Investigating small multiple catch ment runoff generation in a forested temperate watershed
April Lynda James Department of Geography McGill University Montreal, Quebec August 2005
A thesis submitted to McGill University in partial fulfillment of the requirements of the degree ofPh.D. in Geography
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Runoff gen~ration refers to the physical processes by which water travels through the landscape, moving through the subsurface or over the ground surface, ultimately arriving at the stream channel. These physical processes vary in both space and time leading to difficulties in mechanistic modelling of storm response, contaminant transport and nutrient fluxes. Runoff generation has been extensively studied at the hillslope scale and in headwater catchments. However, only recently have empirical studies begun to collect similarly detailed datasets across multiple catchments with which to examine how these processes change with scale. This study examines runoff generation from a series of eight small nested forest catchments and focuses specifically on the controlling influences of antecedent moisture conditions and catchment topography. End-member-mixing-analysis using stream water hydrochemistry from the eight catchments shows changing seasonal and storm-based source water contributions to the stream channel. Analysis identifies hydrochemical solutes with behaviour consistent with the assumptions of the mixing-model approach for aIl eight catchments. Results indicate that testing of solute selection is critical in the application ofthis method to multiple catchments. Runoff generation observed for five storm events shows a strong nonlinear relationship between runoff and antecedent moisture conditions, supporting the hypothesis of varying 'states of wetness'. Detailed hiIlslope-scale investigation during the different 'states of wetness' indicates that while groundwater and soil moi sture profiles show changing active-flow connectivity on a seasonal and storm based time scale, there no significant change in spatial patterns of shallow soil moisture. These results suggest that a priori spatial patterns in shallow soil moi sture in forested terrains may not be a good predietor of eritieal hydrologie eonnectivity that leads to the threshold change in runoff generation, as has been found in rangeland catchments. Differences in storm response from the eight catchments are in part attributable to variation in topography and landscape organization. The multiple catchments have similar distributions of topographic index and yet differences in mean values of
1 topographie index lead to significantly different estimates of mean residence time. Scaling of storm response is dominated by the behaviour of the three largest catchments. These three catchments distinguish themselves with larger MRT and larger valley bottom areas. It is these three catchments that, under dry antecedent moi sture conditions, show significantly larger amounts of new water delivery to the stream channel, suggesting a significant change in dominant runoff mechanisms related to topography and landscape organization.
II Resumé Le ruissellement réfère aux processus physiques au cours desquels l'eau se déplace du sous-sol à la surface et rejoint le cours d'eau en fin de parcours. Ce processus varie selon les lieux et le temps, ce qui entraîne des difficultés pour modeler les effets des orages, le transport de contaminants et les flux de nutriments. Le ruissellement a été largement étudié à l'échelle des versants (hillslope) et au niveau des bassins-versants en amont. Cependant, les études empiriques qui collectent des données détaillées à partir de plusieurs bassins-versants et qui examinent comment les processus changent selon l'échelle, sont plus rares. Cette recherche étudie le processus de ruissellement mesuré à partir de huit (8) petits bassins versants en forêt et plus particulièrement, étudie les effets des conditions d'humidité antérieures et de la topographie des bassins versants. Une analyse utilisant l'hydrochimie de l'eau provenant des huits bassins versants révèle des apports d'eau provenant des changements saisonniers et d'orages. L'analyse identifie l'hydrochimie des corps dissous avec un modèle consistant et avec un modèle de mélange linéaire. Les résultats indiquent que l'analyse des corps dissous est importante dans l'application de cette méthode aux multiples bassins versants. Les ruissellements observés lors des 5 orages montrent une forte relation non linéaire entre les ruissellements et des conditions antérieures 'd'état d'humidité', corrhoborant l'hypothèse de l'existence 'd'état d'humidité' variable. Une recherche fouillée à l'échelle des versants (hillslope) de différents 'états d'humidité' indique que, alors que les profils de l'eau au sol et de l'humidité du sol montrent un changement de débit liés aux saisons et à l'événement de l'orage, il n'y a pas de changement significatif dans la structure spatiale de l'humidité des sols peu profonds (profondeur de 20cm). Ces résultats suggèrent que les structures spatiales d'humidité mesurées au préalable dans les sols peu profonds ne sont probablement pas des indicateurs satisfaisants de la connectivité hydrologique qui mène au changement dans les ruissellements, tel que l'on peut le retrouver dans les bassins versants des paturages. Les différences entre les orages dans les 8 bassins versants sont en partie attribuables aux variations topographiques et à la configuration du paysage. Les
III bassins versants ont des distributions identiques d'index topographique et, cependant, les données conduisent à des estimés de 'temps de résidence moyen' (MRT) qui diffèrent de façon significative. La graduation des ruissellements d'orages est dominée par le comportement des trois plus grands bassins versants. Ces trois bassins versants se distinguent par un plus grand MRT et de plus grandes aires de fond de vallée. Ce sont également ces trois bassins versants qui montrent des quantités plus grandes d'eau neuve apportées jusqu'au cours d'eau, suggérant qu'un changement significatif dans les mécanismes prédominants de ruissellement est lié à la topographie et à la configuration du paysage.
IV Acknowledgements 1 would like to thank the many people that supported and contributed to completion ofthis dissertation. First, 1 wish to thank my advisor Dr. Nigel Roulet for his unwavering support as scientist, teacher, mentor, and colleague - thank you. 1 wish to thank my committee members, Dr. Tim Moore, Dr. Michel Lapointe and Dr. Charles Lin for their suggestions and comments during the varying stages of research. Thank you to Mike Dalva for many conversations on field equipment, data analysis and our common field site. Thank you to Martin Duval, Denis Bergeron, Marc-andré Langlois and Fidèl Dulac at the Mont Saint-Hilaire Centre de La Nature for their logistical support. 1 wish to thank Dr. Marty Lechowitz and the ECONET project for funding of the laser altimetry (LIDAR) digital elevation dataset. Processing of the LIDAR dataset was performed by Benoit Hamel, creating a high resolution (lm x lm) digital elevation model used extensively in this work. Ion ex change chromatography was performed in the laboratory of Dr. Brian Branfireun at University of Toronto Mississauga by Carl Mitchell and fellow students. 1 wish to thank my tremendously talented and eager team of field assistants: Raissa Marks, Sheena Pappa, Catie Burlando, and Nathan Deutsch. Not to be forgotten are the additional field assistants that 1 plied into the field with grandiose promises of adventure, escape and the occasional home-cooked meal: Andrei Zimmerman, Sebastien Laberge, Leif Burge, Barb Ramov, Andrei Fluerasu, and Lynda and Richard James. Financial support for this work was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), a McGill-McConnell Fellowship, and a McGill Graduate Studies Fellowship. The Global Environment and 3 Climate Change Centre (GEC ) was also very supportive in funding conference travel. On a more personal note, 1 wish to thank friends and colleagues that have acted as scientific mentors, encouraged me to stretch further and reach for the Ph.D. stars: Curt Oldenburg, Stefan Finsterle, Rohit Salve, Sally Benson and Ron Falta. Thank you to friends who have always been a constant source of support: Thank you Martha, Sarah, and Janie; Thank you Leif and Barb; Thank you Tim and family; Elaine and the MOC gang - thank you for the c1imbing, skiing, running, biking.
v Thank you Andrei - for the c1imbing and swimming, skiing and science, laughter and friendship always. Most of aU 1 wish to thank my parents, Lynda and Richard James, my strongest supporters and life-Iong mentors and friends.
VI Table of Contents
Abstract 1 Resumé III Acknowledgements V Contributions of Authors X List of Figures XII List of Tables XV
1. Introduction 1 1.1. Research site description 4
2. Runoff generation in small, forested catchments: A review of processes, select spatial controls and scaling hypotheses 8 2.1. Runoff mechanisms in small forested catchments 8 2.2. Catchment 'state ofwetness' as a control 10 2.3. Topographyand landscape organization as a control 13 2.4. Scaling ofrunoff generation in small catchments 14 2.5. Conclusions 17
3. Modeling stream water contributions across scale: application of end-member-mixing-analysis (EMMA) in a nested forested watershed 18 3.1. Introduction 19 3.2. Study Area 21 3.3. Methods 22 3.3.1. Field sampling and laboratory methods 22 3.3.2. Principal component and residual analysis 24 3.4. Results and Discussion 25 3.4.1. EMMA tracer selection 25 3.4.2. Mixing-space dimensionality at the Lk (147 ha) catchment 28 3.4.3. Independent testing of end-members in Lk catchment (147 ha) mixing-space 36 3.4.4. Spatial evaluation of the Lk catchment (147 ha) mixing-model 41 3.5. Conclusions 50
4. Analysing the effects of varying antecedent moisture conditions on runoff generation in small catchments 52 4.1. Introduction 53 4.2. Site Description 56 4.3. Methods 56
VII 4.3.1. Catchment delineation and characterization 56 4.3.2. Stream flow monitoring 56 4.3.3. Antecedent moisture conditions (AMCs) 58 4.3.4. Geochemical and isotopic field sampling and laboratory analysis 59 4.3.5. Isotopie hydrograph separation 60 4.3.6. End-member-mixing-analysis (EMMA) 61 4.4. Results 62 4.4.1. Storm selection 62 4.4.2. Runoff ratios 62 4.4.3. New/event water 67 4.4.4. Timing of storm response 74 4.4.5. Dissolved organic carbon (DOC) 75 4.4.6. EMMA 80 4.5. Discussion 84 4.5.1. Hydrometric response 84 4.5.2. Event-based interpretation 86 4.5.3. Comparison ofMSH results with other scaling studies 88 4.6. Conclusion 89
5. Investigating hydrologie connectivity and its association with threshold change in runoff response in a temperate forested watershed 91 5.1. Introduction 92 5.2. Site Description 95 5.3. Methods 96 5.3.1. Temporal monitoring of catchment discharge and soil moi sture profile__ 96 5.3.2. Spatial surveys of shallow soil moisture 98 5.3.3. Topographic analysis 98 5.3.4. Evaluating patterns in shallow soil moisture 99 5.4. Results 100 5.4.1. Hydrologic storm response at Mont Saint-Hilaire 100 5.4.2. Spatial surveys of shallow soil moi sture 103 5.4.3. Correlation with topographic index 103 5.4.4. Spatial continuity in shallow soil moisture 104 5.4.5. Spatial connectivity in shallow soil moisture 109 5.4.6. In-storm hydrologic connectivity 113 5.5. Discussion and Conclusions 118 5.5.1. Spatial continuity of shallow soil moisture 118 5.5.2. Connectivity in shallow soil moisture 119 5.5.3. In-storm hydrologic connectivity 119
6. Does topography and landscape organization control scaling of small catchment storm runoff generation? 122 6.1. Introduction 123
VIII 6.1.1. Inter-catchment comparison of topography and landscape organization_124 6.2. Site Description 127 6.3. Methods 129 6.3.1. Characterizing catchment topographie variables 129 6.3.2. Hydrograph recession analysis ofMRT 130 6.3.3. Baseflow hydrochemistry and contact time 131 6.3.4. Monitoring ofcatchment storm response 132 6.4. Results 133 6.4.1. Comparing catchment topography and landscape organization 133 6.4.2. Sub-catchment area accumulation and stream network structure 134 6.4.3. Topographie index 134 6.4.4. Baseflow recession analysis of contact time 136 6.4.5. Geochemical evidence of contact time 140 6.4.6. Storm runoff generation 142 6.5. Discussion and Conclusions 144 6.5.1. Do these catchments differ in their topography and landscape? 144 6.5.2. Does MRT scale with mean sub-catchment area? 147 6.5.3. Does storm response scale with MRT, total and/or mean-subcatchment area? ______148
6.6. Conclusions, ______150
7. Summary and Conclusions______151
8. References ______-.,..... ______154
IX Contributions of Authors The four manuscripts in this thesis have been or will be submitted for publication in peer-reviewed joumals. For each manuscript, Dr. Nigel Roulet is listed as co author. As required by the McGill University Graduate and Postdoctoral Studies Office, contributions of co-authors to each manuscript are described below.
Chapter 3: Modeling stream water contributions across scale: application of end-member-mixing-analysis (EMMA) in a nested forested watershed Dr. Nigel Roulet encouraged and supported my idea to test the application of end-member-mixing-analysis (EMMA) on the multiple catchments at MSH, providing solid grounding for the subsequent application of this method to individual storm-event analysis (Chapter 4). InteHectual discussions with both Dr. Nigel Roulet and Dr. Richard Hooper (included in the acknowledgements) were of considerable value in determining what observational data to include in the analysis and why. AH programming and analyses were performed by myself. Dr. Nigel Roulet provided inteHectual and editorial comments on drafts ofthe manuscript.
Chapter 4: Analyzing the effects of varying antecedent moisture conditions on runoff generation in small catchments Many discussions with Dr. Nigel Roulet led to the design of the field study and the objective of examining runoff generation from multiple catchments for varying antecedent moi sture conditions. 1 performed aH analyses (hydrometric, isotopic hydrograph separation, end-member-mixing-analysis, topographie catchment delineation) included in this paper. The MSH DEM was created from a LIDAR dataset by Benoit Hamel (included in acknowledgements) of the Mont Saint-Hilaire nature reserve. Dr. Nigel Roulet provided intellectual and editorial comments on drafts of the manuscript.
x Chapter 5: Investigating hydrologie eonneetivity and its association with threshold change in runoff response in a temperate forested watershed Discussions with Dr. Nigel Roulet were an important foundation for the design of this study. It was at the suggestion of Dr. Roulet that a second detailed survey site be added to the field study for comparison of high and low relief catchments. This comparison will be explored in an additional manuscript beyond the material inc1uded here. 1 used the pseudo-code of Western and Grayson (2001) for the connectivity function analyses. AlI programming, statistical and spatial analysis was performed by myself. Intellectuai email discussions with Dr. Andrew Western (inc1uded in acknowledgements) provided helpful comment on interpretation ofresults. Dr. Nigel Roulet provided intellectuai and editorial comments on drafts of the manuscript.
Chapter 6: Does topography and landscape organization control sealing of small eatehment storm runoff generation? Many discussions with Dr. Nigel Roulet aided in establishing the objectives of this study and the approaches used. AlI GIS analyses involving the MSH DEM was performed by myself. Dr. Nigel Roulet provided intellectuai and editorial comments on drafts of the manuscript.
XI List of Figures
Figure 1.1 Monteregian hills ofSouthern Quebec, Canada. 6 Figure 1.2 Bedrock geologic map of Mont Saint-Hilaire. 7 Figure 2.1 Diagram of runoff mechanisms and sorne environmental controls. Taken from Buttle et al. (2000). 9 Figure 2.2 Nonlinear relationship between runoff and mean catchment soil moi sture at the Tarrawarra rangeland catchment in Australia (Western and Grayson 1998b). Il Figure 2.3. Peak runoffrates and lag times as a function ofbasin size for runoff production (Jones, 1997: data from Dunne (1978), Kirkby (1985), Anderson and Burt (1990), Burt et al. (1992». 17 Figure 3.1 The nested catchments at Mont Saint-Hilaire, Quebec. 23 Figure 3.2 Bivariate solute plots ofLk catchment (147 ha) stream chemistry, located at the outflow to Lake Hertel. 27 Figure 3.3 Characteristic geochemical signatures (median values) ofpotential end-members. 29 Figure 3.4 Case 7 residuals for mixing-space of 1, 2 and 3 dimensions. 31 Figure 3.5 Case 7A residuals for mixing-space of 1, 2 and 3 dimensions. 32 Figure 3.6 Case 7B residuals for mixing-space of 1, 2 and 3 dimensions. 33 Figure 3.7 Case 7C residuals for mixing-space of 1,2 and 3 dimensions. 34 Figure 3.8 Relative root-mean-square-error (RRMSE) for Lk catchment stream solute concentrations in mixing-spaces of variable dimensions (Cases 7, 7A and 7B). 38 Figure 3.9 Solute-solute plots of end-members and observed stream chemistry 2 2 from Lk (147 ha) and Aw (11 ha) catchments: a) Mg + vs HC03-, b) Mg + vs EC. 44 Figure 3.10 Residual analysis for additional catchments in the 1-D Lk catchment (147 ha) mixing-space (Case 7C). 46 Figure 3.11 Diagnostic statistics for fit of additional catchment stream chemistry into the 1-D Lk catchment mixing-space (Case 7C). 47 Figure 3.12 Select mixing-diagrams for additional catchment stream chemistry: 2 (a) HC03- versus Mg +, and (b) HC03- versus EC. 48 Figure 4.1 Nested catchment system at Mont Saint-Hilaire, Quebec. 57
XII Figure 4.2 Threshold-like change in runoff over a small change in antecedent moi sture conditions (AMC) in the Aw (11 ha) catchment. 67 18 Figure 4.3 Hydrographs and Ô 0 tracer response from individual catchments during storm 8 (14 mm on wet AMC). 69 18 Figure 4.4 Hydrographs and Ô 0 response from individual catchments for storm 10 (38 mm on dry conditions). 70 Figure 4.5 Quantifying runoff and new water compositions for Storm 10 (38 mm on dry AMC) at the Lk (147 ha) catchment. 71 Figure 4.6 Total runoff (mm) and new water contributions (mm and %) across aIl catchments for the 5 storm events. 72 Figure 4.7 Lag times (hrs) for initial response and peak in discharge 18 (right hand panels) and peak dilution in Ô 0 (left hand panels). 73 Figure 4.8 Stream water DOC concentrations during Storms 8 (14 mm on wet), 10 (38 mm on dry) and 1 (25 mm on dry). 78 Figure 4.9 Stream water solute-solute plots of DOC (J,leq/l) versus
Ô180 (per mil) for each storm. 79 Figure 4.10 Individual storm end-member-mixing-analysis. 83 Figure 4.11 Runoffratio and lag time as a function of total catchment area and mean sub-catchment area. 85 Figure 5.1 Spatial survey grid of shallow soil moisture. 97 Figure 5.2 Hydrologic response (runoffratio) as a function of surrogate measures ofantecedent moi sture conditions: mean shallow soil moisture (a) and local water table elevation (b) for three riparian and lower hillslope wells. 101 Figure 5.3 Seasonal evolution ofshallow soil moi sture during Spring-Fall2002. 102 Figure 5.4 Wettest and driest surveys and corresponding histograms. 105 Figure 5.5 Histogram and map of topographie index (ln(altanp» for surveyed area. 106 Figure 5.6 Soil moisture and topographic index for wet and dry representative surveys. 106 Figure 5.7 Spatial continuity oftopographic index and shallow soil moisture. 108 Figure 5.8 Spatial patterns of shallow soil moi sture for three representative surveys. 110 Figure 5.9 Omnidirectional (a) and topographic (b) connectivity functions. 111
XIII Figure 5.10 Histograms (left) and variograms (right) for two simulations (RI and R2) of shallow soil moisture with prescribed means and variograms ofthe wettest (22-May-02) and driest (21-Sept-02) surveys, respectively. 112 Figure 5.11 Integral connectivity scales (omnidirectional and topographie) as a function of mean shallow soil moisture. 113 Figure 5.12 Evolution of moi sture within the soil profile on steep upper hiIlslope (location A - Figure 1) and lower hillslope (location B) during storms on wet conditions (storm i) and dry conditions (storm ii). 115 Figure 5.13 Evolution ofwater table elevation during storms on wet antecedent (storm i) and dry conditions (storm ii). 116 Figure 6.1 Nested catchments at Mont Saint-Hilaire, Quebec. 128 Figure 6.2 Comparison of cumulative frequency distributions of sub-catchment area for individual catchments: (a) sub-catchment area in units of hectares (ha), (b) normalized. 135 Figure 6.3 Frequency distributions oftopographic index for each catchment. 136 Figure 6.4 Baseflow recession analysis for the Aw catchment. 137 Figure 6.5 Spatial patterns of catchment characteristic parameters from recessional analysis. 139 Figure 6.6 Seasonal and spatial trends in baseflow concentrations ofEC CilS/cm). 141 Figure 6.7 Correlation between baseflow EC (ilS/cm) prior to individual storms and baseflow recession analysis MRT. 142 Figure 6.8 Total runoff (mm) and new water contributions (mm and %) across aIl catchments for the 5 storm events. 145 Figure 6.9 Scaling of new water (mm) inputs with measures or proxies of catchment hydrologie function. 146
XIV List of Tables
Table 3.1 List of solutes evaluated for use in EMMA. 30 Table 3.2 Analysis of dimensionality (rank) ofLk (147 ha) catchment mixing-space. 35 Table 3.3 Magnitude of residuals compared to analytical precision for Case 7 with 1 eigenvector retained (l-D mixing-space). 35 Table 3.4 Median concentrations ofpotential end-members, orthogonal projections in 3-D Case 7 mixing-space, and percent difference between projections and values. 39 Table 3.5 Fit ofpotential end-members in Lk catchment (147 ha) mixing-spaces. 42 Table 3.6 Seasonal changes in baseflow and groundwater concentrations: Spring-Fall 2002. 43 Table 3.7 Independent eigenvector analysis for additional catchments using Case 7C solutes (5 solutes). 45 Table 4.1 Catchment physical characteristics. 58 Table 4.2 Catchment gauging station, size, and discharge monitoring equipment. 58 Table 4.3 Storm event characterization. 64
Table 4.4 0180 isotopie compositions (per mil) ofnew/event and old/pre-event waters. 64 Table 4.5 Runoffresponse and two-component hydrograph separation using 0180. 65 l8 Table 4.6 Regressions for individual storm events: (a) DOC vs. Ô 0 and (b) DOC vs. Q. 77 Table 4.7 Dimensionality of mixing space for individual storms. 80 Table 5.1 Spatial surveys ofshallow soil moisture. 101
Table 5.2 Linear regression between topographie index (ln(a/tan~» and shallow soil moi sture for each of 9 surveys. 102 Table 5.3 Spatial continuity ofthe topographie index for the surveyed area. 107 Table 5.4 Spatial continuity of shallow soil moisture for each survey. 107 Table 6.1 Catchment topographie characteristics. 133 Table 6.2 Hydrograph recession analysis: empirical parameters for estimating MRT. 137 Table 6.3 Hydrograph recession analysis: Integrated catchment-scale parameters. 137
xv Table 6.4 Ranking of catchments by basetlow EC (ilS/cm) concentrations prior to individual storm events. 141 Table 6.5 Characteristics of 4 storm events and new water delivery for the 8 nested catchments. 143
XVI 1. Introduction
The science of hydrology is plagued by the inability to describe hydrologic phenomena with simple mechanistic relationships (Dooge 1997). This is particularly true of the mesoscale, defined by Dooge (1997) as ranging from catchments of 100 km2 down 2 to a unit hectare (0.01 km ). Because water in the stream channel is accumulated from across the catchment, a range of scales is inherently involved in producing its characteristic description, defined by measures such as peak discharge, runoff coefficient and biogeochemical and isotopic signatures. Hydrology of small catchments encompasses spatial scales ofindividual runoffmechanisms (mm to m sca1e) to integrated scales ofthe catchment (e.g. 105 m) (Bloschl and Sivapalan 1995, Buttle 1998). Although many studies have investigated the storm response of small catchments, the complex interplay of runoff mechanisms, biogeochemical transformations, environmental controls and their variability across the catchment has made reliable predictions of storm discharge (amount, arrivaI time, quality) from small catchments based on physically-based (mechanistic) models unsuccessful. Understanding effects of scale and spatial variability on these signaIs is a research direction that is critical to the framing of small, local response within regional and continental scale phenomenon. Researchers attempting to 2 describe larger scale behaviour (e.g. larger basins > 100 km ) are informed by smaller scale process description (Shaman et al. 2004). Alexandre Chorin summarizes the state of modem-day science that is no doubt apparent in the hydrological sciences: " ... We are at the beginning of the age of multiscale science and multiscale computation, with a growing need to understand not only phenomena on each of many scales but also the interaction between phenomena at very different scales ... " (Bareblatt 2003). A principal weakness in our ability to address the issues of seale, spatial variability and spatial aggregation of hydrologie response is the absence of spatially distributed datasets with whieh to test existing hillslope and watershed models (Bonell 1998), identifying a c1ear need for field based research. It is only very recently that we have begun to see the publication of multi-eatehment empirieal studies (Brown et al. 1999, Shanley et al. 2002, McGlynn et al. 2003, McGlynn et al. 2004) that provide detailed examination of runoff generation beyond the relative simplicity of the headwater
1 catchment. These studies are few and span a large range of c1imatic and geological conditions. Even fewer studies exist in the highly seasonal c1imatic regime of eastem Canada. Catchment topography and moisture conditions are two environmental controls of major significance in their influence on runoff generation and its spatial aggregation. Recent studies have hypothesized states of catchment wetness that potentially facilitate the prediction ofhydrologic response (Grayson et al. 1997, Zehe and Blosch12004). This hypothesis is based on empirical evidence of nonlinear changes in runoff response with small changes in catchment moisture conditions. Although evidence for variation in runoff generation on wet versus dry conditions is available, it is still unc1ear exactly how moi sture conditions affect runoff generation across scale and if a 'state-of-wetness' can be identified and prove useful for hydrologic modeling. Inter-comparison ofrunoff generation from multiple catchments brings to question the influence of catchment-scale topography and landscape organization. Few studies of small catchment hydrologic response have combined detailed examination of runoff generation with multiple catchment scale analysis of topography and landscape organization (Woods and Sivapalan 1997, McGlynn et al. 2004). However, these studies have provided a foundation for how to compare multiple catchments with respect to topography, landscape organization and their impact ofhydrologic response. The objective of this research is to examine how runoff generation from small catchments varies in space and time with focus on the influence of the environmental controls of topography and moisture conditions. To address these objectives, an empirical study of runoff generation was designed and conducted across scale, represented here by a series of nested catchments within the temperate, forested watershed of Mont-St-Hilaire (MSH), Quebec. The nested catchments of MSH range in size from 7 to 147 ha and exhibit significant topographic relief. Over the course of two late-spring-to-early-fall field seasons, the eight nested catchments were monitored for hydrometric, isotopic and hydrochemical evidence of runoff generation. During the study period, the state of wetness of the catchments ranged from wet conditions typical of late spring after snowmelt, to the very dry conditions typical of late summer-early fall. The
2 research objectives of this study are addressed by the following analyses and presented as 4 independent manuscripts. In the study of hillslope and catchment hYdrology, many lines of evidence of runoff generation are indirect. We often rely on the water in the stream to tell us its origin and flowpath. In Chapter 3, 1 examine stream water hYdrochemistry for evidence of source waters contributing to the stream channel. 1 apply a linear mixing-model approach (end member-mixing-analysis or EMMA) and the diagnostic techniques of Hooper (2003) to examine geographical source water (or end-member) definitions across multiple catchments. This approach develops a mixing-model of source waters for the largest catchment (147 ha) based on observations of stream chemistry. The mixing-model is then tested across the 7 sm aller catchments. This analysis provides a rare testing of EMMA across scale and allows for its subsequent application in the detailed examination of runoff generation across the eight nested catchments in Chapter 4. In Chapter 4 runoff generation is examined from the eight nested catchments for five individual storm events. These five storms occur on varying antecedent moi sture conditions. For each storm a range of metrics are used to explicitly quantify antecedent moisture conditions (AMCs) and the state of wetness of the catchments. Delivery flowpaths, contributing water sources and dominant runoff mechanisms are interpreted from hydrometric, isotopic and hydrochemical tracer evidence. This analysis queries the influence of state of wetness on runoff generation and contributes to the study of scaling of hydrologie response from small catchments. In Chapter 5, a detailed study of changing hydrologie connectivity, its physical definition and its influence on total runoff is performed within a single catchment. Spatial surveys of shallow soil moi sture are collected between storm events in an attempt to bracket changes in the state of wetness and evaluate the level of organization in spatial patterns of shallow soil moisture, one definition of connectivity. This approach has been used over small rangeland catchments but not in the more complex terrain of forested catchments. Detailed storm-based temporal observations of water table elevations and evolution of the soil moisture profile provide evidence of an alternative definition of hydrologie connectivity, in which hillslopes and valley bottoms are connected by active flow.
3 In Chapter 6, characterization of topography and landscape organization of each catchment is performed using a high-resolution airborne laser altimetry LIDAR (light detecting and ranging) digital elevation model (DEM). Inter-comparison of catchment characteristics is coupled with the storm response analyses of Chapter 4 to address the influence on catchment-scale runoff generation. In addition, baseflow hydrochemistry and recession analysis provide complementary estimates of mean residence time (MRT) and effective transmissivity of individual catchments. This empirical study focuses explicitly on storm response, collecting a detailed datas et ofrunoff generation for 12 individual storms across eight nested catchments. The spatial extent of this dataset is still rare and offers a valuable resource with which to challenge our understanding of the aggregation and scaling of runoff generation. State-of-the-art empirical work must inc1ude many types of complementary data. This combined analysis, integrating an examination of physical processes, their spatial variability, environmental controls (topography and antecedent moi sture conditions) and the use of hydrometric and isotopic and tracer evidence has been recognised as critical for current day studies ofcatchment hydrology (Cirmo and McDonnellI997).
1.1. Research site description This study was conducted within the Westcreek catchment system of the Mont Saint Hilaire (MSH) Biosphere reserve, located in southern Quebec (Lat: 45°32'49" N, Long: 73°10'07" W). Current c1imate in this region is humid-continental, with daily mean temperature norms for January and July of -1O.3°C and 20.8°C, respectively. The region receives an average annual precipitation of 940 mm, 22% of which cornes in the form of snow (Meteorological Survey of Canada). The distinct seasonality in temperate c1imate in western Quebec produces a large temporal variation in moisture conditions in the catchment system. Although precipitation is relatively uniform throughout the year (~80 1 mm mon- ), a 4-month snow coyer and subsequent melt creates peak moisture conditions rarely obtained again during the summer months when there is large evapotranspirative loss ofwater. With the senescing of the deciduous leaf cover, wetter soils are common in the fall. In the winter, a shallow frost in the soils is common, but concrete frost seldom
4 occurs. Because of the strong seasonality, MSH offers an ideal location to examine the influence of changing states of wetness of runoff generation. MSH is one of 10 Monteregian hills located in southern Quebec, Canada (Figure 1.1). The mountain is plutonic in origin, rising 350 m above the surrounding lowlands of the St. Laurent river valley. The 3 plutonic intrusions that formed MSH during the Cretaceous period have resulted in bedrock made up of essexite, predominantly gabbros, on the western side of the mountain, and syenitic rocks on the eastern side (Figure 1.2). The Westcreek catchment system overlies the Sunrise and Pain de Sucre suites of essexite bedrock. Soils derive from the weathering of the parent igneous intrusions and glacial tills left behind after the recession of the Laurentian !ce sheet, approximately 10,000 B.P. The lower elevations of the mountain (up to -170 m ASL) were submerged by the postglacial Champlain sea (Webber 1965). One of 13 remaining are as of old growth deciduous forest in the St Lawrence Valley, MSH is vegetated by mature beech-maple forest with sorne sugar maples over 400 yrs old. Soils are generally immature (Gyn 1968) and include Brunisols, Gleysols and Podzols. Within the Westcreek catchment system, soils are classified as Dystric Brunisols (Agriculture and Agri-Food Canada 1998) and have a pH of approximately 4.5. Horizons progress from black organic to brown sand at depth with little evidence of mottling or gleying within the soil profile. Soil texture is a sandy loam with very little clay (2-4%), 20-30% silt, and 66-78% sand (Wironen 2005). Bulk density is 3 approximately 1.37 glcm • On the hillslopes, soils are well drained and range in depth from -0 cm to -1.5 m. In the valley bottoms soils can be >2 m and perched water tables can form due to the presence of a low-permeability layer or fragipan (Dingman, 1994) within 30-50 cm of the surface. This layer is limited in extent and does not appear on the hillslopes, possibly a result of illuviation (Mehuys and Kimpe 1976). Rooting depth can be limited by both the fragipan layer and/or bedrock. Sugar maples can affect shallow soil moi sture by 'lifting' water from deep soils and redistributing moisture at shallow depths (Dawson 1993, Lovett and Mitchell 2004). Microtopography is also significant in these forested catchments. Tree falls create mounds and hollows on the length scale of several meters.
5 •10 --.., .-
Quebec ------""" V.SA
1. Oka 6. Mont Saint-Grôgoito 2. Mont~1 7. Mont YalY'la$ka 3. Mont Saint-Bruno 8. Mont Shefford .:t Mônt Scùnt-Huüire 9. Mont BI'OfT'Ie 5. Mont Aougem:.nt 10. Mont Mégantic
Figure 1.1. Monteregian hills of Southem Quebec, Canada. The Monteregian hills, inc1uding MSH, formed from a series of igneous intrusions into overlying sediment during the Cretaceous period. Soils derive from the weathering of the parent igneous intrusions beginning after the last glaciation during the Pleistocene, receding approximately 10,000 B.P. (2005). Figure provided by the B. Hamel, MSH Nature Reserve, adapted from Horvath and Gault (1990).
6 D Lorraine and Richmond Groops (...... 4ndl~) Orci:t.ticàn III Hor.... COfOI14 imet.llmOlpb~rodal) Il Sunns. SUita 1.93 Ma (~p.ffeinillit. pyfQlQll/'lÏte) Il East HlU SUite 122 Ma {peQlJt.atiM. nepbeliM. .nit~ potpbyrt.! Il Pain de &ueta Sui» 120 MA (gabbIQ. n.eplle"". diorite. monzonita)
Figure 1.2. Bedrock geologic map of Mont Saint-Hilaire (MSH). The 3 plutonic intrusions that formed MSH during the Cretaceous period have resulted in bedrock made up of essexite, predominantly gabbros, on the western si de of the mountain, and syenitic rocks on the eastern side. The Westcreek watershed of this study overlies essexite bedrock (Sunrise and Pain de Sucre suites) and drains into an internallake, Lake Hertel, from the western side of the mountain. Figure provided by the B. Hamel, MSH Nature Reserve, adapted from (Horvath and Gault (1990).
7 2. Runoff generation in small, forested catchments: A review of processes, select spatial controls and scaling hypotheses.
2.1. Runoff mechanisms in small forested catchments The mechanisms of runoff generation attempt to physically explain how the input of precipitation, as presented in the hyetograph, results in the output of channel flow of the storm hydrograph. The hydrograph is an integrated response, summing contributions from the various runoff mechanisms simultaneously at play over the entire drainage area. From its point of origin as precipitation to discharge at the stream channel, water can travel a myriad of flowpaths, recharge numerous stores, and take part in many biogeochemical transformations. Currently, no one overarching theory exists, systematically describing links and relationships between runoff mechanisms, their delivery of different sources of water, and the influence of controls such as antecedent moi sture conditions, topography, soils, vegetation and rainfall characteristics. Figure 2 (Buttle et al. 2000) provides a summary diagram of runoff processes in Canadian Shield basins. However, it also serves as a summary of generalised influences of controls on runoff, the closest semblance we have to a conceptual framework. Recent review ofliterature on runoffmechanisms emphasises the heterogeneous nature of the response over the catchment. Delivery mechanisms include Horton or infiltration excess overland flow (HOF), saturation overland flow (SOF), subsurface stormflow (SSSF) by means ofinterflow (throughflow) and/or preferential flow (macropore or pipe), groundwater ridging and groundwater flow (GWF). For humid, temperate forested regions, the variable source area (VSA) concept, put forth by Hewlett and Hibbert (1967), is commonly used to describe the variable nature, in time and space, of runoff production resulting from a series of mechanisms (saturation overland flow and subsurface flow). Source areas are typically located in valley bottoms, areas of groundwater discharge and near-channel environments. As a result, inextricably linked to the VSA concept is the idea of topography as a control on flow through the catchment to the point of discharge. In humid temperate forested regions, subsurface flow and flow from VSAs can dominate the hydrologic response (Peters et al. 1995, Wolock et al. 1997, Norton and Femandez 1999).
8 OVëRaURO!N THICKNeSS
SPATIAL UNIFORIilITY
DOMINANT RUNOFF PROCESSIfS):
SLOPES eEOROCK/\ SOlI.. SsSFl AT • 55SF ABOVE PRO A!..; OOTCROPS "1SLANDS". 501l.·8EOROOI< CISCONnNUiTIES HOF • SS$F AT INTERFACE ·GWF !L.e"ORCCK / INTERFACE ·SOF ~ FOOTSLOPES; ~ SOI' SOI' RlPARIAN ZONES: GWF GWF S'OPE CONCAVITIES ~ ~ COUPUNG COUPUNG :>VRIN" CONTINUOUS eerwEEN 51.01>E$ ?1:RI005 OF HlGrl COUI"_ING AND RECEIVING WATERS INPUT 7HROUGHOUT tE.G. STREAMS. YEM \VEn.ANOS, DEC::l'JPUNG OURINO LAKES) PERICOS OF ORCVGriT
BASIN STREAMFlOW CHARACrERJSTICS;
PEAKFLOWS 1 SPRINGl AND l'AlI. SPRING ANO FAU. BASE FlOWS NOSASEFLOW CONilNUAl OVRING OROUGHT SASEî'LOW
MiNIMAl RES?ONSe VAA1ASl.E. RESPONS; TOSUMMER TOSUMMER AANFAlL INPUTS AANFAU. INPUTS
Figure 2.1 Diagram of runoff mechanisms and sorne environmental controls. Taken from Buttle et al. (2000).
Empirical studies indicate that preferential flow in forested catchments is an important delivery mechanism that remains challenging to describe and varied in its form. Infiltration of water can occur by both matrix flow and flow through systems of macropores created by root-systems, insect and animal activities, well represented in the shallow subsurface of forested catchments (Beven and Germann 1982, McDonnell 1990, Buttle et al. 2000). This type of preferential flow can rapidly move new water vertically to depth. It can also force the lateral displacement of old water, delivering it to the stream channel (McDonnell 1990). In shallow-soil forested slopes, preferential flow along the soil-bedrock interface (BR runoff) (Buttle et al. 2001) can rapidly deliver a mixture of event and pre-event water to the stream channel.
9 2.2. Catchment 'state ofwetness' as a control Antecedent moisture conditions (AMC), one of the most important environmental controls of storm runoff (Sivapalan 1993), can influence flowpaths, dominant mechanisms of delivery and sources ofwater delivered to the stream channel. In northern regions, seasonal trends in soil moisture stores will generally follow the seasonal fluctuation of stream discharge (Cirmo and McDonnell 1997). Moisture stores are replenished by spring snowmelt. With summer, moisture demand from plants will deplete stores. For instance, on forested hillslopes at MSH, Rouse and Wilson (1969) observed soil moisture to decrease 2 fold during the 1966 and 1967 growing seasons. Autumn brings reduced biological demand for water with leaf senescence (reduced evapotranspiration), increasing moi sture stores. Finally, in winter, minimal moi sture is added to the system since much of the precipitation occurs in the form of snow, and is not released to the system until spring. Changes in AMCs can also occur over smaller time scales (e.g. storm events). The magnitude of a single storm or a sequence of storms delivers precipitation that changes moisture storage and provokes changes in runoff from event to event. Observations of runoff generation from different types of catchments around the world have shown strong non-linear changes in runoff with small changes in AMCs. For ex ample, at the Tarrawarra rangeland catchment in Australia, the runoff coefficient (runofflrainfall) f shows a large increase for a correspondingly very small increase in mean catchment soil moisture (Figure 2.2) (Western and Grayson 1998b). Evidence of this nonlinear response also exists from forested catchments (Buttle et al. 200 1, Tromp van Meerveld and McDonne1l2005). leading to a hypothesis of 'states ofwetness' of the catchment. (Make the connection between this and transmissivity feedback and the latest discussion of connectivity - reference to Kendall et al. 1999) Working on rangeland catchments in Australia and New Zealand (Grays on et al. 1997, Western 1998, Western and Grayson 1998a, b, Western and McMahon 1999, Western and Grayson 2001, Woods et al. 2001, Western and Wilson 2004, Western et al. 2005), herein referred to as the 'Melbourne Group', characterize the catchment macrostate based on the organization of patterns in shallow root-zone soil moisture. Grayson et al. (1997) hypothesize two preferred states of spatial soil moisture patterns that imply different
10 dominant mechanisms of water movement. During the wet state (State 1) lateral movement of water (e.g. from upslope to downslope) is the dominant influence on soil moi sture patterns. The staggered response of soil moi sture at different observation points within the Tarrawarra experimental catchment due to the time it takes for water to be redistributed is presented as field evidence of this state of "nonlocal control". During this state, the topographic wetness index (Beven and Kirkby 1979) replicates the observed spatial pattern across the 10.5 ha pasture-covered catchment. However, during the dry state (State II), the wetness index that relates soil moi sture at a given location as a function of the upslope area does not accurately describe the spatial pattern. During this state the authors describe control of water movement as dominated by local vertical fluxes resulting in spatial soil moisture patterns that are much more random. As a result, they suggest the need for different indices depending on the state of moisture, wet or dry, and where a dry state index is defined in terms of local physical properties (soil and terrain). Switching between the states is observed over an average period of a month.
