Search Results (144)

Energy poverty, resulting from poor energy efficiency and economic and social barriers to accessing appropriate, modern, and sustainable energy services, remains a critical issue in Jordan, a country facing growing climate pressures, particularly given its history of rapid urbanization. This study examines energy poverty through a multidimensional lens, considering its spatial and socio-demographic variations across Jordan. Drawing on data from 19,475 households, we apply a novel energy poverty index and binary logistic regression to analyze key determinants of energy poverty and discuss their intersection with climate vulnerability. The energy poverty index (EPI) is structured around four pillars: housing, fuel, cooling, and wealth. The results show that 51% of households in Jordan are affected by energy poverty. Contributing factors include geographic location, gender, age, education level, dwelling type, ownership of cooling appliances, and financial stability. The results indicate that energy poverty is both a socio-economic and infrastructural issue, with the highest concentrations in the northern and southern regions of the country, areas also vulnerable to climate risks such as drought and extreme heat. Our findings emphasize the need for integrated policy approaches that simultaneously address income inequality, infrastructure deficits, and environmental stressors. Targeted strategies are needed to align social and climate policies for effective energy poverty mitigation and climate resilience planning in Jordan. Full article

Efficient water management is critical for sustainable dryland agriculture, especially under increasing water scarcity and climate variability. Shanxi Province, a typical dryland region in northern China characterized by pronounced climatic variability and limited soil water availability, faces severe challenges due to uneven precipitation and restricted water resources. This study aimed to evaluate the spatiotemporal dynamics of soil water resources and their coupling with crop water demand under different hydrological year types. Using daily meteorological data from 27 stations (1963–2023), we identified dry, normal, and wet years through frequency analysis. Soil water resources were assessed under rainfed conditions, and water deficits of major crops—including millet, soybean, sorghum, winter wheat, maize, and potato—were quantified during key reproductive stages. Results showed a statistically significant declining trend in seasonal precipitation during both summer and winter cropping periods (p < 0.05), which corresponds with the observed intensification of crop water stress over recent decades. Notably, more than 86% of daily rainfall events were less than 5 mm, indicating low effective rainfall. Soil water availability closely followed precipitation distribution, with higher values in the south and west. Crop-specific analysis revealed that winter wheat and sorghum had the largest water deficits in dry years, necessitating timely supplemental irrigation. Even in wet years, water regulation strategies were required to improve water use efficiency and mitigate future drought risks. This study provides a practical framework for soil water–crop demand assessment and supports precision irrigation planning in dryland farming. The findings contribute to improving agricultural water use efficiency in semi-arid regions and offer valuable insights for adapting to climate-induced water challenges. Full article

Climate change is projected to increase the intensity and frequency of natural hazards such as heat waves, extreme rainfall, heavy snowfall, typhoons, droughts, floods, and cold waves, potentially impacting the operational safety of critical infrastructure, including nuclear power plants (NPPs). Although quantitative indicators exist to screen-out natural hazards at NPPs, comprehensive methodologies for assessing climate-related hazards remain underdeveloped. Furthermore, given the variability and uncertainty of climate change, it is realistically and resource-wise difficult to evaluate all potential risks quantitatively. Using a structured expert elicitation approach, this study systematically identifies and prioritizes climate-related natural hazards for Korean NPPs. An iterative Delphi survey involving 42 experts with extensive experience in nuclear safety and systems was conducted and also evaluated using the best–worst scaling (BWS) method for cross-validation to enhance the robustness of the Delphi priorities. Both methodologies identified extreme rainfall, typhoons, marine organisms, forest fires, and lightning as the top five hazards. The findings provide critical insights for climate resilience planning, inform vulnerability assessments, and support regulatory policy development to mitigate climate-induced risks to Korean nuclear power plants. Full article

