Quantifying the potential benefits of risk-mitigation strategies on present and future seismic losses in Kathmandu Valley, Nepal

Abstract

Structural risk-mitigation measures have been shown to significantly reduce earthquake-induced physical damage and casualties in various regions worldwide. However, these benefits remain unknown or inadequately quantified for potential future events in some hazard-prone areas such as Kathmandu Valley, Nepal, which this article addresses. The analysis involves modeling an earthquake scenario similar to the 2015 Gorkha earthquake (moment magnitude 7.8) and using four exposure inventories representing the current (2021) urban system or near-future (2031) development trajectories that Kathmandu Valley could experience. The results predict substantial losses (€8.2 billion in repair/reconstruction costs and 89,199 fatalities) in 2021 if the building stock’s quality is assumed to have remained the same as in 2011 (Scenario A). However, a partial improvement of the building stock’s quality in the present (Scenario B) can decrease financial losses and fatalities by 17% and 44%, respectively. Moreover, under a “no change” pathway for 2031 (Scenario C), where the quality of the expanding building stock remains the same as in 2011, and the number of buildings is larger to reflect population growth, financial losses and fatalities will increase by 20% and 25% respectively over those of Scenario A. In contrast, further upgrades to the building stock’s quality by 2031 (Scenario D) would reduce financial and human losses by 14% and 54% respectively, relative to those of Scenario A. In addition, the largest financial and human losses computed in the four scenarios are consistently associated with the low- and middle-income population. The main findings of this article can be used to inform decision makers about the benefits of investing in forward-looking seismic risk-mitigation efforts.

Design and assessment of pro-poor financial soft policies for expanding cities

“…10 -two of the most expensive and strongest building types -while this percentage increases to 88% in TV50_total. Table 1 also provides the construction cost for each building type, which is adapted from Mesta et al [41]. In this case study, we assume that households within a multi-family building equally share the total repair costs of their residential building incurred in a given seismic event.…”

Quantifying the potential benefits of risk-mitigation strategies on future flood losses in Kathmandu Valley, Nepal

“…We use the four exposure scenarios A-D for Kathmandu Valley developed in Mesta et al (2022a), which are based on the National Population and Housing Census 2011 (Central Bureau of Statistics, CBS, 2012) and various assumptions on the estimated population and number of households for 2021 and 2031. Note that the government of Nepal postponed the census planned for 2021 due to the COVID-19 pandemic, so the characterization of 2021 urban development relies on 10year-old data.…”

“…Seven building typologies are included in the considered exposure scenarios: adobe (A), brick and stone masonry with mud mortar (BSM), brick and stone masonry with cement mortar (BSC), wood-frame (W), current-construction-practice reinforced concrete (RC-CCP), well-designed reinforced concrete (RC-WDS), and reinforced masonry (RM). These typologies have been previously used by Chaulagain et al (2016Chaulagain et al ( , 2015 as well as Mesta et al (2022a) to estimate seismic losses in Nepal and Kathmandu Valley. A, BSM, and BSC buildings constitute unreinforced masonry (URM) structures.…”

“…The specifications of each scenario are primarily provided in Mesta et al (2022a); any deviations in these details for this specific study are documented in Sect. 2.3.1 to 2.3.3.…”

“…Using the same reasoning, we group RC-CCP and RC-WDS under one building typology labeled RC-CCP/WDS. We determine the proportions of the building typologies per municipality based on the 2011 census data (type of outer wall, type of foundation), as described in Mesta et al (2022a). The exact height (number of stories) of each building is uncertain and is therefore randomly sampled using typology-specific empirical distributions or single values (not provided in Mesta et al, 2022a) that are derived from data collected for more than 20 000 buildings after the 2015 Gorkha earthquake by the NSET (2016).…”