Climate change and socio-economic transitions are intensifying disaster risks, calling for integrated, cross-disciplinary mitigation strategies. The complex dynamics of disaster vulnerability pose challenges for risk management, particularly in resource-constrained countries. Existing assessments often focus on composite vulnerability scores and obscure the multifaceted dynamics of vulnerability and its components, limiting their relevance for risk management. Addressing this gap, our study analyses spatio-temporal changes in social vulnerability and its components – sensitivity and adaptive capacity – across Nepal by applying Principal Component Analysis to municipal census data from 2011 to 2021. We (i) map vulnerability into five classes for both years, (ii) quantify decadal changes in social vulnerability and its components, and (iii) identify distinct social vulnerability transition pathways. National social vulnerability declined by 35% by 2021, as a 63% adaptive capacity gain outweighed a 28% rise in sensitivity, with spatially heterogeneous patterns. We reveal notable structural changes in dominant indicators: Sensitivity reflects persistent demographic pressures alongside growing influences of urbanization and population mobility. Adaptive capacity reflects stable contributions from critical services and asset ownership, augmented by improvements in housing quality and connectivity. Furthermore, by investigating the interaction between the shifts in sensitivity and adaptive capacity, we identify three social vulnerability transition pathways (escalating sensitivity, adaptive balance, and resilience transition) that yield actionable and spatially explicit insights for prioritising disaster risk reduction. Our refined component-focused approach offers nuanced insights beyond conventional social vulnerability assessments, is transferable to any country with census data, and is valuable in data-scarce settings.

Community flood resilience is defined as a multi-dimensional capacity, shaped by the dynamic interplay of social, human, financial, physical and natural capitals. Yet, previous studies and existing adaptation strategies have predominantly addressed these dimensions in isolation, neglecting the analysis of their interactions. This study contributes to the development of integrated, systems-based adaptation strategies by examining how interactions among capitals shape resilience outcomes. Drawing on Flood Resilience Measurement for Communities (FRMC) data from 293 flood-prone communities from 19 developing countries globally, this study adopts a mixed-methods design combining network analysis of covariation between capitals with a qualitative thematic analysis of stakeholder-perceived causal mechanisms linking those capitals. Our findings reveal that resilience is not simply the sum of independent capitals, but an emergent property of their interactions. Both quantitative and qualitative analysis show dense connections among capitals, with social capital found to play a central role by enabling coordination, resource mobilization, and knowledge exchange. Financial and human capitals were also found to act as foundational enablers, particularly in supporting the development and effective use of physical and natural assets. Feedback loops among capitals highlight the importance of mutually reinforcing dynamics in building adaptive capacity. These findings underscore the need for disaster risk reduction and climate adaptation strategies to move beyond siloed interventions. We advocate for integrated funding and policy frameworks that target coupled capitals, such as pairing infrastructure development with social financial capacity-building, to leverage synergies across sectors.

A significant challenge of resilience measurement lies in taking a complex, multi-dimensional concept and operationalizing it in a concrete and measurable way. The next generation FRMC (Flood Resilience Measurement for Communities) framework and tool is providing such a measurement within a standardized approach (e.g. not dependent on the location it is applied to), and which therefore can be used across the globe. It is based on the Sustainable Livelihood Framework and includes 44 indicators called ‘sources of resilience’ that are distributed across and represent critical aspects of five complementary ‘capitals’ (5C). The sources are selected for the roles they play in helping people on their development path and/or providing capacity to withstand and respond to shocks. We present the dynamics of these resilience indicators over time based on a large-scale empirical assessment of communities across the globe that are exposed to flood risks. In more detail, resilience indicators are measured for a baseline as well as endline period (over 2-4 years) and in case of flood hazard events, a post-event analysis was performed to identify corresponding damages. The baseline survey involved 325 communities across 22 developing countries, with data collected from over 19,000 households as well as focus groups, key informants, and secondary sources. This survey represented a total community population of more than 1 million people and generated over 2.5 million data points from 14,300 graded sources. The endline survey engaged 280 communities of the 325 in 19 developing countries. Post-event surveys were conducted in 66 communities across 7 developing countries that have experienced a flood event. Lastly, an interventions survey analysed in which communities’ interventions were implemented. Based on a Confirmatory Factor analysis, Structural Equation modelling as well as a Boosted Regression Tree approach we found important differences in the dynamics of resilience over time which are not only dependent if hazard events have realized but also in regard to the resilience levels communities are starting from during the baseline period. Our empirical findings should therefore provide a better understanding about actual resilience trajectories that can take place and the important dimensions that may influence them over time.

