Search Results (3,491)

Reliable rainfall information is fundamental for climate-risk analysis and operational monitoring in Mediterranean regions such as Apulia (Southern Italy), one of the areas most affected by climate change-driven shifts in rainfall patterns. Recent evaluations across Italy and comparable Mediterranean settings consistently show that gridded precipitation performance is highly dependent on orography and dataset typology: reanalyses often provide the best overall agreement with gauges, while satellite and blended products can exhibit larger biases, with persistent challenges in complex terrain and for high-intensity events. In this context—and given the documented spatial heterogeneity of rainfall extremes within Apulia—validation of such gridded datasets with respect to ground observations remains essential for early warning and climatological applications. In the present work, we evaluate four widely used precipitation products—CHIRPS-v2, the newly released CHIRPS-v3, IMERG, and ERA5—benchmarking them against the Apulia region Civil Protection rain-gauge network. We provide diagnostics aligned with early warning and climate monitoring: bias and error statistics, rainfall intensity distributions, and dry spell duration. A key contribution is, to our knowledge, the first dedicated validation of CHIRPS-v3 in Apulia, which is timely given that CHIRPS-v3 was explicitly developed to address shortcomings such as underestimated temporal variance and to leverage expanded station inputs. The results indicate that CHIRPS-v3 yields systematic improvements over CHIRPS-v2 across multiple metrics, while ERA5 generally shows the strongest overall agreement with gauges—consistent with broader Italian evidence. Full article

Natural lakes in urbanizing regions face compounding climatic and anthropogenic pressures. Despite their socio-ecological importance, the dual vulnerability of these urban lakes to both long-term areal shrinkage and the shifting frequencies of extreme water events remains a critical research gap, often overlooked in favor of large, remote lake systems. We investigated surface area dynamics, extreme events, and climatic attribution of 7320 natural lakes across China’s five major urban agglomerations (Jing-Jin-Ji, Yangtze River Delta, Greater Bay Area, Chengdu-Chongqing, and Middle Yangtze) from 2001 to 2023. Using a satellite area product, we assessed long-term trends via Seasonal-Trend decomposition by Loess (STL). Regional climate shifts were detected via multi-scale Standardized Precipitation–Evapotranspiration Index (SPEI) breakpoint analysis, and climate attribution was performed by correlating detrended lake areas with SPEI. Results show 59.4% of lakes exhibit significant trends, with shrinkage (50%) vastly outpacing expansion (9.4%), most severely in Jing-Jin-Ji (−0.28%/year). Despite all agglomerations transitioning toward wetter conditions (2008–2013), extreme event responses diverged markedly regionally. Climate-driven lakes (14.5%) displayed stronger shrinkage and greater sensitivity to extremes than lakes with low climate sensitivity, particularly in Jing-Jin-Ji and Chengdu-Chongqing. These findings reveal pronounced spatial heterogeneity in urban lake vulnerability, providing an evidence base for sensitivity-stratified management strategies. Full article

Rainfall variability strongly influences both flood and drought hazards, especially in climatic transition zones where precipitation is highly seasonal and spatially heterogeneous. This study assessed long-term changes in rainfall patterns and compound flood–drought hazard in the Huaihe River Basin, China, using ERA5-Land-derived daily precipitation series at 174 spatial sampling locations during 1950–2025. Rainfall pattern indicators, flood-related rainfall extremes, and SPI-3-based drought indicators were calculated to characterize rainfall amount, frequency, intensity, dry–wet persistence, heavy rainfall events, and meteorological drought conditions. The Mann–Kendall test and Sen’s slope estimator were used to detect long-term trends, and a compound flood–drought hazard classification framework was developed based on a flood-related rainfall hazard index (FHI) and a drought-related hazard index (DHI). The results showed that annual total precipitation, wet days, and consecutive wet days decreased significantly, indicating reduced rainfall occurrence and wet spell persistence. Flood-related rainfall indicators generally showed decreasing tendencies, with more evident declines in persistent multi-day extremes than in single-day rainfall. In contrast, mean SPI-3 showed a significant drying tendency, although drought frequency, severe drought frequency, and drought intensity did not exhibit significant monotonic trends. Spatially, rainfall pattern, flood-related, and drought-related indicators showed clear heterogeneity across the basin. The compound hazard classification identified flood-dominated and drought-dominated areas as the two major hazard types, each accounting for 31.03% of the spatial sampling locations, while low compound hazard and compound flood–drought hazard areas each accounted for 18.97%. These findings indicate that flood- and drought-related hazards coexist but vary spatially across the Huaihe River Basin. The proposed framework provides preliminary rainfall-based information for differentiated flood–drought hazard assessment, climate-adaptive water resources planning, and the sustainable management of water resources in regions facing spatially heterogeneous hydroclimatic hazards. Full article

