Global Estimates of Reach‐Level Bankfull River Width Leveraging Big Data Geospatial Analysis. Issue 7 (2nd April 2020) - Lin, Peirong; Pan, Ming; Allen, George H.; de Frasson, Renato Prata; Zeng, Zhenzhong; Yamazaki, Dai; Wood, Eric F.
Abstract: Recent progress in remote sensing has snapshotted unprecedented numbers of river planform geometry, providing opportunity to revisit the oversimplified channel shape parameterizations in global hydrologic models. This study leveraged two recent Landsat‐derived global river width databases and created a 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 ( R 2  = 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 estimate bankfull river width, creating a new reach‐level dataset for use in global hydrodynamic modeling. Plain Language Summary: Large‐scale river models typically parameterize channel shapes (e.g., bankfull width) based on discharge or drainage area with a power law model or stream order with look‐up tables. These highly simplified representations reflect our limited understanding of spatial variability of channel shapes, leading to great uncertainty in global river modeling. Using the most up‐to‐date global snapshots of channel planform geometry derived from satellite images, this study revisited state‐of‐the‐art channel shape parameterizations used in large‐scale river models. We also used a machine learning approach to improve the predictive power for the width spatial variability. Results from this study can complement the understanding of downstream hydraulic geometry from a data mining viewpoint, which also informs the improvement of channel shape parameterization in models. Key Points: A machine learning approach to predicting global bankfull river width was developed Local physiographic factors and human interferences are also important covariates for width variability Estimates of global reach‐level bankfull river width were provided for use in models