The response of plants to climate change is among the main questions in ecology and evolution. Faced with changing conditions, populations may respond by adapting, going extinct or migrating. Fine-scale environmental variation offers a unique mosaic to explore these alternatives. In this thesis, I used ecological surveys, field experiments and molecular methods to study the range of possible responses at a very local scale in the alpine dwarf willow Salix herbacea L. Since gene flow may impact the potential for adaptation and migration, I first explored whether phenological divergence driven by snowmelt patterns impacts gene flow. I found that sites with late snowmelt work as sinks of the genetic diversity, as compared to sites with early snowmelt. I also used a combined approach that looked at the selection, heritability and genomic architecture of ecologically-relevant traits, as well as genomic divergence across the snowmelt mosaic. In this way, I was able to understand which genomic regions may relate to phenological, growth and fitness traits, and which regions in the genome harbor genetic variation associated with late- and early- snowmelt sites. I found that most of the genomic divergence driven by snowmelt is novel and is localized in few regions. Also, Salix herbacea has a strong female bias. Sex bias may matter for adaptation to climate change because different sexes of many dioecious species differ in several functions that may fluctuate with changing conditions. I found that the bias is uniform across environments and is already present at seeds and seedlings. A polygenic sex determination system together with transmission distortion may be maintaining the bias. Overall, fast-evolving microhabitat-driven genomic divergence and, at the same time, genetically-based trait variation at a larger scale may play a role for the ability of S. herbacea to persist in diverse and variable conditions. / SNSF Sinergia Salix

Snowmelt-dominated systems are a significant source of water supply for the Western United States. Changes in timing and duration of snowmelt are predicted to continue under climate change; however, the impact this change will have on water resources is not well understood. The ability to compare hydrologic processes across space and time is critical to accurately assess the physical and chemical response of headwater systems to climate change. This dissertation builds upon previous work by using long-term data from two snowmelt dominated catchments to investigate the response of hydrologic processes at different temporal and spatial scales. First, results from an hourly spatially-distributed energy balance snowmelt model were spatially and temporally aggregated to provide daily, catchment-wide snowmelt estimates, which, along with measured discharge and hydrochemical data were used to assess and compare hydrologic processes which occur on an annual scale in two headwater catchments for an eleven year study period. Second, the magnitude and timing of snowmelt, discharge fluxes and hydrochemical data were used to assess and compare inter-annual catchment response in two headwater catchments for an eleven year study period. Third, a pseudoinverse method was developed to compare mineral weathering fluxes in a series of nested sub-catchments over an eleven year study period. Advances from this work include the use of an independently-created energy balance snowmelt model for spatially-distributed hydrologic input for catchment-scale water balance, application of a quantifiable measure of catchment-scale hydrologic flux hysteresis and the development of a method to quantify and compare mineral weathering reactions between source waters across space and time. These methods were utilized to quantify and assess its role of dynamic storage in mitigating climate change response.

In cold and snowy countries, water resources management and planning require accurate and reliable spring peak flow forecasts which call for adequate snowmelt estimation techniques. Thus, exploring the potential of snowmelt models to improve the spring peak flow prediction has been an active research area. Snow models vary in degree of complexity from simple empirical models to complex physically based models. Whereas majority of studies on snowmelt modeling have focused on comparing the performance of empirical snowmelt estimation techniques with physically based methods, very few studies have investigated empirical methods and conceptual models for hydrological applications. This study investigates the potential of a simple Degree-Day Method (DDM) to effectively and accurately predict peak flows compared to sophisticated SNOW-17 model at La-Grande River Basin (LGRB), Quebec and Upper Assiniboine river at Shellmouth Reservoir (UASR), Manitoba. Moreover, since hydrologic models highly rely on estimated parameter vectors to produce accurate streamflow simulations, accurate and efficient parameter optimization techniques are essential. The study also investigates the benefits of seasonal model calibration versus annual model calibration approach. The study is performed using two hydrological models, namely MAC-HBV (McMaster University Hydrologiska Byrans Vattenbalansavdelning) and SAC-SMA (Sacramento Soil Moisture Accounting) and their model combinations thereof. Results indicate that the simple DDM performed consistently better at both study sites and showed significant improvement in prediction accuracy at UASR. Moreover, seasonal model calibration appears to be an effective and efficient alternative to annually calibrated model especially when extreme events are of particular interest. Furthermore, results suggest that SAC-SMA model outperformed MAC-HBV model, no matter what snowmelt computation method, calibration approach or study basin is used. Conclusively, DDM and seasonal model optimization approach coupled with SAC-SMA hydrologic model appears to be a robust model combination for enhanced spring peak flow prediction. A significant advantage of aforementioned modeling approach for operational hydrology is that it demonstrates computational efficiency, ease of implementation and is less time-consuming. / Thesis / Master of Science (MSc)

