Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models
Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models
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水文模型不确定性评估中GLUE方法的主观因素评价及与形式贝叶斯方法的比较
DOI：
10.1016/j.jhydrol.2010.06.044
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发表时间：
2010-09
影响因子：
6.4
通讯作者：
Xia, Jun
中科院分区：
文献类型：
--
作者：
Xu, Chong-Yu;Singh, V. P.;Li, Lu;Xia, Jun
水文模型不确定性的量化是近年来水文学研究的热点问题。由于采用了不同的方法和不同的假设，报告了不同的结果和结论。特别是，广义似然不确定性估计（GLUE）和贝叶斯方法评估概念流域模型的不确定性之间的分歧已被广泛讨论。最受批评的是GLUE方法中关于阈值、样本模拟数量和似然函数影响的主观选择。本文系统地研究了阈值和样本数对GLUE不确定性评估的影响，并以华北干旱区流域为例，对两个成熟的概念性水文模型（WASMOD和DTVGM）进行了GLUE估计的后验分布、参数和总不确定性的综合评估和基于大都会Hasting（MH）算法的形式化贝叶斯方法。结果表明，在GLUE方法中，参数的后验分布和模拟放电的95%置信区间是敏感的阈值的选择，作为衡量的可接受的样本率（ASR）。然而，当GLUE方法中的阈值足够高（即，当ASR值小于0.1%时），GLUE方法的参数后验分布、模拟流量的95%置信区间和95%置信区间内的观测值百分比（P-95 CI）均接近贝叶斯方法对两种水文模型的估计值。其次，在GLUE方法中，当ASR固定时，样本模拟数量的不足会影响最大Nash-Sutcliffe（MNS）效率值。然而，当WASMOD模式的模拟样本数增加到2× 104，DTVGM模式的模拟样本数增加到8× 104时，模拟样本数对模式模拟结果的影响就变得不那么重要了。第三，在模拟流量的不确定性所造成的参数的不确定性是远远小于所造成的模型结构的不确定性两个水文模型。第四，模型拟合的最大纳什-萨克利夫效率值所衡量的优度是几乎相同的GLUE和贝叶斯方法的水文模型。该研究为水文模型的不确定性评估提供了有用的信息。
Quantification of uncertainty of hydrological models has attracted much attention in the recent hydrological literature. Different results and conclusions have been reported which result from the use of different methods with different assumptions. In particular, the disagreement between the Generalized Likelihood Uncertainty Estimation (GLUE) and the Bayesian methods for assessing the uncertainty in conceptual watershed modelling has been widely discussed. What has been mostly criticized is the subjective choice as regards the influence of threshold value, number of sample simulations, and likelihood function in the GLUE method. In this study the impact of threshold values and number of sample simulations on the uncertainty assessment of GLUE is systematically evaluated, and a comprehensive evaluation about the posterior distribution, parameter and total uncertainty estimated by GLUE and a formal Bayesian approach using the Metropolis Hasting (MH) algorithm are performed for two well-tested conceptual hydrological models (WASMOD and DTVGM) in an arid basin from North China. The results show that in the GLUE method, the posterior distribution of parameters and the 95% confidence interval of the simulated discharge are sensitive to the choice of the threshold value as measured by the acceptable samples rate (ASR). However, when the threshold value in the GLUE method is high enough (i.e., when the ASR value is smaller than 0.1%), the posterior distribution of parameters, the 95% confidence interval of simulated discharge and the percent of observations bracketed by the 95% confidence interval (P-95CI) for the GLUE method approach those values estimated by the Bayesian method for both hydrological models. Second, in the GLUE method, the insufficiency of number of sample simulations will influence the maximum Nash–Sutcliffe (MNS) efficiency value when ASR is fixed. However, as soon as the number of sample simulations increases to 2×104for WASMOD and to 8×104for the DTVGM model the influence of number of sample simulations on the model simulation results becomes of minor importance. Third, the uncertainty in simulated discharges resulting from parameter uncertainty is much smaller than that resulting from the model structure uncertainty for both hydrological models. Fourth, the goodness of model fit as measured by the maximum Nash–Sutcliffe efficiency value is nearly the same for the GLUE and the Bayesian methods for both hydrological models. Thus this study provides useful information on the uncertainty assessment of hydrological models.
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