 653 79 0.95 1.13 5.06 0.81 0.90 0.10 353.23.3.2. Radiative exchange at the peat surfaceMonthly mean net radiation varied from a minimum near 0 MJ m-2 in winter to a maxi-mum of 500 MJ m-2 in summer (Figures 3.1 and 3.3). Monthly longwave radiation fluxeswere consistently higher than shortwave radiation fluxes, and net longwave radiation wasconsistently negative (Figure 3.3). Interannual variation of net radiation at the peat surfaceduring the growing season varied less than ± 3% of the mean (Table 3.2).Albedo generally ranged from 0.1 to 0.25, except for snowy days when it approached unity(Figure 3.4). Physically unrealistic values in excess of unity occurred at low sun angleswhen uncertainty in the measurements was relatively high. Albedo was lowest in spring,when it was around 0.1, and tended to increase over the growing season to a maximum ofabout 0.14 at the end of summer. In winter, albedo was noisier and fluctuated between 0.1to 0.25, with occasional increases to unity or higher.3.3.3. Energy balance analysisEnergy balance closureMean energy balance closure, expressed as the slope of a linear regression that was forcedthrough the origin and fitted to turbulent fluxes versus available energy, ranged from 0.56to 0.65 on a 30-minute basis. Energy balance closures for the entire study period on a30-minute, daily and monthly basis were 0.59, 0.75 and 0.74, respectively. Significant lackof closure that was associated with under-estimation of turbulent fluxes occurred in everyyear (Table A1.2); however, uncertainty in ground heat flux estimates, heat storage termsand net radiation likely contributed to energy imbalance, too.Seasonal and interannual variability of energy balance componentsTurbulent fluxes were generally low in winter (Figure 3.5). Partitioning of wintertimeturbulent energy fluxes shifted from a dominant latent heat flux in the 2014-2015 winter270100200300P (mm)0306090E (mm)−1000100200300P−E (mm)05101520T a (°C)0100200300400500Q* (MJ m−2 )0.00.20.40.6VPD (kPa)−0.3−0.2−0.10.0Jul2014Jan2015Jul2015Jan2016Jul2016Jan2017Jul2017Jan2018Jul2018Jan2019Jul2019Jan2020Jul2020Jan2021WTD (m)Figure 3.1.: Monthly observations of mean air temperature (𝑇𝑎), precipitation (𝑃 ), evapo-transpiration (𝐸), vapour pressure deficit (𝑉 𝑃𝐷), net radiation (𝑄∗) and watertable depth below the wintertime peat surface elevation (𝑊𝑇𝐷).282015−400−2000200S t (mm)2016−400−2000200S t (mm)2017−400−2000200S t (mm)2018−400−2000200S t (mm)2019−400−2000200S t (mm)2020−400−2000200Jan Mar May Jul Sep NovS t (mm)Figure 3.2.: Time series of cumulative sums of daily anomalies of 𝑃 - 𝐸 for each year (Eq.3.9). Dates of shift are marked by black vertical lines in spring and fall; blueand orange-brown colors depict periods of net gain and net loss, respectively.290300600900Jul2014Jan2015Jul2015Jan2016Jul2016Jan2017Jul2017Jan2018Jul2018Jan2019Jul2019Jan2020Jul2020Jan2021Radiative flux (MJ m−2 )Inc. long Inc. short Net radiation Out. long Out. shortFigure 3.3.: Monthly mean radiative fluxes over time as calculated from 30-minute data.a0.000.250.500.751.002014 2016 2018 2020Albedo (−)b0.050.100.150.200.25Apr May Jun Jul Aug Sep Oct NovAlbedo (−)Year2014201520162017201820192020Figure 3.4.: Peat surface albedo as calculated from daily values over the entire study period(a) and growing season subsets of peat surface albedo by year (b).30towards a dominant sensible heat flux in 2021, which coincided with decreasing wintertime𝑉 𝑃𝐷 over the study period (Table 3.3).Turbulent fluxes were higher in summer than in winter. Latent heat flux dominated in 2015,2017 and 2018, when growing season Bowen ratio (𝛽) ranged from 0.85 to 0.96 (Table 3.3).Sensible heat flux dominated in 2016, 2019 and 2020, when growing season Bowen ratioranged from 1.33 to 1.45. Ground heat flux was directed downwards starting in March andshifted to upwards in September, with no noticeable variability between years.0100200300400500Jul2014Jan2015Jul2015Jan2016Jul2016Jan2017Jul2017Jan2018Jul2018Jan2019Jul2019Jan2020Jul2020Jan2021Energy flux (MJ m−2 )Ground heat flux Latent heat flux Net radiation Sensible heat fluxFigure 3.5.: Monthly energy fluxes between summer 2014 and 2021. Turbulent fluxes weregap-filled and corrected for lack of energy balance closure using the Bowen-ratioapproach.3.3.4. Controls on evapotranspirationDaily 𝐸 was of the same magnitude as 𝐸𝑒𝑞 during inundated conditions in wintertime, whenboth were typically around 0.3 mm day-1. It was lower than 𝐸𝑒𝑞 in summer, which indicateslimited water supply at the bog surface relative to atmospheric demand for water vapour(Figure 3.6). In years when latent heat flux dominated the energy balance, the mean valueof the Priestley-Taylor 𝛼 varied between 0.76 and 0.85; in years dominated by the sensibleheat flux, it varied between 0.62 and 0.65 (Table 3.3).A value of 𝑘 = 0.838 was found to generate the best fit for the multiple regression model(Eqs. 3.7 and 3.8), which was based on 276 observations, yielding an adjusted 𝑅2 of 0.94and residual standard error of 0.3 mm. All variables were significant at 𝛼 = 0.05, and thesigns of the coefficients are physically realistic (Table 3.4). Variance inflation factors wereall less than 3, indicating that multi-collinearity was not severe.31The relation between observed and predicted 𝐸 does not reveal any obvious nonlinearity ornon-constant variance (Figure 3.7). Boxplots of residuals by month suggest the presence ofa weak seasonal pattern, with a tendency to over-predict (negative residuals) in May, Juneand July. The partial residual plots indicate that the relations with 𝐸𝑒𝑞, 𝑉 𝑃𝐷 and 𝐴𝑃𝐼are reasonably linear, but the relation with 𝑊𝑇𝐷 appears to exhibit some nonlinearity(Figure 3.8).The y-axis scales on the partial residual plots indicate that the available energy term, asrepresented by 𝐸𝑒𝑞, accounted for greatest amount of variability in evapotranspiration,followed closely by vapour pressure deficit (Figure 3.8). Water table depth and antecedentprecipitation index, were secondary controls, with 𝐴𝑃𝐼 having the weakest influence. Asseen in Figure 3.8, the relations are reasonably linear for 𝐸𝑒𝑞, 𝑉 𝑃𝐷 and 𝐴𝑃𝐼 . However,the partial residual plot for 𝑊𝑇𝐷 suggests a relatively abrupt drop in evapotranspirationwhen the water table dropped below a depth of about 0.1 m.Interannual variability of growing season evapotranspiration loss was significantly associ-ated with differences in mean vapour pressure deficit (𝑟2 = 0.65, 𝑝-value = 0.05). Therelationships with mean net radiation (𝑟2 = 0.40, 𝑝-value = 0.18) and mean air tempera-ture were not significant (𝑟2 = 0.26, 𝑝-value = 0.31).02460 2 4 6Eeq (mm day−1)E (mm day−1 )JanFebMarAprMayJunJulAugSepOctNovDecFigure 3.6.: Gap-filled daily evapotranspiration adjusted for lack of energy balance closureversus equilibrium evapotranspiration 𝐸𝑒𝑞.Table 3.4.: Fitted model coefficients used to predict evapotranspiration (Eq. 3.7).Variable Coefficient Value 𝑝-valueIntercept 𝑏0 -0.079 0.26𝐸𝑒𝑞 𝑏1 0.515 < 0.00132Variable Coefficient Value 𝑝-value𝑉 𝑃𝐷 𝑏2 1.961 < 0.001𝑊𝑇𝐷 𝑏3 -2.181 < 0.001𝐴𝑃𝐼 𝑏4 0.004 0.023.4. Discussion3.4.1. Albedo and potential influence on evapotranspirationAlbedo exhibited a seasonal increase through the growing season (Figure 3.4). A weakpositive trend of increasing albedo over the course of the summer agreed with observationsfrom sites in boreal (Lafleur, 2008), subarctic (Lafleur et al., 1987) and arctic sites (Kellner,2001; Lloyd et al., 2001). The increasing trend was likely a result of either the continuouslydrying surface or a change of color of Sphagnum experiencing drought stress (Kim andVerma, 1996; Lafleur, 2008). While an increase in albedo as the peat dries could providea negative feedback to evapotranspiration, the magnitude of the effect would be minorat Burns Bog, as the change in albedo, from about 0.10 in April to 0.13 in August andSeptember, would only reduce net radiation by about 3%.In the winter, albedo became noisier when hollows became filled with water, while hum-mocks remained above the water surface. The increase in noise coincided with prolongedrain that caused a rise of water levels that typically occurred in October. Significantlyhigher variability in the winters was caused by periods of snow cover. The effect of higherwintertime albedo on radiative energy available at the peat surface was minimal due toi) the low sun angle that limited incoming solar radiation, and ii) the relative importanceof net longwave radiative loss that was of the same magnitude as net shortwave radiativefluxes in winter.3.4.2. Energy balance closure and uncertainty of evapotranspiration estimatesMean energy balance closure ranged from 0.56 to 0.65 on a 30-minute basis, which isbelow commonly achieved energy balance closure in the range of 0.7 to 0.9 (Twine et al.,2000; Wilson et al., 2002). The issue of energy imbalance in eddy-covariance studies isknown (Mauder et al., 2020) and it tends to be worse in wetlands (Stoy et al., 2013).Temporal aggregation of energy balance closure can provide information on the role heatstorage terms play in surface energy imbalance as storage terms become less relevant overlonger periods (Leuning et al., 2012). In this study, daily (0.75) and monthly (0.74) energybalance closure was better than half-hourly closure (0.59) and aggregated closure valueswere in the range found in other wetlands (0.76 ± 0.13) (Stoy et al., 2013). These resultssuggest that heat storage terms are a significant source of sub-daily energy imbalance thatcould affect wintertime diel energy balance component observations in this study. The33012340 1 2 3 4Epred (mm day1)E obs (mm day−1 )−0.40.00.40.8Apr May Jun Jul Aug SepResiduals (mm day−1 )Figure 3.7.: Observed (𝐸𝑜𝑏𝑠) versus predicted (𝐸𝑝𝑟𝑒𝑑) evapotranspiration from multiple re-gression model (Eq. 3.7) in the top panel and model residuals by month in thebottom panel.34−1 0 1 2 3 4 5 6−2−1012Eeq (mm/d)Comp. + Resid. (mm day−1 )0.0 0.5 1.0 1.5−2−1012VPD (kPa)Comp. + Resid. (mm day−1 )linear fitloess smooth−0.1 0.0 0.1 0.2 0.3 0.4−1.0−0.50.00.51.0Water table depth (m)Comp. + Resid. (mm day−1 )0 10 20 30 40 50 60−1.0−0.50.00.51.0API (mm)Comp. + Resid. (mm day−1 )Figure 3.8.: Partial residual plots (also know as Component + Residual plots) associatedwith the multiple regression model (Eq. 3.7) used to predict evapotranspiration.The dashed line shows the contribution of the variable to the predicted value,𝑏𝑗𝑥𝑗, while the solid curve is a loess fit.35effect of uncertainty on aggregated values over roughly two-week time intervals betweenfield visits of the water balance study (see Chapter 4) and on monthly values used tocapture interannual and climate variability in this chapter, should be minor. Anotherpotential cause of energy imbalance is that estimating the ground heat flux in peatlandsis challenging due to microtopography, related site heterogeneity and uncertainty of soilmoisture estimates (Kellner, 2001). Net radiation estimates were likely subject to spatialheterogeneity of the peat surface and a mismatch of source areas between radiation andturbulent fluxes, which would have introduced uncertainty (Schmid and Oke, 1990; Schmid,1997; Foken, 2008; Richardson et al., 2012).Gap-filling is another source of uncertainty. However, gap-filling mainly occurred duringwinter when the system was off due to power supply issues. While the relative error is likelyto be high, the low magnitude of evapotranspiration during winter minimizes the impactof this error on the water balance calculations.Past research suggests that evapotranspiration estimates based on corrected observationsby forced energy balance closure are reliable (Barr et al., 2012; Gebler et al., 2015; Hirschiet al., 2017; Mauder et al., 2018). This assumption is supported by good summertimewater balance closure based on independent observations of precipitation and estimates ofgroundwater storage change and groundwater fluxes (see Chapter 4).3.4.3. Daily and seasonal controls on evapotranspirationThe multiple regression analysis revealed that the primary drivers of daily evapotranspira-tion were available energy and atmospheric demand, the latter expressed as vapour pressuredeficit (Figure 3.8). The near linear relationship between evapotranspiration and vapourpressure deficit suggested minimal stomatal control as would occur from vascular vegeta-tion, for which stomatal closure in response to increases in 𝑉 𝑃𝐷 produces a concave-downrelation. Surface moisture availability, as represented by water table depth and antecedentprecipitation, was secondary, which agrees with findings from a number of peatland typesin maritime (Sottocornola and Kiely, 2010) and continental settings (e.g. Kellner, 2001;Kurbatova et al., 2002; Wu et al., 2010; Runkle et al., 2014). Partial residuals of evapo-transpiration and water table depth indicate that there appears to have been a reductionin evapotranspiration when the water table dropped to more than 0.1 m below the winterpeat surface, suggesting an increased limit of moisture supply to the surface via capillaryrise from the water table. According to optimized antecedent precipitation index (𝐴𝑃𝐼),the effect of added peat moisture from precipitation events in summer lasted about 20 days(i.e. on day 18, 5% of initial moisture remained, Eq. 3.8), and had a weak positive effecton evapotranspiration.It should be noted that regression analysis involving daily time series often violates the as-sumption of independence of the error terms, which means that computed 𝑝-values may bebiased low. However, the parameter estimates remain unbiased in the presence of autocor-related error terms, and the variance inflation factors indicate that multi-collinearity is nota major concern. Therefore, we argue that, despite the presence of residual autocorrelation,36the fitted model provides robust estimates of the dependence of evapotranspiration on thepredictor variables.Groundwater moisture availability at the peat surface depends on physical properties ofthe peat, especially during periods of low water table elevations when peat water tensionand pore size distribution control water content and unsaturated hydraulic conductivity(Price, 1997; McCarter and Price, 2014; McCarter and Price, 2015; Golubev et al., 2021).Lower moisture content (Kellner, 2001; Kellner and Halldin, 2002) and higher bulk surfaceresistance (Admiral and Lafleur, 2007a, 2007b) of hummocks are likely reasons for moisturelimitation of evapotranspiration in the summer once water tables dropped below 0.1-0.2m.Across all years, timing of the shift in atmospheric water exchange towards net gain con-sistently occurred between early September and early October in association with onset ofthe autumn-winter rainy season. Timing of the shift from net gain to net loss in spring wasless consistent (Table 3.2 and Figure 3.2). Preliminary analysis suggests that timing of theshift in spring is not related to large scale climatic oscillations such as the Pacific DecadalOscillation or the Oceanic Niño Index (Figure A1.1). Through each growing season, the in-crease in water table depth and increase in albedo could have provided a negative feedbackon water loss by reducing surface moisture availability and net radiation. As mentionedearlier, the increase in albedo would only have decreased solar radiation by 3%, and thushave resulted in only a minor decrease in evapotranspiration. Based on the estimated coef-ficient for 𝑊𝑇𝐷 in the multiple regression model (-2.2 mm day-1 m-1), a drop in the watertable of 0.3 m would result in a mean decrease in evaporation of about 0.7 mm day-1 orabout 22 mm per month, which represents around 59% of the mean evapotranspiration inSeptember, when the deepest water tables typically occur.3.4.4. Interannual variability of evapotranspirationPartitioning of turbulent heat fluxes varied considerably between growing seasons. Differ-ences were primarily associated with interannual variability of vapour pressure deficit andto a lesser extent air temperature and net radiation, consistent with previous studies (Kell-ner, 2001; Lafleur, 2008; Wu et al., 2010; Peichl et al., 2013). Moisture availability at thepeat surface might have acted as secondary control on heat flux partitioning similarly toobservations from a boreal oligotrophic fen in northern Sweden, where the peat surface wascovered by a mix of vascular plants and mosses and where heat flux partitioning varied bywater table depth and precipitation between years (Peichl et al., 2013).An early shift in atmospheric water exchange towards net loss in spring of 2016, 2019and 2020 caused early onset of seasonal water table drawdown (Figures 3.1 and 3.2) thatwould be associated with reduced available moisture at the peat surface and thus act asnegative feedback limiting evapotranspiration (Table 3.3). While one might expect anearlier seasonal shift to net surface water loss to be associated with greater water tabledrawdown, this was not observed. For example, the years with the earliest shift, 2019 and2020, had the smallest water table drawdown. Those two years experienced the lowestseasonal mean 𝑉 𝑃𝐷 and the lowest seasonal total evapotranspiration (Table 3.3).37A reduced seasonal water table drawdown in 2019 and 2020 compared to previous yearsmay reflect the effect of re-wetting that was initiated in 2008 after a major drainage ditch85 m north of the site was blocked. Shallower water levels would have reduced the limitingeffect of available moisture on evaporation that could have caused local vapour pressuredeficit to be lower.To the authors’ knowledge, this study is the first that reports seasonally persistent shifts inthe partitioning of available energy and the dominant role of 𝑉 𝑃𝐷 as a control on the vari-ability of seasonal evapotranspiration over multiple years. Indeed, interannual variabilityin seasonal 𝑉 𝑃𝐷 appeared to override the influence of the timing of the seasonal shift tonet loss of water by surface exchanges. In a climate change context, these results suggestthat changes to summertime vapour pressure deficit will be a critical determinant of thebog’s ecohydrological response.3.5. ConclusionsThis study focused on interannual variability of energy balance components and waterexchange between peat surface and atmosphere at a Sphagnum-dominated site in BurnsBog, a raised ombrotrophic bog in a maritime region of western North America, whereenergy balance dynamics and evapotranspiration have been relatively little studied. Shiftsin atmospheric water exchange (𝑃 − 𝐸) from net loss to net gain consistently occurredbetween early September and early October following the onset of the autumn-winter rainyseason. In spring, timing of the shift was less consistent and varied between mid-Februaryto mid-May. Radiative exchange and albedo of the peat surface were consistent throughoutthe six years of observation. Albedo increased over the growing season and became noisier inwinter when microtopographic depressions became water filled. The increase in summer wassmall and significant, but the negative feedback on available energy for evapotranspirationat the peat surface was minimal.Daily evapotranspiration was primarily driven by available energy (as represented by equi-librium evapotranspiration) and atmospheric demand expressed as vapour pressure deficit.Surface moisture availability was a secondary control that varied by water table depth be-low the wintertime peat surface and to a lesser extent by summertime precipitation. In latesummer, surface moisture supply became more limited when the water table dropped tomore than 0.1 m below the winter peat surface. Interannual variability of April-Septemberevapotranspiration was primarily associated with variations in seasonal mean vapour pres-sure deficit. Atmospheric conditions were not related to large scale climatic patterns suchas the Pacific Decadal Oscillation or the Oceanic Niño Index. Maximum seasonal watertable drawdown appeared to be most strongly associated with vapour pressure deficit overthe years; this influence appeared to override that of the timing of the seasonal shift to netwater loss by surface exchange.384. Quantifying runoff generating processesand lateral flow connectivity at an open,disturbed and rewetted whitebeakrush-Sphagnum site near the bogcenter4.1. IntroductionThe ecohydrological and biogeochemical functions of peatlands are intimately intertwinedthrough dependencies and influences on water table depth (Beer and Blodau, 2007;Waddington et al., 2015; Nelson et al., 2021), and healthy undisturbed peatlands arecharacterized by shallow water tables (Rydin and Jeglum, 2013). In many peatlands,water levels are deliberately altered by management practices such as drainage to lowerwater table levels, as has been associated with peat harvesting, agriculture or forestry(Paavilainen and Päivänen, 1995; Joosten and Clarke, 2002; Holden et al., 2004), orditch blocking, which has been used in restoration efforts to promote higher water tablesin drained peatlands (e.g. Howie et al., 2009b; Holden et al., 2017; Gaffney et al.,2020). Changes in precipitation patterns (Parish et al., 2008; Erwin, 2009) and increasedevapotranspiration loss (Roulet et al., 1992; Gerdol et al., 2008; Waddington et al., 2015)associated with future climatic conditions will likely affect peatland water tables as hasbeen the case during the Holocene (Charman, 2007; Frolking et al., 2010), and bogs willlikely be especially affected as they rely on precipitation as the sole significant source ofwater recharge (Rydin and Jeglum, 2013).Waddington et al. (2015) synthesized the results of peatland ecohydrology studies into aseries of conceptual models describing the positive and negative feedbacks between watertable depth (𝑊𝑇𝐷) and key ecohydrological processes. In the synthesis, distinctions in thedirections and strengths of the various feedback mechanisms were identified for peatlandsin maritime versus continental climatic settings, and also between bogs/poor fens versusrich fens within each climatic zone. The synthesis specifically excluded maritime blanketbogs, although work in these systems has been done in the British Isles (e.g. Holden et al.,2001; Goudarzi et al., 2021). In addition to the contrasts in feedbacks based on climate andpeatland type, the strengths of the feedbacks may vary among or even within peatlands ofa given type. For example, Howie and Hebda (2018) documented substantial variations inthe magnitudes of peat surface elevation oscillation in Burns Bog, British Columbia, whichwere tentatively related to disturbance history and effects of restoration efforts.39In relation to bogs, the empirical basis for the Waddington et al. (2015) synthesis was mostlydrawn from studies in the boreal zone or continental settings, in which spring snowmelt isa key feature of the hydrological regime. In contrast to the numerous studies of boreal andcontinental peatlands, less hydrological research has focused on peatlands in the coastalregions of western North America, which have a rain-dominated maritime climate, despitetheir ecohydrological significance at local, regional and global scales, as discussed in Chapter7 and by Hebda et al. (2000). In the Pacific Northwest, peatland research has focusedon peat formation (Vitt et al., 1990; Asada et al., 2003a, 2003b) and on flow in forest-peatland complexes in the hypermaritime climate near Prince Rupert, British Columbia(Emili et al., 2006; Emili and Price, 2006). Other work has focused on water level andhydrochemistry transitions across bog margins (Howie and van Meerveld, 2011; Howieand van Meerveld, 2012, 2013) and restoration efforts in Burns Bog (Howie et al., 2009b;Howie and van Meerveld, 2018). Howie et al. (2020) documented the variations of a watertable moisture deficit, i.e. the cumulative water table drawdown below the peat surfaceover the growing season, among vegetation types, and statistically quantified the effects ofclimatic variability, ditch blocking and fire disturbance. However, none of this work focusedexplicitly on water table dynamics and the associated ecohydrological feedbacks using aprocess-based framework, which is required for realistic representation in ecohydrologicalmodels used, for example, to simulate impacts of climate change on water table dynamicsor runoff generation.The overarching objective of this study was to document and quantify water table dynamicsand the associated feedbacks with the water balance in a raised ombrotrophic bog in south-western British Columbia, Canada. In addition to quantifying the seasonal and interannualvariability of water balance components, the analysis addressed the following hypotheses,which are based on the synthesis by Waddington et al. (2015): (1) lateral flow will benegatively associated with water table depth, and (2) peat surface elevation changes maymoderate the preceding feedback mechanism.4.2. Methods4.2.1. Site descriptionPeat harvesting at that site had occurred between 1957 and 1963 using the Atkins-Durbrowhydropeat method, which uses high pressure water jets to blast the peat into slurry that waspumped off site for further processing (Hebda et al., 2000). Rewetting was initiated whena major drainage ditch 85 m north of the tower was blocked in 2008 and no Sphagnum wastransferred to help revegetation (Hebda et al., 2000; Christen et al., 2016). The site showsdistinct hummock and hollow microtopography (Figure 4.2) and the average peat depositis 6.1 m deep, based on measurements made during installation of wells and piezometers.40Wb−S0 0.75 1.5 2.25 3 kmNBurns Bog49.1049.1249.1449.16−123.025 −123.000 −122.975 −122.950 −122.925LongitudeLatitudeFigure 4.1.: A satellite image of Burns Bog with the extent of the Burns Bog EcologicalConservancy Area (red line) and the eddy-covariance site (blue square), locatedin a rewetted white beakrush-Sphagnum vegetation community (Wb-S). Theinset shows Burns Bog’s maritime location within the regional geography ofsouth-western British Columbia.41Figure 4.2.: Aerial photograph of eddy-covariance site, located in a rewetted white beakrush-Sphagnum vegetation community (Wb-S). North is indicated by the annotatedblack arrow and 𝑃𝑁 , 𝑃𝐸, 𝑃𝑆 and 𝑃𝐶 denote piezometer nest locations in thenorth, east, south and center (the nest in the east was not captured). Thephotographs were taken in September, 2014, towards the end of the dry season(top) and in March, 2017, toward the end of the winter wet season (bottom).Credit. Parks and Environment, Metro Vancouver.424.2.2. Field and laboratory measurementsInstrumentation of climatic and micrometeorologic sensors along with the monitoring setuprequired for ground heat flux calculation and estimation of energy balance components werepart of the eddy-covariance tower installation that was described in detail in Chapters 2.1and 3. The following sections describe additional instrumentation of the flux tower sitethat was used to calculate hydrologic fluxes and storage changes to estimate componentsof the site’s water balance.Peat hydraulic propertiesSaturated hydraulic conductivity of the acrotelm (𝐾𝑎) was obtained from permeametermeasurements performed on three cylindrical 1.8-L peat cores (diameter 𝑑 = 11.0 cm, heightℎ = 19.0 cm), which were collected from hollows in October 2019. Each core was measuredunder steady-state and constant head flow conditions using two hydraulic gradients, onebeing between 1.5 to 2.0 cm cm-1 and the other between 2.0 to 2.5 cm cm-1. Measurementsfor each gradient were repeated four times and the arithmetic mean was used to determine𝐾𝑎. Two cores were sampled horizontally, each from 6 to 17 cm below the hollow surface(𝐾𝑎,ℎ) and one vertically from 5 to 24 cm below the hollow surface (𝐾𝑎,𝑣).Von Post indices, used to describe the degree of peat decomposition on a discrete scalefrom H1 (unhumidified) to H10 (completely humidified) (von Post, 1924; Stanek and Silc,1977), were used to match slug test measurements of saturated hydraulic conductivity of thecatotelm (𝐾𝑐) made using the method for soft organic soils (Baird et al., 2004) elsewherein Burns Bog with peat from the field site.Specific yield (𝑆𝑦) was obtained from four cylindrical 0.8-L peat cores (𝑑 = 4.8 cm, ℎ = 11.0cm), which were collected in July 2020. Three were sampled between 10 to 21 cm depth andone between 20 to 31 cm depth. Each core was saturated over five days by incrementallyincreasing the water level over two saturation-drainage cycles to minimize enclosure of air.Once saturated the peat samples were allowed to drain over 72 hrs and the weight differencewas assumed to be equal to 𝑆𝑦 (Table 4.1). During drainage, sample tops were sealed andall samples were placed in storage boxes with high humidity to minimize overestimation of𝑆𝑦 due to evaporation.4.2.3. CalculationsTime series of water table and peat surface elevationsBetween December 2016 and November 2017, the recorded water table elevations (𝑧𝑤𝑡) bythe BB1 system pressure transducer at Wb-S displayed artificial diel fluctuations that wereassociated with changes in air temperature. These fluctuations were removed using linearregression between sensor noise and changes in air temperature; residual noise was correctedby smoothing the time series using a 31-point binomial filter, which is a symmetric filter43that, for data with 30-min resolution, uses values within ± 7.5 hr of the center point ina weighted average. Filter weights were based on binomial coefficients, and the filter issimilar in shape to a Gaussian distribution. A regression of the geo-referenced manuallyobserved versus recorded water table elevations by the pressure transducer was then usedto adjust the recorded elevations to make them consistent with the manual values. Metricsof goodness-of-fit are provided in the appendix (Table A1.3).A continuous 30-minute time series of peat surface elevation (𝑧𝑝𝑠) was generated by linearlyinterpolating manual measurements. The lowest decile of the ground elevation distributionobtained from the LiDAR DEM was used as the reference for peat surface elevation, forexample to compute water table depth, with negative values indicating inundation.Digital elevation model of the peat surface and inundation mappingA digital elevation model (DEM) of the peat surface was generated from airborne LiDARobservations (Optech Galaxy T1000, ground point density of 5.5 pt m-2) flown on June 16,2018, by Aries Aviation Services Corp. A DEM with a resolution of 0.5 m was computed bykriging, which was then interpolated to 0.1 m using bilinear interpolation from the rasterpackage in R (Hijmans, 2021; R Core Team, 2022), and used to compute inundation maps ofthe site. The maps covered a rectangular area bounded by the four peripheral piezometernests with side lengths of 42.3 and 45.7 m. The interpolation procedure increases theprecision but not necessarily the accuracy of the inundation maps. However, the mapsshould still provide a valid indication of relative increases or decreases of inundated areathrough time.To account for the effect of peat surface oscillation, daily DEMs were computed by offsettingthe original DEM using daily manual ground elevation observations obtained from linearlyinterpolated manual