Navigating multidimensional household recoveries following the 2015 Nepal earthquakes

Abstract

Abstract Natural disaster recovery is multidimensional and takes time depending on vulnerabilities. Change occurs as households embedded within integrated social and environmental systems adapt or transform. We focus on the April/May 2015 Nepal earthquakes to understand rural natural disaster recovery. We conducted household surveys on critical earthquake impacts and recovery trajectories with 400 randomly selected households in four clusters of settlements in two districts with catastrophic impacts to all houses and infrastructure. To track rapid change in the short-term, we completed surveys at two intervals—approximately 9 months and 1.5 years. Using non-metric multidimensional scaling (NMDS) ordination, our analysis explores relationships among critical recovery indicators, households, and clusters of settlements. Disaster recovery for these rural mountain households and settlements was spatially and culturally heterogenous, context specific, and changing over time, for better and worse. First, households dependent on place-based agropastoral livelihoods had more challenges recovering compared to households with more diverse market-based livelihoods. Second, the experiences of households in displacement camps were distinct from non-displaced households. Third, accessibility was a determining factor in recovery but not consistently. Fourth, households in the planned dam inundation zone were stagnant waiting for relocation. We presented results to research participants and stakeholders 2.5 years after the earthquakes in a series of research return workshops, which linked the results of our quantitative analysis with study participant experiences and perspectives. Our research contributes to the disaster and development aid literature in four ways by: 1) providing a unique dataset with a random sample over two time intervals collected immediately following a natural disaster; 2) offering a methodology that documents and analyzes recovery as a multidimensional phenomenon; 3) empirically illustrating linear and non-linear disaster recovery dynamics; and 4) capturing the complexity of variation at the household and settlement levels while also identifying patterns that resonate on the ground.