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Hydrological conditions in the Mackenzie River Basin (MRB) are becoming increasingly variable due to climate change, permafrost degradation, and cumulative industrial impacts. While scientific assessments have documented many of these trends, far less is known about how changing water levels and flow patterns affect the daily lives, livelihoods, and cultural responsibilities of Indigenous Peoples across the Basin. This paper synthesizes basin wide Indigenous Knowledge related to water level and flow variability, drawing on 31 Indigenous-led research projects. The analysis highlights shared concerns across regions, including reduced travel safety, restricted access to harvesting areas, shifting river and lake behaviour, and emotional and spiritual impacts associated with hydrological extremes. These observations align with scientific evidence of earlier breakup, prolonged low-water periods, and increased hydrological unpredictability, while also revealing social and cultural dimensions not captured through conventional monitoring. By synthesizing basin wide Indigenous Knowledge of water level and flow variability, this study provides new insight into the cumulative social ecological consequences of freshwater change in the MRB and underscores the importance of Indigenous-led research and governance in responding to accelerating hydrological variability. Full article

Buildings account for a significant share of final energy consumption, with space heating representing one of the major energy uses in residential buildings. Therefore, improving the thermal performance of building envelopes is an important strategy for reducing energy demand. Green roofs can contribute to this objective by modifying roof thermal properties and reducing heat losses through the building envelope. This study investigates the use of machine learning to predict annual heating demand and potential heating energy savings associated with replacing conventional roof configurations with a selected green roof assembly in a representative stock of Macedonian buildings. A representative dataset comprising 2934 building cases based on post-2013 buildings designed in accordance with the national energy-performance regulations was assembled. The dataset covers a wide range of building typologies, envelope thermal properties, climatic conditions and heating schedules. Three supervised learning models, Random Forest, Artificial Neural Network and Extreme Gradient Boosting (XGBoost), were developed and compared. The results show that XGBoost achieved the highest predictive accuracy and the best computational efficiency, with test coefficients of determination of 0.9901 for the heating demand of conventional roof buildings and 0.9956 for green-roof-related heating energy savings. Most simulated buildings showed heating energy savings of up to 10% following green roof implementation, while only a limited number of cases exhibited increases in heating demand of up to 3%. The feature importance analysis identified heated floor area, heating duration and wall area as the major drivers of heating demand in conventional roof buildings, whereas roof thermal transmittance was the most influential factor governing green-roof-related heating energy savings. The findings demonstrate that machine learning can reliably reproduce the results of the established energy performance assessment methodology and provide rapid estimates of the potential heating energy savings associated with replacing conventional roofs with a selected green roof system across a representative building stock. The proposed approach can support engineers, urban planners and architects in the early-stage assessment of green roofs as an energy-efficient measure. Full article

As climate change intensifies heat extremes, the Urban Heat Island (UHI) effect amplifies local thermal stress. Assessing the UHI using robust observational data, whether ground- and/or satellite-based, is essential for climate risk assessment and evidence-based urban adaptation. Therefore, this study aims to provide a comprehensive climatological assessment of air temperature patterns and UHI intensity across the Lisbon Metropolitan Area (LMA) over a 26-year period (2000–2025). The methodology employs a dense, high-quality integrated network of in-situ weather stations from the Portuguese Institute for Sea and Atmosphere (IPMA) and the National Water Resources Information System (SNIRH). To bridge critical gaps in traditional climate assessments, this research implements a dual-perspective approach that combines the high temporal resolution of MSG-SEVIRI and the spatial precision of MODIS Land Surface Temperature (LST). This framework accurately captures the lag effects between surface heating and atmospheric response. Validation results demonstrate that satellite-derived LST is a robust proxy for monitoring the nocturnal UHI, with differences generally below 1 °C compared with near-surface air temperature observations (T2m). However, daytime LST significantly overestimates atmospheric temperatures, with deviations of 2–8 °C due to solar radiation and urban geometry. The selection of rural reference stations constitutes a critical methodological factor, as a baseline shift can alter perceived UHI intensities by more than 3 °C. Despite these sensitivities, the results unequivocally confirm a persistent and spatially heterogeneous UHI effect in Lisbon, which intensifies during extreme heat events by up to an additional 4 °C. Analysis of the 2003 and 2018 heatwaves reveals surface LST anomalies exceeding 10 °C and urban–rural thermal differentials reaching up to 7 °C under conditions of suppressed maritime breezes. These nocturnal anomalies are particularly pronounced in densely built-up areas, limiting thermal dissipation and preventing physiological recovery. Integrating multi-sensor satellite data with in-situ validation provides a new benchmark for climate risk assessments, delivering the reliable, reproducible data required to strengthen long-term urban resilience under increasingly frequent extreme heat events. Full article

