68. Assessing Uncertainties in a Conceptual Water Balance Model Using Bayesian Methodology
68. Assessing Uncertainties in a Conceptual Water Balance Model Using Bayesian Methodology
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DOI：
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发表时间：
2005
影响因子：
6.9
通讯作者：
K. Engeland;Chong-yu Xu;L. Gottschalk
中科院分区：
文献类型：
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作者：
K. Engeland;Chong-yu Xu;L. Gottschalk
本研究的目的是估计降雨径流模型模拟的水流的不确定性。考虑了水文模型中不确定性的两个来源：模型参数的不确定性和模型结构的不确定性。通过贝叶斯统计计算不确定性，并使用Metropolis-Hastings算法模拟后验参数分布。将 Metropolis-Hastings 算法计算的参数不确定性与假设参数和模型残差均呈正态分布的最大似然估计进行比较。该研究是使用 WASMOD 模型在瑞典中部的 25 个盆地进行的。计算了由于参数不确定性和总不确定性而导致的模拟放电的置信区间。结果表明，(a) Metropolis-Hastings 算法和最大似然法对参数不确定性给出了几乎相同的估计，(b) 对于这个参数较少的简单模型，参数不确定性导致的模拟水流中的不确定性不如来自其他来源的不确定性重要。
The aim of this study was to estimate the uncertainties in the streamflow simulated by rainfall-runoff model. Two sources of uncertainties in hydrological modelling were considered: the uncertainties in model parameters and those in model structure. The uncertainties were calculated by Bayesian statistics, and the Metropolis-Hastings algorithm was used to simulate the posterior parameter distribution. The parameter uncertainty calculated by the Metropolis-Hastings algorithm was compared to maximum likelihood estimates which assume that both the parameters and model residuals are normally distributed. The study was performed using the model WASMOD on 25 basins in central Sweden. Confidence intervals in the simulated discharge due to the parameter uncertainty and the total uncertainty were calculated. The results indicate that (a) the Metropolis-Hastings algorithm and the maximum likelihood method give almost identical estimates concerning the parameter uncertainty, and (b) the uncertainties in the simulated streamflow due to the parameter uncertainty are less important than uncertainties originating from other sources for this simple model with fewer parameters.