Arts, Faculty of
Geography, Department of
DSpace
UBCV
Tamminga, Aaron
Doctor of Philosophy - PhD
University of British Columbia
Recent advances in methodological and theoretical approaches in fluvial sciences have given rise to increased interest in riverscape perspectives that embrace environmental variability and spatial relationships. These approaches facilitate interdisciplinary understanding of complex fluvial processes that supports conscientious management of river systems. In this context, this dissertation presents the development and application of novel methods to study fluvial structure and eco-hydrogeomorphic relationships. Specifically, the research addresses three main questions: (1) how can UAV-based remote sensing advance the study of fluvial forms and processes? (2) what are the reach-scale geomorphic effects of an extreme flood and to what extent are these changes predictable? and (3) what are the linkages between flood-induced geomorphic change, reach hydraulics, and aquatic habitat? Results based on the case of an extreme flood event on Elbow River, Alberta demonstrate the utility of UAVs to efficiently and accurately measure many aspects of fluvial ecosystem form and function. The combination of high resolution imagery and photogrammetrically derived elevation models provides a powerful way to characterize rivers for a wide range of applications, particularly when combined with numerical flow modeling for a seamless representation of fluvial hydrogeomorphology. Pre- and post-flood UAV surveys documented flood effects with unprecedented detail, showing a largely unpredictable fluvial response characterized by complete channel planform reorganization and widespread bank erosion. These geomorphic changes negatively impacted the study reach in terms of hydraulic diversity and habitat suitability for brown trout and constrain future fluvial adjustment during smaller floods. Overall, the dissertation presents a new way to measure rivers and extract meaningful information and provides an integrative assessment of relationships between geomorphology, hydraulics, and aquatic ecology in a complex, dynamic river system.
UAV-based remote sensing of fluvial hydrogeomorphologyand aquatic habitat dynamicsbyAaron TammingaBSc. (Honors) Queen’s UniversityA THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Geography)The University of British Columbia(Vancouver)September 2016c© Aaron Tamminga, 2016AbstractRecent advances in methodological and theoretical approaches in fluvial scienceshave given rise to increased interest in riverscape perspectives that embrace en-vironmental variability and spatial relationships. These approaches facilitate in-terdisciplinary understanding of complex fluvial processes that supports conscien-tious management of river systems. In this context, this dissertation presents thedevelopment and application of novel methods to study fluvial structure and eco-hydrogeomorphic relationships. Specifically, the research addresses three mainquestions: (1) how can UAV-based remote sensing advance the study of fluvialforms and processes? (2) what are the reach-scale geomorphic effects of an ex-treme flood and to what extent are these changes predictable? and (3) what are thelinkages between flood-induced geomorphic change, reach hydraulics, and aquatichabitat?Results based on the case of an extreme flood event on Elbow River, Albertademonstrate the utility of UAVs to efficiently and accurately measure many as-pects of fluvial ecosystem form and function. The combination of high resolutionimagery and photogrammetrically derived elevation models provides a powerfulway to characterize rivers for a wide range of applications, particularly when com-bined with numerical flow modeling for a seamless representation of fluvial hydro-geomorphology. Pre- and post-flood UAV surveys documented flood effects withunprecedented detail, showing a largely unpredictable fluvial response character-ized by complete channel planform reorganization and widespread bank erosion.These geomorphic changes negatively impacted the study reach in terms of hy-draulic diversity and habitat suitability for brown trout and constrain future fluvialadjustment during smaller floods. Overall, the dissertation presents a new way toiimeasure rivers and extract meaningful information and provides an integrative as-sessment of relationships between geomorphology, hydraulics, and aquatic ecologyin a complex, dynamic river system.iiiPrefaceThis dissertation presents research conduced by Aaron Tamminga under the super-vision of Dr. Brett Eaton. Aaron Tamminga was the primary researcher and wasresponsible for the main study design, data collection, analysis, interpretation, andwriting of the content. Conceptual and analytical support were provided by Dr.Brett Eaton, Dr. Marwan Hassan, Dr. Chris Hugenholtz and Dr. Sarah Gergel.Ventus Geospatial provided technical support for the field surveys and photogram-metry.A version of Chapter 2 has been published: Tamminga, A., Hugenholtz, C.,Eaton, B., and Lapointe, M. (2015a). Hyperspatial remote sensing of channel reachmorphology and hydraulic fish habitat using an unmanned aerial vehicle (UAV): Afirst assessment in the context of river research and management. River Researchand Applications 31(3) 379-391. DOI:10.1002/rra.2743. Aaron Tamminga wrotethe manuscript, which was then reviewed by the co-authors.Material from Chapter 2 was also published in: Whitehead, K., Hugenholtz,C., Myshak, S., Brown, O., LeClair, A., Tamminga, A., Barchyn, T., Moorman,B, and Eaton, B. (2014) Remote sensing of the environment with small unmannedaircraft systems (UASs), Part 2: Scientific and commercial applications. Journalof Unmanned Vehicle Systems 2(3) 86-102. DOI:10.1139/juvs-2014-0007. AaronTamminga wrote the relevant section of this review paper.A version of Chapter 3 has been published: Tamminga, A., Eaton, B., andHugenholtz, C. (2015b). UAS-based remote sensing of fluvial change followingan extreme flood event. Earth Surface Processes and Landforms 40(11) 1464-1476. DOI:10.1002/esp.3728. Aaron Tamminga wrote the manuscript, which wasthen reviewed by the co-authors.ivTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Thesis organization . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3.1 Geomorphic principles . . . . . . . . . . . . . . . . . . . 31.3.2 Physical aquatic habitat . . . . . . . . . . . . . . . . . . . 61.3.3 Characterizing river structure . . . . . . . . . . . . . . . . 81.4 Thesis objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Hyperspatial remote sensing of channel reach morphology and hy-draulic fish habitat using an unmanned aerial vehicle (UAV) . . . . 122.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.2.1 Study site . . . . . . . . . . . . . . . . . . . . . . . . . . 16v2.2.2 UAV survey and photogrammetry . . . . . . . . . . . . . 162.2.3 Submerged DEM correction . . . . . . . . . . . . . . . . 182.2.4 Fluvial morphology metrics and habitat mapping . . . . . 192.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.3.1 Accuracy assessment . . . . . . . . . . . . . . . . . . . . 222.3.2 Feature detection and hydraulic habitat . . . . . . . . . . 252.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 UAV-based remote sensing of fluvial change following an extremeflood event . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383.2.1 Study site . . . . . . . . . . . . . . . . . . . . . . . . . . 383.2.2 2013 flood event . . . . . . . . . . . . . . . . . . . . . . 393.2.3 UAV surveys . . . . . . . . . . . . . . . . . . . . . . . . 403.2.4 DEM correction and accuracy assessment . . . . . . . . . 413.2.5 DEM differencing and change detection . . . . . . . . . . 433.2.6 Flow modeling . . . . . . . . . . . . . . . . . . . . . . . 453.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624 Impacts of geomorphic change on reach scale flow structure andaquatic habitat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684.2.1 Study site . . . . . . . . . . . . . . . . . . . . . . . . . . 684.2.2 UAV surveys and DEM analysis . . . . . . . . . . . . . . 704.2.3 Hydrodynamic modeling . . . . . . . . . . . . . . . . . . 714.2.4 Cluster analysis . . . . . . . . . . . . . . . . . . . . . . . 734.2.5 Brown trout habitat . . . . . . . . . . . . . . . . . . . . . 744.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75vi4.3.1 Morphodynamics . . . . . . . . . . . . . . . . . . . . . . 754.3.2 Flow pattern changes . . . . . . . . . . . . . . . . . . . . 784.3.3 Cluster results . . . . . . . . . . . . . . . . . . . . . . . 834.3.4 Brown trout habitat suitability . . . . . . . . . . . . . . . 924.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035.2 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109viiList of TablesTable 2.1 Error statistics equations. DEM elevations are termed zmod ,measured check point elevations are termed zobs . . . . . . . . 23Table 2.2 Error statistics comparing GPS checkpoint elevations withDEM elevations . . . . . . . . . . . . . . . . . . . . . . . . . 25Table 3.1 Error statistics comparing modeled (DEM) elevations with mea-sured GPS check point elevations . . . . . . . . . . . . . . . . 42Table 3.2 Topographic change determined through DEM differencing . . 51Table 4.1 Calibration summary of modeled versus measured depth andwater surface elevations for Nays2DH hydrodynamic simulations 72Table 4.2 Channel pattern parameters . . . . . . . . . . . . . . . . . . . 77Table 4.3 Morphodynamic adjustments . . . . . . . . . . . . . . . . . . 80Table 4.4 Summary of reach average hydraulic parameters for each simu-lation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81Table 4.5 Summary of clusters and representative average hydraulic char-acteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86Table 4.6 Metrics of reachscape composition . . . . . . . . . . . . . . . 88viiiList of FiguresFigure 2.1 Location of the study reach within the Elbow River watershedand example ground-level image . . . . . . . . . . . . . . . . 15Figure 2.2 Aeryon Scout quadcopter . . . . . . . . . . . . . . . . . . . . 17Figure 2.3 Relationship between D50 determined through photosievingand orthomosaic image texture within a 1-m2 window . . . . 20Figure 2.4 Brown trout habitat suitability curves for depth, velocity, andweighted average percent cover . . . . . . . . . . . . . . . . 21Figure 2.5 (a) Photogrammetry generated orthomosaic and (b) DEM,showing the extent of the subregion modelled with River2D . 24Figure 2.6 Density plots of vertical DEM elevations compared with RTKcheck points . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Figure 2.7 Digitized cover features and bar surface D50 values for themodelled subregion . . . . . . . . . . . . . . . . . . . . . . . 26Figure 2.8 Results of River2D modeling . . . . . . . . . . . . . . . . . . 27Figure 2.9 Comparison of River2D modelled depths with measured depths,with 1:1 line for reference . . . . . . . . . . . . . . . . . . . 28Figure 2.10 Spatial patterns of composite suitability index for (a) adult and(b) juvenile brown trout . . . . . . . . . . . . . . . . . . . . . 29Figure 2.11 Detailed view of UAV orthomosaic with (a) the original 5-cmresolution and (b) resampled 80-cm resolution and grayscaleto approximate IKONOS panchromatic imagery . . . . . . . . 32Figure 3.1 (a) Aeyron Scout quadcoptor used in 2012 survey and (b) eBeefixed-wing UAV used in 2013 survey. . . . . . . . . . . . . . 40ixFigure 3.2 Density plots comparing modeled DEM elevations with mea-sured GPS check point elevations for (a) exposed and (b) cor-rected submerged points. . . . . . . . . . . . . . . . . . . . . 43Figure 3.3 Empirical relationships between field-measured surface grainsize and image texture in the orthomosaics for (a) the D50 and(b) the D84 . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Figure 3.4 Comparison of field-measured evidence of peak flow watersurface elevations and modeled water surface elevations forpre- and post-flood simulations. . . . . . . . . . . . . . . . . 48Figure 3.5 Orthomosaics for (a) pre- and (b) post-flood UAV surveys. . . 51Figure 3.6 Detrended pre- and post-flood DEMs and DEM differencing. . 52Figure 3.7 Distributions of (a) areal and (b) volumetric elevation changesdetermined through DEM differencing. . . . . . . . . . . . . 54Figure 3.8 (a) Scatterplot showing flood-induced elevation change in re-lation to pre-flood detrended relative elevation for different ge-omorphic units and (b) distributions of elevation change asso-ciated with each unit type . . . . . . . . . . . . . . . . . . . . 55Figure 3.9 Sample and model variograms in (a) streamwise and (b) nor-mal directions for pre- and post-flood DEMs . . . . . . . . . 55Figure 3.10 Distributions of modeled normalized shear stress for pre- andpost-flood simulations. . . . . . . . . . . . . . . . . . . . . . 57Figure 3.11 Relationships between elevation changes and modeled normal-ized shear stress for (a) the D50 and (b) the D84 . . . . . . . . 58Figure 3.12 Comparison of patterns of (a) pre-flood elevations, (b) mod-eled normalized shear stresses for the D84, and (c) elevationchanges in a subsection of the study reach . . . . . . . . . . . 58Figure 4.1 (a) Flood frequency analysis for Elbow River at Bragg Creek,1935-2012 (not including 2013 flood). (b) 2012-2015 hydro-graph showing flow conditions relative to timing of UAV surveys 69Figure 4.2 UAV orthomosaics for each survey year. . . . . . . . . . . . . 76Figure 4.3 Thresholded DEMs of difference between survey years. . . . 77xFigure 4.4 Histograms of thresholded elevation changes between surveysyears. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78Figure 4.5 Steady flow hydrodynamic model results shown as maps ofspecific discharge (q, m2/s/m) for each year (2012, 2013, 2014)and discharge (10, 30, 50 m3/s) combination. . . . . . . . . . 79Figure 4.6 Joint depth-velocity frequency distributions. . . . . . . . . . . 82Figure 4.7 Individual depth and velocity density distributions . . . . . . 83Figure 4.8 (a) Depth-velocity scatterplot for classified 2012 10 m3/s data,with points stratified by color for each cluster. (b) Mappedpatterns of 2012 10 m3/s classification . . . . . . . . . . . . . 84Figure 4.9 Detailed example view of (a) 2013 orthomosaic (b) specificdischarge overlain on DEM (c-f) Low energy, pool, glide, rifflefuzzy unit patterns . . . . . . . . . . . . . . . . . . . . . . . 85Figure 4.10 Spatial patterns of clusters at 10 m3/s. . . . . . . . . . . . . . 86Figure 4.11 Measures of reach composition at 10 m3/s . . . . . . . . . . . 87Figure 4.12 Spatial distributions of clusters for all three modeled discharges 90Figure 4.13 Measures of changes in reach composition with discharge . . 91Figure 4.14 AdultHS patterns at 10 m3/s. . . . . . . . . . . . . . . . . . . 92Figure 4.15 Changes in WUA and WUAC per unit stream area for adult andjuevenile brown trout. . . . . . . . . . . . . . . . . . . . . . 93Figure 4.16 Distributions of (a) JuvHS and (b) AdultHS for each 2012 10m3/s classified cluster . . . . . . . . . . . . . . . . . . . . . . 94xiAcknowledgmentsMany people aided in the successful completion of this dissertation, both scientifi-cally and otherwise. First and foremost, I wish to thank my supervisor Brett Eaton.Since I first arrived at UBC, Brett has encouraged (and challenged) me to seek outand pursue my fluvial interests. His optimism, flexibility, mathematical mind, andcuriosity have all shaped my scientific experience at UBC and will undoubtedlyfollow me throughout my career.Invaluable guidance and support were also provided by Chris Hugenholtz, bothin terms of technical UAV applications and his enthusiastic and open approach toscience more broadly.I am also grateful to Sarah Gergel, Marwan Hassan, and Michel Lapointe fortheir early contributions to my fluvial education and for the perspectives they of-fered throughout the process.Thank you to the many people I worked with collecting and analyzing data overthe years: Lesley Winterhalt, Dan McParland, Al Davis, Byeong Kim, Ariel Kettle,and Lea Zhecheva especially provided valuable support and friendship during fieldwork no matter how leaky the hip waders were.Thank you also to friends and family more broadly. My parents Ken and Nancyand sisters Meredith and Mila have been an unwavering source of love, encourage-ment, and advice through my life that has made me who I am today.Lastly, thank you Danielle for your love, support, and companionship. I lookforward to sharing a post-gradschool future with you.xiiChapter 1Introduction1.1 PreambleRiverine ecosystems globally are subject to a wide range of stressors that threatenwildlife populations and ecosystem services. To support successful managementand conservation of these environments, a strong cooperative underpinning froma diverse array of scientific disciplines is needed. However, the past century hasbeen characterized by drastic negative impacts to stream ecosystem habitats andorganisms [Williams et al., 1993, Warren Jr and Burr, 1994, Taylor et al., 1996].These declines can be attributed to a suite of cultural, economic, and natural fac-tors [Lackey, 1999], but a fundamental problem has been disparate epistemologi-cal approaches to understanding fluvial systems and a lack of appropriate tools toprovide information at relevant spatial and temporal scales. This problem is com-pounded by the logistic realities of studying and conceptualizing rivers; a historyof theory-laden field observations and spatially discrete targeted sampling methodshas contributed to a recursive focus on smoothly varying trends or average condi-tions. However, work from a range of backgrounds has led to an increased view ofrivers as spatially continuous lateral and longitudinal mosaics with rich heterogene-ity resulting from linked physical-biological processes acting at a range of scales[Schlosser, 1991, Ward, 1998, Fausch et al., 2002, Wiens, 2002, Carbonneau et al.,2012]. This riverscape perspective provides a holistic framework that embraces1complexity and facilitates collaboration across scientific disciplines in an appliedmanagement context.The emergence and acceptance of this view has been supported by recentmethodological and technological advances. In particular, remote sensing and nu-merical modeling approaches provide valuable tools to collect data at scales andresolutions that can characterize heterogeneous fluvial environments. Feedbacksbetween physical and biological processes makes rivers particularly dynamic sys-tems; geomorphology both drives and reflects processes of flow and sediment trans-port, which in turn form the physical template that mediates ecological structureand function. However, mechanistic understanding of eco-hydrogeomorphic re-lationships is still limited [Vaughan et al., 2009]. Remote sensing provides anefficient and accurate way to collect data that can be used as empirical support inthe development and testing of conceptual models and theories. When combinedwith numerical and physical modeling, such an approach is a valuable way to un-derstand landscape and ecosystem evolution in an environmental change context.In this thesis, I contribute to this ongoing maturation of river sciences through thedevelopment and testing of new methods of fluvial remote sensing using small un-manned aerial vehicles (UAVs) to characterize physical river structure. These toolsare then used to address the overall question of how large flood events conditionmorphology and aquatic habitat dynamics in gravel-bed rivers.1.2 Thesis organizationThis thesis consists of five chapters. This introduction chapter provides anoverview of the scope of the research and the context within which it was con-ducted, as well as a brief review of literature pertinent to the research chapters.Each of the following three body chapters is then structured as a self-containedresearch paper with its own focused introduction, literature review, description ofmethods and results, discussion, and conclusion sections. Chapter 2 presents testsof UAV-based remote sensing in a fluvial setting, with a focus on data quality andderived metrics of geomorphic and habitat structure. Chapter 3 applies these meth-ods in a multi-temporal investigation of reach-scale morphodynamics associatedwith an extreme flood event. Chapter 4 then extends the analysis of physical flood2effects to include subsequent geomorphic readjustment and uses a combined nu-merical flow modeling and statistical classification method to quantify spatiotem-poral hydrodynamics and patterns of habitat suitability for brown trout. A finalconcluding chapter then summarizes results from and linkages between each re-search chapter and puts the findings in broader context.1.3 Literature review1.3.1 Geomorphic principlesIn its simplest form, channel morphology in terms of width, depth, and slope, haslong been understood as a function of sediment load and discharge. While earlystudies in fluvial geomorphology were largely concerned with qualitative descrip-tions of historical landscape evolution [Baker, 1988], examples exist of attemptsto disentangle relationships between interrelated factors and to determine their rel-ative importances. Analysis of stable engineered canals led to the formulation ofhydraulic geometry relationships under equilibrium conditions termed regime re-lations. Lindley [1919] described channels as changing width, depth, and gradientthrough sediment transport until a stable channel formed and provided empiricalrelations between channel dimensions and velocity. He also recognized the con-trolling influence of bed material on channel shape [Lindley, 1919]. Lane [1955]continued with this equilibrium approach in natural systems, introducing the rela-tionshipQsD ∝ QSwhere Qs is the quantity of sediment, D is the particle diameter, Q is the discharge,and S is the channel slope. Similarly, Leopold and Maddock [1953] describedsimple power-law relationships between sediment load, discharge, and channel di-mensions, indicating that changes in discharge and sediment load could result inchannel adjustments in terms of width, depth, slope, or sediment size.These hydraulic geometry relations marked an important step in describingriver morphology in terms of cross sectional dimensions, but several early studiesalso examined planform channel patterns. Leopold and Wolman [1957] seminalstudy of braided, meandering, and straight river patterns described potential con-3trols on planform geometry and put forth the empirical relationship (in its originalimperial units)S = 0.06Q−0.44marking the critical slope threshold below which channels were generally mean-dering and above which channels were generally braided or straight. Schumm andKhan [1972] also analyzed planform geometry using flume experiments relatingsediment load and slope, illustrating the potential for changes in sediment load toinduce changes in river pattern.While these early conceptual models of channel morphology performed wellin describing empirical patterns, they lacked a solid physical basis. Numerous re-searchers have since expanded on these concepts, providing interpretations of thephysical processes that create channels and give rise to the empirical scaling rela-tions discovered for most natural fluvial systems. Sediment transport and the flowof water can be described by the laws of conservation of mass and Newton’s lawsof motion [Stevens and Nordin, 1987]. Assumptions of uniform one-dimensionalsteady flow are usually applied, meaning the downstream gravitational force of theflowing water is balanced by the resistance of the channel bed and banks. This canbe described in terms of the shear stress the water exerts on the banks byτ0 = ρgRSwhere τ0 is the total basal shear stress, ρ is the fluid density, g is gravitationalacceleration, and R is the hydraulic radius. Newton’s Second Law can be appliedin several manners to relate velocity and channel resistance [Ferguson, 2007]:u =d2/3S1/2n=C√dS =√8gdSfwhere u is the mean velocity, n is the Manning roughness coefficient, d is the meanflow depth, C is the Chezy resistance factor, and f is the Darcy-Weisbach frictionfactor.4Sediment transport is related to shear stress and can be considered in terms ofa dimensionless shear stress termed the Shields number asQb/Q = f [ρgdS/g(ρs−ρ)D]where Qb is bed material load and D is grain size to be moved. The term in bracketsrepresents the Shields number, often denoted τ∗ [Church, 2006]. This relationshipdescribes the ability of a stream to move sediment, which occurs when τ∗ exceedsthe critical value τ∗c . The Shields number can be rearranged with the constantsreplaced to yieldS = 1.65τ∗D/dwhich indicates that slope, scale, and sediment characteristics determine sed-iment transport in a channel [Church, 2006]. Rivers can therefore adjust slope,roughness, or channel dimensions to accommodate changes in conditions. Theprinciple of continuity can also be applied to conceptualize changes in sedimenttransport through a channel section and bedload sediment transport can be relatedto sediment supply (Qs) throughQs−Qb = ∆Sswhere ∆Ss is the change in sediment storage in a given area. This relationship statesthat if the sediment supply to a reach is greater than sediment transport capacity,sediment will be stored in the reach [Montgomery and Buffington, 2004].The above physical determinants relating sediment transport, discharge, andchannel morphology provide a better understanding of how and why channels mayrespond to perturbations than empirical relationships do. While the interrelation-ships are complex, they provide the necessary basis with which to explain channelforms. For example, Church [2006] described alluvial river channels in terms ofphysical sediment transport processes, relating stream competence (quantified bythe Shields number) and sediment budgets to channel morphology and stability.The concept of river regime has also benefited from a physical explanation; Eatonet al. [2004] developed a rational regime model based on work done by Millar and5Quick [1993] and Millar [2005] to relate physical river processes such as flow resis-tance, sediment transport and bank stability to predictions of hydraulic geometry.This regime model approach has also been extended to a physically based reevalu-ation of planform morphology. Eaton et