Applying a Hydrologic Classification Approach to Low Gradient Boreal Watersheds

APPLYING A HYDROLOGIC CLASSIFICATION APPROACH TO LOW GRADIENT BOREAL WATERSHEDS

BRITTANY RUNDLE GERMAIN

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ENVIRONMENTAL SCIENCES

SCHOOL OF GRADUATE STUDIES NIPISSING UNIVERSITY NORTH BAY, ONTARIO March, 2017

Abstract

The Attawapiskat River catchment makes up a ~57,000 km2 area in Ontario’s Far North extending from Precambrian Shield headwaters through the Hudson Bay Lowlands (HBL) ecozone to the coast. The region is peatland dominated and the low gradient, large expanses require further analysis and study to address uncertainties about their variations in hydrologic response. Recent hydrologic or catchment classification studies aim to assess broad-scale hydrologic systems in terms of smaller ‘building blocks’ to help develop hypotheses of how hydrologic systems function within specific terrains, but few if any have focused on low gradient peatland dominated systems. This study applies Principal Component Analysis (PCA) to representative catchments within the HBL ecozone, the Boreal Shield and the transition between the two in the Attawapiskat River watershed to assess hydrologic similarity based on physical, climatic and hydrologic characteristics. Different assessments of hydrologic similarity between catchments were made based on the combination of metrics/characteristics included in seven scenarios. Physical and terrain-based characteristics grouped catchments by physiographic region (HBL, transition zone and Shield), while hydrologic characteristics (i.e. tracer and flow-based metrics) grouped catchments both by physiographic region and partly by groundwater influence. Physical and terrain-based characteristics were found to exhibit the most control on the PC-space while hydrologic characteristics provided additionally important details about source water contributions to overall catchment hydrology. This study illustrates the importance of tracer- based/flow metrics in hydrologic similarity analyses.

Keywords

Hydrologic classification, peatland environments, Hudson Bay Lowlands

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Acknowledgments

I would like to thank the many people that supported and contributed to the completion of this research: First, I wish to thank my advisors Dr. April James and Dr. Brian Branfireun for the support, direction, and guidance throughout this process. Thank you especially to Dr. James for the opportunity to work for/with you on both this project and many others. Thank you to all members past and present of the Nipissing Watershed Analysis Centre/Integrative Watershed Research Centre for being my work colleagues, school mates and for letting me make a second home out of the lab for the years I have been here. I have had many opportunities as part of this lab group that I would never have experienced if I had ended up somewhere else. Thank you to everyone else who contributed the data for this research: DeBeers Canada, University of Western Ontario, University of Waterloo, Nipissing University, OMNRF, OMOECC and the Vale Living with Lakes Centre. Thank you to Dr. Krystopher Chutko for being a sounding board and someone who is always available to answer questions, no matter how inane they may be. Thank you to the Canadian Network of Aquatic Ecosystem Services for all the contacts, events and conversations that have been made possible by this network over the last few years. While working at a smaller university, it was extremely beneficial to be a part of a larger community and see how other institutions operate their graduate programs.

On a more personal note, thank you to my husband for supporting me completely during this process. Thank you for keeping me fed and keeping me on task for the last few years. You made life much easier on me by keeping up on everything (E.g. laundry) while I attempted to focus. Lastly, thank you to my parents Malcolm and Glenda Rundle: you formed this brain from day one, so everything I do is a testament to your parenting!

Table of Contents

Abstract ...... iii

Acknowledgments...... iv

Table of Contents ...... v

List of Tables ...... vi

List of Figures ...... vii

List of Appendices ...... ix

1 Introduction ...... 1

1.1 Hydrologic Classification Approach ...... 2

1.2 Catchment Scale Connectivity in Large Scale Peatlands ...... 5

1.3 Rationale for Catchment Classification Indices ...... 7

1.4 Research Objectives ...... 10

1.5 Study Area ...... 11

2 Methods ...... 19

2.1 Catchment Metrics ...... 19

2.2 Statistical Analysis ...... 29

3 Results ...... 31

3.1 Catchment Characteristics ...... 31

3.2 Principal Component Analysis Scenarios ...... 41

4 Discussion ...... 53

5 Conclusions and Future Direction ...... 59

References ...... 62

Appendices ...... 67

List of Tables

Table 1: List of study catchments, as defined by streamflow and water chemistry monitoring. Full station name, FIRNNO and PWQMN identification provided where relevant...... 18

Table 2: Physiographic metrics for research sub-catchments (OMNRF, OIHD, 2012)...... 22

Table 3: Percent cover of Quaternary Geology (for values > 10%) and assigned permeability ranking (1:1,000,000 [OGS, 1997]). Permeability rankings of 1-5 are assigned for lowest to highest permeabilities...... 23

Table 4: Percent cover of Bedrock Geology (for values >10%) and assigned permeability ranking of dominant rock types. (1:250,000 [OMNDM, 2011]). Permeability rankings of 1-3 are assigned for lowest to highest values...... 25

Table 5: Percent cover of landcover types for all research catchments (OMNRF, OFAT, 2013).26

Table 6: Climate data and metrics for each region. Data from Environment Canada (RoF and SNF, accessed in 2015) and De Beers VM Research Station (HBL)...... 27

Table 7: Flow-based metrics normalized by drainage areas. Data from Water Survey of Canada (SNF, accessed in 2015) and De Beers VM Research Station (HBL)...... 27

Table 8: Tracer-based metrics. Data collected by Nipissing University (SNF), De Beers VM Research Station (HBL) and OMECC (RoF). See Appendix 7a-j for data...... 28

Table 9: Combinations of catchment characteristics used for different Principal Component Analysis scenarios. Refer to Tables 2-8 for detailed information about each group of metrics. .. 30

List of Figures

Figure 1: Terrestrial ecozones in Ontario, Canada. Boreal Shield in green, Hudson Bay Lowlands (HBL) in blue and Mixedwood Plains in red. Attawapiskat River and watershed indicated (NABCI Canada, 2014)...... 1

Figure 2: Schematic of the T3 concept, taken from Buttle (2006). Hypothetical mapping of a Batchawana River catchment (1) and Abitibi River catchment (2) in terms of relative control of the three T’s (typology, topography and topology)...... 4

Figure 3: Map of Ontario with Attawapiskat watershed outlined and research catchments highlighted. Black dots are climate stations used in study...... 13

Figure 4: Victor Mine research sub-catchments...... 15

Figure 5: Ring of Fire research catchments ...... 16

Figure 6: Sturgeon-Nipissing-French research catchments...... 17

Figure 7: Cumulative frequency distributions of subcatchment area for individual catchments.. 36

Figure 8: Cumulative frequency distributions of subcatchment area for individual catchments normalized with respect to total catchment area...... 37

Figure 9: Cumulative frequency distributions of TWI for individual catchments grouped by physiographic region...... 38

Figure 10: Flow duration curves for SNF (blue and green) and HBL (shades of grey/black) catchments...... 39

Figure 11: Water isotopes in streamflow for SNF (black) (Nipissing University, 2015) and HBL (grey) (Western University, 2013) catchments plotted against Global Meteoric Water Line (GMWL) (Craig, 1961)...... 40

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Figure 12: PCA Scenario 1 (Terrain Metrics). (a) Catchments clustering based on variables, and (b) catchments clustering after PCA...... 46

Figure 13: PCA Scenario 2 (Physical Metrics [No Area]). (a) Catchments clustering based on variables, and (b) catchments clustering after PCA...... 47

Figure 14: PCA Scenario 3 (Physical Metrics [With Area]. (a) Catchments clustering based on variables, and (b) catchments clustering after PCA...... 48

Figure 15: PCA Scenario 4 (Hydrologic Metrics [Flow and Tracer]). (a) Catchments clustering based on variables, and (b) catchments clustering after PCA...... 49

Figure 16: PCA Scenario 5 (Bulk Hydrologic Analysis)...... 50

Figure 17: PCA Scenario 6 (All Metrics [No Area]). (a) Catchments clustering based on variables, and (b) catchments clustering after PCA...... 51

Figure 18: PCA Scenario 7 (All Metrics [With Area]). (a) Catchments clustering based on variables, and (b) catchments clustering after PCA...... 52

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List of Appendices

Appendix 1: Flowpath length comparison for Trib5. Flow path length can be defined as: the distance along a flow path to either the catchment outlet (A) or the stream channel (B). For this analysis, flow path length to the stream channel (B) was used...... 67

Appendix 2: Stream order comparison for Trib5. Stream order is directly linked to the resolution of the stream data layer. For this analysis the Ontario Integrated Hydrology Dataset (OIHD) Enhanced Watercourse Layer (OMNRF, 2012) was used (black line). For VM catchments, a higher resolution DEM was available but for consistency across all catchments, the OIHD layer was used...... 68

Appendix 3: Quaternary geology for the Attawapiskat watershed and SNF catchments (1:1,000,000 [OGS, 1997])...... 69

Appendix 4: Quaternary geology resolution comparison of 1:100,000 (OGS, 2011) and 1:1,000,000 (OGS, 1997) for Ring of Fire catchments. (a) Comparison table of quaternary geology permeability rankings included...... 70

Appendix 5: Bedrock geology for the Attawapiskat watershed and SNF catchments (1:250,000 [OMNDM, 2011])...... 71

Appendix 6: Landcover resolution comparison of (A) OFAT-III and (B) FNLCv1.3 (1: 100,000) for Trib5. OFAT landcover resolution provides high enough resolution to distinguish between peatland features, and provides uniform resolution across all study catchments...... 72

Appendix 7a: Water chemistry and flow summary for Tributary 3, HBL...... 73

Appendix 8: Assessment of normality and linearity for variable variables included in study. Five variables will be transformed, four variables will be omitted and twenty-nine metrics will remain unchanged as per this appendix’s recommendations...... 83

Appendix 9a: Cumulative frequency distributions of TWI...... 101

Appendix 10: TWI distribution for catchment K21. (A) Histogram of TWI distribution and (B) image of TWI values with 3-4 emphasized (blue) to explain "dip" in histogram...... 104

Appendix 11: Total precipitation (mm), PET and moisture index (PET/Precipitation) for Lansdowne House (LH) Climate station (red, RoF), North Bay Airport (NB) Climate station (blue, SNF), and DeBeers Victor Mine (VM) meteorological station (green, HBL)...... 105

Appendix 12a: PCA (A) variance, (B) scores and (C) loadings tables...... 106

1 Introduction

Wetlands are land areas that are permanently or seasonally saturated with water and as such are unique ecosystems where terrestrial and aquatic habitats meet (NWWG, 1997). Typically characterized by unique flood-tolerant vegetation and microbial communities that have adapted to saturated soil conditions, these ecosystems are a link between land and water. In Canada, 14% of the nation’s total land mass is covered by wetlands, making up over a quarter of all wetlands globally (Environment Canada, 2014). These wetlands provide significant economic, social and cultural benefits while also acting to reduce the impacts of storm damage and flooding, maintain water quality in rivers, recharge groundwater, store terrestrial carbon, help stabilize climate conditions and control pests (NWWG, 1997).

2 Canada's largest wetland complex is the Hudson Bay Lowlands (HBL) ecozone (Riley, 2011), a region dominated by mineral poor wetlands and a complex mosaic of bogs, fens, swamps and permafrosted plateaus (Figure 1, shaded blue). The HBL extends from north-eastern Manitoba to western Quebec, however is dominantly situated within Ontario. This region in Ontario’s Far North is considered an area of interest, undergoing an unprecedented period of rapid climate change (Riley, 2011) where changes in hydrology, biology and ecology are widely expected or are already underway (IPCC, 2013). An understanding of how these vast peatland systems link with northern rivers and support aquatic ecosystem functions remains largely unknown.

In contrast to the HBL ~20% of Canada’s land area is Boreal Shield (Figure 1, shaded green), making it the largest ecozone in Canada, extending from northern Saskatchewan through to the Eastern coast (OMNRF, 2009). The Boreal Shield covers more than half (~66.2%) of Ontario and as such is one of the most studied ecozones in the province (OMNRF, 2009). Major river systems that initiate within the Boreal Shield and extend through the HBL into James Bay or Hudson Bay include the Severn, Winisk, Attawapiskat, Albany and Moose Rivers. The inter- basin variability of hydrologic response across the physiographic boundaries of the HBL and Boreal Shield in these northern watersheds has gone largely unstudied.

1.1 Hydrologic Classification Approach

Catchment-scale hydrological classification systems are based on a structured framework that views the Earth as smaller ‘hydrologic landscapes’ to assess water movement – ground water, surface water and atmospheric water – through different landscapes (Winter, 2001). This suggests a physical feature, a ‘fundamental hydrologic landscape unit’ (FHLU), has a unique hydrologic system to itself. In a FHLU, water movement is controlled by the land-surface form (e.g. slope, permeability of soils), the geologic framework (e.g. hydraulic characteristics), and climatic setting (e.g. atmospheric-water exchange). Winter’s (2001) framework views FHLUs as the basic building blocks of all hydrologic landscapes, and aims to view hydrologic systems as independent ‘blocks’ to help develop general hypotheses about hydrologic function and variations across different terrains (Wolock et al., 2004). Classification studies define FHLUs to fit available data or specific hypotheses (e.g. using a set sub-catchment area to compare similar sized units across broad scales, or using catchments as a ‘unit’ to compare to others, independent of size.)

3 There is a growing need for a “unified broad-scale catchment classification system” to address a range of water resource challenges in hydrology (McDonnell and Woods, 2004; Wagener et al., 2007), and there is currently no agreed-on method in place. At the global scale, a universal catchment classification system would be an important organizing principle to assist modelling and experimental approaches to hydrology, by providing guidance on the differences, similarities and idiosyncrasies between catchments (McDonnell and Woods, 2004). In many parts of the world there are large ungauged or poorly gauged watersheds, many of which are affected by local or regional scale human-induced changes to the land surface (Sivapalan et al., 2003). Hydrologic predictions from these watersheds are therefore uncertain and new and innovative methods are being used to advance the capacity to make these predictions in ungauged watersheds (Pomeroy et al., 2004).

Winter (2001) suggests that in areas with similar land slopes, surficial geology and climate there will be similar hydrologic flowpaths, independent of the area’s geographic location. Hydrologic similarity studies considering overall hydrologic function (including precipitation, temperature, streamflow and physical parameters) instead of just each independent variable are therefore important, and can be considered the basis for catchment classification itself (Sawicz et al., 2011). To compare catchments across both places and processes, spatial and temporal scales need to be recognized, and frameworks developed accordingly (Wagener et al., 2007).

Buttle (2006) offers one type of framework suggesting three first-order controls on the characteristics of streamflow within a specific hydroclimate: typology represents the residence times of water in a catchment; topography represents the role of hydraulic gradients in transmitting water from slopes to streams, lakes and wetlands; and topology represents the drainage network and catchment connectivity. The ‘T3’ concept allows catchments to be ‘mapped’ within the framework with respect to the main hydrologic controls (Figure 2). A second framework from Devito et al. (2005) suggests that using traditional topographical variables to define watershed boundaries may not encompass all dominant components of the hydrologic cycle, groundwater conditions, or the type/scale of hydrologic linkages within low gradient systems such as the Boreal Plain. Based on this assessment, the main landscape features Devito et al. (2005) suggest including in a classification study are climate, bedrock geology, surficial geology, soil depth and type, topography and metrics representing the drainage network.

In existing hydrologic classification studies, typically an inductive or deductive approach is chosen to guide the structure of the research. A deductive hydrologic approach is a ‘top-down’ strategy beginning with a theory/hypothesis and leading towards observations that confirm the hypotheses (Wigington et al., 2013). This approach describes and quantifies spatial variations across broad scales where little measured or modelled hydrologic data is available (i.e. ungauged catchments). A deductive approach was applied by Wolock et al. (2004) to identify regions of hydrologic similarity across the United States, focusing on land-surface form (e.g. relief and percentage of flatland), geologic (e.g. soil and bedrock permeability) and climate characteristics (e.g. mean annual precipitation minus potential evapotranspiration). Wigington et al. (2013) applied a comparable approach to group incremental watersheds (i.e. ‘headwater watersheds’ that drain into stream reaches directly) in the state of Oregon into classes with similar hydrologic behaviors, based on climate (e.g. moisture index and water seasonality index), aquifer and soil permeability.

5 In contrast, an inductive ‘bottom-up’ approach uses direct measurements and observations of hydrology to make broader generalizations and develop theories by detecting patterns and regularities in the datasets (Olden et al., 2012). Ali et al.’s (2012) inductive classification evaluated hydrologic similarity between research catchments (with long-term monitoring data) representative of different geomorphic regions of Scotland. This study aimed to identify key catchment characteristics (metrics) that exhibited the most hydrologic control across the catchments, focusing on catchment forcing (climatic), form (physical) and function (hydrologic) characteristics. Ali et al.’s (2012) inductive study used existing long-term datasets to compare representative study catchments instead of classifying the region deductively. The metrics that generated the most accurate or informative results in this case were found to be highly situational and region dependent (Ali et al., 2012). In the Scottish regional context, the best combination of data to approximate the catchment structure and catchment function interactions were topographic characteristics, soil permeability and mean transit time (MTT) estimates (Ali et al., 2012). Classification approaches are flexible for defining frameworks, as the variables and spatial scale of interest can be customized to suit the users’ need (Wolock et al., 2004). It is therefore important to include an extensive set of climatic, physical and hydrologic metrics in a classification approach, as different characteristics could prove to be important in regulating the hydrology of the studied region. Both “top down” and “bottom up” approaches are recognized by the International Association of Hydrological Science (IAHS) decade for Predictions in Ungauged Basins (PUB) – an initiative aimed at making advancements in the capacity to make these predictions – as means to increase hydrological understanding and help establish baseline conditions in remote areas (Pomeroy et al., 2004).