0.8 • i lu ,• )0.4 •- •-• 0.2 - .• 1 0 ..... 15 20 25 30 35 40 45 50 Moisture (%VN)
Figure 2.2 Nonlinear relationship between runoff and mean catchment soil moisture at the Tarrawarra rangeland catchment in Australia (Western and Grayson 1998b).
In their exploration of hydrologic response predictability, Zehe and Bloschl (2004) describe the initial macrostate of the catchment using the mean and variance of the spatial distribution of soil moisture and an experimental variogram to describe spatial continuity. They find that the predictability of hydrologic response is dependent on the state of
11 moisture in the catchment, with lowest predictability at states close to the thresholds where processes activate or switch (e.g. precipitation exceeding infiltration capacity). Most recently, debate has focused on the relationship between shallow soil moisture connectivity and the actual movement of water during storm response, particularly the deve10pment of transient lateral subsurface flow in small forested catchments (Tromp van Meerveld and McDonnell 2005, Western et al. 2005). Tromp van Meerveld and McDonnell (2005) observe strong non-linear behaviour in hydrologic response with hillslope average soil moi sture at the forested Panola catchment. However, they argue that the patterns in shallow soil moisture do not indicate where transient subsurface flow occurs but rather, it is soil depth and bedrock topography that determine the pattern of active flow. In this catchment, subsurface saturation forms on shallow bedrock surfaces, causing lateral subsurface flow. Similar evidence of BR runoff (soil-bedrock interface) exists on Canadian Boreal Shield catchments (Buttle et al. 2001). Hydrometric and isotopic tracer evidence indicate that individual delivery mechanisms can be activated or deactivated by AMCs. Macropore flow is recognized as highly sensitive to AMCs and rainfall intensity, but the nonlinear dependence of flow on these controllers has not been weIl quantified (Beven and Germann 1982). On a steep, forested catchment in Japan, Sidle et al. (1995) observed peak storm contributions delivered by macropore flow ranged from almost zero during dry AMCs to greater than 25% during high intensity storms with AMCs. Sidle et al. (1995) attribute the increase in macropore flow to lateral extension of the macropore system during wetter conditions. Zero-order basins showed a threshold of moisture storage before which they contributed minimally to runoff. Sklash and Farvolden (1979) observed groundwater (pre-event water) dominating the stream hydrograph and the overland flow component for a storm of moderate intensity and very wet AMCs. In contrast, both the hydrograph and the overland flow component were composed predominantly of event water given a very intense storm on dry AMCs. In contrast to work performed under wet AMCs, either due to study areas in regions of high rainfall (Pearce et al. 1986, McDonnell 1990) or under snowmelt conditions (Waddington et al. 1993), Brown et al. (1999) present a study of storm runoffunder dry conditions. Analyses of summer storm events in the Catskill Mountains indicate that
12 event water is a significant component of the summer storm hydrograph (49-62% for the most intense events). De!ivery ofwater is by shallow subsurface stormflow. Brown et al. (1999) contrast their study with those performed under wet antecedent conditions where pre-event water dominates and flow occurs by deeper flowpaths. Another recent study looks specifically at the effects of AMCs on flowpaths and hydro-geochemistry (Biron et al. 1999) (see below for discussion).
2.3. Topography and landscape organization as a control Topography is recognised as a important control on the spatial patterns of hydrological storm response at both catchment and hillslope scales (Beven et al. 1988). Jones (1997a) describes general trends of how basin morphology influences hydrologic response. Smaller watersheds capture less rainfall but hydrograph peaks appear sharper and earlier and result in higher storm levels; larger basins show longer lag times between peak rainfall and peak runoff and overall storm response is extended (Likens and Bormann 1995, Jones 1997a). Greater basin slope results in quicker storm response. More elongated or elliptical basins will increase trave! times, dampening the hydrograph. Hydrographs ofrounder basins will peak more sharply (Jones, 1997a). The most readily available information on spatial variability within a catchment is a contour map of its topography (Kirkby 1997). Based on the VSA concept, Beven and Kirkby (1979) developed the topographic wetness index to describe steady-state moisture storage as a function of topography, where local hillslope segment slope is used as a surrogate for the hydraulic gradient (i.e. the hydraulic gradient is assumed to be parallel to the surface topography). Use of this terrain-based index enabled them to incorporate spatial heterogeneity on the scale of the hillslope in their lumped parameter VSA model, TOPMODEL. Subsurface or bedrock topography has also been suggested as a critical controller of subsurface runoff (McDonnell et al. 1996, Freer et al. 1997). In their study of a forested, shallow soil Canadian shield basin, Peters et al. (1995) conc1ude that roughly all of the storm runoff cornes from water travelling along the soil-bedrock interface. This inc1udes event water travelling vertically by preferential flow paths. Analysis of hillslope trench flow at two catchments (Maimai catchment, New Zealand and Panola Mountain Research
13 Watershed, GA) showed significant influence ofbedrock topography (bedrock index) on hillslope hydraulic gradients and flow patterns (Freer et al., 1997). This relationship, however, decreased with sm aller rainfall events, indicating the importance of antecedent moisture conditions and rainfall intensity. Under dry conditions, the assumption of steady-state flow using any topography-based hydraulic gradient breaks down (Freer et al. 1997). Researchers also suggest topography is the dominant source of variability in biogeochemical processes within the landscape (Creed and Band 1998). As well as exhibiting control on soil moi sture and runoff generation, they suggest it also influences the biogeochemical cyc1ing of nutrients within the catchment. Specifically, they hypothesise that topography regulates the accumulation and loss of N03--N within the catchment. In a study of runoff generation in a forested system on the Canadian shield, Peters et al. (1995) provide an ex ample ofhow event water, travelling by subsurface flow paths along the subsurface topographie feature of the soil-bedrock interface, has significant interaction with the soil and regolith. Wolock et al. (1997) define a subsurface contact time index based on the topographic index of Beven and Kirkby (1979). As the mean topographic index increases moving from the headwater downstream, soil-water contact time index increases. Biron et al. (1999) explore a hypothesis of "horizons oflast contact", where the chemistry of water delivered to the stream will reflect the characteristics of the last horizon encountered. Their sampling, as a result, focuses on near stream environments. What about their results?
2.4. Scaling of runoff generation from small catchments
The processes of storm runoff generation occur heterogeneously within a catchment. Multi-scale studies aim at investigating how to take into account this spatial heterogeneity and what the effects are on the integrated hydrologic and biogeochemical responses. Recent discussion uses the term 'scaling relationships' to refer to the" ... dependence of a catchment hydrologic property on catchment area ... " Sivapalan et al. (2002). Empirical observations have shown conflicting trends in storm response with catchment area. In a compilation of early work, Dunne (1978) observed peak runoff rate to decrease with increasing catchment area for systems dominated by Horton overland flow, subsurface
14 stormflow and variable source storm runoff. The runoff coefficient (discharge/throughfall) also decreased with increasing area. Figure 2.3 (Jones 1997a) summarizes the peak hydrograph characteristics of various runoff mechanisms as a function of catchment area, including Dunne's (1978) compilation. Jones (1997b) specifically add to this diagram a description of pipeflow based on data from the Maesnant experimental basin in mid-Wales. Brown et al. (1999) studied the response of seven nested headwater catchments (8 to 161 ha) in the Catskill Mountains of New York to summer storm events. In contrast with Dunne (1978), peak runoff and the runoff coefficient increased with increasing catchment area. For the most intense storms, Brown et al. (1999) observed decreasing event-water contributions with increasing catchment area and attributed this to an increasing influence of groundwater as one moved to lower parts ofthe watershed. Recent studies of multi-catchment systems have provided a basis of inter-comparison of catchment topography and landscape organization and its effects on runoff generation and scaling relationships of hydrologic response (Woods and Sivapalan 1997, McGlynn and Siebert 2003). McGlynn and Seibert (2003) quantify differences in landscape organization of catchments by comparing the distributions of sub-catchment area. Sub catchment area is ca1culated for each pixel of the stream channel with a catchment. In contrast to total catchment are a, this measure takes into account how upslope catchment area is collected by the stream network. In addition, they ca1culate the accumulating hillslope and riparian area along the stream channel providing distributions of riparian to hillslope ratios (or buffering capacity) for each catchment. A larger buffering capacity removes hillslope connectivity or influence from the stream channel. Sivapalan (2003) proposes travel time as a scalable quantity from hillslope to watershed scales. Mean residence time of a catchment integrates mean catchment behaviour in response to the effects of topography and soil characteristics on flowpaths. Wolock et al. (1997) define a subsurface contact time index based on the topographic index of Beven and Kirkby (1979). As the mean topographic index increases moving from the headwater downstream, soil-water contact time index increases. Wolock et al. (1997) suggest that a reduction in variability of concentrations results from increase in subsurface contact time, which in tum is linked to reduced variation in mean topography
15 and soil characteristics. Based on the approach of Wolock et al. (1997), Vitvar et al. (2002) estimate catchment MRT using runoffhydrograph recession analysis. They offer an alternative method of estimating catchment-scale hydraulic parameters of storage and hydraulic conductivity, replacing soil-derived values with a spatiaUy integrated catchment-scale estimate from the hydrograph recession analysis. Difficulty in understanding scaling relationships has led to a search for natural or preferred scales at which response and thus process representation simplifies. The concept of a representative elementary area (REA), introduced by Wood et al. (1988) suggests a spatial scale above which resolution of the pattern of local heterogeneity is not necessary. Bloschl et al. (1995) suggest that comparison of adjacent are as is required to truly evaluate changes in variability between smaU and large-scale processes. For catchment hydrology, adjacent areas are defined by the down gradient flow of water indicating a nested system of sub-catchments. Simulated storm-flow (Wood et al. 1990, Bloschl et al. 1995), observed inter-storm discharge (Woods et al. 1995) and solute concentrations (Wolock et al., 1997) for nested watershed systems have been used as data with which to search for an REA. In an analysis of inter-storm flow and stream chemistry on the Neversink River watershed in New York State, Wolock et al. (1997) analysed concentrations of solutes as a function of catchment area (e.g. acid neutralization capacity, sum of base cations, pH, Al, DOC, Si). With increasing catchment area, concentrations in solutes decreased in variabilityand at approximately 3 km2 concentrations had stabilized to relative1y constant values. Wolock et al. (1997) interpreted this as the area required to attain equilibrium with the hydrologic and geologic system. By looking at inter-storm flow, Wolock et al. (1997) analyse steady-state behaviour in contrast to the episodic perturbation of a storm event. The REA concept has been applied to both inter-storm and storm event analysis. In the case of inter-storm analysis, solute concentrations reflect an equilibrium with the landscape. At the Maimai catchment in New Zealand, McGlynn et al. (2003) observed a positive relationship between mean residence time (MR T) and median sub-catchment size for 4 catchments ranging in size from 2.6 to 280 ha, supporting the idea that landscape organization is an important control on flowpaths, stores and runoff generation (McGlynn
16 et al. 2003). Although residence time and surrogates such as baseflow hydrochemistry are integrated measures ofhydrologic behaviour of a catchment (McGuire et al. 2005), do they inform us on the scaling relationships of individual storm response? At Sleepers River, Vermont, Shanley et al. (2002) report increasing percent new water inputs with baseflow alkalinity, a surrogate for catchment till transmissivity.
Figure 2.3. Peak runoff rates and lag times as a function of basin size for runoff production (Jones, 1997: data from Dunne (1978, Kirkby (1985), Anderson and Burt (1990), Burt et al. (1992)).
2.5. Conclusions Examination of the literature on runoff generation illustrates the spatial and temporal variability of mechanisms of delivery and environmental controls, represented here by AMes and topography. Increasing the spatial expanse of study to multiple catchments emphasizes these challenges and yet may offer new insight into mechanistic and scaling descriptions. It is unc1ear whether or not dominant runoff mechanisms change as function of catchment area, and if so, how these changes are reflected in field observations.
17 3. Modeling stream water contributions across scale: application of end-member-mixing-analysis (EMMA) in a nested forested watershed
Submitted to Water Resources Research April L. James and Nigel T. Roulet
Keywords: end-member-mixing-analysis, stream chemistry, runoff generation, scale, catchment hydrology.
Context In this chapter, a linear mixing-model approach (end-member-mixing-analysis or EMMA) is used to examine hydrochemical evidence of source waters contributing to the stream channel. The analysis presented here tests if stream water can be described as a linear mixture of source waters that are identified independently in the landscape. The simplified modelling approach is applied to compare hydrochemistry from the multiple catchments and determine what solutes are compatible with the assumption of linear mixing, where source waters do not change as they travel to the stream channel.
Abstract As water moves through the landscape, it transports biogeochemical constituents to the stream channel. The physical processes that deliver water to a stream channel vary in both space and time. As a result, the modelling of runoff generation and biogeochemistry remains a challenge. Mixing models define different sources or end-members of water (e.g. throughfall, groundwater and soil water) that mix together to make up stream water. The application of mixing models has focused on the analysis of runoff from headwater catchments. Current interest in multi-catchment studies challenges the use of hydrochemical mixing-models across scale, where changes in stream chemistry from catchment to catchment may indicate changes in contributions of different end-members or changes in the hydrochemical definitions of end-members in both space and time. We
18 examine seasonal and spatial variability of contributing end-members to streamflow in a 1.47 km2 multi-catchment temperate forest watershed. We create a mixing-model for the largest, highest order catchment against which independent field measurements of en(,i members are tested. The model is then applied to 7 additional sub-catchments, representing progressively sm aller areas. Only spatial testing across catchments allowed us to identify solutes compatible with the application of a single mixing-model across scale. Only two of the geochemical trac ers (electrical conductivity and HC03-) appear to exhibit conservative behaviour in all 8 catchments. Furthermore, we observed strong seasonal changes in end-members geochemistry, emphasising the need for a priori updating of end-members for the application of EMMA to individual storm-events. Our results suggest cautious application of EMMA for multi-catchment studies.
3.1. Introduction Detailed empirical studies of runoff generation are moving beyond the simplifying confines ofheadwater catchments to include larger spatial extent (Mulholland et al. 1990, Fan 1996, Brown et al. 1999, Shanley et al. 2002, McGlynn et al. 2004). In doing so, these studies address how heterogeneity of catchment hydrological and biogeochemical properties and processes vary across scale. One approach to the analysis of multi catchment datasets is the use of mixing-models that rely on natural tracer techniques. Mixing models identify different temporal and/or geographical sources of water that contribute to stream flow and can be used for hydrographic separation (Genereux and Hooper 1998, Hooper 2003). Natural tracer techniques take advantage of the chemicals and chemical isotopes in the catchment environment to investigate flowpaths, water residence times, transport and transformations of chemical solutes and nutrients (Sklash et al. 1986, Stewart and McDonnell 1991, Bazemore et al. 1994, Gibson et al. 2000). The use of natural tracer techniques has arisen, in part, because the difficulty of measuring storm flow generation at the hillslope and catchment scales (Bazemore et al. 1994). They provide an integrated response at the scale of individual catchments with an immediate link to hydrologic flowpaths (Sklash 1990, Kendall 1993, Genereux and Hooper 1998).
19 Mixing-models rely on the assumptions of linearity of the mixing process, time independent geographical sources and conservative behaviour of tracers. End-members represent the chemical compositions of source waters existing within a catchment and are assumed to be approximately constant in time and along the flowpaths to the stream. Because of the working premise that the movement of water from these points to the stream undergoes physical mixing only, end-member-mixing-analysis (EMMA) infers that source areas of the catchment such as the riparian zone do not affect chemical composition ofthe water on the way to the stream (Hooper, 2003). The assumptions involved in mixing-models are challenged by the complex spatial and temporal heterogeneity of the catchment enviroment. Evidence exists for spatially varying end-members (Kendall et al. 2001). Changes in geology, soils, weathering, and c1imate will alter geochemical signatures of water and potentially require more spatially complex mixing models. Evidence also exists for temporal changes in the relationship between hydrological response and hydrochemistry. Recent studies offer varying spatial and temporal extent of analysis in the use of EMMA for estimating contributing water sources (Hooper et al. 1990, Brown et al. 1999, Kendall et al. 1999, Katsuyama et al. 2001). For a single storm event, Brown et al. (1999) use EMMA to examine runoff generation across a series of 7 nested headwater catchments (8 to 161 ha) at Shelter Creek, Catskill Mountains, New York. Three end members, throughfall, O-horizon and groundwater, contribute to summer storm runoff. For a single catchment at Sleepers River, Vermont, Kendall et al. (1999) identify 2 contributing sources during spring snowmelt: a shallow flowpath, attributed to overland flow or perched soil water, characterized by low base cation and silica concentrations and high DOC, and a deep flowpath, exhibiting high base cation and silica concentration and 10wDOC. The extensive analysis performed at Panola Mountain, Georgia, made use of a multi year stream chemistry record to identify contributing end-members (Christophersen et al. 1990, Hooper et al. 1990, Christophersen and Hooper 1992, Hooper 2001, 2003). End members and their geochemical definition were based on the principal component analysis of basin stream chemistry. Initially 3 end-members were identified: a shallow organic horizon, a hillslope mineraI soil horizon and a groundwater or floodplain mineraI
20 horizon (Hooper et al. 1990). More recent analysis revisits the Panola Mountain dataset, applying a series of diagnostic tools to evaluating end-member contributions across 6 gauging stations. Breakdown of the multi-year dataset illustrates the temporal variability of end-members (Hooper 2003). In our study, we apply EMMA and the diagnostic tools of Hooper (2003) to examine contributing source waters across are as of 7 to 147 ha within a series of small nested catchments of temperate forest in southem Quebec, Canada. There exist only a few studies that apply EMMA across scale and fewer still that have tested the working assumptions of conservative solute behaviour across scale (e.g. (Hooper 2003). For each catchment we estimate the dimensionality (or number of end-members) required to explain the observed outflow stream chemistry. Physical interpretation of the contributing end-members is performed by comparing stream chemistry analysis with independent field measurements of potential end-members. Using the mixing-model from the largest catchment outflow (147 ha), we test its application across the 7 smaller, nested catchments. Our analysis allows us to generate a single mixing-model and to test its applicability across scale.
3.2. Study Area Mont-Saint-Hilaire (MSH) is a forested UNESCO biosphere reserve and one of 10 Monteregian hills located in Southem, Quebec, Canada. Climate in this region is humid continental, with daily mean temperature norms for January and July of -10.3°C and 20.8°C, respectively. The region receives an average annual precipitation of 940 mm, 22% of which cornes in the form of snow (Environment Canada 2005). Precipitation is relatively uniform throughout the year although a strong seasonality in baseflow exists due to spring release of snowmelt and increased evapotranspirative demands during the growing season. One of 13 remaining are as of old growth deciduous forest in the St Lawrence River Valley, sugar maple and American beech are the dominant species oftree with sorne sugar maples aged over 400 yrs old. The mountain is plutonic in origin, rising 350 m above the surrounding lowlands of the St. Laurent river. The 3 plutonic intrusions that formed MSH during the Cretaceous period have resulted in bedrock made up of essexite, predominantly gabbros, on the
21 western side of the mountain where the nested catchments of the Westcreek watershed are situated (Figure 3.1). Soils at MSH originate from weathering of the parent igneous intrusions beginning after the Pleistocene glaciation (Webber 1965). In addition, lower elevations within the mountain (up to ~170 m) were influenced by the postglacial Champlain Sea (Webber 1965). Soil thickness in the upper catchments can range from zero to depths of several meters.
Within the lower catchments, soil thickness can also be very shallow (~40 cm) but maximum depths are unknown. For the purpose ofthis study, the Westcreek watershed is divided into nested catchments ranging in size from 7 to 147 ha (Figure 3.1). The Lk catchment is the largest catchment (147 ha) and the most downstream gauging station located at the outflow of the Westcreek watershed into Lake Hertel.
3.3. Methods 3.3.1. Field sampling and laboratory methods From each catchment, stream water was collected prior to, during, and after 12 storm events during spring to fall field seasons of 2001 and 2002. Throughfall samples were recovered after each storm event from a series of 18 rain collectors located throughout the 147 ha watershed. The resulting dataset inc1udes runoffresponse over varying antecedent moi sture conditions, similar to Scanlon et al. (2001). The geochemical signatures of potential end-members were measured from a series of groundwaters wells, piezometers, a perennial spring and soil water lysimeters. Two transects of groundwater wells and piezometers were sampled on a bi-monthly schedule (Figure 3.1). The transect Tl is located in the Aw catchment and extends from the steep hillslope to the variably saturated valley bottom. Soil water lysimeters along this transect sample water from a depth of 35- 40 cm in mineraI soils. Transect T2 is located in the Vc catchment, an area of lower relief and extends from gently sloping upland to variably saturated valley bottom. In the uplands, soils are dark colored, organic and shallow (~30-40 cm) in depth overlying gently sloping bedrock. Within the valley bottom, soils are > lm in depth. Soil water lysimeters along transect T2 are located in the shallow organic soils of the upland.
22 Transect 1
• Gauging station
N
o 500 1000 1500 Meters
Figure 3.1. The nested catchments at Mont Saint-Hilaire, Quebec. Eight nested catchments were gauged and sampled for stream water chemistry during the spring-fall seasons of 2001 and 2002. Two transects of water table wells (groundwater and perched water) and piezometers were sampled bi-monthly for independent identification of potential end-members. Suction lysimeters located along these transects were sampled for soil water contentrations. Along transect 1 (Tl) are located groundwater wells b,d,e, piezometers b,d, perched well Cc. Suction lysimeters sample water on the steep hillslope (hillslope soil) and valley bottom (valley soil). Along transect 2 (T2), organic soil water is sampled, and groundwater is sampled (wells c and b). The perennial spring is located immediately above the Sc gauging station, and downstream of the YV gauging station. See also Figure 3.1 a for detailed instrument position along transects Tl and T2.
23 Water samples were collected in 250 ml HDPE bottles, rinsed with stream water 3 times prior to sample collection. The 250 ml samples were filtered using 0.45 Jlm cellulose acetate syringe filters and separated for cation and anion analysis. Cation samples were acidified to 2-3 pH and refrigerated at 4°C until ion ex change chromatography analysis was performed. Anion samples were frozen until analysis. The remaining unfiltered sample was set aside for electrical conductivity (EC) measurements. EC measurements were made at 25° C, allowing samples to equilibrate to controlled laboratory room temperature. Samples were analyzed for a suite of anions and cations, dissolved organic carbon (DOC) and electrical conductivity (EC). Ion exchange chromatography was performed at University of Toronto, Mississauga Geography Department (Dionex DX-500). DOC was measured using a Shimadzu TOC 5050 organic carbon analyzer. Analytical precision of measurements is expressed in % of average concentrations for each solute (Table 3.3).
3.3.2. Principal component and residual analysis We apply the diagnostic tools of Hooper (2003) to estimate the number of end members contributing to stream water for each catchment's multivariate datas et. For each catchment dataset, principal component analysis is performed resulting in a series of eigenvectors that reduce the dimensionality of the mixing-space, transforming a description of stream chemistry with dimensions equal to the number of solutes included (e.g. 9) to a lower dimension (e.g. 2). To determine the dimensionality of the dataset, or how many eigenvectors to retain, we examine the residuals for each solute (Hooper 2003). For an individual observation of stream water chemistry, a residual is defined by subtracting the original value from its orthogonal projection, the mapping of the original observation onto the reduced dimensional mixing space. Additional eigenvectors are retained until there is no structure to the residuals (Hooper, 2003). The residual analysis is first performed on the Lk catchment (total outflow of 147 ha). The fit between observed values and orthogonal projections are evaluated using two measures of error (Hooper 2003). The relative bias for solute j (bj) is a measure of the systematic error between observed (xij) and projected values (xij)'
24 i(x .. -x .. ) (1) b - i=1 Y Y j - n·x. 1
where xj is the mean of observations for solute j and n is the number of observations. The relative root-mean-square-error (RRMSE), is the standard deviation between the observations and their projected values (Taylor 1997) and is normalized by the mean of the observations,
(2) r.} =
A series of four mixing-space studies was completed to test the influence of individual solutes on the dimensionality of the mixing-space. We use the three criteria outlined by Hooper (2001) to evaluate the independence of potential end-members. End-members must exhibit concentrations bounding that of the observed stream chemistry. They must have lower variability than stream chemistry and be distinct from other end-members (Hooper 2001). Using the Lk catchment analysis, we project the independent field measurements of end-members into the newly defined, lower dimensional mixing-space. Field-based hydrologie observations and site-specifie knowledge are used to interpret the fit of end-members in the mixing-space. Temporal variation of the end-members is also examined. The resulting Lk catchment (147 ha) mixing-model is then applied to the 7 additional catchments to evaluate the explanatory power of a single mixing-model across scale.
3.4. Results and Discussion 3.4.1. EMMA tracer selection EMMA is a linear mixing approach and reqmres trac ers to exhibit conservative behaviour. Linear trends in bivariate solute plots have been used to indicate potential site-specifie conservative trac ers (Christophersen and Hooper 1992, Hooper 2003). Bivariate solute plots of stream water chemistry from the Lk catchment (147 ha) show
25 linear trends for base cations (Ca2+, Mg2+, Na+, K+), EC, and HC03- (Figure 3.2). Weaker trends are observed for DOC, sol- and N03- (e.g. N03- vs. sol·; DOC vs. EC). However, linear trends in solute-solute plots do not necessarily confirm conservative behaviour (R. Hooper, pers. corn., 2004). There is precedence for assuming conservative behavior of sorne biological active species (e.g. DOC, SOl) in storm event runoff analysis (e.g. Brown et al, 1999). Based on 1inear trends in stream chemistry, Kendall et al. (1999) inc1ude DOC in their PCA ana1ysis ofrunoff generation during snowmelt at Sleepers River, VT. Brown et al. (1999) use DOC (and SOl) to identify an O-horizon contribution to summer stormflow. In our dataset, the Lk catchment shows linear trends for DOC with two other solutes: EC and Na+. As a result, we inc1ude DOC in our selection ofpotentially appropriate trac ers for
EMMA. sol- and N03- are inc1uded in the following sensitivity analysis. Solutes inc1uded in EMMA must also,exhibit distinct concentrations for potential end members (Hooper, 1990). We compare the characteristic geochemical signatures (median values) of end-members as measured from transects Tl and T2, and the perennial spring (Figure 3.3). Concentrations ofbase cations, EC, DOC, and HC03-, show a wide range of values depending on geographical source. cr, however, shows little difference in end member compositions sampled independently in the field with the exception of throughfall. As a result, we exc1ude cr as a tracer in our analysis. The more biologically 2 active species of K+, S04 - and N03- could potentially help distinguish end-members but will not exhibit conservative behaviour. Throughfall exhibits the lowest concentrations of cations, anions and EC while DOC concentrations are higher than groundwaters with the exception of perched waters. Tl hillslope soil water exhibit high Ca2+, Mg2+ and cation concentrations when compared to other end-members, inc1uding groundwaters. Higher still are the concentrations in soil water in the valley bottom below the hillslope. This is likely due to the weathering of 2 plagioc1ase-feldspar (Tsujimura et al. 2001), a source of Ca + and HC03- ions in mineraI soils of the hillslopes. Ca2+ is the base cation most susceptible to weathering while other cations may be stored in secondary mineraIs (Birkeland 1999). Leaching of Ca2+ and HC03- from the hillslopes likely contributes the high concentration in soil waters that concentrate in the valley bottom, often under variable saturated and perched conditions.
26 K
400 • ;" .•...' • 400 "+. Mg 300 ";.f. 300.0:': . [EJ.• p;' 200 '100 150 200 200 1 00 SOOlZJ"';'. Ca 600 •• j!" 400 :;,.. 100 150 200
N03
504 3000200 100 !
log(DOC)
EC
HC03
Na K Mg Ca N03 504 log(DOC) EC Figure 3.2. Bivariate solute plots of Lk catchment (147 ha) stream chemistry, located at the outflow to Lake Hertel (n=123). Ions are in units of Jleq/l; DOC is in units of log (Jlmolll); EC is in units of JlS/cm. Linear trends in solute concentrations suggest conservative tracer behaviour (e.g. Mg2+ versus Ca2+).
Under high storage (after spring snowmelt) or wet antecedent moisture conditions, these perched areas are connected directly to the stream channel. As a result, these hillslope and valley bottom soil waters provide a possible upper bounding end-member to stream 2 2 concentrations of Ca +, Mg +, EC, N03- and HC03-. The hillslope and valley soil waters also have high concentrations ofN03-, also distinguishing them from other end-members. In contrast, Tl groundwater, is surprisingly low in base cations as compared to the high values ofthe mineraI soils in the hillslope and the valley bottom. Tl groundwater offers a lower terrestrial end-member bound to base cations (Na+, Mg2+, and Ca2+); it does not overlap with throughfall (i.e. not as low as throughfall) but acts as a lower bound to
27 stream concentrations. As a result, contributions of both groundwater and throughfall could both have a dilution effect on stream chemistry. Mean concentrations at the perennial spring located lower in the catchment system are slightly higher in both Ca2+ and Mg2+ than Tl groundwater. Sampling of T2 shallow organic soil shows very different median concentrations than the Tl soil waters. This location samples organic soils immediately overlying bedrock without the thick underlying mineraI soil horizon. Concentrations of base cations are much more similar to groundwater and as a result do not offer a unique signature. This soil do es offer a unique DOC concentration, the highest value beyond perched waters. Perched water from transect Tl (perched well Cc) provides an upper bound on Na+ that otherwise is not accounted for by other potential end-members with large variability. This potential end-member also provides an estimated upper bound to stream chemistry for RC03·, DOC (with the exception of the organic soil water) and N03- but not for the base cations (Na+, Mg2+, and Ca2+).
3.4.2. Mixing-space dimensionality at the Lk (147 ha) catch ment We estimate the dimensionality (or rank) of the mixing-scale at the Lk catchment (147 ha) using as inputs the 9 solutes indicated in Table 3.1. To determine the robustness of the mixing-space dimensionality, we repeat this analysis with varying solutes excluded (Table 3.2). The rank of the dataset is typically determined using the mIe of 1, where each additional eigenvector retained should explain at least l/(number of solutes) of variance in the stream chemistry (Joreskog et al. 1976). For example, for Case 7 with 9 solutes, the mIe of 1 retains eigenvectors explaining 1/9th (11.1 %) of variance. This results in a rank of 3 and explains a total of 77.2% of the variance. Retaining 3 eigenvectors results in (rank+1) 4 end-members. If we exclude N03- and sol- (Case 7A), rank is reduced to 2, requiring 3 contributing end-members. With the exclusion of K+ (Case 7B), explanation of variance of stream chemistry increases from 73.0% to 78.5% but the rank of the dataset remains at 2. By exc1uding DOC (Case 7C), rank equals 1, only two contributing end-members and 72.8% ofvariance explained.
28 Cations 3000 , K
.., .., .D ~Jl~~~u~:j.D .D w il u a li ~ â ~ l; ~ l ~ l ~ <1) ~ ~ ;:: ;:: ;:: ~ N ~ ;:: ;:: 'J ~ l- 1l: ;:: ] 0 ;:: ~ ~ ;::
Anions
.0 .0 ." ." ... .0 u W il ~ N N 0 Ji ~ -.; -.; ~ a .. ~ .. u JI 'i>. 'i>. .. F- >- ~ ~ ~ ~ ~ ;:: ;:: ~ ~ .~ J ~ ;:: ;:: ;:: ~ ~ ~ ~ ;:: 0 ;:: ] ~ ;:: DOC 2000
1500 so E :::l.
.0 V .~ -.; 'il u ~ ~ N l I:! l- 0 I:!
EC 300 ~'------"------"-"--'-----'--.
E 250 ~ ~::1. 200150 ~
1~~ ,.n...... nUnnnUUnUÜ. .D .D ." ... .D U w .:l ~ :E ~ 1 'il 'il " 'il -.; JI 1 'il 'il >- i i F- ~ ~ ~ ~ ~ .~ ~ ~ ;:: ;:: ;:: ;:: N ~ ;:: "u ~ 1- I:! I ;:: ~ 0 ;:: J'. P! ;::
Figure 3.3. Characteristic geochemical signatures (median values) of potential end members: Transect 1 and 2 groundwaters, soil waters and a natural perennial spring (seep) located between Sc and Yv gauging stations (see Figure 3.1).
29 To further evaluate mixing-space dimensionality, we visuaUy examine the residuals of each solute. An accurate mixing-model should have a random residual pattern (Hooper 2003). If residuals are not random, solutes may not be conservative trac ers or the model may require greater dimensionality (Hooper 2003). Figures 3.4 through 3.7 show the solute residuals for each mixing-space (Cases 7, 7A, 7B and 7C) described in Table 3.2. For aU but one solute (DOC), the residuals are much larger (Case 7 with 1-D mixing space) than analytical precision indicating that we can examine the residuals for structure (Table 3.3).
Table 3.1. List of solutes evaluated for use in EMMA. Type Solutes Cations Na+, K+, Mg2+, Ca2+ Anions N03-, sol-, HC03- Other DOC, EC
For Case 7, a 1-D mixing-space produces structure in residuals for N03-, sol-, and DOC. Because the residuals in DOC are approximately the same size as the analytical 2 precision, patterns in DOC residuals are disregarded. The patterns for N03- and S04 - are eliminated only by increasing the dimensionality of the mixing-space to 3, in this case still consistent with the rule-of-one criterion (Table 3.2). Relative root-mean-square errors (RRMSE) for each of these solutes, much larger than for other solutes in the 1-D case, are correspondingly reduced (Figure 3.8). However, N03- and sol- are known to be chemically and biologically active and will potentially violate the linear assumptions of the PCA.
With N03- and sol- removed from the analysis (Cases 7A and 7B), residual analysis indicates dimensionality has been reduced to 1. With the exclusion of DOC residuals, there are no existing patterns to support an increase in dimensionality beyond a 1-D mixing-space (Figures 3.5 and 3.6). Exclusion ofK+ (Case 7B) increases the explanatory power ofthe model from 73.0% to 78.5%. For 7A and 7B, the residual analysis does not agree with the rule-of-one. Residual analysis suggests a dimensionality of 1 while the rule-of-one criterion indicates a dimensionality of 2.
30 ID 2D 3D
] ...... -. ~ .. Na . . 1., . 1 ,ol! ···50 "r~··1 -50-r~~J '" -50"I~~- • 100 150 200 100 ISO 100 150 200
~ -50· ~ O. ~ 0 • K ~ -100 • ]-50·. ]-50·. Ji -150°D· ~ o~ .. 200 ····100 -100D 100 200 100 . 100 200
l00EJ~ 50 .,. ~ 50EL]. ~ .;.:.'", ~ 0 .t:~·*-· ~ 0 \~ •• ;" 0 '..r4;..T ....r...... , ~ .'.' . Mg ~ -: ..: à! -50 -:.. &! -50 ..: 0 -100 : .0.: -100 : 0". -100 :.0 • 200 300 400 2 2 300 400
~ 200100 ..... ~'OO·200Btd .. • ~100"."o ... Ca :c ,.""",." . .:,." . ., ..... "..... & O~• •"J.:=r...... j 0 ~ .;.t)\." : 1 0 .~ ~:::"'j-; : -100 , ....::,fI'W' • -10 t"'."::.. -100" - ••:." 400 600 800 400 600 400 600 800 10F:"·~---' i J.;...... ~ 10 :.. ~ - 0' ~.'1'0 -5 EJYo... • • ~ 0 II ....."i..~.. .~ 0 ...... :'1.. 1 N03 & ••• ,.~. Of. n;:..... '" -5 -10 ·-10· • • ...... 1""0 ~20'""""""~30 10 20 30 10 20 30
~ l00[Sd:.. ",100 • : ••• , ~ 50 504 " .:.... ~ ..• ...... , .. . ~ 0 '~~.] 0 ~J.::-ii.,... ~ o· ::.:;fJe;., . "',... . .:J.'~: .. -100 100 200 300 -100L....nlOO~2___ UO~ 3" L....n,00;t;;-~200~3i;d,00
~ O.S. $!O.5 ~ 0.5 ~ ...". '" 0.0 '- ~ .~. & .~. ,.05],,~O.O. # 0.0 • , In(DOC) i GJ GJ -0.5 ft •••". .. • • l 1.5 2.0 2.5 ;.0 1..5 1.0 2~5 3. 1.5 2.0 2:5 3.0
~ 0 ~ 0 -" 0 EC 1 -50 .•• ~ -50 ••. j -50 • '" -100 · • . ~-100D· • . -1u· • . D100 150200 100 150 200 100 150 200
200 .10150[:J. ~100 -. ] 50· • .- HC03~Ji o •..,.:. .• .,. •• -200~~,..,.,...... J ...;,.~ .". 500 1000 500 1000 Figure 3.4. Case 7 residuals for mixing-space of 1, 2 and 3 dimensions. Ions are in units of Ileq/l; DOC is in units of log (Ilmol/l); EC is in units of ilS/cm. The patterns for N03- and S042- are eliminated only by increasing the dimensionality of the mixing-space to 3, in this case consistent with the rule-of-one criteria (Table 3.2). Because the residuals in DOC are approximately the same size as the analytical precision, pattern in DOC residuals are disregarded.
31 1D 2D
-6 ° ~-;.~. .~ .: .. Na 1 ·"" -50 rf~. •
100 150 200 100 150 200
~ ..x! -50 " ;ij.° . ~ -6 • K :2 -100 .. ~-SO .. • J1 -150 -200 -100 °DD'100 200 100 200
.",50 '1' ~50 ,," l00E] l00F!J Mg ~ 0 ·id: .. ·. ~O ~:d: .... ~ -50 .... "''II « -50 """ ": -100 : .,,' ,-100 : .,," • 200 300 400 200 300 400
100 .' .'. ~ \~" ...... Ca ~ 0 ~.;.,.. 11 .f, .. .. cf ~ .. ";-.'"... -100 •• '
400 600 800
0.5 ., ~ 1.05JQ~ o. • Jn(DOC) ] 0.0 ] .~ 0.0 ~" -.05 .1 .~ .. . : 1.5 2.0 2.5 3.0 1.5 2.0 2.5 3.0
EC -50 • ~ -50 • i -100 .. &-100 .. ~.:(i . . "" °0'100150200 ""'°0' 100 150200
,; ~~~~.~;. ~~:: .'V. -6 ••" -:,:)C' ". ~ 0 '.'" t.-:.r.~. HC03 Jl 0 -.",,;., •• -: ~.. Cl! -:\~. ~ • -100 ~.. • • -100 •••"' .. ;:: • 500 1000 500 1000
Figure 3.5. Case 7A residuals for mixing-space of 1, 2 and 3 dimensions. Ions are in units of ~eq/l; DOC is in units of log (~mol/l); EC is in units of ~S/cm. There are no existing patterns in residuals to support an increase in dimensionality beyond a 1- dimensional mixing-space. Residual analysis suggests a dimensionality of 1 while the rule-of-one criteria indicates a dimensionality of2.
32 ID 2D
~ 0 .:~...~~. .... 0 ~";'.J.Ïy;,f_"~>rfi!f- Na :§ '. • ~ •••• J! -50 • ~-50 .'
L:J100 150 200 100 150 200
Mg ~ ::':' ~ .~ .. : . -Ji -50~E~ ..:. --1· & -50:E~ ":. --1 -100 :.. .. : ••• 200 300 400 -100200 300 400
100 100 ~ , ...... \~ Ca ...... lolo ... ~ 0 .~-!:: ~ 0'. ~ ... ~ • ...... l .. 1 lot .. ~ I.~, ~ . •• cr .. lolo -1 0(] -100 400 600 800 400 600 800
~ "" 0.0 ." ~ !!jO.5'1.05J ., ~ ~ c:J-':'• In(DOC) ~O.O ~-0.1 .. 0.5 .~. '" -0.2 •
1.5 2.0 2.5 3.0 1.5 2.0 2.5 3.0
o • 1ê 0 ~ -50 • ~-50'· EC r. Ji-loo .. ':.100 D•• D100150200 100 150 200
200 ~ 200 i 100 .:;••• ~ 100 ,( -6 0 ~:\' 1C"~ • :2 "'j.,... '.' HC03 ''il ~!!-~,' èë -100 ... ~ 0 ~ .. ,~. -200 -100 ...... : '.: ':::50~0~1~000::-:-...J 500 1000
Figure 3.6. Case 7B residuals for mixing-space of 1, 2 and 3 dimensions. Ions are in units of )leq/l; DOC is in units of log ()lmol/l); EC is in units of IlS/cm. Similar to Case 7A, there are no existing patterns in residuals to support an increase in dimensionality beyond a 1-dimensional mixing-space. Residual analysis suggests a dimensionality of 1 while the rule-of-one criteria indicates a dimensionality of 2.