measurements. Seasonal surface elevation changes were assumed to bespatially consistent across the field site. Surface water storage capacity (𝑆𝑝𝑠) in topographicdepressions (i.e. hollows) was estimated by filling depressions of the original DEM using theWang and Liu (2006) algorithm within SAGA GIS (Conrad et al., 2015) and subtractingthe filled DEM from the original DEM.Surface inundation was determined by intersecting a plane with height 𝑧 = 𝑧𝑤𝑡 for a givendate with the peat-surface DEM that was adjusted to account for bog breathing, as de-scribed in the preceding section. Areas with 𝑧𝑝𝑠 < 𝑧𝑤𝑡 were considered to be inundated.For illustration, inundation maps were generated for ten dates, five each on the wettingand drying phases. Dates were chosen to have similar values of 𝑧𝑤𝑡 on the wetting and dry-ing phases, and to coincide with important water table elevations including minimum andmaximum water table elevations and threshold elevations at which runoff was generated.Groundwater storage changesChanges in peat water storage, Δ𝑆𝑝 (m), were computed from changes in hydraulic headas follows:44Δ𝑆𝑝 = Δℎ(𝑆𝑦 + 𝑏𝑆𝑠) (4.1)where Δℎ = change in hydraulic head (m), here approximated by water table elevationchange, 𝑏 = aquifer thickness (m), 𝑆𝑦 = specific yield (m3 m-3) and 𝑆𝑠 = specific storagechange due to non-rigid properties of peat (m3 m-3). Following Price and Schlotzhauer(1999), specific storage was estimated as:𝑆𝑠 =1𝑏Δ𝑧𝑝𝑠Δℎ (4.2)where Δ𝑧𝑝𝑠 = change in peat surface elevation (m) associated with a change in hydraulichead of Δℎ (m) based on differences between successive manual observations in the wellsof each piezometer nest.Groundwater flowUnsaturated lateral groundwater flow was assumed to be negligible. Vertical groundwaterflux rate was calculated from hydraulic heads of the shallow and deep piezometers usingDarcy’s law as:𝑄𝑣 = −𝐾𝑣ΔℎΔ𝑧 (4.3)where 𝑄𝑣 = vertical groundwater flux (m s-1) (positive flux leaves site) and 𝐾𝑣 = verticalsaturated hydraulic conductivity (m s-1). It was calculated as harmonic mean of 𝐾𝑎,𝑣 and𝐾𝑐.Lateral groundwater flow divergence (𝑑𝑖𝑣𝑄𝑔𝑤) was calculated by computing the fluxes intoand out of a rectangular control volume surrounding the central groundwater well. Thehorizontal boundaries of the rectangle were defined by line segments constructed as per-pendicular bisectors of line segments connecting the central well to each of the surroundingwells. The flux divergence was computed as follows:𝑑𝑖𝑣𝑄𝑔𝑤 =(𝑄𝑤 −𝑄𝑒 +𝑄𝑠 −𝑄𝑛)𝐴 (4.4)where 𝑑𝑖𝑣𝑄𝑔𝑤 = lateral groundwater flow divergence (m s-1), 𝑄𝑤, 𝑄𝑒, 𝑄𝑠 and 𝑄𝑛 = ground-water fluxes across the western, eastern, southern and northern edges of the rectangle,respectively (m3 s-1) and 𝐴 = area of the rectangle (m2), computed as:𝐴 = 0.5 ⋅ (𝑥𝑒 − 𝑥𝑤) × 0.5 ⋅ (𝑦𝑛 − 𝑦𝑠) (4.5)45where 𝑥𝑒 and 𝑥𝑤 = easting coordinates of the eastern and western wells (m) and 𝑦𝑛 and𝑦𝑠 = northing coordinates of the northern and southern wells (m). The average distancebetween peripheral wells to the central well was 22.0 m. Positive fluxes are from west toeast and south to north in the 𝑥 and 𝑦 directions, respectively.The lateral fluxes were calculated by applying Darcy’s law in finite difference form. Forexample, the flux across the western edge was computed as:𝑄𝑤 = −0.5 ⋅ (𝑦𝑛 − 𝑦𝑠) ⋅ 𝜏(𝑧𝑟) ⋅𝑧𝑤 − 𝑧𝑐𝑥𝑤 − 𝑥𝑐(4.6)where 𝑧𝑐 and 𝑧𝑤 = water table elevations (m) at the central and western wells, respectively,𝜏(𝑧𝑟) = transmissivity associated with the water table elevation at the edge of the rectangle(𝑧𝑟), computed as the average of 𝑧𝑐 and 𝑧𝑤.The lateral hydraulic conductivity was assumed to differ between the acrotelm and catotelm,with constant values for each layer. Thus, the vertical integral of the lateral hydraulicconductivity, or transmissivity, was computed as follows:𝜏(𝑧𝑤𝑡) =⎧{⎨{⎩𝐾𝑐 ⋅ (𝑧𝑤𝑡 − 𝑧0) if 𝑧𝑤𝑡 < 𝑧𝑎𝑐(𝑧𝑎𝑐 − 𝑧0) ⋅ 𝐾𝑐 + (𝑧𝑤𝑡 − 𝑧𝑎𝑐) ⋅ 𝐾𝑎,ℎ if 𝑧𝑤𝑡 ≥ 𝑧𝑎𝑐 ∩ 𝑧𝑤𝑡 < 𝑧𝑝𝑠(𝑧𝑎𝑐 − 𝑧0) ⋅ 𝐾𝑐 + (𝑧𝑝𝑠 − 𝑧𝑎𝑐) ⋅ 𝐾𝑎,ℎ if 𝑧𝑤𝑡 ≥ 𝑧𝑎𝑐 ∩ 𝑧𝑤𝑡 ≥ 𝑧𝑝𝑠(4.7)where 𝑧0 = elevation of the base of the peat deposit (m a.s.l.), 𝑧𝑎𝑐 = elevation of acrotelm-catotelm boundary (m a.s.l.) referenced to the same datum as water table and peat surfaceelevations, and 𝐾𝑐 and 𝐾𝑎,ℎ are the horizontal saturated hydraulic conductivities for theacrotelm and catotelm, respectively (m s-1).The acrotelm-catotelm boundary was defined as being the mean of the minimum water tableelevation measured in every year (3.09 m a.s.l.), approximately 0.30 m below the wintertimepeat surface at the flux tower site. It was assumed to be stationary through time, followingPrice (2003), who found that peat matrix volume changes causing peat surface elevationchanges were limited to the acrotelm, which was the upper 50 cm of peat in undisturbedand cutover peatlands in Québec, Canada.Water balance analysis and estimation of rapid subsurface and overland flowWater balance closure was evaluated for the periods between site visits, approximately everytwo weeks, as follows:𝜖𝑤𝑏 = 𝑃 −𝐸 −Δ𝑡 ⋅ 𝑄𝑣 −Δ𝑡 ⋅ 𝑑𝑖𝑣𝑄𝑔𝑤 −Δ𝑆𝑝 (4.8)where 𝜖𝑤𝑏 = water balance residual (mm), Δ𝑡 = time interval (s) between site visits, andthe fluxes are totals (mm) for the periods between visits.46As will be shown below, the errors of closure were close to zero up to a threshold watertable elevation, above which they were consistently positive. These positive deviations athigher water table elevations were assumed to represent lateral flow divergence from thesite over and above that calculated using Eq. 4.4. This divergence, referred to as 𝑑𝑖𝑣𝑄𝑙,could be associated with lateral macropore flow in the upper parts of the acrotelm andoverland flow (Holden and Burt, 2003; Holden, 2009).Recursive partitioning (Breiman et al., 1984) was used to identify threshold values abovewhich this lateral flow divergence occurred. Recursive partitioning is a data-based methodthat develops a predictive model by iteratively classifying values of the predictand based onbinary splits of each of a set of predictor variables. It is particularly effective for situationsin which the relations between the predictand and predictors are nonlinear (especially forthreshold responses) and/or hierarchical. Some hydrological applications include Elder etal. (1998), Trubilowicz and Moore (2017), and McMahon and Moore (2017).Uncertainty in 𝑑𝑖𝑣𝑄𝑙 was estimated by the root mean square error of water balance closurefor observations when water table elevation was below the threshold at which 𝑑𝑖𝑣𝑄𝑙 becameconsistently positive.4.3. Results4.3.1. Peat properties and relevant elevationsHorizontal saturated hydraulic conductivity of the two acrotelm samples (𝐾𝑎,ℎ) were similar(0.114 vs 0.154 m d-1), and vertical saturated hydraulic conductivity (𝐾𝑎,𝑣) was about fourtimes higher than 𝐾𝑎,ℎ (Table 4.1). Saturated hydraulic conductivity of the catotelm(𝐾𝑐) was assumed to be isotropic and two orders of magnitude lower than mean acrotelmconductivity as von Post decomposition increased from H1 in the acrotelm to H7/H8 indeeper peat layers. This assumption was in line with slug tests performed elsewhere in thebog, where the catotelm had similar von Post indices (H6). Vertical saturated hydraulicconductivity of the peat deposit was of the same order of magnitude as 𝐾𝑐 due to thethickness of the catotelmic layer which increased the relative weight of 𝐾𝑐 in the harmonicmean of 𝐾𝑎,𝑣 and 𝐾𝑐 used to calculate saturated hydraulic conductivity of perpendicularlylayered soil (Freeze and Cherry, 1979).Specific yield (𝑆𝑦) had a mean of 0.48 m3 m-3 and a standard deviation of 0.10 m3 m-3.The site areal average of surface water storage capacity in topographic depressions was21.1 mm. This storage became relevant between October and March, when water tableswere high. Peat surface elevation distribution was skewed due to microtopographic features(mean = 3.37 m a.s.l., 25th-quantile = 3.31 m a.s.l., 75th-quantile = 3.41 m a.s.l.) andmedian elevation on June 16, 2018 was 3.36 m a.s.l.47Table 4.1.: Summary of relevant site characteristics. 𝐾𝑐,𝑠𝑙𝑢𝑔 = slug test based saturatedhydraulic conductivity of catotelm with similar von Post indices as catotelm atfield site. Ground elevation metrics based on LiDAR (𝑧𝐿𝑖𝐷𝐴𝑅) flown on June16, 2018. 𝑧𝑝𝑠,10 and 𝑧𝑝𝑠,75 = 10th and 75th percentile of peat surface elevationdistribution defined to represent hollow and hummock elevation, respectively,𝑆𝑝𝑠 = peat surface water storage capacity and lateral flow divergence thresholdsderived from recursive partitioning are shown as 𝑧1 and 𝑧2. Values for hydraulicconductivity and specific storage are means of all samples, unless specified dif-ferently.Variable Unit Value Min Max n𝐾𝑎,ℎ m d-1 0.134 0.114 0.154 2𝐾𝑎,𝑣 m d-1 0.604 - - 1𝐾𝑐 m d-1 0.004 0.001 0.006 2𝐾𝑐,𝑠𝑙𝑢𝑔 m d-1 0.005 0.003 0.008 3𝐾𝑣 m d-1 0.004 - - 1𝑆𝑦 (m3 m-3) 0.482 0.387 0.583 4𝑆𝑦 sd (m3 m-3) 0.097 - - -𝑆𝑝𝑠 mm 21.1 - - -𝑧𝐿𝑖𝐷𝐴𝑅 median m a.s.l. 3.36 3.180 3.860 10304𝑧𝑝𝑠,10 m a.s.l. 3.29 - - -𝑧𝑝𝑠,75 m a.s.l. 3.41 - - -𝑧1 m a.s.l. 3.345 - - -𝑧2 m a.s.l. 3.402 - - -𝑧𝑝𝑠,𝑚𝑎𝑥 m a.s.l. 3.404 - - -4.3.2. Water table fluctuationsWater table elevation displayed a clear seasonal pattern, with levels remaining high throughwinter and early spring, followed by a consistent decline into late summer, and then byrecharge following onset of autumn rain season (Figure 4.3). The timing of the onset ofwater table drawdown in the spring varied from early April in 2020 to mid-June in 2017.The timing of recovery to winter levels also varied, but generally occurred in the secondhalf of November. Timing was largely dependent on precipitation patterns and determinedwhen the site began generating lateral flow. The maximum water table drawdown belowwintertime peat surface elevation varied from 0.25 m in 2020 to 0.35 m in 2018.4.3.3. Peat surface elevation changes and surface inundationPeat surface elevation varied seasonally; between 2016 and 2019, the maximum annualrange was 0.25 m. The seasonal pattern was relatively similar among all nests but the onelocated 24 m north of the tower, which was closest to a blocked drainage ditch 85 m north4820163.03.13.23.33.43.5z wt (m a.s.l.)20173.03.13.23.33.43.5z wt (m a.s.l.)20183.03.13.23.33.43.5z wt (m a.s.l.)20193.03.13.23.33.43.5z wt (m a.s.l.)20203.03.13.23.33.43.5Mar 01 Apr 01 May 01 Jun 01 Jul 01 Aug 01 Sep 01 Oct 01 Nov 01 Dec 01z wt (m a.s.l.)Figure 4.3.: Time series of daily water table elevations (blue) and wintertime peat surfaceelevation (black) between March 1 and November 30.49of the tower. At that location seasonal peat surface change was less than at the other nests(Figure 4.4).3.23.33.4Jan2016Jul2016Jan2017Jul2017Jan2018Jul2018Jan2019Jul2019z ps (m a.s.l.)Nest locationCenterEastNorthSouthWestFigure 4.4.: Time series of peat-surface elevations (𝑧𝑝𝑠) at each of the five piezometer nestsbased on manual measurements approximately every two weeks.The relation between peat surface and water table elevations differed between drying andwetting phases and among nests (Figure 4.5). At all but the northern nest, the slope ofthe 𝑧𝑝𝑠-𝑧𝑤𝑡 relation was lower during the drying phase. The slopes of the 𝑧𝑝𝑠-𝑧𝑤𝑡 relationswere similar among all but the northern nest, ranging from 0.72 to 0.76 m m-1 duringthe drying phase and 0.75 to 0.79 m m-1 during the wetting phase. At the northern nest,the slope was 0.57 m m-1 and 0.53 m m-1 on the drying and wetting phases, respectively.Although the linear fits are strong, with r2 greater than 0.92 and mostly greater than 0.97,the distribution of points around the best-fit lines indicates some nonlinearity, especiallyfor the wetting phase.The site became seasonally inundated when 𝑧𝑤𝑡 rose above the peat surface during thewet months of the year. The difference in the 𝑧𝑝𝑠-𝑧𝑤𝑡 relation between drying and wettingphases became apparent as hysteresis in the relationship between inundated areal fractionversus water table elevation (Figures 4.6 and 4.7). The areal fraction of peat surface beinginundated ranged from 0% to 45%.4.3.4. Water balance analysisWater balance closureAs seen in Figure 4.8a, the errors of closure were generally distributed around 0 mm up toa water table elevation of about 3.34 m a.s.l., above which the errors became systematicallypositive and increased with increasing water table elevation. The pattern suggests a thresh-old response. Application of recursive partitioning using closure error as a predictand andwater table elevation as a predictor yielded two thresholds, one at 3.345 m a.s.l. and an-other at 3.402 m a.s.l. Figure 4.8b shows that these thresholds are associated with changes50dry : y = 0.76 ⋅ x + 0.83, r2 = 0.99wet : y = 0.79 ⋅ x + 0.70, r2 = 0.99Center3.03.13.23.33.43.53.0 3.1 3.2 3.3 3.4 3.5zwt (m a.s.l.)z ps (m a.s.l.)dry : y = 0.75 ⋅ x + 0.83, r2 = 0.99wet : y = 0.79 ⋅ x + 0.70, r2 = 0.99West3.03.13.23.33.43.53.0 3.1 3.2 3.3 3.4 3.5zwt (m a.s.l.)z ps (m a.s.l.)dry : y = 0.57 ⋅ x + 1.44, r2 = 0.93wet : y = 0.53 ⋅ x + 1.53, r2 = 0.98North3.03.13.23.33.43.53.0 3.1 3.2 3.3 3.4 3.5zwt (m a.s.l.)z ps (m a.s.l.)dry : y = 0.74 ⋅ x + 0.89, r2 = 0.99wet : y = 0.79 ⋅ x + 0.74, r2 = 0.99South3.03.13.23.33.43.53.0 3.1 3.2 3.3 3.4 3.5zwt (m a.s.l.)z ps (m a.s.l.)Cycledryingwettingdry : y = 0.72 ⋅ x + 0.96, r2 = 0.98wet : y = 0.75 ⋅ x + 0.85, r2 = 0.98East3.03.13.23.33.43.53.0 3.1 3.2 3.3 3.4 3.5zwt (m a.s.l.)z ps (m a.s.l.)dry : y = 0.71 ⋅ x + 0.95, r2 = 0.99wet : y = 0.73 ⋅ x + 0.86, r2 = 0.99Mean3.03.13.23.33.43.53.0 3.1 3.2 3.3 3.4 3.5zwt (m a.s.l.)z ps (m a.s.l.)Figure 4.5.: Manual peat surface elevation (𝑧𝑝𝑠) versus water table elevation (𝑧𝑤𝑡) for eachof the five piezometer nests and site mean. Measurements were conductedapproximately twice a month between May 2016 and October 2019. Leastsquares linear regression was fitted to drying and wetting phases separately.The phases were defined based on water table elevation changes of a runningmean with a window size of two weeks to remove small fluctuations and extractthe seasonal signal.5110 m 3.53.63.73.83.9Elevation (m a.s.l.)Inundated = 42%, zwt = 3.413 m10 m 3.53.63.73.83.9Elevation (m a.s.l.)Inundated = 32%, zwt = 3.411 m10 m 3.53.63.73.83.9Elevation (m a.s.l.)Inundated = 28%, zwt = 3.405 m ≈ z210 m 3.53.63.73.83.9Elevation (m a.s.l.)Inundated = 13%, zwt = 3.403 m ≈ z210 m 3.43.53.63.73.83.9Elevation (m a.s.l.)Inundated = 21%, zwt = 3.353 m ≈ z110 m 3.43.53.63.73.83.9Elevation (m a.s.l.)Inundated = 10%, zwt = 3.358 m ≈ z110 m 3.33.43.53.63.73.8Elevation (m a.s.l.)Inundated = 9%, zwt = 3.235 m10 m 3.33.43.53.63.73.8Elevation (m a.s.l.)Inundated = 0.9%, zwt = 3.232 m10 m 3.23.33.43.53.63.7Elevation (m a.s.l.)Inundated = 1%, zwt = 3.146 m10 m 3.23.33.43.53.63.7Elevation (m a.s.l.)Inundated = 0.4%, zwt = 3.14 mFigure 4.6.: Maps of inundated area across the range of observed 𝑧𝑤𝑡. 𝑧𝑤𝑡 decreases fromtop to bottom and each row shows roughly the same 𝑧𝑤𝑡. Maps on the leftare based on the wetting phase in autumn (starting in late summer (bottomleft map) and continuing into winter (top left map)) and maps on the rightare based on the drying phase in late spring and summer (starting in latespring (top right map) and continuing into summer (bottom right map)). Blueareas are inundated. The second and third rows show maps for 𝑧𝑤𝑡 𝑧2 and𝑧𝑤𝑡 𝑧1, which represent the thresholds associated with lateral flow divergence(see Figure 4.8 and associated discussion).520.00.10.20.30.43.1 3.2 3.3 3.4zwt (m a.s.l)A sub (m2 m−2 )dryingwettingFigure 4.7.: Hysteretic relationship between inundated area and 𝑧𝑤𝑡 based manual observa-tions of 𝑧𝑤𝑡 and 𝑧𝑝𝑠 with data points corresponding to Figure 4.6 highlighted.Inundated area and 𝑧𝑤𝑡 correspond to maps shown in Figure 4.6. The verticallines depict thresholds 𝑧1 (dark green) and 𝑧2 (purple) above which lateral flowdivergence occurs.in the relation between the closure error and the calculated net input (𝑃 −𝐸 −𝑄𝐺𝑊 ). Forwater table elevations above the upper threshold, the relation is roughly 1:1, suggestingthat net input is leaving the site as lateral flow divergence in excess of that computed forsubsurface flow (henceforth referred to as 𝑑𝑖𝑣𝑄𝑙).Water flux magnitudes and timingMore precipitation occurred between October and March to May, reaching monthly valuesbetween around 100 to 300 mm month-1 in winter (Figure 4.9). Evapotranspiration (𝐸) didnot exceed 10 mm month-1 between late autumn and early spring, but dominated waterloss in summer, reaching values around 100 mm month-1.Groundwater flux divergence was negligible throughout the year. Vertical groundwaterflow varied between -0.6 and 0.2 mm month-1. It was upward-oriented in summer and earlyautumn, and the mean of the annual maximum reached gradients of 0.006.Storage change was driven by 𝑃 and 𝐸. It was negative in late spring when 𝐸 lossesexceeded 𝑃 and became positive in autumn when ample 𝑃 recharged groundwater. Theearliest lateral flow divergence, as inferred from systematically positive water balance errors,was generated in autumn during periods of positive storage change and it persisted untilmid- to late-spring. The timing of the onset of positive 𝑑𝑖𝑣𝑄𝑙 was relatively consistentamong years, while the timing of cessation was more variable (Table 4.2). Hydrological53a−10001002003.1 3.2 3.3 3.4zwt (m a.s.l.)ε wb (mm)b−1000100200−100 0 100 200P − E − QGW (mm)ε wb (mm) zwt < z1z2 > zwt ≥ z1zwt > z2Figure 4.8.: Residuals of water balance closure versus 𝑧𝑤𝑡 on day of field measurements(left). Vertical lines indicate break-points determined by application of recur-sive partitioning (Breiman et al., 1984). Residuals of water balance closureversus net hydrologic input (right). Curves in both plots are locally weightedscatter plot smoothers.fluxes at this site showed distinct seasonal shifts from predominantly vertical fluxes (𝑃and 𝐸) in summer to lateral fluxes in winter, when 𝑑𝑖𝑣𝑄𝑙 was as high as 250 mm month-1(Figure 4.9).Table 4.2.: Dates of start (onset) and end (cessation) of lateral flow divergence (𝑑𝑖𝑣𝑄𝑙)between 2016 and 2020 based on daily water table elevation (𝑧𝑤𝑡). The numberof days when 𝑑𝑖𝑣𝑄𝑙 occurred was calculated as sum of days at which 𝑧𝑤𝑡 wasabove 𝑧2 or 𝑧1. The number of days with 𝑑𝑖𝑣𝑄𝑙 for the wet season of 2016 to2017 is a conservative estimate as 𝑧𝑤𝑡 was not available between Dec. 10, 2016and Jan. 20, 2017.Onset Cessation No. days with 𝑑𝑖𝑣𝑄𝑙Dec 06, 2015 Apr 25, 2016 136Nov 02, 2016 Jul 03, 2017 > 200Nov 20, 2017 May 21, 2018 183Nov 22, 2018 May 04, 2019 160Nov 17, 2019 May 17, 2020 153Annual water balanceAnnual water year balance components varied between years of complete field observations(Table 4.3). Precipitation in 2017 was higher than in 2019 and lateral flow divergence variedaccordingly. Evapotranspiration in 2019 was lower than during the other years, althoughthe fraction of precipitation lost to the atmosphere remained similar during all years (on540100200300P (mm)−1000100∆ S (mm)0100200300E (mm)EEeq−1012divQgw (mm)0100200300ε wb (mm)3.13.23.33.4Oct2016Mar2017Oct2017Mar2018Oct2018Mar2019Oct2019z (m a.s.l.)z2z1zps,maxFigure 4.9.: Monthly water balance components. From top to bottom, the panels showmonthly precipitation, storage change, 𝐸 and 𝐸𝑒𝑞, groundwater flux divergence,and residuals of water balance closure which are assumed to be equal to lat-eral flow divergence when 𝑧𝑤𝑡 > 𝑧1. Error bars indicate uncertainty in 𝜖𝑤𝑏,estimated by RMSE of water balance closure when 𝑧𝑤𝑡 < 𝑧1. Positive ground-water and surface water fluxes are leaving the site. A common y-axis range isused for all variables except groundwater flux divergence. The bottom paneldisplays manual water level observations along with elevations of 𝑧1, 𝑧2 andmaximum peat surface elevation 𝑧𝑝𝑠,𝑚𝑎𝑥.55average 41%). Groundwater fluxes and storage change were small except for the 2019water year, when storage change was roughly 52 mm. Annual water year balance closurewas between ± 5 mm, except for 2019 when a lack of closure of about 114 mm or 11% ofprecipitation input occurred. The majority of lack of closure in 2019 occurred in summerwhen 𝜖𝑤𝑏 was positive but 𝑧𝑤𝑡 < 𝑧1 (Figure 4.9).Table 4.3.: Calculated water balance for water years of 2017 to 2019. All quantities are inmm. WY = water year. Lateral flow divergence (𝑑𝑖𝑣𝑄𝑙, here equivalent to 𝜖𝑤𝑏in Eq.4.8) was calculated without threshold condition (𝑑𝑖𝑣𝑄𝑙 = 𝜖𝑤𝑏, 𝑖𝑓𝑧𝑤𝑡 > 𝑧1).𝑄𝑔𝑤,𝑣 = vertical groundwater flux and 𝑄𝑔𝑤,ℎ = horizontal groundwater flux. 𝜖= residual of water year balance. Positive values of fluxes other than 𝑃 indicatethat water leaves the site.WY Δ𝑆 𝑃 𝐸 𝐸𝑒𝑞 𝑄𝑔𝑤,𝑣 𝑄𝑔𝑤,ℎ 𝑄𝑔𝑤,𝑡𝑜𝑡 𝑑𝑖𝑣𝑄𝑙 𝑄𝑡𝑜𝑡 𝜖 𝐸/𝑃 𝑄𝑡𝑜𝑡/𝑃2017 6.4 1289.7 501.0 612.2 -1.7 1.7 -0.1 781.5 781.4 0.8 0.39 0.612018 9.2 1198.0 515.1 651.1 -1.8 -0.3 -2.1 681.3 679.2 -5.4 0.43 0.572019 52.2 1075.8 449.3 679.9 -1.9 -2.0 -3.9 463.9 460.0 114.1 0.42 0.434.4. Discussion4.4.1. Peat surface elevation changePeat surface elevation change was closely related to change in water table elevation, butdiffered between periods of drying and wetting (Figure 4.5). The relation was approximatelylinear during the drying phase, but was slightly concave-up during the wetting phase, whichhas been observed elsewhere (Fritz et al., 2008). Linear regression models provided goodfits (𝑟2 > 0.97) with similar coefficients among sites (slopes ranging from 0.72 to 0.79)except for the diverging relationship of the northern nest from the others, where the slopeswere 0.57 and 0.53 for the drying and wetting phases, respectively. The lower slope at thenorthern nest may be related to reductions in peat elasticity or decomposition resultingfrom the historic effects of drainage due to its proximity to the drainage ditch (Waddingtonet al., 2010). However, differences in peat thickness (Howie and van Meerveld, 2018), bulkdensity or vegetation cover (Fritz et al., 2008) could also explain the lower slope. Variabilityin peat surface elevation change over horizontal distances at the scale of tens of meters havebeen observed in other peatlands (Fritz et al., 2008; Nijp et al., 2019).Peat surface elevation response to changes in water table elevation at this re-wetted opensite was stronger compared to other sites in Burns Bog. For example, 𝛿𝑧𝑝𝑠/𝛿𝑧𝑤𝑡, where 𝛿indicates the annual range, at open harvested sedge-dominated sites with a peat thicknessroughly between 2.5 m and 3.5 m ranged from 0.36 to 0.39 (Howie and Hebda, 2018), andsites with shrub and/or tree vegetation, a mixed history of disturbance and peat thicknessbetween 1.0 and 4.5 m had a 𝛿𝑧𝑝𝑠/𝛿𝑧𝑤𝑡 ratio between 0.08 and 0.26 (Howie and Hebda, 2018).These findings suggest that peat water storage change might be predictable by developing56empirical relations to predict peat surface elevation and hydraulic properties of the acrotelmas functions of water table elevation, providing an alternative to computationally expensivenumerical simulations of peat deformation for modelling purposes (Camporese et al., 2006),as peatland water dynamics cannot be modelled accurately without accounting for the effectof bog breathing on storage change (Kellner and Halldin, 2002).4.4.2. Peat surface inundationEstimates of the relative area of surface inundation ranged from 0.0 to 0.45 m2 m-2. Theestimates were based on the assumption that peat surface displacement across the fieldsite was equal. The assumption of identical peat surface displacement across the fieldsite was oversimplified as is evident from peat surface elevation measurements at eachpiezometer nest (Figure 4.4). An attempt to use oblique photography from the flux towerto map inundated areas to validate the DEM-based estimates was unsuccessful. However,the maximum inundation extent appears reasonable in comparison to the extent of surfacewater visible in Figure 4.2, so we believe that the maps at least qualitatively capture theseasonal expansion and contraction of surface storage.Estimating the inundated fraction of the peat surface as a function of water table elevationwould be useful in modelling peatland hydrology because (a) inundation can influenceevaporation rates by reducing the area-averaged surface resistance to vapour loss and (b)runoff generation via overland flow is facilitated by the increasing connectivity of waterstored in surface depressions (Frei et al., 2010; Van der Ploeg et al., 2012; Oosterwoud etal., 2017). Numerical simulations by Frei et al. (2010) suggested that the relation betweensurface flow and surface storage is hysteretic for a virtual system that was not subject tosurface elevation changes. In this study, the estimated inundation area had a hystereticrelation with water table elevation that was caused by hysteresis in the relation betweenpeat surface elevation and water table elevation (Figures 4.6 and 4.7). An interestinghypothesis is that reduced surface water connectivity during the drying period could be aself-organization mechanism that acts to preserve water longer into summer.4.4.3. Lateral flow divergenceAs seen in Figure 4.8, water balance residuals were near zero for lower water table elevations,but there appeared to be two threshold elevations (𝑧1 = 3.345 and 𝑧2 = 3.402 m a.s.l.) thatmarked shifts to increasingly positive residuals, which are interpreted to predominantlyrepresent water export from the site by lateral divergence in excess of that computed fromDarcy’s law for subsurface flow for water table elevation at or above 𝑧2, and a combinationof water export by lateral divergence and filling of surface storage for water table elevationsbetween 𝑧1 and 𝑧2. In this study, the hydraulic conductivity was determined from relativelysmall soil cores; such measurements are known to underestimate the effective hydraulicconductivity because they do not capture the effects of preferential flow via macropores(Bromley et al., 2004). For example, Holden (2009) found macropore flow at the surfaceand at 5 cm depth to account for almost 80% of the flow. Therefore, lateral flow divergence57as inferred from the shift to systematically positive water balance residuals is imputed tobe lateral macropore flow or overland flow.These water table thresholds, when referenced to the peat surface elevation observed atthese times (3.404 m a.s.l.), are in line with other studies that reported runoff generationwhen water levels neared the surface. For example, increased runoff was reported whenwater levels stayed within 5 to 15 cm of the peat surface in a peatland-forest complex incoastal British Columbia (Emili and Price, 2006), and the majority of runoff was observedeither on top of the peat surface or at less than 5 cm depth in blanket peat in the UK (Holdenand Burt, 2003). In a raised patterned bog in Estonia, hydrologic connectivity increasedstrongly when water levels were less than 5 cm below the peat surface (Oosterwoud etal., 2017), and the majority of tracer transport in a ladder fen in the James Bay Lowlands,Canada, occurred within the top 10 cm of the saturated peat (McCarter and Price, 2017).Lateral flow divergence via overland flow would require that the hollows be inundated to thepoint that there is sufficient connectivity to support overland flow export from a relativelylarge fraction of the bog surface (e.g. Frei et al., 2010; Van der Ploeg et al., 2012). Asseen in Figure 4.6, the upper threshold of 3.402 m a.s.l. is associated with a shift torelatively extensive and spatially connected inundation, consistent with the hypothesis offlow divergence by overland flow. Between the lower and upper thresholds, inundation andconnectivity varied, particularly between the wetting and drying phases (Figure 4.6). Thisseasonal contrast may hold important implications for bog water retention in spring, asinundation is lower during the drying phase for a given water table elevation, suggestingless connectivity, which could be an important self-regulatory feedback for retaining water.It would appear that both macropore flow and overland flow can produce lateral flowdivergence between the thresholds, with the relative contributions varying with water tableelevation and between the wetting and drying phases.These results suggest that runoff simulation in a distributed hydrological model requiresdistinct process representations for saturation-excess overland flow, near-surface lateral flowand peat matrix flow, which is consistent with a study recommending separate modellingof low flow and high flow from a boreal fen in Norway (Kværner and Kløve, 2008). Whileprevious studies have identified threshold-like behaviour that was associated with a criticalwater table depth (e.g. Emili and Price, 2006; Frei et al., 2010; Van der Ploeg et al., 2012;Balliston and Price, in press), an apparently novel result from this study is the existenceof two thresholds.4.4.4. Water balance and timing of shift in hydrologic fluxesWater balances for peatlands are usually limited to the growing season, and annual waterbalances are rarely reported (Shantz and Price, 2006a; McCarter and Price, 2013). In a bo-real peatland in eastern Finland, annual water balances varied considerably between years(Wu et al., 2010). In a dry year, precipitation, evapotranspiration and runoff were 580 mm,370 mm and 194 mm respectively; during a wet year, precipitation, evapotranspiration andrunoff were 707 mm, 272 mm and 387 mm, respectively (Wu et al., 2010). The 𝐸/𝑃 ratiosin the dry and wet years were 0.64 and 0.38 (Wu et al., 2010). The boreal, subcontinental58climate in Finland created a different climatic framework with lower precipitation, evapo-transpiration and runoff than the oceanic climate in this study (Table 4.3). The fraction ofprecipitation evaporating back into the atmosphere varied more strongly in Finland thanin Burns Bog, even though Burns Bog experienced dry and wet conditions in the wateryears of 2019 and 2017 (Table 4.3), which suggests that Burns Bog is more resilient tointer-annual climatic variability than the peatland in Finland.The magnitude of hydrological fluxes in Burns Bog changed between seasons, and a shift inwater loss occurred from vertical fluxes (𝐸) during dry months when 𝑑𝑖𝑣𝑄𝑙 was negligibleto dominance by horizontal fluxes (𝑑𝑖𝑣𝑄𝑙) during wet months of the year when 𝐸 wasnear zero (Figure 4.9). In autumn, the shift coincided roughly with seasonal changes inprecipitation patterns as the increase in 𝑃 , combined with a decrease in 𝐸 due to limitedavailable energy and atmospheric demand, recharged the water table to levels at which𝑑𝑖𝑣𝑄𝑙 was generated (i.e. 