Towards Assimilation of Soil Moisture Remote Sensing Data Into a Semi-Distributed Flood Model (PAP014895)

Código

PAP014895

Autores

Maurizio Mazzoleni, Leonardo Alfonso, Michele Ferri, Martina Monego, Daniele Norbiato, Dimitri P. Solomatine

Tema

Flood forecasting and early warning systems

Resumo

The aim of this work is to consider assimilation of soil moisture observations into a semi-distributed hydrological model used for flood modelling and forecasting. In addition, the effect of the sub-basins discretization used in the hydrological model is analyzed. The methodology is applied in the Bacchiglione catchment, located in the North of Italy, having a drainage area of about 1400 km2, length of main reach of 118km and average discharge of 30m3/s at Padova. The proposed methodology has four steps. Firstly, two different scenarios of spatial discretization of the Bacchiglione basin into set different sub-basins, characterized by its own hydrologic response, are considered. In this way, two different structures of the semi-distributed hydrological model are implemented. Secondly, three sets of soil moisture remote sensing observations are analyzed. Thirdly, each one of these sets is assimilated, using a nudging scheme approach, in each one of the previous model structures. The main idea of this study is to update the model state, expressed in terms of soil water capacity, as response of the distributed information of soil moisture, and the consequent estimation of the water level along the main river channel using a 1D model. Finally, the results of the data assimilation module are analyzed to understand the correlation between soil moisture observations and model structure. The results of this work show that assimilation of distributed information about the soil moisture improves the flood modeling accuracy, and demonstrates that model structures requires certain updates in the used assimilation procedures. This study is partly supported by the WeSenseIt project funded by FP7 Research Programme of the European Commission.