Science, Irving K. Barber Faculty of (Okanagan)
Earth, Environmental and Geographic Sciences, Department of (Okanagan)
DSpace
UBCO
Liu, Wanyi
Master of Science - MSc
University of British Columbia
Forest thinning is a method to reduce competition in overstocked young forests. While the short-term effect of thinning is often studied by field thinning experiments, the long-term effect of thinning is rarely studied and remains unclear, particularly in the context of climate change. In this study, we used data from field experiment to train two Artificial Intelligence models: artificial neural network (ANN) and deep neural network (DNN) for assessing the long term effect of thinning on transpiration and water use efficiency (WUE) in young lodgepole pine forests at both tree and stand levels. We showed that DNN is more accurate than ANN in terms of model performance measures. We then used the trained DNN model to predict the transpiration and WUE under different thinning and climate change scenarios. The simulations showed that thinning has a long-lasting effect on increasing tree-level transpiration and both tree- and stand-level WUE but decreasing stand-level transpiration. Thinning effect decreases as thinning intensity decreases and trees age. Climate change reduces both tree- and stand-level WUE. Thinning mitigates the impact of climate change on stand-level transpiration. Based on the simulated long-term effects, we recommend that the thinning with the remaining of 2500–3500 stem/ha as suitable densities for a better balancing or trade-off between total carbon and water (stand transpiration) for young and overstocked lodgepole pine stands in the southern interior of British Columbia. However, selection of the final thinning densities must consider broader ecological processes and functions such as wood quality, biodiversity as well as their interactions with climate change.
Using machine learning to model longterm effects of thinning and climate changeon transpiration and water use efficiencybyWanyi LiuB.Sc. in Agriculture, Beijing Forestry University, 2017A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Earth and Environmental Sciences)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)Feburary 2023© Wanyi Liu, 2023The following individuals certify that they have read, and recommend to theCollege of Graduate Studies for acceptance, the thesis entitled:USING MACHINE LEARNING TO MODEL LONG TERM EFFECTS OFTHINNING AND CLIMATE CHANGE ON TRANSPIRATION AND WATER USEEFFICIENCYsubmitted by WANYI LIU in partial fulfillment of the requirements of the degreeof MASTER OF SCIENCEAdam Wei, I. K. Barber School of Arts & SciencesSupervisorDavid Scott, I. K. Barber School of Arts & SciencesSupervisory Committee MemberRita Winkler, Ministry of Forests, Lands, Natural Resource Operations and RuralDevelopmentSupervisory Committee MemberGuangyu Wang, Faculty of Forestry, University of British Columbia VancouverUniversity ExamineriiAbstractForest thinning is a method to reduce competition in overstocked young forests.While the short-term effect of thinning is often studied by field thinning exper-iments, the long-term effect of thinning is rarely studied and remains unclear,particularly in the context of climate change. In this study, we used data fromfield experiment to train two Artificial Intelligence models: artificial neural net-work (ANN) and deep neural network (DNN) for assessing the long term effectof thinning on transpiration and water use efficiency (WUE) in young lodgepolepine forests at both tree and stand levels. We showed that DNN is more accuratethan ANN in terms of model performance measures. We then used the trainedDNN model to predict the transpiration and WUE under different thinning and cli-mate change scenarios. The simulations showed that thinning has a long-lastingeffect on increasing tree-level transpiration and both tree- and stand-level WUEbut decreasing stand-level transpiration. Thinning effect decreases as thinning in-tensity decreases and trees age. Climate change reduces both tree- and stand-levelWUE. Thinning mitigates the impact of climate change on stand-level transpira-tion. Based on the simulated long-term effects, we recommend that the thinningwith the remaining of 2500–3500 stem/ha as suitable densities for a better balanc-ing or trade-off between total carbon and water (stand transpiration) for young andoverstocked lodgepole pine stands in the southern interior of British Columbia.However, selection of the final thinning densities must consider broader ecologicalprocesses and functions such as wood quality, biodiversity as well as their interac-tions with climate change.iiiLay SummaryClimate change has severely impacted our forests and many other related ecolog-ical functions and services. In the BC interior, young and overstocked lodgepolepine trees are regenerated following severe forest disturbance. An important man-agement question is if thinning should be conducted, and if so, what suitable re-maining densities would be. In this study, we used machine learning method andthe field experimental data collected in the Upper Penticton Experimental water-shed to simulate the long-term effects of thinning and climate change scenarios.We found that thinning provides potentially important effects on both forest carbonand water (transpiration). While it decreases total forest carbon, it can increase wa-ter supply by reducing stand-level transpiration. We also found that thinning canpositively mitigate the climate change impacts. Based on our simulation results,we suggest the thinning densities (remaining) of 2500–3500 stem/ha for thinningtreatments in the study region.ivPrefaceThis study was directed by Prof. Adam Wei in the Department of Earth, Environ-mental and Geographical Sciences. The long-term monitoring site for studying theeffects of thinning on water and WUE was designed by Prof. Adam Wei and WanyiLiu, with collaborations from Dr. Rita Winkler from the BC Ministry of Forests .The data was collected by Prof. Adam Wei, Yi Wang and Wanyi Liu. The modelsetup and analysis were done by Wanyi Liu.vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiList of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Experiment and methodology . . . . . . . . . . . . . . . . . . . . . . 62.1 Site description . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2.1 Tree growth . . . . . . . . . . . . . . . . . . . . . . . . . 82.2.2 Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2.3 Sap flow rate and transpiration . . . . . . . . . . . . . . . 92.2.4 WUE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10vi2.3 Machine learning setup . . . . . . . . . . . . . . . . . . . . . . . 102.3.1 Introduction to ANN . . . . . . . . . . . . . . . . . . . . 112.3.2 Introduction to DNN . . . . . . . . . . . . . . . . . . . . 112.3.3 DNN and statistical regression . . . . . . . . . . . . . . . 132.3.4 Feature selection . . . . . . . . . . . . . . . . . . . . . . 142.3.5 DNN specification . . . . . . . . . . . . . . . . . . . . . 162.4 Generation of future data . . . . . . . . . . . . . . . . . . . . . . 183 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.1 DNN results and validation . . . . . . . . . . . . . . . . . . . . . 213.2 Future transpiration prediction . . . . . . . . . . . . . . . . . . . 213.2.1 Tree level transpiration . . . . . . . . . . . . . . . . . . . 223.2.2 Stand level transpiration . . . . . . . . . . . . . . . . . . 233.3 Future WUE prediction . . . . . . . . . . . . . . . . . . . . . . . 243.3.1 Tree level WUE . . . . . . . . . . . . . . . . . . . . . . . 253.3.2 Stand level WUE . . . . . . . . . . . . . . . . . . . . . . 273.4 Range of thinning densities . . . . . . . . . . . . . . . . . . . . . 294 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.1 NN discovers important factors . . . . . . . . . . . . . . . . . . . 324.2 DNN: advantage and disadvantage . . . . . . . . . . . . . . . . . 334.3 The effect of thinning . . . . . . . . . . . . . . . . . . . . . . . . 334.3.1 The effect of thinning on transpiration . . . . . . . . . . . 334.3.2 The effect of thinning on WUE . . . . . . . . . . . . . . 354.4 The effect of climate change . . . . . . . . . . . . . . . . . . . . 364.4.1 The effect of climate change on transpiration . . . . . . . 364.4.2 The effect of climate change on WUE . . . . . . . . . . . 374.5 Thinning mitigates the effect of climate change . . . . . . . . . . 384.6 Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 Conclusions and management implications . . . . . . . . . . . . . . 41Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50viiAppendix B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Appendix C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Appendix D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Appendix E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54viiiList of TablesTable 2.1 Feature combinations. . . . . . . . . . . . . . . . . . . . . . . 15Table 2.2 Feature selection result. . . . . . . . . . . . . . . . . . . . . . 16Table 2.3 The relative importance of features of #2 DNN. . . . . . . . . 16Table 2.4 Specification of DNN. . . . . . . . . . . . . . . . . . . . . . . 17Table 3.1 Predicted daily Ttree(L/d) in C, T1 and T2 from 2020 to 2090under four climate scenarios. . . . . . . . . . . . . . . . . . . 25Table 3.2 Predicted daily Tstand(mm/d) in C, T1 and T2 from 2020 to 2090under four climate scenarios. . . . . . . . . . . . . . . . . . . 27Table 3.3 Predicted daily WUEtree (mm2/L) in C, T1 and T2 from 2020to 2090 under four climate scenarios. . . . . . . . . . . . . . . 27Table 3.4 Predicted daily WUEstand (kg/m3) in C, T1 and T2 from 2020to 2090 under four climate scenarios. . . . . . . . . . . . . . . 30Table 1 Training data summary . . . . . . . . . . . . . . . . . . . . . 50Table 2 Testing data summary . . . . . . . . . . . . . . . . . . . . . . 50Table 3 Daily Ttree increasing rate (%) in C, T1 and T2 from 2030 to2090 under four climate scenarios. . . . . . . . . . . . . . . . 51Table 4 Daily Tstand increasing rate (%) in C, T1 and T2 from 2030 to2090 under four climate scenarios. . . . . . . . . . . . . . . . 52Table 5 Daily WUEtree increasing rate (%) in C, T1 and T2 from 2030to 2090 under four climate scenarios. . . . . . . . . . . . . . . 53Table 6 Daily WUEstand increasing rate (%) in C, T1 and T2 from 2030to 2090 under four climate scenarios. . . . . . . . . . . . . . . 54ixList of FiguresFigure 2.1 Research site and experiment design of three blocks of C, T1and T2 after thinning in June 2016. Adopted from (Wang etal., 2020) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Figure 2.2 diameter at breast height (DBH) mesurement . . . . . . . . . 