Improving Global Flood Forecasting using Satellite Detected Flood Extent

Abstract

Flooding is a natural global phenomenon but in many cases is exacerbated by human activity. Although flooding generally affects humans in a negative way, bringing death, suffering, and economic impacts, it also has potentially beneficial effects. Early flood warning and forecasting systems, as well as the development of real-time monitoring systems, are recognised measures to reduce the number of flood victims and to support flood disaster responses. Importantly, they may buy time to take appropriate mitigation measures to reduce flood peaks, thus reducing the associated negative impacts. One of the main constraints for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is an even larger issue for real-time flood monitoring and forecasting. This lack of data could potentially be overcome if satellite-retrieved surface water changes signal or streamflow estimates were sufficiently accurate to serve as a surrogate for ground-based measurements. In the first part of the study, the potentials and constraints of river streamflow estimates based on the remote sensing signal of the Global Flood Detection System are evaluated. We used the merged product from the Global Flood Detection System (GFDS) that uses both AMSR-E (Advance Microwave Scanning Radiometer – Earth Observing System) and TRMM (Tropical Rainfall Measuring Mission) to derive surface water extent during the study period. The influence of the local physiographic factors which might influence the retrieval of the satellite signal was also studied. Validation is done based on ground-based streamflow observations. In the second part of the study, it was tested if the GFDS derived streamflow proxy improved the model calibration of the distributed rainfall-runoff routing model LISFLOOD, used by the Global Flood Awareness System (GloFAS). Finally, the GFDS surface water extent data were assimilated into the large-scale hydrological model LISFLOOD using an Ensemble Kalman filter (EnKF). It was evaluated if flood forecasting skill would improve, as well as the timing of the flood peak, as compared to baseline initial conditions (without data assimilation). Furthermore, two additional studies looked at the use of globally near real-time available products for flood forecasting, monitoring, and assessment to support decision makers and humanitarian organisations such as Red Cross Red Crescent and the World Food Programme.