High-resolution spatial assessment of population vulnerability to climate change in Nepal

Current, spatially explicit, and high-resolution assessments of population vulnerability to climate change and variability in developing countries can be difficult to create due to lack of data or financial and technical capacity constraints. We propose a comparative, multiple-approach framework to assess the spatial variation of population vulnerability to climatic changes using several high-resolution variables related to climate, topography, and socioeconomic conditions with an objective to detect the spatial variability of climate vulnerability in Nepal. Nepal is one of the most vulnerable countries to the effects of climate change due to frequent climatic hazards and poor socio-economic capacity. We used a climate vulnerability index (CVI) approach to derive climate vulnerability maps at the one-kilometer resolution and test an additive and a principal components-based composite method of data aggregation. In this work, we attempt to answer three questions. 1) How do different methods of assessment inform the spatial variation of the climate vulnerability in Nepal? 2) How do different variables interact to shape climate vulnerability in Nepal? 3) What proportions of the population in Nepal are vulnerable to climatic disasters and why? Our analysis uncovered significant spatial variations in population vulnerability to climate change across Nepal, with the highest vulnerability being experienced by the High Mountain region followed by the regions in the lower elevations. We find that although the lack of adaptive capacity is the biggest cause of population vulnerability to climate change in Nepal, a resilient community is shaped by both biophysical and socioeconomic characteristics. By performing an iterative sensitivity analysis of our thirteen variables both at the aggregate level (nationally) as well as at the more disaggregated (physiographic region) level, we contribute to identifying important, multi-scalar driving factors for vulnerability that can be employed as leverage points for lowering vulnerability at different scales. After performing analyses at multiple regions, we conclude that region-specific variable selection is needed for more detailed assessments and in order to prioritize adaptation strategies at scales that go beyond the hierarchy of administrative divisions. (C) 2017 Elsevier Ltd. All rights reserved.