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The Himalayan country like Nepal, due to its poor adaptive capacity and limited access to alternate means of livelihoods, is more vulnerable to climate change. It is found that climate change exacerbates the situation and presents a daunting challenge to predominantly rain-fed agricultural livelihoods. This study aims to analyze the extent and sources of households’ livelihood vulnerability to climate change in western Nepal. We conducted a household survey (n = 200), focus group discussions (n = 4), and key informant interviews (n = 10) across the Phusre Khola watershed and a formative composite index of livelihood vulnerability (LVI) was constructed. The result shows drought as the most common climatic hazard followed by landslide and hailstone, respectively. It was found that agriculture was the most impacted resources. Livelihood strategies with LVI value (0.49) and climate variability (0.48) were seen as the most vulnerable and health (0.12) as the least vulnerable component in the study area. The Upper Phusre sub-watershed was observed as the most vulnerable with the LVI (0.33). Overall, the study shows that vulnerability assessment indices can be broadly applied in similar settings in different regions of the country, and they could contribute to identifying and prioritizing adaptation and mitigation needs.

Recent cases of dam failures indicate that the safe operation and proactive maintenance of critical infrastructure is of significant importance considering the growing number of ageing dams and the increase in intensity and frequency of extreme climatic events. The current procedures to assess the performance of dam infrastructure, usually based on geodetic and geotechnical instrumentation, do not provide repeatable and reliable information with regard to subsurface hazards that evolve within the body of earth-fill dams that could compromise the integrity of the structure. This increases the risk of failure with significant socio-economic impacts and long-term disruption on downstream communities. On the contrary, geophysical methods can provide advanced information about subsurface hazards and can therefore significantly assist to define the safety level of dam infrastructure. This will enable early remedial maintenance and repair actions to be carried out enhancing public safety and eventually reducing costs for asset owners. This study presents for the first time the investigation of the condition of three reservoir dams in Scotland with the application of two complementary non-invasive geophysical methods coupled with visual inspection. Electromagnetic (EM) sensing was initially employed to provide an assessment of the upper soil layers of the crest and downstream shoulder of the dam. Electrical Resistivity Tomography (ERT) arrays were then installed on the crest to assess the subsurface conditions of the dam based on the resistivity signatures. The analysis of the geophysical models identified weak zones within the body of dams associated with high-resistivity patterns associated with potential animal burrowing activity and fissuring on the crest of the dam. The electrical resistivity surveys revealed low-resistivity zones influenced by seepage conditions inside the body of dams but also provided an indication of potential internal erosion areas. Finally, the geophysical models provided an insight of the homogeneity of the fill material and determined the dam foundation characteristics. The geophysical results presented in this investigation provide important baseline measurements and key information about the current condition and on-going performance of dam infrastructure.

Coastal farmers are the first group of people who feel climate-related calamities most severely, such as sea-level rise, salinity intrusion, coastal flooding, tidal surges and tropical cyclones. They are operating agricultural activities under these climatic conditions that affect farm productivity. This study explores farmer perceptions of changes in farm productivity and perceptions of causes of decreased farm productivity (if any) over the past 10 years compared with more than 10 years back. We partitioned the causes of decreased farm productivity into climatic and non-climatic based on the primary data collected through household survey in ten coastal subdistricts along the coast of the Bay of Bengal. We visited 381 households during September–October 2018 using a pre-tested structured interview schedule. Average monetary farm productivity in the study area was 1.98. A small proportion (11%) of the sampled farmers mentioned that farm productivity had decreased over the past years. A majority (64%) of them believed that climate change was responsible for such decreases in farm productivity. The farmers who thought that climate change was causing the decreased farm productivity were characterized by greater education, more awareness of climate change, less communication with extension agents, stronger belief in decreased cyclone and salinity, and weaker belief in decreased flood. The farmers perceived that dry period salinity, flood and coastal inundations were the major products of climate change to adversely affect crop productivity. Since agricultural adaptation to climate change requires clear understanding of the climatic impacts on farm productivity, and more than one-third of the farmers failed to identify climatic impacts on decreased farm productivity, their improvement of climate change awareness is essential. Extension organizations and other agents should promote updated climate knowledge among farmers to make them more aware of climate change issues, so that they can adapt to climate change through their agricultural activities.

