地理学报 ›› 2011, Vol. 66 ›› Issue (9): 1270-1280.doi: 10.11821/xb201109012

• 水文研究 • 上一篇    下一篇

水文模型参数敏感性快速定量评估的RSMSobol方法

孔凡哲1, 宋晓猛1, 占车生2, 叶爱中3   

  1. 1. 中国矿业大学资源与地球科学学院, 江苏徐州 221008;
    2. 中国科学院地理科学与资源研究所陆地水循环及地表过程重点实验室, 北京 100101;
    3. 北京师范大学全球变化与地球系统科学研究院, 北京 100875
  • 收稿日期:2011-05-02 修回日期:2011-06-08 出版日期:2011-09-20 发布日期:2011-11-04
  • 通讯作者: 占车生(1975-), 男, 湖北黄冈人, 博士, 主要从事遥感水文学和大尺度水文模型模拟研究。E-mail: zhancs2006@gmail.com E-mail:zhancs2006@gmail.com
  • 作者简介:孔凡哲(1964-), 男, 江苏徐州人, 博士,教授,主要从事流域水文模拟与流域产汇流过程研究。E-mail: kongfz3@126.com
  • 基金资助:

    国家水体污染控制与治理重大专项(2009ZX07210-006); 国家重点基础研究发展计划(973 计划) 项目(2010CB428403); 中国矿业大学基本科研业务费大学生创新项目

An Efficient Quantitative Sensitivity Analysis Approach for Hydrological Model Parameters Using RSMSobol Method

KONG Fanzhe1, SONG Xiaomeng1, ZHAN Chesheng2, YE Aizhong3   

  1. 1. School of Resource and Earth Science, China University of Mining & Technology, Xuzhou 221008, Jiangsu, China;
    2. Key Laboratory of Water Cycle & Related Land Surface Processes,Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    3. State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science,Beijing Normal University, Beijing 100875, China
  • Received:2011-05-02 Revised:2011-06-08 Online:2011-09-20 Published:2011-11-04
  • Supported by:

    National Grand Science and Technology Special Project of Water Pollution Control and Improvement, No.2009ZX07210-006; National Key Basic Research Program of China (973 Program), No.2010CB428403; China University of Mining and Technology Student Innovation Project of Fundamental Research Funds for Central Universities

摘要: 水文模型参数敏感性分析是模型不确定性量化研究的重要环节,其可以有效识别关键参数,减少模型率定的不确定性,提高模型优化效率。然而如何快速有效地定量评估参数敏感性已成为当前大尺度分布式水文模型优化的瓶颈。针对传统的全局定量敏感性分析方法在多参数复杂水文模型的不足,本文采用基于统计学习理论的支持向量机(SVM) 建立非参数响应曲面(称为代理模型),再结合基于方差的Sobol 方法,建立了基于响应曲面方法的Sobol 定量全局敏感性分析方法(RSMSobol 方法),实现复杂模型系统参数敏感性的快速定量化评估。本文选用淮河流域的日尺度分布式时变增益水文模型进行实例研究,采用水量平衡系数(WB),Nash-Sutcliffe 效率系数(NS) 和相关系数(RC) 三个目标函数综合评价模拟效果。研究结果显示RSMSobol方法在实现定量全局敏感性分析的同时降低了模型运行时耗,提高了模型评估效率,且与传统定量方法Sobol 方法具有同样的评估效果。该方法的有效应用为大型复杂水文动力模拟系统的参数定量化敏感性评价提供了参考,为模型参数进一步优化提供了可靠依据。

关键词: 代理模型, 响应曲面方法, 敏感性分析, 支持向量机, 淮河流域

Abstract: Sensitivity analysis of hydrological models is a key step for model uncertainty quantification. It can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. However, how to effectively validate a model and identify the dominant parameters for a large-scale complex distributed hydrological model is a bottle-neck to achieve the parameters optimization. There are some shortcomings for classical approaches, e.g. time-consuming and high computation cost, to quantitatively assess the sensitivity of the multi-parameters complex hydrological model. For this reason, a new approach was applied in this paper, in which the support vector machine was used to construct the response surface (a surrogate model) at first. Then it integrated the SVM-based response surface with the Sobol method, i.e. the RSMSobol method, to achieve the quantification assessment of sensitivity for complex models. Taking the distributed time-variant gain model in the Huaihe River Basin as a case study, we selected three objective functions (i.e. water balance coefficient WB, Nash-Sutcliffe efficiency coefficient NS, and correlation coefficient RC) to assess the model as the output responses for sensitivity analysis. The results show that the RSMSobol method can not only achieve the quantification of the sensitivity, and also reduce the computational cost, with good accuracy compared to the classical approaches.

Key words: meta-modeling approach, response surface methodology, sensitivity analysis, support vector machines, Huaihe River Basin