Search Results (691)

The increasing frequency of extreme climate events (ECEs) is expected to significantly affect crop yields in the future, threatening regional and global food security. However, uncertainties in yield projections persist due to regional variability, model differences, and scenario assumptions. Leveraging historical agricultural disaster and meteorological data from China (1995–2014), this study employs the vulnerability curve assessment to determine the most appropriate models for assessing crop yields affected by different ECEs (drought, extreme precipitation, extreme low temperature, and extreme wind) across six regions. By integrating multi-model and multi-scenario (SSP1-2.6, SSP3-7.0, SSP5-8.5) future climate data from Coupled Model Intercomparison Project Phase 6 (CMIP6), we conducted pooled prediction of the individual and combined impacts of different ECEs on crop yields for the near-term (2020–2040) and mid-term (2041–2060). The median of multi-model prediction of crop yield reductions in China was −16.0% (range: −32.5% to −2.6%), with more severe losses in Northeast, Northwest, and North China, particularly under higher radiative forcing scenarios. Drought is the most destructive of the four types of ECEs. These results will aid decision-makers in identifying high-risk zones for crop yields affected by ECEs and provide a scientific basis for the developing targeted adaptation strategies in various regions. Full article

Southwestern China, characterized by highly undulating terrain and mountainous areas, faces frequent landslide disasters. However, previous studies in this region mostly neglected the role of extreme rainfall in landslide susceptibility assessment and the socio-economic risks threatened by landslides. To address these gaps, this study integrated 688 recorded landslides for Tengchong City in the southwest of China and 10 influencing factors (topography, lithology, climate, vegetation, and human activities), particularly two extreme precipitation indices of maximum consecutive 5 day precipitation (Rx5day) and maximum length of wet spell (CWD). These influencing factors were selected after ensuring variable independence via multicollinearity analysis. Four machine learning models were then built for landslide susceptibility assessment. The Random Forest model performed the best with an Area Under Curve (AUC) of 0.88 and identified elevation, normalized difference vegetation index (NDVI), lithology, and CWD as the four most important influencing factors. Landslides in Tengchong are concentrated in areas with low NDVI (<0.57), indicating increased vegetation cover might reduce landslide frequency. Landslide risk was further quantified via the Analytic Hierarchy Process (AHP) by integrating multiple socio-economic factors. High-risk zones were pinpointed in central-southern Tengchong (e.g., Heshun and Tuantian townships) due to their high social exposure and vulnerability. Overall, this study highlights extreme rainfall and vegetation as key modifiers of landslide susceptibility and identifies the regions with high landslide risk, which provides targeted scientific support for regional early-warning systems and risk management. Full article

Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to study population exposure to heat stress. This research focuses on Naples, Italy’s most densely populated city, where intense human activity and unique geomorphological conditions influence local temperatures. The presence of a Surface Urban Heat Island (SUHI) is assessed by deriving high-resolution Land Surface Temperature (LST) in a time series ranging from 2013 to 2023, processed with the Statistical Mono Window (SMW) algorithm in the Google Earth Engine (GEE) environment. SMW needs brightness temperature (Tb) extracted from a Landsat 8 (L8) Thermal InfraRed Sensor (TIRS), emissivity from Advanced Spaceborne and Thermal Emission Radiometer Global Emissivity Database (ASTERGED), and atmospheric correction coefficients from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR). A total of 64 nighttime images were processed and analyzed to assess long-term trends and identify the main heat islands in Naples. The hottest image was compared with population data, including demographic categories such as children, elderly people, and pregnant women. A risk index was calculated by combining temperature values, exposure levels, and the vulnerability of each group. Results identified three major heat islands, showing that risk is strongly linked to both population density and heat island distribution. Incorporating Local Climate Zone (LCZ) classification further highlighted the urban areas most prone to extreme heat based on morphology. Full article