𝑧1 or 𝑧2). The shift from 𝑑𝑖𝑣𝑄𝑙 to 𝐸 as the dominant water lossbetween March and May varied more between years as transitions in seasonal precipitationpatterns were more variable and 𝑃 was required to sustain water levels at or above 𝑧1 for𝑑𝑖𝑣𝑄𝑙 to occur. Seasonality and shift in water loss processes is a distinctive feature thatsets apart mid-latitude coastal peatlands from peatlands in boreal or continental settings,where freezing temperatures reduce water loss in winter and promote water storage in snowpack that recharges groundwater as it melts in spring (e.g. Shantz and Price, 2006b; Wuet al., 2010; Ketcheson et al., 2012; Van Huizen et al., 2020).An ensemble of 12 climate projections for Burns Bog suggests that air temperature shouldincrease over the next century in all months under both RCP4.5 and RCP8.5 scenarios(Pacific Climate Impacts Consortium, 2019). Precipitation should increase in October toApril under both climate scenarios and decrease from June to September, especially inJuly and August. Because the bog has a maximum storage capacity, an increase in winterprecipitation would likely result in greater water export via lateral flow rather than anincrease in storage that would carry over into spring and summer. What may be morecritical to the bog’s ecohydrological functioning is how much higher temperatures affectsummertime vapour pressure deficit, which would have a stronger effect on summertimewater loss than an earlier seasonal shift to net water loss by evapotranspiration during thespring (Chapter 3), as well as the effectiveness of water-table-related feedback mechanismsin limiting water loss through the summer.4.5. ConclusionsThis study focused on hydrological processes and water table dynamics at a Sphagnum-dominated site in Burns Bog, a raised ombrotrophic bog in a maritime region of westernNorth America, where the physical hydrology of peatlands has been relatively little studied.Water losses from the site were dominated by evapotranspiration in summer and by lateralflow divergence in winter. Groundwater flow divergence via matrix flow was computed tobe a minor term of the water balance and negligible over the course of a year, and lateral59flow divergence was inferred to be dominated by water export from the site via near-surfacemacropore flow and overland flow.The initiation of lateral flow divergence associated with macropore flow and possibly over-land flow was associated with a threshold water table elevation, below which water balanceerrors averaged near zero, and above which the errors were systematically positive. Therewas a second threshold water table elevation, above which it appeared that all of the netwater input to the site was exported as lateral flow. This second threshold appeared tobe associated with an increase in the connectivity of ponded water at the surface and thusthe efficiency of lateral water export by overland flow. Further research should focus onthe dynamics of surface ponding using a combination of remotely sensed and ground-basedobservations, particularly the apparently hysteretic relation between inundation area andwater table elevation.Strong seasonality in water balance components suggests that potential changes in win-tertime precipitation due to climate change will not significantly affect the ecohydrologyof coastal peatlands such as Burns Bog. Of greater concern is the potential for increasedtemperatures, increased vapour pressure deficit and reduced summer precipitation to causegreater water table drawdown in late summer.605. Quantifying throughfall, stemflow andinterception loss in five vegetationcommunities in a maritime raised bog5.1. IntroductionEvaporation of rainfall that is intercepted by a vegetation canopy can be an importantcomponent of total evapotranspiration loss for a range of ecosystems, including both shrub-dominated and tree-dominated communities (e.g. Levia et al., 2011; Sadeghi et al., 2020;Yue et al., 2021). Interception loss is challenging to measure directly (e.g., using eddycovariance), and is usually estimated using canopy water balance equations and measure-ments of open-site precipitation, throughfall and stemflow. Many studies that measuredthroughfall and/or stemflow in peatlands focused on quantifying chemical inputs to the soil(Urban et al., 1995; Miller et al., 1996) such as sea salt (Haraguchi et al., 2003; Iyobe et al.,2003; Iyobe and Haraguchi, 2005; Iyobe and Haraguchi, 2008), sulfur (Mörth et al., 1999),mercury (Grigal et al., 2000; Braaten and Wit, 2016), and dissolved organic carbon (Dalvaand Moore, 1991; Koprivnjak and Moore, 1992; Hill et al., 2002). Such studies were oftennot designed to estimate interception loss and there is a lack of comprehensive throughfallstudies for application of analytical interception loss models in wetlands (Bryant et al.,2005).Studies quantifying interception loss in peatlands have been relatively rare with little em-phasis on process understanding (Table 5.1). Many past studies were limited to single-layerthroughfall observations (e.g. Dubé et al., 1995; Van Seters and Price, 2002; Farrick andPrice, 2009; Baisley, 2012), and only a few studies measured stemflow from trees (Päivänen,1966; Robinson, 1998) and shrubs (Päivänen, 1966; Zwieback et al., 2019). Most studiesderived plot-scale averages of throughfall from troughs (e.g. Dubé et al., 1995; Van Setersand Price, 2002; Farrick and Price, 2009), while some used randomly placed collectors(e.g. Päivänen, 1966; Baisley, 2012) or limited sampling of throughfall in gaps and underthe drip line of individual trees (Thompson et al., 2014). These approaches provide anestimate of the spatially averaged throughfall for the specific site being sampled, but theresults may not be transferable to locations with different vegetation species, height ordensity (e.g. Stogsdill Jr et al., 1989; Bryant et al., 2005; André et al., 2011; Blume etal., 2021). Troughs have the advantage of providing higher-resolution throughfall values(e.g., hourly) than is possible using manual storage gauges. However, because troughs aregenerally at fixed locations, there is the potential for sampling bias, as well as potential61issues with evaporation or splash-out (Herbst et al., 2008). However, these issues appear tohave received relatively little attention, particularly in forest stands typical of peatlands.Tree throughfall tends to exhibit temporally persistent spatial patterns (i.e. relatively staticspatial patterns), typically with a systematic increase in throughfall with distance from thenearest tree (e.g. Keim et al., 2005; David et al., 2006; Carlyle-Moses and Lishman, 2015).This observation leads to the hypothesis that plot- or stand-scale estimates of throughfallcould be generated by defining strata based on distance from the nearest tree or somemeasure of canopy conditions, then using random sampling within those strata. To ourknowledge, no previous studies have applied such an approach. While David et al. (2006)and Carlyle-Moses and Lishman (2015) sampled throughfall based on distance from thenearest tree, their analyses focused on spatial patterns rather than up-scaling. Hassan et al.(2017) sampled throughfall under two trees in an area with discontinuous canopy cover, thenscaled up those measurements to the plot and catchment scales based on the canopy coverdetermined from remotely sensed imagery. However, they only sampled one tree for eachof two species, and only had four gauges under each tree, located at fixed positions midwaybetween the stem and the tree’s canopy edge. Further, they did not compare the plot- andcatchment-scale estimates to independent estimates – e.g., based on random sampling.In contrast to the substantial body of research focused on tree throughfall and stemflow,shrub throughfall and stemflow have received far less attention, and past research mainlyfocused on semi-arid or arid regions (e.g. Návar and Bryan, 1990; Mauchamp and Janeau,1993; Dunkerley and Booth, 1999; Návar et al., 1999; Garcia-Estringana et al., 2010) oralpine environments (Uehara and Kume, 2012). Shrub throughfall studies in humid climatesare rare (Zwieback et al., 2019; Yue et al., 2021). However, the few studies conducted inpeatlands suggest that interception loss from shrubs can be a significant component ofpeatland water budgets (Table 5.1).The overarching objective of this study was to quantify the variability of throughfall, stem-flow from trees and interception loss in a range of vegetation communities in an om-brotrophic bog, including communities with complex multi-layered vegetative structure.The study focused on Burns Bog in southwestern British Columbia, Canada, which hasa history of drainage and peat harvesting (Hebda et al., 2000), and is located near thesouthern extent of raised bogs in western North America (Rocchio et al., 2021). Specifi-cally, this study had three goals: (1) to compare the significance of interception loss fromtrees and shrubs for net precipitation in peatlands; (2) to use independent observationsto evaluate the accuracy of a tree-based throughfall sampling design that can predict spa-tially distributed throughfall in conjunction with tree stand metrics obtained from airborneLiDAR at the plot level; (3) to assess the reliability of trough sampling to provide treethroughfall estimates in peatland environments.62Table 5.1.: Overview of interception loss studies in peatlands. 𝑆𝐹 = stemflow, 𝐼𝐿𝑡𝑟𝑒𝑒 = interception loss from trees and 𝐼𝐿𝑠ℎ𝑟𝑢𝑏= interception loss from shrubs. 𝐼𝐿𝑡𝑟𝑒𝑒 in Thompson et al. (2014) based on dripline throughfall, 𝐼𝐿𝑠ℎ𝑟𝑢𝑏 estimates inDubé et al. (1995) and Van Seters and Price (2002) comprise logging debris and shrub. 𝐼𝐿𝑠ℎ𝑟𝑢𝑏 estimates in Päivänen(1966) for 15 mm (0.07) and 1 mm (0.46) of rain. Note that 𝐼𝐿 is given as fraction of gross precipitation.Country Type Disturbance Vegetation 𝑆𝐹 𝐼𝐿𝑡𝑟𝑒𝑒 𝐼𝐿𝑠ℎ𝑟𝑢𝑏 ReferenceCanada bog pristine black spruce no 0.44 Baisley (2012)Canada bog disturbed black spruce no 0.47 Baisley (2012)Canada fen disturbed black spruce no 0.54 Baisley (2012)Canada bog burned 1935 black spruce no 0.20-0.26 0.1 Thompson et al. (2014)Canada bog burned 2006 black spruce no 0 0.05 Thompson et al. (2014)Canada bog before logging black spruce no 0.41 Dubé et al. (1995)Canada fen before logging red spruce no 0.35 Dubé et al. (1995)Canada swamp before logging black spruce no 0.35 Dubé et al. (1995)Canada bog after logging black spruce no 0.15 Dubé et al. (1995)Canada fen after logging red spruce no 0.12 Dubé et al. (1995)Canada swamp after logging black spruce no 0.08 Dubé et al. (1995)Finland peatland drained Norway spruce yes 0.23-0.35 Päivänen (1966)Finland peatland drained Scots pine yes 0.2-0.25 Päivänen (1966)Finland peatland drained white birch yes 0.2 Päivänen (1966)Finland peatland drained Labrador tea yes 0.07-0.46 Päivänen (1966)Canada bog pristine spruce, pine no 0.32 Van Seters and Price (2002)Canada bog pristine spruce, pine no 0.23 Van Seters and Price (2002)Canada bog disturbed spruce, pine no 0.19 Farrick and Price (2009)UK bog forestry Sitka spruce yes 0.21-0.25 Robinson (1998)UK bog forestry Sitka spruce yes 0.23-0.28 Robinson (1998)Canada peatland pristine birch yes 0.16 Zwieback et al. (2019)Canada peatland pristine birch yes 0.29 Zwieback et al. (2019)635.2. Methods5.2.1. Study siteWithin the bounds of BBECA, 32% of the area is covered by mature tree vegetation that isassociated with dense shrubby understory of salal or Labrador tea. Another 31% is coveredby mostly stunted and sparse trees with an understory dominated by stunted shrubs, herbsand Sphagnum. The rest of the bog consists of Sphagnum-dominated or sedge vegetationcommunities (18%), sparsely vegetated or bare peat and open water. Most mature foreststands occur along the lagg and rand of the bog, whereas areas near the center are open.In this study, sample plots were established at one site in each of five plant communities(Table 5.2; Figure 5.1). The selection of the communities to sample was based on twocriteria: first, the spatial extent, in order to maximize the area of Burns Bog representedin this study; and second, similarity of vegetation communities in relation to hydrologicalprocesses (Table 5.2). Collectively, the field sites included in this study cover the completerange of vegetation structures existing in Burns Bog (Figure 5.3).5.2.2. Field measurementsInstrumentation to monitor the canopy water balance was installed at one site each in thefive vegetation types shown in Table 5.2; site locations are shown in Figure 5.1. Photographsof monitoring sites are shown in Figure 5.3 and details on meteorological observations fromthe BB1 flux tower system near the bog’s center and micro-meteorological stations at eachfield site are provided in Chapter 2.2.In this chapter, throughfall is defined as the sum of direct throughfall through gaps in thecanopy plus canopy drip. Stemflow is defined as water that reaches the peat surface byflowing down the tree trunk. Interception loss is defined as water that is intercepted byvegetation and lost to the atmosphere by evaporation from wetted surfaces such as leavesor needles and the trunk.Plot-averaged throughfallPlot-averaged throughfall was measured using a stratified random sampling approach. Eachfield plot (30 by 30 m) was divided into six 5-m wide north-south oriented strata in whichthroughfall collectors were randomly relocated during each field visit, approximately everytwo weeks. Funnels, 22.0 cm in diameter, were connected to water jugs using garden hoseand installed above the shrub layer at approximately 1.2 m to measure overstory throughfall(Figure A2.2a).Shrub throughfall was measured using 10-cm-long sections of PVC pipe that were cappedand sealed at the bottom and had a bevelled top edge. Depending on shrub size, collectorswere 5.2 cm or 4.0 cm in diameter and styrofoam balls were used to minimize evaporationin summer (Figure A2.2b and A2.2c).64B−SaL−TP−tallP−lowP−SaN2 km49.09°N49.10°N49.11°N49.12°N49.13°N49.14°N49.15°N123.02°W 123.00°W 122.98°W 122.96°W 122.94°W 122.92°WVegetation Hebda (2000)B−Sa (0.03)P−Sa (0.08)P−wood (forest) (0.06)P−tall (0.15)P−low (0.31)Mixed forest (−)Hardhack (< 0.01)Wb−ts (0.12)Wb−S (0.18)Water lily (0.01)Open water (0.03)Disturbed (0.01)AG (−)Bare (0.02)Figure 5.1.: A map of Burns Bog with the extent of the Burns Bog Ecological ConservancyArea (red line) and locations of field sites (black squares). In the legend, B-Sa =Birch salal, P-Sa = Pine salal, P-wood = Pine woodland (mature forest), P-tall= Pine tall shrub, P-low = Pine low shrub, Mixed forest = Redcedar, fir andalder, Hardhack = Hardhack shrub, Wb-ts = White beakrush threeway-sedge(open), Wb-S = White beakrush Sphagnum (open), Water lily = vegetatedponds (open water), Disturbed = disturbed heathland, AG = agriculture, andBare = bare peat. In the legend, the areal fraction of BBECA covered by eachvegetation community is provided in brackets. The map is based on Hebdaet al. (2000); an updated version is currently being developed but not yetavailable.65Table 5.2.: Characteristics of vegetation communities in which plots were established. Descriptions are based on Hebda et al. (2000) (see also Figure 5.1). The areal extent is provided in km2 and as fraction of the total area of BBECA (fBB).Community Area 𝑓𝐵𝐵 (−) DescriptionB-Sa 0.63 0.03 Birch (Betula papyrifera) and (Betula pendula) occupiesrelatively dry sites and possesses a dense Salalunderstory (Gaultheria shallon).P-Sa 1.82 0.08 Dominated by lodgepole pine (Pinus contorta) andoccational western hemlock spruce (Tsuga heterophylla)occupies relatively dry sites and possesses a dense Salalunderstory (Gaultheria shallon).P-tall 3.32 0.15 Dominated by lodgepole pine (Pinus contorta) andoccational western hemlock spruce (Tsuga heterophylla)occupies sites with intermediate wetness with a Labradortea understory (Rhododendron groenlandicum) andSphagnum moss covering the bog surface.P-low 6.94 0.31 Stunted and sparse pine (Pinus contorta) occupies wetsites with an understory consisting of stunted Labradortea (Rhododendron groenlandicum), herbs and Sphagnumcovering the ground.L-T 0.11 0.01 Tall Labrador tea (Rhododendron groenlandicum) andSphagnum growing on the bog surface. Inundated duringperiods of high groundwater levels. Classified asHardhack in Hebda et al. (2000).66Sites with sparse canopy were equipped with a total of 12 collectors (two per stratum)for each canopy layer. The continuous-canopy B-Sa site was equipped with a total of 24collectors (four per stratum) for each canopy layer, and throughfall below the single shrublayer at the L-T site was measured using a total of 30 collectors (five per stratum).Tree-based throughfall samplingTree-based throughfall estimates were made at randomly selected trees in each plot at siteswith a sparse tree canopy (P-Sa, P-tall and P-low), using PVC pipe collectors describedabove. At least three trees were monitored at the same time in each plot. Spatial clusteringof samples was prevented by subdividing each plot (30 by 30 m) into six 10 by 15 m strataand by selecting only one tree per stratum. Not all strata were sampled in the intervalsbetween field visits, and the sampled strata for each interval were selected randomly. Afterfive or six field trips collectors were moved to new trees to sample throughfall variabilitybetween trees. Occasionally, the number of observations from field trips was less than fivefor a given tree due to missing data caused by snow or freezing in January and February,but usually measurements for a given tree would be available from all nine collectors. AtP-Sa, P-tall and P-low a total of twelve, nine and fifteen trees were sampled, respectively.Tree-based throughfall sampling was based on a modified version of the scheme used byCarlyle-Moses and Lishman (2015), who placed collectors near the stem, mid-distance be-tween stem and the canopy edge, at the canopy periphery, and one in the open area awayfrom the tree. At each tree, collectors were located below both the tree and shrub lay-ers, and were arranged in three transects separated by angular distances of about 120∘and radiating outwards from the trunk (Figure A2.3). Along each transect, measurementswere taken at different distances from the trunk that roughly corresponded to throughfallfrom the inner canopy (0 to 0.5 times the mean canopy radius), outer canopy (0.5 to 1.0times the mean canopy radius), and outside canopy (greater than the mean canopy radius,i.e. an area without overhead canopy). During field trips, approximately every two weeks,transects and collector distances were changed randomly.StemflowStemflow from trees was measured at each site with overstory vegetation (B-Sa, P-Sa, P-talland P-low) between April and November 2019. Five trees were randomly selected at allsites except for P-tall. At the P-tall site, six trees were sampled to increase the number ofwestern hemlock trees in the sample, which made up a larger proportion of trees at P-tallcompared to the other sites. Stemflow was collected by a collar of braided vinyl tubing cutlongitudinally that was stapled and sealed to the trunk using silicone after some bark wasremoved for better fit (Figure A2.4). Tubing was wrapped around the trunk twice in anupward spiral pattern and drained into a 10-L jug. The tubing end connected to the jug wasnot cut and stemflow collars were cleaned during every field trip to prevent clogging andspillage. During periods of high precipitation some jugs used to collect stemflow overflowed;these censored observations were excluded from analysis.67Canopy opennessCanopy openness was calculated from hemispherical photographs (Figure A2.5). At theB-Sa, P-Sa, P-tall and P-low sites, photographs were taken for each tree-layer throughfallobservation for both tree-based and plot-averaged sampling approaches. Shrub canopyopenness at these sites was determined from photographs taken above shrub throughfallcollectors using the plot-averaged sampling approach at one time in mid-July 2018. Shrubcover was assumed to be constant throughout the year. At the L-T site, photographswere taken for each shrub throughfall observation throughout the field campaign. Due tothe frequency at which photographs were taken (approximately every other week duringfield visits at each gauge location), it was not always possible to take photographs duringdiffuse skylight conditions to minimize penumbral effects from the sun. The data weremainly used as a visual reference in scatterplots, and were only used in the analysis as acovariate in a regression model of throughfall versus gross precipitation for B-Sa. At thatsite, even with penumbral effects, the photographs should robustly capture the differencebetween leaf-on and leaf-off conditions. Hemispherical photographs were processed usingthe R-package Hemiphot (ter Steege, 2018). Canopy openness was calculated from thewhole sky dome captured by the hemispherical photograph and the blue channel was usedto generate a binary image. Binarization can be sensitive to a threshold used to split pixelsof the digital photograph into “canopy” (black) or “open sky” (white) category and thethreshold is commonly adjusted manually for each photograph (Díaz and Lencinas, 2018).For a subsample of photographs during various sky light conditions an appropriate thresholdwas manually determined to be 0.45; however, it was not possible to apply this procedureto all photographs due to the large number of photographs taken (𝑛 > 2650). Instead,mean canopy openness from a wide range of thresholds was calculated for each photograph(0.35 to 0.55 by 0.01 increments) and only if the standard deviation of computed canopyopenness was more than 0.05 a photograph was checked manually, as standard deviation ofcanopy openness was deemed an appropriate metric to estimate threshold sensitivity. Toobtain canopy openness from binary hemispherical images, the hemisphere was divided into90 concentric rings (each ring corresponding to a circular sphere segment with an arc of1∘) and each ring contained 360 individual points. Canopy openness was calculated frommeans of each ring that were weighted by the relative area of the sky dome covered by thering (ter Steege, 2018).5.2.3. CalculationsFormulae for the computation of throughfall means and uncertainties in this section wereadapted from Gilbert (1987).Plot-averaged mean throughfallMean tree and shrub throughfall between field trips was calculated from random observa-tions arranged in 𝐿 equal-sized strata. In each stratum ℎ, throughfall 𝑦 was sampled 𝑛ℎ68times, such that 𝑦𝑖ℎ represented the 𝑖th measured value in stratum ℎ. The estimated meanthroughfall for the plot, ̄𝑦𝑝, was given by:̄𝑦𝑝 =𝐿∑ℎ=1𝑤ℎ ̄𝑦ℎ (5.1)where 𝑤ℎ is the fraction of total plot area represented by stratum ℎ and ̄𝑦ℎ is the mean forstratum ℎ, calculated as̄𝑦ℎ =1𝑛ℎ𝑛ℎ∑𝑖=1𝑦𝑖ℎ (5.2)For equal-sized strata, the weights are all equal, and were calculated as:𝑤ℎ = 𝐿−1 (5.3)Plot-averaged throughfall uncertaintyAn estimate of the variance for each stratum mean throughfall (𝑠2ℎ) was calculated as:𝑠2ℎ =1𝑛ℎ − 1𝑛ℎ∑𝑖=1(𝑦𝑖ℎ − ̄𝑦ℎ)2 (5.4)The variance of the plot mean (𝑠2𝑝) was then estimated as:𝑠2𝑝 =𝐿∑ℎ=1𝑤2ℎ𝑠2ℎ (5.5)The standard uncertainty of the plot mean (𝑠𝑝) was then computed as:𝑠𝑝 = √𝑠2𝑝 (5.6)Confidence intervals were estimated as follows:𝐶𝐼 ̄𝑦 = ̄𝑦𝑝 ± 𝑘 𝑠𝑝 (5.7)where 𝑘 is a “coverage factor.” Using a value of 𝑘 = 2 in Eq. 5.7 defines an interval havinga confidence level of approximately 95% (National Institute of Standards and Technology,2017).69Tree-based mean throughfallEach plot was divided into four strata based on distance from the nearest tree, as follows:𝐶𝑐 =⎧{{⎨{{⎩0.0 ≤ 𝑑 < 0.5, inner0.5 ≤ 𝑑 < 1.0, outer1.0 ≤ 𝑑 < 3.0, outside𝑑 ≥ 3.0, open(5.8)where 𝑑 was the normalized distance from the trunk of each collector, calculated as:𝑑 = 𝑑𝑐𝑟𝑡(5.9)where 𝑑𝑐 was the distance (m) of collector 𝑐 from the nearest trunk of tree 𝑡 and 𝑟𝑡 was themean tree canopy radius (m) of tree 𝑡 calculated from eight measurements with an angulardistance of 45∘.In each sampling period, below-canopy throughfall was measured at 𝑛𝑡 trees. At each site,the number of samples from all trees in the “inner”, “outer” and “outside” canopy zonewere 𝑛𝑖, 𝑛𝑜 and 𝑛𝑜𝑠, respectively.For each sampling period, the mean throughfall in each zone for tree 𝑡 was computed asfollows:̄𝑥𝑡𝑖 =1𝑛𝑡𝑖𝑛𝑡𝑖∑𝑗=1𝑥𝑡𝑖,𝑗 (5.10)̄𝑥𝑡𝑜 =1𝑛𝑡𝑜𝑛𝑡𝑜∑𝑘=1𝑥𝑡𝑜,𝑘 (5.11)̄𝑥𝑡𝑜𝑠 =1𝑛𝑡𝑜𝑠𝑛𝑡𝑜𝑠∑𝑚=1𝑥𝑡𝑜𝑠,𝑚 (5.12)where 𝑥𝑡𝑖,𝑗, 𝑥𝑡𝑜,𝑘 and 𝑥𝑡𝑜𝑠,𝑚 refer to individual throughfall measurements in each zone foreach tree, and ̄𝑥𝑡𝑖, ̄𝑥𝑡𝑜 and ̄𝑥𝑡𝑜𝑠 refer to the mean throughfall in each zone for each tree.For each sampling period, the mean throughfall in each canopy zone was computed asfollows:̄𝑥𝑖 =1𝑛𝑡𝑛𝑡∑𝑡=1̄𝑥𝑡𝑖 (5.13)70̄𝑥𝑜 =1𝑛𝑡𝑛𝑡∑𝑡=1̄𝑥𝑡𝑜 (5.14)̄𝑥𝑜𝑠 =1𝑛𝑡𝑛𝑡∑𝑡=1̄𝑥𝑡𝑜𝑠 (5.15)where ̄𝑥𝑖, ̄𝑥𝑜 and ̄𝑥𝑜𝑠 refer to the mean throughfall in each zone.The mean throughfall at the plot scale was computed as follows:̄𝑥𝑝 = 𝑓𝑖 ̄𝑥𝑖 + 𝑓𝑜 ̄𝑥𝑜 + 𝑓𝑜𝑠 ̄𝑥𝑜𝑠 + (1 − 𝑓𝑖 − 𝑓𝑜 − 𝑓𝑜𝑠)𝑃𝑜𝑝𝑒𝑛 (5.16)where 𝑓𝑖, 𝑓𝑜 and 𝑓𝑜𝑠 are the fractions of the plot within the inner, outer and outside canopyzones, respectively, and 𝑃𝑜𝑝𝑒𝑛 is the precipitation measured at an open site adjacent to theplot.Tree-based throughfall uncertaintyIt was assumed that the sampling variance of 𝑃𝑜𝑝𝑒𝑛 and the estimation variances for 𝑓𝑖, 𝑓𝑜and 𝑓𝑜𝑠 are all negligible compared to the sampling variances of the throughfall measure-ments. The variance of the plot-scale mean throughfall from tree-based sampling was thengiven by:𝑠2𝑝𝑡 = 𝑓2𝑖 𝑠2𝑖 + 𝑓2𝑜𝑠2𝑜 + 𝑓2𝑜𝑠𝑠2𝑜𝑠 (5.17)where 𝑠2𝑖 , 𝑠2𝑜 and 𝑠2𝑜𝑠 are the variances associated with mean throughfall for each canopyzone.The variance of the inner canopy zone mean throughfall (𝑠2𝑖 ) was computed as:𝑠2𝑖 =1𝑛2𝑡𝑛𝑡∑𝑡=1𝑠2𝑡𝑖 (5.18)where 𝑠2𝑡𝑖 was the estimated variance of the mean throughfall in the inner zone for tree 𝑡,given by:𝑠2𝑡𝑖 =1𝑛𝑡𝑖 − 1𝑛𝑡𝑖∑𝑗=1(𝑥𝑡𝑖,𝑗 − ̄𝑥𝑡𝑖)2 (5.19)The variance of the outer canopy zone mean (𝑠2𝑜) was computed as:71𝑠2𝑜 =1𝑛2𝑡𝑛𝑡∑𝑡=1𝑠2𝑡𝑜 (5.20)where 𝑠2𝑡𝑖 was the estimated variance of the mean throughfall in the outer zone for tree 𝑡,given by:𝑠2𝑡𝑜 =1𝑛𝑡𝑜 − 1𝑛𝑡𝑜∑𝑘=1(𝑥𝑡𝑜,𝑘 − ̄𝑥𝑡𝑜)2 (5.21)The variance of the outside canopy zone mean (𝑠2𝑜𝑠) was computed as:𝑠2𝑜𝑠 =1𝑛2𝑡𝑛𝑡∑𝑡=1𝑠2𝑡𝑜𝑠 (5.22)where 𝑠2𝑡𝑜𝑠 was the estimated variance of the mean throughfall in the outside zone for tree𝑡, given by:𝑠2𝑡𝑜𝑠 =1𝑛𝑡𝑜𝑠 − 1𝑛𝑡𝑜𝑠∑𝑚=1(𝑥𝑡𝑜𝑠,𝑚 − ̄𝑥𝑡𝑜𝑠)2 (5.23)The standard uncertainty of the plot mean was then computed as:𝑠𝑝𝑡 = √𝑠2𝑝𝑡 (5.24)Confidence intervals were estimated as follows:𝐶𝐼?̄? = ̄𝑥𝑝 ± 𝑘 𝑠𝑝𝑡 (5.25)where 𝑘 is a coverage factor, defined earlier.Standard uncertainties of the mean throughfall for each canopy zone were calculated basedon canopy zone throughfall variances obtained from Eqs. 5.18, 5.20 and 5.22. The standarduncertainties of mean throughfall in the inner canopy zone (𝑠𝑖), outer canopy zone (𝑠𝑜) andoutside canopy zone (𝑠𝑜𝑠) were calculated as:𝑠𝑖 = √𝑠2𝑖 (5.26)𝑠𝑜 = √𝑠2𝑜 (5.27)72𝑠𝑜𝑠 = √𝑠2𝑜𝑠 (5.28)Estimating fractional cover by canopy zoneThe equations presented in the preceding section indicate how the measurements were scaledup. This section focuses on estimating the fractional coverage by different canopy classes.Plot-averaged throughfall was estimated from tree-based sampling using a semi-distributedapproach that combined manual throughfall observations from canopy zones with remotelysensed vegetation metrics based on LiDAR data, which were processed with the R-packageLidR (Roussel et al., 2020; Roussel and Auty, 2021). LiDAR observations were obtainedon June 16, 2018, using an aircraft mounted Optech Galaxy T1000, with a non-groundpoint density of 9.9 pt m-2. Individual tree tops were identified using a local maximumfilter on the LiDAR point cloud (Popescu and Wynne, 2004). Window size of the filter wasiteratively adjusted so that the Lidar-based tree count agreed within one tree with ground-based surveys of the overstory tree population at each site. Calibrated window sizes atB-Sa, P-Sa, P-tall and P-low were 2.40 m, 2.35 m, 1.90 m and 2.70 m, respectively. Fromthe point cloud each tree was segmented using previously identified tree tops following Silvaet al. (2016) and each tree’s canopy area was obtained by computing the convex hull of eachsegmented tree following Park and Oh (2012). The canopy radius of each individual treewas calculated from the canopy area by assuming a circular crown shape. Relative arealfractions (𝑓𝑐) of canopy zones (inner, outer, outside, open) were estimated by measuringthe distance of 10,000 random locations to the closest tree and by scaling the distance usingthe tree’s canopy radius, as spatial throughfall patterns are better captured as a functionof normalized distance by crown diameter rather than as a simple distance from the trunk(e.g Zirlewagen and Wilpert, 2001; Carlyle-Moses and Lishman, 2015). Canopy zones weredefined as in Eq. 5.8.StemflowStemflow observations from trees were limited to the period between April to November2019. For each tree at each field site, a linear relation was fit between volumetric stemflowand open precipitation, as follows:𝑆𝐹𝑗,𝑡 = 𝑏0 + 𝑏1𝑃𝑜𝑝𝑒𝑛,𝑡 + 𝑒𝑡 (5.29)where 𝑆𝐹𝑗,𝑡 is the stemflow volume (L) measured at tree 𝑗 during the sampling period 𝑡,and 𝑃𝑜𝑝𝑒𝑛,𝑡 is gross precipitation (mm) during the same period. Some intercepts of linearregression models were positive but not significantly different from zero. In these instances,linear regression models were forced through the origin.Stemflow depth per unit plot area was calculated as:73𝑆𝐹𝑝𝑡 = (?̄?0 + ?̄?1𝑃𝑜𝑝𝑒𝑛,𝑡)𝑛𝐴 (5.30)where ?̄?0 and ?