Nutrient pollution from agricultural and urban sources continues to degrade freshwater ecosystems worldwide. Phosphorus and nitrogen, particularly in their bioavailable forms, drive these processes, with phosphorus often acting as the primary limiting nutrient in freshwater systems. While best management practices (BMPs) such as reduced fertilizer use, cover crops, and riparian buffers aim to limit nutrient losses, their effectiveness is highly variable and increasingly challenged by climate change, extreme precipitation, and shifting hydrology.This dissertation investigates nutrient management at multiple scales in New York State, focusing on both watershed modeling for long-term planning and short-term, forecast-informed decision-making. Chapter 1 presents an alternative SWAT model calibration approach for a data-limited watershed (Canandaigua Lake, central New York) and evaluates the effects of calibration uncertainty on estimated BMP effectiveness. Results show that total phosphorus (TP) loads, their spatial distribution, and both absolute and percent load changes vary under parameter uncertainty; however, the relative ranking of BMP effectiveness remains largely consistent. Chapter 2 builds on this by using a model-as-truth experiment in the Cayuga Lake watershed to evaluate how differences in data availability influence model performance and BMP assessment. The findings demonstrate that TP load estimates and estimated changes in load under BMPs vary considerably across data availability scenarios, but the amount of data available for calibration does not explain how close model estimates approach their true values. This outcome is largely attributed to the challenge of calibrating a high-dimensional model in the presence of significant equifinality. Still, the ranking of BMP effectiveness remains stable across data availability scenarios, consistent with Chapter 1 results. Chapter 3 evaluates the skill of short-term probabilistic runoff forecasts across New York State, a tool meant to inform short-term, farm-scale decisions around manure and fertilizer application. Results show that the runoff forecasting system effectively identifies snowmelt- and thaw-driven runoff events but tends to underestimate low-probability and rainfall-driven events, with forecast performance varying across watersheds. Overall, this work demonstrates that combining robust calibration, uncertainty quantification, and real-time runoff forecasting can support adaptive nutrient management. Future research should explore alternative calibration methods and machine-learning post-processing to improve model reliability and operational decision support