1.2 Catchment Scale Connectivity in Large Scale Peatlands

Many wetlands in Ontario’s Far North are large contiguous peatlands (defined as: waterlogged soils where anoxic conditions result in organic detritus accumulations greater than 0.40m thick [NWWG, 1997]) with relatively flat terrains that allow for long-term saturation (Gorham, 1991). The unique hydrological response of peatlands can be quite complex, and despite their importance for hydrology, peatland processes and functions are yet under-represented in most distributed models used for hydrologic predictions (Whitfield et al., 2009). Peatlands develop and are regulated by interactions between soil biogeochemistry, plant ecology and hydrology

6 (Morris et al., 2011). Water movement is important for the transport of oxygen and nutrients throughout the system, which influences peat development (Ingram, 1978) and plant variations at the surface.

Northern peatland environments are typically made up of varying series of contiguous features such as bogs, fens and peat plateaus that heavily influence the overall hydrology of the region (Quinton et al., 2003). Peat plateaus (elevated flat sections of peatlands that form over permafrost) have been observed to have a limited capacity to store water (Quinton and Baltzer, 2013), with water additions from precipitation or snowmelt generating runoff that flows into the nearby bogs and fens (Hayashi et al., 2004). Bogs then act as primary storage features during low water tables when subsurface flow is the main flowpath; but during high water supply when available storage becomes limited, the bogs drain into the nearby fens (contributing as a source of basin runoff) (Quinton et al., 2003). Contiguous wetland systems in the Scotty Creek watershed have been observed to store snowmelt runoff when storage is available, but when storage was exceeded, the individual wetland features would connect in a dynamic flow system (Hayashi et al., 2004). The varying landscape features of bogs, fens and peat plateaus behave differently and exhibit different characteristics during periods of high or low flows (Hayashi et al., 2004).

Hydrologic connectivity (defined as: the linkage of separate regions of a catchment through water flow [Blume and van Meerveld, 2015]) is therefore of key importance in peatlands, as hydrologic systems can be decoupled depending on the moisture availability within the organic soil. Observations of structural connectivity (based on landscape patterns and distribution), functional connectivity (spatially and temporally variable connectivity during rain events) (Blume and van Meerveld, 2015), and storage capacity provide evidence of fluctuating conditions in Northern basins (Spence, 2010). These conditions depend on antecedent moisture availability and water levels at any given point in time and space (Quinton et al., 2003), which make it very difficult to predict runoff responses. Richardson et al. (2012) suggest that the lateral movement of water within and between distinct landcover types is one of the most poorly understood aspects of northern peatland hydrology. In reference to Buttle’s (2006) T3 concept, both structural connectivity and functional connectivity are important to assess in a classification approach involving peat environments.

7 1.3 Rationale for Catchment Classification Indices

In catchment classification studies, Ali et al. (2012) note that many papers to date rarely justify the inclusion or omission of specific metrics in similarity studies, except in the case of data availability. A preliminary application of Devito et al.’s (2005) hierarchy of fundamental controls to the HBL extending into the Boreal Shield in Ontario’s Far North explores the rationale of metrics relevant for northern watersheds:

A) Climate: Devito et al. (2005) suggest that local and regional climatic differences should be the first control to consider. HBLs climate is moderated by Hudson Bay itself and isolated convective systems are unlikely to cause sufficiently large short- to medium-term differences in precipitation inputs (Richardson et al., 2012). Extending beyond the HBL into the Shield (Figure 1), climate may be more varied (Rouse, 1991).

B) Bedrock and Surficial Geology: Differences in bedrock are most significant at the Shield/Lowland divide, as the HBL is mostly sedimentary limestone (some permeability [Riley, 2011]) while the Boreal Shield is relatively impervious granitic bedrock (Devito et al., 2005). Surficial geology varies in depth, texture and heterogeneity. Greater groundwater contributions are expected in the central HBL (where silts and clays overlay the limestone) than in the Shield. Groundwater inputs in the HBL have been found to increase with catchment area (Orlova and Branfireun, 2012), so geologic variations could also be an important regional control in a classification approach (influencing groundwater interactions and lateral flows).

C) Soil Type and Depth: The peat-dominated HBL contains organic soils ranging from 1 – 3 meters in depth (Riley, 2011). Peat and peaty gley soils (subject to poor vertical drainage and are close to saturation all year) were found to be important in the Scotland classification (Ali et al., 2012). They are associated with overland flows and shallow lateral subsurface flows, unlike the freely draining soils that allow deeper subsurface flow and groundwater recharge (Ali et al., 2012). Based on the extent of bogs and fens across the landscape, similar soils are expected in the HBL. In contrast, the headwater Shield has been exposed from the postglacial Tyrrell Sea longer than the HBL (isostatic rebound) so the Shield has older/more developed soils. Landcover (% cover) will show the succession and wetland development across the

8 watershed – less wetlands/more forests in the Shield (less peat), and more wetlands in HBL (more peat).

D) Topography and Drainage Network: For the low-gradient HBL, studies suggest metrics of connectivity may be more important in differentiating hydrologic similarity than topography. Landcover (% cover) representing local vegetation differences, together with bedrock and surficial geology will indicate hydraulic gradients and water flows, as there is little variation in slope and topography to delineate hydrologic landscapes. Where limited topographic variations and relatively uniform soils were encountered, Ali et al. (2012) found that the topology (e.g. drainage density) of the landscape features close to rivers controlled the overall hydrology of the catchment.

In relation to peatland studies and Buttle’s (2006) T3 concept, Hrachowitz et al. (2009) observed soil type, drainage density, precipitation intensity and topographic wetness index (TWI) to be dominant landscape controls on transit time estimates in Scottish peatlands. With typology represented by soil type (permeability), topology represented by drainage density and topography associated with TWI, they support the T3 approach as a valid tool - their catchments were dominantly controlled by typology and topology, with topography acting as a secondary influence (Hrachowitz et al., 2009).

To apply the T3 concept to the HBL/Shield divide, topology (drainage networks) relates back to Blume and van Meerveld’s (2015) explanation of connectivity. In wetland dominated ecosystems, the connection/disconnection of overland flowpaths is dependent on water table positions, antecedent conditions and available storage within the profile (Quinton et al., 2003). Drainage density (the ratio of cumulative length of stream and the total drainage area) is an important metric (structural connectivity), because stream orders and channel networks are expected to be different between the Shield and Lowlands. In the HBL, controls on water storage and runoff depend on the distribution and physiographic makeup of fen systems, the dominant conveyors of water to the flowing surface waters. In the Shield, the local slope and mesoscale topography may play a bigger role in hydrologic forcing, and the drainage networks have had more time to develop post glaciation.

9 Functional connectivity is more complex to identify but can be represented by the inclusion of high flow (Q10) and low flow (Q90) discharge values for seasonal separation (Richardson et al., 2012). Richardson et al. (2012) observed a high correlation between daily discharge and drainage areas within the HBL during low flow conditions (Q10), and degradation of this relationship with higher flows (Q90) and large runoff events. This was attributed to differences in runoff timing (quickflow response), as significant differences in total runoff (mm) were observed during the wetter period (Richardson et al., 2012), but emphasizes the importance of assessing high and low flows in peatland classifications.

Typology relates to flowpath lengths or residence times of water within the landscape. Percent responsive soil cover was an important typological control in Hrachowtiz et al.’s (2009) study, yet is expected to vary minimally within the HBL because of dominance of peat soils (i.e. poor vertical drainage and are close to saturation year-round [Ali et al., 2012]). In place of this, flowpath length and mean transit time estimations (or surrogate metrics [Tetzlaff et al., 2014]) are important to inform how storage and transit times vary across the physiographic divide. The last consideration for the T3 concept, topography, is not expected as a primary control within the HBL, but is expected to be more important across HBL/Shield transition.

Hydrologic literature specific to northern peatland environments also identified stream order and catchment sizes as important geomorphic characteristics to consider. Richardson et al. (2012) observed a direct influence of stream order on the extent of channel incision within the HBL: larger order systems (based on the Shreve stream order classification) contained steeper gradient channel fens with deep channel incisions, while lower order areas coincided with bog- dominated areas with weakly incised channels. This is a result of the peatlands acting as aggrading ecosystems that adapt to maximize water ‘optimization’ – slowing down lateral runoff and seepage loss in the low permeability catotelm (lower layer) and reducing the duration of surface pooling after large rain events/melt in the higher permeability acrotelm (upper layer) (Ingram, 1978). Likewise, Orlova and Branfireun (2014) used a nested catchment approach and water chemistry data to quantify groundwater and surface-water contributions within the Nayshkootayaow River catchment in the HBL and confirm that an increase in deep groundwater contributions to the stream was evident with increased catchment area. Consistent with observations in the Laird River basin (St Amour et al., 2005) and the upper Attawapiskat (Singer

10 and Cheng, 2002) in the HBL as well, it was also reported that deep groundwater has a smaller relative contribution to streams during wet periods, which are instead dominated by surface runoff. Deep groundwater and surface runoff were found to be comparable contributors during dry periods. Overall, the relative contributions of the end members were directly related to both watershed size and stream order, which then dictates the extent of groundwater interaction with the underlying geology (Orlova and Branfireun, 2014).

From general hydrologic literature (that doesn’t necessarily emphasize ‘northern peatland environments’), further metrics of importance include: median sub-catchment area (spatial organization and topology), flowpath length and gradients, and topographic wetness index (TWI). McGlynn et al. (2003) observed that median sub-catchment area was more closely correlated with Mean Residence Times (MRT) than total catchment area in the Maimai Valley in New Zealand. These findings link to spatial organization and Buttle’s (2006) typology/topology, in that the distribution of sub-catchment area (measured here as median sub-catchment area) provided a stronger representation of first order controls on storage and overall hydrologic function than total catchment area (McGlynn, 2003). Tetzlaff et al. (2014) acknowledge that relationships between landscape organization, storage and hydrologic responses at the catchment scale can be especially important in northern landscapes where topography is often relatively subdued as a result of glacial history. Similarly, McGuire et al. (2005) found that MRT showed no correlation to total catchment area (also in the Maimai Valley), and instead was higher correlated to flowpath lengths and gradients. They suggest that the internal topographic arrangement (instead of total catchment area) controls transport at the catchment scale, and that ‘simple’ topographic attributes (such as flowpath lengths and gradients) demonstrate this internal form and structure of the catchments (McGuire et al., 2005). Lastly, Hrachowitz et al.’s (2009) study found soil permeability, drainage density, precipitation intensity and TWI explained ~85% percent of the variance in Mean Transit Time (MTT) estimates across Scotland. Hrachowitz et al. (2009) consider TWI to represent the influence of topography in their study, in respect to Buttle’s (2006) T3 concept.

1.4 Research Objectives

This research applies an inductive classification approach to representative research catchments within the HBL ecozone, the Boreal Shield and the transition between the two ecozones in the

11 Attawapiskat River watershed to assess hydrologic similarity based on physical, climatic and hydrologic characteristics. This classification will be used to gain insight into the controls on/ and inter-basin variability of hydrologic response in specific ecozones and across the Attawapiskat catchment. Specifically, this study seeks to:

1. Assess whether research catchments are hydrologically similar or different across geomorphic provinces within the Attawapiskat watershed. It is expected that catchments will demonstrate more hydrologic similarity to catchments within their physiographic region (e.g. HBL).

2. Determine which characteristic metrics are most important in evaluating hydrologic similarity (both within the HBL and across the larger Attawapiskat watershed), with respect to Buttle’s (2006) T3 concept.

3. Assess controls on streamflow within the hydroclimate of the HBL in the context of the T3 framework (Buttle, 2006). For example, will topology (defined as: the role of a drainage network or structural connectivity) be a first order control on streamflow, with topological metrics (e.g. drainage density) more important in distinguishing hydrologic similarity among HBL catchments than traditional topographic metrics (e.g. slope, topographic wetness index)?

The results of this research will provide a unique similarity analysis in an expansive peat dominated environment in Ontario’s Far North, resulting in the generation of new metrics for first to third order research catchments in the central James Bay Lowland. Overall, an assessment of hydrologic similarity analysis (catchment classification) using provincial government derived tools (e.g. Ontario Flow Assessment Tool [OMNRF, OFAT-III, 2013]), and regional scale data products (e.g. Integrated Hydrology Dataset [OMNRF, OIHD, 2012]) will be made.

1.5 Study Area

The largest wetland complex in North America is the Hudson Bay Lowlands (HBL) ecozone located primarily in Northern Ontario, and dominated by organic soils and wetlands (Abraham et al., 2010). The region has been undergoing isostatic rebound over the last millennium, after being depressed by the Laurentide ice sheet (Riley, 2011). The Lowland emerged as the

12 postglacial Tyrrell Sea in Ontario’s Far North drained down to become James Bay/Hudson Bay, leaving behind glaciolacustrine and marine silts/tills with very low hydraulic conductivity (Riley, 2011). Coastal marshes and mineral wetlands developed and succeed into large peatland expanses as the Tyrrell Sea retreated (Riley, 2011).

The unique structure of the HBL is determined by its flat geological structure, poorly drained marine silts and clays, organic soils, discontinuous permafrost coverage, and relatively cold climate (Abraham et al., 2010). These conditions have resulted in an extensive flat plain dominated by mineral poor wetlands and peatlands mosaicked by bogs, fens, swamps and permafrosted plateaus. Peatlands predominate this region, with the thinnest organic matter accumulation generally located along the James Bay coast in recently emerged areas and increasing peat accumulation further inland (Abraham et al., 2010). Boreal peatlands provide globally important ecosystem functions (i.e. regulation and maintenance of the hydrologic cycle and carbon storage) as peat stores ~30% of the Earth’s terrestrial soil carbon while only covering ~3% of the Earth’s surface (Gorham, 1991), or ~12% of Canada’s land surface (Tarnocai, 1998).

Located in the central-western HBL, the Attawapiskat River watershed (~57,000 km2) is representative of large northern watersheds with Boreal Shield headwaters and large percentages of Lowland, and as such is the chosen watershed for this hydrogeomorphic classification approach (Figure 3). Climate is classified as cold continental, demonstrated by long cold winters (October - April), short warm summers (May – Sept) and moderate levels of precipitation (Environment Canada, 2014). Mean annual total rainfall from Lansdowne House meteorological monitoring station (west of Ring of Fire research catchments) is ~700 mm (1971-2000), with ~250 mm (34%) falling as snow (Environment Canada, 2014). Sporadically discontinuous permafrost can also be found across the watershed, extending from the HBL into the Shield (Riley, 2011). Peat depth in the HBL ranges from 0 – 3m and is underlain by fine-grained sediments (Riley, 2011), with less peat accumulated in the Shield. Select research catchments (Table 1) are used to represent of the HBL and Shield ecozones and the transition zone between.

Figure 3: Map of Ontario with Attawapiskat watershed outlined and research catchments highlighted. Black dots are climate stations used in study.

De Beers Victor Mine HBL catchments

The De Beers Victor Mine site (VM) is located within the Hudson Bay Lowlands, 90 km west of Attawapiskat, Ontario in the Nayshkootayaow River catchment, a tributary of the Attawapiskat River. Six catchments were selected to be representative of the HBL ecozone (Figure 3, green shading) as they are located within a mapped peatland complex (AMEC, 2004). Bogs and fens make up > 90% of the landscape surrounding the De Beers mine site, and are situated above a locally karstic Silurian limestone aquifer known as the Attawapiskat Formation (AMEC, 2004). Catchments NR-003 (NR3), NR-002 (NR2) and NR-001 (NR1) are nested, and along the Nayshkootayaow River itself; while Tributary-3 (Trib3), Tributary-5 (Trib5), and Tributary-7 (Trib7) are tributary catchments off the main stream (Figure 4). These catchments range in size from ~90 – 2000 km2 (Table 2) and have been instrumented and studied by University of Waterloo, University of Toronto and Western University since 2007. These sites are at the lowest

14 elevation of this study, with max elevations ranging from ~90 – 173 m as they are in the more depressed HBL end of the Attawapiskat catchment.

The De Beers VM catchments are of particular interest because of what can be gained from previous research efforts. In a chemical analysis (i.e. Mg, Cl, and SC) of the stream water at these sites, it was found that the main source of solutes to the river systems was the bedrock aquifer, and the differences in chemical composition between streams can be attributed to their connection to the overburden and bedrock aquifers – the larger streams are deeply incised into bedrock and possibly directly linked to groundwater, while the smaller tributaries have no direct contact to the deep bedrock aquifer (Orlova and Branfireun, 2014). The lower reaches and tributaries of the Nayshkootayaow River (NR3, Trib5 and Trib7) were found to be more deeply cut into the bedrock (showing higher concentrations of solutes), while the upper reaches and tributaries (NR1 and Trib3) remain incised within the organic layer on top of marine sediments and are dominated by surface waters (showing lower concentrations of solutes). Overall, Orlova and Branfireun (2014) found an increase of deep groundwater contribution to the streams with an increase in catchment size (a downstream increase in deep groundwater input to the Nayshkootayaow River during both wet and dry seasons), with only Trib3 departing from this pattern.

OMNRF Ring of Fire Transition catchments

Eight OMNRF/OMOECC Ring of Fire (RoF) baseline study research catchments (Figure 3, red shading) are slightly east of the HBL/Shield boundary and for the purposes of this study are representative of the physiographic province ‘transition’ area (Figure 1). Koper KM 21 (K21) and Koper KM 2 (K2) are nested catchments (~159 km2 and ~211 km2, respectively), while Coomer KM 4 (C4), FNOS069 (F69), FNOS070 (F70), FNOS078 (F78), FNOS090 (F90) and FNOS091 (F91) are independent catchments (Figure 5) nearby (~100–1000 km2) (Table 2). These catchments are at a slightly higher elevation than the HBL catchments, with max elevations ranging from ~168–228 m (Table 2). While stream flow, water chemistry and stable water isotope data is currently being collected for RoF sites by the OMNRF, it was not available for the time frame of this study.

Figure 4: Victor Mine research sub-catchments.

