Hydrologic Model Parameter Estimation in Ungauged Basins Using Simulated SWOT Discharge Observations

In situ gauge networks are often used in hydrologic model calibration, but these networks are limited or nonexistent in many regions. The upcoming Surface Water Ocean Topography (SWOT) mission promises to fill this observation gap by providing discharge estimates for rivers wider than 100 m. SWOT observation utility for model parameter selection in regions devoid of in situ gauges is assessed using proxy SWOT discharge estimates derived from an observing system simulation experiment and Monte Carlo methods. The sensitivity of the parameter selection to measurement error and observation frequency is also evaluated. Single- and multi-point parameter selection are performed for ten sub-basins within the Susitna and upper Tanana river basins in Alaska. SWOT is expected to observe Alaskan river points 4-7 times per 21-day repeat cycle with 120-km swath coverage. For an expected SWOT measurement error of 35%, parameter estimation is successful for 50% (90%) of sub-basins using single- (multi-) point parameter selection. Decreasing observation frequency to simulate lower latitudes resulted in success for only 10% of midlatitude and tropical sub-basins for single-point selection, whereas multi-point selection was successful in 80% (60%) of midlatitude (tropical) sub-basins. Single-point parameter selection is more sensitive to measurement error than multi-point parameter selection. The results strongly support the use of multi-point over single-point parameter selection, yielding robust results nearly independent of observation frequency. Most importantly, this study suggests SWOT can be used to successfully select hydrologic model parameters in basins without an in situ gauge network.

Automated summary

The study assesses the utility of SWOT for model parameter selection in regions without in situ gauge networks, concluding that multi-point parameter selection is more robust than single-point selection and can yield reliable results nearly independent of observation frequency.