Dual assimilation of satellite soil moisture to improve streamflow prediction in data‐scarce catchments

This paper explores the use of active and passive microwave satellite soil moisture products for improving streamflow prediction within four large (>5000km2) semiarid catchments in Australia. We use the probability distributed model (PDM) under a data-scarce scenario and aim at correcting two key controlling factors in the streamflow generation: the rainfall forcing data and the catchment wetness condition. The soil moisture analysis rainfall tool (SMART) is used to correct a near real-time satellite rainfall product (forcing correction scheme) and an ensemble Kalman filter is used to correct the PDM soil moisture state (state correction scheme). These two schemes are combined in a dual correction scheme and we assess the relative improvements of each. Our results demonstrate that the quality of the satellite rainfall product is improved by SMART during moderate-to-high daily rainfall events, which in turn leads to improved streamflow prediction during high flows. When employed individually, the soil moisture state correction scheme generally outperforms the rainfall correction scheme, especially for low flows. Overall, the combined dual correction scheme further improves the streamflow predictions (reduction in root mean square error and false alarm ratio, and increase in correlation coefficient and Nash-Sutcliffe efficiency). Our results provide new evidence of the value of satellite soil moisture observations within data-scarce regions. We also identify a number of challenges and limitations within the schemes.

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