Figure 5: Ring of Fire research catchments

Sturgeon-Nipissing-French Shield catchments As there are no long-term monitoring catchments located in the headwater Shield region of the Attawapiskat watershed, catchments from the Sturgeon River-Lake Nipissing-French River (SNF) watershed located near North Bay, Ontario are used to represent Shield catchments (Figure 3, blue shading). Located within the Boreal Shield ecozone, on Precambrian bedrock with glacial sand and clay deposits, this region exhibits similarities to the Boreal Shield of the Attawapiskat watershed. The four SNF catchments are dominantly covered by Great Lakes-St. Lawrence forest, have minimal urban/agricultural land use and little hydropower development (Figure 6). Despite this, North Bay’s location east of Sudbury, Ontario – a city with a history of mining, smelting and acid rain – is important to mention as acidic emissions may have impacted the region (i.e. pH of soils, surface waters) historically (Gunn et al., 2004). The SNF catchments are located further south than the extent of the Attawapiskat watershed, which will influence

17 climate-related metrics (e.g. PET/P, water isotopes). Nonetheless, large northern watersheds like the Attawapiskat, the Winisk, the Albany, etc. all have significant variations in latitude, and a version of this will be represented by the inclusion of the SNF catchments. SNF catchments range in size from ~80 – 3000 km2, with max elevations ranging from ~307 – 690 m (Table 2).

Figure 6: Sturgeon-Nipissing-French research catchments.

18 Table 1: List of study catchments, as defined by streamflow and water chemistry monitoring. Full station name, FIRNNO and PWQMN identification provided where relevant. Data source Site Description/Location Full Station Name (This Study) ID Western/Waterloo Tributary of Nayshkootayaow River Tributary-3 Trib3 Western/Waterloo Tributary of Nayshkootayaow River Tributary-5 Trib5 Western/Waterloo Tributary of Nayshkootayaow River Tributary-7 Trib7 Nayshkootayaow River (Tributary of Western/Waterloo NR001 NR1 Attawapiskat River) Nayshkootayaow River (Tributary of Western/Waterloo NR002 NR2 Attawapiskat River) Nayshkootayaow River (Tributary of Western/Waterloo NR003 NR3 Attawapiskat River) Koper-KM2 MNRF Koper Creek (JBL) K2 FIRNNO#: FNOS084 Koper-KM21 MNRF Koper Creek (JBL) K21 FIRNNO#: FNOS083 Coomer-KM4 MNRF Coomer Creek (JBL) C4 FIRNNO#: FNOS087 Vale Living with Lakes FIRNNO#: FNOS069 Centre McFaulds Creek (JBL) F69 PWQMN#: 19004301002 (LWLC)/MNRF/MOECC FIRNNO#: FNOS070 LWLC/MNRF/MOECC Streatfield River (JBL) F70 PWQMN#: 19004300902 FIRNNO#: FNOS078 LWLC/MNRF/MOECC Highbank Creek (JBL) F78 PWQMN#: 19004301402 FIRNNO#: FNOS090 LWLC/MNRF/MOECC Muketei River (JBL) F90 PWQMN#: 19004400202 FIRNNO#: FNOS091 LWLC/MNRF/MOECC Gleason Creek (JBL) F91 PWQMN#: 19004300602 Water Survey of Canada Glen Afton Sturgeon River near Glen Afton, ON GA (WSC)/Nipissing University WSC#: 02DC004 La Vase WSC/Nipissing University La Vase River at North Bay, ON LV WSC#: 02DD013 Veuve WSC/Nipissing University Veuve River at Verner, ON V2 WSC#: 02DD012 Little Sturgeon River below Booth Little Sturgeon WSC/Nipissing University LS Lake WSC#: 02DC013

19 2 Methods 2.1 Catchment Metrics

For each catchment, physiographic metrics (Table 2) were calculated in ArcGIS version 10.4 (ESRI, 2014) using a 30m raster enhanced flow direction grid (D8 flow direction method) derived from the filled, 30m, enhanced DEM product available in Ontario Flow Assessment Tool (OFAT-III) (originating from the Ontario Integrated Hydrology Dataset [OIHD] [OMNRF, OIHD, 2012]). The enforced DEM provided in the OIHD is not a filled DEM; however the enhanced flow direction product provided in the package was created using a filled DEM as the input dataset (OMNRF, OIHD, 2012). The filled DEM is not distributed as part of the OIHD package, however the provided DEM was filled as a pre-processing step before generating select physiographic metrics (e.g. drainage area, slope, TWI, mean flowpath length and median subcatchment area). For this study, mean flowpath length is defined as the distance along a flowpath to either the stream channel or the catchment outlet, consistent with similar hydrologic studies (e.g. McGuire et al., 2005; Hrachowitz et al., 2009) (see Appendix 1). Median subcatchment area is defined as the median of the subcatchment areas of all stream cells upstream of the catchment outlet (McGlynn et al., 2003). Drainage density and Shreve stream order were calculated using a map of all connected watercourse features (e.g. streams, lakes, etc.), from the enhanced watercourse layer from OIHD. Stream order is a function of stream network resolution, so for this study the OIHD layer (photogrammetrically captured from aerial photographs) was used as it has a consistent resolution for all of Ontario (see Appendix 2). Richardson et al. (2012) suggest the use of Shreve stream order for the Attawapiskat region over Strahler stream order, as it takes into account a larger number of minor tributaries contributing to the stream network. Mean topographic wetness index (TWI) was calculated following the method from Beven et al. (1979). Low values of TWI occur on steep hillslopes indicating areas of low saturation, while high values occur in valley bottoms and areas of convergence indicating that these areas are predicted to saturate first (Beven, 1979). The physiographic variables included in Table 2 are not normalized by area, unless they are normalized by definition (i.e. drainage density).

Percent cover of quaternary geology for all catchments was calculated by clipping the Quaternary Geology of Ontario Seamless Coverage ERLIS Dataset 14 (Ontario Geologic Survey

20 [OGS], 1997) – 1: 1,000,000 to each basin area in ArcGIS (Table 3, Appendix 3). A ranking of permeability was assigned to the dominant quaternary geology cover for each catchment based on an OGS permeability ranking system (OGS, 2003, Appendix E). Rank values from 1 to 5 were assigned, with 1 representing low, 2 representing moderate-low, 3 representing moderate, 4 representing moderate-high and 5 representing high permeability. An overall catchment ranking was applied based on the dominant permeability ranking for each basin area. Greater resolution quaternary geology data (1:100,000 [OGS, 2011]) is available for select locations (catchments K2, K21 and C4), but does not extend across the Attawapiskat watershed. The 1:100,000 product refines the ‘peat, muck, marl’ category to distinguish between bog and fen surface covers and resolves more till coverage, yet the resulting catchment rankings were consistent for both resolutions (Appendix 4). For this reason, the lower resolution dataset (1: 1,000,000) consistent for all catchments was used for this study.

Percent cover for bedrock geology for all catchments (Table 4) was calculated by clipping the Bedrock Geology of Ontario Rev1 Dataset (OMNDM, 2011) – 1: 250,000 to each catchment area (Appendix 5). A permeability ranking system was assigned to the dominant bedrock geology covers for each catchment based on generalized rock permeability information (Plummer et al., 2005; Dingman, 2008). Rank values from 1 to 3 were assigned to the dominant bedrock types, with 1 representing ‘less permeable’ materials, 2 representing ‘moderately permeable’ materials and 3 representing ‘more permeable’ materials. Rock types with >10% cover in the catchments are recorded in Table 4 with a corresponding permeability ranking. An overall catchment ranking was applied based on the dominant permeability ranking for each.

Landcover (%) (Table 5) was generated in OFAT-III, which combines three provincially available datasets: Far North Landcover Version 1.3[FNLCv1.3] – 1: 100,000; Provincial Landcover Database 2000 Edition [PLC2000] – 1: 50,000; and Southern Ontario Land Resource Information System [SOLRISv1.2], resampled to a common pixel size of 15 m and reclassified (OMNRF, OFAT, 2013). To assess if OFAT-III could differentiate between large bog and fen complexes, percent cover from the FNLCv1.3 dataset was compared to percent cover from OFAT-III for Trib5 (HBL) (Appendix 6). OFAT-III was determined to have a high enough resolution to distinguish between these important peatland features, and has a uniform resolution across all catchments included in the study. In Table 5, landcover type ‘disturbance’ is defined

21 by OFAT-III as forest clear cuts and burns (natural or anthropogenic) estimated to be less than 10 years of age, old burns (> 10 years) with sparse vegetation, or non- and sparse woody areas.

Climate metrics (Table 6) specific to the three regions were generated using meteorological data from three weather stations (two long-term and one research site). Lansdowne House Environment Canada climate station (1993-2014) is located west of the Ring of Fire catchments, and the De Beers Victor Mine meteorological station maintained by the University of Waterloo (2008-2012) is located on the De Beers VM site in the Attawapiskat watershed (Figure 3). North Bay Airport “A” Environment Canada climate station (1977-2012) is in the Sturgeon-Nipissing- French watershed, within the city of North Bay (Figure 3). A ten year record (2000-2010) was used for both the Lansdowne House climate station and North Bay Airport climate station, while the DeBeers Victor Mine record was shortened to three years (2009-2011) due to data gaps. Mean annual precipitation (mm) was calculated and averaged for all years for each of the meteorological stations (Table 6). Hamon’s (1961) equation was used as a simple (temperature based) method to calculate potential evapotranspiration (PET) for the three climate stations. A linear extrapolation was used to fill the temperature data gaps for the 2000-2010 period of record (at least 87% of daily data was available for any given year. The average annual moisture index is the ratio of annual potential evapotranspiration (PET) to annual precipitation (P) (Willmott and Feddema, 1992).

Flow metrics were generated from De Beers VM discharge stations (averaging 10 years of data for NR1, NR2, NR3, Trib3, Trib5 and Trib7), and Water Survey of Canada stations (for shield catchments GA [~40 years], LV [~20 years], V2 [~20 years] and LS [~4 years]) (Table 7). Mean and median annual flow (l/s/km2) normalized by catchment area were generated and Q10 and Q90 values were extracted (points where river discharge is equal or exceeded 10% of the time, or 90% of time, respectively). Select datasets with large periods of record (GA, LV and V2) were investigated with Stream Analysis Assessment Software (Metcalfe and Schmidt, 2012) to recheck Q10 and Q90 values. All values included in Table 7 have been normalized by drainage area of the individual catchments.

Table 2: Physiographic metrics for research sub-catchments (OMNRF, OIHD, 2012). Shading indicates exclusion from PCA (light grey) or transformed (dark grey) (See Appendix 8). Transition – Ring of Fire HBL – Victor Mine SNF Physiographic K2 K21 C4 F69 F70 F78 F90 F91 NR1 NR2 NR3 Trib3 Trib5 Trib7 GA LV V2 LS Metrics Drainage Area (km2) 211 159 206 109 1005 120 296 249 731 1090 2036 106 217 91 2999 84 1021 197 Mean Elevation (m) 170 173 181 159 184 188 182 172 129 121 113 94 104 82 342 236 234 324 Max Elevation (m) 189 189 204 168 208 222 228 194 173 173 173 105 139 91 688 307 325 446 Mean Catchment 1.8 1.9 2.1 1.5 1.7 2 2.3 1.9 1.8 1.8 1.8 1.8 1.7 1.6 8.2 2.4 3.8 2.8 Slope (%) Main Channel Slope 0.05 0.06 0.01 0.06 0.02 0.06 0.05 0.04 0.06 0.05 0.05 0.06 0.09 0.07 0.14 1.27 0.07 0.39 (%) Main Channel 67 43 77 31 176 42 54 100 123 145 192 31 65 31 240 31 111 63 Length (km) Mean Aspect (°) 176 176 179 171 177 187 179 172 173 173 174 178 176 175 176 191 178 188 Median Subcatchment Area 21 17 18 10 19 9 13 27 11 11 10 8 9 6 13 10 9 9 (km2) Mean Flowpath 1.2 1.2 1.5 1.2 1.5 1.6 1.5 1.5 2 1.9 2.4 1.3 2.5 4 0.9 0.8 0.9 0.9 Length (km) Mean TWI 4.9 4.9 5.0 5.2 5.1 5.3 5.0 4.9 5.0 5.0 5.0 5.0 5.1 5.2 4.8 5.2 5.1 5.5 Drainage Density -1 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.9 0.5 0.5 0.5 0.6 0.4 0.3 1.1 1.5 1.4 1.2 (km ) Shreve Order 22 18 16 10 55 9 24 12 77 121 163 21 11 2 1561 82 820 122

23 Table 3: Percent cover of Quaternary Geology (for values > 10%) and assigned permeability ranking (1:1,000,000 [OGS, 1997]). Permeability rankings of 1-5 are assigned for lowest to highest permeabilities. Transition – Ring of Fire HBL – Victor Mine SNF Quaternary Rank K2 K21 C4 F69 F70 F78 F90 F91 NR1 NR2 NR3 Trib3 Trib5 Trib7 GA LV V2 LS Geology Lake None 0 0 0 0 <10 11.4 0 0 0 0 0 0 0 0 <10 <10 <10 0 Peat, Muck, Marl 5 98.8 98.4 93.5 100 96.8 85.4 68.5 100 99.5 99.7 99.8 100 100 100 <10 <10 <10 <10 Till: Silt/Clay 1 <10 <10 <10 0 0 0 27.9 0 0 0 0 0 0 0 0 0 0 0 Silt & Clay: 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 <10 11.1 <10 Glaciolacustrine Sand & Gravel: 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 <10 34.7 Glaciofluvial Bedrock 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 80.4 92 70.4 53.3 OVERALL CATCHMENT 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 RANKING

Tracer-based metrics were generated from stable water isotopes δ18O and δ2H, Conductivity (EC) and pH data available for HBL catchments (late April-mid Oct 2010/2011 for all, plus 2012 for Trib5) and SNF catchments (year-round 2013-2015) (Appendix 7a-j). Few point measurements for EC and pH were available for RoF catchments, with no water isotope data. Mean, range, standard deviation (SD), and coefficient of variation (CV) for δ18O, δ2H, pH and EC were calculated for each catchment with available data (Table 8). It is assumed that the period of sampling in the HBL covers enough of a range from freshet through fall to accurately calculate variables (i.e. CV, SD) that provide insight into storage and residence time proxies, as described by Tetzlaff et al., (2009). CV is calculated as the Standard Deviation / Average. D- excess mean is calculated as the average of δ2H – (8* δ18O).

25 Table 4: Percent cover of Bedrock Geology (for values >10%) and assigned permeability ranking of dominant rock types. (1:250,000 [OMNDM, 2011]). Permeability rankings of 1-3 are assigned for lowest to highest values. Transition – Ring of Fire HBL – Victor Mine SNF Bedrock Rank K2 K21 C4 F69 F70 F78 F90 F91 NR1 NR2 NR3 Trib3 Trib5 Trib7 GA LV V2 LS Geology (Ultra)Mafic None <10 <10 <10 0 0 15.9 <10 0 0 0 0 0 0 0 <10 <10 <10 0 Mafic/Intrusive 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16.2 0 0 0 Felsic Igneous 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 <10 50.4 <10 17 Shale, Sand-, 3 0 0 0 0 18.3 0 0 0 100 100 100 100 100 100 0 0 0 0 Dolo &Siltstone Shale, Lime-, 3 91.5 95.6 85.3 100 81.7 84.1 19.3 100 0 0 0 0 0 0 0 0 0 0 Dolo &Siltstone Magmatic 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 <10 47.6 90.9 75.7 Conglomerate 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 40.2 0 0 0 Gneisses 3 0 0 0 0 0 0 74.1 0 0 0 0 0 0 0 <10 0 <10 <10 Quartz 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27.5 0 0 0 OVERALL CATCHMENT 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 1 1 1 RANKING

26 Table 5: Percent cover of landcover types for all research catchments (OMNRF, OFAT, 2013). Transition – Ring of Fire HBL – Victor Mine SNF Landcover K2 K21 C4 F69 F70 F78 F90 F91 NR1 NR2 NR3 Trib3 Trib5 Trib7 GA LV V2 LS Clear Open Water 5.9 6.2 9.7 14.4 10.4 13.8 3.9 4.3 10.2 8.9 8 8.3 6.2 6.2 12.4 1.3 3 1.3 Turbid Water 0 0 0 0 0 0 0 0 0.3 0.2 0.1 0 0 0 0 0 0 0 Swamp 16.1 18.1 18.1 5.1 11.7 25 17.5 16.3 10.8 9.9 8.6 4.5 7 6.8 0 0 0 0 Fen 27.5 27.1 28.9 31.1 28.4 21.13 33 37 35.9 34.9 26.4 37.3 41.7 0.1 0 0.2 0 Bog 46.3 43.7 36.7 45.5 47.6 34.9 46.6 41.9 33.5 36.3 40.3 57.2 44.5 44 2.1 3.3 3.2 3.3 Sparse Treed 0.2 0.3 0.4 0.4 0.1 0.2 1.3 0.4 1.2 1.3 1.1 1.1 0.9 0.2 1.9 14.5 13.1 7 Treed Upland 0 0 0 0 0.03 0.61 0.11 0 0.1 0.1 0.1 0 0.1 0 7.6 15.2 8.5 11.1 Deciduous Treed 0.1 0.1 0.1 0.1 0.1 0.93 0.53 0.35 0.5 0.4 0.2 0 0 0 40.6 36.1 46.4 50.9 Mixed Treed 3.5 4.4 3.6 0.4 1.6 10.4 6.2 3.5 5.7 6.3 5.4 2.5 4 1.1 34 4.7 12.3 23.5 Disturbance 0.4 0.2 2.5 3 0.1 2.4 2.6 0.3 0.7 0.8 1.4 0 0.1 0 1.4 0 2 0.2 Community/Infrastructure 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5.4 1.4 0.2 Agriculture & 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 9.7 0 Undifferentiated Land Use Bedrock 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0.1 0

27 Table 6: Climate data and metrics for each region. Data from Environment Canada (RoF and SNF, accessed in 2015) and De Beers VM Research Station (HBL). Lansdowne House (RoF) De Beers Victor Mine (HBL) North Bay Airport “A” (SNF) Climatic Metrics K2, K21, C4, F69 - F91 NR1, NR2, NR3, Trib3, Trib5 & Trib7 GA, LV, V2 & LS Period of Record 2000-2010 2009-2011 2000-2010 Annual Potential Evapotranspiration (Avg.) (mm) 480 459 584 Annual Total Precipitation (mm) (Avg.) (mm) 634 560 1093 PET/Precipitation (Annual Moisture Index) 0.77 0.84 0.55

