《Water Science and Engineering》刊登“使用元建模法对新安江模型进行参数识别和整体灵敏度分析”

作者：Xiao-meng SONG , Fan-zhe KONG , Che-sheng ZHAN , Ji-wei HAN , Xin-hua ZHANG

刊物：《Water Science and Engineering》，2013年1月，第6卷第1期，1-17页

关键词：新安江模型；整体灵敏度分析；参数识别；元建模法；响应面模型

摘要：参数识别，模型校准和不确定性的量化是建模过程中的重要步骤，也是获得可靠结果和有价值信息的重要保障。水文模型的灵敏度分析是模型不确定性量化的关键一步，其通过找出主要参数，减小模型校核的不确定性，进而有效的优化了模型。但是，传统方法存在多参数水文模型定量评价灵敏度所需的计算时间长，计算成本高等缺点。基于这些缺点，本文介绍了使用全球技术的两步统计评估框架。该方法主要基于以下两点：（1）筛选法（Morris）对参数进行定性分级；（2）方差法结合元模型进行定量的灵敏度分析，即Sobol法与响应面法（RSMSobol）相结合。首先，将Morris筛选法用于参数灵敏度的定性区分，然后选取十个参数确定灵敏度指数。其次，利用RSMSobol法对灵敏度进行确定，即：计算基于响应面模型的一阶和总体灵敏度指数。RSMSobol法不仅可以确定灵敏度，还可以减少计算成本，比起传统方法有较好的准确性。这种方法对大规模的复杂分散水文模型是有效可靠的。

Parameter identification and global sensitivity analysis of Xin’anjiang model using meta-modeling approach

Authors:Xiao-meng SONG, Fan-zhe KONG ,Che-sheng ZHAN,Ji-wei HAN,Xin-hua ZHANG

Journal：Water Science and Engineering，2013, 6(1): 1-17

Key Word：Xin’anjiang model; global sensitivity analysis; parameter identification; meta-modeling approach; response surface model        

Abstract：Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1) a screening method (Morris) for qualitative ranking of parameters, and (2) a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol). First, the Morris screening method was used to qualitatively identify the parameters’sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM) were calculated. The RSMSobol method can not only  quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity  analysis of a complex large-scale distributed hydrological model.