8Figure 2.3 ANN example. . . . . . . . . . . . . . . . . . . . . . . . . . 11Figure 2.4 DNN example. . . . . . . . . . . . . . . . . . . . . . . . . . 12Figure 2.5 Density simulation result by TASS III. . . . . . . . . . . . . . 19Figure 2.6 DBH simulation by TASS III. . . . . . . . . . . . . . . . . . 20Figure 3.1 Result of optimization. . . . . . . . . . . . . . . . . . . . . . 22Figure 3.2 Scatter plots of Te against Tm of daily lodgepole pine. The solidred lines are regression lines. . . . . . . . . . . . . . . . . . . 23Figure 3.3 Predicted daily Ttree in C, T1 and T2 from 2020 to 2090 underfour climate scenarios. . . . . . . . . . . . . . . . . . . . . . 24Figure 3.4 DBH growth rate . . . . . . . . . . . . . . . . . . . . . . . . 25Figure 3.5 Predicted daily Tstand in C, T1 and T2 from 2020 to 2090 underfour climate scenarios. . . . . . . . . . . . . . . . . . . . . . 26Figure 3.6 Predicted daily WUEtree in C, T1 and T2 from 2020 to 2090under four climate scenarios. The unit of WUEtree is mm2/kg.The numeric of WUEtree is the same as that in mm2/L. . . . . 28Figure 3.7 Predicted daily WUEstand in C, T1 and T2 from 2020 to 2090under four climate scenarios. . . . . . . . . . . . . . . . . . . 29Figure 3.8 Three metrics ((a) Tstand, (b) WUEstand and (c) total carbon) fordetermining the range of thinning densities. . . . . . . . . . . 31xFigure 4.1 Thinning effect on transpiration under different climate scenar-ios compared with C. . . . . . . . . . . . . . . . . . . . . . . 34Figure 4.2 Thinning effect on WUE under different climate scenarios com-pared with C. . . . . . . . . . . . . . . . . . . . . . . . . . . 35Figure 4.3 The effect of climate change on (a) Ttree, (b) Tstand, (c) WUEtreeand (d) WUEstand compared with representative concentrationpathway (RCP) 2.6. . . . . . . . . . . . . . . . . . . . . . . . 36Figure 4.4 The effect of climate change on T in 2090 compared with RCP2.6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Figure 4.5 The effect of climate change on WUE in 2090 compared withRCP 2.6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Figure 4.6 Linear regression of Tstand against climate change intensity un-der different thinning regimes. . . . . . . . . . . . . . . . . . 39xiList of Symbols∆T Temperature difference between two thermocouples ◦CTmax Maximum temperature ◦CTmin Minimum temperature ◦CAs Sapwood area per tree cm2F Sap flow rate g/hK Dimensionless variable calculated from the measured temperature differ-ence between two thermocouples of the probeN Total days of the growing seasonP Precipitation mmT Transpiration mm/dTe Estimated transpiration mm/dTm Measured transpiration mm/dT1 Moderate thinning treatmentT2 Heavy thinning treatmentxiiList of AbbreviationsANN artificial neural networkBAI Basal area incrementDBH diameter at breast heightDNN deep neural networkLAI leaf area indexMSE mean square errorNN neural networkNPP net primary productionNRMSE normalized root mean square errorSR solar radiationRCP representative concentration pathwayxiiiRH relative humiditySFD sap flow densitySGD stochastic gradient descentUPEW Upper Penticton Experimental WatershedVPD vapour pressure deficitWUE water use efficiencyxivAcknowledgmentsThis project would not have been possible without the support of many people.Many thanks to my supervisor Prof. Adam Wei, who gave me the chance to studyat UBC. I have benefited greatly from your wealth of knowledge and your goodcharacter. Also thanks to my committee members, Prof. David Scott and Dr. RitaWinkler, who offered guidance and encouragement.I would like to thank Yi Wang, Dr. Qiang Li, Krysta Giles-Hansen, YipingHou, Yang Liu, Yingchun Liao, Dr. Fubo Zhao, Prof. Wenfei Liu, and everyfaculty member in this department for their help, and to Dr. David Spittlehouse forproviding useful data.I would like to thank the Chinese Scholarship Council for supporting my stayin Canada.I would like to thank my parents, my husband, and my son for their endlesssupport and love.I would like to thank everyone that I met during this long journey.xvChapter 1IntroductionExtreme weather conditions with catastrophic consequences have become moreand more frequent in BC, Canada, i.e., the wildfire in Lytton in 2021 and the floodin Abbotsford in 2021. The smoky weather in the entire Okanagan region duringsummers of 2020 and 2021 as a result of wildfires is another evidence. These large-scale forest disturbances have caused damage on the structure and compositionof forests, and as a result, have had a negative impact on a variety of ecologicalprocesses, including the carbon and water cycles (Bredemeier et al., 2012; Bearupet al., 2014; Lasslop et al., 2019). As climate change worsens and has a greaterimpact on people’s lives, it is important to take adaptation strategies to mitigate thepossible effects caused by climate change.Thinning is widely acknowledged as a useful forest management tool. Priorto the 1980s, when growth and yield were the primary forest management objec-tives, thinning was performed primarily for timber production. Therefore, thinningwas one of key silvicultural activities to increase the diameter growth of the re-maining trees by reducing competition for water, light and nutrients (Emminghamand Elwood, 1983; Aussenac, 2000; Cabon et al., 2018). As an abrupt change inthe local environment, thinning causes the plant to undergo various physiologicalprocesses to adapt to the new environment with more light availability. To main-tain the temperature in the presence of more radioactive energy, plants open theirstomata, resulting in more water and carbon exchange. This homeostatic reactionpushes the plants to a state that is better for their growth and resilience. It was1argued that after thinning, the increased ratio of the leaf area to sapflow area as ahomeostatic response to the increased water availability that maximizes water useefficiency (WUE) and whole-tree carbon uptake (McDowell et al., 2006). Withclimate change impacts and more concerns over forest carbon, water supply andbiodiversity, the objectives of thinning have been expanded to include carbon se-questration (Zhang et al., 2018), stand-level water consumption (Medhurst et al.,2002; Wang et al., 2019), soil moisture (Rasco´n-Ramos et al., 2021) and WUE(Wang et al., 2020).It is important to highlight some limitations of thinning. D’Amato et al. (2013)and Elkin et al. (2015) found that the effect of a single thinning treatment mayreverse as stands mature, resulting in higher vulnerability. The accumulation ofaboveground biomass following thinning may lead to increasing fire hazard andseverity (Safford et al., 2012; Martinson and Omi, 2013). The consequences ofthinning depend on how we employ the thinning as management treatment.In the last decade, thinning has been used as a tool for mitigating climatechange impacts as it enables the remaining trees to better cope with droughts(Campo et al., 2014; Fernandes et al., 2016; Sohn et al., 2016; Vernon et al., 2018)and regulate carbon sequestration (Wang et al., 2020). However, knowledge of thelong-term effects of thinning on growth, water, carbon and other ecological pro-cesses is crucial to designing of sustainable forest management, especially underclimate change. Since thinning is a human intervention, it is important to justifyits application and to carefully analyze its long-term effect on various ecologicalprocesses and functionsIn order to understand the carbon-water coupling, WUE is a good index forevaluating the trade-off between carbon assimilation and water consumption (Fer-nandes et al., 2016). Transpiration is of great significance for understanding wateruse and predicting water yields. Through transpiration, water evaporates from theundersides of leaves and is drawn up from the roots. This process cools the leaves,exchanges oxygen and carbon dioxide and moves nutrients up the tree (Caemmererand Baker, 2007). Transpiration is affected by several environmental and plantfactors, including temperature, humidity, precipitation, wind speed, light inten-sity, soil moisture and sapwood area, stomatal conductance. Some of these factorsare not independent, e.g. high precipitation increases humidity and soil moisture.2However, higher humidity decreases transpiration whereas higher soil moisture in-creases transpiration. For lodgepole pine in particular, although it has been consid-ered to have a shallow root system, Byers et al. (2011) showed that snowmelt playsan important role for its transpiration. A formula that incorporates all these con-flicting factors is complex. Granier et al. (1990) gave an empirical equation thatcalculates the sap flow rate based only on temperature differences and sapwoodarea, which is a huge simplification. For lodgepole pine, the Granier equation alsogives good result (Reid et al., 2006). Thinning increases the tree level transpiration(Campo et al., 2014; Wang et al., 2019) and decreases the stand level transpiration(Forrester et al., 2012; Wang et al., 2019). Thinning also improves the tree levelWUE accompanied with the increased tree growth (Wang et al., 2020); whereasthe effect of thinning on the stand level WUE is still unclear. Moreover, previousstudies mainly focus on the short-term effect on the transpiration and WUE; thelong term effect of thinning remains poorly understood.Various approaches have been used to assess the long-term effect of thinningincluding field experiments and computer simulations. The experimental approachoften provides robust results. However, long-term field measurements are expen-sive and time-consuming. Alternatively, computer models have been used to eval-uate the long-term effect of thinning and climate change. The models are dividedinto two categories.1. Process-based models, e.g., SVAT model (Koster and Suarez, 1994), theShuttleworth–Wallace model (Chen et al., 2020) and the modified Jarvis-Stewart model (Whitley et al., 2007; Guyot et al., 2017);2. Statistical models, e.g., multiple linear regression (Tu et al., 2019) and RATPmodel (Sinoquet et al., 2001).Process-based models utilize domain-specific knowledge but are hard to build. Toadapt a process-based model into another situation, model calibration is necessary.Statistical models are general-purpose but are restrictive in functional forms, i.e.,parametric models. recently, neural network (NN) has become a promising alter-native modelling method. The advantages of NN are as follows.1. Flexibility. The architecture of NN can be modified easily to adapt to theproblem at hand;32. Expressibility. The