33 lD 2D
~ o.~· 0 .~_. ~ i ...7:.-. ~.. ..!O t:Ji.'~---~7: •• 11-- :li '.. g ' •• Na .!) :li co: -50 • J1-50 • r=J100 150 200 100 150 200
5iEJ':~.~... ii 50~..~I.;a,- ;;;-6 0 •"'~~'" JO. ::0""0 :_~'.- ___ 1 Mg ~ 50 ...... & .~ .... ~1 : •••• -50 .:. : •• , 200 300 400 200 300 400
100 100 •• .... ," .. ~ ~ ., .... Ca ~ 0 .;(.~ -.:: .. .. ~ 0 -..=i: :~.".:: .... & ~\. .; ... ~ .. i :~: ." .. ...,... .. -100 •• ' -100 • ~.
400 600 BOO 400 600 800
-'" 0 !!! -50".. ~ 5 •• ' EC l ~ .-100 • . j! l°D0 _... • • . -5 .~., D100 150200 100 150 200 200 ~ 200 § 100 ~ 100 . '. .. :"':'" .. ." :t .r~·, HC03 ." 0 .. ~ ••••1'): • " 0 .. L,J!...... -:~: & ~ :. -100 ":,.:-. ~ -100 . .' ...... -200 500 1000 -200 500 1000
Figure 3.7. Case 7C residuals for mixing-space of 1, 2 and 3 dimensions. Ions are in units of J-leq/l; DOC is in units of log (J-lmolll); EC is in units of J-lS/cm. There is no pattern in residuals in a I-D mixing-space indicating that variance in these solutes is already well represented by mixing two end-members.
34 Table 3.2. Analysis of dimensionality (rank) of Lk (147 ha) catchment mixing-space. This table describes 4 analyses of dimensionality, inc1uding the number of solutes, the solutes exc1uded from the original set and the variance explained by each additional nd rd eigenvector (1 st, 2 , 3 ) etc. Bracketed numbers indicate the accumulated percent variance in stream water chemistry explained by the retained eigenvectors. For each case, bolded numbers indicate the rank determined by the rule of 1 (Joreskog et al. 1976).
Additional Rank Case Total # solutes No. of solutes Ist 2nd 3rd 4th 5th excluded 47.0 7 9 16.2 (63.2) 14.0 (77.2) 8.4 (85.6) 6.9 (92.5) (47.0) 7A N03-,SO/ 7 54.9 (54.9) 18.1 (73.0) 10.6 (83.6) 8.9 (92.5) 3.7 (96.2) 7B K+ 6 61.3 (61.3) 17.2 (78.5) 12.2 (90.7) 4.7 (95.4) 2.9 (98.3) 7C DOC 5 72.8 (72.8) 14.7 (87.5) 6.7 (94.2) 3.8 (98.0) 2.1 (100)
Table 3.3. Magnitude of residuals compared to analytical precision for Case 7 with 1 eigenvector retained (1-D mixing-space). Both error and residuals are expressed as % of average concentrations. Solute Anal. Error (%) Residuals (%) Na t8.3 (7.0) 72.1 K 14.9 (4.5) 546.2 Mg 29.1 (11.5) 42.7 Ca 7.8 (4.4) 37.6 N03 84.9 (7.1) 139.2 S04 29.4 (1.8) 99.2 DOC 7.6 8.3* EC 1.0 163.6 tHC03 15.2(3.7) 43.1
t Temporal error - estimates are larger than analytical error in brackets due to sorne changes in concentration observed during extended refrigerated storage time.
tHC03 error is the sum of error for all other cations and anions as concentrations are not measured directly but rather are calculated using the charge balance equation. An additional 5% uncertainty could be added here for random error in the charge balance. * the only solute for which the analytical error and residuals are similar.
35 In Case 7C, we remove DOC from the analysis. There is no pattern in residuals in a I D mixing-space (Figure 3.7), indicating that variance in these solutes is already weIl represented by mixing two end-members. RRMSE for aIl solutes is less than 14% (Lk in Figure 3.11c). The only significant change in residuals that occurs with an increase in rank is a reduction in EC. In the case of EC, the error is controIled by a cluster of large values and there is no pattern to the residuals that would support an increase in dimensionality. From these four case analyses we observe that the dimensionality of the mixing space will depend on the solutes that we assume to be conservative. 3 of the 4 cases indicate a I-D mixing space with two contributing end-members. Next, we further explore the legitimacy of each mixing-space, by examining how the independent measurements of potential end-members fit into each mixing-space.
3.4.3. Independent testing of end-members in Lk catchment (147 ha) mixing-space End-members are mapped into the Lk (147 ha) mixing-space, testing their ability to fit in the mixing-space and to bound the observed stream chemistry. Table 3.4 lists median values (lower and upper quartiles in brackets) for aIl potential end-members visuaIly presented in Figure 3.3. Using the approach developed by Christophersen and Hooper (1992) and applied to the Panola Mountain dataset (Hooper, 2001), we compare the median values of end-members to their orthogonal projections in each mixing-space (Tables 3.4 and 3.5). Criterion for an acceptable fit is set at 15% difference between median values and orthogonal projections (bolded values in Tables 3.4 and 3.5). Two findings are clearly indicated by this analysis. First, there is greater success in fitting end-members into the mixing-spaces for solutes EC and HC03-. This suggests that the assumption of conservative behaviour is most appropriate for these two solutes. Second, several end-members consistently fit aIl 4 mixing-spaces as indicated by <15% differences between median values and orthogonal projections. For example, for Case 7c (Table 3.5c) Tl groundwater shows 9% and 5% difference between observed and 2 projected values for EC and and HC03 -, respectively. Na+ and Mg + also show differences <15%. The fit of TI groundwater for these 4 solutes is a consistent result for aIl 4 mixing-spaces. T2 groundwater and the perennial spring also fit weIl in the Lk mlxmg-spaces. As a final end-member T2 organic soil water also provides a fairly good
36 fit (values only slightly above the 15 % fit criterion in sorne cases). End-members that do not fit weIl in Lk mixing-spaces are throughfall, Tl soil waters, and Tl perched weIl water. For example, Tl hillslope soil water shows absolute differences ranging from 21 to 172 % (Table 3.5c) and the Tl valley bottom soil water fits only for EC. End-members must also bound observed stream chemistry. Two solute-solute plots illustrate stream chemistry from the Lk (147 ha) and Aw (11 ha) catchments and the 4 end-members with good mixing-space fit (Figure 3.9). Tl and T2 groundwaters are represented by mean values from wells and piezometers. Error bars indicate observed temporal variability of individual instrument observed concentrations. We observe very few observations of stream chemistry that are not boundèd by the identified end members. Tl groundwater provides a lower bound to stream concentrations from both 2 catchments. T2 groundwater has higher concentrations of aIl three solutes (HC0 3·, Mg +, EC), acting as an upper bound on stream water observations, albeit with large variability. The perennial spring water (seep) resembles the higher bounding concentrations of the T2 groundwater with slightly lower mean concentrations. Temporal variability of end-members can complicate interpretation of source mixing. At Panola Mountain, temporal variability of end-members was observed over multiple years (Hooper, 2001). On time scales greater than a single storm event (e.g. seasonal or multiple year), solute concentrations, particularly major base cations (e.g. Ca2+, Mg2+, Na+, K+) are often monitored as non-conservative trac ers (Ogunkoya and Jenkins 1993, Eisenbeer et al. 1994, Wolock et al. 1997, Burns et al. 1998, Rice and Hornberger 1998, Kendall et al. 1999) reflecting contact time or residence time because of slow mineraI dissolution rates (Lasaga 1984). The greater the contact (or residence) time, the larger the tracer concentration until sorne maximum steady-state value is attained (Trudgill et al. 1996, Burns et al. 1998). Baseflow on the other hand will likely exhibit a maximum tracer concentration due to long residence time in the catchment system (Trudgill et al. 1996).
37 120 ' 100 a) Case 7
120 ,......
100; b) Case 7A
120
100 cl Case 78 OID IIIZD g BO UJ 20 : o :OEI .c::œ .. ,.DiDILII .. ,ClIi ~ ; <3 ~ III ~ Figure 3.8. Relative root-mean-square-error (RRMSE) for Lk catchment stream solute concentrations in mixing-spaces of variable dimensions (Cases 7, 7A and 7B). RRMSE for individual solutes can be reduced with increasing dimensionality. The patterns for 2 N03- and S04 - are eliminated only by increasing the dimensionality of the mixing-space to 3, in this case still consistent with the rule-of-one criteria (Table 3.2). In case 7, 2 relative root-mean-square-error (RRMSE) for N03- and S04 -, much larger than for other solutes in the 1-D case, are reduced with an increase in mixing-space dimension from 1-D to 3-D. However, N03- and sol- are known to be chemically and biologically active and will potentially violate the linear assumptions of the PCA. See Figure 3.11 for RRMSE of Case 7c. 38 Table 3.4. Median concentrations of potential end-members, orthogonal projections in 3-D Case 7 mixing-space, and percent difference between projections and values. Median values are accompanied by first and third quartiles in brackets. AH ions in Ileq/l; log(DOC) in log(llmol/I); EC in ilS/cm. Percent differences below 15% are bolded indicating a good fit in the mixing-space. End- Na K Mg Ca N0 log(DOC) EC HC0 member 3 S04 3 ThoughFall 22 49 58 128 39 43 2.9 27 249 Value (20,31) (38,4) (26,68) (96,196) (27,53) (35,69) (2.7,3.0) (22,40) (170,315) Orthogonal 28 68 74 226 18.9 173 3.4 43 134 Projection Percent 28.1 39.5 28.3 76.7 -51.5 301 18.3 60.3 -46.1 Difference Tl hillslope soil water (suction Iysimeters) 96 75 793 1918 1745 99 2.6 217 1102 Value (90,100) (71,9) (695,868) (1506,1973) (1092,1906) (88,104) (2.5,2.7) (210,225) (868,1295) Orthogonal 293 1903 887 4007 838 5997 18 727 -1437 Projection Percent 206 2438 11.9 109 -52.0 5957 594 235 -230 Difference Tl valley bottom soil water (suction Iysimeters) 106 91 988 2560 2123 160 2.62 255 1217 Value (78,148) (67,102) (871,1020) (2057,2638) (1707,2232) (139,182) (2.61,2.7) (196,284) (916,1543) Orthogonal 363 2318 1091 4898 1023 7321 21.3 886 -1733 Projection Percent 242 2447 10.5 91 -51.8 4476 718 247 -242 Difference Tl groundwater (wells b,d,e; piezometers b,d) 111 36 243 354 7.4 108 2.1 66 640 Value (85,144) (16,53) (171,288) (248,461) (3,13) (69,150) (2.0,2.5) (53,77) (376,809) Orthogonal 107 18 234 453 4.0 135 2.35 69 644 Projection Percent -3.7 -48.9 -3.8 27.9 -46.3 25.0 11.8 4.1 0.5 Difference 39 Table 3.4 continued ... End-member Na K Mg Ca N0 3 S04 log{DOq EC HC0 3 Tl perched water (weil Cc) 510 79 292 406 31 98 2.80 120 1102 Value (449,601) (73,80) (265,388) (349,505) (15,51) (59,137) (2.76,2.80) (102,122) (1048,1403) Orthogonal 210 87.6 491.6 946 20.9 204 138 1507 Projection 2.34 Percent -58.8 10.9 68.4 133.1 -32.5 108 -16.5 14.8 36.7 Difference Perennial Spring 99 29 487 427 6.8 180 1.6 76 842 Value (90,112) (27,34) (389,541) (356,469) (4,11) (121,208) . (1.5,1.7) (70,88) (732,943) Orthogonal 147 6.6 314 589 4.6 187 86 835 Projection 1.9 Percent 48.4 -77.3 -35.6 38.0 -31.7 18.9 Difference 3.8 13.4 -0.8 T2 organic soit water (suction Iysimeters) 84 19 372 328 2.7 40 3.23 59 619 Value (70,101) (17,35) (254,428) (274,366) (1,155) (35,41) (3.20,3.25) (43,75) (510,733) Orthogonal 87 48 213 416 2.5 32 2.9 65 724 Projection Percent 3.1 153 -42.9 26.9 -6.3 -18.9 -9.9 17.0 Difference 9.5 T2 groundwater (wells b, c) 80 43 409 531 1 32 3.0 95 1115 Value (55,107) (20,66) (297,659) (379,674) (0,1.6) (19,56) (2.9,3.1) (72,140) (809,1539) Orthogonal 120 63 298 563 2.6 4.3 2.8 84 1056 Projection Percent 49.7 47.0 -27.3 6.1 162 -86.6 -11.2 Difference -5.6 -5.3 40 At MSH, stream chemistry shows a relatively linear trend between end-members, as exemplified by the Lk and Aw catchments, a continuum in concentrations that evolves seasonally from spring to fall (Figure 3.9). During this time, water storage in the catchment changes considerably, from highest storage (WET) immediately after spring snowmelt to lowest storage (DRY) in late-summer and early fall Seasonal changes in baseflow, Tl groundwater and perennial spring concentrations during spring-fall2002 are summarized in Table 3.6. Both Tl groundwater and perennial spring water exhibit seasonally shifting geochemical definitions. For instance, HC03- concentrations in the perennial spring increase 536 Ileq/l over the course of the 2002 season. This suggests a seasonally increasing mean contact or residence time of groundwater. In comparison, we observe increases in baseflow HC03 - concentrations of 888 and 463 Ileq/l at the Lk and Aw catchments, respectively. 3.4.4. Spatial evaluation of the Lk catch ment (147 ha) mixing-model For each of the 7 additional catchments, an independent estimate of dimensionality is performed using the 5 solutes inc1uded in Case 7C (Table 3.7). The rule-of-one estimates a rank of 1 (l-D mixing space) for all catchments with the exception of the Aw and Sb catchments, which have a rank of 2. For the 1-D mixing-spaces, explained variance is quite high (>70% with only the Yv catchment below this). For the Aw and Sb catchments three end-members (2-D mixing-space) would explain over 90% of the observed variability. Interestingly, estimated dimensionality of the perennial spring (seep) dataset indicates a 1-D mixing space or 2 end-members with 73.3% of variance explained, a result ofthe seasonal changes in end-member definition. Stream chemistry from each additional catchment is projected into the Lk (147 ha) catchment 1-D mixing-space (Case 7C). Residual ana1ysis for each catchment is illustrated in Figure 3.10 and the corresponding diagnostic statistics are described in Figure 3.11. The relative bias (3.11a) and the RRMSE (3.11 b) provide measures of how well the 1-D Lk (147 ha) mixing-model can describe the stream chemistry from each additional catchment. Projected RRMSE is compared to the 'At-Site' RRMSE (3.11c) where stream chemistry from each additional catchment is projected into a 1-D mixing space created by its own eigenvectors. As a result, Figure 3.11(c) evaluates the 41 variability of each catchment's stream chemistry and the ability of a linear I-D model to explain this variability. Table 3.5. Fit ofpotential end-members in Lk catchment (147 ha) mixing-spaces. Listed are the percent differences between observed and projected median values of end- members. Percent differences below 15% are bolded indicating a good fit in the mixing space. a) Case 7A, I-D Potential End- 2 2 Na+ K+ Mg + Ca + DOC EC RC0 " members 3 Throughfall 97 -114 38 39 -13 15 -58 Tl hillslope soil water 173 36 -23 -39 -20 -23 69 Tl valley soil water 205 46 -24 -44 -25 -19 93 Tl groundwater -5 -33 -5 29 14 6 -6 Tl perched water -58 -2 68 132 -22 14 33 Natural Spring (Seep) 36 34 -38 38 46 16 -0.4 T2 organic soil water 21 17 -41 34 -25 14 -8 T2 groundwater 71 -8 -25 13 -22 -6 -23 b) Case 7B , I-D M 2+ Potential End-members Na+ g Ca2+ DOC EC Re03" Throughfall 63 5 16 -10 -2 -75 Tl hillslope soil water 177 -22 -39 -23 -22 69 Tl valley soil water 210 -22 -43 -30 -18 93 Tl groundwater -6 -7 28 15 5 -6 Tl perched water -58 68 131 -24 14 31 Natural Spring (Seep) 39 -37 41 46 18 2 T2 organic soil water 20 -41 34 -24 14 -8 T2 groundwater 69 -26 12 -22 -6.6 -24 c) Case 7C, I-D 2 2 Potential End-members Na+ Mg + Ca + EC RC03" Throughfall 84 15 138 4 -78 Tl hillslope soil water 172 -21 -33 -25 65 Tl valley soil water 187 -20 -40 -15 58 Tl groundwater -11 -9 36 9 5 Tl perched water 61 86 177 37 31 Natural Spring (Seep) 29 37 39 12 1 T2 organic soil water 3 -28 31 13 18 T2 groundwater 88 26 10 4 15 42 Table 3.6. Seasonal changes in baseflow and groundwater concentrations: Spring-Fall 2002. Solute t Lk catchment Perennial Spring tTl groundwater tAw catchment baseflow Ll [ ] Ll [ ] baseflow Ll [ ] Ll [ ] Na+(lleq/l) 98 39 18(58) 24 Mg2+ (!!eq/l) 324 389 136 (211) 234 Ca2+(!!eq/l) 568 232 304 (442) 283 EC (ilS/cm) 39 28 20 (38) 35 HC03-(!!eq/l) 888 536 471 (849) 463 t Maximum difference in baseflow concentrations during spring to faU 2002. ~Maximum range observed from wells and piezometers in transect 1 (Tl). Average of 3 stations (WeIl d, Piez d, WeIl e) with maximum change in brackets. Observations taken from 4 June to 1 November, 2002. At-site RRMSE (Figure 3.11c) is similar across aH catchments with most values ranging between 5 to 15%. This indicates that a linear modeling approach, specifie to each catchment, gives high performance in explaining stream chemistry variability across space (see also Table 3.7). This is a direct result of the strong seasonal signal in stream chemistry present throughout the watershed. The projected RRMSE (Fig lIb) shows much greater variability in fitting the 7 additional catchments in the Lk (147 ha) catchment mixing-space. The Ef catchment (91 ha) located immediately upstreàm from the Lk catchment and delivering approximately two-thirds of the total discharge, gives the best fit in the Lk 1-D mixing-space. The projected RRMSEs are very similar to at-site 2 values and the relative bias (Figure 3.11a) is low for aH solutes except Mg +. There are no patterns in residuals for this catchment (Figure 3.10). These results indicate that at the 2 Ef catchment solutes, with the exception of Mg + are mixing with the same ratios as for the Lk catchment. 43 a) 1800 1600 1400 E' 1200 : ! 1000 c LK (147 ha) Wet (\") o Lk (147 ha) Dry 0 800 ~ " AW (II ha) Wet l 600 L AW (11 ha) Dry • TlGW 400 .T2GW III T2 Organic SW 200 l' ,t Seep 0 0 200 400 600 800 1000 Mg (J.req/I) b) a LK (147 ha) Wet 300 o Lk (147 ha) Dry Q AW (11 ha) Wet 250 G AW (11 ha) Dry • TlGW ·T2GW 200 ---E.., liII T2 Organic SW ...... Seep :g 150 w~ 100 50 0 0 200 400 600 800 1000 Mg (J,Ieq/I) Figure 3.9. Solute-solute plots of end-members and observed stream chemistry from Lk 2 (147 ha) and Aw (11 ha) catchments: a) Mg2+ vs HC03-, b) Mg + vs EC. Only end members that fit into the Lk mixing-space area inc1uded. Groundwaters from Transects 1 and 2 are represented by mean values from wells and piezometers. Error bars indicate observed temporal variability of individual instrument observed concentrations. Stream chemistry is separated into high storage (wet) and low storage (dry) conditions, as defined by water table elevation and threshold like change in storm runoff generation. 44 Table 3.7. Independent eigenvector analysis for additional catchments using Case 7C solutes (5 solutes). Bracketed numbers indicate the accumulated percent variance in nd rd stream water chemistry explained by additional eigenvectors (1 st, 2 , and 3 ). For 6 of 8 catchments, the rule of 1 indicates a 1-D mixing-space with two end-members (bolded numbers). Analysis of perennial spring (seep) stream chemistry also indicates a 1-D mixing-space, or mixing of two contributing sources, a result of the seasonal change in the spring water geochemical signature. Rank Catchment 1st 2nd 3rd 6.1 4.4 Ef 87.3 (93.4) (97.8) 17.1 7.2 Pw 72.2 (89.3) (96.5) 14.8 6.7 Sc 72.2 (87) (93.7) 27.5 4.0 Sb 64.1 (91.6) (95.6) 17.6 14.6 Yv 58.9 (76.5) (91.1) 15.7 8.5 Vc 71.8 (87.5) (96) 20.0 6.1 Aw 71.8 (91.8) (97.9) 15.2 9.2 Seep 73.3 (88.5) (97.7) Upstream of the Ef catchment, fits deteriorate. The residual analysis in Figure 3.10 2 indicates structure for solute Mg + at an catchments upstream of Ef (91 ha). There is 2 sorne pattern in Ca + for Sb (7 ha), and Vc (11 ha) catchments and the perennial spring (seep). Solute-plots for the additional 7 catchments (e.g. Aw catchment in Figure 3.9) show a significant offset of a sti1llinear structure in stream chemistry as compared to the Lk stream chemistry. Figure 3.12a shows stream chemistry from an 7 additional 2 catchments for the Mg + vs. HC03- mixing-diagram. Compared to the Lk stream 2 chemistry, Mg + concentrations for Ef and Sb are shifted to the left (lower concentrations) 2 while an other catchments are shifted to the right (higher Mg + concentrations). This is indicated in Figure 3.10 where residuals are only without structure and dominantly 2 positive for catchments Ef and Sb and Figure 3.11a where the relative bias for Mg + 45 EF PW SC SB YV VC AW Seep 60 ";ij • . ... 80 " .. 40 80 O' 80 .:'. '.' . ~20~ ..·i··' .., 50 • .' ., . It! 100E]:t. .: .. 150 '. 100 .. O'· -150 "- . .g .. , o 1- 0 ' ---200, '~. _loot!J .~ :' 0 "...... '~','"' -100100[5] '." 100: ;' S0[;J_l00l:]''... '. ' .~ a .. -200 .~..., • ~., 50 50 ~." -300 • .!I! -50 ~ 5J Mg -100 -200 0'• - • ,'. 1..' vI. '. ,' • -250 '~ ~. -300 • 0 -100 '. 1-"-400 -100· .,: -300 • , -100 20() 300 400 EJ400 600 00 400 1 50 25 200 300 400 0 200 SO~ ~ •• ...... 3oo[!J"2oo~' 4OO[fJ300 ..:~.' 400300 3OO[!] .. ;. 4OO~300 _ 2503OO~' .~" :!2 O ...... Ca 2oo~100 ' ,~. .-t>:. ,200 ':';" ." 200 .~.d- ~ '. \~: 100 ~~-L.. a:: -50 ".,,:" ...... ~~ : 200 :" 200 ..... ~::'J( ,~~,200'~ 150' '\:..~.. " a • 100 ." :~I,?:''!'. 100 ,'l:-- , :" 100 .~'~ o 200 600 300 4 300 400 500 8 0 0 40 00 400 500 600 ~ 400 600 800 o • , cr -100 • • , • -100 EC ;', 40' ••••: { . -100 -100 -50 60[] ,:.~: • -200 00- ~ • --200 1 00 00 •• ' '0 °D -50 -200 -200. -100 80 100 120 • 100 1 2030 • 40' ,,' -300 1 0 3 loo"'"'ïô 1 0 100150200250 ~400 [6J 100 200 300 400 LJ ~ 100 300 200Ej~'=' 400 l00~ .- \" ,. 200, 600[2]'400 '. '. , 150 '~'.:.... • 200[iJ100 :,,' 3 ~.. &200' " "" f" ",... ..:-,...-,:...... , ••• ...... ".:. 00 ... _ .. Jo 100....- o !fi: ~ 0 • :li"'~~100 '?t'!4:-:', 200 ... "'" 100' ",:' • ',: 0 • -~•• ::' 200 • ,.fl~·.~,' , ,,~_,. .,..,...... " ~ ...... 1... HC03 .. t' '~'.1 l:·n:. .... o ,.~.. 50 -100. ':.;". ~~. • -100 ':' • 500 1000 - 00 ••• ' ' o' 100,' _: 1000 500 1000 400 500 600 600 800 1000 600 1000 200 600 1000 800 1000 Figure 3.10. Residual analysis for additional catchments in the I-D Lk catchment (147 ha) mixing-space (Case 7C). Ions are in units of Ileq/l; EC is in units of ilS/cm. Catchment Ef provides the best fit in the Lk catchment mixing-space, with no 2 patterns in residuals for this catchment. These results indicate that at the Ef catchment solutes, with the exception of Mg + are mixing with the same ratios as for basin Lk (Hooper 2003). Upstream from the Ef catchment, fits deteriorate for sorne solutes, as indicated by the presence of pattern in residuals (e.g. Mg2+). 46 100 ~---_.------~ ·... Na (a) 80 i DMg w [D~ ~ , ,: IIEC i :. Il Il Il li i ~ ,.'= o :3 ... :f±j m ~ i -20 ; . . -40 120 , L1Na . (b) DMg 100 ; D~ IlEC Il HC03 "- ~ al '"-' W "- " Figure 3.11. Diagnostic statistics for fit of additional catchment stream chemistry into the I-D Lk catchment mixing-space (Case 7C). The RRMSE and the relative bias provide measures ofhow weIl the I-D mixing-model can describe the stream chemistry from each additional catchment. Panels (a) and (b) show the relative bias and RRMSE, respectively, for each catchment projected into the Lk catchment I-D mixing-space. Projected RRMSEs are compared to the 'At-Site' RRMSE illustrated in panel (c) where each catchment is projected into a I-D mixing-space created by its own eigenvectors. As a result, panel (c) evaluates the variability of each catchment's stream chemistry and the ability ofa linear I-D model to explain this variability. 47 a) 1800 1600 1400 0 (.' 0 ,...,. 1200 o h ~o./~ :::::: ~~ 9 ....,! 1000 0 ~~*~ CT) 0 u 800 I o LK (147 ha) 600 L Additional catchmenlS enGW 400 - T2GW 200 l1li T2 Organic SW ,~ >è o Seep 0 0 200 400 600 800 1000 Mg (peq/I) b) 1800 1600 1400 ,...,. 1200 0 :::::: L , ! 1000 CT} 0 800 0 0 U I o Lk (147 ha) 600 ;, Additional catchmenlS enGW 400 -T2GW 200 l!I T2 Organic SW o Seep 0 0 50 100 150 200 250 300 350 400 EC (J,lS/cm) Figure 3.12. Select mixing-diagrams for additional catchment stream chemistry: (a) 2 HC03- versus Mg +, and (h) HC03- versus EC. The HC03- vs. EC mixing-diagram shows a tight linear pattern for aH catchment stream chemistry, with no offset, indicating variation in mixing ratios for difference catchments, as ohserved in the HC03 - versus 2 Mg + mixing-diagram. This suggests HC03- and EC wi11likely he most appropriate for use in a single linear mixing model application across aH catchments. 48 switches from large positive values for Ef and Sb catchments to negative values for all other catchments. 2 2 These results indicate that within the other catchments, Mg + and Ca + are not mixing with the same ratios as the Lk catchment. The Aw catchment is a strong example of this phenomenon, with large projected RRMSEs for both cations (Figure 3.llb). The at-site RRMSEs for these two solutes are also high (30 and 34%, respectively) indicating a poor fit with the conservative mixing assumption. The strong weathering effect of Tl mineraI soils may account for the large departure of the Aw stream chemistry from a linear mixing-model (Table 3.4). The analysis suggests that this source of cations is not present everywhere, varying across the watershed. Compared to the other solutes, HC03- and EC offer smaller and more consistent projected RRMSE and for the most part low relative bias. The large relative bias and projected RRMSE of Sb catchment result from more dilute concentrations compared to the Lk catchment. More dilute concentrations are consistent with the ephemeral nature of the SB catchment, where stream flow is observed only under high storage (wet) conditions. Solute plots of Sb stream chemistry still indicate a linear pattern, simply extending the lower range of concentrations observed at the Lk catchment. The HC03- vs. EC mixing-diagram (Figure 3 .12b) shows a tight linear pattern where all catchment stream chemistry overlap and are bounded by the Tl and T2 groundwater end-members with fewexceptions. This suggests HC03- and EC are the most appropriate solutes for use in a single linear mixing model application across scale. There are a number of observations of stream chemistry that suggest inclusion of additional end-members. For instance, in Figure 3.12 a cluster of observations exhibit more di lute concentrations than Tl groundwater, possibly explained by throughfall. At higher concentrations, a cluster of observations has much higher EC concentrations than are bounded by the T2 groundwater. Detailed application of EMMA for individual storm events may elicit additional time-sensitive end-members. 49 3.5. Conclusions End-member-mixing-analysis (EMMA) of the multivariate MSH dataset indicates that stream water can be described as a linear mixture of two end-members: interpreted here as groundwaters of varying contact and/or residence time. The l-D mixing-space results from a strong seasonal evolution of solute concentrations. Four independently defined end-members successfully fit in the l-D Lk (147 ha) catchment mixing-space and bound stream water observations. Tl groundwater sampled from a high elevation, headwater catchment provides a lower bound (dilute concentrations) to stream chemistry, suggesting a young groundwater with short contact time. Located lower down in the stream network, the perennial spring shows higher concentrations, consistent with longer mean contact time. Both these end-members exhibit a seasonal evolution in their geochemical signatures, suggesting that mean contact time increases with time from spring snowmelt. Upper bounds on stream water chemistry are provided by T2 groundwater, representing longer residence time water. Independent measurements of this end-member are more variable than Tl or perennial spring waters. This results from the complicating measurement within a perched area. As a result of the temporal variability in end member signatures, for storm-based EMMA, it will be important to update end-member definitions with a priori information. Further, detailed application of EMMA for individual storm events may elicit additional time-sensitive end-members (e.g. perched water and/or soil water). The spatial application of the l-D Lk (147 ha) catchment mixing-model to the additional 7 catchments shows that we were unable to a priori determine appropriate conservative tracers. Only the extensive testing of end-member fit into the Lk mixing space and spatial testing across catchments has identified HC03 - and EC as compatible with the application of a single mixing-model across the multiple catchment watershed. 2 2 Although Mg + and Ca + appeared to be good candidate solutes, showing linear trends in solute-solute plots (Figure 3.2) and showing distinct concentrations in end-members (e.g. Tl hillslope and valley soil water in Figure 3.2), our analysis indicates that they do not mix in the same ionic ratios in all catchments. This result clearly emphasizes the importance of testing our assumptions of conservative solute behaviour across scale. 50 Acknowledgements AJ would like to thank Raissa Marks, Sheena Pappas, Catie Burlando and Nathan Deustch for their tireless assistance in the field and the staff of the Mont Saint-Hilaire nature reserve for their logistical support. Thanks also to Dr. Brian Branfireun, Carl Mitchell and fellow students for ion ex change chromatograph analysis at University of Toronto Mississauga. The authors benefited greatly from discussion with Dr. Richard Hooper on EMMA, PCA and dimensional analysis. Drs. Bob Whitehead and Doug Goldsack provided helpful discussions on geochemistry and principal component analysis. Thanks also to Dr. Jake Peters for an early reading of the manuscript. This work was funded in part by the N atural Sciences and Engineering Research Council of Canada (NSERC), a McGill-McConnell Fellowship, a McGill Graduate Studies 3 Fellowship and the McGill Global Environment and Climate Change Centre (GEC ). 51 4. Analysing the effects of varying antecedent moisture conditions on runoff generation in small catchments To submit to Journal ofHydrology April L. James and Nigel T. Ronlet Keywords: runoff generation, hydrograph separation, end-member-mixing-analysis, antecedent moisture conditions, scale. Context At the hillslope and catchment scales, antecedent moi sture conditions have been identified as an influential controller on runoff generation. Recent studies have hypothesized varying states of catchment wetness (wet versus dry) that would facilitate the prediction of hydrologie response to storm events. The analysis presented in Chapter 4 attempts to explain the varying storm response of small catchments at MSH as a function of antecedent moisture conditions. Examination of runoff generation from multiple and often nested catchments has resulted in varied scaling relationships. In this study we contribute to the discussion of scaling of storm response and runoff generation from small catchments. Abstract We examine storm runoff generation from eight small, forest catchments with varying antecedent moisture conditions (AMCs). AMCs, quantified by mean shallow soil moi sture, groundwater water elevations and antecedent precipitation index, vary considerably for the five storms observed over the course of two late-spring to early-fall field seasons. Hydrometrie analysis shows a strong nonlinear change in storm runoff ratio consistent with the hypothesis of varying states of catchment wetness: wet versus dry. New water, defined by isotopie hydrograph separation, generally contributes smaller fractions of total runoff during wet AMCs when water tables are high, and variably 52 saturated areas are directly connected to the stream channel. Under dry AMCs, groundwater storage is depleted in sorne ephemeral catchments, saturated are as are smaller or absent and new water can account for much larger percentages of total runoff 18 (up to 76% of total runoff). High correlation of DOC and Ù 0 suggests that during dry conditions, new water is being delivered to the stream channel by shallow subsurface flow, assisted at MSH by the presence of a shallow fragipan in the valley bottoms. This is supported by end-member-mixing-analysis where throughfall shows a good fit in EMMA mixing-space and in the case of a large, high intensity storm, stream water chemistry is pre-dominantly a mixture of throughfall and groundwater. Under wet conditions perched water is an important addition al end-member. Scaling of total runoff with catchment size shows a varied relationship. For these five storms at MSH, scaling patterns for new water delivery with catchment size appear only for dry conditions. 4.1. Introduction Recent empirical studies have begun to specifically examme hydrological and biogeochemical processes beyond the traditional confines of the headwater catchment in an attempt to see if processes aggregate with increasing catchment size. An increasing number of researchers are attempting to integrate detailed analysis of storm response from multiple and often nested catchments (Mulholland et al. 1990, Brown et al. 1999, Shanley et al. 2002, McGlynn et al. 2004) in an examination of spatial and temporal controls on process aggregation. Examination of how runoff generation processes aggregate across scale is complicated by the temporal factor of antecedent moisture conditions (AMCs) (Shanley et al. 2002b). Empirical field studies of storm response have provided evidence of a non-linear 'threshold' response in runoff generation with AMCs (Buttle et al. 2001, Western and Grayson 2001, Tromp van Meerveld and McDonnell2005). A dramatic change in runoff ratios are observed with small changes in measures of AMCs inc1uding mean shallow soil moi sture and local groundwater table elevation. The increase of water delivered to the stream channel has been attributed to changing hydrologie connectivity, defined by Stieglitz (2003) as the active connection of moving water and may be a catchment-scale example ofnon-linearity due to switching of controlIing processes (Dooge 2004). 53 Runoff generation during periods of snowmelt (Waddington et al. 1993, Shanley et al. 2002b) and within continuously wet rainforest catchments (Pearce et al. 1986, McDonnell 1990) results in the well-documented large old water contribution to the hydrograph, suggesting deep flowpaths as dominant (Brown et al, 1999). Studies of dry AMes suggest a change in delivery mechanisms and flowpaths. In contrast to their observations under wet conditions, Sklash and Farvolden (1979) observe both the hydrograph and the overland flow component were composed predominantly of event water given a very intense storm on a dry basin. With deliberate focus on runoff generation under dry AMes, Brown et al. (1999) examined five storm events for seven nested headwater catchments. They observe large instantaneous event water fractions (49-62% for the most intense events) delivered by shallow subsurface stormflow. Although macropore flow is recognized as highly sensitive to AMes and rainfall intensity, the nonlinear dependence of flow on these controllers has not been aptly quantified (Beven and Germann 1982). On a steep, forested catchment, Sidle et al. (1995) showed increasing subsurface contributions to stormflow during increasing wetness. Peak storm contributions by macropore flow ranged from almost zero during dry conditions to greater than 25% during high intensity storms on wet AMes. They attribute the increase in macropore flow to lateral extension of the macropore system during wetter conditions. Zero-order basins showed a threshold of moisture storage before which they contribute minimally to runoff (Sidle et al. 1995) The spatial patterns in new/old water partitioning and metrics of runoff generation across scale, represented by small nested catchment systems, are one focus of study. Based on the topographic wetness index ln(a/tanp) of Beven and Kirkby (1979), the topographie scaling hypothesis (Shanley et al. 2002b) proposes that as catchment size increases, increasing contributing upslope area and decreasing mean slope will cause greater saturated overland flow and result in increasing new water inputs. Shanley et al. (2002b) further propose ground-frost and AMes as additional factors that will impact these spatial trends. They suggest the impact of AMes on spatial trends will vary depending on the transmissivity of the catchment itself, which in tum is affected by AMe. Wet conditions result in greater catchment transmissivity and potentially greater new water input. 54 Under dry conditions, high storage deficits reduce catchment transmissivity and may promote greater infiltration of rainfall, resulting in less new water input. However, evidence suggests that under dry conditions, surface and shallow subsurface flowpaths can activate quickly delivering new water to the stream channel. These pathways may be activated by hydrophobicity of organic soils (Biron et al. 1999, Buttle and Turcotte 1999, Buttle et al. 2005) or reduced permeability at depth. Biron et al. (1999) suggest both the hydrophobicity effect and reduced hydraulic conductivity below the organic layer could explain the conflicting observations of high solute concentrations (DOC, N03 and Ca) corresponding with discharge peaks and dry conditions. On a forested slope in south central Ontario, Buttle and Turcotte (1999) observed overland flow for intense throughfall on dry conditions. At Shelter Creek in the Catskill Mountains of New York, Brown et al. (1999) examined storm response across scale (8 to 161 ha) under dry summer conditions. Their analysis indicates that during these dry AMCs, high instantaneous fractions of new water can be accounted for by direct precipitation onto the channel and a shallow subsurface O-horizon end-member. For a 33 mm event, they observed maximum peak new water contributions to decrease with catchment size. Smaller catchments with potentially smaller stores of groundwater provided larger peak contributions of new water than larger catchments. Shanley et al. (2002) examined two snowmelt periods and a summer storm event across four catchments (41 to 11,125 ha) in the Sleepers River Research Watershed, Vermont. For the dry summer storm event (36 mm delivered in 4 hours) and 1 snowmelt event new water fractions increased with catchment size. In this paper, we examine runoff generation from catchments of different size for a variety of AMCs. We attempt to test the hypothesis that AMCs may be the key to explaining the inconc1usive observed patterns of hydrologic response across catchment size. We provide a detailed analysis ofrunoff generation for five storm events collected 2 on wet ta dry AMCs across 8 nested catchments ranging in size from 0.07 to 1.47 km • AMCs are quantified and runoff generation assessed by hydrometric, isotopic, and hydrochemical methods. We hypothesize that under wet AMCs, greater hydrologic connection across the landscape (e.g. hillslope to riparian area, stream channel and variable saturated areas) wi11lead to a similar hydrologic response (e.g. more uniform 55 new water partitioning) while dry conditions will result in greater variability among catchments. 4.2. Site Description This study was conducted within the Westcreek watershed of Mont Saint-Hilaire (MSH), Quebec (Figure 4.1). Located in southem Quebec (Lat: 45°32'49" N, Long: 73°10'07" W), MSH is one of 13 remaining areas of old growth deciduous forest in the St Lawrence Valley. Sugar maple and American beech are the dominant species oftree with sorne sugar maples over 400 yrs old. Historic land-use change within the mountain watershed has been minimal. The c1imate in this region of eastem Quebec has large seasonality and can result in very dry conditions in the watershed in sorne summers. Daily mean temperature for January and July are -lO.3°C and 20.8°C, respectively. The region receives an average annual precipitation of 940 mm, 22% of which cornes in the form of snow. Precipitation is relatively uniform throughout the year. 4.3. Methods 4.3.1. Catchment delineation and characterization The eight nested catchments located within the Westcreek watershed at MSH (Figure 4.1) were delineated using standard GIS software and a high-resolution (lm x lm) digital elevation model (DEM). The DEM was created from a LIDAR dataset of MSH flown with an Optech ALTM 2050, 50 KHz (50,000 pulses/sec) system in the spring of 2003 before leaf-out to optimize observational accuracy of ground elevations. Table 4.1 shows characteristic parameters for each gauged catchment inc1uding total and mean sub catchment area, mean slope, minimum and maximum elevation and mean topographic wetness index, ln(a/tan~) (Beven and Kirkby 1979). Based on the approach of McGlynn et al. (2004) mean sub-catchment size is the mean of the distribution of upslope contributing area for each pixel representing the stream channel in a given catchment. 4.3.2. Stream flow monitoring During the 2001 and 2002 field seasons runoff was monitored from the eight nested catchments using a series of V -notch weirs and existing culverts (Table 4.2). Permanent 56 staff gauges were installed and rating curves were developed for each station. Stage was recorded on 15 minute intervals. The Ef catchment (91 ha) is located immediately above the confluence of the east and west branches of the Westcreek stream system (Figure 1). At this location, the streambed is too wide for weir installation and electronic monitoring. As a result, we calculate the discharge of the 91 ha area by subtracting discharge from the west branch, measured immediately upstream at the Pw station (53 ha) from the Lk station (147 ha) located approximately 20 m downstream. Transect 1 • Gauging station o 500 1000 1500 Meters ~~~~~~-...