Worldwide phenomena called algae bloom has been recently a serious matter for inland water bodies. Temporal and spatial variability of the bloom makes it di cult to use in-situ monitoring of the lakes. This study aimed to evaluate the potential of Sentinel-3 Ocean and Land Colour Instrument (OLCI) and Sentinel-2 Multispectral Instrument (MSI) data for monitoring algal blooms in Lake Erie. Chlorophyll-a (Chl-a) related products were tested using NOAA-Great Lakes Chl-a monitoring data over summer 2016 and 2017. Thematic water processor, fluorescence line height/maximum chlorophyll index (MCI) and S2 MCI, plug-in SNAP were assessed for their ability to estimate Chl-a concentration. We processed both Top of the Atmosphere (TOA) reflectance and radiance data. Results show that while FLH algorithms are limited to lakes with Chl-a < 8 mg m-3, MCI has the potential to be used effectively to monitor Chl-a concentration over eutrophic lakes. Sentinel-3 MCI is suggested for Chl-a > 20 mg m-3 and Sentinel-2 MCI for Chla > 8 mg m-3. The different Chl-a range limitation for the MCI products can be due to the different location of the maximum peak bands, 705 and 709 for MSI and OLCI sensors respectively. TOA radiances showed a signi cantly better correlation with in situ data compared to TOA reflectances which may be related to the poor pixel identi cation during the process of pixel flagging affected by the complexity of Case-2 water. Our fi nding suggests that Sentinel-2 MCI achieves better performance for Chl-a retrieval (R2 = 0.90). However, the FLH algorithms outperformed showing negative reflectance due to the shift of reflectance peak to longer wavelengths along with increasing Chl-a values. Although the algorithms show moderate performance for estimating Chl-a concentration; this study demonstrated that the new satellite sensors, OLCI and MSI, can play a signi ficant role in the monitoring of algae blooms for Lake Erie.

Agricultural tile drains are a source of phosphorus (P) contributing to eutrophication. Preferential flowpaths can rapidly transport P to tile drains, but their activation in different soil textures and under variable seasonal conditions (antecedent moisture conditions and presence of soil frost) is not well understood. Subsurface placement of fertilizer has been proposed as a management practice to reduce P loss, compared to surface applications. However, how subsurface placement reduces P loss is not well understood. The goal of this thesis is to relate subsurface flowpaths and fertilizer placement to identify source and transport mechanisms controlling P movement to tile drains, across soil textures and seasonal conditions. A lab experiment was done on intact soil monoliths (clay, silt loam) to investigate interactions between fertilizer placement, subsurface flowpaths, and soil frost. Conservative water tracers (Brˉ, Clˉ and D₂O) applied through successive events identified matrix flow as the dominant flowpath in unfrozen silt loam, while preferential flow dominated in unfrozen clay and in both soil types under partially frozen conditions. Subsurface placement of fertilizer reduced dissolved reactive P losses by 60% in silt loam and 64% in clay over the simulated non-growing season compared to surface broadcast applications. A field study used blue dye as a tracer to investigate subsurface flowpaths in clay and silt loam plots under wet and dry conditions. Dye stain patterns were analyzed to determine the relative importance of matrix and macropore flow. Soil samples were collected to determine soil P distribution post-fertilization. Preferential flow occurred under all soil texture and moisture conditions. Dry clay soil showed the deepest staining (92 ± 7.6 cm), followed by wet clay (77 ± 4.7 cm). In silt loam soil, depth of staining did not differ between wet (56 ± 7.2 cm) and dry (50 ± 6.6 cm) conditions. Soil water-extractable P distribution varied with fertilizer application in the top 10 cm of the soil profile, but did not differ at depth. Together, the results of this research suggest subsurface placement is a suitable practice for minimizing subsurface nutrient loss, by reducing contact between the nutrient supply and preferential flowpaths, particularly in clay soils prone to preferential flow. This work provides an improved understanding of subsurface flowpaths carrying P to tile drains, and more broadly, solute transport through preferential flowpaths.

In the coupled human-environment system, humans play a central role in creating various environmental problems, and in turn, are impacted by these environmental consequences. In Canada, water quality degradation caused by agricultural activities has become a severe problem for a long time. It has been noted that the application of pesticides, manure and fertilizers have led to an increasing amount of chemicals and other pollutants in surface runoff which eventually converge into surface water bodies and result in water eutrophication. To maintain water quality and develop a sustainable agricultural system, Best Management Practices (BMPs) have been suggested. However, the high complexity of the agriculture system makes it difficult for policymakers and researchers to monitor and evaluate the performance of BMPs across large spatial scales and develop appropriate improvement strategies accordingly. Under these circumstances, agent-based models (ABM) stand out for their ability to deal with the complexities in the agri-environment system. To better understand the dynamics of farmer’s decision-making on BMP application under different socio-economic and environmental situations, an ABM has been developed to simulate the decision-making processes in the Upper Medway Creek subwatershed in this study. The ABM uses an optimizing decision-making structure that relies on choice by highest utility. In addition, the ABM integrates a weighted sum function to evaluate the influences of economic, environmental and social factors on farmers’ decision-making. Results from the model pre-test were compared to those obtained from a random generator to examine how does the developed ABM perform against the random generator. Then, a sensitivity analysis has been performed using the one-factor-at-a-time method to examine the impacts of different potential interventions, including government subsidies and educational activities, on farmers’ decision-making for certain BMP adoptions. The results demonstrated that the developed ABM is robust in simulating farmers’ decision-making on BMP application within the Upper Medway Creek subwatershed. According to the sensitivity analysis, providing subsidies and improving knowledge level of BMPs have positive effects on the implementations of certain BMPs in general. While comparing to improving knowledge levels of BMPs, providing subsidies makes greater contribution to motivating farmers to adopt BMPs. For each BMP, a subsidy rate, which indicates the proportion of implementation costs needs to be subsidized to effectively encourage the BMP adoption, has been suggested. The results of this study provide a better understanding of how different socio-economic conditions affect farmers’ decision-making on BMP adoptions and offer insights for policymakers to develop effective strategies incentivising farmers’ adoptions of BMPs and further preserving water quality in the Upper Medway Creek subwatershed.