Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment
文献类型：SCI/SSCI论文
| 作者 | Jiang Y.; Liu, C. M.; Li, X. Y.; Liu, L. F.; Wang, H. R. |
| 发表日期 | 2015 |
| 关键词 | HIMS rainfall-runoff model Parameter estimation Particle swarm optimization Master-slave swarms shuffling evolution Semiarid catchment Sensitivity analysis particle swarm optimization global sensitivity hydrologic model arid catchment multiobjective optimization genetic algorithm input variables northern china water-quality river-basin |
| 英文摘要 | 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. (C) 2015 Elsevier Ltd. All rights reserved. |
| 出处 | Environmental Modelling & Software |
| 卷 | 67 |
| 页 | 72-88 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 1364-8152 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/38672]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Jiang Y.,Liu, C. M.,Li, X. Y.,et al. Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment. 2015. |
入库方式： OAI收割
来源：地理科学与资源研究所
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