The rapid urbanization in developing countries has resulted in altered land-use patterns, surface energy imbalances, and heightened urban heat stress, exacerbating the urban heat island effect and vulnerability to heatwaves. The abandonment of agricultural lands, while a global challenge, presents cities with a unique opportunity to meet tree cover targets and improve resilience to these climatic challenges. Building on prior studies, this research employs the combined use of ENVI-met 4.4.6 and Ray-Man 3.1 simulation models to assess the efficacy of nature-based solutions in revegetating abandoned urban agricultural lands with the aim of enhancing outdoor thermal comfort. As a vital component of urban ecosystem services, thermal comfort, particularly through microclimate cooling, is essential for improving public health and livability in cities. This investigation focuses on the integration of broadleaf, evergreen, and edible woody species as bioclimatic interventions to mitigate urban heat stress. Simulation results showed that species such as Quercus spp. (broadleaf) and Cupressus arizonica (evergreen) substantially reduced the Mean Radiant Temperature (Tmrt) index by up to 26.76 °C, primarily due to their shading effects and large canopies. Combining these vegetation types with crops emerged as the most effective strategy to mitigate heat stress and optimize land-use. This study demonstrates how cities can incorporate nature-based solutions to adapt and mitigate the health risks posed by climate change while fostering resilience. These findings offer valuable knowledge for other developing countries facing similar challenges, highlighting the importance of revegetating abandoned urban agricultural lands for thermal comfort and ecosystem service provision, with the advantages of reducing mortality and morbidity during heatwaves. Consequently, these results should inform urban climate policies aimed at promoting resilience, public health, and ecological sustainability in a changing climate. Full article

This paper presents a comprehensive methodology for assessing the resilience of landscapes to human impact in western Kazakhstan. The approach developed is based on integrating remote sensing data (MODIS, SMAP, NDVI and NDSI), the results of field surveys, and multi-criteria analysis methods in a GIS environment. The assessment covered over 50 landscape types and subtypes using ten key indicators reflecting climatic, geomorphological, soil, hydrological, and biotic characteristics. These indicators were normalised, aggregated and summarised to create an integral index of landscape resilience, which allowed four resilience classes to be identified, ranging from highly vulnerable to relatively resilient. The spatial analysis revealed that over 60% of the region’s territory is classified as high-vulnerability, predominantly within semi-desert and desert zones, which are vulnerable to climatic risks, degradation of vegetation cover and human activity. Verification of the results based on remote monitoring data for the period 2000–2024 and field observations confirmed the reliability of the developed methodology. The results obtained allow the identification of areas prioritised for environmental monitoring, restoration and sustainable land use in arid climate conditions. A plan of measures for regulation and restoration of ecosystems and spatial planning tools are proposed. The obtained data can be used for the development of regional environmental policy and sustainable land use strategies. Full article

Food production systems in Ecuador’s high Andean region are pivotal for food security, rural livelihoods, and agrobiodiversity, yet they are increasingly exposed to climate stress. We assessed four representative crops (tree tomato, quinoa, potato, and maize) across three Andean zones (North, Center, South) in 2015–2022 using monthly NASA POWER (MERRA-2) climate fields. After confirming non-normality, Spearman correlations and multiple linear regressions with leave-one-year-out validation were applied to quantify the influence of maximum/minimum temperature and precipitation on cultivated and harvested area, production, sales, and loss categories. To place monthly signals in a process context, daily extreme-event diagnostics (ETCCDI-style) were also computed: heat days ($\mathrm{TX}90$), ≥5-day dry spells, and the annual maximum consecutive dry days (${\mathrm{CDD}}_{max}$). Models explained a wide range of variability across crops and zones (approx. $R^2\sim 0.55$–0.99), with quinoa showing the most consistent fits (several outcomes $R^2>0.90$). Extremes provide an eye-catching, actionable picture: the Southern zone concentrated dryness hazards, with 1–5 dry spells $\ge 5$ days per year and ${\mathrm{CDD}}_{max}$ up to ∼8 days, while heat-day frequency showed non-significant declines across zones in 2015–2022. Reanalysis frost days were virtually zero—consistent with under-detection of local valley frosts at coarse resolution—so frost risk was interpreted via monthly signals and reported losses. Overall, the results show precipitation-driven vulnerabilities in the South and support quinoa’s role as a resilient option under increasing climate stress, offering concrete guidance for water management and climate-smart planning in mountain agroecosystems. Full article

