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Tropical cyclone risk mapping for a coastal city using geospatial techniques
Journal of Coastal Conservation ( IF 1.7 ) Pub Date : 2021-01-08 , DOI: 10.1007/s11852-020-00788-y
Aysha Akter , Ahammed Dayem

Tropical cyclone associated storm surges and their disastrous influences are becoming a major concern amongst the worldwide coastal community. The detailed risk modelling study seems an urgent need to support the storm surge mitigation actions. Using a Geographic Information System (GIS) based risk model, this study attempted to obtain both present and future storm surge wave heights in the Cox’s Bazar Sadar Upazilla in Bangladesh. Linear storm surge model setup was done for the different return periods includes 5, 10, 20, 50, and 100 years. Also, to assess climate change effects, a 0.34 m sea-level rise in 2050 was tested using the regional scale surge models. The simulated storm surge model provided a time series dataset for the risk model and obtained risk maps comprised of the relationship among risk zone and the return periods. With the present inundation depth trend, the acquired risk maps of 50 and 100 year return period showed that 10.13% and 36.4% of the study area belongs to very high-risk zone respectively. Conversely, for future inundation depth conditions 30.83% and 49.9% of the study area would be within a very high-risk zone in 50 and 100 year return period respectively. This is envisaged that a detailed historical inundation dataset could enrich the adopted approach considering both present and future storm surge risk modelling for the Decision Support System (DSS). Also, this approach might be adopted for other identical coastal environments to aid mitigation programs and strategies.



中文翻译:

利用地理空间技术绘制沿海城市的热带气旋风险图

与热带气旋相关的风暴潮及其灾难性影响正成为全球沿海社区的主要关注点。详细的风险模型研究似乎迫切需要支持风暴潮缓解措施。本研究使用基于地理信息系统(GIS)的风险模型,试图获得孟加拉国Cox的Bazar Sadar Upazilla中当前和未来的风暴潮波高。针对包括5年,10年,20年,50年和100年的不同回报期进行了线性风暴潮模型设置。另外,为评估气候变化的影响,使用区域规模浪涌模型测试了2050年海平面上升0.34 m。模拟的风暴潮模型为风险模型提供了时间序列数据集,并获得了由风险区和收益期之间的关系组成的风险图。根据当前的淹没深度趋势,获得的50年和100年回归期的风险图表明,研究区域的10.13%和36.4%分别属于极高风险区。相反,对于未来的淹没深度条件,分别在50年和100年的回归期内,研究区域的30.83%和49.9%将处于高风险区域内。可以设想,详细的历史淹没数据集可以丰富决策方法,同时考虑决策支持系统(DSS)的当前和未来风暴潮风险建模。同样,可以在其他相同的沿海环境中采用这种方法,以帮助缓解计划和策略。相反,对于未来的淹没深度条件,分别在50年和100年的回归期内,研究区域的30.83%和49.9%将处于高风险区域内。可以设想,详细的历史淹没数据集可以丰富决策方法,同时考虑决策支持系统(DSS)的当前和未来风暴潮风险建模。同样,可以在其他相同的沿海环境中采用这种方法,以帮助缓解计划和策略。相反,对于未来的淹没深度条件,分别在50年和100年的回归期内,研究区域的30.83%和49.9%将处于高风险区域内。可以设想,详细的历史淹没数据集可以丰富决策方法,同时考虑决策支持系统(DSS)的当前和未来风暴潮风险建模。同样,可以在其他相同的沿海环境中采用这种方法,以帮助缓解计划和策略。

更新日期:2021-01-08
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