Search Results (1,932)

The rapid global expansion of hydropower poses questions about the resilience and sustainability of riparian vegetation, especially in the context of ongoing climate change. Satellite remote sensing provides a valuable means for monitoring long-term and spatially continuous changes in vegetation, offering insights into how dams influence RV dynamics worldwide. Here, we integrated satellite-derived environmental indicators, including Normalized Difference Vegetation Index (NDVI), to quantify and compare riparian vegetation trends upstream and downstream of dams globally. By applying paired linear regression analyses to pre- and post-construction NDVI time series, we identified dams associated with significant RV degradation following impoundment. Furthermore, we employed Gradient Boosting Regression Models (GBRM), calibrated using current observational data and driven by CMIP6 climate projections, to forecast global riparian vegetation trends through the year 2100 under various climate scenarios. Our analysis reveals that, although widespread vegetation degradation was not evident up to 2017—and many regions showed slight improvements—future projections under higher-emission pathways (SSP3-7.0 and SSP5-8.5) indicate substantial RV declines after 2040, particularly in high-latitude forests, grasslands, and arid regions. Conversely, tropical and subtropical riparian forests are predicted to maintain stable or increasing NDVI under moderate emission scenarios (SSP1-2.6). These results highlight the potential for adaptive dam development strategies supported by continued satellite-based monitoring to help reduce climate-related risks to riparian vegetation in regions. Full article

Islands and island communities are often perceived as homogenous in mainstream discourse. While many islands share characteristics, such as smallness or isolation, these are experienced differently across and within island contexts and intersect with spatial, socio-cultural, political, and economic landscapes. The concept of islandness is developed to both understand shared island characteristics and their differences across places, communities, and situations. This makes islandness highly relevant to discussions of green transitions as it highlights the need to examine the diverse, intersecting, and local realities that might interfere with green citizenship. However, analytical approaches to islandness are limited, with few spatial, scalable, and transferable frameworks available. This paper argues that spatial microsimulation offers a productive way to engage with islandness using the case of climate change and environmental attitudes across Iceland. We present the SimIceland model, developed within the EU-funded project PHOENIX: The Rise of Citizens’ Voices for a Greener Europe. The model is developed to better understand how Iceland’s citizens’ feel about climate change by taking socio-cultural, environmental, and different geographical administrative regions into account. Through a simple example of an analytical demonstration, we show how this model can support a deeper understanding of islandness in the specific context of climate attitudes in Iceland. Furthermore, we discuss how the model can contribute to public participation initiatives. The model and data are open access, and we conclude by inviting further developments and the use of spatial microsimulation to explore islandness, green citizenship, and participatory approaches to sustainability in island contexts. Full article

This paper outlines the implementation and core results of a combined archaeological, historical, and ethnographic study of the histories of well construction and water management among Boran, Gabra, and Rendille pastoralists in arid and semi-arid areas of Northern Kenya and Southern Ethiopia. Co-developed with representatives from different local communities from the outset, this project sought to document the spatial distribution of different types of hand-dug wells found across the study areas, their associated oral histories and, if possible, establish through archaeological means their likely date of initial construction. Concurrent with addressing these academic objectives, this project aimed to train a cohort of local heritage stewards in archaeological, historical, and ethnographic data collection and interpretation, equipping them with the necessary skills to monitor sites of heritage value and further record additional elements of the tangible and intangible heritage of the study areas. This paper discusses the archaeological work that the community trainees participated in, the strategies developed with them to create wider awareness of this heritage, and its implications for identifying ways to ”weather” climate change in the future. Full article

Climate change significantly affects hydrological processes in forest ecosystems, particularly in sensitive coastal areas such as the Croatan National Forest (CNF) in North Carolina. Accurate projections of future water yield are essential for managing agriculture, forestry, and natural ecosystems. This study investigates the potential impacts of climate change on water yield using a combination of statistical downscaling and machine learning. Two downscaling methods, a Statistical DownScaling Model (SDSM) and Multivariate Adaptive Constructed Analogs (MACA), were evaluated, with the SDSM providing superior performance for local climate conditions. To improve precipitation input accuracy, twenty ensemble scenarios were generated using the SDSM, and various machine learning algorithms were applied to identify the optimal ensemble. Among these, the Extreme Gradient Boosting (XGBoost) algorithm exhibited the lowest error and was selected for producing high-quality precipitation time series. This methodology is integrated into the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach, which leverages machine learning to enhance climate input precision and reduce uncertainty in hydrological modeling. Water yield was simulated over the period 1961–2060, combining observed and projected climate data to capture both historical trends and future changes. The results show that combining statistical downscaling with machine learning algorithms can help improve the accuracy of water yield projections under climate change and be useful for water resource planning, forest management, and climate adaptation. Full article

