Estimated Input of Mercury to Kusawa Lake, Northern Canada, from Snow

Examensarbete vid Institutionen för geovetenskaper Degree Project at the Department of Earth Sciences ISSN 1650-6553 Nr 388

Estimated Input of Mercury to Kusawa Lake, Northern Canada, from Snow and Glacial Melt Water En uppskattning av kvicksilvers inflöde i Kusawa Lake, norra Kanada, från snö- och glaciär-isavsmältning

Ingrid Beckholmen

INSTITUTIONEN FÖR

GEOVETENSKAPER

DEPARTMENT OF EARTH SCIENCES

Examensarbete vid Institutionen för geovetenskaper Degree Project at the Department of Earth Sciences ISSN 1650-6553 Nr 388

Ingrid Beckholmen

ISSN 1650-6553

Estimated Input of Mercury to Kusawa Lake, Northern Canada, from Snow and Glacial Melt Water Ingrid Beckholmen

Unusually high levels of mercury (Hg) have been detected in several northern Canadian lakes, remote from human industrial activities. At Kusawa Lake, a subalpine glacier-fed mountain lake situated in the Yukon Territory (western Canada), measurements of Hg in caught fish are below, but worryingly close to, the Health Canada guideline for safe fish consumption of 0.5 µg g-1. The sources of the Hg found in the fish of Kusawa Lake (and many other northern lakes) are still unidentified. A suspected pathway for Hg input to freshwater ecosystems is through long-range atmospheric transport and direct or indirect deposition in water and/or snow-covered surfaces, later released to aquatic ecosystems through spring/summer melt and runoff. The objective of this thesis is to estimate the maximal potential contribution of atmospheric Hg released by snow- and ice melt that could enter Kusawa Lake through runoff from snow and glacier ice melt. Hg data previously obtained from the Kusawa Lake catchment were used to estimate the possible range of Hg concentrations in snow and ice. To quantify the input of Hg from snow and ice melt into Kusawa Lake, the total water inflow from these sources first had to be estimated. This was done by using the HBV hydrological model (Hydrologiska Byråns Vattenbalansavdelnings modell), initially developed by the Swedish Meteorological and Hydrological Institute (SMHI), and subsequently modified to include a glacier routine. The HBV model was run using temperature and precipitation data from the nearby city of Whitehorse (~60 km east of Kusawa Lake), and was calibrated using historical hydrometric data from the Kusawa Lake catchment itself. Using these data and the HBV-simulated runoff, the total flux of Hg entering Kusawa Lake trough snow and glacial melt water was estimated to be 550 ± 495 g yr-1 (3.6 ± 3.5 μg m-2 yr-1). This flux comparable in magnitude with model-based estimates of the total atmospheric deposition of Hg in the Yukon region, which range between 4.5 and 7 μg m-2 yr-1. This suggests that for subarctic glacier-fed lakes like Kusawa, the supply of Hg from snow and ice melt actually represents a large percentage of the total annual Hg input.

Keywords: Mercury, HBV-model, Kusawa Lake, Canada, runoff, glaciers

Degree Project E1 in Earth Science, 1GV025, 30 credits Supervisor: Christian M. Zdanowicz Department of Earth Sciences, Uppsala University, Villavägen 16, SE-752 36 Uppsala (www.geo.uu.se) ISSN 1650-6553, Examensarbete vid Institutionen för geovetenskaper, No. 388, 2017

The whole document is available at www.diva-portal.org Populärvetenskaplig sammanfattning

En uppskattning av kvicksilvers inflöde i Kusawa Lake, norra Kanada, från snö- och glaciär-isavsmältning Ingrid Beckholmen

Höga halter av grundämnet kvicksilver (Hg) har uppmätts i ett flertal nordliga sjöar, i områden där det inte finns någon uppenbar mänsklig påverkan som kan ha orsakat detta. Kusawa sjön, som ligger i Yukon Territory i Kanada, är en fjällsjö vars vatten i huvudsak kommer ifrån den årliga snösmält- ningen och omkringliggande smältande glaciärer. Här har man observerat höga halter av Hg i fisk. Halterna är under Kanadas riktlinjer för säker konsumtion av fisk på 0,5 µg per g Hg men oro- väckande nära. Vad som är orsaken till detta i Kusawa sjön (och i många andra nordliga sjöar) är oklart. En möjlig källa skulle kunna vara att Hg, i gasform, transporteras till dessa platser genom globala vindsystem och kan efter kemiska reaktioner avsättas som snö i och med nederbörd eller direkt på ytan av snötäcken. När snön sedan smälter kan Hg följa med i vattendragen och slutligen hamna i dessa sjöar. Syftet med den här uppsatsen var att uppskatta hur mycket Hg som genom smältvatten från snö och glaciärisar kan årligen transporteras i vattendrag till Kusawa sjön. Arbetet använder sig av tidigare insamlad data över kvicksilverhalter i snö- och isprover i Kusawa sjöns närområde. Detta har gjorts genom att använda en avrinningsmodell, HBV, för att uppskatta hur mycket vatten som transporteras till sjön i genomsnitt varje år. Modellen tar hänsyn till både snö och glaciärisavsmältning och är baserad på meteorologisk data, så som nederbörd, temperatur och avdunstning, från den närliggande staden Whitehorse (ca 60 km öster om Kusawa sjön). Det totala flödet av THg (Total Hg, var alla typer av kemiska föreningar av Hg är inräknade), genom snö- och glaciärsmältvatten, uppskattas att vara 550 ± 495 g per år för Kusawa sjöns yta. Detta ger ett genomsnittligt flöde på 3,6 ± 3,5 μg per m2 och år, vilket skulle kunna motsvara storleks- ordningen av atmosfäriskt Hg som uppskattas avsättas varje år i regionen (7 μg per m2 och år ). Denna undersökning är den första uppskattningen och har därför ingen tidigare litteratur att jämföra med. Den använda metoden är en mycket grov och förenklad bild av de processer som sker ute i naturen men ett första steg för att undersöka varför Hg halten är så hög i den subalpina sjön Kusawa i Kanada.

Nyckelord: Kvicksilver, HBV-model, Kusawa Lake, Kanada, avrinning, glaciärer

Examensarbete E1 i geovetenskap, 1GV025, 30 hp Handledare: Christian M. Zdanowicz Institutionen för geovetenskaper, Uppsala universitet, Villavägen 16, 752 36 Uppsala (www.geo.uu.se) ISSN 1650-6553, Examensarbete vid Institutionen för geovetenskaper, Nr 388, 2017

Hela publikationen finns tillgänglig på www.diva-portal.org Table of Contents

1 Introduction ...... 1 2 Aim ...... 2 3 Background ...... 3 3.1 Mercury in the subarctic and arctic freshwater environment ...... 3 3.2 Previous research in the Kusawa Lake area ...... 5 3.3 Study area ...... 6 3.3.1 Climate and hydrology ...... 8 3.3.2 Geology and geomorphology ...... 9 3.3.3 Human occupation and activity ...... 9 4 Methods ...... 11 4.1 HBV model ...... 11 4.1.1 Model overview ...... 11 4.1.2 Model Setup: Catchment description ...... 13 4.1.3 Model Setup: Meteorological and hydrological data ...... 17 4.1.4 Model calibration and validation ...... 17 4.2 Calculation of Hg inflow to Kusawa Lake ...... 21 4.3 Calculation of uncertainties ...... 22 5 Results ...... 24 5.1 HBV model calibration and validation ...... 24 5.2 Estimating potential fluxes of Hg to Kusawa Lake ...... 27 6 Discussion ...... 29 6.1 Uncertainties and limitations of this study ...... 29 6.6 Comparison to other studies in the area ...... 30 6.7 Future perspectives ...... 31 7 Conclusions ...... 33 8 Acknowledgements ...... 34 9 References ...... 35 Appendix: Calibration of HBV parameters ...... 40

1 Introduction

Mercury (Hg) is the only metal known to be liquid at standard temperature and pressure, which makes it unusually volatile. In the atmosphere, Hg exists predominantly (98%) in the form of gaseous 0 elemental mercury (Hg (g) or GEM), which is the atmospheric Hg species with the longest lifetime, 6 to 12 months (Schroeder & Munthe, 1998; Cole et al., 2014). There are both natural and anthropogenic Hg sources to the atmosphere. The dominant natural sources are evasion from the surface Ocean, and emissions from volcanoes and forest fires, which together are estimated to release 3000 to 4000 t Hg yr-1 (Pirrone et al., 2010). Human activities, such as the burning of fossil fuels and a variety of industrial processes, are estimated to release an additionally 2000 t yr-1. Due to its long 0 atmospheric residence time, Hg (g) has the ability to be transported to remote regions and can pollute environments where no local sources of Hg emissions are present (Schroeder & Munthe, 1998; Evans et al., 2005; AMAP, 2011). In the subarctic and the Arctic, GEM can be deposited in snowfall and/or onto snow-covered II surfaces after photo-oxidation to reactive gaseous Hg (Hg or RGM) or particulate Hg (Hgp). Once released into the aquatic environment by melt, Hg can be transformed by biotic and abiotic processes into bioavailable organic species. One of these species, methylmercury (MeHg), is particularly toxic to organisms. Once MeHg and other organic forms of Hg enter the aquatic food chain, they tend to bioaccumulate and become biomagnified in the body of top predators. Trout, walleye and burbot are common freshwater predator species that have been reported to contain worryingly high levels of Hg, even though the levels are under the commercial limit for sold fish at 0.5 µg g-1 Hg (wet weight) given from the Canadian Health guidelines. If Hg accumulates in the human body it can cause damages to brain, kidneys and disrupt the neurological development of unborn babies (UNEP, 2002; Yukon Contaminants Committee, 2010). Wheatley (1984) reported that indigenous people, with a traditional diet, in isolated communities in Arctic Canada have high levels of mercury in blood and hair. As a part of their traditions, fishing and hunting is very important, and consumption of fish and game have been vital in these peoples' lives. However, the source of high Hg levels in fish is still being debated for some regions (Evans et al., 2005; Lockhart et al., 2005).

