HydroGraphNet boosts watershed predictions of daily flow and nitrogen in sparse data regions‎

Spatially distributed prediction of streamflow and nitrogen (N) export dynamics is essential for precision management of agricultural watersheds. While temporal deep learning models have shown strong basin-scale performance, their ability to generalize spatially is limited, particularly under data-scarce conditions. To address this gap, a team of researchers led by the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) propose HydroGraphNet, a knowledge-guided graph machine learning framework integrating process-based knowledge and explicit spatial learning into temporal modeling.