Integrating Information From Hydrologic Models and SWOT Remote Sensing Measurements to Estimate Discharge Across River Networks

Abstract

The Surface Water and Ocean Topography (SWOT) satellite has the potential to improve knowledge of river discharge globally. However, SWOT discharge algorithms are informed by global hydrological model data, and a portion of the model's systematic bias is passed along to the SWOT discharge time series. To address this issue, we present a new algorithm designed to reduce systematic bias in SWOT discharge. The algorithm analytically integrates basin‐scale information by applying mass conservation to time‐averaged SWOT discharge and estimates of regionalized runoff. We test the algorithm on three river basins with drainage areas on the order of 10 ⁵  km ² . We find that when the time‐averaged SWOT discharge observations are spatially unbiased (i.e., their spatially averaged error is close to zero) the algorithm significantly reduces error in SWOT discharge: the median (interquartile range) absolute value of the normalized error is 15% (10%–19%) for the Willamette River basin, and 12% (7%–48%) for the upper Ohio River basin, significant reduction in SWOT discharge error (median values of 30% and 31%, respectively). However, for the Loire River basin, which has high (∼60%) spatial bias, the algorithm does not reduce error associated with SWOT discharge. These results indicate that understanding and reducing spatial bias at the basin scale is key for reducing time series bias in river discharge. Based on a preliminary assessment of spatial bias globally, we find that SWOT discharge could achieve its target accuracy (30% discharge error, with most of it temporal bias) even in ungaged basins, enabling SWOT discharge to significantly increase accuracies of the global water budget.