2 Aim

This aim of this thesis is to increase the current understanding of possible source contributions of Hg to northern subalpine Canadian lakes. Specifically, the thesis will seek to answer the following question: How large is the potential annual input of dissolved Hg in melt water from snow and glacier ice into Kusawa Lake?

In order to accomplish the study goal, the following work objectives will be addressed:

1. Using a hydrological model (HBV), estimate the runoff contributions of snow and glacier ice to the hydrological budget of Kusawa Lake,

2. Combine the results from HBV simulations and Hg data from snow and ice collected by Karlsson (2014) to estimate the possible annual mass flux of Hg entering Kusawa Lake through runoff from snow and ice melt,

3. Estimating the total uncertainties on the calculated Hg flux, taking into account the spread of Hg concentrations in snow and ice, as well as that in the simulated runoff, determined, through a Monte Carlo procedure with HBV, and

4. Compare findings with other reported data on Hg inputs to northern lakes in the scientific literature.

3 Background

3.1 Mercury in the subarctic and arctic freshwater environment

Mercury (Hg) is a naturally-occurring metal that mainly exists within the Earth's crust in the stable form of cinnabar (HgS). It is the only metal that is liquid at standard temperature and pressure, and it is consequently volatile (Schroeder & Munthe, 1998). In the atmosphere, Hg is predominantly in 0 gaseous forms, 98 % of which is Hg (g) or gaseous elemental mercury (GEM), but it can also exist as II reactive gaseous mercury (RGM or Hg (g)) and as particulate mercury (Hgp) (AMAP, 2011). Not only is GEM the dominant atmospheric species, it has also the longest lifetime, between 6 to 12 months, whereas the lifetime of RGM is much shorter, from a few hours to a week due to its reactivity, and that of Hgp at most 1-2 weeks (Schroeder & Munthe, 1998; Cole et al., 2014). The relatively long lifetime of GEM makes Hg a pollutant that can be transported by air currents and deposited in remote locations, far from emissions sources. In particular, GEM is thought to be capable of transport over tens of thousands of kilometers, while typical transport distances are noticeably shorter for the other two forms of atmospheric Hg (Schroeder & Munthe, 1998; Evans et al., 2005; AMAP, 2011). Some Hg is released in the surface environment by volcanic eruptions, by the weathering of rocks and forest fires. However, as a result of prehistorical to modern human industrial activities, the amount of Hg has increased in the atmosphere and in shallow sediments worldwide (Fitzgerald et al., 1994). In particular, the purposeful extraction of Hg for commercial use, and its release as a byproduct when burning fossil fuels such as coal, or by other industrial processes, has greatly augmented the quantity of free Hg in the atmosphere that is available for deposition (Amos et al., 2013). The total amount of Hg released into the atmosphere each year by human activities is presently estimated to ~2000 t, whereas natural releases represent an additionally 3000 to 4000 t (Pirrone et al., 2010, Streets et al., 2011). While Hg emissions in North America and Europe have decreased in recent decades, Hg emissions in East Asian countries are increasing due to their rapid industrialization, and in particular due the use of coal as a fossil energy source in these emerging economic regions (Pirrone et al., 2010; Streets et al., 2011; Zhang et al., 2016) (Fig. 1). In the subarctic and the Arctic, maximum annual GEM concentrations in the atmosphere occur in the late winter and early spring season (Schroeder & Munthe, 1998; Cole et al., 2014). Some of the

GEM in air can be photo-oxidized to more reactive species such as RGM or Hgp which are then deposited onto snow, notably in the weeks following polar sunrise (Steffen et al., 2008). Some of the Hg deposited in this way can later become biologically available, if released during snowmelt. However, up to two-thirds of the Hg deposited in snow by the photo-oxidation process is quickly reduced and re-emitted back to the atmosphere as GEM (Steffen et al., 2005, 2008). Atmospheric Hg that is deposited and enters lakes and streams can later be transformed into bioavailable and toxic species; one of which is methylmercury (MeHg). In organisms, this form of Hg is 10-100 times more

3 toxic than inorganic (elemental) Hg (Douglas et al., 2012). The transformation of Hg into MeHg is favored in environments where ambient oxygen levels are low, such as in wetlands or in lake sediments. MeHg can subsequently enter the aquatic food chain, where it can undergo bio- magnification and bio-accumulation (Douglas et al., 2012). In the arctic environment, where the summers are short and the winters long and harsh, the low rates of primary productivity affect the life cycle of vertebrate and invertebrates alike. As a result of the slow growth rates of many arctic organisms, there is a tendency for a longer lifetime accumulation of contaminants and/or higher contaminant concentrations, when compared to faster growing organisms or populations further south in warmer, more productive regions (Evans et al., 2005). Consequently, predatory fishes in subarctic or arctic lakes, such as trout, pike and walleye, are often found to contain relatively high levels of Hg (Evans et al., 2005; Yukon Contaminants Committee, 2010).

Figure 1. Estimates of annual mercury emission to air from anthropogenic sources over the world (AMAP, 2011)

Recent measurements and historical data have shown that during the last ~150 years, the average concentration of Hg has increased at a rate of 1 to 4% per year in Arctic biota (AMAP, 2011). If Hg accumulates in the human body through the consumption of contaminated food such as fish, it can cause damages to the brain and kidneys and also disrupt the neurological development of unborn babies (UNEP, 2002; Yukon Contaminants Committee, 2010). When worryingly high levels of Hg were first discovered in the fish populations of several big lakes in Canada, it became an issue of public health concern. Health Canada thereafter established the following guidelines; (1) commercially sold fish can not have Hg levels that exceed 0.5 µg g-1 (wet weight) and (2) for people who frequently catch and consume fish as part of their traditional diet, as do many First Nations groups, the Hg levels

4 in the fish should not exceed 0.2 µg g-1. In addition, Health Canada has made the further specific recommendations for women of child-bearing age and for children under 12 years, to limit their intake of large fish such as trout and burbot that are heavier than 2 lbs (0.91 kg) or longer than 40 cm.

3.2 Previous research in the Kusawa Lake area

Awareness of unusually high Hg concentrations in fish from remote locations such as in the Arctic first became public in the 1990s (Evans et al., 2005). Since then, several studies have been conducted to monitor Hg levels in fish from northern lakes of Canada, including in the Yukon Territory (Evans et al., 2005; Lockhart et al., 2005; Stern, 2011). However, none of these studies have directly addressed the issue of identifying the sources of the Hg found in fish. It is often simply assumed that atmospheric Hg transport and deposition from distant emission sites has been the dominant source in historical times. Measurements on fish caught within Kusawa Lake indicate that they contain variable amounts of Hg. The measured levels are below, but worryingly close to, Health Canada’s guidelines for safe fish consumption of 0.5 µg g-1, and they vary with species and size/age of the fish (Lockhart et al., 2005; Yukon Contaminants Committee, 2010). Despite this fact, fish consumption remains an important part of the diet for local inhabitants, and continues to be recommended by the Yukon health authorities, because of the high concentrations of beneficial nutrients and essential lipids found in local fish, which can reduce the risk of heart diseases, type-2 diabetes and certain cancers (Evans et al., 2005). The sources of the Hg found in the fish of Kusawa Lake (and many other northern lakes) are still unidentified. Some of it may be geogenic, i.e., derived from local bedrock or sediments (e.g., Nasr et al., 2011), but another suspected pathway for Hg input to freshwater ecosystems is through long-range atmospheric transport and direct or indirect deposition in water and/or snow-covered surfaces. It is the latter pathway which is the object of the present study. Stern et al. (2009) and Joe-Strack (2015) analyzed Hg in sediment cores from Kusawa Lake and found increasing trends in Hg concentration since the 19th century. In the core analyzed by Joe-Strack (2015), Hg levels increased from ~0.002 to 0.034 µg g-1, while MeHg was below detection limits. The rising Hg levels in Kusawa Lake sediments suggest increasing inputs to the catchment since the 19th century. However, Stern et al. (2009) argue that the apparent trend results instead from increased scavenging of dissolved Hg in the water column by algae due to changes in the rate of primary productivity accompanying regional climate warming. Direct measurements of atmospheric GEM have been conducted at several monitoring stations across northern Canada starting in the early 1990s, but it is only in 2007 that monitoring began in the Yukon at Little Fox Lake (N 61.35°, W 135.63°), ~185 km northeast of Kusawa Lake (Cole et al., 2014). The mean concentration of GEM measured at this station over the period 2007-11 was 1.28 ± 0.17 ng m-3, with important daily and seasonal variations. The period of monitoring is still too short

