Streamflow Calibration in Ungauged Basins Using SWOT Discharge and SMAP Surface Soil Moisture Products

Abstract

Significant advances have been made in using terrestrial remote sensing to reduce random errors in land surface models (LSMs). However, less progress has been made in dealing with systematic LSM errors that are instead correlated with true system states. Such errors are particularly prominent in ungauged hydrologic basins lacking suitable in‐channel measurements of discharge (Q) for LSM calibration. While satellite remote sensing has been proposed as a solution to this problem, no single satellite retrieval type has yet proven to be a reliable calibration substitute for in‐channel Q measurements. Past work has shown that LSM calibration against Soil Moisture Active Passive (SMAP) surface soil moisture retrievals can ensure high precision for storm‐scale Q. Here, we build on this work by adding two additional calibration constraints based on a long‐term water balance and synthetic Q retrievals designed to realistically mimic the accuracy and availability of Surface Water Ocean Topography (SWOT) Q products. Results for 56 medium‐scale (500–10,000‐km ² ) basins in the eastern United States demonstrate that multi‐objective calibration utilizing these new constraints can simultaneously optimize both LSM daily Q bias and precision without relying on in‐channel Q measurements. The addition of calibration constraints employing a water balance and SWOT Q is shown to be particularly valuable for ensuring LSM Q estimates with low bias in their mean and temporal variability, respectively. Likewise, preliminary real‐data results based on an early version of the SWOT Q product suggest that our approach for generating synthetic SWOT Q is appropriate.