With climate change, hydro-climatic hazards, such as floods in the Himalayas regions, are expected to worsen, thus likely to overwhelm humans and socioeconomic system. Precisely, the Koshi River basin (KRB) is often impacted by floods over time. However, studies on estimating and predicting floods are still scarce in this basin. This study aims at developing a flood probability map using machine learning algorithms (MLAs): Gaussian process regression (GPR) and support vector machine (SVM) with multiple kernel functions including Pearson VII function kernel (PUK), polynomial, normalized poly kernel, and radial basis kernel function (RBF). Historical flood locations from available (topography, hydrogeology, and environmental) datasets were further considered to build a flood probability model. Two datasets were carefully chosen to measure the feasibility and robustness of MLAs: the training dataset (flood locations between 2010 and 2019) and the testing dataset (flood locations of 2020) with thirteen flood influencing factors. Validation of the MLAs was performed with statistical indices such as the coefficient of determination (r2: 0.546 –.995), mean absolute error (MAE: 0.009 –373), root mean square error (RMSE: 0.051–0.466), relative absolute error (RAE: 1.81–8.55%), and root-relative square error (RRSE: 10.19–91.00%). Results showed that the SVM-Pearson VII kernel (PUK) yielded better prediction than other algorithms. The resultant map from SVM-PUK revealed that 27.99% area with low, 39.91% area with medium, 31.00% with high, and 1.10% area with very high probabilities of flooding in the study area. The flood probability map, derived in this study, could add great value to the effort of flood risk mitigation and planning processes in KRB.

Wind erosion is one of the most critical problems in arid and semi-arid regions. The wind transports only dry soil, and no soil with a wet surface can be moved. The study aimed to investigate the changes in wind erosion potential in the coastal plains of Hormozgan province, Iran. Data from four synoptic stations from 1988 to 2017, as well as outputs from four climate models, namely HadGEM-2-CC, CSIRO-MK3- 6-0, ACCESS1-3 and CNRM-CM5, based on two Representative Concentration Pathways, RCP 4.5 and RCP 8.5, were used for the analysis. The outputs of climate models were ensembled and downscaled using the Change Factor statistical downscaling method. The downscaled data showed good accuracy in representing the large-scale GCM data. Furthermore, the “Potential of Wind Erosion Occurrence” was calculated for monthly and annual time scales using the Mann-Kendall nonparametric trend test. The results indicated that there were no significant trends in wind erosion occurrence during the base period (i.e., 1988 to 2017). However, it was projected that the “potential for wind erosion occurrence” and the “percentage of windy days associated with drought” would increase in the near future (i.e., 2031 to 2060) under both Representative Concentration Pathways (RCP 4.5 and RCP 8.5). Interestingly, both scenarios showed a decreasing trend in wind erosion occurrence in the far future (i.e., 2071 to 2100). This suggests that changes in wind patterns play a significant role in shaping wind erosion potential, while daily precipitation does not have a significant impact on this trend.

The best available information on climate change projections is used here in conjunction with the observation-based conceptual site modeling framework that draws on the source-pathway-receptor-consequences approach, to conduct an impact assessment of future climatic hazards on the Panchang Bedena landfill in Selangor, Malaysia. Climate change impact assessment of landfills is currently limited by the reliability of downscaled models and inadequate information on such sites. Using the worst-case scenario where the submerged landfill is recognized as the source of contaminated water and material, surface runoff from the facility is identified as the prominent pathway for transport of pollutants, while subsurface migration is restrained by the impermeable underlying material. Receptors include the nearby settlement, agricultural land, and coastal ecosystem. The adverse consequences expected relate to the health and well-being of surrounding communities, damage of built assets, degradation of mangrove habitat, and long-term productivity loss for the agricultural sector. The findings provide a useful starting point to engage stakeholders, specifically the affected communities and government authorities, to identify adaptation options, including detailed investigations to improve the conceptual site model, monitoring, and early warning, depending on the availability of resources. The approach lays the foundation for future work to make landfills climate-resilient, specifically in developing countries with paucity of information on landfills, unreliable downscaled climate projections, and resource limitation for conducting an intensive conventional scientific investigation.

South Africa is highly susceptible to climate variability and long-term climatic shifts, necessitating a comprehensive understanding of changing extreme precipitation patterns to guide effective mitigation and adaptation responses. This study examined variations in extreme precipitation indices from 1981 to 2023 across the eastern Free State Province using daily rainfall records derived from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Ten extreme precipitation indices were evaluated, with trend detection conducted through the Innovative Trend Analysis (ITA) technique. Findings indicate that the majority of municipalities exhibited statistically significant declining trends (p < 0.05) in total wet-day precipitation (PRCPTOT), R99P, R95P, the Simple Daily Intensity Index (SDII), CDD, RX5day, R20mm, and R10mm, suggesting an overall reduction in both heavy and moderate rainfall occurrences. In contrast, significant upward trends (p < 0.05) were identified in CWD, and RX1day, reflecting a shift toward prolonged wet periods and more intense short-duration rainfall events. Taken together, these divergent patterns point to the simultaneous emergence of heightened drought vulnerability driven by reduced cumulative rainfall and increased flood risk linked to intensified precipitation extremes. These results underscore the importance of forward-looking, climate-resilient water resource management and context-specific adaptation strategies suited to the eastern Free State’s complex mountainous terrain.