The increasing complexity of Water Distribution Systems (WDSs), driven by urbanization, climate change, and aging infrastructure, necessitates robust methods for risk assessment and visualization. This study presents a practical methodology for mapping the risk of water supply disruption or reduction using five parameters: Probability (P), Consequences (C), Water Pipe category (WP), Inhabitants exposed (I), and response Efficiency (E). The approach enables comprehensive analysis of the risk associated with specific pipeline segments within an Analyzed Supply Area (ASA). The method integrates statistical and operational data, allowing utilities to evaluate vulnerability, identify Critical Infrastructure (CI), and prioritize maintenance. The investigation conducted during the study revealed that cast iron and steel pipes with large diameters (e.g., 400 mm) show the highest failure probability and impact. Despite a calculated risk value (r_LW = 80), effective response measures—including specialized repair teams and equipment—kept the risk acceptable. The results demonstrate that historical failure and response data enhance risk identification and management. The generated risk maps facilitate spatial visualization of high-risk areas, supporting decision-making processes, renovation planning, and emergency preparedness. Integration with GIS tools, including GeoMedia and Google Earth programmes, enables dynamic map creation and simulation of response scenarios. The methodology is scalable and adaptable to any WDS, and potentially to other municipal systems such as wastewater and heating networks. By accounting for both technical and social dimensions of risk, the method supports improved water safety planning and infrastructure resilience. Future development should include real-time data integration and climate-related risk scenarios to increase predictive accuracy and system adaptability. Full article

Despite increasing concerns over recurrent phosphorus (P) pollution, the Ju River—a small tributary of the Yangtze River—has received limited scientific attention. To correct this, the present study integrates field-based observations with the Hydrological Simulation Program—FORTRAN (HSPF) model to comprehensively assess the conjunct effects of urban expansion and changing precipitation patterns on watershed hydrology and phosphorus dynamics at the small-catchment scale. A total of five urban expansion scenarios and three precipitation enhancement scenarios were simulated to capture both seasonal and event-driven variations in daily discharge and total phosphorus (TP) concentrations. The model was calibrated and validated using in situ water quality data, ensuring high reliability of the simulations. The results indicate that agricultural non-point sources are the primary contributor to total phosphorus (TP) loads. During the overlapping period of intensive farming and heavy rainfall (June–July), TP concentrations more than doubled compared to other months, with these two months accounting for over 70% of the annual TP load. Urban expansion significantly amplified hydrological extremes, increasing peak discharge by up to 224% under extreme rainfall, thereby intensifying flood risks. Although increased precipitation diluted TP concentrations, it simultaneously accelerated overall phosphorus export. This study offers a novel modeling–monitoring framework tailored for small watersheds and provides critical insights into how land use transitions and climate change jointly reshape nutrient cycling. The findings support the development of targeted, scenario-based strategies to mitigate eutrophication risks in vulnerable river systems. Full article

Coastal marshes provide essential ecosystem services, yet they are vulnerable to anthropogenic stressors and climate change, particularly sea level rise (SLR). Restoration approaches like marsh terracing have emerged as nature-based strategies to enhance resilience and reduce habitat loss. This study applies the Sea Level Affecting Marshes Model (SLAMM) to assess the potential of marsh terraces to mitigate future losses, while also examining the model’s limitations, including its assumptions and capacity to reflect complex marsh processes. A geospatial approach was used to generate 3D representations of terraces through morphostatic modeling within digital elevation models (DEMs). Under a no-restoration scenario, SLAMM projections show that all marshes analyzed are at risk of total loss by 2100. In contrast, scenarios including terracing demonstrate a delay in net marsh loss, extending the persistence of key marsh habitats by approximately a decade. Although marsh degradation remains likely under high SLR conditions, the results underscore the utility of marsh terraces in prolonging habitat stability. Additionally, the study demonstrates the feasibility of integrating restoration features like terraces into DEMs and wetland models. Despite SLAMM’s simplified erosion and accretion assumptions, the model yields important insights into restoration effectiveness and long-term marsh dynamics, informing more adaptive, forward-looking coastal management strategies. Full article