The article presents an analysis of current (during the period 1985–2022) and projected (during the period 2025–2099) changes in the hydrological regime of the Buktyrma, Yesil, and Zhaiyk river basins in Kazakhstan under the conditions of global climate change. This study is based on the integration of data from General Circulation Models (GCMs) of the sixth phase of the CMIP6 project, socio-economic development scenarios SSP2-4.5 and SSP5-8.5, as well as the results of hydrological modelling using the SWIM model. The studies were carried out with an integrated approach to hydrological change assessment, taking into account scenario modelling, uncertainty analysis and the use of bias correction methods for climate data. A calculation method was used to analyse the intra-annual distribution of runoff, taking into account climate change. Detailed forecasts of changes in runoff and intra-annual water distribution up to the end of the 21st century for key water bodies in Kazakhstan were obtained. While the projections of river flow and hydrological parameters under CMIP6 scenarios are actively pursued worldwide, few studies have explicitly focused on forecasting intra-annual flow distribution in Central Asia, calculated using a methodology appropriate for this region and using CMIP6 ensemble scenarios. There have been studies on changes in the intra-annual distribution of runoff for individual river basins or local areas, but for the historical period, there have also been studies on modelling runoff forecasts using CMIP6 climate models, but have been very few systematic publications on the distribution of predicted intra-annual runoff in Central Asia, and this issue has not been fully studied. The projections suggest an intensification of flow seasonality (1), earlier flood peaks (2), reduced summer discharges (3) and an increased likelihood of extreme hydrological events under future climatic conditions. Changes in the seasonal structure of river flow in Central Asia are caused by both climatic factors—temperature, precipitation and glacier degradation—and significant anthropogenic influences, including irrigation and water management structures. These changes directly affect the risks of flooding and water shortages, as well as the adaptive capacity of water management systems. Given the high level of water management challenges and interregional conflicts over water use, the intra-annual distribution of runoff is important for long-term planning, the development of adaptation measures, and the formulation of public policy on sustainable water management in the face of growing climate challenges. This is critically important for water, agricultural, energy, and environmental planning in a region that already faces annual water management challenges and conflicts due to the uneven seasonal distribution of resources. Full article

Hippophae neurocarpa S. W. Liu & T. N. Ho exhibits established medicinal characteristics, valuable dietary attributes, and remarkable adaptability, displaying strong resistance to cold, drought, and to acidic and alkaline soils. These traits and others make it a valuable species for soil erosion control and a distinctive economic forest tree in western China. However, research on its geographic distribution remains limited. To address this gap, we employed the MaxEnt model to map its current distribution and to predict the future geographic distribution of suitable habitats for this species under SSP1-2.6, SSP2-4.5, and SSP5-8.5 climate scenarios. Collectively, these data suggest that the species’ current and future suitable habitats are predominantly concentrated at the junction of the northeastern Qinghai-Tibet Plateau and the Loess Plateau. Under present climatic conditions, highly suitable habitats are primarily located in the northeastern Qinghai-Tibet Plateau, with smaller patches in the Hengduan and Himalaya mountains. The AUC value of this model reached 0.954; projections under three future emission scenarios indicate an overall expansion trend in suitable habitat area. Notably, by the 2070s under the SSP2-4.5 scenario, the total suitable habitat area is projected to increase by 11.64%—the highest among all scenarios. Additionally, climate change is expected to drive a slight northward shift in the species’ distribution center toward higher latitudes. Key environmental factors influencing its projected distribution include elevation (elev), temperature seasonality (bio04), mean temperature of the coldest quarter (bio11), and precipitation of the warmest quarter (bio18). These insights are critical for conserving H. neurocarpa’s genetic resources and guiding future biodiversity conservation strategies. Full article