5 for the trend to be determined, but atmospheric modeling suggests as much as 35 % of the GEM at Little Fox Lake could be supplied by long-range transport from Asia (Dastoor et al., 2015). Regardless of atmospheric inputs, the levels of Hg in fish for a given lake catchment may also depend on the geological properties of the watershed (for example when Hg-rich rocks are found; Domagalski et al., 2016), as well as on soil and water pH, and on the availability of dissolved organic carbon (DOC). Schuster et al. (2011) studied the relationship between Hg and DOC in the Yukon River Basin, of which the Kusawa Lake drainage area is a sub-catchment, and found a linear relationship between the two. As the Yukon River Basin is, to a large extent, covered by boreal forest, its soils are rich in organic carbon. Due in part to the thawing of permafrost as a result of climate warming, this organic carbon and Hg present in soils can now be remobilized and move into streams and river systems (Douglas et al., 2012). Thus, Schuster et al. (2011) found that the Hg concentration for the Yukon River is significantly greater than for other large rivers in the northern hemisphere. The present thesis is based in part on data collected by Karlsson (2014), who investigated the Hg content in snow and glacial firn/ice in the southwestern Yukon and in northern British Columbia. The main objectives of her study were to quantify the accumulation of atmospheric Hg in snow and glacial ice, and to establish and if any systematic correlations existed between THg levels, major ions and oxygen isotope ratios, in order to help identify the source(s) of the Hg. Snow and ice samples were collected in June 2013 from four glacier sites, three of which were in the headwaters of the Kusawa Lake catchment, and the fourth site being in the central St. Elias Mountains further to the west. The sum of all reactive Hg species (total mercury, abbreviated THg) was measured in the melted samples, and these data will be used in the analysis presented in this thesis. Reported THg values for seasonal snow were between 0.24−6.17 ng L-1, and between 0.2−1.57 ng L-1 for glacial ice. These values do not differ markedly out from other measurements in snow from the Yukon River Basin (Wang et al., 2005). In the present study, the Hg data of Karlsson (2014) will be used in combination with hydrological model runoff simulations to estimate the magnitude of the flux of atmospheric Hg, initially deposited in snow, which can be subsequently transported by snow and ice melt into Kusawa Lake.

3.3 Study area Kusawa Lake is a glacier-fed mountain lake, situated in the Yukon Territory of western Canada, near the border of the province of British Columbia (Fig. 2). The lake has an area of 140 km2 and a drainage basin of over 4000 km2 (Moore et al., 2002), part of which extends into British Columbia, and reaches to the border of Alaska. The elevation range of the Kusawa Lake catchment is 610-2700 m asl (Roots et al., 2004), and the lake itself lies at an altitude of 671 m asl. The catchment lies in the headwaters of the Yukon River, the 4th largest river in North America (Brabets et al., 2000). A large part of the Kusawa Lake basin is included in a protected Yukon territorial park. This area is known for its abundance of wildlife, and sport-fishing in the lake is a popular activity (McDowell et al., 2009).

The closest city is Whitehorse (23,310 inhabitants), which lies roughly 60 km to the northeast of Kusawa Lake (Roots et al., 2004). Kusawa Lake has several sub-catchments, the largest being that of the Primrose River (1300 km2), which covers one-third of the total drainage basin area. The Primrose River itself flows through one relatively large lake, Primrose Lake, before discharging into Kusawa Lake.

A B

Figure 2. A) Location map of Kusawa Lake, Yukon Territory, Canada. Dark bold line = U.S.-Canada border. Red line = Yukon-British Columbia border. B) Close-up of the Kusawa Lake area. The dark circles show the location of the glacier-covered areas which were sampled for Hg by Karlsson (2014). Basemap credits: ESRI (2016).

The Kusawa Lake catchment is a part of two Canadian eco-regions: the Yukon Southern Lakes and Yukon Stikine Highlands regions. The Yukon Southern Lakes region is a vast area of mountains with rounded summits, rolling hills and broad valleys occupied by lakes and rivers, and is part of the larger Yukon Plateau (Roots et al., 2004). At altitudes below ~850 m, along rivers and valley floors, the vegetation cover is dominated by open coniferous and mixed woodlands (Fig. 3). White spruce is the main tree species. Higher up on valley slopes, between ~850-1200 m, the forest transitions into a subalpine zone that consists mainly of medium-size shrubs. Above the subalpine zone, at altitudes ~>1200 m and on the mountain summits, the vegetation is characterized by sparse and patchy tundra over bare rock, with dry dwarf shrub, moss and lichen. The plants in this zone can suffer from water stress, which limits the extent of the vegetation cover (Roots et al., 2004; McDowell et al., 2009). The Stikine Highland eco-region is located to the south of the Southern Lakes. The Yukon part of this eco-region lies in the Boundary Ranges, a subrange of the Pacific Coast Mountains that stretches between Kusawa Lake and the Gulf of Alaska. The terrain there consists of rugged mountains cut by deep valleys. On the higher mountains, at elevations of ~1300-2400 m, cirque glaciers are found. The total glacier-covered area within the Kusawa Lake catchment was estimated to be 148 km2 (see section 4.1.1 below). The vegetation pattern in the Stikine Highland eco-region is similar to that in the Southern Lakes eco-region, but water stress is less severe here due to moist winds blowing from the nearby Pacific Ocean (Roots et al., 2004).

Figure 3. View of the western flank of Kusawa Lake valley, showing the three main elevation/land cover zones. Photo modified from McDowell et al. (2009).

3.3.1 Climate and hydrology

Kusawa Lake is situated in a subarctic transitional zone between the relatively moist climate of the St. Elias Mountain range to the west, and the much drier continental interior climate of the southeastern Yukon. The St. Elias Mountains act as an orographic barrier against Pacific moisture, resulting in a rain shadow effect over the Kusawa Lake area, making the local climate comparatively dry (Roots et al., 2004). Precipitation varies locally across the region. Kusawa Lake has a reported mean annual precipitation of 500-600 mm (Yukon River Panel, 2003), whereas the surrounding Yukon Southern Lakes eco-region and the southeastern Yukon experiences mean precipitation rates of 200-325 mm (Moore et al., 2002; Roots et al., 2004). The annual stream flows in the study region are dominated by snow- and glacier ice melt, with peak flow conditions occurring in June. A secondary peak often occurs late in the summer as a result of intense rainfall (Roots et al., 2004; McDowell et al., 2009). The air temperature is influenced by moist air masses traveling inshore from the nearby Pacific Ocean (Wahl, 2004). The mean annual

8 temperature is ~1̊ C, with a summer mean of 10̊ C, and –13̊ C during winter (Yukon River Panel, 2003). The Yukon Territory as a whole can experience extreme annual temperature variations, with historical minimum and maximum values of –62.8 and 36.1 ˚C (Wahl, 2004). The snow cover season extends from October until mid-April in the valley floors, and about a month later at higher altitudes (Roots et al., 2004). The study region is underlain by discontinuous permafrost, predominantly on alpine slopes with thick glacial sediments (McDowell et al., 2009).

3.3.2 Geology and geomorphology

The bedrock composition of the Yukon Southern Lakes eco-region has a great diversity, as it extends over four different tectonic terranes. In the western and eastern parts of the region, the bedrock is predominantly made of crystalline metamorphic and granitic rocks, while in the central part of the region it consists of mafic volcanic rocks, ancient limestone reefs, and clastic sediments. In the Yukon Stikine Highlands the bedrock is predominantly made of granitic intrusions (~60 % of the region), together with other magmatic rocks, sandstones and breccias. The surface deposits in both eco-regions are largely associated with the last major Pleistocene expansion of Cordilleran glaciers, locally known as the McConnell glaciation (26,500-10,000 years ago). During this time, glaciers expanding out of the Coast Mountains and Cassiar Mountains covered most of the study area, flowing from the highlands in a north-northwestward direction. During the last deglaciation, Kusawa Lake was a part of the southern extension of the great Glacial Lake Champagne. The exact level and extent of the ice-dammed lake in the Kusawa River valley is unknown, but a former shoreline is found at 760 m above current sea level at nearby Bennett Lake (Roots et al., 2004). Glacial Lake Champagne left large deposits of clay and silt, and in some depressions and in the valley of the Takhini River, the stream that drains Kusawa Lake, the sediment thickness can be up to 75 m (Roots et al., 2004). The Kusawa Lake of today was created when the ice dam that jammed glacial Champagne Lake collapsed, allowing it to drain. After deglaciation, the higher altitudes were left with bare bedrock exposed to weathering, and many mid- altitude slopes were covered with glacier till and outwash (McDowell et al., 2009).

3.3.3 Human occupation and activity

The ancestors of the Cree and Chipewyan First Nations are thought to be the first people to have populated this part of the western Canadian subarctic. They primarily hunted moose and caribou, and fished in the numerous lakes and rivers. This traditional native diet is still important for the modern descendants of the First Nations occupying the region (Evans et al., 2005). Today, the consumption of fish from Kusawa and other lakes is increasing as a result of a growing sport fishing industry, fueled by ecotourism. The Kusawa Lake Territory Park is a popular tourist and recreation destination, totaling 33,000 person-days of visit per year. The objectives of the park when it was created were to “protect an area of cultural significance to First Nations”, as well as to uphold the

9 ecological values and traditional activities of the native people, including hunting and fishing (McDowell et al., 2009). However, it is estimated that 92 % of the anglers who fish in Kusawa Lake nowadays are from Whitehorse.