Upland environments are important sources of mercury (Hg) to downstream wetlands and water bodies. Hydrology is instrumental in facilitating Hg transport within, and export from watersheds. Two complementary studies were conducted to assess the role hydrological processes play in controlling Hg mobility and transport in forested uplands. A field study compared runoff and Hg fluxes from three, replicate hillslope plots during two contrasting spring snowmelt periods, in terms of snowpack depth and timing. Hillslope Hg fluxes were predominately flow-driven. The melting of soil frost significantly delayed a large portion of the Hg flux later into the spring following a winter with minimal snow accumulation. A microcosm laboratory study using a stable Hg isotope tracer applied to intact soil cores investigated the relative controls of soil moisture and precipitation on Hg mobility. Both hydrologic factors control the mobility of contemporary Hg; with greatest Hg flushing from dry soils under high-flow conditions.

Rivers deliver significant macronutrients and sediments to lakes that can vary substantially throughout the year. These nutrient and sediment loadings, exacerbated by winter and spring runoff, impact aquatic ecosystem productivity and drive the formation of harmful algae blooms. The source, extent and magnitude of nutrient and sediment loading can vary drastically due to extreme weather events and hydrologic processes, such as snowmelt or high flow storm events, that dominate during a particular time period, making the temporal component (i.e., time over which the loading is estimated) critical for accurate forecasts. In this work, we developed a data-driven framework that leverages the temporal variability embedded in these complex hydrologic regimes to improve loading estimates. Identifying the "correct" time scale is an important first step for providing accurate estimates of seasonal nutrient and sediment loadings. We use water quality concentration and associated 15-minute discharge data from nine watersheds in Vermont's Lake Champlain Basin to test our proposed framework. Optimal time periods were selected using a hierarchical cluster analysis that uses the slope and intercept coefficients from individual load-discharge regressions to derive improved linear models. These optimized linear models were used to improve estimates of annual and "spring" loadings for total phosphorus, dissolved phosphorus, total nitrogen, and total suspended loads for each of the nine study watersheds. The optimized annual regression model performed ~20% better on average than traditional annual regression models in terms of Nash-Sutcliffe efficiency, and resulted in ~50% higher cumulative load estimates with the largest difference occurring in the "spring". In addition, the largest nutrient and sediment loadings occurred during the "spring" unit of time and were typically more than 40% of the total annual estimated load in a given year. The framework developed here is robust and may be used to analyze other units of time associated with hydrologic regimes of interest provided adequate water quality data exist. This, in turn, may be used to create more targeted and cost-effective management strategies for improved aquatic health in rivers and lakes.

An energy-based model accounting for snow accumulation and snowmelt in a coniferous forest and in an open area An energy balance approach was used to simulate snow water equivalent (SWE) evolution in an open area, forest clearing and coniferous forest during winter seasons 2011/12 and 2012/13 in the Bystřice River basin (Krušné Mountains). The aim was to describe the impact of vegetation on snow accumulation and snowmelt under different forest canopy structure and density of trees. Hemispherical photographs were used to describe the forest canopy structure. Energy balance model of snow accumulation and melt was set up. For forest sites the snow model was altered for accounting the effects of the forest canopy on the driving meteorological variables of the snow model. Leaf area index derived from 32 hemispherical photographs of the vegetation and sky was used for forest influence implementation in the snow model. The model was evaluated using snow depth and SWE field data measured at 16 localities in winter seasons from 2011 to 2013. The model was able to reproduce the SWE evolution in both winter seasons beneath the forest canopy, forest clearing and open area with correlations to observations ranging from 0.16 to 0.99. The SWE maximum in forest sites is by 18% lower than in open areas and forest...