This study investigates the impact of climate change on the Mudurnu Stream Basin in northwest Türkiye by analyzing climate parameters in the Mediterranean region. Historical data from EC-Earth2, HadGEM2-ES, and MPI-ESM-MR GCMs from the CMIP5 Euro-CORDEX archive were assessed, and future precipitation and temperature data were derived using five statistical bias correction methods for the selected EC-Earth2 model under RCP4.5 and RCP8.5 scenarios. The SWAT model was employed to simulate future runoff amounts for the Mudurnu Stream Basin. The findings reveal notable changes in precipitation and temperature. The annual and seasonal variations of total precipitation and average, maximum, and minimum temperatures for the RCP4.5 and RCP8.5 scenarios in the Sakarya and Mudurnu regions were analyzed and determined. The projections for future river flow indicate a significant increase in precipitation during the rainy seasons. The Mudurnu Stream mainstem will experience an increase in flow of between 70 and 140% under RCP4.5 and between 80 and 160% under RCP8.5. In the Dinsiz Stream tributary, a 32–55% increase is observed for the spring and summer months. In this context, the rainfall and runoff projections required for the estimation of potential drought and flood risks in the near and distant future were calculated. Full article

This paper aims to analyze the Black Sea region’s potential for renewable energy, focusing on offshore wind and waves. The study highlights the Black Sea as a new region for marine renewables exploitation in the context of climate objectives and the European shift to renewable energy. It also incorporates results from different previous studies, when data from in situ measurements, satellite observations, and numerical simulations and climate reanalysis have been considered and analyzed. The reviewed studies cover a wide time span from historical data in the late 20th century to projections extending until 2100, considering the climate change impact. They focus on both localized coastal regions (predominantly Romanian waters) and the larger Black Sea Basin. The comparative analysis identifies the northwestern part of the sea as the most favorable region for the development of offshore wind farms. The present work also discusses the environmental implications and technological development of different types of wave energy converters (WECs) and their use in hybrid systems integrating multiple marine energy resources. The review concludes by highlighting the region’s outstanding potential for renewable energy and stressing the need for technological development, regional policy integration, and investment in infrastructure to enable sustainable marine energy harnessing. Full article

Land use/cover (LUCC) change in arid oasis–desert ecotones has significant implications for spatial governance in ecologically fragile regions. To better capture the temporal and spatial complexity of land transitions, this study developed a vector tracing method by integrating time-series remote sensing data with vector-based transfer pathways. Analysis of northern Oman from 1995 to 2020 revealed the following: (1) Arable land and impervious surfaces expanded from 0.51% to 1.09% and from 0.31% to 0.98%, respectively, while sand declined from 99.03% to 97.01%. Spatially, arable land was concentrated in piedmont irrigation zones, impervious surfaces near coastal cities, and shrubland and grassland along the Al-Hajar Mountains, forming a complementary land use mosaic. (2) Human activities were the dominant driver, with typical one-way chains accounting for 69.76% of total change. Sand was mainly transformed into arable land (7_C1, 7_D1, 7_E1; where the first part denotes the original type, the letter denotes the year of change, and the last digit denotes the new type), impervious surfaces (7_C6, 7_D6, 7_E6), and shrubland (7_E4). (3) Water scarcity and an arid climate remained primary constraints, manifested in typical reciprocating chains in the oasis–desert interface (7_D1_E7, 7_A1_B7, 7_C1_D7) and in the arid vegetation zone along the Al-Hajar Mountain foothills (7_D3_E7, 7_C3_D7), together accounting for 24.50% of total change. (4) The region exhibited coordinated transitions among oasis, urban, and ecological land, avoiding the common conflict of cropland loss to urbanization. During the study period, transitions among arable land, impervious surfaces, forest, shrubland, and wetland were rare (Type 16: 3.31%, Type 82: 2.89%, Type 12: 0.04%, Type 18: 0.01%). The case of northern Oman provides a valuable reference for collaborative spatial governance in ecologically fragile arid zones. Future research should integrate socio-economic drivers, climate change projections, and higher-temporal-resolution data to enhance the applicability of the chain-spectrum method in other arid regions. Full article