al. [2010] reviewed Leopold and Wolman[1957] data in a regime model context and proposed more refined slope-dischargerelationships differentiating channel types. However, such regime analyses aremost applicable to rivers that have sufficiently adjusted to their conditions to beconsidered in equilibrium and may not apply to very short or very long timescalesover which most landscapes can be considered to be in transient conditions [Lane,1955]. The stochastic nature of sediment transport in rivers also complicates simplerelationships between sediment transport and channel form, particularly in moun-tainous regions where mass wasting events dominate sediment transport processes[Einstein, 1950, Engelund and Fredsøe, 1976, Benda, 2003, Church, 2010].1.3.2 Physical aquatic habitatPhysical habitat is strongly controlled by stream hydrogeomorphology. Habitat canbe described in terms of many features and at many scales; watershed habitat char-acteristics can be considered nested hierarchies of geomorphic features rangingfrom microhabitats such as individual cover features and mesohabitats at the scaleof riffles and pools, to the overall catchment scale determined by factors such assurficial geology and bioclimatic control [Gregory et al., 1991]. Factors commonlyconsidered to be important to physical habitat include water depth and velocity[Hogan and Church, 1989], riparian vegetation cover [Roy et al., 2005], sedimentsize and availability [Kondolf, 2000], water temperature [Hari et al., 2006], andin-stream large wood [Abbe and Montgomery, 1996]. Many of these factors havereciprocal relationships with hydrogeomorphology. For example, feedbacks be-tween riparian vegetation and physical processes shape both in-stream and flood-plain ecosystems through complex relationships between sediment stabilization,physical disturbance patterns, and vegetation succession [Stallins, 2006]. Under-standing these recursive relationships is necessary for informed management ofriverine habitat. Although the direct influence of physical habitat on population-level ecological changes can be difficult to demonstrate and is dependent on the6species and organism being studied, many studies have found effects of some as-pects of physical habitat on species composition. Litvan et al. [2008] found thatrip-rap grade control structures led to increases in macroinvertebrate biomass, den-sity, and diversity, and Rowe et al. [2009] found that 18 different physical habitatvariables were significant predictors of fish assemblages. Smokorowski and Pratt[2007] also found strong links between habitat alterations and fish biomass in ameta-analysis of direct habitat manipulation experiments.Given the importance of physical habitat for overall aquatic ecosystem health,many methods have been developed to quantify habitat. Fish habitat is the majorfocus of most methods, as fish can be easily captured and measured, are responsiveindicators of potential stressors, and are ‘charismatic’ and economically importantorganisms [Davis, 1995]. One widely used method is the Physical Habitat Sim-ulation System (PHABSIM, [Bovee, 1982]) which relates in-stream hydraulics tofish habitat using empirical habitat preference curves for different species. PHAB-SIM is commonly used to inform stream habitat analysis using the in-stream flowincremental methodology [Bovee et al., 1998] and can give weighted usable area(WUA) curves describing available habitat as a summation of physical microhab-itat quantity and quality. Although common, these types of methods that rely onempirical abundance-environment relations have been criticized for ignoring bio-logical interactions such as density-dependent competition and for unfounded in-terpretations of cause-and-effect when other causal mechanisms may be at play[Van Horne, 1983, Lancaster and Downes, 2010]. They are also difficult to modifyfor broader scale controls on fish populations such as lateral and longitudinal con-nectivity and metapopulation dynamics. However, regardless of individual methodapplicability, available physical habitat is a primary control driving many key bio-logical processes.In practical terms, one of the most relevant scales for the assessment of riverecosystem structure is the mesoscale [Frissell et al., 1986, Fausch et al., 2002].Working at this spatial scale allows for efficient characterization of in-stream habi-tats while still providing relevant measures that are directly relatable to smallerscale controls on fish populations. At this scale, physical habitat patches can be ex-plicitly identified in terms of geomorphic units (pools, glides, runs, and riffles) withdistinct depth, velocity, and sediment patterns [Rosenfeld et al., 2011] and there-7fore different quality as habitat. Because of the general correspondence betweenthe size of fluvial geomorphic features and the time scale at which they change[Ward, 1998], working at this scale facilitates understanding of linkages betweenreach-scale geomorphic processes such as lateral channel migration or changesin sediment supply and habitat changes relevant to fish populations. It also al-lows for the identification of other factors of importance such as floodplain andhyporrheic connectivity, backchannel characteristics, riparian vegetation patterns,and large wood features. Although in-stream habitat unit classification based ongeomorphic features has been criticized as being subjective and often improperlyused in monitoring and management applications [Poole et al., 1997], mesoscalephysical habitat is a vital component of aquatic ecosystems and may provide auseful eco-geomorphological framework for interdisciplinary studies [Thoms andParsons, 2002]. This is particularly true over longer time scales where more de-tailed measures of physical habitat are impossible to assess. However, as with anyscale-dependent measure, it is important to complement measures of mesohabitatwith perspectives from other scales, such as the stream segment scale where moregeneral morphologic controls may be evident, and the microhabitat scale whereunique combinations of velocities, depths, and sediment sizes may provide moredetailed quantitative measures of actual fish habitat distributions.1.3.3 Characterizing river structureThis thesis focuses on remote sensing of river structure in terms of topographyand bathymetry. Fluvial remote sensing has expanded rapidly since the mid-1990s,fueled both by the proliferation of new sensors and platforms and commensurateadvances in computing power and geographic information systems (GIS) [Fitz-patrick, 2001]. The most common methods include aerial photography, satelliteimagery, RADAR and LiDAR [e.g., Fryer, 1983, Leclerc and Hickin, 1997, Lo-rang et al., 2005, Williams et al., 2011, Hugue et al., 2016], each of which comeswith trade-offs in data quality, ease of acquisition, resolution (temporal and spa-tial), extent, and cost. As the broader perspective of discontinuities and spatialcomplexity has gained traction in geomorphology, more focus has been placed onthe accurate determination of river morphology in three dimensions to quantify8morphological adjustments over multiple scales, to visualize vertical in addition toplanform channel adjustments, and as input to two or three dimensional numericalmodels of channel processes [Lane, 2000]. In particular, digital photogrammetry(or structure-from-motion) techniques have emerged as an efficient and accurateway to quantify river channel topography, making use of multiple images overlap-ping covering features of interest from different angles to reconstruct three dimen-sional structure [Lane, 2000, Fonstad et al., 2013, Javernick et al., 2014, Woodgetet al., 2014, Javernick et al., 2015]. Because these methods can be applied withconsumer-grade imagery and without rigorous estimation of camera parameters orposition [e.g., Micheletti et al., 2015], digital photogrammetry can provide an ac-cessible, accurate, and efficient way to measure channel elevations. However, somelogistical challenges exist depending on site and setting; issues such as turbid ordeep water, high relative vertical relief, and vegetation obstructions can all limitthe applicability of digital photogrammetry in fluvial environments.In order to generate a seamless characterization of river structure in three di-mensions, measures of submerged bathymetry are often required to correct or mo-saic with elevation data measured for exposed areas. Passive optical remote sens-ing such as that used for photogrammetry measures incoming reflected solar radi-ation. When light passes through water it is absorbed exponentially according toBeer-Lambert’s law, causing deeper areas to look darker. Empirical models [e.g.,Lyzenga, 1981] take advantage of this fact to generate a regression between field-measured depths and radiance values measured at the sensor. Because differentfrequencies of light are absorbed at different rates in the water column, a bandratio is often used. The band frequencies that are most applicable vary based oncondition and environment, but red and green bands (longer wavelenths, absorbedmore quickly) from optical imagery vary more strongly in shallow water than blue(shorter wavelength, absorbed less quickly), so many applications rely on a ratio ofred and green bands to relate to water depth [e.g., Winterbottom and Gilvear, 1997,Legleiter et al., 2004, Carbonneau et al., 2006]. Although this empirical calibrationprocedure is site specific (as opposed to physically based radiative transfer modelsthat should be broadly applicable) and requires ground-measured data (which iscounterproductive to the basic advantage of remote sensing), it implicitly accountsfor factors such as changing stream bottom conditions and atmospheric influences9on radiative transfer. Also, field data are usually collected regardless to assess ac-curacy in targeted remote sensing applications, so ground truthing and collectionof calibration data can often go hand in hand [Flener, 2015].1.4 Thesis objectivesThe strong interrelationships between channel morphodynamics and ecosystemfunction mean that lotic ecosystems are sensitive to perturbations and changes ingoverning conditions. It is therefore essential to understand the process-form link-ages that drive both geomorphic change and aquatic habitat dynamics. This is bestachieved by interdisciplinary study through a combination of empirical observa-tions, conceptual, physical, and numerical modeling, and long-term monitoring.While these factors have long been a foundation of fluvial study and managementin some form, recent methodological and conceptual advances have led to an in-creased call for multi-scale investigations that embrace spatiotemporal variability.In particular, remote sensing and numerical modeling approaches have recentlyemerged as efficient and accurate ways to measure and model dynamic river sys-tems. However, the body of research applying and interpreting such analyses is stillunderdeveloped, and more studies of eco-hydrogeomorphic variability are needed,particularly in an environmental change or disturbance context. With this broadmotivation in mind, the overall research objective of this dissertation is to deter-mine how large flood events shape channel morphology and what the implicationsare for aquatic habitats. Given the rarity of large flood events, their relative contri-bution to geomorphic processes is difficult to determine; smaller competent floodsmay contribute more overall geomorphic work due to their frequency, but largeevents can leave lasting legacies that condition future geomorphic responses andshape ecosystems for decades to come [Wolman and Miller, 1960]. To address thisresearch objective, the dissertation focuses on three primary questions:1. How can remote sensing using UAVs advance the methodological under-pinnings of data collection and information extraction in spatially complexfluvial environments?102. What are the detailed reach-scale geomorphic effects of an extreme floodevent, and can fluvial adjustments be explained by pre- and post-flood con-ditions?3. What is the relationship between flood-induced geomorphic change, hydro-morphic diversity, and aquatic habitat structure?11Chapter 2Hyperspatial remote sensing ofchannel reach morphology andhydraulic fish habitat using anunmanned aerial vehicle (UAV)2.1 IntroductionRecent advances in digital image collection and processing techniques have ledto the increased use of remote sensing and photogrammetry for river research andmanagement. Applications range from broad scale analyses of change from con-ventional aerial photography [e.g., Lapointe et al., 1998] and satellite-based as-sessments of stream temperature [e.g., Cherkauer et al., 2005], to close range pho-togrammetry of small streams under vegetation cover [e.g., Bird et al., 2010], or ofgravel structures in flume experiments [e.g., Butler et al., 2002]. Such studies havedemonstrated the utility of remotely sensed data at many scales and supported thecall for spatially continuous perspectives of fluvial systems that embrace hetero-geneity of physical processes and aquatic habitat [Fausch et al., 2002, Carbonneauet al., 2012].12Central to any remote sensing application is the need to balance data type,quality, and ease of acquisition at appropriate spatial and temporal scales. Al-though satellite imagery and conventional aerial photography are widely availableand can provide sub-meter spatial resolutions with a wealth of historic data, theycan be costly and are not suited to tailored, site-specific studies at fine scales. Bothapproaches also offer limited utility for determination of topography, which is be-coming increasingly necessary in studies focusing on three-dimensional river fea-tures [Lane, 2000]. While elevation data have traditionally been collected throughground-based surveys, this approach is time consuming and does not benefit fromthe complete spatial coverage offered by remote sensing. Airborne LiDAR surveyscan also provide high quality topographic data [Biron et al., 2013], but typicallyrequire a substantial financial investment, thus potentially limiting the adoption ofairborne LiDAR as a monitoring tool. To address such issues, a focus on pho-togrammetric production of DEMs has emerged, and the accuracy and limitationsof these techniques in fluvial systems are well documented [e.g. Pyle et al., 1997,Westaway et al., 2001, Carbonneau et al., 2003, Fonstad et al., 2013].Using photogrammetry to efficiently produce high quality DEMs from re-motely sensed data provides an ideal method with which to address questions ofdynamic linkages between geomorphic processes and aquatic habitat in three di-mensions. In particular, a focus at intermediate spatial scales (i.e. 101 - 103 m) isneeded to bridge existing gaps between research and conservation at scales relevantto fish populations and communities [Fausch et al., 2002, Wheaton et al., 2010a].At such scales, fish habitat is often estimated in terms of flow depth and veloc-ity distributions [e.g. Rosenfeld et al., 2011], or mesohabitat characteristics [e.g.Hauer et al., 2009]. However, difficulties exist in efficiently collecting the neces-sary topographic data, and important qualitative aspects of habitat such as coverare often neglected [Ayllo´n et al., 2012, Boavida et al., 2012]. Similarly, chan-nel morphology is often treated as a constant factor, due largely to the difficultiesassociated with coupling morphologic change with habitat analyses. To gain a bet-ter understanding of intermediate-scale morphology-habitat relationships, tailoreddata collection with high spatial and temporal resolution is required. These datashould be able to be easily integrated with other spatial data in a GIS frameworkto facilitate cross-scale applications [e.g. Hardy and Addley, 2001] and repeated13frequently in monitoring and adaptive management contexts [Downs and Kondolf,2002].Many remote sensing platforms aimed at intermediate or reach scales havebeen developed and tested. Tethered balloons [Church et al., 1998] and kite aerialphotography [Smith et al., 2009] have been applied to feature mapping and DEMgeneration, and Vericat et al. [2008] and Fonstad et al. [2013] demonstrated thepotential of lighter-than-air helikites to determine topography and map reach scalefluvial features such as wetted channel outlines. However, these systems are easilyaffected by weather conditions and may lack the stability needed to produce highquality DEMs. Appropriate imagery can also be acquired from helicopters or smallaircraft [e.g. Booth et al., 2007], but these can be limited by flying height restric-tions and do not offer the operational flexibility needed to capture many scales ofenvironmental change. One emerging method for the collection of suitable scaleand quality imagery is the use of unmanned/uninhabited aerial vehicles (UAVs).UAV technology has diversified rapidly in the past decades as miniaturized com-ponents (autopilots, inertial measurement units, GPS receivers, and telemetry) andplatforms have advanced, and UAV-based sensors have been applied to a range ofcivil and research applications [e.g. Hugenholtz et al., 2012, Watts et al., 2012,Hugenholtz et al., 2013].In fluvial settings, Lejot et al. [2007] tested imagery from a fixed-wing Pixydrone in a variety of contexts, mapping bathymetry of a 5 km length of river andan oxbow lake, and using photogrammetry to create DEMs of two small exposedgravel bars. They were able to produce high quality bathymetric maps over a rangeof bed substrates and 5 cm pixel resolution DEMs, with average vertical errorsbetween 0.157 and 1.069 m. However, they noted drawbacks associated with plat-form instability during image acquisition, and their photogrammetry was limitedby poorly defined external image parameters due to the fact that the drone could notsupport an inertial navigation system or differential GPS [Lejot et al., 2007]. Withthe technological advances that have occurred in even the past five years, continuedinvestigation of the usefulness of UAVs to river research is warranted. In this paper,we address the applicability of UAVs to fluvial systems. Specifically, we test theaccuracy of an orthomosaic and DEM generated from images captured by a fullyautonomous quadcopter and evaluate the ability to measure key metrics of reach-14Figure 2.1: Location of the study reach within the Elbow River watershedand example ground-level imagescale morphology. We then assess the utility of the data in producing estimates ofavailable hydraulic habitat through the initialization of a 2D hydrodynamic modeland mapping of important aquatic habitat features.152.2 Methods2.2.1 Study siteElbow River is a gravel bed river in southern Alberta, Canada that flows east outof the Rocky Mountains (Figure 2.1. At the study site near the town of RedwoodMeadows (50.988◦N, 114.509◦W), the river flows through a 200 m wide activechannel with extensive gravel bars and is split into 2-4 anabranches. The drainagearea is 791 km2 at the Water Survey of Canada gauging site directly upstream atBragg Creek, and elevation at the site is 1,250 m. The study reach was 1 km longand the survey extended across the active channel and into floodplain forest for awidth of approximately 350 m. All survey work was performed on September 25,2012 when water levels were low at a discharge of 5.9 m3/s.2.2.2 UAV survey and photogrammetryThe UAV used to acquire the imagery for the orthomosaic and DEM generation wasan Aeryon Scout (Aeryon Labs Inc.) with a Photo 3S high-resolution (non-metric)camera (Figure 2.2). The Scout is a small quadcopter that can be pre-programmedto fly set waypoints using a small tablet PC. These waypoints correspond to imagecenter coordinates calculated from the specified flying height, image overlap andcamera parameters. At each waypoint, the 3-axis stabilized camera with an 8.4mmlens and a field of view of 37◦ x 29◦ captures true-color images with embedded geo-tags and metadata. To ensure correct camera positioning for each image, onboardservos automatically adjust the platform position and camera orientation through-out the flight.For the aerial survey, we positioned 45 highly visible ground control points(GCPs) throughout the study reach to assist with the photogrammetric processing.We measured the position of each GCP on the ground with a real-time kinematic(RTK) GPS, as well as 297 geolocated check points for testing of the generatedDEM. The UAV survey itself was performed in eight flight lines running parallelwith the river for a total of 192 separate images, with the quadcopter flying at aspeed of 5 m/s and a height of 100 m above the ground surface. Although theScout can fly up to 3 km from the base station, regulations dictate that the aircraft16Figure 2.2: Aeryon Scout quadcoptermust be within line of sight during flight (∼800 m). At the study reach, the wide,straight channel allowed for easy sightlines and all images were captured in a singleflight that took about 30 minutes.The autopilot log and geotagged images were then processed with the Enso-MOSAIC UAV package (MosaicMill Ltd., Finland). This software was used forall photogrammetry steps, including calculation of internal orientation of the Photo3Sł camera to create a calibration file for subsequent processing. The photogram-metry sequence involved the automatic identification of image keypoints and bun-dle block adjustment (BBA) and automatic aerial triangulation (AAT). BBA wasused to calculate the location and rotation of the camera for each image and solvethe AAT based on a combination of manual and automatic tie points (includingGCPs). After each iteration the adjustment error was assessed, tie points withlargest residuals were removed, and the process was repeated until the final BBAconverged. This continued until the largest residuals were deleted and the accuracyof the mosaic was optimized. Following this step, the final DEM was generatedby the software and used to orthorectify the image mosaic. Although processingtime varies based on computer memory, number of images, and experience of theoperator, the data for this study were processed completely in approximately a day.172.2.3 Submerged DEM correctionOne of the most commonly cited challenges of photogrammetric analyses of riversis the lack of reliable through-water photogrammetry, limiting the accurate deter-mination of submerged topography [e.g. Lane, 2000, Westaway et al., 2000, Birdet al., 2010]. Although the shallow, clear water at Elbow River study reach allowedfor an apparent submerged topography to be calculated in the initial DEM genera-tion based on homologous points, refraction at the air-water interface, water surfacewaves, and white-water areas in riffles can complicate automated photogrammetry[e.g. Fryer, 1983, Fryer and Kniest, 1985], often resulting in an underestimation ofactual water depths and therefore erroneously high bed elevations. To account forthis, we tested two methods of DEM correction: a simple first-order correction forthe refractive index for water [Westaway et al., 2000], and an empirically calibrateddepth estimate based on pixel color values in the orthomosaic. As both methodsrequire a water surface from which to subtract depths, we extracted elevations atpoints spaced 1 m apart along the waters edge, removed unreliable points (e.g.under overhanging vegetation), and then interpolated a water surface profile usingordinary kriging [Westaway et al., 2001, Legleiter, 2012, Williams et al., 2013b].For the refractive index correction method, we subtracted the uncorrected DEMfrom the water surface elevations, yielding a map of apparent water depth. We thenmultiplied apparent water depths by a constant refractive index for water (1.34)to determine actual depths, which were subsequently subtracted from the watersurface elevations to give the corrected DEM. For the second correction method,we produced a map of water depths based on an empirically calibrated relationbetween measured water depth at GPS check points and orthomosaic pixel colorvalues [e.g. Winterbottom and Gilvear, 1997, Legleiter et al., 2004, 2009, Fleneret al., 2013]. After testing all available RGB band combinations, we found that aratio of log-transformed red/green values within a moving filter of 1 m2 to accountfor substrate and water surface irregularities yielded the strongest relationship (R2= 0.77). These water depths were then subtracted off of the water surface elevationsto produce the corrected DEM.182.2.4 Fluvial morphology metrics and habitat mappingTo assess the ability to extract key metrics of reach-scale morphology and avail-able aquatic habitat, we combined a depth-averaged hydrodynamic model withfeatures mapped from the orthomosaic. We determined sediment sizes on exposedbars using techniques similar to Carbonneau [2004], taking 30 close range, ge-olocated vertical photos of 1 m2 bar surface patches with visually consistent sed-iment size distributions. We then determined the median grain size (D50) of eachimage using a manual photosieving graphical user interface programmed in MAT-LAB (Mathworks, 2008) to measure the b-axis of 50 clasts spaced on a 5x10 gridin each photo. These D50 values at each close range image location were thenrelated to image texture calculated as standard deviation within a 1 m2 movingwindow in the UAV aerial orthomosaic (Figure 2.3). Image texture, defined as athe variation between the spectral properties of a pixel and its neighboring pixels,was calculated with the Spatial Analyst ArcGIS extension’s neighborhood statis-tics tool. Although Carbonneau [2004] compared the use of local image textureto two-dimensional semivariance and chose semivariance because it is less sensi-tive to illumination changes associated with daylight or camera exposure times, wefound image texture to produce a high quality empirical relationship (R2 = 0.82)and had no issues with lighting given the consistent sunlight during the 20 minuteacquisition flight. To produce a final map of grain sizes throughout the reach, weextracted exposed gravel areas using a supervised classification and applied thetexture/(D50) relationship.With the 5 cm resolution of the orthomosaic, large wood (LW) pieces and jamswere clearly distinguishable and were manually digitized. Jams were defined asgroups of LW with three or more pieces and were