Flooding is a major global natural hazard, with resulting disasters disproportionately affecting communities in developing countries. Enhancing community resilience is crucial for reducing flood risk, managing impacts and ultimately protecting sustainable development gains. Yet, there is little validated empirical evidence, particularly at the community scale, of the relationship between resilience characteristics before a natural hazard-event occurs and realized resilience after it. We present real-world testing of how a community’s pre-flood resilience capacities influence post-flood outcomes, using actual flood events from 66 communities in seven developing countries across the world. In doing so, we applied the Flood Resilience Measurement for Communities (FRMC) approach, a validated framework and associated tool that dynamically assesses pre-flood resilience across multiple capitals to support the design of interventions for enhancing community disaster resilience. We specifically address the question how baseline community resilience, measured by 44 indicators called ‘sources of resilience’ influences flood impacts and post-flood outcomes that are measured across six themes (assets, livelihoods, life and health, lifelines, governance, and social norms). We observed that higher levels of natural, physical, and financial capital are associated with better post-event community outcomes and reduced flood impacts, such as the prevention of fatalities and serious injuries, the protection of public and private buildings and land, and livelihood stability. Importantly, in most cases, multiple sources of resilience worked together to influence a single outcome, highlighting the multidimensional nature of disaster resilience. Hence, our results emphasize the need for a multi-faceted and dynamic approach to building community flood resilience.

Practice and policy have emphasized the need for building resilience to climate-related events in a further warming world. Scholarship has studied resilience largely in terms of process, latent capacity informing vulnerability, or the outcome of risk management interventions, with little work integrating these perspectives. Implementation science work by the Climate Resilience Alliance has developed the Flood Resilience Measurement for Communities (FRMC) process and tool to measure resilience as an outcome (post-flood mortality and morbidity reduction) and as capacity (pre- and post-intervention levels). This article builds on FRMC analytics to investigate the effect of resilience capacity, represented by five forms of capital (5Cs) and five stages of the disaster risk management (DRM) cycle, on injury and mortality outcomes across 66 flood-affected communities in seven Global South countries. Data were collected using household surveys, community focus groups, key informant interviews, and secondary sources. We applied a quasi-experimental regression design, controlling for demographic and flood hazard/exposure variables, to estimate the effect of 5Cs and DRM stages on health outcomes. Results show that social and human capital help reduce injuries after floods, and preparedness lowers both deaths and injuries. Some results were unexpected, such as the positive association between natural capital and delayed deaths, where limited gains in natural capital may not yield meaningful protection in communities with degraded ecosystems. This study finds that preparedness is the most consistent predictor of positive health outcomes, while forms of 5Cs may not translate into reduced mortality. By combining 5Cs, DRM stages, and health indicators, this paper contributes to bridging a gap in the literature and offers policy-relevant insights for improving community-level disaster response.

