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基于 FFPI 的 Flash Flood Vulnerability Mapping 及 GIS 空间分析案例研究:罗马尼亚雷亚谷流域。

Flash Flood Vulnerability Mapping Based on FFPI Using GIS Spatial Analysis Case Study: Valea Rea Catchment Area, Romania.

机构信息

Faculty of Geography, Babes-Bolyai University, 400006 Cluj-Napoca, Romania.

Cluj-Napoca Subsidiary Geography Section, Romanian Academy, 400015 Cluj-Napoca, Romania.

出版信息

Sensors (Basel). 2022 May 7;22(9):3573. doi: 10.3390/s22093573.

Abstract

The risk associated with extreme hydrological processes (flash floods, floods) is more present than ever, taking into account the global climatic changes, the expansion of inhabited areas and the changes emerging as a result of inadequate land management. Of all the hydrological risks, slope flash floods represent the processes that have the highest impact because of the high speed of their development and their place of origin, which makes them difficult to predict. This study is performed in an area susceptible to the emergence of slope flash floods, the Valea Rea catchment area, spatially located in Northwest Romania, and exposed to western circulation, which favours the development of such processes. The entire research is based on a methodology involving the integration of spatial databases, which indicate the vulnerability of the territory in the form of a weighted average equation to highlight the major impact of the most relevant factor. A number of 15 factors have been used in raster spatial databases, obtained by conversion (land use, soil type, lithology, Hydrologic Soil Group, etc.), derived from the digital elevation model (slope, aspect, TWI, etc.) or by performing spatial analysis submodels (precipitation, slope length, etc). The integration of these databases by means of the spatial analysis equation based on the weighted average led to the vulnerability of the territory to FFPI, classified on five classes from very low to very high. The final result underlines the high and very high vulnerability (43%) of the analysed territory that may have a major impact on the human communities and the territorial infrastructure. The results obtained highlight the torrential nature of the analysed catchment area, identifying several hotspots of great risk, located mainly within the built-up areas of intensely inhabited regions; a fact which involves a major risk and significant potential material damage in the territory. The model was validated by directly comparing the results obtained with locations previously affected, where the flood effects have been identified, highlighting the fact that the model may be taken into account to be applied in practice, and also to be implemented in territories that share the same features.

摘要

与极端水文过程(山洪、洪水)相关的风险比以往任何时候都更加突出,考虑到全球气候变化、人口居住区域的扩张以及因土地管理不当而产生的变化。在所有水文风险中,边坡山洪暴发是影响最大的过程,因为它们的发展速度很快,且起源于难以预测的地方。本研究在一个容易发生边坡山洪暴发的地区进行,该地区位于罗马尼亚西北部的雷亚流域(Valea Rea catchment area),受西风环流影响,有利于这类过程的发展。整个研究基于一种综合空间数据库的方法,该方法以加权平均方程的形式表示了该地区的脆弱性,以突出最重要因素的主要影响。在栅格空间数据库中使用了 15 个因素,这些因素是通过转换(土地利用、土壤类型、岩性、水文土壤组等)、从数字高程模型中得出(坡度、方位、TWI 等)或通过执行空间分析子模型(降水、坡度长度等)获得的。通过基于加权平均的空间分析方程对这些数据库进行整合,得到了该地区对 FFPI 的脆弱性分类,分为五个等级,从极低到极高。最终结果强调了分析区域的高脆弱性和极高脆弱性(43%),这可能对人类社区和领土基础设施产生重大影响。研究结果突出了分析流域的暴雨性质,确定了几个风险极高的热点,主要位于人口密集地区的建成区;这一事实涉及到该地区的重大风险和重大潜在物质损失。该模型通过直接比较先前受洪水影响的地点的结果进行了验证,突出了该模型可用于实际应用的事实,也可在具有相同特征的地区实施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e23a/9101478/7d0f1aa74b64/sensors-22-03573-g001.jpg

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