4 Methods

In order to estimate the maximum potential contribution of Hg into Kusawa Lake from snow- and glacial melt water, a modified version of the HBV hydrological model (Hydrologiska Byråns Vattenbalansavdelning) was used (Konz & Seibert, 2010; Seibert & Vis, 2012). The work was divided into three main steps, described in greater detail below: 1. Key descriptive properties of the Kusawa Lake catchment, such as area, hypsometry, and glacier coverage, were determined from various geospatial databases using ArcGIS. 2. Next, the HBV model was calibrated and validated using temperature, precipitation and snowpack data obtained from the study region. Once calibrated, the model was used to simulate runoff contributions from snow and glacial ice melt into Kusawa Lake over the recent decade 2001-11. 3. The THg measured in snow and glacial samples by Karlsson (2014) was scaled by the HBV- simulated discharge to obtain an estimate of the maximum yearly average input of Hg from snow and ice into Kusawa Lake. Uncertainties of the calculated Hg fluxes were estimated from the spread of the THg data and using a Monte Carlo procedure for the runoff variability.

4.1 HBV model

4.1.1 Model overview HBV is a conceptual hydrological model that uses different physical routines to simulate the specific discharge from a catchment from meteorological input data. The model was initially developed by the Swedish Meteorological and Hydrological Institute (Bergström, 1992). In this study the version HBV- light was used which includes a glacier routine (Konz & Seibert 2010; Seibert & Vis 2012). The model uses temperature, precipitation and long-term evaporation rates as inputs, and estimates the resulting total discharge (Qsim), as well as the contributions from snow (Qsnow) and glacial melt (Qglac). The discharge is expressed as specific discharge in the unit of mm day-1, instead as a volumetric unit per timestep. The model can be calibrated and its performance evaluated against actual hydrometric data from the same catchment. For this work, the most important routines in the model are the snow, glacier and response routines. The snow routine is dependent on precipitation and temperature as input since these parameters control the accumulation and melt of the snowpack. When the air temperature (Tair) falls below a specified threshold value (Tt), precipitation accumulates as snow. Conversely, when Tair rises above Tt, any existing snow cover melts according to:

= ( ) (1)

𝑄𝑄𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑃𝑃𝐷𝐷𝐷𝐷𝐷𝐷 𝑇𝑇𝑎𝑎𝑎𝑎𝑎𝑎 − 𝑇𝑇𝑡𝑡

11 where Qsnow is the amount of meltwater produced, and the parameter PDDF is a degree-day "melt factor" (in units of mm ˚C-1 ∆t-1) which determines how much snow, in water equivalent, is melted per degree- day. The value of Tt is typically close to 0˚C (Bergström, 1992).

Two other unitless parameters, PCWH and PCFR, control the maximum liquid water retention capacity of the snowpack that must be exceeded for runoff to occur, and the amount of water that may refreeze within it (Qref), respectively. The latter quantity is determined by:

= [ ( )] (2)

𝑄𝑄𝑟𝑟𝑟𝑟𝑟𝑟 𝑃𝑃𝐶𝐶𝐶𝐶𝐶𝐶 𝑃𝑃𝐷𝐷𝐷𝐷𝐷𝐷 𝑇𝑇𝑎𝑎𝑎𝑎𝑎𝑎 − 𝑇𝑇𝑡𝑡 For glaciated catchments, the glacier routine can be used to estimate the amount of water added to the total discharge as a result of glacial ice melt, in addition to inputs in the form of seasonal snow melt and direct precipitation. The glacier routine can simulate annual glacier growth and recession following a mass balance approach, in which case time series of glacier extent must be provided as an input file. Alternatively, the routine can simply simulate glacial meltwater production based on the percentage of the catchment area covered by ice, which was the preferred option for this thesis. To set up the HBV model with the glacial routine, the slope aspect distribution over the catchment must be specified in order to account for the effect of different sun expositions for snow- and ice- covered surfaces. Slope aspect is simply categorized into south-facing, north-facing and east- or west- facing slopes (or horizontal surfaces). The glacial routine, like the snow routine, uses a degree-day factor approach to compute melt, while making allowance for slope exposure effects. For glaciers with north-facing slopes, the effectiveness of temperature-driven melt is reduced, and vice versa for south- facing glacier slopes. To simulate the dynamics of internal glacial storage, the routine transforms 0.1% of the accumulated snow into glacial ice for each daily time step (day-1). Conversely, the onset of glacier melt only occurs once the simulated snow pack on the glaciers has been totally melted (Konz & Seibert, 2010). The hydrological response routine translates all available water within the catchment to actual discharge, taking into account the effects of evaporation, soil moisture infiltration and groundwater recharge. The routine treats the observed discharge as if it were determined by the combined response of two separate reservoirs: a shallower one with a more rapid, non-linear response, and a deeper one with a slower, more linear response. For any day, the discharge rate as of function of time, Q(t), is calculated following Darcy’s law as:

( ) = ( ) ( ) (3) 𝑡𝑡0−𝑡𝑡 𝐾𝐾 𝑄𝑄 𝑡𝑡 𝑄𝑄 𝑡𝑡0 𝑒𝑒

-1 where t-to defines a time step interval (∆t), and K is the reservoir storage coefficient (in units of ∆t ). In HBV, three recession coefficients, called K0, K1 and K2, controls the shape of the flow recession curves. The value of K2 determines the recession to base flow.

4.1.2 Model Setup: Catchment description The first step in setting up the HBV model for the present study was to determine physical descriptive parameters for the Kusawa Lake catchment. These include total area and sub-catchment area, lake- covered area, land cover type, hypsometry (percentage of area as a function of altitude), slope angle and aspect, and of course the percentage of glacier cover. Most of these properties were derived in ArcGIS from a Digital Elevation Model (DEM) with a spatial resolution of 30 m obtained from the Yukon Government (Yukon Department of the Environment, 2015). Delineation of the whole Kusawa Lake watershed was derived using the ArcGIS Hydrology function (Spatial Analysis toolset), and using the coordinates of the outlet of Kusawa Lake, where it discharges into the Takhini River (Fig. 4). The watershed area of the Primrose River sub- catchment, for which some hydrological data are also available (see below), was delineated by the same method, using coordinates for the river outlet in Kusawa Lake as determined from Google Earth imagery. The area of the only two large lakes within the catchment, Primrose (11 km2) and Kusawa itself (137 km2), were also derived from the DEM in ArcGIS, and verified against visible satellite imagery (Google Earth, 2013). Smaller lakes and ponds were neglected. Using this method, the total calculated area of the Kusawa Lake catchment and Primrose River sub-catchment were estimated to be 4076 km2 and 1235 km2, respectively.

A B

Figure 4. A) Digital Elevation Model of the Kusawa Lake catchment with a resolution of 30 m. B) Outlines of the Kusawa Lake catchment (red) and the Primrose River sub-catchment (shaded in blue).

The Kusawa Lake catchment was subdivided in the HBV model into three distinct elevation zones, each with a uniform land cover/vegetation cover type: forest, subalpine or alpine (Table 1). The alpine zone, which lies above ~1200 m asl, covers two thirds (67 %) of the entire lake catchment. The spatial distribution of the forested zone (between ~642-850 m asl) differs somewhat between the Kusawa Lake catchment and Primrose sub-catchment.

Table 1. Descriptive properties of the Kusawa Lake catchment used for the HBV model setup.

Elevation range (m a.s.l.) Area distribution Glaciers Lakes Total area Zone Min Max km2 % km2 % km2 % Forest 642 850 0 0 137 3 391 10 Subalpine 850 1200 0 0 0 0 941 23 Alpine 1200 2529 148 4 0 0 2748 67

While this subdivision is a simplification, it is in fact reasonably close to the land cover zonation observed in the Kusawa Lake area (Fig. 3). The boundaries of the three elevation zones were estimated using recent Landsat visible satellite imagery in Google Earth (scenes acquired on 2013-04- 10). To do this, spatially distributed points within and adjacent to the study catchment were selected, and the mean altitude for each zone transition, from the valley floors up to the alpine zone, was

14 determined using the corresponding elevations in the Yukon DEM. Both flanks of the Kusawa Lake and Primrose River valleys were examined to get representative mean values, not dependent on cardinal points. Glaciers in the Kusawa Lake catchment are confined to the upper, alpine elevation zone. Their distribution and total area were obtained from the Randolph Glacier Inventory, version 5.0 (Arendt et al., 2015), accessible in geospatial data format through the Global Land Ice Measurements from Space (GLIMS) database. To use the glacier routine built in the HBV model, the glacier-covered area was specified as fraction of the land cover area within the alpine elevation zone (Fig. 5, Table 1).

Figure 5. Spatial distribution of glaciers (black) in the Kusawa Lake catchment, as delineated in the Randolph Glacier Inventory (Arendt et al., 2015).

As specified earlier, the HBV model takes into account the effect of sunlight exposure on snow and ice melt rates as a function of slope aspect (orientation). To this end, the slope aspect distribution for each elevation zone was extracted from the DEM using ArcGIS. Slopes were classified into five groups (Fig. 6), and their distribution within the three elevation zones was calculated as a fraction of the total area of the catchment. The alpine zone was further subdivided into glacier-free and glacier-covered areas (Fig. 7, Table 2).

N (2) N (1) N (1) (0˚- 45˚) E (45˚- 135˚) W E S (135˚- 225˚) W (225˚- 315˚) N (2) (315˚- 360˚) S

Figure 6. Aspect classification as a function of the compass direction in degrees.

A B

Figure 7. A) Delineation of the three main elevation/land cover zones within the Kusawa Lake catchment used for discharge simulations with the HBV model. B) Slope aspect classes in the Kusawa Lake catchment used for HBV simulations.

Table 2. Slope aspect distribution in the Kusawa Lake catchment and Primrose River sub-catchment, expressed as percentages of total catchment area.