This paper examines building performance under extreme conditions by treating thermal resilience as a process-based and time-dependent property. Existing approaches remain fragmented: resentation, extreme event definition, resilience metrics, passive strategies, and energy systems are typically handled in isolation, leaving current methods with limited capacity to explain how buildings respond to prolonged disruptions such as heatwaves or power outages. The study offers a cross-domain synthesis of thermal resilience research, drawing together climate modelling, indoor thermal response, resilience metrics, passive design strategies, distributed energy systems, and regulatory constraints. Building on this synthesis, a trajectory-based framework is developed that links climate inputs, event definition, indoor thermal response, and performance metrics within a unified structure. This integration is extended into architectural design through a decision-oriented framework that interprets resilience as the outcome of a hierarchy of decisions structured by reversibility and operational dependence. Early design decisions define the constraints and the range of achievable performance within which subsequent optimisation occurs. Building performance emerges from the interaction of passive strategies and energy-supported systems under constrained conditions. The results establish that thermal resilience cannot be inferred from conventional indicators; it must be understood through the temporal evolution of indoor conditions. The proposed framework provides a consistent basis for linking resilience assessment with design decision-making, supporting a unified approach to resilience-oriented design. Full article

Against the background of climate change intensifying extreme hail events, the photovoltaic module industry faces a critical trade-off between improving hail resistance and maintaining environmental sustainability. This study establishes an emergy–life cycle coupling assessment framework to systematically evaluate the environmental sustainability of six typical hail resistance enhancement designs across four hail risk scenarios in China. Five hierarchical hypotheses are proposed and validated through quantitative analysis. The optimal design point occurs at 30 mm hail resistance using 3.2 mm tempered glass, achieving a minimum unit environmental impact per impact resistance UEIC of 9.63 × 10¹² sej/mm. The ranking divergence index SDR between coupled emergy–LCA and conventional LCA methods is 0.267, with ecosystem service dependence ESD reaching 0.241 for composite backsheet designs, revealing natural capital overlooked by traditional methods. A complete ranking reversal occurs at a threshold hail frequency of 1.3 events per year, above which the 3.2 mm glass design outperforms standard modules with life cycle emergy input LCEA of 3.20 × 10¹⁴ sej versus 3.41 × 10¹⁴ sej under high-risk scenarios. Material type dominates environmental impact over structural parameters by a factor of 2.32, with recycled aluminum frames reducing ELCI by 12.4%. The dual-optimum design is identified as the 3.2 mm tempered glass scheme, achieving a combined sustainability score CSS of 0.782 and emergy yield ratio EYR of 3.86, outperforming the industry average of 3.61. Multi-objective optimization using NSGA-II yields a Pareto front of 12 non-dominated solutions, with the 3.2 mm glass design maintaining Pareto optimal status in 72% of Monte Carlo iterations. This research provides a quantitative decision-making framework recommending standard modules for regions below one annual hail event, the 3.2 mm glass design for regions between one and four annual events, and steel frame combinations above four annual events, demonstrating that moderate enhancement achieves the optimal balance between hail protection and environmental sustainability. Full article