̄?1 are the means of the intercepts and slopes for all trees in the plot, 𝑛 isthe number of trees in the plot, and 𝐴 is the plot area (m2). This approach to scaling upstemflow volumes to a mean depth over the plot is consistent with Carlyle-Moses and Price(1999).Stemflow from shrub cover was not measured in this study. It was assumed to be a constantratio of gross precipitation as:𝑆𝐹𝑠 = 𝑏𝑠𝑃𝑜𝑝𝑒𝑛 (5.31)where 𝑆𝐹𝑠 = stemflow from shrub (mm), and 𝑏𝑠 was assumed to lie between 0.05 and 0.1(Päivänen, 1966; Zwieback et al., 2019).Interception loss and net precipitationTree and shrub interception loss were calculated from gross precipitation, throughfall andstemflow as:𝐼𝐿𝑡 = 𝑃𝑜𝑝𝑒𝑛 − ( ̄𝑦𝑡,𝑝 + 𝑆𝐹𝑡) (5.32)𝐼𝐿𝑠 = ̄𝑦𝑡,𝑝 − ( ̄𝑦𝑠,𝑝 + 𝑆𝐹𝑠) (5.33)Net precipitation was calculated as:𝑃𝑛𝑒𝑡 = ̄𝑦𝑠,𝑝 + 𝑆𝐹𝑡 + 𝑆𝐹𝑠 (5.34)where 𝑃𝑛𝑒𝑡 = net precipitation at the bog surface (mm), ̄𝑦𝑡,𝑝 and ̄𝑦𝑠,𝑝 = mean tree and shrubthroughfall for the plot respectively (mm), and 𝑆𝐹𝑡 and 𝑆𝐹𝑠 = tree and shrub stemflow(mm).5.2.4. Regression analysisThe relationship between plot-averaged tree throughfall and gross precipitation at sites withconifer vegetation (P-Sa, P-tall, P-low) and the relationship between plot-averaged shruband tree throughfall at each site was described using linear regression models of the form:𝑇𝐹𝑖 = 𝑏0 + 𝑏1𝑃𝑖 + 𝑒𝑖 (5.35)74where 𝑖 denotes periods of observation, 𝑇𝐹𝑖 = plot-averaged throughfall (mm), 𝑃𝑖 = grossprecipitation or plot-averaged tree throughfall (mm), and 𝑒𝑖 = error term.At B-Sa canopy openness was a significant predictor (𝑝 < 0.05) for plot-averaged treethroughfall observations between June 2018 and May 2019. A multiple-linear regressionmodel was fit, as follows:𝑇𝐹𝑖 = 𝑏0 + 𝑏1𝑃𝑖 + 𝑏2𝐶𝑜𝑝𝑒𝑛 + 𝑒𝑖 (5.36)where 𝑃𝑖 = gross precipitation and 𝐶𝑜𝑝𝑒𝑛 = canopy openness (-), and 𝑒𝑖 = error term. Therelationship between tree-based tree throughfall and gross precipitation or tree-based shruband tree throughfall at each site with conifer vegetation (P-Sa, P-tall, P-low) was describedusing linear regression models of the form:𝑇𝐹𝑖,𝑐 = 𝑏0 + 𝑏1𝑃𝑖,𝑐 + 𝑒𝑖,𝑐 (5.37)where 𝑖 denotes periods of observation, 𝑇𝐹𝑖,𝑐 = tree-based throughfall in canopy zone 𝑐(i.e. inner, outer or outside) (mm), 𝑃𝑖,𝑐 = gross precipitation or tree-based tree throughfallin canopy zone 𝑐 (mm), and 𝑒𝑖,𝑐 = error term.Plot-scale throughfall estimates from plot-averaged (Eq. 5.1) and tree-based (Eq. 5.16)observations were compared by fitting a linear regression model of the form:̄𝑦𝑝,𝑖 = 𝑏0 + 𝑏1 ̄𝑥𝑝,𝑖 + 𝑒𝑖 (5.38)where 𝑖 denotes periods of observation, ̄𝑦𝑝,𝑖 = plot-averaged plot-scale throughfall (mm),̄𝑥𝑝,𝑖 = tree-based plot-scale throughfall (mm), and 𝑒𝑖 = error term.For all relations, the range of scatter around the regression line tended to increase withgross precipitation or tree throughfall. Therefore, relations were fit using weighted leastsquares regression, with weights that were inversely proportional to the square of grossprecipitation or tree throughfall as appropriate.5.2.5. Missing dataOn rare occasions, heavy or prolonged rainfall caused tree-based throughfall collectors andplot-averaged shrub throughfall collectors to overflow. To avoid underestimation of truethroughfall due to censored data without discarding all observations, mean throughfall( ̄𝑦𝑝) was estimated as follows: i) a linear regression model was fit to compute the meanfrom the median throughfall for field visits with no missing data, ii) missing values fromoverflowed collectors were replaced with values exceeding collector capacity (100 mm), iii)when the number of overflowed collectors (𝑛𝑜𝑓) was 𝑛𝑜𝑓 < 0.5 ⋅ 𝑛 + 1, where 𝑛 = totalnumber of collectors, mean throughfall was estimated from the linear relationship usingmedian throughfall of all observations including replaced ones due to collector overflow.75This approach implicitly assumes that the distribution of throughfall observations wassimilar between field visits. Metrics of fit are provided in the appendix (Table A2.1). Whenmore than 𝑛𝑜𝑓 = 0.5 ⋅ 𝑛 + 1 collectors overflowed, data were excluded from the analysis, asthe median could not be calculated.Missing throughfall observations due to disconnected or toppled-over plot-averaged treethroughfall collectors, or wildlife interference in rare occasions, were assumed to be randomand to not introduce bias to calculated mean throughfall.For the calculation of canopy water fluxes, tree and shrub throughfall on dates with insuffi-cient data to compute mean throughfall (e.g. due to frozen collectors in winter or when themajority of shrub throughfall collectors overflowed) were estimated using weighted linearregression models of plot-averaged tree throughfall and gross precipitation or plot-averagedshrub and tree throughfall (Eqs. 5.35 to 5.36).5.3. Results5.3.1. Overview of study periodIn the two years of observation, daily air temperature (𝑇𝑎) varied between highs of around30∘C in summer and lows of -5∘C to -10∘C in winter; however, air temperature stayed abovezero on most winter days (Figure 5.2). Extended periods of rain and overcast skies inwinter generated conditions of low incident solar radiation (𝐾↓) and high relative humidity(𝑅𝐻) with the exception of occasional frost days when relative humidity dropped sharply.In summer, long periods of clear skies produced high incident solar radiation and lowerhumidity. Atmospheric conditions were more turbulent in summer when daily minimumwind speed (𝑣) was above 1 m s-1 than in winter when it stayed below 1 m s-1 and windy dayswith wind speed up to about 6 m s-1 occurred occasionally. The majority of precipitation(𝑃 ) typically fell as rain between October to mid-spring.5.3.2. Vegetation characteristicsPlot-averaged tree canopy cover at the field sites ranged from almost none (< 0.01) tonearly complete cover (0.98) and plot-averaged shrub cover varied in density and height(Figure 5.3 and Table 5.3). At sites with sparse tree canopies (i.e. P-Sa, P-tall and P-low),fractions of each canopy zone were estimated using airborne LiDAR (Table 5.3).76−100102030T a (°C)maxmin406080100RH (%)0.00.30.60.91.2VPD (kPa)246v (m s−1 )0100200300K ↓ (W m−2 )0204060Jan2018Jul2018Jan2019Jul2019Jan2020P (mm)Figure 5.2.: Daily meteorological data during the two years of study. Plots show from top tobottom air temperature (𝑇𝑎), relative humidity (𝑅𝐻), wind speed (𝑣), incidentsolar radiation (𝐾𝑑𝑜𝑤𝑛𝑎𝑟𝑟𝑜𝑤), and precipitation (𝑃 ). Values of 𝑅𝐻, 𝑉 𝑃𝐷, 𝑣and 𝐾𝑑𝑜𝑤𝑛𝑎𝑟𝑟𝑜𝑤 are daily means, while 𝑃 is shown as daily totals.77bac deFigure 5.3.: Photos of vegetation communities at monitoring sites. Red funnels are treethroughfall collectors used in the plot-average approach. a) Birch salal (B-Sa),b) Pine salal (P-Sa), c) Pine tall shrub (P-tall), d) Pine low shrub (P-low), ande) Labrador tea (L-T).78Table 5.3.: Vegetation metrics obtained from airborne LiDAR include tree count, treeheight, relative tree canopy area (𝐶𝑎𝑟𝑒𝑎), tree canopy radius (𝐶𝑟𝑎𝑑𝑖𝑢𝑠) andcanopy zone fractions at sites with sparse tree cover (e.g. 𝑓𝑖𝑛𝑛𝑒𝑟 is relative plotarea classified as inner canopy zone). Ground-based observations include heightof tree canopy base, seasonal tree canopy openness (𝐶𝑜𝑝𝑒𝑛), basal area (𝐴𝑏𝑎𝑠𝑎𝑙),mean (𝐷𝐵𝐻), 25th (𝐷𝐵𝐻25) and 75th (𝐷𝐵𝐻75) quantile of the distribution oftree trunk diameter at breast height, and shrub height. Height of canopy baseis a ground-based observation that was obtained from a sub-sample of trees andit cannot be directly compared to tree height from airborne LiDAR.Variable B-Sa P-Sa P-tall P-low L-TTree count 88 61 77 31Tree density (stems ha-1) 1058 746 961 383Tree height (m) 18.74 12.6 6.89 4.32Tree canopy base (m) 8.43 2.58 0.78Relative Carea (-) 0.98 0.67 0.58 0.17Cradius (m) 1.73 1.72 1.39 1.2finner 0.13 0.11 0.04fouter 0.37 0.29 0.14foutside 0.49 0.56 0.55fopen 0.01 0.05 0.26DBH (cm) 9.18 16.3 10.21 4.14DBH25 (cm) 2.1 12 6.08 1.73DBH75 (cm) 15.34 20.13 13.85 4.42Abasal (m2 ha-1) 34.38 22.1 21.87 4.92Copen,leafed (-) 0.3 0.58 0.61 0.79Copen,leafless (-) 0.68Copen,shrub (-) 0.16 0.18 0.22 0.33 0.38Shrub height (cm) 66.02 62.56 61.8 33.45 105.775.3.3. Plot-averaged throughfallPlot-averaged tree throughfall was highest at P-low, where trees were most sparse andstunted (Figure 5.4). Throughfall at P-Sa and P-tall was lower than at P-low, and it wassimilar at P-Sa and P-tall despite differences in canopy area, tree height and density (Figure5.4 and Table 5.3). Tree throughfall at B-Sa varied between 72% to 83% of gross precipi-tation during the leafed and leafless period, respectively. Plot-averaged shrub throughfallwas less than tree throughfall at each site (Figure 5.5). It varied among sites, but there wasno clear relationship with plot-averaged canopy openness below the shrub layer or shrubheight (Figure 5.5 and Table 5.3).79y = − 0.96 + 0.81 x, r2 = 0.989y = − 1.8 + 0.91 x, r2 = 0.992y = − 0.34 + 0.87 x, r2 = 0.991y = − 1.6 + 0.96 x, r2 = 0.984P−tall P−lowB−Sa P−Sa0 100 200 300 0 100 200 30001002003000100200300Popen (mm)TFy,t,p (mm)0.40.60.8CopenFigure 5.4.: Plot-average tree throughfall observations ( ̄𝑦𝑡,𝑝, Eqs. 5.1 to 5.3) versus grossprecipitation (𝑃𝑜𝑝𝑒𝑛) by site with 1:1-line (dashed line). A weighted linearregression (Eqs. 5.35 to 5.36) was fit (solid line), including a 95% confidenceinterval (grey bands). Error bars show the standard uncertainty of the mean(Eq 5.6). 𝐶𝑜𝑝𝑒𝑛 shows mean tree canopy openness calculated from hemisphericalphotographs for each observation. In this and other scatterplots, where errorbars are not visible, they are covered by the plotting symbol.80y = − 0.71 + 0.66 x, r2 = 0.92y = − 0.78 + 0.73 x, r2 = 0.91y = − 1.4 + 0.69 x, r2 = 0.93y = − 2.6 + 0.63 x, r2 = 0.89y = − 1 + 0.6 x, r2 = 0.93P−low L−TB−Sa P−Sa P−tall0 50 100 150 0 50 100 1500 50 100 150050100150050100150TFy,t,p (mm)TFy,s,p (mm)0.20.30.4CopenFigure 5.5.: Plot-average shrub throughfall observations ( ̄𝑦𝑠,𝑝, Eqs. 5.1 to 5.3) versus treethroughfall ( ̄𝑦𝑡,𝑝) (at L-T ̄𝑦𝑡,𝑝 = 𝑃𝑜𝑝𝑒𝑛) by site with 1:1-line (dashed line).A weighted linear regression (Eq. 5.35) was fit (solid line), including a 95%confidence interval (grey bands). Error bars show the standard uncertainty ofthe mean (Eq 5.6). 𝐶𝑜𝑝𝑒𝑛 shows mean canopy openness below the shrub layercalculated from hemispherical photographs taken once in mid-July 2018, exceptat L-T where shrub canopy openness was computed for each field visit.815.3.4. Tree-based throughfall in sparse canopy standsTree throughfall at each site with sparse canopy increased with distance from the trunk(Figure 5.6). The increase was most pronounced at P-tall, where the difference in throughfallbetween inner and outside canopy zones was highest. The difference in throughfall betweeninner and outside canopy zones at P-Sa and P-low was similar; however, low throughfall atP-Sa was mostly limited to the inner canopy zone. Tree throughfall in the outside canopyzone was similar despite differences in canopy openness of that zone between sites (Table5.3).y = − 2.1 + 0.54 x, r2 = 0.90y = − 2.1 + 0.35 x, r2 = 0.88y = − 2.4 + 0.53 x, r2 = 0.95y = − 1.8 + 0.74 x, r2 = 0.95y = − 3.8 + 0.67 x, r2 = 0.95y = − 2 + 0.63 x, r2 = 0.94y = − 1.1 + 0.91 x, r2 = 0.98y = − 3.9 + 0.96 x, r2 = 0.97y = − 2.9 + 0.94 x, r2 = 0.98inner outer outsideP−SaP−tallP−low0 30 60 90 0 30 60 90 0 30 60 90030609003060900306090Popen (mm)TFx,t (mm)0.20.40.60.8CopenFigure 5.6.: Tree-based tree throughfall observations ( ̄𝑥𝑡, Eqs. 5.13 to 5.15) by site andcanopy zone with 1:1-line (dashed line). A weighted linear regression (Eq. 5.37)was fit (solid line), including a 95% confidence interval (grey bands). Error barsshow the standard uncertainty of the mean (Eqs. 5.26 to 5.28). 𝐶𝑜𝑝𝑒𝑛 is treecanopy openness above collector.Shrub throughfall at P-tall and P-low showed the opposite trend compared to tree through-fall; it decreased with increasing distance from the trunk, as indicated by the slopes of the82fitted regressions (Figure 5.7). At P-tall the decrease in shrub throughfall was stronger thanat P-low and shrub throughfall at P-low was generally higher (Figure 5.7). At P-Sa a cleartrend was not observed; canopy shrub cover showed no trend with distance from the trunk(Figure A2.4b). Shrub throughfall near the trunk was highest at P-low, followed by P-talland P-Sa. Shrub throughfall outside the tree canopy varied between sites in accordancewith site-averaged differences in canopy openness below the shrub layer (Table 5.3).y = − 0.15 + 0.65 x, r2 = 0.86y = 2.7 × 10−5 + 0.82 x, r2 = 0.94y = − 0.23 + 0.95 x, r2 = 0.77y = − 0.55 + 0.81 x, r2 = 0.85y = 0.17 + 0.74 x, r2 = 0.79y = − 0.49 + 0.81 x, r2 = 0.92y = − 1 + 0.6 x, r2 = 0.89y = − 0.4 + 0.64 x, r2 = 0.89y = − 0.32 + 0.78 x, r2 = 0.96inner outer outsideP−SaP−tallP−low0 30 60 90 0 30 60 90 0 30 60 90030609003060900306090TFx,t (mm)TFx,s (mm)0.20.40.60.8CopenFigure 5.7.: Tree-based shrub throughfall observations ( ̄𝑥𝑠, Eqs. 5.13 to 5.15) by site andcanopy zone with 1:1-line (dashed line). A weighted linear regression (Eq. 5.37)was fit (solid line), including a 95% confidence interval (grey bands). Error barsshow the standard uncertainty of the mean (Eqs. 5.26 to 5.28). 𝐶𝑜𝑝𝑒𝑛 is treecanopy openness identical to canopy openness of tree throughfall collector asshown in Figure 5.6, as no hemispherical photographs were taken for shrubcollectors.835.3.5. Comparison of throughfall estimatesTree throughfall estimates from subplot-based stratified random observations ( ̄𝑦𝑡,𝑝) wereconsistently higher than tree throughfall estimates from tree-based observations ( ̄𝑥𝑡,𝑝). Thebias was generally within the range of uncertainty and results show a strong and clearlydefined linear relationship (Figure 5.8). Shrub throughfall estimates from both approachesdid not exhibit clear bias; however, uncertainty and scatter between estimates based onrandom subplot sampling ( ̄𝑦𝑠,𝑝) and tree-based sampling ( ̄𝑥𝑠,𝑝) were considerable, withstandard uncertainties of up to 62% and 61% for the random and tree-based estimates,respectively (Figure 5.9).y = 1.41 + 1.08 x, r2 = 0.99 y = 0.9 + 1.18 x, r2 = 0.98 y = 0.573 + 1.06 x, r2 = 0.99P−Sa P−tall P−low0 25 50 75 100 0 25 50 75 100 0 25 50 75 1000255075100TFx,t,pTFy,t,pFigure 5.8.: Subplot-based average ( ̄𝑦𝑡,𝑝) versus tree-based ( ̄𝑥𝑡,𝑝) site average tree through-fall by site. Error bars depict 95% confidence intervals calculated from Eqs.5.4 to 5.7 and 5.17 to 5.25 for plot-average and tree-based values, respectively.Formulas show the weighted linear relationship between ̄𝑦𝑡,𝑝 and ̄𝑥𝑡,𝑝 computedby applying Eq. 5.38.Table 5.4.: Metrics of fit of weighted linear regression model fit to plot-average throughfall( ̄𝑦𝑡,𝑝, ̄𝑦𝑠,𝑝) versus tree-based site averages ( ̄𝑥𝑡,𝑝 and ̄𝑥𝑠,𝑝). 𝑅𝑆𝐸 = residualstandard error (mm), 𝑎 = intercept, 𝑠𝑎 = standard error of the intercept, 𝑏 =slope, 𝑠𝑏 = standard error of the slope, 𝑛 = number of observations (-).Model RSE 𝑟2 𝑎 𝑠𝑎 𝑏 𝑠𝑏 𝑛P-Sa ̄𝑦𝑡,𝑝 = 𝑎 + 𝑏 ⋅ ̄𝑥𝑡,𝑝 0.1050 0.99 1.41 0.19 1.08 0.04 9P-Sa ̄𝑦𝑠,𝑝 = 𝑎 + 𝑏 ⋅ ̄𝑥𝑠,𝑝 0.2829 0.95 0.34 0.10 1.18 0.09 13P-tall ̄𝑦𝑡,𝑝 = 𝑎 + 𝑏 ⋅ ̄𝑥𝑡,𝑝 0.1650 0.98 0.90 0.26 1.18 0.06 9P-tall ̄𝑦𝑠,𝑝 = 𝑎 + 𝑏 ⋅ ̄𝑥𝑠,𝑝 0.3210 0.94 -0.42 0.24 1.26 0.11 11P-low ̄𝑦𝑡,𝑝 = 𝑎 + 𝑏 ⋅ ̄𝑥𝑡,𝑝 0.1103 0.99 0.57 0.28 1.06 0.04 10P-low ̄𝑦𝑠,𝑝 = 𝑎 + 𝑏 ⋅ ̄𝑥𝑠,𝑝 0.2629 0.92 -0.22 0.51 0.96 0.09 1184y = 0.343 + 1.18 x, r2 = 0.95 y = − 0.419 + 1.26 x, r2 = 0.94 y = − 0.219 + 0.957 x, r2 = 0.92P−Sa P−tall P−low0 20 40 60 80 0 20 40 60 80 0 20 40 60 80020406080TFx,s,pTFy,s,pFigure 5.9.: Subplot-based average ( ̄𝑦𝑠,𝑝) versus tree-based ( ̄𝑥𝑠,𝑝) site average shrubthroughfall by site. Error bars depict 95% confidence intervals calculated fromEqs. 5.4 to 5.7 and 5.17 to 5.25 for plot-average and tree-based values, respec-tively. Formulas show the weighted linear relationship between ̄𝑦𝑠,𝑝 and ̄𝑥𝑠,𝑝computed by applying Eq. 5.38.Continuous tree throughfall observations from troughs at P-Sa and P-tall, and from asingle tipping bucket at P-low agreed well with estimated tree throughfall based on randomsampling ( ̄𝑦𝑡,𝑝) (Figure 5.10). At B-Sa trough throughfall showed good agreement with theexception of two prolonged rain periods, during which the trough-based estimate was lessthan the estimates from random sampling.5.3.6. StemflowVolumetric stemflow varied by species and was not related to diameter at breast height(𝐷𝐵𝐻). Stunted trees at P-low and P-tall generated similar amounts of stemflow as maturetrees at B-Sa (Figure 5.11). Trees at P-Sa generated less stemflow with the exception of Tree3, which had a high canopy base and was located adjacent to an opening in the directionstorms approached.Between July 2018 and April 2019 trees at B-Sa generated 19 mm of stemflow as a meandepth over the plot area, and at P-Sa, P-tall and P-low stemflow was 2 mm, 10 mm and 5mm (Table 5.5).5.3.7. Canopy water fluxes and net precipitationThe range in gross precipitation among sites was about 10% of the mean and sites in theeastern parts of the bog received more precipitation than sites in the south and west (Table5.5). Stemflow contributed between 0.4% and 3% of net precipitation. Interception lossfrom the tree canopy ranged from 7% to 16% of gross precipitation, while interception loss85y = 1.19 xy = 1.02 xy = 1.02 xy = 0.947 xP−tall P−lowB−Sa P−Sa0 100 200 0 100 20001002000100200TFtrough (mm)TFy,t,p (mm)0.40.60.8Pint (mm 30min−1)Figure 5.10.: Manual plot-averaged tree throughfall ( ̄𝑦𝑡,𝑝) versus continuous tree throughfall(𝑇𝐹𝑡𝑟𝑜𝑢𝑔ℎ) with 1:1-line (dashed line) and fit non-weighted linear regression(solid line), with a 95% confidence interval (grey bands). Colors show themean 30-minute precipitation intensity during the sampling period.86Tree 1 Tree 2 Tree 3 Tree 4 Tree 5 Tree 6B−SaP−SaP−tallP−low0 50 100 150 0 50 100 150 0 50 100 150 0 50 100 150 0 50 100 150 0 50 100 150051015051015051015051015Popen (mm)SF (L)10 15 20 25dbh Species Birch Hemlock PineFigure 5.11.: Stemflow volume versus gross precipitation by site and tree. 𝐷𝐵𝐻 increasesfrom left to right. Non-weighted linear regression was fit (solid line), with a95% confidence interval (grey bands). Colors show DBH and symbol shapesindicate tree species.87from the shrub canopy was up to roughly double that from the trees. The fraction of grossprecipitation intercepted by trees was higher in summer (Table 5.6) than in winter (Table5.7). Despite differences in phenology, summer and winter tree interception loss fractions ofgross precipitation were nearly the same at B-Sa and P-Sa. Seasonal changes in the fractionof gross precipitation lost to the atmosphere due to shrubs were not consistent across sites(Tables 5.6 and 5.7).Table 5.5.: Summary of net precipitation flux totals and interception loss over the courseof the field campaign between May 1, 2018 and April 24 to 30, 2019, dependingon site. 𝑃𝑜𝑝𝑒𝑛 = gross precipitation measured at a nearby clearing of eachsite, 𝑇𝐹𝑡 and 𝑇𝐹𝑠 are tree and shrub throughfall calculated from plot-averagedobservations (Eqs. 5.1 to 5.3), 𝑆𝐹𝑡 = tree stemflow per unit plot area calculatedusing Eqs. 5.29 to 5.30, and 𝑆𝐹𝑠 = range of shrub stemflow per unit plot areabetween 5-10% of 𝑇𝐹𝑡, or 𝑃𝑜𝑝𝑒𝑛 at L-T, becoming stemflow (Päivänen, 1966;Zwieback et al., 2019), and 𝑃𝑛𝑒𝑡 = net precipitation. All quantities are in mm.Variable B-Sa P-Sa P-tall P-low L-T𝑃𝑜𝑝𝑒𝑛 1075.6 930.1 1059.5 1060.0 1012.5𝑇𝐹𝑡 887.2 803.4 920.6 979.4𝑇𝐹𝑠 556.0 501.9 537.7 686.7 571.2𝑆𝐹𝑡 19.3 2.3 9.6 4.7𝑆𝐹𝑠 44.4 - 88.7 40.2 - 80.3 46.0 - 92.1 49.0 - 97.9 50.6 - 101.2𝐼𝐿𝑡 169.0 124.4 129.3 75.9𝐼𝐿𝑠 242.6 - 286.9 221.1 - 261.3 290.8 - 336.9 194.7 - 243.7 340.1 - 390.7𝑃𝑛𝑒𝑡 619.7 - 664.0 544.4 - 584.6 593.3 - 639.4 740.4 - 789.4 621.8 - 672.4𝐼𝐿𝑡/𝑃𝑜𝑝𝑒𝑛 0.16 0.13 0.12 0.07𝐼𝐿𝑠/𝑃𝑜𝑝𝑒𝑛 0.23 - 0.27 0.24 - 0.28 0.27 - 0.32 0.18 - 0.23 0.34 - 0.39𝑇𝐹𝑡/𝑃𝑛𝑒𝑡 0.82 0.86 0.87 0.92𝑇𝐹𝑠/𝑃𝑛𝑒𝑡 0.52 0.54 0.51 0.65 0.56𝑃𝑛𝑒𝑡/𝑃𝑜𝑝𝑒𝑛 0.58 - 0.62 0.59 - 0.63 0.56 - 0.60 0.70 - 0.74 0.61 - 0.66Table 5.6.: Summary of net precipitation flux totals and interception loss between May 1and September 24 to 27, 2018, depending on site. 𝑃𝑜𝑝𝑒𝑛 = gross precipitationmeasured at a nearby clearing of each site, 𝑇𝐹𝑡 and 𝑇𝐹𝑠 are tree and shrubthroughfall calculated from plot-averaged observations (Eqs. 5.1 to 5.3), 𝑆𝐹𝑡 =tree stemflow per unit plot area calculated using Eqs. 5.29 to 5.30, and 𝑆𝐹𝑠 =range of shrub stemflow per unit plot area between 5-10% of 𝑇𝐹𝑡, or 𝑃𝑜𝑝𝑒𝑛 atL-T, becoming stemflow (Päivänen, 1966; Zwieback et al., 2019), and 𝑃𝑛𝑒𝑡 =net precipitation. All quantities are in mm.Variable B-Sa P-Sa P-tall P-low L-T𝑃𝑜𝑝𝑒𝑛 164.2 156.9 143.1 143.1 170.5𝑇𝐹𝑡 119.7 125.7 127.2 134.588Variable B-Sa P-Sa P-tall P-low L-T𝑇𝐹𝑠 80.5 71.8 82.7 87.7 91.4𝑆𝐹𝑡 2.9 0.4 1.3 0.6𝑆𝐹𝑠 6.0 - 12.0 6.3 - 12.6 6.4 - 12.7 6.7 - 13.5 8.5 - 17.1𝐼𝐿𝑡 41.6 30.8 14.6 8.0𝐼𝐿𝑠 27.2 - 33.2 41.4 - 47.7 31.7 - 38.1 33.4 - 40.1 62.1 - 70.6𝑃𝑛𝑒𝑡 89.4 - 95.4 78.4 - 84.7 90.4 - 96.7 95.0 - 101.7 99.9 - 108.4𝐼𝐿𝑡/𝑃𝑜𝑝𝑒𝑛 0.25 0.20 0.10 0.06𝐼𝐿𝑠/𝑃𝑜𝑝𝑒𝑛 0.17 - 0.20 0.26 - 0.30 0.22 - 0.27 0.23 - 0.28 0.36 - 0.41𝑇𝐹𝑡/𝑃𝑛𝑒𝑡 0.73 0.80 0.89 0.94𝑇𝐹𝑠/𝑃𝑛𝑒𝑡 0.49 0.46 0.58 0.61 0.54𝑃𝑛𝑒𝑡/𝑃𝑜𝑝𝑒𝑛 0.54 - 0.58 0.50 - 0.54 0.63 - 0.68 0.66 - 0.71 0.59 - 0.64Table 5.7.: Summary of net precipitation flux totals and interception loss between October30 to November 1, 2018 and January 23 to 25, 2019, depending on site. 𝑃𝑜𝑝𝑒𝑛= gross precipitation measured at a nearby clearing of each site, 𝑇𝐹𝑡 and 𝑇𝐹𝑠are tree and shrub throughfall calculated from plot-averaged observations (Eqs.5.1 to 5.3), 𝑆𝐹𝑡 = tree stemflow per unit plot area calculated using Eqs. 5.29to 5.30, and 𝑆𝐹𝑠 = range of shrub stemflow per unit plot area between 5-10%of 𝑇𝐹𝑡, or 𝑃𝑜𝑝𝑒𝑛 at L-T, becoming stemflow (Päivänen, 1966; Zwieback et al.,2019), and 𝑃𝑛𝑒𝑡 = net precipitation. All quantities are in mm.Variable B-Sa P-Sa P-tall P-low L-T𝑃𝑜𝑝𝑒𝑛 637.8 561.2 629.5 629.5 631.2𝑇𝐹𝑡 549.8 469.0 542.4 592.1𝑇𝐹𝑠 296.0 307.7 294.4 401.7 314.5𝑆𝐹𝑡 11.5 1.4 5.7 2.8𝑆𝐹𝑠 27.5 - 55.0 23.4 - 46.9 27.1 - 54.2 29.6 - 59.2 31.6 - 63.1𝐼𝐿𝑡 76.6 90.9 81.5 34.6𝐼𝐿𝑠 198.9 - 226.4 114.4 - 137.8 193.7 - 220.8 131.3 - 160.9 253.6 - 285.1𝑃𝑛𝑒𝑡 334.9 - 362.4 332.6 - 356.0 327.2 - 354.3 434.1 - 463.7 346.0 - 377.6𝐼𝐿𝑡/𝑃𝑜𝑝𝑒𝑛 0.12 0.16 0.13 0.05𝐼𝐿𝑠/𝑃𝑜𝑝𝑒𝑛 0.31 - 0.35 0.20 - 0.25 0.31 - 0.35 0.21 - 0.26 0.40 - 0.45𝑇𝐹𝑡/𝑃𝑛𝑒𝑡 0.86 0.84 0.86 0.94𝑇𝐹𝑠/𝑃𝑛𝑒𝑡 0.46 0.55 0.47 0.64 0.50𝑃𝑛𝑒𝑡/𝑃𝑜𝑝𝑒𝑛 0.53 - 0.57 0.59 - 0.63 0.52 - 0.56 0.69 - 0.74 0.55 - 0.60895.4. Discussion5.4.1. Canopy water fluxesGross precipitationTotal precipitation during the study period was generally higher at the more easterly sites(B-Sa, P-tall, P-low) than at the more westerly sites (P-Sa and L-T). This pattern is con-sistent with the regional southwest-northeast precipitation gradient across the bog (Cheng,2011; Howie et al., 2020). Although the recording periods differed by a few days amongsites, this is not believed to have a major effect given that precipitation recorded at aflux tower near the open centre of the bog differed by less than 10 mm over the differentobservation periods.Tree throughfallAt B-Sa, average tree throughfall between June 20, 2018, and May 23, 2019, was 82%of gross precipitation and ranged from 72% to 83% during the leafed and leafless season,respectively. These values were higher than observations from a peatland in central Finland,where throughfall was 72% and 75% of gross precipitation under birch with relative canopycover of 0.26 and 0.37 and a basal area of 17 and 10 m2 ha-1 (Päivänen, 1966).At P-Sa, P-tall and P-low, tree throughfall was 86%, 87% and 92% of gross precipitation,respectively. In a peatland in central Finland, throughfall was 78% and 68-69% of grossprecipitation in pine stands with relative canopy cover of 0.14 and 0.24-0.27, and basalareas of 14-19 m2 ha-1 (Päivänen, 1966).The differences between Burns Bog and the Finnish sites could reflect the fact that resultsfrom Finnish sites are based on summer rainfall, whereas the values for Burns Bog includewinter, when less energy for evaporation of intercepted water was available.Shrub throughfallIn Burns Bog, mean shrub throughfall per plot varied between 51% and 65% of grossprecipitation, which is substantially lower than tree throughfall. The amount of grossprecipitation becoming shrub throughfall was less than what was found in four other studies.Zwieback et al. (2019) found shrub throughfall to be 76% of gross precipitation at sparseand 63% of gross precipitation at dense shrubby birch tundra sites. In a peatland complexin Alberta, Canada, shrub throughfall was similarly higher than observed in Burns Bogat both a burned (95%) and unburned (90%) site (Thompson et al., 2014), and in anabandoned cutover bog in Québec, shrub throughfall was estimated to be 81% of grossprecipitation (Farrick and Price, 2009), while throughfall under shrub and logging debriswas 77% of gross precipitation (Van Seters and Price, 2002).90Shrub throughfall was generally subject to higher uncertainty than tree throughfall andfuture studies would benefit from an increased number of collectors when quantifying shrubthroughfall.Stemflow from treesOn a per tree basis, stemflow generation was similar at B-Sa, P-tall and P-low, despitedifferences in 𝐷𝐵𝐻 and tree size (Figure 5.11). At P-low, the isolated location of stuntedand small trees likely facilitated stemflow generation (Van Stan II et al., 2011). At P-Sa,trees generated about an order of magnitude less stemflow.Stemflow as a mean depth over the plot between July 2018 and April 2019 ranged from 2mm to 19 mm. Stemflow contributed less than 1% of net precipitation at P-Sa and P-low,and 1.6% and 3% of net precipitation at P-tall and B-Sa, respectively.Interception lossNet precipitation ranged from 58% to 72% of gross precipitation, indicating substantialinterception loss. Interception loss from trees varied between 7% and 16% of gross pre-cipitation and was higher at sites with taller trees and more closed canopies (Tables 5.3and 5.5). Stemflow from shrubs was not measured directly despite it being shown to berelatively higher than stemflow from trees (Sadeghi et al., 2020) and its significance, ac-counting for 10% of gross precipitation in Labrador tea in Finland (Päivänen, 1966) and8% in shrubby birch in the Arctic (Zwieback et al., 2019). Based on the assumption thatshrub stemflow was 7.5% of tree throughfall, shrub interception loss ranged from 21% to36% of gross precipitation, considerably greater than interception loss from trees.Shrubification is likely part of the future trajectory of many northern boreal peatlands(Zhang et al., 2013) due to lower water tables (e.g. Weltzin et al., 2003; Kettridge et al.,2015) and changed fire regimes (Kettridge et al., 2015; Enayetullah et al., 2022). Inter-ception loss from shrub cover as measured in this study suggests that shrubification couldtrigger a positive feedback by reducing precipitation input (Waddington et al., 2015; Nelsonet al., 2021); however, effects of increased interception loss may be offset by lower evapo-transpiration from the peat surface due to increased shading (Kettridge et al., 2013), butcould also be exacerbated by shrub transpiration, a process that appears to have been littlestudied.Tree interception loss in Burns Bog was less compared to observations from similar vegeta-tion communities in a peatland in central Finland (Päivänen, 1966). Less interception lossin Burns Bog despite denser vegetation cover is likely attributable to the difference betweenclimates. Burns Bog is located in a moderate oceanic climate (Köppen classification Cfb)where the majority of precipitation occurs as rainfall over prolonged periods during the cooland wet winter months (Stahl et al., 2006; Howie et al., 2020), whereas central Finlandexperiences subarctic climate (Köppen classification Dfc), where rain occurs almost entirelyduring the summer months.91In Burns Bog, vegetation development was found to be connected to summertime watertable drawdown expressed as water table moisture deficit (𝑊𝑇𝑀𝐷), which is the integratedwater table depth over time between March and November of a given year expressed in m-days (Howie et al., 2020). In winter, the effect of interception loss on vegetation developmentis likely limited as rainfall exceeds evapotranspiration and thus maintains high water tables.In summer, transpiration from vegetation likely exerts a stronger control on 𝑊𝑇𝑀𝐷 thaninterception loss due to the relatively dry summers that characterize the region. It ishypothesized, however, that interception loss could increase 𝑊𝑇𝑀𝐷 by causing watertable drawdown to begin earlier in spring and by delaying the onset of seasonal recharge inautumn.5.4.2. Variation of throughfall with distance from tree trunksThroughfall increased with increasing distance from the trunk, which agrees with findingsreported for juvenile pine in British Columbia (Carlyle-Moses and Lishman, 2015) andother conifer trees elsewhere (e.g. Johnson, 1990; Bo Pedersen, 1992; Beier et al., 1993;Zirlewagen and Wilpert, 2001). Such a relationship is not always evident (Ford and Deans,1978; Loustau et al., 1992; Bialkowski and Buttle, 2015), and it is not clear what causesthese differences, as direct comparison is not possible due to different experimental designsand data analyses (Keim et al., 2005). It appears that sampling strategies that involvescaling distance from the trunk by canopy size, for example, by assigning canopy zonessimilar to this study or Zirlewagen and Wilpert (2001) and Carlyle-Moses and Lishman(2015), were more successful in detecting a relationship between throughfall amount in agiven canopy zone and distance from the trunk. These results are consistent with findingsthat report spatial correlation of randomly sampled throughfall to be at the scale of one-half- and one crown diameter in mid-age and old conifer stands in the Pacific Northwest(Keim et al., 2005).Increase in tree throughfall with distance from the trunk varied between sites (Figure 5.6)and was likely related to canopy structure of individual trees in combination with standmetrics such as density and tree configuration (Keim et al., 2005). For example, the averagecanopy base height of sampled trees at P-Sa was 9.04 m, which provides less shelter nearthe trunk than canopies of sampled trees at P-tall, which reached down to 0.97 m onaverage (Figures 5.3 and A2.4). Higher canopy bases would allow rain to be redistributedhorizontally (Herwitz and Slye, 1995), which is evident from the less pronounced throughfallgradient between canopy zones inner and outside at P-Sa compared to the gradient at P-tall(Figure 5.6).Density of shrub cover increased with distance from the trunk, which caused shrub through-fall to decrease with increasing distance from the trunk (Figure 5.7). Establishment of shrubnear trunks seemed related to tree canopy diameter and height of the canopy base, whichis supported by observations from P-Sa and P-tall (Figures 5.7 and A2.4). These findingssuggest that spatial heterogeneity in net precipitation introduced by throughfall of individ-ual trees can be partially offset by distinct spatial patterns of throughfall from the shrublayer.925.4.3. Comparison of tree-based and plot-averaged throughfallRandom sampling is a popular approach to quantify throughfall (e.g. Zimmermann etal., 2010), because the sampling design ensures that estimates will be unbiased and thecalculations for the mean and its uncertainty are straightforward. Because each through-fall gauge samples the same population, missing data due to gauge malfunction can beexcluded without affecting the validity of the mean and its estimated uncertainty. Further-more, developing a relation between the sample mean and median provides an approachfor estimating the mean when data are censored due to gauge overflow, as was done in thisstudy. The results from random sampling are, however, specific to the sample plot, whichlimits extrapolation to other areas.Tree-based sampling has been used to estimate throughfall for an isolated tree (David et al.,2006) and to scale up measurements at two trees to plot and catchment scales (Hassan et al.,2017), as well as to quantify the spatial pattern of throughfall (Carlyle-Moses and Lishman,2015). However, to our knowledge, the current study is the first to use stratified samplingbased on distance from the nearest tree to scale up throughflow measurements to the plotscale, with a comparison to an independent estimate based on random sampling. However,careful planning is required to i) randomly select trees for a sub-sample of the population,ii) stratify and sample canopy zones of selected trees, and iii) compute areal fractions ofcanopy zones within the area of interest (e.g. from LiDAR). Data collection and processingare more expensive than for random sampling; however, an advantage is that results can beextrapolated to areas with different tree configuration (i.e. different tree density), becausethe only vegetation-specific aspects are empirical relationships between throughfall andgross precipitation for each canopy zone. A tree-based approach may thus be more usefulin parameterizing distributed hydrologic models for which canopy characteristics can bespecified using LiDAR.As seen in Figure 5.8, tree-based throughfall estimates tended to be similar to or lowerthan estimates from stratified random sampling. The source of this bias could be related todifferent evaporative losses from the two collector designs. It is assumed that evaporativeloss from stratified tree throughfall collectors (red funnels connected with a hose to waterjugs, Figure A2.2a) was minimal given their enclosed nature. Stratified shrub throughfallcollectors and tree-based throughfall collectors were constructed from open PVC pipes(Figure A2.2b) that could have lost water via evaporation. However, the collectors wereequipped with Styrofoam balls in the summer to minimize evaporation (see Figure A2.2c).The balls were removed in winter to increase the capacity of collectors because it wasassumed that evaporative loss would be low because winter in Burns Bog is characterizedby overcast skies, low but generally above-freezing air temperature and high humidity.Stratified shrub throughfall collectors were installed below the shrub layer, where the airwas likely near saturation, wind speed would be minimal, and minimal amounts of radiationreached the collectors. However, tree-based tree throughfall collectors (e.g. Figure A2.3)were more exposed than those below the shrub layer, and the assumption that evaporationwas minimal may not have been valid, leading to the bias shown in Figure 5.8.It was not possible to estimate the uncertainties for many of the tree-based averages because93of insufficient sample size within the canopy zones, a result of canopy zone stratification afterthe field campaign was concluded. However, when sufficient observations were available tocompute the uncertainty of tree-based throughfall (𝑇𝐹?