All areas Glacier-covered areas only Lakes North South East/West North South East/West Kusawa Lake catchment Forest 1.64 1.93 2.70 0 0 0 3.3 Subalpine 5.14 5.83 12.14 0 0 0 0 Alpine 16.73 21.28 25.63 2.28 0.28 1.06 0 Primrose River sub-catchment Forest 0.18 0.28 0.27 0 0 0 0 Subalpine 5.67 7.43 14.80 0 0 0 0.8 Alpine 17.76 21.39 28.08 2.20 0.13 0.95 0

4.1.3 Model Setup: Meteorological and hydrological data

To run the HBV model, some specific meteorological and hydrometric input data are required, which include air temperature and precipitation data from a nearby weather station, together with estimates of the long-term mean evaporation rate and air temperature for each month of year in the study area. Daily meteorological data, including air temperature and total precipitation, were obtained from the Environment Canada weather station at Whitehorse, approximately 60 km east of the Kusawa Lake catchment, at an altitude of 706 m asl (Environment Canada, Online Climate Data Archive). The same source provided the long-term monthly means for the evaporation rate and air temperature, Whitehorse being the only permanent station in the southern Yukon with nearly continuous records spanning more than 30 years. It was assumed that the general pattern of precipitation events across the region is similar in Whitehorse and in the Kusawa Lake catchment, although the precipitation phase (solid vs. liquid) with elevation was adjusted as a function of the temperature lapse rate in HBV. The temperature and precipitation data from Whitehorse were incomplete and had occasional days with missing recorded values. For precipitation, the occasional missing values were assumed to be 0 mm, and for temperature they were linearly interpolated from the previous and following days. To calibrate the HBV model for the study catchment, and to evaluate its performance, at least some discharge data are also required. In the Kusawa Lake catchment, historical discharge data are available from two locations: (1) from the outlet of the Primrose River, on the east side of the lake, and (2) from the main outlet of Kusawa Lake itself (Fig. 4). Neither the Primrose River nor Kusawa Lake outlet have regulated flow. Data from the outlet of Primrose River cover the period 1990-98 (Yukon Water Resources Hydrometric Program, 2005; station 29AC006), while those from the outlet of Kusawa Lake cover the period 1952-86, but are only continuous after 1965 (Canadian Historical Hydrometric Data online archive; station 09AC004). The Primrose river data are available at daily resolution, but are only complete for some of the summer months (typically: June, July, August and September), while winter discharge measurements are lacking altogether. The discharge data from the Takhini River at the outlet of Kusawa Lake are essentially continuous after 1965, and cover both summer and winter months. In this work, Kusawa Lake is assumed to be in hydrological balance, where inflow is equal to outflow. The total catchment discharge into the Takhini River (outflow of Kusawa Lake) is therefore considered to be equal to the total catchment inflow into Kusawa Lake and therefore has no change is storage over time as it is assumed to be constant.

4.1.4 Model calibration and validation

Optimization of the HBV model parameters can be done manually, or automatically using the Genetic Algorithm and Powell (GAP) optimization method (Seibert, 2000). In this work, the GAP optimization method was used, with a convergence limit set at 5000 iterations of the algorithm. The initial ranges (min and max) of parameter values for the calibration were the default ones suggested by the HBV

17 model, with some modifications based on available information from the study region (Appendix: Table A1). The parameters for which the range of possible values were adjusted are shown in table 2.

Table 2. HBV model parameters for which the range of values was modified relative to default values.

Parameter Variable name in HBV

Degree-day melt factor for snow/ice CFMAX Snowfall correction factor SFCF Precipitation correction factor for glaciers CFGlacier Slope correction factor for snow/ice melt CFSlope Maximum soil moisture storage FC Length of triangular weighting function in the hydrological response routine MAXBAS Precipitation correction factor with altitude PCALT Temperature correction factor with altitude TCALT

The adjusted upper and lower limits for most of these parameters were based on hydrological modelling work performed in the 195 km2 Wolf Creek Research Basin, located ~85 km east of the Kusawa Lake catchment, and which shares most of its physiographic and climatic characteristics (Pomerory et al., 2010; Rasouli et al., 2014). For the temperature lapse rate (parameter TCALT in the HBV model), the limiting values were chosen based on regional surveys by Bonnaventure and Lewkowicz (2013). Various metrics can be used in HBV to evaluate the performance of the model against observed discharge data, and to guide the calibration. In this work, the Nash-Sutcliffe coefficient (Reff) was used as the principal performance metric, following widespread usage. The coefficient is defined as:

( ( ) ( )) = 1 (4) ( ( ) ( )) 2 ∑ 𝑄𝑄𝑜𝑜𝑜𝑜𝑜𝑜 𝑡𝑡 −𝑄𝑄𝑠𝑠𝑠𝑠𝑠𝑠 𝑡𝑡 2 𝑅𝑅𝑒𝑒𝑒𝑒𝑒𝑒 − ∑ 𝑄𝑄𝑜𝑜𝑜𝑜𝑜𝑜 𝑡𝑡 −�𝑄𝑄��𝑜𝑜𝑜𝑜𝑜𝑜��𝑡𝑡��

Where Qobs and Qsim are the observed and simulated discharge, respectively, and t is the time step. A value of 1 for Reff implies a perfect agreement between Qobs and Qsim. Two different calibration strategies were tested. First, the Kusawa Lake catchment was subdivided, with the aim to optimize the model parameters using data from the Primrose River sub-catchment only (Fig. 4). This seemed justifiable, given that it represents ~1/3 of the total catchment area for the Kusawa Lake watershed, and includes part of the glacier-covered region. It was therefore expected that the model parameters optimized for this sub-catchment should be valid and applicable over the larger watershed. For this initial trial, the calibration was performed using meteorological data over the period 1990-98, for which discharge data were available (Fig. 8). The resulting calibration settings were then used to simulate the discharge from the entire Kusawa Lake watershed over a different period (1975-86), and the performance of the model was evaluated against the discharge data from the Kusawa Lake outlet for that period. For the HBV model run, a warm up-period of one year was

18 adopted in order to initialize the different state variables before the actual simulation period. The Qsim values for the warm up-period were therefore excluded from the output.

After visual inspection of the results, it was found that Qsim over the validation period (1981-86) did not agree well with Qobs at the outlet of Kusawa Lake (see section 5.1). The simulation gave a Reff of 0.75. To improve the match, the recession curve coefficients K0, K1 and K2 were re-optimized separately from other variables, using a Monte Carlo simulation (50,000 runs), and further adjustments to the value of K2 were made manually to maximize the agreement between Qobs and Qsim. The adjusted parameters were then used to re-simulate discharge in the Primrose River sub-catchment for the period 1990-98, but the results remained unsatisfactory. Consequently, the attempt to use the Primrose River discharge data for calibration was abandoned.

A 15 B5

10 4

C] 5 °

3 0

-5 2

-10 Temperature [ Evaporation [mm] 1 -15

-20 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

50 C] C ° 0

Temp [ -50 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

20 D 10

Precipitation [mm] 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

] 10 6 -1

d 10

E 5

0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Discharge [m Date

Figure 8. Input meteorological and discharge data used to calibrate the HBV model for the Primrose sub- catchment. A) and B) Mean monthly temperature and evaporation in Whitehorse (Source: Environment Canada, Climate Normals, 1981-2010). (C) and (D) Daily temperature and total precipitation in Whitehorse for the period of 1-Jan-1990 to 31-Dec-1998 (Source: Environment Canada). (E) Daily discharge data from the outlet of Primrose River over the same period (Source: Yukon Hydrometric Network).

As an alternative, the HBV model was calibrated more directly using the discharge data from the outlet of Kusawa Lake itself over the period 1975-86 (Fig. 9). The data were split into two periods, and the first, spanning 1975-81, was used for model calibration using the GAP optimization tool. The resulting calibration settings were then used to simulate the discharge for the second period, spanning 1982-86, and the results were evaluated against the measured discharge for that interval.

50 C] ° 0

A Temp [ -50 1975 1977 1979 1981 1983 1985 1987 40

B 20

Precipitation [mm] 1975 1977 1979 1981 1983 1985 1987

7 ] 10

-1 4 d 3 C 2 0 Discharge [m 1975 1977 1979 1981 1983 1985 1987 Date

Figure 9. Input meteorological and discharge data used to calibrate the HBV model for the entire Kusawa Lake catchment. The mean monthly temperature and evaporation data used were the same as in Fig. 6. A) and B) Daily temperature and total precipitation in Whitehorse for the period of 1-Jan-1975 to 31-Dec-1986 (Source: Environment Canada). (C) Daily discharge data from the outlet of Kusawa Lake over the same period (Source: Canadian Hydrometric Network).

To verify the HBV model's ability to realistically simulate the seasonal snowpack evolution in the study catchment, a simulation was performed over the period 1995-2004. The simulated snow cover time series was compared with measurements made over this same period by a snow pillow situated at an altitude of ~1250 m asl in the subalpine zone of the nearby Wolf Creek Research Basin, south of Whitehorse (Water Resources Branch, Environment Yukon). Finally, the last calibrated parameters for the HBV model were used to simulate the Kusawa Lake catchment discharge over the period 2000-11, based on the temperature and precipitation input data from Whitehorse over this period (Fig 10).

50 C] ° A 0 Temp [

-50 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

B 10

0 Precipitation [mm] 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Figure 10. Input meteorological data used for discharge simulation for the Kusawa Lake catchment using the HBV model. A) and B) Daily temperature and total rainfall in Whitehorse for the period of 1-Jan-2000 to 31- Dec-2011 (Source: Environment Canada).