Understanding and strengthening community-level resilience to natural hazard-induced disasters is critical for the management of adverse impacts of such events and the growth of community well-being. A key gap in achieving this is limited standardized and validated disaster resilience measurement frameworks that operate at local levels and are universally applicable. The Flood Resilience Measurement for Communities (FRMC) is a foremost tool for community flood resilience assessment. It follows a structured approach to comprehensively assess community flood resilience across five classes of capacities (capitals) to support strategic investment in resilience strengthening initiatives. The FRMC is a further development of an earlier version (the FRMT, the Flood Resilience Measurement Tool). The FRMT has been developed and applied between 2015 and 2017 in 118 flood prone communities across nine countries. It has been validated in terms of content and face validity as well as in terms of reliability. To reduce redundancy and survey effort, the FRMC holds a lesser number of indicators (44 versus 88) and has now been applied in over 320 communities across 20 countries. We examine the validation for the revised resilience construct and the new community applications and present a comprehensive overview of the statistical and user validation process and outcomes in both practical and scientific terms. The results confirm the validity, reliability as well as usefulness of the FRMC framework and tool. Furthermore, our approach and results provide insights for other resilience measurement approaches and their validation efforts. We also present a comprehensive discussion about the dynamic aspects of flood resilience at community level, and the many validation aspects that need to be incorporated both in terms of quantification efforts as well as usability on the ground.

Enhancing resilience and reducing disaster risks are major societal challenges. Hence, it is crucial to understand resilience at the community level, as the impact of disasters and the potential for resilient development are particularly high at this scale. Key for understanding community resilience is the systematic assessment and measurement of resilience. The Flood Resilience Measurement for Communities (FRMC) framework offers a holistic approach to measure community flood resilience, facilitating the identification of interventions to strengthen resilience. This paper discusses empirically measured flood resilience data from 292 communities across 20 developing countries worldwide using the FRMC framework. Furthermore, it will provide examples of how such data can serve as a valuable resource for understanding the typology of community flood resilience across the world, and for identifying targeted interventions that address the unique challenges faced by different communities.

Reducing disaster risk and enhancing resilience are major global societal challenges. To inform this challenge, understanding resilience at the community level is especially important because the impact of disasters and the potential for resilient development are particularly acute at this scale. The last decade has seen a surge in efforts in measuring resilience to a variety of hazards, yet measurement frameworks lack empirical validation and widespread application. To bridge this information gap, we provide analysis into an unprecedented dataset: a standardized, empirically validated approach to community flood resilience measurement, applied in over 290 communities across 20 developing countries. The analysis is based on the Flood Resilience Measurement for Communities (FRMC) framework and tool designed to provide a holistic approach to measuring community flood resilience and to support implementation of resilience-strengthening interventions. Our analysis starts with an assessment of the validity and reliability of the data and leads into querying whether and how to organize the wealth of information of community contexts into a discrete set of clusters. Although we appreciate that fostering resilience has to be strongly context-aware, we also present a taxonomy related to flood risk and socioeconomic community characteristics, which, using multinomial and random forest methods, leads us to identifying five distinct community clusters based on their resilience profiles and capital scores. This clustering taxonomy provides a way to group communities by similarities and differences between absolute and distributional resilience levels and socioeconomic community characteristics. These clusters may serve as a resource for further examining efforts for building resilience, analyzing resilience dynamics over time, and informing policy options across the world.

The Adaptation Gap Report (AGR) series contributes to addressing these questions by annually assessing progress on adaptation and informing key processes, notably under the UNFCCC. In line with this, the AGR 2024 continues to assess information on planning, implementation and finance (chapters 2, 3 and 4, respectively), to explore whether countries are collectively on track to adapt to the global challenge of climate change. The AGR 2024 extends its assessments in important ways compared with the previous AGRs. First, it includes a topical chapter to discuss the central issue of ‘means of implementation’ other than finance itself, namely capacity-building and technology transfer (see section 1.2 and chapter 5). Second, it further considers underlying causes and processes behind the numbers, as well as a more downscaled analysis of subnational adaptation action (sporadically using the example of cities).