Table 7: Flow-based metrics normalized by drainage areas. Data from Water Survey of Canada (SNF, accessed in 2015) and De Beers VM Research Station (HBL). Shading indicates exclusion from PCA (light grey) or transformed (dark grey) (see Appendix 8). Transition – RoF HBL – Victor Mine SNF Flow Metrics K2, K21, C4, F69 - F91 NR1 NR2 NR3 Trib3 Trib5 Trib7 GA LV V2 LS 2004- 2006- 2004- 2005- 2005- 2005- 1942- 1974- 1973- 2009- Period of Record 2015 2015 2015 2015 2014 2014 2013 2012 2013 2013 Mean Annual Flow (l/s/km2) 11 9 10 13 12 8 13 11 11 14 Median Annual Flow 2 5 3 4 5 3 2 9 4 4 7 (l/s/km ) Q10 (l/s/km2) 28 25 28 26 30 21 27 27 29 33 Q90 (l/s/km2) 0.7 0.7 0.6 1.1 0.4 <0.1 4.3 0.9 0.6 1.2

28 Table 8: Tracer-based metrics. Data collected by Nipissing University (SNF), De Beers VM Research Station (HBL) and OMECC (RoF). See Appendix 7a-j for data. Shading indicates exclusion from PCA (light grey) or transformed (dark grey) (See Appendix 8). Transition – Ring of Fire HBL – De Beers Victor Mine SNF Tracer Metrics K2 K21 C4 F69 F70 F78 F90 F91 NR1 NR2 NR3 Trib3 Trib5 Trib7 GA LV V2 LS 2010- 2010- 2010- 2010- 2010- 2010- 2013- 2013- 2013- 2013- Period of Record 2010 2010 2010 2010 2010 2010 2010 2010 2011 2011 2011 2011 2012 2011 2015 2015 2015 2015 δ18O & δ2H n-size 27 24 28 23 59 24 57 30 28 20 δ2H Mean (‰) -92.9 -93.8 -93.4 -92.0 -96.4 -93.9 -79.2 -76.3 -74.3 -76.8 δ2H Range (‰) 30.4 26.1 33.2 26.9 37.5 38.5 23.4 45.4 37.6 40.4 δ2H SD (‰) 6.1 5.7 6.7 5.5 7.6 8.3 5.2 12.1 8.5 9.8 δ2H CV (‰) -0.07 -0.06 -0.07 -0.06 -0.08 -0.09 -0.07 -0.16 -0.11 -0.13 δ18O Mean (‰) -12.3 -12.5 -12.4 -12.3 -12.7 -12.4 -10.5 -10.4 -10.1 -10.8 δ18O Range (‰) 4.2 3.5 5.2 5.0 5.8 6.0 3.8 6.7 5.7 5.2 δ18O SD (‰) 0.9 0.8 1.0 1.0 1.2 1.2 0.8 1.8 1.3 1.4 δ18O CV (‰) -0.07 -0.06 -0.08 -0.08 -0.09 -0.10 -0.08 -0.18 -0.13 -0.13 D-excess Mean 5.1 6.2 5.9 6.4 5.6 5.3 4.7 6.9 6.7 9.6 Cond. n-size 2 2 2 2 3 3 2 2 18 17 21 20 48 18 28 24 23 18 Cond. Mean 101.0 90.0 83.0 57.1 90.7 87.5 141.0 131.0 122.8 98.3 140.4 58.0 122.8 129.7 37.5 159.6 85.6 29.9 (µS/cm) Cond. Range 34.0 28.0 22.0 12.1 12.2 15.4 40.0 64.0 179.0 257.9 333.9 133.0 373.5 191.2 132.5 343.7 211.6 65.1 (µS/cm) Cond. SD (µS/cm) 24.0 19.8 15.6 8.6 6.7 7.8 28.3 45.3 53.7 65.9 89.6 34.0 75.5 61.5 25.3 110.2 56.5 15.1 Cond. CV (µS/cm) 0.2 0.2 0.2 0.2 0.1 0.1 0.2 0.4 0.4 0.7 0.6 0.6 0.6 0.5 0.7 0.7 0.7 0.5 pH n-size 2 2 2 2 3 3 2 2 20 16 20 20 53 19 29 25 23 18 pH Mean 7.4 7.3 7.4 7.0 7.6 7.5 7.8 7.3 7.4 7.6 7.8 7.2 7.5 7.4 7.1 6.9 7.1 6.2 pH Range 0.3 0.7 0.1 0.4 0.2 0.4 0.7 0.2 1.4 1.2 1.8 1.8 2.1 1.2 1.0 1.3 1.0 2.5 pH SD 0.2 0.5 0.1 0.3 0.1 0.2 0.5 0.1 0.4 0.3 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.8 pH CV <0.1 0.1 <0.1 <0.1 <0.1 <0.1 0.1 <0.1 0.1 <0.1 0.1 0.1 0.1 0.1 <0.1 0.1 <0.1 0.1

2.2 Statistical Analysis

Principal component analysis (PCA) is a statistical transformation that reveals patterns in a dataset and reduces the dimensions (number of variables), with minimal loss of information (Abdi and Williams, 2010). PCA transforms data into smaller or similarly sized sets of artificial variables (called principal components [PCs]) that account for the most variance in the original data. The process defines that PC1 accounts for the largest amount of variance in the dataset, PC2 accounts for the next highest amount (that was not accounted for in PC1) and so on, with each PC remaining uncorrelated with the ones before (Shaw, 2003). This operation is sometimes viewed as revealing the internal structure of the data while it also explains the largest amount of variance within the dataset.

Hydrologic modelling can be a tool for predictions of hydrologic behaviour within a set catchment area. Different approaches have been used in hydrology to analyze these behaviours by comparing many different variables that contribute to the overall hydrology of a catchment, often involving geomorphological, geological and meteorological characteristics. Many of the geomorphic parameters strongly correlate with each other so there is often need to reduce the number of variables (Singh et al., 2008). Many hydrologic studies have used principal component analysis to represent large datasets by PCs and to classify an area into different, climatologically homogeneous regions (Rao and Burke Jr., 1997). PCA has been used extensively in hydrologic studies for End Member Mixing Analyses (EMMA) (e.g. Orlova and Branfireun, 2014), classification approaches and similarity analyses (e.g. Tetzlaff et al., 2009; Wolock et al., 2004, Singh et al., 2008, and Toth, 2013) to help interpret large datasets. Tetzlaff et al. (2009) used PCA to differentiate between landscape structures of study catchments using only topographic indices. By plotting PC1 against PC2, catchments in different geomorphic provinces grouped together, and they determined that PCA has strong value in inter-catchment comparison to link geomorphic structure and hydrologic function (Tetzlaff et al, 2009)

In this study, PCA was used to assess how different catchments (already delineated by drainage area) group together in PC space based on their physical, climatic and hydrologic characteristics in a simple form of similarity analysis. This study follows a similar approach to Ali et al.’s (2012) catchment classification study by defining seven different scenarios using different

groupings of catchment metrics (Table 9). Ali et al., (2012) used Affinity Propagation (AP) as their mathematical technique for similarity analysis instead of PCA. AP allows for a different analysis in that it is a clustering algorithm that partitions similar objects (catchments) together in groups and identifies an “exemplar” catchment that is most representative of the catchments within that same group. Although this study closely parallels Ali et al. (2012) in the design of scenarios of metrics considered, PCA was used as the method of analysis in support of assessing similarity.

Three scenarios (Scenarios 1-3) compare catchments based on a combination of physiographic and terrain based metrics, two (Scenarios 4 and 5) compare catchments based on hydrologic (flow and tracer) metrics, and the final two (Scenarios 6 and 7) compare catchments based on all the above metrics, both physiographic and hydrologic. Two PCs were retained for each scenario, accounting for at least 70% of the variance.

Table 9: Combinations of catchment characteristics used for different Principal Component Analysis scenarios. Refer to Tables 2-8 for detailed information about each group of metrics. Metrics included Scenario Name Area Physiography Quaternary/ Landcover Climate Flow Tracer-based Bedrock Geology 1. Terrain X 2. Physical (No Area) X X X X 3. Physical (Area) X X X X X 4. Hydrologic X X 5. Bulk Hydrologic * X X 6. All (No Area) X X X X X X 7. All (Area) X X X X X X X * Bulk Hydrologic scenario uses point measurements of streamflow and water chemistry instead of average values.

PCA has been widely used for a variety of hydrologic analyses, but the use of a large dataset is important: the larger the sample size, the more reliable the PCA results will be. In this study, only HBL (6 catchments) and SNF (4 catchments) datasets were available to calculate the statistic-based metrics of streamflow and tracer data, with one value reported for each statistical metric for each catchment in the Hydrologic analysis (i.e. Mean, range, standard deviation, and coefficient of variation). To address whether sample size may affect results, a bulk hydrologic scenario (Scenario 5) using 217 point measurements was used in a complimentary PCA.

PCA requires that the data demonstrates approximate normality and linearity between the variables. The majority of the metrics (29 of 38) generated for this analysis are considered acceptable for PCA (approximately normally distributed and linear), however four metrics (channel length, CV pH, CV EC and SD pH) were omitted and five metrics (LOG drainage density, LOG shreve stream order, LOG channel slope, LN median subcatchment area, and SQRT Q90) were transformed to meet the criteria of approximate normality and linearity. See Appendix 8 for further discussion.

To further assess catchment groupings in component space, Cluster Analysis was used to divide catchments into groups where catchments within the same group are more similar to each other than those in other groups (Yeung and Ruzzo, 2000). Two K-Mean Cluster (KMC) approaches were applied on the same datasets for comparison (one on the original variables and one on the resulting PCA component scores from the scenarios). In preparation for the KMCs, Hierarchical Cluster (HC) was implemented and the “elbow rule” (where values are plotted and the largest increase between values indicates the number of clusters, creating an “elbow” in the plotted line [Chiang and Mirkin, 2010]) was used to determine the number of relevant clusters for each scenario. HC was used before KMC because the HC approach has a high level of clustering error but the output provides the number of clusters required for the set-up of the KMC, which is a more reliable clustering approach (Chiang and Mirkin, 2010). For each scenario (Table 9) the HC determined that 6 clusters were required. In the two subsequent KMCs for each scenario, the first analysis uses the original metrics of interest for each catchment, while the second analysis uses the component loadings resulting from the PCA (which has reduced the dimensionality of the dataset but only considers some variance between metrics [Yeung and Ruzzo, 2000]).

3 Results 3.1 Catchment Characteristics

The catchments used in this study range considerably in size (Table 2), with the largest being GA and NR3 (2999 km2 and 2036 km2, respectively), and the smallest being Trib7 and LV (91 km2 and 84 km2, respectively). There are key differences in catchment characteristics between the HBL/RoF and the SNF shield catchments: compared to HBL/RoF, SNF catchments have a larger

range of mean elevations (234–342 m versus 82-129 m [HBL] and 159-188 m [RoF]), have greater mean catchment slopes (2.4–8.2% versus 1.5–2.3% [RoF]) and channel slopes (0.14– 1.27% versus 0.01–0.06% [RoF] and 0.05-0.09% [HBL]). They also have greater drainage densities (1.1–1.5 versus 0.3–0.6 [HBL] and 0.4-0.9 [RoF]) and Shreve stream orders (82–1561 versus 2–163 [HBL] and 9-55 [RoF]), with smaller mean flowpath lengths (0.8–0.9 km versus 1.2–1.5 km [RoF] and 1.3-4 km [HBL]). Within the Attawapiskat itself, HBL catchments have lower mean elevations than the RoF catchments (82–129 m versus 170–188 m) as they are positioned closest to the coast, smaller median subcatchment areas (6–11 km2 versus 9–27 km2), higher main channel slopes and higher stream orders (see above).

Median subcatchment area helps describe catchment-scale differences in drainage structures and residence times (e.g. McGlynn et al., 2003), and for this study ranges from 6–27 km2 (Trib7 and F91, respectively). Cumulative frequency distributions of subcatchment area for each catchment are shown in Figure 7 and 8 (normalized by area). RoF catchments show the most variation in distributions with subcatchment areas ranging between ~35 km2 (F69) and ~145 km2 (F91) by the 75th percentile, which is shown by the large range of RoF median subcatchment areas (9–27 km2). Figure 8 shows for F90 and F70 (RoF), GA and V2 (SNF), and all HBL catchments that a high percentage of cumulative subcatchment area (75–95%) is collected by 25% of the stream channel. The two largest catchments (GA and NR3) represent the high end of this range (~94 and ~95%, respectively) (Figure 8, dotted lines). Generally speaking, HBL and SNF catchments have lower median subcatchment areas (6–11 km2 and 9–13 km2, respectively) than RoF catchments (9–27 km2) (Figure 7).

Mean topographic wetness index (TWI) values cover the same ranges for HBL and RoF catchments (5.0-5.2 and 4.9-5.2, respectively), while SNF catchments range similarly (LV [5.2] and V2 [5.1]) to both higher (LS: 5.5) and lower values (GA: 4.8). Cumulative frequency distributions of TWI values are shown in Figure 9, with distributions for individual catchments in Appendix 9a-c. The HBL and RoF catchments show very similar normalized cumulative frequency distributions of TWI, indicating that these catchments have proportionally similar areas of high and low values, or areas that act hydrologically similar. The “jag” in HBL and RoF frequency distributions is from a low amount of TWI values 3-4, as these values are found along the larger rivers in the catchments (Appendix 10). SNF catchments distinguish themselves with

33 proportionally more low TWI values and less high TWI values than the HBL or RoF catchments, and the mean values show the most variability within a single physiographic region of all the catchments. GA is the steepest catchment which is reflected in having the lowest mean TWI value.

Ranking of quaternary geology permeability for HBL and RoF catchments is identical, with the main difference between the HBL/RoF and SNF catchments (Table 3). The quaternary geology of HBL/RoF catchments is dominated by peat, muck and marl (68.5-100% coverage), which results in the same high permeability ranking of 5, while SNF catchments are largely dominated by bedrock (70-92% coverage for 3 of the 4 catchments) with smaller percentages of sand and gravel glaciofluvial, and/or silt and clay glaciolacustrine deposits. Based on the dominance of bedrock, the quaternary geology of all four SNF catchments is given a permeability ranking of 1, indicating low permeability.

Similarly, permeability rankings for bedrock geology (Table 4) were uniform across RoF/HBL catchments but different for SNF. HBL catchments are underlain by shale, sandstone, dolostone and siltstone bedrock (100% coverage) while RoF catchments are dominantly shale, limestone, dolostone and siltstone (81.7–100% coverage) except for F90, which is 74.1% gneissic rock. Bedrock permeability ranking of 3 applies to all HBL/RoF catchments, representing the most permeable materials. SNF catchments have different dominant bedrock geology types. V2 and LS are 90.9% and 75.7% magmatitic rock, respectively, and are given a ranking of 1, representing lowest permeability. LV is a mix of felsic igneous (50.4%) and magmatitic (47.6%), with both bedrock types characterized with low permeability ranking of 1. GA is conglomerate (40.2%), quartz (27.5%) and mafic/intrusive (16.2%), with an overall rank of 2 (representing low to moderate permeability) because both conglomerate and mafic/intrusive (56.4%) have this rank. Overall, SNF catchments are underlain by much less permeable bedrock than those in the HBL/RoF.

For landcover, the main difference is again seen between the HBL/RoF and SNF shield locations (Table 5). SNF sites are dominantly treed (70.5-92.5%, versus 0.9-12.1%) with less wetland coverage (2.2-3.4% versus 71.6-92.5%) and more community/infrastructure, agriculture and bedrock than the other catchments. Within the HBL/RoF the most treed catchments (078, 090

and NR2) are those with higher mean catchment slope values than the rest. Although these catchments are still dominated by wetlands (71.6-85%), the greater percentage of tree coverage (8.1-12.1%) compared to the rest (0.9-7.5%) suggests better soil drainage.

Climate data (Table 6) shows higher PET across the Attawapiskat watershed (0.84 in the HBL and 0.77 around the RoF sites), and lower for the SNF shield sites (0.55). SNF catchments receive almost 2 times more precipitation than the HBL/RoF catchments, with 560 and 634 mm of mean annual precipitation for HBL/RoF catchments and 1093 mm in the SNF catchments. Estimated mean annual PET is slightly lower for HBL/RoF catchments (459 mm and 480 mm, respectively) compared to SNF (584 mm) (Appendix 11). The lower PET at the more northern latitude is expected (compared to general PET maps of Ontario), and PET values are consistent with spatial trends across Ontario for the catchments (Natural Resources Canada, Potential Evapotranspiration Maps, 1974).

Flow duration curves for HBL and SNF catchments are shown in Figure 10 (flow data was not available for RoF catchments at the time of study). At low flows and highest exceedance probability, GA has much more discharge being generated per unit area (from 50% exceedance probability [EP] onwards) compared to the rest of the catchments. LS shows more discharge generated per unit area from 40-80% EP than the other catchments as well, but drops down at low flows (Q80-Q100). GA is the largest catchment with the highest stream order and longest channel length (allowing for the generation of higher discharge per unit area), while LS is much smaller and what is contributing to the higher flows is less obvious (Table 7). For the remainder of the catchments, discharge behaviours are more similar to each other during high flows (Q10- Q70) with more variation during low flows (Q80, Q90). Trib7 is the driest in terms of how much water is accumulating per unit area, and is less able to generate flows compared to the other catchments. LV is smaller than Trib7 in total catchment area, but generates more water per unit area than Trib7 and has a much larger drainage density and stream order.