functional form provided by NN is versatile enough torepresent interesting relationships (Universal Approximation Theorem).The tree species under study is the lodgepole pine (Pinus contorta ssp. Latifolia),which is common in British Columbia. Lodgepole pine has robust adaptability andcan thrive in poor soil. However, young generations often form dense and homoge-nous cohorts with densities exceeding 50000 stem/ha (Johnstone and Thienen,2004). Carbon sequestration and hydrological processes could be impacted bythe extensive forest cover (Berryman, 1982). Drought caused by climate changeexacerbates this by increasing the frequency of forest fire and insect outbreaks.Consequently, carbon starvation and hydraulic failure (failed water transport whentrees are under water stress), the two mechanisms of tree mortality, are frequentlyobserved in affected forests (McDowell, 2011). Seidl et al. (2017) showed that re-alistic techniques or practices are required to manage these extremely dense foreststands, particularly in the context of climate change. Therefore, the objectives ofthis study are as follows.1. Find an effective and easy-to-implement way (machine learning model) tostudy the functional relation between transpiration/WUE and other easy-to-measure environmental and biological variables, which complements the ex-isting method;2. Explore the long-term effects of thinning and climate change on transpirationand WUE at both tree-level and stand-level.3. Find out the relative optimal range of stand density for lodgepole pine inorder to balance forest carbon and water (e.g., transpiration), especially tran-spiration in this study.Thanks to the expressibility and the easiness to implement, this machine learningmethod can be conducted if data is adequate. The results from this study provideinformation about carbon and water coupling at the stand level for lodgepole pinethinning management under climate change base on the the data collected from theexperiment collected at Upper Penticton Experimental Watershed (UPEW).The layout of the thesis is as follows. Section 2 is on the experimental detail4and the machine learning methodology. Section 3 shows the main result of thisstudy. In Section 4, we discuss the major limitation of this study.5Chapter 2Experiment and methodology2.1 Site descriptionA four-year1 field experiment was conducted at 241 Creek in the UPEW on theOkanagan Plateau, approximately 26 km northeast of Penticton, BC (49◦39′34′′N,119◦24′34′′W) (Figure 2.1). UPEW is located within British Columbia’s drier En-gelmann spruce subalpine fir zone, which is characterized by steep, rocky terrainand cold, snowy winters (Coupe´ et al., 1991). Between 1992 and 2008, the averageannual precipitation was 697.2 mm, less than a third of the amount which fell dur-ing the growing season (June to October). The annual mean temperature is 1.9 ◦C(Winkler et al., 2021), with daily temperature ranging in −8–28 ◦C throughoutthe growing season. The elevation is about 1675 m above the sea level, and snowcover occurs from early November to mid-June. The study site is south-facingand is populated with even-aged lodgepole pine trees that have been re-establishedafter the 2002 logging. Luvisolic soils are derived from granite parent rock andhave a coarse sandy-loam texture with inadequate water storage capacity. Soilsare formed on morainal and glaciofluvial sediments derived from coarse-grainedgranitic rocks. Soils are coarse sandy loam in texture with low water holding ca-pacities and are well and rapidly drained. For more detailed description, see (Win-kler and Moore, 2006). The experiment was designed and set up by Yi Wang etal. (2019) in 2016. Three replicates of the treatments were assigned in a random-12016 and 2017 by Yi Wang, 2018 and 2019 by me6Figure 2.1: Research site and experiment design of three blocks of C, T1 andT2 after thinning in June 2016. Adopted from (Wang et al., 2020).ized block design, each 25 m by 75 m. Each block contained one unthinned controlplot and two different thinned plots, each 20 m by 20 m. The thinning was carriedout manually and the slash was left in place. The densities of trees in treatment1 (T1), treatment 2 (T2) and the control (C) were approximately 4500 stem/ha,1100 stem/ha, and 27000 stem/ha, respectively.Another research location was situated at UPEW site P7 forest with lodgepolepine trees of age 125 years old and with density 801 stem/ha. Data on sap flow,biomass and growth collected in 2000 (Spittlehouse, 2002) were also used for thisstudy.2.2 ExperimentWe introduce the measurement conducted in the experiment. A summary of themeasurement result can be found in Appendix 5.72.2.1 Tree growth45 trees were randomly chosen in each plot to have their diameter at breast heights(DBHs) measured using an electronic calliper (Model: 500-196-30, Mitutoyo Cor-poration, Japan). The measurement was taken at the beginning of and the end ofeach growing season. The result is summarized in Figure 2.2. Along with DBH,Figure 2.2: DBH mesurementtree growth was measured in terms of Basal area increment (BAI) in cm2 (West,1980).2.2.2 ClimateThroughout the study period, climate stations (HOBO weather station, Onset Com-puter, Bourne, MA, USA) were installed in each plot in Block 1. The sensors wereplaced about 2.5 m at the canopy level. Daily average, maximum and minimumair temperatures (T, Tmax and Tmin), relative humidity (RH), solar radiation (SR)in MJ/m2 and precipitation P were recorded every 10 min. The vapour pressuredeficit (VPD) in Pa was calculated using RH and T via the Goff-Gratch equation.82.2.3 Sap flow rate and transpirationThe sap flow density (SFD) was measured using the heat-based sap flow sensor(Dynamax, Houston, Texas, USA), which measures the temperature difference in(2.1). 5 trees from each treatment were instrumented at the breast height withGranier-type sap flow probes (Model TDP-30) for sap flow measurement through-out the growing seasons during 2016 and 2019. All probes were south-facing andwere completely protected by thermal insulation materials to minimize the effect ofambient temperature gradients (Wang et al., 2019). Sap flow rate F was calculatedusing the following empirical equations calibrated from (Granier et al., 1990).K =∆Tmax−∆T∆T,SFD = 0.0119×K1.231,F = As×SFD×3600/sh−1,(2.1)where ∆T is the temperature difference at two probes in ◦C, SFD is in g/(cm2 s), Fis in g/h and As is the sapwood area per tree in cm2. Wang et al. (2019) estimatedAs ,the sapwood area per tree, by a linear regression model(R2 = 0.98)As/mm2 = 102.81×DBH/mm−3709.3. (2.2)For trees in controlled and thinned plots, Equation 2.2 is expected to hold true(Bre´da and Granier, 1996). Tree and stand level transpirations were estimated bythe following equation.Ttree = SFD×As,Tstand = SFD×As,(2.3)where SFD was averaged half-hourlySFD =SFD(0 : 00)+SFD(0 : 30)+ · · ·+SFD(24 : 00)48×1800 (2.4)9and As, the stand sapwood area for each plot, was estimated byAs = As× trees in each plot. (2.5)2.2.4 WUETree level WUE was calculated as the ratio of BAI and the tree level transpirationTtree during the growing seasons2WUEtree =BAITtree, (2.6)where BAI was calculated by the difference between the basal area of the currentyear and that of the last year, divided by the total growing days in each year.Stand level WUE was calculated as the ratio of the daily net primary production(NPP), which included the overground biomass and the underground biomass, bothestimated by TASS III (section 2.4), to the stand level transpiration Tstand duringthe growing seasonsWUEstand =NPPTstand, (2.7)where the daily NPP was calculated by the difference between NPP of the currentyear and that of the last year, divided by the total growing days in each year.2.3 Machine learning setupMachine learning is a black-box way of modelling. Machine learning can be im-plemented in a variety of ways, one of which is through the use of neural networks.Despite the simplicity of a neural network, it can model phenomena and performtasks that are almost impossible to achieve by a traditional programme.2WUEtree is defined as the ration of the carbon mass to the water mass/volume. (2.6) is thedefinition used in Wang et al. (2020). Since this work is a continuation of Wang et al. (2020), weadopted their definition. We remark that using univariate biomass equation, BAI can be convertedinto carbon mass (Monserud et al., 2006).10x1x2...xnHidden...yˆInput OutputFigure 2.3: ANN example.2.3.1 Introduction to ANNMcCulloch and Pitts (1943) defined artificial neural network (ANN) as a massivelyparallel interconnected network of simple (usually adaptive) elements which are in-tended to interact in the same way as biological nervous systems. ANN model isbroadly applied for solving regression and classification problems in many fields(Whitley et al., 2007; Tu et al., 2019). The architecture of ANN is depicted in Fig-ure 2.3, which consists of three layers: an input layer, a hidden layer and an outputlayer. The input layer is linked to the hidden layer where the data are processedthrough weighted connections. Each neuron in the hidden layer is connected toall neurons in the output layer, which can be more than one if we are interested inseveral outputs simultaneously.2.3.2 Introduction to DNNHinton et al. (2006) introduced the idea of deep learning, an emerging area of ma-chine learning, among which deep neural network (DNN) has emerged as a basicbut nevertheless strong deep learning model with hydrology applications (Achieng,112019). Several hidden layers are employed in DNN to learn complicated nonlinearrelationships between inputs and outputs. The difference between ANN and DNNis the number of hidden layers. ANN has one hidden layer; whereas DNN has sev-eral hidden layers. Figure 2.4 shows the architecture of a DNN model with input,hidden layers and output. Although this generalization seems trivial, the express-ing power of DNN is much stronger than that of ANN. The slogan is “deeper isbetter than wider”.x1x2...xn......