digitized as polygons, whileeach individual piece of LW was digitized as intersecting lines representing the di-ameter at breast height and the length. In addition to LW, we digitized features thatcould provide cover to fish, with a focus on brown trout (Salmo trutta). Althoughpreferences may vary based on factors such as geographical region, fish life stage,time of year, and changing flow conditions [e.g. Ayllo´n et al., 2009], brown troutare heavily dependent on cover throughout their life cycle [Armstrong et al., 2003].We digitized important cover features that included overhead vegetation, undercut19Figure 2.3: Relationship between D50 determined through photosieving andorthomosaic image texture within a 1-m2 windowbanks, pools, and water surface turbulence. All of these features can provide visualoverhead cover, and, in addition to in-stream and overhanging LW, constitute themain sources of cover use [Courtney et al., 1998].We then used the two-dimensional depth averaged hydrodynamic modelRiver2D to model flows at the 5.9 m3/s discharge during the time of image ac-quisition. River2D is a finite element model that is based on a conservative Petrov-Galerkin upwinding formulation and solves the Saint-Venant equations for con-servation of mass and momentum. The model was developed for use in naturalstreams and has been tested in a wide range of systems [e.g. Katopodis, 2003,Lacey and Millar, 2004, Waddle, 2009]. River2D also includes a component toevaluate fish habitat that facilitates the incorporation of habitat preference curvesand calculation of suitabilities and overall weighted usable area (WUA) through-out the modeled reach based on depths, velocities and a channel index describingphysical features such as substrate or cover. The model takes an input topographyfile and creates a finite element mesh of triangular polygons used to solve for flow20Figure 2.4: Brown trout habitat suitability curves for depth, velocity, andweighted average percent coverdepths and velocities. Other necessary inputs include equivalent roughness height(ks), initial flow conditions, and boundaries to define the model extent.To model flows in the Elbow River study reach, we selected a 640 m long sub-section downstream of a point where all flow was contained in a single channelto ensure inflow discharge at the upstream boundary was accounted for correctly.We extracted elevations from the corrected DEM at points spaced 1 m apart andcreated a bed topography file supplemented with breaklines and boundaries thatwere digitized directly from the orthomosaic. Breaklines were placed to aid incorrect interpolation along linear features such as steep cutbanks. We then useda uniform fill with a 1 m spacing to develop the computational mesh, resulting in105,525 nodes and 209,353 elements throughout the modeled area. We calibratedthe model by varying ks until the water surface elevations of the steady-state solu-tion matched observed water surface elevations from the orthomosaic and DEM.Although a wide range of estimates for equivalent roughness height have beenused, the final value we used was 0.12 m, which is equivalent to ∼2*D84 and lieswithin commonly cited ranges [Millar, 1999]. The model was run to steady stateconvergence based on the inflow discharge of 5.9 m3/s measured at the Water Sur-vey of Canada gauge upstream and an outflow water surface elevation of 1239.5 mmeasured directly from the DEM.Available hydraulic habitat was calculated as WUA based on the modeleddepths and velocities and digitized cover features as in the commonly used physical21habitat simulation system (PHABSIM). Because of the strong impact that regionaldifferences can have on fish preference, we used habitat preference curves devel-oped for juvenile and adult brown trout in the Kananaskis River, AB [Courtneyet al., 1998]. These curves describe the suitability of different depth, velocity,and cover values as a habitat suitability index (HSI) from 0 to 1 and were basedon field data of fish habitat selection and supplemented by data from a telemetrystudy by Bunt et al. [1999]. The preference curves are shown in Figure 2.4. Tocreate a channel index file representing cover availability, we used our digitizedcover features to determine percent cover, assuming 100% cover directly underfeatures and 50% cover within a buffer of 1 m. Courtney et al. [1998] developed aweighting system based on relative value of cover types, so we grouped our coverfeatures into instream objects (instream, provided by instream LW), instream over-head (overhead, provided by pools, water surface turbulence, and cutbanks), andoffstream overhead (o f f stream, provided by overhanging LW or live vegetation).We then used the equation:cover = (10∗ instream+10∗overhead+2∗o f f stream)/22 (2.1)to calculate weighted average percent cover as in Courtney et al. [1998]. Thisformat matched their determined cover preference curves, but we also modified thechannel index curve to have a suitability of 0.2 at 0% cover, effectively assigningareas with no cover the suitability determined for gravel substrates [e.g. Ayllo´net al., 2012]. A composite suitability index (CSI) was calculated for each modelnode as the product of the HSI values for depth, velocity, and channel index, andmultiplied by the area associated with each node for a local WUA. The sum of alllocal WUAs gave the total WUA for the study reach.2.3 Results2.3.1 Accuracy assessmentThe orthorectified image mosaic and DEM of the study reach are shown in Figure2.5. Visually, the high resolution orthomosaic and DEM clearly display the formand distribution of fluvial features of interest, with no visible artifacts from the pho-22Table 2.1: Error statistics equations. DEM elevations are termed zmod , mea-sured check point elevations are termed zobsError metric EquationMean error (ME) ME = ∑ni (zmod−zobs)nRoot-mean-square error (RMSE) RMSE =√∑ni (zmod−zobs)2nStandard deviation of error (SDE) SDE =√∑ni ((zmod−zobs)−ME)2n−1Maximum error (Emax) Emax = max|zmod− zobs|togrammetry process. To assess the vertical accuracy of the DEM, we comparedthe modeled (DEM) elevations with the 297 independent GPS check points, strati-fied by subaerial exposed points and subaqueous submerged points. We calculatedfour accuracy parameters (Table 2.1) for exposed and submerged points: the meanerror (ME), the root mean square error (RMSE), the standard deviation of the error(SDE), and the maximum absolute error. ME is a measure of the accuracy of thedata that indicates any positive or negative systematic error, RMSE is a measureof dispersion of the frequency distribution of residuals that is sensitive to large er-rors, SDE provides information about precision and the distribution of residualsabout the mean, and maximum absolute error describes the largest residual presentfor an understanding of the limits of the data quality [e.g. Li, 1992, Lane, 2000].Residuals were calculated as field-measured GPS elevations subtracted from mod-eled DEM elevations; positive values indicate an overprediction of elevations in theDEM, negative values indicate an underprediction of elevations in the DEM. Forsubmerged areas, we compared the error statistics for the uncorrected DEM, theDEM corrected using the red-green optical empirical method, and the DEM cor-rected using the simple refraction index method as described by Westaway et al.[2000]. Values for exposed and submerged points are shown in Table 2.2. Densityplots for the exposed and the three submerged residual distributions were also cre-ated, as well as a scatterplot of elevation residuals against measured water depthfor the three submerged DEMs (Figure 2.6).Overall, exposed points were more accurate and precise than submerged points,with an RMSE of 8.8 cm and SDE of 0.68 cm. For submerged points, both cor-23Figure 2.5: (a) Photogrammetry generated orthomosaic and (b) DEM, show-ing the extent of the subregion modelled with River2DFigure 2.6: Density plots of vertical DEM elevations compared with RTKGPS check points for (a) exposed points, (b) uncorrected submergedpoints and the two depth correction methods, and (c) scatter plot of ver-tical errors versus depth for uncorrected submerged points and the twodepth correction methods. Vertical lines on the density plots representthe mean error of each data set; elevation differences were calculated asDEM elevations minus RTK GPS elevations24Table 2.2: Error statistics comparing GPS checkpoint elevations with DEMelevationsn ME(m)RMSE(m)SDE(m)Emax(m)Exposed 221 0.032 0.088 0.007 0.320Uncorrected submerged points 76 0.144 0.169 0.008 0.306Red-green empirical correction 76 0.007 0.119 0.016 0.305Refraction correction 76 0.064 0.130 0.003 0.387rection methods improved the ME and RMSE compared to the uncorrected DEM,which consistently over-predicted bed elevations due to the effect of refraction onapparent water depths. Although the red-green empirical correction had a lowerRMSE and ME than the refraction index method, it introduced substantial scatterto the residuals and had a high SDE. It also showed a slight increase in vertical errorwith water depth, while the refraction index did not. As both methods had strengthsand weaknesses and overall similar errors, the choice of corrected surface to usewas based on a visual comparison of the integrity of fluvial forms in the contextof the desired application of hydrodynamic modeling. Although it was affected byshadows in certain areas, the topography generated by the red-green optical correc-tion was chosen to be the most reliable, particularly along steep cutbanks where therefraction index method could not adequately correct the oversimplification gener-ated in the original photogrammetry. For the River2D applications, we were ableto remove unreliable shaded areas and interpolate between them, ensuring a sub-merged topography that captured the meaningful features in terms of bathymetryand instream habitat.2.3.2 Feature detection and hydraulic habitatThe digitized cover features and calculated D50 values for exposed sediment areshown for the modeled subregion in Figure 2.7. The distribution and orientationof LW pieces and jams are evident; mid-channel braid bars contain large amountsof LW, and several large key log jams exist at channel confluences and adjacentto deep pools. This spatial complementarity of cover features is particularly ev-25Figure 2.7: Digitized cover features and bar surface D50 values for the mod-elled subregionident at the outer banks of bend apices, where scour pools are frequently foundin association with cutbanks, overhanging vegetation, LW accumulations on thebanks, and broken water surfaces associated with turbulent water coming off ofsteep upstream riffles. D50 values also conform to expected trends, showing coarsesediment at bar heads and downstream fining. However, fine-scale patterns are af-fected by local shadows associated with topographic breaks and vegetation, whichcause unrealistically high image texture values and therefore erroneously high D50values.The hydrodynamic model results from River2D are shown in Figure 2.8, withdepths and velocities overlain on the orthomosaic. The modeling accurately cap-tured complex flow patterns along the braided reach, following all major channels26Figure 2.8: Results of River2D modeling showing (a) depths and (b) veloci-tiesand replicating longitudinal pool-riffle sequences. Some sections of small channelswith shallow flow were predicted to be dry by the model; depths in these sectionsare estimated to be less than 10 cm and the misrepresentation could be attributedto vertical errors in the DEM or limitations associated with the use of a uniformroughness length or threshold for groundwater flow across the study area. The ef-fect of using an interpolated water surface during the DEM correction procedureis also evident in some areas, where unnatural transverse ridges show undulationsin depth and velocity. These changes are on the scale of 1 m horizontally, whichreflects the 1 m-spaced waters edge points used for water surface elevation in-terpolation. Although flow modeling can be sensitive to topographic uncertainty[Legleiter et al., 2011], these fine scale errors do not detract substantially from thereproduction of aspects of flow that are important to reach-scale hydraulic habitatdistributions such depth and velocity distributions in relation to longitudinal pool-riffle morphology, transverse cross section shape and thalweg position.27Figure 2.9: Comparison of River2D modelled depths with measured depths,with 1:1 line for referenceAccuracy of the hydrodynamic modeling was assessed with measured depths at76 geolocated points. A comparison is shown in Figure 2.9. Overall, the points fol-low a 1:1 line, indicating correspondence between measured and modeled depths(R2 = 0.73). Residuals are generally largest for deeper water; the largest absoluteerror was 0.50 m at a measured depth of 0.98 m. The RMSE for the depths was12.5 cm, ME was -0.37 cm, and SDE was 12.5 cm. These errors likely reflect thepropagation of errors from the submerged DEM correction or the effect of com-paring GPS point measurements with depth values interpolated from the 1 m nodespacing used in the modeling process [e.g. Bailly et al., 2010]. However, thesedepth errors are of similar magnitude to other reach-scale hydrodynamic models ofbraided channels (e.g. RMSE = 9 - 13 cm, [Williams et al., 2013a]), and allow fora reliable assessment of available hydraulic habitat throughout the reach.With the combination of the flow patterns from River2D and the map of coverfeatures from the orthomosaic, we were able to develop continuous maps for avail-able habitat suitability throughout the reach for adult and juvenile brown trout.Patterns of CSI (reflecting combined preferences for depth, velocity, and cover)28Figure 2.10: Spatial patterns of composite suitability index for (a) adult and(b) juvenile brown troutare shown in Figure 2.10. High quality potential habitat for adult brown trout isfound in pools and along cutbanks where adequate cover sources are present. Shal-low riffle areas generally have low CSI values, reflecting their lack of multiplesources of cover and their high water velocities and low depths. High quality ju-venile brown trout habitat is found in the same general areas, but CSI values arelower in high velocity thalweg areas or in areas of flow convergence and higher inshallower areas (e.g. pool exit/run areas, shallow side channels). Overall WUA is338.5 m2 for adult brown trout and 581.1 m2 for juveniles. Because of the wide,anabranching nature of the study reach and lack of access to cover in most areas,the WUA values are substantially smaller than similar calculations based on depthand velocity suitability alone would be (due to the multiplication by another factorless than one representing cover). While this may preclude direct comparisons withstudies that focus only on depth and velocity, it serves to highlight the importanceof cover as a strong determinant of spatial patterns of habitat availability.292.4 DiscussionThis study demonstrated the utility of a small UAV to efficiently and accuratelycharacterize reach-scale morphology and hydraulic habitat. We were able to gen-erate a 5 cm orthomosaic and a DEM with vertical RMSE of 8.8 cm in dry areasand 11.9 cm in submerged areas, sufficient to initialize and run a 2D hydrody-namic model. Important channel features such as grain size on exposed bars, LW,and overhead cover elements were also easily identified from the high resolutionorthomosaic and used to inform analyses of available habitat.The accuracy of the topographic data generated represents an improvementover many other common survey methods. Vertical RMSE values for subaerialportions of airborne LiDAR surveys in similar settings are generally larger: Bowenand Waltermire [2002] found a value of 43 cm along the Green River floodplainin Utah, Legleiter [2012] reported 21 cm for exposed portions of the Soda ButteCreek in Wyoming, and Notebaert et al. [2009] found values of 15, 13, and 50 cmfor tests of different LiDAR datasets in Belgian river valleys. However, terrestriallaser scanning can provide slightly better accuracy, with RMSE values in the rangeof 4 cm [Williams et al., 2013b] to 6 cm [Schu¨rch et al., 2011]. Accuracies of pho-togrammetry generated surfaces at similar scales vary widely, from 4.5 cm RMSEwith terrestrial oblique digital imagery [Chandler et al., 2002] to mean errors up to1.07 m from aerial photogrammetry [Lejot et al., 2007], but are generally of deci-metric scale [Lane, 2000]. The DEM from this study is also an improvement overthe UAV-generated DEM of Hugenholtz et al. [2013], who found a vertical RMSEof 29 cm. These improvements over previous UAV photogrammetry could be at-tributed to platform stability; both Lejot et al. [2007] and Hugenholtz et al. [2013]used fixed-wing aircraft that may not be as stable as the quadcopter we used, andthe use of detailed image parameters from the inertial measurement unit and on-board differential GPS clearly aided in DEM construction compared to Lejot et al.[2007].Because UAVs allow for frequent surveys that could be used for monitoring andgeomorphic change detection, it is important to consider the limitations imposedby the errors in the topographic data. If we assume that a repeat flight of the sameElbow River reach would have the same vertical error of 8.8 cm, we can calculate30a simple limit of topographic change (T) that can be reliably distinguished fromerror:T =±3√(RMSE1)2+(RMSE2)2 (2.2)where RMSE1 and RMSE2 represent the vertical errors from the first and sec-ond survey, and the multiplier 3 represents the extreme tails of a normal probabilitydistribution, corresponding to a 99.7% confidence interval. This gives a thresholdvalue of 37.3 cm, which, in the context of hydrogeomorphic change in a dynamicgravel bed river like the Elbow, would allow for identification of processes such asbar migration, chute cutoff, and bank retreat. Although some diffuse erosion anddeposition processes may fall below the limit of topographic change [e.g. Brasing-ton et al., 2003, Wheaton et al., 2010a, Perignon et al., 2013], the main changes onthe river associated with competent floods would be detectable. If more detailedmeasures were required, a ground-based method such as total station surveyingor terrestrial laser scanning may offer better results, however, such methods aremuch less efficient and do not offer the valuable vertical vantage point and highresolution imagery associated with UAV-based photogrammetry.In terms of habitat analyses, the primary advantage of UAVs is that they canbe used to collect a combination of topographic information and aerial imagery atthe reach scale, with an appropriate resolution for many applications. Our calcu-lation of habitat availability in terms of WUA demonstrates how new technologycan be used to refine long-standing methods; although WUA has been criticized asoverly simplistic and easily misinterpreted, coupling a 2D flow model with detailedmeasures of cover availability in a GIS context allows for intuitive visualization ofhabitat suitability throughout a reach in addition to a single WUA total. Despite itsimportance for many fish species, cover is frequently neglected in WUA calcula-tions due to difficulties in characterizing it properly [Boavida et al., 2012]. UAVsurveys ameliorate this problem while also providing the necessary topographic in-formation to calculate depth and velocity distributions. The combination of coverfeatures throughout the active channel and a continuous DEM also allows for flex-ibility in terms of modeling and scenario testing. For example, different flow sce-narios could be run to determine how habitat and access to cover change with flow31Figure 2.11: Detailed view of UAV orthomosaic with (a) the original 5-cmresolution and (b) resampled 80-cm resolution and grayscale to ap-proximate IKONOS panchromatic imagerylevel without the need for further intensive field surveys. Such detailed analysesof habitat features in relation to hydraulics could not be achieved with most otherremote sensing sources. For example, Figure 2.11 shows a 70 m by 70 m sectionof the Elbow River orthomosiac with the original 5 cm resolution and the samearea resampled to 80 cm resolution and converted to grayscale to approximate thepanchromatic imagery offered by the IKONOS satellite. The loss of visual detail isstriking: individual pieces of large wood and exposed grain sizes are indistinguish-able, water depth patterns are masked, and cover features such as water surfaceturbulence are difficult to identify. Panchromatic imagery also precludes the use ofband ratios to estimate water depths, and multispectral satellite imagery is of evenlower spatial resolution (∼4 m). Similarly, although DEMs can be generated withhigh resolution satellite imagery, vertical errors are typically on the scale of 5-10m [Toutin, 2004], which is inadequate for reach-scale analyses of river structure.Despite the potential benefits of UAVs for study of channel morphology andhabitat, some restrictions and drawbacks exist. In terms of practical issues, UAVsare best suited to rivers with a wide active channel where trees do not obscuremuch of the aerial perspective. Vegetation can also affect the generation of DEMs,as photogrammetry does not allow for the determination of a bare earth DEM withvegetation removed in the way that airborne LiDAR does. In the Elbow River studyreach, vegetation effects were minimal given the wide channel belt and prevalence32of unvegetated gravel bars, but floodplain trees adjacent to the wetted channel insome areas did cause problems with unreliable elevations and shading. Similarly,water turbidity and depth are limiting factors in the applicability of any photogram-metry in river settings [Legleiter et al., 2009], but Elbow River presented an idealcase for these as well, with depths less than 1.5 m and clear water allowing fordetermination of submerged topography.The regulatory framework associated with UAVs can also limit potential ap-plications in many cases. Because of the rapid expansion of UAV technology anduse in recent years, the regulatory landscape in many countries remains relativelyimmature and restrictive [Rango and Laliberte, 2010]. In Canada, the line of sightrestriction of 800 m yields a maximum surveyable area of approximately 2 km2.While this can be overcome by moving the operator and base station to cover newareas, the potential for beyond line of sight flights would greatly increase efficiencyand applicability. Wait times to receive certification to operate and use UAVs canalso take up to 6 months, limiting the ability to quickly respond to environmentalchange. In the United States, the recent Federal Aviation Administration Modern-ization and Reform Act of 2012 (FAARMA) is set to begin the standardizationof regulations relevant to UAV use [Hugenholtz et al., 2012], which will providean opportunity for further development and testing of UAV applications and leg-islation. However, with the need to balance safety and applicability across a widerange of uses in a rapidly evolving field, it is unclear whether regulations will be-come more or less stringent in the future, and continued input from the researchcommunity will be necessary throughout the process.Overall, the ability to rapidly and accurately assess reach-scale habitat and mor-phology with relatively low costs and training requirements makes UAVs a usefulplatform to effectively fill a longstanding gap in fluvial remote sensing in termsof spatial and temporal scales. The Elbow River case study provides an exampleof how the combination of operational flexibility and data quality could tangiblybenefit river management. The river experienced unprecedented flooding in lateJune 2013, extensively damaging infrastructure and causing drastic geomorphicchanges. For comparison with the pre-flood (September 2012) imagery used inthis study, we were able to mobilize quickly to redo the UAV survey at the studyreach to explore questions of geomorphic change detection and aquatic habitat ef-33fects in the context of flood disturbance, potentially aiding restoration efforts orfuture preventative measures. The continued expansion of such relatively low cost,tailored UAV surveys will support an increased availability of high quality spatialdata that can inform effective river management.2.5 ConclusionThis study assessed the quality of UAV-collected data in a fluvial setting anddemonstrated how such information can be used to identify key reach scale met-rics of fluvial morphology and characterize important aspects of aquatic habitat. Inparticular, the combination of high resolution aerial photos and topographic data al-lows for detailed examinations of habitat-morphology linkages that can be repeatedat temporal resolutions that are difficult to achieve with other remote sensing meth-ods. The aerial perspective also allows for intuitive visualization of river structurethat can facilitate effective communication, teaching, and outreach. As the tech-nology advances and becomes more widespread, UAVs have the potential to fill adiverse range of data collection niches relevant to river scientists and managers.34Chapter 3UAV-based remote sensing offluvial change following anextreme flood event3.1 IntroductionUnderstanding the physical processes that dictate channel form and function is acentral theme of fluvial geomorphology, with important implications for predictivecapacity and management. In Chapter 2, UAVs were shown to be an efficient andaccurate way to measure spatial patterns of fluvial features of interest. While thisstatic snapshot of ecosystem conditions provides a wealth of information, a pri-mary benefit of the operational flexibility of UAV-based remote sensing is the abil-ity to easily repeat surveys and measure dynamic environmental systems to capturechanges over time. In a fluvial geomorphology context, such methods potentiallyallow for detailed investigations of process-form relationships and the inference ofthe underlying mechanisms that drive geomorphic change. In gravel-bed rivers,controls on morphodynamics include flow magnitude and duration, sediment cal-iber and supply, vegetation, and channel pattern. Many models aimed at untanglingthe influence of these factors exist, based on a rich history of theoretical, empirical,and conceptual evaluations [e.g. Lane, 1957, Leopold and Wolman, 1957, Wolman35and Miller, 1960, Millar, 2005, Gurnell et al., 2012]. The insights provided by suchmethods allow for reliable determination of process interactions in a wide range ofsystems and conditions.One of the major limitations of the majority of these studies, however, is thatthe focus is on equilibrium or near-threshold changes; the understanding of mor-phodynamics related to large flood events is less well developed. Due to the in-herent rarity and unpredictability of high magnitude events, reliable data coveringpre- and post-flood conditions are generally lacking, inhibiting the developmentof theory and predictive models that can be applied in hazard assessments, streamecosystem management, and restoration applications. Many early examples that doexist are based on historical air photo analysis [e.g. Desloges and Church, 1992]and limited topographic survey data [e.g. Miller, 1990], allowing for the assessmentof channel planform and, to some extent, vertical change. These studies illustratethe potential variability of responses to large floods: depending on geomorphicsetting, channels can experience dramatic restructuring [e.g. Pitlick, 1993], andpersistent changes in river regime and geometry [e.g. Newson, 1980, Desloges andChurch, 1992], or more minor changes with limited lasting impacts [e.g. Costa,1974, Gupta and Fox, 1974, Gardner, 1977]. A common theme documented inthese studies is channel widening [e.g. Warburton, 1994, Krapesch et al., 2011],but the degree of change is conditioned by variable factors such as vegetation, val-ley constraints, and stream power patterns [e.g. Miller, 1990, Magilligan, 1992,Lapointe et al., 1998].More detailed studies supported by recent methodological advances have al-lowed for the investigation of other aspects of channel adjustment to large floods.In particular, spatially continuous topographic data collected with methods suchas light detection and ranging (LiDAR), Structure-from-Motion (SfM) photogram-metry, and terrestrial laser scanning (TLS) facilitate the identification of the three-dimensional river structure and changes due to processes such as scour and filland bank erosion [e.g. Brasington et al., 2000, Lane, 2000, Charlton et al., 2003,Lane et al., 2003, Wheaton et al., 2010a, Harrison et al., 2011, Williams et al.,2011, Fonstad et al., 2013, Wheaton et al., 2013, Micheletti et al., 2015, Javernicket al., 2014]. For example, Grove et al. [2013], Croke et al. [2013], and Thompsonet al. [2013] quantified bank erosion over a large scale due to a catastrophic flood36using LiDAR, highlighting the contribution of mass failures in addition to fluvialentrainment on erosion processes and sediment supply. Perignon et al. [2013] in-corporated vegetation analysis into a LiDAR-based study of a large flood on theRio Puerco, New Mexico, finding spatially variable patterns of sediment erosionand deposition that depended on devegetated sediment source areas and valley wallerosion. Topographic analyses are also complemented by computational methodssuch as numerical flow modeling, which can be used to simulate fluid forces as-sociated with floods [e.g. Duan, 2001, Horritt and Bates, 2002, Williams et al.,2013a].Despite advances provided by case study availability and data quality, predict-ing large flood effects in a management context remains difficult. Piegay et al.