Accurate daily runoff prediction is essential for flood control, reservoir operation, and scientific water resources management. However, runoff processes are increasingly affected by climate change and human activities, leading to pronounced nonlinearity and nonstationarity that limit the performance of single data-driven models. This study aims to improve the reliability and hydrological credibility of daily runoff prediction by systematically evaluating recurrent neural network (RNN) structures and explicitly modeling prediction residuals. Three commonly used RNN architectures—long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM)—are systematically compared for daily runoff prediction in the Andi Reservoir watershed under identical hydrometeorological conditions. Based on the comparative results, BiLSTM is selected as the base model to capture dominant temporal dependencies. To further address systematic prediction errors under complex hydrological conditions, a residual-learning framework is constructed by integrating BiLSTM with extreme gradient boosting (XGBoost), in which XGBoost is employed to model and correct the nonlinear residuals of BiLSTM predictions. In addition, the Shapley Additive Explanations (SHAP) method is applied to interpret the contributions of input variables and to examine the learning mechanisms of both the base model and the residual-correction stage. Results indicate that BiLSTM performs better than LSTM and GRU for daily runoff prediction and that residual correction using XGBoost further enhances prediction accuracy and robustness, particularly under nonstationary conditions and peak-flow scenarios. The contribution of this study lies in providing a systematic modeling framework that combines model comparison, residual learning, and interpretability analysis to support more reliable daily runoff prediction in complex watersheds. Full article

With global warming and rapid urbanization, short-duration summer rainstorms are becoming more intense and localized, posing growing challenges to urban flood resilience. However, their spatiotemporal characteristics, vertical structures, and environmental drivers remain poorly understood. Here, we combine multi-source remote sensing datasets and China’s new-generation satellite-borne dual-frequency precipitation radar observations to investigate summer rainstorms in Nanjing, China, during 2017–2024. Results reveal pronounced spatiotemporal heterogeneity, with higher rainfall intensities concentrated over urban and adjacent areas. During the study period, rainstorm intensity and duration increased by 7.44% and 38.63%, respectively, while the affected area decreased by 8.18%, indicating a transition toward more localized yet more intense rainfall events. Environmental analyses suggest that large-scale thermodynamic conditions and regional topographic forcing provide a favorable background for convection development, while local urban thermal effects may further modulate rainfall enhancement. Three-dimensional radar detection of an illustrative rainstorm event indicates an inverted-cone vertical structure, suggesting a mixed convective-stratiform precipitation structure involving both warm-rain and ice-phase processes. An Explainable Bayesian-Optimized XGBoost (EBOX) model further identifies near-surface air temperature and specific humidity as the primary environmental factors associated with rainstorm occurrence and development. Overall, this study highlights the value of integrating satellite remote sensing with explainable artificial intelligence to improve understanding of urban extreme rainfall and provide new insights into how climate change, topography, and urbanization jointly shape precipitation extremes in rapidly urbanizing monsoon regions. Full article

Flood risk is increasingly shaped by the combined effects of climate change and the vulnerability of built environments, while building-level flood damage severity analysis is often constrained by limited data availability. This study develops a transparent and reproducible framework for analyzing building-level flood damage severity under climate-adjusted hazard conditions in data-constrained environments. The framework integrates administrative post-event damage records, GIS-based terrain information, a terrain-based proxy flood-depth reconstruction procedure, and a standardized Rhine Atlas/ICPR depth–damage relationship. Representative terrain-based proxy flood depths are reconstructed using building locations, terrain elevation, and settlement-level exposure assumptions. Observed damage categories are not used to assign proxy flood depths directly, but serve exclusively as empirical ordinal reference information for ordinal consistency assessment of model-derived damage severity. Climate effects are incorporated through a simplified hazard adjustment based on projected changes in extreme precipitation intensity. The framework is applied to 413 residential buildings affected by flood events in Serbia during the period 2016–2021. Results show a consistent nonlinear relationship between terrain-based proxy flood depth and ICPR-derived structural damage severity, as well as a strong influence of terrain elevation on relative hazard intensity. Climate-adjusted sensitivity scenarios indicate that even moderate increases in extreme precipitation lead to measurable increases in structural damage severity and an upward shift in model-derived damage levels. The proposed framework provides a practical approach for flood damage severity analysis in data-constrained environments, supporting improved decision-making in sustainable flood risk management and climate adaptation planning. Full article

