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The amount of agricultural drought vulnerability in an underdeveloped rain-fed agro-based economy at the local, regional, and national level is most prominent factor for measurement. The desiccation of rain in agricultural sector becomes apprehensive to intercontinental food supply chain. So, adequate investigation and development of sustainable agricultural methodology are key factors to sustain the food security of a territory. In this research, delineation of agricultural drought vulnerability (ADV) status has been carried out by multidimensional mixed-method index approach using remote sensing and geographic information system. An integrated three-dimensional model is utilized to enrich this study. The three indices of this model include exposure index (EI), sensitivity index (SI), and adaptive capacity index (ACI). The ACI has been constructed by combining the environmental adaptive capacity (EAC), social adaptive capacity (SAC), and economic adaptive capacity (EcAC) index. The 40 parameters for ADV modeling are picked up by analyzing meteorological, geo-environmental, social, and remote sensing data. There are six exposure parameters, seven sensitivity parameters, twelve environmental adaptive capacity parameters, six social adaptive capacity parameters, and nine economic adaptive capacity parameters. Each index has been computed by assigning the weights based on their relative importance by using the analytic hierarchy process (AHP) approach. Final results were classified into five vulnerability zones, e.g., very low, low, moderate, high, and very high covering an area 362.32 km 2 , 186.68 km 2 , 568.69 km 2 , 547.05 km 2 , and 266.89 km 2 respectively. Results have been validated with long-term Aman paddy yield data (2004 to 2014) through the yield anomaly index (YAI). Finally, the model ADV is a good model fit ( R square = 0.894) and all the relationships were found significant, when SI, EI, and ACI are considered its predictors. While SI ( B  = 0.391, p  < 0.001) and EI ( B  = 0.223, p  < 0.001) are positively associated with ADV, ACI is negatively associated with ADV ( B  =  − 0.721, p  < 0.001). This regional agricultural drought vulnerability model can be useful to identify drought-responsive areas and improve drought mitigation measures.

Drought is one of the major barriers to the socio-economic development of a region. To manage and reduce the impact of drought, drought vulnerability modelling is important. The use of an ensemble machine learning technique i.e. M5P, M5P -Dagging, M5P-Random SubSpace (RSS) and M5P-rotation forest (RTF) to assess the drought vulnerability maps (DVMs) for the state of Odisha in India was proposed for the first time. A total of 248 drought-prone villages (samples) and 53 drought vulnerability indicators (DVIs) under exposure (28), sensitivity (15) and adaptive capacity (10) were used to produce the DVMs. Out of the total samples, 70% were used for training the models and 30% were used for validating the models. Finally, the DVMs were authenticated by the area under curve (AUC) of receiver operating characteristics, precision, mean-absolute-error, root-mean-square-error, K-index and Friedman and Wilcoxon rank test. Nearly 37.9% of the research region exhibited a very high to high vulnerability to drought. All the models had the capability to model the drought vulnerability. As per the Friedman and Wilcoxon rank test, significant differences occurred among the output of the ensemble models. The accuracy of the M5P base classifier improved after ensemble with RSS and RTF meta classifiers but reduced with Dagging. According to the validation statistics, M5P-RFT model achieved the highest accuracy in modelling the drought vulnerability with an AUC of 0.901. The prepared model would help planners and decision-makers to formulate strategies for reducing the damage of drought.

The increasing frequency and intensity of urban floods due to the climate crisis necessitate effective adaptation. In South Korea, flood vulnerability assessments have focused on preparedness, underscoring the need for adaptive capacity research. This study proposes the Urban Flood Adaptive Capacity Index (UFACI), a Fuzzy Logic-based framework that quantifies urban resilience. Developed from a socio-ecological systems (SES) perspective, the UFACI integrates economic resources, social capital, risk perception, and infrastructure. Fourteen indicators are applied using Fuzzy Logic to address uncertainties and enhance decision-making. The methodology is tested in 12 rainwater pumping station drainage areas in Seoul, providing actionable insights for flood management. This study contributes by shifting the focus from vulnerability to adaptive capacity, offering a systematic, data-driven approach to flood resilience assessment. Unlike conventional methods, the UFACI integrates socio-economic and physical factors, enabling targeted policy interventions and resource allocation. Its application in Seoul demonstrates its practical value, with potential adaptability for broader urban flood risk management.

