In cold regions, the breakup of river ice can be a significant event, resulting in flooding and damage to communities. Given the severity of such events, it is desirable to be able to predict the timing and severity of breakup. Limited progress has been made on forecasting breakup related flooding as no deterministic model of the breakup process and ice jam formation exist. Current tools for predicting breakup rely on developing a relationship between the previous winter conditions and the current spring conditions, with the understanding that a rapid or large runoff with a thick ice cover has the potential for a more severe breakup than if ice has had time to melt. These tools are largely empirical, statistical, or soft computing methods which rely on historical data sets of discrete observations to relate the complex relationship between climate and hydrology to breakup conditions and are limited by access to the extensive data required. Within the current prediction methods, the application of hydrological models for forecasting breakup timing and severity is limited. Hydrological models can address some of the limitations of current tools, as they are able to simulate the complex relationships between climate and hydrology which has a strong influence on the breakup period. Additionally, hydrological models may be more practical in regions with limited data, as they can simulate variables of interest instead of relying on large historical data sets. This thesis demonstrates how a hydrological model can be used to predict the timing and severity of breakup, through the coupling of a 1D river ice model with a hydrological model. Emphasis is placed on the development of the hydrological model to ensure that it provides realistic results throughout the basin. The Liard basin, a large relatively data sparse river basin, in northern Canada is used as a case study. A thorough calibration strategy, based on an iterative, multi-objective approach is used in the development of the model. The final model exhibits strong performance in both calibration and validation throughout the basin. A simple 1D river ice model in MATLAB is coupled with the hydrological model. The hydrological model can forecast the timing of breakup well based on the timing of the initial rise in the hydrograph. Breakup severity is predicted using a simple threshold model based on ice thickness, flow, and accumulated shortwave radiation. The prediction method was applied to an independent location as verification of the methodology with promising results.

Flows of reactive nitrogen (N) have significantly increased over the last century, corresponding to increases in the global population. The pressures on the N cycle include human waste, fossil fuel combustion as well as increasing food production (i.e., increasing fertilizer consumption, biological N fixation, and livestock manure production). The result is humans causing a 10-fold increase in the flow of reactive N globally. The influx of anthropogenic N into aquatic environments degrades water quality, alters fresh and saline ecosystem productivity, and poses an increasing threat to drinking water sources. In the U.S., decades of persistent hypoxic zones, created by elevated concentrations of nitrate from the landscape, have altered ecosystem trophic structure and productivity. Additionally, increasing N contamination of groundwater aquifers places over 20% of the U.S. population at increased risk of diseases and cancers. Despite billions of dollars of investment in watershed conservation measures, we have not seen proportional improvements in water quality. It has been argued that delayed improvements in water quality can be attributed to legacy stores of N, which has accumulated in the landscape over many decades. There is considerable uncertainty associated with the fate of N in the landscape; however, studies quantified increasing stores of N in the subsurface, suggesting increasing stores of N in groundwater aquifers, in soil organic nitrogen pools, and the unsaturated zone. Nevertheless, the spatial distribution of legacy N across the conterminous U.S. is poorly quantified. Here, we have synthesized population, agricultural, and atmospheric deposition data to develop a comprehensive, 88-year (1930 to 2017) dataset of county-scale N surplus trajectories for the U.S. N surplus, defined as the difference between N inputs and usable N outputs (crop harvest), provides insight into the trends and spatial distribution of excess N in the landscape and an upper bound on the magnitude of legacy N accumulation. Our results show that the spatial pattern of N surplus has changed drastically over the 88-year study period. In the 1930s, the N inputs were more or less uniformly distributed across the U.S., resulting in a few hotspots of N surplus. The following decades had sharp increases in N surplus, driven by the exponential use of fertilizer and combustion of fossil fuels. Contemporary N surplus distribution resembles a mosaic of varying degrees of excess, concentrated in the heavily cultivated areas. To understand dominant modes of behavior, we used a machine learning algorithm to characterize N surplus trajectories as a function of both surplus magnitudes and the dominant N inputs. We find ten primary clusters, three in crop dominated landscapes, four in livestock dominated landscapes, two in urban dominated landscapes, and one in areas minimally impacted by humans. Using the typologies generated can facilitate nutrient management decisions. For example, watersheds containing urban clusters would benefit from wastewater treatment plant upgrades. In contrast, those dominated by livestock clusters would have more success in managing nutrients by implementing manure management programs. The estimates of cumulative agricultural N surplus in the landscape highlights agronomic regions that are at risk of large stores of legacy N, possibly leading to groundwater and surface water contamination. In these agronomic regions, the average cumulative N surplus exceeds 1200 kg-N/ha by 2017. Despite having minimal agricultural activity in urban areas, urban fertilizer use has led to an average cumulative N surplus of over 900 kg-N/ha. While our estimates are an upper bound to legacy stores, significant uncertainty remains regarding the magnitude of the estimate of N accumulation. However, our results suggest that legacy N is at varying degrees, impacting most counties in the U.S. The significant investment and corresponding lack of returns can lead to disillusionment in farmers, watershed managers, and the general public. Developing such N surplus typologies helps improve understanding of long-term N dynamics. Beyond refining the supporting science, appropriately communicating uncertainties and limitations of water quality improvements to the stakeholders, authorities, and policymakers are essential to continuing efforts to improve national water quality.