· · ·· · ·· · ·· · ·......yˆInput Hidden OutputFigure 2.4: DNN example.The setup of a neural network is as follows.1. Choose an architecture of the network, which means specifying the numberof layers L and amount of neurons in each layer 1≤ l ≤ L;2. Choose an activation function σ : R→ R for each neuron;3. Choose a loss function L for the output layer;4. Compute the weights and biases via a minimization procedure.122.3.3 DNN and statistical regressionThere are many similarities between DNN and statistical regression (linear regres-sion, generalized linear model etc.). After all, both model the relationship betweensome responses and some explanatory variables. They are however different.1. Linear regression, e.g., Y = β0 +β1X +σ2ε , where ε ∼N (0,1),X (inde-pendent of ε),Y are random variables and β0,β2,σ2 are parameters to be in-ferred, has a good statistical inference theory. For example, the least squareestimators of the parameters coincides with the maximal likelihood estima-tors. Hence, we can use good statistical properties of the maximal likelihoodestimator to calculate the asymptotic variances and to construct confidentintervals.2. On the other hand, there is no statistical inference happening in DNN. There-fore, there is no statistical inference theory for DNN. For one thing, theparameters in DNN are weights and biases for neurons. These parametersare deemed as fix numbers and the algorithm tries to find them. Strictlyspeaking, the only randomness (excluding measurement errors) in our DNNmodel comes from the algorithm. Had we not used a probabilistic algorithmstochastic gradient descent (SGD), each run of the program would have pro-duced the same result.3. When parameters are not too abundant, e,g, three of them in linear regressionmodel Y = β0 +β1X +σ2ε , it is possible to assign a meaning for each pa-rameter. It is in principle possible to construct a statistical estimator (whichis a random variable) for each weight and bias in DNN. However, this isnot practical because the number of neurons can be of the same magnitudeof or even more than the number of samples. Thus, the current statisticalestimation theory based on large sample is not applicable.4. The expressibility in linear regression comes from the assumed Gaussianrandom variable ε , without with X would be a fixed linear function of Y . Theexpressibility of DNN comes from its architecture and enormous amount ofneurons.13In short, parameters in statistical regression are few and they individually carry im-portant information. There exists mature statistical theory for them. Whereas pa-rameters in DNN are enormous but individually they carry very little information.However, collectively, they can perform complex tasks such as image processingand speech recognition.2.3.4 Feature selectionFeature selection is the first phase in developing a machine learning model. Itaddresses a basic question: What explanatory variables, called features in data sci-ence and machine learning community, should one use. These variables constitutethe input layer of an NN. The motivation of feature selection is as follows.1. Improve model accuracy and reduce overfitting;2. Reduce training time (the bigger the NN, the longer time it takes to run).In this study, the feature selection was divided into the following steps.1. Group the features into different types (meteorological, tree and categorical);2. Choose features from each type to form combinations of features (The com-bination needs not be exhaustive. The process could be halted as long asimportant features were selected).3. Put each different combination into a trail NN with varying input layers toassess the quality of the combination.The features were divided into three types.1. Meteorological variables: SR, T, Tmax, Tmin, RH, P and VPD;2. Tree variables: DBH and age;3. Categorical variable: stand density3We chose twelve different combinations of features (Table 2.1).3Even though Stand density is a continuous variable, it is essentially a stand-level notion. There-fore, all trees in the same plot share the same stand density. Treating it as a continuous variablewould most likely render it a dummy variable.14# Features1 DBH, T, SR, RH, P, Age, Density2 DBH, Tmax, Tmin, SR, RH, P, Age, Density3 DBH, T, SR4 DBH, Tmax, Tmin, SR5 DBH, T, SR, Age6 DBH, Tmax, Tmin, SR, Age7 DBH, T, SR, RH8 DBH, Tmax, Tmin, SR, RH9 DBH, T, SR, RH, Age10 DBH, Tmax, Tmin, SR, RH, Age11 DBH, VPD, SR12 DBH, VPD, SR, AgeTable 2.1: Feature combinations.We used the normalized root mean square error (NRMSE) and R2 to measurethe performance of combinations of features. NRMSE measures the differencebetween the model prediction and the measured data. The lower NRMSE, the moreaccuracy the model. R2 measures the covariance between the model prediction andthe measured data. The greater R2, the more influence the combination of featureshas on the predicted data. Therefore, the lower NRMSE and the greater R2, thebetter model.NRMSE =√1N ∑i (T im−T ie )2ETmR2 =Cov (Te,Tm)Var (Te)Var (Tm),(2.8)where E is expectation, Cov is covariance and Var is variance.After two rounds of feature selection, we found that treating density as a con-tinuous variable was not appropriate as we only had three values. Therefore, westopped considering it in other feature combinations and treated it as a categoricallabel for data instead.The result of the feature selection is summarized in Table 2.2. We observed thatDNN performs systematically better than the corresponding ANN. Moreover, even15the best result #3 produced by ANN is worse than the worst result #9 produced byDNN (lower NRMSE and only a slightly lower R2).ANN DNN# NRMSE R2 NRMSE R21 0.852 0.680 0.789 0.7542 0.816 0.696 0.801 0.7413 0.805 0.711 0.713 0.7834 0.814 0.702 0.722 0.7645 0.869 0.655 0.803 0.7016 0.844 0.671 0.765 0.7057 0.842 0.684 0.755 0.7028 0.836 0.680 0.767 0.7099 0.842 0.670 0.801 0.70110 0.866 0.659 0.798 0.70311 0.782 0.693 0.736 0.72312 0.805 0.689 0.780 0.718Table 2.2: Feature selection result.Using #2 DNN, we performed a relative importance analysis of features. Theresult is summarized in Table 2.3.DBH T SR Age Density RH P81.63% 9.13% 5.85% 3.09% 2.88% 1.66% −4.24%Table 2.3: The relative importance of features of #2 DNN.From Table 2.3, we see that precipitation has a negative impact on the model.The first three most imortant features are DBH, T and SR. Age is comparable toSR. However, since the age for a plot was relatively uniform (same as for density),we did not adopt it in the final result of the feature selection. Therefore, we adoptedDBH, T and SR as our input for later phases of DNN.2.3.5 DNN specificationThe full specification of DNN used in this experiment is given in Table 2.4.Due to its superior performance compared with other activation functions (sig-moid, ReLU etc.), the hyperbolic tangent function tanh was adopted as the activa-16Architecture 3×64×32×8×1 Fully connectedRegularization method Batch normalizationActivation function tanhLoss function mean square error (MSE)Optimization procedure adamTable 2.4: Specification of DNN.tion function in this experiment. All data are normalized (shift the mean to zero andscale the variance to 1) before feeding to the NN to mitigate the effect of differentunit scales. The estimated transpiration Te is given by the architecture:Te ={W [3]{W [2] tanh[W [1] tanh(W [0]a[0]+b[0])+b[1]]+b[2]}+b[3]}, (2.9)where W [i] and b[i] are the weight matrix and bias vector between the i-th layer andthe i+1-th layer, respectively.The loss function is:L =1NN∑i=1[T im−Te(xi)]2, (2.10)where T im and Te(xi) are the measured/estimated transpiration using data point xi,respectively.The procedure of determining weight matrices and biases is known as training.We used a minimization procedure to achieve this:({Wˆ [i]}3i=0,{bˆ[i]}3i=0)= argmin L, (2.11)i.e., we find{Wˆ [i]}3i=0 and{bˆ[i]}3i=0 such that L becomes as small as possible.The data was normalized using the maximum-minimum normalization methodto minimize the effect of absolute scales. The number of hidden neurons was cho-sen by a trial-and-error manner, varying from 2 to 28.172.4 Generation of future dataFuture scenarios consisted of meteorological and tree data. The meteorologicaldata is simulated by MarkSimGCM web application (MarkSim in short).The tree data was simulated by TASS III (B.C. Ministry of Forests and Range,2010).MarkSimGCM web application is a user-friendly interactive applica-tion that runs the MarkSim (Jones and Thornton, 2000) on Google Earth. It takesthe output of a general circulation model to generate future daily weather datastochastically. The simulation location was set at the experiment site. T, Tmax,Tmin and SR during the growing season (June to October) from 2020 to 2090 weresimulated at a 10-years step. Four climate scenarios indexed by the representativeconcentration pathway (RCP) were simulated, including RCP 2.6, RCP 4.5, RCP6.0 and RCP 8.5. According to the Intergovernmental Panel on Climate Change,RCP 2.6 mandates that global carbon dioxide emissions begin to decline in 2020and reach zero by 2100. In contrast, in RCP 8.5, emissions continue to increaseduring the course of the 21st century. RCP 4.5 and RCP 6.0 are intermediate sce-narios and are included for the purpose of interpolation.TASS III is a biologically oriented, spatially explicit (distance dependent), in-dividual tree, growth and yield model, especially for lodgepole pine and whitespruce. Since TASS III is an empirical model with few inputs, essentially a blackbox, its use under different thinning scenarios must be validated. Goudie (1998)found that TASS III generally performs well under different thinning scenarios butmay occasionally overestimate the response to thinning moderately. We simulatedlodgepole pine, starting from 2002, thinning in 2016, and running for 150 years.Data collected at the control plot from 2016 to 2019 was used to calibrate TASS III.Data from one nearby plot, a 125-year-old lodgepole pine plot, collected in 2000was used to validate TASS III. Two thinning scenarios were simulated using datacollected at T1 and T2 from 2016 to 2019. Simulation results of density and DBHin control and two thinning scenarios are summarized in Figure 2.5 and Figure 2.6.18(a) Density result for T1, T2 and C.