4.2 Calculation of Hg inflow to Kusawa Lake To calculate the possible Hg contribution from snow and glacial meltwater to Kusawa Lake, the simulated discharge from HBV was used in conjunction with the measured snow and ice Hg data from Karlsson (2014). These data were found to be approximately log-normally distributed over a range of 0.2-6.14 ng L-1, with a geometric mean of 0.44 ng L-1 (arithmetic mean = 0.75 ng L-1) (Fig 11).

10 Frequency [n]

0 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 - Samples [log THg ng L 1] 1 0 Figure 11. Histogram of Karlsson's (2014) measured THg in snow and ice samples collected within the Kusawa Lake catchment and the nearby St. Elias Mountains (n = 116).

For both snow- and glacier-produced meltwater, the resulting maximum annual flux FHg of dissolved Hg entering Kusawa Lake was calculated following:

= [THg] (5)

𝐹𝐹𝐻𝐻𝐻𝐻 𝑄𝑄𝑠𝑠𝑠𝑠𝑠𝑠�� where Qsim is the simulated annual discharge contribution from either snow or glaciers, and [THg] is the geometric mean concentration of total Hg in all snow and glacier samples, after Karlsson� �(2014).�� The Hg fluxes from snow and glacier melt were then summed to obtain a total annual Hg flux from both sources.

4.3 Calculation of uncertainties Uncertainties in the calculated annual Hg fluxes to Kusawa Lake arise from possible variations in the

THg concentration in snow and ice (Fig. 9) and from the interannual variability of Qsnow and Qice. For THg, the uncertainty was considered to be ± 2 ng L-1, which is approximately equal to two standard deviations around the arithmetic mean concentration (2σHg), and this interval encompasses nearly 99 % of the probability distribution of THg. To estimate the uncertainty in the annual discharge, a Monte

Carlo simulation (1000 runs) of the total discharge (Qtot) from the Kusawa Lake catchment was performed with the HBV model using Whitehorse climatological input data for the decade 2001-2012. In this simulation, all but a few prescribed model parameters were allowed to vary freely between preset limits (Appendix: Table A1). The 10th, 50th and 90th percentiles of the simulated total discharge

(Q10, Q50, Q90) were computed for each day of the simulation, and then summed for each year. Values of Q50 were taken as the most probable annual estimates of Qtot, and the ~2σ uncertainties around these values were estimated as:

2 = (6)

𝜎𝜎𝑄𝑄 𝑄𝑄90 − 𝑄𝑄10

The Monte Carlo simulation did not generate separate estimates of Qsnow and Qice, but only of the total discharge (Qtot). Therefore, the 2σ uncertainties on Qsnow and Qice were simply scaled from that around Qtot as follows:

2 = 2 (7)

2𝜎𝜎𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄= 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄2 𝜎𝜎 𝑡𝑡 𝑡𝑡 𝑡𝑡 (8) 𝜎𝜎𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝜎𝜎𝑡𝑡𝑡𝑡𝑡𝑡 where and are the mean fractional contributions of Qsnow and Qice to Qtot obtained over the model 𝑓𝑓calibration𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑓𝑓period𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 (see section 5.2). With these estimates of 2σHg, 2 , and 2 , the uncertainties of the Hg fluxes from snow melt (Fsnow) and from glacier ice melt𝜎𝜎𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 (Fice) could𝜎𝜎𝑄𝑄 then𝑄𝑄𝑄𝑄𝑄𝑄 be calculated following:

2 = + [ ] (9) 2𝜎𝜎𝐻𝐻𝐻𝐻 2𝜎𝜎𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 and 𝜎𝜎𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ��THg�� 𝑄𝑄𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 �� 2 = + [ ] (10) 2𝜎𝜎𝐻𝐻𝐻𝐻 2𝜎𝜎𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝜎𝜎𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝑖𝑖𝑖𝑖𝑖𝑖 ��THg�� 𝑄𝑄𝑖𝑖𝑖𝑖𝑖𝑖 ��

Finally, for the total Hg flux (Ftot) from both snow and ice meltwater, the 2σ uncertainty (2σFtot) was calculated as the quadratic sum of 2 and 2 :

𝜎𝜎𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝜎𝜎𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 2 = ((2 ) + (2 ) ) (11) 2 2 𝑡𝑡𝑡𝑡𝑡𝑡 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝜎𝜎 � 𝜎𝜎 𝜎𝜎

5 Results

5.1 HBV model calibration and validation

Results of calibrating the HBV model using hydrometric data from the Primrose River sub-catchment over the period 1991-1998 are shown on Fig. 12. The simulated specific discharge had an Reff value of 0.75. The main features of the observed summer discharge of the river were adequately captured by the model, such as the high spring flow due to snow melt and the duration of low flow periods. However, since observations of winter base flow were missing, the shape of the recession curve of the hydrograph was largely unconstrained. Furthermore, the model did not manage to reproduce the highest peak flows, such as the peaks in July 1991 and 1992, and in June 1998. The HBV parameter set obtained from the Primrose River sub-catchment was validated by simulating the specific discharge for the entire Kusawa Lake catchment for the years 1976-1986. The resulting simulated hydrograph, and actual specific discharge observations over the same period, are shown on Fig. 13. The simulated specific discharge had an Reff value of 0.76. However, as seen in the figure, the simulated and observed specific discharges had a relatively poor fit. The magnitude of spring and summer flows were generally comparable, but the recession curves produced by the model tended to underestimate the actual specific discharge in both rate and magnitude. The observed specific discharge showed gradual, smooth recession curves, while those simulated by the model tended to decrease too sharply. Several peak flow periods were also underestimated. Accordingly, the calibrated parameter set produced from calibration with the Primrose River sub-catchment was rejected, as it did not generate a satisfying simulation of total specific discharge for the Kusawa Lake catchment as a whole.

8 Qobs

Qsim 6 Reff=0.75

Spec. discharge [mm/day] 0 1991 1992 1993 1994 1995 1996 1997 1998 1999 Date Figure 12. Observed (red) and simulated (black) specific discharge for the Primrose River sub-catchment over the calibration period 1991-1998.

7 Qobs 6 Reff= 0.76 Qsim

1 Spec. discharge [mm/day] 0 1976 1978 1980 1982 1984 1986 Date Figure 13. Observed (red) and simulated (black) specific discharge from Kusawa Lake catchment over the period 1976-1986. The simulation was performed using the optimal parameter values obtained from the calibration in the Primrose sub-catchment (previous figure).

For the alternative model calibration approach, the Kusawa Lake hydrological discharge dataset covering the period 1976-1986 was split in two. The simulated discharge for the first half-period

(1976-1981) used for calibration us shown on Fig. 14. The Reff value for this simulation was 0.97, very near the maximum of 1, and the observed and simulated discharges followed each other very closely. The peak flows for 1980 and 1981 were not captured exactly, however except for these minor deviations, the HBV model simulation gave a very satisfying rendition of the recorded discharge, both in terms of timing and magnitude. A comparison of results of the model simulation for the latter half of the Kusawa Lake hydrological dataset period (1982-1986) is shown on Fig. 15. The Reff value was 0.93. Here also, the main features of the observed catchment discharge were adequately captured by the simulated runoff. Further evaluation of the model was made by comparing the simulated snowpack development for the Kusawa Lake catchment with snow pillow observations from the Wolf Creek basin over the period 1996-2005 (Fig. 16). As can be seen, the simulated snowpack in the Kusawa Lake catchment and the observed snowpack in the Wolf Creek catchment show very similar start (accumulation) and end (melt out) dates, and the characteristics of their development curves follow each other very closely. Minor deviations could be expected, given the physiographic differences between the two basins. Based on these validation experiments, the parameter set obtained for the HBV model using the 1976-1981 calibration period was retained and used for the final simulation of the catchment discharge over the period 2001-2012.

Qobs 5 Qsim Reff= 0.97 4

Spec. discharge [mm/day] 0 1976 1977 1978 1979 1980 1981 1982 Date

Figure 14. Observed (red) and simulated (black) specific discharge for the Kusawa Lake over the calibration period 1976-1981.

Qobs 6 Qsim Reff= 0.93

Spec. discharge [mm/day] 0 1982 1983 1984 1985 1986 1987 Date

Figure 15. Observed (red) and simulated (black) specific discharge from Kusawa Lake catchment over the period 1982-1986. The simulation was performed using the optimal parameter values obtained from the calibration in the Kusawa Lake (previous figure).

200 Snowpillow Simulated snow

150

100 SWE [mm] 50

0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Date

Figure 16. Subalpine snow pillow data (red) from the Wolf Creek catchment, 1996-2005, compared with simulated snow pack (black) in Kusawa Lake over the same period.

5.2 Estimating potential fluxes of Hg to Kusawa Lake

The final HBV model simulation of the Kusawa Lake basin discharge, covering the period 2001-2012, is shown on Fig. 17, with snow and glacier meltwater contributions shown separately. As explained earlier, it is assumed that the lake is in a state of hydrological balance, and the catchment discharge is therefore treated as being equal to the total lake inflow. Based on to the HBV model results, the annual discharge contribution from the seasonal melting of glacier ice is larger than the annual contribution from snowpack meltwater. On average, the glacier contribution to the annual discharge is estimated to be 38 %, while that from snowpack melt is estimated to be 22 %, such that the combined contributions from snow and ice melt amount to 60 % of the total annual discharge (Table 3). 6 Qsnow Qglacier 5 Qtotal

2 Discharge [mm/day] 1

0 2001 2003 2005 2007 2009 2011 Date Figure 16. Simulated total specific discharge (black), melt water specific discharge from snow (blue) and glaciers (red) from the Kusawa Lake catchment for the period 2001-2011.