Moisture transport in building materials significantly influences their durability, mechanical integrity, and thermal performance. This study presents an experimental investigation of moisture permeability in a range of traditional and modern wall elements, including autoclaved aerated concrete (ACC), ceramic blocks, silicate blocks, perlite concrete blocks, and concrete units. Both vapor diffusion and capillary transport mechanisms were analyzed under controlled climatic conditions using gravimetric and hygrometric methods. Among the tested materials, autoclaved aerated concrete (AAC) was selected for detailed numerical modeling because of its high porosity, strong capillarity, and widespread use in modern construction, which make it especially vulnerable to moisture-related degradation. Based on the experimental findings, a digital twin was developed to simulate hygrothermal behavior of walls made of ACC under various environmental conditions. The model incorporates advanced moisture transport equations, capturing diffusion and capillary effects while considering real-world variables, such as relative humidity, temperature fluctuations, and wetting–drying cycles. Calibration demonstrated strong agreement with experimental data, enabling reliable predictions of moisture behavior over extended exposure scenarios. This integrated approach provides a robust engineering tool for assessing the long-term material performance of AAC, predicting degradation risks, and optimizing material selection in humid climates. The study illustrates how coupling experimental data with digital modeling can enhance the design of moisture-resistant and durable building envelopes. Full article

Due to climate warming, extreme precipitation events have intensified in frequency and intensity. This trend has raised significant concerns about its impact on natural reserves in eastern China’s monsoon region. A risk assessment is, therefore, needed to evaluate the vulnerability of these protected areas. Based on observed and simulated daily precipitation data, this study analyzed the spatiotemporal trends of heavy rainfall in the eastern monsoon region of China and assessed the exposure risk of the protected areas to rainstorm events both in the historical and future periods. Results indicate that the annual average number of heavy rainfall days gradually increases from northwest to southeast, displaying a distinct zonal distribution pattern. The proportion of heavy rainfall days to total precipitation days and the average intensity of heavy rainfall show peak centers in the southeastern coastal areas, western Sichuan region, and North China Plain, with minimum values observed in the northwestern direction. Protected areas in China’s Eastern Monsoon Region display a north–south gradient of precipitation exposure risk that intensifies from historical (1995–2014) to near future (2031–2050) to far future (2081–2100) under SSP245 scenario, with highest vulnerability in southeastern coastal areas. National reserves generally experience lower exposure than provincial and municipal ones, though all categories face increasing precipitation risks over time. Full article

Escalating extreme wildfires, fueled by the confluence of climate change, land use patterns alterations, ignitions by humans, and flammable fuels accumulation, pose significant and increasingly destructive risks to critical infrastructure (CI). This study presents a comprehensive comparative analysis of wildfire impacts and the corresponding CI resilience strategies employed across the EU and the US. It examines the vulnerability of CIs to the devastating effects of wildfires and their inadvertent contribution to wildfire ignition and spread. The study evaluates the EU’s CER Directive and the US National Infrastructure Protection Plan and assesses European Commission wildfire resilience-related initiatives, including FIRELOGUE, FIRE-RES, SILVANUS, and TREEADS flagship projects. It synthesizes empirical evidence and extracts key lessons learned from major wildfire events in the EU (2017 Portuguese fires; 2018 Mati wildfire) and the US (2023 Lahaina disaster; 2025 Los Angeles fires), drawing insights regarding the effectiveness of various resilience measures and identifying areas for improvement. Persistent challenges impeding effective wildfire resilience are identified, including governance fragmentation, lack of standardization in risk assessment and mitigation protocols, and insufficient integration of scientific knowledge and data into policy formulation and implementation. It concludes with actionable recommendations aimed at fostering science-based, multi-stakeholder approaches to strengthen wildfire resilience at both policy and operational levels. Full article

