Bayesian calibration of a flood inundation model using spatial data
Bayesian calibration of a flood inundation model using spatial data
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DOI：
10.1029/2009wr008541
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发表时间：
2011-05-21
影响因子：
5.4
通讯作者：
Hankin, Robin K. S.
中科院分区：
文献类型：
--
作者：
Hall, Jim W.;Manning, Lucy J.;Hankin, Robin K. S.
贝叶斯模型校准理论提供了一个连贯的框架来区分和编码洪水概率预测中的多个不确定性来源。本文演示了使用贝叶斯方法进行计算机模型校准，校准数据的形式是洪水范围的空间观测。贝叶斯过程涉及生成洪水模型校准参数和观测误差的后验分布，以及高斯模型不足函数，该函数表示最佳模型预测与现实之间的差异。该方法首先说明了一个简单的教学的例子，然后应用到洪水模型的一段泰晤士河在英国。对于给定严重程度的洪水，生成洪水的预测空间分布。
Bayesian theory of model calibration provides a coherent framework for distinguishing and encoding multiple sources of uncertainty in probabilistic predictions of flooding. This paper demonstrates the use of a Bayesian approach to computer model calibration, where the calibration data are in the form of spatial observations of flood extent. The Bayesian procedure involves generating posterior distributions of the flood model calibration parameters and observation error, as well as a Gaussian model inadequacy function, which represents the discrepancy between the best model predictions and reality. The approach is first illustrated with a simple didactic example and is then applied to a flood model of a reach of the river Thames in the UK. A predictive spatial distribution of flooding is generated for a flood of given severity.