HI35, defined as the mean annual number of days on which apparent temperature exceeds 35 °C, is introduced in this paper as a country-level heat exposure metric. Unlike vulnerability indicators, HI35 is treated as an exogenous climatic exposure variable and not as a direct measure of vulnerability or as an endogenous outcome. By combining HI35 with World Bank Environmental, Social, and Governance (ESG) indicators, this study applies unsupervised clustering algorithms to derive an exposure–vulnerability typology of countries. The Environmental, Social, and Governance pillars are analyzed separately through dedicated clustering procedures, supported by robustness checks and an additional country taxonomy based on principal component analysis and hierarchical clustering. The results identify heterogeneous country profiles in which similar levels of heat exposure coexist with different ESG-based vulnerability conditions, including environmental pressures, fragility, institutional capacity, innovation, water access, nutrition, and governance quality. Conversely, countries with relatively low heat exposure may display differentiated social or institutional vulnerability profiles. The empirical evidence suggests that heat exposure and ESG-based vulnerability are conceptually distinct but jointly relevant dimensions for classifying climate-risk profiles. No causal relationship is inferred between ESG indicators and HI35; the analysis is descriptive, classificatory, and based on unsupervised learning. Conceptually, HI35 captures the occurrence of extreme heat events, while ESG indicators describe the environmental, social, and institutional conditions associated with sensitivity, resilience, and adaptive capacity. Full article

Agronomic practices and interannual climate variability significantly modulate the bioactive composition of agricultural by-products. This study evaluated the effects of farming systems (organic vs. conventional) and geographic locality across two harvest seasons (2022–2023) on the fatty acid (FA) profiles of peel and seeds from the “Mollar de Elche” pomegranate (Punica granatum L.) Protected Designation of Origin (PDO). Gas chromatography (GC-FID) analyses demonstrated that the harvest year, characterized by significantly reduced extreme temperature days in 2023, exerted a dominant, overriding effect on lipid biosynthesis compared to agronomic management. In the seeds, punicic acid was the unequivocal predominant FA, increasing dramatically from an average of ~75,700 mg/kg dry matter (DM) under severe heat stress (2022) to ~150,000 mg/kg DM under milder conditions (2023) (p < 0.001). In the peel, polyunsaturated fatty acid (PUFA) accumulation was strictly dependent on the interaction between localized geographic micro-conditions and climate, rendering the farming system a secondary factor. Crucially, the milder 2023 season significantly enhanced the unsaturated-to-saturated (U/S) ratio in both tissues and markedly improved cardiovascular lipid quality, lowering both the Atherogenic (AI) and Thrombogenic (TI) indices. These findings demonstrate that while organic farming can optimize lipid unsaturation under favorable climatic conditions, severe environmental stress nullifies these agronomic benefits, highlighting the need for climate-resilient strategies to valorize pomegranate by-products. Full article

Mediterranean islands face increasing climate risks from rising temperatures, prolonged droughts, extreme precipitation, and sea-level rise, while seasonal tourism intensifies water and energy demand during the most vulnerable periods of the year. This study examines whether incorporating tourism as an intrinsic component of the Water–Energy–Food nexus changes the assessment of climate risks in desalination-dependent island water systems. To address this question, the Water–Energy–Food (WEF) nexus is extended to Water–Energy–Food–Tourism (WEFT) and integrated into an EU-aligned Climate Risk and Vulnerability Assessment framework. The approach is applied to the Hermoupolis Water Supply System on Syros Island, Greece, where potable water supply depends largely on energy-intensive desalination. A technically bounded climate risk assessment is compared with a WEFT-adjusted assessment that accounts for tourism-driven demand amplification and water–energy interdependencies while keeping hazard exposure and likelihood climate-driven. The results show that heatwaves constitute the dominant near-term risk because they coincide with peak water demand and high electricity requirements for desalination. When tourism amplification is included, drought-related risks shift from medium to high already in the near future for key production and pumping components, indicating earlier emergence of critical risk conditions without changes in hazard probability. Coastal risks become more important toward the end of the century, especially under high-emission scenarios. The main contribution of the study is to show that tourism-driven amplification can be operationally incorporated into sensitivity and impact assessment while preserving comparability with a conventional CRVA. The proposed WEFT–KTM framework provides a transferable basis for assessing and prioritizing adaptation in desalination-dependent, tourism-driven Mediterranean island systems. Full article