For the isotope and tracer-based metrics, SNF catchments are consistently more enriched in δ18O and δ2H (Figure 11) compared to the other research catchments. This is consistent with more depleted δ18O and δ2H in mean annual precipitation with increasing latitudes due to a global isotope-temperature correlation (Clark, 2015). As an example, records from the Canadian

Network of Isotopic Precipitation (CNIP) show Bonner Lake ON mean annual precipitation to be more depleted than Ottawa ON by 2.8‰ for δ18O and 21.9‰ for δ2H (Gibson et al., 2005). GA has the lowest ranges and standard deviations for δ18O and δ2H of the SNF catchments, which results in CV values closer to the VM catchments (Table 8). GA is a large, unaltered catchment with a potentially large amount of subsurface storage contributing to isotope dampening compared to the other SNF catchments. SNF catchments also have consistently lower mean pH values than HBL/RoF catchments (6.2-7.1 versus 7.0-7.8), which could be a remnant of acid rain deposition in the past (Gunn et al., 2004). Conductivity varies mostly by site, with no clear differences between the HBL, RoF and SNF catchments. LV has the highest mean conductivity values (159.6 µS/cm) and LS has the lowest (29.9 µS/cm), with the rest of the sites varying in between.

______-______

10000 SNF Catchments RoF Catchments HBL Catchments

1000 ) ) 2

100 catchment Area (km catchment

Sub - 10

1 0 0.25 0.5 0.75 1 Normalized Frequency

Figure 7: Cumulative frequency distributions of subcatchment area for individual catchments. Median (50th percentile) subcatchment area is indicated by the vertical line marking 0.5.

1.00 SNF Catchments RoF Catchments HBL Catchments ) ) 2 0.75

0.50

0.25 catchment Normalized SubArea (km - catchment

0.00 0 0.25 0.5 0.75 1 Normalized Frequency

Figure 8: Cumulative frequency distributions of subcatchment area for individual catchments normalized with respect to total catchment area. Median (50th percentile) subcatchment area is indicated by the vertical line marking 0.5. Dotted lines mark the two largest catchments GA (blue) and NR3 (green).

Figure 9: Cumulative frequency distributions of TWI for individual catchments grouped by physiographic region.

1000.00 NR1 NR2 NR3 Trib3 100.00 Trib5 Trib7 GA LV

) ) LS 2 10.00 V2

1.00 Discharge (l/s/km

0.10

0.01 0 10 20 30 40 50 60 70 80 90 100 Exceedence Probability (%)

Figure 10: Flow duration curves for SNF (blue and green) and HBL (shades of grey/black) catchments. Q10 (high flows) are when discharge (Q) is equaled or exceeded 10% of the time, Q90 (low flows) are when discharge is equaled or exceeded 90% of the time (Smakhtin, 2001).

3.2 Principal Component Analysis Scenarios

Terrain and Physical Metrics:

PCA of Terrain metrics (Scenario 1, Figure 12) includes all physiographic metrics (Table 2) excluding drainage area for all catchments. The two principal components (PCs) retained for this analysis explain 72.5% of total variance (Appendix 12a). Mean catchment slope and LOG drainage density (controls on topology), LOG stream order (topology and typology), and mean flowpath length (typology) are strongly correlated with PC1 suggesting this axis represents both topology (i.e. drainage networks and connectivity) and typology (i.e. residence time and internal storage) within the catchments. Mean TWI, mean aspect and LOG channel slopes are strongly loaded on PC2, representing topographical (i.e. hydraulic gradients) controls. HBL and RoF catchments have lower elevations, lower catchment slopes, and smaller drainage density and stream orders than SNF catchments, and plot nearby each other in PC space (Trib and NR catchments are separated by RoF catchments, which have shorter mean flowpath lengths and generally larger median subcatchment areas). SNF catchments are spread out (more variable), but still plot closer to other SNF catchments than HBL/RoF.

In the cluster analysis based on the variables, Trib3, 5 and 7 group together (yellow), NR1, NR2, and NR3 group together (purple), and all RoF catchments group together (green), leaving the SNF catchments independent of each other (Figure 12a). The HBL Trib catchments (Trib3, 5 and 7) have some of the lowest values (elevations, channel lengths, smallest slopes, etc.), while NR catchments (NR1, 2 and 3) are very similar to each other for all terrain characteristics (see left axis, Figure 12).. SNF catchments are independent of each other, indicating high variability (and possibly low similarity) between them, despite having the same physiographic region (see top and bottom axes, Figure 12). Clustering the PC scores after the PCA groups NR1, NR2, and NR3 (purple); Trib3, F69, F90 and F78 (red); K2, K21, C4, F70 and F91 (green); Trib 5 and Trib 7 (yellow) and LV and LS (orange) with GA, V2 left separate (Figure 12b). Trib3, F69, F90 and F78 have the smallest mean channel lengths and “middle of the range” median subcatchment areas, grouping them together. F78 has the highest mean aspect of the HBL/ RoF catchments, explaining why it plots away from the rest on PC2. LV and LS are most similar in mean catchment slope, channel length, aspect and stream order compared to the rest of the SNF sites.

42 Trib7 and Trib 5 have the lowest elevation, catchment slope, stream order and highest mean flowpath length of all the catchments.

PCA on Physical Metrics (No Area) (Scenario 2, Figure 13) includes all physiographic metrics (except drainage area) (Table 2), quaternary and bedrock geology rankings (Table 3, 4), percent cover of landcover types (Table 5) and climate metrics (Table 6) for all catchments. The two PCs retained for this scenario explain 70.7% of the total variance (Appendix 12b). This scenario builds on Scenario 1 (Terrain Metrics), with forests (i.e. more deciduous/treed upland/sparse treed towards right axis, less treed on left), wetlands (i.e. more swamp/bogs/fens towards left axis, less wetland on right), bedrock/quaternary permeability (i.e. more permeable on left axis, less permeable on right), and all climate metrics correlating on PC1. In the PC space, the HBL/RoF catchments plot tighter together than in Scenario 1 (overlap in upper left) with the SNF still separate (lower right). Clustering the variables resulted in the same groups as Scenario 1a (Figure 13a). Adding climatic, geologic and landcover metrics removes some variation between the HBL/RoF sites as the geology and climate act as homogenizing variables and landcover varies minimally across the HBL. The NR and Trib catchments cluster separately despite being near because some physiographic metrics are still a function of catchment size (Table 2). Clustering after PCA is similar to Scenario 1 (Figure 12b), only now the Trib catchments group together again (Figure 13b). There are two RoF groupings (green and orange), one NR (purple) and one Trib group (yellow). LS and LV still form the only SNF group (blue) despite plotting further away from each other than in Scenario 1.

PCA on Physical Metrics (With Area) (Scenario 3, Figure 14) includes the same metrics for Scenario 2, with the addition of drainage area (Table 2, 3, 4, 5 and 6). The two PCs retained for this scenario explain 70.3% of the total variance (Appendix 12c). The PCA results for this set of metrics are extremely close to the Scenario 2 analysis, with drainage area strongly correlated with PC2. The catchments plot the same as Scenario 2 in PC space (HBL/RoF together, SNF separate) just reversed on PC2 axis. In the cluster analyses, the only change from Scenario 2 is one less cluster (6 instead of 7) after PCA - combining the Trib and NR catchments (purple) (Figure 14b).

In the first three Scenarios (Terrain, Physical [No Area] and Physical [Area]) HBL/RoF catchments generally plot together, separating away from the SNF catchments in PC-space. This

43 clearly distinguishes between the two physiographic provinces, even though the SNF catchments are highly varied among themselves. Scenario 1 separates HBL Trib catchments from RoF slightly, and adding other metrics (climate, landcover, and quaternary/bedrock geology) reduces the separation of NR catchments from ROF in Scenario 2 and 3, but the initial Terrain metrics appear to be the dominant controls in all three.

Hydrologic Metrics:

PCA on Hydrologic Metrics (Scenario 4) includes flow and tracer-based metrics (Table 7, 8) for the ten HBL and SNF catchments for which this data was available. The two PCs retained for this scenario explain 70.5% of the total variance (Appendix 12d). Mean and Range for pH are correlated with PC1, while all EC values are correlated with PC2 (Figure 15). All flow metrics included in this analysis are positive on PC1 and negative on PC2, with mean annual flow and Q10 more strongly correlated with PC1, and median annual flow and SQRT Q90 more strongly correlated with PC2 (using >0.60 as the value for “strong” correlations). For water isotopes (δ18O and δ2H) range values are positively correlated, and CV values are negatively correlated with both PCs. In PC-space, catchments group consistent with physiographic provinces: HBL catchments plot in the upper left while SNF catchments are generally more acidic and more enriched in stable water isotope ratios (δ18O and δ2H) plotting in the lower right (with a few exceptions). For HBL catchments, Orlova and Branfireun (2014) concluded that NR2, NR3, Trib5 and Trib7 demonstrated less bedrock groundwater influence than NR1 and Trib3 and a similar grouping is suggested in Figure 15, with the former four catchments loaded positively, and the latter two loaded more negatively on PC2 (NR1 is loaded 0.02 on PC2). For SNF catchments, flow characteristics place GA and LS negative on PC2 due to significantly higher low flows (Q90) compared to other (HBL and SNF) catchments. GA shows variation (range and SD) in water isotopes more similar to HBL catchments than the other SNF catchments, resulting in a more negative position along PC1. While PC-space shows general grouping by physiographic province like in Scenarios 1-3, clustering the variables and clustering after PCA resulted in very different groupings. Clustering the variables (Figure 15a) groups NR3 and LV together due to high mean, range and SD conductivity values (green). NR1, Trib7 and V2 group around the centroid of the PC-space, possibly due to mid-range values for all characteristics (blue). The last grouping of GA and Trib3 (orange) have similar range, CV and SD of δ18O and δ2H, similar pH and EC metrics and at least some annual flow metrics (mean, Q10). Trib5, NR2

44 and LS remain independent. Clustering the PC scores (Figure 15b) groups Trib5 and NR3 (green), with similar flow metrics and high SD and range of EC. NR1, NR2, Trib3 and Trib7 form a second group (blue), and LV and V2 form the third group (yellow) although they plot separately, as they are the most similar in flow properties than the rest of the SNF catchments. GA and LS remain independent.

PCA of individual point measurements (discharge, pH, conductivity, δ18O and δ2H) (Scenario 5, Figure 16) during the same period of record supports the general catchment grouping patterns observed by the previous analysis of catchment flow and tracer statistics (Figure 15). Retaining two PCs explains 77.7% of the total variance (Appendix 12e). Stable water isotopes (δ18O and δ2H) strongly correlate with PC1, while pH and EC correlate with PC2. Again, HBL catchments group separately from SNF catchments, driven in strong part by differences in mean stable water isotope and pH values. Seasonality in streamflow water isotopes is visible in both regions, with spring samples (March-May) plotting in the lower left (most negative on PC1 and PC2) representing isotopically depleted spring freshet (purple circle, Figure 16), and more enriched summer samples (June-August) plotting in the top right (positively on PC2) (green circle, Figure 16). Fall samples (September-November) plot between these seasons with HBL samples plotting slightly higher on PC2 and slightly to the left (more negative) (light red circle, Figure 16) compared to SNF samples (dark red circle, Figure 16). The HBL winter season is not represented as winter sampling is difficult in remote environments; however, some SNF winter samples (December-February) are available and plot the most circumneutral of the seasons.

Scenarios 4 and 5 demonstrate that basic terrain and physical descriptions of HBL catchments (Scenarios 1-3) fails to capture important information for hydrologic response/similarity. Hydrologic and tracer metric statistics separate HBL catchments by groundwater influence consistent with Orlova and Branfireun (2014), while SNF catchments remain highly variable. Headwater catchments with greater bedrock groundwater influence (NR1, Trib3) plot separately in PC-space from the downstream catchments with less groundwater influence (NR3 and Trib5).

All Metrics:

PCA of All Metrics (No Area) (Scenario 6) includes all metrics (Tables 2-8) except drainage area for the ten catchments for which data is available (HBL and SNF only). The two PCs retained for this scenario account for 73.1% of the total variance (Figure 17, Appendix 12f). The

45 majority of metrics in this analysis are strongly correlated with PC1, similar to the Physical Metrics (No Area) analysis (Scenario 2). Based on landcover (wetlands), mean flowpath length and bedrock/quaternary geology permeabilities, PC1 is negatively associated with typology (storage and residence times), and positively associated with topology (connectivity) (see Scenario 1). Flow and tracer-based metrics correlate with both PC1 and PC2, but are more strongly loaded along PC2 (higher correlations). Catchments once again plot similarly to Scenario 1 in PC-space: NR and Trib catchments are separate, and SNF remain highly variable. Clustering the variables groups NR1, NR3 and Trib5 (green); NR2, Trib3 and Trib7 (blue), and leaves the SNF catchments independent. Clustering after PCA groups the NR catchments (green); Trib5 and 7 (blue) and excludes Trib3 (which consistently has the lowest δ18O, δ2H and EC values, and the highest pH of the Tribs [red]) (Figure 17b). Lastly, LV and V2 group together (purple) with the most similar flow, pH and EC of the SNF sites, similar to Scenario 4 (Hydrologic Metrics). LV and V2 have the highest percentages of community/infrastructure and agriculture/undifferentiated land use, but since this grouping did not play out in Scenario 2 or 3 (Physical [No Area] and [Area]) and did appear in Scenario 4 (Hydrologic), this grouping is assumed to be influenced by the flow/tracer-based metrics.

The PCA for All Metrics (Area) (Scenario 7) includes all metrics including area (Tables 2-8) for the ten catchments that the data is available for (HBL and SNF only) and the results are very like All Metrics (No Area), just reversed on the PC2 axis (Figure 18, Appendix 12g). The two PCs retained for this scenario account for 72.9% of the total variance. The metric loadings parallel Scenario 6 (only reversed), with drainage area strongly correlating with PC2. Clustering the variables groups NR1 and NR2 together (blue), with NR3 separate (orange). NR3 has the highest SD and ranges for all water chemistry of the NR catchments, and has the largest total area, longest main channel length, second longest mean flowpath length, and highest stream order of the HBL catchments. For the SNF catchments, LV groups with LS (purple) because they are the smallest of the SNF sites. Clustering results after PCA grouped catchments the same as in Scenario 6 (Figure 18b).

HBL and SNF remain separate in the PC-space and SNF remains highly variable. Within the HBL, the groundwater influence grouping consistent with Orlova and Branfireun (2014) and seen in Scenario 4 (Hydrologic) does not reappear even though flow and tracer based metrics are incorporated here in Scenario 7. The catchments plot most similar to Scenarios 1-3.

(a)

(b)

Figure 12: PCA Scenario 1 (Terrain Metrics). (a) Catchments clustering based on variables, and (b) catchments clustering after PCA.

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Figure 13: PCA Scenario 2 (Physical Metrics [No Area]). (a) Catchments clustering based on variables, and (b) catchments clustering after PCA.

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Figure 14: PCA Scenario 3 (Physical Metrics [With Area]. (a) Catchments clustering based on variables, and (b) catchments clustering after PCA.

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Figure 15: PCA Scenario 4 (Hydrologic Metrics [Flow and Tracer]). (a) Catchments clustering based on variables, and (b) catchments clustering after PCA.

Figure 16: PCA Scenario 5 (Bulk Hydrologic Analysis). Water isotope seasons are indicated by coloured circles: isotopically depleted spring samples (Purple circle [Mar-May]), isotopically enriched summer samples (Green circle [Jun-Aug]), and varied fall samples (light red circle [HBL Sep-Nov] and dark red circle [SNF Sep-Nov]).

(a)

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Figure 17: PCA Scenario 6 (All Metrics [No Area]). (a) Catchments clustering based on variables, and (b) catchments clustering after PCA.

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Figure 18: PCA Scenario 7 (All Metrics [With Area]). (a) Catchments clustering based on variables, and (b) catchments clustering after PCA.

53 4 Discussion

The Attawapiskat watershed in Ontario’s Far North provides a unique landscape that spans physiographic provinces (Boreal Shield and Hudson Bay Lowlands), with the HBL (De Beers VM) catchments situated in the middle of a large bog/fen complex. For all the large watersheds in Ontario’s Far North, this range of physiographic provinces is an important feature for analysis specifically in the context of hydrologic similarity and understanding water generation. The hydrology of Boreal Shield headwater catchments is well studied across Ontario; however, the complexity of large, undisturbed peatlands is not commonly addressed in hydrological classification studies. The focus here on the Attawapiskat watershed then becomes an important case study, generating results that are likely to be relevant across the larger region. In the context of the IAHS Predictions in Ungauged Basins decade of research, studies like these are imperative to expand our capacities to make predictions to assist in land use planning and water governance purposes, as well as helping to establish baseline conditions in remote areas.

Ali et al. (2012) highlight that other studies are typically very specific about what metrics they include (i.e. land use, climate, storage, etc.), however no universally accepted metric or combination of metrics have been identified specifically for catchment similarity analyses. In this study, it was confirmed that assessments of hydrologic similarity as illustrated by PCA results will vary depending on the metrics included in the analysis. Using PCA to assess where catchments plot in the component space resulted in physiographic groupings: HBL/RoF catchments together and SNF catchments together with a clear distinction between them. In the physical/terrain scenarios (1-3), the HBL and RoF catchments grouped together with little to differentiate them, except for slight separation of the Trib catchments. In the All Metrics scenarios (6-7) there is again separation of Trib and NR catchments, but Trib5 is closer to the NRs than the other Tribs, and Trib7 is the most distant. The most ‘shuffled’ results are from the Hydrologic scenario (4) where groupings such as NR3/Trib5 and NR2, NR1 and Trib3 occur, demonstrating more hydrologic-based variations within the catchments independent of physical metrics.

Based solely on terrain/physical metrics, the HBL/RoF catchments plot closely in PC-space with the NR and Trib catchments slightly separate from each other despite their proximity within the

54 HBL. The NR catchments are much larger (~ 730-2036 km2) and several terrain metrics (not normalized by area) can echo this variation in catchment size (i.e. channel length and stream order) (Table 2). SNF catchments plot separately from the HBL/RoF sites, but show high variability between them, resulting from large variations in a suite of metrics (Table 2). As a result, PCA in Scenarios 1-3 suggests catchments within the HBL are hydrologically similar (Trib and NR sites); RoF and HBL catchments are hydrologically similar; and SNF catchments are more hydrologically similar to each other than to HBL or RoF catchments.