̄?,𝑡,𝑝 and 𝑇𝐹?̄?,𝑠,𝑝 in Figures 5.8 and5.9), it was similar in magnitude to uncertainty from random observations (𝑇𝐹 ̄𝑦,𝑡,𝑝 and𝑇𝐹 ̄𝑦,𝑠,𝑝 in Figures 5.8 and 5.9).5.4.4. Comparison of trough-based and plot-averaged throughfallTrough observations agreed well with random manual observations (Figure 5.10), withthe exception of two prolonged rainfall periods at B-Sa during which debris was found inthe troughs that could have clogged the tipping-bucket rain gauge and caused collectedthroughfall water to spill. The combined area of trough collectors and tipping bucket wasapproximately 3.6 times the size of an individual tipping bucket (same as in Herbst et al.(2008)). It is possible that spill-over occurred due to the increased collection area of thetrough setup (Calder and Kidd, 1978; Herbst et al., 2008). A combination of clogging andspill-over is maybe the most likely explanation for underestimation of throughfall by thetrough method. Others reported evaporative losses of water retained by debris as a reasonfor underestimation of throughfall by troughs (Reynolds and Neal, 1991).Splash-out can be another cause of throughfall underestimation. Herbst et al. (2008) re-ported splash-out during the leaf-on period only, and argued that increased drop size due toconcentration from leaves in concert with high intensity rainfall (up to 6 mm hr-1 based on5-minute observation intervals) caused throughfall underestimation of approximately 20%in an oak-birch forest in a United Kingdom. In contrast to Herbst et al. (2008), underes-timation of throughfall from troughs in this study took place in the leafless period duringmean rainfall intensities between 0.7 and 0.8 mm 30 min-1, based on 30-minute observa-tions. In addition, troughs did not underestimate throughfall during periods with rainfallintensities above 0.8 mm per 30 min, but occurred in events with smaller throughfall totals,which makes splash-out an unlike cause of throughfall underestimation in this study.Randomly relocated funnels provide reliable and unbiased throughfall estimates, but areprone to outliers over short periods or small throughfall amounts (Zimmermann et al.,2010; Zimmermann and Zimmermann, 2014). The temporal resolution is limited by thefrequency of site visits, which is typically weekly to monthly. Trough observations are lessprone to outliers and fewer collectors are required to obtain comparably accurate resultsto randomly relocated funnels (Zimmermann et al., 2010; Zimmermann and Zimmermann,2014). Another advantage is that trough collectors can provide sub-daily resolution, whichmay be important in some applications.Results presented here suggest that continuous trough measurements in tandem with ran-dom observations to check for and, if necessary, correct for bias, are a suitable way ofquantifying throughfall at sub-daily time intervals in forest stands typical to peatland en-vironments.945.5. ConclusionsThis study quantified tree and shrub canopy water fluxes in five complex structured veg-etation communities in a maritime raised bog in the Pacific Northwest. Interception lossmade up an important component of the bog’s water balance and reduced net precipita-tion to between 58% and 72% of gross precipitation, assuming 7.5% of tree throughfallbecame stemflow from shrubs. Interception loss from the shrub layer was between 21% and36% of gross precipitation and exceeded that from trees, which ranged from 7% to 16%of gross precipitation. The fraction of gross precipitation intercepted by trees was higherin summer than in winter. Average throughfall under birch varied between 72% of grossprecipitation during the leafed season and 83% of gross precipitation during the leaflessseason. Stemflow contribution from trees to net precipitation was small and ranged from0.4% to 3% of net precipitation. Small stunted conifer trees and mature birch generatedsimilar amounts of stemflow per tree and tree density seemed to be an important variablefor stemflow estimation per unit plot area at the plot scale.Tree throughfall increased with increasing distance from the trunk and shrub throughfallshowed an opposite trend, suggesting that some spatial variability of tree throughfall was off-set by the shrub layer. This effect varied between sites and was likely related to tree canopyradii and the height of the canopy base. Observations from the independent tree-basedthroughfall sampling strategy were within the range of uncertainty of plot-scale estimatesbased on random sampling, even though tree-based throughfall estimates tended to be toolow. This bias may have resulted from evaporation from the tree-based throughfall collec-tors, and could potentially be avoided by modifying the collector design and deployment.The approach required parametrization for stand-specific vegetation metrics, in this casegenerated from LiDAR, but results suggested it could be a potential tool to extrapolatethroughfall observations to locations with similar vegetation characteristics but differenttree density and configuration. Given the potential value in being able to estimate through-fall beyond the individual study plot, further work should focus on testing and refiningthe tree-based approach to estimating throughfall, for example, by relating throughfall toLiDAR-derived canopy characteristics above the gauge locations rather than distance fromthe nearest tree.Continuous trough observations of tree throughfall agreed well with mean throughfall fromrandomly placed collectors, with the exception of two periods with prolonged rainfall dur-ing which the troughs under-measured compared to the randomly relocated gauges. It isrecommended that random sampling of throughfall be used in conjunction with troughcollectors to check for and, if necessary correct for, any bias.It is hypothesized that interception loss may exert an important positive feedback on veg-etation development in peat bogs by causing summertime water table drawdown to occurearlier in spring and by delaying seasonal recharge in autumn. However, more research isrequired to quantify this effect in contrast to transpiration loss, which is expected to bethe major vegetation-related driver of water table drawdown in summer.956. Tree transpiration loss in four vegetationcommunities in a maritime raised bog inthe Pacific Northwest6.1. IntroductionUndisturbed ombrotrophic bogs are naturally composed of bryophytes such as Sphagnummoss that tolerate lack of oxygen in saturated peat (Rydin and Jeglum, 2013), which limitsdevelopment of vascular plants (e.g. Heikurainen, 1955; Finér and Laine, 1998). Drier andwarmer climate (Roulet et al., 1992; Charman, 2007; Frolking et al., 2010; Helbig et al.,2020) or drainage (Paavilainen and Päivänen, 1995; Joosten and Clarke, 2002; Holden etal., 2004) can lower water levels and thus enable trees (Murphy et al., 2009) and shrubs(Weltzin et al., 2003) to establish and alter the hydrology of a peatland by increasingtranspiration loss (Landhäusser et al., 2003a; Kučerová et al., 2010; Sarkkola et al., 2013)and interception loss (Robinson, 1998; Exler and Moore, 2022). These changes can triggerpositive feedback mechanisms, for example by lowering water levels further and therebycausing tree establishment and shrubification to intensify by promoting suitable growingconditions for vascular plants (Landhäusser et al., 2003a; Murphy et al., 2009; Waddingtonet al., 2015). However, shading from trees and shrubs may limit the effect of vegetation onwater levels by lowering evaporation from the peat surface (Kettridge et al., 2013). Indeed,Strilesky and Humphreys (2012) reported reduced ecosystem-level evapotranspiration froma treed portion compared to an open site of a temperate peatland in Ontario, Canada, dueto the combined effects of shading and low stomatal conductance of trees.Tree transpiration is commonly quantified by measuring sap flow, and observations frommultiple trees are used to capture variability among trees of different size or species (e.g.Hatton et al., 1995; Wullschleger et al., 2001; Kumagai et al., 2005b). Tree size is a first-order structural control on sap flow variability in both single-species (Hatton et al., 1995)and multi-species (Wullschleger et al., 2001; Chiu et al., 2016) forests. Tree-level sap flowmeasurements are typically scaled up to the plot or stand levels by multiplying a mean sapflow velocity by a plot-level estimate of sapwood area, or by developing a relation betweenvolumetric sap flow rate and either diameter at breast height (DBH) or basal area, whichcan be applied to all trees within a plot (e.g. Hatton et al., 1995; Hogg et al., 1997; Kumagaiet al., 2005b).Past studies in temperate forests found differences of sap flow velocity (Hernandez-Santanaet al., 2015) and transpiration (Jung et al., 2011) among species to be a minor compo-nent of within-stand variability in transpiration during non-moisture-limited conditions96(Wullschleger et al., 2001; Jung et al., 2011; Hernandez-Santana et al., 2015; Chiu et al.,2016). However, species-specific transpiratory behavior has been reported in a variety ofclimatic regimes (Granier and Bréda, 1996; Dierick and Hölscher, 2009; Cavaleri and Sack,2010), for example due to differences in hydraulic structure (Oren and Pataki, 2001; Traveret al., 2010; Gebauer et al., 2012) and leaf conductance control (Dang et al., 1991; Orenand Pataki, 2001; Gebauer et al., 2012), suggesting that among-species variability of tran-spiratory behavior can influence stand-level transpiration.The primary environmental drivers of transpiration are widely understood to include ra-diation — e.g. photosynthetically active radiation (Bovard et al., 2005), solar radiation(Oogathoo et al., 2020) or global radiation (Granier and Bréda, 1996; Gebauer et al., 2012)— and vapour pressure deficit, which is especially relevant because it can be used to modelnighttime or diel variation in sap flow rates (Fisher et al., 2007; Forster, 2014). Duringdrought conditions, low moisture supply can exert a limiting control on the relationshipbetween atmospheric drivers and sap flow or transpiration (Gartner et al., 2009; Mitchellet al., 2012; Wang et al., 2017), which has been studied through direct measurement ofsoil moisture (e.g. Granier et al., 2000; Lundblad and Lindroth, 2002; Bovard et al., 2005;Brinkmann et al., 2016; Hassler et al., 2018), or indirectly by means of spatial or temporalsurrogates for varying moisture availability (e.g. Zeppel et al., 2008; Angstmann et al., 2013;Patankar et al., 2015). Available research suggests that moisture supply in mid-latitudeforests acts as a secondary control on transpiration (Gartner et al., 2009; Brinkmann et al.,2016; Wang et al., 2017) by controlling the soil-to-leaf pathway differently among species(e.g. Reich and Hinckley, 1989; Cochard et al., 1996; Irvine et al., 1998; Addington et al.,2004; Bovard et al., 2005,) and it is suggested that between-species variability at the standlevel increases during drought conditions (Oren and Pataki, 2001; Brinkmann et al., 2016;Chiu et al., 2016). Soil moisture is often correlated with water table depth: deeper watertables are often associated with reduced water content in the rooting zone of mineral soils,which can limit transpiration (Vincke and Thiry, 2008). In contrast, shallow water tablesand waterlogging in peat have been associated with hypoxic or anoxic conditions that limittranspiration by decreasing root and stomatal hydraulic conductance (Liu et al., 2022).Studies quantifying tree transpiration in peatlands are limited (Table 6.1). Some studiesmeasured transpiration to estimate its significance in relation to total evapotranspirationat the stand level (Kučerová et al., 2010; Sarkkola et al., 2013) or landscape level (Warrenet al., 2018; Perron et al., 2023). Others quantified transpiration in the context of waterbudgets in drained silviculture peatlands (Sarkkola et al., 2013) or burned versus unburnedpeatlands (Thompson et al., 2014). Transpiration loss from encroaching birch seedlings (Fayand Lavoie, 2009) and young aspen after burning (Depante et al., 2019) has been measured.Patankar et al. (2015) used sap flow observations on permafrost to investigate the effectof active layer thickness on transpiration and Perron et al. (2023) investigated temporaland spatial variability to identify stand structural and edaphic controls. Depante et al.(2019) measured stomatal conductance to quantify the importance of hydrologic controlsand hydraulic redistribution on transpiration of young aspen and the effect of ecosystemwarming on vegetation-water relations was investigated by Warren et al. (2021). The long-term effect of permafrost degradation on growth of larch forests and tree transpiration inSiberia was investigated by Iijima et al. (2014). Tree transpiration studies in peatlands97have been limited to continental settings in which seasonal snow accumulation and meltare significant components of the hydrologic regime.The broad objective of this study was to quantify the controls on tree-level sap flow and plot-level transpiration to assess their significance in the water balance of a maritime peatlandenvironment as a basis for understanding and predicting the ecohydrological response toclimatic variability and change. As indicated in the review of past studies, no previouswork quantified peatland tree transpiration under maritime climatic conditions, and theresults of previous studies may not be applicable due to differences in tree species andseasonal patterns of moisture availability. The study focused on Burns Bog in southwesternBritish Columbia, Canada, which is located near the southern extent of raised bogs inwestern North America (Rocchio et al., 2021) and has a history of drainage and peatharvesting that promoted tree encroachment along the bog’s margins and into the bog’sinterior along drainage ditches (Hebda et al., 2000). Specifically, we use vapour pressuredeficit to represent atmospheric controls on sap flow, and hypothesize that the relationsbetween sap flow and vapour pressure deficit vary by tree species, water table depth anddiameter at breast height. In addition, we aimed to quantify the variability of transpirationloss among vegetation communities by making first-order estimates over the course of agrowing season.98Table 6.1.: Overview of sap flow field studies conducted in peatlands. Climate lists the Köppen classification where Dfc =subarctic dry continental climate, and Dfb = humid continental climate.Country Type Region Vegetation Climate n ReferenceCanada Permafrost complex Subarctic Black spruce, tamarack Dfc 39 Perron et al. (2023)Canada Permafrost plateau Subarctic Black spruce Dfc 12 Patankar et al. (2015)Canada Ombrotrophic peatland Southern Québec Birch seedling Dfb 6 Fay and Lavoie (2009)Canada Upland peatland complex Western Boreal Plains Trembling aspen Dfb/Dfc Depante et al. (2019)Canada Permafrost complex Subarctic Black spruce Dfc 22 Warren et al. (2018)Czechia Raised bog Central Europe Bog pine Dfb 6 Kučerová et al. (2010)Finland Drained peatland Central and northern Scots pine Dfc 5 to 8 Sarkkola et al. (2013)USA Bog Northern Minnesota Black spruce, Larch Dfb 24 to 38 Warren et al. (2021)Canada Ombrotrophic bog Alberta Black spruce Dfc 3 Thompson et al. (2014)Russia Permafrost Siberia Larch Dfc/Dfd 15 Iijima et al. (2014)996.2. MethodsTree transpiration was estimated from sap velocity measurements based on a combinationof the compensation heat pulse method (CHPM, Swanson, 1962; Green et al., 2003) andthe average gradient method (AGM, Testi and Villalobos, 2009), in conjunction with sap-wood area estimates obtained from increment borer samples. Observations from sampledtrees were scaled to the plot level extend by using allometric relationships between modelcoefficients and diameter at breast height (𝐷𝐵𝐻).6.2.1. Field measurementsSap flow measurements were conducted at B-Sa, P-Sa, P-tall and P-low, the four sitesin Burns Bog that included trees. Details on the sites are provided in Chapter 2.1 andvegetation metrics are presented in the results section of Chapter 5. Vapour pressuredeficit for modelling sap flow was computed from meteorological observations made at aneddy-covariance flux tower site near the bog’s center. For details on the instrumentation ofthe eddy-covariance flux tower see Chapter 2.2.Water table depthFor this analysis water table elevation was referenced to summertime peat surface eleva-tion, which was calculated as the median of peat surface observations between June andSeptember 2018, to accommodate the annual cycle of peat surface elevation in Burns Bog(see Chapter 4). Between May 2018 and June 2019 the amplitude of peat surface elevationchange was 4.5, 5.0, 6.6 and 9.5 cm at B-Sa, P-Sa, P-tall and P-low, respectively.Sap flow velocitySap flow velocity was measured every 30 minutes using sap flow systems manufactured byTranzflo NZ Ltd. (Green, 2009), which consisted of a linear heater and two temperaturesensors that were installed radially in the tree trunk at breast height. Each sensor measuredtemperature at 10, 20, 30 and 45 mm depth to capture non-uniformity in the sap flowvelocity profile (e.g. Green and Clothier, 1988; Čermák and Nadezhdina, 1998; Nadezhdinaet al., 2002). Sensors were placed 5 mm upstream (below) and 10 mm downstream (above) ofthe heater, respectively. A 50 cm section of the trunk around the sensors was wrapped withreflective bubble wrap after installation to minimize the effect of solar radiation on trunktemperature. Sap flow systems were connected to CR10X loggers (Campbell Scientific Inc.)that were programmed to trace the temperature signal every 0.25 s for 300 s and average thetemperature difference over 180 s. Sap flow systems were powered by a set of batteries andsolar panels, which caused battery voltage to exhibit a diel signal. Loggers calculated therequired heater activation time, commonly between 1-2 s (e.g. Green et al., 2003), beforeeach measurement, to apply a consistent input of heat of 100 J between measurements.100Each site was equipped with four sets of sensors, each set comprising a linear heater andtwo temperature sensors. Similar to Simpson (2000), a combined sampling approach ofstationary and roving sensors was used to sample sap flow variability both through timeand among trees (Kumagai et al., 2005a; Komatsu et al., 2016). Between April 2019 andNovember 2019, sap flow was monitored in one stationary tree and between four and sixrandomly selected trees, depending on the site. At P-Sa, P-tall and P-low, western hemlocks(Tsuga heterophylla) were selected as stationary trees as pine trees produce resin that candamage the sap flow sensors over time. This decision was based on communication with themanufacturer, who used a similar set-up in previous studies (e.g., Green, 2009). Stationarytrees were equipped with two sets of sensors, one of which was moved once to capturevariation in sap flow with azimuthal direction (e.g. Shinohara et al., 2013; Komatsu et al.,2016). A continuous record of sap flow from all three radial directions was obtained byfitting a linear regression model between the stationary set of sensors and the set of sensorsthat was moved. Sap flow systems in randomly selected trees were installed in randomradial directions and measured sap flow velocity over a six to eight week period.6.2.2. CalculationsVolumetric sap flow, sapwood area and propertiesTrunk wood samples were taken for each set of sensors using an increment auger followingcompletion of the sap flow measurements. Volumetric fractions of gas, liquid and woodymatrix were obtained from laboratory measurements of the fresh weight, “immersed” weightusing Archimedes’ principle of displacement, and oven-dried weight (Edwards and Warwick,1984). The samples did not contain visually discernible heartwood, likely due to the smallsize of the stunted tree trunks of most trees (e.g. Hatton and Vertessy, 1990), except forbirch located at B-Sa.Sapwood area of the sampled trees was calculated from trunk circumference at breast heightand bark thickness, which was obtained from increment auger cores. Volumetric sap flowper tree was calculated from area and sap velocity at four depths similar to the weightedaverage integration method in Hatton and Vertessy (1990). The corresponding sapwoodarea (𝐴𝑠𝑎𝑝,𝑘) of sensor 𝑘 for each of 𝑛 sap flow sensors was obtained by dividing the trunkcross section into 𝑛− 1 concentric annuli and a circle at the center of the trunk. The innerand outer radii of annulus 𝑘 were located half-way between a pair of adjacent sensors 𝑘 and𝑘+1 or 𝑘 and 𝑘-1, and 𝑘 = 1, 2, 3, ..., 𝑛. For a given tree with 𝑛 = 4 sap flow sensors andsensor 𝑘 = 1 being the furthest from the trunk center, volumetric sap flow was given by:𝐴𝑠𝑎𝑝,1 = 𝜋(𝑟20 − 𝑟21,2) (6.1)𝐴𝑠𝑎𝑝,2 = 𝜋(𝑟21,2 − 𝑟22,3) (6.2)𝐴𝑠𝑎𝑝,3 = 𝜋(𝑟22,3 − 𝑟23,4) (6.3)𝐴𝑠𝑎𝑝,4 = 𝜋𝑟23,4 (6.4)101𝑄𝑠𝑎𝑝 = 𝑣𝑠,1𝐴𝑠𝑎𝑝,1 + 𝑣𝑠,2𝐴𝑠𝑎𝑝,2 + 𝑣𝑠,3𝐴𝑠𝑎𝑝,3 + 𝑣𝑠,4𝐴𝑠𝑎𝑝,4 (6.5)where 𝑟0 = sapwood outer radius (cm), 𝑟1,2, 𝑟2,3 and 𝑟3,4 = radii (cm) half-way betweensensors 1 at 10 mm depth, 2 at 20 mm depth and 3 at 30 mm depth, 𝐴𝑠𝑎𝑝,1 to 𝐴𝑠𝑎𝑝,3 =annuli areas centered on sap flow sensors 1, 2 and 3, 𝐴𝑠𝑎𝑝,4 = area of the circle at the centerof the trunk associated with sensor 4 (cm2), 𝑣𝑠,1 to 𝑣𝑠,4 = sap velocity on a total sapwoodarea basis (Eq. 6.8) of sensors 1 to 4, and 𝑄𝑠𝑎𝑝 = volumetric sap flow at the tree level (Ls-1).On a sub-daily time scale, sap flow lags leaf transpiration of trees due to short-term waterredistribution and water storage changes within trees (e.g. Čermák et al., 1982; Schulzeet al., 1985; Hatton and Vertessy, 1990); however, these effects do not significantly affectdaily observations (Schulze et al., 1985; Cienciala et al., 1992). In the following discussiondaily sap flow volumes are assumed to be equivalent to daily tree transpiration loss (e.g.Cienciala et al., 1992).Sap flow velocityCompensation heat pulse methodIn sapwood of uniform physical and thermal properties, the raw heat pulse velocity can becalculated following Swanson (1962) as:𝑣𝑧 =(𝑥𝑑 − 𝑥𝑢)2 ⋅ 𝑡𝑧(6.6)where 𝑣𝑧 = raw heat pulse velocity (cm hr-1), 𝑥𝑢, 𝑥𝑑 = distance of upstream and downstreamtemperature sensor from linear heater, here 0.5 cm and 1 cm, respectively, and 𝑡𝑧 = timedelay (s) for temperatures at points 𝑥𝑢 and 𝑥𝑑 to become equal.Installation of the temperature sensors and heater disrupt xylem tissue, which violates as-sumptions of idealized heat transport on which sap flow calculations are based on. Wound-ing of xylem tissue causes a systematic underestimation of the true heat pulse velocity ifmeasurements are not corrected appropriately (Cohen et al., 1981; Green and Clothier,1988). The error on convection due to wounding can be corrected for, following Swansonand Whitfield (1981) and Green et al. (2003), as:𝑣𝑐 = 𝑎0 + 𝑎1 ⋅ 𝑣𝑧 + 𝑎2 ⋅ 𝑣2𝑧 (6.7)where 𝑣𝑐 = corrected heat pulse velocity (cm hr-1) and coefficients 𝑎0, 𝑎1 and 𝑎2 wereobtained from a numerical modelling study (Swanson and Whitfield, 1981) and later con-firmed experimentally (Green et al., 2003). The coefficients are sensor-specific and wereprovided for the sap system used in this study (Green et al., 2003; Green, 2009).102To calculate sap flow from corrected heat pulse velocity, volumetric fractions of wood andliquid in the trunk need to be considered as follows (Edwards and Warwick, 1984):𝑣𝑠 = (𝑘 ⋅ 𝑉𝑤 + 𝑉𝑙) ⋅ 𝑣𝑐 (6.8)where 𝑣𝑠 = sap velocity on a total sapwood area basis (cm hr-1), 𝑉𝑤, 𝑉𝑙 = volumeticfractions of wood and liquid (cm3 cm-3) respectively, and 𝑘 is a coefficient related to thermalproperties of the woody matrix that was corrected for ambient temperature as (Becker andEdwards, 1999):𝑘 = 0.4 + 0.00214 ⋅ 𝑇 + 6 ⋅ 10−6 ⋅ 𝑇 2 (6.9)where 𝑇 = stem temperature, here assumed to be equal to ambient air temperature (∘C).Calibrated average gradient methodThe monitoring setup used in this study was able to measure a minimum heat pulse velocityof 𝑣𝑐,𝑚𝑖𝑛 = 4.5 cm hr-1 using CHPM and a trace period of 300 s. On rainy or overcast daysin the early and late growing season (Benyon, 1999; Fisher et al., 2007; Forster, 2014) andthroughout the growing season at night (e.g. Becker, 1998; Daley and Phillips, 2006; Fisheret al., 2007; Forster, 2014) sap flow velocity tended to be less than the detection limit ofthe compensation heat pulse technique (Green et al., 2003). Lower sap flow velocities wereestimated using AGM on a subset of data, which was limited to observations with a traceperiod of 180 s or less and observations were restricted to a window of ± 2 days of theday of interest similar to Green et al. (2017). To predict heat pulse velocities below thedetection limit of CHPM (4.5 cm hr-1), a linear regression model was fit between the subsetof corrected heat pulse velocities of CHPM and the average temperature difference at thetwo sensors as:𝑣𝑐,𝑐ℎ𝑝𝑚 = 𝑎Δ𝑇 + 𝑏 (6.10)where 𝑣𝑐,𝑐ℎ𝑝𝑚 = corrected heat pulse velocity based on CHPM (cm hr-1), Δ𝑇 = averagetemperature difference (∘C), and 𝑎 and 𝑏 = slope and intercept of the fit linear regressionmodel. Corrected heat pulse velocities below the CHPM detection limit of 4.5 cm hr-1 werepredicted as:𝑣𝑐,𝑎𝑔𝑚 = 𝑎Δ𝑇 + 𝑏 (6.11)where 𝑣𝑐,𝑎𝑔𝑚 = corrected heat pulse velocity based on AGM (cm hr-1).Scatter in the linear relationship between 𝑣𝑐,𝑐ℎ𝑝𝑚 and Δ𝑇 (Eq. 6.10) occasionally causedpredictions from Eq. 6.11 to be higher than the detection limit of CHPM or unrealisticallynegative (i.e. reverse flow). In these instances, predictions that fell outside a range with an103upper bound of 1.5 ⋅ 𝑣𝑐,𝑚𝑖𝑛 and a lower bound of -1 cm hr-1 (Nadezhdina et al., 2009) wereset to 0 cm hr-1, which resulted in replacement of 2.9%, 1.5%, 7.1% and 8.9% of observationsat B-Sa, P-Sa, P-tall and P-low with 0 cm hr-1. Filtering data may have introduced bias tosap flow estimates if predicted outliers were not random; however, the effect was assumedto be minimal since only low-flow observations were affected.6.2.3. Modelling sap flowSap flow for individual trees was modelled as an asymptotic function of vapour pressuredeficit using a model of the form:𝑄𝑠𝑎𝑝 = 𝑆𝐹𝑚𝑎𝑥 + (𝑅0 − 𝑆𝐹𝑚𝑎𝑥)𝑒−𝑘𝑉 𝑃𝐷 (6.12)where 𝑄𝑠𝑎𝑝 = volumetric sap flow (L day-1), and 𝑉 𝑃𝐷 = mean daily vapour pressure deficitmeasured at eddy-covariance flux tower near the bog’s center at 2.0 m (kPa), 𝑆𝐹𝑚𝑎𝑥 is theasymptote, 𝑅0 is the y-intercept (i.e., the sap flow associated with 𝑉 𝑃𝐷 = 0), and 𝑘 isrelated to the rate at which 𝑄𝑠𝑎𝑝 increases with increasing 𝑉 𝐷𝑃 . Models of the form of Eq.6.12 were fit using nonlinear least squares regression, as implemented in the nls functionin R. For each species, the asymptotes (𝑆𝐹𝑚𝑎𝑥) and intercepts (𝑅0) were related to 𝐷𝐵𝐻using power-law models of the following forms:𝑆𝐹𝑚𝑎𝑥 = 𝑎𝐷𝐵𝐻𝑏 (6.13)𝑅0 = 𝑐𝐷𝐵𝐻𝑑 (6.14)where 𝑎, 𝑏, 𝑐 and 𝑑 are empirically determined coefficients. The scatter of both relationshipstended to increase with 𝐷𝐵𝐻. To address this heteroscedasticity, models were fit usinggeneralized nonlinear least square regression as implemented in the gnls function in thenlme package in R. The models were fit using weights that were inversely proportional to1/ ̂𝑦2𝑡, where 𝑡 is a fitting parameter. The coefficient 𝑘 did not vary systematically with𝐷𝐵𝐻 and was implemented as species-specific constant, taken as the median value for alltrees for each species.Sap flow at the stand level was estimated in three steps. First, 𝑆𝐹𝑚𝑎𝑥 and𝑅0 were estimatedfor each tree by applying Eqs. 6.13 and 6.14 to a complete 𝐷𝐵𝐻 survey at each site. Ina second step, tree-specific 𝑆𝐹𝑚𝑎𝑥 and 𝑅0 and species-specific 𝑘 were applied to Eq. 6.12to estimate sap flow of each tree from daily vapour pressure deficit. Finally, daily sap flowfrom all trees at a given site was summed to obtain stand level daily sap flow.1046.2.4. Effect of water table depth on sap flowTo test the hypothesis that sap flow was influenced by water table depth, the Spearmanrank correlation test was applied to the residuals from the relation between sap flow andvapour pressure deficit of each tree (Eq. 6.12). The Spearman test was used because itcan detect nonlinear trends. However, because the Spearman test only detects monotonictrends, scatterplots of the residuals versus water table depth were examined to assess thepresence of a non-monotonic relation. The test was applied only to trees for which theobservation period included a range in water table depths of at least 0.20 m.6.3. Results6.3.1. Overview of study periodFrom May through September 2019, daily air temperature (𝑇𝑎) varied between highs ofaround 30∘C and lows of 3 to 5∘C (Figure 6.1). Daily vapour pressure deficit (𝑉 𝑃𝐷) rangedfrom 0.25 to 0.9 kPa during most days, except for a few hot days when it exceeded 1.0 kPaand with the onset of rain in fall when it dropped below 0.25 kPa. Clear skies producedhigh incident solar radiation (𝐾↓) with mean daily values above 30 MJ m-2. On occasionalcloudy days in summer and overcast skies in fall, mean daily incident solar radiation stayedbelow 10 MJ m-2. The majority of precipitation occurred in May and September and a fewrainy days occurred throughout summer. Groundwater levels stayed within 0.30 m of thesummertime peat surface elevation in early May and dropped to between 0.60 and 1.00 min the first half of September.6.3.2. Sap flow observationsSap flow was monitored in four vegetation communities that varied in structure (i.e. treesize, density and species) and wetness status. Over the course of the growing season (May1 to September 30, 2019) 25 trees were monitored and observations from 21 trees covered asufficiently wide range of atmospheric conditions to fit a nonlinear relationship of the formshown in Eq. 6.12. Of these 21 trees, six were hemlock, ten were pine and five were birch.The effect of water table depth on sap flow was assessed for a subset of 17 trees. Sap flowmeasurements from four trees, one tree at each field site, had an insufficient range of VPDto support model fitting. These observations were used to provide an independent test ofmodel predictions.Sap flow and atmospheric demandSap flow increased asymptotically with increasing vapour pressure deficit, and higher sapflow occurred in trees with larger diameter at breast height (Figure 6.2). Differences inobserved sap flow resulted from a combined effect of trunk diameter (i.e. sapwood area)105102030T a (°C)maxmin0.00.30.60.91.2VPD (kPa)102030K ↓ (MJ m−2 )0510152025P (mm)−0.9−0.6−0.30.0May2019Jun2019Jul2019Aug2019Sep2019Oct2019WTD (m) B−SaP−lowP−SaP−tallFigure 6.1.: Daily air temperature (𝑇𝑎), daily mean vapour pressure deficit (𝑉 𝑃𝐷), dailyincident solar radiation (𝐾↓) and daily precipitation totals (𝑃 ) at an eddy-covariance tower near the bog center, and groundwater levels below the sum-mertime peat surface (𝑊𝑇𝐷) at each field site. 