As one of Europe’s rare floodplain forest ecosystems, the İğneada Longos Forests face increasing ecological pressures; this study examines land use and land cover (LULC) changes in the İğneada Longos Forests, a protected national park in Turkey, between 1984 and 2014, while also assessing future climate change impacts under different shared socioeconomic pathways (SSPs). In this context, the MaxEnt model, which exhibits a very high sensitivity, was used to determine the land use/land change and the change in natural distribution habitats of the forest tree species in the İğneada Longos Forests, which constitute the research area, due to the effects of climate change. The analysis of forest management plans revealed significant LULC shifts, including wetland loss, cropland expansion, and declines in pioneer tree species, such as the lowland maple and the European ash, due to anthropogenic pressures and increasing droughts. Climate modeling using the Emberger and De Martonne indices projected severe aridity by 2100, with Mediterranean climate dominance expanding (up to 89.25% under SSP3–7.0) and humid zones disappearing. These changes threaten biodiversity, carbon sequestration capacity, and ecosystem stability, particularly in floodplain forests, which are critical for carbon storage. The findings underscore the urgent need for adaptive conservation strategies, stakeholder collaboration, and climate-resilient forest management to mitigate ecological degradation and sustain ecosystem services under escalating climate stress. Full article

Oak forests are an important part of temperate European ecosystems, where they are actively improving biodiversity, carbon storage, and ecological stability. However, current concerns such as climatic changes, and especially rising temperatures and changing precipitation patterns, are impacting their resilience. In this context, our study intends to evaluate the impact of climatic variability on temperate oak forests, focusing on the influence of temperature and precipitation. This covers different sites that have different environmental conditions. By using both a bibliometric approach and a systematic analysis of publications that have studied the influence of climate change on oak forests, our study has identified specific species and site responses to climate stressors. Furthermore, we have also evaluated trends in drought sensitivity. All these aspects have allowed us to understand and suggest improvements for the impact of climate change on the resilience and productivity of oak ecosystems. We have analyzed a total number of 346 publications that target the impact of climate change on oak forests. The articles were published between 1976 and 2024, with the majority originating from the USA, Spain, Germany, and France. These studies were published in leading journals from Forestry, Environmental Sciences, and Plant Sciences, among which the most cited journals were Forest Ecology and Management, the Journal of Biogeography, and Global Change Biology. As for the keywords, the most frequent ones were climate change, drought, growth, forest, and oak. However, we have observed a trend towards drought sensitivity, which indicates the intensification of climate changes on oak ecosystems. Moreover, this trend was more present in central and southern regions, which further highlights the impact of regional conditions. As such, certain local factors (soil properties, microclimate) were also taken into account in our study. Our literature review focused on the following aspects: Oak species affected by climate change; Impact of drought on oak forests; Influence of climate change on mixed forests containing oaks; Effects of climate change on other components of oak ecosystems; Radial growth of oaks in response to climate change; Decline of oak forests due to climate change. Our results indicate that oak forests decline in a process caused by multiple factors, with climate change being both a stressor and a catalyst. Across the globe, increasing temperatures and declining precipitation affect these ecosystems in their growth, functions, and resistance to pathogens. This can only lead to an increased forest decline. As such, our results indicate the need to implement forest management plans that take into account local conditions, species, and climate sensitivity. This approach is crucial in improving the adaptivity of oak forests and mitigating the impact of future climate extremes. Full article

This study investigates the impact of climate variability on wheat production in Algeria’s semi-arid interior plains from 2014 to 2024, aiming to curb the challenges of rainfed wheat cultivation, optimize irrigation, and improve water productivity. The Soil–Water–Atmosphere–Plant (SWAP) model-driven approach refined irrigation scheduling to mitigate climate-induced losses and improve resource efficiency. Using historical climate data, soil properties, and wheat growth observations from the experimental farm of the Technical Institute for Field Crops, the SWAP model was calibrated and validated using one-factor-at-a-time sensitivity analysis, achieving a coefficient of determination (R²) of 0.93 and a Normalized Root Mean Squared Error (NRMSE) of 17.75. Two drought-based irrigation indices, Soil Moisture Drought Index (SMDI) and Crop Water Stress Index (CWSI), guided adaptive irrigation strategies, showing a significant reduction in crop failure during drought periods. Results revealed a strong link between rainfall variability and wheat yield. Adopting a 9-day irrigation interval could increase water productivity to 18.91 kg ha⁻¹ mm⁻¹, enhancing yield stability under varying climatic conditions. The SMDI approach maintained soil moisture during extreme drought, while CWSI optimized water use in normal and wet years. This study integrates SMDI and CWSI into a validated irrigation framework, offering data-driven strategies to enhance wheat production resilience. Findings support sustainable water management and provide practical insights for policymakers and farmers to refine irrigation planning and climate adaptation, contributing to long-term agricultural sustainability. Full article