Rapid urbanization in arid climates is accompanied by comprehensive degradation of the urban environment, where traditional approaches to greening demonstrate low sustainability due to water scarcity and extreme temperature conditions. This paper presents a systematic review of contemporary concepts, classification approaches, and methods for the quantitative assessment of eco-constructive modules designed for the local rehabilitation of urban systems. A critical synthesis of the evolution of nature-inspired solutions was conducted, revealing conceptual and terminological fragmentation and the absence of climate-adapted classifications for arid regions. The necessity of transitioning from local analytical monitoring to multi-criteria qualimetric assessment is substantiated. As a result, a conceptual matrix for classifying modules based on parameters of function, scale, technological autonomy, and climate adaptation has been developed, and a methodological framework for calculating an integral index of ecological potential has been proposed. The critical role of parametric design and microclimatic modeling in the pre-project validation of solutions is demonstrated. The proposed scientific and applied framework addresses the identified research gaps, providing a reproducible methodology for the design, quantitative assessment, and regulatory integration of modular systems. The results lay the foundation for transforming passive urban surfaces into an active network of local sanitation. Full article

With rapid urbanization, the generation of municipal solid waste is growing, placing ever-increasing pressure on cities to close, remediate and repurpose landfill sites in environmentally sustainable and climate-adaptive ways. Traditional landfill final covers such as compacted clay and geosynthetic systems are intended to limit infiltration; yet their conceptual designs often fail in performance longevity due to effects such as desiccation, settlement, root intrusion, freeze–thaw cycling and extreme rainfall. Phytocapping, or evapotranspiration/store-and-release cover technology is the use of vegetated soil profiles to provide storage for percolating rainfall, return water to the atmosphere through evapotranspiration and support biologically mediated oxidation of methane. Phytocapping is a green-inclusive nature-based climate adaptation strategy for urban landfill closure. This study explores hydrological performance, methane mitigation, ecological co-benefits, economic feasibility, climate sensitivity, monitoring requirements and regulatory barriers linked to phytocapping systems. Field evidence is strongest in Australia and the United States, especially through ACAP- and A-ACAP-style programs, while evidence from humid tropical, monsoon, freeze–thaw and low-resource urban contexts is comparatively lacking. As reported in published studies, well-designed phytocaps can result in reduced percolation compared to traditional clay caps. Reported publications also mention considerable construction-cost savings, depending on site conditions and design assumptions. Methane-related outcomes vary by measurement method and site context, with studies reporting surface flux reductions, methane oxidation and landfill gas attenuation as distinct performance indicators. These advantages are counter-balanced by design uncertainties that vary from site to site, limited long-term monitoring data, climate transferability concerns, and regulatory systems still firmly anchored in prescriptive low-permeability barriers. This review proposes a policy-oriented analytical framework that bridges the gap between technical performance evidence, urban co-benefits, staged monitoring and performance-based landfill closure regulation. As such, phytocapping should be considered not as a general-purpose substitute for engineered covers, but as a climate-responsive nature-based solution that can complement urban waste servicing infrastructure, ecological restoration and adaptive governance of landfills when properly designed, monitored and regulated. Full article

Expansive soils represent a critical challenge in geotechnical engineering due to their significant volumetric changes in response to moisture variations, which cause recurrent structural damage to foundations, pavements and infrastructure. The intensification of wet–dry cycles associated with climate change increases the likelihood of failures, reinforcing the need for more efficient and sustainable stabilization methods. Conventional techniques based on lime or salts present environmental and performance limitations when used independently. This study evaluates a combined approach using sodium chloride (NaCl) and lime sludge (LS), an abundant industrial by-product, to improve the behavior of expansive soils while simultaneously valorizing a difficult-to-manage waste material. Mixtures containing 3%, 6% and 9% NaCl and 5%, 10% and 15% LS were prepared. Atterberg limits, free swell, swelling pressure and infiltration tests were carried out to analyze the response of the treated soil. The results show significant reductions in plasticity and swelling potential: the liquid limit decreased by up to 35%, the plasticity index by up to 36% and the free swell by up to 65% for the optimal combination (9% NaCl + 15% LS). In addition, infiltration increased from 25 to 40 mm, indicating improved hydraulic behavior of the treated soil. The direct reuse of lime sludge prevented its disposal in landfills and reduced the environmental impact associated with its management. Overall, the findings demonstrate that the combination of NaCl and LS is an effective and economical alternative under short-term laboratory conditions, with potential for sustainable application subject to long-term validation for mitigating the swelling of expansive soils. Pilot-scale validation under extreme climatic conditions is recommended to advance toward its integration into resilient infrastructure projects. This approach offers a more efficient, cost-effective, and sustainable technical solution, distinguished by its dual action (chemical and recycling) and its contribution to waste valorization. Future research will focus on validating the method at the pilot scale and assessing its performance under extreme climatic conditions, consolidating its applicability in resilient infrastructure projects. Full article