•-.....• -.--.--- •.--.•.. -.. -•... --...... ~~~~~ !...... 1 1 Figure 4.1. Eight nested catchments at Mont Saint-Hilaire, Quebec. A perennial spring is located immediately above the Sc gauging station, and downstream of the YV gauging station provided an additional groundwater sampling point. Water table wells recorded seasonal and storm-based changes in catchment storage. Transect 1 inc1udes 3 sites of nested soil moisture (TDR) probes and water table wells (A,B,C). Additional TDR probes were installed vertically at ground surface between nests. Transect 1 is extended along the stream channel by two additional riparian area water table wells (wells D and E). 57 Table 4.1. Catchment physical characteristics. AlI values derived from the lm x lm resolution LIDAR-derived DEM. See explanation in text for definition of mean sub- catchment area. Total Mean Average Minimum Maximum Mean Catehment Area Sub-eatehment Slope Elevation Elevation Topographie (ha) Area (ha) (%) (m) (m) Wetness Index Lk 147 22.8 15.9 177 414 3.71 Ef 91 24.0 16.2 178 409 3.69 Pw 48 15.9 15.7 179 414 3.72 Sc 38 12.5 18.2 190 414 3.58 Yv 30 14.8 19.5 194 414 3.54 Ve 11 4.1 14.7 207 409 3.79 Aw 11 6.9 23.1 256 414 3.39 Sb 7 4.2 13.5 195 312 3.73 Table 4.2. Catchment gauging station, size, and discharge monitoring equipment. Catehment (size) Equipment Lk (147 ha) 1.0 m diam. eulvert; Keller 173-L pressure transdueer (0-2.5 psi range) EF (91 ha) tnla Pw (53 ha) 90° V-noteh weir; stilling weIl with potentiometer Sc (38 ha) 0.8 m cm diameter eulvert; Keller 173-L pressure transdueer (0-2.5 psi range) Yv (30 ha) 90° V -noteh weir; stilling weIl with potentiometer Vc (11 ha) 0.5 m eulvert; potentiometer Aw (11 ha) 53.8° V-noteh weir; stilling weIl with potentiometer Sb (7 ha) 53.8° V-noteh weir; stilling weIl with potentiometer fDiseharge at the Ef eatehment is estimated by subtraetion ofPw from Lk eatehments. 4.3.3. Antecedent moisture conditions (AMes) AMCs were quantified by a series of measures. Recorded throughfall for the 7-day period prior to storm events was calculated from on-site tipping bucket gauges (15-minute interval) located in the Aw and Lk catchments. Tipping bucket gauges were calibrated against manual throughfall measurements. In two ofthe eight nested catchments (Awand Vc catchments) local water table elevations were monitored for seasonal and storm-based changes in catchment storage. We use the two catchments as a surrogate for changing AMCs throughout the 8 nested catchments. In the Aw (11 ha) catchment (Figure 4.1) weIl B is located at the break in slope between hillslope and valley bottom. Wells D and E are located along the valley bottom following the stream channel towards the gauging 58 station within several meters of the permanent riparian area. Well D is located at the perennial-ephemeral transition point of the stream. Well E is located in an area ofnatural subsurface recharge to the stream, immediately above the catchment gauging station. Each well was instrumented with potentiometers and water levels recorded on a 15- minute interval. In addition, extensive spatial surveys of shallow soil moisture taken between storm events throughout the field season provided snap shots of seasonal changes in storage in the shallow subsurface. The surveys collected in the Aw catchment inc1uded over 300 point measurements each and covered a spatial area of approximately 180 m x 300 m and elevation change of 50 m. Surveys were collected using a portable soil water reflectometry probe with 20 cm probe length. Site-specifie calibration of volumetrie moi sture content was performed using an intact soil column extracted directly from a representative hillslope. A total ofnine individual surveys were collected during the 2001 and 2002 field seasons and provided an estimate of mean soil moisture for each survey. Detailed analysis of spatial surveys and hydrologie connectivity is presented by James and Roulet (Chapter 5) herein referred to as James and Roulet (2005c). 4.3.4. Geoehemieal and isotopie field sampling and laboratory analysis During and between storm events, stream water from the eight catchments was sampled for isotopie and biogeochemical tracers. Water was collected in 250 ml HDPE bottles. A second sample was collected in a 20 ml HDPE bottle with cone shaped plastic liner for isotopie analysis. Each bottle was rinsed with stream water three times prior to sample collection. In the laboratory, the 250 ml samples were filtered using 0.45 pm cellulose acetate syringe filters and separated into vials for cation and anion analysis. Cation samples were acidified to 2-3 pH and refrigerated at 4°C until ion exchange chromatography analysis was performed. Anion samples were frozen until analysis. The remaining unfiltered sample was set aside for electrical conductivity measurements. EC measurements were made at 25°C, allowing samples to equilibrate to controlled laboratory room temperature. Groundwater from wells and piezometers and soil waters (collected from suction lysimeters) were processed in the same way. Throughfall was 59 collected from 15 different manual collectors distributed throughout the 147 ha watershed. Samples were analyzed for a suite of anions and cations, dissolved organic carbon 18 (DOC), electrical conductivity (EC) and Ô 0. DOC was measured using a Shimadzu TOC 5050 organic carbon analyzer. Ion ex change chromatography (Dionex DX-500) was performed at University of Toronto, Mississauga Geography Department. Isotopic 18 analysis of Ô 0 was performed by the University of Waterloo's Environmental Isotope 18 Lab. Blind samples provided a mean repeatability of Ô 0 compositions of ± 0.09 %0. 4.3.5. Isotopie hydrograph separation 18 At each gauging station, we use Ô 0 compositions to separate the hydrograph into the two time-source reservoirs of old/pre-event and new/event water (Pinder and Jones 1969, Sklash and Farvolden 1979). Total runoff is a sum of contributions from these two components: (1) where t, 0 and n subscripts refer to total, old and new water, respectively. Based on a similar expression for isotopic composition, the fractional contribution of old or new waters can be written as: (2) (3) New water composition for each storm was calculated from volume weighted average of throughfall compositions collected at 15 locations across the 147 ha nested watershed. Standard deviation in new water composition due to spatial variability was within 0.16 per mil with the exception of one event where standard deviation was 0.42 per mil. Old water composition was defined by baseftow of each individual basin. For each storm we estimate uncertainty in total new water contribution due to uncertainty in runoff and isotopic compositions. A confidence interval for instantaneous runoff is estimated from ±2 standard deviations of the best-fit rating curve coefficients. 60 At high discharge, uncertainty in runoff (WQ) is largest. For each sampling of stream water, uncertainty in the fraction of new water due to the isotopic composition of old, new and stream water was estimated using the root-mean-square-error formulation (Genereux 1998): (4) The two sources of uncertainty are then propagated using the same formulation for a product of two independent variables to give an estimate of uncertainty in new water contributions (Lyons 1991). New water runoff (Xn) at any point in time is the product of total runoff (Q) and the fraction of new water (fn): X n =Qfn (5) where uncertainty in new water runoff (WXn) is estimated by: (6) Finally, uncertainty III total new water delivered during the storm IS estimated by summing the errors for each instantaneous measurement during the storm hydrograph. 4.3.6. End-member-mixing-analysis (EMMA) We apply the diagnostic tools of Hooper (2003) to evaluate the contributions of source waters (or end-members) during each individual storm event. In James and Roulet (Chapter III, submitted manuscript to Water Resources Research, July 2005), herein referred to as James and Roulet (2005a), we tested the validity of applying a single mixing-model across the 8 nested catchment system at MSH. Our results indicated that both electrical conductivity (EC) and alkalinity (HC03) appear conservative across all catchments and we use them here in our application of EMMA (Christophersen et al. 1990, Hooper et al. 1990, Christophersen and Hooper 1992, Hooper 2003). In addition, we include DOC as a tracer due to its association with shallow subsurface flowpaths particularly under dry AMC (Brown et al. 1999). For each individual storm event, principal component and residual analysis (Hooper, 2003) of observed stream water 61 chemistry results in an estimate of the dimensionality of the mixing-space (number of contributing end-members). Independent field sampling of end-members allows us to physically interpret these source waters and estimate their relative contributions. 4.4. Results 4.4.1. Storm Selection Five storms (Table 4.3) were selected for analysis on the basis of storm size, AMCs, 18 completeness of the observations and Ô 0 source separation between event and pre-event waters. Storm size and intensity can vary due to their different origins (e.g., frontal versus convective). The 5 storms range in size from 5 to 38 mm. The 38 mm storm, a convective summer storm was delivered within a 2.4 hr period, while the 5 mm frontal storm occurred over a 17 hr period. AMCs for each storm are assessed by i) precipitation records from the previous 7 days (antecedent precipitation index, API7); ii) local riparian water table elevations and mean shallow soil moisture from spatial surveys from the Aw catchment. API7 for the 5 storms ranges from 65 mm to 0 mm. In the Aw catchment, local riparian water table elevations drop from depths of -0.33 m to 0.88 m (Well D) and mean shallow soil moi sture varies from 0.24 to 0.11 vol/vol. In addition, these 5 storms provide a large isotopie difference between event and pre-event waters ranging from 5.00 to 7.61 %0 (Table 4.4), ideal for isotope hydrograph separation. New/pre-event water compositions are the volume-weighted average of all manual throughfall collectors (-15) with standard deviations representing spatial variability. Old water compositions are an average of all 8 gauging stations and the natural spring prior to each storm event. Standard deviations represent spatial variation. 4.4.2. Runoff ratios Total storm runoff from each individual catchment for the five storms (Table 4.5 and Figure 4.2) is defined as all discharge (no graphie al hydrograph separation) where start of hydrograph rising limb and end of recession limb are identified by changes in the first derivative (dQ/dt) or slope of the hydrograph. In the case of the recession limb, end time is defined with a dQ/dt of zero or a value estimated as indicating minimal change. For these 5 storms, hydrologie response clearly varies with AMe. Wet conditions pro duce 62 significantly larger runoff ratios than dry conditions. Stonn 5 (5.5 mm) (Table 4.5a) produces the largest runoff ratios of 20 to 59%. For stonn 8 (14 mm) (Table 4.5b) we observe runoff ratios of 2 to 16%. In comparison, at Sleepers River, Vennont, very large stonn events (~156 mm) during snowmelt conditions produce runoff ratios as high as 67% and 86% from a forested 41 ha catchment for two consecutive years (1993, 1994) (Shanley et al. 2002b). High runoff ratios of 28% and 61 % have been observed for consecutive stonns of 27 and 70 mm, respectively, from an 80 ha catchment within the Maimai watershed of west coast New Zealand (McGlynn et al. 2004). On dry conditions, the two largest stonns (10 and 1) (Tables 4.5c,d) produce much smaller runoff ratios, ranging from 0.2% to ~2.8%, and 0 to 2.1 %, respectively. Stonn Il (7 mm) (Table 4.5e) produces the smallest runoffratios (0 to 1.4%). In comparison, a 33 mm stonn occurring on dry summer conditions at the 161 ha Shelter Creek catchment (Catskill mountains, New York) gives a estimated runoff ratio of ~ 5% (Brown et al, 1999). For a summer stonn of36 to 44 mm at the Sleepers River Research Watershed in Vennont, Shanley et al. (2002b) observed runoff ratios ranging from 4.7% to 8.7% for catchments of 41 to 11,125 ha. The runoff ratio from stonns observed at MSH show a nonlinear threshold-like change with AMCs (Figure 4.2). In Figure 4.2, we plot stonn runoff ratios from the Aw catchment by two measures of AMCs: (a) mean shallow soil moisture content and (b) local riparian water table elevation. A second detailed study of local AMCs in the VC catchment (not included here) shows similar results. Although we do not have independent measurements of AMCs for aIl catchments, except for API7, our analysis suggests that this threshold-like response is consistent for aIl 8 catchments. 63 Table 4.3. Storm event characterization Storm No. 5 8 10 Il 5-Jun-02 23-Jun-02 16-Jun-Ol 17-Jul-02 3-Sept-02 Magnitude (mm) 5.5 14.1 25.16 38.11 7.03 Duration (hrs) 16.6 1.2 10.3 2.4 2.9 A vg Intensity 0.08 2.0 0.6 3.8 0.6 (mm per 15 min interval) Max intensity 0.8 6.6 12.2 6.3 2.6 (mm per 15 min interval) APht (mm) 65 15 5 0 Mean shallow soil moisture Aw (11 ha) catchment 0.24 0.24 0.23 0.20 0.11 (voUvol) Aw (Il ha) riparian water table elevation (m) -0.33 -0.29 -0.76 -0.81 -0.88 WeIlD t Antecedent precipitation index defined here as throughfall records from the previous 7-day period. Table 4.4. (") 180 isotopie compositions (per mil) ofnew/event and old/pre-event waters. 5-Jun-02 23-Jun-02 17-Jul-02 3-Sept-02 16-Jun-01 (5) (8) (10) (11) (1) NewWatert -4.26 -6.90 -5.73 -5.47 -5.36 (spatial std. deviation) (0.13) (0.14) (0.16) (0.42) (0.12) Old Watert -11.87 -11.90 -11.75 -11.99 -12.96 (spatial std. deviation) (0.17) (0.18) (0.25) (0.17) (0.17) New-Old difference 7.61 5.00 6.02 6.52 7.60 tVolume weighted average of spatial samples with standard deviations of compositions included in brackets; t Average of aIl gaugings stations and natural spring prior to storm with standard deviation in brackets. 64 18 Table 4.5. Runoff response and two-component hydrograph separation usmg Ô 0. Corresponding uncertainties in total runoff and new water contributions are represented by t1 and s, respectively. a) Small storm (Storm 5) ton wet antecedent moi sture conditions (AMCs). Total NewWater New/Old Catchment Area Runoff ±â Runoff Contribution ±s Water (ha) (mm) (mm)a Ratio (mm) (mm)b Contributions ~%~ ~%~ Lk 147 3.24 0.82 59 0.09 0.05 3%/97% Ef 91 Pw 48 Sc 38 2.02 1.23 37 0.05 0.05 3%/97% Yv 30 1.81 0.05 33 0.07 0.03 4%/96% Vc* 11 1.63 0.43 30 0.10 0.04 6%/94% Aw 11 3.40 0.65 62 0.05 0.05 2%/98% Sb 7 1.11 0.31 20 0.09 0.03 8%/92% TThfaIl = 5.5 mm; EF, PW have incomplete data; • VC has a second peak at 24.75 hrs; b) Medium storm (Storm 8) ton wet antecedent moi sture conditions. Total NewWater New/Old Catchment Area Runoff ±â Runoff Contribution ±s Water (ha) (mm) (mmt Ratio (mm) (mm)b Contributions (%~ {%~ Lk 147 2.27 0.55 16 0.22 0.10 10%/90% EF 91 2.08 1.02 15 0.29 0.13 9%/91% PW 48 1.85 0.81 13 0.28 0.14 15%/85% sc 38 0.34 0.21 2 0.07 0.05 21%/79% YV 30 1.38 0.04 10 0.19 0.04 14%/86% VC 11 0.39 0.10 3 0.11 0.03 27'Yo / 73 AW 11 1.64 0.32 12 0.11 0.06 7'Yo/93% SB 7 0.52 0.14 4 0.12 0.04 23'0/77% TThfaIl = 14 mm. c) Large storm (Storm 10) t on intermediate antecedent moisture conditions. Total NewWater New/Old Catchment Area Runoff ±â Runoff Contribution ±s Water (ha) (mm) (mmt Ratio (mm) (mm)b Contributions ~%} ~%~ Lk 147 0.92 0.23 2.4 0.35 0.09 39'0/61% Ef 91 0.98 0.46 2.6 0.31 0.16 31%/69% Pw 48 1.07 0.46 2.8 0.49 0.21 46%/54% Sc 38 0.23 0.14 0.6 0.09 0.05 38'0/62% Yv 30 0.44 0.01 1.2 0.18 0.01 41'0/59% Vc 11 0.24 0.06 0.6 0.10 0.03 44%/56'0 Aw 11 0.30 0.06 0.8 0.13 0.03 44%/55'0 Sb 7 0.06 0.02 0.2 0.04 0.01 76% /24'0 TThfall = 38 mm. 65 d) Large storm (Storm 1) t on dry antecedent moi sture conditions. Total NewWater New/Old Catchment Area Runoff ±A Runoff Contribution ±e Water (ha) (mm) (mmt Ratio (mm) (mm)b Contributions {%} {%} Lk 147 0.52 0.13 2.1 0.13 0.03 24% / 76'0 EF; 91 PW* 48 sc 38 0.28 0.17 1.1 0.07 0.C4 25%/75% YV 30 0.13 0.00 0.5 0.04 0.00 33%67% vc 11 AW 11 0.06 0.01 0.2 0.02 0.00 34%/66% SB 7 0 nia 0 0 nia 0%/0% tThfalI = 25 mm; EF, PW and VC have incomplete data; e) Small storm (Storm Il) t on dry antecedent moi sture conditions. Total NewWater New/Old Catchment Area Runoff ±A Runoff Contribution ±e Water (ha) (mm) (mmt Ratio (mm) (mm)b Contributions {%} {%} Lk 147 0.09 0.02 1.3 0.01 0.01 9%/91% EF 91 0.10 0.04 1.4 0.01 0.01 12%/88% PW 48 0.04 0.02 0.5 0.00 0.00 11%/89% sc 38 0.05 0.03 0.7 0.01 0.01 15%/85% YV 30 0 nia 0 0 nia 0%/0% vc 11 0 nia 0 0 nia 0%/0% AW 11 0.06 0.01 0.9 0.01 0.00 15%/70% SB 7 0 nia 0 0 nia 0%/0% tThfall = 7 mm; _ a Error associated with uncertainty in power model coefficients; b Error propagated from runoff and isotopie compositions; Error associated with isotopie compositions only ranges between 1-6 % (Genereux, 1998). 66 0.7 0.70 • WellS a) • 5 b) 5 0.6 0.60 • .. wWell1) .9 0 -;j 0.5 '';::::; 0.50 b. Weil E .... ~ 1 1::: 0.4 ::t:: 0 0 0.40 § c: 1 0.3 ::l 0.30 ~ 0: 1 Wb. 0.2 • 0.20 1 • ! 0.1 .8 0.10 j 8, ill • 0.0 0.00 1 •• 10.. ' • ~ 0.05 0.10 0.15 0.20 0.25 0.30 -2.0 -1.5 -1.0 .0.5 0.0 0.5 mean soil moisture local water table elevation (vol/vol) (m below ground surface) Figure 4.2. Nonlinear threshold-like change in runoff ratio over a small change in antecedent moi sture conditions (AMes) in the Aw (11 ha) catchment. Storms 1, 5, 8, 10 and Il are identified. AMes are quantified by (a) mean shallow soil moi sture content from snap-shot spatial surveys, and (b) riparian water table elevations (Wells B, D and E from Figure 4.1). 4.4.3. New/event water l8 For each of the five storm events, new water has a depleted Ô 0 signature resulting in a strong dilution response in stream water compositions illustrated for storms 8 (14 mm on wet AMes) and 10 (38 mm on dry AMes) in Figures 4.4 and 4.5, respectively. Instantaneous measurements of runoff are separated into new and old water contributions, illustrated in Figure 4.6 for storm 10 at the Lk (147 ha) catchment. Panel 6(a) shows the rating curve for the Lk catchment with a 95% (±2cr) confidence interval. The corresponding uncertainty in the instantaneous runoff is illustrated in panel 6(b). Uncertainty in new water composition (6b) results from propagation of uncertainty from both runoff and isotopic compositions as described in Section 3.5. Integrated through time, we estimate the uncertainty in total new water contributions for each catchment for each storm, allowing us to compare new water contributions across scale (Figure 4.7). Table 4.5 summarizes uncertainties in total runoff and new water contributions for each storm and catchment. Storm 5: 5.5 mm /ow intensity storm on wet AMes. The small, low intensity storm 5 occurred on 5 June 2002 and was preceded by 65 mm ofthroughfall during the previous 7 67 days (Table 4.3). Total runoff on these wet conditions is the highest of all 5 storms, ranging from 1.1 to 3.4 mm and there are significant differences in total runoff across catchments. For instance, the highest total runoff (3.4 mm) is observed from the small (11 ha) but highest relief Aw catchment. Total new water contributions are similar across catchments (~0.1 mm) and fractions individually range from 2 to 8% of total runoff (Figure 4.7). Storm 8: 14 mm moderate intensity storm on wet AMCs. Storm 8 occurs on 23 June 2002 and is preceded by 15 mm of throughfall (API7) and moderately wet AMC. Although it is a larger and more intense storm (14 mm de1ivered in 1.2 hrs), total runoff (Table 4.5b) from the multiple catchments has dropped (0.4 to 2.2 mm) from those observed during Storm 5 indicating a seasonal decrease in stores due to lower API7, increased time from snowmelt and demands of evapotranspiration. New water contributions are higher than during storm 5, ranging up to 0.28 mm and account for a larger percentage of total runoff (7% to 21 %). There are significant differences in total runoff across catchments (Figure 4.7) with large st amounts from the largest three catchments (Lk, Ef and Pw). Taking into consideration uncertainties from new-old water compositions and runoff measurements, no catchment size dependency of total new water contributions (mm) is distinguishable for storm 8. Storm 10: 38 mm high intensity storm on dry AMCs. On 17 July 2002, the largest and on average, most intense convective storm (storm 10) delivered 38 mm within a 2.4 hr period. AMC are dry with little throughfall in the previous week (5 mm) (Table 4.3) and the lowest riparian water table elevations in this mid-summer timeframe. Prior to this storm, the ephemeral catchment stations Yv, Vc and Sb have gone dry. Total storm runoff from the multiple catchments ranges from 0.06 to 1 mm and runoff ratios range from 0.2% to 2.8%. During storm 10, we observe the highest magnitudes ofnew water (0.05 to 0.49 mm). The large st three catchments (Lk, Ef, Pw) have distinctly higher amounts of new water (mm) than the remaining smaller 5 catchments, suggesting topographie scaling. New water fractions (%) are slightly higher for the smaller catchments, with 76% new water delivered from the smallest ephemeral catchment (Sb). 68 l~-~-----_~_]~: i 12:00 16:48 21:36 2:24 7:12 12:00 7.0T-························· ... ······-···~'~~E~~~1 -12 Ê 6.0 -11 ...... s 5.0 ~Aw(l1ho) -10 0 --Yc (11 ho) ~ 4.0 --Sb(7ho) ~Aw -9 - '!!,O 3.0 -c.-Yc 10 -<>-Sb -8 2.0 1.0 -7 0.0 t __ ,_ .. ~_--.-!L~~~~~~~~~_.. i -6 12:00 16:48 21:36 2:24 7:12 12:00 7.0 -b-~--~---~-8-·· -12 Ê 6.0 -11 ~Pw(53ho) ,...... s 5,0 ;r --Sc (38ho) -10 ~...... 4,0 --Yv(30ho) 0 -tr-Pw -9 00 3.0 -D-Sc -10 ..g 2.0 -<>-Yv 1.0 IIIIIIIIIIIIIII ~~i!::=~ -7 0.0 ...... ",) -6 12:00 16:48 21:36 2:24 7:12 12:00 7.0 .. _...... _.. _ .... --_ ...... -...... -.... -...... _... _...0---==="='-"=8"'- -12 Ê 6.0 -11 --Ef(9Iho) .s 5,0 :t:: --Lk (147 ho) 0 C 4.0 -o-Ef ::l ·9 CC 0 3.0 -D-Lk 00 fij .... -8 10 t2 2.0 - 1.0 -7 0,0..1------' -6 12:00 16:48 21:36 2:24 7:12 12:00 Figure 4.3. Hydrographs and ôl8a tracer response from individual catchments during storm 8 (14 mm on wet AMes). Throughfall (mm) is shown in the top panel. Total ruriOff (mm) for each of the 8 catchments is illustrated on the left hand axis. ôl8a tracer response (%0) is illustrated on the right hand axis (darkened symbols). 69 0 1 Ê ~~r i 5 i .5 10 ~ i1 [ _.. __ .... ~ ~ 15 9:36 14:24 19:12 0;00 4:48 9:36 4.0 ...... _...... -13 3.5 -11 Ê 3.0 '""' .5 ~Aw{lIha) 2.5 -9-Vc (II ha) .....,~ :t: -9 c:0 2.0 --Sb[Tha) ::J ~Aw ~O Cl: 1.5 -G-Vc IQ Ri -o-Sb -7 ... 1.0 ~ 0.5 -5 0.0 -d Ob .L ... m 9:36 14:24 19:12 0:00 4:48 9:36 4.0 -13 3.5 Ê 3.0 -11 '""' .5 .....,~ :t: 2.5 0 -9 c 2.0 ~O ::J IQ Cl: 1.5 Ri -7 ... 1.0 ~ 0.5 -5 0.0 9:36 14:24 19:12 0:00 4:48 9:36 4.0 -13 3.5 Ê 3.0 -11 .5 '""' :t: 2.5 0 ~....., c: -9-Lk{147ha) -9 ::J 2.0 0.5 -5 0.0 " ...... _~ .. __ .__ .. __ ._-~---_. __ ._-_.. _._ ... _.. - 9:36 14:24 19:12 0:00 4:48 9:36 Figure 4.4. Hydrographs and 8180 response from individual catchment for storm 10 (38 mm on dry conditions). Throughfall (mm) is shown in the top panel. Total runoff (mm) for each of the 8 catchments is illustrated on the left hand axis. 8180 tracer response (%0) is illustrated on the right hand axis (darkened symbols). 70 160 r········ .. ············································...... " ...... , 0.35 rib-l--,r''·T''T..----...... ------,· 0 o Data al 140 i '1W ---Totol Runoff i 5 --Mean 0.30 1 ... A' ... New Water 120 ...... Upper confidence interval --ThFall l 10 1'0.25 i ...... • Lower confidence intervol ~100 E 1 .15 Ê ;;::. E 0.20 i ~ ; : E ~ 80 L i ; 20 ':::: E' ~ i o ti 0.15 ! "0 -5 60 .. 25 ~ III :~ 0.10 i; 45 40 Z ! : 30 ! 20 0.051 i 35 o I. __~~:.~ ...... 1 0.00 L_ .. __...... ~ .-.: 40 o 50 100 150 200 250 198.7 198.8 198.9 199.0 199.1 199.2 199.3 199.4 Stage (mm) Julian day Figure 4.5. Quantifying runoff and new water compositions for Storm 10 (38 mm on dry AMes) at the Lk (147 ha) catchment. Panel (a) shows the rating curve for the Lk catchment with a 95% (±2a) confidence interval. The corresponding uncertainty in the instantaneous runoff is illustrated in panel 5(b). Uncertainty in new water composition (5b) results from propagation of uncertainty from both instantaneous runoff measurements and isotopic compositions as described in Section 3.5. Storm 1: 25 mm /ow intensity storm on dry AMCs. Storm 1, the second largest storm (25 mm) occurred on 16 June, 2001. Of the three ephemeral catchments, the Sb was dry and the Vc and Yv catchments had very low flow «0.5 lis) prior to the storm. Total runoffranges from 0 to 0.52 mm with the Sb catchment (7 ha) delivering no runoff during this storm. New water contributions (mm) increase with catchment area, consistent with the topographie scaling hypothesis (Figure 4.6). New water fractions (%) from flowing catchments, range from 24% to 34%, decreasing with catchment size. Storm 11: 7 mm /OW intensity storm on dry AMCs. Storm 11 occurred in early faH (2 September, 2002). The three ephemeral catchments (Yv, Vc, Sb) delivered no runoff. The perennial spring located above the Sc gauging station assures that this station continues to flow regardless of the zero flow from both Yv and Sb catchments. Largest total runoff(-O.1 mm), is delivered from the large st two catchments. New water 71 Largest Smallest Storm 5 (5.5 mm) O~ 4.0 Ê 0.7 ..-.. 3.5 3~ .5. 0.6 ê 3.0 :::- 2.5 '+- ,.0 ~ 1.5 a 1.0 t; 0.5 1"'" 0.0 LW._",_4_"'_4.J-L..LJ....LJ....LJ.....u.J Lk Ef Pw Sc y~ "'Ir. Aw Sb Uc Et Pw Sc y" Ve Aw Sb Storm 8 (14 mm) Wet 0.8,------4.0,------, AMe ~ 3.5 ~ 3.0 ~ 2.5 JO'4 9'1 U5".(, ~ 2.0 c è 1." J! 1.0 o 0.5 1- 0.0 !.l...l....w.....w...J...l....1..L...L.L...L.L..u.; Lk Ef Pw Sc Vv 'Ile Aw Sb I..kEfPwScYvVcAwSb Storm 10 (38 mm) 0.8 --~--,--_. __ . ..-.. 0.7 ~ 0.6 "';:' 0.5 ~ O." "s: 0.3 s: 02 Storm 1 (25 mm) Dry AMe Lk Ef P.rr Sc Yv Ve Aw Sb Storm 11 (7 mm) -lIOt.dNngtIUltc:4I&pfl:lorhvcrfic:c!4XU. 0.05 r 04 î 0. 1 ~ 0.03 j :::1J..J -=..0 • 10.00 --~-L~.L.JPi'-l..J.:Lm -"O....J.~-'--"--'O Lk Ef Pw Sc y", Vr. Aw Sb Lk Et Pw Sc Ytt 'Ile Aw Sb Figure 4.6. Total runoff (mm) and new water contributions (mm and %) across all catchments for the 5 storm events. Error bars on new water contributions result from uncertainty in isotopie compositions of new and old waters and uncertainty in instantaneous runoff measurements. New water is expressed as a percent of total runoff in the left hand panel. 72 Largest Smallest Storm 5 (5.5 mm) 1200 c····················································· ...... • 1200 f ••. oPrme.Q DQ 1 lk EF PW sc VII VC AW 58 '::~Lk EF PWij~I~1 sc YV VC AW S8 Wet Storm 8 (14 mm) AMC "00 , ...... DQ ~;48 8018 3:36 Lk EF PW sc VV vc AW SB Ut EF PW sc YV VC AW SB Storm 10 (38 mm) 6:00 .. 6:00 .... ]' ., .. l ~~~::Q ., .. 1 ::18 i ruMillillm !~ Ut EF PW SC YV ve AW SB ~ ~H~IUc EF PW sc vv ve AW SB Storm 1 (25 mm) Dry AMC lie EF PW sc VII ve AW Ut EF PW sc YV lie AW 58 Storm 11 (7 mm) 6:00 . 6:00 j DQ ~ i QPeokQ ~ ... ! DIl\itiolQ 4:48 ! .018 ~ Lk EF PW It.W 58 j Ut EF PW sc YV Jve AW SB ! J~ ~ ~ ~sc "yv ve ~ " ~~ ~ ~" Figure 4.7. Lag times (hrs) for initial response and peak in discharge (right hand panels) and peak dilution in 0180 (left: hand panels). Initial response lag time is defined as the time between start of storm and change in slope of stream discharge. Zero indicates no flow from ephemeral catchments. Nia indicates incomplete data. Note for Storm 5 the vertical scale (time) is different. Resolution of lag times is based on a 15-minute sampling interval. 73 contributions are very smaH (~0.01 mm) and indistinguishable for the 5 flowing catchments. 4.4.4. Timing of storm response The five storms offer different size and intensity of delivery, making more chalIenging a comparison of response under the simplified framework of wet-dry AMCs. As a result, we focus on a comparison of the timing of response between catchments for each individual storm event. These five storms are examples of storm response under varying AMCs but clearly are a limited sample. To compare timing of response from the seven catchments, we identify the catchment with the earliest hydrometric response as indicated by change in slope (dQ/dt) and set this as time zero. AlI storm response is then compared with respect to this start time. This approach is similar to that presented by McGlynn et al. (2004). Figure 4.7 ilIustrates lag times to initial and peak response in discharge (right 18 hand panels) and a comparison of peak response in discharge and Ô 0 peak dilution (left hand panels). Resolution oflag times is based on a 15-minute sampling interval. Storm 5: 5.5 mm low intensity storm on wet AMCs. Storm 5 shows initial response time in Q from aIl catchments within 45 min of the Yv catchment. Peak response varies between 7 to 16 hrs after initial Q response, and is quickest for the Yv and Vc catchments. Peak in new water contributions lag Q with only 1 exception (Aw) , l8 indicated by Ô 0 (Figure 4.7). In the case of the Vc and Sb catchments, the lag time between Q and new water is ~30 hrs (Figure 4.7, left panel). In contrast, for the Aw catchment, new water peaks approximately 4 hrs before Q. Storm 8: 14 mm moderate intensity storm on wet AMCs. Storm 8 delivers 14 mm within a 2 hr period on wet conditions delivering the highest maximum intensity (6.6 mm per 15 min interval) of aU 5 storms. Initial Q response is similar across aH catchments (within 15 min of each other). Time to peak Q varies between 15 min and 1hr 45 min from initial response. Two catchments show distinct differences in Q and new water lag times. The Yv catchment is once again the fastest to respond but shows the largest lag 74 between Q and new water (~1 hr after peak Q). In contrast, new water peaks ~30 min before Q in the Aw catchment. Storm la: 38 mm high intensity storm on dry AMCs. Storm 10 is the largest and most intense of the 5 storms. Initial Q response varies considerably from catchment to catchment. The longe st initial Q response time is 1 hr 30 min for the Sb catchment suggesting a threshold response (see McGlynn and McDonne1l2003). Prior to the storm, this ephemeral catchment is dry (no flow). Peak Q lag times are within 45 min of each other with the exception of the Yv catchment. The Yv catchment shows a strong double peak in Q (Figure 4.4) with lag times of 1 hr 30 min and ~3 hrs. This second peak is likely a result of its downstream location from the Aw catchment. Peak in new water at ~2 hrs arrives between these two peaks in Q. New water and Q peaks coincide for all additional catchments with only the Lk and Sc catchments showing peak new water preceding Q by ~45 min. Storm 1: 25 mm /ow intensity storm on dry AMes. There is no difference in timing of initial response in Q. Peak Q from the catchments varies by ~45 min, with the largest catchment giving the slowest response. For each catchment peaks in new water and Q are within 15 min of each other. Storm 11: 7 mm /ow intensity storm on dry AMCs. We observe the three largest catchments to have significantly longer lag times for both initial response in Q (>1 hr) and peak Q (>30 min). For 4 of the 5 catchments flowing under these dry AMCs, new water peaks prior to Q by 45 min (Sc) to 1 hr 30 min (Lk). The Aw catchment shows similar timing of Q and new water peaks. 4.4.5. Dissolved organic carbon (DOC) The magnitude and time-evolution of DOC concentrations in the stream channel during a storm can provide supporting evidence of active flowpaths and contributing source waters (Figure 4.9). Water moving through the organic rich, O-horizon can deliver high concentrations of DOC to the stream channel. This can occur by rising water 75 tables (Hornberger et al. 1994) or under dry conditions, rapid movement ofwater through macropores or high permeability layers in the shallow subsurface (Brown et al. 1999). Timing of the delivery is also important. Flushing of DOC, defined here as the arrivaI of the peak in concentrations prior to peak in discharge can indicate transport-limited delivery to the stream channel (Burns 2005) where mechanisms delivering water to the stream channel are activated during a storm event. Figure 4.8 illustrates time-evolution of DOC for individual catchments during storms 8, 10 and 1. Solute-solute plots of DOC 18 and Ô 0 illustrate the bounding of stream water concentrations during each storm by three contributing source waters (Figure 4.9): perennial spring water (groundwater), throughfall and perched surface waters. In addition to these end-members, groundwater taken from water table wells in the Aw catchment is inc1uded. Storm 5: 5.5 mm /ow intensity storm on wet AMes. Storm 5 shows no correlation 18 between either DOC and Ô 0 or DOC and Q (Table 4.6). Changes in DOC concentrations during this storm are small « 200 J.leq/l) as compared to the other four storms and there are minimal patterns in DOC-Q plots (not shown). Solute-solute plots of DOC and ô 180 show little departure of stream water concentrations from groundwater (Figure 4.9). Storm 8: 14 mm moderate intensity storm on wet AMes. Storm 8 exhibits high 18 correlation of DOC and Ô 0 (Table 4.6) suggesting contributions from a water source 18 with both enriched Ô 0 and high DOC. Flushing of DOC where concentrations are higher on the rising limb of the hydrograph (c1ockwise hysteresis) is observed from 2 of the smallest catchments (Aw and Sb), even under these wet conditions. Figure 4.9 shows a greater departure of stream water from groundwater with highest concentrations of DOC deriving from the VC catchment and c10sely resembling a perched water end member. Storm 10: 38 mm high intensity storm on dry AMes. High correlation of DOC and 18 Ô 0 (Table 4.6) suggest new water delivery via a shallow subsurface flowpath from which higher DOC concentrations can originate. Based on similar results, Brown et al. 76 (1999) hypothesised that an important component ofstonn runoffwas being delivered to the stream via lateral flow through the O-horizon. We observe counter-clockwise hysteresis (Figure 4.8) of DOC concentrations with discharge from three catchments (Lk, Pw, Aw) with higher concentrations of DOC on the falling limb of the hydrograph. There is no evidence of DOC flushing (clockwise hysteresis) during this stonn event. Stream water concentrations show very strong departure from groundwater towards end- members of perched water and throughfall. Table 4.6. Regressions for individual stonn events: (a) DOC and ôlSO and (b) DOC and Q. (a) DOC and ôlSO Storm r2 p-value n 5-Jun-02 (5) 0.03 0.113 79 23-Jun-02 (8) 0.59 <0.001 81 17-Jul-02 (10) 0.69 <0.001 67 16-Jun-01 (1) 0.38 <0.001 53 3-Sept-02 (11) 0.33 <0.001 39 (b) DOC and Q Storm r2 p-value n 5-Jun-02 (5) 0.33 <0.001 74 23-Jun-02 (8) 0.14 0.001 81 17-Jul-02 (10) 0.002 0.73 59 16-Jun-Ol (1) 0.001 0.86 36 3-Sept-02 (11) 0.24 0.001 45 Storm 1: 25 mm low intensity storm on dry AMes. Stonn 1 shows smaller but still significant correlation between DOC and ôlSO (Table 4.6) and no correlation between DOC and Q. DOC flushing is evident for the larger catchments ofLk, Sc and Yv (Ef and Pw datasets are incomplete) suggesting that a large stonn on these dry conditions has mobilized DOC that has built up over time. Figure 4.9 shows a similarly strong departure of stream water concentrations from groundwater towards perched water and throughfall end-members but with greater variability. 77 Lk (147 ha) Ef (91 ha) Pw (53 ha) Sc (38 ha) Yv (30 ha) Vc (11 ha) Aw (11 ha) Sb (7 ha) 1200 lZOO 1200 1200 1200 1200 1200 lZOO; -+-ltilillg ...... 11111"9 --~sllIg! --Aitlng --IliSIIIg 1000 C" 1000 --FWing 1000 ...... AilIIIg ..},OOO 1000 : _Folh,,!!, !::::-IOOO ~ooo! -e-Flilling 1000 Foll'lI9 -8-FaUi,,!! -O-Falli"9 --Fcdling -e- i ...... -&-Faltillg ::::: i ~800i Storm 8 S800 800 1"'0800 ~800 1 ~600 ! 600 14mm 1600 :5 600 I!""iU 1: ,,«JO "2400 i g" «10, )g4OO " ~( g"" ...... 11:1;119 WEr " "" ~ ":5 . 200 Folling zoo! ""'.V200, zoo / 200 P 200 V l~~ __ ~ o OL Oi M W W U M W W U u 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 00 JO 2.0 l,O 00 lO 2.0 3.0 40 0.0 1.0 2.0 3.0 0.0 1.0 2.0 3.0 Q(mmlhr) Q(mmlhr) Q(mm/hrl Q(mm/hr) Q(mm/hr) Q(mm/hr) Q (mm/hr) Q (mm/hr) 1200 1200 1200 1200,···· 1200: 1200 __ Aising 1000' 1000 1000 1000 1000 10001 ~ .... -+-FIIII'119 =2800 S800! ""800 ~::, Storm 10 ::::::_ooolv: ! 1",,~ 38mm 1... ' 600 : '800;/ 1""1 .. 1"" 1 <4OOi R, ng i:I'.f/:S.v;o) :5 u __ .. -+-Ailillg :5"" ~4001 Ri.. f'Ig o • Ib~..i:J DRY __ --Ibting CJ i __ ! Cl -+-Rllillg i g: __ Fatli"9 Qo 200\ -&-FClll'ng """.(a 200\ i:r -+-FallilOJ Q zoo zooi 1 -+-Falli"" -FolI,ng I:{ m\ 200 ...... ~.~~~~j.:.__.. ai .. 0 1... 0'································· 0' o o 1.0 2.0 3.0 4.0 1.0 2.0 1 ,.0 0.0 2,0 3.0 4.0 0.0 lO 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 0.0 10 2,0 3.0 4.0 0.0 1.0 20 Q(mm/hr) Q(mm/hr) Q(mm/hr) Q (mm/hr) Q(mm/hr) Q(mm/hr) Q(mm/hr) Q(mm/hr) 1200, 1200 ...... ''''sillg __ Ri.UIg 1000 1000 1000 ...... '" ....'.11'"9 -+-Follillg Sl:~!.=:~,~ Storm 1 ~ 800, 1.,,1 25 mm ! ...' NIA NIA No Flow i: No Flow " . g~i g 400 j DRY I~ro-:-g"" " zoo 200 . p" " . l 0'""'--···- o --- M .. W .. M U W U 0.0 .. 1. .. 0.0 0" 1.0 1.5 Q (mm/hr) Q (mm/hr) Q(mmlhr) Q(mm/hr) Figure 4.8. Stream water DOC concentrations during Storms 8 (14 mm on wet), 10 (38 mm on dry) and 1 (25 mm on dry). Note that visuals of Storm 1 have a smaller range of Q as compared to Storms 8 and 10. Darkened symbols indicate the rising limb of the hydrograph and light symbols, the falling limb. For storm 8, counter-c1ockwise hysteresis is visible for 5 of the 7 catchments (Lk, Ef, Pw, Yv, Vc). Catchments Aw and Sb show a c10ckwise hysteresis or flushing of DOC even under these wet conditions. For storm 10, sorne catchments continue to show counter-c1ockwise hysteresis (Lk, Pw) as well as the Aw catchment. We do not observe c10ckwise hysteresis or flushing during storm 10. DOC-Q patterns from storm 1 suggest flushing from Lk, Sc, and Yv catchments. We can have shallow subsurface flowpaths active without a flushing effect (i.e. a c10ckwise hysteresis DOC-Q pattern). 78 1800 1800 ~--_.~-~'"._-- ·5-.0 OS~M 1600 Storm 5 a'Thfali 1600 Storm 8 D'1"W'c.11 eSpri"WJCi2 eSp""O02 1400 o-Aw&WOZ 1400 ~Awr;W02 APer 1800 ,.....------...... ,----, 1800 o strevm °S1rtOtr. a_" 1600 a_u 1600 Storm 10 Storm 1 • S,ring .S~"90Z 01 1400 OAwGW02 1400 0 OAw&WOl ÂPc..-ehc,dGW02 ÀP ..,hcd&WOI ::::::: 1200 E.' 1200 CT CT CIJ 1000 CIJ 1000 3 3 U 800 u 600 o 0 00 0 0 0 0" • + Cl 600 + Cl 600 .+.•• o,p • 400 400 • J. 'II• 200 200 ;~oO 0 -14 ~ ~ ~ ~ 4 -8 ~ -6 ~ -4 818 0 (per mil) 818 0 (per mil) 1800 o S1rcG .. 1600 a1W<:1I ••", ... 0' 1400 OAw&WCZ ÂPeI'tMedGW02. 12OO storm+ 'â-CIJ 3 1000 U 0 800 Cl 600 + 400 200 oJ;f: OL-~~~';'.t~ ______~ ~ ~ ~ ~ ~ 4 -8 ~ -6 ~ -4 818 0 (per mil) Figure 4.9. Stream water solute-solute plots of DOC Clleq/l) versus 0180 (per mil) for each storm. Independently measured geographical sources of water are inc1uded with temporal variation for years 2001 or 2002 and spatial uncertainties represented by error bars Ce.g., Spring 01 indicates spring water where error bars represent temporal variation observed during the 2001 spring-fall field season). 79 Storm Il.' 7 mm low intensity storm on dry AMes. Again there is small but significant 18 correlation between DOC and Ô 0 (Table 4.6a). There is a low correlation between DOC and Q (Table 4.6b). Flushing of DOC is suggested from catchments Lk, Ef and Sc (not shown). Stream water concentrations depart from groundwater to a lesser degree than storms 10 and 1. 