(b) Density result for T2.Figure 2.5: Density simulation result by TASS III.19Figure 2.6: DBH simulation by TASS III.20Chapter 3Results3.1 DNN results and validationThe loss function L (MSE) describes the mean squared difference between themodel predictions and the measured data. In each epoch, all data were fed intothe DNN and its parameters (weights and biases) were updated by the SGD tominimize the loss function. This process is known as the training. After training,L decreased by a factor of 10 (Figure 3.1).To avoid overfitting, we performed a 5-fold cross validation. The dataset wasrandomly partitioned into 5 groups, each containing 821 data points. We trainedthe model 5 times and each time 80% of data was used to train the model. One passof the 5-fold cross validation is given in Figure 3.2, where the results on the trainingdata and the test data are negligible, which indicates the absence of overfitting.3.2 Future transpiration predictionIn this section, we used our trained and cross-validated DNN to predict transpi-ration in the future under different climate change scenarios. However, since themost explanatory variable in our machine learning model was DBH and the futuretree data was generated by TASS III, a crude growth and yield model, the resultmight not be very informative about the future climate change scenarios.21Figure 3.1: Result of optimization.3.2.1 Tree level transpirationThe daily tree level transpiration Ttree in C, T1 and T2 was predicted from 2020to 2090, in a ten-year time steps under four future climate change scenarios: RCP2.6, RCP 4.5, RCP 6.0 and RCP 8.5. The result is shown in Figure 3.3. The detailfor each scenario is presented in Table 3.1.Ttree in T1 and T2 exhibits a similar pattern. From 2020 to 2030, tree transpira-tion increases rapidly. Then Ttree increases gradually until reaching a stable level in2050, with an increase rate less than 10% (Table 5). In contrast, Ttree in C increasesrapidly from 2020 to 2040. Then it increases gradually until reaching a stable levelin 2060. In C, Ttree ranges in 1.444–1.446 L/d in 2020 and in 9.066–10.746 L/d in2090. In T1, Ttree ranges in 2.508–2.604 L/d in 2020 and in 9.273–11.213 L/d in2090. In T2, Ttree is the highest among three scenarios, ranging in 2.600–2.604 L/din 2020 and in 9.488–11.746 L/d in 2090.The predicted tree transpiration under four climate scenarios (Figure 3.3) are22(a) Result on training set. (b) Result on testing set.Figure 3.2: Scatter plots of Te against Tm of daily lodgepole pine. The solidred lines are regression lines.similar because only three features (DBH, T, SR) were used in the DNN model.According to relative importance of the parameters Table 2.3, T and SR take up to15% of the total importance while DBH takes more than 80%. Thus, the significantdifference between four climate scenarios only occur at around 2080 and 2090.In RCP 6.0 and RCP 8.5, there is a gradual shift around 2080 and 2090. Thismay be due to the higher temperature in those two scenarios, leading to highertranspiration. There is an inflection point at around 2030 and 2040 in Figure 3.3.This is related to DBH growth curves because DBH is the most important factor inour model, which agrees with Figure 3.4. With tree age increasing, younger treesare expected to have higher transpiration rate than the older.3.2.2 Stand level transpirationThe daily stand level transpiration Tstand in C, T1 and T2 was predicted from 2020to 2090 under four future climate change scenarios: RCP 2.6, RCP 4.5, RCP 6.0and RCP 8.5. The result is shown in Figure 3.5. The detail for each scenario ispresented in Table 3.2.A similar pattern of Tstand can be seen in C and T1. Tstand in C and T1 increasesrapidly until 2030, where it reaches a maximum of approximately 6 mm/d and3 mm/d, respectively, followed by a decrease. This pattern agrees with Figure 3.4as we remarked in the beginning of section 3.2 that DBH was the most important23(a) RCP 2.6. (b) RCP 4.5.(c) RCP 6.0. (d) RCP 8.5.Figure 3.3: Predicted daily Ttree in C, T1 and T2 from 2020 to 2090 underfour climate scenarios.component in our model. In 2090, Tstand in C is lower than that of 2020, whereasTstand in T1 is greater than that of 2020. However, the trend in T2 is different, whereit increases rapidly from 2020 to 2030, then gradually until 2090. The increaserates from 2020 to 2030 in C, T1 and T2 are approximately 80%, 170% and 190%,respectively (Table 5). In addition, it is clear that Tstand is the greatest in C, followedby T1 and the smallest in T2.3.3 Future WUE predictionIn this section, we used our trained and cross-validated DNN to predict WUE inthe future.24Figure 3.4: DBH growth rateClimate Thinning 2020 2030 2040 2050 2060 2070 2080 2090C 1.445 5.200 7.617 8.364 8.708 8.903 8.998 9.066RCP 2.6 T1 2.508 7.387 8.508 8.852 9.037 9.156 9.211 9.273T2 2.600 7.825 8.807 9.068 9.189 9.287 9.377 9.488C 1.444 5.237 7.619 8.530 8.978 9.268 9.458 9.596RCP 4.5 T1 2.508 7.436 8.533 9.044 9.332 9.563 9.738 9.906T2 2.600 7.880 8.842 9.271 9.511 9.743 9.994 10.232C 1.447 5.150 7.559 8.409 8.951 9.311 9.566 9.806RCP 6.0 T1 2.509 7.313 8.434 8.903 9.306 9.610 9.859 10.148T2 2.600 7.741 8.724 9.120 9.480 9.793 10.135 10.519C 1.446 5.223 7.693 8.712 9.329 9.823 10.249 10.746RCP 8.5 T1 2.512 7.450 8.639 9.241 9.711 10.162 10.614 11.213T2 2.604 7.898 8.957 9.474 9.914 10.397 10.998 11.746Table 3.1: Predicted daily Ttree(L/d) in C, T1 and T2 from 2020 to 2090 underfour climate scenarios.3.3.1 Tree level WUEThe daily tree level WUEtree in C, T1 and T2 was predicted from 2020 to 2090under four future climate change scenarios: RCP 2.6, RCP 4.5, RCP 6.0 and RCP8.5. The result is shown in Figure 3.6. The detail for each scenario is presented in25(a) RCP 2.6. (b) RCP 4.5.(c) RCP 6.0. (d) RCP 8.5.Figure 3.5: Predicted daily Tstand in C, T1 and T2 from 2020 to 2090 underfour climate scenarios.Table 3.3.WUEtree in C, T1 and T2 exhibits a similar pattern. All of them show a down-ward trend aggregation, with a sharp decrease from 2020 to 2030. The declinefrom 2030 to 2090 in C and T1 exhibits some fluctuations (Table 5). WUEtreeis highest in T2, followed by T1 and C. WUEtree is approximately 0.86 mm2/L,0.98 mm2/L and 1.00 mm2/L in C, T1 and T2 in 2020, respectively and decreasesto approximately 0.16 mm2/L, 0.20 mm2/L and 0.20 mm2/L in 2090, respectively.26Climate Thinning 2020 2030 2040 2050 2060 2070 2080 2090C 3.409 6.161 5.351 3.973 3.113 2.448 2.115 1.926RCP 2.6 T1 1.097 3.010 2.935 2.633 2.101 1.831 1.658 1.484T2 0.279 0.822 0.925 0.952 0.942 0.929 0.867 0.878C 3.409 6.206 5.352 4.052 3.210 2.549 2.223 2.039RCP 4.5 T1 1.097 3.030 2.944 2.691 2.170 1.913 1.753 1.585T2 0.279 0.827 0.928 0.973 0.975 0.974 0.924 0.947C 3.416 6.103c 5.310 3.994 3.200 2.560 2.248 2.084RCP 6.0 T1 1.097 2.980 2.910 2.649 2.164 1.922 1.775 1.624T2 0.279 0.813 0.916 0.958 0.972 0.979 0.937 0.973C 3.413 6.190 5.404 4.138 3.335 2.701 2.408 2.284RCP 8.5 T1 1.099 3.036 2.980 2.749 2.258 2.032 1.911 1.794T2 0.280 0.829 0.940 0.995 1.016 1.040 1.017 1.086Table 3.2: Predicted daily Tstand(mm/d) in C, T1 and T2 from 2020 to 2090under four climate scenarios.Climate Thinning 2020 2030 2040 2050 2060 2070 2080 2090C 0.862 0.372 0.286 0.269 0.281 0.241 0.206 0.170RCP 2.6 T1 0.978 0.408 0.358 0.363 0.351 0.307 0.248 0.219T2 1.049 0.536 0.485 0.425 0.381 0.337 0.286 0.212C 0.862 0.369 0.286 0.264 0.272 0.231 0.196 0.161RCP 4.5 T1 0.979 0.405 0.357 0.355 0.340 0.294 0.235 0.205T2 1.049 0.533 0.483 0.416 0.368 0.321 0.268 0.197C 0.861 0.375 0.288 0.267 0.273 0.230 0.194 0.157RCP 6.0 T1 0.978 0.412 0.361 0.361 0.341 0.293 0.232 0.200T2 1.049 0.542 0.490 0.423 0.369 0.319 0.264 0.192C 0.861 0.370 0.283 0.258 0.262 0.218 0.181 0.144RCP 8.5 T1 0.977 0.404 0.353 0.348 0.327 0.277 0.216 0.181T2 1.047 0.531 0.477 0.407 0.353 0.301 0.244 0.172Table 3.3: Predicted daily WUEtree (mm2/L) in C, T1 and T2 from 2020 to2090 under four climate scenarios.3.3.2 Stand level WUEThe daily stand level WUEstand in C, T1 and T2 was predicted from 2020 to 2090under four future climate change scenarios: RCP 2.6, RCP 4.5, RCP 6.0 and RCP8.5. The result is shown in Figure 3.7. The detail for each scenario is presented inTable 3.4 and Table 5.WUEstand in C, T1, and T2 exhibits a downward trend overall, with a slightlyincrease from 2050 to 2070 in C and from 2030 to 2040 in T2. WUEstand is27(a) RCP 2.6. (b) RCP 4.5.(c) RCP 6.0. (d) RCP 8.5.Figure 3.6: Predicted daily WUEtree in C, T1 and T2 from 2020 to 2090 underfour climate scenarios. The unit of WUEtree is mm2/kg. The numericof WUEtree is the same as that in mm2/L.highest in T2, followed by T1 and C. WUEstand is approximately 0.22 kg/m3,0.28 kg/m3 and 0.30 kg/m3 in C, T1 and T2 in 2020, respectively and approxi-mately 0.06 kg/m3, 0.08 kg/m3 and 0.14 kg/m3 in 2090, respectively.The trend of WUEstand in T2 is different from that of T1 and C during 2030 to2050. It is likely that this is only an artifact of the model. WUEstand was calculatedby the ratio of NPP and Tstand (2.7), where NPP was given by TASS III and Tstandwas estimated from Ttree, the output of NN. Since both TASS III and NN are blackbox, it is difficult to explain this numerical artifact.28(a) RCP 2.6. (b) RCP 4.5.(c) RCP 6.0. (d) RCP 8.5.Figure 3.7: Predicted daily WUEstand in C, T1 and T2 from 2020 to 2090under four climate scenarios.3.4 Range of thinning densitiesWe determined the optimal thinning range using Tstand, WUEstand and total car-bon as three metrics (Figure 3.8). If the thinning is insufficient, Tstand is relativelyhigh and WUEstand is rather low. If the thinning is too severe (500 stem/ha), thenWUEstand will grow with age, indicating that there is an excess of water. Addition-ally, heavy thinning would reduce total carbon. If the stand density of lodgepolepine is too low, trees will grow with a large number of branches, degrading thewood quality. Therefore, 2500–3500 stem/ha (remaining) was determined to bethe suitable thinning range from the perspective of forest carbon and water.29Climate Thinning 2020 2030 2040 2050 2060 2070 2080 2090C 0.215 0.069 0.054 0.053 0.060 0.064 0.062 0.062RCP 2.6 T1 0.283 0.161 0.144 0.115 0.111 0.102 0.092 0.090T2 0.298 0.229 0.254 0.257 0.225 0.197 0.191 0.154C 0.215 0.068 0.054 0.052 0.059 0.062 0.059 0.059RCP 4.5 T1 0.283 0.160 0.144 0.113 0.108 0.098 0.087 0.084T2 0.298 0.228 0.253 0.251 0.217 0.188 0.179 0.143C 0.215 0.069 0.055 0.053 0.059 0.061 0.058 0.058RCP 6.0 T1 0.283 0.163 0.146 0.114 0.108 0.097 0.086 0.082T2 0.298 0.232 0.256 0.255 0.218 0.187 0.176 0.139C 0.215 0.069 0.054 0.051 0.056 0.058 0.054 0.053RCP 8.5 T1 0.282 0.160 0.142 0.110 0.104 0.092 0.080 0.074T2 0.298 0.227 0.250 0.246 0.208 0.176 0.163 0.125Table 3.4: Predicted daily WUEstand (kg/m3) in C, T1 and T2 from 2020 to2090 under four climate scenarios.30(a) Tstand.(b) WUEstand.