Multi-objective parameter optimization of distributed hydrological models based on data-poor watersheds

Abstract

The distributed hydrological model's high-resolution representation of watershed heterogeneity is especially suitable for hydrological simulation in watersheds with small areas, and the hydrological model needs to use flow observation data for parameter calibration to obtain adapted parameters, but the lack of observation basins is the core problem we face in China, where there are few hydrological stations and they are concentrated in important rivers. This work attempts to optimize the parameters of the distributed hydrological model using limited flow observation data in a small watershed where flow observation is relatively scarce and to explore the value of combining multiple sources of data to optimize the parameters of the distributed hydrological model. Based on the combination of meteorological observations and remote sensing data collected, a distributed hydrological model is constructed based on the WetSpa model for the Jianshan River basin, and a multi-objective genetic algorithm NSGA-II is applied to rate the model to predict the simulated basin runoff process. The results show that the hydrological model optimized by multi-objective parameters has good adaptability in the study area, and the simulation has certain accuracy, which can provide basic support for the simulation of the water environment in the basin and also provide a reference for hydrological simulation.