𝑉 𝑃𝐷 and 𝐾↓ were calculatedfrom observations over 24 hr for each day.106and tree species. Sap flow ranged from 1 L per day in small hemlock and pine trees up toaround 40 L per day in large pine and 330 L per day in large hemlock. The range in treesize was narrower for birch; sap flow ranged from around 7 L per day up to about 115 Lper day.Birch Hemlock Pine0.3 0.6 0.9 1.2 0.3 0.6 0.9 1.2 0.3 0.6 0.9 1.20102030400100200300306090VPD (kPa)Q sap (L day−1 )DBH 10 20 30 400.25 0.50 0.75 1.00WTDFigure 6.2.: Volumetric sap flow (𝑄𝑠𝑎𝑝) versus vapour pressure deficit by tree species. Di-ameter at breast height of tree trunks is indicated by the size of data pointsand the color shows groundwater levels below the summertime peat surface.Note that the y-axis scale differs among species.Sap flow and water table depthThe effect of water table depth on sap flow was complex, varied by species, and was notalways significant for trees with a groundwater level range of at least 0.20 m during sapflow observations (Table 6.2 and Figure 6.3). For all hemlock trees, residuals from the sapflow-𝑉 𝑃𝐷 relation (Eq. 6.12) decreased with increasing water table depth across mediandepths ranging from 0.15 to 0.55 m below the peat surface. The effect was significant at alevel of 𝑝 < 0.05 for all but two trees: tree 36 (𝑝 = 0.148) and tree 67 (𝑝 = 0.35). Tree 67was the largest tree, with a trunk diameter at breast height of 0.43 m (Table 6.2). For thattree, the relationship between residuals and water table depth was complicated (Figure6.3). At P-low, the residuals exhibited a negative relation with increasing 𝑊𝑇𝐷 when𝑊𝑇𝐷 dropped below 0.3 cm, suggesting a shift to water-limited conditions. At P-tall, sapflow residuals in hemlock were negatively related to water table depth, once it dropped to0.5 or 0.6 m of the peat surface (Trees 6 and 15 in Figure 6.3). The residual trends fortree 35 at P-low and tree 6 at P-tall suggest a decreasing trend with higher water tablesfor ground water levels above 0.2 and 0.35 m below the peat surface, respectively. However,confidence bands around the trend lines suggest this trend may not be significant; i.e., thebands at either end of the range overlap.107The effect of water table depth was less clear for pine trees compared to hemlock. Residualsfrom the sap flow-𝑉 𝑃𝐷 relation decreased significantly with increasing water table depthfor two pine trees (Trees 99 and 14, 𝑝 < 0.05); however, the effect occurred only whenthe median water table was more than 0.4 m below the peat surface (Table 6.2). Tree 30showed a strong significant positive (𝑝 = 0.001) relationship between water table depth andsap flow residuals at a median water table of 0.15 m below the peat surface (Table 6.2).At P-low, the relationship between residuals from the sap flow-𝑉 𝑃𝐷 relation with 𝑊𝑇𝐷observed in trees 30 and 99 suggests that sap flow may have been limited when water tabledepth was above 0.3 m or below 0.4 m (Figure 6.3). Sap flow in the remaining pines didnot appear to be significantly affected by the water table.The stationary birch tree at B-Sa showed a statistically significant (𝑝 < 0.05) relationshipbetween sap flow residuals and water table depth (Table 6.2). On average, sap flow of tree76 increased with increasing water table depth across a range between 0.37 and 1.05 m, andthe median water table depth was 0.74 m. The relationship was non-monotonic and trees86 and 87 showed a similar trend that was not statistically significant; however, sap flowwas not monitored across the entire range of water table depth for those trees. Sap flowresiduals for tree 76 appeared to have a concave-up relation with water table depth, whichwould be consistent with limitations to transpiration at both low and high water tableelevations. With the exception of the large hemlock at P-Sa, all stationary trees (i.e. treeswith measurements across the entire range of seasonal water table drawdown) showed astrongly significant (𝑝 < 0.001) effect of water table depth on sap flow (Table 6.2).Table 6.2.: Spearman correlation between sap flow and water table depth from trees withsap flow observations across a water table depth range of at least 0.20 m. Site= field site at which tree was located, Species = tree species, Tree = tree iden-tification, 𝐷𝐵𝐻 = trunk diameter at breast height (cm), 𝑟𝑠 = Spearman rankcorrelation coefficient, p-value = statistical significance based on Spearman rankcorrelation test, n = number of daily observations, 𝑊𝑇𝑟𝑎 = range of water tabledepth during observations (m), 𝑊𝑇𝑚𝑒𝑑 = median water table depth below sum-mertime peat surface (m), 𝑊𝑇𝑚𝑖 and 𝑊𝑇𝑚𝑎 = minimum and maximum watertable depth below summertime peat surface (m).Site Species Tree 𝐷𝐵𝐻 𝑟𝑠 p-value n 𝑊𝑇𝑟𝑎 𝑊𝑇𝑚𝑒 𝑊𝑇𝑚𝑖 𝑊𝑇𝑚𝑎B-Sa Birch 76 33 0.336 < 0.001 124 0.68 0.74 0.37 1.05B-Sa Birch 84 16 -0.113 0.484 41 0.20 0.41 0.37 0.58B-Sa Birch 86 23 0.135 0.34 52 0.50 0.83 0.54 1.04B-Sa Birch 87 19 -0.049 0.73 52 0.50 0.83 0.54 1.04P-low Hemlock 35 15 -0.696 < 0.001 117 0.55 0.39 0.06 0.61P-low Hemlock 36 9 -0.230 0.148 41 0.27 0.15 0.06 0.33P-Sa Hemlock 67 43 -0.085 0.35 124 0.49 0.53 0.26 0.75P-tall Hemlock 6 24 -0.679 < 0.001 110 0.44 0.54 0.25 0.69P-tall Hemlock 15 10 -0.422 0.002 53 0.20 0.55 0.45 0.65P-low Pine 30 17 0.495 0.001 41 0.27 0.15 0.06 0.33P-low Pine 31 6 -0.230 0.302 22 0.21 0.58 0.41 0.61108Site Species Tree 𝐷𝐵𝐻 𝑟𝑠 p-value n 𝑊𝑇𝑟𝑎 𝑊𝑇𝑚𝑒 𝑊𝑇𝑚𝑖 𝑊𝑇𝑚𝑎P-low Pine 49 10 0.111 0.621 22 0.21 0.58 0.41 0.61P-low Pine 99 7 -0.402 0.004 49 0.25 0.43 0.30 0.55P-Sa Pine 55 24 -0.276 0.213 22 0.20 0.72 0.55 0.75P-tall Pine 12 18 0.087 0.664 27 0.21 0.38 0.25 0.47P-tall Pine 13 15 0.300 0.128 27 0.21 0.38 0.25 0.47P-tall Pine 14 13 -0.281 0.042 53 0.20 0.55 0.45 0.656.3.3. Species-based modelling of sap flow from vapour pressure deficit and𝐷𝐵𝐻Sapwood area and species composition varied between sites (Table 6.3). At P-Sa, P-talland P-low, pine was dominant with similar fractions of hemlock at P-Sa and P-tall. AtP-low hemlock was less prevalent compared to P-Sa and P-tall; instead a small fraction oftrees consisted of birch. At B-Sa the entire tree population was birch.Table 6.3.: Summary of vegetation characteristics at each field site. 𝐴𝑠𝑎𝑝 = total basal areaof living trees (m2 ha-1), and 𝑓𝑏, 𝑓ℎ and 𝑓𝑝 = fraction (%) of total basal area ofbirch, hemlock and pine, respectively.Site 𝐴𝑠𝑎𝑝 𝑓𝑏 𝑓ℎ 𝑓𝑝BSa 30.64 100.0 0.0 0.0PSa 17.80 0.0 31.3 68.7Ptall 20.23 0.0 28.1 71.9Plow 3.66 6.2 11.7 82.2Both the intercept (𝑅0) and asymptote (𝑆𝐹𝑚𝑎𝑥) of the fitted relations between sap flow andvapour pressure deficit were positively related to 𝐷𝐵𝐻, and the coefficient 𝑘 varied amongtree species (Figure 6.4 and Table 6.4). For a given value of 𝐷𝐵𝐻, hemlock and birchhad the highest values of both 𝑅0 and 𝑆𝐹𝑚𝑎𝑥 and pine the lowest. Table 6.4 summarizesthe parameters of power-law models fit to the 𝑅0-𝐷𝐵𝐻 and 𝑆𝐹𝑚𝑎𝑥-𝐷𝐵𝐻 relations. Thecoefficient 𝑘 was noticeably higher for hemlock compared to pine and especially birch.Table 6.4.: Fitted parameters of species-specific power-law relations (Eqs. 6.13 and 6.14)used to estimate 𝑆𝐹𝑚𝑎𝑥 and 𝑅0 from 𝐷𝐵𝐻, and 𝑘 for application in the sapflow-𝑉 𝑃𝐷 model (Eq. 6.12).The parameters 𝑡1 and 𝑡2 are the fitted values of 𝑡used to compute weights in the generalized nonlinear squares model for 𝑆𝐹𝑚𝑎𝑥and 𝑅0, respectively.Variable Birch Hemlock Pine𝑎 0.020 0.005 0.066109Variable Birch Hemlock Pine𝑏 2.548 2.900 1.858𝑡1 0.527 0.647 1.244𝑐 0.002 0.008 0.007𝑑 2.657 2.329 2.229𝑡2 0.959 0.895 1.036𝑘 2.402 5.284 2.353Figure 6.5 illustrates the agreement between daily sap flow as observed and as predictedusing the modelled coefficients in the asymptotic model. There is broad agreement forobservations from trees used to fit the models (Figure 6.5a). Independent observations ofpine trees with insufficient observations for model fitting were generally reproduced by themodel; however, the predicted sap flow for P-Sa was more variable than observed sap flow(Figure 6.5b). The model consistently underestimated observed sap flow of a birch tree atB-Sa well.There is broad agreement as expected given that the observed data were those used to fitthe models. However, some biases are evident, such as a tendency to overpredict sap flowfor hemlock at the P-Sa site and underpredict for hemlock at the P-tall site, which suggeststhat sap flow behaviour for a given species varied among sites.Most model predictions fell within ± 50% of the observed sap flow; however, they werebiased for individual trees (Figure 6.6). For example, relative errors of predicted sap flowin trees “b” and “c” at B-Sa were 100% that of observed sap flow during periods of deepwater levels (WTD ≥ 1.0 m) and shallow water levels (WTD ≤ 0.6 m), respectively. At P-tall sap flow predictions for tree “b” exceeded observations by up to 200%. These systematicover-estimates occurred during low sap flow conditions. When sap flow in pine was high,for example at tree “b” at P-Sa, the model tended to under-estimate sap flow; however, therelative error remained less than 50%. Sap flow predictions for hemlock generally stayedwithin a relative uncertainty of ± 50%.The four trees with insufficient data for model fitting (Eq. 6.12) were tree “b” at B-Sa, tree“c” at P-Sa, tree “a” at P-tall and tree “e” at P-low. Except for sap flow predictions in tree“b” at B-Sa, model predictions fell within the ± 50% relative uncertainty bound.6.3.4. Prediction of plot-scale tree transpirationAt the plot scale, daily volumetric sap flow was highest at B-Sa, where it varied from 700to just under 3300 L per day (Figure 6.7). At P-Sa and P-tall, daily volumetric sap flowwas of the same order of magnitude, ranging from around 350 to 1450 L per day. At P-low,daily volumetric sap flow was lowest; it varied between 50 and 200 L per day. Expressedas mean depth over the plot area, trees transpired between 24 and 421 mm between May1 to September 30, 2019 (Table 6.5). Tree transpiration made up between 8% of grossprecipitation at P-low and 146% of gross precipitation at B-Sa (Table 6.5). The ordering of110Tree 76 Tree 35 Tree 30Tree 86 Tree 67 Tree 99Tree 87 Tree 6 Tree 14Tree 15Birch Hemlock Pine12340.5 0.7 0.9 0.2 0.4 0.6 0.2 0.4 0.6−101−0.20.00.20.4−0.40.00.4−202−3003060−10010−0.4−0.20.00.2−10010−5.0−2.50.02.55.0−404WTD (m)Q sap,obs − Q sap,pred (L Day−1 )SiteBSaPlowPSaPtallFigure 6.3.: Residuals of the sap flow-𝑉 𝑃𝐷 relation (Eq. 6.12) versus water table depthfrom a subset of trees that were either i) selected for stationary monitoring ofsap flow, ii) had a wide range of 𝑊𝑇𝐷 over the monitoring period, or iii) hada strong significant trend in residuals with 𝑊𝑇𝐷. Each panel shows data fromone tree and colors indicate the field site it was located at. A locally weightedsmoothing (LOESS) was fit with a span of 1.0 to illustrate the general trend.The grey ribbon indicates a 95% confidence interval around the fitted relation.11101002003004000 10 20 30 40 50DBH (cm)SFmax (L day−1 )0204060800 10 20 30 40 50DBH (cm)R0 (L day−1 )246Birch Hemlock PineSpeciesk (kPa−1 ) SpeciesBirchHemlockPineFigure 6.4.: Variation of asymptotic model coefficients by tree species. The top panels showthe relations between 𝑆𝐹𝑚𝑎𝑥 and 𝐷𝐵𝐻 (left panel) and 𝑅0 and 𝐷𝐵𝐻 (rightpanel); the bottom left panel shows boxplots of the estimated values of 𝑘 byspecies.112a01002003000 100 200 300Qsap,obs (L day−1)Q sap,pred (L day−1 )b02550750 25 50 75Qsap,obs (L day−1)SpeciesBirchHemlockPineSiteB−SaP−lowP−SaP−tallFigure 6.5.: Predicted sap flow versus observed sap flow by site and tree species of trees usedduring model fitting (a) and independent observations of trees with insufficientobservations for model fitting (b).plot-scale transpiration by site is consistent with the among-site differences in water tabledrawdown (Figure 6.1). For example, B-Sa had the highest transpiration and water tabledrawdown, while P-low had the lowest.Table 6.5.: Plot-scale volumetric sap flow between May 1 and September 30, 2019. 𝐴𝑠𝑖𝑡𝑒= area of field site, 𝑄𝑠𝑎𝑝 = cumulative volumetric sap flow, and 𝑃𝑜𝑝𝑒𝑛 = grossprecipitation measured at a nearby open site.Site 𝑄𝑠𝑎𝑝(𝑚𝑚) 𝑃𝑜𝑝𝑒𝑛(𝑚𝑚) 𝑄𝑠𝑎𝑝/𝑃𝑜𝑝𝑒𝑛B-Sa 421 288 1.46P-Sa 216 257 0.84P-tall 154 297 0.52P-low 24 297 0.086.4. Discussion6.4.1. Effect of tree size and species on sap flowDaily sap flow exhibited an apparently asymptotic relation with vapour pressure deficit(Eq. 6.12). Similar relations were found, for example, for Douglas-fir trees in the PacificNorthwest (Moore et al., 2004) and for black spruce in a boreal peatland on permafrost(Patankar et al., 2015).113Birch Hemlock PineB−SaP−SaP−tallP−low0.5 0.7 0.9 0.2 0.4 0.6 0.2 0.4 0.6−1.5−1.0−0.50.00.5−1.5−1.0−0.50.00.5−1.5−1.0−0.50.00.5−1.5−1.0−0.50.00.5WTD (m)εQ sap,obs (−)ObservationTestTrainingTreeabcdefFigure 6.6.: Relative model residuals versus water table depth below the summertime peatsurface arranged by field site (rows) and tree species (columns). Dashed linesbound the ± 50% relative error range. Color indicates whether sap flow obser-vations were used for fitting of the asymptotic model (black), or for independentmodel testing (red), and shape depicts individual trees in each panel.1140100020003000May2019Jun2019Jul2019Aug2019Sep2019Oct2019Q sap (L day−1 )B−SaP−lowP−SaP−tallFigure 6.7.: Time series of daily sap flow (𝑄𝑠𝑎𝑝) between May 1 and September 30, 2019,that was estimated by applying a nonlinear model (Eq. 6.12) to daily vapourpressure deficit computed from observations at an eddy-covariance flux towernear the bog center.The asymptotic maximum daily sap flow (𝑆𝐹𝑚𝑎𝑥) increased as an apparently species-specificconcave-up power-law function of 𝐷𝐵𝐻 (Eq. 6.13 and Figure 6.4). For larger trees, thevalue of 𝑆𝐹𝑚𝑎𝑥 for a given 𝐷𝐵𝐻 was higher in hemlock and birch compared to pine. Forexample, the asymptotic maximum sap flow of a hemlock or birch tree with 𝐷𝐵𝐻 = 35 cmwould be about three times that of a pine of similar diameter.These results suggest that even large pine trees that are not stunted by shallow water tablesgenerate less transpiration loss than large hemlock or birch. As a consequence, at the treelevel, large hemlock and birch contribute disproportionately to transpiration loss in BurnsBog. From a restoration perspective with the goal of peatland rewetting, these trees shouldbe prioritized in tree removal or bark stripping activities aimed at reducing water losses bytranspiration.The coefficient 𝑅0 represents daily sap flow under conditions of low atmospheric demand,e.g., on a rainy day with high relative humidity. The value of 𝑅0 increased as species-specificconcave-up power-law functions of 𝐷𝐵𝐻 (Figure 6.4). In contrast to the among-speciesvariation in 𝑆𝐹𝑚𝑎𝑥, differences between hemlock or birch and pine were less pronounced.However, sap flow observations between October through April would be required to char-acterize the relationship between sap flow and vapour pressure deficit outside the growingseason and to assess the significance of shallow water tables as a control (i.e. waterlogging).Regardless, the effect of wintertime transpiration loss on the water levels in Burns Bogor other maritime raised bogs would likely be minor due to precipitation in those monthsexceeding evapotranspiration.The value of the parameter 𝑘 is related to how rapidly sap flow approaches the asymptoticmaximum as 𝑉 𝑃𝐷 increases (Eq. 6.12). The value of 𝑘 was highest in hemlock, while birch115and pine had similar values about half that of hemlock (Figure 6.4) suggesting that hemlockexerted stronger stomatal control on sap flow compared to birch and pine. Stomatal activityhas been shown to vary with tree height (e.g. Delzon et al., 2004; Addington et al., 2006;Ambrose et al., 2010) and species (Oren et al., 1999). The wider range of 𝑘 values inhemlock and pine may be explained by the wider range of peat moisture status across thesites these species were located at as hydraulic conductance along the soil-to-leaf pathwayvaries by species (Reich and Hinckley, 1989) and soil water status (e.g. Reich and Hinckley,1989; Cochard et al., 1996; Irvine et al., 1998).6.4.2. Effect of water table depth on sap flowThe effect of water table depth on sap flow was complicated and varied among trees andsites. Most hemlocks showed a significant negative effect of increased water table depth onthe residuals from the relation between sap flow and 𝑉 𝑃𝐷, suggesting available moisture tobe a limiting control on sap flow when water table depth increased over the growing season(Table 6.2). The depth at which sap flow became moisture-limited was around 0.3 m atP-low (Tree 35 in Figure 6.3), between 0.5 and 0.6 m at P-tall (Trees 6 and 15 in Figure6.3) and at P-Sa moisture limitation was not significant in a large tree (Tree 67 in Figure6.3). It is hypothesized that the larger hemlock at P-Sa (𝐷𝐵𝐻 = 43 cm) was able to drawwater from deeper peat due to better developed roots. The inferred differences in rootdevelopment could be connected to different drainage histories between P-Sa which wasdrained more severely and for a longer duration than P-tall (Hebda et al., 2000; Murphy etal., 2009), and deeper seasonal water table drawdown between P-Sa and P-low (Murphy andMoore, 2010). Drier conditions at P-Sa were also evident from the absence of Sphagnummoss, which covered the peat surface at the other two sites. It should be noted that threeout of the six hemlock trees were equipped with stationary sap flow sensors that seemedmore successful at detecting a dependence of sap flow on water table depth due to thebroader range of water table depths sampled during the longer monitoring period in thesetrees compared to trees equipped with roving sensors.The effect of water table depth on sap flow in pine was less clear compared to hemlock.At P-low shallow water levels limited sap flow, as indicated by a positive slope in therelationship between water table depth and the residuals from the relation between sapflow and 𝑉 𝑃𝐷 (Tree 30 in Figure 6.3). The effect was marginally significant and may beconnected to waterlogging (Patankar et al., 2015; Liu et al., 2022) and associated anoxiaearly in the growing season (Coutts and Philipson, 1978; Wang et al., 2016). Later in thegrowing season, sap flow in pine at P-low became moisture-limited (Tree 99 in Figure 6.3),which could be connected to limited root growth to a few tens of centimeters below the peatsurface due to the generally higher groundwater levels at P-low (Coutts and Philipson, 1978;Warren et al., 2021), especially early in the growing season as active roots are particularlyvulnerable to waterlogging (e.g. Radville et al., 2016; Iversen et al., 2018; Domisch et al.,2020; Malhotra et al., 2020). At P-tall sap flow in pine was not moisture-limited early inthe growing season (Trees 12 and 13 in Table 6.2), which could be connected to deeper rootdevelopment due to deeper seasonal water table drawdown compared to P-low (Murphyand Moore, 2010; Iversen et al., 2018). However, sap flow became moisture-limited later in116the growing season when water table depth dropped below 0.4 m (Tree 14 in Figure 6.3).Results suggest the range at which sap flow in pine was not affected by groundwater levelsto be narrower and shallower at P-low compared to P-tall. It appears that the groundwaterlevels at which the sap flow-𝑉 𝑃𝐷 relations in pines were affected by water table depthvaried according to differences in seasonal water table drawdown at P-tall and P-low.The birch tree used for stationary sap flow observations (Tree 76) was the only birch treethat showed a significant positive effect of water table depth on sap flow, which could havebeen connected to waterlogging (Landhäusser and Lieffers, 2001; Landhäusser et al., 2003b)early in the growing season when water tables were less than 0.6 m below the peat surface(Figure 6.1 and Figure 6.3); roots were likely developed close to the maximum depth ofseasonal water table drawdown (Murphy and Moore, 2010; Iversen et al., 2018). Trees 86and 87 showed a similar but insignificant trend of the effect of water table depth on thesap flow-𝑉 𝑃𝐷 relation based on the Spearman rank correlation test; however, the relationbetween the sap flow-𝑉 𝑃𝐷 relation and water table depth was apparently non-monotonic,which violates an assumption underlying the Spearman rank correlation test. Failure todetect significance of the effect of water table depth on the sap flow-𝑉 𝑃𝐷 relation for trees86 and 87 may be related to the narrower range of observed water table depths (0.54 to1.04 m below the peat surface) compared to tree 76. As seen in Figure 6.3, sap flow forall three birch appeared to become moisture-limited during conditions of maximum watertable drawdown; however, the effect was only marginally significant for tree 76.With the exception of stationary trees, sap flow was measured between six and eight weeksper tree. Over these periods, the change in water table depth did not exceed 0.2 m insome cases, which limited the range of water levels and number of observations availableto assess the effect of water table depth on sap flow. The effect seemed to vary by treesize, species, timing of the onset of seasonal water table drawdown and the site’s drainagehistory, which likely affected root development as shallow groundwater tables impede deeproot development in peatlands (e.g. Steele et al., 1997) due to a lack of oxygen (e.g. Kinget al., 1986; Lieffers and Rothwell, 1987; Ray and Schweizer, 1994). Individual speciesadapt differently which becomes evident in their tolerance to waterlogging (e.g. Couttsand Philipson, 1978; Philipson and Coutts, 1978; Sanderson and Armstrong, 1980; Rayand Schweizer, 1994; Wang et al., 2016). Longer monitoring periods per tree to sample abroader range of water table depths would have provided better evidence on how changesin water table depth affect sap flow.For more realistic transpiration modelling in peatlands, future research should focus onimproving the understanding on how tolerance of roots to lack of oxygen in the dormantseason (e.g. Coutts and Philipson, 1978; Wang et al., 2016) and onset of seasonal watertable drawdown affect root development in peatlands, as timing of seasonal root activationis still poorly understood (e.g. Radville et al., 2016; Iversen et al., 2018; Malhotra et al.,2020) and root vulnerability depends on timing, duration and frequency of waterlogging(e.g. Nicoll and Coutts, 1998; Domisch et al., 2020).Results presented in Table 6.2 and Figure 6.3 provide evidence that short-term (i.e. seasonal)water table depth variability acted as secondary control on sap flow by affecting the sap flow-𝑉 𝑃𝐷 relation. The effect seems to vary among tree species; the sap flow-𝑉 𝑃𝐷 relation in117hemlock was prone to moisture limitation, in birch the relation was affected by waterlogging,and the sap flow-𝑉 𝑃𝐷 relation in pine seemed generally less sensitive. These species-specificeffects of water table depth on the sap flow appeared to have been different between sites,which suggests that there exists an additional long-term effect (i.e. drainage history) ofwater table depth on the sap flow that are conveyed via groundwater table-controlled rootdevelopment.6.4.3. Modelling sap flow using species-specific relations with 𝑉 𝑃𝐷 and 𝐷𝐵𝐻Model predictions agreed reasonably well with observed sap flow that was used for modelfitting; however, higher sap flows of larger trees tended to be overestimated (e.g. birch atB-Sa or hemlock at P-Sa in Figure 6.5a). Model predictions of independent observationsof trees with insufficient data to fit the asymptotic model captured pine sap flow of smalltrees well, but the model overpredicted sapflow of the second largest birch with a 𝐷𝐵𝐻of 29 cm at B-Sa (Figure 6.5b). It is likely that the power-law functions (Eqs. 6.13 and6.14) overpredicted coefficients 𝑆𝐹𝑚𝑎𝑥 and 𝑅0, especially for large birch and hemlock dueto the small sample size of large trees in particular. This would have led to overpredictionof sap flow by the asymptotic model (Eq. 6.12). Water table depth was low for sap flowobservations from the independent birch (Figure 6.6) which makes overprediction due towater table depth effects unlikely. However, it is possible that the birch was a special caseor that the poorly constrained relations for larger trees in Eqs. 6.13 and 6.14 caused pooragreement between observed and predicted sap flow. Part of the among site difference inmodel predictions may be related to water table depth that varied among sites and thathas not been explicitly represented in the model.Relative prediction error of the model stayed within ± 50% of the observed sap flow with afew exceptions (Figure 6.6), suggesting that vapour pressure deficit can be used to obtainfirst-order estimates of actual sap flow in Burns Bog. An underlying assumption of themodel was that species behave similarly across sites, which results suggest was not alwaysthe case (Figure 6.5 and 6.6). Nevertheless, it appears that residuals were associatedwith second-order effects of water table depth such as moisture limitation, for examplefor hemlock and pine at P-low or P-tall (Figure 6.6), which were identified to be significantin some trees (Table 6.2).6.4.4. Growing season transpiration lossPlot-level growing season transpiration loss ranged from 24 mm at P-low up to 421 mmat B-Sa, equivalent to between 8 and 146% of gross precipitation (Table 6.5). Even whenaccounting for an assumed ± 50% uncertainty in the plot-level transpiration estimates, theresults indicate that transpiration is a minor component of the water balance at P-low anda major component at B-Sa, relative to growing season precipitation. This range reflectsthe range in tree size, density, and species composition (Table 6.3) among sites (Humphreyset al., 2006). For example, at B-Sa, P-Sa, P-tall and P-low, basal area was 34 m2 ha-1, 22m2 ha-1, 22 m2 ha-1, and 5 m2 ha-1, respectively (Table 6.3). The range in basal area among118sites was likely mediated by differing drainage histories (Hebda et al., 2000; Murphy et al.,2009) and seasonal water table drawdown (Murphy and Moore, 2010) (Table 6.1). Among-site variation in transpiration loss and its correlation with among-site variation in watertable drawdown in Burns Bog, as illustrated in Table 6.5 and Figure 6.1, are evidenceof the strong positive feedbacks between ecohydrologic processes and water table depthsuggested by Waddington et al. (2015). To the authors’ knowledge, this study is the firstto specifically document this feedback using field observations.Other studies have shown that the fraction of growing-season precipitation being lost tothe atmosphere via tree transpiration can be significant in treed peatlands. For example,Kučerová et al. (2010) reported that growing season transpiration from bog pine in acentral European peatland made up 101% of gross precipitation over the same period. InFinland, transpiration loss from June through August was 60 mm (mean of 2010 and 2011)in northern Finland, and 85 mm (mean from 2007 through 2011) in central Finland, whenmean precipitation of the years from 2007 through 2011 in June through September was269 mm and 240 mm, respectively (Sarkkola et al., 2013).6.5. ConclusionsThis study focused on quantifying sap flow in four vegetation communities of varying treesize, species and density, peat moisture status, and drainage history in a maritime raisedbog in southwestern British Columbia, Canada. At the tree level, sap flow exhibited anasymptotic relation with vapour pressure deficit (𝑉 𝑃𝐷). The residuals from the sap flow-𝑉 𝑃𝐷 relations indicated an influence of water table depth on sap flow that was complicatedand varied by site, possibly due to differences in drainage-induced root development andspecies-specific tolerance to waterlogging and drought. Paper birch was apparently affectedby waterlogging that reduced sap flow during periods with high water tables, whereasresiduals for western hemlock showed a negative relationship with water table depth, andlodgepole pine was apparently less sensitive to seasonal changes in water table depth. Thefindings suggest that water table depth can influence transpiration on daily to seasonal timescales via waterlogging and moisture limitation during periods of shallow and deep watertable, respectively; over longer, multi-year, time scales, water table depth may be able toinfluence transpiration through drainage-induced development of deeper root networks.For the asymptotic model of sap flow as a function of 𝑉 𝑃𝐷, the intercept and asymptoteexhibited similar increases with increasing 𝐷𝐵𝐻 for birch and hemlock. The increase ofboth metrics was considerably less for pine. The gradient 𝑘 was similar between birch andpine and about half that of hemlock, suggesting that hemlock exert a stronger physiologicalcontrol on sap flow with changing 𝑉 𝑃𝐷 than birch or pine did.Estimates of plot-scale transpiration over the growing season (May 1 to September 30)ranged from 24 mm in a pine low shrub community with sparse and stunted trees up to 421mm in a mature birch stand with dense shrub cover, which constituted between 8% and146% of gross precipitation, respectively. The ranking of the plot-scale estimates of growing119season transpiration was consistent with the ranking of seasonal water table drawdown –e.g., the site with the highest transpiration also had the greatest water table drawdown.To the authors’ knowledge, this is the first study that quantified plot-level transpirationloss in multiple vegetation communities in a peatland. The precision of the estimates waslimited by the sample size, but was adequate to infer that transpiration is minimal in termsof water loss at the P-low site, significant at the B-Sa site, and intermediate at the othertwo sites. Results suggest that seasonal water table depth changes can exert an importantspecies-specific control on transpiration that needs to be captured by process-based modelsin the future, and drainage-induced differences in water table depth among sites reduceaccuracy of model predictions likely via differences in root development among sites, if notexplicitly represented in the model. Future studies should explore the application of theapproach but with larger samples sizes and with sap flow observations covering sufficientlywide ranges of water table depth for each tree to provide a basis for assessing its influenceon transpiration.1207. Peat surface evaporation and moisturedynamics in five vegetation communitiesin a maritime raised bog in the PacificNorthwest7.1. IntroductionEvapotranspiration is the dominant summertime water loss process in many peatlands (e.g.Lafleur et al., 2005; Brown et al., 2010). Sub-canopy evaporation from the peat surfacecan be an important component of the water balance (e.g. Kim and Verma, 1996; Heij-mans et al., 2004) even compared to transpiration from woody shrub vegetation (Lafleurand Schreader, 1994; Admiral et al., 2006). Both overstory and understory vegetation canaffect peat surface evaporation by shading that reduces incoming solar radiation (Esseryet al., 2008; Kettridge and Baird, 2010; Kettridge et al., 