The suitability of remotely sensed soil moisture for improving operational flood forecasting

摘要

We evaluate the added value of assimilated remotely sensed soil moisture forthe European Flood Awareness System (EFAS) and its potential to improve theprediction of the timing and height of the flood peak and low flows. EFAS isan operational flood forecasting system for Europe and uses a distributedhydrological model (LISFLOOD) for flood predictions with lead times of up to10 days. For this study, satellite-derived soil moisture from ASCAT(Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer -Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) isassimilated into the LISFLOOD model for the Upper Danube Basin and resultsare compared to assimilation of discharge observations only. To assimilatesoil moisture and discharge data into the hydrological model, an ensembleKalman filter (EnKF) is used. Information on the spatial (cross-) correlationof the errors in the satellite products, is included to ensure increasedperformance of the EnKF. For the validation, additional dischargeobservations not used in the EnKF are used as an independent validation dataset.Our results show that the accuracy of flood forecasts is increased when moredischarge observations are assimilated; the mean absolute error (MAE) of theensemble mean is reduced by 35%. The additional inclusion of satellitedata results in a further increase of the performance: forecasts of baseflowsare better and the uncertainty in the overall discharge is reduced, shown bya 10% reduction in the MAE. In addition, floods are predicted with ahigher accuracy and the continuous ranked probability score (CRPS) shows aperformance increase of 5–10% on average, compared to assimilation ofdischarge only. When soil moisture data is used, the timing errors in theflood predictions are decreased especially for shorter lead times andimminent floods can be forecasted with more skill. The number of false floodalerts is reduced when more observational data is assimilated into thesystem. The added values of the satellite data is largest when theseobservations are assimilated in combination with distributed dischargeobservations. These results show the potential of remotely sensed soilmoisture observations to improve near-real time flood forecasting in largecatchments.