4.4.6. EMMA For each individual storm event, principal component and residual analyses (Hooper, 2003) of observed stream water chemistry provide estimates of the dimensionality of the mixing-space (or the number of contributing end-members). Table 4.7 shows the % variance explained for each eigenvector retained. For instance, for storm 10, retaining 1 eigenvector (Rank 1) explains 79.6% of variance in stream water solute concentrations and creates a l-dimensional (2 end-member) mixing-space. The dimensions of each storm's mixing-space is evaluated by the rule of 1 (Joreskog et al. 1976) and residual analysis (Hooper 2003). In Table 4.7, bold numbers indicate the mixing-space dimensionality for which each eigenvector explains at least 33% (l/no.solutes) of the observed variance in stream water chemistry (Joreskog et al. 1976). Table 4.7. Dimensionality of mixing space for individual storms. Percent variance explained by PCA eigenvectors. Bolded numbers indicate the rank at which components explain at least 33% of the observed variance in stream water chemistry. Rank 1 Rank 2 Rank 3 Storm. (%) (%) (%) 26.2 10.0 5 63.8 (90) (100) 33.6 7.7 8 58.7 (92.3) (100) 13.8 6.6 10 79.6 (93.4) (100) 34.5 23.2 42.4 (76.9) (100) 28.9 7.3 11 63.8 (92.7) (100) 80 For storm 10, both the mIe of 1 and residual analysis indicate a 1-D mixing-space with 2 contributing end-members. Storms 8 and 1 require a 2-dimensional mixing space or 3 contributing end-members. The mIe of 1 indicates storms 5 and Il require a 1-D mixing space but residual analysis (plots not shown) indicate that linear patterns in DOC residuals disappear only with the additional of a second eigenvector (2-D mixing-space and 3-endmembers). DOC residuals (9-18%) are close to analytical precision (~8%) but for these storms we interpret the pattern in residuals to be real. Independent field sampling of end-members allows us to physically interpret these source waters and estimate their relative contributions. Figure 4.10 illustrates stream water chemistry from each storm event in storm-specific 2-dimensional mixing-spaces. Independent sampling of throughfall, perennial spring water, perched water and soil water are projected into each mixing-space with small residuals indicating excellent fit into the linear-mixing spaces created by the PCA of stream water chemistry. PerchedlVSA water is sampled from variable saturated areas in the Aw and V c catchments that, depending on AMC, are often directly connected to the stream channel. We refer to it as perched because in the Aw and VC catchments the local fragipan in the valley bottoms create perched conditions. Soil water is sampled from valley bottom and riparian are as in the Aw and Vc catchments. In each mixing space, stream water concentrations are bounded by independently sampled end-members. Storm 5: 5.5 mm /ow intensity storm on wet AMes. PCA indicates a 2-D 3 end member mixing-space with 90% (63.8% + 26.2%; Table 4.7) of variance explained. Throughfall composition does not fit into the PCA mixing-space (residuals>40%) and as a result is not included in Figure 4.1 O. AIso, soil water also does not fit to the mixing space. Stream water is very close to groundwater priOf to the storm and deviates towards perched water. Catchments show a linear, non-hysteretic evolution between groundwater and a perched water end-member with a variable initial mixing of these end-members for the different catchments, as shown for the Aw, Pw and Vc catchments (variable spaced along the U1 axis, Figure 4.1 Ob). This and the significant variability in groundwater and perched water concentrations likely explain the 2-D nature of the mixing-space. 81 Storm 8: 14 mm moderate intensity storm on wet AMCs. PCA indicates 92.3% (58.7% + 33.6%; Tàble 4.7) of variance in stream water concentrations can be explained by the mixing of three end-members (2-D mixing-space). For this storm throughfall, groundwater and perched groundwater end-members fit into the mixing-space and bound stream water concentrations (Figure 4.1 Oc). Catchments show temporal evolution of stream water concentrations initially most similar to groundwater, progressing towards a mixing of throughfall and perched water, and finally retuming towards groundwater (Figure 4.10d). Soil water samples taken at shallow depths (20-30 cm) in the hillslope and valley bottom do not provide a bounding end-member. Storm 10: 38 mm high intensity storm on dry AMCs. This is the only storm PCA and residual analysis indicates is a mixing oftwo end-members (1-D) with 79.6% of variance explained. For the purposes ofvisual presentation, Figure 4.10e illustrates a 2-D mixing space for this storm. Stream water observations are once again bounded by groundwater, throughfall and perched water. The linear evolution of stream water concentrations along the U1 axis, shown for the Lk (147 ha) and Aw (11 ha) catchments, illustrates the 1-D nature of the mixing-space (Figure 4.1 Of). It appears that streamwater begins as a mixture of groundwater and perched water prior to the storm. The shallow subsurface storm flow delivers water that looks like a combination of throughfall and transitionally developed perched water. This is especially visible for the Vc catchment and less so for Lk and Aw catchments. Soil water sampled from suction lysimeters does not fit into the PCA mixing-space, with high residuals (>30%) and do es not appear to be a legitimate end member to stream water concentrations in this linear mixing application. Our results suggest that a transient end-member such as the O-horizon end-member of Brown et al. (1999) provides an important contribution for this storm. Storm 11: 7 mm /ow intensity storm on dry AMes. For storm Il, a 2-D mixing space explains 92.7% (63.8% + 28.9%) of stream water variance. End-members ofthroughfall, groundwater and perched water fit into the mixing-space and bound stream water chemistry (Figure 4.1 Dg). Stream water evolution shows a mixture of throughfall and perched water reaching the stream channel (Figure 4.1 Oh). 82 (a) Storm 5 (bl GW 4 ASpringGW .t:.Aw(lIho)GW GW OPerched 0:;;'O i t start 2~ -6 -il o~ 6' 0 &l!o 4 6 8 10 12 14 .t:. -1' 00 -4 .t:. U2 -4 U2 \~(,v.{:/· .t:. .t:. " -+-Pw (53 ho) -4 o ...... "w(lIho) PerchedGW --Ve(Uho) -10 A SpringGW " ! " "w(lIho)GW Perchi!dGW o Perched "--&--- UI ut (cl Storm 8 (dl 4 0 o 0 0 0 0 o Perched GW ThFali PerchedGW ThFali 00 00 00 0 o fP 0 J ~oo °0 U2 U2 'l,"o 0 dbJ,0 °0° cg ·00 ~ " -2"'0~?f!~ 8 ! t.f!œ -1 0 o S1ream ---Lk o/, A- 0 ...... AW Thf.1I ·2 -2 o _Ve ASpringGW GW o Thf.1I GW AA -3 Il' -3 " Aw (11 ha) GW A SpringGW OPerch.d " Aw (11 ho)GW <> Perched UI UI Storm 10 (el (f) o Stream ---Lk <> ••••.•• AW o Thfall 4 PerchedGW _Vc ASpringGW 0 ThfailiO "Aw(1I ho)GW A SpringGW OPerched Aw(11 ho)GW <> 0 0 Perched U2 o U2 o / 4 -6 -4 AA o 0 -2 GW GW ThFali -3 ThFall -3 , ",------4--,- UI ut Figure 4.10. Individual stonn end-member-mixing-analysis. 2-dimensional U-space plots indicate that in each mixing space, stream water concentrations are bounded by independently obtained concentrations of end-members. 83 (9) Storm 11 (hl ...•... Lk(147 ho) --0-Aw (II ho) o S1reom -Ef(9Iho) 4 o Thfoll 4 o Thfoll ASpri ll9 GW A Sprill9GW ~. Aw (11 ho) GW ,Aw (11 ho)GW 3 Perched GW o <> Perchee! PerchedGW ~P.rched ThFa11 2 ~ 0 ThFali 2 <> <> o &' olo~ U2 <>&' 000 0 L\ .J!'~,.•~ . \ '\. U2 0 00 0 00 CIl 0 0 '0 &,00 °0 ° 0 .1 .... 3 0 00 ;77 ~.:,\~\' "~- -3 -1 0 1 0 3 0 1 .. -1 00 IY:J,. 0 t:A .. -2 , GW -2 00 GW • -3 -3 Ul U1 (i) Storm 1 Gl ························6···· o S1ream ---- Aw (11 ha) --tr- Sc o Thfall <> <> 1 4 o Thfall 4 ASpringGW Perched PerchedGW A SpringGW OPerched ! <> Perched 2 o o U2 j o o 0190 0 T~Fali n1 Fall {. oooca ""0 0 ° 0 '0 o 0 iD -5 -3 -1 0 1 3 5 7 9 11 -5, 5 7 9 11 'bo -2 o .--4.- ...... Ul ut Figure 4.10 continued. 4.5. Discussion 4.5.1. Hydrometrie response The threshold-like change in runoff with AMes at MSH has been observed in very different c1imate and catchment settings inc1uding range land catchments in Australia (Western and Grayson 2001) and the forested temperate catchment of Panola Mountain, Georgia USA (Tromp van Meerveld and McDonnell 2005). Although metrics of AMes are not explicitly measured in all catchments (except for APl?), data from the two catchments in which AMes was quantified suggests that the nonlinear response with AMes occurs across catchment size. The dramatic change in runoff ratios with AMes indicates much greater efficiency in the ability of water to be transported to the stream 84 channel, i.e. greater hydrologic connectivity. What is the effect of changing hydrologic connectivity on runoff generation and how does this manifest across sc ale? 1.0 1.000 • • • • • •• • • • • .6. .6. • .6..6. .6. • 510rm 5 .6. .6. • 51orm5 0.1 .6. 0.100 .6. .g .6.51ormB .g .6. 510rm 8 +51orml ~ ~ .6. .6. +51orml .6. 0 0 li:: li:: .6. o 51orml0 0 .6. o .6. 0 !j! 0 til o 510rm 10 X c c X :1( 510rm 11 +:1(:1( X510rrn Il 2 0.01 0+ 2 0 0.Dt0 ; !If ~ +0:1( 0 )j(!!S + + + 0 0 0.001 0.001 100 150 200 a 50 a 5 10 15 catch ment area (ha) median sub-catchment area (ha) 2.5 2.5 2.0 0 2.0 0 .6. 510rm 8 .6. 510rm 8 ~ 1.5 II. )j( + Storm 1 ~ 1.5 0 .6. xœ: + Slorm 1 é- œ: é- D 51ormi0 o 51orrni0 <11 [JJ œ: .6. i. <11 0 0 œ: i..6. )j( slorm Il E X510rrn Il .~ '':; .6.0 0"> 1.0 i. Di. + 0"> 1.0 i. + ...J'" i. o li!: .:3 .6. li!: 0 0.5 ++ 0.5 + + :t: .6. * .6. 0.0 )j( 0.0 a 50 100 150 200 a 5 ID 15 catch ment area (ha) median sub-catchment area (ha) Figure 4.11. Runoff ratio and lag time as a function of total catchment area and median sub-catchment area. Areas are derived from analysis of the lm x lm DEM. Median sub catchment area is defined as the median of the distribution of upslope area coUected by the stream channel. Under wet conditions (Storms 5 and 8), we observe only slight scaling of total runoff with either total catchment area (Figure 4.lla) and no scaling with mean sub-catchment area (Figure 4.11 b). Peak lag time of discharge Q also shows slight scaling with catchment size (Figure 4.llc) but none with mean subcatchment area (Figure 4.lld). 85 High groundwater storage conditions across the catchments result in variable runoff ratios and lag times with little to moderate indication that scale is a relevant variable. Under dry conditions, runoff and peak lag times generally appear to increase with mean sub catchment size. Under these conditions (storms 1, 10 and 11), 3 of the smaller catchments « 50 ha) have minimal to no flow prior to storm response (depleted groundwater storage) while the largest 3 catchments with greater storage show larger runoff ratios. 4.5.2. Event-Based Interpretation Storm 5: 5.5 mm low intensity storm on wet AMCs. This small, low intensity storm produces the largest runoff ratios due to high storage volumes of water existing in the catchments (wet conditions). Isotope hydrograph separation indicates new water delivery is small (~0.1 mm) and uniform across all catchments. The timing ofnew water arrivaI is significantly slower for the V c and Sb catchments (Figure 4.7) due to small low lying areas that are variably saturated and yet take greater time to deliver this water to the stream channel. EMMA results indicate that throughfall is not an end-member in the linear mixing-space created by stream chemistry. Stream water concentrations show increasing input from a perched water end-member during storm response. This perched water forms in valley bottoms due to high water tables and the shallow fragipan located at ~40 cm depth. Under wet conditions, the variable saturated areas that form can be directly connected to the stream channel, increasing the areas for saturation overland flow and direct precipitation, and delivering new water mixing with existing perched water. Storm 8: 14 mm moderate intensity storm on wet AMCs. The magnitude of runoff during this storm is smaller than storm 5, indicating decreasing storage across all catchments. The magnitude ofnew water (~0.2 mm) delivered during the storm event is indistinguishable for individual catchments. However, higher fractions (21-27%) ofnew water from 3 of the smaller catchments suggest the depletion of storage is reducing hydrologie connectivity for these smaller catchments. This is discussed in detail in 18 Chapter 5. High correlation of DOC and Ô 0 (Table 4.6) suggests new water is being delivered by a shallow subsurface flowpath. EMMA indicates three contributing end- 86 members: groundwater, throughfall and perched groundwater with strong mixing these last two end-members. Storm 10: 38 mm high intensity storm on dry AMes. During the high intensity storm, although infiltration may be greater due to dry conditions, the presence of the shallow fragipan results in a fast shallow subsurface flowpath, delivering new water to the stream channel. High correlation of DOC and 0180 (Table 4.6) supports this interpretation of source (new) and geographical flowpath (shallow subsurface). The smallest catchment (Sb), ephemeral with zero flow prior to the storm, delivers the least amount of new water «0.1 mm) but new water is the dominant and largest fraction (76%) of total stream flow. This catchment also shows a large lag time(> 1 hr) before an initial response in discharge (Figure 4.7). The depleted storage of old water in the smaller catchments results in higher fractions of new water given this delivery flowpath. The three largest catchments deliver a greater magnitude of new water than the 5 smaller catchments but fractions of new water are generally similar or lower than most of the smaller catchments (with the exception of the Pw catchment). Generally, for this storm, we see greater new water output (magnitude) for the three largest catchments, consistent with the topographie scaling hypothesis where new water increases with increasing catchment size due to greater saturation overland flow. EMMA results illustrate the strong 1-D nature of this storm event with dominant mixing of throughfall and groundwater end-members. Storm 1: 25 mm /ow intensity storm on dry AMes. Although AMCs are similar to those of storm 10, the magnitude of total runoff and new water delivered is considerably smaller (Figure 4.6). The lower intensity of this storm, suggests infiltration with a more gentle triggering of the fast shallow subsurface flowpaths active for storms 8 and 10. The correlation of DOC and 0180 (Table 4.6) is lower than for storms 8 and 10. Both the magnitude of total runoff and new water output suggest an increase with catchment size, consistent with topographie scaling, and a decrease in fraction of new water with catchment size showing depleted storage and connectivity of old water for the smaller catchments. However, for this storm, new water amounts are small and the error analysis indicates we cannot clearly identify a trend. 87 Storm 11: 7 mm /ow intensity storm on dry AMes. This small, low intensity stonn on dry conditions produces extremely small magnitudes of total runoff and new water (1 order of magnitude smaller) with no discernable pattern across scale. The three ephemeral catchments (Yv, Vc, Sb) remain inactive. We observe flushing of DOC from 3 of the larger catchments (Lk, Ef and Sc). The correlation of DOC and ô 180 (Table 4.6) is lower than for stonn 10, similar to stonn 1. EMMA suggests activation of a perched water end-member mixing with throughfall. 4.5.3. Comparison of MSH results with other scaling studies The few studies of runoff generation across nested catchments have resulted in varied and inconsistent scaling patterns (Brown et al. 1999, Shanley et al. 2002b, McGlynn et al. 2004, Shaman et al. 2004). For a series of summer stonns (9-34 mm) at Shelter Creek in the Catskills mountains of New York, Brown et al. (1999) observed peak event contributions of new water inversely related to catchment size with smaller "near-stream" groundwater storage in these smaller catchments accounting for smaller percentages of old water. These high percentages of new water (maximum instantaneous event water contributions ranged between Il % and 62%) are similar to MSH results. At MSH, up to 76% of total runoffwas identified as new water (stonn 10, Sb ephemeral catchment) and maximum instantaneous new water contributions were as high as 99% (stonn 1, Aw catchment). Brown et al. (1999) also observed new water contributions increased with average rainfall intensity. This also appears to be true for the 5 stonns studied at MSH with higher new water output (in mm or fractions) from stonns 8 and 10. Brown et al. (1999) attributed delivery of new water under dry conditions to shallow subsurface flow and direct precipitation (channel throughfall). They observed no significant change in saturated are as during summer stonn events and conc1uded that saturation overland flow did not contribute greatly to summer stonnflow. The MSH results are not inconsistent with the AMCs-framework hypothesed by Shanley et al. (2002). Under dry conditions and high stonn intensity (stonn 10), we observe increasing new water inputs (mm) with catchment size, but smaller fractions with respect to total runoff. Lower catchment transmis si vit y (and hydrologie connectivity) due 88 to depleted storage in small catchments may be an explanation for these high percentages of new water. The shallow subsurface flow path strongly activated under high storm intensity negates the lower transmissivity of these smaller catchments emphasizing the storm-based temporal connectivity created above the fragipan. However, total runoff and response timing generally scale with mean sub-catchment area. The MSH results under wet conditions show no scaling of new water contributions. High storage across all catchments appears to result in more uniform response of total runoff and new water delivery. Wet conditions deliver water with a strong mix of groundwater and perched water, itse1f a mixture of throughfall and existing perched water. Independent sampling of soil water does not fit into the linear mixing-space of EMMA suggesting it is not an end-member to stream water composition under these variable AMes or that it does not conform to the assumptions of a linear mixing-model (i.e. may change in composition with travel). Lag times of new water delivery can be quite difference for individual catchments (e.g. Storm 5, Vc and Sb catchment). The strong correlation McGlynn et al. (2004) observed between new water lag times and catchment size, reflecting changing connectivity across sc ale, is generally not seen at MSH. This may result from very different catchment organization ofMSH as compared to the well-organized Maimai catchment. 4.6. Conclusion The MSH study focuses on how AMes alter spatial patterns in runoff generation from eight nested catchments. Analyses of these five storms suggest that AMes can play an important role in explaining these spatial patterns at MSH. Under dry conditions, smaller catchments at MSH have smaller storage of old water, sorne so depleted that flow is ephemeral. As a result, trends of increasing magnitudes of new water output with catchment size can translate into decreasing fractions of new water relative to total stream flow. This is seen c1early for one of the five storm events (storm 10). Given a moderate to high intensity storm, highest fractions of new water are delivered from the smallest, ephemeral catchments. The two storms observed on wet conditions show no spatial trends in new water delivery, suggesting more spatially uniform groundwater storage and runoff generation. 89 Acknowledgements Al wou Id like to thank Raissa Marks, Sheena Pappas, Catie Burlando and Nathan Deustch for their tireless assistance in the field and the staff of the Mont Saint-Hilaire nature reserve for their logistical support. The authors would like to thank Dr. Martin Lechowicz and the ECONET project for funding of the LIDAR dataset and Benoit Hamel for data processing and DEM creation. Thanks also to Dr. Brian Branfireun, Carl Mitchell and fellow students for ion exchange chromatograph analysis at University of Toronto Mississauga. This work was funded in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), a McGill-McConnell Fellowship, a McGill Graduate Studies Fellowship and the McGill Global Environment and Climate 3 Change Centre (GEC ). 90 5. Investigating hydrologie eonneetivity and its association with threshold change in runoff response in a temperate forested watershed To submit to Hydrological Processes April L. James and Nigel T. Roulet Keywords: runoff generation, hydrologie connectivity, soil moisture, antecedent moisture conditions, catchment hydrology. Context The strong nonlinear relationship between storm runoff and antecedent moisture conditions at MSH supports the hypothesis of varying states of catchment wetness (wet versus dry) that would facilitate the prediction of hydrologie response during storm events. This threshold-like response has been attributed to changes in hydrologie connectivity, a term that has been variably defined by researchers. In Chapter 5, observations at MSH are used to examine the various definitions of hydrologie connectivity and their relevance in explaining storm response. Abstraet Across varied hydrologie landscapes, the spatial and temporal variation of antecedent moi sture conditions can alter the dominant mechanisms by which water is delivered to the stream channel. Studies indicate in many catchments, a threshold-like response occurs where small changes in average soil moi sture leads to dramatic changes in the amount of water delivered to the stream channel. This nonlinear behaviour of the storm response has been characterized by a change in spatial organization of shallow soil moisture and hydrologie connectivity. However, much of the analysis of the role of soil moisture organization and connectivity has been performed in small rangeland catchments. Therefore, we examined the relationship between hydrologie connectivity and runoff response within a temperate forested watershed of moderate relief. We have undertaken 91 spatial surveys of shallow soil moisture over a sequence of storms with varymg antecedent moi sture conditions. We analyse each survey for evidence of hydrologic connectivity, as has been defined by Western et al. (2001), and we monitor the storm response from the catchment outlet. Our results show evidence of a non-linear response in runoff generation over small changes in measures of antecedent moisture conditions, yet unlike the previous studies of rangeland catchments, in this forested landscape we do not observe a significant change in geostatistical hydrologic connectivity with variations in antecedent moisture conditions. These results suggest that a priori spatial patterns in shallow soil moisture in forested terrains may not be a good predictor of critical hydrologic connectivity that leads to threshold change in runoff generation, as it has been found to be in rangeland catchments. 5.1. Introduction In this paper we examine the association between antecedent moi sture conditions (AMes), patterns of shallow soil moi sture and the amount of storm runoff generated in a small, temperate forest catchment. AMe is one of the most important environmental controllers of storm runoff (Sivapalan 1993), influencing flowpaths and dominant mechanisms of delivery and sources of water delivered to the stream channel. Many studies have observed changes in runoff generation from wet to dry conditions as inferred by new/old water separation and hydrochemical evidence (Sklash and Farvolden, 1979; Sidle, 1995; Brown et al., 1999; Biron et al., 1999). At the scale of the catchment, zero order basins have shown a threshold of moi sture storage below which they contribute minimally to runoff, and after which individual mechanisms such as macropore flow become active (Sidle et al. 1995). Quantifying AMe is of interest because it tells us about the system state of the catchment or hillslope. AMes are typically quantified using measures such as the antecedent precipitation index (API) (e.g. McDonnell 1990, McGlynn et al. 2004), discharge (e.g. (Meyles et al. 2003)), groundwater elevation, extent of saturated areas and shallow soil moisture. AlI of these metrics attempt to address the continuously changing water balance/storage deficit of the catchment in a way that is relevant to runoff 92 generation. Stimulated by the evaluation of spatially-distributed models, recent studies have emphasized the importance of the spatial characterization of initial moi sture conditions focusing on the observation and/or simulation of the changing moisture conditions across the spatial extent of a catchment (Western et al. 2001, Grayson et al. 2002, Stieglitz et al. 2003). In their exploration of hydrologic response predictability, Zehe and Bloschl (2004) describe the initial macrostate of the catchment using the mean and variance of the spatial distribution of soil moi sture and an experimental variogram to describe spatial continuity. They find that the predictability of hydrologic response is dependent on the state of moisture in the catchment, with lowest predictability at states close to the thresholds where processes activate or switch (e.g. precipitation exceeding infiltration capacity). Working on rangeland catchments in Australia and New Zealand, a research group (Grays on et al. 1997, Western et al. 1998b, a, Western and Grayson 1998, Western et al. 1999, Western et al. 2001, Woods et al. 2001, Western et al. 2004, Western et al. 2005), herein referred to as the 'Melbourne Group', characterize the catchment macrostate based on the organization of patterns in shallow root-zone soil moisture. They provide experimental evidence of a non-linear 'threshold' response in runoff generation with AMe, quantifying the patterns by hydrologic connectivity (Western et al. 2001), and random versus organized spatial distribution (Grayson et al. 1997). Under dry conditions, vertical fluxes control water movement and result in random patterns with low connectivity. Under wet conditions, lateral fluxes of water lead to organized patterns and higher connectivity. Western et al. (2005) argue that patterns in shallow soil moisture can be used as a fingerprint of saturation excess processes controlling the fluxes of water in their catchments. Most recently, debate has focused on the relationship between shallow soil moisture connectivity and the actual movement of water during storm response, particularly the development of transient lateral subsurface flow in smaU catchments (Tromp van Meerveld and McDonnell 2005, Western et al. 2005). Tromp van Meerveld and McDonnell (2005) observe strong non-linear behaviour in hydrologic response with hillslope average soil moi sture at the forested Panola catchment. However, they argue that the patterns in shallow soil moisture do not indicate where transient subsurface flow 93 occurs but rather, it is soil depth and bedrock topography that determine the pattern of active flow. In this catchment, subsurface saturation forms on shallow bedrock surfaces, causing lateral subsurface flow. Others studies have examined the hydrologic connectivity of hillslopes to riparian areas and streams (Buttle et al., 2004; Spence and Woo, 2002). Does shallow soil moisture (top 20-30 cm), as an indicator of AMC, give us any information about hydrologic connectivity or system state in forested catchments? Spatial pattern in shallow soil moisture has been correlated to topographic position (e.g. topographic index). This correlation can vary in time within the same catchment (Western et al. 2001, Blyth et al. 2004). Weak correlations are attributed to the dominance of vertical fluxes, while significant correlations are attributed to lateral movement ofwater. Western and Grayson (2001) suggest that hydrologic connectivity, as defined by spatial pattern and its effect on hydrologic response, is relevant to many hydrological processes. But few studies beyond the rangeland catchments used to develop this conéept have the appropriate data to test the association of connectivity and patterns in shallow soil moisture. For instance, forested catchments exhibit larger variability in soil hydrologic properties than rangeland catchments (Roberts 2000) that can translate into very different storm response behaviour. We approach this issue by first examining the record of hydrologie response within a forested catchment for evidence of threshold-like behaviour under wet to dry conditions. We inc1ude shallow soil moi sture and water table elevation as measures of AMC. A non linear hydrologic response is confirmed suggesting distinct initial conditions, "states-of the-system" or macrostates of the catchment. So as a second step, using spatial surveys ofshallow soil moisture similar to those done by Western et al. (2001, 2004), we evaluate the spatial connectivity of soil moi sture in an attempt to define these distinct macrostates. We wish to answer the following question: Does connectivity in shallow soil moisture characterize macrostates differently under wet and dry conditions in this catchment system where subsurface flow is an important mechanism for runoff generation? Finally, we examine the temporal evolution of moi sture content within the soil profile for evidence of in-storm changes in hydrologie connectivity and the generation of lateral 94 subsurface flow. In this work, we address the working definition of hydrologic connectivity and its relevance to the development of runoff generation in a small, temperate forest catchment. 5.2. Site Description This study was conducted within the Westcreek catchment system of the Mont Saint Hilaire Biosphere reserve, located in southern Quebec (Lat: 45°32'49" N, Long: 73°10'07" W). The distinct seasonality in temperate climate in western Quebec produces a large temporal variation in moi sture conditions in the catchment system. Although precipitation is relatively uniform throughout the year (-80 mm mono!), a 4 month snow coyer and subsequent melt creates peak moi sture conditions rarely obtained again during the summer months when there is large evapotranspirative loss of water. With senescence, wetter soils are common in the fall. In the winter, a shallow frost in the soils is common, but concrete frost seldom occurs. MSH is vegetated by a mature beech-maple forest. Soils derive from the weathering of the parent igneous intrusions and glacial tills left behind after the recession of the Laurentian !ce sheet, approximately 10,000 B.P. The lower elevations of the mountain (up to -170 m ASL) were submerged by the postglacial Champlain sea (Webber 1965). Soils are generally immature (Gyn 1968) and include ferro-humic podzols, humic gleysols and brunisols. Within the Westcreek catchment system, soils are classified as dystric brunisols (Agriculture and Agri-Food Canada 1998) and have a pH of approximately 4.5. Horizons progress from black organic to brown sand at depth with little evidence of mottling or gleying within the soil profile. Soil texture is a sandy loam with very little clay (2-4%), 20-30% silt, and 66-78% sand (Wironen 2005). Bulk density 3 is approximately 1.37 g/cm • 1 Our study was performed in an Il ha catchment with significant topographic relief (Figure 5.1). The catchment is shaped like an elongated amphitheatre with maximum elevation change of 160 m from valley bottom to steep sloped boundary and an average slope of 23°. On the hillslopes, soils are weIl drained and range in depth from -0 cm to -1.5 m. In the valley bottoms soils can be >2 m and perched water tables can form due to 95 the presence of a low-permeability layer or fragipan (Dingman, 1994) within 30-50 cm of the surface. This layer is limited in extent and does not appear on the hillslopes, possibly a result of illuviation (Mehuys and Kimpe 1976). Gravimetic laboratory analysis approximates porosity of hillslope soils at 47%. Using a mean particle diameter of 0.25 mm (med. sand) and the Kozeny-Carmen equation (Bear, 1972) we approximate the permeability and saturated hydraulic conductivity (K) of the hillslope soils to be on the order of 1.0x10-10 m, and 1.0x10-3 mis, respectively. From experimental measures on illuviated fragipans in the Appalachian and Laurentian Highlands of Quebec, we estimate K of the fragipan layer on the order of 1.0x10-7 to 1.0x10-4 mis (Mehuys and Kimpe 1976). Rooting depth can be limited by both the fragipan layer and/or bedrock. Sugar maples can affect shallow soil moi sture by 'lifting' water from deep soils and redistributing moi sture at shallow depths (Dawson 1993, Lovett and Mitchell 2004). Microtopography is also significant in these forested catchments. Tree falls create mounds and hollows on the length scale of several meters. As a result, soil moisture can vary significantly on a subgrid-scale of the surveys. 5.3. Methods 5.3.1. Temporal monitoring of catchment discharge and soil moisture profile Discharge from the Il ha catchment was continuously monitored using a V -notch weir located at the catchment outflow (Figure 5.1). The gauging station was instrumented with a potentiometer connected to a datalogger and recorded stage on a 15-minute interval. Throughfall was measured using a series of manual (15) and tipping bucket (2) raingauges located within the larger 147 ha Westcreek catchment system. During a storm event, changes in soil moisture at depth and in the shallow subsurface allow for observation of changing hydrologic connectivity as defined by the active flow of water. A series of water table wells and TDR soil profiles were continuously monitored for seasonal and storm-based dynamics. Locations A, B and C include water table wells and TDR profiles along the East-West transect from steep hillslope (A), to break in slope (B) and finally valley bottom (C) (Figure 5.1). Locations D and E indicate water table wells 96 along a North-South trajectory, following the stream channel towards the gauging station. Both of these wells (D, E) are located along the valley bottom, within several meters of the permanent riparian area. Well D is located at the perennial-ephemeral transition point of the stream. Well E is located in an area of natural subsurface recharge to the stream, immediately above the catchment gauging station . • • ! ! ! l, ! 1 Figure 5.1. Spatial survey grid of shallow soil moisture. Squares represent point measurements of soil moi sture and illustrate elevation change (legend, in m). Contour lines are 2 m intervals. Catchment outflow is at the bottom of the survey (circ1e). Small black circles indicate the location of water table wells and vertical TDR soil profiles (A,B,C only). Survey grid spacing is 10 m x 10 m. 97 5.3.2. Spatial surveys of shallow soil moisture In order to evaluate the spatial organization/patterns of shallow soil moisture, we designed a densely sampled survey with >300 sample locations, what Grayson et al. (2002) refer to as LOP "lots-of-points" collection of pattern information (Figure 5.1). Shallow soil moi sture was measured using a portable CSI soil water reflectometry probe with 20 cm probe length. Site-specifie calibration of volumetrie moi sture content was performed using an intact soil column extracted directly from a representative hillslope. The survey was positioned along north-south and east-west transects with 10 m x 10 m spacing between grid points. Transects extend across significant topographie relief, from valley bottom to steep hillslope and across areas of ephemeral and perennial stream channel. The survey grid covers an area of 180 m x 300 m with an elevation change of approximately 50 m. Each survey was collected within a single day. A total of 9 individual surveys were collected during the 2001 and 2002 field seasons with the purpose of evaluating AMCs during which hydrologie response was monitored for significant threshold-like change (Table 5.1). During wet conditions, the catchment shows extensive variable saturated are as due to the formation of perched water tables. 5.3.3. Topographie Analysis Surrogate pattern information (Grayson et al. 2002) for the two catchments was derived from the 1 m x 1 m resolution MSH digital elevation mode (DEM). The MSH DEM was produced from an airborne laser altimetry LIDAR (light detecting and ranging) dataset collected prior to leaf out in early Spring 2003. The datas et was obtained using an Optech ALTM 2050, 50 KHz (50,000 pulses/sec) system and processed to extract last returns indicating ground elevation. Filling of the DEM was performed for catchment delineation and characterization. Due to the extensive canopy coyer and large changes in elevation within the catchment, it was impractical to survey aIl individual survey grid points with GPS methods. A subset of grid points was georeferenced using a Trimble Pathfinder Geoexplorer GPS unit. Using these data as reference points and the approximate uniform 98 distances between grid points, latitude and longitude for each survey point was calculated. From the georeferenced LIDAR survey, elevation, slope, and upslope contributing area, was obtained for each survey point location. The topographic index, In(aJtan~) of Beven and Kirkby (1979) was calculated using the maps of upslope contributing area (a) and slope (~) for any given point in space. 5.3.4. Evaluating patterns in shallow soil moisture We examine both the continuity and connectivity of shallow soil moisture at MSH. Experimental variograms of each soil moi sture survey are generated to examine the continuity of soil moi sture during seasonal changes within the catchment. These are compared to the variogams describing the structure of the underlying catchment morphology. Western et al. (2001) defined hydrologic connectivity as " ... hydrologically relevant spatial patterns ... " of properties (e.g. high permeability) or state variables (e.g. soil moi sture ) that facilitate flow and transport in a hydrologic system (e.g. an aquifer or watershed). They describe the method employed here to calculate the omnidirectional and topographic connectivity functions for each survey. In this approach each observation of soil moi sture is identified as being above or below an indicator value (e.g. 50th percentile). A semi-variogram describes the probability of spatial dissimilarity (or variability) of soil moisture (in this case) with separation distance (Goovaerts 1997). The connectivity function, an indicator-variogram (Goovaerts, 1997) describes the probability of spatial connection of higher-than-indicator-values with separation distance (Western et al. 2001). Connectivity for a mean separation distance is defined as the 'lag dependent probability' (Grayson et al. 2002) or ratio of connected pairs to total pairs with higher than-indicator soil moisture. Omnidirectional connectivity evaluates connectivity in all directions while the topographic connectivity restricts the evaluation of connection to the two directions of steepest descent as indicated by slope (Western et al. 2001). The integral connectivity scale " ... represents the average distance over which pixels are connected ... "(Western et al., 2001). 99 5.4. Results 5.4.1. Hydrologie storm response at Mont Saint-Hilaire Observations of hydrologie storm response (runoff ratio) collected during 2001 and 2002 show evidence of a threshold-like change over a small change in AMC, defined in this study as the mean antecedent shallow soil moi sture of the spatial survey closest in time to the storm occurrence (Figure 5.2a). Figure 5.2b illustrates storm response as a function of a priori local water table elevation. Runoffratios range from 0.002 to 0.62. Table 5.1 illustrates the seasonal trend of decreasing mean shallow soil moisture (MSM) from spring through later summer with sorne rewetting in the fall. Figure 5.3 shows shallow soil moisture during the 2002 season for select spatial survey grid points categorized by topographie location (steep hillslope, low hillslope and valley bottom), as weIl as the continuous TDR measurements from depths of 20 cm (horizontal probe) and 0-15 cm (vertical probe) for the corresponding soil pit locations (A,B,C), respectively (Figure 5.1). There is a marked seasonality. The drop in mean shallow soil moisture below runoff threshold values (SMS of 0.22-0.23) occurs during the period between surveys collected in mid-June and mid-July (Figure 5.3). The largest temporal changes in soil moisture are observed to occur in the valley bottom and low hillslope locations with smaller variability observed on the steep hillslope locations. Rouse and Wilson (1969) observed similar decreases in soil moi sture (2 fold) during the 1967 and 1966 growing seasons on the forested hillslopes at MSH. These observations suggest that we have captured conditions across a critical switch in behaviour of the hydrologic system. The threshold-like change in storm response leads us to search distinct macrostates and changes in hydrologic connectivity that may characterize these macro states. This behaviour suggests a two-state (wet-dry) system similar to that described by Grayson et al. (1997) where hydrologic connectivity (and its characteristic pattern) changes between system states. 100 0.7 0.70 o WeliB a) i.• b) 0.6 o I!. :'0>0 0.60 I!.Piez B 0 0 'Z; 0.5 .~ 0.50 XPiezC c:I Threshold: '-' .... Cl Perched C !t: 0.4 0 ~ 0.40 t:: C oWeliC 0.3 ci! ~ 0.30 XWeliD 0.2 • 0.20 0 x+ +WeIiE (ii) ; k(i) ., O(Cl 0.1 li 0.10 el!. rw..Q ~ ... _ 0.0 \ 0.00 .. 