Table 3. Simulated annual discharge from snow and glacial meltwater to Kusawa Lake, 2001-2012. fsnow, fice and fsnow+ice are the estimated fractional contributions to the total catchment discharge Qtot. Uncertainties (2σ) are based on a Monte Carlo simulation (1000 runs) performed with the HBV model.

Year Qsnow fsnow Qice fice Qsnow+ice fsnow+ice Qtot (km3/year) (km3/year) (km3/year) (km3/year)

2001 219 ± 62 0.12 630 ± 178 0.34 848 ± 189 0.46 1833 ± 520 2002 214 ± 75 0.16 624 ± 220 0.46 838 ± 232 0.62 1348 ± 474 2003 183 ± 67 0.15 673 ± 246 0.54 856 ± 255 0.69 1248 ± 457 2004 311 ± 111 0.19 886 ± 316 0.53 1197 ± 355 0.72 1669 ± 595 2005 553 ± 160 0.24 704 ± 204 0.31 1257 ± 259 0.56 2262 ± 655 2006 346 ± 107 0.21 601 ± 185 0.36 947 ± 213 0.57 1647 ± 507 2007 440 ± 138 0.24 706 ± 221 0.38 1146 ± 261 0.61 1866 ± 585 2008 521 ± 162 0.28 506 ± 158 0.27 1027 ± 226 0.54 1892 ± 590 2009 599 ± 189 0.29 728 ± 230 0.35 1327 ± 298 0.64 2064 ± 652 2010 382 ± 120 0.22 715 ± 224 0.41 1097 ± 254 0.63 1745 ± 547 2011 527 ± 160 0.26 588 ± 178 0.29 1115 ± 240 0.55 2018 ± 612 2012 500 ± 153 0.27 599 ± 183 0.32 1099 ± 239 0.58 1882 ± 577

Average 400 ± 38 0.22 663 ± 62 0.38 1063 ± 73 0.60 1789 ± 64

Table 4 gives the calculated maximum annual fluxes of dissolved Hg entering Kusawa Lake from snow and glacier ice melt over the period 2001-2012, calculated from equation 5 and using the simulated discharge listed in Table 3. The average total Hg flux from both snow and ice melt -1 (Fsnow+ice) is 550 ± 495 g yr , approximately 60 % of it being associated with glacier meltwater. Translated into an equivalent input rate per unit area over Kusawa Lake, this flux estimate is equal to 3.6 ± 3.5 μg m-2 yr-1. The total uncertainties (2σ) on the calculated Hg fluxes were estimated from the spread of Hg data collected by Karlsson (2014) and from the possible interannual variability of the catchment discharge, as inferred using a Monte Carlo simulation with 1000 realizations (Fig. 17).

Table 4. Estimated annual fluxes of Hg into Kusawa Lake from snow (Fsnow) and glacier ice (Fice) meltwater, and combined snow and ice meltwater (Fsnow+ice). Uncertainties (2σ) were calculated taking into account the spread of Hg values as well as the interannual variability of the catchment discharge.

Year Fsnow Fice Fsnow+ice g/year g/year g/year

2001 113 ± 469 326 ± 1352 439 ± 1431 2002 111 ± 466 323 ± 1362 434 ± 1439 2003 95 ± 402 348 ± 1473 444 ± 1527 2004 161 ± 680 459 ± 1935 620 ± 2051 2005 286 ± 1188 365 ± 1515 651 ± 1925 2006 179 ± 747 311 ± 1297 490 ± 1497 2007 228 ± 952 365 ± 1256 594 ± 1799 2008 270 ± 1125 262 ± 1095 532 ± 1570 2009 310 ± 1295 377 ± 1576 687 ± 2040 2010 198 ± 826 370 ± 1546 568 ± 1753 2011 273 ± 1136 304 ± 1268 577 ± 1703 2012 259 ± 1079 310 ± 1293 569 ± 1684

Average 207 ± 264 344 ± 419 550 ± 495

Figure 17. Range of simulated total specific discharge (Qtot) from the Kusawa Lake catchment produced with a Monte Carlo simulation (1000 iterations) with the HBV model. These results were used to estimate uncertainties in Hg fluxes to the lake (Table 4).

6 Discussion

6.1 Uncertainties and limitations of this study

Some considerations associated with the HBV discharge modeling, and with the computations of Hg input to Kusawa Lake, are discussed below, as some of these could introduce uncertainties and limitations in the interpretation of the final simulation results. Concerning the physical properties of the Kusawa Lake catchment, there is is some uncertainty on the extent and actual surface area of the watershed. The calculated area obtained by ArcGIS using the Yukon 30 m DEM was 4076 km2, whereas the area as reported in the literature varies between 4066 and 4070 (Brabets et al., 2000; Moore et al., 2002). Likewise, an area of 1299 km2 was determined for the sub-catchment of Primrose River using ArcGIS, whereas the area reported in the literature was 1235 km2 (Yukon River Panel, 2003). For the Kusawa Lake catchment, the discrepancies are small (< 1 %) compared to the size of the catchment, but they are larger for the Primrose River sub-catchment (~ 1 %). This is probably owing to the fact that the outlet of the Primrose River into Kusawa Lake is flat and delta-like, making it difficult to locate precisely. However, since the Primrose River sub- catchment was not used for the final HBV model calibration, these uncertainties probably have a negligible effect on the final results. Another potential issue concerns the input meteorological and hydrometric data that were used for calibrating, validating and forcing the HBV model. The original time series of temperature and precipitation data from Whitehorse were not fully continuous and contained many gaps. There were in fact three different weather stations operated at different times and places in the Whitehorse area over the past 15 years, and the precipitation and temperature data used in the study were averages of the time series from these stations, produced to compensate for gaps in individual records. Even then, some gaps remained which, for the final simulation 2001-2011, totaled 56 days for temperature (~2% of daily records), and 20 days for precipitation (~2% of daily records). Many minor gaps in the air temperature record were relatively easy to fill by interpolation, because the signal was very spatially coherent across the three Whitehorse weather stations in the period of overlap. However there remained one time period, in April 2007, for which only two values of daily temperature where reported, on the 4th and the 7th of the month. This was an important part of the snow-melt period in that year, and also a period of rapid temperature fluctuations, which went from -7.4 ˚C on March 31st, to 5˚C on April 4th, i.e. within three days. How the missing air temperature data may have affected the HBV simulation for that year is unknown, but since the objective was to look at the mean results over a decade, rather than for any single year, it can probably be assumed that the impact was not large. Missing values in the precipitation data were simply set to 0 mm day-1. Interpolation was not considered a reliable infilling strategy because the time series for the three Whitehorse weather stations shows a considerable degree of spatial heterogeneity. Interpolating these data would have

29 given a false perception of reality. To properly quantify the uncertainty in the modeled discharge that arises from the spatial variability of precipitation, a separate Monte-Carlo exercise would need to be performed, which was not done in this study owing to time constraints. Likewise, the Monte Carlo procedure that was used for estimating the interannual variability in discharge to Kusawa Lake could only provide results for the total specific discharge Qtot, and did not provide separate estimates of variability for Qsnow and Qice. Therefore, the total uncertainty was simply apportioned between the Qsnow and Qice terms based on the relative magnitude of their mean contributions to Qtot. This shortcut was due to limitations in the current version of HBV. To separately estimate the potential spread of values for snowmelt and glacial Hg fluxes (Fsnow and Fice), the HBV model would need to be run iteratively for each possible set of input parameters generated by the

Monte Carlo simulation, and the individual simulated time series of Qsnow and Qice would have to be saved and compiled, which was too onerous a task to undertake in the time frame of the present study. As can be seen in Table 4, the uncertainty in the estimated total Hg flux from snow and ice

(Fsnow+ice) is almost as great as the flux estimate itself, which implies that the Hg flux could be negligible, or up to nearly twice the estimated mean value of 550 ± 495 g yr-1. This estimated annual meltwater flux of Hg into Kusawa Lake is of course based on a simplification of reality, in which the Hg concentration in meltwater is considered to be relatively constant in time and space, and the mean annual meltwater Hg flux is then largely determined by the meltwater discharge into Kusawa Lake. In reality, the concentration of Hg in the snowpack is likely to vary from year to year, site to site, and over the course of the melt period, with the first meltwater pulse typically having the largest Hg load, and decreasing subsequently (Durnford & Dastoor, 2011). Furthermore, it is likely that some fraction, possibly quite large, of the Hg present in snow or ice at the onset of the melt period will be lost by photo-reduction and gaseous evasion from the snowpack or from glacier ice before it is transferred to snowmelt (Mann et al., 2015), such that the mass of Hg actually susceptible of entering Kusawa Lake will be less than the estimated figure.