Studies have reported that economic development can contribute to reducing vulnerability to natural hazards. However, there exist considerable variations in the association between economic growth and vulnerability, especially at the sub-country scale. Based on climatic hazard impact (indicated by mortality and direct economic losses (DEL)) and economic development data for 31 provinces in the mainland of China from 1990 to 2015, trends of disaster impact, and trends of vulnerability (indicated by mortality rate and DEL rate), were detected using the Mann–Kendall trend test at the provincial level. Next, the relationship between income and vulnerability was characterized. At the provincial level during 1990–2015, there was a clear transformation of the climatic hazard impact landscape from high average annual mortality to high average annual DEL. Both the mortality and the DEL rates presented downward trends for most provinces. The magnitude of vulnerability decrease was higher in the economically developed provinces compared to the underdeveloped provinces, and vulnerability declined nonlinearly with income increase. A vulnerability trap appears to exist approximately below 1600 US$ for income—above this threshold, a province’s vulnerability significantly decreased. Mortality and economic vulnerability to climatic hazards are correlated with economic development, but causal links between economic development and vulnerability reduction need further investigation. Our findings suggest that shortening the transitional period from the low-income trap and reducing inter-provincial disparity of economic development may be powerful tools for reducing vulnerability to climatic hazards, especially for economically underdeveloped regions. These should also be important considerations in developing efficient adaption strategies for climate change.

The Western Ghats have been declared as a World Heritage Site by UNESCO. Besides, it is classified as one of the world’s 36 biodiversity hotspots by Conservation International. The Western Ghats of Kerala have experienced devastating landslides and floods in recent years, which are triggered by climate change. This alarming situation calls for policymakers to develop a comprehensive climate adaptation policy at the local level. However, no study has yet thoroughly investigated this critical issue. Therefore, this study explores the prospects and trade-offs of climate rehabilitation policies for families living in the highly landslide- and flood-prone areas of the Western Ghats in Kerala, India. We have undertaken a mixed methodology comprising four focus group discussions followed by empirical analyses. Towards this, a semi-structured questionnaire is framed to gather relevant information based on the outcomes. The data are analyzed using robust logistic regression models. The findings indicate that most agricultural worker families support the rehabilitation policy, given their lower opportunity costs due to the absence of farmland ownership. On the other hand, agricultural families face considerable trade-offs regarding rehabilitation. Most agricultural families prefer to rehabilitate within a short distance from the current residence or construct a retaining wall as they fear rehabilitation to distant places will gravely affect their livelihood. This research highlights the potential for implementing a rehabilitation policy for marginalized communities heavily exposed to climate risks. Additionally, constructing retaining walls should also be a primary focus of the Government.

Char (Bengali term for riverine island) is a unique socioecological system associated with a large alluvial river. About 4 % of the 160 million people of Bangladesh, who are mostly poor, live in chars, which are located within and alongside its major rivers—the Jamuna, Ganges and Meghna. Agriculture is the mainstay of livelihoods in these riverine chars. However, the cropping system of the chars is at risk to different hydro-climatic hazards due to their vulnerable physical and climatic settings. This study assesses the various hazards to major cash crops in the chars of the Jamuna River and identifies the current coping practices and suggests potential adaptation measures against such hazards. The study is conducted using a combination of conventional and participatory research approaches through analysis of available hydro-climatic data from secondary sources and synthesis of primary information on hazards, their impacts, coping practices and planned adaptations collected directly from the fields using participatory research tools by a multi-disciplinary team. The findings of the study reveal that flood, untimely rain, cold, fog, drought, pest infestation, wind, hailstorm and erosion are the principal hazards to agricultural crops in the Jamuna chars. The hazards vary depending on the crops, and chilli is found to be more vulnerable than other crops. Among the different actors, the crop producers are the worst affected by the hazards. Use of plastic sheet, irrigation and pesticide, reseeding, early harvesting and growing chance crops are among the prominent coping strategies of the producers. The study recommends provisioning of drying, storage and credit facilities, introducing short-duration and disaster-resilient crop varieties, developing local capacity through training and extension services, and improving weather forecasting and dissemination to reduce the agricultural risks in the chars. The findings will be useful in designing programs and interventions in agricultural system to improve the livelihoods of the char dwellers, particularly in south Asia.