[2005] outline a framework for assessing bank erosion risk, promoting a nestedapproach to address different spatial scales of control. This concept is also em-phasized by Krapesch et al. [2011], who studied hydraulic parameter thresholdsfor major morphologic adjustment and found that while specific stream power isa strong predictor at broad scales, more detailed parameters must be addressedat finer scales. Nardi and Rinaldi [2014], however, found that spatial patterns ofmorphologic changes due to a large flood on the Magra River were unrelated to avariety of potential controlling factors over 34 km of the river. At the reach scale,thresholds for significant change are still unclear; Eaton and Lapointe [2001] usedpre- and post-flood DEMs to assess the adjustment and estimate sediment trans-port rates associated with two floods on the Sainte Margeurite River and foundoverall morphologic stability and similar modes of adjustment for both a 7- and275-year flood. Legleiter [2014b] documented a homogenization of morphologyon the Round Prairie reach of Soda Butte Creek, likely associated with a wave ofsediment deposition, emphasizing the importance of sediment supply.Understanding the potential for scour, fill, and bank erosion must also ac-count for differing degrees of bed mobility during flood events. As flood flowsincrease, gravel-bed rivers typically shift from partial to full mobility as surfacegrains in a given size class become increasingly entrained [Wilcock and McArdell,1993, 1997]. The relationship between surface grain size and boundary shearstress is therefore a key factor; full mobility is typically reached when the ratioof shear stress to critical shear stress for incipient motion is approximately two37[Wilcock and McArdell, 1993]. Using numerical flow modeling, Lisle et al. [2000]found that sediment-rich channels have greater areas of full mobility compared tosediment-poor channels at bankfull discharge, but that significant areas of immo-bility persisted. May et al. [2009] also used flow modeling to predict bed mobi-lization, and found that mobility thresholds can predict spatial patterns of scour.However, these concepts are best applied to near-threshold conditions, and theirapplicability to more drastic floods and channel adjustment is not well understood.In this contribution, we use remote sensing imagery collected with small un-manned aerial vehicles (UAVs) to address the controls on channel adjustment as-sociated with a major flood. Small UAVs have recently been applied to a widerange of environmental science applications, supported by advances in computa-tional power and image processing software [e.g. Hugenholtz et al., 2012, Wattset al., 2012, Hugenholtz et al., 2013, Whitehead and Hugenholtz, 2014, Whiteheadet al., 2014]. In fluvial settings, UAV-based remote sensing has proven to be an ef-ficient, flexible, and reliable method of collecting high resolution topographic dataand imagery [Lejot et al., 2007, Flener et al., 2013, Woodget et al., 2014], withvertical elevation errors of less than 10 cm and image resolution on the scale of 5cm/pixel [James and Robson, 2014]. Such continuous high quality data facilitateinvestigation of aspects of earth surface processes that were previously difficult orimpossible to measure [Fonstad and Marcus, 2010, Passalacqua et al., 2014]. Bycombining UAV surveys bracketing a flood event with hydrodynamic models, weaim to address three related questions: (1) What are the reach-scale geomorphic ef-fects of a large flood event? (2) Can potential high flow conditions be constrainedusing pre- and post-flood data without knowledge of bed topography during theevent? and (3) Are simulated high flow conditions related to patterns of geomor-phic change?3.2 Methods3.2.1 Study siteElbow River is a gravel-bed river located in Alberta, Canada, that flows east outof the Rocky Mountains (Figure 2.1). At the 1 km long study site located near38the town of Redwood Meadows (50.999◦N, 114.509◦W), the channel has a weaklyanabranching/wandering pattern typical of the unconfined section of the river be-tween the foothills and the city of Calgary, an elevation of 1250 m, and a drainagearea of 791 km2. At the nearby Water Survey of Canada gauging site at BraggCreek, the mean annual flood is 87.2 m3/s and the largest flow on record since1950 is 377 m3/s. Before the flood effects, the bed surface D50 was 34.72 mmand the bed slope was 0.64%. Active channel width was approximately 150-200m with extensive gravel bars exhibiting sparse low shrub vegetation. The studyreach was chosen prior to flooding as a test site for UAV remote sensing of fluvialstructure and aquatic habitat; details can be found in Chapter 2.3.2.2 2013 flood eventBetween June 19-23, 2013, Southwestern Alberta experienced unprecedentedflooding leading to widespread social, ecological, and physical infrastructure im-pacts. The Insurance Bureau of Canada estimated the insured losses exceeded $1.7 billion, making it the most expensive natural disaster in Canadian history atthe time of writing. A combination of factors led to record flows in the Elbow,Bow, and High River basins: high rainfall caused by unique meteorological condi-tions, wet antecedent conditions, and remaining snowpack and frozen ground in themountainous regions where peak rainfall occurred. Precipitation totals exceeded300 mm during the event, and flashy runoff response caused rapid flooding. OnElbow River, recorded reservoir inflows at Glenmore Dam (30 km downstream ofthe Redwood Meadows study site) peaked on June 20 at 1,240 m3/s, correspond-ing to an approximately 500 year return period. Although peak flow dischargeswere not available at the Bragg Creek gauging site near the study reach, maximumdischarge was estimated at 850 m3/s with a rating curve relating recorded stage todischarge. Throughout the region, mountain tributaries contributed large sedimentloads to higher order streams and bank erosion was widespread, causing substantialgeomorphic change to fluvial systems.39Figure 3.1: (a) Aeyron Scout quadcoptor used in 2012 survey and (b) eBeefixed-wing UAV used in 2013 survey.3.2.3 UAV surveysTo study the effects of the flooding on the Elbow River study reach, we used UAVsurveys bracketing the event. The pre-flood survey was conducted in late Septem-ber 2012 using an Aeryon Scout quadcoptor (Aeryon Labs, Inc.) that acquired im-agery for subsequent photogrammetry (Figure 3.1). The Scout was pre-programedto cover set waypoints; at each waypoint, true-color images were acquired witha three-axis stabilized Photo 3S camera with an 8.4-mm lens and a field of viewof 37◦ x 29◦. Eight flight lines running parallel to the river covered the 800 mlong study reach, and 192 geotagged images were collected. The flying speed ofthe quadcoptor was 5 m/s, with a height of 100 m above the ground surface. Thesurvey was supported by 45 visible ground control points (GCPs) which were mea-sured to a vertical precision of 2 cm with a real-time kinematic (RTK) GPS.The post-flood survey was performed in late September 2013. No geomoph-ically effective floods occurred in the two months between flood event and UAVsurvey; field visits in July and August 2013 confirmed that the morphology of theriver did not change significantly. The UAV used for this survey was an eBee40(senseFly, Ltd.), a fixed-wing platform with a 96 cm wingspan (Figure 3.1). Thegeographic extent of the 2012 survey was used to guide the setup of 10 flight linescovering the study reach. Flying height was 100 m and speed was 10 m/s, and 310geotagged RGB images were collected with a 16.1 megapixel Canon IXUS 127HS camera. Forward image overlap was approximately 80% along the flight pathwith 70% sidelap across the flight paths. We also laid out 35 GCPs for georegister-ing, spaced throughout the survey area. Both the 2012 and 2013 UAV surveys andassociated ground measurements were completed in less than a day each.Photogrammetric processing was carried out in a similar overall manner forboth surveys. The 2012 images were processed with the EnsoMOSAIC UAVpackage (MosaicMill, Ltd., Finland), while the 2013 images were processed withPix4D software (Pix4D SA, Switzerland). Both software suites calculate internalcamera orientations and calibration parameters for subsequent steps. The Pix4Dsoftware automates traditional photogrammetric steps and incorporates novel al-gorithms (like the scale invariant feature transform, SIFT, Lowe [2004]) to deter-mine camera positions and match keypoints between images. The process theninvolves automatic aerial triangulation (AAT) to calculate ground coordinates withreference to the measured GCPs, and bundle block adjustment (BBA) in an itera-tive manner to optimize mosaic accuracy. Once BBA error is minimized, a digitalelevation model (DEM) is generated and used to orthorectify the image mosaic.The resulting orthomosaics and DEMs had spatial resolutions of 5 cm/pixel and 4cm/pixel for the 2012 and 2013 surveys, respectively.3.2.4 DEM correction and accuracy assessmentBefore the photogrammetry generated DEMs could be used in subsequent analy-sis, several correction and quality assessment steps were performed. A ubiquitousproblem in photogrammetric analyses of fluvial systems is the refractive effects ofwater, which lead to overpredictions of elevations in submerged areas [e.g. Lane,2000, Westaway et al., 2000, 2001, 2003, Bird et al., 2010]. Although our UAV sur-veys were performed during low-flow conditions, both DEMs required correctionfor this problem. Following tests discussed in Chapter 2, we used an optical/em-pirical bathymetric correction process similar to that used by Legleiter [2012] and41Table 3.1: Error statistics comparing modeled (DEM) elevations with mea-sured GPS check point elevationsn ME (m) RMSE (m) SDE (m) Emax (m)2012Exposed 221 0.032 0.088 0.007 0.320Uncorrected submerged 76 0.144 0.169 0.008 0.386Corrected submerged 76 0.0001 0.098 0.009 0.3542013Exposed 213 0.007 0.047 0.002 0.191Uncorrected submerged 82 0.154 0.218 0.009 1.002Corrected submerged 82 0.0007 0.095 0.023 0.318Williams et al. [2013b]. This process involved regression of field-measured waterdepths at geolocated points against natural log-transformed red and green intensitypixel value ratios from the orthomosaics to take advantage of the linear relationshipbetween transformed water color and water depth [Lyzenga, 1981, Legleiter et al.,2009]. We developed a map of water depth by applying this empirical relation tothe rest of the wet pixels in the orthomosaic, which we then subtracted off a watersurface elevation raster produced by interpolation through water’s edge elevationpoints using kriging. This corrected bed surface was then mosaicked with the dryareas to produce a seamless corrected DEM.To assess the accuracy of the topographic data generated from the UAV im-agery, we used elevations from field-measured checkpoints. These points wereindependent of DEM generation or image mosaicking and were measured with theRTK GPS. By stratifying points into submerged and exposed dry areas and com-paring modeled (DEM) elevations with measured (GPS) values, we were able tocalculate error statistics for both surveys, averaged over wet or dry areas. Equationsfor these metrics are shown in Table 2.1; values for both UAV surveys are shown inTable 3.1. Similarly, density plots of elevation residuals for both years are shown inFigure 3.2. For all calculations, errors were calculated as field-measured elevationssubtracted from modeled elevations; positive values indicate overprediction of el-evations in the DEM and negative values indicate underprediction of elevations inthe DEM.4202.557.510−0.2 0 0.2Density20122013−0.2 0 0.2Elevation difference (m)(a) (b)Figure 3.2: Density plots comparing modeled (DEM) elevations with mea-sured GPS check point elevations for (a) exposed and (b) corrected sub-merged points. Vertical dashed lines indicate the mean error for eachdataset; elevation differences were calculated as modeled minus mea-suredOverall, vertical accuracy in the DEMs was high for both survey years, with amaximum RMSE of 0.098 m in submerged areas of 2012 following DEM correc-tion. For context, this is in the same range as the D84 of 0.068 m in the study reach.Correction of submerged areas improved the RMSE by 0.07 - 0.11 m and reducedmaximum errors as well. In general the 2013 survey had lower errors than the 2012survey, which could be attributed to different UAVs, photogrammetry software, orflight conditions between the two years. The errors of these topographic surfaceswere deemed appropriate for the DEM differencing and flow modeling performedfor this reach-scale investigation.3.2.5 DEM differencing and change detectionGeomorphic change associated with the flood event was assessed using DEM dif-ferencing between the two surveys. We used the Geomorphic Change Detection5.0 (GCD) software (http://gcd.joewheaton.org) to determine elevation changesand account for DEM uncertainty propagated through the calculation [Wheatonet al., 2010b]. This involved creation of error surfaces based on RMSE valuescalculated in Table 3.1. Errors were stratified based on submerged or exposed ar-43eas for both years. Because the channel widened significantly between 2012 and2013, the 2013 DEM had areas where limited elevation data were available for2012. To account for this mismatch, we added interpolated floodplain surfacesto the 2012 DEM based on field observations of pre-flood bank heights and pointmeasurements of elevation. These interpolated areas were assigned a conserva-tive 0.5 m error value and allowed us to estimate volumes of sediment associatedwith bank erosion. Patterns of elevation change with changes below the mini-mum levels of detection (LoD) thresholded out were then mapped throughout thereach, and areas and volumes of erosion and deposition were calculated from theDEM of difference (DoD). A 99.7% confidence interval was chosen for the errorpropagation from successive surveys, resulting in a stringent assessment of topo-graphic change, particularly when compared to the magnitude of elevation changesobserved throughout most of the reach.As a second method of characterizing reach-scale morphology and change be-tween the two surveys, we used geostatistical models of channel structure [Chap-pell et al., 2003, Legleiter and Kyriakidis, 2008, Legleiter, 2014a,b]. To do so, wecalculated variograms to assess dissimilarity of elevations as a function of distancebetween observation pairs. In fluvial settings, variogram analysis can be applied totopographic data to determine characteristic horizontal and vertical length scalesand variance of elevations. The models are flexible and can account for varyingchannel complexity and anisotropy in different directions. They can also be ap-plied to detrended and scaled data to provide dimensionless characterization ofchannel structure and facilitate comparison between different systems and at dif-ferent times.We calculated sample variograms in streamwise (along-stream) and normal(across-stream) directions for the pre- and post-flood topographies. Dissolvingthe analysis into two direction vectors accounted for the inherent anisotropy inriver channels, which are typically more variable across the channel than along it.We were therefore able to fit isotropic models to sample variograms computed ineach direction. Although coordinate transformation from a Cartesian to channel-centered system is often applied to allow for a consistent frame of reference re-lated to in-stream distances [Legleiter, 2014b], the wide channel belt and irreg-ular braided morphology of the Elbow River study reach made this impractical.44Instead, we rotated the DEMs so that valley slope was oriented north-south andthe across-stream direction was east-west [Chappell et al., 2003]. We then de-trended the DEMs based on average valley slope and calculated residuals from themean detrended elevation. To remove scale effects associated with channel size,we normalized elevations by estimated bankfull depth and normalized horizontaldistances by average channel width [Legleiter, 2014b]. We then calculated samplevariograms γˆ(h) for given lag vectors (h), which show the dissimilarity betweensample pairs different distances apart. Because of the density of our elevation data,we used small values of directional tolerance (∆φ ) and band width (bm) to ensurecorrect resolution of calculations into streamwise and normal components. Withsample variograms calculated, we then fit exponential model variograms to thedata, allowing for determination of summary parameters.3.2.6 Flow modelingTo simulate high flow conditions and investigate their relationship to the quantifiedgeomorphic change, we used the 2D hydrodynamic model Nays2DH implementedwithin the International River Interface Cooperative (iRIC) framework (http://i-ric.org/en/). This was a change of model from Chapter 2 where River2D was used;this choice was made to capitalize on application stability and computational ef-ficiency in model performance. Nays2DH is a depth-averaged model that solvesthe Saint-Venant equations of free surface flow with finite differencing based on ageneral curvilinear coordinate system. Inputs to the model are discharge, topog-raphy, and roughness in the form of Manning’s roughness parameter. The modelapproach is based on assumptions of steady hydrostatic flow and accounts for tur-bulence through an isotropic eddy viscosity.We first ran simulations of the flows through the pre- and post-flood topogra-phies for the low-flow conditions at the time of each UAV survey. This allowedus to tune the model parameters with accurately measured values of water sur-face elevations and depths for discharges of 5.9 m3/s (2012) and 6.1 m3/s (2013).To account for roughness, we created continuous maps of sediment size (D50 andD84) throughout the study reach by relating measured grain sizes with measuresof image texture in the orthomosaics [e.g. Carbonneau, 2004]. This involved tak-45ing close-range geolocated vertical photos of 1 m2 sediment patches on exposedbars during field sampling, determining D50 and D84 with a photosieving GUI pro-gramed in MATLAB that facilitates measurement of particle b-axes, and then relat-ing sediment size for each location to image texture in the orthomosaic calculatedas standard deviation within a 1 m2 window (Figure 3.3). This relationship wasthen applied to the rest of the exposed areas to map out sediment sizes. For wettedareas, where sediment was not visible, we assigned constant sizes for D50 and D84drawn from coarse sediment on exposed bar heads, with the assumption that thesedeposits are representative of submerged sediments transported at high flows. Us-ing these coarse values provided a conservative estimate of submerged grain sizes.D84 distributions were then used as model inputs in Nays2DH to determine Man-ning’s roughness parameter.Following model calibration to low flow conditions, we performed steady sim-ulations of the peak discharge for the June 2013 flood of 850 m3/s run through boththe 2012 and 2013 DEMs. Although bed conditions during the flood event are un-known and were undoubtedly different than pre- and post-flood configurations, thisprocedure allowed us bracket potential distributions of flow parameters during theflood peak and investigate channel adjustment to such high flows. Similarly, theflood pulse had a very rapid rising limb, meaning a topographic condition similarto the pre-flood DEM was likely subject to the majority of fluid forces at peak flow.By making the assumption that the pre-flood topography experienced the bulk ofthe flood flow before substantial adjustment could occur, we were able to examinethe extent to which geomorphic change for a flood of a given magnitude can bepredicted through hydrodynamic modeling based on pre-flood conditions alone.Model topography was extracted directly from the DEMs. Computational gridspacing for both simulations was set to 2 m x 2 m. To restrict flood flows to theactive channel for the simulations, we created artificial 10 m tall channel banksbounding the areas of interest. Although overbank flooding occurred during theevent, water in the floodplain is much less active than that of the channel anddoes not contribute to geomorphic change. Adding these bank restrictions to theDEMs also allowed us to measure water surface elevations associated with mod-eled flows. This provided a method with which to assess the model performancerelative to high water marks (visible from mud stains, rafted debris, etc.) sur-4602040608020122013050100150200100 20 30 40Image textureD 84 (mm)D 50 (mm) R 2 = 0.79R 2 = 0.77R 2 = 0.69R 2 = 0.81(a)(b)Figure 3.3: Empirical relationships between field-measured surface grainsize and image texture in the orthomosaics for (a) the D50 and (b) theD8447124312441245124612471243 1244 1245 1246 1247Measured WSE (m)Modeled WSE (m)20122013Figure 3.4: Comparison of field-measured evidence of peak flow water sur-face elevations and modeled water surface elevations for pre- and post-flood simulations.veyed in July 2013 following the flood [e.g. Baker, 1987]. These measured highwater marks were also used as starting downstream water surface elevations re-quired in the Nays2DH model parameterization. While not a perfect measure oftrue water surface elevations due to splashing and run-up around obstacles, thecomparison between modeled and measured water surface elevation showed thatthe model predictions were in the right range (Figure 3.4) considering the potentialdegree of variability associated with such large flood events. The 2012 simulationclosely followed a 1:1 line (y = 0.96x+ 47.8,R2 = 0.96) indicating correspon-dence between measured and modeled values and supporting the assumption thatthe pre-flood topography was subject to the majority of peak flows. Modeled watersurface elevations were slightly underpredicted in upstream portions of the reachand overpredicted in downstream portions of the reach for the 2013 simulation(y = 0.83x+ 209.3,R2 = 0.94); this effect could be attributed to the changes inchannel width that occurred through the flood.48With the combination of continuous grain size maps and model-output hy-draulic parameters, we determined distributions of shear stress associated with highflows for both the 2012 and 2013 simulations. Because shear stress was not a directoutput from Nays2DH, it was calculated as:τo = ρ(V(8/ f )1/2)2(3.1)where τo is shear stress, ρ is the density of water, V is velocity, and f is theDarcy-Weisbach friction factor [Ferguson, 2007]. The term (8/ f )1/2 was calcu-lated from grain size as proposed by Parker [1991]:(8/ f )1/2 = 8.1(d/2D84)1/6 (3.2)where d is water depth. Shear stress was therefore determined for each pointoutput from the model in a manner consistent with the Manning’s n approach toflow resistance used in Nays2DH.The calculated shear