A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps.
A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps.
复制标题
将气候变化和无计划的城市扩张纳入滑坡敏感性地图的机械方法。
DOI：
10.1016/j.scitotenv.2022.159412
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Bozzolan E
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文献类型：
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作者：
Bozzolan E
经验证据表明，气候、森林砍伐和非正规住房（即快速增长的发展中国家典型的不受管制的建筑做法）可增加滑坡的发生。然而，这些环境变化没有被认为是共同的，并在区域或国家的滑坡敏感性评估的动态方式。这一差距可能是由于缺乏能够以计算效率高的方式代表大面积（> 100平方公里）的模型，同时考虑降雨渗透、植被和住房的影响。因此，我们提出了一种新的方法，使用一个山坡规模的机械模型，以产生区域的敏感性地图下不断变化的气候和非正式的城市化，这也占现有的不确定性。在加勒比地区的应用表明，用新方法估计的滑坡易感性与过去的飓风密集型飓风相关联，确定了该事件后观察到的滑坡的约67.5%。我们随后证明，假设的非正式住房（包括森林砍伐）的扩张增加了山体滑坡的易感性（+20%），而不是由于气候变化而加剧的暴雨（+6%）。然而，它们的综合作用导致的滑坡发生率（高达+40%）比单独考虑这两个驱动因素时要高得多。结果表明，包括土地覆盖和气候变化的滑坡敏感性评估的重要性。此外，通过对城市增长和气候变化之间被忽视的动态进行机械建模，我们的方法可以提供主要滑坡驱动因素的定量信息（例如，量化森林砍伐与非正式城市化的相对影响）以及这些驱动因素对边坡稳定性最不利或可能最不利的位置。在缺乏数据的发展中国家，这类信息往往缺失，但对于支持国家长期环境规划、确定财政努力的目标以及促进国家或国际对滑坡减缓的投资而言，这类信息至关重要。
Empirical evidence shows that climate, deforestation and informal housing (i.e. unregulated construction practices typical of fast-growing developing countries) can increase landslide occurrence. However, these environmental changes have not been considered jointly and in a dynamic way in regional or national landslide susceptibility assessments. This gap might be due to a lack of models that can represent large areas (>100km2) in a computationally efficient way, while simultaneously considering the effect of rainfall infiltration, vegetation and housing. We therefore suggest a new method that uses a hillslope-scale mechanistic model to generate regional susceptibility maps under changing climate and informal urbanisation, which also accounts for existing uncertainties. An application in the Caribbean shows that the landslide susceptibility estimated with the new method and associated with a past rainfall-intensive hurricane identifies ~67.5 % of the landslides observed after that event. We subsequently demonstrate that the hypothetical expansion of informal housing (including deforestation) increases landslide susceptibility more (+20 %) than intensified rainstorms due to climate change (+6 %). However, their combined effect leads to a much greater landslide occurrence (up to +40 %) than if the two drivers were considered independently. Results demonstrate the importance of including both land cover and climate change in landslide susceptibility assessments. Furthermore, by modelling mechanistically the overlooked dynamics between urban growth and climate change, our methodology can provide quantitative information of the main landslide drivers (e.g. quantifying the relative impact of deforestation vs informal urbanisation) and locations where these drivers are or might become most detrimental for slope stability. Such information is often missing in data-scarce developing countries but is key for supporting national long-term environmental planning, for targeting financial efforts, as well as for fostering national or international investments for landslide mitigation.