Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment. (May 2015)

• Record Type: Journal Article
• Authors: Jiang, Yan; Liu, Changming; Li, Xuyong; Liu, Lifang; Wang, Hongrui
• Is Part Of: Environmental modelling & software. Volume 67(2015:May)
• Page Start: 72
• Page End: 88
• Publication Date: 2015-05
• Subjects: HIMS rainfall-runoff model -- Parameter estimation -- Particle swarm optimization -- Master–slave swarms shuffling evolution -- Semiarid catchment -- Sensitivity analysis; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.01.008
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: 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.