stresses were then related to the spatially continuous mapsof surface grain size to investigate patterns and distributions of potential for sedi-ment transport. We assumed a critical Shields stress value of 0.045 for entrainmentof the D50 in unstructured gravel mixtures [e.g. Komar, 1987, Church, 2006], al-lowing calculation of the critical shear stress for the D50 at each model point:τc50 = 0.045(ρs−ρ)gD50 (3.3)where ρs is the sediment density (assumed to be 2,650 kg/m3) and g is theacceleration due to gravity. We also calculated critical shear stress for the D84 asin Wilcock and Crowe [2003]:τc84 =(D84D50)0.67(3.4)We then normalized calculated shear stresses by critical shear stresses for easeof interpretation in the context of proposed thresholds for partial and full mobility,where a normalized shear stress greater than one indicates potential partial mobility49for the size fraction and greater than two indicates potential full mobility for thatsize fraction.3.3 ResultsOrthomosaics developed for both survey years are shown in Figure 3.5. Qualita-tively, the geomorphic change is striking: the channel widened substantially andpre-flood features were erased completely. Reach average width, measured as ac-tive channel area divided by length, increased from 149.15 m to 192.03 m. Vege-tation and accumulations of large wood were stripped from bar top locations; newwood jams formed throughout the channel, particularly along outer bank bends.Low flow patterns were also rearranged. Whereas the pre-flood low flow channelsplit into 2-3 main anabranches around mid-channel bars, the post-flood configura-tion is much more concentrated in a single thread pattern interrupted occasionallyby smaller-size bars. This new low flow channel also appears to have a long me-ander wavelength (on the scale of 800 m) within the active channel, approximatelytwice that of the dominant thread in 2012.Observable flood effects from the orthomosaics can be examined in more detailwith the use of the DEMs and the change detection analyses applied. Pre- and post-flood DEMs and the DoD are shown in Figure 3.6. The planform changes evidentin the orthomosaics are supported by the topographic data, with low flow channelsclearly demarcated in detrended DEMs. Bar features appear to have a larger, moreelongated characteristic scale following the flood and are less dissected. The DoDshows variable changes in elevation, with overall reliable change detection due tothe high magnitude elevation changes relative to the minimum threshold. Of the183,447 m2 study area, 89% of vertical change was above the detection threshold.Spatial variations in the DoD reflect infilling of pre-flood low flow channels andcarving of new channels, along with significant bank erosion.Summaries of areal and volumetric erosion and deposition are shown in Table3.2; distributions for each measure are shown in Figure 3.7 For these analyses,areal changes reflect the total surface area within a given vertical change bin, andvolumetric changes are calculated as areal distributions multiplied by the depth ofelevation change for each area. Overall, the study reach experienced a net vol-50Figure 3.5: Orthomosaics for (a) pre- and (b) post-flood UAV surveys. Theblack dashed outline on the post-flood image corresponds to the extentof the 2012 survey, for referenceTable 3.2: Topographic change determined through DEM differencingArea (m2) Volumetric change(m3)Mean elevationchange (m)Including bankerosionAggradation 77,122 50,306 ± 8,528 0.65 ± 0.11Degradation 86,582 -89,294 ± 22,284 -1.03 ± 0.26Net change -38,988 ± 23,860 -0.24 ± 0.15Excludingbank erosionAggradation 64,371 43,580 ± 3,314 0.68 ± 0.06Degradation 58,561 28,805 ± 4,841 -0.64 ± 0.07Net change 14,775 ± 4,277 0.13 ± 0.0451Figure 3.6: Detrended pre- and post-flood DEMs and DEM differencing. De-trended elevations reflect the removal of valley slope and normalizationrelative to mean reach elevation; positive values are above mean eleva-tion, negative values are below mean elevation52umetric change of -38,988 ± 23,860 m3, much of which is attributable to bankerosion. The conservative error values for interpolated bank areas explain the highuncertainty in this estimate. This net volumetric change corresponds to a net ele-vation change of -0.24 ± 0.15 m averaged over the study reach. However, scourand fill were both significant throughout the reach, as 36% of volumetric changewas depositional and 64% was erosional. If the areas of bank erosion are excludedfrom the change detection to focus on the active channel bed, the net volumetricchange is 14,775 ± 4,277 m3. Given the accuracy of elevation data for the activechannel, uncertainty is substantially reduced, showing a net depositional budget inthe channel bed itself.To further investigate patterns of erosion and deposition, we stratified elevationchanges based on geomorphic unit classification of the pre-flood reach. Areas wereclassified as either low flow channels, bar edges/chutes, or bar tops to relate to gen-eral elevation distributions of features. This allowed for an investigation of whetherelevation changes due to flooding depend on pre-flood geomorphic configuration.Plotting elevation change against detrended relative pre-flood elevation (Figure3.8) shows that the classified features generally have characteristic elevations asexpected, with low flow channel points at low elevations, bar edges/chutes at inter-mediate elevations, and bar tops at higher elevations. In terms of elevation changedue to flooding, low flow channel and bar edge/chute areas experienced similar,generally positive elevation changes, whereas bar tops experienced changes closerto zero or negative. While differences in elevation change between low flow chan-nel and bar edges/chute areas were not statistically significant, bar tops experi-enced significantly more scour than both other classes (ANOVA p <0.001, posthoc Tukey HSD p <0.001). These results suggest an evening out of pre-flood to-pography: higher elevation bar top areas were generally scoured whereas lowerelevation bar edges, chutes, and low flow channel areas were generally infilled.Analysis of the geomorphic changes and their relation to pre-flood topographiccondition can be further supported by the variograms applied to pre- and post-floodconfigurations. Dimensionless sample variograms and fitted models are shown inFigure 3.9 for both years. In the streamwise direction, the sill increased substan-tially from 2012 to 2013, with a slightly larger range. This suggests an increasein topographic heterogeneity and a longer characteristic streamwise length scale53050001000002000400060008000−4 −2 0 2 4Elevation change (m)Volume (m3 )Area (m2 )(a)(b)Figure 3.7: Distributions of (a) areal and (b) volumetric elevation changes de-termined through DEM differencing. Changes were calculated as 2013elevation minus 2012 elevation; positive values represent deposition andnegative values represent erosion. Dark grey portions of histograms aredata that were below LoD thresholds.of bar features. However, a distinct along-stream pattern in variability is shown in2013 beyond the initial sill, with peak in dissimilarity at lags of 2 and 4 channelwidths, and a minimum at 3. This effect is likely attributable to the increasinglysingle-thread, sinuous nature of the main channel that developed as a result ofthe flood; this underlying pattern was not removed through the analysis becausewe centered the coordinate system along the entire active channel rather than thisnewly developed thalweg. The across-stream variograms also reflect an increasein heterogeneity resulting from the flood. For both 2012 and 2013, the normal sill54−2−1012−2 −1 0 1 22012 relative elevation (m)Low flowchannelBar edges/chutesBar topsElevation change (m)Low flow channelBar edges/chutesBar tops(a) (b)Figure 3.8: (a) Scatterplot showing flood-induced elevation change in rela-tion to pre-flood detrended relative elevation for different geomorphicunits and (b) distributions of elevation change associated with each unittype00.020.040.060.080 1 2 3 4Streamwise lag distance, h (channel widths)γ (h)201220130 0.3 0.6 0.9 1.2Normal lag distance, h (channel widths)(a) (b)Figure 3.9: Sample and model variograms in (a) streamwise and (b) normaldirections for pre- and post-flood DEMsis greater than the streamwise sill, suggesting the characteristic anisotropy of riverchannels remains. However, the difference between streamwise and normal sill ismuch less for 2013, caused by the increase in streamwise variability.Results of the flow modeling provide insight into the relationship between theflood magnitude, forces applied to the channel bed, surface sediment sizes, andchannel morphodynamics. Density plots and cumulative distributions comparing55simulations of 2012 (pre-flood topography) and 2013 (post-flood topography) floodforces are shown in Figure 3.10. These distributions reflect the variation in mod-eled shear stress and mapped sediment size for pre- and post-flood simulations. Forboth 2012 and 2013, a large proportion of normalized shear stresses far exceededthresholds of entrainment, partial mobility, and full mobility. In general, normal-ized shear stresses for both the D50 and D84 were much higher in the 2012 model:66.6% of bed area had a value over two for D50 in the pre-flood configuration com-pared to 49.7% in the post-flood configuration. For the D84, 50.3% of bed areaexceeded a normalized shear stress of two for the pre-flood topography whereas26.3% did for the post-flood. Despite the high shear stresses for both years, calcu-lated distributions of normalized shear stress predicted some of the bed to remainimmobile (τo/τci <1), particularly in the post-flood configuration where 40.0% ofbed area was below thresholds for entrainment of the D84. In the simulation usingthe pre-flood topography, only 26.6% of bed area was below this threshold. Theseresults suggest a topographic or grain size adjustment due to the flood, resulting indiffering distributions of local boundary shear stresses in relation to surface grainsize.Because thresholds for sediment transport have been shown in previous studiesto relate to the proportion of bed surface area experiencing geomorphic adjustmentand to the magnitude of elevation change, we examined the relationship betweenour modeled normalized shear stresses for the pre-flood simulation and the ero-sion and deposition determined through DEM differencing. If elevation changesare dependent on modeled shear stress values from pre-flood conditions, this rela-tionship could be used predictively to assess potential distributions of geomorphicchanges for future flood events. Scatterplots for both the D50 and D84 normalizedshear stresses are shown in Figure 3.11, and mapped patterns of normalized shearstresses in comparison to pre-flood topography and observed elevation changes areshown in Figure 3.12. If τo/τci were a direct control on elevation change, morescour would be expected for higher τo/τci values. However, our results show nodetectable relationship. A wide range of scatter exists in the data; some areas withsmall τo/τci values experienced elevation changes representing up to 2m of scouror 2m of aggradation, whereas some areas with τo exceeding 10-20x thresholdsfor entrainment showed minimal elevation changes. In fact, the largest elevation5600.10.20.30.40.500.250.500.7510 5 10 15τo / τc50 τo / τc84Cumulative proportionDensity0 5 10 1520122013(a) (b)(c) (d)Figure 3.10: Distributions of modeled normalized shear stress for pre- andpost-flood simulations. Vertical dashed lines represent thresholds forpartial and full mobilitychanges appear to correspond to values of τo/τci in the 5-10 range. While suchvalues are still far above thresholds for full mobility and likely correspond to zonesof intensive sediment transport, this analysis shows that the signal of sediment en-trainment thresholds is masked by confounding factors such as changing patternsof scour and fill through the flood event related to sediment supply and completereworking of bed morphology.57Figure 3.11: Relationships between elevation changes and modeled normal-ized shear stress for (a) the D50 and (b) the D84Figure 3.12: Comparison of patterns of (a) pre-flood elevations, (b) modelednormalized shear stresses for the D84, and (c) elevation changes in asubsection of the study reach583.4 DiscussionThe June 2013 flooding on Elbow River caused substantial geomorphic change.By combining UAV-based remote sensing, photogrammetry, and flow modeling,we characterized three-dimensional topographic changes and placed them in thecontext of simulations bracketing potential high flow conditions. Overall, the floodevent was degradational with large elevation changes throughout the study reach.Bank erosion was prominent and contributed to major morphologic changes in-cluding widening, increased elevation variability, and a restructuring of channelpattern. These patterns of change were largely unrelated to initial conditions andresulted in a morphologic adjustment towards bed surface stability.The observed topographic change of the reach suggests that the flooding re-sulted in a catastrophic change in the sense that the channel morphology is nowadjusted to a new regime associated with a larger formative discharge [e.g. Deslo-ges and Church, 1992]. To put this event in the general context of other studiesof flood changes, unit stream power ω was calculated as ω = γQS/w, where γ isthe specific weight of water, Q is the peak discharge, S is the gradient, and w isthe channel width. Based on the pre-flood topography, the reach experienced apeak unit stream power of 4,816 W m−2. This is far above proposed thresholds formajor morphologic adjustment and floodplain stripping (e.g. 300 W m−2, Magilli-gan [1992]), and fits into ranges measured by Krapesch et al. [2011] for reachesthat widened substantially due to flood events. Given the degree of bank erosionand vertical change associated with the June 2013 flood, this is not surprising, andsuggests that unit stream power could be a useful predictor of general flood ef-fects (widening, entrainment of most sediment sizes) on Elbow River if pre-floodtopography and a modeled discharge were used predictively.While reach-average factors such as unit stream power can be used as generalscreening tools, a primary goal of our study was to assess the possibility of usingmore detailed parameters to address local interactions between flow patterns andmorphologic changes. This approach was facilitated by the imagery and elevationdata provided by UAV-based remote sensing, which allowed for accurate assess-ments of elevation changes at a hyperspatial resolution. Combining these elevationchanges with hydrodynamic modeling at a similar scale provides a powerful tech-59nique with which to investigate a wide range of geomorphic questions that wouldbe difficult or impossible to address in such a spatially continuous manner withmore traditional approaches such as manual surveying.In terms of potential controls on bed adjustments, we found that there was norelationship between patterns of modeled shear stress and topographic change. Al-though classified geomorphic unit types stratified elevation changes to some extent(Figure 9), with bartops generally experiencing erosion, these patterns could not beexplained by modeled shear stresses relative to surface grain sizes in the pre-floodconfiguration. This result contrasts with studies that took similar approaches tofluvial adjustments associated with smaller floods. For example, May et al. [2009]found that although the relationship between scour depth and modeled Shields pa-rameter was highly variable, there were distinct probability distributions of scourfor given ranges of Shields stress. Similarly, Lisle et al. [2000] suggested thatmobile areas of river channels with high boundary shear stress and fine bed sur-face material are prone to rapid morphologic change. In the case of Elbow River,the magnitude of the flood was such that significant areas of the reach experiencedconditions far above thresholds for mobility (Figure 11). Complex patterns of com-pensating scour and fill occurred, related to large volumes of sediment suppliedfrom upstream areas and local bank erosion. The spatially variable topographicadjustments are largely unpredictable, suggesting a nonlinear relationship betweeninitial conditions, flow forces, and morphologic change. This finding supports theconcept that sediment transport is controlled by entrainment in near-threshold con-ditions, but shifts to depend on the rate at which momentum is transferred from thefluid to the boundary materials (related to stream power) at higher magnitude flows[e.g. Bagnold, 1966, 1980, Laronne and Carson, 1976, Eaton and Church, 2010].Morphologic adjustment in this case is therefore more dependent on sediment fluxthan entrainment, meaning relationships between shear stress and sediment sizeslike the ones we tested are inadequate predictors of elevation changes. Determiningpotential scour hazards for large flood events in streams like Elbow River is there-fore difficult as the system transitions to a largely chaotic regime where spatialchanges cannot be accurately predicted from initial conditions.The geomorphic restructuring of the reach appears to have led to an adjustmenttowards future stability in the face of large flows. Analysis of the thresholds for60partial and full mobility (Figure 11) shows that if the pre-flood topography weresubject to the entire peak flow, at least 50% of the bed surface D84 would be fullymobile. If the same flood discharge were run through the post-flood morphology,only 25% of the bed surface would be fully mobile, leaving 75% of the reach eitherpartially mobile or below thresholds for entrainment. Since coarse grains controlmuch of bed stability, larger flows will be required to produce major restructuringin the post-flood reach. This contrast in normalized shear stresses between pre-and post-flood conditions could be caused by a combination of bed surface coars-ening related to large sizes in sediment supply and armoring during waning flows[e.g. Parker and Klingeman, 1982, Dietrich et al., 1989] and the widening asso-ciated with bank erosion leading to a conveyance of flood flows with lower shearstresses. These findings support the notion that the channel adjustments associatedwith large floods may remain as a long-term control on geomorphic processes [e.g.Hickin and Sichingabula, 1988, Desloges and Church, 1992, Gardner, 1977]. Fu-ture monitoring of morphodynamics associated with more common, smaller floodson Elbow River will provide further insight into the persistence of these effects andthe potential of channel readjustment through sinuosity changes, revegetation, andbed sediment sorting.Overall, this study demonstrates how recent advances in data collection andanalysis methods can be applied to novel questions about Earth surface processes.In fluvial settings, changes that occur at intermediate spatial and temporal scalesare of critical importance for both the physical geomorphic template and ecologicalprocesses [Fausch et al., 2002]. Similarly, the importance of spatial heterogeneityhas increasingly been recognized as fluvial science and remote sensing technolo-gies have developed [Carbonneau et al., 2012]. UAV-based remote sensing pro-vides a flexible, efficient, and accurate tool for geomorphic studies in this context.As shown in this work, detailed spatial relationships between pre-existing topogra-phy and fluvial change are easily identified, both qualitatively through comparisonof high resolution orthoimagery and quantitatively through DEM differencing andgeostatistics. This scale of analysis also meshes easily with numerical flow mod-eling, particularly two and three-dimensional approaches that can be applied togeomorphically complex reaches and extended to ecological applications. Whenhigh-resolution elevation models are combined with meter-scale hydraulics, ques-61tions of controls on geomorphic change can be addressed in ways that were pre-viously not possible. With the operational flexibility associated with UAV surveysand the rapid proliferation of the technology, multi-temporal datasets capturinggeomorphic processes in three dimensions will become more common [e.g. Pas-salacqua et al., 2014, Tarolli, 2014]. The case study of Elbow River shows poten-tial strengths and limitations of how such datasets and modeling can contribute tothe wider understanding of fluvial processes at intermediate scales. Future workextending these techniques to flood events in other systems and incorporating as-pects of habitat quality and complexity will allow for identification of thresholdsfor physical and ecological changes and inform sustainable management of fluvialsystems.3.5 ConclusionThe inherent rarity of large flood events means that studying and predicting theireffects is difficult. Reliable pre-flood data are often lacking, prohibiting detailedanalyses of geomorphic change. In this study, we used high resolution pre- andpost-flood topographic data and imagery collected with UAVs to document de-tailed morphologic adjustment resulting from a major flood on Elbow River. Theflood resulted in large (>2 m) elevation changes, widespread bank erosion, and acomplete reorganization of channel pattern. These effects seem likely to persist inthe future as the reach morphology is now stabilized and adjusted to larger flows.Because such datasets can now be collected reliably and efficiently with currentmethods and technology, it is also important to know whether predictive capacitycan be enhanced as more examples of large flood effects are obtained. We foundthat simulations of peak flows can be used to constrain ranges of flood forces inrelation to bed surface sediment sizes and are related to reach-scale adjustments,but that local patterns of erosion and deposition are unrelated to initial conditionsin such a high magnitude event. Continued collection of such detailed examplesof fluvial change will support the development of more advanced predictive andtheoretical understanding of the geomorphic effects of large floods.62Chapter 4Impacts of geomorphic change onreach scale flow structure andaquatic habitat4.1 IntroductionAs shown in Chapter 3, the geomorphic effects of a high magnitude flood event onElbow River were major, resulting in complex adjustments to vertical and planformgeomorphic structure. Simulations of peak flood forces showed that the majority ofthe active channel of the reach experienced bed shear stresses far in excess of sedi-ment entrainment thresholds; significant erosion and deposition resulted in a com-plete rearrangement of channel morphology and substantial bank retreat. Giventhe evidence of similar geomorphic response along the length of the river, the2013 flood event potentially impacted aquatic ecosystems throughout the region,both through immediate acute flood impacts to aquatic organisms [e.g. Jensen andJohnsen, 1999, Korman et al., 2011] and more lasting effects on physical habitat.However, relationships between overall geomorphic adjustments and ecologicallyrelevant changes that may directly influence aquatic species are complicated. Thischapter therefore focuses on expanding previous interpretations of geomorphic im-pacts in an ecohydraulics context, addressing the way that changes to the physical63structure of Elbow River alter reach-scale hydraulics at the sub-bankfull flows thatmost frequently define aquatic habitat.Flow velocity and depth patterns are fundamental controls on stream ecosys-tem structure and function. Hydraulic attributes determine habitat availability andquality for aquatic organisms ranging from macrophytes and benthic invertebratesto fish communities [Hughes and Dill, 1990, Madsen et al., 1993, Silva et al., 2014],with important implications for linked aquatic and terrestrial ecosystems [Nakanoand Murakami, 2001, Gende et al., 2002]. Flow patterns also control physical pro-cesses such as sediment flux in alluvial channels that can reshape morphology andconsequently restructure local hydraulics. These relationships between hydrogeo-morphology and ecology create dynamic fluvial environments that are sensitive tochanging external pressures and controls.A rich history of research linking flow conditions with species preference anduse exists [e.g. Chapman, 1966, Statzner et al., 1988, Leclerc, 2005, Clifford et al.,2008]. In particular, a focus on lotic fish habitat based largely on correlative habi-tat/use studies has shown that specific physical conditions are required for thespawning, rearing, and feeding of different fish species and life stages [Morantzet al., 1987, Heggenes and Saltveit, 1990, Armstrong et al., 2003]. Although ex-tension of these microhabitat preferences to broader-scale community and popula-tion dynamics is difficult and the distribution of individuals is strongly mediated byother factors such as predation and competition [Van Horne, 1983], the ’fundamen-tal niche’ of habitat preference [cf. Hutchinson, 1957] remains a strong foundationupon which to relate species’ ecology to physical stream conditions [Rosenfeld,2003], and physical habitat has been shown to be a key control on patterns of fishdistribution and abundance [Milner et al., 2003].The importance of physical habitat is recognized in river management andrestoration applications. As rivers globally have been increasingly subject to reg-ulation, impoundment, flood control measures, and other modifications, collabora-tion between physical and biological sciences at the ecohydraulics/geomorphologyinterface has blossomed [Wassen and Grootjans, 1996, Clarke et al., 2003, Par-sons et al., 2003, Noffke, 2005, Vaughan et al., 2009]. Disruption of the hydro-logical cycle is also commonly predicted as an impact of climate change, whichwill further stress freshwater ecosystems and the services they provide [Jackson64et al., 2001, Wilby et al., 2010]. To properly address potential effects of suchchanges, a thorough understanding of the underlying mechanisms that drive fluvialadjustments is needed, along with investigation of how these processes shape dis-tributions of ecologically relevant patterns. However, the importance of a dynamicphysical template is often neglected in correlative empirical studies. Building