In recent years, the Yellow River Basin has experienced frequent extreme climate events, with an increasing intensity and frequency of droughts, exacerbating regional water scarcity and severely constraining agricultural irrigation efficiency and sustainable water resource utilization. The accurate estimation of reference crop evapotranspiration (ET₀) is crucial for developing scientifically sound irrigation strategies and enhancing water resource management capabilities. This study utilized daily scale meteorological data from 31 stations across the Yellow River Basin spanning the period 1960–2023 to develop various machine learning models. The study constructed four machine learning models—random forest (RF), a Support Vector Machine (SVM), Gradient Boosting (GB), and Ridge Regression (Ridge)—using the meteorological variables required by the Priestley–Taylor (PT) and Hargreaves (HG) equations as inputs. These models represent a range of algorithmic structures, from nonlinear ensemble methods (RF, GB) to kernel-based regression (SVR) and linear regularized regression (Ridge). The objective was to comprehensively evaluate their performance and robustness in estimating ET₀ under different climatic zones and drought conditions and to compare them with traditional empirical formulas. The main findings are as follows: machine learning models, particularly nonlinear approaches, significantly outperformed the PT and HG methods across all climatic regions. Among them, the RF model demonstrated the highest simulation accuracy, achieving an R² of 0.77, and reduced the mean daily ET₀ estimation error by 0.057 mm/day and 0.076 mm/day compared to the PT and HG models, respectively. Under drought-year scenarios, although all models showed slight performance degradation, nonlinear machine learning models still surpassed traditional formulas, with the R² of the RF model decreasing marginally from 0.77 to 0.73, indicating strong robustness. In contrast, linear models such as Ridge Regression exhibited greater sensitivity to changes in feature distributions during drought years, with estimation accuracy dropping significantly below that of the PT and HG methods. The results indicate that in data-sparse regions, machine learning approaches with simplified inputs can serve as effective alternatives to empirical formulas, offering superior adaptability and estimation accuracy. This study provides theoretical foundations and methodological support for regional water resource management, agricultural drought mitigation, and climate-resilient irrigation planning in the Yellow River Basin. Full article

Trees and forests are nature-based solutions of strategic importance for climate change mitigation. Policy and popular media are focused on the number of trees to plant, but that cannot be a definitive solution. A growing number of scientific papers address the problems concerning tree plantations and forest restoration for climatic purposes. In this review, we analyze ecological limitations and trade-offs to be considered for the realization and management of these interventions. Terrestrial sinks (forests and other terrestrial natural ecosystems) can absorb only a fraction of the carbon emitted, and the establishment of new effective forests is constrained by ecological limitations. Moreover, the stimulation of tree growth due to carbon fertilization is offset by the harshening of ecological conditions due to climate change (higher temperatures beyond the optimum for photosynthesis, increasing drought, and nutritional imbalances). The increase in frequency and severity of disturbances can turn forests from sinks to sources of carbon. Finally, physiological mechanisms connected to albedo and the emission of organic volatile compounds (VOCs) reduce the efficacy of climate cooling. Although such constraints exist, the establishment of new plantations and the restoration of existing forests are still necessary but are just one of the actions to fight climate change and must not be seen as an alternative to reducing carbon emissions. Considering limitations and trade-offs in the models to estimate tree growth and carbon storage will allow us to produce more realistic plans for climate mitigation. Full article

Plantation forests (PFs) play a crucial role in China’s climate change mitigation strategy due to their significant capacity to sequestrate carbon (C). Understanding the long-term trend in PFs’ C uptake capacity and the key drivers influencing it is crucial for optimizing PF management and planning for climate mitigation. In this study, we quantified the long-term (1981–2019) C sequestration of PFs in Jiangsu Province, where PFs have expanded considerably in recent decades, particularly since 2015. Seasonal and interannual variations in gross primary productivity (GPP), net primary productivity (NPP), and net ecosystem productivity (NEP) were assessed using the boreal ecosystem productivity simulator (BEPS), a process-based terrestrial biogeochemical model. The model integrates multiple sources of remote-sensing datasets, such as leaf area index and land cover data, to simulate the critical biogeochemical processes governing land surface dynamics, enabling the quantification of vegetation and soil C stocks and nutrient cycling patterns. The results indicated a significant increasing trend in GPP, NPP, and NEP over the past four decades, suggesting enhanced C sequestration by PFs across the study region. The interannual variability in these indicators was associated with that of nitrogen (N) deposition in recent years, implying that nutrient availability could be a limiting factor for plantation productivity. Seasonal GPP and NPP exhibited peak values in spring (April to May) or late summer (August to September), with increases in growing season productivity in recent years. In contrast, NEP peaked in spring (April to May) but declined to negative values in early summer (July to August), indicating a seasonal C source–sink transition. All three indicators showed a general negative correlation with late-growing-season temperature (August to September), suggesting that summer droughts probably highly constrained the C sequestration of the existing PFs. These findings provide insights for the strategic implementation and management of PFs, particularly in regions with a warm temperate climate undergoing afforestation expansion. Full article