6.6 Comparison to other studies in the area

Nagorski et al. (2014) studied the spatial distribution of Hg in Alaskan streams around Glacier Bay, on the southeastern Alaskan coast, to the south-southwest of the Kusawa Lake basin. They sampled water from three different types of catchments; with glaciers, recently deglaciated, and with wetlands. The stream water samples collected from glaciated catchments had the lowest concentrations of THg (0.08- 0.89 ng L-1) and also the lowest MeHg levels (<0.01 ng L-1) of all. Kusawa Lake falls into this glaciated catchment category, with ~3 % of the watershed covered by glaciers. The mean THg level for the glaciated streams near Glacier Bay was 0.43 ng L-1, very close to the estimated mean of 0.44 ng L-1 for the snow and ice meltwater samples from the Kusawa Lake catchment and the St. Elias Mountains analyzed by Karlsson (2014), which suggests that the estimates of Hg release from snow

30 and ice presented here are realistic. Although no THg data are available from Kusawa Lake itself, Halm and Dornblaser (2007) reported summertime THg concentrations of 0.53-0.54 ng L-1 in the Takhini River (in which Kusawa Lake discharges), which are also very close to the values obtained by Karlsson (2014) for snow- and ice-meltwater feeding into Kusawa Lake. Published observations or estimates of Hg release from snow/ice meltwater to subarctic lakes are unfortunately very limited. However, other Hg fluxes have been estimated for regions of western Canada or the USA that can be used to provide some perspective to the results of this study. An attempt to identify the atmospheric contribution (by wet or dry deposition) to the annual average Hg load of western North American catchments was made by Domagalski et al. (2016). They used the Global/Regional Atmospheric Heavy Metals Model (GRAHM; Dastoor et al., 2015) to estimate a net atmospheric Hg deposition rate of 7 μg m-2 yr-1 for the entire Yukon river catchment, of which the Kusawa Lake basin is one of the headwater sub-catchments. For the Yukon Territory alone, Nasr et al. (2011), also using the GRAHM model, computed a somewhat lower Hg deposition rate of 4.5 μg m-2 yr-1. In their study, Domagalski et al. (2016) also estimated that the ratio of the total Hg load in the Yukon River to the annual atmospheric deposition over the entire catchment is ~0.74. This high ratio suggests that there must be other, non-atmospheric, source(s) that supply large Hg contributions to the Yukon River, presumably geogenic sources. The estimated figures for atmospheric Hg deposition (4.5-7 μg m-2 yr-1) derived from the GRAHM model are of comparable magnitude to the estimated input of Hg from snow and glacier ice melt (3.6 ± 3.5 μg m-2 yr-1). This suggests that for glacier-fed lakes like Kusawa, the supply of Hg from snow and ice melt actually represents a large percentage of the total annual Hg input. In this thesis, no attempt was made to estimate the possible loss of Hg (for example, by Hg0 evasion) from stream water during transportation from the snowpack or glaciers to Kusawa Lake. However the estimated large ratio of riverine Hg load to atmospheric input for the Glacier Bay area streams by Domagalski et al. (2016) suggests that this loss is probably quite limited. Other possible Hg inputs, for example associated with groundwater seepage into Kusawa Lake, remain unquantified at present.

6.7 Future perspectives

Global emissions of Hg to the atmosphere have been decreasing during recent decades, with the exception of emissions from eastern Asia that have risen in step with industrialization (AMAP, 2011; Zhang et al., 2016). Recent modelling of atmospheric Hg transport and deposition suggests that in the southwestern Yukon Territory, the dominant source region of Hg is eastern Asia, with Asian contributions exceeding those from other continents by a factor of ~4-5 (Dastoor et al., 2015). Hence if the Hg deposited in snow in the Kusawa Lake catchment originates predominantly from Asia, this input could be expected to increase in the future. Furthermore, if the current regional climate warming trend observed in the Yukon persists (+2°C over the past 50 years; Streicker, 2016), glaciers located in

31 the headwaters of the Kusawa Lake catchment will continue to shrink and melt, as is anticipated in nearby, glaciated catchments (DeBeer et al., 2012). This will release the historical burden of Hg presently stored in firn and ice. On the other hand, as the regional climate warms, an increase in mixed boreal forest vegetation cover will likely occur in the Yukon Southern Lakes ecoregion (Rowland et al., 2016). As well, mountain permafrost, where present, may degrade (Bonnaventure & Lewkowicz, 2013). Naturally-occurring geogenic Hg, presently stored in permafrost, are then likely to be released to the aquatic drainage system, increasing the dissolved and/or particulate load of Hg associated with OM in streams, as is presently being observed in the Yukon River (Toohey et al., 2016). A greater supply of labile OM would probably facilitate the removal of Hg from the water column by binding, flocculation and settling in lake bottom sediments (Joe-Strack, 2006). Hence, some effects of the anticipated climate warming could counteract any increase in the input of Hg from snow and glacier ice meltwater. However, if the area of wetlands expands in the lake catchment, as might be expected in a future, warming climate with a denser vegetation cover, the potential for transformation of Hg into bio-available MeHg via methylation may increase in the aquatic network, which would raise the risk level of Hg exposure in fish consumption. These considerations illustrate the fact that making long-term predictions of the risks associated with Hg exposure from fish consumption in subarctic lakes such as Kusawa Lake is far from being a simple matter, as both the Hg supply and the mechanisms affecting its fate (such as methylation rates, or scavenging by OM) are likely to evolve in response to future climate change and anthropogenic emission trends. Some useful steps that could be taken to reduce uncertainties associated with such long-term predictions would be to (1) assess the amount of geogenic Hg and OM stored in soils of the catchment area that could be remobilized in the future, (2) monitor Hg wet deposition rates in precipitation, which are presently unknown, and (3) estimate Hg evasion to the atmosphere from the lake itself.

7 Conclusions

This thesis, which builds on the work of Karlsson (2014) contributes a further step in quantifying the possible sources of Hg entering Kusawa Lake. Specifically, in this thesis an attempt was made to estimate the maximum possible flux of Hg from glacial and nival sources delivered by streamflow into Kusawa Lake. This was done by using the HBV hydrological model, calibrated with temperature and precipitation data from the city of Whitehorse and using historical hydrometric data from the Kusawa Lake catchment itself. Results of simulations for the period 2001-2012 suggest that together, snow and ice melt account for ~60% of the total annual discharge. Using this figure and the THg data obtained by Karlsson (2014) yields an estimated maximum potential flux of Hg into Kusawa Lake from snow and glacier ice melt of 550 ± 495 g yr-1 (3.6 ± 3.5 μg m-2 yr1). This flux comparable in magnitude with model-based estimates of the total atmospheric deposition of Hg in the Yukon region, which range between 4.5 and 7 μg m-2 yr-1. This suggests that for subarctic glacier-fed lakes like Kusawa, the supply of Hg from snow and ice melt actually represents a large percentage of the total annual Hg input. The climate is warming rapidly in the Yukon (+2°C over the past 50 years; Streicker, 2016), and this is anticipated to cause widespread glacier shrinkage, mountain permafrost degradation, an expansion of mixed boreal forest cover, and probably an increased flux of OM in mountain and forest streams. At the same time, current modeling suggests that long-range atmospheric Hg transport from eastern Asia, the dominant source region for Hg deposition in northwestern Canada, might increase in the future due to the rapid industrialization trends. Together, these changes will determine how exposure risks to Hg by fish harvesting and consumption in lakes such as Kusawa will evolve. To improve projections of such risks, it is suggested to (1) assess the amount of geogenic Hg and OM stored in soils of the catchment area that could be remobilized in the future, (2) monitor Hg wet deposition rates in precipitation, which are presently unknown, and (3) estimate Hg evasion to the atmosphere from the lake itself.

8 Acknowledgements

I wish to thank Christian Zdanowicz for his guidance, supervision and the opportunity to write this thesis. Thanks for valued comments from Jan Seibert, and also for helping with problems associated with using HBV, and to Roger Herbert, for many helpful comments on the thesis draft. I further wish to say thanks to Evelina Gallon, my opponent, for useful suggestions for improvements. I am also grateful for support from, and discussions with, Cecilia Bayard. Finally, I would like to thank Monica Beckholmen for all the support during this thesis and for all the help you provided.

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Appendix: Calibration of HBV parameters

Table A1. Calibration interval and optimal values for HBV model parameters as determined using the GAP algorithm with 5000 iterations. Figures marked by an asterisk (*) were adjusted from default values. Optimized value Calibration range Parameter Default from GAP algorithm Units code value Glacier-free Glacier Used in: Lower Upper areas areas

TT °C 0 -2.0 0.5 -1.4 -0.7 Snow routine -1 -1 CFMAX mm day °C 3.0 0.5 6.0* 0.8 5.4 Snow routine SFCF unitless 1.0 0.5 1.5* 0.6 1.5 Snow routine CFR unitless 0.05 0.05 0.05 0.05 0.05 Snow routine CWH unitless 0.1 0.1 0.1 0.1 0.1 Snow routine CFGlacier unitless 1.0 0.5* 1.5* 0.5 1.0 Glacier routine CFSlope unitless 1.0 0.5* 1.5* 1.2 0.7 Glacier routine FC mm 200 100 600* 470 400 Soil moisture routine LP unitless 1.0 0.3 1.0 0.8 0.5 Soil moisture routine BETA unitless 1.0 1.0 5.0 1.6 4.4 Soil moisture routine

KGmin day-1 0.05 0.01 0.20 0.04 Glacier routine dKG day-1 0.10 0.01 0.50 0.03 Glacier routine AG mm-1 1.00 0 10.00 0.08 Glacier routine -1 PERC mm day 1 0 4 3 Response routine UZL mm 20 0 70 69 Response routine K0 day-1 0.2 0.1 0.5 0.3 Response routine K1 day-1 0.1 0.01 0.20 0.03 Response routine K2 day-1 0.05 5.00E-05 0.10 0.01 Response routine MAXBAS day 1.0 1.0 7.0* 6.8 Routing routine -1 Cet °C 0 0 0.3 1.2 E-06 Other PCALT % per 100 m 10 10 80* 15 Catchment settings TCALT °C per 100 m 0.6 0.4* 0.7* 0.6 Catchment settings Elev. of P m 706* 706* 706 Catchment settings Elev. of T m 706* 706* 706 Catchment settings

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