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Hydrologic Model Parameter Estimation in Ungauged Basins using Simulated SWOT Discharge Observations
  • Nicholas J Elmer,
  • James L McCreight,
  • Christopher R. Hain
Nicholas J Elmer
NASA Postdoctoral Program, NASA Postdoctoral Program

Corresponding Author:nicholas.j.elmer@nasa.gov

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James L McCreight
National Center for Atmospheric Research (UCAR), National Center for Atmospheric Research (UCAR)
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Christopher R. Hain
Marshall Space Flight Center, NASA, Marshall Space Flight Center, NASA
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In situ gauge networks are often used in hydrological 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 with widths greater than 100 meters. Proxy SWOT discharge estimates derived from an observing system simulation experiment and Monte Carlo methods are used to assess SWOT observation utility for model parameter selection in regions devoid of in situ gauges. The sensitivity of the parameter selection to measurement error and observation temporal frequency is also evaluated. Single-point and multi-point parameter selection is performed for ten sub-basins within the Susitna River 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 discharge error of 35%, parameter estimation is successful for 60% and 90% of sub-basins using single-point and multi-point selection, respectively. Decreasing observation frequency to simulate lower latitudes resulted in success for only 20% of midlatitude and 10% of tropical sub-basins for single-point selection, whereas multi-point selection was successful in 80% of midlatitudes and 70% of tropical sub-basins. Single-point parameter selection was much more sensitive to SWOT discharge error than multi-point parameter selection. The results strongly support the use of multi-point parameter selection over single-point parameter selection, yielding robust results nearly independent of observation error with approximately half the sensitivity to observation frequency.