부산대학교 도서관

A reliable estimation of soil moisture conditions is fundamental for rivers’ discharge predictions, especially in small catchments where flash floods occur. In this context, microwave remote sensing can be exploited to estimate soil moisture at large scale. These estimates can be used to enhance the predictions of hydrological models using data assimilation techniques. Flash flood early warning systems can, thus, be improved. This study tested the effect of the assimilation of three different ASCAT-derived soil moisture products, processed and distributed within the EUMETSAT H-SAF framework (SM-OBS-1, SM-OBS-2, SM-DAS-2), into a distributed physically based hydrological model (Continuum). The study areas were three Italian catchments, representative of the typical Mediterranean small basins prone to flash floods. The products were first preprocessed in order to be comparable with the model soil moisture state estimate. Subsequently, they were assimilated using three Nudging-based techniques. Then, observed discharges were compared with the modeled one in order to understand the impact of the assimilation. The analysis was executed for a multiyear period ranging from July 2012 to June 2014 in order to test the assimilation algorithms for operational purposes in real-cases scenarios. Findings showed that the assimilation of H-SAF soil moisture products with simple preprocessing and assimilation techniques can enhance discharge predictions; the improvements significantly affect high flows. Although SM-OBS-2 and SM-DAS-1 are added-value products with respect to SM-OBS-1 (respectively, higher spatial and temporal resolution), they may not necessarily perform better. The impact of the assimilation strongly relies on the permanent catchment characteristics (e.g., topography, hydrography, land cover).