Climate change is affecting farmers’ livelihoods and their ability to adapt. Therefore, solutions for adaptation and resilience are required. The objective of the work was to assess how nature-based solutions contribute to improving the adaptive capacity of farmers, taking coffee production in Mexico as a case study. It followed the theoretical approach of the Sustainable Livelihoods Framework, which involves identifying the capacities, resources, and activities that a population possesses, considering the following six dimensions: natural, social, human, economic, physical, and political. A rapid systematic review was carried out to identify measurement indicators for each dimension. A semi-structured survey was constructed to collect information on the indicators in the field. The surveys were administered to a sample of 60 randomly selected farmers who utilized various management types incorporating nature-based solutions, including diversified polyculture, simple polyculture, and simplified shade. In addition, farmers who do not use nature-based solutions and who grow coffee in full sun were considered. An index of adaptive capacity was then calculated for each coffee agroecosystem assessed, and finally, actions were proposed to strengthen the livelihood dimensions and increase the adaptive capacity of farmers. It was found that farmers using the management types diverse polyculture and simple polyculture had an average value of the adaptive capacity index classified as high (15.06 and 11.61, respectively). Farmers using the simplified shade management type had an average index value classified as medium (8.59). Whereas, farmers producing coffee in full sun were classified with low adaptive capacity in the average index value (−0.49). The results obtained in this research can contribute to informed government decision making (local, state, or federal) in generating policies to improve or design nature-based solutions in the agricultural sector, thereby increasing the adaptive capacity of producers in the face of climate variability.

Community-based observing networks (CBONs) use a set of human observers connected via a network to provide comprehensive data, through observations of a range of environmental variables. Invariably, these observers are Indigenous peoples whose intimacy with the land- and waterscape is high. Certain observers can recall events precisely, describe changes accurately, and place them in an appropriate social context. Each observer is akin to a sensor and, linked together, they form a robust and adaptive sensor array that constitutes the CBON. CBONs are able to monitor environmental changes as a consequence of changing ecological conditions (e.g., weather, sea state, sea ice, flora, and fauna) as well as anthropogenic activities (e.g., ship traffic, human behaviors, and infrastructure). Just like an instrumented array, CBONs can be tested and calibrated. However, unlike fixed instruments, they consist of intelligent actors who are much more capable of parsing information to better detect patterns (i.e., local knowledge for global understanding). CBONs rely on the inclusion of Indigenous science and local and traditional knowledge, and we advocate for their inclusion in observing networks globally. In this paper, we discuss the role of CBONs in monitoring environmental change in general, and their utility in developing a better understanding of coupled social-ecological systems and developing decision support both for local communities as well as regional management entities through adaptive capacity indices and risk assessment such as a community-based early warning system. The paper concludes that CBONs, through the practice of Indigenous science in partnership with academic/government scientists for the purpose of knowledge co-production, have the potential to greatly improve the way we monitor environmental change for the purpose of successful response and adaptation.

The coastal stretch of Saurashtra, Gujarat, is seriously threatened by storm surges. Hence, assessing the preparedness to storm surge impacts is a major task in coastal disaster management where identification of relative vulnerability of coastal stretches is a prime concern. The aim of this study is to assess coastal vulnerability related to storm surge events along the coastal talukas of Saurashtra coast, by analyzing physical features and demographic variables using Geographical Information System (GIS) techniques. Vulnerability of a taluka is defined in terms of its exposure, sensitivity, and adaptive capacity. We calculated vulnerability of Exposure Index, Sensitivity Index, and Adaptive Capacity Index separately in ArcMap s/w, and the vulnerability map of different indices in the study region was drawn. The Total Vulnerability Index (TVI) is prepared by integrating the above index. The TVI map shows that Kalyanpur, Porbandar, and Talaja talukas are highly vulnerable in comparison with other talukas as they have large area under low-lying, high sensitivity value, and low adaptive capacity value. On the other hand, Diu and Maliya are lower vulnerable due to the presence of rocky/cliffy coast, sand dune, small coastal length and located in elevated region although there exists high population density and built-up area. Our research finding will assist coastal disaster managers and decision makers to plan appropriate measures to minimize the losses due to storm surge impacts.

