Application of geospatial technologies in developing a dynamic landslide early warning system in a humanitarian context: the Rohingya refugee crisis in Cox’s Bazar, Bangladesh

Since August 2017, more than 744,400 stateless Rohingya refugees – an ethnic Muslim minority group from the Rakhine State – have entered Bangladesh to escape serious crimes against humanity conducted by the Myanmar Army. Extensive level of deforestation and hill cutting activities took place in Cox’s Bazar District (CBD) in Bangladesh to accommodate them. The refugee camps are sitting on hills and loose soil and are highly vulnerable to rainfall-triggered landslides. Notably in June 2017, landslides in the same region killed at least 160 people. From this perspective, the study aims to develop a localised landslide early warning system (EWS) for the Rohingya refugees and their host communities in CBD. A novel method, combining landslide inventory and susceptibility maps, rainfall thresholds and dynamic web-based alert system, has been introduced to develop the landslide early warning system (EWS) by applying advanced geoinformation techniques. Results suggest that approximately 5,800 hectares of forest land cover disappeared due to the 2017 Rohingya influx. Land cover changes through hill cutting andslope modifications, and unplanned urbanisation are predominantly responsible for slope failures and consecutive 5-day periods of rainfall between 95–220 mm could initiate landslides in high susceptible areas. The EWS would support the local authorities and international organisations in reducing disaster risks and saving lives from landslides in a humanitarian context.

自2017年8月起，超过74.44万名无国籍罗兴亚难民（Rohingya refugees）——来自若开邦的穆斯林少数民族群体——为躲避缅甸军方实施的严重反人类罪行，进入孟加拉国境内。为安置这批难民，孟加拉国科克斯巴扎尔县（Cox’s Bazar District, CBD）境内开展了大规模森林砍伐与山地开挖活动。这些难民营选址于山坡与松散土层之上，极易遭受降雨引发的滑坡灾害侵袭。值得注意的是，2017年6月该区域曾发生滑坡灾害，造成至少160人遇难。
基于此背景，本研究旨在为科克斯巴扎尔县的罗兴亚难民及其收容社区开发一套本地化滑坡早期预警系统（Landslide Early Warning System, EWS）。本研究运用先进地理信息技术（geoinformation techniques），结合滑坡编目（landslide inventory）、滑坡敏感性图（landslide susceptibility maps）、降雨阈值（rainfall thresholds）与动态网络警报系统，提出了一套构建滑坡早期预警系统的创新方法。
研究结果显示，受2017年罗兴亚难民涌入的影响，该区域约5800公顷的森林植被覆被消失。山地开挖、边坡改造以及无规划城市化引发的土地覆被变化，是边坡失稳的主要诱因；当连续5天降雨量达95~220毫米时，高敏感性区域可能触发滑坡灾害。该滑坡早期预警系统可协助当地政府与国际组织在人道主义场景下降低灾害风险，挽救滑坡灾害中的生命。