0.05 0.10 0.15 0.20 0.25 0.30 -2.0 -1.5 -1.0 -0.5 0.0 0.5 mean soil moisture local water table elevation (vol/vol) (m below ground surface) Figure 5.2. Hydrologic response (runoff ratio) as a function of surrogate measures of antecedent moi sture conditions: (a) mean shallow soil moisture and local (b) water table elevation for three riparian and lower hillslope wells. Storms i and ii indicated here are used in subsequent analysis (Figures 5.12 and 5.13). Table 5.1. Spatial surveys of shallow soil moisture. MSM indicates the mean shallow th th th soil moisture of each survey (vol/vol). The 50 , 75 and 90 percentiles of individual survey distributions are used as indicator values for evaluating pattern. The directional integral connectivity scale Ir represents the average distance over which there is connectivity (Western et al., 2001). RI and R2 are random realizations of shallow soil moisture surveys created using geostatistical characteristics of the wettest (22-May-02) and driest (21-Sept-02) surveys (Figures 5.9 and 5.10). No. of MSM Std.Dev SM Percentile Directional h (m) Date obsv. (vol/vol) (vol/vol) 50th 75th 90th 50th 75th 90th 22-May-02 341 0.24 0.10 0.20 0.27 0.39 178 84 14 20-Jun-02 340 0.24 0.10 0.20 0.28 0.38 200 71 13 3-Jul-02 341 0.20 0.10 0.17 0.28 0.33 197 69 10 25-Jul-02 343 0.19 0.09 0.16 0.23 0.32 192 53 11 21-Sept-02 340 0.10 0.07 0.08 0.12 0.16 192 54 36 I-Nov-02 341 0.19 0.07 0.17 0.21 0.27 158 86 56 7-May-Ol 342 0.23 0.10 0.19 0.26 0.38 188 64 10 19-Jul-Ol 342 0.22 0.09 0.19 0.25 0.35 208 65 11 l-Aug-Ol 342 0.14 0.10 0.11 0.16 0.26 180 49 21 RI 342 0.26 0.08 0.24 0.30 0.36 74 11 2 R2 342 0.16 0.07 0.14 0.19 0.25 111 6 2 101 Table 5.2. Linear regression between topographie index [ln(aJtan~)] and shallow soil moi sture for the 9 spatial surveys. r indicates the relationship between the topographie index and mean soil moi sture (MSM). MSM Survey Date p-values (vol/vol) r 22-May-02 .24 .28 <0.01 20-Jun-02 .24 .29 <0.01 3-Jul-02 .20 .33 <0.01 25-Jul-02 .19 .37 <0.01 21-Sept-02 .10 .33 <0.01 I-Nov-02 .19 .26 <0.01 7-May-Ol .23 .21 <0.01 19-Jul-0l .22 .26 <0.01 l-Aug-Ol .14 .31 <0.01 (a) 5teep Hilisiape (b) Law Hillslape (c) Valley Bottom o Ai 0.6 0.6 0 D ,~ o '2 A5 D F2 o Cil c 85 0.5 Q K7 0.5 b. Rl~ Q ~ x Xl2 o.• x• ..12 i!! • G4 :.: urn o., 1 • X3 ~ X L3 • !)!)lI • Xl .~ L5 • ODI2 -O~15"" 0.3 ! ~ 'c 1 . ············20cl!\. -O-IScm ) .. ~:i · ~ ~.: . i -0-15 cm 0i 201:01 · o., l .w·20ctl\ B Thrioold """"'" ., > Î ~ i: D :~ ...... 1 ! 0.1 0.1 """"'" 0.0-·--··_--··············_·· 0.0 * 140 160 180 200 220 240 260 260 300 1-40 160 180 ZOO 220 240 260 280 300 140 160 180 200 220 240 260 280 300 Julian day Julian day Julian day Figure 5.3. Seasonal evolution of shallow soil moisture during Spring-Fall 2002. Evolution of shallow soil moisture on (a) steep, (b) low hillslope and Cc) valley bottom topographie positions is illustrated by a representative series of survey grid-points. Locations of individual grid points are distinguished by letter (column)-number (row) identifiers. Columns fUn from A to DD moving from South to North. Rows increase from 1 to 18 moving from West to East. Overlain are continuous TDR soil moisture records taken at depths of 0-15 cm (vertical) and 20 cm (horizontal) along the A-B-C transeet (Figure 5.1) from steep hillslope to valley bottom. 102 5.4.2. Spatial surveys of shallow soil moisture Figure 5.4 illustrates two shallow moi sture surveys and corresponding histograms representing wet (22-May-02) and dry (21-September-02). Under wet conditions, a large wet area is present in the lower portion of the catchment. The upper and most northern portion of the catchment appears connected - this is the ephemeral part of the stream, angling off to the west. Although soils are ~2 m deep in the valley bottom, an extensive variably saturated area forms midway along the length of the survey due to the impermeable fragipan layer and a shallow water table. Under dry conditions, the upper portion of the catchment dries up and the mid-range variably saturated area also dries up, leaving only the lower perennial portion of the stream with surrounding riparian area. 5.4.3. Correlation with topographie index Figure 5.5 shows the histogram and maps of topographie index [ln(a/tanp)] corresponding to each survey point of spatial soil moisture (e.g. Figure 5.1). Maps of th th th topographie index illustrate pattern defined by indicator values of 50 , 75 and 90 percentiles. These percentiles were chosen to correspond to the analysis of the spatial patterns of shallow soil moisture discussed in Section 5.4.2. Low values of topographie index derive from small contributing areas and/or steep slopes, describing the upper hillslopes. High values derive from larger contributing area and/or low slopes (e.g. the valley bottoms or areas of flow convergence such as the ephemeral portion of the stream channel). Regression analysis of soil moisture with topographie index ranges between ~ of 0.21 and 0.37 for individual surveys (Table 5.2). Figure 5.6 shows soil moi sture as a function of topographie index for select wet and dry surveys. Spearman rank correlation of surveys with the catchment topographie variables produce the following values: for elevation, slope, ln(a) and ln(a/tanp), correlations range between -0.46 to -0.55, -0.54 to -0.63, 0.25 to 0.36, and 0.58 to 0.65, respectively. These results compare weIl with those ofNyberg (1996). 103 5.4.4. Spatial continuity in shallow soil moisture We describe the spatial continuity of the underlying topographic index and surveyed shallow soil moi sture using experimental variograms (Figure 5.7) calculated from topographic index or survey data and their model fits (Tables 5.3 &5.4). The nugget, sill and range refer to the common components of a variogram: the nugget is the non-zero intercept, the sill is the maximum variance, and the range is the separation distance at which the sill is reached. In Figure 5.7 a, the topographic index shows significant differences in North-South and East-West directional varigrams due to the elongated shape of the catchment (Figure 5.1). In the North-South direction, the spherical and exponential functions (with nugget) give a range of approximately 85 m. In the East West direction, the range is roughly 2 times smaller (40 m). In this direction, variance decreases at larger separation distances due to the greater likelihood that sample pairs are located on the hillslopes that flank the low lying valley bottom of this elongated catchment and where topographic indices are more similar, reducing variance at large separation distances. The spatial structure of shallow soil moi sture reflects, in part, the spatial structure of the topographic index (Figure 5.7b,c). Experimental soil moi sture variograms from the 9 surveys conducted indicate a range at which a sill develops within 50-80 m in the North South direction. The largest separation distances span the length of the catchment, contrasting valley bottom, perennial stream and variable saturated are as to the upper portion of the survey where no valley bottom exists, where the stream channel is ephemeral and variable saturated are as do not form. Ranges in the East-West direction are smaller (30-40 m) and variance in soil moisture decreases following the trend observed in the variogram of the topographic index. The seasonal evolution of soil moisture structure indicates decreased variability with drier conditions, similar to those of Western et al. (1998a, Western et al. 2004). The wettest surveys (22-May-02; 20-June-02; 7-May-Ol), as defined by the mean shallow soil moi sture (0.24, 0.24, 0.23 voUvol, respectively), exhibit the highest variance. Surveys taken mid-summer (3-Jul-02, 25-Jul-02, 19-Jul-Ol) with lower mean soil moi sture (0.19, 0.20 and 0.22, respectively) show lower variance. Markedly lower variance occurs for surveys taken on 21-September-02 (0.10 voUvol) and 01-November-02 (0.19 voUvol). 104 For these two surveys a deterioration of the linear increase in variance occurs at the largest distances in the North-South direction. Although the 01-August-01 survey is the second driest (0.14 vol/vol), the variance is similar to that of the early or mid-season surveys. Wet: 22-May-02 Dry: 21-September-02 +N • >0.25 Histograms 12:> 100 04 100 03 4J 00 100 03I MSM=0.24 0.2 ! ~ ro g MSM=0.10 ü üà 02 g ~ ~ 40 00 0.1 !if If 01 0 0 S.O 01 02 0.3 0.4 05 0.6 of ~O 0.1 0.2 0.3 04 0.5 0.6 0.9 Figure 5.4. Wettest and driest soi! moisture surveys and corresponding histograms. Observed values of shallow soil moisture range between 0.03 and 0.7 vol/vol using site specifie calibrations. Legend classification is the same for both surveys, based on the 22- May-02 distribution of soil moisture. Mean shallow soil moi sture for the survey collected during wet conditions on 22-May-02 is 0.24 vol/vol and under dry conditions (21- September-20) is 0.10 vol/vol. 105 100.-.--.-.--'--'-'-' 00 00 70 ë ro S ID 40 6 8 10 12 14 16 Topographie index h th 50t pereentile 75 pereentile 90 th pereentile Figure 5.5. Histogram and map of topographie index [ln(a/tanp)] for surveyed area. Pattern is shown using the 50th (a), 75 th (b), and 90th (e) pereentile as indieator values. Dark gridbloeks indieate values higher than (or equal) to the indieator value. Light gridbloeks indieate values lower than the indieator value. A DEM resolution of 10 m x 10 m was used to generate elevation, slope, eontributing area and topographie index. Values of topographie index ranges between 5 and 16. 0.6 r------_~-.... 0.6 ,...... o 0 0- 21-September-02 :::::: 0.5 o 0 'O~ 0 0 o 00 I!P El 0 ~ 0.5 8 ~ o lb 0 0 ~ • (5 0.4 00 0.:000 0 0 o Z. El oOJj 0 1 0.4 o ~ 0.3 o :l ~ 0.3 o 0 00 t: (QI 00 0 ~ t: 0 ë5 0.2 •....0 00 E è? .~ 0.2 '5 0.1 '" o 22-May-02 Sl 0.1 1 0.0 L...... , 0.0 L...... o. 4 6 8 W ~ M ~ œ 4 6 8 W ~ M ~ œ In(a/tanB) In(a/tanB) Figure 5.6. Soil moisture and topographie index for wet and dry representative surveys (see Table 5.2). 106 Table 5.3. Spatial continuity of the topographic index for the surveyed area (Figure 5.7a). Values are derived from a visual fit with a spherical variogram model (with nugget). Range Direction Nugget sm (m) North-South 1.5 2.9 84 East-West 1.8 3.4 40 Table 5.4. Spatial continuity of shallow soil moi sture for each survey. Values are derived from a visual fit with spherical variogram model (with nugget). a) North-South direction Nugget sm Range Survey Date (v/v}2 {V/V}2 {m} 22-May-02 0.0015 0.0068 60 20-Jun-02 0.0015 0.0065 55 3-Jul-02 0.001 0.0069 65 25-Jul-02 0.0015 0.0055 60 21-Sept-02 0.00078 0.0034 67 I-Nov-02 0.0015 0.0023 65 7-May-01 0.002 0.0056 65 19-Jul-0 1 0.0015 0.0054 60 I-Aug-Ol 0.001 0.007 58 b) East-West direction Nugget sm Range Survey Date {V/V}2 (V/V}2 {m} 22-May-02 0.001 0.01 40 20-Jun-02 0.0015 0.009 40 3-Jul-02 0.0015 0.009 40 25-Jul-02 0.0015 0.009 40 21-Sept-02 0.001 0.004 30 I-Nov-02 0.001 0.004 30 7-May-01 0.003 0.007 40 19-Jul-Ol 0.0015 0.007 40 I-Aug-Ol 0.0015 0.008 30 107 a) Topographie Index --Omnidirectional -e- North-South -i<- &!st-West 4 2 o '------' o w ~ H W 100 IW Separation distance (m) b) Soil moisture (North-South) 0.012 ,...------, ,--__--, N~ 0.010 ~"0 0.008 Z. ~ 0.006 Cl .g 0.004 ~ -'-I-Aug-Ol 0.002 0.000 '------'. o M ~ ~ W 100 lM Separation distance (m) e) Soil moisture - East -West 0.012 --22-May-O~ 0.010 -e-20-June-O~ N~ "0 -*-3-July-02 <. 0.008 -i<-25-Jul)'-02 ~ -+-ll-Sept-02 E 0.006 -&-]-Nov-02 ~ -+-7-May-01 g 0.004 .;: __ 19-Jul-0 1 -'-I-Aug-Ol ~ 0.002 0.000 o M ~ ~ W 100 lM Separation distance (m) Figure 5.7. Spatial eontinuity of topographie index and shallow soil moisture. (a) Directional and omnididrectional variograms of the topographic index; (b) Variograms of shallow soil moisture in the North-South and (c) East-West directions. Wet conditions occur in the early spring after snowmelt (e.g. 22-May-02). Driest conditions appear in the late summer and fall (e.g. 22-September-02). 108 5.4.5. Spatial connectivity in shallow soU moisture Spatial connectivity in shallow soil moisture is assessed using the approach of Western et al. (2001). For three surveys, Figure 5.8 presents visual patterns in shallow soil th th th moisture based on indicator values of 50 , 75 and 90 percentile of individual distributions. For each indicator-value, patterns in shallow soil moisture appear similar for all surveys. Patterns created using the 50th percentile show connection on the length scale of the entire survey (-310 m) regardless ofwet or dry conditions (Figures 5.8 and 5.9). Maximum length scales over which we observe connectivity for the 75 th percentile is 150 m, the length of the lower potion of the elongated catchment. For the 90th percentiles surveys show a maximum length scale of 60 m with the exception of 1 survey (l-Nov-02) with a length scale of 130 m. Observation of similar spatial organization under both wet and dry conditions differs from the findings of Western et al. (2001) where wet and dry conditions pro duce organized and random patterns, respectively. Although similarly organized patterns exist under both wet and dry conditions, hydrologic connectivity as defined by pattern c1early does not indicate water movement. The driest surveys (e.g. 1-Aug-02) have extremely low soil moi sture (i.e. no active flow of water) and organized patterns. Figure 5.9 compares omnidirectional (a) and topographic (b) connectivity functions. To provide a base1ine connectivity of a random or non-organized pattern, two multi gaussian simulations of shallow soil moi sture are inc1uded. These two simulations (RI and R2) were created with prescribed means and semi-variograms of the wettest (22- May-02) and driest (01-August-02) surveys, respectively, but with random organization (Figure 5.10 and Table 5.1). In Figure 5.9, we observe the connectivity function to change significantly with the indicator value (Western et al. 2001). Greater connectivity is observed for the 50th percentile compared to the 75 th and 90th percentile indicator values, with integral connectivity scales ranging from 188-208 m, 49-86 m, and 2-56 m, respectively (Table 5.1, Directional Ir). This analysis raises the question: what indicator value should we consider to evaluate changes in patterns? Selecting the 75 th percentile results in absolute values of soil moisture from each individual survey that together, bound the mean value (- 0.24 vol/vol) at which we observe the threshold change in runoff response (Table 5.1). 109 th th 50 thpercentile 75 percentile 90 percentile 22 May 2002 MSM =0.24 3 July 2002 MSM = 0.20 21 Sept 2002 MSM = 0.10 Figure 5.8. Spatial patterns of shallow soil moi sture for three surveys. Surveys on 22- May-02, 03-July-02 and 21-Sept-02 have mean shallow soil moistures (MSM) of 0.24, 0.20 and 0.10, respectively (see Table 5.1), spanning the range of MSM over which threshold change in runoff ratio is observed (Figure 5.2). Patterns are shown using the t th th 50 \ 75 and 90 percentiles of individual survey distributions as indicator values for light versus dark. 110 a) Omnidirectional connectivity b) Topographie connectivity -_._------22-May-02 --l2-May-02 0.9 50th percentile Il 20-IUIl-02 0.9 --tr--20-IUIl-02 O.S D 0.8 o 03-JuI-02 03-lul-02 > ~ 0.7 ~ 25-Jul-02 .... 0.7 o 25-1\11-02 .::; 0.6 :~ X 21-Sep-02 0.6 x 21-Sep-02 B x··· . OI-Nov-02 ~ (1J 0.5 c: 0.5 X 01-Nov-02 c: c: • 07-May-OI c: 0.4 • 07-May-01 (3 0.4 (3 + 19-1ul-01 + 19-JuI-0 1 0.3 0.3 • Ol-Aug-Ol • Ol-Aug-Ol 0.2 ...... RI 0.2 -'-'-RI 0.1 • _ •• _. R2 0.1 • ...... R2 0~------~--~--4~ o 50 100 150 200 250 300 350 50 100 ISO 200 250 300 350 Separation distance {ml Separation distance (m) 1.0 --22-May-02 --22-May-02 0.9 75th percentile 0.9 ,r, 75th percentile A 20-Jun-02 y l>. 20-JUIl-02 0.8 DOl-lui-OZ 0.8 1\'. D 03-luI-02 ~ 0.7 o 25-luI-02 ~ 0.7 ni ~ o 25-JuI-02 :,§ 0.6 X OI-Sep-02 -::; 0.6 ,\ x l( Ol-Sep-OZ 1 ~ 0.5 X 01-Nov-02 'e ; * X 01-Nov-02 c: ~ 0.5 . i, f ~~ c: 0.4 • 07-May-OI c: 0.4 l II' ZI • (l7-May-OI (3 0.3 + 19-Jul-OI + 19-1ul-01 (3 0.3 1 \ .a x x 0.2 • OI-AIIg-OI • Ol-Aug-Ol -'-'-RI 0.2 : }. A'). -'-'-RI 0.1 0.1 .' ,,\.. Il \ ...... R2 ~l>..A .~: .. • ...... HZ o t-~"""'-:''''''''::...JIo''''I+i_... ~1-' 0.0 o 50 100 150 200 250 300 350 o 50 100 150 200 250 300 350 Separation distance (m) Separation distance (m) 0.9 90th percentile 90th percentile --22-May-02 --tr--20-1un-02 0.8 --a--03-lul-02 ~ 0.7 .::; -+--25-lul-02 'e 0.6 __ Ol-Sep-OZ ~ 0.5 -*-01-Nov-02 c: 0.4 (3 ---+-07-May-OI 0.3 -+--19-Jul-Ol 0.2 --OI-Aug-OI 0.1 '. -'---RI .'. ." ~. 50 100 150 200 250 300 350 o 50 100 150 200 250 300 350 Separation distance {ml Separation distance (m) Figure 5.9. Omnidireetional (a) and topographie (direetional) (b) eonneetivity funetions. th th th Indieator values are set at 50 , 75 and 90 pereentiles of eaeh individual survey. RI and R2 indieate eonneetivity funetions for two simulations of shallow soil moisture with preseribed means and variograms of wet (22-May-02) and dry (I-August-02) surveys, respeetively (see Figure 5.10). 111 a) Random pattern Rl 80 1 1 1 1 1 1 0.012 70 0.2 0.010 60 E 0.008 50 ...ro ë f Cl g 0 0.006 40 '':: 8 0.1 16 30 -, ~ 0.004 -B-22-May-02 -NS !iP-, 20 ~ 22-May02 - EW 0.002 -4-RI-NS 10 ~RI-EW [ 0.000 1..-______-' o rR 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 o 20 40 60 80 100 120 Separation distance (m) b) Random pattern R2 120 1 1 1 1 1 1 0.012 -B-21-Sept-02 - NS 100 0.3 0.010 ~ 21-Sept-02 - EW -4-RI- NS 80 E 0.008 ~RI-EW ....ro ë 021 Cl 0 0.006 8 60 '':: 16-, 40 ~ 0.004 0.1 !iP.... 20 0.002 0.000 L...-______....J o r 1L~J 1 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 o 20 40 60 80 100 120 Separation distance (m) Figure 5.10. Histograms (left) and variograms (right) for two simulations (RI and R2) of shallow soil moisture with prescribed me ans and variograms of the wettest (22-May-02) and driest (21-Sept-02) surveys (inc1uded in Figure 5.9), respectively. Consistent with the visual observation of similar patterns for wet and dry AMCs, the omnidirectional connectivity function (Figure 5.9a) shows no increase in connectivity with wetter conditions. This is true for all 3 indicator values. For each survey, the topographie connectivity function (Figure 5.9b) is larger than the omnidirectional connectivity function. Although there are fewer cells with higher-than-indicator values inc1uded in the analysis (inc1udes only the 2 directions of steepest descent) a larger number of these cells are connected leading to a larger value of connectivity. In the second panel of Figure 5.9b (75 th percentile), there appears to be sorne decrease in 112 connectivity with drier conditions. Wetter surveys show slightly larger connectivity up to separation distances of 150 m. The corresponding integral connectivity scale values (indicating the average distance over which there is connectivity) shows sorne decrease with mean survey soil moisture (Figure 5.11 and Table 5.1) but the trend is not particularly convincing. Highest connectivity is observed for one of the wettest surveys (22-May-02) and lowest connectivity for the driest survey (1-Aug-01). However, analysis using the 50th and 90th percentile indicator values do not show a decreasing trend in connectivity with drier AMes. Omnidirectional Topographie ••...•....•...•.• __ ...... _.... -- ...... _-_ ...... 0 200 o Ormi 50lh Ê 200 00 0 o Topo 50th 0 0 IV 1 0 0 o Ormi 75th ïii i o Topo 75th 0 u 0 150 \Il 150 1 cP 0 AOrmi 90th ;>.. 1 A Topo 90th 00 ... 1 0 0 .> 0 .'::; 100 u 100 IV 0 0 C 1 oQ]l c 0 é 50 0 50 0 u A o 0 ~a:e A ïii... 1 AA4fI. 0------~ ... ~...... AÀ.. AtJÀ...... 0"1 0 ...IV 0.00 0.05 0.10 0.15 0.20 0.25 0.30 .E 0.00 0.05 0.10 0.15 0.20 0.25 0.30 Mean soil moisture (vol/vol) Mean soil moisture (vol/vol) Figure 5.11. Integral connectivity scales (omnidirectional and topographic) as a function of mean shallow soil moisture. The integral connectivity scale represents the average distance over which there is connectivity in root-zone soil moi sture (Western et al., 2001). 5.4.6. In-storm hydrologie eonnectivity In this section, we examine the soil moisture profile and water table elevations along a transect from steep hillslope to valley bottom for evidence of changes in hydrologic connectivity as defined by active flow on the time-scale of a storm event, as proposed by Stieglitz (2003). Active flow connection to the stream channel may occur via a number of mechanisms such as shallow subsurface flow, saturation overland flow from variably saturated areas, and groundwater mounding. In Figure 5.12, we present the temporal evolution of moi sture within the soil profile for two storms: one wet (Storm i) and one dry (Storm ii) with runoff ratios bounding the threshold change in hydrologic response (Figure 5.2). 113 On the steep hillslope with thick soil coyer (Location A, Figure 5.1) a water table was never observed to fonn, even under early-spring wet conditions. At this location, slopes are ~ 27-30° at both the ground surface and the soil-bedrock interface and soil thickness is approximately 1.5 m. The lack of measured water table fonnation at the bedrock interface on this steep slope does not prec1ude movement ofwater along the soil-bedrock interface, but it does indicate that even ùnder wet conditions, a connected water table does not extend up these steep slopes. However, on steep hillslopes with little soil coyer and convergent slopes we have observed saturated conditions, resulting in water seeping direct1y out of the soi1s. In this case, steep shallow soils are certainly hydro10gically connected. Figure 5.12 illustrates the soil moisture profile evolution along the high relief transect A-B-C (Figure 5.1) for stonns (i) and (ii). Under wet conditions, stonn (i) delivers 14 mm of throughfall in 1.2 hrs with an average intensity of 9 mmIhr. On drier conditions, stonn (ii) delivers 38 mm in 2.4 hrs with an average intensity of 15 mmJhr. The high intensity of these two stonns suggests activation of macropore flow when compared to the 5 mmIhr initiation rate used by Beven and Gennann (1982) and based on experimental evidence from Omiti and Wild (1979). For both these stonns, shallow soil moisture on the steep upper hillslope (location A) shows strong and immediate response with changes in soil moi sture at 20 cm of 0.05 voVvol and 0.09 cm voVvol for stonns (i) and (ii), respectively. This indicates approximately 18 mm (136% ofvoVvol throughfall) volume change under wet conditions and 36 mm (95% of throughfall) under dry conditions at this shallow depth. The larger volume change under wet conditions suggests greater lateral movement of water on the well-drained steep hillslopes. At 60 cm depth, volume change is 3 mm and 8 mm or approximately 20% of throughfall for stonn (i-wet) and stonn (ii-dry). Response-time at this depth is lagged, peaking faster for wet conditions (21 hrs) than dry (2.5 days). Below this depth there is little evidence of significant volume changes. At the break in hillslope (lower hillslope, location B), TDR probes at 20 cm indicate similar volume changes of 10 mm (67% ofthroughfall) and 29 mm (77% ofthroughfall) for wet and dry stonns. At 110 cm, we observe a slow dewatering from near-saturated conditions prior to stonn (i) followed by negligible stonn response. Under drier 114 conditions, a priori moisture at this depth is significantly lower and shows a lagged increase to storm (ii) over the subsequent 2 days. The lowest depth of TDR probe (170 cm) indicates continuous saturated conditions (~0.4 vol/vol) during storm (i). In the case of storm (ii), a priori conditions at 170 cm are unsaturated, with saturation quickly re established within 7 to 26 hours of the storm, and a volume change of 52 mm (138% of throughfaIl). Response at this depth is also shown by the water table elevation (Figure 5.13, lower left panel, WeIl B). Wet Storm (il: 14 mm delivered in 1.2 hrs Upper hillslope lower hillslope 0.45 0 0.45 o --bS(gs) -+-05 (gs) 'lJ o b4(20 Dry Storm (ii): 38 mm delivered in 2.4 hrs Upper hillslope lower hillslope 0.45 0.45 1 --+-.S(g') --05 (g.) c b4(:!Ocm) 0.40 D 44 (20 cm) 75 0.40 ~ -t-bJ (70crn) + .3 (60 cm) :g O.3S ~ 0.35 --+-b2 (110 cm) 5 -ThFALL 5:;1 -.!>-bl (J70cm) g ;1 ~ -; 0.30 \a...... 0.30 ~ ~ ~ -ThfAll. ::> D "3 '0 .; 0.25 -- E 0.25 "3 o lw..Aa 3 2- .~ E 10 lU ~ 0.20 --~ - 0.20 0.15 0.15 D .. 010 ~ 010 ~ 197 19~ 199 200 2tJl 202 203 204 197 198 199 ZOO ZOI 202 203 204 Julian Day Julian Day Figure 5.12. Evolution of moisture within the soil profile on steep upper hillslope (location A - Figure 5.1) and lower hillslope (location B) during storms on wet conditions (storm i) and dry conditions (storm ii). Figure 5.2 shows the corresponding runoff ratios and mean antecedent shallow soil moisture for each storm. Time-domain-reflectometry 115 probes (30 cm length) are installed vertically at ground surface (gs) and horizontally at depth. Storm (i) 14 mm delivered in 1.2 hrs • - - 'WeIlB . Weill! 0.0 -Wcll-D 0.0 ~-WelJ..O , --Weil·!! l - ...... Wcll·E -111Fan l fr ~ CIl .;).4 5 u ~).4 -êa ;1 -ê "C ~ ir E a c "C :l -0.8 3' c -O.~ il "'t,._ e r'~t\ 3 :l ($ •'" 0> e '\,. ~~ 1O~ Cl ~ 'wl 0 ---- ~ "ii ·1.2 0 «> "ii -1.2 «> .1.6 L-______--' ·1.6 15 174.0 1745 175.0 175.5 176.0 176.5 IJ.Il 0.5 1.0 15 2.0 2.5 3.0 Discharge (mm/hr) Julian day Storm (H) 38 mm delivered in 2.4 hrs •••• Wens ·.. ·•· .. ·Well·B 0.0 0.0 -Wcll·D -fr-Well·[) --Well·E l --Wcll·f, CIl u -0.4 -ThFall -ê f a fI>.- "Cc f?/m .. -C.S / :le / 0> '/ 10 ~ / ~ .d 0 101:11 •. ,.Jil "ii ·1.2 './tr .-.... ~ .... ~ ..... - «> :~V ~ ,\ • ---.J L-______~ 15 .1.6 '---______·1.6 IQ7 IQS 19'1 200 201 202 203 204 0.0 0.2 0.4 0.6 0.8 1.0 12 Julian day Discharge (mm/hr) Figure 5.13. Evolution ofwater table elevation during storms on wet antecedent (storm i) and dry conditions (storm ii). Left-hand panels show the time-series evolution. Right hand panel shows water table elevation change with stream discharge (mm/hr). Wells B, D and E are located along two transects: cross-valley and along the length of the stream channel (Figure 5.2). Antecedent water table elevations (Figure 5.13) differ considerably for these two storms, with levels lower by 0.33 (Well B) to 0.62 m (Well D) under dry conditions. Under wet conditions (storm i), Well E responds immediately, and peaks on the rising limb (c1ockwise hysteresis) (Figure 5.13, top, right panel). Well D shows a slower 116 response with higher elevations on the falling limb of the hydrograph. This suggests strong subsurface control on well D from the upper portion of the catchment. Well E is more removed from this effect and has a much smaller immediate area from which subsurface flow will accumulate. The immediacy of the water table response is not so surprising considering how high the water table is prior to the storm (~ -0.30 m below ground surface) and its position in an area of local convergence. Well B also shows immediate response followed by a second and relatively separate peak in water table e1evation. The first response coincides with that of well D, suggesting influence from a groundwater connection with the low-lying upper portion of the basin, followed by a lagged response from the more immediate steep hillslope above. Under dry conditions (storm ii), well B sits at -1.50 m and soil moi sture at the 1.70 m probe (bl) has fallen to ~0.27, unsaturated conditions (Figure 5.12). Well E shows no change for a small pre-storm throughfall but both well D and B respond with rises of ~ 12 cm and 68 cm, respectively (Figure 5.13). During storm (ii), all three wells (E, D, and B) show a counter-c1ockwise hysteresis, with higher water table elevations during the falling limb of the hydrograph. At well B, once again we see an immediate response that coincides with a rise in well D. A secondary response that suggests a lagged response from the steep hillslope above is of longer duration, maintaining a water table ~ 1.2 m above the pre-storm level for approximately 24 hrs. Soi! moisture at the 1.7 m depth (TDR probe b1, Figure 5.12, right top panel) quickly retums to saturated conditions and remains saturated for over 48 hours. TDR probes in the valley bottom (location C - not shown in Figures) indicate that saturated conditions are quickly restored during this storm. These two sample storms suggest that shallow subsurface flow occurs under both wet and dry conditions. The temporal evolution of moi sture in the soil profile in response to both storm events is fast and appears to last re1atively short periods of time (days after the storm). In both cases, the water table at the break in hillslope is very responsive. However, distinguishing the response of these two storms is a) the unsaturated (unconnected) conditions at depth on the lower hillslope under dry conditions, b) the small response in wells D and E and finally c) and the size of the antecedent variably saturated areas (e.g. Figure 5.4). 117 5.5. Discussion and Conclusions 5.5.1. Spatial continuity of shallow soil moisture The surveys of shallow soil moi sture at MSH show spatial continuity with ranges on the order of 60 m and 40 m in the N-S and E-W directions and appear to be influenced by the spatial structure of catchment morphology, most strongly under wet conditions. Variograms of the topographic index show ranges in the N-S and E-W directions of85 m and 40 m, respectively. The smaller ranges in the East-West direction result from the narrow valley bottom with bounding hillslopes. In the N-S direction, ranges are longer due to the elongated shape of the catchment. Although our ranges are larger than the 20 m range of influence that Nyberg (1996) observed on the forested 6300 m2 Gârsdsjon catchment in Sweden, he also observed soil moisture variograms that follow those of the topographic index and its directional anisotropy. In the rangeland catchments of Australia and New Zealand, Western et al. (2004) have reported correlation lengths between 25 and 330 m, or ranges (3 x correlation length) from 75 to 990 m, placing the ranges from the above two forested catchments at the low end of observations made on rangeland catchments. In Southwest England, Meyles et al., (2003) have found ranges of 8 to 180 m on a grass and peat covered catchment. At MSH we did not observe a distinguishable seasonal trend in ranges. However, the variance (variogram sills) in soil moisture shows considerable decrease with drier conditions. This is consistent with the general finding of Western et al. (2004) that spatial variance increases with mean soil moi sture until sorne value, then decreases as wetter conditions act to reduce variability. At MSH, the presence of variably saturated areas in the valley bottom appears to increase spatial variance under wet conditions. The correlation between topographic index and soil moisture at MSH is statistically significant (p<0.01) with r2 values of 0.21 to 0.37. Spearman rank correlation results are similar to those of Nyberg (1996) where slope, elevation, upslope area and topographic index contribute to the spatial variability of soil moisture. 118 5.5.2. Conneetivity in shallow soil moisture By visual inspection all surveys show organization as large connected areas in the valley bottom and along topographically convergent zones (e.g. ephemeral stream channel). As a result, based on the definition of organization (" ... a non-random spatial pattern that becomes apparent when examined visually ... " (Grayson and Bloschl 2000) the patterns at MSH do not show changes in organization with AMCs. Calculation of the omnidirectional and topographic connectivity functions confirms this finding. When an indicator value is selected (75 th percentile) that results in survey MSM values bounding the threshold change in hydrologic response, the topographic connectivity function appears to exhibit sorne distinction between surveys collected under wet versus dry conditions. These findings differ significantly from the rangeland catchment studies in which linear patterns form under wet conditions and are absent under dry conditions (Western et al. 2001). Our results suggest that critical information on the macrostate of the MSH catchment cannot be identified by the connectivity of shallow soil moisture. The patterns at MSH also suggest that lateral redistribution of water is a dominant process under all antecedent conditions. This is consistent with the steep slopes and the variable lower boundaries impeding vertical movement such as the shallow fragipan in the valley bottom and shallow bedrock on hillslopes. 5.5.3. In-storm hydrologie eonneetivity Although the spatial organization or connectivity of shallow soil moisture at MSH is relatively constant in time, c1early the occurrence and connection of subsurface flow and variably saturated areas to the stream channel during a storm event is not, as indicated by the threshold runoff response with changes in AMC. The temporal evolution and persistence of lateral subsurface flow is short, and yet it will occur on both wet and dry AMCs. However, the spatial extent of this active flow on the hillslope and its connection with the valley bottom variably saturated are as and/or the stream channel changes significantly between wet and dry conditions. Changes in shallow soil moi sture in response to storm events under both wet and dry conditions indicate lateral water 119 movement on the steep hillslopes. At the break in slope (location B) between hillslope and valley bottom, both soil moi sture at depth and water table elevations show a critical change in the connectivity of saturated conditions under wet and dry conditions. Under wet conditions, saturated conditions at 170 cm are constant, while under dry conditions, only in response to the storm are saturated conditions re-established. Although the water table at this location shows a larger response during dry conditions (due to the larger size of the storm), a priori levels are significantly lower, suggesting lower connection to hillslope soils and consistent with no variably saturated areas. From these observations, we would suggest that it is lateral subsurface tlow in combination with extensive connection of variably saturated areas to the stream channel that controls hydrologic response in this forested catchment system. As a result, the definition of hydrologic connectivity as varying connection of different parts of the lands cape via active lateral tlow (Stieglitz et al. 2003) appears to be a more relevant definition for the MSH catchment. In this paper we have contributed a detailed study of soil moisture in a forested catchment, a contrasting system to the catchments on which analysis of system states as defined by AMCs have been previously conducted. In this type of catchment, subsurface lateral tlow is an important mechanism of runoff generation. The results of our study show a regime change in catchment system behaviour is observed, giving support to the framework linking connectivity and hydrologic response suggested by the Melbourne Group. However, within this forest catchment, connectivity as defined by spatial patterns in shallow soil moisture does not distinguish the system state. Acknowledgements The authors would like to thank Dr. Martin Lechowicz and the ECONET project for funding of the LIDAR dataset and Benoit Hamel for data processing and DEM creation. Thanks atso goes to the staff of the MSH nature reserve and our field assistants Catie Burlando, Nathan Deustch, Raissa Marks and Sheena Pappas who contributed much effort in collection of the soil moisture surveys in steep terrain and variable field conditions. Thanks also to Clare Salustra for GIS assistance and Mike Wironen for soil texture classification data. We also benefited from e-mail discussion on spatial aspects of 120 "state of wetness" with Dr. Andrew Western. Thanks also to Dr. Lei Wen for helpful comments on the manuscript. This work was funded in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), a McGill-McConnell Fellowship, a McGill Graduate Studies Fellowship and the McGill Global Environment and Climate 3 Change Centre (GEC ). 121 6. Does topography and landscape organization control scaling of small catch ment storm runoff generation? To submit to WaterResources Research April L. James and Nigel T. Roulet Keywords: runoff generation, mean residence time (MRT), topography, landscape organization, topographie wetness index Context Inter-catchment comparison of hydrologie behaviour is challenging in part because of the spatial variability in topographie and landscape organization. When significant topographie relief exists within a catchment, it will influence runoff generation. At MSH, the 8 nested catchments appear to exhibit significant and varying relief. As a result, Chapter 6 contributes an analysis of the influence of topographie and landscape organization on storm runoff generation. Abstract The influence of topography on runoff generation is well documented. Both surface and subsurface topography can drive flow on hillslopes delivering water to the stream. At the catchment scale, studies have defined landscape organization as how the stream collects upslope area, in sorne instances distinguishing hillslope from riparian or valley bottom area. However, few studies have combined detailed inter-comparison of multiple catchment storm response (e.g. as new/old water contributions) with a detailed characterization of topography and landscape organization. In this paper, we examine storm runoff generation from multiple small, forested catchments in an attempt to evaluate the influence of catchment topography and landscape organization. These small catchments are located within the same watershed with uniform hydrologie input from storm events. As a result, inter-comparison of storm response is assumed to be a direct function of individual catchment properties. A high-resolution airbome laser altimetry 122 LIDAR (light detecting and ranging) digital elevation model (DEM) is used to characterize each of the 8 catchments. Storm runoff generation is quantified by hydrometric, isotopic and hydrochemical methods. Mean residence time (MRT) ofwater in individual catchments is estimated by baseflow recession analysis. Our topographic analysis quantifies differences in total catchment area, mean slope, and distributions of sub-catchment area as collected by the stream channel. Our analysis shows the 8 catchments at MSH to have similar distributions of topographic index and yet values lead to significantly different estimates of MRT. Baseflow hydrochemistry provides a surrogate measure to which MRT estimates are compared. The three largest catchments distinguish themselves with larger MRT and larger valley bottom areas. It is these three catchments that also show significantly larger amounts of new water delivery to the stream channel suggesting a significant change in dominant runoff mechanisms related to topography and landscape organization. 6.1. Introduction In this paper we examine the influence of topography on the spatial patterns and scaling of runoff generation from small catchments. In many catchments topography is an important control on spatial heterogeneity of runoff generation. Based on the variable source-area (VSA) concept of Hewlett and Hibbert (1967), Beven and Kirkby (1979) developed the topographic wetness index to describe steady-state moisture storage as a function of topography, where local hillslope segment slope is used as a proxy for the hydraulic gradient. Use of this terrain-based index enabled them to incorporate spatial heterogeneity on the scale of the hillslope in their lumped parameter VSA model, TOPMODEL. Although surface topography is often the most readily available information on spatial variability within a catchment (Kirkby 1997), subsurface or bedrock topography has been suggested as a more critical controller of subsurface runoff (McDonnell et al. 1996, Freer et al. 1997). In their study of a forested, shallow soil Canadian shield basin, Peters et al. (1995) conclude that most of the storm runoff cornes from water travelling along the soil bedrock interface. This includes event water travelling vertically by preferential flow paths. Analysis of hillslope trench flow at two catchments (Maimai catchment, New 123 Zealand and Panola Mountain Research Watershed, GA) showed significant influence of bedrock topography on hillslope hydraulic gradients and flow patterns (Freer et al., 1997). This relationship however, decreased with smaller rainfall events, indicating the importance of antecedent moisture conditions and rainfall intensity. Under dry conditions, the assumption of steady-state flow using any topography-based hydraulic gradient breaks down (Freer et al. 1997). Research also suggests topography IS the dominant source of variability in biogeochemical pro cesses within the landscape (Creed and Band 1998). In the case of nitrogen, ne ar-stream environments such as wetlands, riparian zones and seepage faces are a critical area of transformation and retention (Cirmo and McDonnell 1997). Creed and Band (1998) hypothesize that topography regulates the accumulation and loss ofN03- -N within the catchment. What happens when we move beyond a single catchment? In a comparison of hydrologic response from multiple small catchments, does topography influence the spatial patterns and scaling of runoff generation? These questions are important to the further development of spatially accurate hydrologic and biogeochemical models and it forces us to examine how to compare catchment-scale hydrologic and biogeochemical behaviour. 