A Time-Space varying Distributed Unit Hydrograph (TS-DUH) for lare-scale operative flash flood forecast

Abstract

Increasing threats of flash flood call for effective and operative ways to offer accurate forecasting and warning. In this study, a Time-Space varying Distributed Unit Hydrograph (TS-DUH) based on publicly-available-only data is proposed for efficient flash flood forecasting. As in the traditional spatially distributed unit hydrograph (SDUH) method, TS-DUH initially estimates the runoff travel time (and flow velocity) from each location within a catchment to the outlet based on topographic and hydroclimate characteristics. However, the delineation of the runoff-drainage process is further adjusted by considering the heterogeneous and dynamic runoff contribution caused by rainfall and soil moisture variations. The excess rainfall is estimated by the widely used Global Flood Monitoring System (GFMS) which provides long-term (2000-present) well-archived and real-time operative global runoff datasets from a state-of-the-art DRIVE model (DRIVE-Runoff). An alternative excess rainfall input is taken from the Soil Conservation Service's curve number method (CN-Runoff). The performance of the TS-GUH method is evaluated using 6,324 flash flood events of 281 small-to-medium-sized catchments in the CONUS, with 1,686 events used for calibration. The validation results show that using DRIVE-Runoff is better than CN-Runoff, 99% and 71% of events have KGE values greater than 0 and 0.5, respectively, with a median KGE value of 0.6 and the probability of detection (POD) of flood events 0.9. More importantly, using near real-time satellite rainfall-driven DRIVE-Runoff, long-term flow simulation (2003-2020) without calibration at 803 gauges shows better performance of TS-DUH than the original GFMS, with a median KGE improvement of 0.15. This combined UH and numerical hydrological model approach showed great potential for flash food monitoring and forecasting at regional or global scales.