Snow depth variability at the plot scale: Assesment of topography and vegetation influence Abstract This master thesis deals with the evaluation of snow depth variability at the plot scale. It focuses on influence of topography and vegetation factors as slope, exposure, curvature, solar radiation and leaf area index. There is also assesment the impact of the size scale. Measurement was carried out in period of accumulation and snowmelt in winter 2014/2015 in the Krkonoše Mountains on Hanapetrova glade. To evaluate the effect of selected factors on variability of snow depth there was used multiple linear regresion and other descriptive statistical methods. The research shows that the variability of snow depth during the accumulation is greater in forest which is probably due to vegetation. The dependency was not confirmed by regression analysis. Higher variability of snow cover in the forest was also observed in the melting period. The variability of snow cover increased in the forest in general. The results show that the snow depth variability decreasses with increasing grid size. Keywords: snow accumulation, snowmelt, topography, vegetation, multivariate analysis

No description available.

The hydrology of the Canadian Prairies has been well described in the scientific literature. 20th C observations show that snowmelt over frozen soils accounted for over 80% of the annual runoff, and streamflow hydrographs peaked in April and ceased in May due to a lack of runoff or groundwater contributions. Since then, the region has undergone rapid changes in land use and climate, both which affect streamflow generating processes. This study evaluates the detailed hydrological impact of regional changes to climate on an instrumented research catchment, the Smith Creek Research Basin (SCRB); an unregulated, wetland and agriculture dominated prairie catchment in south-eastern Saskatchewan. Wetlands have been drained for decades, reducing wetland extent by 58% and maximum storage volume by 79%, and increasing drainage channels lengths by 780%. Long term meteorological records show that there have been gradual changes to the climate: though there are no trends in annual precipitation amount, increasing temperatures since 1942 have brought on a gradual increase in the rainfall fraction of precipitation and an earlier snowmelt by two weeks. In the summer months, the number of multiple day rainfall events has increased by 5 events per year, which may make rainfall-runoff generation mechanisms more efficient. Streamflow records show that annual streamflow volume and runoff ratios have increased 14-fold and 12-fold, respectively since 1975, with major shifts in 1994 and 2010. Streamflow contributions from rainfall-runoff and mixed-runoff regimes increased substantially. Snowmelt runoff declined from 86% of annual discharge volume in the 1970’s to 47% recently while rainfall runoff increased from 7% to 34%. Annual peak discharge tripled over the period from 1975 to 2014, with a major shift in 1994, while the duration of flow doubled in length to 147 days after a changepoint in 1990. Recent flooding in the SCRB has produced abnormally large streamflow volumes, and flooding in June 2012 and 2014 was caused solely by rainfall, something never before recorded at the basin. Although the observed changes in climate and wetland drainage are substantial, it is unlikely that a single change can explain the dramatic shifts in the surface hydrology of the SCRB. Further investigation using process hydrology simulations is needed to help explain the observed regime changes.

Inverse geochemical modelling has been used frequently in groundwater systems between wells along a known flowpath and between precipitation and stream waters in catchments. This research expands the use of inverse geochemical modelling through a reaction path model (RPM) between waters in an alpine catchment to determine the geochemical connections and disconnections within the catchment. The data for this study are from the Green Lake 4 catchment in the Colorado Front Range during the 1996 snowmelt season, which has been divided into discrete time intervals based on snowmelt hydrology. Unique combinations of geochemical connections occur during these time intervals, and they show a dynamic hydrologic system. RPM results show notable disconnections; soil water is not geochemically connected to any other end member. These changes reflect changes in weathering reactions in the catchment that are dependent on the duration and timing of snowmelt. Previously end-member mixture analysis (EMMA) models have been used to discern the water sources in catchments. The combination of RPM and EMMA approaches offers the opportunity to connect the source of water to the internal hydrologic structure of the catchment, to better understand how catchments might respond to changes in climate or atmospheric deposition.