The escalating frequency and severity of natural disasters present significant challenges to the stability and sustainability of global financial systems, with the US stock market being especially vulnerable. This study examines sector-level exposure and contagion dynamics during climate-related disaster events, providing insights essential for sustainable investing and resilient financial planning. Using an advanced econometric framework—dynamic conditional correlation GARCH (DCC-GARCH) augmented with Generalized Hyperbolic Processes (GHPs) and an asymmetric specification (ADCC-GARCH)—we model daily stock returns for 20 publicly traded US companies across five sectors (insurance, energy, automotive, retail, and industrial) between 2017 and 2022. The results reveal considerable sectoral heterogeneity: insurance and energy sectors exhibit the highest vulnerability, with heavy-tailed return distributions and persistent volatility, whereas retail and selected industrial firms demonstrate resilience, including counter-cyclical behavior during crises. GHP-based models improve tail risk estimation by capturing return asymmetries, skewness, and leptokurtosis beyond Gaussian specifications. Moreover, the ADCC-GHP-GARCH framework shows that negative shocks induce more persistent correlation shifts than positive ones, highlighting asymmetric contagion effects during stress periods. The results present the insurance and energy sectors as the most exposed to extreme events, backed by the heavy-tailed return distributions and persistent volatility. In contrast, the retail and select industrial firms exhibit resilience and show stable, and in some cases, counter-cyclical, behavior in crises. The results from using a GHP indicate a slight improvement in model specification fit, capturing return asymmetries, skewness, and leptokurtosis indications, in comparison to standard Gaussian models. It was also shown with an ADCC-GHP-GARCH model that negative shocks result in a greater and more durable change in correlations than positive shocks, reinforcing the consideration of asymmetry contagion in times of stress. By integrating sector-specific financial responses into a climate-disaster framework, this research supports the design of targeted climate risk mitigation strategies, sustainable investment portfolios, and regulatory stress-testing approaches that account for volatility clustering and tail dependencies. The findings contribute to the literature on financial resilience by providing a robust statistical basis for assessing how extreme climate events impact asset values, thereby informing both policy and practice in advancing sustainable economic development. Full article

Climate change is an ongoing threat to heritage assets with compound climate extremes. Risk assessment and vulnerability approach show the consistent deterioration of ancient monuments. This study focuses on the open-air Greek theatre, a recognizable structure with cultural values and exposed to the climatic conditions. The methodology of this study is the collection of historical meteorological data from weather stations to assess the frequency and impacts of warm–wet, warm–dry, cold–wet, and cold–dry climate extremes on these structures. Climate historic documentation indicates an increasing frequency of these compound extremes, intensifying the structural degradation. Heritage management should include the mitigation–adaptation strategy based on the climate data and the assessment of climate change challenges for the protection of heritage and, in particular, of Greek open-air theatres. Full article

This study investigates the multifaceted factors influencing the success of government-funded construction projects and addresses the challenges posed by climate-induced flooding, proposing integrated solutions encompassing structural vulnerability, system capacity, and organizational preparedness. By examining the challenges faced by coastal infrastructure, such as aging infrastructure, sea-level rise, and extreme weather events, this research seeks to identify strategies that enhance resilience and minimize the impact of flooding on coastal communities. The study presents a systematic review of 80 scholarly articles integrating quantitative and qualitative findings. Utilizing the PRISMA guidelines, the review highlights structural analysis, hydraulic modeling, and organizational surveys, to assess the resilience of coastal infrastructure systems. The results of this study offer actionable insights for policymakers, infrastructure managers, and coastal communities, facilitating informed decision-making and promoting climate-resilient development. Coastal regions around the world are increasingly vulnerable to climate-induced hazards such as sea level rise, storm surges, and intense flooding events. Among the most at-risk assets are transport infrastructure and buildings, which serve as the backbone of urban and regional functionality. This research paper presents a multidimensional assessment framework that integrates structural vulnerability, system capacity, and organizational preparedness to evaluate the resilience of coastal infrastructure. Drawing upon principles of resilience such as robustness, redundancy, safe-to-fail design, and change-readiness, the study critically reviews and synthesizes existing literature, identifies gaps in current assessment models, and proposes a comprehensive methodology for resilience evaluation. By focusing on both transport systems and building infrastructure, the research aims to inform adaptive strategies and policy interventions that enhance infrastructure performance and continuity under future climate stressors. Full article