Hydrological real-time modeling using remote sensing data (original) (raw)

Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. Sequential data assimilation using the Ensemble Kalman Filter provides a both efficient and robust tool for a real-time modeling framework. One key parameter in a hydrological system is the soil moisture which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by the radar scatterometer on board the ERS satellite. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modeling framework yields good results in watersheds where the soil storage is an important factor. For the largest watershed a forecast over almost six weeks can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In watersheds with little soil storage and a quick response to rainfall events the performance is relatively poor.