Use of hydrologic metrics and tracer data (excluding RoF because of data availability) very different patterns emerge: the scale-based NR and Trib groupings within the HBL are no longer apparent, and are instead spaced out across the PC-space. Orlova and Branfireun (2014) report NR3, Trib5 and Trib7 are deeply incised channels with greater links to groundwater, while NR1 and Trib3 are less incised (within an organic surface layer) and more dominated by surface waters. Using a limited part of the same dataset, this was reflected in PC-space (Scenarios 4 and 5), with NR3 and Trib5 close to each other (Trib7 separate, but still in proximity) and NR1 and Trib3 close together, with NR2 bridging the gap between the two sets (Figure 15). With NR1/Trib3 representing the upper, less incised reaches of the river network and NR3/Trib5 representing the lower, more incised reaches, NR2 logically represents the middle reaches of the river (potentially with a greater combination of surface water and groundwater interaction) (Figure 4).

Investigating the seasonality of stream chemistry showed Trib5 and Trib7 behaving similarly to the downstream Nayshkootayaow (NR) sites during both high and low flows, despite the significant size differences between catchments (Orlova and Branfireun, 2014). In general, Orlova and Branfireun (2014) found an increase of deep groundwater contribution to the streams with an increase in catchment size during both wet and dry seasons, yet in Scenario 4, catchments plot in the component space regardless of catchment size. SNF catchments are extremely variable, and plot nearly as far apart from each other as they do from the HBL catchments. The division between SNF and HBL catchments is obvious (diagonally from -PC1/- PC2 to +PC1/+PC2), which was originally thought to be explained by the fact that SNF catchments are at a different latitude than Attawapiskat shield (impacting mean isotope values, as SNF are consistently more enriched in δ18O/δ2H than the HBL sites). An assessment of this

55 latitudinal influence was made by running a PCA with all hydrologic metrics except δ18O or δ2H, to get more of an indication of storage and residence times. The resulting PC-space demonstrated the same divide between the SNF/HBL catchments as Scenario 4, indicating that the latitudinal influence is not a dominant control. The divide, however, may still be influenced by the history of acid rain deposition in the SNF (consistently lower pH values in the SNF than the HBL [more acidic]) (Gunn et al., 2004).

In a full analysis of all metrics used in this study, the catchments plot in the PC-space very similarly to how they plot in the physical-based scenarios (1-3). Trib7 is consistently the most removed from the HBL catchments in scenarios 1-3 and 6-7, suggesting terrain and landcover characteristics separate this catchment out as the least hydrologically similar of the HBL catchments. Including hydrologic metrics reflects the same general NR and Trib groupings, with Trib3 and Trib5 more hydrologically similar to the NR catchments. Scenarios 6 and 7 demonstrate that catchments within the same physiographic provinces plot closer together than catchments in other physiographic provinces (i.e. HBL catchments more hydrologically similar to HBL catchments than SNF, etc.).

Do PCA, Cluster Analyses or PCA + Cluster Analyses provide the most meaningful information?

Using different data analysis techniques and different clustering algorithms to analyze the same set of data can lead to very different conclusions. In this study, the application of K-Means Cluster (KMC) approach to the original variables and then again after PCA to the ‘transformed’ dataset resulted in different cluster groups (clustering catchments based on hydrologic similarity). In the Physical scenarios (1-3), the KMC on the original variables (Figures 12a, 13a and 14a) resulted in clusters more similar to the groupings evident in PC-space. In contrast, for the Hydrologic and All Metrics scenarios (4, 6-7), the KMC after PCA (Figures 15b, 17b and 18b) resulted in clusters more similar to the groupings in PC-space instead. In general, the KMC after PCA resulted in clusters more like the PC-space groupings in four of the seven scenarios, so the usefulness of the cluster analyses (beyond an assessment of catchment location in PC-space) depended on which variables were used in each scenario.

56 What metrics are important in evaluating hydrologic similarity for catchments within the HBL and across the watershed? (Expected topology – i.e. role of a drainage network or structural connectivity [DD], to be more important in hydrologic similarity than topography – i.e. slope)

To address the question of what metrics were the most important for evaluating hydrologic similarity in this study, it is helpful to relate back to Buttle’s (2006) T3 concept (i.e. topology, typology and topography) in Figure 2. In the Attawapiskat watershed, drainage network development and structural connectivity (Topology) across the landscapes were expected to be of key importance because the hydrologic systems within and between the various wetland types in the region could fluctuate with high or low flows – as found in Hrachowitz et al.'s (2009), and Ali et al.'s (2012) studies on peatland environments in Scotland. In comparison to Buttle’s (2006) hypothetical “mapping” of catchments in the T3 space, the Attawapiskat watershed is expected to be similar to the Abitibi River catchment (Figure 2, blue circle), which is also a large low gradient northern watershed in the HBL.

Drainage density was specifically included to represent the drainage network extent and development, and high flow/low flow (i.e. Q10 and Q90) values included to represent functional connectivity and seasonal separation (Blume and van Meerveld, 2015). Drainage density (DD) is distinctly higher for SNF (1.1-1.5) than either RoF or HBL catchments (0.4-0.9 and 0.3-0.6, respectively), and was identified as a metric separating out SNF when it was included (Scenarios 1-3, 6-7). However, with all the other physical metrics strongly distinguishing HBL/RoF from SNF as well, it was not considered a dominant characteristic. In the scenarios including Hydrologic metrics (Scenarios 4-7), low flow values (SQRT Q90) were identified as important in how the catchments plotted/grouped together, while high flow values (Q10) had less of a role. Richardson et al. (2012) suggest higher correlations with drainage area during low flows instead of high flows, which is paralleled here in Scenario 7 where drainage area and SQRT Q90 are strongly positively loaded on PC2 while Q10 is strongly positive on PC1. An additional PCA was run on the bulk hydrologic data for HBL catchments (removing the SNF catchments) to assess if separation within the physiographic region could be seen without physical metric influences, but no obvious patterns emerged. Ultimately these metrics representing topology were useful in distinguishing between the HBL and Shield catchments, and less so separating the catchments within the HBL or between the HBL and RoF sites (specifically referring to drainage

57 density, as flow was not available for RoF catchments). Referring to the Buttle’s T3 schematic (Figure 2), this separation is paralleled by the mapping of the Abitibi and Batchawana catchments, roughly suggesting where the HBL and SNF catchments would map on the topology axis.

It was similarly expected that flowpath lengths and residence times of water within the landscape, representing Typology (Buttle, 2006), would prove to be important hydrologic controls in the Attawapiskat watershed. Earlier on in this study it was explained that although Hrachowitz et al. (2009) found percent responsive soil cover to be an important typological control, it was not expected to be a dominant factor in distinguishing between HBL catchments because of the similarity of the peat dominated soils throughout the area. In the analysis presented, the combination of landcover percentages and quaternary geology permeability demonstrated the prevalence of 'peat, muck and marl' and wetland coverage within the HBL and did not distinguish between HBL or RoF catchments. These factors did distinguish between the HBL and Shield, however, as they are metrics that represent the 'invisible conductivities' below surface and there are greater differences between these two regions.

Mean flowpath length was an important control in Ali et al.'s (2012) study, and was seen to play out more dominantly between the HBL catchments in the physical metrics scenarios (Scenarios 1-3) and had less of a role in Scenarios 6 and 7 (all metrics scenarios). Overall, mean flowpath length helped separate both HBL and RoF from the Shield: SNF catchments have the smallest mean flowpath length (0.8-0.9), then the RoF (1.2-1.6) and lastly, the HBL (1.3-2.5). It was also expected that these metrics representing typology and non-surficial drivers would be more important than surficial characteristics across the HBL/Shield divide, which was not the case. The physical properties (i.e. terrain-based metrics and landcover percentages) were seen to be more influential in the assessment of hydrologic similarity in most the scenarios (Scenarios 1-3, 6-7) by washing out the influence of the hydrologic metrics and tracer-based data.

Lastly, Topography was not expected to be a major control distinguishing between catchments in the HBL (due to the low-gradient nature) but was expected to be of importance across the Attawapiskat watershed, extending into the Shield. Buttle (2006) suggests that topographic conditions would have the greatest relative control on streamflow for the Batchawana catchment

58 (Figure 2), and the landscape is similar to that in the SNF shield catchments. Other studies that include low gradient environments like the HBL (e.g. Ali et al., 2012 and Hrachowitz et al., 2009) emphasized that of the three controls, topography was the least dominant player. In this study, channel slopes and mean/max elevation values were identified as strong characteristics separating HBL/RoF from SNF in the physical-based scenarios (1-3) but ultimately disappeared in the final two scenarios including all the metrics (6, 7). In the physical-based scenarios (1-3), the terrain based metrics all heavily distinguish between the HBL/RoF and SNF Shield sites, so it is difficult to interpret which metric specifically is the driver. Analysis of landcover, geology and climate metrics as they were added to the scenarios makes it clear that they are homogenizing variables that mask variation between the catchments instead of emphasizing differences. TWI was included as a topographic metric as well (paralleling Hrachowitz et al.’s [2009] study), and was found to not be a distinguishing characteristic between the RoF and HBL, but more-so between the RoF/HBL and SNF catchments. Altogether, all three T’s were found to be drivers separating out the RoF and HBL from the SNF, but none of the mentioned metrics or controls stood out in clearly distinguishing the RoF from the HBL.

Study Limitations

Hydrologic research in Ontario’s Far North sometimes provides a challenge, as not all measurements can be made in all catchments (due to geographic isolation [accessibility] and lack of long term monitoring). In this study, one of the biggest weaknesses was not having flow and isotopic/tracer-based metrics for all sites as anticipated, which would have allowed for greater interpretations of the hydrologic variations between the RoF and HBL divide. A stronger dataset may have also included water sampling over the same time period for all sites. Although HBL sampling did include spring freshet period (late April/early May), less data was available during this period compared to SNF stations.

In this study, certain metrics do not fit the assumptions of normality and linearity as required by PCA (specifically geologic, climate and land use data), but were nonetheless included. The geologic data provided ranked permeabilities for each catchment; climate data provided three values characterizing each region (precipitation, PET and annual moisture index ratio); and land use data provided percent coverages for each catchment. These metrics could not be assessed for

59 normality or linearity and as such may not fit the requirements for the PCA. It is interesting to note that the two scenarios that demonstrated the most resounding differences between catchments were the Terrain and Hydrologic scenarios (1 and 4), and are therefore unaffected by this weakness (that these metrics cannot be assessed for normal distribution or linearity). Overall, future analysis should consider other methods where these metrics could be considered more consistent with the assumptions of the statistical methods.

On a similar note, the use of provincial government digital elevation models (DEMs) and other datasets (i.e. landcover, geologies, and climate) provided enough terrain and physiographic metrics to attempt an assessment of hydrologic similarity based on the T3 concept (Buttle, 2006), however spatial resolutions are coarse and higher resolution information on geology and soils in particular could allow for more detailed assessment on the transition between the HBL and Shield itself.

As a specific example, peat soils can form various structures based on the plant materials from which it is formed which can result in hydraulic conductivities ranging over 9-10 orders of magnitude (Gorham, 1991). For this study, a permeability ranking system from 1-5 (representing low permeability to high permeability) was applied to categorize the quaternary (surficial) geology of the Attawapiskat river watershed. This data represents the unconsolidated geologic materials lying on top of the bedrock with categories including tills, sand/gravel and peat, muck and marl. During the creation of this ranking system, peat/muck/marl were categorized as "organic soils" and given the ranking of 5 (high permeability), while exposed bedrock was ranked as 1 (low permeability). While an organic soil could, in theory, have a higher transmission rate than bedrock, experimental studies have evaluated peat permeability in the James Bay Lowlands measuring from 0.01 – 10 m/day while the predominantly limestone bedrock permeabilities can range from 5 – 99 m/day (Riley, 2011). The implications for this analysis are that the provincially available quaternary geology layer included in this analysis may not capture important differences in surficial permeability.

5 Conclusions and Future Direction

This study used Principal Component Analysis and subsequent Cluster Analysis in a method similar to Ali et al. (2012) to evaluate hydrologic similarity across the Attawapiskat watershed in

60 Ontario’s Far North. Ultimately, studies such as this are important for developing models of hydrological prediction, as there is limited information on watershed hydrology in Ontario’s Far North and current research tends to focus on smaller scale analysis, at the bog/fen level. This study harnessed existing data (both provincial and from industry partners) to further understand the hydrologic and biogeochemical processes that occur within northern aquatic ecosystems and across large ecozone boundaries. Improved understanding of these processes is the foundation of making sound policy decisions concerning land use and climate change impacts in the north. Watershed classification across the Hudson Bay Lowlands will be essential for both source water protection planning for community and industry-based development planning, as they assess use of local water resources against environmental regulations (i.e. minimum baseflow requirements, etc.). Deeper insight into watershed hydrology in this region will provide the basis for hydrologic modelling in both private and public sectors.

Like Ali et al. (2012), this study was successful in grouping catchments together using both PCA and Cluster analysis (depending on the metrics chosen), and making different assessments of hydrologic similarity between catchments based on the combination of metrics/characteristics included. Research catchments were found overall to be more hydrologically similar within their physiographic region, with greater differences occurring across the geomorphic boundaries. Physical and terrain-based characteristics clearly grouped catchments by physiographic region (HBL, transition zone and Shield), while hydrologic characteristics (i.e. tracer and flow-based metrics) grouped catchments both by physiographic region and partly by groundwater influence. Physical and terrain-based characteristics were found to exhibit the most control on the PC-space in all PCA scenarios, while hydrologic characteristics provided additionally important details and showcased differences in source water contributions that were otherwise not distinguished by physiographic data.

The characteristic metrics that were most important in evaluating hydrologic similarity within the HBL itself were general terrain based characteristics (i.e. channel length, elevation, and mean flowpath lengths) (Table 2). Landcover, geologic permeability and climate were found to be homogenizing metrics instead of distinguishing out the separate HBL catchments. Across the larger scale Attawapiskat watershed, drainage density and low flow values (Q90) (topology), mean flowpath length, landcover and geologic permeability (typology) and channel slopes,

61 catchment elevations and TWI (topography) were all identified as key metrics in determining hydrologic similarity. Despite important metrics from each “control”, topology was found to be the strongest driver, topography as the second strongest, and typology as the weakest driver across the catchments and for determining hydrologic similarity between the catchments in this study (Figure 2).

With respect to Orlova and Branfireun’s (2014) link between channel incision/groundwater interaction and this study’s findings about the importance of flow/tracer-based metrics in a classification approach, it would be useful to determine an easily generated/measured predictive metric for future use. Stream order could be one such metric indicating channel incision (larger stream order implying deeper incision), however in the case of the HBL catchments, the large number of small tributaries in the Attawapiskat watershed affects this interpretation – Trib7 (stream order 2) is much more incised than Trib3 (stream order 21) because it is further downstream and closer to the most incised outlet, NR3 (stream order 163). Likewise, a simple metric such as the distance from each catchment pour point to the mainstem outlet is complicated this same way when tributaries are much less incised than the mainstem itself. This regional study specific to the Attawapiskat watershed and large northern catchments emphasizes the need for predictive metrics to limit the in situ data necessary for a successful catchment comparison.

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67 Appendices

A B

Appendix 3: Quaternary geology for the Attawapiskat watershed and SNF catchments (1:1,000,000 [OGS, 1997]).

(a)

Appendix 5: Bedrock geology for the Attawapiskat watershed and SNF catchments (1:250,000 [OMNDM, 2011]).

Appendix 7 (7a-j) includes graphical representations of streamflow and tracer data included in this analysis. HBL data collected by University of Western/University of Waterloo 2010-2011 (water chemistry) and De Beers Canada ~2004-2015 (flow). SNF data collected by Nipissing University 2013-2015 (water chemistry) and Water Survey of Canada ~1942-2013 (flow).

Appendix 7a: Water chemistry and flow summary for Tributary 3, HBL.

Appendix 7b: Water chemistry and flow summary for Tributary 5, HBL.

Appendix 7c: Water chemistry and flow summary for Tributary 7, HBL.

Appendix 7d: Water chemistry and flow summary for NR001 (NR1), HBL.

Appendix 7e: Water chemistry and flow summary for NR002 (NR2), HBL.

Appendix 7f: Water chemistry and flow summary for NR003 (NR3), HBL.

Appendix 7g: Water chemistry summary for Glen Afton (GA), SNF.

Appendix 7h: Water chemistry and flow summary for Little Sturgeon (LS), SNF.

Appendix 7i: Water chemistry and flow summary for La Vase (LV), SNF.

Appendix 7j: Water chemistry and flow summary for Veuve (V2), SNF.

83 Appendix 8: Assessment of metric normality and linearity. Five metrics recommended for transformation (i.e. LOG), four metrics to be omitted and twenty-nine metrics to remain unchanged.

The object of PCA is to reduce the dimensions (or number of variables) of a large dataset to a smaller set of artificial variables to represent the most information from the original dataset. For PCA approximations of normality and linearity among variables is ideal (McClune and Grace, 2002).

The primary reason to test whether data follow a normal distribution is to determine if parametric tests can be used on the data (Helsel and Hirsch, 2002). PCA requires this normality; however some consider this assumption to be relaxed if the purposes of the analysis are descriptive (Helsel and Hirsch, 2002). In this study, an approximation of normality is assessed for each of the 38 metrics through visual interpretation of histograms, skewness (asymmetry), kurtosis (peakiness) and Shapiro-Wilks P-values generated in SPSS (Table 1; Figure 1). Skewness, kurtosis and Shapiro-Wilks P-values closest to 1 are ideal. For some variables, transformation of the dataset with non-normal distributions can improve its distribution (e.g. closer to normal) and linearity. For that reason the LOG, LN and square root of the original variables were also assessed for normality.

For a PCA, the quality of the output is dependent on how well relationships between variables can be represented by straight lines (McClune and Grace, 2002). Linearity between two variables is represented by bivariate plots and Pearson’s R-value generated in SPSS. Pearson’s R assumes the data follow a normal distribution, and measures the linear association between two variables. Data with a positive slope that follows a straight line would be R = 1. Helsel and Hirsch (2002) promote the use of bivariate plots as well as a Pearson’s R to judge linearity because many different patterns can produce the same correlation coefficient due to outliers and curved relationships. In this study, all 38 variables were assessed for linearity (Figure 2a, 2b). This assessment of linearity was repeated for the 38 variables with recommended transformations based on normality assessment (Table 1) (Figures 3a, 3b).