It has been predicted that approximately 65% of the developing world and 85% of the developed world will be living in cities by 2050. Toronto, the largest city in Canada and the fourth largest in North America, is expected to double in population in the next 50 years. Although such rapid urbanization can lead to enormous social, economic, and environmental change, little is understood about how population growth in Toronto and the “Golden Horseshoe” region around Lake Ontario will impact the ecological systems of Southern Ontario. In our study, we are particularly interested in the ways in which increasing population densities in the Greater Toronto Area are impacting nutrient flows across Southern Ontario’s urban/rural continuum and how changing nutrient dynamics may lead to increasingly impaired water quality in Lake Ontario and beyond. In this work, we utilize a mass balance approach to quantify the flow of nutrients through urban, suburban, and agricultural areas of the Greater Toronto Area. A wide range of factors are considered, including human behaviour, domestic animals, stormwater management, and wastewater treatment processes. The present results suggest that any study of urban metabolism must take into account not only nutrient flows within urban boundaries, but must also identify externalities of urban development associated with a range of processes, from global trade to regional waste management.

Intensification of farming operations and increased nutrient application rates have led to higher crop yields and greater food security. At the same time, widespread use of commercial nitrogen (N) and phosphorus (P) fertilizers and large-scale livestock production have led to unintended environmental consequences, including eutrophication of both coastal and inland waters, threats to drinking water, and increased production of N2O, a potent greenhouse gas. In the past, crop and livestock production were typically more integrated, allowing most livestock to be fed by local crops, and most livestock manure to be applied directly to nearby cropland. Under current intensive agriculture practices, however, there is frequently a spatial decoupling of crops and livestock, leading to hot spots of manure production and a lack of opportunities for cost-efficient and environmentally sensitive disposal. In recent years, there has also been increased interest in the use of both farm and regional-scale bioreactors to convert excess manure to energy, thus exploiting a renewable energy source and increasing the potential to recycle animal waste. In the present work, I develop a spatially distributed optimization approach to identify hotspots of manure production, and, using both economic and environmental criteria, evaluate the economic feasibility of (1) transporting manure for spreading on cropland to meet established nutrient requirements, and (2) constructing biogas reactors to process excess manure in areas where long-range transport is found to be infeasible. This work is focused on manure redistribution, and potential for biogas construction at the continental US scale. In order to identify the spatial disconnect between livestock and crop production, I developed a gridded data set where each cell was 6 km x 6 km and calculated the crop requirements and manure production in each cell. After finding the P requirements in each cell, I found that 530,000 tonnes of phosphorus in manure was located in areas where, if applied, it would be in excess of the local crop requirements. I then examined the feasibility of transporting manure from excess locations (cells) to other locations to use as fertilizer by formulating an optimization problem to maximize the financial benefits of transporting the manure. Savings from transporting manure was calculated as the financial benefit from buying less mineral fertilizer minus the cost of transporting the manure. The solution to this optimization problem shows that transporting manure was able to reduce the excess phosphorus applied to fields by at least 88% with savings of up to $3 billion USD. Finally, I examined the costs and benefits of using the remaining excess manure (after transportation for fertilizer) as fuel to operate biogas plants. For this, I formulated an optimization model to site biogas plants across the continental US such that net profits from the biogas plants were maximized. Biogas net profits were defined as the money made from selling electricity minus the annualized costs for constructing and operating the biogas plants and transporting the manure to the biogas plants. The solution to this problem shows that constructing and operating 387 biogas plants yielded a net profit of $100 million USD and would utilize all of the manure remaining after transportation for fertilizer. This 100% utilization rate of excess manure would have great environmental benefits in terms of removing potential sources of non-point source pollution from farms that would otherwise be available to runoff into waterways.