2013; Waddington et al., 2015),and shading by understory vegetation can be the dominant control on available energy atthe peat surface in open forest peatlands (Baldocchi et al., 2000). In addition to shading,the aerodynamic roughness of a peatland is controlled by its vegetation (Mölder and Kell-ner, 2002; Thompson, 2012; Moore et al., 2013), which can alter evaporation by affectingaerodynamic resistance near the peat surface (Kettridge and Baird, 2006) and zero-planedisplacement height (Kettridge et al., 2013; Waddington et al., 2015). Another influenceof vegetation results from the reduction of peat moisture content via transpiration andinterception loss, which increases bulk surface resistance and can act as a secondary controlon evaporation (Admiral et al., 2006; Bond-Lamberty et al., 2011).Most past studies measured vapour exchange between vegetated peatlands and the atmo-sphere by means of the eddy-covariance technique (e.g. Humphreys et al., 2006; Brownet al., 2013; Moore et al., 2013); however, this approach does not directly distinguish thecomponents of vapour exchange. A limited number of studies partitioned bulk vapour ex-change as determined by eddy covariance or the Bowen ratio-energy balance approach intothe components contributed by areas of bare peat and vegetation surrounding the flux towerbased on two- (Kim and Verma, 1996) or three-source models (Admiral and Lafleur, 2007a,2007b; Moore et al., 2022). However, this approach still fails to separate transpirationand peat surface evaporation from the vegetated areas. The development and testing ofprocess-based modelling would benefit from the ability to separate peat surface evaporationand transpiration in the vertical dimension.121Lysimeters provide a means to measure evaporation from the peat surface that is coveredby vegetation, but come with a set of drawbacks. One major concern with most lysimeterdesigns is that moisture content in the lysimeters may not be representative of the peatmoisture content outside the lysimeter. Another concern arises from precipitation that cancause waterlogging or overflow, and may render measurements futile if the exact influx isunknown. In past studies in peatlands, lysimeter moisture content was managed by addingwater to match water levels (Price, 1992a) or moisture content (Petrone et al., 2004) ofthe surrounding peat, or by adding an amount of water equivalent to daily evaporationloss (Lafleur and Schreader, 1994). However, adding water to the peat surface could alterthe vertical moisture content profile, likely affecting observations especially at daily mea-surement intervals. In some studies, excess water was removed when water levels in thelysimeter were above those of the surrounding peat, for example after heavy rainfall (Price,1992a; Lafleur and Schreader, 1994; Heijmans et al., 2004). An alternative approach isthe use of more sophisticated automated lysimeter designs (e.g. Schwärzel and Bohl, 2003;Schwärzel et al., 2006; Proulx-McInnis et al., 2014); however, the number of lysimeters fordeployment of these systems would likely be limited by cost and labour restrictions.Existing studies of sub-canopy evaporation in peatlands are limited to boreal ecosystems(Lafleur and Schreader, 1994; Heijmans et al., 2004; Petrone et al., 2004), where seasonalsnow accumulation and melt are significant components of the hydrologic regime. Obser-vations were conducted in an open tamarack black spruce forest (Lafleur and Schreader,1994), and in dense or open black spruce forest and a bog (Heijmans et al., 2004). There is ascarcity of peat surface evaporation research from a wider range of vegetation communities,especially in maritime climatic settings, where seasonal snow cover is a minor componentof the hydrologic regime.The overarching objective of this study was to quantify peat surface evaporation loss infive structurally distinct vegetation communities comprising different species and varyingpeat surface cover in a maritime climate. The study focused on Burns Bog, a raised bog insouthwestern British Columbia, Canada, where a history of drainage and peat harvestingfacilitated encroachment of shrub and tree vegetation (Hebda et al., 2000). Burns Bog islocated near the southern extent of raised bogs in western North America (Rocchio et al.,2021). To achieve the study’s objective, a novel empirical approach was developed to adjustlysimeter evaporation estimates to account for differences in peat moisture content betweenthe lysimeters and the surrounding site.7.2. Methods7.2.1. Peat moisture contentField observationsContinuous volumetric peat moisture content was measured at 10, 20, 30 and 40 cm depthbelow the peat surface using a capacitance sensor (PR2/4, Delta-T Devices) that took a122reading every 30 minutes in a hollow microtopography near the micrometeorological stationbetween May, 2018 and October, 2019.Plot-averaged peat moisture content at 10, 20, 30 and 40 cm depth was measured using aspatially stratified random sampling approach. Each field plot (30 by 30 m) was dividedinto six 5-m wide north-south oriented strata in which sampling locations were randomlyrelocated during each field visit. Each time, two hollows were sampled in each of the sixstrata, yielding a total of twelve hollow observations per field trip. The initial samplingdesign included peat moisture observations on hummocks; however, these measurementswere deemed unreliable due to poor contact between sensor and hummock moss and werethus excluded from the analysis. Manual observations were conducted between May, 2018and May, 2019.Calibration of moisture sensorsAccuracy of soil moisture sensor observations relies heavily on soil- and/or site-specificcalibration, especially in soils characterized by high organic matter and moisture contentand low bulk density (e.g. Kellner and Lundin, 2001; Yoshikawa et al., 2004; Nagare etal., 2011; Vaz et al., 2013; Bircher et al., 2016). In this study, laboratory calibrationwas conducted using undisturbed peat samples that were collected from each site using thesame extraction as used for lysimeter sampling, described below. At each field site, betweenfive and ten peat samples were collected from hollow microtopography, yielding a total of29 hollow samples. Samples were covered on-site and transported to a laboratory wherevolumetric peat moisture content was measured with every sensor in every peat samplesuch that a total of 174 hollow observations were available for model fitting. Gravimetricmoisture content was determined by extracting three 220 cm3 peat cores from each sampleand by oven-drying the cores at 80∘C for 24 hours until core mass change was less than 0.1g over two hours.An initial attempt was made to fit independent site-specific calibration relationships. AtP-tall, the site for which only five samples were available, fitting yielded uncertain cali-bration coefficients due to the large scatter and the small sample. At the remaining sites,independent calibration relationships were more robust, even though estimates for B-Sawere based on only six samples.Due to the high uncertainty in the calibration relationship for P-tall, it was decided to applya mixed-effects model to obtain more robust sensor calibration coefficients. The model wasinitially applied using both site and sensor as random effects; however, the relationship didnot differ significantly among sensors, resulting in the fitting of a simpler model with fieldsite as only random effect. The final calibration relation had the following form:𝜃𝑖𝑗 = 𝐵0 + 𝑏0𝑖 + (𝐵1 + 𝑏1𝑖)√𝜖 + 𝑒𝑖𝑗 (7.1)where 𝜃𝑖𝑗 is the volumetric moisture content for a sample from site 𝑖 using sensor 𝑗, 𝐵0 and𝐵1 are the fixed-effects estimates for the intercept and slope, 𝑏𝑜𝑖 and 𝑏1𝑖 are the random123effects intercept and slope for site 𝑖, and 𝑒𝑖𝑗 is the residual for the sample from site 𝑖 usingsensor 𝑗. Refractive index (√𝜖) was calculated as an empirical polynomial function of rawsensor output that was provided by the manufacturer (Delta-T-Devices, 2004) as:√𝜖 = 1.125 − 5.53𝑉 + 67.1𝑉 2 − 234.42𝑉 3 + 413.56𝑉 4 − 356.68𝑉 5 + 121.53𝑉 6 (7.2)where 𝑉 = raw sensor output (mV). The fixed-effects coefficients, 𝐵0 and 𝐵1, were usedfor all sites and sensors.Estimation of continuous changes in moisture contentStationary continuous peat moisture observations were made consistent with site-averagedmanual observations from the roving sensor by applying a simple least-squares linear re-gression model of the form:𝜃𝑚𝑎𝑛 = 𝑏0 + 𝑏1𝜃𝑠𝑡𝑎 + 𝑒 (7.3)where 𝜃𝑚𝑎𝑛 = mean of manual peat moisture observations from the roving sensor (vol. %)available at roughly two-weekly intervals, 𝜃𝑠𝑡𝑎 = 30-min peat moisture observation fromthe stationary sensor (vol. %), 𝑏0 and 𝑏1 = intercept and slope obtained from the fit linearregression model, and 𝑒 = residual.7.2.2. Lysimeter measurementsConstruction and deploymentAt each site (B-Sa, P-Sa, P-tall, P-low and L-T), evaporation from the peat surface wasmeasured using twelve bucket lysimeters, two of which were randomly placed within each ofthe six 5-m wide strata such that one contained peat from a hummock and one from a hollow.During sampling it was ensured that buckets did not contain mature shrub (i.e. salal orLabrador tea) and other vascular vegetation was kept at a minimum to minimize unwantedtranspiration water loss. Each lysimeter consisted of one simple plastic bucket withoutdrainage feature (22.5 and 24 cm inner diameter and height, respectively), which was filledwith an undisturbed peat core to approximately ± 2 cm of its rim. The lysimeter was placedin an identical “holder”-bucket to prevent unwanted moss to be stuck on the outside whenremoved for weighing (Lafleur and Schreader, 1994). The insulating effect of the bucket onsoil heat flux and peat temperature inside the lysimeter was assumed to be minimal.In the rainy half of the year, between October 29 and May, 26, lysimeters were shelteredby a 30-cm diameter lid that was installed approximately 15 cm above the lysimeter top toprevent influx from throughfall. Had the lysimeters not been sheltered, they would likelyhave overflowed during periods with high throughflow, such that evapotranspiration during124those periods would have been overestimated. The sheltering effect of the lid on incomingradiation and atmospheric turbulence was assumed to be small due to the low sun angleand dense and tall shrub vegetation that would have had a stronger effect on radiationand humidity near the peat surface at most sites. Although sheltered during the rainyseason, lysimeters remained near saturation, which would have roughly corresponded topeat moisture content of the surrounding peat.Undisturbed peat cores were obtained in four steps. First, a peat cube with side lengths ofapproximately 25 cm was separated from the surrounding peat by carefully cutting downvertically into the peat using a serrated knife or clippers when roots were present. Thesurrounding peat was removed on one side which allowed sliding a wooden board under theblock. The peat block was lifted out of the hole and a lysimeter bucket was used as leadfor cutting off excess peat. The peat block was inserted into a lysimeter bucket and thesample was deemed intact when vertical compression between undisturbed pre-sample peatand transplanted peat core was less than 10% (i.e. 2.5 cm).Calculation of evaporationEvaporation rate from lysimeters was calculated from the change of mass between con-secutive manual observations by converting gravimetric peat moisture content to a depthas:𝐸𝑙𝑦𝑠,𝑡 =(𝑀𝑡−1 −𝑀𝑡)Δ𝑡 ⋅ 𝐴𝑙𝑦𝑠 ⋅ 𝜌𝐻2𝑂(7.4)where 𝐸𝑙𝑦𝑠,𝑡 = average evaporation rate from lysimeter between observation 𝑡 − 1 and 𝑡(mm day-1), 𝑀𝑡−1 and 𝑀𝑡 = mass of lysimeter (kg) at observation 𝑡 − 1 and 𝑡, respectively,Δ𝑡 = time difference between observation 𝑡−1 and 𝑡 in days, 𝜌𝐻2𝑂 = density of water hereassumed to be 1000 kg m-3, and 𝐴𝑙𝑦𝑠 = area of lysimeter (m2) calculated as:𝐴𝑙𝑦𝑠 = (0.5 ⋅ 𝑑𝑙𝑦𝑠)2𝜋 (7.5)where 𝑑𝑙𝑦𝑠 = lysimeter diameter, here 0.225 m. Estimating evaporation from mass changein lysimeters implicitly assumes that mass gain due to moss growth is negligible, which wasassumed to be legitimate for two-week measurement intervals over the course of about ayear.Evaporation was measured in twelve lysimeters at each site and between 17 and 20 timesover the course of the field campaign. Occasionally observations from individual lysimeters –and in rare cases observations from most lysimeters – were missing, due to unknown influxes(i.e. precipitation or flooding). Missing observations were assumed to be random and tonot bias the mean of the remaining measurements. Only when more than half of lysimetermeasurements were erroneous (e.g. due to short-lived snow in winter), all observations werediscarded.125Moisture content in lysimetersVolumetric peat moisture content in lysimeters was approximated from measured weightsduring every field trip (𝑀𝑡) along with the weight of completely filled lysimeters and thedry mass of the peat in every lysimeter as:𝜃𝑙𝑦𝑠 =(𝑀𝑡 −𝑀𝑝,𝑑𝑟𝑦 −𝑀𝑏)𝑀𝐻2𝑂⋅ 100% (7.6)where 𝜃𝑙𝑦𝑠 = relative volumetric peat moisture content in lysimeter (vol. %), 𝑀𝑏 = mass ofempty lysimeter bucket (kg), 𝑀𝐻2𝑂 = mass of water held in a lysimeter bucket filled to therim (kg), and 𝑀𝑝,𝑑𝑟𝑦 = mass of oven-dried peat from lysimeter (kg). Eq. 7.6 assumes thatlysimeters were filled to the rim with peat, which was roughly the case when the lysimeterswere installed.7.2.3. Equilibrium evaporationEquilibrium evaporation (𝐸𝑒𝑞, m s-1) refers to evaporation from a freely evaporating surfacewhen the ambient air is saturated (Priestley and Taylor, 1972). At the open BB1 flux towersite it was calculated as follows:𝐸𝑒𝑞,𝐵𝐵1 =1𝐿𝑣 ⋅ 𝜌𝐻2𝑂𝑠𝑠 + 𝛾 ⋅ (𝑄∗ −𝑄𝐺) (7.7)where 𝐿𝑣 = latent heat of vaporization (J kg-1), which was computed as a function of airtemperature, 𝜌𝐻2𝑂 = density of water, here 1000 kg m-3, 𝑠 = slope of saturation vapourpressure curve (kPa ∘C-1), 𝛾 = psychometric constant (kPa ∘C-1), 𝑄∗ = net radiation (Wm-2) and 𝑄𝐺 = ground heat flux at the peat surface (W m-2). It is commonly found thatwet-surface evaporation is somewhat higher than 𝐸𝑒𝑞 because horizontal advection and/orvertical down-mixing typically maintain unsaturated conditions, even over extensive wetsurfaces. For example, Priestley and Taylor (1972) reported that potential evaporationaveraged 1.26 times 𝐸𝑒𝑞.7.2.4. Estimating site evaporation and uncertaintyTo derive an approach to adjust lysimeter evaporation to account for differences in volu-metric moisture content between the lysimeters and the site, the first step was to derivea predictive model to estimate peat surface evaporation based on lysimeter measurements,available energy and peat moisture content as follows:𝐸𝑜𝑏𝑠 = 𝑏0 + 𝑏𝑒𝑞𝐸𝑒𝑞,𝐵𝐵1 + 𝑏𝜃𝜃𝑙𝑦𝑠 + 𝑒 (7.8)126where 𝐸𝑜𝑏𝑠 = observed lysimeter evaporation (mm day-1), 𝑏0 = model intercept, 𝑏𝑒𝑞 and 𝑏𝜃= model coefficients associated with predictors 𝐸𝑒𝑞,𝐵𝐵1 and 𝜃𝑙𝑦𝑠, and 𝑒 = random error.Available lysimeter evaporation measurements were then corrected for the difference involumetric peat moisture content between lysimeters and the surrounding peat during thegrowing season (May 1 to September 30) as follows:𝐸𝑠𝑖𝑡𝑒 = 𝐸𝑙𝑦𝑠 + 𝑏𝜃(𝜃𝑚𝑎𝑛 − 𝜃𝑙𝑦𝑠) (7.9)where 𝐸𝑠𝑖𝑡𝑒 = site evaporation (mm day-1), and 𝑏𝜃 = fitted coefficient from Eq. 7.8. Apply-ing the correction assumes that the limiting influence of moisture content on evaporation isthe same in lysimeters and the surrounding peat, which implicitly assumes that hydraulicproperties of the peat are the same. Given that the lysimeters were just over 20 cm deep,the manual measurements at 10 cm depth were used in Equation (7.9). During times whenlysimeter measurements were missing, site evaporation was estimated directly based on thefitted model shown in Eq. 7.8 and by using manually measured moisture content as:𝐸𝑠𝑖𝑡𝑒 = 𝑏0 + 𝑏𝑒𝑞𝐸𝑒𝑞,𝐵𝐵1 + 𝑏𝜃𝜃𝑚𝑎𝑛 + 𝑒 (7.10)Using standard approaches for uncertainty propagation (Gilbert, 1987; National Instituteof Standards and Technology, 2017), the standard uncertainty in 𝐸𝑠𝑖𝑡𝑒, denoted as 𝑠(𝐸𝑠𝑖𝑡𝑒),can be computed as:𝑠(𝐸𝑠𝑖𝑡𝑒) = √[𝑠(𝐸𝑙𝑦𝑠)]2 + 𝑠2(𝑏𝜃)(𝜃𝑠𝑖𝑡𝑒 − 𝜃𝑙𝑦𝑠)2 + 𝑏2𝜃[𝑠2(𝜃𝑠𝑖𝑡𝑒) + 𝑠2(𝜃𝑙𝑦𝑠)] (7.11)where 𝑠(𝐸𝑙𝑦𝑠) = standard uncertainty of 𝐸𝑙𝑦𝑠, which can be computed as the standarderror of the mean of 𝐸𝑙𝑦𝑠; 𝑠(𝑏𝜃) = standard error of 𝑏𝜃, which can be extracted from thelinear model output; 𝑠(𝜃𝑠𝑖𝑡𝑒) = standard uncertainty of 𝜃𝑠𝑖𝑡𝑒, which can be computed asthe standard error of the mean of the manual site measurements; and 𝑠(𝜃𝑙𝑦𝑠), which can becomputed as the standard error of the mean of the lysimeter measurements.7.3. Results7.3.1. Meteorological conditions during the study periodDaily mean air temperature (𝑇𝑎) varied between highs of around 23∘C in summer and lowsof -5∘C in winter (Figure 7.1). On most days 𝑇𝑎 stayed above zero. Extended periodsof rain and overcast skies in winter generated conditions of low available energy and lowatmospheric demand with daily mean vapour pressure deficits (𝑉 𝑃𝐷) of less than 0.25 kPa.In contrast, long dry periods with clear skies in summer produced high available energyand 𝑉 𝑃𝐷 with daily mean values between 0.25 and 0.5 kPa that reached over 0.75 kPaat times. Daily mean net radiation reached 20 MJ m-2 in summer and stayed below 2.5127MJ m-2, rarely negative, in winter. Ground heat flux was less than 10% of net radiationin summer. It was oriented away from the peat surface (positive) in summer and becamenegative between September and March.Groundwater levels rose to within 0.25 m of the peat surface between November 2018 andMay 2019. In summer groundwater levels dropped to between -0.75 and -1.0 m depth in2018 and between -0.5 and -1.0 m in 2019. Seasonal water table drawdown was less in 2019at all sites except for B-Sa.7.3.2. Peat moisture contentVolumetric peat moisture content at 10-cm depth started to decrease between May andJune and reached minimum moisture content in late summer. Near surface peat was drierin 2018 than in 2019 (Figures 7.1 and 7.3). In winter, peat was continuously saturated atP-low and L-T; at B-Sa it was saturated for a short period of heavy rainfall in late 2018 andearly 2019. P-Sa and P-tall did not reach saturation; however, peat water content peakedduring short periods of heavy rainfall.Manual volumetric peat moisture content observations followed the seasonal trend observedin continuous observations at each site (Figure 7.3). The seasonal amplitude was highest atB-Sa and P-tall and lowest at L-T. Variability between the twelve hollow observations wasgenerally low; it tended to increase in summer when peat water content was lower (e.g. P-low). There appeared to be weak hysteretic behaviour in the relation between manual(𝜃𝑚𝑎𝑛) and continuous stationary observations (𝜃𝑠𝑡𝑎), especially at P-Sa, with 𝜃𝑚𝑎𝑛 beinglower during drying than during wetting (Figure 7.2).Volumetric moisture content in lysimeters was significantly different to manual observationsduring most field trips (Figure 7.4) and observations tended to be clustered near saturationand at low moisture contents with few outliers. Variability of volumetric moisture contentamong lysimeter buckets and field observations increased with decreasing moisture content.Slopes of the fitted relationships in Figure 7.4 suggest that lysimeters dried more quicklythan the surrounding peat, with the exception for observations at B-Sa.7.3.3. Lysimeter evaporationCanopy openness differed between field sites (Table 7.1). At B-Sa, tree canopy opennessvaried between 0.21 and 0.74 in the leafed and leafless season respectively; at P-Sa, P-talland P-low it was constant and ranged from 0.58 to 0.79. Canopy openness below theshrub layer ranged from 0.16 at B-Sa to 0.38 at L-T, and was the limiting factor of canopyopenness at all sites.Lysimeter evaporation varied between field sites during periods of minimal moisture limita-tion (i.e. 𝜃𝑙𝑦𝑠 > 55% in Figure 7.5). The slope of the relation between lysimeter evaporationand equilibrium evaporation at the flux tower site tended to be higher for P-low (canopyopenness = 0.33) than for the sites with lower canopy openness. The anomaly is the L-T128−505101520T a (°C)0.000.250.500.751.00VPD (kPa)05101520Q* , Q G (MJ m−2 )Q*QG0204060P (mm)0255075100θ sta (vol. %) B−SaP−SaP−tallP−lowL−T−1.00−0.75−0.50−0.250.00May2018Jul2018Sep2018Nov2018Jan2019Mar2019May2019Jul2019Sep2019WTD (m)Figure 7.1.: Daily observations of mean air temperature (𝑇𝑎), vapour pressure deficit(𝑉 𝑃𝐷), net radiation (𝑄∗) and ground heat flux (𝑄𝐺), precipitation (𝑃 ) andcorrected volumetric peat moisture content at 10 cm depth (𝜃𝑠𝑡𝑎, Eq. 7.3). Allobservations were taken at the BB1 flux tower site at an open site near thebog’s center, except for volumetric eat moisture content measurements (𝜃𝑠𝑡𝑎)and groundwater level that were taken in each vegetation community.129B−Sa02550751000 25 50 75 100θsta (vol. %)θ man (vol. %)P−Sa02550751000 25 50 75 100θsta (vol. %)θ man (vol. %)P−tall02550751000 25 50 75 100θsta (vol. %)θ man (vol. %)P−low02550751000 25 50 75 100θsta (vol. %)θ man (vol. %)L−T02550751000 25 50 75 100θsta (vol. %)θ man (vol. %)CycledryingwettingFigure 7.2.: Manual (𝜃𝑚𝑎𝑛) versus stationary (𝜃𝑠𝑡𝑎) volumetric peat moisture observationsby site. Colors indicate drying cycle (between May and early September 2018)and wetting cycle (between mid-August and May 2019). Error bars indicatethe standard error of the mean of manual observations. Black lines depict thelinear relationship that was used to make continuous peat moisture contentobservations consistent with the site average (Eq. 7.3). Grey ribbons show the95% confidence interval.130B−Sa0255075100θ (vol. %)P−Sa0255075100θ (vol. %)P−tall0255075100θ (vol. %)P−low0255075100θ (vol. %)L−T0255075100May2018Jul2018Sep2018Nov2018Jan2019Mar2019May2019θ (vol. %)Figure 7.3.: Time series of linearly interpolated manual, site-averaged volumetric peat mois-ture content observations (𝜃𝑚𝑎𝑛 in black) and unadjusted stationary daily val-ues (𝜃𝑠𝑡𝑎 in blue) by site. Error bars indicate the standard error of the meanof manual observations.131y = 22 + 0.65 x, r2 = 0.65y = − 46 + 1.4 x, r2 = 0.80y = − 18 + 1.4 x, r2 = 0.85y = − 77 + 1.6 x, r2 = 0.78y = − 1.1 + 1.1 x, r2 = 0.94P−low L−TB−Sa P−Sa P−tall0 25 50 75 0 25 50 750 25 50 7502550750255075θman (vol. %)θ lys (vol. %)Figure 7.4.: Volumetric moisture content in lysimeters (𝜃𝑙𝑦𝑠 in vol. %) versus manual fieldobservations (𝜃𝑚𝑎𝑛 in vol. %) by site. Black lines indicate fitted linear regressionrelationships and gray ribbons show the 95% confidence interval. Error barsshow the standard error of the mean.132site, which had the greatest canopy openness, but an apparently lower slope than for P-low.During drier periods in summer 2018, lysimeters were driest at P-low and retained highestmoisture content at L-T (Figures 7.4 and 7.5). During the dry periods, lysimeter evapora-tion remained in the same range of values as during wet periods, despite being associatedwith substantially higher values of open-site equilibrium evaporation.Table 7.1.: Relative tree and shrub canopy openness based on hemispherical photographs ateach field site. 𝑇𝑟𝑒𝑒𝑚𝑒𝑎𝑛 = mean tree canopy openness, 𝑇𝑟𝑒𝑒𝑚𝑖𝑛 and 𝑇𝑟𝑒𝑒𝑚𝑎𝑥= minimum and maximum tree canopy openness, which correspond to leafedand non-leafed canopy openness at B-Sa. 𝑆ℎ𝑟𝑢𝑏𝑚𝑒𝑎𝑛 = mean shrub canopyopenness that was determined from a single survey in July 2018 at all sitesexcept for L-T, where it was measured every other week during field visits.Site 𝑇𝑟𝑒𝑒𝑚𝑒𝑎𝑛 𝑇𝑟𝑒𝑒𝑚𝑖𝑛 𝑇𝑟𝑒𝑒𝑚𝑎𝑥 𝑆ℎ𝑟𝑢𝑏𝑚𝑒𝑎𝑛B-Sa 0.51 0.21 0.74 0.16P-Sa 0.58 0.51 0.64 0.18P-tall 0.61 0.54 0.73 0.22P-low 0.79 0.59 0.89 0.33L-T - - - 0.387.3.4. Prediction of lysimeter evaporationThe regression model (Eq. 7.8) provided reasonable to good fits (adjusted 𝑅2 from 0.67 to0.81) at all sites except P-low, where the adjusted 𝑅2 was 0.34 (Figure 7.6 and Table 7.2).The model coefficients are all positive and statistically significant except for the coefficientfor moisture content at L-T. Variance inflation factors were less than 10, which has beensuggested as a threshold above which multicollinearity can have an adverse effect on theestimated coefficients (Neter et al., 1996).Table 7.2.: Summary metrics of predictive model used to gap-fill growing season and annualtotal evaporation loss (Eq. 7.8). 𝑠𝑒 = residual standard deviation (mm day-1),𝑅2𝑎𝑑𝑗 = adjusted R2, 𝑉 𝐼𝐹 = variance inflation factor, and 𝑛 = number of obser-vations used to fit model. 𝑏0 = model intercept, 𝑏𝑒𝑞 and 𝑏𝜃 = model coefficientsassociated with predictors 𝐸𝑒𝑞,𝐵𝐵1 and 𝜃𝑙𝑦𝑠; values in brackets indicate 𝑝-value.𝑆𝑖𝑡𝑒 𝑏0 𝑏𝑒𝑞 𝑏𝜃 𝑠(𝑏𝜃) 𝑅2𝑎𝑑𝑗 𝑉 𝐼𝐹 𝑠𝑒 𝑛B-Sa -0.39(0.057)0.21(0.001)0.0062(0.030)0.00247 0.67 5.09 0.062 14P-Sa -0.31(0.017)0.20 (<0.001)0.0044(0.009)0.00133 0.81 8.66 0.034 12P-tall-0.79(0.002)0.42 (<0.001)0.0112(0.001)0.00242 0.75 5.10 0.087 14133𝑆𝑖𝑡𝑒 𝑏0 𝑏𝑒𝑞 𝑏𝜃 𝑠(𝑏𝜃) 𝑅2𝑎𝑑𝑗 𝑉 𝐼𝐹 𝑠𝑒 𝑛P-low-1.07(0.073)0.66(0.013)0.0165(0.032)0.00681 0.34 8.21 0.232 15L-T -0.28(0.462)0.26(0.011)0.0046(0.364)0.00485 0.77 7.21 0.082 127.3.5. Estimated site evaporationEstimated rates of site evaporation peaked between May and mid-July 2018 and aroundApril 2019 across all sites (Figure 7.7). Between November 2018 and March 2019, whenavailable energy was low, evaporation did not exceed 0.1 mm day-1 at B-Sa, P-Sa and P-tall or 0.2 mm day-1 at P-low and L-T. At B-Sa, P-Sa and P-tall, evaporation was low inAugust and September 2018 when seasonal peat moisture content reached its lowest levels;at P-low and L-T it was less limited by available moisture content.At B-Sa and P-Sa, uncertainty in evaporation estimates was consistently lower compared tothe other sites. At P-tall, uncertainty of wintertime estimates tended to be higher comparedto the rest of the observation period; it was comparable to wintertime uncertainty at P-lowand L-T, where uncertainty was the highest among all sites in summer 2018 and spring2019.During the growing season of 2018 (May 1 to September 30), evaporation loss and thedifference between sites were relatively small for B-Sa, P-Sa and P-tall; at L-T and P-low,evaporation was roughly two to three times higher, respectively (Table 7.3). Estimatedgrowing season evaporation loss was less than open site precipitation and tended to behigher at less vegetated and wetter sites. Over the course of a year estimated evaporationloss was less than 22% of open site precipitation; the decrease was proportional among sites(Table 7.4).Table 7.3.: Estimated site evaporation (𝐸𝑠𝑖𝑡𝑒), equilibrium evaporation (𝐸𝑒𝑞,𝐵𝐵1) and grossprecipitation (𝑃𝑜𝑝𝑒𝑛) over the course of a growing season in five vegetation com-munities (May 1 to September 28-30, 2018, depending on site). The fraction𝐸𝑠𝑖𝑡𝑒/𝐸𝑒𝑞,𝐵𝐵1 would be equivalent to Priestley-Taylor’s 𝛼 (Priestley and Taylor,1972).Site 𝐸𝑠𝑖𝑡𝑒 𝐸𝑒𝑞,𝐵𝐵1 𝑃𝑜𝑝𝑒𝑛 𝐸𝑠𝑖𝑡𝑒/𝑃𝑜𝑝𝑒𝑛 𝐸𝑠𝑖𝑡𝑒/𝐸𝑒𝑞,𝐵𝐵1B-Sa 35 250 165 0.21 0.14P-Sa 25 252 139 0.18 0.10P-tall 40 249 144 0.28 0.16P-low 121 249 144 0.84 0.49L-T 74 252 149 0.49 0.29134y = 0.0013 + 0.31 x, r2 = 0.92y = − 0.039 + 0.83 x, r2 = 0.91y = 0.005 + 0.18 x, r2 = 0.84y = − 0.0024 + 0.4 x, r2 = 0.87y = − 0.03 + 0.45 x, r2 = 0.97P−low L−TB−Sa P−Sa P−tall0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.50.0 0.5 1.0 1.5 2.0 2.50.00.51.01.52.02.50.00.51.01.52.02.5Eeq,BB1 (mm day−1)E lys (mm day−1 )20 40 60θlys (vol. %)Figure 7.5.: Lysimeter evaporation (𝐸𝑙𝑦𝑠) versus equilibrium evaporation at BB1 (𝐸𝑒𝑞,𝐵𝐵1)for each field site. Color indicates lysimeter volumetric moisture content (𝜃𝑙𝑦𝑠in vol. %). Error bars indicate the standard error of the mean and are smallerthan symbol size in most cases. Black lines and equations show linear regressionrelationships for observations with 𝜃𝑙𝑦𝑠 > 55%, and gray ribbons show the 95%confidence interval.135P−low L−TB−Sa P−Sa P−tall0.00 0.25 0.50 0.75 0.00 0.25 0.50 0.750.00 0.25 0.50 0.750.000.250.500.750.000.250.500.75Eobs (mm day−1)E lys (mm day−1 )20 40 60θlys (vol. %)Figure 7.6.: Observed lysimeter evaporation (𝐸𝑙𝑦𝑠) versus predicted lysimeter evaporation(𝐸𝑜𝑏𝑠) based on predictive multi-linear regression model (Eq. 7.8). Color indi-cates lysimeter volumetric moisture content (𝜃𝑙𝑦𝑠 in vol. %).136B−Sa0.00.51.01.5E site (mm day−1 )P−Sa0.00.51.01.5E site (mm day−1 )P−tall0.00.51.01.5E site (mm day−1 )P−low0.00.51.01.5E site (mm day−1 )L−T0.00.51.01.5May2018Jul2018Sep2018Nov2018Jan2019Mar2019May2019E site (mm day−1 )Figure 7.7.: Time series of corrected lysimeter (blue, Eq. 7.9) and estimated (gray, Eq.7.10) site evaporation (𝐸𝑠𝑖𝑡𝑒). Error bars indicate the standard uncertainty ofthe mean of observed evaporation (Eq. 7.11), or the 68% confidence interval ofpredicted evaporation.137Table 7.4.: Estimated site evaporation (𝐸𝑠𝑖𝑡𝑒), equilibrium evaporation (𝐸𝑒𝑞,𝐵𝐵1) and grossprecipitation (𝑃𝑜𝑝𝑒𝑛) over the course of a year in five vegetation communi-ties (May 1, 2018 to April 28-30, 2019, depending on site). The fraction𝐸𝑠𝑖𝑡𝑒/𝐸𝑒𝑞,𝐵𝐵1 would be equivalent to Priestley-Taylor’s 𝛼 (Priestley and Taylor,1972).Site 𝐸𝑠𝑖𝑡𝑒 𝐸𝑒𝑞,𝐵𝐵1 𝑃𝑜𝑝𝑒𝑛 𝐸𝑠𝑖𝑡𝑒/𝑃𝑜𝑝𝑒𝑛 𝐸𝑠𝑖𝑡𝑒/𝐸𝑒𝑞,𝐵𝐵1B-Sa 63 344 1077 0.06 0.18P-Sa 39 344 912 0.04 0.11P-tall 66 343 1061 0.06 0.19P-low 237 343 1061 0.22 0.69L-T 129 345 1033 0.12 0.377.4. Discussion7.4.1. Sensor calibrationsThe limited number of soil moisture sensor calibration observations for some of the fieldsites made it necessary to combine calibration data in a mixed-effects model instead offitting independent relationships to data from each site separately. The decision to use amixed-effects model affected estimates of growing season and annual evaporation loss at thesites for which sufficient calibration samples were available. To evaluate the effect of thisdecision, the analysis was repeated with soil moisture calibrations based on independentrelationships derived for each site. Growing season evaporation estimates differed by 5%at P-Sa and L-T, 10% at B-Sa and P-low, and at P-tall estimates were 30% lower whenindependent relationships were used. Similar results were found for annual evaporationtotals. These results suggest that the decision to pool calibration data for all sites had arelatively minor effect on estimates at B-Sa, L-T, P-Sa and P-low, and is assumed to haveproduced more reliable values than would have been the case using the small sample ofcalibration data for P-tall.7.4.2. Peat moisture contentIn winter, near surface peat was saturated when groundwater levels rose to within 10 cmof the surface, which was the case continuously at P-low and L-T between November andJune and for shorter periods of heavy rainfall at B-Sa. At P-Sa and P-tall, groundwaterlevels stayed below 10 cm of the wintertime peat surface, but volumetric moisture contentspiked during heavy rainfall. Moisture content at saturation varied between sites and washigher where Sphagnum was present (i.e. P-low and L-T versus B-Sa). Saturation moisturecontent was likely a function of level of decomposition and bulk density, which would likelyhave been higher at B-Sa due to decades of drainage (e.g. Boelter, 1969; Minkkinen andLaine, 1998; Price and Schlotzhauer, 1999).138Summertime volumetric peat moisture content dropped to less than 25% at vegetated sites(B-Sa, P-Sa and P-tall), associated with groundwater levels dropping to more than 75 cmbelow the wintertime peat surface. Peat moisture content was lowest at P-tall, despiteshallower groundwater levels, compared to B-Sa and P-Sa, suggesting that capillary risemust have been limited by a larger fraction of the pore size distribution being composedof large pores (McCarter and Price, 2014; Golubev and Whittington, 2018). At B-Sa andP-Sa, decades of peripheral drainage of the bog would likely have increased decompositionand shifted pore size distribution (Boelter, 1968; Boelter, 1969; Minkkinen and Laine,1998). In fall, volumetric peat moisture content recovered with increasing rain in September;groundwater levels followed in close order, except for B-Sa, where groundwater levels didnot recover until November.At each site except B-Sa, the slope of the linear relationship between lysimeter volumetricmoisture content and manual observations suggest that the lysimeters dried more rapidlythan the surrounding peat. This contrast in drying rates likely reflects the maintenanceof moisture content by capillary rise from the water table. At B-Sa, the apparently morerapid drying in the surrounding peat could reflect the removal of moisture by transpirationfrom the overstory trees, which could also be responsible for that site having the greatestwater table drawdown (Figure 7.4).7.4.3. Lysimeter evaporationLysimeter evaporation varied between field sites during periods of minimal volumetric mois-ture limitation (i.e. 