Bottom-up multilevel flood hazard mapping by integrated inundation modelling in data scarce cities
Bottom-up multilevel flood hazard mapping by integrated inundation modelling in data scarce cities
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DOI：
10.1016/j.jhydrol.2023.129114
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发表时间：
2023-02
影响因子：
6.4
通讯作者：
M. Guan;Kaihua Guo;Haochen Yan;N. Wright
中科院分区：
文献类型：
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作者：
M. Guan;Kaihua Guo;Haochen Yan;N. Wright
随着城市化和气候变化导致的城市洪水风险的增加，洪水风险预测对于洪水风险管理和应急响应变得越来越重要。然而，当缺乏足够的高质量数据时，标准洪水淹没模型方法具有很大的不确定性。因此，本研究开发了一种自下而上的多层次(网格-公里-区)的城市洪水风险制图方法，建立在基于网格的洪水模拟与来自开放来源的数据获取的基础上。这减少了数据稀缺和质量对危险建模的不利影响。在本文中，我们首先提出了一种集成的方法来绘制城市流域的网格化洪水图，该方法使用了一个由大众来源的社交媒体数据支持的水动力学模型。然后，将该模型应用于2020年8月的成都洪涝灾害，阐述了输入数据偏差和参数不确定性对城市洪水模型的影响。结果表明，地形数据的选择和城市排水流量的量化对城市淹没范围、淹没深度和淹没持续时间有显著影响。这表明，在仅仅依靠单一尺度洪水水动力学模型进行城市洪水淹没测绘时，即使有群众来源的数据支持，也有可能出现较大的变化。本文提出的多层次风险映射方法提供了多层次和全面的淹没映射，从而减少了数据偏差或可用性的影响，较粗的风险映射对数据输入质量和模型不确定性的敏感度较低，表明在这个较高的空间尺度上可靠性相对较高。网格-公里-区域三级方法提供了更可靠的洪水风险图，可以支持数据稀缺城市的暴雨洪水管理。
With increasing urban flood risk due to urbanisation and climate change, flood hazard prediction is ever more crucial for flood risk management and emergency response. However, when sufficient high-quality data are lacking, a standard flood inundation modelling approach has significant uncertainties. Therefore, this study develops a bottom-up approach for urban flood hazard mapping at multiple levels (grid-kilometre-district), built upon the integration of grid-based flood modelling with data acquisition from open sources. This reduces the adverse effects of data scarcity and quality on hazard modelling. In the paper, we first set out an integrated approach for gridded inundation mapping in an urban basin using a hydrodynamic model supported by crowd-sourced social media data. Then, applying the model to flooding in Chengdu in August 2020, we articulate how input data bias and parameter uncertainty both affect urban inundation modelling. The results show that the choice of terrain data and the quantification of urban drainage flows significantly influence the modelled urban inundation extent, depth and duration. This indicates the potential for large variation when using urban inundation mapping merely relying on a single-scale flood hydrodynamic modelling even when supported by crowd-sourced data. The multilevel hazard mapping approach developed here presents multi-layered and comprehensive inundation mapping; thus the effects of data bias or availability are reduced and the coarser hazard mapping shows less sensitivity to the data input quality and model uncertainty, indicating relatively higher reliability at this higher spatial scale. The grid-kilometre-district three level approach provides more reliable flood hazard mapping, which can support rainstorm-induced flood management in data scarce cities.