Streamflow variations in the Middle Minjiang River Basin (MMR) are vital for the flood mitigation and water resources management of the Chengdu metropolitan area which is important for the development of Southwest China. However, how climate change, Chengdu metropolitan area and Zipingpu Reservoir influence streamflow in the MMR remains unclear. Hence, we coupled the Geomorphology-Based Ecohydrological Model (GBEHM), the Physic-aware Hybrid Learning (PaHL) model and the Extreme Gradient Boosting (XGBoost) model to reproduce streamflow variations at Pengshan station—the outlet cross section of MMR—from 1980 to 2019, subsequently performing attribution analysis. Annual streamflow at Pengshan station exhibits a decreasing trend from 1980 to 2019. Coupled simulations effectively reproduce daily streamflow at Pengshan station during 35 years, with values of NSE, R² and KGE exceeding 0.96. The dominant influence of anthropogenic disturbance on daily streamflow decrease is generally steady at Pengshan station, explaining 62.3% and 430.8% of it before and after the impoundment of Zipingpu Reservoir (in 2006), respectively. Majority of the climate change’s influence is notably concentrated from June to September, suggesting a potential temporal imbalance in water resources and a threat of extreme hydrological events. Our study contributes to flood mitigation and water resources management in the MMR. Full article

Under the “dual carbon” targets, using green finance to support renewable energy use is an important way to reduce extreme climate risks. This study builds a balanced panel dataset of 271 Chinese cities from 2010 to 2021. We measured the level of Green Finance (GF) and renewable energy utilization (RE). Employing two-way fixed effects, the Spatial Durbin Model (SDM), and the Heterogeneous Spatial Autoregressive (HSAR) model, we systematically examine the promoting effects, transmission mechanisms, spatial heterogeneity, and economic–environmental consequences of GF on RE. The empirical results reveal that GF significantly enhances RE and generates pronounced positive spatial spillovers. Mechanism analysis indicates that R&D investment and environmental regulation serve as the primary transmission channels. The promotion effect is more pronounced in the eastern and central regions, as well as in areas with higher R&D investment and stricter environmental regulation, whereas the spatial spillover effect is particularly evident in coastal regions. Further consequence analysis demonstrates that GF contributes to reducing conventional energy intensity, improving green total factor productivity, and alleviating extreme climate events. Building on these findings, this study proposes spatially differentiated and sustainability-oriented policy strategies to advance China’s energy transition and foster coordinated economic and environmental sustainability. Full article

Indian metropolitan cities such as Mumbai grapple with rapid urbanisation, extreme urban density, high built-up areas, loss of green cover, and shrinking open spaces, resulting in increased impermeable surfaces, urban heat island effects, and frequent flooding occurrences. Modern stormwater management has increasingly been characterised by integrated grey-green approaches; however, cities in the Global North benefit from established policies, technical expertise, and financial resources that enable the systematic and large-scale integration of Blue-Green Infrastructure (BGI) through district-wide geospatial assessment frameworks, unlike many cities in the Global South. Despite growing interest in nature-based stormwater solutions, there remains a dearth of geospatial empirical research from India examining the placement, distribution, performance, and functionality of BGI integrated with existing stormwater management systems in cities such as Mumbai. Furthermore, hydrological modelling using tools such as the Storm Water Management Model (SWMM) for the design, planning, and implementation of BGI in Indian cities remains largely unexplored. This study explores the role of BGI strategies in improving urban stormwater management within high-density Indian cities under a 25-year return period extreme rainfall scenario. Using an integrated approach that combines QGIS-based spatial analysis with EPA-SWMM hydrologic-hydraulic modelling, the research examines runoff behaviour, identifies flooding hotspots, and evaluates the effectiveness of Low Impact Development (LID)-based BGI measures such as permeable pavements, infiltration trenches, and green roofs applied at the ward level in Mumbai’s F/North and G/North Wards. Detailed land use classification, spatial mapping, and rainfall simulation corresponding specifically to a 25-year return period rainfall event was used to assess pre- and post-intervention conditions. The findings indicate that the applied BGI measures led to a 12.6% reduction in peak runoff (137.6 m³/s to 120.2 m³/s) and a 5.5% decrease in total runoff volume (783,510 m³ to 740,410 m³). More importantly, the peak flooding flow rate decreased by 45% (94.1 m³/s to 51.7 m³/s), demonstrating that BGI measures can efficiently reduce peak flooding flows by extending runoff hydrographs during extreme rainfall events. These findings are specifically applicable to the simulated 25-year return period extreme rainfall scenario and may vary under different rainfall intensities or return periods. Less extreme events could potentially experience even greater relative reductions or prevent flooding altogether, while also easing downstream hydraulic loads. Overall, strategically placed BGI interventions can significantly reduce surface runoff and peak flow, thereby enhancing stormwater resilience within spatially constrained urban environments. This study provides a replicable, data-driven framework for catchment-scale stormwater planning in dense Indian cities under extreme rainfall conditions, offering practical insights into methods, local contextual considerations, and spatial planning strategies for policymakers and urban planners seeking to retrofit and adapt existing infrastructure under increasing hydrologic stress and climate variability. Full article