6.1.1. Inter-catchment comparison of topography and landscape organization Recent studies of multi-catchment systems have provided a basis of inter-comparison of catchment topography and landscape organization and its effects on runoff generation and scaling relationships of hydrologic response (Woods and Sivapalan 1997, McGlynn and Siebert 2003). McGlynn and Seibert (2003) quantify differences in landscape organization of catchments by comparing the distributions of sub-catchment area. Sub catchment area is calculated for each pixel of the stream channel with a catchment. In contrast to total catchment are a, this measure takes into account how upslope catchment area is collected by the stream network. In addition, they calculate the accumulating hillslope and riparian area along the stream channel providing distributions of riparian to hillslope ratios (or buffering capacity) for each catchment. A larger buffering capacity removes hillslope connectivity or influence from the stream channel. 124 Sivapalan (2003) proposes travel time as a scalable quantity from hillslope to watershed scales. Mean residence time (MRT) of a catchment integrates mean catchment behaviour in response to the effects of topography and soil characteristics on flowpaths. Wolock et al. (1997) define a subsurface contact time index based on the topographic index of Beven and Kirkby (1979). As the mean topographic index increases moving from the headwater downstream, soil-water contact time index increases. Wolock et al. (1997) suggest that a reduction in variability of concentrations results from increase in subsurface contact time, which in turn is linked to reduced variation in mean topography and soil characteristics. Based on the approach of Wolock et al (1997), Vitvar et al. (2002) estimate catchment MRT using runoff hydrograph recession analysis. They offer an alternative method of estimating catchment-scale hydraulic parameters of storage and hydraulic conductivity, replacing soil-derived values with a spatially integrated catchment-scale estimate from the hydrograph recession analysis. Difficulties in understanding spatial patterns of hydrologic response within nested catchment systems has led to a search for natural or preferred scales at which response and thus process representation simplifies. The concept of a representative elementary area (REA), introduced by Wood et al. (1988), suggests a spatial scale above which resolution of the pattern of local heterogeneity is not necessary. Bloschl et al. (1995) suggest that comparison of adjacent are as is required to truly evaluate changes in variability between small and large-scale processes. For catchment hydrology, adjacent areas are defined by the down gradient flow of water indicating a nested system of sub catchments. Simulated storm-flow (Wood et al. 1990, Bloschl et al. 1995), observed inter-storm (or low-flow) discharge (Woods et al. 1995) and solute concentrations (Wolock et al., 1997) for nested watershed systems have been used as data with which to search for an REA. In an analysis of inter-storm flow and stream chemistry on the Neversink River watershed in New York State, Wolock et al. (1997) analysed concentrations of solutes as a function of catchment scale (e.g. acid neutralization capacity, sum of base cations, pH, Al, DOC, Si). With increasing basin Slze, concentrations in solutes decreased in variability and at approximately 3 km2 concentrations had stabilized to relatively constant values. Woloek et al. (1997) interpreted this as the area required to attain equilibrium with the hydrologie and geologie 125 system. By looking at inter-storm flow, Wolock et al. (1997) analyse steady-state behaviour in contrast to the episodic perturbation of a storm event. The REA concept has been applied to both inter-storm and storm event analysis. In the case of inter-storm analysis, solute concentrations reflect an equilibrium with the landscape. In more recent work on the Neversink watershed (NY), Shaman et al. (2004) observe self-similarity in 2 low-flow runoff at basin sizes above 8 to 21 km • At smaller sizes, Shaman et al. (2004) hypothesis that self-similarity does not occur. At the Maimai catchment in New Zealand, McGlynn et al. (2003) observed a positive relationship between mean-residence-time (MRT) and median sub-catchment size for four catchments ranging in size from 2.6 to 280 ha, supporting the idea that landscape organization is an important control on flowpaths, stores and runoff generation. Although residence time and surrogates such as baseflow hydrochemistry are integrated measures of hydrologie behaviour of a catchment (McGuire et al. 2005), do they inform us on the scaling pattern of storm response? At Sleepers River, Vermont, Shanley et al. (2002) report increasing percent new water inputs with baseflow alkalinity, a surrogate for catchment till transmissivity. In this paper, we investigate how topography and landscape organization affect the spatial patterns of runoff generation across a series of eight small nested forest catchments at Mont Saint-Hilaire, Quebec. The nested catchment system, a total area of 147 ha, offers different climatic, topographie and hydrogeologic conditions than the previous nested-catchment studies (Brown et al. 1999, Shanley et al. 2002, McGlynn et al. 2003, McGuire et al. 2005). We set out to quantify runoff generation from the 8 catchments and determine if dominant mechanisms change across scale, focusing on the influence of topography and landscape organization on the spatial patterns observed. We characterize each catchment with a series of physical and behavioural measures. Storm response is quantified for five storms. From these results, we attempt to explain the spatial patterns we observe. We wish to address the following questions: Are these eight nested catchments topographically and organizationally different? What bearing do any differences have on runoff generation? And do baseflow hydrochemistry and MRT indicate scaling of storm response? 126 6.2. Site Description This study was conducted within the Westcreek catchment system of the Mont Saint Hilaire Biosphere reserve, located in southem Quebec (Lat: 45°32'49" N, Long: 73°10'07" W) (Figure 6.1). The climate in this region of eastem Quebec has large seasonality and can result in very dry conditions in the watershed in sorne summers. Daily mean temperature for January and July are -1O.3°C and 20.8°C, respectively. The region receives an average annual precipitation of 940 mm, 22% of which cornes in the form of snow. Precipitation is relatively uniform throughout the year. In the winter, a shallow frost in the soils is common, but concrete frost seldom occurs. MSH is vegetated by mature beech-maple forest. Soils derive from the weathering of the parent igneous intrusions and glacial tills left behind after the recession of the Laurentian Ice sheet, approximately 10,000 B.P. The lower elevations of the mountain (up to ~170 m ASL) were submerged by the postglacial Champlain sea (Webber 1965). Soils are generally immature (Gyn 1968) and include ferro-humic podzols, humic gleysol and brunisols. Within the Westcreek catchment system, soils are classified as dystric brunisols (Agriculture and Agri-Food Canada 1998) and have a pH of approximately 4.5. Horizons progress from black organic to brown sand at depth with little evidence of mottling or gleying within the soil profile. Soil texture is a sandy loam with very little clay (2-4%), 20-30% silt, and 66-78% sand (Wironen 2005). On the hillslopes, soils are well drained and range in depth from ~O cm to ~ 1.5 m. In the valley bottoms soils can be >2 m and perched water tables can form due to the presence of a low-permeability layer or fragipan (Dingman, 1994) within 30-50 cm of the surface. This layer is limited in extent and do es not appear on the hillslopes, likely a result of illuviation (Mehuys and Kimpe 1976). 127 • Gauging station N o 500 1000 1500 Meters ~L~~~~~~"""""~~_~_~....~ __ ~__~_~:S~ Figure 6.1. Nested catchments at Mont Saint-Hilaire, Quebec. Catchments range in size from 7 to 147 ha. Catchment delineation, stream network and topographic characteristics are derived using the 1 m x lm resolution MSH digital elevation model (DEM). The MSH DEM was produced from a laser altimetry LIDAR (light detecting and ranging) dataset collected prior to leaf out in early Spring 2003. The datas et was obtained using an Optech ALTM 2050, 50 KHz (50,000 pulses/sec) system and processed to extract last retums indicating ground elevation. 128 6.3. Methods 6.3.1. Charaeterizing eatehment topographie variables Topographic characteristics ofthe 8 nested catchments are derived using the 1 m x lm resolution MSH digital elevation model (DEM). The MSH DEM was produced from a laser altimetry LIDAR (light detecting and ranging) dataset collected prior to leaf out in early Spring 2003. The dataset was obtained using an Optech ALTM 2050, 50 KHz (50,000 pulses/sec) system and processed to extract last retums indicating ground elevation. Each catchment was delineated and characterized using standard GIS software. For each pixel of the DEM, we calculated the topographic wetness index ln(a/tan~) (Beven and Kirkby 1979), where a is the upslope contributing area and ~ is the slope, resulting in a frequency distribution and mean value for each individual catchment. Accumulated area for each pixel upon which the stream network is based was calculated for each catchment using a single-direction flow algorithm from a standard GIS software package based on the algorithm of J enson and Domingue (1988). The cumulative upslope contributing catchment area collected along each pixel of the stream network results in a frequency distribution of accumulated area along the stream channel, a mean sub-catchment area and total catchment area, an approach similar to that of McGlynn and Seibert (2003). Appropriate threshold area for the initiation of the stream network was determined by comparing the DEM-generated stream network with field information collected from 2 of the 8 catchments. At MSH this threshold appears to depend on topography of the catchment. For each catchment, valley bottom and hillslope areas are estimated using a simple 0 slope-based criterion. We define valley bottom by the break in slope of 8 • The valley bottom is distinguished from the riparian area, a permanently saturated area surrounding the stream channel. This criterion is based on detailed field observations in the Aw and Vc catchments. In the Aw catchment, a permanently saturated riparian area is very distinct in the lower part of the catchment where the stream had eut into the overlying sediments by up to ~ 1 m. This part of the stream is perennial. There is a distinct valley bottom area within the Aw catchment between the break in steep hillslopes and the permanently saturated riparian area. Upstream, flow becomes ephemeral. In this location, under wet conditions large variable saturated areas in the valley bottom connect 129 to the stream channel and perched water tables fonn adjacent to the steeper hillslopes. Because of the ephemeral nature of portions of the stream channel and the temporal variability of the saturated areas, for the MSH study, we make a distinction between the riparian area and the valley-bottom. This approach is consistent with changes in variable source are as that develop under wet conditions. 6.3.2. Hydrograph recession analysis of MRT Following the method of Vitvar et al. (2002), we calculate mean baseflow residence time for each catchment using hydrograph recession analysis. This method combines Wolock's (1997) approaeh to estimating contact time (Tc) as a funetion of topographie wetness index (À) with the use of hydrograph recession analysis to estimate an integrated catchment-scale storage coefficient (S) and hydraulic conductivity (K), S À Tc =-·e . (1) K Detennination of the mean eatehment topographie wetness index (À.) is described in section 6.3.1. To detennine catchment-scale storage and hydraulic eonductivity, this method relies on the analogy of a groundwater pump test in an unconfined aquifer, assuming a description of the eatchment as a dynamic groundwater reservoir. Baseflow recession is modelled using an exponential relationship, (2) where QI is discharge (m3s-l) at time t, Qo is the maximum baseflow, and a is the baseflow recession coefficient (day-l). Time to full reeession is estimated using an approximation of minimum baseflow and solving for t in equation (2). Catchment storage volume V m is then estimated as = Qo ·f Vm . (3) a A semi-Iog plot of recession (or drawdown in the case of a pump test) versus time allows us to detennine the hydrograph reeession (dH) for 1 log unit of time. Mean catchment transmissivity (T) is then given by, (4) 130 where Qm is mean baseflow discharge (or pumping rate) (Freeze and Cherry 1979, Vitvar et al. 2002). Using Darcy's law, and assuming an idealized symmetric geometry of flow into the stream channel from both sides of the catchment, the hydraulic gradient (1) of groundwater in the catchment and is given by 1 = L·Qo (5) A·T where L is the flowpath length, approximated as Y2 the catchment width (Vitvar et al. 2002). Vitvar et al. (2002) calculate hydraulic conductivity from the expression of mean groundwater saturated flow Qm (6) where A is the catchment area. Catchment-scale storativity, the volume of water released from storage per unit surface area per unit dec1ine hydraulic head (Freeze and Cherry 1979) is s= Vm (7) L·I·A Using the expressions of Vitvar et al., (2002), the ratio of SIK in equation (1) can be expressed as a direction function of variables determined from the hydrograph recession analysis (Qo, Qm, a) and the maximum flowpath length L, (8) 6.3.3. Baseflow hydrochemistry and contact time As part of the storm-based field study, baseflow was collected prior to storm events during spring-fall field seasons of 2001 and 2002. Water samples were collected in 250 ml HDPE bottles, rinsed with stream water 3 times prior to sample collection. The 250 ml samples were filtered using 0.45 !lm cellulose acetate syringe filters and separated for cation and anion analysis. Cation samples were acidified to 2-3 pH and refrigerated at 4 oC until ion ex change chromatography analysis was performed. Anion samples were 131 frozen until analysis. The remaining unfiltered sample was set aside for electrical conductivity (EC) measurements. EC measurements were made at 25 0 C, allowing samples to equilibrate to controlled laboratory room temperature. Samples were then analyzed for a suite of anions and cations, dissolved organic carbon (DOC) and electrical conductivity (EC). Ion exchange 'chromatography (Dionex DX-500) was performed in the Department of Geography at University of Toronto, Mississauga. DOC was measured using a Shimadzu TOC 5050 organic carbon analyzer. Hydrogeochemical solute concentrations typically vary as a function of contact time. The greater the contact (or residence) time, the larger the tracer concentration until sorne maximum steady-state value is attained (Trudgill et al. 1996, Burns et al. 1998). As a result, catchment baseflow willlikely exhibit a maximum tracer concentration due to long residence time in the catchment system (Trudgill et al. 1996). In these instances, pathlength can act as a surrogate measure for contact or residence time (Trudgill et al. 1996, Wolock et al. 1997). Major base cations (e.g. Ca2+, Mg2+, Na+, K+) are often monitored as non-conservative tracers (Ogunkoya and Jenkins 1993, Eisenbeer et al. 1994, Wolock et al. 1997, Burns et al. 1998, Rice and Hornberger 1998, Kendall et al. 1999) since their concentrations are dependent on residence time because of slow mineraI dissolution rates (Lasaga 1984). However, uptake of cations such as Ca2+, Mg2+ by biological processes (Likens et al. 1977) and short flowpaths may impact on solutes attaining a maximum concentration (Trudgill et al. 1996). In various studies si li ca has been used as a non-conservative tracer in the analysis of storm runoff (Kennedy et al. 1986, Wels et al. 1991a, Wels et al. 1991b, O'Brien and Hendershot 1993, Hinton et al. 1994, Buttle and Peters 1997). Contrastingly, it has also been used as a conservative tracer, replacing environmental isotopes (Hooper and Shoemaker 1986, Durand et al. 1993, Pionke et al. 1993) 6.3.4. Monitoring of catchment storm response Storm response from the 8 catchments was examined in detail for 5 storms under varying antecedent moisture conditions. James and Roulet (submitted manuscript to Journal of Hydrology 2005) herein referred to as James and Roulet (2005b) provided 132 detailed examination of hydrometrie storm response, isotopie hydrograph separation, and end-member-mixing-analysis for eaeh storm event aeross the 8 nested eatehments. 6.4. Results 6.4.1. Comparing catchment topography and landscape organization The 8 nested eatehments vary in total area from 7 to 147 ha (Table 6.1) and mean slope varies between 13.5° and 23.1". Median sub-eatehment area alters the ranking of the 8 eatchments. The Ef eatehment (91 ha) and the Lk eatehment (147 ha) have similar median sub-eatehment areas (10.9 and 10.2 ha, respeetively). Both the Pw (53 ha) and Se (38 ha) eatehments have smaller sub-eatehment areas (8.7 and 9.5 ha, respeetively). The elongated Yv eatehment (30 ha) has the largest median sub-eatehment area (12.3 ha). Interestingly, this eatehment is also ephemeral. The Ve (11 ha) and Sb (7 ha) eatehments, are similar in median sub-eatehment size (~3.7 ha). The Aw eatehment (11 ha) differs from the other two small eatehments, with a median sub-eatehment area of 6.8 ha. The Aw eatehment is a steep-sloped bowl-shaped amphitheatre in the upper reaehes of the eatehment system and is eharaeterized with a distinetly steeper mean slope (23.1°) than the other 7 eatehments. Valley bottom area, defined by slope < 8° is sizably larger for the Lk, Ef and Pw eatehments. Both the Pw and Ef eatehments have extensive valley-bottom areas that variably saturate, distinguishing them from the upstream eatehments. The Lk eatehment, a 2-3 order stream, reeeives water from both these eatehments. Table 6.1. Catehment topographie eharaeteristies. AlI values derived from the 1 m x 1 m resolution LIDAR-derived DEM. Valley- Total MedianSub- Mean Hillslope bottom Catchment area catchment slope area (H) area (V) (ha) area (ha) ( 0 ) (ha) (ha} Lk 147 10.9 15.9 38.7 108.3 Ef 91 10.2 16.2 21.5 69.5 Pw 53 8.7 15.7 15.3 32.7 Sc 38 9.5 18.2 7.3 30.7 Yv 30 12.3 19.5 5.2 24.8 Vc 11 3.8 14.7 2.4 8.6 Aw Il 6.8 23.1 1.2 9.8 Sb 7 3.7 13.5 1.8 5.2 133 6.4.2. Sub-catchment area accumulation and stream network structure Comparison of the stream network generated by the DEM analysis (Figure 6.1) and field observations suggests a channel initiation area that may be sensitive to topographie relief. In the high relief Aw catchment an initiation area of 2.5 ha is estimated while in the lower reliefVc catchment 5.0 ha is more consistent with field observations. Using a 2.5 ha stream initiation area, the cumulative frequency distribution of sub-catchment area for each catchment is shown in Figure 6.2. The four largest catchments (Lk, Ef, Pw, Sc) show cumulative frequency distributions (Figure 6.2, panel a) that are fairly similar up to the 70th percentile (or sub-catchment areas up to 20 ha). Above this, cumulative sub catchment area diverges, and approximately 30% of stream channel pixels account for the differences in total accumulated area. In panel (b), sub-catchment area is normalized by total catchment area. This panel shows that for the largest 4 catchments, a high percentage of cumulative sub-catchment area (74-85%) is collected by a small fraction of the stream channel (25-50%). This is particularly true for the two largest catchments (Lk and Et) with 85% and 77% of the total accumulated area collected by approximately 25% of the stream channel for the Lk and Ef catchments, respectively. For the Lk catchment, the 85% of the total accumulated area collected by approximately 25% of the stream channel are are as > 21 ha. For the Ef catchment, the 77% of the total accumulated area collected by 25% of the stream channel are areas > 18 ha. These similarities in collection ofupslope area may result in similar influence oftopography on hydrologic behaviour. Of the four remaining catchments, the elongated Yv (30 ha) catchment distinguishes itself with higher frequency of sub-catchment areas <17 ha than all other catchments (panel a) and a fairly uniform accumulation of are a, following al: 1 line in panel (b). The remaining catchments (Aw, Sb and Vc) faU above this 1:1 line. For these smaller catchments, the channel initiation area of 2.5 ha represents a much larger fraction of total accumulated area (Figure 6.2, panel b). 6.4.3. Topographie index The eight catchments show very similar normalized cumulative frequency distributions of À, the topographic wetness index (Figure 6.3, panel a). This indicates that all 8 catchments have proportionally similar areas of high and low À, are as that act 134 hydrologically similar. Low values of topographie index occur on steep hillslopes with small upslope areas (Figure 6.3, panel b). High values occur in areas of convergence and valley bottoms with large upslope are as and lower slopes (Figure 6.3, panel c). Mean values of,,- range from 3.15 to 3.46 (Table 6.3). As noted in previous studies (Wolock and McCabe 1995), we observed values of,,- to be very sensitive to DEM resolution. The mean value of,,- for the Aw cathment is 3.15, 6.05 and 6.96 for 1 m x 1 m, 5 m x 5 m, 10 m x 10 m DEM resolutions, respectively. Lower-resolution DEMs were derived by averaging and/or reducing the number of pixels included from the original lm x lm DEM. Most reported analyses of catchment terrain have been performed with the lower resolution DEMs (e.g. 10 m x 10 m or larger). Studies have shown that use of a multiple flow direction (mfd) algorithm instead of a single-flow algorithm (sfd) results in a distribution of topographie index with higher mean and lower variance and skew independent ofDEM resolution (Wolock and McCabe 1995). At this instant in time, we have not repeated this analysis using an mfd algorithm. (a) (b) 1000 ,------,------, 1.00 ,------,------::--::-:>1 --Lk --Lk •••• Ef •••• Et 'Il !il --Pw ~ --Pw é , .. , .. , Sc l'Il ...... Sc --Yv ~ --Yv ~ 0.75 •••• Aw •••• Aw E ,-",,------Vc ~ 100 -----"------' Vc .s:::. , '.', Sb 10 ,,,,, .. Sb ..... ~ c: (3 0.0 0.5 1.0 0.0 0.5 1.0 Normalized frequency Normalized frequency Figure 6.2. Comparison of cumulative frequency distributions of sub-catchment area for individual catchments: (a) sub-catchment area in units of hectares (ha), (b) normalized with respect to total catchment area. Median (50th percentile) sub-catchment area for individual catchments is indicated at the intersection of frequency distributions and the 0.5 verticalline. 135 25 -Lk •••• El (a) -Pw 20 -y,·······Se ...... "'Aw ·················Vc 18 ,...... 2 , " Sb c 15 16 (c) 14 i 10 12 ~10 8 :.-_~r-' 6 "" ...... "...... J 0.94 0.96 0.98 1.00 O~---+------~ \ Normalized frequency 2.5 , .. ", ...... ",,,, ...... ,,, ...... ,, ...... ,,";) 2.0 1.5 1.0 0.0 ..... 0.0 0.1 0.2 0.3 0.4 Figure 6.3. Frequency distributions of topographie index for each catchment. Nonnalized distributions across catchments are very similar. Low values of topographie index occur on steep hillslopes with small upslope areas. High values occur in areas of convergence and valley bottoms with large upslope areas and lower slopes. Distinction between nonnalized frequency distributions is observed in high and low values (2 insets). 6.4.4. Baseflow recession analysis of contact time Baseflow discharge records from 2001 and 2002 were used to detennine the recession coefficient a for each of the 8 nested catchements. The catchments at MSH exhibit a strong seasonal recession in discharge. Three of the 8 catchments (Sb, Yv, Vc) are ephemeral with flow disappearing mid-summer. Figure 6.4 illustrates the recession analysis for the Aw (11 ha) catchment. Maximum baseflow is estimated using observed values from late spring wet conditions. We approximate full recession using the lowest discharge observed under the driest conditions, late in the growing season. For the three ephemeral catchments, we prescribe a minimum baseflow of 0.01 Vs to indicate full recesslOn. 136 0.0100 .. ······· ...... -.· .. ·.. ·...... ·· .. ··· .... ·· .. ·...... ·...... ·.. · ...... ·...... , maximum baseflow ~ ....,.!!!. 0.0010 ,5 cr minimum boseflow O.OOOI.L..------' 150 160 170 180 190 200 210 220 230 Day of the Year (2002) Figure 6.4. Baseflow recession analysis for the Aw catchrnent. Horizontallines indicate maximum and minimum baseflow. Dashed Hnes indicate the recession predicted by the exponential re1ationship of equation (2) with recession coefficient u. For the Aw catchrnent, U = 0.173, corresponding to a full recession time of 16.3 days (Table 6.2). Table 6.2. Hydrograph recession analysis: empirical parameters for estimating MRT. Cl Qb max Q bmin Qmean L Catchment t. (days·l) (Ils) (Ils) (Ils) (m) (days) Lk 0.036 25.0 1.0 9.09 170 76.4 Ef 0.035 20.0 3.0 7.46 170 76.6 Pw 0.039 12.0 0.2 2.93 269 105.0 Sc 0.096 10.0 1.2 4.72 267 22.1 Yv 0.157 5.0 0.01* 0.80 207 39.6 Vc 0.058 1.4 0.01 * 0.28 154 85.2 Aw 0.173 2.5 0.15 0.89 177 16.3 Sb 0.432 0.3 0.01* 0.09 80 7.9 * Indicates ephemeral catchrnents; full recession baseflow set at O.OI1/s. Table 6.3. Hydrograph reeeSSlOn analysis: Integrated eatehrnent-seale parameters. Parameters inc1ude mean storage coefficient (S), mean hydraulic conductivity (K), mean topographie wetness index (À.) and estimated MRT. K MRT Catchment S À. (mmIhr) (days) Lk .66 30 3.41 27.2 Ef .40 19 3.38 26.5 Pw .06 3 3.42 23.9 Sc .043 11 3.31 4.5 Yv .006 1 3.28 10.2 Vc .021 1 3.46 35.2 Aw .01 3 3.15 4.3 Sb .011 3 3.36 5.7 137 Full recession times (tr) of the 8 catchments ranges from 8 to 105 days (Table 6.2, Figure 6.5a,b). In Figure 6.5a, there is a general increasing trend in full recession time with increasing topographic index. The Sb catchment is the smallest catchment, with a higher topographic index but a very short recession time, falling off this linear trend. This implies a disproportionately small storage capacity compared to the other catchments (Figure 6.5c). The Sc catchment is located immediately downstream of a perennial spring. The input from the spring ensures a higher minimum baseflow, which may reduce the estimates of full recession time for this catchment station. In panel 6.5b, there is a general trend of increasing time to full recession with median sub-catchment area for 4 of the 8 catchments. The three largest catchments (Pw, Efand Lk) and the Vc catchment exhibit larger recession times relative to the other 4 catchments. AlI 4 of these catchments have significant low-lying variably saturated areas regardless of their size. This may lead to disproportionately slow baseflow recession for their given median sub catchment areas. The three largest and most down stream catchments exhibit significantly larger maximum transient storage volumes Vrn (at least 3 times larger) than the remaining 5 smaller catchments (Figure 6.5c,d, Table 6.2). The mean storage eapaeity (unitless) and hydrologie eonduetivity of the individual eatchments are listed in Table 6.3. Mean hydraulic conductivities range between 0.8 to 30 mmIhr or 2.0xl0-7 to 8.5xlO-6 mis, a very large difference between eatchments. In the case of the Lk and Ef catehments, we use the maximum width to the stream network (not the width ofthe catehment) to estimate maximum flowpath length. The final two panels of Figure 6.5 (e, f) show the resulting mean residenee time in days. Panel 5e shows the exponential relationship between topographie wetness index À and contact time ofequation (1). Recessional analysis estimates MRT of the largest three catehments Lk, Ef and Pw to be 27.2, 26.5 and 23.9 days, respeetively. These eatehments have larger values of À. The V c catehment with a small median sub-catchment size has the largest value of À. Although the Ve catehment has small transient storage volume, K is also small giving the largest SIK ratio; it has the largest À at 3.46, resulting in the highest MRT (35.2 days). 138 MRTs estimated by baseflow reeession analysis are very sensitive to mean values of topographie index and as a result, DEM resolution. For instance, the Aw eatehment is eharaeterized by a mean topographie index of3.15 and 6.96 for DEMs ofresolution lm x 1 m, and 10 m x 10 m, respeetively. The eorresponding MRTs are 4.3 and 193.4 days, respeetively. (a) Tr (b) Tr ...... ,~~~~~-- ...... VI .Pw VI ·Pw ~ 100 ~100 Vc ~ ~ • Vc Ef Lk s:: 80 Ef s:: 80 0 •• 0 •• 'iij Lk 'iij VI \II 60 ~ 60 u u \II \II <- <- 40 Yv. Vv. :; :; 40 Sc .... • Sc ...... 0 20 Aw ...0 20 Aw • • • Sb • \II \II Sb' E 0 E 0 i= i= 3.1 3.2 3.3 3.4 3.5 3.6 0 5 10 15 In(a/tan~) median sub-catchment area (ha) (c)Vm (d) Vm 70 70 '", Lk 60 • • Lk E • ..E 60 " Ef. ~ 50 ~" 50 Ef • ..en 40 ~ 2'40'" 0 +- 0 1/) 30 Pw +- 1/) 30 E • • Pw 20 E E" 20 )( "E 10 .Sc ï, Aw Yv 10 .Sc ::lE" Sb • Vc ~ Aw 0 . • ...... MRT 50 J':)~RT 50 'V;' >- 40 !40 1 Lk E.. E += 30 +='" 30 u.. ..u "j s: ,ü " s: .. 20 ~ 20 .~ J " Yv ~ l- 1- HI c 10 li 10 Sb 3 Aw l Sc Sb .. Aw Sc p' ::lE f ! 1 ::lE 1 f ! 0 o·· ...... •....•...... _...... _-.. _.. 3.1 3.2 3.3 3.4 3.5 3.6 0 5 10 15 In(a/tan~) median sub-catchment area (ha) Figure 6.5. Catehment eharaeteristie parameters from reeessionai analysis. Inc1udes time to full reeession (a,b), maximum storage volume (e,d), and MRT (e,f) as a funetion of topographie wetness index and mean sub-eatehment area (ha). Uneertainty in MRT has been estimated based on 30% estimated error in flowpath length L (Table 6.2). 139 6.4.5. Geochemical evidence of contact time Previous analysis presented by James and Roulet (manuscript submitted to Water Resources Research, July 2005), herein referred to as James and Roulet (2005a), indicates that alkalinity (HC03) and electrical conductivity (EC) exhibit consistent mixing behaviour across all 8 catchments of the MSH watershed. At MSH, the perennial spring and baseflow from all catchments show progressively higher alkalinity and electrical conductivity (shown in Figure 6.6) through the spring-summer-fall season, inferring increasing mean contact time of water in the catchments with increasing time from spring snowmelt. Shanley et al. (2002) used alkalinity as a proxy for effective hydraulic conductivity for individual catchments, observing increasing concentrations (and thus increasing effective conductivity) with total catchment area. At MSH, tracer concentrations also suggest increasing effective conductivity with total catchment area with increasing similarity of baseflow concentrations for the large st 3 catchments. Concentrations from catchments located upstream of the perennial spring (Yv, Aw, Sb) are lower than that of the perennial spring or the larger catchments into which they drain (Sc, Pw, Ef and Lk). With the exception of the Vc and Yv catchments, electrical conductivity also increases with median sub-catchment size (Figure 6.6c). Figure 6.7 and Table 6.4 compare results of MRT recessional analysis and geochemical evidence of contact time. Generally the geochemistry and recessional analysis agree that the Lk, Ef, Pw and Vc catchments show the longest MRTs with rankings varying somewhat (Table 6.4). Geochemical analysis ranks the Ef catchment as the longest MRT and the Pw catchment the 4th longest. Aw and Sb catchments clearly offer the shortest MRT. The geochemistry of the Sc catchment (Figure 6.7) suggests a longer residence time (e.g. ~1O-20 days) than estimated by the recessional analysis (4.5 days). With the natural spring located immediately above the Sc catchment, the additional flow increases estimates of minimum baseflow at the Sc gauging station, reducing estimates oftime to full recession and MRT. 140 120 120 120.---- a 510",,5 1 al bl 61 6.510",,8 cl t~& O! oU: o SlIOnnlO • 100 ",0 00 ! 0 III 6EI 100 100 -S1Drml1 • ·5101'11\1 œGl'i -1 oP\r;- Ê Ê lA ~ t ~ 1 Ê .. U u 1 XSc 0 u 80: *it.~ • Ii ...... 9 • 8 ~ i~~·· IOSpring :g80 Il :g 80 vcG) 0 • l+VV + u ~ 60 i:;".& 1rl 0 i w ~ 0,\;) Yv UJ ~.::. AVe i ~ 6 D Sbrm!S "~ i "5,,",,8 40 ·"w 60 OC 60 C i ! " o S10nn 10 " ! ·Sb 0* "C + ·5'k1rmll + • • 1 +5,,",,1 20 l ...... ••••.. .J 40 40 150 170 190 210 230 250 270 290 310 o 25 50 75 100 125 150 0 5 10 15 Day of the Year (2002) Total catchment area (ha) Median sub-catchment area (ha) Figure 6.6. Seasonal and spatial trends in baseflow concentrations of EC (ilS/cm). Included as reference in panel (a) are concentrations from the perennial spring located above the Sc catchment gauging station (black open squares). Increasing concentrations at aIl stations suggest that mean residence time is increasing with time from mid-June (Day 170). Prior to this, concentrations show a mi Id decrease, suggesting that mean contact time de creas es after snowmelt and into the early growing season. Panels (b,c) illustrates baseflow concentrations as a function of total and median sub-catchment area (ha) prior to 5 individual storm events. Concentrations can be used as a proxy for effective catchment transmissivity. Self-similarity at larger catchment are as suggested in (b) disappears when plotted as a function of mean sub-catchment area (c). Table 6.4. Ranking of catchments by baseflow EC (ilS/cm) concentrations prior to individual storm events. Concentrations are used as a proxy of effective catchment transmissivity where a rank of 1 indicates highest baseflow concentration and most hydraulicaIly conductive catchment. Ranking of MRT from recessional analysis is included for comparison. Catchment MRT Storm 5 Storm 8 Storm 10 Storm 1 Storm Il Lk 2 2 3 1 2 2 Ef 3 1 1 3 1 1 Pw 4 3 4 NA 4 3 Sc 7 4 5 4 5 4 Yv 5 5 6 5 6 NA Vc 1 2 2 2 3 NA Aw 8 6 8 6 7 5 Sb 6 7 7 NA 8 NA 141 120 o SlormtO o 51onn8 :t( 1 A510rml :t( 100 : xS1orm5 ,.... 1 ::KS1ormll ::K 0 1 ::K 0 ~ ~ 80 1 ~ ~ Dl! :g 0 A U 1 LU 1 0 8 D (} 1 A 60 1 x O Lk Vc 40 I~~:!~ Yv Pw Ef 0 5 10 15 20 25 30 35 40 MRT (days) Figure 6.7. Correlation between baseflow EC (ilS/cm) prior to individual storms and baseflow recession analysis MRT. Positive correlation between EC and MRT supports use ofEC as a surrogate for MRT. Variability in this trend is due to changes in ranking of geochemistry, illustrated here and in Table 6.4 for baseflow prior to 5 different storm events. Uncertainties in rankings ofMRT from recessional analysis may derive from the simplified assumptions of catchment geometries. 6.4.6. Storm runoff generation Isotopic hydrograph separation for 4 of 5 individual storm events, described in detail in Chapter 4, provides varying spatial patterns in new water delivery across the 8 nested catchments (Table 6.5, Figure 6.8). Storm 5, a small, low intensity storm (Table 6.5a) delivered on wet conditions during early June 2002 resulted in high variation in total runoff per area (mm) across the catchments but spatially uniform new water delivery (~0.1 mm). 18 days later, storm 8, a 14 mm storm (Table 6.5b) delivered in 1.2 hrs, resulted in smaller amounts of runoff per unit area but greater amounts of new water than for storm 5. Analysis suggests higher new water inputs from the Pw, Ef and Lk catchments as compared to the other catchments. Storm 10 (17-Jul-02), a high intensity storm delivers 36 mm in 2.4 hrs on dry conditions. New water inputs from the Pw, Ef and Lk catchments are distinctly higher than the remaining 5 catchments. Storm 1 (16- Jun-Ol), a 25 mm event of average intensity on dry conditions, shows a topographic scaling of both total runoff and new water inputs, inputs increasing with total catchment area. 142 Table 6.5. Characteristics of 4 storm events and new water delivery. a) 5.5 mm storm (5)delivered in 16 hrs (average intensity of 0.08 mm/15 min.) Total NewWater New/Old Catchment Area Runoff ±L\ Runoff Contribution ±E Water (ha) (mm) (mmt Ratio (mm) (mm)b Contributions (%2 {%2 Lk 147 3.24 0.82 59 0.09 0.05 3%/97% EF 91 PW 48 SC 38 2.02 1.23 37 0.05 0.05 3%/97% YV 30 1.81 0.05 33 0.07 0.03 4%/96% VC* 11 1.63 0.43 30 0.10 0.04 6%/94% AW 11 3.40 0.65 62 0.05 0.05 2%/98% SB 7 1.11 0.31 20 0.09 0.03 8%/92% EF, PW have incomplete data; * VC has a second peak at 24.75 hrs. b) 14 mm storm (8) delivered in 1.2 hrs (average intensity of2.0 mm/15 min.) Total NewWater New/Old Catchment Area Runoff ±L\ Runoff Contribution ±E Water (ha) (mm) (mm)a Ratio (mm) (mm)b Contributions {%2 {%2 Lk 147 2.17 0.55 16 0.22 0.10 10% / 90% EF 91 2.08 1.02 15 0.19 0.13 9%/91% PW 48 1.85 0.81 13 0.28 0.14 15%/85% SC 38 0.34 0.21 2 0.07 0.05 21%/79% YV 30 1.38 0.04 10 0.19 0.04 14%/86% VC 11 0.39 0.10 3 0.11 0.03 27%/73 AW 11 1.64 0.32 12 0.11 0.06 7%/93% SB 7 0.52 0.14 4 0.12 0.04 23%/77% c) 38 mm storm (10) delivered in 2.4 hrs (average intensity of 3.8 mm/15 min.) Total NewWater New/Old Catchment Area Runoff ±L\ Runoff Contribution ±E Water (ha) (mm) (mmy Ratio (mm) (mm)b Contributions (%2 {%} Lk 147 0.9 0.2 2.4 0.4 0.09 39% / 61% Ef 91 1.0 0.5 2.6 0.3 0.16 31% / 69% Pw 48 1.1 0.5 2.8 0.5 0.21 46%/54% Sc 38 0.2 0.1 0.6 0.1 0.05 38%/62% Yv 30 0.4 0.01 1.2 0.2 0.01 41% / 59% Vc Il 0.2 0.1 0.6 0.1 0.03 44%/56% Aw 11 0.3 0.1 0.8 0.1 0.03 44%/55% Sb 7 0.1 0.02 0.2 0.04 0.01 76%/24% 143 d) 25 mm storm (1) delivered in 10.3 hrs (average intensity of 0.6 mm/15 min.) Total NewWater New/Old Catchrnent Area Runoff ±11 Runoff Contribution ±e Water (ha) (mm) (mmt Ratio (mm) (mm)b Contributions {%} {%} Lk 147 0.5 0.1 2.1 0.1 0.03 24%/76% EF~ 91 PW~ 48 SC 38 0.3 0.2 1.1 0.1 0.04 25%/75% YV 30 0.1 0.004 0.5 0.04 0.003 33% 67% VC 11 AW 11 0.1 0.01 0.2 0.02 0.004 34%/66% SB 7 0 nia 0 0 nia 0%/0% EF, PW and VC have incomplete data; a Error associated with uncertainty in power model coefficients; b Error propagated from runoff and isotopic compositions; Error associated with isotopic compositions only, was 1 to 6 % (Genereux, 1998). 6.5. Discussion 6.5.1. Do these catchments differ in their topography and landscape organization? The 8 catchments at MSH do exhibit differences in their topography and landscape organization that may contribute to spatial patterns and scaling in runoff generation. Cumulative distributions of sub-catchment area suggest several groupings of similar network structure. The largest 4 catchments (Lk, Ef, Pw and Sc) have a similar shape to their cumulative frequency distributions of sub-catchment area and large median sub catchment are as (8.7-10.9 ha). In particular, the Lk and Ef catchments are very similar in these measures of organization with mean sub-catchment sizes of 10.9 ha and 10.2 ha, respectively. All4 ofthese catchments collect water from a stream network with multiple channels, sorne of which are ephemeral. The elongated Yv catchment is fairly unique in its organizational structure with the highest median sub-catchment area and uniform accumulation of sub-catchment area along 1 long channel. Finally, the 3 small catchments (Vc, Aw and Sb) have smaller median sub-catchment sizes (3.7 to 6.8 ha) and accumulate their area along a single channel mostly from small sub-catchment areas, resulting in a flatter cumulative distribution (Figure 6.2a). McGlynn et al. (2003) examined the organizational structure of 4 catchments (2.6, 17, 80 and 280 ha) in the Maimai Valley, New Zealand with median sub-catchment areas of 1.2, 8.2, 3.9 and 3.2 ha, respectively. In comparison to these catchments dominated by small headwater sub-catchments, the larger catchments at MSH show higher median 144 Storm 5 (5.5 mm) 0.8 ,.•...... 4.0,....------.., ,..... 