This thesis examines the use of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) for the study of lake ice with a particular focus on the estimation of ice thickness. Experiments were conducted in two lake regions: (1) sub-Arctic lakes located near Yellowknife and Inuvik in the Northwest Territories during March 2017 and 2019, and (2) MacDonald Lake, Haliburton, Ontario, which is known as a mid-latitude lake, during the ice season of 2019-2020. For both regions, GNSS-IR results are compared and validated against in-situ ice and on-ice snow measurements, and also with ice thickness derived from thermodynamic lake ice models. In the first experiment, GNSS antennas were installed directly on the ice surface and the ice thickness at each site was estimated by analyzing the signal-to-noise ratio (SNR) of the reflected GNSS signals. The GNSS-IR capability of ice thickness estimation tested on sub-Arctic lakes results in a root mean square error (RMSE) of 0.07 m, a mean bias error (MBE) of -0.01 m, and a correlation of 0.66. At MacDonald Lake, a GNSS antenna was mounted on a 5-m tower on the shore to collect reflected signals from the lake surface. The Least-Squares Harmonic Estimation (LS-HE) method was applied to retrieve higher SNR frequencies in order to estimate the depths of multiple layers within lake ice and the overlaying snowpack. Promising results were obtained from this experiment; however, ice thickness estimation using GNSS-IR at this mid-latitude lake site was found to be highly dependent on the presence or absence of wet layers such as slush at the snow-ice interface and wet snow above that interface. On colder days, when there was a lower chance for the formation of wet layers, ice thickness could be estimated with a correlation of 0.68, RMSE of 0.07 m, and MBE of -0.02 m. In addition, GNSS-IR showed the potential for determining the freeze-up and break-up timing based on the SNR amplitude of reflected signals. The novel work presented in this thesis points to the potential of using reflected signals acquired by recent (e.g. Cyclone Global Navigation Satellite System (CYGNSS) and TechDemoSat-1 (TDS-1)) and future GNSS-R missions for lake ice investigations.

Increased phosphorus (P) loadings from agricultural runoff into the Great Lakes can lead to eutrophication, resulting in harmful algal blooms and hypoxic conditions. Many studies have demonstrated that subsurface tile drains contribute to total P loss, particularly under no-till. However, most studies have been conducted on soils receiving synthetic fertilizers, and less is understood regarding P loss in tile drains following manure application and if and how tillage and/or manure placement can impact these losses. The goal of this study was to determine if different management practices i.e., conservation till, conventional till, and incorporation, mitigates P loss through tile drains following fall manure application over the Non-Growing Season (NGS). The objectives of this field-based study were to: 1) quantify annual runoff, and P loss from tile drains in a silt loam soil throughout the NGS; 2) investigate if losses differ between conventional and conservation tillage; and 3) determine if incorporation of manure impacts P loss in tile runoff. Tile discharge was monitored from 3 adjacent tile drains with different management treatments (annual till without incorporation, conservation till (with and without manure incorporation) over the span of 8 years (2011-2018), with water samples collected during runoff events for most years during this period. Two years that followed fall manure application (2014-15, 2017-18) were selected for more intensive study. Most P loss occurred during discrete hydrologic events over the NGS, predominantly during the first large discharge event. During this event deep annual tillage increased P loss compared to conservation tillage, with manure incorporation further reducing P loss resulting in differences in cumulative P loss in the tiles over the NGS. This study highlights the importance of in-field long term monitoring in order to capture temporal and spatial variability within a system and recommends that fall manure is incorporated to reduce P losses in tile drains.

