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Analyzing the sensitivity of a flood risk assessment model towards its input data

机译：分析洪水风险评估模型对其输入数据的敏感性

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摘要

The Small Island Developing States are characterized by an unstable economy and low-lying, densely populated cities, resulting in a high vulnerability to natural hazards. Flooding affects more people than any other hazard. To limit the consequences of these hazards, adequate risk assessments are indispensable. Satisfactory input data for these assessments are hard to acquire, especially in developing countries. Therefore, in this study, a methodology was developed and evaluated to test the sensitivity of a flood model towards its input data in order to determine a minimum set of indispensable data. In a first step, a flood damage assessment model was created for the case study of Annotto Bay, Jamaica. This model generates a damage map for the region based on the flood extent map of the 2001 inundations caused by Tropical Storm Michelle. Three damages were taken into account: building, road and crop damage. Twelve scenarios were generated, each with a different combination of input data, testing one of the three damage calculations for its sensitivity. One main conclusion was that population density, in combination with an average number of people per household, is a good parameter in determining the building damage when exact building locations are unknown. Furthermore, the importance of roads for an accurate visual result was demonstrated.
机译：小岛屿发展中国家的特点是经济不稳定，地势低洼，人口稠密的城市，因此很容易受到自然灾害的伤害。洪水对人们的影响比任何其他危害都多。为了限制这些危害的后果，必须进行充分的风险评估。这些评估难以获得令人满意的输入数据，尤其是在发展中国家。因此，在这项研究中，开发并评估了一种方法，以测试洪水模型对其输入数据的敏感性，以便确定必不可少的最少数据集。第一步，针对牙买加安诺托湾的案例研究创建了洪水破坏评估模型。该模型基于2001年热带风暴米歇尔（Michelle）造成的洪水泛滥程度图，为该区域生成了一个破坏图。考虑了三种损坏：建筑物，道路和农作物的损坏。生成了十二种情况，每种情况都有不同的输入数据组合，并测试了三种损害计算之一的敏感性。一个主要结论是，人口密度与每个家庭的平均人数相结合，是确定建筑物确切位置未知时确定建筑物损坏的好参数。此外，证明了道路对于获得精确视觉效果的重要性。

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