Article ID: 2025-033
Impact-based forecasts provide information on exposure, vulnerability, and risk, which are essential for quantifying risks and facilitating timely evacuation. For small- and medium-sized rivers, accurate deterministic flood forecasting more than three hours in advance is quite difficult owing to forecast errors in the location and intensity of precipitation systems. Operational weather forecasting services employ ensemble prediction systems for early flood warning, although the number of ensemble members is limited to a few dozen. From the perspective of probability prediction with minimized sampling errors, increasing the number of ensemble members for flood forecasting is an important research topic. This study investigated the impacts of the number of ensemble members on flood prediction. The precipitation dataset was 100 and 1000-member local ensemble transform Kalman filter (LETKF100 and LETKF1000, respectively) and 21-member operational weather forecast (MEPS). The flood forecasting model was the Japan Meteorological Agency's (JMA) operational flood forecasting system, “Runoff Index Model (RIM)”, which incorporates a tank model and the Manning equation. The case study was the extreme flooding caused by record-breaking rainfall in the Kuma River Basin (1880 km2), Japan, that occurred in July 2020. Using LETKF1000, the RIM successfully forecasted a high flood risk in the flood damaged area, with a probability of 60 % half a day ahead; six hours earlier than the JMA's operational flood forecast. Furthermore, we investigated the number of members required for ensemble flood forecasting. The prediction accuracy for the occurrence of risks during the flood forecasting period was similar between LETKF1000 and LETKF100. Whereas, LETKF1000 has higher prediction accuracy than LETKF100 regarding the timing of flood peaks. When selecting 500 members from the initial 1000 members, results nearly identical to those from the 1000 members were obtained. These results demonstrate that the LETKF1000 has the potential to provide valuable information for facilitating early evacuation.