Nutrient losses from agricultural operations contributes to the issue of eutrophication of freshwater systems. Although many studies have been conducted on diffuse nutrient losses from fertilizer application, there is a paucity of studies on point source phosphorus (P) loss from bunker silos. Furthermore, the build-up of legacy P in the landscape from historical land management practices can create critical source areas of P that contribute to P loads long after those practices cease. The goal of this thesis is to quantify the contribution of a dairy farm (dominated by bunker silo losses) to watershed P losses, and to monitor P concentrations in surface and groundwater across a riparian zone to characterize the sorption potential of its sediments and infer whether the riparian zone may be acting as a sink for P, or a source of previously retained (legacy) P to the stream. Stream discharge was monitored continuously throughout the study, and automatic water samplers were deployed in the stream above, and below the bunker silo to analyze soluble reactive P (SRP), total dissolved P (TDP), and total P (TP) on an event basis. The riparian zone was equipped with a series of nested wells and piezometers along a three transects to monitor groundwater P levels, and to determine the hydraulic conductivity of the riparian groundwater. A transect was also installed on the unaffected side of the transect as a reference. The farmyard contribution to watershed P losses over a one-year period was 32% (SRP) and 22% (TP). Cumulative loads over the entire study suggest that the farmyard P losses were 21.2 kg/ha SRP and 120 kg/ha TP. Peak P concentrations occurred during snowmelt and thaw events and were smaller during periods of baseflow. However, after the bunker silo was refilled in mid-summer months, both SRP and TP were considerably elevated. Large amounts of P were found to be stored in the riparian soil, however, estimated contributions of riparian P to the overall loads were negligible. This may be a result of missed flowpaths during site set-up, or an occurrence of upwelling of P in the streambed. The results of this research suggest that this particular farmyard bunker silo contributes large amounts of P to the adjacent stream on an annual basis. This study should be used as a starting point for future studies examining livestock farmyard nutrient losses.

Alpine regions contribute 60 % of annual surface runoff, playing an important role in regulating the global water balance. Many of the world’s major river networks originate from alpine headwater basins, popularizing mountains as the “Water Towers of the World”. The Rocky Mountains represent Western Canada’s “Water Tower” since they store and distribute water resources to over 13 million people across Western Canada and the Pacific Northwest USA. At the headwater, topography causes land surfaces to cycle in and out of shadows, creating distinct microclimates that strongly influence evapotranspiration (ET) and carbon fluxes. Yet, relatively few studies have observed the relationship between the energy, water, and carbon fluxes of mountain catchments; and have rather focused on periods of snow and ice cover. Therefore, understanding the contribution of subalpine wetlands to the water budget remains a leading hydrological need in mountain areas worldwide. This thesis attempts to address these knowledge gaps by investigating the influence of complex terrain on the spatial and temporal variability of shade across a subalpine wetland (2,083 m a.s.l.) in the Canadian Rocky Mountains and the effect of shade on seasonal flux dynamics. Meteorological and eddy covariance equipment was installed from June 7th to September 10th to establish baseline environment conditions and to monitor the turbulent and radiative fluxes over the 2018 snow free period. Hill shade and solar radiation models for clear-sky days were compared to field observations to understand how shade impacted the energy, water, and carbon fluxes. Water Use Efficiency (WUE) was used as a metric to understand the relationship between water and carbon cycling. Overall, shade shortened the growing season and prolonged snowmelt. Shade was greatest near the headwall and reduced cumulative solar radiation by 86.4 MJ over the study period. When shade was low and constant during the period of Stable Shade (June 7th – July 30th), it had a non-significant relationship with incoming solar radiation (K↓) and net radiation (Q*); however, when shade rapidly increased during the period of Dynamic Shade (July 31st – September 10th) it strongly influenced K↓ and Q*. On average, during Dynamic Shade, each hourly increase of shade per day, reduced K↓ and Q* by 32 W/m2 and 28 W/m2, equivalent to 13 % and 16 %, respectively. Water and carbon fluxes had a similar response to shade as the energy fluxes. Each hourly increase of shade reduced ET and Gross Primary Production (GPP) by similar margins: 17 % and 15 %, respectively. Therefore, WUE remained relatively unaffected by horizon shade, because shade equally reduced ET and GPP. These findings indicate that under uncertain future climate scenarios (i.e. increased risk of flood, drought, and forest fires), shade may be an important mechanism for moisture conservation in a variety of subalpine ecosystems that are at risk of late season water stress.

