Post-Disaster Damage Assessments as Catalysts for Recovery: A Look at Assessments Conducted in the Wake of the 2015 Gorkha, Nepal, Earthquake

Abstract

In the wake of large earthquake disasters, governments, international agencies, and large nongovernmental organizations scramble to conduct impact and damage assessments that help them understand the nature and scale of the emergency in order to orchestrate a complex series of emergency, response, and recovery activities. Using the Gorkha earthquake as a case study, this research seeks to provide greater clarity into the types of post-disaster damage assessments, their purposes, and their potential as catalysts for critical recovery activities. We argue that damage assessment methodologies need to be tailored to the diverse information needs in post-disaster contexts, which vary by user group and change over time. This research builds upon the authors’ direct experience supporting the government of Nepal in the Post-Disaster Needs Assessment (PDNA) process, support with the rapid visual inspections conducted by the National Engineering Association, and interviews with humanitarian organizations who conducted damage assessment in Nepal.

Framework for improving the resilience and recovery of transportation networks under geohazard risks

“…The 2015 Gorkha earthquake in the central part of that country caused extensive damage and hundreds of fatalities, and also triggered a large number of landslides, specifically in rural areas, where it had ramifications on their transportation networks [12]. Extensive effort was put forth for post-earthquake damage assessment and recovery, which illustrated the need for decision-making processes and early planning in Nepal [27]. Hence, it provided a suitable area for testing our new methodology.…”

Evaluating the INLA-SPDE approach for Bayesian modeling of earthquake damages from geolocated cluster data

“…In the hours to weeks following major earthquake disasters, detailed information on the spatial variability, extent, and severity of damages is often sparse. This lack of consistent and verifiable post-disaster information poses major challenges for rapid emergency response efforts (Comfort, Ko, and Zagorecki, 2005;Goodchild and Glennon, 2010;Lallemant, Soden, et al, 2017). Until field surveyors can be mobilized at scale, disaster response decisions are informed by coarse modeled damage estimates, scattered eye-witness reports, and any remotely-sensed damage assessments that might be available (Goodchild and Glennon, 2010;Xie et al, 2016;Lallemant, Soden, et al, 2017;Monfort, Negulescu, and Belvaux, 2019).…”

“…This lack of consistent and verifiable post-disaster information poses major challenges for rapid emergency response efforts (Comfort, Ko, and Zagorecki, 2005;Goodchild and Glennon, 2010;Lallemant, Soden, et al, 2017). Until field surveyors can be mobilized at scale, disaster response decisions are informed by coarse modeled damage estimates, scattered eye-witness reports, and any remotely-sensed damage assessments that might be available (Goodchild and Glennon, 2010;Xie et al, 2016;Lallemant, Soden, et al, 2017;Monfort, Negulescu, and Belvaux, 2019). Understanding how to best leverage and synthesizing these diverse sets of impact data is an active area of research, highlighted by work like Loos et al (2020) which proposes a spatial integration framework for combining modeled damage estimates, field surveys, remotesensing proxies, and auxiliary shaking estimates into a single estimate of damage.…”

“…These relationships can be derived empirically, semi-empirically, or analytically depending on the availability of relevant local seismological studies (Jaiswal et al, 2011;Porter, 2014). While modeled areal damage estimates are successfully applied for rapid order-of-magnitude impact estimates in systems like PAGER, their accuracy at high spatial resolutions is strongly dependent on the quality of input data (Erdik et al, 2011;Jaiswal et al, 2011;Lallemant, Soden, et al, 2017). On the other hand, field-based engineering assessments provide detailed ground-truthed damage data, but are relatively sparse in the weeks following a major earthquake.…”

“…As a result, the damage statistics that inform requests for disaster aid are often based on modeled estimates. The timeline for delivery of a PDNA to funding stakeholders (approximately a month) is simply too short to fully survey the damages (Lallemant, Soden, et al, 2017).…”