Effective and sustainable water reserve management faces increasing challenges due to climate-induced variability, data fragmentation, and the limitations of traditional, static modeling systems. This study introduces a conceptual framework designed to address these challenges by integrating digital twins, IoT-driven real-time monitoring, game engine simulations, and AI-driven decision support systems (AI-DSS). The methodology involves constructing a digital twin ecosystem using IoT sensors, GIS layers, remote-sensing imagery, and game engines. This ecosystem simulates water dynamics and assesses policy interventions in real time. AI components, including machine-learning models and retrieval-augmented generation (RAG) chatbots, are embedded to synthesize real-time data into actionable insights. The framework enables the continuous assessment of hydrological dynamics, predictive risk analysis, and immersive, scenario-based decision-making to support long-term water sustainability. Simulated scenarios demonstrate accurate flood forecasting under variable rainfall intensities, early drought detection based on soil moisture and flow data, and real-time water-quality alerts. Digital elevation models from UAV photogrammetry enhance terrain realism, and AI models support dynamic predictions. Results show how the framework supports proactive mitigation planning, climate adaptation, and stakeholder communication in pursuit of resilient and sustainable water governance. By enabling early intervention, efficient resource allocation, and participatory decision-making, the proposed system fosters long-term, sustainable water security and environmental resilience. This conceptual framework suggests a pathway toward more transparent, data-informed, and resilient decision-making processes in water reserves management, particularly in regions facing climatic uncertainty and infrastructure limitations, aligning with global sustainability goals and adaptive water governance strategies. Full article

Beavers play a key role in creating temporary water reservoirs that significantly impact the natural environment and local river hydrology. The primary aim of this study was to assess the potential of increasing the number of beaver dams (Castor spp.), as an alternative method of water retention in the environment. Research conducted on three small lowland streams in central Poland revealed that beaver dams, even in modified riverbeds, enable the formation of shallow floodplains and ponds. Innovative analyses considered the structural materials of the dams and their impact on river hydromorphology and sediment transport. The findings emphasise the importance of beavers in water retention processes, the stabilisation of water levels during low flows and the protection of biodiversity. The study also demonstrated that beaver dams play a critical role in storing surface- and groundwater, mitigating drought impacts, reducing surface runoff, and stabilising river flows. These constructions influence local hydrology by increasing soil moisture, extending water retention times, and creating habitats for numerous species. The collected data highlight the potential of beaver dams as a tool in water resource management in the context of climate change. Further research could provide guidance for the sustainable utilisation of beavers in environmental conservation strategies and landscape planning. Full article

Inadequate water supply during droughts, leading to socio-economic drought, has become a global issue. In this context, the drought resistance and disaster mitigation capabilities of reservoirs play a crucial role during drought events. Taking the downstream Yellow River Basin (DYRB) as the study area, this research analyzes the evolution and characteristics of socio-economic drought in the region from 1956 to 2016 at different time scales (3 months, 6 months, 9 months, and 12 months). The copula function is used to calculate the joint recurrence period of socio-economic drought in the downstream area. In addition, this study constructs a reservoir optimization operation model to explore the drought resistance capabilities of water supply strategies in response to downstream socio-economic droughts. The results show that the indices across the four time scales indicate that the DYRB faced the most severe socio-economic drought from the 1990s to the early 21st century, with long durations and widespread impacts. Compared with conventional scheduling methods, water supply restriction strategies can cope with more severe socio-economic droughts. However, the maximum drought resistance capacity corresponding to its recurrence period still cannot cope with the socio-economic droughts of the early 21st century. Therefore, the implementation of basin-wide unified water planning is of great importance to improve drought resistance capacity. Full article

