Bayesian updating of flood inundation likelihoods conditioned on flood extent data
Bayesian updating of flood inundation likelihoods conditioned on flood extent data
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DOI：
10.1002/hyp.1499
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发表时间：
2004-12-15
影响因子：
3.2
通讯作者：
Beven, K
中科院分区：
文献类型：
--
作者：
Bates, PD;Horritt, MS;Beven, K
以前，我们已经详细介绍了广义似然不确定性估计（GLUE）程序的应用，以估计洪水淹没图中包含的二进制模式数据为条件的模型中的空间分布不确定性。将该方法应用于两个站点，其中有一个单一的一致的淹没范围天气图像，以测试该方法的模拟性能。在本文中，我们将此扩展到检查该方法的预测性能的河流塞文河，英格兰中西部。独特的是，通过星载合成孔径雷达获得了两次大洪水的一致淹没图像，以及使用激光测高技术获得的高分辨率数字高程模型。因此，这些数据允许对以前的GLUE应用程序进行严格的拆分样本测试。为了实现这一点，在GLUE框架内对应用于每次洪水事件的典型水力模型进行了参数不确定性的蒙特卡罗分析。然后，在每个分析中确定的最佳10%的参数集被用于绘制洪水范围预测中的不确定性，使用先前为独立验证数据集和设计洪水提出的方法。最后，研究了将来自每个蒙特卡罗集合的可能性信息组合在一起的方法，以确定这是否有可能减少设计洪水的洪水风险的空间分布测量的不确定性。结果表明，对于这一河段和这些事件，先前建立的方法能够产生明确的洪水风险图，并与观测到的淹没范围相比较。更一般地说，我们表明，即使是单一的、质量差的淹没程度图像，在约束水力模型校准方面也是有用的，而且在模拟的两个事件之间，有效摩擦参数的值大致是平稳的，这很可能反映了它们相似的水力特性。版权所有：John Wiley Sons， Ltd. 2004
Previously we have detailed an application of the generalized likelihood uncertainty estimation (GLUE) procedure to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. This method was applied to two sites where a single consistent synoptic image of inundation extent was available to test the simulation performance of the method. In this paper, we extend this to examine the predictive performance of the method for a reach of the River Severn, west-central England. Uniquely for this reach, consistent inundation images of two major floods have been acquired from spaceborne synthetic aperture radars, as well as a high-resolution digital elevation model derived using laser altimetry. These data thus allow rigorous split sample testing of the previous GLUE application. To achieve this, Monte Carlo analyses of parameter uncertainty within the GLUE framework are conducted for a typical hydraulic model applied to each flood event. The best 10% of parameter sets identified in each analysis are then used to map uncertainty in flood extent predictions using the method previously proposed for both an independent validation data set and a design flood. Finally, methods for combining the likelihood information derived from each Monte Carlo ensemble are examined to determine whether this has the potential to reduce uncertainty in spatially distributed measures of flood risk for a design flood. The results show that for this reach and these events, the method previously established is able to produce sharply defined flood risk maps that compare well with observed inundation extent. More generally, we show that even single, poor-quality inundation extent images are useful in constraining hydraulic model calibrations and that values of effective friction parameters are broadly stationary between the two events simulated, most probably reflecting their similar hydraulics. Copyright (C) 2004 John Wiley Sons, Ltd.