arobust and practically applicable eco-hydromorphic science requires explanationof static patterns through experimentation and longer-term monitoring of environ-mental change [Gaston and Blackburn, 1999, Vaughan et al., 2009].Recent advances in data collection and analysis methods provide ideal toolsto address issues of fluvial change. In particular, remote sensing allows for ef-ficient and accurate characterization of riverine environments that facilitates highresolution, spatially-continuous perspectives and investigation [Fausch et al., 2002,Mertes, 2002, Carbonneau et al., 2012]. Remote sensing has been widely used forstream structure and habitat classification and mapping at scales ranging from in-dividual sediment grains to entire watersheds [Winterbottom and Gilvear, 1997,Legleiter et al., 2002, Marcus et al., 2003, Westaway et al., 2003, Legleiter et al.,2004, Dugdale et al., 2010, Williams et al., 2013b, Javernick et al., 2015, Hugueet al., 2016]. This view has contributed to increased interest in discontinuities andvariations rather than gradually varying or smooth conceptualizations of river sys-tems; heterogeneity results from a diversity of linked physical and ecological pro-cesses, giving rise to a riverscape characterized by multi-scale complexity. In thiscontext, approaches from landscape ecology provide valuable tools, such as con-cepts of patch dynamics, connectivity, spatial arrangements, and threshold changesin relation to disturbance regimes [Wiens, 2002, Brierley et al., 2006, Wohl, 2013].In an eco-hydromorphic context, focusing on spatio-temporal variability pro-vides a valuable way to link process and structure. Geomorphic literature is repletewith examples of landform mapping and analysis, especially at the reach scalewhere river sections are often seen as mosaics of morphological units such as poolsand riffles [Richards, 1978, Lisle, 1982, Halwas and Church, 2002]. Such channelunits result from specific interactions between hydraulic and sedimentological fac-tors [Richards, 1976, Lisle et al., 2000, MacWilliams et al., 2006, Hassan et al.,2008]. Stratifying river systems at this scale allows for investigation of geomor-phic mechanisms and provides a bottom-up framework for spatial ecological ap-65plications [Buffington and Montgomery, 2013, Wheaton et al., 2015]. Because ge-omorphic processes result in non-random assemblages of flow properties and sed-iment sizes throughout a river reach [Wyrick and Pasternack, 2014], ecologicallysignificant patches also arise at this scale, with differing quality in terms of habitatfor various organisms. As a result, many examples of habitat analyses for targetspecies are based on meso-scale (1-10 channel widths) discretization of stream en-vironments [Newson and Newson, 2000, Parasiewicz, 2001, Dunbar et al., 2011,Silva et al., 2014], with similar approaches forming a core physical-biological linkin management activities [Crowder and Diplas, 2002, Hauer et al., 2012]. In theseapplications, high spatial heterogeneity is usually viewed as desirable, providing arange of unique ecological conditions and habitat niches [Maddock, 1999, Thoms,2006].Despite the recognized importance of fluvial complexity, the understanding ofrelationships between geomorphic change and spatio-temporal dynamics of streamhabitats is limited. In particular, information about process rates and magnitudesin response to external change is needed to support conscientious management inthe face of long-term challenges. One key aspect of this is the role of disturbance;quickly acting pulse events such as large floods can completely restructure fluvialsystems. Due to their inherent rarity and unpredictability, the effects of large infre-quent disturbances are poorly understood [Turner and Dale, 1998]. Flood impactsare highly variable and affect components of the ecosystem in different ways thatmay leave lasting legacies that condition future processes [Church, 1980, Turneret al., 1998]. For example, Parsons et al. [2006] highlighted the importance of pre-flood reach morphology in determining different biotic response to a large floodevent, and many examples focusing on geomorphic changes have demonstratedlasting shifts in channel form relative to sediment transport or bank erosion thresh-olds [Gupta and Fox, 1974, Desloges and Church, 1992, Magilligan, 1992, Groveet al., 2013, Thompson and Croke, 2013]. In this context, long-term investiga-tion is necessary to address potential issues of non-linearities at play; rivers canexhibit multiple stable states based on thresholds in internal or external physicalforcing or bio-geomorphic feedback mechanisms [Lane and Richards, 1997, Dentet al., 2002, Stallins, 2006]. Similarly, rivers are controlled by processes interactingacross a hierarchy of scales, and short time-scale and small space-scale processes66can influence broader-scale behavior [Lane and Richards, 1997]. Making sense ofthe interactions between environmental change and eco-hydromorphology there-fore requires detailed field measurement at a range of nested scales supported bynumerical and physical modeling to build and test conceptual models of ecosystemevolution.In this paper, we focus on the relationship between geomorphic change due toa large flood event and spatio-temporal hydraulic variation. Reach-scale channelform strongly controls depth and velocity distributions; Stewardson and McMahon[2002] showed that the degree of lateral versus longitudinal structural variation isimportant, with simple channels that lack pronounced pool-riffle morphology dis-playing a positive correlation between depth and velocity. In such systems, flowpatterns are mainly related to thalweg position, with deep, fast water at the thalweggrading to slower, shallower water towards the banks. In channels with strongerlongitudinal structure, a more negative correlation between depth and velocity gen-erally develops, with faster water in shallow riffles and slower flows in deeper pools[Stewardson and McMahon, 2002, Schweizer et al., 2007]. This relationship is alsohighly discharge dependent; geomorphic structure influences flow variation moststrongly at low flows, whereas higher discharges lead to convergence in flow condi-tions throughout a reach [Keller, 1971, Wallis et al., 2010]. However, these generalconceptualizations may not apply in all systems or morphologies, and a more thor-ough mechanistic examination of how channel structure controls flow distributionsis needed [Rosenfeld et al., 2011].To investigate how geomorphic change influences flow patterns, we take acombined remote sensing and hydrodynamic modeling approach. Numerical flowmodeling has proven to be a valuable tool for ecohydraulic analysis, allowing fordetailed characterization of flow patterns at varying discharges [Pasternack et al.,2006, Clifford et al., 2009, Benjankar et al., 2014]. When combined with multi-temporal elevation datasets that capture changes in geomorphic structure, such anapproach allows for extraction of information at scales and resolutions that areideal to investigate ecohydromorphic questions. To this end, we examine changesin flow structure at three discharges (10, 30, 50 m3/s) for three distinct reach-scalemorphologies (surveyed in Sept. 2012, Sept. 2013, and Sept. 2014) bracketingan extreme flood event that occurred in June 2013 on a section of Elbow River,67Alberta. To address spatial patterns of unique hydromorphic conditions, we applystatistical clustering techniques [Legleiter and Goodchild, 2005] to modeled flowdistributions and then interpret the results in terms of habitat variability, configura-tion, and suitability for brown trout.4.2 Methods4.2.1 Study siteElbow River is a gravel bed river draining the eastern slopes of the Canadian RockyMountains in Southwestern Alberta. Part of the South Saskatchewan River basin,it originates in sub-alpine areas of the mountains, flowing through foothills intoprairie and farmland before joining with Bow River in Calgary. Although fish as-semblages vary geographically throughout the watershed, the river supports nativebull trout (Salvelinus confluentus), westslope cutthroat trout (Oncorhynchus clarkiilewisi) and mountain whitefish (Prosopium williamsoni) populations, as well asnon-native brook trout (S. fontinalis), rainbow trout (Oncorhynchus mykiss) andbrown trout (Salmo trutta) [Nelson and Paetz, 1992]. Bull trout and cutthroat troutpopulations have declined over the past century due to inter-species competition,exploitation, and habitat degradation [Post and Johnston, 2002]. All of the lo-cal salmonids are valued as sport fish, so understanding their contrasting and/orcompeting ecological requirements and geographical distributions is important tosustainable fisheries management.Our study reach was a 1 km section of Elbow River near Bragg Creek, AB(Figure 2.1. At this point, the river has exited the mountains and exhibits ananabranching/braided morphology flowing through a wide (200-300 m) sediment-rich gravel/cobble active channel. The reach was chosen initially as a test site forfluvial remote sensing methods using unmanned aircraft systems (UAVs) and wassurveyed in detail in September 2012 (Chapter 2). The following summer (June19-22, 2013) the region experienced extreme flooding caused by a large sustainedrainfall event and rapid melting of a lingering snowpack in the mountains; waterlevels were the highest in 60 years and the estimated damages of CAD$6 billionmake the event the costliest natural disaster in Canadian history at the time of writ-68Figure 4.1: (a) Flood frequency analysis for Elbow River at Bragg Creek,1935-2014. Grey dashed boundaries indicate 90% confidence intervals,the two flood years covered in this study are annotated by red points(b) 2012-2015 hydrograph showing flow conditions relative to timingof UAV surveys (denoted by vertical grey arrows).ing [Pomeroy, 2015]. At the Elbow study reach, peak discharge was estimated atgreater than 800 m3/s. Although the short record of stream gauge data and diffi-culty in applying statistical analysis to events generated by different mechanisms(snowmelt vs. rainfall vs. rain-on-snow) make flood frequency analysis for theevent highly uncertain, annual peak discharges for the Bragg Creek Water Surveyof Canada gauging station (05BJ004) for 1935-2014 are shown for context with aLog Pearson Type III distribution in Figure 4.1. In this analysis the previous largestflow was 231 m3/s in 2005, which corresponds to a return period of approximately40 years. The 2014 freshet had a return period of about 5 years. The 2013 flood isa large outlier from the data; the return period as shown is just under 100 years butextending the fitted distribution would predict a return period of over 500 years forthis magnitude of event. A longer time period including other large events wouldhelp reduce this uncertainty. Regardless of exact return periods, the 2013 floodwas very uncommon, a fact that is reflected in the high magnitude geomorphicadjustments documented along Elbow River.694.2.2 UAV surveys and DEM analysisIn this study, we make use of three datasets collected through UAV-based remotesensing. For this scale of analysis, UAVs provide an ideal platform to providehigh resolution optical imagery covering the study reach; 200-300 individual geo-referenced images taken from approximately 100 m flying height with 60-80%overlap were collected each year. These photos were processed with photogram-metry software (EnsoMOSAIC, Pix4D) to generate ortho-rectified image mosaics(4-5 cm/pixel resolution) and digital elevation models (DEMs). After areas withsubmerged topography were corrected using an optical/empirical depth estimationprocedure, DEM vertical root mean square errors were 5-10 cm. Further detailsof photogrammetry and DEM accuracy assessment can be found in Chapter 2 andChapter 3. Timings of the surveys relative to the hydrograph for the period 2012-2015 are shown in Figure 4.1; each survey was conducted in late September at lowflow conditions (6-10 m3/s). The September 2012 survey captured pre-flood con-ditions, the September 2013 survey shows the reach structure following the June2013 flood event (no geomorphically effective floods occurred between the floodand the data collection), and the September 2014 survey shows how the reach ad-justed following a more common (yet still significant) spring freshet.To assess geomorphic change between each year, we created DEMs of differ-ence (DoDs) by subtracting successive DEMs. Subtracting the earlier DEM fromthe later one (i.e. 2013-2012, 2014-2013) gives elevation change values throughoutthe reach, where negative changes indicate degradation and positive values indicateaggradation. To account for uncertainty, we thresholded the DoDs using verticalerror values surveyed for each year, stratified by whether areas were exposed orsubmerged because errors are generally larger in submerged cells. After propa-gating errors from each DEM to determine a minimum level of detection, we dis-carded elevation changes below this level that could not reliably be distinguishedfrom noise [Brasington et al., 2003, Lane et al., 2003, Wheaton et al., 2010b]. Theresulting thresholded DoDs were used evaluate spatial patterns of erosion and de-position that resulted from the 2013 flood event and subsequent reworking and toquantify the specific morphodynamic adjustments that condition reach-scale flowpatterns.704.2.3 Hydrodynamic modelingThe photogrammetry generated DEMs were also used as topographic input toNays2DH, a depth-averaged hydrodynamic model implemented within the Inter-national River Interface Cooperative (iRIC). Nays2DH solves the shallow waterequations using a general curvilinear grid for numerical discretization; grid spac-ing for our models was set to 1 m by 1 m based on estimates of the Peclet number[e.g., Papanicolaou et al., 2010] to minimize numerical diffusion while maintain-ing computational efficiency and the ability to resolve the small-scale geomorphicforms such unit bars and pool/riffle-associated breaks in slope that control flow pat-terns over a sufficiently long study reach. Other model inputs included downstreamwater surface elevation, discharge, and roughness in the form of Manning’s n. Foreach year, we initially calibrated steady flow models at the discharge captured dur-ing the UAV surveys (6 m3/s in 2013, 10 m3/s in 2013 and 2014). This allowedfor comparison of modeled and measured water surface elevations (extracted di-rectly from the DEMs along the water’s edge in the orthomosaic) and fine tuningof the flow resistance parameter. Flow resistance was treated as spatially uniformand starting values were estimated from D84 values using the Manning-Stricklerapproach, and lateral eddy viscosity was estimated as LEV = 0.01 ∗Y ∗U , whereY is reach average depth and U is reach average velocity [Nelson and McDonald,1996]. We also compared field-measured depths with modeled depths to furthercheck these low-flow model results (Table 4.1). Relative to the size of sedimentthroughout the reach (D84 = 0.068 m), which introduces inherent uncertainty inpoint-measured elevations and fluctuations in water surface, model performancefollowing calibration was good and adequately captured the scale of hydraulic vari-ation that we were interested in.We then ran steady flow simulations based on the calibrated model parametersfor three specified discharges for each year: 10, 30, and 50 m3/s. These valuesrange from the base flow typical of the late summer or fall (10 m3/s) to an approx-imately two-year flood (50 m3/s) and therefore reflect the hydraulic conditions fishin Elbow River would be subject to throughout a normal year. Although extendinglow-flow resistance values to higher discharges is an uncertain process [Fergu-son, 2010], the high resolution model topography meant that form roughness was71Table 4.1: Calibration summary of modeled versus measured depth and watersurface elevations for Nays2DH hydrodynamic simulationsYear Q (m3/s) WSE R2 WSE RMSE(m)Depth R2 DepthRMSE (m)2012 6.1 0.99 0.12 0.72 0.112013 10.3 0.97 0.18 0.80 0.092014 10.1 0.98 0.17 0.75 0.08largely captured in our calibration procedure, enabling more reliable extension tohigher flows [Williams et al., 2013a]. Similar scale modeling studies have alsoreported few meaningful differences in indicators of boundary roughness at lowand high flows [Hauer and Habersack, 2009, Krapesch et al., 2011, Wyrick andPasternack, 2014].These nine simulations were used to examine flow structure in terms of reach-average parameters and depth-velocity distributions. According to the simple con-ceptual model of Stewardson and McMahon [2002], two end-member bivariatedepth-velocity distributions can be considered: one with a positive association be-tween depth and velocity resulting mainly from bank effects and minimal longitu-dinal structure, and one with a negative association due to strong pool-riffle effects.They suggest most reaches can be described by some mixture of these two generalforms. Schweizer et al. [2007] explained this concept by using mixed bivariate nor-mal and bivariate lognormal distributions and a mixture parameter (smix) describingthe relative contribution of each end-member distribution (a higher smix reflects ashift away from mostly normal to more skewed lognormal). They found that anincrease in reach Froude number corresponds to a more symmetrical/normal distri-bution shape (lower smix), and that streams with larger roughness elements producemore spatial variation in depth and velocity, resulting in a shift to a more skewedlognormal shape (higher smix). Although these concepts were developed primarilyin single-thread rivers, they provide a useful framework to examine the relationshipbetween geomorphic form and flow distributions. As an analogous measure of thenormally distributed, positive association between depth and velocity, we predicteda simple flow resistance-based set of velocities for the range of reach depths using72the reach average slope. Deviations of each depth-velocity distribution from thiscurve (calculated as R2) therefore reflect local changes in bed topography that pro-duce spatial flow variability and hydraulic diversity. To put the hydraulic parame-ters in geographic context, spatial flow patterns were also examined by generatingmaps of specific discharge (q, m3/s/m) calculated as modeled depth multiplied bymodeled velocity.4.2.4 Cluster analysisTo interpret flow patterns in terms of ecologically relevant units, we used fuzzy settheory to classify modeled flow distributions into cohesive groups. Classificationis fundamental to the understanding of the geospatial organization of fluvial en-vironments, but the subjectivity and reliance on expert knowledge associated withmany conventional habitat classification methods compromises their transferabilityand reliability [Poole et al., 1997]. Similarly, rigid Boolean classification schemestend to obscure uncertainty, transitions between classes, and within-class variabil-ity [Fisher, 1998]. For this reason, we applied fuzzy logic to classify the aquaticvariability in Elbow River, as proposed by Legleiter and Goodchild [2005]. Thisapproach allows individual data points to exhibit partial membership in multipleclasses, retaining a measure of uncertainty in the creation of fuzzy sets. With anunsupervised fuzzy c-means algorithm [Bezdek et al., 1984], data points (in thiscase, 1 m2 pixels of Nays2DH model outputs) are grouped into a number of setsbased on Euclidian distance from the cluster center and have membership valuesranging from 0 (completely unlike the set) to 1 (perfect example of the set) ineach set. If desired, rigid classes can then be formalized by choosing a thresholdmembership value [Cheng et al., 2001].We applied the clustering algorithm (implemented in the R package ‘e1071’)to the modeled 10 m3/s discharge in the pre-flood (2012) morphology. We choseto use low flow data to define the clusters because it reflects the common condi-tions that fish experience throughout most of the year while performing their dailyactions such as feeding, resting, and rearing. Periods of low flow are also whengeomorphic structure exerts the strongest control on hydraulic patterns. Classi-fying based on low flow data in the pre-flood morphology therefore stratifies the73river reach into hydraulic units that have strong associations with underlying land-forms, providing a baseline to compare with different geomorphic configurationsand discharges.For the clustering, we chose three variables that strongly reflect hydraulic vari-ability: flow depth, depth-averaged velocity, and Froude number. Including Froudenumber provides an explicit weighting to the relationship between depth and veloc-ity; stream habitat structure and use are often associated with distinct combinationsof depth and velocity as opposed to either variable independently [Statzner et al.,1988, Kemp et al., 1999]. We scaled each of these factors and then applied theclustering algorithm, varying the number of clusters and degree of fuzziness (m)until we settled on four distinct clusters and m = 2 as a way of balancing detail andinterpretability while minimizing the within-group sum of squares to ensure sep-arate and internally compact clusters. We then extended the analysis to the 2012higher flows (30, 50 m3/s) and all flows for 2013 and 2014 by calculating partialmemberships for each flow model point to each of the four cluster centers deter-mined through the 2012 10 m3/s classification. To facilitate interpretation of spa-tial patterns, we then also assigned each point to the cluster to which it belongedmost strongly, creating rigid hydraulic units with discrete boundaries while stillbeing able to access the original partial membership values that describe the uncer-tainty in the classification. Cluster patterns were then mapped as hydraulic patchesdisplaying particular flow conditions and landscape metrics describing geospatialorganization were calculated with the FRAGSTATS-based ClassStat tool in the‘SDMTools’ R package.4.2.5 Brown trout habitatAlthough the hydraulic units developed with the clustering approach objectivelydivided reach-scale flow patterns into ecologically relevant patches, they were cre-ated without a target species in mind. This was done intentionally to focus on therelationship between morphodynamics and spatial flow structure. However, it isalso useful to interpret the cluster results and general fluvial adjustments througha fish habitat lens. For this reason, we also used the modeled depths and veloci-ties to calculate measures of habitat suitability for brown trout. We chose brown74trout because it is a common salmonid in the region with well-documented habitatpreferences. Using preference curves generated for the nearby Kananaskis River[Courtney et al., 1998], we calculated habitat suitability indices reflecting pref-erences for depth (DHS) and velocity (VHS) for juvenile and adult brown trout.The values for each factor range from 0 (completely unsuitable) to 1 (ideal habi-tat). Overall adult and juvenile composite suitability values (AdultHS, JuvHS) werethen calculated as HS= (DHS)1/2(VHS)1/2. Calculating habitat suitability based ondepth and velocity alone is useful because it isolates the role of hydraulic changes,but an equally important aspect of salmonid habitat is access to overhead cover.In Elbow River, cover is primarily provided by large wood (LW) or steep under-cut banks. We used the high resolution orthomosaics and topographic informationfrom the DEMs to digitize these features and then calculated cover suitability CHSas an exponential decay with distance from cover within the wetted area for eachmodeled flow. Composite suitability values including cover (AdultHSC, JuvHSC)were then calculated as HSC = (DHS)1/3(VHS)1/3(CHS)1/3 in a manner similar tosuitability calculations that include a channel index or substrate factor [Leclercet al., 1995, Pasternack et al., 2004].4.3 Results4.3.1 MorphodynamicsThe general geomorphic changes for the study period are shown in Figure 4.2.Overall, pre-flood reach morphology was characterized by a sinuous primary low-flow thread with several major anabranches associated with bar dissection andchute cutoff. The 2013 flood event resulted in substantial bank erosion (10s ofmeters of bank retreat) on both sides of the 2012 channel as width adjusted to thelarge discharges, along with a complete reworking of sediment and morphologywithin the active channel. The bulk of the low-flow discharge in 2013 appears tobe mainly confined into one or two wide, shallow anabranches with a long meanderwavelength reflecting the path cut by the waning flood flows. The 2014 morphol-ogy reflects more subtle changes that occurred during the 2014 freshet includingbank erosion at bend apices and associated bar deposition, along with infilling of75Figure 4.2: UAV orthomosaics for each survey year. Flow direction is frombottom to topsmaller high elevation secondary channels produced by the 2013 flood but likelynot accessed during the 2014 freshet. These general channel pattern changes arealso reflected in Table 4.2, where a braiding parameter (B) and a sinuosity pa-rameter (P) are calculated for low flow wetted channels as: B = Lctot/Lcmax andP = Lcmax/LR where Lctot is the sum of the lengths of all channels in the reach,Lcmax is the length of the widest channel through the reach, and LR is the straight-line length of the channel belt [Friend and Sinha, 1993]. The flood event caused areduction in sinuosity (P) from 2012 to 2013 along with an increase in braiding (B)due to the presence of two to three linear medial bars splitting the main low-flowanabranch. In 2014, an increase in P reflects cutbank retreat and bar deposition,and the concentration of flows into one main channel results in a large decrease inB.DEM differencing (Figure 4.3, Figure 4.4) supports and quantifies the observedmorphodynamics. Summaries of elevation adjustments are shown in Table 4.3. Ge-omorphic turnover from the 2013 flood was extensive; 88% of the reach area expe-rienced detectable elevation changes in the 2013-2012 time period, but it is likely76Table 4.2: Channel pattern parameters. Values were calculated for wettedtwo-way connected anabranches at low flow. B is the braiding parameter,P is the sinuosity parameterYear B P2012 2.02 1.282013 2.31 1.082014 1.69 1.10Figure 4.3: Thresholded DEMs of difference between survey years. Negative(red) elevation changes represent areas of erosion, positive (blue) valuesindicate deposition. DoDs are overlain on detrended DEMs within theactive channel.77Figure 4.4: Histograms of thresholded elevation changes between surveysyears. Negative (red) elevation changes represent erosion, positive(blue) values indicate deposition.the entire active channel bed was affected and areas without discernible changescorrespond to areas where scour and fill largely canceled out to a resultant elevationchange below thresholds of detection. The adjustment was degradational overalldue to bank erosion, but large volumes of sediment (from local bank erosion) weredeposited within the channel as well. The magnitude of change during the 2014-2013 epoch was much smaller and impacts were less extensive. The DoD supportsthe fact that the 2014 freshet did not access (at least, in a geomorphically effectiveway) the higher elevation bartops and channels that remain from the 2013 flood;elevation changes are confined to the area around the low flow anabranch and re-flect lateral adjustments through outer bank erosion. During this time period, 20%of the active channel experienced detectable turnover and the net change was alsodegradational.4.3.2 Flow pattern changesResults from the Nays2DH model runs give insights into the relationships betweenmorphodynamics and flow patterns. Maps of specific discharge (Figure 4.5) re-flect the rearrangement of low flow topography; 10 m3/s flows in 2012 follow two78Figure 4.5: Steady flow hydrodynamic model results shown as maps of spe-cific discharge (q, m2/s/m) for each year (2012, 2013, 2014) and dis-charge (10, 30, 50 m3/s) combination.79Table 4.3: Morphodynamic adjustments for each time period. % Area is thepercentage of reach area experiencing detectable elevation change strati-fied by direction (aggradation vs. degradation), Σ Vol represents totals ofvolumetric change, ∆ Z is average elevation change throughout the reach.