Climate warming and the increasing frequency of extreme climate events have exerted a systemic shock on global Agricultural Production Resilience (APR). Clarifying the impact mechanism is essential to ensuring global food security. This study employs a cross-country network meta-analysis framework. We systematically synthesize 76 empirical studies published between 2005 and 2025. This paper aims to quantify the impacts of five climatic factors on APR. These factors include extreme high temperature, extreme drought, extreme flooding, precipitation variability, and temperature anomaly. Heterogeneity and moderating effects across latitudinal regions, agricultural production modes, agricultural structures, and irrigation conditions are examined, followed by robustness tests and publication bias analysis. The results show that: (1) At a cross-country scale, all five climatic factors have significant negative impacts on APR. The intensity of impact ranks in descending order as extreme flooding, extreme high temperature, extreme drought, precipitation variability, and temperature anomaly, with extreme climates as the dominant risk factor. (2) The impact effects exhibit significant latitudinal heterogeneity. The absolute value of adverse shocks to APR in low-latitude regions is markedly larger than that in mid- and high-latitude countries; extreme floods constitute the primary risk for low-latitude areas, while extreme high temperatures dominate mid- and high-latitude regions. (3) Rain-fed agriculture and crop farming suffer substantially stronger climatic impacts than irrigated agriculture and animal husbandry. (4) Agricultural structure and production modes exert prominent moderating effects. A higher share of crop cultivation and rain-fed farmland corresponds to stronger adverse climatic impacts, whereas animal husbandry, facility agriculture, and well-developed irrigation facilities can partially mitigate such disturbances. This study provides empirical evidence for countries and regions to implement differentiated adaptation policies within agricultural climate governance frameworks and enhance APR. Full article

Extreme and sudden temperature fluctuations observed as a result of global climate change increase the environmental pressure on energy transmission infrastructure. These meteorological changes significantly increase the risk of failure for porcelain insulators, which exhibit low thermal resistance and are susceptible to sudden arcing and surface deformations. In this study, a hybrid CNN–BiLSTM–SE architecture augmented with the Squeeze-and-Excitation attention mechanism is proposed using surface leakage current signals to diagnose healthy, cracked, and broken structural conditions in three-unit porcelain insulators. The SE block in the architecture dynamically rescales feature maps from CNN layers on a channel-by-channel basis. Thus, it highlights the signal characteristic that is dominant for fault diagnosis just before the BiLSTM units learn temporal dependencies. Leakage current data were obtained under an experimental setup at 60 kV for 15 different conditions covering all possible combinations of healthy, cracked, and broken insulator units. The raw signals were preprocessed with the Savitzky–Golay filter to suppress noise while preserving the diagnostic waveform morphology. 24 features covering time-domain statistics, frequency-domain spectral characteristics, and wavelet-domain energy components were extracted and used as model inputs. The CNN–BiLSTM–SE architecture achieved a classification accuracy of 93.83%, surpassing the standalone CNN (88.89%), BiLSTM (87.65%), and CNN–BiLSTM (91.36%) models, as well as classical machine-learning baselines (SVM: 87.65%, Random Forest: 90.12%, Boosted Trees: 87.65%). Full article

Extreme wildfire events are increasingly shaping Mediterranean fire regimes under the combined influence of climatic variability, vegetation stress, and growing anthropogenic pressure, posing critical risks to ecosystem stability and landscape resilience. Understanding the spatial determinants of wildfire susceptibility is therefore essential for advancing evidence-based fire risk assessment in fire-prone Mediterranean environments. This ongoing doctoral research investigates the environmental controls of wildfire occurrence in Muğla Province (Türkiye) through the integration of multi-source remote sensing data, geospatial analysis, and interpretable machine learning techniques. Burned-area reference data for 2021–2024 were derived from the MODIS MCD64A1 product within the Google Earth Engine environment and represented using a binary burned/non-burned classification. Predictor variables include ERA5-Land meteorological indicators, SRTM-derived topographic parameters, MODIS-based NDVI vegetation condition, and WorldPop population density as a proxy for human exposure, harmonized at a common 1 km spatial resolution. A Random Forest model was implemented to examine wildfire susceptibility patterns with emphasis on model interpretability and variable contribution rather than predictive optimization. Preliminary results indicate that vegetation condition, wind-related dynamics, and population density are dominant contributors to wildfire occurrence, reflecting coupled ecological vulnerability and human influence. Extreme fire conditions observed during 2021 are intentionally reserved for subsequent validation and stress-testing analyses. The proposed framework provides a transparent and transferable methodological basis for analyzing extreme wildfire susceptibility in Mediterranean landscapes and supports future development of interpretable, data-driven wildfire risk assessment approaches. Full article