Climate-related disaster impacts, such as loss of human life as its most severe consequence, have been rising globally. Some studies attribute this increase to population growth, while others point to climate change as the primary cause. However, empirical evidence linking climate change to disaster impacts remains limited, particularly in the Global South. This study addresses the impact attribution question in Nepal, a low-income and highly disaster-prone country. We applied a robust regression-based method that accounts for the role of hazard, exposure and vulnerability in flood and landslide mortality, using subnational scale empirical data from 1992 to 2021.Historically, flood and landslide mortality has been highest in central and eastern Nepal due to the stronger influence of the Indian monsoon. However, disaster impacts have surged in recent years in western Nepal, driven largely by an increase in extreme precipitation events. For example, a one standardized unit increase in maximum one-day precipitation increases flood mortality by 33%, and heavy rain days increases landslide mortality by 45%. In contrast, a one standardized unit increase in per capita income reduces landslide and flood mortality by 30% and 45%, respectively. While reductions in vulnerability have helped lower disaster mortality, population exposure has not played a significant role. Therefore, the rise in flood and landslide mortality, particularly in western Nepal, is primarily attributable to the increase in precipitation extremes linked to climate change. With climate change expected to further intensify such extremes, disaster mortality is likely to increase unless significant efforts are made to reduce vulnerability.

Most existing climate impact assessments in Nepal only consider a limited number of generic climate indices such as means. Few studies have explored climate extremes and their sectoral implications, which in turn are key for informing policy and practice. This study evaluates future scenarios of extreme climate indices from the list of the Expert Team on Sector-specific Climate Indices (ET-SCI) and their sectoral implications in the Karnali Basin in western Nepal. First, future projections of 26 climate indices relevant to six climate-sensitive sectors in Karnali are made for the near (2021–2045), mid (2046–2070), and far (2071–2095) future for low- and high-emission scenarios (RCP4.5 and RCP8.5, respectively) using bias-corrected ensembles of 19 regional climate models from the COordinated Regional Downscaling EXperiment for South Asia (CORDEX-SA). Second, a qualitative analysis based on expert interviews and a literature review on the impact of the projected climate extremes on the climate-sensitive sectors is undertaken. We also used widely available global data sets such as DesInventar and national census data and disaster-specific mixed-effects regression models to assess the impact of precipitation extremes on landslide and flood mortality. Both the temperature and precipitation patterns are projected to deviate significantly from the historical reference already from the near future with increased occurrences of extreme events. Results show winter in the highlands is expected to become warmer and dryer. The hot and wet tropical summer in the lowlands will become hotter with longer warm spells and fewer cold days. Low-intensity precipitation events will decline, but the magnitude and frequency of extreme precipitation events will increase. Furthermore, an increase in one standardized unit in maximum one-day precipitation increases flood mortality by 33%, and heavy rain days increase landslide mortality by 45%.

Human mortality and economic losses due to climatic disasters have been rising globally. Several studies argue that this upward trend is due to rapid growth in the population and wealth exposed to disasters. Others argue that rising extreme weather events due to anthropogenic climate change are responsible for the increase. Hence, the causes of the increase in disaster impacts remain elusive. Disaster impacts are higher in low-income countries, but existing studies are mostly from developed countries or at the cross-country level. This study will assess the attribution of rising climatic disaster mortality to indicators of climatic hazards, exposure, and vulnerability at the subnational scale in a low-income country, using Nepal as a case study. This empirical study at the scale of 753 local administrative units of Nepal will follow a regression-based approach that will overcome the limitations of the commonly used loss normalization approach in studying the attribution of disaster-induced loss and damage. In Nepal, landslides and floods account for more than two-thirds of the total climatic disaster mortality. Hence, we will use the past 30 years (1991-2020) landslides and floods mortality data from DesInventar and Nepal's Disaster Risk Reduction portal as the dependent variable. As explanatory variables to represent climatic hazards, we will estimate and use mean and extreme precipitation indices from observational data by the Department of Hydrology and Meteorology Nepal. We will use the local unit’s population as a proxy of disaster exposure. Socio-economic and environmental indicators such as annual per capita income, percentage of people with access to mobile phones and internet, land cover distribution, and slope will be used as indicators of vulnerability. Exposure and vulnerability indicators data will be accessed from Nepal’s Central Bureau of Statistics and other sources. This study is expected to identify indicators of climatic hazards, exposure, and vulnerability that could explain the spatial and temporal variability of climatic disaster mortality in Nepal. Similarly, it will provide new insights on the role of climate change on rising climatic disaster mortality from the low-income countries’ context.