% Area Σ Vol (m3) ∆ Z (m)2013 - 2012Aggradation 40.1 37,223.8 0.61Degradation 48.0 -73,899.6 -1.01Net change -36,675.8 -0.402014-2013Aggradation 9.3 4,496.6 0.32Degradation 11.0 -9,676.8 -0.58Net change -5,180.1 -0.26to three main anabranches splitting around large medial bars with associated flowconvergence and scour pools where threads meet. In 2013, flow is more restrictedto a wide primary channel produced by the flood but occasionally diverges as flowsaccess other high-elevation secondary channels. The 2014 10 m3/s flow is confinedthe to same general path as in 2013, but here fluvial reworking through the 2014spring freshet appears to have entrenched flows even more into a single thread con-veying the bulk of the discharge with less access to secondary channels. These pat-terns are accentuated as modeled discharges increase to 30 and 50 m3/s; 2012 flowsdisplay a wider range of hydraulic conditions with deep scour and bend apex poolshaving locally high specific discharges but a significant portion of the flow is morespread through gradually sloping bar surfaces, secondary anabranches, and areasof slackwater. In 2013, these higher discharges also activate more anabranches andfill shallow areas, whereas the 2014 high discharges more strongly follow a mainthalweg with high specific discharge values.These changes in flow pattern can be further examined through reach averagehydraulic parameters, joint depth-velocity frequency distributions, and individualunivariate density distributions for depths and velocities. Overall, flows in 2012 aredeeper and reach average hydraulic widths are less than 2013 for a given discharge80Table 4.4: Summary of reach average hydraulic parameters for each simula-tion. Y is depth, W is width, U is velocity, Fr is Froude number, τ isshear stressYear Q (m3/s) Y (m) W (m) U (m/s) Fr (-) τ (Pa) W/Y (-)2012 10 0.42 31.0 0.67 0.46 16.8 73.130 0.51 45.1 1.03 0.49 32.6 87.550 0.56 64.0 1.06 0.49 33.5 115.12013 10 0.30 38.4 0.69 0.42 16.6 128.430 0.45 54.6 0.94 0.47 26.3 122.650 0.52 68.8 1.06 0.49 31.5 132.42014 10 0.37 28.3 0.82 0.44 20.6 77.330 0.51 42.5 1.07 0.49 31.9 82.550 0.58 55.1 1.19 0.51 37.2 94.2(Table 4.4). In 2014, widths are less than 2012 or 2013, corresponding to increasedaverage velocities. This is reflected in a large change of width/depth (W/Y ) ratio,which is much higher in the 2013 post-flood configuration than in 2012. By 2014,W/Y is once again reduced to values similar to before the flood.Bivariate depth-velocity frequency distributions (Figure 4.6) confirm thesegeneral changes. The associations between depth and velocity (Figure 4.6) for themodeled flow data vary with discharge and by geomorphic configuration (year). In2012, the positive association between depths and velocities is weak at 10 m3/s,with a negative R2 (-0.25) between the reach average slope-predicted curve andthe actual flow distribution. In 2013 and 2014, the frequency distributions corre-spond more closely with the predicted curve (R2 = 0.28, 0.23). All three yearsshow evidence of a drowning out of topographic influence and roughness elementsas discharge increases (more similar flow distributions despite different morphol-ogy; higher R2), but the 2012 morphology remains distinct in its wide range ofhydraulic conditions even at 50 m3/s (for example, many high depth, low velocitypoints remain). 2013 and 2014, on the other hand, show a relatively low diversityof conditions by 30 and 50 m3/s.Examining the individual distributions of depth and velocity separately (Figure4.7) is also valuable. All three years show a shift away from left-skewed velocity81Figure 4.6: Joint depth-velocity frequency distributions. A simple flowresistance-predicted curve of depths and velocities is overlain (dashedline); R2 between the curve and the modeled distributions is reportedfor each year-discharge combination.and depth distributions as discharge increases. At higher discharges, velocity distri-butions begin to exhibit a more pronounced peak at velocities in the 1-2 m/s range,especially in the 2014 model runs. In the 2014 configuration, this high velocitypeak is also evident at 10 m3/s, indicating largely channelized flow patterns evenat low discharges and corresponding to higher overall reach average velocities.822012 2013 20140.00.51.01.52.00.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0Depth (m)density10cms30cms50cms2012 2013 20140.00.30.60.90 1 2 3 4 0 1 2 3 4 0 1 2 3 4Velocity (m/s)densityFigure 4.7: Individual depth and velocity density distributions4.3.3 Cluster resultsTo further interpret the general changes in flow patterns in terms of reachscapecomposition and spatial structure, we classified the 2012 10 m3/s flow data. Re-sults of the fuzzy c-means clustering are shown in Figure 4.8. The four clustersstratify the depth-velocity flow field into groups of characteristic flow conditions(Table 4.5), and a detailed example view of the relationship between geomorphicsetting, flow patterns, and clusters is shown in Figure 4.9. These clusters reflectspecific hydraulic conditions associated with controls exerted by mesoscale bed-forms and therefore largely correspond with the common descriptive geomorphicunit terms pool, riffle, and glide. Cluster 1 (low energy) represents low slope, lowvelocity environments with a shallow depths, typically found along stream margins,in shallow secondary channels, or in backwater areas of scarcely perceptible flow.Cluster 2 (pool) is deeper flow with a range of velocities below about 1 m/s and83Figure 4.8: (a) Depth-velocity scatterplot for classified 2012 10 m3/s data,with points stratified by color for each cluster. Point transparency isscaled by the membership of each point to its class. Lines of equalFroude number are overlain for reference. Black points indicate centersfor each cluster. (b) Mapped patterns of 2012 10 m3/s classificationlow water surface slope. In the 2012 morphology, pool units are primarily foundalong outer bends or in areas of forced vertical or lateral expansion such as thoseassociated with scour at anabranch confluences or around large wood jams. Cluster3 (glide) is flow with moderate depths, velocities, and water surface slopes, gen-erally found in areas of diverging flow or along lateral gradients flanking threadsof higher intensity flow. Cluster 4 (riffle) is high velocity, high energy flow thatoften exhibits broken water surface texture. In general, riffle and pool clusters arethalweg flow conveying similar specific discharges, and the four clusters can bedifferentiated by Froude number, increasing from an average of 0.21 in low en-ergy units, 0.33 in pools, 0.51 in glides, to 0.67 in riffles (Table 4.5). The pairedblue/green color scheme also reflects the general conditions and locations of theclusters: blue for the low Froude number low energy and pool clusters, green for84Figure 4.9: Detailed example view of (a) 2013 orthomosaic (b) specific dis-charge overlain on DEM (c-f) Low energy, pool, glide, riffle fuzzy unitpatterns with transparency scaled by membership to each class85Table 4.5: Summary of clusters and representative average hydraulic charac-teristicsCluster Description Depth(m)Velocity(m)Fr (-) S f (-) q (m2/s)1 Low energy 0.20 0.23 0.21 0.0017 0.0512 Pool 0.73 0.83 0.33 0.0021 0.583 Glide 0.29 0.76 0.51 0.0066 0.244 Riffle 0.45 1.39 0.67 0.012 0.64Figure 4.10: Spatial patterns of clusters at 10 m3/s. Clusters are overlain ondetrended DEMs within the active channelhigher Froude number glide and riffle clusters. The deeper mainstem pool and riffleclusters are then differentiated from their more marginal shallow low energy andglide counterparts as dark blue or green versus light blue or green.Figure 4.10 shows the mapped patterns of these clusters for all three years at10 m3/s. At this discharge, geomorphic control on the flow patterns is most evi-dent and the arrangement of clusters has the most relevance to fish that depend onspecific low-flow hydraulic conditions throughout most of the year. For all three86Figure 4.11: Measures of reach composition at 10 m3/s87Table 4.6: Metrics of reachscape composition. Superscript a denotes classmetrics (calculated for for each cluster class), superscript b indicateslandscape metrics (calculated including all classes)Spatial metric Unit DescriptionProportion of landscapea % proportional abundance of each classEdge densitya m/ha standardized total edge length for eachclassMean fractal dimensionindexa- shape complexity from 1 (low) to 2(high)Patch densitya n/100ha standardized number of patches of eachclassMean patch areaa m2 average size of patches for each classShannon Diversity Indexb - overall heterogeneity and diversitygeomorphic configurations, the clustering created spatially well-defined hydraulicunits. Reachscape cluster composition is summed up as percentage of wetted areafor each cluster in Figure 4.11a. In general, flow in 2012 morphology appearsthe most fluvially organized, with deep pools found at low gradient outer bendsof the main anabranch transitioning into higher velocity riffle units in higher gra-dient sections. Areas where flow diverges into two or three anabranches are typi-cally wider, shallower environments associated with glides. However, low energypatches are the most prevalent in this morphology (30% of wetted area), due toseveral prominent low gradient secondary channels and lateral transitions withinthe main channel driven by uneven cross sectional bed elevation distributions. In2013, the geomorphic disruption from the large flood event and transition to highwidth/depth ratios manifests itself in reductions in pool and riffle clusters; moreflow is carried through shallow, high energy slope stream areas corresponding toglide units (43% of area in 2013 vs. 24% in 2012). Pool and riffle units still ex-ist, but they are more sporadic and less easily explained by planform configurationor bar-pool-riffle units. Low energy patches remain common given the shallowoverall depths and several side channels. By 2014, hydraulic diversity increasesslightly relative to 2013 as pool and riffle conditions reform in the main anabranch88thalweg, but the reach is still dominated by glide-type flow conditions and lowenergy environments are reduced in area.Figure 11 also includes several metrics of landscape pattern chosen to interpretpotentially ecologically relevant changes in spatial composition and shapes of thecluster patches. Descriptions of the metrics are shown in Table 4.6. Patch density,which reflects the number of patches present in a class, shows that at 10 m3/s,low energy and glide units are much more frequent compared to pools or rifflesin all three years. This generally corresponds to smaller mean patch areas forlow energy and glide units, compared to the larger, more spatially contiguous pooland riffle units. However, the flood impacts appear to have resulted in a mutingof this pattern; patch densities for low energy and glide classes are reduced in2013 and even more in 2014 as glide units in particular become larger and morecohesive. These change are also reflected in edge density, a measure of edge lengthfor each class, and fractal dimension index, a measure of shape complexity. For allthree years, the elongate, linear shape of the more marginal glide and low energyunits is quantified as higher edge density and fractal dimension index; pools andriffles are generally more rounded and simple-shaped with lower edge densities andfractal dimension index values. Between 2012 and 2013, a reduction in mean patcharea, patch density, and edge density for pool and riffle clusters likely reflects thedisorganized nature of post-flood morphology relative to the well-defined, elongatefeatures in the pre-flood conditions. The final metric, Shannon Diversity index,is calculated at the landscape level (for all classes combined), and is highest in2012, lower in 2013, and then recovers slightly in 2014. This is associated with amore even distribution of class areas in 2012 compared to the homogenization ofconditions and dominance by glide units in 2013 and 2014.Flow patterns change at higher discharges, resulting in different proportionsand arrangements of patches throughout the reach. Figure 4.12 shows mapped clus-ter patterns for all three discharges, and Figure 4.13 shows changes in landscapemetrics with discharge. Patterns of patch change with discharge are different be-tween the three years, shown most simply by the proportion of landscape occupiedby each class. In 2012, all four classes are relatively equal in proportion (20-30%of area) at all three discharges. In 2013, the low energy and glide units that dom-inate at 10 m3/s become less common as discharge increases and more pool and89Figure 4.12: Spatial distributions of clusters for all three modeled discharges90Figure 4.13: Measures of changes in reach composition with dischargeriffle-like conditions develop, with a more equal distribution of the four classes by50 m3/s. In 2014 the glide conditions that are most common at 10 m3/s quicklygive way to more pool and riffle areas, with riffle-like hydraulic conditions domi-nating by 50 m3/s. Secondary anabranches that at 10 m3/s are primarily low energyor glide units tend to transition to pool or riffle conditions with higher dischargesas well, and the more marginal low energy or glide clusters are only found alongshallow sloping bar edges, in backwater areas, or in smaller high elevation channelsthat had no flow at 10 m3/s. Patch density and edge density display similar trendswith discharge for the three years; pool and riffle units remain less frequent in num-ber with low edge densities, whereas low energy and glide classes decline in patchand edge density as discharge increases. Pools and riffles also show increases inpatch area with discharge as flow conditions coalesce into a quickly flowing main-91Figure 4.14: AdultHS patterns at 10 m3/s. Only cells with HS>0.25 areshown.stem that is less influenced by topographic highs or lows. These overall changesare reflected in the Shannon Diversity index, which is high for all discharges in2012 and lowest in 2013 at 10 m3/s but climbs with discharge as pool and riffleunits become more common. 2014 has intermediate Shannon Diversity values for10 m3/s but reaches similar values to 2012 or 2013 by 30 m3/s.4.3.4 Brown trout habitat suitabilityCalculated habitat suitability values for adult and juvenile brown trout put the doc-umented hydrogeomorphic changes in a direct habitat impact context. Figure 4.14shows mapped areas of habitat suitability for adult trout (AdultHS) at 10 m3/s. Inthis figure, only those areas with habitat suitability greater than 0.25 are displayedto emphasize potential habitat hotspots in relation to flow patterns. In 2012, highlysuitable habitat is available along much of the reach; outer bends associated withdesirable pool-like hydraulic conditions provide long linear stretches of good habi-tat, as do smaller-scale scour pools which are often associated with large woodjams. Lateral gradients also appear important; areas with sufficient depth and low922012 2013 20140.00.10.20.30.410 30 50 10 30 50 10 30 50Discharge (m3 s)WUA per unit stream area (m2m2 ) Juv WUAAdult WUAJuv WUAcAdult WUAcFigure 4.15: Changes in WUA and WUAC per unit stream area for adult andjuevenile brown trout.enough velocities are found along either side of high velocity thalweg threads. In2013, these high suitability areas are less widespread and few areas have the veryhigh (i.e. HS>0.75) suitability values that were present in 2012. This is largely dueto the wide, shallow nature of the post-flood flow and the lack of well defined pools,although small scour pools associated with LW accumulations still offer some habi-tat hotspots. By 2014, bank erosion and vertical scour once again provide accessto more suitable flow conditions. However, although some small LW-associatedscour pools still exist, sediment deposition and planform adjustment have cut offlow flows from several large jams that previously provided good habitat. Spatialpatterns of JuvHS are largely the same as those for adults, but slightly more re-stricted due to lower velocity tolerances.As a measure of total reach-scale suitability, habitat suitability changes can alsobe analyzed by calculating weighted usable area as the sum of each cell’s habitatsuitability multiplied by cell area (1 m2) for each flow model. This also allows easyexamination of overall changes in habitat suitability with discharge for the threeyears (Figure 4.15). Two measures of weighted usable area are calculated: onebased only on depth and velocity preferences (WUA, from AdultHS and JuvHS) toisolate purely hydraulic effects on habitat and one including the effect of overheadcover (WUAC, from AdultHSC, JuvHSC). To account for the effect of increasingtotal wetted area as discharge increases, WUA is expressed on a per unit area basis930.000.250.500.751.00Juv HSlow energypoolglideriffle0.000.250.500.751.00Adult HSFigure 4.16: Distributions of (a) JuvHS and (b) AdultHS for each 2012 10 m3/sclassified clusteras a measure of intrinsic habitat quality. Summing up habitat suitability values thisway confirms an overall reduction in habitat availability and quality as a result ofthe 2013 flood, especially at low flow; adult and juvenile WUA are highest in 2012at 10 m3/s and then low in 2013 and 2014. In 2012, WUA declines as dischargeincreases, especially for juveniles as velocity tolerances are quickly exceeded. Be-cause 2013 and 2014 have less suitable 10 m3/s conditions to begin with, this dropoff with discharge is less strong; adult WUA is largely constant with discharge andjuvenile WUA declines slightly between 30 and 50 m3/s. Including the effect ofcover preferences in the WUA calculations results in lower overall values becausecover suitability declines quickly with distance from overhead cover, so much ofthe stream is less suitable by this more stringent measure. Although this effect issimilar across the three years and discharges, cover availability is slightly higher in2012 than 2013 and 2014, and always increases with discharge. Including CHS inthe WUAC calculation therefore exacerbates the observed reduction in habitat suit-ability between 2012 and 2013-2014: flow depths and velocities are less suitableand access to overhead cover from LW and undercut banks is less widespread inthe post-flood geomorphic configurations.Figure 4.16 shows distributions of JuvHS and AdultHS at 10 m3/s associatedwith each fuzzy c-means cluster to evaluate whether the hydraulically defined clus-ters display distinct habitat suitability ranges. For juvenile trout, the low energy94cluster provides the most suitable conditions with low velocities; small bodied ju-veniles can also occupy the shallow depths in this cluster. These conditions arealso highly suitable for adult trout but slightly less so due to a lower preference forvery shallow depths. Pool habitats are the second most suitable for juveniles whocan inhabit deep waters, but the range of velocities within this cluster means somepool areas are largely inhospitable due to low tolerance to velocities greater thanabout 0.5 m/s. Adult trout can use higher velocity conditions, so the pool clusterprovides consistently high quality habitat. The higher Froude number glide andriffle clusters are generally less suitable for both juveniles and adults; juveniles inparticular cannot tolerate high velocity riffle units at all.4.4 DiscussionThis study aimed to investigate the relationships between river morphodynamics,flow organization, and aquatic habitat. Through repeat surveys bracketing a largeflood event, we characterized geomorphic change resulting from the flood and sub-sequent readjustment in the following year. Overall, we found that restructuring ofmorphology strongly impacted flow patterns in both a reach-average sense and interms of the spatiotemporal arrangement of zones with distinct hydraulic charac-teristics. These changes resulted in a reduction in adult and juvenile brown trouthabitat quality as measured through habitat suitability indices.The geomorphic adjustments due to the 2013 flood were drastic. The detailedpre- and post-flood topographic datasets document spatial patterns of bank ero-sion, scour, and fill at a resolution that is rare for such high magnitude disturbanceevents. In general, the active channel widened by approximately 50 meters be-tween 2012 and 2013. This is consistent with many other examples of large floodsin the geomorphic literature [e.g. Magilligan, 1992, Kale, 2007, Hooke, 2008] andreflects an adjustment to forces far in excess of thresholds for entrainment of largegrain sizes and bank erosion [Olsen et al., 1997, Eaton and Church, 2004]. Similarpatterns of bank erosion were also observed along the rest of Elbow River and onrivers throughout the region. This primary effect of large floods reflects a chan-nel form adjustment to a shallower, wider flow that reduces mean boundary shearstress until the flood pulse recedes or bank stability is reattained [Tamminga et al.,952015, Eaton and Mackenzie, in review]. However, the more detailed geomorphicimpacts and system responses of such widening are less understood and depend onvalley and floodplain configuration, geomorphic history, and initial active channelgeometry [Fuller, 2007]. At the Elbow study reach, bank erosion supplied largevolumes of alluvium to the channel bed, resulting in substantial local aggradationdespite the event being net degradational (Figure 4.3, Table 4.3). This sedimentand the associated high flood transport rates erased the existing pre-flood morphol-ogy completely as large bars were constructed and dissected during the flood flows.These bar features have a long streamwise length scale that reflect a much longersediment path length (the downstream displacement of particles over time) than thepre-flood channel forming flows would have exhibited [Pyrce and Ashmore, 2003].These depositional features are also relatively tall due to the deep flows in whichthey formed [Church and Jones, 1982, Nicholas et al., 2016]. Similar effects havebeen reported by Wheaton et al. [2013], who documented a shift in braiding mech-anisms to favor channel widening and transverse bar building as the magnitude andduration of flows above bankfull increased. They postulated that the large sedimentloads and reduced topographic steering during large floods result in a reduction ofbraidplain heterogeneity, consistent with findings from Madej [1999] and Legleiter[2014b].The 2013 flood therefore set the geomorphic constraints for the processes ofreadjustment during the following year (2013-2014). Because the channel con-figuration was set by such a high magnitude event that built high elevation bar-forms, the 2014 spring freshet (which peaked at approximately 100 m3/s) waslikely constricted to zones within the over-steepened low sinuosity path cut by thewaning 2013 flood flows. Consequently, the 2014 geomorphic adjustment reflectsa lower flow magnitude and shorter duration above sediment transport thresholdscombined with topographic confinement, resulting in less areally extensive geo-morphic turnover and smaller volumetric changes (Table 4.3). As opposed to thesignificant lateral instability evident in the 2012-2013 changes, the 2013-2014 mor-phodynamics appear to be progressing towards the reestablishment of lateral dy-namic equilibrium through sedimentation in secondary channels, bank erosion, baredge trimming, and unit bar accumulation that results in increases in sinuosity and96a more single thread channel pattern. This effect is net degradational, indicatingthat this readjustment process remains on-going.These large, channel forming events (2013 flood and 2014 freshet) configurethe geomorphic template within which the lower magnitude flows that are moreecohydraulically relevant operate. These smaller discharges are what we focusedon through numerical flow modeling in this study, allowing for investigation ofhow flow patterns are conditioned by geomorphic change. Overall, we found thatthe 2013 flows were very wide and shallow compared to the same discharges in the2012 geomorphic configuration (Figure 4.5, Table 4.4). This shows that the reach-scale widening and local deposition associated with the 2013 flood are reflectedin smaller discharges as well. This wider shallower flow is also less hydraulicallydiverse (Figure 4.6), with a near complete loss of deep, slow areas at all threemodeled discharges. Overall it appears the 2013 flow patterns are less fluviallyorganized; insufficient time of competent flows following the flood means there isa lack of sediment transport and sorting processes that lead to shear stress concen-tration and predictable patterns of pools scour and bar deposition or avulsion andnew anabranch creation [e.g. Whiting and Dietrich, 1993, Burge, 2006, Harrisonet al., 2011]. As a result, the topographic highs and lows or planform bifurcationsthat result in local differences in gradient and flow resistance are largely absent inthe post-flood morphology. 