(c) Total carbon.Figure 3.8: Three metrics ((a) Tstand, (b) WUEstand and (c) total carbon) fordetermining the range of thinning densities.31Chapter 4DiscussionIn this chapter, we discuss the advantage and disadvantage of the DNN model,the effect of thinning and climate change and the limitation of this work. It isimportant to note that DNN is data-driven and our data lacks soil water, competitionfactors etc., which are most likely affected by thinning. Moreover, our DNN modelfavors DBH, which was given by TASS III, a growth and yield model. As a result,the prediction under climate change scenarios was severely limited. However, theresult showed some reasonable trends, albeit with some modelling artifacts. Wewill discuss some further limitations in section 4.6.4.1 NN discovers important factorsThe tree level transpiration Ttree depends on various conflicting factors. Our NNmodel tried to predict Ttree based on optimizing the network parameters to fit themeasurement data. However, Ttree was not measured directly but was estimatedby the Granier equation (2.1), which is an empirical equation that only dependson temperature and As. We also followed Wang et al. (2019) to use a regressionequation (2.2) to estimate As by DBH. Therefore, our measurement of Ttree wasin fact a simple function of temperature and DBH. However, the NN model didnot know of the Granier equation nor the regression equation that we used. Ittreated the data of Ttree as if it were measured directly. From this perspective, ourNN model automatically discovered Ttree as a function of temperature and DBH as32shown in our feature selection result.4.2 DNN: advantage and disadvantageAs shown in Table 2.2, DNN performed systematically better than the correspond-ing ANN. Compared with ANN, the result of DNN increases by 7.13−11.47% and7.02−12.19% in terms of NRMSE and R2 under various input combinations in thetraining set, while the corresponding values are 8.75−13.92% and 7.32−14.56%in the testing set.This is in agreement with the result in Achieng (2019), where a DNN with5 hidden layers outperformed the corresponding ANN estimating the soil mois-ture retention curve. This is promising because compared with a detailed processmodel, a DNN model is much easier to build. The drawback is that since DNNis data-driven, it is difficult to use DNN to model variables that must be measuredmanually. Hence, DNN cannot replace detailed analyses. In our study, the densityof stand is such a variable and we found that it had negative impact on the accuracyof DNN during feature selection.4.3 The effect of thinning4.3.1 The effect of thinning on transpirationThinning has a positive effect on the tree level transpiration Ttree and a negativeeffect on the stand level transpiration Tstand. Moreover, this effect decreases withtime. The effect of thinning on the transpiration T is shown in Figure 4.1.In our study, thinning effect is more significant in T2 than in T1. At the treelevel, this effect decreases from 73.59% in 2020 to 3.34% in 2090 in T1 and from79.94% in 2020 to 6.96% in 2090 in T2, respectively. It is remarkable that thiseffect decreases rapidly to above 10% by 2040 and then slows down. At the standlevel, from 2020 to 2090, the thinning effect gradually decreases from 67.82% to22.19% in T1 and from 91.80% to 53.44% in T2, respectively.The positive effect of thinning on Ttree is due to several reasons.1. After thinning, the competition for resources between each individual be-33(a) Tree level. (b) Stand level.Figure 4.1: Thinning effect on transpiration under different climate scenarioscompared with C.comes lesser (Andre´s et al., 2017; Park et al., 2018; Sullivan et al., 2020).2. DBH is the most important parameter in predicting Ttree in our study, ac-counting for 81.68% in relative importance (Table 2.3). This is consistentwith Diaconu et al. (2015), Chase et al. (2018), and Cabon et al. (2018),which showed that thinning improves the tree radial growth significantly.3. Thinning changes the stand structure by increasing the open canopy exposureand lowering the leaf water potential values (Simonin et al., 2007). As aresult, Ttree increases.Besides the above reasons, soil moisture is also important for tree growth. How-ever, soil moisture data was not available in this study.The negative effect of thinning on Tstand is due to the decreased stand density(Simonin et al., 2007; Forrester et al., 2012). In 2020, the stand densities of T1 andT2 are 81.46% and 95.44% lower than that of C, respectively. In 2090, the standdensity of T1 and T2 are 24.70% and 56.47% lower than that of C.The effect of thinning on T depends on the intensity of thinning: the heavierthe thinning, the greater the effect. This is consistent with Park et al. (2018), whichshows that heavy thinning (240 stem/ha remaining) is more effective at increasingTtree and reducing Tstand than light thinning (320 stem/ha). However, Park et al.(2018) studied the effects of thinning in the short term, whereas we studied longterm effects. The difference between Ttree in T1, T2 and C becomes negligible after342050 (Figure 3.3), whereas the difference between Tstand is still significant till 2090.This seems to contradict the homeostasis hypothesis1. However, the stand mightnot reach the homeostasis when the environment undergoes continuous changes(Lesch and Scott, 1997).4.3.2 The effect of thinning on WUEThe effect of thinning on WUE is shown in Figure 4.2. In general, thinning hasa positive effect on WUEtree and WUEstand (Forrester et al., 2012). WUEtree has(a) Tree level. (b) Stand level.Figure 4.2: Thinning effect on WUE under different climate scenarios com-pared with C.a downward trend but is highest in T2, followed by T1 and C (Figure 3.6). Theincrease of WUEtree after thinning is due to the increased growth rate. Thinningimproves the light interception for individual tree, resulting in more carbon as-similation (Warren et al., 2001). Hence, T2 with the lowest stand density has thehighest WUEtree.WUEstand has a downward trend (Figure 3.7) but thinning has a positive effecton WUEstand. The maximal effect (165.91%) occurs in 2040 in T1; whereas themaximal effect (381.33%) occurs in 2050 in T2. This means that heavy thinninghas greater and more long-lasting effect on WUEstand.1A forest stand tends to develop to fully utilize the resource after the thinning (Whitehead et al.,1984).354.4 The effect of climate changeClimate change has increased Ttree and Tstand, from about 0% in 2020 to more than20% in 2090 and has decreased WUEtree and WUEstand, from about 0% in 2020 tomore than 15% in 2090 (Figure 4.3). This may be due to the higher temperature andhigher SR in 2090 compared to 2020. As the temperature increases, transpirationincreases due to a higher concentration in sunlight and warm air. Since the effectof the climate change is most significant in 2090, we used the prediction in 2090to discuss the effect of the climate change below.(a) Ttree. (b) Tstand.(c) WUEtree. (d) WUEstand.Figure 4.3: The effect of climate change on (a) Ttree, (b) Tstand, (c) WUEtreeand (d) WUEstand compared with RCP 2.6.4.4.1 The effect of climate change on transpirationThe effect of climate change on Ttree and Tstand is quite similar. The result is sum-marized in Figure 4.4. Under climate change, both Ttree and Tstand increase. Thiseffect is most significant under RCP 8.5, approximately 20% higher than that under36RCP 2.6.(a) Tree level. (b) Stand level.Figure 4.4: The effect of climate change on T in 2090 compared with RCP2.6.Climate change has the following effects on transpiration rates.1. High temperature increases biophysical forces driving transpiration (Bauerleand Bowden, 2011).2. VPD in the air increases as temperature rises, resulting in an increase in therate of transpiration from plant canopies (Kirschbaum, 2004).3. Partial stomatal closure under elevated level of CO2 restricts the evaporationof water, thereby limiting the transpiration rate (Kirschbaum and McMillan,2018).We did not take the level of CO2 into account. As a result, we could only assessthe effect of climate change on T via temperature. The level of CO2 should beconsidered in future works.4.4.2 The effect of climate change on WUEThe effect of climate change on WUEtree and WUEstand is also similar. The resultis summarized in Figure 4.5. Under climate change, both WUEtree and WUEstanddecrease. This effect is most significant under RCP 8.5, approximately 17% lowerthan that under RCP 2.6.37(a) Tree level. (b) Stand level.Figure 4.5: The effect of climate change on WUE in 2090 compared withRCP 2.6.WUE is the ratio of carbon assimilation and transpiration. The effect of climatechange on transpiration was discussed in subsection 4.4.1. A higher CO2 concen-tration can boost photosynthetic rates, which facilitates carbon assimilation. Thisis especially pronounced at high temperature and under water-limited condition(Kirschbaum, 2004). Photosynthesis is affected by rising temperature, but plantshave a remarkable ability to adapt to growing conditions and can function even athigh temperature if water supply suffices. High temperature speeds up the decom-position of organic matter in the soil. In nutrient-limited systems, this increasescarbon assimilation (Kirschbaum, 2000). However, CO2 was not considered inthis study.4.5 Thinning mitigates the effect of climate changeIn long term, thinning cannot prevent the effect of the climate change on Tstand(Elkin et al., 2015). However, thinning can mitigate this effect. As shown in Fig-ure 4.6, Tstand is almost in a perfect linear relationship with the intensity of theclimate change. The slope in each line is different. The lowest slope (T2) indi-cates that heavy thinning mitigates the influence of climate change on Tstand moreeffectively than moderate thinning.38Figure 4.6: Linear regression of Tstand against climate change intensity underdifferent thinning regimes.4.6 LimitationThere are some limitations in this study.1. The distribution of data over tree ages is uneven. The data set contains fouryears of data for juvenile age (14−18) and one year of data for mature age(125). If middle age (80) data were to be included, the result would be morerobust.2. Fan et al. (2021) and Tang et al. (2018) showed that more input parametersimproves prediction, our study used only three parameters (DBH, T and SR),the best among all combinations of features (Table 2.2).(a) Soil water content and leaf area index (LAI) are also important forTtree. These parameters were not considered as inputs due to the lackof data. Nevertheless, the strong relationship between DBH and LAI39might suggest that LAI be indirectly reflected in our simulation.(b) CO2 directly influences WUE and is critical for forecasting future cli-mate scenarios. This was however not captured in MarkSim and wedid not measure CO2 in the experiment.