How far is distributed hydrological model from real-time flood forecasting

Physically based, distributed hydrological model(PBDHM) was proposed for long time, and was regarded to have the potential to improve the flood forecasting accuracy. But unfortunately, this is still in the dream due to some existing challenges, and the biggest one is model parameter determination. Initially, it was assumed that parameter of PBDHM should be derived from the terrain properties directly, such as the DEM, land use/cover(LUC) types and soil types, not calibrated like lumped conceptual model(LCM) by employing optimization algorithm. In fact, PBDHM’s parameter calibration is also infeasible considering its huge number of model parameters, that could be up to millions or even to billions. As there is no “optimal” references for deriving PBDHM’s parameters directly from terrain properties, PBDHM’s capability for real-time flood forecasting has been weakened, so limiting its use mainly in scientific studies. In this study, the author assumes that PBDHM also needs parameter “calibration”, and the theory and framework for PBDHM parameter optimization have been presented. Based on the Liuxihe model, which was proposed for watershed flood forecasting, an automatic parameter optimization algorithm has been proposed by employing Particle Swarm Optimization (PSO). With parameter optimization, flood simulation accuracy of Liuxihe model has been improved largely, and very importantly, its performance is very stable. Not like LCM, model performance fluctuates sharply, thus limiting its capability being used for real-time flood forecasting. From dozens case studies in China, it also has been found that hydrological data from only one flood event is enough for parameter optimization, not like LCM, which requires hydrological data from a series of flood events. This finding is significant particularly for data-poor watershed, which makes PBDHM’s parameter optimization feasible for most of the world watersheds. With this advances, Liuxihe model has been used in several Chinses watersheds for real-time flood forecasting, and successful forecasting have been achieved. These successful implementations have proven that PBDHM has entered a new era for the real world application.