Abstract
The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity) data in an agricultural region of the state of Iowa in the central U.S. They explore three different strategies for updating model soil moisture states using satellite-based soil moisture observations. The first is a “hard update” method equivalent to replacing the model soil moisture with satellite observed soil moisture. The second is Ensemble Kalman Filter (EnKF) to update the model soil moisture, accounting for modeling and observational errors. The third strategy introduces a time-dependent error variance model of satellite-based soil moisture observations for perturbation of EnKF. The study compares streamflow predictions with 131 USGS gauge observations for four years (2015–2018). The results indicate that assimilating satellite-based soil moisture using EnKF reduces predicted peak error compared to that from the open-loop and hard update data assimilation. Furthermore, the inclusion of the time-dependent error variance model in EnKF improves overall streamflow prediction performance. Implications of the study are useful for the application of satellite soil moisture for operational real-time streamflow forecasting.
Highlights
Accurate rainfall-runoff partitioning is one of the most critical factors in predicting the magnitude of streamflow fluctuations
In the following two subsections, we present streamflow prediction performance maps for hard update and Ensemble Kalman Filter with time-dependent variance (EnKFV)
Compared to streamflow predictions with SMOS EnKFV, SMAP soil moisture assimilation with EnKFV achieves a higher reduction in streamflow peak differences in the eastern part of the study domain
Summary
Accurate rainfall-runoff partitioning is one of the most critical factors in predicting the magnitude of streamflow fluctuations. Soil moisture is the main state variable in hydrologic models that determines estimated runoff magnitudes. The value of the hydrologic model states at any point in time are subject to uncertainties, as they encode the history of all the variables and hydrometeorological input forcings that determine them. Epistemic decisions such as model structure, model parameters, closure equations, initial conditions, among others (e.g., [1,2,3]), play a factor in determining the value on state variables everywhere and every time as flow equations are integrated. Crow et al [4]
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