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.

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.

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