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The hydrologic models were calibrated to the observed data (e.g. soil moisture and streamflow) at the basin and subbasin-scales.
Assimilation of satellite soil moisture and streamflow data into a distributed hydrologic model has received increasing attention over the past few years.
The combined assimilation of surface soil moisture and streamflow outperforms the assimilation with only surface soil moisture or streamflow especially in the estimate of full profile soil moisture.
Compared to open-loop model and direct-insert method, the estimate of both soil moisture and streamflow has been improved by EKF assimilation.
The combined assimilation of streamflow and soil moisture provides more accurate forecasts of both soil moisture and streamflow, particularly for shorter lead times.
A drought index developed from the Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture product showed reasonable correlations with the observed soil moisture and streamflow.
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An extension of this approach to apply root zone soil moisture estimates from limited field sensor data showed considerable improvement in the simulation of root zone moisture content and streamflow with corresponding observed data.
The results show that there are significant differences in soil moisture variations and streamflow estimates when the three assimilation approaches were applied.
The correlations of the TWS streamflow, soil moisture and snow water equivalent with precipitation were all identified.
The best data assimilation scheme for hydrologic modeling involved using a combination of streamflow, soil moisture, and snow water equivalent while performing both state and parameter updating.
Perhaps it's the record setting lows in snowpack, streamflow, soil moisture and reservoir levels.
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