A Kalman Filter Approach for Estimating Daily Discharge Using Space-Based Discharge Estimates

The SWOT mission is the first satellite to conduct a global survey of the Earth’s surface waters, measuring water surface elevation, river width, and water surface slope for estimating discharge. As SWOT can only sample mid-latitude locations approximately twice per its 21-day cycle, obtaining discharge estimates with an ideal temporal and spatial resolution along the river network employing SWOT discharge products is not feasible. To overcome this limitation, we develop a linear dynamic system for daily discharge estimation over continuous reaches in a single-branch river network using SWOT observations. The linear dynamic system includes a process model based on a physically-based spatiotemporal discharge correlation model and observation equations utilizing SWOT products. We solve this dynamic system through a Kalman filter, which is simultaneously executed in the time and space domain to obtain daily discharge. Since SWOT discharge products are currently inaccessible, we use a perturbed version of synthetic SWOT datasets obtained by Monte Carlo simulation with biased measurements to test the feasibility of our approach. The validation of the estimated discharge against true discharge in the datasets over all test cases leads to a median correlation as high as 0.95, a median NSE for residuals as high as 0.82, and a median relative bias as high as 5.22%, respectively. Our method delivers promising results and the hope of obtaining daily discharge for continuous reaches in a river network once the required SWOT data is available.