Climate change is reshaping agricultural systems by altering temperature and rainfall regimes, increasing the frequency of extreme events, and intensifying risks to crop productivity, water use, and farm decision-making. As climate–agriculture research expands rapidly, it becomes increasingly difficult to identify consolidated knowledge domains, emerging priorities, and evidence gaps. This study maps the structure and evolution of this literature using 219,261 Scopus-indexed documents selected from 290,560 records published between 1990 and 2025. A text-mining workflow combined BERTopic-based semantic modeling with supervised thematic classification into 18 macro-themes, while annual shares, z-scores, and document-level primary–secondary co-framing were used to assess temporal salience and cross-theme coupling. The results show sustained growth in research output, with 53.67% of publications produced between 2016 and 2025, and strong geographical concentration in the United States and China, which together account for 41.98% of the corpus. Hydrology and water management, crop production, impact assessment, and atmospheric processes remain central pillars, while socio-economic vulnerability, food security, sustainability, biotechnology, and greenhouse gas mitigation have gained prominence. The resulting evidence map provides a reproducible overview of the climate–agriculture knowledge landscape and can support research prioritization and policy design for climate-resilient agrifood systems. Full article

As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationships, bypassing continuous crop physiological responses and limiting their capacity to capture fine-grained temporal impacts of meteorological anomalies. To address this, we propose a novel two-stage spatiotemporal functional framework that integrates high-resolution daily weather trajectories with satellite-derived indicators, utilizing the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to represent canopy structural vigor and hydraulic status, respectively. In the first stage, a Historical Functional Linear Model (HFLM) dynamically maps daily meteorological trajectories (temperature, precipitation, and solar radiation) onto continuous physiological curves under strict temporal causality constraints. This generates bivariate coefficient surfaces that reveal dynamic windows of vulnerability and capture divergent, lagged physiological responses to climate stress. In the second stage, a spatially heterogeneous functional additive model integrates these weather-shaped physiological trajectories alongside raw meteorological dynamics as joint predictors for county-level yields. By extracting functional principal components and modeling flexible non-linear biological responses while accounting for continuous spatial heterogeneity, this dual-channel frameworkcaptures key aspects of both chronic physiological stress and acute meteorological shocks. Validated across a 25-year (2000–2024) U.S. Corn Belt panel, the proposed DC-FAM achieves a mean weighted mean squared prediction error (WMSPE) of 242.33 (bu/acre)² and a median out-of-sample $R_{\mathrm{cv}}^2$ of 0.422, outperforming all benchmarks including a random forest. Attribution of the 2012 flash drought further demonstrates the framework’s capacity to mechanistically trace the complete disaster propagation chain from anomalous spring warming to mid-summer hydraulic failure. The proposed framework provides a transparent, biophysically grounded tool for decoding dynamic climate stress trajectories and disaster propagation chains, offering potential implications for adaptive farm management and precision agricultural insurance. Full article

