Streamflow calibration in ungauged basins using SWOT discharge and SMAP surface soil moisture products

Authors: Crow, Wade; DURAND, M - The Ohio State University; COSS, S - The Ohio State University; REICHLE, R - Goddard Space Flight Center

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/6/2025
Publication Date: N/A
Citation: N/A

Interpretive Summary: Hydrologic models convert observations of rainfall into estimates of future stream flow. As such, they are extremely useful tools for forecasting surface water availability for agricultural use. However, a long-standing problem with hydrologic models is that they require large amounts of historical stream flow data to be accurately calibrated. Unfortunately, such data is not available in many agricultural river basins. This paper presents a new calibration technique for hydrologic models that relies instead on satellite-based observations of in-stream river water height (i.e., river stage) and soil water availability. Results demonstrate that such an approach can effectively compensate for a lack of historical stream flow data and, therefore, significantly increase the geographic distribution of agricultural basins where hydrologic models can be successfully calibrated. The results of this study will be used by irrigation management districts to better forecast surface water availability and mitigate the impacts of agricultural drought.

Technical 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 (SSM) retrievals can ensure high precision for storm-scale Q. Here, we build on this work by adding two additional calibration constraints based on long-term water balance considerations 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- to 10,000-km^2) basins in the eastern United States demonstrate that multi-objective calibration utilizing these new constraints can simultaneously optimize 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 the mean and temporal variability of Q, respectively. As a result, the joint use of SMAP SSM, SWOT Q, and a long-term water balance constraint is shown to be capable of calibrating LSM Q across a range of Q metrics without reliance on in-channel Q. In addition, preliminary real-data results based on an early version of the SWOT Q product suggest that our approach for generating synthetic SWOT Q products is appropriate.