The impacts of climatic disasters have been rising globally. Several studies argue that this upward trend is due to rapid growth in the population and wealth exposed to disasters. Others argue that rising extreme weather events due to anthropogenic climate change are responsible for the increase. Hence, the causes of the increase in disaster impacts remain elusive. Disaster impacts relative to income are higher in low-income countries, but existing studies are mostly from developed countries or at the cross-country level. Here we assess the spatiotemporal trends of climatic disaster impacts and vulnerability and their attribution to climatic and socioeconomic factors at the subnational scale in a low-income country, using Nepal as a case study. Loss of life is the most extreme consequence of disasters. Therefore, we employed human mortality as a measure of disaster impacts, and mortality normalized by exposed population as a measure of human vulnerability. We found that climatic disaster frequency and mortality increased in Nepal from 1992 to 2021. However, vulnerability decreased, most likely due to economic growth and progress in disaster risk reduction and climate change adaptation. Disaster mortality is positively correlated with disaster frequency and negatively correlated with per capita income but is not correlated with the exposed population. Hence, population growth may not have caused the rise in disaster mortality in Nepal. The strong rise in disaster incidence, potentially due to climate change, has overcome the effect of decreasing vulnerability and caused the rise in disaster mortality.

Existing climate projections and impact assessments in Nepal only consider a limited number of generic climate indices such as means. Few studies have explored climate extremes and their sectoral implications. This study evaluates future scenarios of extreme climate indices from the list of the Expert Team on Sector-specific Climate Indices (ET-SCI) and their sectoral implications in the Karnali Basin in western Nepal. First, future projections of 26 climate indices relevant to six climate-sensitive sectors in Karnali are made for the near (2021–2045), mid (2046–2070), and far (2071–2095) future for low- and high-emission scenarios (RCP4.5 and RCP8.5, respectively) using bias-corrected ensembles of 19 regional climate models from the COordinated Regional Downscaling EXperiment for South Asia (CORDEX-SA). Second, a qualitative analysis based on expert interviews and a literature review on the impact of the projected climate extremes on the climate-sensitive sectors is undertaken. Both the temperature and precipitation patterns are projected to deviate significantly from the historical reference already from the near future with increased occurrences of extreme events. Winter in the highlands is expected to become warmer and dryer. The hot and wet tropical summer in the lowlands will become hotter with longer warm spells and fewer cold days. Low-intensity precipitation events will decline, but the magnitude and frequency of extreme precipitation events will increase. The compounding effects of the increase in extreme temperature and precipitation events will have largely negative implications for the six climate-sensitive sectors considered here.

The impacts of climatic disasters have been rising globally. Several studies argue that this upward trend is due to rapid growth in the population and wealth exposed to disasters. Others argue that rising extreme weather events due to anthropogenic climate change are responsible for the increase. Hence, the causes of the increase in disaster impacts remain elusive. Disaster impacts are higher in low-income countries, but existing studies are mostly from developed countries or at the cross-country level. Here we assess the spatiotemporal trends of climatic disaster impacts and vulnerability and their attribution to climatic and socioeconomic factors at the subnational scale in a low-income country, using Nepal as a case study. Loss of life is the most extreme consequence of disasters. Therefore, we employed human mortality as a measure of disaster impacts, and mortality normalized by exposed population as a measure of human vulnerability. We found that climatic disaster frequency and mortality increased in Nepal from 1991 to 2020. However, vulnerability decreased, most likely due to economic growth and progress in disaster risk reduction and climate change adaptation. Disaster mortality is positively correlated with disaster frequency and negatively correlated with per capita income but is not correlated with exposed population. Hence, population growth may not have caused the rise in disaster mortality in Nepal. The strong rise in disaster incidence, potentially due to climate change, has overcome the effect of decreasing vulnerability and caused the rise in disaster mortality.