(c) Only tree metrics were taken into account (Ttree, WUEstand and totalcarbon) when searching the suitable range of densities. Therefore, oursuggested range of densities (2500–3500 stem/ha remaining) was a re-sult of the trade-off between Tstand and total carbon. In the future, woodquality, biodiversity, wood production and other parameters should alsobe included into analysis and decision.3. The growth model TASS III was used to generate the future growth data.Even though TASS III is a widely used tree growth model in BC, Canada,it does not take climate change into account. Therefore, there is a level ofuncertainty in our future DBH data, requiring attention in future studies40Chapter 5Conclusions and managementimplicationsIn this study, we found that DNN outperformed ANN in predicting tree level tran-spiration of lodgepole pine. And DNN with features DBH, T and SR performedrelatively better than other combinations.Based on DNN simulation results, we found that thinning has a long-lastingeffect on increasing Ttree, WUEtree and WUEstand and on decreasing stand leveltranspiration. This effect decreases as thinning intensity decreases and as treesage. The greater the impact of climate change, the greater the potential for Ttreeand Tstand. Climate change also results in a drop on WUEtree and WUEstand. Thin-ning, particularly heavy thinning, can help mitigate the effects of climate change onstand level transpiration in the long run. However, since our measurement of tran-spiration was based on the Granier equation (2.1), many factors affecting transpira-tion were only taken into account by the empirical coefficients in (2.1). Therefore,the result might not be very accurate. Interestingly, NN automatically found thatDBH and temperature were two most significant factors in our model. Had therebeen some more direct and accurate ways to measure transpiration, this data-drivenapproach would be expected to perform better.Therefore, we concluded that thinning might have an effect on forest carbonand water processes at both tree and stand levels. We suggest the thinning densitiesof 2500–3500 stem/ha based on the balance of total forest carbon and water sup-41ply (transpiration). However, it is important to recognize that other managementobjectives such as wood quality, biodiversity not studied in this research should bealso considered for the final selection of thinning densities in the studied forests.42BibliographyAchieng, K. (2019). “Modelling of soil moisture retention curve using machinelearning techniques: Artificial and deep neural networks vs support vectorregression models”. In: Computers & Geosciences 133, p. 104320. DOI:10.1016/j.cageo.2019.104320 (pages 11, 33).Andre´s, E. de, J. Camarero, and J. Blanco (2017). “Tree-to-tree competition inmixed European Beech-Scots pine forests has different impacts on growth andwater-use efficiency depending on site conditions”. In: Journal of Ecology 106(1), pp. 59–75. DOI: 10.1111/1365-2745.12813 (page 34).Aussenac, G. (2000). “Interactions between forest stands and microclimate:Ecophysiological aspects and consequences for silviculture”. In: Annals ofForest Science 57 (3), pp. 287–301. DOI: 10.1051/forest:2000119(page 1).B.C. Ministry of Forests and Range (2010). Tree & Stand Simulator. Version III.URL: https://www2.gov.bc.ca/gov/content/industry/forestry/managing-our-forest-resources/forest-inventory/growth-and-yield-modelling/tree-and-stand-simulator-tass (page 18).Bauerle, W. and J. Bowden (2011). “Predicting transpiration response to climatechange: Insights on physiological and morphological interactions thatmodulate water exchange from leaves to canopies”. In: HortScience 46 (2),pp. 163–166. DOI: 10.21273/hortsci.46.2.163 (page 37).Bearup, L., R. Maxwell, D. Clow, and J. McCray (2014). “Hydrological effects offorest transpiration loss in bark beetle-impacted watersheds”. In: NatureClimate Change 4 (6), pp. 481–486. DOI: 10.1038/nclimate2198(page 1).Berryman, A. (1982). “Mountain pine beetle outbreaks in Rocky Mountainlodgepole pine forests”. In: Journal of Forestry 80 (7), pp. 410–419. DOI:10.1093/jof/80.7.410 (page 4).Bre´da, N. and A. Granier (1996). “Intra- and interannual variations oftranspiration, Leaf Area index and radial growth of a sessile oak stand43(Quercus petraea)”. In: Annals of Forest Science 53 (2-3), pp. 521–536. DOI:10.1051/forest:19960232 (page 9).Bredemeier, M., A. Merino, and Gallardo L. (2012). “Water and carbon in forests:Challenges for forest management under the pressures of climate change”. In:European Journal of Forest Research 131 (6), pp. 1639–1642. DOI:10.1007/s10342-012-0655-0 (page 1).Byers, A., A. Harpold, and H. Barnard (2011). “Ecohydrology of Lodgepole PineForests: Connecting Transpiration to Subsurface Flow Paths and Storagewithin a Subalpine Catchment”. In: American Geophysical Union FallMeeting (page 3).Cabon, A., F. Mouillot, M. Lempereur, J.-M. Ourcival, G. Simioni, andJ. Limousin (2018). “Thinning increases tree growth by delayingdrought-induced growth cessation in a Mediterranean evergreen oak coppice”.In: Forest Ecology and Management 409, pp. 333–342. DOI:10.1016/j.foreco.2017.11.030 (pages 1, 34).Caemmerer, S. von and N. Baker (2007). “The biology of transpiration. Fromguard cells to globe”. In: Plant Physiology 143 (1), p. 3. DOI:10.1104/pp.104.900213 (page 2).Campo, A. del, T. Fernandes, and A. Molina (2014). “Hydrology-oriented(adaptive) silviculture in a semiarid Pine Plantation: How much can bemodified the water cycle through forest management?” In: European Journalof Forest Research 133 (5), pp. 879–894. DOI:10.1007/s10342-014-0805-7 (pages 2, 3).Chase, C., M. Kimsey, T. Shaw, and M. Coleman (2018). “The response of light,water, and nutrient availability to pre-commercial thinning in dry inlandDouglas-fir forests”. In: Forest Ecology and Management 363, pp. 98–109.DOI: 10.1016/j.foreco.2015.12.014 (page 34).Chen, H., J. Huang, and E. McBean (2020). “Partitioning of dailyevapotranspiration using a modified Shuttleworth-Wallace model, randomForest and support vector regression, for a cabbage farmland”. In:Agricultural Water Management 228 (20), p. 105923. DOI:10.1016/j.agwat.2019.105923 (page 3).Coupe´, R., A. Stewart, and B. Wikeem (1991). Engelmann spruce–subalpine firzone. Ecosystems of British Columbia. Tech. rep. BC Ministry of Forests,pp. 223–236. URL:www.for.gov.bc.ca/hfd/pubs/docs/srs/Srs06 (page 6).D’Amato, A., J. Bradford, S. Fraver, and B. Palik (2013). “Effects of thinning ondrought vulnerability and climate response in north temperate forestecosystems”. In: Ecological Applications 23, pp. 1735–1742. DOI:doi.org/10.1890/13-0677.1 (page 2).44Diaconu, D., H. Kahle, and H. Spiecker (2015). “Tree- and stand-level thinningeffects on growth of European Beech (Fagus sylvatica L.) on a northeast- anda southwest-facing slope in southwest Germany”. In: Forests 6 (9),pp. 3256–3277. DOI: 10.3390/f6093256 (page 34).Elkin, C., A. Giuggiola, A. Rigling, and H. Bugmann (2015). “Short- andlong-term efficacy of forest thinning to mitigate drought impacts in mountainforests in the European Alps”. In: Ecological Applications 25 (4),pp. 1083–1098. DOI: 10.1890/14-0690.1 (pages 2, 38).Emmingham, W. and N. Elwood (1983). Thinning : An important timbermanagement tool. Tech. rep. 184. Oregon State University, p. 8. URL:http://catalog.extension.oregonstate.edu/pnw184(page 1).Fan, J., J. Zheng, L. Wu, and F. Zhang (2021). “Estimation of daily maizetranspiration using support vector machines, extreme gradient boosting,artificial and deep neural networks models”. In: Agricultural WaterManagement 245, p. 106547. DOI: 10.1016/j.agwat.2020.106547(page 39).Fernandes, T., A. del Campo, R. Herrera, and A. Molina (2016). “Simultaneousassessment, through sap flow and stable isotopes, of water use efficiency(WUE) in thinned pines shows improvement in growth, tree-climatesensitivity and WUE, but not in WUEi”. In: Forest Ecology and Management361, pp. 298–308. DOI: 10.1016/j.foreco.2015.11.029 (page 2).Forrester, D., J. Collopy, C. Beadle, C. Warren, and T. Baker (2012). “Effect ofthinning, pruning and nitrogen fertiliser application on transpiration,photosynthesis and water-use efficiency in a young eucalyptus nitensplantation”. In: Forest Ecology and Management 266, pp. 286–300. DOI:10.1016/j.foreco.2011.11.019 (pages 3, 34, 35).Goudie, J. (1998). “Model validation: A search for the magic grove or the magicmodel”. In: Stand density management : planning and implementatio. Ed. byC. Bamsey. Alberta, Canada: Clear Lake Ltd (page 18).Granier, A., V. Bobay, J. Gash, J. Gelpe, B. Saugier, and W. Shuttleworth (1990).“Vapour flux density and transpiration rate comparisons in a stand of maritimepine (pinus pinaster AIT.) in Les Landes forest”. In: Agricultural and ForestMeteorology 51 (3-4), pp. 309–319. DOI:10.1016/0168-1923(90)90115-m (pages 3, 9).Guyot, A., J. Fan, K. Oestergaard, R. Whitley, B. Gibbes, M. Arsac, andD. Lockington (2017). “Soil-water content characterisation in a modifiedJarvis-Stewart model: A case study of a conifer forest on a shallow unconfinedaquifer”. In: Journal of Hydrology 544, pp. 242–253. DOI:10.1016/j.jhydrol.2016.11.041 (page 3).45Hinton, G., S. Osindero, and Y. Teh (2006). “A fast learning algorithm for deepbelief nets”. In: Neural Computation 18 (7), pp. 1527–1554. DOI:10.1162/neco.2006.18.7.1527 (page 11).Johnstone, W. and F. van Thienen (2004). A summary of early results from recentlodgepole pine thinning experiments in the British Columbia interior.Tech. rep. 016. British Columbia Ministry of Forests, Research Branch. URL:www.for.gov.bc.ca/hre/pubs/pubs/1286.htm (page 4).Jones, P. and P. Thornton (2000). “MarkSim:Software to Generate Daily WeatherData for Latin America and Africa”. In: Agronomy Journal 92 (3). DOI:doi.org/10.2134/agronj2000.923445x (page 18).Kirschbaum, M. (2000). “Will changes in soil organic carbon act as a positive ornegative feedback on global warming”. In: Plant biology 48, pp. 21–51. DOI:10.1023/A:1006238902976 (page 38).— (2004). “Direct and indirect climate change effects on ohotosynthesis andtranspiration”. In: Plant biology 46 (3), pp. 242–253. DOI:10.1055/s-2004-820883 (pages 37, 38).Kirschbaum, M. and A. McMillan (2018). “Warming and elevated CO2 haveopposing influences on transpiration. Which is more important?” In: CurrentForestry Reports 4, pp. 51–71. DOI: 10.1007/s40725-018-0073-8(page 37).Koster, R. and M. Suarez (1994). “The components of a ‘SVAT’ scheme and theireffects on a GCM’s hydrological cycle”. In: Advances in Water Resources 17(1-2), pp. 61–78. DOI: 10.1016/0309-1708(94)90024-8 (page 3).Lasslop, G., A. Coppola, A. Voulgarakis, C. Yue, and S. Veraverbeke (2019).