Canada’s Rocky Mountains provide a large and essential supply of freshwater to downstream ecosystems and communities. Previous research has demonstrated that warmer temperatures, associated with climate change, are expected to increase the recruitment of trees towards alpine zones, by way of tree islands and krummholz. Tree islands and krummholz are coniferous trees that grow in isolated patches. Tree islands are stunted and deformed, yet their stems grow above the shrub layer, leaving them exposed to winter snowdrifts, unlike krummholz, which grow stunted or in matts, below the snowpack. These trees are unique, relative to conifers below the treeline limit, as they have growth mechanisms which allow them to persist in areas that are otherwise too harsh for full treeline expansion. This thesis addresses the complex relationships between the spatial variability of evapotranspiration (ET) in tree islands and krummholz on a subalpine ridge slope in Kananaskis, Alberta. As well, relationships between these canopies and controls on ET, such as snowcover, meteorological fluxes and vegetation characteristics are assessed. By addressing these objectives, this study will reduce existing knowledge gaps on how forest transition zones in mountain ecosystems may contribute to ecosystem water loss, should these tree patches continue to prosper at higher elevations. Atmometers, which measure the rate of ET from heterogenous landcover to the atmosphere, were used at FRS to determine the rate of potential crop evapotranspiration (ETC) from krummholz and tree islands. ETC was then converted to actual evapotranspiration (ETA) using patch-specific correction coefficients (KC) in order to address the influence of canopy dynamics and water availability on ET. ETA during the growing season was greater in the krummholz (190 mm) than the tree islands (131 mm). Krummholz were observed to be moisture rich tree patches that were shorter in height and more exposed than tree islands. Because of this, krummholz ET was controlled by the advection of sensible heat transported from drier areas downward over the krummholz resulting in oasis-effect ET (QE > Q*). Horizontal advection of sensible heat from the taller tree islands to the shorter krummholz increases clothesline-effect ET at FRS. In addition, the exposure of the krummholz to the effects of solar radiation to the their subsurface increases the rate of early growing season ETA by increasing soil water evaporation. Tree islands, which extend above the annual snowpack were capable of sheltering windblown snow, increasing the amount of water available to the tree islands and krummholz for the growing season. Water balances for the tree islands and krummholz indicated that SWE was the primary source of water to the patches and did not suggest water deficits during the observed growing season. Tree island ET rates were controlled by the evaporation of intercepted precipitation (2 - 58%), and growth characteristic such as increased canopy density, which increased subsurface sheltering, reducing soil water evaporation, while maintaining inner-canopy VPD (increases transpiration). The results of this study improve our knowledge of how tree islands and krummholz will influence ecosystem water storage, especially in terms of ET, and determined what dominant controls exist on ET in subalpine systems. As climate change is expected to decrease annual snowpack levels and increase seasonal air temperatures, ET from tree island and krummholz may contribute to water deficits in subalpine ecosystems.

The spatio-temporal heterogeneity of seasonal snow and its impact on socio-economic and environmental functionality make accurate, real-time estimates of snow water equivalent (SWE) important for hydrological and climatological predictions. Passive microwave remote sensing offers a cost effective, temporally and spatially consistent approach to SWE monitoring at the global to regional scale. However, local scale estimates are subject to large errors given the coarse spatial resolution of passive microwave observations (25 x 25 km). Regression downscaling techniques can be implemented to increase the spatial resolution of gridded datasets with the use of related auxiliary datasets at a finer spatial resolution. These techniques have been successfully implemented to remote sensing datasets such as soil moisture estimates, however, limited work has applied such techniques to snow-related datasets. This thesis focuses on assessing the feasibility of using regression downscaling to increase the spatial resolution of the European Space Agency’s (ESA) Globsnow SWE product in the Red River basin, an agriculturally important region of the northern United States that is widely recognized as a suitable location for passive microwave remote sensing research. Multiple Linear (MLR), Random Forest (RFR) and Geographically Weighted (GWR) regression downscaling techniques were assessed in a closed loop experiment using Snow Data Assimilation System (SNODAS) SWE estimates at a 1 x 1 km spatial resolution. SNODAS SWE data for a 5-year period between 2013-2018 was aggregated to a 25 x 25 km spatial resolution to match Globsnow. The three regression techniques were applied using correlative datasets to downscale the aggregated SNODAS data back to the original 1 x 1 km spatial resolution. By comparing the downscaled SNODAS estimates to the original SNODAS data, it was found that RFR downscaling produced much less variation in downscaled results, and lower RMSE values throughout the study period when compared to MLR and GWR downscaling techniques, indicating it was the optimal downscaling method. RFR downscaling was then implemented on daily Globsnow SWE estimates for the same time period. The downscaled SWE results were evaluated using SNODAS SWE as well as in situ derived SWE estimates from weather stations within the study region. Spatial and temporal errors were assessed using both the SNODAS and in situ reference datasets and overall RMSEs of 21 mm and 37 mm were found, respectively. It was observed that the southern regions of the basin and seasons with higher downscaled SWE estimates were associated with higher errors with overestimation being the most common bias throughout the region. A major contribution of this study is the illustration that RFR downscaling of Globsnow SWE estimates is a feasible approach to understanding the seasonal dynamics of SWE in the Red River basin. This is extremely beneficial for local communities within the basin for flood management and mitigation and water resource management.

