188 - Assessing Climate Change's Short - and Long-Term Effects on Meteorological Variables and Their Influence on Runoff and Nutrient Loads in Agricultural Watersheds

Water quality degradation and ecosystem instability within agricultural watersheds are predominantly driven by high levels of nitrogen and phosphorus. Amplifying the biogeochemical cycles of these elements, climate change stands as a significant threat to watershed health. This research delves into the intricate interaction between climate change, various meteorological variables, their attributes (such as rainfall intensity and frequency), and their implications for water quality in watersheds. This study examines the effects of climate change on meteorological patterns and the subsequent changes in runoff, total nitrogen (TN), and total phosphorus (TP) loads in the Kissimmee River watershed of South Florida (7,580 km2), known for harmful anthropogenic nutrient loads. Integrating physically based hydrological model (Soil & Water Assessment Tool) and advanced machine learning techniques, we develop a predictive framework for nutrient loads based on watershed and meteorological characteristics. This framework evaluates climatic shifts in the watershed using two future projections (RCP 4.5 with SSP245 and RCP 8.5 with SSP585) based on anticipated changes in precipitation and temperature, comparing short-term (up to 2050) and long-term periods (up to 2100) against the 1985–2021 baseline. Our findings underscore the pronounced influence of rainfall intensity and distribution on runoff, TN, and TP levels. Conclusively, the integration of machine learning with watershed-scale models offers superior predictive capabilities for water quality parameters.Water managers could use this study to track future trends in water quality and prioritize pollution sources in agricultural watersheds. This would pave the way for the adaptation of strategies to maintain water quality under climate change.