“Influence of fire on the carbon cycle and climate”. In: Current ClimateChange Reports 5 (2), pp. 112–123. DOI:10.1007/s40641-019-00128-9 (page 1).Lesch, D. and D. Scott (1997). “The response in water yield to the thinning ofPinus radiata, Pinus patula and Eucalyptus grandis plantations”. In: ForestEcology and Management 99 (3), pp. 295–307. DOI:10.1016/S0378-1127(97)00045-5 (page 35).Martinson, E. and P. Omi (2013). Fuel treatments and fire severity: Ameta-analysis. Tech. rep. U.S. Department of Agriculture, Forest Service,p. 38. DOI: 10.2737/RMRS-RP-103 (page 2).McCulloch, W. and W. Pitts (1943). “A logical calculus of the ideas immanent innervous activity”. In: The bulletin of mathematical biophysics 5, pp. 115–133.DOI: 10.1007/BF02478259 (page 11).McDowell, N. (2011). “Mechanisms Linking Drought, Hydraulics, CarbonMetabolism, and Vegetation Mortality”. In: Plant Physiology 155 (3),pp. 1051–1059. DOI: 10.1104/pp.110.170704 (page 4).46McDowell, N., H. Adams, J. Bailey, and T. Kolb (2006). “HomeostaticMaintenance Of Ponderosa Pine Gas Exchange In Response To Stand DensityChanges”. In: Ecological Applications 16 (3), pp. 1164–1182. DOI:10.1890/1051-0761(2006)016[1164:HMOPPG]2.0.CO;2(page 2).Medhurst, J., M. Battaglia, and C. Beadle (2002). “Measured and predictedchanges in tree and stand water use following high-intensity thinning of an8-year-old Eucalyptus nitens plantation”. In: Tree Physiology 22 (11),pp. 775–784. DOI: 10.1093/treephys/22.11.775 (page 2).Monserud, R., S. Huang, and Y. Yang (2006). “Biomass and biomass change inlodgepole pine stands in Alberta”. In: Tree Physiology 26, pp. 819–831. DOI:10.1093/treephys/26.6.819 (page 10).Park, J., T. Kim, M. Moon, S. Cho, D. Ryu, and H. Kim (2018). “Effects ofthinning intensities on tree water use, growth, and resultant water useefficiency of 50-year-old pinuskoraiensis forest over four years”. In: ForestEcology and Management 408, pp. 121–128. DOI:10.1016/j.foreco.2017.09.031 (page 34).Rasco´n-Ramos, A., M. Martı´nez-Salvador, Sosa-Pe´rez G., Villarreal-Guerrero F.,A. Pinedo-Alvarez, E. Santellano-Estrada, and R. Corrales-Lerma (2021).“Soil Moisture Dynamics in Response to Precipitation and Thinning in aSemi-Dry Forest in Northern Mexico”. In: Water 13 (1), p. 105. DOI:10.3390/w13010105 (page 2).Reid, D., U. Silins, and V. Lieffers (2006). “Sapwood hydraulic recoveryfollowing thinning in lodgepole pine”. In: Annals of Forest Science 63.4,pp. 329–338. DOI: 10.1051/forest:2006013 (page 3).Safford, H., J. Stevens, K. Merriam, M. Meyer, and A. Latimer (2012). “Fueltreatment effectiveness in California yellow pine and mixed conifer forests”.In: Forest Ecology and Management 274, pp. 17–28. DOI:10.1016/j.foreco.2012.02.013 (page 2).Seidl, R., D. Thom, M. Kautz, D. Martin-Benito, M. Peltoniemi, G. Vacchiano,J. Wild, D. Ascoli, M. Petr, J. Honkaniemi, M. Lexer, V. Trotsiuk, P. Mairota,M. Svoboda, M. Fabrika, T. Nagel, and C. Reyer (2017). “Forest disturbancesunder climate change”. In: Nature Climte Change 7, pp. 395–402. DOI:10.1038/nclimate3303 (page 4).Simonin, K., T. Kolb, M. Montes-Helu, and G. Koch (2007). “The influence ofthinning on components of stand water balance in a ponderosa pine foreststand during and after extreme drought”. In: Agricultural and ForestMeteorology 143 (3-4), pp. 266–276. DOI:10.1016/j.agrformet.2007.01.003 (page 34).47Sinoquet, H., X. Le Roux, B. Adam, T. Ameglio, and F. Daudet (2001). “A modelfor simulating the spatial distribution of radiation absorption, transpirationand photosynthesis within canopies: Application to an isolated tree crown”.In: Plant, Cell & Environment 24 (4), pp. 395–406. DOI:10.1046/j.1365-3040.2001.00694.x (page 3).Sohn, J., S. Saha, and J. Bauhus (2016). “Potential of forest thinning to mitigatedrought stress: A meta-analysis”. In: Forest Ecology and Management 380,pp. 261–273. DOI: 10.1016/j.foreco.2016.07.046 (page 2).Spittlehouse, D. (2002). “Sap flow and transpiration of old lodgepole pine trees”.In: Proceedings 25th Conference on Agricultureal and Forest Meteorology,pp. 123–124. URL: www.for.gov.bc.ca/hfd/pubs/docs/Misc/Spittlehouse2002.pdf (page 7).Sullivan, T., D. Sullivan, P. Lindgren, D. Ransome, and L. Zabek (2020).“Twenty-five years after stand thinning and repeated fertilization in lodgepolepine forest: Implications for tree growth, stand structure, and carbonsequestration”. In: Forests 11 (3), p. 337. DOI: 10.3390/f11030337(page 34).Tang, D., Y. Feng, D. Gong, W. Hao, and N. Cui (2018). “Evaluation of artificialintelligence models for actual crop evapotranspiration modeling in mulchedand non-mulched maize croplands”. In: Computers and Electronics inAgriculture 152, pp. 375–384. DOI: 10.1016/j.compag.2018.07.029(page 39).Tu, J., X. Wei, B. Huang, H. Fan, M. Jian, and W. Li (2019). “Improvement of sapflow estimation by including phenological index and time-lag effect inback-propagation neural network models”. In: Agricultural and ForestMeteorology 276-277, p. 107608. DOI:10.1016/j.agrformet.2019.06.007 (pages 3, 11).Vernon, M., R. Sherriff, P. van Mantgem, and J. Kane (2018). “Thinning,tree-growth, and resistance to multi-year drought in a mixed-conifer forest ofnorthern California”. In: Forest Ecology and Management 422, pp. 190–198.DOI: 10.1016/j.foreco.2018.03.043 (page 2).Wang, Y., A. del Campo, X. Wei, R. Winkler, W. Liu, and Q. Li (2020).“Responses of forest carbon and water coupling to thinning treatments fromleaf to stand scales in a young montane pine forest”. In: Carbon Balance andManagement 15.24. DOI: 10.1186/s13021-020-00159-y (pages x, 2,3, 7, 10).Wang, Y., X. Wei, A. del Campo, R. Winkler, J. Wu, Q. Li, and W. Liu (2019).“Juvenile thinning can effectively mitigate the effects of drought on treegrowth and water consumption in a young Pinus contorta stand in the interior48of British Columbia, Canada”. In: Forest Ecology and Management 454,p. 117667. DOI: 10.1016/j.foreco.2019.117667 (pages 2, 3, 9, 32).Warren, C., J. McGrath, and M. Adams (2001). “Water availability and carbonisotope discrimination in conifers”. In: Oecologia 127, pp. 476–486. DOI:10.1007/s004420000609 (page 35).West, P. (1980). “Use of diameter increment and basal area increment in treegrowth studies”. In: Canadian Journal of Forest Research 10 (1), pp. 71–77.DOI: 10.1139/x80-012 (page 8).Whitehead, D., P. Jarvis, and R. Waring (1984). “Stomatal conductance,transportation, and resistance to water uptake in a Pinus sylvestris spacingexperiment”. In: Canadian Journal of Forester Research 14 (5), pp. 692–700.DOI: 10.1139/x84-124 (page 35).Whitley, R., M. Zeppel, N. Armstrong, C. Macinnis-Ng, I. Yunusa, and D. Eamus(2007). “A modified Jarvis-Stewart model for predicting stand-scaletranspiration of an Australian native forest.” In: Plant and Soil 305 (1-2),pp. 35–47. DOI: 10.1007/s11104-007-9399-x (pages 3, 11).Winkler, R., D. Allen, T. Giles, B. Heise, R. Moore, T. Redding, D. Spittlehouse,and X. Wei (2021). “Approaching four decades of forest watershed research atUpper Penticton Creek, British Columbia: A synthesis”. In: HydrologicalProcesses 35 (5). DOI: 10.1002/hyp.14123 (page 6).Winkler, R. and R. Moore (2006). “Variability in snow accumulation patternswithin forest stands on the Interior Plateau of British Columbia, Canada”. In:Hydrological Processes 20 (17), pp. 3683–3695. DOI: 10.1002/hyp.6382(page 6).Zhang, X., D. Guan, W. Li, D. Sun, C. Jin, F. Yuan, A. Wang, and J. Wu (2018).“The effects of forest thinning on soil carbon stocks and dynamics: Ameta-analysis”. In: Forest Ecology and Management 429, pp. 36–43. DOI:10.1016/j.foreco.2018.06.027 (page 2).49AppendicesAppendix ASR/MJm−2 d−1 T/◦C Tmin/◦C Tmax/◦C RH/% P/mm VPD/Pa DBH/cm T /mmd−1min 0.37 −21.89 −8.07 −2.71 25.25 0.00 0.00 4.24 0.00max 36.77 22.84 15.15 33.50 100 23.60 1991.83 48.70 65.44mean 12.22 9.07 3.70 17.88 66.23 1.13 502.57 10.43 4.66sd 7.59 7.01 4.43 7.31 18.03 2.92 424.15 9.22 7.03Table 1: Training data summarySR/MJm−2 d−1 T/◦C Tmin/◦C Tmax/◦C RH/% P/mm VPD/Pa DBH/cm T /mmd−1min 0.37 −18.02 −8.07 −2.71 25.25 0.00 0.13 4.24 0.00max 30.73 22.84 15.15 33.50 99.98 23.60 1991.83 48.70 60.27mean 12.36 9.35 3.73 17.88 66.13 1.22 508.25 10.26 4.57sd 7.51 6.68 4.47 7.32 18.14 3.08 426.24 9.05 6.84Table 2: Testing data summary50Appendix BClimate Thinning 2030 2040 2050 2060 2070 2080 2090C 259.91 46.49 9.82 4.11 2.24 1.07 0.75RCP 2.6 T1 194.52 15.18 4.04 2.09 1.31 0.60 0.67T2 200.95 12.56 2.96 1.33 1.06 0.98 1.18C 262.58 45.49 11.95 5.25 3.24 2.04 1.46RCP 4.5 T1 196.50 14.75 5.99 3.18 2.47 1.83 1.73T2 203.10 12.21 4.85 2.58 2.45 2.57 2.39C 255.82 46.76 11.24 6.45 4.02 2.75 2.51RCP 6.0 T1 191.53 15.33 5.55 4.53 3.26 2.60 2.93T2 197.74 12.70 4.53 3.95 3.30 3.49 3.79C 261.20 47.27 13.25 7.07 5.31 4.33 4.85RCP 8.5 T1 196.53 15.96 6.97 5.08 4.65 4.45 5.64T2 203.27 13.41 5.78 4.64 4.88 5.77 6.80Table 3: Daily Ttree increasing rate (%) in C, T1 and T2 from 2030 to 2090under four climate scenarios.51Appendix CClimate Thinning 2030 2040 2050 2060 2070 2080 2090C 80.72 −13.16 −25.75 −21.65 −21.35 −13.63 −8.90RCP 2.6 T1 176.32 −2.48 −10.28 −20.22 −12.85 −9.46 −10.51T2 193.96 12.56 2.96 −1.08 −1.41 −6.60 1.18C 82.06 −13.75 −24.30 −20.78 −20.59 −12.80 −8.25RCP 4.5 T1 176.17 −2.85 −8.60 −19.37 −11.85 −8.35 −9.58T2 196.06 12.21 4.85 0.14 −0.05 −5.12 2.39C 78.66 −12.99 −24.78 −19.88 −19.99 −12.20 −7.31RCP 6.0 T1 171.54 −2.36 −8.98 −18.31 −11.18 −7.66 −8.51T2 190.82 12.70 4.53 1.48 0.78 −4.27 3.79C 81.36 −12.69 −23.42 −19.41 −19.00 −10.85 −5.19RCP 8.5 T1 176.19 −1.83 −7.75 −17.88 −9.98 −6.00 −6.09T2 196.21 13.41 5.78 2.15 2.32 −2.16 6.80Table 4: Daily Tstand increasing rate (%) in C, T1 and T2 from 2030 to 2090under four climate scenarios.52Appendix DClimate Thinning 2030 2040 2050 2060 2070 2080 2090C 56.87 23.11 5.96 −4.33 14.20 14.48 17.31RCP 2.6 T1 58.32 12.14 −1.30 3.25 12.54 19.14 11.80T2 48.89 9.52 12.43 10.40 11.54 15.17 25.63C 57.19 22.58 7.75 −3.19 15.03 15.30 17.89RCP 4.5 T1 58.60 11.81 0.56 4.27 13.53 20.11 12.72T2 49.25 9.24 14.01 11.49 12.74 16.49 26.51C 56.38 23.26 7.16 −2.03 15.66 15.88 18.72RCP 6.0 T1 57.89 12.25 0.15 5.51 14.18 20.71 13.74T2 48.34 9.63 13.75 12.66 13.46 17.23 27.50C 57.03 23.52 8.81 −1.44 16.69 17.15 20.54RCP 8.5 T1 58.60 12.72 1.48 6.01 15.33 22.11 15.95T2 49.28 10.19 14.76 13.23 14.76 19.02 29.54Table 5: Daily WUEtree increasing rate (%) in C, T1 and T2 from 2030 to2090 under four climate scenarios.53Appendix EClimate Thinning 2030 2040 2050 2060 2070 2080 2090C 67.98 21.23 1.74 −13.55 −6.23 4.00 −1.25RCP 2.6 T1 43.12 10.22 20.35 3.11 8.29 9.59 2.54T2 23.08 −10.73 −1.18 12.46 12.26 3.38 19.10C 68.21 20.69 3.61 −12.31 −5.20 4.92 −0.54RCP 4.5 T1 43.50 9.89 21.81 4.14 9.32 10.68 3.55T2 23.62 −11.08 0.65 13.52 13.45 4.88 20.06C 67.61 21.38 3.00 −11.05 −4.41 5.57 0.49RCP 6.0 T1 42.53 10.34 21.49 5.38 10.01 11.35 4.67T2 22.25 −10.60 0.34 14.66 14.16 5.73 21.14C 68.09 21.65 4.72 −10.40 −3.13 7.00 2.72RCP 8.5 T1 43.50 10.82 22.53 5.87 11.21 12.92 7.12T2 23.66 −9.91 1.52 15.22 15.45 7.76 23.36Table 6: Daily WUEstand increasing rate (%) in C, T1 and T2 from 2030 to2090 under four climate scenarios.54
Thesis/Dissertation
eng
Earth and Environmental Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Wei, Xiaohua
Using machine learning to model long term effects of thinning and climate change on transpiration and water use efficiency
Text