Heatwaves and tropical nights are emerging as significant public health challenges under accelerating climate change, with middle-aged and older adults demonstrating heightened vulnerability. This scoping review maps the existing evidence on how nocturnal heat affects sleep in middle-aged and older adults aged 45 and above, synthesizing findings from experimental and observational studies published in English over the past decade. A comprehensive search of PubMed and Scopus, supplemented by reference screening, identified 31 relevant studies. Data on study design, population characteristics, heat exposure metrics, sleep outcomes, and interventions were charted and synthesized narratively due to methodological heterogeneity. Across studies, elevated nighttime temperatures consistently reduced total sleep time and sleep efficiency, increased wake after sleep onset, and disrupted sleep architecture, particularly REM and N3 stages. Environmental, behavioral, and physiological interventions such as improved ventilation, targeted cooling strategies, and pre-sleep thermal management partially mitigated heat-related sleep disruption. Overall, the findings highlight gaps in standardized exposure metrics and harmonized sleep assessment, providing guidance for future research and public health strategies aimed at protecting sleep health in middle-aged and aging populations amid increasingly frequent extreme heat events. Full article

The increasing frequency of extreme weather events, particularly hailstorms, driven by climate change, poses growing threats to the resilience, environmental sustainability, and long-term performance of photovoltaic (PV) systems. This study evaluates the environmental impacts of a 12 kWp rooftop PV installation in Brescia, northern Italy, through a comparative Life Cycle Assessment (LCA) of three system configurations: a standard unprotected system (Scenario A), one equipped with a retractable polycarbonate hail-protection panel with automated weather-sensor activation (Scenario B), and one using thicker reinforced front-glass modules (Scenario C). The analysis follows a cradle-to-gate plus operational maintenance phase (30-year horizon, excluding end-of-life) system boundary and employs the ReCiPe 2016 Midpoint (H) methodology across 18 environmental impact categories. A novel integration of the Social Discount Rate (SDR) to the LCA framework—constituting a Discounted LCA (D-LCA)—incorporates both temporal discounting and risk dimensions into the environmental evaluation. A structured PESTEL-based risk taxonomy is applied to derive scenario-specific SDRs, with the Environmental risk category as the key differentiator between configurations. The static LCA identifies Scenario A as the lowest-impact option, while the D-LCA framework reverses this ranking: Scenario C achieves the highest Net Present Value of Emissions, followed by Scenario A. A negative NPV-E for Scenario B reflects the temporal cost of a large, front-loaded construction debt rather than absolute environmental harm. D-LCA framework should be interpreted as a complement to the full 18-category static LCIA profile, not a replacement. These results demonstrate that risk-informed D-LCA provides a more policy-relevant environmental sustainability assessment than static LCA for long-lived energy infrastructure subject to climate-driven operational risks. Full article

