SWAT模型多目标与单目标标定的比较——以印度木斯河流域为例

Shashikanth Kulkarni, T. Anurag, M. Hussain, S. Prasanna, Vittal Hari
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引用次数: 0

摘要

考虑到所涉及的水文过程,流域水文模型的校准至关重要。土壤和水分评估工具(SWAT)就是这样一个流行的模型,需要适当的校准,没有这些模型就难以正确地模拟径流。本研究旨在利用非支配遗传算法-II (NSGA-II)和swat -校准不确定度程序(SWATCUP)的多目标校准框架进行校准。该研究从2013年至2016年在印度的Musi河流域(1万平方公里)进行了7年。它包括三年的初始预热期,2015-2016年的校准期和2014-2015年的验证期。NSGA-II旨在优化Nash Sutcliffe Efficiency (NSE)和Percentage Bias (PBias)等多个目标函数。每月的模拟结果以校正和验证期的统计参数NSE、R2和PBias表示。结果表明,该方法具有令人满意的性能。此外,NSGA-II结果与SWATCUP (Sequential Uncertainty Fitting ver.2)进行了比较(SUFI-2)。我们发现NSGA-II的性能优于SWATCUP。灵敏度分析结果表明,CN2、GW_DELAY、GW_REVAP、ALPHA_BF、RCHRG_DP和CH_K2的敏感性较高,而SURLAG、ESCO、SLSUBBS、HRU_SLP的敏感性最低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of multi-objective and single objective calibration for SWAT model: a case study on Musi river basin, India
ABSTRACT Calibration of hydrological models for watersheds is critical considering the hydrological processes involved. The Soil and Water Assessment Tool (SWAT) is one such popular model and requires proper calibration, without which models have difficulty in proper simulation of runoff. The present study aims to utilize multi-objective calibration framework using Non-Dominated Genetic Algorithm- II (NSGA-II) and SWAT-Calibration Uncertainty Procedures (SWATCUP) for calibration. The study is conducted on Musi river basin located in India (10,000 Sq km) for seven years from 2013–2016. It includes an initial warm-up period of three years, the calibration period from 2015–2016, and validation period from 2014–2015. NSGA-II aims to optimize the multiple objective functions i.e. Nash Sutcliffe Efficiency (NSE) and Percentage Bias (PBias). The Monthly simulations results are expressed in terms of statistical parameters NSE, R2 and PBias for calibration and validation period. The results indicate satisfactory performance. Further, NSGA-II results are compared with SWATCUP (Sequential Uncertainty Fitting ver.2 (SUFI-2). We find NSGA-II performance is better than SWATCUP. The sensitive analysis indicates that CN2, GW_DELAY, GW_REVAP, ALPHA_BF, RCHRG_DP, and CH_K2 are very sensitive whereas SURLAG, ESCO, SLSUBBS, HRU_SLP are observed to be least sensitive.
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来源期刊
ISH Journal of Hydraulic Engineering
ISH Journal of Hydraulic Engineering Engineering-Civil and Structural Engineering
CiteScore
4.30
自引率
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发文量
59
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