A summary of recommended metrics to be included in PCA based on both assessment of normality and linearity is provided in Table 2. The majority (29) of the 38 metrics considered are

84 judged to demonstrate approximate normal distribution and linearity (see metric labels 1-38). In the case of four metrics (channel length [and ln], CV conductivity [and sqrt], SD pH [and log], and CV pH) neither the original nor the transformed metric were normally distributed or demonstrated many linear relationships with other variables and were recommended to be excluded from PCA. For five of the 38 variables (log channel slope, log drainage density, log Shreve stream order, ln median subcatchment area and SQRT Q90) transformations were found to have better normality, yet minimal change in linearity (see metric labels A-LL).

A8, Table 1: Skewness, kurtosis, Shapiro-Wilks P-value and visual assessment of normality for each variable and three select transformations (LOG, LN and square root) for each. Light grey shading indicates metrics recommended excluding, and dark grey shading indicates metrics with recommended transformations. Metric Transformation Skewness Kurtosis Shapiro- Transformati Visually Wilks on with Normal? P-Value largest P- Value? Drainage Original 2.097 4.351 0.000 Area LOG 0.697 -0.763 0.053 LOG LOG LN 0.693 -0.766 0.051 SQRT 1.365 1.116 0.002 Mean Original Value 1.024 0.928 0.058 Original Elevation LOG 0.073 -0.271 0.586 LN 0.088 -0.276 0.611 LN LN SQRT 0.567 0.168 0.314 Max Original Value 2.322 6.228 0.000 Original Elevation LOG 0.801 1.543 0.169 LN 1.632 3.484 0.008 SQRT 0.805 1.508 0.179 SQRT SQRT Slope Original Value 3.506 13.182 0.000 LOG 2.548 7.321 0.000 None None LN 2.549 7.302 0.000 SQRT 3.059 10.347 0.000 Channel Original Value 3.766 14.758 0.000 Slope LOG 1.248 3.567 0.005 LOG LOG LN 1.262 3.604 0.004 SQRT 3.053 10.171 0.000 Channel Original Value 1.111 0.409 0.014 Length LOG 0.236 -1.166 0.200 None LN 0.242 -1.153 0.202 LN SQRT 0.676 -0.597 0.092

85 A8, Table 1: Continued Metric Transformation Skewness Kurtosis Shapiro- Transformati Visually Wilks on with Normal? P-Value largest P- Value? Aspect Original Value 1.315 1.091 0.007 Original LOG 1.050 1.189 0.005 LN 1.180 0.714 0.014 LN LN SQRT 1.280 1.037 0.009 Median Sub Original Value 1.284 1.219 0.012 catchment LOG 0.572 -0.254 0.269 Area LN 0.569 -0.157 0.278 LN LN SQRT 0.936 0.304 0.074 Flowpath Original Value 1.912 4.684 0.003 Original Length LOG 0.645 0.504 0.384 LOG LOG LN 0.683 0.566 0.364 SQRT 1.301 2.303 0.046 TWI Original Value 0.930 1.294 0.162 Original LOG 0.510 0.383 0.282 LOG LOG LN 0.722 0.596 0.280 SQRT 0.816 1.433 0.205 Drainage Original Value 1.310 0.363 0.000 Density LOG 0.856 -0.334 0.005 LOG LOG LN 0.846 -0.353 0.005 LN LN SQRT 1.092 -0.124 0.001 Shreve Original Value 3.134 9.915 0.000 LOG 0.619 0.417 0.385 LOG LOG LN 0.622 0.424 0.376 SQRT 2.356 5.484 0.000 Quaternary Original Value -0.484 -2.277 0.000 Geology LOG -0.484 -2.277 0.000 None None Ranking LN -0.484 -2.277 0.000 SQRT -0.484 -2.277 0.000 Bedrock Original Value -0.742 -1.640 0.001 Geology LOG -0.850 -1.446 0.000 None None Ranking LN -0.852 -1.443 0.000 SQRT -0.796 -1.538 0.001 PET Original Value -0.484 -2.277 0.000 LOG -0.484 -2.277 0.000 None None LN -0.484 -2.277 0.000 SQRT -0.484 -2.277 0.000 Mean Annual Original Value 0.484 -2.277 0.000 Precipitation LOG 0.484 -2.277 0.000 None None LN 0.484 -2.277 0.000 SQRT 0.484 -2.277 0.000

86 A8, Table 1: Continued Metric Transformation Skewness Kurtosis Shapiro- Transformati Visually Wilks on with Normal? P-Value largest P- Value? PET/MAP Original Value -0.484 -2.277 0.000 LOG -0.484 -2.277 0.000 None None LN -0.484 -2.277 0.000 SQRT -0.484 -2.277 0.000 Mean Original Value -0.233 -0.564 0.848 Original Original Annual LOG -0.549 -0.133 0.736 Flow LN -0.565 -0.137 0.718 SQRT -0.398 -0.429 0.775 Median Original Value 1.146 1.262 0.227 Original Annual LOG 0.108 0.199 0.875 Flow LN 0.111 0.238 0.882 LN LN SQRT 0.654 0.460 0.652 Q10 Original Value -0.354 1.611 0.831 Original Original LOG -0.787 2.036 0.603 LN -0.839 2.285 0.511 SQRT -0.616 1.841 0.717 Q90 Original Value 2.683 7.911 0.000 LOG -2.106 6.223 0.003 LN -2.094 6.194 0.003 SQRT 1.158 3.951 0.066 SQRT SQRT δ H Range Original Value -0.182 -1.099 0.600 Original LOG -0.284 -1.134 0.700 LN -0.301 -1.096 0.674 SQRT -0.169 -1.106 0.729 SQRT SQRT δ H SD Original Value -0.032 -1.054 0.750 Original Original LOG -0.284 -1.134 0.700 LN -0.301 -1.096 0.674 SQRT -0.169 -1.106 0.729 δO Range Original Value -0.253 -0.725 0.779 Original Original LOG -0.591 -0.620 0.501 LN -0.597 -0.592 0.543 SQRT -0.422 -0.712 0.666 δO SD Original Value 0.988 1.008 0.321 Original LOG 0.460 -0.232 0.659 LOG LOG LN 0.477 -0.243 0.652 SQRT 0.654 0.212 0.544 D-Excess Original Value 1.708 3.935 0.064 Original LOG 1.056 1.906 0.485 LOG LOG LN 0.155 2.197 0.399 SQRT 1.428 3.005 0.182

87 A8, Table 1: Continued Metric Transformation Skewness Kurtosis Shapiro- Transformati Visually Wilks on with Normal? P-Value largest P- Value? Conductivit Original Value -0.401 -1.168 0.483 Original Original y Mean LOG -0.962 -0.431 0.093 LN -0.979 -0.376 0.093 SQRT -0.692 -0.811 0.264 Conductivit Original Value 0.166 -1.126 0.633 Original y Range LOG -0.812 0.570 0.449 LN -0.801 0.573 0.446 SQRT -0.257 -0.614 0.728 SQRT SQRT Conductivit Original Value 0.199 -0.288 0.962 Original Original y SD LOG -0.928 0.359 0.436 LN -0.932 0.417 0.447 SQRT -0.364 -0.333 0.920 Conductivit Original Value -0.712 -0.450 0.074 y CV LOG -0.991 0.387 0.052 None LN -1.051 0.560 0.046 SQRT -0.782 -0.048 0.077 SQRT pH Mean Original Value -1.268 2.433 0.285 Original Original LOG -1.566 3.184 0.089 LN -1.508 3.161 0.123 SQRT -1.325 2.560 0.226 pH Range Original Value 0.923 -0.050 0.174 Original LOG 0.516 -0.866 0.397 LN 0.535 -0.828 0.410 LN LN SQRT 0.737 -0.466 0.280 pH SD Original Value 2.148 5.287 0.001 LOG 1.499 2.668 0.013 LOG None LN 1.505 2.700 0.013 LN SQRT 1.843 3.952 0.004

A8, Figure 1: Normality Assessment - distributions and Q-Q plots for all metrics (1 of 6).

A8, Figure 1: Continued (2 of 6).

A8, Figure 1: Continued (3 of 6).

A8, Figure 1: Continued (4 of 6).

A8, Figure 1: Continued (5 of 6).

A8, Figure 1: Continued (6 of 6).

A8, Figure 2a: Pearson R-values for all 38 metrics (no transformations). Black text indicates positive values, red text indicates negative values. R-values > |0.5| are considered as an indication of linearity. See Table 2 for variable labels that associate with number labels 1-38.

A8, Figure 2b: Bivariate plots demonstrating linearity for all 38 metrics (no transformations). See Table

2 for variable labels that associate with number labels 1-38.

A8, Figure 3a: Pearson R-values for 38 metrics including recommended transformations that demonstrated greater normality. Black text indicates positive values, red text indicates negative values. R-values > |0.5| are considered as an indication of linearity. See Table 2 for variable labels that associate with alphabetical labels A-LL.

A8, Figure 3b: Bivariate plots demonstrating linearity for all 38metrics including recommended transformations that demonstrated greater normality. See Table 2 for variable labels that associate with alphabetical labels A-LL.

A8, Table 2: Summary of labelling, normal distributions, linearity and recommended metrics for PCA. (G) indicates a good choice, (F) indicates a fair choice, (P) indicates a poor choice and (N) indicates no good choice. In the case of the untransformed and transformed variables both being G or F choices, the untransformed variable is recommended.

Dark grey shading indicates variables where the transformed variable is a better choice than the untransformed variable (greater normality and linearity). Light grey shading indicates variables where neither the transformation nor untransformed variable demonstrates normality or linearity (both poor choices). Metric Metric Normally Linear Summary of Label Distributed? Relationships? selection Y/N (# of R values (G)ood /(F)air >0.5) /(P)oor /(N)one Drainage Area 1 N LR’s with 8 out of F 38 other variables Max Elevation 2 Y 17 G Mean Elevation 3 Y 19 G Mean Catchment Slope 4 Y 12 F Channel Length 5 N 6 N Channel Slope 6 N 15 P (LOG better) Drainage Density 7 N 17 P (LOG better) Shreve 8 N 14 P (LOG better) TWI 9 Y 9 F Mean Flowpath Length 10 Y 15 G Median Subcatchment Area 11 N 7 P (LN better) Mean Aspect 12 Y 18 G Mean Annual Flow 13 Y 17 G Median Annual Flow 14 Y 19 G Q10 15 Y 9 F Q90 16 N 16 P (SQRT better) Mean δ18O 17 Y 19 G Range δ18O 18 Y 12 F SD δ18O 19 Y 14 F CV δ18O 20 Y 19 G Mean δ2H 21 Y 17 G Range δ2H 22 Y 14 F SD δ2H 23 Y 19 G CV δ2H 24 Y 19 G Mean Conductivity 25 Y 5 F Range Conductivity 26 Y 6 F

A8, Table 2: Continued Metric Metric Normally Linear Summary of Label Distributed? Relationships? selection Y/N (# of R values (G)ood /(F)air >0.5) /(P)oor /(N)one SD Conductivity 27 Y 5 F CV Conductivity 28 N 5 N Mean D-Excess 29 Y 14 F Mean pH 30 Y 21 G Range pH 31 Y 8 F SD pH 32 N 6 N CV pH 33 N 6 N Bedrock Geology Ranking 34 Ranked values 20 - Quaternary Geology Ranking 35 Ranked values 20 - Average PET 36 3 values 21 - Mean Annual Precipitation 37 3 values 22 - PET/Precipitation 38 3 values 22 - 38 Metrics with transformations (shaded – transformations recommended) Drainage Area (LOG) A Y 6 F Max Elevation (SQRT) B Y 16 G Mean Elevation (LN) C Y 19 G Mean Catchment Slope (Orig) D N 7 P Channel Length (LN) E Y - messy 7 N Channel Slope (LOG) F Y 16 G Drainage Density (LOG) G Y 18 G Shreve (LOG) H Y 14 G TWI (LOG) I Y 9 F Mean Flowpath Length (LOG) J Y 15 G Med. Subcatchment Area K Y 5 F (LN) Mean Aspect (LN) L Y 18 G Mean Annual Flow (Orig) M Y 18 G Median Annual Flow (LN) N Y 11 G Q10 (Orig) O Y 5 F Q90 (SQRT) P Y 16 G Mean δ18O (Orig) Q Y 18 G Range δ18O (Orig) R Y 10 F SD δ18O (LOG) S Y 13 F CV δ18O (Orig) T Y 20 G Mean δ2H (Orig) U Y 17 G Range δ2H (SQRT) V Y 13 F SD δ2H (Orig) W Y 19 G CV δ2H (Orig) X Y 17 G Mean Conductivity (Orig) Y Y 6 F Range Conductivity (SQRT) Z Y 6 F

100

A8, Table 2: Continued Metric Metric Normally Linear Summary of Label Distributed? Relationships? selection Y/N (# of R values (G)ood /(F)air >0.5) /(P)oor /(N)one SD Conductivity (Orig) AA Y 5 F CV Conductivity (SQRT) BB N 3 N Mean D-Excess (LOG) CC Y 13 F Mean pH (Orig) DD Y 20 G Range pH (LN) EE Y 8 F SD pH (LOG) FF N 5 N CV pH (Orig) GG N 2 N Bedrock Permeability (Orig) HH Ranked values 21 - Quaternary Permeability II Ranked values 20 - (Orig) Average Yearly PET (NT) JJ 3 values 20 - Average Yearly Precip.(NT) KK 3 values 21 - PET/Precipitation (NT) LL 3 values 21 -

101

Appendix 9a: Cumulative frequency distributions of TWI for Ring of Fire (RoF) Catchments.

102

Appendix 9b: Cumulative frequency distributions of TWI for Hudson Bay Lowlands (HBL) catchments.

103

Appendix 9c: Cumulative frequency distributions of TWI for Sturgeon-Nipissing-French (SNF) catchments.

104

(B)

(A)

Appendix 10: TWI distribution for catchment K21. (A) Histogram of TWI distribution and (B) image of TWI values with 3-4 emphasized (blue) to explain "dip" in histogram. TWI values of 3-4 are located along larger rivers within the catchment.

105 Year LH PET LH PET/Pr NB NB PET/Pr VM VM PET/Pr Precip PET Precip PET Precip 2000 454 781 0.58 550 951 0.58 2001 513 681 0.75 608 1213 0.50 2002 462 611 0.76 579 1096 0.53 2003 495 573 0.86 572 1086 0.53 2004 425 779 0.55 554 1094 0.51 2005 515 714 0.72 633 917 0.69 2006 501 528 0.95 598 1328 0.45 2007 486 634 0.77 598 1065 0.56 2008 457 457 0.90 565 1335 0.42 2009 456 456 0.78 552 1085 0.51 432 640 0.68 2010 522 522 0.89 615 850 0.72 481 463 1.04 2011 464 577 0.80 AVG: 480 634 0.77 584 1093 0.55 459 560 0.84

106

Appendix 12 (12a-g) includes all tables for PCA scenarios. The order of PCA scores in the table is based on metrics strongly loaded on PC1 and then strongly loaded on PC2 (see shading).

Appendix 12a: Terrain (Scenario 1) PCA (A) variance, (B) scores and (C) loadings tables. Component % of Variance Cumulative % 1 48.8 48.8 A 2 23.8 72.6 3 11.6 84.2 4 5.6 89.8 5 4.1 93.9 6 2.6 96.5 7 2.1 98.6 8 1.3 99.9 9 0.07 99.9 10 0.03 100

Variables PC1 PC2 Mean Elevation 0.94 -0.05 B Max Elevation 0.93 -0.16 LOG Drainage Density 0.88 0.06 Catchment Slope 0.78 -0.34 LOG Shreve 0.77 -0.29 Flow Length -0.72 0.21 LOG Channel Slope 0.61 0.59 TWI 0.11 0.89 LN Med. Subcatchment Area 0.03 -0.74 Aspect 0.52 0.66

Catchments PC1 PC2 NR1 -0.20 -0.62 C NR2 -0.16 -0.68 NR3 -0.14 -0.69 Trib3 -0.49 -0.04 Trib5 -0.71 -0.16 Trib7 -1.22 -0.19 F69 -0.56 -0.03 F70 -0.38 -0.42 F78 -0.53 1.18 F90 -0.23 -0.24 F91 -0.33 -0.88 K2 -0.37 -0.65 K21 -0.32 -0.54 C4 -0.51 -0.63 GA 3.19 -0.60 LS 0.98 2.49 V2 1.28 0.38 LV 0.70 2.32

107 Appendix 12b: Physical No Area (Scenario 2) PCA (A) variance, (B) scores and (C) loadings tables. Component % of Variance Cumulative % 1 57.6 57.6 A 2 13.1 70.7 3 6.9 77.6 4 6.3 83.9 5 4.8 88.7 6 3.5 92.2 7 2.7 94.9 8 1.8 96.7 9 1.3 98.0 10 1.0 99.0

Variables PC1 PC2 Catchments PC1 PC2 Precipitation 0.99 0.08 B NR1 -0.35 0.28 C Quaternary Geology -0.99 -0.03 NR2 -0.36 0.28 PET 0.98 0.09 NR3 -0.41 0.30 Deciduous Treed 0.98 0.05 Trib3 -0.65 0.24 Annual Moisture Index -0.97 -0.12 Trib5 -0.71 0.62 Bedrock Geology -0.97 0.18 Trib7 -1.03 0.87 Treed Upland 0.97 -0.19 F69 -0.44 0.24 Bog -0.94 -0.09 F70 -0.36 0.45 Fen -0.93 -0.13 F78 -0.24 0.58 LOG Drainage Density 0.91 0.03 F90 -0.33 0.27 Sparse Treed 0.86 -0.43 F91 -0.41 0.28 Mean Elevation 0.83 0.44 K2 -0.42 0.35 Max Elevation 0.79 0.57 K21 -0.39 0.41 LOG Channel Slope 0.73 -0.37 C4 -0.39 0.27 Bedrock 0.72 -0.29 GA 2.95 0.38 Agriculture 0.70 -0.49 LS 1.70 -0.51 Mixed Treed 0.69 0.59 V2 1.19 -2.61 LOG Shreve 0.69 0.41 LV 0.66 -2.69 Swamp -0.65 0.17 Community/Infrastructure 0.64 -0.55 Aspect 0.61 -0.34 Flow Length -0.59 -0.31 Turbid Water -0.25 0.03 Catchment Slope 0.63 0.66 Clear Open Water -0.44 0.52 TWI 0.29 -0.51 LN Median Subcatchment Area -0.18 0.45 Disturbance -0.03 0.36