Given limited scientific agreement on approaches and methodologies, estimates of climate-change adaptation costs vary widely. Here, we present a meta-analysis of aggregate adaptation costs in developing countries, across three roughly homogeneous groups of estimates, i.e. national plan-based, bottom-up science-based, and global top-down estimates. We show that the level of global warming, a country's economic status, and methodology applied, are the main determinants for the estimated costs of adaptation. Not surprisingly, adaptation costs are much higher at high levels of global warming by 2050 and 2100, diverging from low levels of warming from the 2030s. Consequently, strong global mitigation action could reduce the adaptation costs by three quarters by 2100. Next, adaptation costs are higher for high-income countries in absolute dollar value, but costs are higher relative to gross domestic product for low-income countries. The integrated assessment model based estimates are at the higher end of the range at the global scale, but the estimates based on the sectoral impacts aggregation approach are higher in case of bottom-up estimates. Regardless of the methodology applied, current climate finance pledges of USD100 billion by 2020 - for both mitigation and adaptation - would fall far short of estimated global adaptation costs.

Field screening of seven cultivars of cabbage namely: Green Crown, Green Top, Green Coronet, Pioneer, Nepa Round, Copenhagen Market and Golden Acre were carried out against cabbage butterfly (Pieris brassicae) and cabbage aphid (Brevicoryne brassicae) at the research farm of entomology section, Gokuleshwor Agriculture and Animal Science College, Baitadi in RCBD design from October 2017 to February 2018. Five plants were tagged randomly after transplanting in field excluding border plants in each plot. Data were collected for the population dynamics of cabbage butterfly larvae and cabbage aphid on weekly basis. None of the seven cultivars were found resistant to cabbage butterfly and cabbage aphid, however their population density varied on tested cultivars. Cabbage butterfly population was recorded the highest on the cultivar Pioneer (22.88 larvae/plant) and the lowest on the cultivar Copenhagen Market (10.06 larvae/plant), and other cultivars were of intermediate types. Similarly, the population density of aphid ranged from 36.70 to 105.58 aphids/leaf. The highest population density of aphid was recorded on cultivar Green Crown (105.58 aphids/leaf) and the lowest on cultivar Copenhagen Market (39.82 aphids/leaf. From the results, Copenhagen Market proved to be the best against both cabbage butterfly and cabbage aphids.

Given limited scientific agreement on approaches and methodologies, estimates of climate-change adaptation costs vary widely. Here, we present a meta-analysis of aggregate adaptation costs in developing countries, across three roughly homogeneous groups of estimates, i.e. national plan-based, bottom-up science-based, and global top-down estimates. We show that the level of global warming, a countryu0027s economic status, and methodology applied, are the main determinants for the estimated costs of adaptation. Not surprisingly, adaptation costs are much higher at high levels of global warming by 2050 and 2100, diverging from low levels of warming from the 2030s. Consequently, strong global mitigation action could reduce the adaptation costs by three quarters by 2100. Next, adaptation costs are higher for high-income countries in absolute dollar value, but costs are higher relative to gross domestic product for low-income countries. The integrated assessment model based estimates are at the higher end of the range at the global scale, but the estimates based on the sectoral impacts aggregation approach are higher in case of bottom-up estimates. Regardless of the methodology applied, current climate finance pledges of USD100 billion by 2020 - for both mitigation and adaptation - would fall far short of estimated global adaptation costs.