Non-point source anthropogenic nutrient loading through intensive farming practices is a global source of water quality degradation by creating harmful algal blooms in aquatic ecosystems. Phosphorus, as the key nutrient in this process, has received much attention in different studies as well as conservation programs aimed at mitigating the transfer of polluting nutrients to freshwater resources. Central to conservation initiatives developed to maintain and improve water quality is the application of the Conservation Practices (CPs), introduced widely as practical, cost-effective measures with overall positive impacts on the rate of nutrient load reductions from farmlands to freshwater resources. Crop rotation is one of the field-based BMPs applied to maintain the overall soil fertility and preventing the displacement of the topsoil layers by surface water runoff across the agricultural watersheds. The underlying concept in the application of this particular BMP is a deviation from the monoculture cropping system by integrating different crops into the farming process. This way, cultivated soils do not lose key nutrients, which are necessary for crop growth, and the overall crop productivity remains unchanged in the landscape. The successful implementation of crop rotation highly depends on planning the rotation process, which is influenced by a variety of environmental, structural, and managerial factors, including the size of farmlands, climate variability, crop type, level of implementation, soil type, and market prices among other factors. Each of these decision variables is subject to variation depending upon the variability of other factors, the complexity of watersheds upon which this BMP is implemented, and the overall objectives of the BMP adoption. This study aims to investigate two of these decision variables and their potential impacts on phosphorus load reductions through a scenario-based hydrologic modeling framework developed to iv assess the post-crop rotation water quality improvements across the Medway Creek Watershed, situated in the Lake Erie Basin in Ontario, Canada. These variables are the spatial pattern of crop rotation and its level of implementation, assessed at the watershed scale through the modifications made to the delineation of the basic Hydrologic Response Units (HRUs) in the modeling process as well as certain assumptions in the management schedules, and decision rules required for the integration of crop rotation into the proposed modeling framework and optimal placement of this non-structural BMP across the watershed. The main modeling package utilized in this study is the Soil and Water Assessment Tool (SWAT), used in conjunction with the ArcGIS and IBMSPSS tools to allow for spatial assessment and statistical analyses of the proposed hydrologic modeling results, respectively. Following in-depth statistical analyses of the scenarios, the results of the study elicit the critical role of both factors by proposing optimal ranges of application on the watershed under study. Accordingly, to achieve optimal implementation results compared to the baseline scenario, which has the zero rate of implementation, conservation initiatives in the watershed are encouraged to consider the targeted placement of crop rotation on half of the lands under cultivation. Despite, having a statistically significant impact on water quality compared to the baseline scenario, the random distribution scenario is less effective than the targeted scenario in mitigation of total phosphorus load. Similarly, compared to the medium rate of implementation the targeted placement in a higher proportion of the cultivated areas did not lead to statistically significant results but may be considered depending upon the purpose and scope of implementation.

Spatial data is characterized by rich contextual information with multiple characteristics at each location. The interpretation of this multifaceted data is an integral part of current technological developments, data rich environments and data driven approaches for solving complex problems. While data availability, exploitation and complexity continue to grow, new technologies, tools and methods continue to evolve in order to meet these demands, including advancing analytical capabilities, as well as the explicit formalization of geographic knowledge. In spite of these developments Discrete Global Grid Systems (DGGS) were proposed as a new comprehensive approach for transforming scientific data of various sources, types and qualities into one integrated environment. The DGGS framework was developed as the global data model and standard for efficient storage, analysis and visualization of spatial information via a discrete hierarchy of equal area cells at various spatial resolutions. Each DGGS cell is the explicit representation of the Earth surface, which can store multiple data values and be conveniently recognized and identified within the hierarchy of the DGGS system. A detailed evaluation of some notable DGGS implementations in this research indicates great prospects and flexibility in performing essential data management operations, including spatial analysis and visualization. Yet they fall short in recognizing interactivity between system components and their visualization, nor providing advanced data friendly techniques. To address these limitations and promote further theoretical advancement of DGGS, this research suggests the use of Q-analysis theory as a way to utilize the potential of the hierarchical DGGS data model via the tools of simplicial complexes and algebraic topology. As a proof of concept and demonstration of Q-analysis feasibility, the method has been applied in a water quality and water health study, the interpretation of which has revealed much contextual information about the behaviour of the water network, the spread of pollution and chain affects. It is concluded that the use of Q-analysis indeed contributes to the further advancement and development of DGGS as a data rich framework for formalizing multilevel data systems and for the exploration of new data driven and data friendly approaches to close the gap between knowledge and data complexity.