Reach Scale Hdyrology

Two recent Landsat-derived global river width databases were leveraged to create a new reach-level width dataset to measure the validity of model parameterizations at ~1.6 million kilometers of rivers in length. By showing state-of-the-art parameterization schemes only capture 30%–40% of the width variance globally, we developed a machine learning (ML) approach surveying 16 environmental covariates, which considerably improved the predictive power (R²=0.81 and 0.77 for two testing cases). Beyond the commonly discussed upstream basin conditions, ML revealed that local physiographic factors and human interference are also important covariates for width variability. Finally, we applied the ML model to estimated bankfull discharge, creating a new reach-level bankfull width dataset for use in global hydrodynamic modeling.