Mitigating the environmental impacts of agricultural contaminants has become increasingly vital at both local and global levels to support increased food production needed for a growing population. A key focus in achieving sustainable agriculture is reducing nutrient pollution and emerging contaminants. A research gap exists between using hydrological modeling to evaluate and improve agricultural land management practices for contaminant load reduction, and its application to regions with scarce data, significant uncertainty in watershed and land management conditions, and new land use practices. This dissertation aimed to deepen our understanding of these uncertainties and identify innovative strategies to address them through three separate modeling exercises and field experiments. The first study found that the spatial variability of fertilizer and manure application within a watershed introduces significant uncertainty in inferred nutrient load reduction effects of cover cropping at large watershed scales. The second study revealed that novel, regression-based approaches could consistently improve rainfall-runoff model parameter estimates in ungauged locations to improve runoff estimation during extreme events. Finally, the last study produced early evidence that the placement of solar panels on small agricultural catchments may not significantly affect runoff, especially compared to other physical controls that impact hydrologic response. Taken together, the results of this dissertation highlight the potential to significantly improve our understanding of hydrologic response in dynamic and evolving agricultural landscapes, and to use this improved understanding to advance modeling techniques that can better guide management actions.