The integration of remotely sensed data into a watershed modeling approach to characterize winter cover crop nitrate uptake function and wetland inundation at the landscape scale
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The Chesapeake Bay (CB) is the largest and most productive estuary in the United States (US), supporting more than 3,600 species of plants and animals (CEC, 2000). Degrading water quality of the CB estuaries requires implementing conservation practices to reduce excessive nutrients loads from agricultural lands. The role of both winter cover crops (WCCs) and wetland restoration and enhancement in reducing agricultural nutrient loads on the Coastal Plain of the Chesapeake Bay Watershed (CBW) has been widely recognized. In order to effectively reduce nutrient loads using two conservation practices, it is important to understand their long-term, cumulative impacts at the watershed scale. A watershed modeling approach has been recommended to simulate the cumulative effects of conservation practices on nutrient loads at the watershed scale. When using a watershed modeling approach, accurate characterization of physical processes of conservation practices within a modeling context and consideration of multiple stressors (e.g., climate change and human activities) are critical for obtaining reliable information. This dissertation has sought to characterize and evaluate the long-term impacts of WCCs and wetlands on hydrology and water quality at the watershed scale, using a watershed modeling approach in conjunction with remotely sensed data. The WCCs are planted during winter fallow seasons to absorb residual soil nitrate. The WCC nitrate uptake capacity is dependent on its biomass as soil nitrate is being converted to WCC biomass. The WCC growth was first estimated using landscape-level biomass observations derived from remotely sensed data and field measurements to accurately represent WCC nitrate uptake efficiency. Then, the long-term effect of WCC on nitrate loads was evaluated at the watershed scale by considering WCC planting methods, soil properties, and crop rotations. The simulation results represent the typical growth pattern of WCCs observed in this region, and demonstrate the most effective WCC implementation method for enhanced WCC water quality benefits, regarding local characteristics. Inundation is a key abiotic factor characterizing wetland ecosystem functions including water purification. Thus, the accurate prediction of the spatial distribution of inundation can indicate the capacity of wetlands to remove nutrient loads at the local landscape scale. An integrated wetland-watershed modeling approach is presented to show how remotely sensed data can be used to improve spatial prediction of wetland inundation while reducing prediction uncertainty. The simulation results demonstrate that the model prediction with wetland parameters derived from remotely sensed data accurately replicates the observed spatial inundation pattern. These findings provide useful information for identifying the locations in need of wetland restoration and enhancement. A watershed modeling approach that incorporated remotely sensed data accurately demonstrates the effective way to implement WCCs and wetland restoration and enhancement for reducing agricultural nutrient loads. Therefore, this dissertation would contribute to achieving nutrient reduction goals of the CB.