Flood risk in Kuala Lumpur, Malaysia: A consideration of flood defences in a broadscale hydraulic model
Flood risk in Kuala Lumpur, Malaysia: A consideration of flood defences in a broadscale hydraulic model
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马来西亚吉隆坡的洪水风险：大尺度水力模型中防洪的考虑
DOI：
10.1111/jfr3.12907
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发表时间：
2023
影响因子：
4.1
通讯作者：
Massam A
中科院分区：
文献类型：
--
作者：
Massam A
一种在城市环境中考虑本地规模防御基础设施的新方法，加上大规模的水力模型框架，应用于马来西亚首都吉隆坡。大规模的水力建模框架通常能够采用更复杂的模型，但通常仅限于同质决策，以确保跨大区域的标准化输出。相反，小规模的水力建模框架倾向于更好地整合局部尺度的特征，但如果要扩展到区域视图之外，计算成本可能会很高。通过结合防御系统对大规模水力模型框架进行改进，可以更准确地表示河流洪水的风险。这项研究纳入了吉隆坡的防御措施，使我们对河流洪水范围的估计减少了约40%。本研究的结果通过一组高质量的观测结果进行了验证，证明了该模型框架能够在该市95%以上的已知洪水风险区捕获洪水风险。在未来的模型构建中，利用数据驱动的决策和水力模型中的现有功能整合国防基础设施可以实现自动化。这种新方法弥合了局部尺度模型框架与大规模、同质2D水力建模研究之间的差距。
A novel approach to consider local‐scale defence infrastructure in an urban environment, coupled with a broadscale hydraulic model framework, is applied to the capital city of Kuala Lumpur, Malaysia. Broadscale hydraulic modelling frameworks are often able to employ more complex models, but are typically limited to homogenous decision‐making to ensure standardised outputs across large regions. Conversely, small‐scale hydraulic modelling frameworks tend to better integrate local‐scale features but can be computationally expensive to scale up beyond a regional view. Improvements to the broadscale hydraulic model framework through the incorporation of defence systems yield a more accurate representation of fluvial flood risk. This study incorporates defences in Kuala Lumpur, yielding a reduction in our estimates of fluvial flood extent by around 40%. The results of this study are validated against a set of high‐quality observations, demonstrating the capability of the model framework in capturing flood risk in more than 95% of known flood risk zones in the city. Incorporating defence infrastructure using data‐driven decision making and existing functionality in the hydraulic model could be automated in future model builds. This new approach bridges the gap between local‐scale model frameworks and the broadscale, homogenous 2D hydraulic modelling studies.
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DOI：
10.1111/1752-1688.12717
发表时间：
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期刊：
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DOI：
--
发表时间：
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期刊：
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DOI：
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发表时间：
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