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<p>Effective on-ground landslide forecasting requires a detailed understanding of risk at a granular level, which is often impeded by scarce impact data, incomplete landslide inventories, and difficulties in quantifying the exposed elements. This study presents a rapid methodology for evaluating landslide exposure at the village (Mauza) level in Darjeeling District, West Bengal, India. This approach integrates comprehensive landslide susceptibility maps with demographic and infrastructure data from the national census to identify and rank areas based on their exposure. To achieve this, separate susceptibility maps for landslide initiation and runout were developed using statistical models and simulation tools. These were then combined to delineate high-susceptibility zones. Subsequently, exposure was quantified by overlaying key social vulnerability indicators, such as population, number of households, literate population, children below six years, and working population on the composite susceptibility map. This integrated analysis enabled a systematic assessment and ranking of 197 Mauzas according to the exposure of their communities and assets. The results identified critical exposure hotspots, with Kurseong, Sukhiapokhri, Simana Basti, and Darjeeling consistently ranking among the most exposed areas. By leveraging existing and open data sources, this methodology provides a practical and efficient decision support tool for disaster managers. Village-level exposure ranking allows for targeted prioritization of resources and risk-reduction interventions in the most critical zones. This approach significantly enhances regional landslide forecasting and provides stakeholders with a reliable foundation for informed life-saving decisions, particularly in data-deficient environments.</p>

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Rapid assessment of landslide exposure to elements at risk for decision support in regional landslide forecasting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月23日

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  • Gargi Singh,
  • Sumit Kumar,
  • Rabisankar Karmakar,
  • Akshaya Kumar Mishra

摘要

Effective on-ground landslide forecasting requires a detailed understanding of risk at a granular level, which is often impeded by scarce impact data, incomplete landslide inventories, and difficulties in quantifying the exposed elements. This study presents a rapid methodology for evaluating landslide exposure at the village (Mauza) level in Darjeeling District, West Bengal, India. This approach integrates comprehensive landslide susceptibility maps with demographic and infrastructure data from the national census to identify and rank areas based on their exposure. To achieve this, separate susceptibility maps for landslide initiation and runout were developed using statistical models and simulation tools. These were then combined to delineate high-susceptibility zones. Subsequently, exposure was quantified by overlaying key social vulnerability indicators, such as population, number of households, literate population, children below six years, and working population on the composite susceptibility map. This integrated analysis enabled a systematic assessment and ranking of 197 Mauzas according to the exposure of their communities and assets. The results identified critical exposure hotspots, with Kurseong, Sukhiapokhri, Simana Basti, and Darjeeling consistently ranking among the most exposed areas. By leveraging existing and open data sources, this methodology provides a practical and efficient decision support tool for disaster managers. Village-level exposure ranking allows for targeted prioritization of resources and risk-reduction interventions in the most critical zones. This approach significantly enhances regional landslide forecasting and provides stakeholders with a reliable foundation for informed life-saving decisions, particularly in data-deficient environments.