The impacts of assimilating satellite soil moisture into a rainfall–runoff model in a semi-arid catchment

Abstract

Soil moisture plays a key role in runoff generation processes, and the assimilation of soil moisture observations into rainfall–runoff models is regarded as a way to improve their prediction accuracy. Given the scarcity of in situ measurements, satellite soil moisture observations offer a valuable dataset that can be assimilated into models; however, very few studies have used these coarse resolution products to improve rainfall–runoff model prediction. In this work we evaluate the assimilation of satellite soil moisture into the probability distributed model (PDM) for the purpose of reducing flood prediction uncertainty in an operational context. The surface soil moisture (SSM) and the soil wetness index (SWI) derived from the Advanced Microwave Scanning Radiometer (AMSR-E) are assimilated using an ensemble Kalman filter. Two options for the observed data are considered to remove the systematic differences between SSM/SWI and the model soil moisture prediction: linear regression (LR) and anomaly-based cumulative distribution function (aCDF) matching. In addition to a complete period rescaling scheme (CP), an operationally feasible real-time rescaling scheme (RT) is tested. On average, the discharge prediction uncertainty, expressed as the ensemble mean of the root mean squared difference (MRMSD), is reduced by 25% after assimilation and little overall difference is found between the various approaches. However, when specific flood events are analysed, the level of improvement varies. Our results reveal that efficacy of the soil moisture assimilation for flood prediction is robust with respect to different assumptions regarding the observation error variance. The assimilation performs similarly between the operational RT and the CP schemes, which suggests that short-term training is sufficient to effectively remove observation biases. Regarding the different rescaling techniques used, aCDF matching consistently leads to better assimilation results than LR. Differences between the assimilation of SSM and SWI, however, are not significant. Even though there is improvement in streamflow prediction, the assimilation of soil moisture shows limited capability in error correction when there exists a large bias in the peak flow prediction. Findings of this work imply that proper pre-processing of observed soil moisture is critical for the efficacy of the data assimilation and its performance is affected by the quality of model calibration.