Based on 224 face-to-face household surveys in an ecologically fragile grazing area in China's Inner Mongolia Autonomous Region, this study analyzes herder (pastoralist) adaptive capacity for coping with climate change. The Composite Indicator Framework Method and Entropy Weighting Method were applied to calculate each herder's Adaptive Capacity Index (ACI) based on 16 indicators from seven determinants of adaptive willingness and adaptive capital. Results show that herders have an average ACI value of 0.40. Classifying herders into three groups of relatively high, medium, and low ACI values, we find that financial capital and social capital are the most significant determinants of herder adaptive capacity. Improving financial services and building herder social capital would be the most effective ways of enhancing adaptive capacity for coping with climate change. The methods and indicators developed can be generalized to other similar areas to facilitate better policy interventions for reducing poverty and improving rangeland management.

People in the Arctic and sub-Arctic continue to face uncertainty in their livelihoods as they contend with environmental variability and change operating at multiple scales. The arctic water resources vulnerability index (AWRVI) was proposed as a tool that arctic communities could use to assess their susceptibility to both changing biophysical conditions affecting their water resources and socioeconomic conditions measuring their ability to respond to such changes. The application of AWRVI in six communities in Northwest Alaska and one in Southcentral Alaska is explored with a view to enhancing the tool as an adaptive capacity index, and a set of AWRVI indicators and parameters was refined by modifying the suite of biophysical measures and societal capacities to enhance the ability of the tool to gauge community adaptive capacity, and incorporate the use of more diverse datasets. A critical update was the development of an indicator for change in timing of precipitation in response to advice from Alaskan practitioners and scientists. Index scores based on the updated AWRVI are compared with the original AWRVI for the seven communities and show small to modest changes in the adaptive capacity scores. The role of the updated AWRVI is discussed as a tool to assist communities as they attempt to understand, negotiate, and reconcile adaptation measures for environmental change at local scales, potentially providing a guide for communities to target adaptive responses.

Purpose The West Africa coastal area faced with the serious health challenge is the most underdeveloped place. Through making the visualized spatial analysis of this area, this study aims to identify which factor of social vulnerability predominantly affects infant mortality. Design/methodology/approach This study uses the spatial data available from NASA-affiliated institution and a geographic information system for analysis. Findings This study reveals that the Poverty and Adaptive Capacity Index, as economic aspect of social vulnerability, is spatially correlated with the infant mortality rate, whereas the Population Exposure Index, as population aspect of social vulnerability, does not. Thus, the economic rather than population factor is probably the driving force of high infant mortality. Originality/value This study clarifies the determinant of infant mortality in the West Africa coastal area in space dimension.

The objective of this study is vulnerability and exposer intensity due to land use change in Olomouc, Czech Republic. Vulnerability assessment with exposer intensity to land use/cover change is an important step for enhancing the understanding and decision-making to reduce vulnerability. This study work includes quantification of Exposure Index (EI), Sensitivity Index (SI) and Adaptive Capacity Index (AI). EI is based on intensity of land use/cover change, SI and AI based on natural factors such as elevation, slope, vegetation and land use/cover. Vulnerability Index (VI) derived on the quantification of SI and AI and compared from 1991, 2001 and 2013. Comparing of EI and VI for last three decades, settlements have highest vulnerability index due to high socio-economic activities and water have lowest vulnerability index due to less human interferences. Agriculture has highest exposer index and second highest vulnerability, which show its high rate of exploitation and production. In the study areas, vulnerability tends to increase with the increase of exposure to land use change, but can peak off once the land use start to benefit socio-economically from development. Only in this way we can enhance the adaptive capacity of study area to use change of land.