2013 flows instead reflect the bar features and patternscreated by a completely different scale of flow event than those that normally createhydraulic diversity in this system.Although bed mobilization likely occurs somewhere between the 30 and 50m3/s discharges, patterns of elevated specific discharge at these discharges can hintat where geomorphic change will occur and explain observed changes. In 2012 at50 m3/s we see high specific discharges associated with outer bends and anabranchconfluences (Figure 4.5). It is likely that these zones allow for compensating ero-sion and deposition while the channel’s average dimensions remain generally con-stant and that this mode of adjustment was dominant during the previous yearsbefore the 2013 flood. In the 2013 50 m3/s modeled discharge there is mainly onezone of elevated unit discharge in the upstream half of the reach; by 2014 this zonehas shifted to the right outer bank as lateral migration progresses. This is associatedwith the reestablishment of shorter length-scale sediment transport processes and97the reorganization of coherent bar-pool-riffle units. However, the process is stillon-going, as evidenced by the high shear stress in this configuration (Table 4.4)and the location of elevated forces at upstream end of outer bends. These moreminor geomorphic adjustments between 2013 and 2014 result in slight changes inflow distributions and diversity. The 2014 10 m3/s depth/velocity distribution hasa slightly wider range of hydraulic conditions than 2013 (lower R2, Figure 4.6).However, this effect is largely drowned out by 30 and 50 m3/s, where the devel-opment of a cohesive deep, high velocity, thread is evident (Figure 4.7) and smallscale elevation changes become less important.Joint depth-velocity distributions provide a framework for interpreting the hy-draulic conditions that control classified patch composition and distributions. In2012, the diverse range of depth-velocity pairs (Figure 4.6a) at 10 m3/s is reflectedin the relatively even apportioning of flow points into the four clusters (Figure4.11). The diverse class distribution remains relatively constant as discharge in-creases (Figure 4.13). 2012 flow conditions are therefore the most complex, asmeasured both on a continuous basis (low R2, Figure 4.6) and when interpretedbased on patterns of discrete classified units (high Shannon Diversity Index, Fig-ure 4.13). This diversity can be explained by the geomorphic configuration in thepre-flood reach: alternating pool-riffle units in the main anabranch along with sev-eral substantial low gradient side channels create a wide range of flow conditions.In 2013, the lower hydraulic diversity and wide shallow flow conditions result in anunequal proportional assignment of cluster memberships; the reach is dominatedby low energy and glide units at 10 m3/s. By 2014 geomorphic adjustments resultin a slight recovery of pool and riffle units relative to 2013, but diversity is still low.These differences in flow distributions and reachscape composition are most evi-dent at 10 m3/s; conditions tend to converge to more equal proportions of classesby 30 and 50 m3/s as small-scale geomorphic forms become less important.A benefit of classifying flow data is that it allows for explicit investigation ofspatial flow patterns. With the fuzzy c-means approach used in this study and ouranalysis of the resulting clusters, we were able to address measures of patch shapesand sizes that may reflect underlying geomorphic features and have direct ecolog-ical relevance. We found some patterns that are relatively universal in our flowmodels; low energy and glide patches are generally smaller and more numerous,98with more complex shapes and higher edge densities than pool or riffle patches(Figure 4.13). This effect is likely a combination of the actual small scale of thesehydromorphic units along with a classification artefact associated with fitting theseslender, linear stream’s edge features into 1 m2 pixels [Wyrick and Pasternack,2014]. Pool and riffle units, on the other hand, are generally more spatially con-tiguous and less fragmented due to the strong flow organization they are associatedwith, resulting in lower edge densities and fractal dimension index values. How-ever, the geomorphic change throughout the study period appears to modify thesepatterns to some degree; pre-flood pool and riffle units are more elongate and lin-ear at low flow than in 2013 or 2014. This is quantified by a decrease in edgedensity and mean patch area for these units as a result of the flood and reflects thedisorganized nature of flow conditions in the post-flood morphologies.Because each of the classified clusters has distinct hydraulic characteristics, theresulting hydromorphic unit maps can be linked with species’ preferences for spa-tial investigation of habitat suitability. In the brown trout habitat context examinedherein, applying preference curves to continuous depth and velocity data (Figure4.14, Figure 4.15) gives a traditional appraisal of habitat suitability throughout thereach. By this metric, adult and juvenile habitat suitability declined as a result ofthe 2013 flood, especially at low flows. However, many fish species have beenshown to have strong affinities for discrete habitat types [Schlosser, 1991, Nick-elson et al., 1992, Rosenfeld and Boss, 2001] that may relate to more than justthe hydraulic conditions in those habitat units. For example, availability of preyfor drift-feeding salmonids is a primary driver of growth differences between slowand fast water habitat units [Nielsen, 1992, Reid and Thoms, 2008, Rosenfeld andRaeburn, 2009]. Spatial arrangements of habitats that generate benthic invertebratedrift that fish can feed on (riffles) versus energetically favorable feeding locations(pools), along with factors such as pool length that affect rates of depletion ofprey through deposition or consumption by individual fish occupying that unit,are therefore important. Similarly, lateral velocity gradients such as fast currentsclose to velocity shelters are profitable in terms of energy gain [Fausch and White,1981, Hughes and Dill, 1990], and the distribution of hydraulically sheltered sitesand suitable sediment size patches can control spatial patterns of spawning suit-ability [Cienciala and Hassan, 2013]. For these reasons, predicting fish density99based on discrete habitat classes is often more accurate than based on continuousvariables alone [Rosenfeld, 2003], especially when univariate habitat suitabilitycriteria that ignore interactions between hydraulic variables are the basis for as-sessment of depth and velocity suitability [Ayllo´n et al., 2009]. In this context, thebenefit of a bottom-up statistical classification approach is that the clustering basedon depth, velocity, and Froude number integrates these factors and their interac-tions explicitly without a myopic focus on potentially species-specific suitabilitycriteria [Poole et al., 1997, Schweizer et al., 2007]. This allows for an objectivestratification of habitat units that relates to both underlying geomorphic processesand resulting habitat dynamics.At the Elbow River study site, the overall picture of a decrease in habitat suit-ability as the result of the 2013 flood can be refined by accounting for the spatialcharacteristics of the classified habitat patches. For example, pool units declinedin total areal proportion at 10 m3/s between 2012 and 2013 (Figure 4.11), largelydriving the observed decrease in WUA. However, riffle units also decreased inproportion. Although riffle units themselves are not highly suitable habitat whenmeasured by hydraulic preference curves (Figure 4.16), their function as a zone ofbenthic invertebrate prey production makes them ecologically important to drift-feeding salmonids. The observed reductions in mean patch area and edge densityfor riffle units at 10 m3/s could therefore be interpreted as further negative geomor-phic consequences of the flood that result in less prey availability and fewer suitableriffle-adjacent holding and feeding zones for brown trout. However, territorial re-quirements and competition between individuals and different age classes are alsomediated by spatial patterns of habitat zones; Ayllo´n et al. [2010] documented apositive scaling of territory size with brown trout body size, but salmonid territoryarea typically decreases as food abundance or habitat quality and complexity in-crease [Keeley, 2000, Imre et al., 2002, 2004, Venter et al., 2008]. The smaller, lessnumerous pool units and overall decrease in hydraulic diversity in the post-floodmorphologies could therefore further stress salmonid populations due to increasedcompetition for prime territories. Although unraveling these linkages and and thecontrasting ecological requirements for different age classes and species requiresmore detailed direct information on spatiotemporal patterns of fish space use onElbow River specifically, the classification procedure used in this study provides100a robust generic framework to address issues of the relationship between habitatdynamics for any lotic species and hydrogeomorphology.The results of this study provide one way of quantifying the effects of a largeflood disturbance event. Although the short time period (three years) covered inour UAV surveys makes prediction of future morphodynamic trajectories difficult,the primary immediate effect of the flood was a homogenization of sub-bankfullflow conditions and a decrease in hydraulic and habitat diversity. The readjustmentduring the one year following the flood event appears to be progressing towards ameandering laterally stable single thread channel, with the high elevation bar sur-faces built during the 2013 flood acting as floodplain features largely disconnectedfrom the low flow habitats. This reduction in geomorphic complexity is in contrastwith many examples of ecological responses to large infrequent disturbance eventsin other systems, which are often cited as creating or maintaining heterogeneity[Romme and Knight, 1982, Foster et al., 1998]. In the case of Elbow River, thedisturbance appears to be a clock-resetting event that returns the system to an ini-tial state with complete geomorphic and habitat turnover [Phillips and Van Dyke,2016]. This is different than examples of smaller magnitude flood disturbancedynamics, where shifting mosaic steady-state conditions are maintained [Arscottet al., 2002]. For Elbow River, smaller magnitude floods will be necessary to re-work the geomorphic structure set by the 2013 flood, allowing for the recreation ofheterogeneity through vertical and lateral erosion, meso-scale bedform construc-tion, and avulsion mechanisms to create new secondary channels [Wheaton et al.,2013]. Judging by the rate of bank erosion and bar building observed between 2013and 2014 and the historical record of large flows in the region, the time scale forsuch readjustment is likely on the scale of 50 years [Beechie et al., 2006]. However,potential changes in broader driving conditions such as shifts in flood frequency orrates of vegetation recolonization [Hervouet et al., 2011] with climate change mayaffect recovery trajectories. Continued careful monitoring of fluvial ecosystemconditions in an environmental change context will therefore be necessary to fullyunderstand linked fluvial and ecological processes.1014.5 ConclusionStream morphodynamics, hydraulic organization, and aquatic habitat are inti-mately linked and sensitive to changes in governing conditions. In this contribu-tion, we took advantage of detailed datasets characterizing river structural changesassociated with a large flood event to examine these interrelationships in a distur-bance context. By applying an objective stream habitat unit classification methodto modeled distributions of hydraulic variables, we mapped patterns of hydgeo-morphic features at a range of discharges for three unique geomorphic conditionsbracketing the flood. We documented a general homogenization of flow conditionsas a result of the flood associated with a loss of pool and riffle units. The effects ofgeomorphic change on flow patterns were most evident at low (10 m3/s) discharges.These hydrogeomorphic changes resulted in a decline in habitat suitability for adultand juvenile brown trout when measured by conventional habitat suitability crite-ria; we also showed that a spatially explicit assessment of hydrogeomorphic con-ditions can complement and refine these traditional estimates of habitat impacts.Overall, this study provides an integrative assessment of the mechanisms drivingfluvial ecosystem dynamics in an environmental change context.102Chapter 5Conclusion5.1 OverviewA thorough understanding of the complex interrelationships between geomor-phology, hydrology, and aquatic ecology is necessary to support sustainable loticecosystem management in the face of natural and anthropogenic environmentalchange. Advancing this understanding requires short- and long-term investigationat a range of spatial scales and in different settings to develop and refine mod-els of ecosystem evolution and predict potential responses to changes in drivingconditions [Poff and Matthews, 2013]. To support this goal, focusing on pattern-process linkages, spatial structuring, scaling relationships and fluvial system dy-namics is a priority to develop mechanistic understanding, as is the identificationof proper model systems or species to use to answer research questions [Vaughanet al., 2009]. The development of new methodologies and conceptual frameworksfor measuring and interpreting fluvial ecosystem complexities and dynamics willalso facilitate research and development in this context [Fonstad and Marcus, 2010,Passalacqua et al., 2014, Tarolli, 2014].The overall aim of the research in this dissertation was to use the case of anextreme flood event on a gravel bed river to study spatially explicit river morpho-dynamics and assess the impacts of geomorphic change on aquatic habitat structureand quality. This required the testing and application of new remote sensing meth-ods using UAVs to efficiently and accurately measure ecosystem properties at tem-103poral and spatial scales appropriate to the research. Taken together, the three coreresearch chapters present a novel approach to studying rivers: a comprehensive,multi-temporal, meter-scale-or-better model of submerged and subaerial topogra-phy and relevant ecological features produced efficiently through UAV-based pho-togrammetry and complemented by numerical hydrodynamic modeling to simulatehydraulic conditions and inform eco-hydrogeomorphic research questions.5.2 SummaryIn Chapter 2, the focus was on exploring UAV fluvial remote sensing applicationsand testing data quality. It was shown that UAV-based digital photogrammetry canproduce high quality DEMs and orthomosaics that contain a wealth of informa-tion on previously hard to measure aspects of channel morphology and ecosystemstructure. Vertical accuracy in the DEMs was high enough relative to the scaleof most geomorphic features to allow for meaningful characterization of three di-mensional morphology. Sediment sizes on exposed bars were also mapped em-pirically; however, the lack of information on submerged sediment sizes precludesdetailed analysis of important factors such as sediment sorting in relation to flowpatterns through different channel units or assessments of spatially variable hy-draulic roughness. Similarly, neither the DEM nor empirical measures of grain sizecan resolve features such as stone cells or other stabilizing micro-scale bedformsthat can have important hydrogeomorphic consequences [Church et al., 1998, Law-less and Robert, 2001, Hassan et al., 2008, Hardy et al., 2010]. Although methodsaimed at estimating submerged grain sizes and characterizing their spatial variabil-ity exist [e.g., Carbonneau et al., 2005, Nelson et al., 2014], such applications aremost suitable to shallow, optically clear parts of the stream and accurate submergedgrain size determination remains a challenge.Another focus of Chapter 2 was to test methods of bathymetric correction thatare required to account for overpredictions of submerged elevations in the opti-cal photogrammetry. Although both the tested methods improved vertical accu-racy, they introduce further uncertainty into the process and submerged verticalerrors remained about twice as large as those from exposed areas. Application andtesting of other methods such as theoretical approaches or active remote sensing104techniques [e.g., Fonstad and Marcus, 2005, Feurer et al., 2008, Legleiter, 2015]could provide alternative ways to quantify bathymetry and correct underwater ele-vations, but this gap between submerged and exposed topographic accuracy needsto be addressed further [Flener, 2015]. Similarly, the reliance on estimation of awater surface elevation for the depth correction process necessitates accurate wa-ter’s edge elevations along the entire stream. While this was not a problem in thewide, shallow, non-forested Elbow River active channel, it could prove difficult inother fluvial settings. Chapter 2 also showed that the high resolution orthomosaiccan be used to map features that contribute to aquatic habitat for fish species suchas LW distributions, cutbank locations, water surface roughness, and offstream orinstream vegetation. While manually digitizing these features is simple and accu-rate, broader application and more efficient workflows could be developed throughthe application of automated image classification techniques [e.g., Fernandez-Diazet al., 2014, Overstreet and Legleiter, 2014, James and Robson, 2014, Casado et al.,2015]. The final important aspect of Chapter 2 was the demonstration that the pho-togrammetry derived, corrected DEM was of sufficient accuracy and resolution toinitialize and run a 2D numerical hydrodynamic model. This opens up the overallmethod to a much wider range of investigations that explicitly consider the impor-tance of spatial flow properties; for the purposes of the chapter the model resultswere combined with mapped habitat features to produce refined estimates of habi-tat suitability and availability for brown trout.Chapter 3 built on the methodological framework developed in Chapter 2 tofocus on the purely geomorphic impacts of an extreme flood event in June 2013.Many studies of flood morphodynamics exist, and field-based examples are com-mon in the geomorphic literature as a way of examining process-form relationships.However, the accuracy and resolution of the pre- and post-flood UAV-collected dataused to characterize the geomorphic effects of the Elbow River flood are unprece-dented. When combined with numerical flow modeling, such an approach allowedfor detailed investigation of reach-scale spatial patterns of erosion and depositionand potential hydraulic controls on observed geomorphic change. It was shownthat significant bank erosion and channel widening occurred, resulting in a netdepositional event despite large local volumes of sediment being supplied to thechannel bed. Reorganization of channel pattern to reflect the large flows resulted105in an increase in streamwise and across-stream variability measured through semi-variograms as tall, long bar feature were constructed and dissected. To test the pre-dictability of geomorphic changes from pre-flood data, spatially continuous grainsize maps were combined with flow model estimated shear stress to assess thresh-olds for sediment entrainment and mobility and see if these explained observedpatterns of erosion and deposition. Although such an approach may provide valu-able insight into controls on morphodynamics in near-threshold conditions, thegeomorphic changes during the 2013 flood event were of such high magnitudethat mechanisms of sediment flux become much more important than just entrain-ment. Prediction of potential flood effects in a management context is thereforebest treated in a probabilistic manner to set bounds on potential vertical and laterallimits of geomorphic impacts [e.g., Krapesch et al., 2011, Clifford, 2012, Blan-ton and Marcus, 2013, Buffin-Be´langer et al., 2015]. To explain and predict moredetailed geomorphic changes, better inclusion of the spatial distribution of bedload transport is needed. Progress is being made in this direction through physi-cal modeling, coupled hydrodynamic/morphodyamic numerical models and fieldmeasures of sediment transport in relation to local hydraulic controls [e.g., Ren-nie and Church, 2010, Asahi et al., 2013, Nicholas et al., 2013, Wheaton et al.,2013, Church and Ferguson, 2015, Williams et al., 2015, Eaton and Mackenzie, inreview], but this avenue of research remains largely unexplored.Chapter 4 took the general geomorphic changes documented in Chapter 3 andextended them to include fluvial readjustment in the year following the flood event.These changes to the physical river structure were then combined with 2D hydrody-namic models to assess how changes to reach-scale morphology affect the organi-zation of flow at the sub-bankfull discharges that define aquatic habitat throughoutmost of the year. Although the temporal resolution of the UAV surveys (1 sur-vey/year) means smaller event-scale geomorphic changes may be subsumed intothe overall yearly perspective, the relatively simple, nival flow regime on ElbowRiver means the majority of geomorphic work is confined to one large event eachyear. The three topographic configurations (2012, 2013, 2014) therefore providea meaningful characterization of morphodynamics over the study period. Treat-ing the three geomorphic configurations as static and then ‘pouring’ water throughthem with the 2D hydrodynamic model is then an efficient way to document hy-106drogeomorphic relationships. Although 2D flow modeling remains an uncertainprocess that is heavily influenced by data quality and model calibration and ver-ification [e.g., Oreskes et al., 1994, Kondolf et al., 2000, Beven, 2001, Lane andRichards, 2001, Legleiter et al., 2011, Cienciala and Hassan, 2016], the errors inpredicted hydraulic properties are generally within 20 to 30% and are comparableto those based on field data [Pasternack et al., 2006]. Given the enhanced spatialcoverage and resolution of such methods, they provide a valuable means of assess-ing spatial and temporal hydraulic dynamism, particularly when combined withecological perspectives [Bergen et al., 2001, Reynolds, 2002, Clifford et al., 2005,Pasternack et al., 2006].The results of Chapter 4 indicated that flood-induced changes to the physicalform of the reach had profound impacts on flow patterns. Interestingly, the overallincrease in streamwise and across-stream variability within the entire active chan-nel documented in Chapter 3 was not reflected directly in the sub-bankfull flowconditions; post-flood hydraulics were instead less diverse than pre-flood condi-tions. This was associated with a loss of the pool and riffle-like conditions thatgive rise to diverse depth-velocity pairs. In terms of aquatic habitat, these hy-dromorphic changes were shown to be deleterious for adult and juvenile browntrout, with a general loss of suitable hydraulic conditions and a reorganization ofthe spatial characteristics of high quality patches. The fuzzy c-means clusteringmethod provided an objective fluvial landform classification procedure with whichto assess spatiotemporal changes; a secondary benefit of this approach is that itforces an explicit heuristic examination of hydraulic properties and depth-velocitydistributions. Although this requires some subjective decisions about the most ap-propriate number of classes and clustering parameters, the end result is a deeperunderstanding of the underlying data in relation to the study setting.5.3 ConclusionsOverall, this dissertation provides a thorough, integrative analysis of fluvial hy-drogeomorphology and aquatic habitat in the context of an extreme flood eventon a gravel-bed river. The research contributes to the fields of fluvial remotesensing, hydrology, geomorphology, and aquatic ecology and embraces the multi-107directional linkages between them. Viewing rivers in a spatially continuous, highresolution manner such as that provided by UAV-based remote sensing facilitatesthe inclusion of all aspects of fluvial heterogeneity. This scale of analysis alsomeshes easily with reach-scale numerical and physical models, further allowing forthe comparison of landscape patterns and ecological structure with experimentalinvestigations and hypothesis testing of underlying mechanisms. These approachesare also relatively new in the fluvial sciences; as technologies and methodologiesadvance and mature, eco-hydrogeomorphic investigations will become more com-mon and sophisticated. When combined with new frameworks for extracting andassimilating data and information at different scales, approaches similar to thoseused in this dissertation will provide a valuable contribution to the understandingand management of rivers as interconnected, human-influenced complex systemsin an ever-changing environment.108BibliographyT. B. Abbe and D. R. Montgomery. Large woody debris jams, channel hydraulicsand habitat formation in large rivers. Regulated Rivers Research &Management, 12(23):201–221, 1996. → pages 6J. D. Armstrong, P. S. 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Thesis/Dissertation
eng
Geography
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
UAV-based remote sensing of fluvial hydrogeomorphology and aquatic habitat dynamics
Text