Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

We present a study on the Hydro-Informatic Modelling System (HIMS) rainfall-runoff model for a semiarid region. The model includes nine parameters in need of calibration. A master-slave swarms, shuffling evolution algorithm based on self-adaptive dynamic particle swarm optimization (MSSE-SDPSO) is proposed to derive model parameters. In comparison with SCE-UA, PSO, MSSE-PSO and MSSE-SPSO algorithms, MSSE-SDPSO has faster convergence and more stable performance. The model is used to simulate discharge in the Luanhe River basin, a semiarid region. Compared with the SimHyd and SMAR models, HIMS model has the highest Nash-Sutcliffe efficiencies (NSE) and smallest relative errors (RE) of volumetric fitness for the periods of calibration and verification. In addition, the studies indicate that the HIMS model with all-gauge data improves runoff prediction compared with single-gauge data. A distributed HIMS model performs better than a lumped one. Finally, the Morris method is used to analyze model parameters sensitivity for the objective functions NSE and RE. Present a MSSE-SDPSO method to select hydrologic model parameters.Check the suitability of HIMS model for streamflow simulation in semiarid catchment.In HIMS model, several rain gauges can predict runoff more accurately than a gauge.A distributed HIMS model performs better than a lumped HIMS model.Analyze the sensitivity of HIMS model parameters.

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