Edge-of-field runoff prediction by a hybrid modeling approach using causal inference

To mitigate the negative impacts caused by runoff events, we developed a hybrid modeling approach by combining physics-based and statistical models to predict the occurrence and level of severity of daily runoff events, supporting agricultural producers to avoid nutrient application before significant runoff events.

We chose to use the National Oceanic and Atmospheric Administration's National Water Model (NWM) as the physical model given its flexible architecture design, technical robustness, model resolution, data availability, and wide application scale. For the statistical model, we developed a data-driven tool built from Directed Information and eXtreme Gradient Boosting (XGBoost) to estimate the occurrence and the level of severity of daily edge-of-eld (EOF) runoff events.