Introduction: Marine heatwaves are increasing in frequency and intensity, posing major challenges for fishes inhabiting shallow coastal ecosystems. Short-term exposure to extreme warming can alter metabolic performance and thermal tolerance, with potential consequences for species persistence and school composition in thermally variable habitats. Understanding the capacity of coastal fishes to withstand acute warming events is therefore essential for predicting ecological responses to climate change. Objective: We aimed to determine the effects of simulated marine heatwaves on thermal tolerance and metabolic performance in juvenile grey mullets, Chelon labrosus and Chelon aurata, two abundant sympatric species inhabiting the Ria Formosa lagoon (southern Portugal). Methodology: Juvenile mullets acclimated at 17 °C were exposed to simulated heatwave treatments of 23, 27, or 33 °C and sampled either at peak temperature or after 48 h and 1-week recovery at 17 °C. Critical thermal maximum (CTmax, using a 1 °C/min thermal ramp), static oxygen consumption (MO2), and intermittent respirometry parameters were measured. Standard metabolic rate (SMR), maximum metabolic rate (MMR), and aerobic scope (AS) were derived from intermittent respirometry. A complementary temperature-ramp (>3 h at each temperature step 17, 23, 27 and 33 °C) was performed to evaluate routine metabolic rate and estimate Q10 values across increasing temperatures. Additional plasma and tissue analyses are being conducted to assess energetic substrate mobilization and cellular responses to thermal and oxidative stress. Results: CTmax increased significantly with warming in both treatment modes, demonstrating rapid heat hardening in juvenile mullets. Fish exposed to 27 and 33 °C exhibited higher CTmax than control fish, and this elevated tolerance persisted after recovery. Chelon labrosus showed slightly higher CTmax values than C. aurata. Oxygen consumption increased with temperature, with the strongest responses occurring at 33 °C. SMR increased markedly with warming, particularly in heatwave-exposed fish, while MMR increased mainly at the highest temperature treatment. In contrast, AS showed no clear thermal optimum or decline across treatments. Routine metabolic rate increased non-linearly with temperature in the complementary ramp experiment, with a mean Q10 of 2.28, confirming strong thermal dependence of metabolism. Conclusions: Juvenile mullets possess substantial short-term thermal plasticity and can rapidly increase heat tolerance during marine heatwaves but this enhanced tolerance is accompanied by elevated metabolic costs under extreme warming, indicating potential energetic trade-offs near upper thermal limits. Differential physiological responses between species may influence school composition and ecological performance across thermal landscapes. Ongoing plasma and tissue analyses will further clarify the energetic and cellular mechanisms underlying thermal and oxidative stress resilience in coastal fishes. Full article

Current climate change has led to significant changes in precipitation patterns in the Loess Hilly Region, resulting in frequent extreme rainfall events, which have a significant impact on restoring the soil hydrological function of grasslands in this area. This study focuses on the restoration of grasslands through the conversion of farmland in the Loess Hilly Region. Using natural rainfall as the control, seven precipitation gradient treatments were established with rainout shelters: +20%, +40%, and +60% rainfall increases, and −20%, −40%, and −60% rainfall decreases. The changes in infiltration characteristics were then analyzed. Long-term increased rainfall promoted vegetation restoration and improved soil physicochemical properties. Compared with the natural rainfall control, the +20%, +40%, and +60% rainfall increase treatments enhanced the total porosity of the 0–5 cm soil layer by 0.29%, 4.64%, and 3.18%, respectively, and increased the soil organic carbon content by 28.42%, 62.46%, and 63.16%, respectively. Soil infiltration rate was also enhanced accordingly. Relative to the steady-state infiltration rate of the control (4.76 mm/min), the +20%, +40%, and +60% treatments increased the rate by 1.13%, 16.67%, and 22.54%, respectively, with the +60% treatment achieving the highest steady-state infiltration rate of 5.83 mm/min. The macroaggregate content in the +40% treatment was 47.70%, which was significantly higher than that in the other treatments. The increase in infiltration was related to the increase in total porosity, organic carbon, and the content and stability of large aggregates. Moderate rainfall increases can promote organic carbon accumulation and the formation of large aggregates, enhancing soil infiltration capacity; however, rainfall intensities exceeding 60% can damage the soil structure, and infiltration no longer significantly increases. Full article

Environmental risk management in urban areas has become increasingly important in recent decades, mainly due to climate change and the anticipated rise in the frequency and severity of extreme rainfall events. In highly urbanized environments, these conditions can intensify hydraulic stress on drainage systems, leading to flooding and surcharge within combined sewer networks. Continuous simulations (2008–2018) were performed using coupled hydrological–hydraulic modeling. Discharge outputs and rainfall series were aggregated at hourly resolution and segmented into independent events. Results show marked seasonality: ~86 events/year and ~118 events/year were identified, with higher occurrence in autumn and winter and fewer events in summer. Event duration tends to be longer from late autumn to spring, whereas summer events are generally shorter. Conversely, peak rainfall and peak discharge exhibit higher median values and variability during summer and early autumn, consistent with intense convective Mediterranean storms. Full article