3.5 3'- ~ 30 :;:- 2.5 ~ 2.0 ~ 1.5 -a 1.0 ~ 0.5 nia nia f" 0.0 UJ... __J...LLLL.L..CLL.JL..U LI< EfPwScYvVcA.Sb LI< Ef Pw Sc Yv V, Aw Sb Storm 8 (14 mm) Wet ~.O r······ ..·············································· ...... , AMC '""" 3.5 ~ 3.0 :;::' 2.5 10'491. 151. g 2.0 2 1.5 -a 1.0 ~ 0.5 f- 0.0 UJ.....I...1-.LLJ...LLLLJ...JLL.JL..U Lk Ef Pw Sc VII VC Aw Sb LIc: Ef Pw Sc Vv Ve Aw Sb Storm 1 (25 mm) 0.8 0.7 Ê 0.6 .t 0.5 r... 0) Dry -+- 0.4 CI :l 0.3 AMC :l 0.2 0) Z 0.1 ./a 0.0 LI< EfPw5cYvVcA.Sb Storm 11 (7 mm) a ..t.~j":kalecrfba"'".,..II;III_. 005 0.12 H~' __UH~"H_' _____ Ê 12~ Ê 004 1 .5 0.10 .5 1 .... r...... 0.08 0) 0.03 0 <- c 15~ :> 0.06 ~ r... -a 0.04 n~ .,:l .m li 1 <- z ~ 0.02 :: A.L~jJ 0.00 LI< EfPw 5< Yv Vc Alli Sb l LI<~Jïl Ef Pw Sc Yv VC Aw Sb Figure 6.8. Total runoff (mm) and new water contributions (mm and %) across all catchments for the 5 storm events. Error bars on new water contributions result from uncertainty in isotopie compositions of new and old waters and uncertainty in instantaneous runoff measurements. New water is expressed as a percent of total runoff in the left hand panel. 145 0.8 ...... -.-...... -.. -...... -.- ...... -...... -...... 0.8 r-·---·...... · .. ··· ..· .... ------··· .. ··---...... ·-- ..· .. j o Storm 8 (a) ! 0 Storm 10 (b) 0.7 o Storm 10 0.7 1 1 ! 0 Storm 8 i ; i A Storm 1 Ê 0.6 Ê 0.6 1  Storm 1 1 E : .§, 0.5 '-' 0.5 ! t.. ~ of- 0 0.4 ~ 0.4 i :t :t :t 0.3 :t 0.3 ~ f ~ Z Z 0.2 0.2 i Aw i ~ 1 1- 0.1 0.1 Pw i Lk ~ Ef Vc 0.0 0.0 i 51 Il : 1 40 60 80 100 o 5 10 15 20 25 30 35 40 Baseflow EC (ilS/cm) MRT (days) 0.8 0.8 r----· .. ---- .... ---·-..· .. ·- .. ·· (c) Pw o Storm 10 1 0 Storm 10 (d)l 0.7 0.7 o Storm 8 o Storm 8 0.6 0.6 j  Storm 1 '""'  Storm 1 '""'E E Ef E Pw E 0.5 '-' 0.5 1 Ef Lk '-' t.. t.. ~ ~ 0.4 0.4 1 of- t 0 :t :t 0.3 1 Yv :t 0.3 1 :t ~ ~ Z Aw Z 0.2 0.2 ! Sb ~YV ! Sc ai ~ Sc i 0.1 0.1 i t Q Vc ~ il 0.0 1 il i 0.0 .~Vc ...... ~l...... _...... o 25 50 75 100 125 150 0 5 10 15 Total catch ment area Median sub-catchment area Figure 6.9. Scaling of new water (mm) inputs with measures or proxies of catchment hydrologie function: (a) pre-storm basetlow electrical conductivity (uS/cm), (b) MRT, (c) total and (d) median sub-catchment areas. For storms 8 and 10, Lk, Ef and Pw catchments show distinctly larger new water inputs (mm) than the remaining 5 catchments. 146 catchment area, indicating higher frequency of large sub-catchment areas. A second major difference between the organizational structure at MSH and the Maimai Valley is the channel initiation threshold (2.5 versus 0.5 ha, respectively). At MSH, the 8 catchments vary significantly in the total area of valley bottom (Table 6.2). The three largest catchments (Lk, Ef and Pw ) show valley-bottom areas that are significant larger than the smaller 5 catchments (2-5 times larger than the Sc catchment). Values ofmean topographie index (MT!) for these 8 catchments are very similar ranging from 3.15 to 3.46 and show the sensitivity to DEM resolution observed by previous work (Wolock and McCabe 1995). Using a 10 m x 10 m DEM of the MSH catchments, MTI values range between to 6.96 to 7.82. Independent of magnitude, the small range ofMTI values for the eight catchments is similar to that observed of other nested catchment systems for which scaling of hydrologie behaviour has been examined. Shaman (2004) observe a range ofMTI of6.49 to 6.90 for Il catchments ranging in size from 1.64 to 176 km2 in the Neversink River watershed, Catskill Mountains of New York. At Sleepers River Watershed, Vermont, Shanley et al. (2002) presented a nested study of 4 catchments ranging in size from 41 to Il,125 ha, with MTIs between 6.08 to 6.35. Furthermore, the small range of MT! values do result in distinct differences in MRT for individual catchments. 6.5.2. Does MRT scale with median sub-catchment area? McGlynn et al. (2003) suggest that median sub-catchment area is a more relevant measure of catchment hydrologie function than total catchment area. Using a tritium based technique to quantify MRT, they observe a positive relationship between MRT and median sub-catchment area for the 4 catchments in the Maimai Valley. In this study we use stream hydrochemistry and baseflow recession analysis to approximate MRT and its relative ranking between catchments. At MSH, baseflow electrical conductivity suggests a positive relationship with median sub-catchment area with two catchments (Yv, and Vc) as outliers to this trend (Figure 6.6c). Similarly, baseflow recession analysis MRT values show an increase in MRT from the small Sb and Aw catchments to the larger, Pw, Efand Lk catchments (Figure 6.5f). For the Aw and Sb catchments, the small maximum storage volumes and short MRT, although sensitive to MTI, are consistent with the ephemeral 147 nature of three of the catchments and upper portions of the stream channels. Pw, Ef and Lk catchments show a strong difference with the remaining catchments; MRT values are distinctly higher. Uncertainties in rankings of MRT will result from the simplified assumptions of catchment geometries in recessional analysis, illustrated here by a 30% uncertainty on flowpath length L. The Sc catchment may fit into a positive trend with median sub-catchment area if, as suggested earlier, the baseflow recession analysis MRT is under-estimated due to flow from the perennial spring. Concentrations ofEC appear to support this suggestion. However, as evidenced by both methods, the Vc and Yv catchments c1early do not fit this general positive trend. Both hydrochemistry and recessional analysis indicate the small V c catchment has one of the longest MRT and yet a small median sub-catchment area. The elongated Yv catchment has the largest median subcatchment area and a fairly short MRT. It also has a relatively high mean slope and the second sma11est value of MTI (Figure 6.5a). This could help explain the short MRT in contrast to the large median sub-catchment area. 6.5.3. Does storm response scale with MRT, total and/or median-subcatchment area? The 4 storm events described in section 6.4.6 show variable scaling of response (Figures 6.8 and 6.9). During storm 5, one of the sma11est catchments (Aw) delivers the largest amount of total runoff per unit area (mm) and new water input (mm) is uniform across a11 8 catchments (Table 6.5a, Figure 6.8). During storm 8, high total runoff (mm) from the Aw and Yv catchments is more similar to that of the larger catchments (Lk, Ef and Pw) (Figure 6.8). That smaller catchments can produce similar amounts ofwater per unit area as larger catchments under wet conditions has been observed at the Maimai Valley (McGlynn et al. 2004). The Aw and Yv catchments distinguish themselves with the highest mean slopes suggesting that under these wet conditions, the steeper topography of these two catchments results in stronger hydraulic gradients delivering more water per unit area. During storm 10, we observe distinctly larger total runoff and new water inputs from the Pw, Ef, and Lk catchments and compared to the remaining 5 catchments. This suggests a change in dominant delivery mechanisms for the Pw, Ef and 148 Lk catchments, catchments that distinguish themselves from the remaining 5 catchments in landscape organization. Figure 6.9 presents scaling of storm new water inputs with measures or proxies of hydrologic function: a) baseflow electrical conductivity; b) baseflow recession analysis MRT; c) total catchment area and d) median sub-catchment area. It is the largest three catchments, (Lk, Ef and Pw) that distinguish themselves as higher transmissivity catchments, exhibiting higher EC baseflow concentrations, higher MRT and higher total runoff and new water per unit area inputs. These results depend on antecedent moi sture and storm conditions. Greatest differences in storm response are observed for the high intensity storm on dry antecedent moi sture conditions (Storm 10). Under these conditions, shallow subsurface flow is inferred as a dominant runoffmechanism (Chapter 4). A positive relationship for new water inputs during storms 8, 10 and 1 is suggested for MRT, total and median sub-catchment are as (Figures 6.9b,c,d). In the case of MRT (Figure 6.9b), used here as a proxy of effective catchment transmissivity, the Vc catchment does not fit into a positive scaling trend. Both solute concentrations and recessional analysis indicate that water flowing from the V c catchment has the longest mean residence time (~35 days) of aIl 8 catchments. This catchment has the largest mean topographic index (3.46) and one of the longe st times to full recession (Figure 6.5b). The storm response of this small catchment with small storage volume (Figure 6.5d) suggests that in contrast to the other catchments, it is strongly controlled by a small, localized perched water system. This could account for the high MRT and high baseflow electrical conductivity and yet small new water inputs. In the case of median sub-catchment area (Figure 6.9d), it is the Yv catchment that presents a notable exception to a positive scaling relationship. In comparison to the MSH results, Shanley et al. (2002) observed summer storm new water inputs to increase with catchment MTI and baseflow alkalinity. Within the 4 catchments in the Maimai Valley, McGlynn et al. (2004) observed new water inputs to be unrelated related to catchment size. 149 6.6 Conclusions The analyses presented here make a strong case for the influence of topography and landscape organization on scaling of runoff generation at the catchment scale. The scaling results from the MSH catchments are dominated by the contrast in behaviour of the largest three catchments (Lk, Ef and Pw) from the remaining 5 smaller catchments. These three catchments distinguish themselves topographically by larger mean sub catchment areas and distinctly longer MRTs. They exhibit significantly larger valley bottom areas in which variably saturated are as can form with the help of the shallow fragipan located in valley bottoms. In this study of 5 storms events, strong differences in storm response for the 8 catchments occur for the 2 larger, more intense storms. Scaling relationships do change with varying moisture and storm conditions. Acknowledgements AJ would like to thank Raissa Marks, Sheena Pappas, Catie Burlando and Nathan Deustch for their tireless assistance in the field and the staff of the Mont Saint-Hilaire nature reserve for their logistical support. The authors would like to thank Dr. Martin Lechowicz and the ECONET project for funding ofthe LIDAR datas et and Benoit Hamel for data processing and DEM creation. Thanks aiso to Dr. Brian Branfireun, Carl Mitchell and fellow students for ion ex change chromatograph analysis at University of Toronto Mississauga. This work was funded in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), a McGill-McConnell Fellowship, a McGill Graduate Studies Fellowship and the McGill Global Environment and Climate 3 Change Centre (GEC ). 150 7. Summary and conclusions This study examines runoff generation under varying AMCs from multiple small catchments, addressing how runoff generation changes in space and time. Empirical studies such as this one are of value in their ability to test our understanding of the spatial and temporal variability of hydrologic response. Collection of spatial datasets in hydrological sciences has been fuelled by their usefulness in distinguishing between "behavioural" and "non-behavioural" hydrological models (Grayson et al. 2002). They are one attempt to recognize and tackle the reality of equifinality, where many models may be consistent with observational data. In this study, observations of runoff generation extend to multiple catchments. The spatial extent of this study challenges the application of methods previously used for analysis of smaller study areas. Application of end-member-mixing-analysis (EMMA) in Chapters 3 and 4 relies on assumptions oflinear-mixing of source waters that are constant in time and space as defined by conservative solutes. Seasonal-based analyses (Chapter 3) indicate that, at MSH, two solutes show consistent trends of mixing for the 8 catchments: electrical conductivity and alkalinity. These two solutes are subsequently used in individual storm event analysis of source waters (Chapter 4). Other solutes, such 2 2 as Mg + and Ca +, show variation in mixing ratios from catchment to catchment, providing spatial information on weathering processes. MSH stream water exhibits a strong seasonal trend in solute concentrations that appear to relate to mixing of water of varying residence times. End-members definitions also show this strong seasonal trend emphasizing the need for a priori updating of end-members for the application of EMMA to individual storm-events. Results suggest cautious application of EMMA for multi catchment studies. In Chapter 4, observations of runoff generation from the 8 small catchments show the strong nonlinear change in total runoff for small changes in AMCs, consistent with the hypothesis of variable states of catchment wetness. These observations support similar evidence from varying types of catchments around the world. Wet conditions exhibit shallow water tables and variably saturated areas directly connected to the stream channel, allowing for larger total runoff, often independent of storm size. At MSH, 151 variably saturated areas are often fonned by perched water tables due to the presence of a shallow low penneability fragipan in the valley-bottoms. Under these wet conditions, water delivered to the stream channel is a mixture of groundwater, perched water and, depending on the size and intensity of the event, throughfall. Large, high intensity stonns on dry conditions show evidence of rapid de1ivery of throughfall to the stream channel by shallow subsurface pathways. This is supported by large fractions of new water (up to 76% of total runoff), high correlation of DOC and 8180 in stream water concentrations and a 1-D EMMA mixing-space with bounding end-members of groundwater and throughfall. The ephemeral nature of sorne of the catchments at MSH emphasizes the depletion of storage under these dry conditions. Scaling of total runoff with catchment size shows a nonlinear relationship, reminiscent of the representative elementary area concept of Wood et al. (1995), even at these relatively small catchment sizes. For these 5 stonn events at MSH, scaling patterns for new water delivery with catchment size appear only for dry conditions. Analyses of spatial surveys within the Aw and V c catchments presented in Chapter 5 indicate a spatial organization or connectivity of shallow soil moi sture that is relatively constant in time. Under both wet and dry conditions, patterns indicate valley bottoms that are wetter and more geostatistically connected than hillslopes. This is the case even under very dry conditions when no active flow is present. These findings differ from observations on the rangeland catchments of Australia and suggest that critical infonnation on the state of wetness or macrostate at MSH cannot be identified by the connectivity of shallow soil moisture. The patterns at MSH and detailed stonn-based temporal observations of soil moi sture and water table elevations suggest that lateral redistribution of water is a dominant process under all antecedent conditions. This is consistent with the steep slopes and the variable lower boundaries impeding vertical movement such as the shallow fragipan in the valley bottom and shallow bedrock on hillslopes. Results suggest that it is lateral subsurface flow in combination with extensive connection of variable saturated are as to the stream channel that controls hydrologic response in this forested catchment system As a result, the definition of hydrologic connectivity as varying connection of different parts of the landscape via active lateral 152 flow (Stieglitz et al. 2003) appears to be a more relevant definition for the MSH catchment The 8 catchments at MSH exhibit differences in topography and landscape organization that are reflected in their hydrological behaviour. Distributions of topographic wetness index for each catchment are very similar, possibly a result of the nested nature of these catchments. Mean values of topographic wetness index calculated with a DEM of the same resolution (10 m x 10 m) are comparable to those reported at other high relief sites in the North-Eastern USA (e.g. Neversink River watershed, Catskill Mountains of New York and Sleepers River Watershed, Vermont). The three large st catchments at MSH (Pw, Ef and Lk) distinguish themselves as higher transmissivity catchments as compared to the remaining 5 catchments. They exhibit higher EC baseflow concentrations and longer estimates of MRT from baseflow recessional analysis. They also exhibit larger valley-bottom areas. Storm response from these 3 catchments during 2 storm events shows higher total runoff and new water inputs, resulting in sorne positive scaling patterns. However, these results vary with storm and antecedent moi sture conditions. The work presented here contributes to an increasingly strong collection of empirical studies on scaling of runoff generation from multiple catchments. How hydrological response, and more specifically runoff generation, change across multiple small catchments is complex and the MSH study provides sorne insights specific to forested catchments. This study and others like it provide an important connection between hillslope scale process description and small catchment and watershed scale response. Sivaplan (2003) suggests the parallel development of both upward (working from the scale of hillslopes and small catchments) and downward (working from the larger watershed scale) approaches as a critical way forward. This dual approach, of which the MSH study is a part, attempts to address the challenges in describing the complexity of hillslope scale processes, how they aggregate into catchment and watershed response and how to reconcile observational data and modeling efforts at multiple scales. 153 8. References Agriculture and Agri-Food Canada, (Ed). (1998), The Canadian System of Sail Classification, NRC Research Press. Bareblatt, G. 1. (2003), Scaling, Cambridge University Press, Cambridge. Bazemore, D. E., K. N. Eshleman, and K. J. Hollenbeck (1994), The role of soil water in stormflow generation in a forested headwater catchment: synthesis of natural tracer and hydrometric evidence, J. Hydrol., 162, 47-75. Bear, J. (1972), Dynamics ofFluids in Porous Media, New York: American Elsevier. Beven, K., and P. Germann (1982), Macropores and water flow in soils, Water Resour. Res., 18 (5), 1311-1325. Beven, K. J., and M. J. Kirkby (1979), A physically based, variable contributing area mode1 ofbasin hydrology, Hydrol. Sci. Bull., 24 (1), 43-68. Beven, K. J., E. F. Wood, and M. Sivapalan (1988), On hydrological heterogeneity - catchment morphology and catchment response, J. Hydra!., 100,353-375. Birkeland, P. W. (1999), Soils and Geomorphology, Oxford University Press, New York. Biron, P. M., Roy, A.G., F. Courschesne, W. H. Hendershot, B. Côté, and J. Fyles (1999), The effects of antecedent moisture conditions on the relationship of hydrology and hydrochemistry in a small forested watershed, Hydra!. Proc., 13, 1541-1555. Bloschl, G., R. B. Grayson, and M. Sivapalan (1995), On the representative elementary area (REA) concept and its utility for distributed rainfall-runoff modelling, Hydrol. Proc., 9, 313-330. Bloschl, G., and M. Sivapalan (1995), Scale issues in hydrological modelling: a review, Hydrol. Proc., 9, 251-290. Blyth, E. M., J. Finch, M. Robinson, P. Rosier (2004), Can soil moisture be mapped onto the terrain?, Hydrology and Earth System Sciences, 8 (5),923-930. Bonell, M. (1998), Selected challenges in runoff generation research in forests from the hillslope to headwater drainage basin scale, J. Am. Wat. Resour. Assac., 34 (4), 765- 785. 154 Brown, V. A, J. J. McDonnell, D. A Burns, and C. Kendall (1999), The role of event water, a rapid shallow flow component and catchment size in summer stormflow, J. Hydro/., 217,171-190. Burns, D. A, R. P. Hooper, J. J. McDonnell, J. E. Freer, K. C., and K. Beven (1998), Base cation concentrations in subsurface flow from a forested hillslope: the role of flushing frequency, Water Resour. Res., 34 (12),3535-3544. Burns, D. (2005), What do hydrologists mean when they use the term flushing?, Hydro/. Proe., 19, 1325-1327, DOI:10.1002/hyp.5860. Buttle, 1. M., and D. L. Peters (1997), Inferring hydrological processes in a temperate basin using isotopie and geochemical hydrograph separation: a re-evaluation, Hydro/. Proe., Il, 557-573. Buttle, 1. M. (1998), Fundamentals of small catchment hydrology, Isotope Tracers in Catehment Hydr%gy, C. Kendall and J. J. McDonnell, (Eds.), 1-49, Elsevier Science. Buttle, 1. M., and D. S. Turcotte (1999), Runoff processes on a forested slope on the Canadian Shie1d, Nordie Hydr%gy, 30, 1-20. Buttle, 1., 1. Creed, and J. Pomeroy (2000), Advances in Canadian forest hydrology, 1995-1998, Hydro/. Proe., 14, 1551-1578. Buttle, J. M., S. W. Lister, and A. R. Hill (2001), Controls on runoff components on a forested slope and implications for N transport, Hydro/. Proe., 15 (DOl: 10.1 002/hyp.450), 1065-1070. Buttle, J. M .. , P. J. Dillon, and G. R. Eerkes (2004) Hydrologie coupling of slopes, riparian zones and streams: an example from the Canadian Shield, J. Hydro/., 287, 161-177. Buttle, J. M., 1. F. Creed, and R. D. Moore (2005), Advances in Canadian forest hydrology, 1999-2003, Hydro/. Proe., 19, 169-200. Christophersen, N., C. Neal, and R. P. Hooper (1990), Modeling stream-water chemistry as a mixture of soil water endmembers, a step towards second generation acidification models, J. Hydro/., 116, 307-320. Christophersen, N., and R. P. Hooper (1992), Multivariate analysis of stream water chemical data: the use of principal components analysis for the end-member mixing problem, Water Resour. Res., 28 (1),99-107. 155 Cirmo, C. P., and J. J. McDonnell (1997), Linking the hydrologic and biogeochemical controls of nitrogen transport in near-stream zones of temperate-forested catchments: a review, J. Hydrol., 199, 88-120. Creed, 1. F., and L. E. Band (1998), Exploring functional similarity in the export of nitrate-N from forested catchments: A mechanistic modeling approach, Water Resour. Res., 34 (11),3079-3093. Dawson, T. E. (1993), Hydraulic lift and water-use by plants - implications for water balance, performance and plant-plant interactions, Oecologia, 95, 565-574. Dingman, S. L. (1994), Physical Hydrology, Prentice Hall, Englewood Cliffs, NJ. Dooge, J. C. 1. (1997), Scale problems in hydrology, Reflections on Hydrology, N. Buras, (Ed). 85-143, American Geophysical Union, Washington, D.C .. Dooge, J. C. 1. (2004), Bringing it all together, Hydrology and Earth System Sciences Discussions, 1 (SRef-ID: 1812-2116/hessd/2004-1-41), 41-73. Dunne, T. (1978), Field studies ofhillslope flow processes, Hillslope Hydrology,lrst, M. J. Kirkby, (Ed). 227-294, John Wiley & Sons Ltd .. Durand, P., M. Neal, and C. Neal (1993), Variations in stable oxygen isotope and solute concentrations in small submediterranean montane streams, J. Hydrol., 144, 283-290. Eisenbeer, H., A. West, and M. Bonell (1994), Hydrologic pathways and stormflow hydrochemistryat South Creek, northeast Queensland, J. Hydrol., 162, 1-2l. Environment Canada (2005), The National Climate Data and Information Archive, Canadian Climate Normals, 1971-2000, http://www.climate.weatheroffice.ec.gc.ca. Fan, J. (1996), Effects of subbasin size on stream chemistry in the Neversink river watershed, New York, Ph.D., University of Kansas, Kansas City. Freer, J., J. McDonnell, K. J. Beven, D. Brammer, D. Burns, R. P. Hooper, and C. Kendall (1997), Topographic controls on subsurface storm flow at the hillslope scale for two hydrologically distinct small catchments, Hydral. Proe., Il (1347-1352). Freeze, R. A., and J. A. Cherry (1979), Groundwater, Prentice Hall Inc., Englewood Cliffs. Genereux, D. (1998), Quantifying uncertainty in tracer-based hydrograph separations, Water Resour. Res., 34 (4), 915-919. 156 Genereux, D. P., and R. P. Hooper (1998), Oxygen and hydrogen isotopes in rainfall runoff studies, Isotope Tracers In Catchment Hydrology,lrst, C. Kendall and J. J. McDonnell, (Eds.), 319-346, Elsevier. Gibson, J. J., J. S. Price, R. Aravena, D. F. Fitzgerald, and D. Maloney (2000), Runoff generation in a hypermaritime bog-forest upland, Hydrol. Proc., 14,2711-2730. Goovaerts, P. (1997), Geostatistics for Natural Resources Evaluation, Oxford University Press. Grayson, R. B., A W. Western, F. H. S. Chiew, and G. Bloschl (1997), Preferred states in spatial moisture patterns: Local and nonlocal controls, Water Resour. Res., 33 (12), 2897-2908. Grayson, R. B. (2000), Spatial Processes, Organization and Patterns, Spatial Patterns in Catchment Hydrology, G. B. R. Grayson, (Ed). 404, Cambridge University Press, Cambridge. Grayson, R. B., G. Bloschl, AW. Western, T.A McMahon (2002), Advances in the use of observed spatial patterns of catchment hydrological response, Advances in Water Resources, 25, 1313-1334. Gyn, Q. H. (1968), The role of moisture and temperature cycles in soil movement on Mont Saint-Hilaire, M.Sc., McGill University, Montreal. Hewlett, J. D., and A. R. Hibbert (1967), Factors affecting the response of small watersheds to precipitation in humid areas, Forest Hydrology, S. W.E. and H. W. Lull, (Eds.), 275-290, Pergamon, New York. Hinton, M. J., S. L. Schiff, and M. C. English (1994), Examining the contributions of glacial till water to storm runoff using two- and three-component hydrograph separations, Water Resour. Res., 30 (4), 983-993. Hooper, R. P., and C. A Shoemaker (1986), A comparison of chemical and isotopie hydrograph separation, Water Resour. Res., 22, 1444-1454. Hooper, R. P., N. Christophersen, and N. E. Peters (1990), Modelling streamwater chemistry as a mixture of soi1water end-members - an application to the Panola mountain catchment, Georgia, U.S.A, J. Hydrol., 116, 321-343. Hooper, R. P. (2001), Applying the scientific method to small catchment studies: a review of the Panola Mountain experience, Hydra!. Proc., 15,2039-2050. 157 Hooper, R. P. (2003), Diagnostic tools for mixing models of stream water chemistry, Water Resour. Res., 39 (3), 1055, doi:lO.l029/2002WROOI528. Homberger, G. M., K. E. Bencala, and D. M. McKnight (1994), Hydrological controls on dissolved organic carbon during snowmelt in the Snake River near Montezuma, Colorado, Biogeoehem., 25, 147-165 Horvâth, L., and R. A. Gault (1990), The mineralogy of Mont Saint-Hilaire, Quebec, The Mineralogieal Record, 21,284-359. James, A. L., and N. T. Roulet (2005a), Modeling stream water contributions across scale: application of end-member-mixing-analysis (EMMA) in a nested forested watershed, submitted to Water Resourees Researeh. James, A. L., and N. T. Roulet (2005b), Analysing the effects of varying antedecent moi sture conditions on runoff generation in small catchments, to be submitted to Journal ofHydrology James, A. L., and N. T. Roulet (2005c), Investigating hydrologie connectivity and its association with threshold change in runoff response in a temperate forested watershed, submitted to Hydrologieal Proeesses. Jenson, S. K., and J. O. Domingue (1988), Extracting topographie structure from digital elevation data for geographic infromation system analysis, Photogrammetrie Engineering and Remote Sensing, 54 (11), 1593-1600. Jones, J. A. A. (1997a), Global Hydrology: proeesses, resources and environmental management, Addison Wesley Longman Ltd., Edinburg Gate, Harlow. Jones, J. A. A. (1997b), Pipeflow contributing areas and runoff response, Hydrol. Proe., 11,35-41. Joreskog, K. G., J. E. Klovan, and R. A. Reyment (1976), Geologieal Factor Analysis. Methods in Geomathematics 1, Elsevier, Amsterdam. Katsuyama, M., N. Ohte, and S. Kobashi (2001), A three-component end-member analysis of streamwater hydrochemistry in a small Japanese forested headwater catchment, Hydrol. Proe., 15, 249-260. Kendall, C. (1993), Impact of isotopie heterogeneity in shallow systems on modeling of stormflow generation, Ph.D., University of Maryland. 158 Kendall, K. A., J. R Shanley, and J. J. McDonnell (1999), A hydrometric and geochemical approach to test the transmissivity feedback hypothesis during snowmelt, J. Hydrol., 219, 188-205. Kendall, C., J. J. McDonnell, and W. Gu (2001), A look inside 'black box' hydrograph separation models: a study at the Hydrohill catchment, Hydrol. Proc., 15, 1877-1902. Kennedy, V. c., C. Kendall, G. W. Zellweger, T. A. Wyerman, and R. J. Avanzino (1986), Determination of the components of storm flow using water chemistry and environmental isotopes, Mattole River Basin, California, J. Hydrol., 84, 107-140. Kirkby, M. J. (1997), TOPMODEL: a personal view, Hydrol. Proc., 11,1087-1097. Lasaga, A. C. (1984), Chemical kinetics ofwater-rock interaction, J. Geophys. Res., 89, 4009-4025. Likens, G. E., F. H. Bormann, R. S. Pierce, J. S. Eaton, and N. M. Johnson (1977), Biogeochemistry ofa Forested Ecosystem, Springer-Verlag, New York. Likens, G. E., and F. H. Bormann (1995), Biogeochemistry of a Forested Ecosystem, Springer-Verlag, New York. Lovett, G. M., and M. J. Mitchell (2004), Sugar maple and nitrogen cyc1ing in the forests ofeastern North America, Front. Ecol. Environ., 2 (2),81-88 Lyons, L. (1991), A practical quide to data analysis for physical science students, Cambridge University Press. McDonnell, J. J. (1990), A rational for old water discharge through macropores in a steep, humid catchment, Water Resour. Res., 26 (11),2821-2832. McDonnell, J. J., J. Freer, R. Hooper, C. Kendall, D. Burns, K. Beven, and J. Peters (1996), New method developed for studying flow on hillslopes, Eos, 77 (47), 465- 466. McGlynn, R, J. J. McDonnell, S. Stewart, and J. Seibert (2003), On the relationship between catchment scale and streamwater mean residence time, Hydrol. Proc., 17, 1756-1181. McGlynn, B. L., and J. Siebert (2003), Distributed assessment of contributing area and riparian buffering along stream networks, Water Resour. Res., 39 (4), 1082, doi:10.1029/2002WR001521. 159 McGlynn, B. L., J.J. McDonnell, J. Seibert, and C. Kendall (2004), Scale effects on headwater catchment runoff timing, flow sources, and groundwater-streamflow relations, Water Resour. Res., 40, W07504, doi:lO.l029/2003WR002494. McGuire K. J., J. J. McDonnell, M. Weiler, C. Kendall, B. L. McGlynn, J. M. Welker, and J. Seibert (2005), The role of topography on catchment-scale water residence time, Water Resour. Res., 41, W05002, doi:10.1029/2004WR003657. Mehuys, G. R, and C. R D. Kimpe (1976), Saturated hydraulic conductivity III pedogenetic characterization of podzols with fragipans in Quebec, Geoderma, 15, 371-380 Meyles, E., A Williams, L. Ternan, J. Dowd (2003), Runoff generation in relation to soil moisture patterns in a small Dartmoor catchment, Southwest England, Hydro!. Proc., 17,251-264. Mulholland, P. J., G. V. Wilson, and P. M. Jardine (1990), Hydrogeochemical response of a forested watershed to storms: effects of preferential flow along shallow and deep pathways, Water Resour. Res., 26 (12),3021-3036. Norton, S. A, and I. J. Fernandez, (Eds). (1999), The Bear Brook Watershed in Maine. A Paired Watershed Experiment - The First Decade (1987-1997), Kluwer Academic Publishers. Nyberg, L. (1996), Spatial variability of soil water content in the covered catchment at Gardsjon, Sweden, Hydro!. Proc., 10 (1),89-103. O'Brien, c., and W. H. Hendershot (1993), Separating stream flow into groundwater, solum and upwelling flow and its implications for hydrochemical modelling, J. Hydro!., 146, 1-12. Ogunkoya, O. O., and A Jenkins (1993), Analysis of storm hydrograph and flow pathways using a three-component hydrograph separation mode1, J. Hydro!., 142, 71- 88. Omoti U., and A. Wild (1979), Use of fluorescent dyes to mark the pathways of solute movement through soils under leaching conditions. 2. Field experiments., Soil Science, 128 (2),98-104. 160 Pearce, A. J., M. K. Stewart, and M. G. Sklash (1986), Stonn runoff generation in humid headwater catchments 1. Where does the water come from?, Water Resour. Res., 22 (8), 1263-1272. Peters, D. L., J. M. Buttle, and C. H. Taylor (1995), Runoff production in a forested, shallow soil, Canadian Shield basin, Water Resour. Res., 31 (5), 1291-1304. Pinder, G. F., and J. F. Jones (1969), Detennination of the groundwater component of peak discharge from chemistry of total runoffwater, Water Resour. Res., 5, 438-445. Pionke, H. B., W. J. Gburek, and G. J. Folmar (1993), Quantifying stonnflow components in a Pennsylvania watershed when 0-18 and stonn conditions vary, J. Hydrol., 148, 169-187. Rice, K. c., and G. M. Hornberger (1998), Comparison of hydrochemical trac ers to estimate source contributions to peak flow in a small, forested, headwater catchment, Water Resour. Res., 34 (7), 1755-1766. Roberts, J. (2000), The influence of physical and physiological characteristics of vegetation on their hydrological response, Hydrol. Proc., 14,2885-2901. Rouse, W. R, and R G. Wilson (1969), Time and space variations in the radiant energy fluxes over sloping forested terrain and their influence on seasonal heat and water balances at a middle latitude site, Geografiska Annaler, 51 (Ser. A, 3), 160-175. Scanlon, T. M., J. P. Raffensperger, and G. M. Hornberger (2001), Modeling transport of dissolved silica in a forested headwater catchment: Implications for defining the hydrochemical response of observed flow pathways, Water Resour. Res., 37 (4), 1071-1082. Shaman, J., M. Stieglitz, and D. Burns (2004), Are big basins just the sum of small catchments?, Hydrol. Proc., 18 (DOl: 1O.1002/hyp.5739), 3195-3206. Shanley, J. B., C. Kendall, T. E. Smith, D. W. Wolock, and J. J. McDonnell (2002), Controls on old and new water contributions to stream flow at sorne nested catchments in Vennont, USA, Hydrol. Proc., 16, 589-609. Sidle, R c., y. Tsuboyama, S. Nogushi, 1. Hosoda, M. Fujieda, and T. Shimizu (1995), Seasonal hydrologic response at various spatial scales in a small forested catchment, Hitachi Ohta, J apan, J. Hydrol., 168, 227-250. 161 Sivapalan, M., (Ed). (1993), Linking hydrologic parameterizations across a range of scales: hillslope to catchment to region, IAHS Publ. 212. Sivapalan, M., C. Jothityangkoon, and M. Menabde (2002), Linearity and nonlinearity of basin response as a function of scale: Discussion of alternative definitions, Water Resour. Res., 38 (2), 10.1029/2001WR000482. Sivapalan, M. (2003), Process complexity at hillslope scale, process simplicity at watershed scale: Is there a connection?,Geophysical Research Abstracts,5,07973, EGS-AGU-EUG Joint Assembly,Nice, France. Sklash, M. G., and R. N. Farvolden (1979), The role of groundwater in storm runoff, J. Hydrol., 43, 45-65. Sklash, M. G., M. K. Stewart, and A. J. Pearce (1986), Storm runoff generation in humid headwater catchments 2. A case study of hillslope and low-order stream response, Water Resour. Res., 22 (8), 1273-1282. Sklash, M. G. (1990), Environmental isotope studies of storm and snowmelt runoff generation, Process studies in hillslope hydrology, M. G. Anderson and T. P. Burt, (Eds.), 401-435, Wiley, Chichester. Spence, c., and M-k. Woo (2002), Hydrology of subarctic Canadian shield: bedrock upland, J. Hydrol., 262, 111-127. Stewart, M. K., and J. J. McDonnell (1991), Modeling base flow soil water residence times from deuterium concentrations, Water Resour. Res., 27 (10),2681-2693. Stieglitz, M., J. Shaman, J. McNamara, V. Engel, J. Shanley, G.W. Kling (2003), An approach to understanding hydrologie connectivity on the hillslope and the implications for nutrient transport, Global Biogeochemical Cycles, 17 (4), 1105. Taylor, J. R. (1997), An Introduction to Error Analysis, University Science Books, Sausalito, CA. Tromp van Meerveld, I., and J. J. McDonnell (2005), Comment to "Spatial correlation of soil moi sture in small catchments and its relationship to dominant spatial hydrological processes", J. Hydrol., 303, 307-312. Trudgill, S., J. Ball, and B. Rawlins (1996), Modelling the Solute Uptake Component of Hillslope Hydrochemistry: Are Flow Times and Path Lengths Important during 162 Mineral Dissolution?, Advances in Hillslape Praeesses, M. G. Anderson and S. M. Brooks, (Eds.), 295-324, Wiley & Sons. Tsujimura, M., Onda, Y. Ito, J. (2001), Stream water chemistry in a steep headwater basin with high relief, Hydral. Proe., 15, 1847-1858. Vitvar, T., D. A. Burns, G. R Lawrence, 1. J. McDonnell, and D. M. Wolock (2002), Estimation of baseflow residence times in watersheds from the runoff hydrograph recession: method and application in the Neversink watershed, Catskill Mountains, New York, Hydral. Proe., 16 (DOI:10.1002/hyp.5027), 1871-1877. Waddington, J. M., N. T. Roulet, and A. R Hill (1993), Runoffmechanisms in a forested groundwater discharge wetland, J Hydral., 147, 37-60. Webber, J. (1965), Comparison of chemical composition of soils and bedrock of Mont St. Hilaire, Quebec, Cano J Earth Sei., 2, 44-58. Wels, c., R J. Cornett, and RD. Lazerte (1991a), Hydrograph separation: A comparison of geochemical and isotopic tracers, J Hydral., 122, 253-274. Wels, C., C. H. Taylor, and R J. Cornett (1991b), Streamflow generation in a headwater basin on the Precambrian Shield, Hydra/. Prae., 5, 185-199. Western, A. W., Bloschl, G., and R R Grayson (1998a), How well do indicator variograms capture the spatial connectivity of soil moisture?, Hydra/. Proe., 12, 1851- 1866. Western, A. W., Bloschl, G., and R RGrayson (1998b), Geostatistical characterisation of soil moi sture patterns in the Tarrawarra catchment, J Hydral., 205, 20-37. Western, A. W., and R R Grayson (1998), The Tarrawarra data set: Soil moisture patterns, soil characteristics, and hydrological flux measurements, Water Resaur. Res., 34 (10), 2765-2768 . Western, A. W., Grayson, RB., Bloschl, G., Willgoose, G.R, and T. A. McMahon (1999), Observed spatial organization of soil moisture and its relation to terrain indices, Water Resour. Res., 35 (3), 797-810. Western, A. W., Bloschl, G., and R R Grayson (2001), Toward capturing hydrologically significant connectivity in spatial patterns, Water Resaur. Res., 37 (1),83-97. 163 Western, A. W., S-L. Zhou, RB. Grayson, T.A. McMahon, G. Bloschl" and D. J. Wilson (2004), Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes, J. Hydro/., 286, 113-134. Western, A. W., S.-L. Zhou, RB. Grayson, T.A.McMahon, G. Bloschl, D.J. Wilson (2005), Reply to comment by Tromp van Meerveld and McDonnell on Spatial correlation of soil moi sture in small catchments and its relationship to dominant spatial hydrological processes, J. Hydro/., 303, 313-315. Wironen, M. (2005), Exotic earthworm invasion of an old-growth forest in southern Quebec: Invasion status and effects on soil carbon and nitrogen distribution and storage, Undergraduate Honors Thesis, McGill University, Montreal, Quebec. Wolock, D. M., and G. J. McCabe, Jr. (1995), Comparison of single and multiple flow direction algorithms for computing topographic parameters in TOPMODEL, Water Resour. Res., 31 (5), 1315-1324. Wolock, D. M., J. Fan, and G. B. Lawrence (1997), Effects of basin size on low-flow stream chemistry and subsurface contact time in the Neversink river watershed, New York, Hydro/. Proc., 11, 1273-1286. Wood, E. F., M. Sivapalan, K. Beven, and L. Band (1988), Effects of spatial variability and scale with implications to hydrologic modeling, J Hydro/., 102, 29-47. Wood, E. F., M. Sivapalan, and K. Beven (1990), Similarityand scale in catchment storm response, Rev. of Geophys. , 28 (1), 1-18. Woods, R., M. Sivapalan, and M. Duncan (1995), Investigating the representative elementary area concept: an approach based on field data, Hydro/. Proc., 9, 291-312. Woods, R A., and M. Sivapalan (1997), A connection between topographically driven runoff generation and channel network structure, Water Resour. Res., 33 (12), 2939- 2950 Woods, R, R Grayson, A. Western, M. Duncam, D. Wilson, R Young, R Ibbitt, R Henderson, T. McMahon (2001), Experimental Design and Initial Results From the Mahurangi River Variability Experiment: MARVEX, Land Surface Hydr%gy, Meteor%gy and Climate, J. A. V. Lakshimi, J. Schaake, (Ed). 201-213, American Geophysical Union. 164 Zehe, E., and G. Bloschl (2004), Predictability of hydrologie response at the plot and catchment scales: Role of initial conditions, Water Resour. Res., 40 (doi: 10. 1029/2003 WR002869), 21. 165