Significant wave height (SWH) is a key physical parameter influencing the safety of shipping, fisheries, and marine engineering projects, and is closely related to climate change and marine disasters. Existing models struggle to balance a high prediction accuracy with low parameter counts, and are challenging to deploy on platforms such as buoys. To address these issues, this study proposes an innovative method for SWH prediction by combining Singular Spectrum Analysis (SSA) with a residual correction mechanism in a Long Short-Term Memory (LSTM) network. This method utilizes SSA to decompose SWH time series, accurately extracting its main feature modes as inputs to the LSTM network and significantly enhancing the model’s ability to capture time-series data. Additionally, a residual correction module is introduced to fine-tune the prediction results, effectively improving the model’s 12 h forecasting accuracy. The experimental results show that for 1, 3, 6, and 12 h SWH predictions, by incorporating SSA and the residual correction module, the model reduces the Mean Squared Error (MSE), Root-Mean-Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) by 60–95%, and increases the coefficient of determination (R²) by 2–60%. The proposed model has only 10% of the parameters for LSTM based on Variational Mode Decomposition (VMD), striking an excellent balance between prediction accuracy and computational efficiency. This study provides a new methodology for deploying SWH prediction models on platforms such as buoys, and holds significant application value in marine disaster warning and environmental monitoring. Full article

Carbon stock plays a crucial role in regulating atmospheric carbon dioxide concentrations and represents a vital ecological function for mitigating climate change and supporting long-term environmental sustainability. Jiangsu Province, a typical region experiencing rapid urbanization and land-use transformation in eastern China, serves as a representative case for regional-scale carbon assessment. This study employs the InVEST model, integrated with multi-source remote sensing data, a random forest algorithm, and a control variable approach, to simulate the spatiotemporal dynamics of carbon stock in Jiangsu Province under a set of climate, productivity, and population scenarios. Three scenario groups were designed to isolate the individual effects of climate change, gross primary productivity, and population density from 2020 to 2060, enabling a clearer understanding of the dominant drivers. The results indicate that the coupled model estimates Jiangsu’s 2020 carbon stock at 1.52 × 10⁹ t C, slightly below the 1.82 × 10⁹ t C estimated by the standalone InVEST model, with the coupled results closer to previous estimates. Compared with InVEST alone, the integrated model significantly improves numerical accuracy and spatial resolution, allowing for finer-scale pattern recognition. By 2060, carbon stock is projected to decline by approximately 24.4% across all scenarios. Among the features, climate change exerts the most significant influence, with an elasticity coefficient range of −37.76–1.01, followed by productivity, while population density has minimal impact. These findings underscore the dominant role of climate drivers and highlight that model integration improves both predictive accuracy and spatial detail, offering a more robust basis for scenario-based assessment. The proposed approach provides valuable insights for supporting sustainable carbon management, real-time monitoring, and provincial-scale decarbonization planning. Full article

As local risk assessments are fundamental for risk management and mitigation strategies, this work introduces a methodology for assessing multi-hazard scenarios of extreme compounded events and their duration using daily time series of surface variables from high-resolution climate simulations during historical and future periods under RCP8.5. The aim was to investigate the return level extremes of 20- and 50-year periods of hazards occurring within specific durations and concurrent extreme values of other surface variables, for selected locations in Greece. In addition, future changes in the temporal occurrence of compounded hazards involving precipitation and wind with temperature extremes were performed based on temperature extreme percentiles. The assessment revealed the geographical dependence in the projected occurrence, intensity, and duration of compounded multi-hazard extremes, emphasising the need for high spatial resolution climate data for their investigation. The highlights of the findings include a significant increasing trend of compounded multi-hazard extremes, e.g., hot days and tropical nights, milder winter minimum temperatures with lower rainfall extremes, hotter and windier events of shorter duration, and longer precipitation extremes with increased extreme temperatures. The projections showcased the impact of climate change on extreme compounds with a multitude of interesting findings associated with significant changes in their duration, intensity, and temporal occurrence. Full article