𝜃𝑙𝑦𝑠 > 55 vol. % in Figure 7.5). The differences in evaporation rate wererelated with canopy openness, except for L-T, where evaporation was lower compared toP-low despite the more open, but higher, shrub canopy at L-T (Figure 7.5 and Table 7.1).Shrub cover at P-low was dominated by Salal, whereas the L-T site was covered by Labradortea. These differences in shrub height and species likely complicated the relationship, andsuggest that canopy openness alone is insufficient to predict evaporation loss. For a morerobust representation of vegetation cover effects on evaporation, one should consider thatshrub transmissivity varies not only with height, but also canopy geometry and solar angles(Essery et al., 2008; Kettridge and Baird, 2010; Kettridge et al., 2013). Nevertheless, resultssuggest that it may be possible to obtain first-order estimates of peat surface evaporationunder conditions of minimal moisture limitation by combining equilibrium evaporation froman open site with canopy openness (Figure 7.6 and Table 7.2).The predictive model, Equation (7.8), provided a reasonable fit at all of the sites butP-low (Figure 7.6). The lysimeters at that site experienced greater drying than at theother sites, and the greater scatter in the relation could reflect the existence of complexrelations between peat moisture content in the lysimeter and the availability of moisturefor evaporation at the peat surface – e.g., due to soil moisture hysteresis or variations invertical water contents.1397.4.4. Site evaporation estimates adjusted for moisture contentThe effect of differences in volumetric moisture content between lysimeters and the sur-rounding site is a well-recognized challenge in using lysimeters to determine evaporation.As reviewed in the introduction, previously used approaches to address this challenge havebeen to manipulate the lysimeter moisture content either manually or using an automatedsystem. In this study, an alternative approach has been introduced, which involves adjust-ing the lysimeter evaporation based on a linear correction derived from a regression model.This approach is based on three key assumptions: (1) that the effect of moisture contenton evaporation in the lysimeter is the same as in the surrounding peat, (2) that the effectof atmospheric demand can be represented as a linear function of equilibrium evaporationat an open site, and (3) that the limiting effect of moisture differences can be representedas a linear effect. Unfortunately, it would be challenging to verify these assumptions.As can be seen in Figure 7.5, there was limited overlap in the range of equilibrium evapo-ration between the “wet” and “dry” observations for each site, which means that the twopredictor variables are collinear; this increases the uncertainty in the estimated coefficients.If this approach is used in the future, more attention should be paid to ensuring that thecombinations of equilibrium evaporation and moisture content be more effectively crossed— for example, by manipulating the moisture content of a subset of the lysimeters.Based on standard error propagation approaches, the estimated uncertainty in site evapo-ration estimates was less than 0.1 mm day-1 at B-Sa and P-Sa, but was higher at P-tall,P-low and L-T, where it reached up to 0.3 mm day-1 at times. Error propagation (Eq. 7.11)suggests that higher uncertainty at P-low and L-T arose from a higher standard error of 𝑏𝜃;the model coefficient associated with volumetric peat moisture content (Table 7.2). Higher𝑠(𝑏𝜃) was likely a result of more scatter in the 𝐸𝑙𝑦𝑠-𝐸𝑒𝑞,𝐵𝐵1 relationship for observationsfrom dry lysimeters at P-low (Figure 7.6). At L-T the range of volumetric moisture contentacross which observations were available was relatively narrow, which would make 𝑏𝜃 moresensitive to uncertainty. The fact that moisture content was not a significant componentof the model at L-T supports this argument.When lysimeter measurements were unavailable, direct predictions based on the regressionmodel were used as estimates. The uncertainty of these predictions, based on computed pre-diction limits, was of the same magnitude as uncertainty estimated from error propagationwhen lysimeter measurements were available.7.4.5. Peat surface evaporationNotwithstanding the uncertainties associated with the site evaporation estimates, they ex-hibit reasonable seasonal variations and differences among sites. Therefore, they shouldprovide reasonable first-order estimates of the importance of this flux in the overall waterbalance.The ratio of estimated growing season site evaporation to equilibrium evaporation at theflux tower (𝐸𝑠𝑖𝑡𝑒/𝐸𝑒𝑞,𝐵𝐵1) ranged from 10% to 16% for B-Sa, P-Sa and P-tall, but was14029% and 49% at L-T and P-low, respectively. The ratio was 1% to 4% higher for annualtotals than for the growing season at B-Sa, P-Sa and P-tall. However, at L-T and P-low theratios increased to 37% and 69%, respectively. About half of the annual evaporation lossoccurred in the seven months outside the growing season (Lafleur et al., 2005; Sottocornolaand Kiely, 2010), when volumetric peat moisture content was relatively high and when itshould have been less of a limitation on evaporation than during the growing season. Theseresults suggest that shading from vegetation and, to a lesser extent, peat moisture contentlimited peat surface evaporation, although the effect varied substantially among sites.The evaporation totals represented between 18% and 84% of open-site precipitation for thegrowing season and 4% to 22% on an annual basis. These results indicate that peat surfaceevaporation was a significant mode of water loss, especially in the growing season, andneeds to be included in water balance analyses and process-based hydrologic modelling.7.5. ConclusionsThis study used lysimeter observations to investigate the effect of canopy cover and peatmoisture content on peat surface evaporation loss in five vegetation communities of variousstructure and density in a maritime raised bog in southwestern British Columbia, Canada.This study introduced a new approach to adjust measured lysimeter evaporation to correctfor differences in volumetric moisture content between the lysimeter and in the surroundingpeat. Although the approach is based on a number of assumptions that are difficult to verify,the estimated evaporation rates exhibited reasonable seasonal variations and differencesamong sites, and are believed to provide reasonable first-order estimates.The variation of lysimeter evaporation among sites was correlated with differences in canopyopenness for four of the sites, although the Labrador Tea site deviated from that pattern.This result suggests that, while a simple index like canopy openness may provide a first-order basis for modelling peat surface evaporation, a more detailed consideration of canopygeometry may be appropriate. Lysimeter evaporation was also strongly influenced by volu-metric moisture content within the lysimeters.The regression model used to derive a linear correction for peat moisture content provideda reasonable to good fit for four sites, but the fit was poor at one site. Uncertainties in theestimated site evaporation rates were estimated to be mainly about ± 0.1 mm day-1, butoccasionally reached up to ± 0.3 mm day-1.Estimated peat surface evaporation loss was highest in the early growing season, when avail-able energy was high and sufficient peat moisture content remained from the rainy winter.Evaporation loss declined in August and September, the period of minimum seasonal peatmoisture content. Evaporation loss made up between 18 and 84% of growing season grossprecipitation, or between 4 and 22% of annual gross precipitation, and therefore must beconsidered in water balance analyses and process-based hydrologic models.1418. ConclusionsThis research studied interannual variability of energy and water exchange with the at-mosphere along with runoff processes near the open center of an ombrotrophic maritimepeatland in the Pacific Northwest, and the effect of vegetation on the hydrologic exchangewith the atmosphere at five sites with different vegetation communities located along themargin of the bog. The following sections summarize key findings, implications of vegeta-tion changes due to restoration efforts or climatic change on peatland ecohydrology, andoutline potential avenues for future research.8.1. Summary of key findingsIn Chapter 3, multiple years of sub-daily observations of energy and water exchange be-tween the peat surface and the atmosphere at an open Sphagnum-dominated site were usedto fit a regression model that represented daily evapotranspiration as a function of avail-able energy (represented by equilibrium evaporation), atmospheric demand (representedby vapour pressure deficit) and water supply at the surface (represented by depth to wa-ter table and antecedent precipitation index). The model coefficients and examination ofpartial residual plots indicated that available energy and atmospheric demand were thedominant controls on daily evapotranspiration. Water table depth was a secondary controlon daily and seasonal evapotranspiration; summertime precipitation had a weak positiveeffect at the seasonal level. Interannual variability of growing season evapotranspirationwas most strongly associated with differences in vapour pressure deficit, which was also cor-related with seasonal water table drawdown – that is, the years with greater VPD also hadthe greatest water table drawdown. The effect of growing season vapour pressure deficitoverwhelmed the effect of the timing of the onset of water deficit.In Chapter 4, observations from Chapter 3 were combined with groundwater and peatsurface elevation measurements as well as remotely sensed peat surface microtopography ina water balance approach to study seasonal shifts in primary water loss processes and betterunderstand runoff generation. In summer, evapotranspiration was the main water lossprocess, whereas in winter lateral near-surface and overland flow were the only significantpathways of water leaving the site; groundwater flow was negligible year-round. Wintertimerunoff generation was controlled by two groundwater table elevation thresholds. The firstthreshold was within a few centimeters of the peat surface and was inferred to coincide withinitiation of near-surface macropore flow, limited overland flow and some surface storagein topographic depressions. The second threshold corresponded to peat surface elevationand marked a groundwater level at which nearly all surplus input left the site as overland142flow through a connected network of topographic depressions. The relationship betweengroundwater level and peat surface elevation as well as groundwater level and inundatedpeat surface area was hysteretic.In Chapter 5, two independent manual tree and shrub throughfall measurement approacheswere combined with tree stemflow observations, remotely sensed tree canopy metrics, andcontinuous trough-based throughfall observations to estimate interception loss from treesand shrubs separately, and to test a new approach of throughfall measurements that istransferable to areas with different stand structure. Shrub interception loss per unit groundarea was higher than interception loss from trees at each field site. Tree throughfall increasedwith distance from the trunk, whereas shrub throughfall decreased and potentially offsetsome of the spatial heterogeneity introduced by trees. Smaller trees generated stemflowmore efficiently than bigger trees. Trough observations agreed well with random manualobservations except for periods when trough collectors were clogged by debris. Independenttree-based tree throughfall measurements generally agreed with random observations andcan be combined with remotely sensed tree canopy metrics to transfer local throughfallobservations to areas with similar tree canopy, but different stand structure.Chapter 6 used sub-daily sap flow observations in combination with allometric tree surveysand groundwater level measurements to create a predictive model of sap flow as functionof vapour pressure deficit by tree size and species to obtain first-order estimates of treetranspiration at the plot scale and explore the secondary effect of waterlogging on the rela-tionship. At the tree level, tree size and species were primary controls on transpiration andthe increase with tree size was higher in birch and hemlock than pine. Water table depthwas a secondary control at the stand level that affected species differently. Transpirationof birch was affected by waterlogging during periods with shallow water levels, whereastranspiration rates of hemlock were limited by deep groundwater levels; pine was less sensi-tive to differences in groundwater levels. Results suggested that water table depth can bea secondary control on tree transpiration on the daily and seasonal scale via waterlogging;differences between field sites suggested that water table depth likely affected transpirationvia drainage history and associated root development on longer time scales.Chapter 7 used evaporation observations from lysimeters and peat moisture content mea-surements to derive a new approach to adjust lysimeter measurements for moisture contentdifference to the surrounding ground during periods of limited moisture supply. Resultswere used to study the effect of vegetation cover on peat surface evaporation, and to esti-mate growing season and annual evaporation loss. Peat surface evaporation was primarilycontrolled by shading from vegetation, but moisture content was an important limitationduring the growing season. Evaporation was highest in the early growing season whenavailable energy was high and sufficient peat moisture content from the rainy season wasnot a limiting factor.Chapters 5 to 7 highlight how vegetation affects specific aspects of atmospheric waterexchange of a peatland. The effect of vegetation type and stand structure on verticalwater exchange becomes apparent when hydrologic fluxes are compared between differentvegetation communities (Table 8.1). Tree transpiration is the primary water loss process ina well-drained mature vegetation community (e.g. B-Sa), but it may be a minor loss at a143wet and sparsely vegetated site (e.g. P-low). In contrast, evaporation from the peat surfacemay constitute the major water loss in areas similar to P-low, L-T or Wb-S, whereas it maybe less relevant at more vegetated and drier sites such as B-Sa or P-Sa. The difference ofthe effect of interception loss and stemflow between vegetation communities may be minorin climates where growing season precipitation is small such as the maritime climate in thePacific Northwest where this study was conducted.It is interesting to compare the total water losses shown in Table 8.1 in relation to themagnitude of seasonal water table drawdown among the sites as shown in Figures 3.1 and7.1. While the B-Sa site had both the greatest water loss and the greatest water tabledrawdown, it is interesting to note that the Wb-S site had the second-highest water lossbut the least water table drawdown. This apparent discrepancy may possibly be due tothe fact that shrub transpiration was not evaluated, and thus that vertical water lossesat P-tall, P-low and P-Sa may be underestimated. An alternative explanation could bethat the water table drawdown was dominantly caused by greater lateral flux divergence atsites other than Wb-S, as they were located in marginal regions of the bog, where lateralgroundwater gradients would have been greater. Resolving this issue would help to clarifythe strength of the feedbacks between water table levels and vegetation dynamics.Table 8.1.: Open site precipitation and water loss fluxes to the atmosphere in the growingseason 2018 (May 1 to September 30). 𝑃𝑜𝑝𝑒𝑛 = open site precipitation (mm),𝐸𝑝 = peat surface evaporation (mm), 𝑇 = tree transpiration (mm), and 𝐼𝐿 =interception loss (mm), which considers interception loss and stemflow from treeand shrub layers. Uncertainty of 𝐼𝐿 + 𝐸𝑝 + 𝑇 would be in the order of ± 50%of 𝑇 , due to uncertainty associated with upscaling of first-order transpirationestimates. Shrub transpiration is excluded; it was not within the scope of thisresearch.Site 𝑃𝑜𝑝𝑒𝑛 𝐼𝐿 𝐸𝑝 𝑇 𝐼𝐿 + 𝐸𝑝 + 𝑇B-Sa 165 72 35 444 552P-Sa 139 75 25 224 324P-tall 144 50 40 160 249P-low 144 45 121 25 191L-T 149 66 74 - 140Wb-S 172 - 386 - 3868.2. Implications of climatic change and vegetation shifts onatmospheric water exchange of peatlandsWhile this study has been conducted in the Pacific Northwest of North America, whichis characterized by a maritime climate and extends along the southern limit of northernpeatlands (Rocchio et al., 2021), it is believed that findings have broader applicability toother geoclimatic regions, where drainage or climate change can induce shifts in vegetation.144In addition, results hold implications to peatlands undergoing restoration interventions suchas ditch blocking raises water levels to kill-off invasive vegetation or where active vegetationcontrol and removal is being conducted.Results of this research show that pathways of vertical water exchange are a function ofvegetative cover and suggest that the relative magnitude of water loss processes to theatmosphere would likely change as a result of recently reported vegetation encroachment innorthern peatlands (e.g. Berg et al., 2009; Myers-Smith et al., 2011; Ropars and Boudreau,2012; Zhang et al., 2013).Climatic change is projected to increase temperatures and reduce precipitation in summerin the Pacific Northwest, whereas winters are projected to become warmer with increasedprecipitation (Mote and Salathé, 2010). Higher air temperatures and increased precipitationin winter would likely have minimal influence on the hydrology and vegetation communitiesof peatlands in the Pacific Northwest, as the effect of increased wintertime precipitationwould be limited on already high groundwater levels in winter due to increased waterloss by lateral flux divergence, and wintertime groundwater levels do not appear to be astrong predictor of seasonal water table drawdown in summer. The results in Chapter3 demonstrated that summer vapour pressure deficit appeared to be the key control onseasonal water table drawdown at the Wb-S site. On the one hand, decreased precipitationduring the growing season would mean that interception loss would be less important asa source of water loss. On the other hand, higher summertime temperatures would beassociated with higher growing season vapour pressure deficits, which would increase thepotential for higher loss via peat surface evaporation and transpiration (Helbig et al., 2020);however, the effect would likely vary among sites. Based on the statistical modelling resultspresented in Chapter 3, the increase in peat surface evaporation may be linear for Sphagnum-covered or bare surfaces, at least for the range of water table drawdown included in the dataset used to fit the model. However, the response of peat surface evaporation to increasingvapour pressure deficit is likely to be moderated by surface moisture supply for sites suchas at B-Sa, P-Sa and P-tall, which experienced deeper seasonal water table drawdown. AtP-low and L-T, the negative feedback of moisture limitation on summertime peat surfaceevaporation appeared to be limited, suggesting that these sites may be more prone toincreased evaporation under climatic change conditions. Sap flow observations showed thatthe effect of increased summertime vapour pressure deficit on tree transpiration would bemoderated by stomatal activity for all tree species that were monitored. The effect ofmoisture limitation on tree transpiration would likely be complicated due to their ability totap into deeper groundwater which would, however, depend on the rate of change of seasonalwater table drawdown and rates of root growth as shallow groundwater levels impede rootingat a certain depth. A major source of uncertainty is the lack of knowledge regardingtranspiration from shrubs, which remains underrepresented in peer-reviewed literature withfew exceptions (e.g. Kelliher et al., 1986). However, given their relatively shallow rootingdepths, they are more likely than trees to have transpiration rates limited by soil moistureavailability, especially at sites that experience deep water tables.Restoration efforts focusing on ditch blocking would raise water levels and could kill themajority of mature vegetation. If ditch blocking is not feasible or if a drainage network145does not exist, targeted vegetation removal or killing through bark stripping may be analternative in peatlands of limited size. Either restoration intervention would pose a viableoption to offset or mitigate some adverse aspects of climatic change on peatlands. Killingof mature trees would reduce tree transpiration that should reduce water table drawdown;the effect would differ by the prevalent tree species and size. Simultaneously, removal ofthe tree canopy would increase interception loss by shrub; however, the effect on seasonalwater table drawdown may be limited in summer, especially if summers become drier. Lessshading from trees would enable more solar radiation to reach shrubs, which could increaseshrub transpiration; however, the effect would depend on moisture supply, which couldimpose a negative feedback on the increase due to a shallow rooting depth. There remainsuncertainty around the net effect on shrub hydrology. Increased groundwater levels shouldreduce the negative feedback of moisture supply on evaporation from the peat surface.While these considerations are based on observations from a raised bog environment in amaritime climate, the process-based character of this approach should allow some aspects ofthese implications to be transferable to other climatic settings with different configurationsof vegetation communities.8.3. Avenues for future researchThis research contributed to the understanding of how mature vascular vegetation affectsthe hydrology of rain-fed peatlands by drawing from an extensive set of field observationsand combining empirical results with analytical modelling to help constrain some plant-hydrology interactions that have previously been mostly described conceptually (Wadding-ton et al., 2015). This work expanded the geoclimatic range of peatland hydrology researchin the northern hemisphere, which was predominantly conducted in high-latitude or conti-nental locations, to the southern boundary of these ecosystems (Rocchio et al., 2021), whichis where some aspects of peatland hydrology are believed to be most affected by climaticchange (Helbig et al., 2020).In recent years, evidence of a widespread shift in plant functional type in northern peatlandsthat is mostly associated with shrub encroachment is being reported (Berg et al., 2009;Myers-Smith et al., 2011; Ropars and Boudreau, 2012; Zhang et al., 2013), and the potentialimplications for hydrologic ecosystem fluxes may be significant. Results presented heresuggest that interception loss from shrubs was a significant component of water loss at allsites where mature vascular vegetation established; it exceeded interception loss from treeseven in densely treed forest stands. Despite the potentially high impact of shrub cover onthe hydrology of northern peatlands, there has been a lack of research focusing on shrubhydrology, with few exceptions (e.g. Zwieback et al., 2019; Moore et al., 2022), particularlyin respect of interception loss, but also regarding shrub transpiration. Future researchshould focus on expanding the current understanding of the role of shrub interception lossin context of regional climatic regimes in areas where shrub encroachment predominantlyoccurs, specifically in context of summertime precipitation. In addition, future researchefforts should include studies on shrub transpiration, particularly with a focus on peat146moisture control in the context of seasonal watertable drawdown and the potential feedbackbetween increased drought and increased shrub development.Peat surface evaporation was an important water loss at most sites and results identifiedshading from shrub and peat moisture content as the two dominant controls. A fruitfulavenue of future research could work at refining the use of adjusted lysimeter measurementsto estimate peat surface evaporation in a wider range of conditions. Additionally, peatlandhydrology research would benefit from efforts aimed at identifying better metrics to repre-sent shading than canopy openness alone. A more robust metric for the effect of shadingon surface evaporation could enable first-order quantitative modelling with the potential toextrapolate localized observations into areas for which shading was estimated remotely.Tree throughfall was successfully estimated from remotely sensed canopy metrics and strati-fied throughfall observations at the stand level. This pilot study highlighted the potential oftransferring local observations to areas with similar tree species but different stand metrics;however, future approaches should optimize throughfall sampler design to reduce bias fromevaporation. The new approach should be tested for a wider range of vegetation cover,for example by replicating the study in forests of different tree species and by assessingapplicability to broad-leaved stands.Tree transpiration was the dominant water loss process in areas covered by mature trees.Uncertainty arose from the limited sample size when species-specific tree size was used toscale tree level transpiration to the stand level. The small sample size also limited the abil-ity to robustly quantify the species-specific effect groundwater table depth on transpiration.Future research should employ a sufficient number of sap flow sensors that would enablederivation of more robust species-specific relationships for upscaling. 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𝑇𝑎 = air temperature (∘C), 𝑅𝐻 = relativehumidity (%), 𝑊𝑉 = wind speed (𝑚 𝑠−1), RMSE = root mean square error.Data from the Environment and Climate Change Canada weather station inBurns Bog is denoted by a subscript, for example 𝑇𝑎𝑒𝑐𝑏𝑏 = air temperature(∘C).Model RMSE 𝑟2 intercept slope𝑇𝑎 vs 𝑇𝑎𝑒𝑐𝑏𝑏 0.944 0.981 1.014 0.960𝑅𝐻 vs 𝑅𝐻𝑒𝑐𝑏𝑏 4.943 0.899 14.704 0.809𝑊𝑉 vs 𝑊𝑉𝑒𝑐𝑏𝑏 0.811 0.499 0.617 1.412Table A1.2.: Energy balance closure of entire study period at 30-minute resolution andaggregated to daily and monthly values. 𝑛 = number of observations.Data 𝑟2 slope 𝑛30 min 0.93 0.59 70435Daily 0.95 0.75 1743Monthly 0.98 0.74 76Table A1.3.: Raw automated water table elevation and peat surface elevation recordingswere site-specifically calibrated and geo-referenced using manual observationstaken approximately every two weeks from surveyed benchmarks. During thestudy period pressure transducers were replaced a several times due to tech-nical issues and separate calibrations were established for each sensor. RMSE= root mean square error (m)Model RMSE 𝑟2 intercept slope𝑧𝑤𝑡,𝑚𝑎𝑛𝑢 vs 𝑧𝑤𝑡,𝑎𝑢𝑡𝑜 Jul 2015 to Dec 2016 0.038 0.883 3.303 0.010𝑧𝑤𝑡,𝑚𝑎𝑛𝑢 vs 𝑧𝑤𝑡,𝑎𝑢𝑡𝑜 Jan 2017 to Oct 2017 0.009 0.993 2.640 0.004175Model RMSE 𝑟2 intercept slope𝑧𝑤𝑡,𝑚𝑎𝑛𝑢 vs 𝑧𝑤𝑡,𝑎𝑢𝑡𝑜 Oct 2017 to Jan 2021 0.009 0.992 3.656 0.009176−2−1012PDO−1012Jul2014Jan2015Jul2015Jan2016Jul2016Jan2017Jul2017Jan2018Jul2018Jan2019Jul2019Jan2020Jul2020Jan2021Anomaly (°C)Figure A1.1.: Time series of Pacific Decadal Oscillation (top) and Oceanic Niño Index (bot-tom). There was no clear relationship between interannual meteorologicalvariability in Burns Bog and these climatic patterns. The multi-year de-creasing trend in 𝑉 𝑃𝐷 in Burns Bog was not apparent in observations atthe Vancouver International Airport, which is located approximately 15 kmnorthwest of the field site.177B. Appendix B - Supplementary informationfor throughfall observations in study threeTable A2.1.: Metrics of fit of linear regression models of plot-averaged shrub throughfallobservations used to estimate mean throughfall ( ̄𝑦𝑡,𝑝) from median throughfall( ̃𝑦𝑡,𝑝) of data containing censored values. 𝑅𝑆𝐸 = residual standard error(mm).Model RSE 𝑟2 𝑎 𝑏B-Sa ̄𝑦𝑡,𝑝 vs. ̃𝑦𝑡,𝑝 2.435 0.919 2.394 0.987P-Sa ̄𝑦𝑠,𝑝 vs. ̃𝑦𝑠,𝑝 2.809 0.928 -0.781 1.279P-tall ̄𝑦𝑠,𝑝 vs. ̃𝑦𝑠,𝑝 2.879 0.953 2.466 0.875P-low ̄𝑦𝑠,𝑝 vs. ̃𝑦𝑠,𝑝 2.179 0.981 2.399 0.921L-T ̄𝑦𝑠,𝑝 vs. ̃𝑦𝑠,𝑝 2.163 0.981 2.000 0.931178Figure A2.1.: Photograph of the weather station at P-tall. Two 2.44 m long troughs wereinstalled at 2 m and collected rain that was diverted into a tipping bucketthat recorded throughfall at 30-min intervals.a b cFigure A2.2.: Photograph of tree throughfall collectors used in the random approach (a);shrub throughfall collector with screen to prevent entrapment of small rodents(b), and shrub throughfall collector equipped with a styrofoam ball to preventevaporation in summer (c).179innerouteroutsidetrunkFigure A2.3.: Photograph of tree-based throughfall setup at P-tall. Orange arrows showthe trunk of the tree being sampled and tree throughfall collectors samplinginner, outer and outside canopy zone. The dashed red line approximates thetransect along which the collectors were arranged.180baFigure A2.4.: a) Photograph showing stemflow measurement setup. It shows sparse shrubcover under a tree with low hanging canopy at P-tall, and b) dense shrub coverabout 1 m tall under a tree with canopy not reaching below approximately 9m at P-Sa.181a bdce fFigure A2.5.: Hemispherical photographs of tree canopies (and shrub canopy at L-T) withcanopy openness corresponding to values shown in Table 5.3. a) B-Sa leaf onperiod, b) B-Sa leafless period, c) P-Sa, d) P-tall, e) P-low and f) L-T.182
Thesis/Dissertation
eng
Geography
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Moore, R. Daniel
Hydrological processes as function of vegetation type in six vegetation communities in a maritime raised bog
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