Wildfires represent an increasing threat to ecosystems and communities, driven by climate change, fuel dynamics, and human activities. In Ambato, Ecuador, a city in the Andean highlands, these risks are exacerbated by prolonged droughts, vegetation dryness, and urban expansion into fire-prone areas within the Wildland–Urban Interface (WUI). This study integrates climatic, ecological, and socio-economic data from 2017 to 2023 to assess wildfire risks, employing advanced geospatial tools, thematic mapping, and machine learning models, including Multinomial Logistic Regression (MLR), Random Forest, and XGBoost. By segmenting the study area into 1 km² grid cells, microscale risk variations were captured, enabling classification into five categories: ‘Very Low’, ‘Low’, ‘Moderate’, ‘High’, and ‘Very High’. Results indicate that temperature anomalies, reduced fuel moisture, and anthropogenic factors such as waste burning and unregulated land-use changes significantly increase fire susceptibility. Predictive models achieved accuracies of 76.04% (MLR), 77.6% (Random Forest), and 76.5% (XGBoost), effectively identifying high-risk zones. The highest-risk areas were found in Izamba, Pasa, and San Fernando, where over 884.9 ha were burned between 2017 and 2023. The year 2020 recorded the most severe wildfire season (1500 ha burned), coinciding with extended droughts and COVID-19 lockdowns. Findings emphasize the urgent need for enhanced land-use regulations, improved firefighting infrastructure, and community-driven prevention strategies. This research provides a replicable framework for wildfire risk assessment, applicable to other Andean regions and beyond. By integrating data-driven methodologies with policy recommendations, this study contributes to evidence-based wildfire mitigation and resilience planning in climate-sensitive environments. Full article

Climate change, particularly the increasing frequency of droughts, poses a critical challenge for agriculture. Rising temperatures and water scarcity threaten both agricultural productivity and ecosystem stability, making the identification of effective drought mitigation strategies essential. This study introduces an innovative approach to agricultural drought monitoring in Poland, utilizing remote sensing (RS) satellite data, collected from 2001 to 2020, and the Drought Identification Satellite System (DISS) index at a 1 km × 1 km spatial resolution, in combination with Copernicus High-Resolution Layers (HRL). To assess areas’ capacities to mitigate drought risks, a multi-criteria decision (MCD) analysis of regional environmental conditions was conducted. Focusing on the Mazowieckie Voivodeship, an algorithm was developed to evaluate regional susceptibility to drought. Spatial datasets were used to analyze environmental indicators, producing a map of communal temperature mitigation capacities. Statistical analysis identified drought vulnerability, highlighting areas in need of urgent intervention, such as increased mid-field tree planting. The study revealed that the frequency of droughts in this region during the growing season from 2001 to 2020 exceeded 40%. As a result, 40 LAU 2 administrative units have been affected by multiple negative environmental factors that contribute to drought formation and its long-term persistence. The proposed methodology, integrating diverse satellite data sources and spatial analyses, offers an effective tool for drought monitoring, mitigation planning, and ecosystem protection in a changing climate. This approach provides valuable insights for policymakers and land managers in addressing agricultural drought challenges and enhancing regional resilience to the impacts of climate change. Full article

Drought raises significant challenges and consequences in the socioeconomic environment in Dobrogea, Romania. This research aimed to assess the spatiotemporal dynamics of agrometeorological droughts from 2001 to 2021 using a multi-index approach that includes the Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI). Severe-to-extreme drought events were detected in 2001, 2007, 2012, 2015, 2016, 2019, and 2020, when temperatures in the area reached as high as 40.91 °C. Regarding area coverage, 2012 and 2020 were the worst drought years, with 66% and 71% of the region affected. Mild and moderate droughts were consistently identified across almost the entire period, while normal wet conditions were indicated in 2004–2006. The spatial analysis and the drought frequency maps revealed that the central, southern, and northwestern areas were particularly vulnerable, underlining the need for targeted drought mitigation measures. The trend analysis results indicated a nonuniform spatial feature of the negative (drying)/positive (wetting) trends at the regional level, with statistically significant trends identified only over small areas. Further results showed a robust relationship among the VHI and SPEI, particularly on 1-month and seasonal timescales. The extended correlation analysis results showed very strong positive relationships among all the vegetation indices, positive relations with rainfall, and strong negative ties with land surface temperature. Moreover, the seasonal VHI proved to be effective for drought monitoring across areas with diverse crop types. The results we obtained are consistent with previous studies on the incidence of drought in the area and hold practical significance for decision-makers responsible for drought management planning within Dobrogea, including setting up an early warning system using the VHI. Full article