Urban flooding, intensified by climate change, poses significant threats to sustainable development, necessitating accurate precipitation projections for effective risk management. This study utilized Bayesian Model Averaging (BMA) to optimize CMIP6 multi-model ensemble precipitation projections for Shanghai, integrating Delta statistical downscaling with observational data to enhance spatial accuracy and reduce uncertainty. After downscaling, RMSE values of daily precipitation for individual models range from 10.158 to 12.512, with correlation coefficients between −0.009 and 0.0047. The BMA exhibits an RMSE of 8.105 and a correlation coefficient of 0.056, demonstrating better accuracy compared to individual models. The BMA-weighted projections, coupled with Soil Conservation Service Curve Number (SCS-CN) hydrological model and drainage capacity constraints, reveal spatiotemporal flood risk patterns under Shared Socioeconomic Pathway (SSP) 245 and SSP585 scenarios. Key findings indicate that while SSP245 shows stable extreme precipitation intensity, SSP585 drives substantial increases—particularly for 50-year and 100-year return periods, with late 21st century maximums rising by 24.9% and 32.6%, respectively, compared to mid-century. Spatially, flood risk concentrates in peripheral districts due to higher precipitation exposure and average drainage capacity, contrasting with the lower-risk central urban core. This study establishes a watershed-based risk assessment framework linking climate projections directly to urban drainage planning, proposing differentiated strategies: green infrastructure for runoff reduction in high-risk areas, drainage system integration for vulnerable suburbs, and ecological restoration for coastal zones. This integrated methodology provides a replicable approach for climate-resilient urban flood management, demonstrating that effective adaptation requires scenario-specific spatial targeting. Full article

Vector-borne parasitic diseases (VBPDs), including malaria, schistosomiasis, leishmaniasis, Chagas disease, African trypanosomiasis, lymphatic filariasis, and onchocerciasis, impose a significant global health burden. This study analyzes the global disease burden of VBPDs from 1990 to 2021 using Global Burden of Disease (GBD) 2021 data and projects trends to 2036. Metrics include prevalence, deaths, disability-adjusted life years (DALYs), and age-standardized rates (ASRs) across regions, sexes, age groups, and Socio-demographic Index (SDI) levels. Key findings reveal persistent disparities: malaria dominated the burden (42% of cases, 96.5% of deaths), disproportionately affecting sub-Saharan Africa. Schistosomiasis ranked second in prevalence (36.5%). While African trypanosomiasis, Chagas disease, lymphatic filariasis, and onchocerciasis declined significantly, leishmaniasis showed rising prevalence (EAPC = 0.713). Low-SDI regions bore the highest burden, linked to environmental, socioeconomic, and healthcare access challenges. Males exhibited greater DALY burdens than females, attributed to occupational exposure. Age disparities were evident: children under five faced high malaria mortality and leishmaniasis DALY peaks, while older adults experienced complications from diseases like Chagas and schistosomiasis. ARIMA modeling forecasts divergent trends: lymphatic filariasis prevalence nears elimination by 2029, but leishmaniasis burden rises across all metrics. Despite overall progress, VBPDs remain critical public health threats, exacerbated by climate change, drug resistance, and uneven resource distribution. Targeted interventions are urgently needed, prioritizing vector control in endemic areas, enhanced surveillance for leishmaniasis, gender- and age-specific strategies, and optimized resource allocation in low-SDI regions. This analysis provides a foundation for evidence-based policy and precision public health efforts to achieve elimination targets and advance global health equity. Full article

This paper introduces a methodology for forecasting spring hydrographs based on projections from regional climate models. The primary study objective was to evaluate how climate change may affect spring discharge. A statistical–analytical modeling approach was developed and applied to the Jósva spring catchment in the Aggtelek Karst region of Hungary. Historical data served to establish a regression relationship between rainfall and peak discharge. This approach is particularly useful for predicting discharge in cases where only historical rainfall data are available for calibration. Baseflow recession was analyzed using a two-component exponential model, with hydrograph decompositionand parameter optimization performed on the master recession curve. Future discharge time series were generated using rainfall data from two selected regional climate model scenarios. Both scenarios suggest a decline in baseflow discharge during different periods of the 21st century. The findings indicate that climate change is likely to intensify hydrological extremes in the coming decades, irrespective of whether moderate or high CO₂ emission scenarios unfold. Full article