108 Appendix 12c: Physical +Area (Scenario 3) PCA (A) variance, (B) scores and (C) loadings tables. Component % of Variance Cumulative % 1 55.9 55.9 A 2 14.4 70.3 3 7.4 77.7 4 6.2 83.9 5 4.7 88.6 6 3.2 91.8 7 2.5 94.3 8 1.8 95.8 9 1.2 97.0 10 1.1 98.1

Variables PC1 PC2 Catchments PC1 PC2 Precipitation 0.99 0.05 B NR1 -0.29 -0.37 C Quaternary Geology -0.99 -0.03 NR2 -0.30 -0.32 PET 0.98 0.06 NR3 -0.35 -0.22 Deciduous Treed 0.98 0.04 Trib3 -0.26 -0.69 Annual Moisture Index -0.97 -0.07 Trib5 -0.63 -0.71 Bedrock Geology -0.97 0.19 Trib7 -0.90 -1.02 Treed Upland 0.96 -0.20 F69 -0.24 -0.48 Bog -0.95 -0.09 F70 -0.47 -0.26 Fen -0.93 -0.07 F78 -0.57 -0.21 LOG Drainage Density 0.91 0.01 F90 -0.28 -0.33 Sparse Treed 0.85 -0.42 F91 -0.28 -0.43 Mean Elevation 0.84 0.37 K2 -0.36 -0.43 Max Elevation 0.79 0.56 K21 -0.41 -0.41 LOG Channel Slope 0.72 -0.36 C4 -0.27 -0.40 Bedrock 0.71 -0.28 GA -0.25 3.11 Agriculture 0.69 -0.54 LS 0.60 1.58 LOG Shreve 0.69 0.48 V2 2.60 1.07 Mixed Treed 0.69 0.59 LV 2.67 0.51 Swamp -0.65 0.08 Community/Infrastructure 0.63 -0.54 Aspect 0.60 -0.42 Flow Length -0.59 -0.21 Turbid Water -0.24 0.14 Drainage Area 0.28 0.77 Catchment Slope 0.64 0.68 TWI 0.28 -0.56 Clear Open Water -0.43 0.54 LN Median Subcatchment Area -0.17 0.37 Disturbance -0.03 0.32

109 Appendix 12d: Hydrologic (Scenario 4) PCA (A) variance, (B) scores and (C) loadings tables. Component % of Variance Cumulative % 1 37.5 37.5 A 2 33.1 70.6 3 13.5 84.1 4 8.0 92.1 5 3.4 95.5 6 1.9 97.4 7 1.2 98.6 8 1.0 99.6 9 0.4 100

Variables PC1 PC2 Mean pH -0.94 0.14 B Mean H2 0.87 -0.04 Mean O18 0.81 -0.05 CV H2 -0.79 -0.57 DExcess 0.77 0.09 CV O18 -0.76 -0.60 Mean Annual Flow 0.67 -0.49 Q10 0.63 -0.13 Range pH 0.36 0.03 Range O18 0.36 0.84 SD Conductivity -0.30 0.83 Range H2 0.51 0.83 Mean Conductivity -0.44 0.82 Median Annual Flow 0.52 -0.75 SD O18 0.66 0.73 Range Conductivity -0.38 0.71 Q90 SQRT 0.37 -0.71 SD H2 -0.09 0.34

Catchments PC1 PC2 NR1 -0.77 0.02 C NR2 -1.07 0.12 NR3 -0.46 0.88 Trib3 -0.67 -0.84 Trib5 -0.26 1.41 Trib7 0.06 0.07 GA -0.79 -0.55 LS 1.10 -2.02 V2 0.87 -0.14 LV 1.98 1.04

110 Appendix 12e: Bulk Hydrologic (Scenario 5) PCA (A) variance, (B) scores and (C) loadings tables. Loadings tables (C) continued on next pages. Component % of Variance Cumulative % 1 43.9 43.9 A 2 33.8 77.7

Variables PC1 PC2 18 δ O 0.95 -0.26 B δ2H 0.92 -0.37 Discharge -0.59 -0.59 pH 0.04 0.86 Conductivity 0.32 0.63

Sample PC1 PC2 Sample PC1 PC2 Sample PC1 PC2 N1.050110 -0.89 0.44 N2.050110 -0.89 0.51 N3.042610 -1.07 0.40 C N1.051510 -0.68 0.42 N2.051510 -0.76 0.55 N3.050110 -0.77 0.36 N1.052410 -0.43 -0.09 N2.052410 -0.62 0.44 N3.050810 -0.88 0.46 N1.061710 0.06 0.50 N2.062810 -0.15 0.08 N3.052410 -0.64 0.82 N1.062210 0.02 0.79 N2.070810 -0.01 -0.05 N3.061810 -0.42 1.20 N1.062810 0.09 0.25 N2.072010 -0.09 0.30 N3.062710 -0.32 1.52 N1.070810 0.14 0.23 N2.072710 -0.13 0.40 N3.062810 -0.04 1.51 N1.072010 -0.19 1.09 N2.081410 -0.21 0.13 N3.070910 -0.14 0.41 N1.072710 0.05 1.23 N2.082410 -0.23 0.66 N3.072010 -0.25 1.49 N1.081410 -0.16 0.17 N2.110610 -0.48 0.30 N3.072710 -0.47 2.36 N1.082410 -0.14 0.50 N2.050411 -2.44 -1.11 N3.081710 -0.22 -0.12 N1.110610 -0.40 0.25 N2.060211 -0.87 -0.46 N3.082210 -0.08 0.09 N1.050411 -2.31 -1.29 N2.080311 -0.30 1.52 N3.082310 -0.13 0.35 N1.060211 -0.76 -0.22 N2.081811 -0.53 0.85 N3.082410 -0.14 0.48 N1.080311 -0.17 0.14 N2.101411 -0.61 0.55 N3.110510 -0.50 -0.15 N1.081811 -0.45 0.80 N3.060211 -1.02 -0.13 N1.101411 -0.54 0.50 N3.080311 -0.24 2.11 N3.081811 -0.70 1.41 N3.101411 -0.70 0.83

111 Appendix 12e: Loadings tables continued (C) Sample PC1 PC2 Sample PC1 PC2 Sample PC1 PC2 T3.041910 -0.64 0.11 T5.042310 -0.81 0.10 T7.042310 -0.74 0.08 T3.042710 -0.59 0.38 T5.050110 -0.78 0.14 T7.050110 -0.46 0.21 T3.050110 -0.27 -0.34 T5.051710 -0.86 0.56 T7.051710 -0.51 0.57 T3.051710 0.09 0.29 T5.062810 -0.16 1.15 T7.062710 -0.08 0.92 T3.052410 -0.04 0.42 T5.071010 -0.04 1.00 T7.062810 -0.01 0.79 T3.061910 -0.34 0.74 T5.072010 -0.31 1.17 T7.071110 -0.11 0.94 T3.062810 -0.15 0.67 T5.072710 -0.32 1.69 T7.072010 -0.20 0.95 T3.071110 -0.37 -0.49 T5.081210 -0.36 0.30 T7.072710 -0.29 1.36 T3.072010 -0.22 -0.52 T5.081310 -0.26 -0.20 T7.081310 -0.43 -0.53 T3.072710 -0.46 -0.65 T5.082410 0.00 0.07 T7.082410 -0.26 -0.03 T3.081210 -2.43 -2.52 T5.110210 -0.14 -0.71 T7.110210 -0.24 -0.27 T3.081310 -0.50 -0.88 T5.050411 -2.46 -1.24 T7.050411 -2.07 -1.34 T3.082410 0.12 0.60 T5.060211 -0.81 -0.11 T7.060211 -0.48 0.12 T3.110610 -0.59 -0.01 T5.073011 0.27 -0.11 T7.080311 -0.07 1.31 T3.050411 -0.65 -0.68 T5.080311 -0.13 2.11 T7.081811 -0.32 1.15 T3.060211 -0.95 -0.15 T5.081111 -0.53 -0.24 T7.101411 -0.65 0.13 T3.080311 -0.67 -0.24 T5.081811 -0.53 1.78 T3.081811 0.04 -0.17 T5.101411 -0.66 0.59 T3.101411 -0.41 -0.82 T5.101911 -1.50 -0.78 T5.041812 -2.89 -0.74 T5.042712 -2.14 -0.03 T5.050412 -2.02 0.00 T5.051212 -1.58 -0.14 T5.052112 -1.62 0.14 T5.060612 -0.97 0.13 T5.061212 -0.73 0.40 T5.062212 -0.84 0.71 T5.063012 -0.71 0.13 T5.070812 -0.64 1.13 T5.080112 -0.29 1.50 T5.080712 -0.09 0.22 T5.081412 -0.52 1.24 T5.082812 -0.51 1.21 T5.091012 -0.97 0.85 T5.091912 -0.67 0.98 T5.092912 -0.69 1.13 T5.100412 -0.70 1.04 T5.101212 -1.10 0.46 T5.101312 -1.15 0.46 T5.101712 -1.03 0.48

112 Appendix 12e: Loadings tables continued (C) Sample PC1 PC2 Sample PC1 PC2 Sample PC1 PC2 Sample PC1 PC2 GA.052313 0.41 -0.82 LV.091613 2.29 1.34 LS.091613 2.11 -1.82 V2.052713 0.63 -0.86 GA.081413 1.15 0.07 LV.101613 2.40 0.81 LS.101613 2.03 -0.96 V2.091613 2.01 0.71 GA.082813 1.20 0.06 LV.111513 0.91 -0.28 LS.111513 0.67 -1.49 V2.101613 2.23 0.44 GA.102413 1.05 0.05 LV.011614 0.12 0.10 LS.121913 0.37 -0.30 V2.111513 1.25 -0.74 GA.120513 1.02 -0.56 LV.041514 -2.29 -3.29 LS.050614 -1.05 -1.92 V2.121913 1.25 -0.54 GA.121813 0.91 -0.14 LV.050714 -0.14 -0.54 LS.052214 0.70 -1.63 V2.011614 0.26 0.00 GA.011614 0.82 -0.44 LV.052114 0.91 -0.04 LS.061214 0.75 -0.08 V2.031914 0.93 0.03 GA.041614 -0.39 -0.86 LV.061114 1.42 1.27 LS.070914 1.11 -0.63 V2.041614 -1.87 -3.57 GA.050614 -0.95 -1.50 LV.062314 2.01 0.38 LS.072414 1.27 -0.40 V2.050614 -0.69 -1.02 GA.052214 -0.18 -1.34 LV.070914 1.78 0.50 LS.080614 1.05 0.38 V2.052214 0.53 -0.68 GA.061214 0.43 -0.50 LV.072314 2.01 0.52 LS.082214 0.78 -0.76 V2.061214 0.97 0.70 GA.062414 0.55 -0.11 LV.080514 1.77 0.41 LS.091114 1.01 -0.38 V2.062414 1.17 0.91 GA.070914 0.71 0.04 LV.082114 1.32 1.47 LS.092514 1.30 -2.38 V2.070914 1.36 0.94 GA.072414 0.99 -0.20 LV.091214 1.70 1.33 LS.100914 1.12 -1.82 V2.072414 0.98 0.16 GA.080614 0.78 0.34 LV.092614 1.29 -0.21 LS.102314 1.38 -2.58 V2.080614 1.04 0.41 GA.082214 0.70 -0.14 LV.101014 1.37 -0.25 LS.110614 1.00 -1.38 V2.082214 1.12 0.80 GA.091114 0.97 -0.20 LV.102414 1.57 -0.74 LS.041715 -1.60 -4.29 V2.091114 1.63 -0.47 GA.092514 1.01 -0.55 LV.110714 1.34 -0.99 LS.051315 0.29 -2.88 V2.092514 1.68 -0.41 GA.100914 0.99 -0.66 LV.011515 0.20 -0.21 V2.100914 1.44 -0.78 GA.102314 0.61 -1.47 LV.021215 0.35 -0.34 V2.102314 1.45 -1.22 GA.110614 0.85 -0.77 LV.031815 -0.42 1.09 V2.110614 1.25 -0.64 GA.112014 0.71 -0.32 LV.041515 -2.12 -2.95 V2.112014 1.08 -0.24 GA.121614 0.75 -0.66 LV.051415 0.45 -0.56 V2.011515 0.21 -0.27 GA.011515 0.54 -1.00 GA.031815 0.63 0.48 GA.041715 -0.57 -1.13 GA.051315 -0.15 -1.26

113 Appendix 12f: All No Area (Scenario 6) PCA (A) variance, (B) loadings and (C) score tables. Component % of Variance Cumulative % 1 50.6 50.6 A 2 22.4 73.0 3 10.2 83.2 4 5.0 88.2 5 4.3 92.5 6 3.5 96.0 7 2.3 98.3 8 0.9 99.2

Catchments PC1 PC2 NR1 -0.40 -0.76 B NR2 -0.50 -0.83 NR3 -0.53 -0.63 Trib3 -0.78 -0.52 Trib5 -0.71 -0.44 Trib7 -1.14 -0.11 GA 2.11 -0.78 LS 0.97 1.18 V2 0.62 0.87 LV 0.35 2.02

Variables PC1 PC2 Variables Continued PC1 PC2 Precipitation 0.98 -0.09 Mean Annual Flow 0.54 -0.37 Quaternary Geology -0.98 0.09 Q10 0.51 -0.09 PET 0.98 -0.09 Turbid Water -0.50 -0.24 Annual Moisture Index -0.98 0.09 Range O18 0.28 0.87 Mean H2 0.98 -0.05 Range H2 0.42 0.86 Deciduous Treed 0.98 -0.12 SD O18 0.60 0.78 Fen -0.98 0.15 Q90 SQRT 0.50 -0.77 Treed Upland 0.97 0.16 Clear Open Water -0.58 -0.75 Mean O18 0.96 -0.09 Median Annual Flow 0.55 -0.74 LOG Drainage Density 0.96 -0.09 Catchment Slope 0.53 -0.71 Swamp -0.93 0.02 Mixed Treed 0.63 -0.70 Bog -0.92 0.14 Mean Conductivity -0.37 0.68 Mean Elevation 0.90 -0.37 SD Conductivity -0.18 0.66 Sparse Treed 0.83 0.41 LN Median Subcatchment Area 0.25 -0.62 Mean pH -0.82 -0.03 LOG Shreve 0.58 -0.61 Aspect 0.80 0.44 TWI 0.38 0.61 CV H2 -0.78 -0.59 Range Conductivity -0.27 0.52 Flow Length -0.78 0.31 Disturbance 0.16 -0.50 LOG Channel Slope 0.77 0.40 SD H2 -0.19 0.31 CV O18 -0.77 -0.62 Range pH 0.04 0.22 Max Elevation 0.75 -0.58 Bedrock 0.66 0.21 Agriculture 0.63 0.49 Community/Infrastructure 0.59 0.56 DExcess 0.58 0.29

114 Appendix 12g: All + Area (Scenario 7) PCA (A) variance, (B) loadings and (C) scores tables. Component % of Variance Cumulative % 1 49.6 49.6 A 2 23.3 72.9 3 10.4 83.3 4 4.9 87.9 5 4.2 92.1 6 3.4 95.5 7 2.4 97.9

Catchments PC1 PC2 NR1 -0.73 0.43 B NR2 -0.79 0.49 NR3 -0.71 0.45 Trib3 -0.67 -0.30 Trib5 -0.82 -0.20 Trib7 -0.88 -1.23 GA 0.88 2.26 LS 1.19 -1.01 V2 1.10 -0.14 LV 1.43 -0.75

Variables PC1 PC2 Variables Continued PC1 PC2 Precipitation 0.98 0.10 Median Annual Flow 0.55 0.72 Quaternary Geology -0.98 -0.10 Mean Annual Flow 0.54 0.33 PET 0.98 0.10 Q10 0.51 0.08 Annual Moisture Index -0.98 -0.10 Range H2 0.41 -0.86 Mean H2 0.98 0.05 Range O18 0.27 -0.86 Fen -0.98 -0.15 Drainage Area 0.07 0.81 Treed Upland 0.97 -0.16 SD O18 0.60 -0.79 Deciduous Treed 0.97 0.12 Q90 SQRT 0.49 0.77 Bedrock Geology -0.97 0.15 Clear Open Water -0.58 0.76 LOG Drainage Density 0.96 0.09 Catchment Slope 0.54 0.72 Mean O18 0.96 0.10 Mixed Treed 0.63 0.70 Swamp -0.93 -0.01 LN Median Subcatchment Area 0.25 0.64 Bog -0.92 -0.15 TWI 0.38 -0.64 Mean Elevation 0.90 0.37 Mean Conductivity -0.37 -0.64 Sparse Treed 0.83 -0.40 LOG Shreve 0.58 0.63 Mean pH -0.82 0.07 SD Conductivity -0.18 -0.61 Aspect 0.79 -0.46 Disturbance 0.17 0.54 Flow Length -0.79 -0.30 Range Conductivity -0.27 -0.48 LOG Channel Slope 0.77 -0.37 SD H2 -0.19 -0.31 CV O18 -0.77 0.62 Range pH 0.04 -0.25 CV H2 -0.77 0.59 Max Elevation 0.76 0.58 Bedrock 0.66 -0.21 Agriculture 0.64 -0.46 Community/Infrastructure 0.59 -0.54 DExcess 0.58 -0.33

115