Search Results (233)

Land use/land cover (LULC) is one of the core driving factors affecting terrestrial ecosystem carbon storage and exacerbating global warming. As an area with the most intense land–sea interactions, China’s coastal zone has experienced drastic LULC transition and carbon storage fluctuations during the rapid urbanization process. Based on the InVEST model, this study analyzes the spatiotemporal dynamics of LULC and carbon storage (CS) in China’s coastal regions from 2000 to 2024, and simulated multi-scenario carbon storage trajectories for 2050 integrating the SSP-RCP scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, the XGBoost-SHAP and generalized additive models (GAMs) were introduced to deeply analyze the nonlinear characteristics and temporal heterogeneity of the driving mechanisms of CS evolution. The results show the following: (1) During the study period, the LULC structure of the coastal region was dominated by cropland and forestland consistently accounting for over 85%, but exhibited a competitive pattern characterized by the continuous expansion of built-up land severely squeezing ecological spaces. (2) The total regional CS showed an overall phased downward trend, accompanied by increasing fragmentation of high carbon sink areas. Notably, as the core carbon pool, the reduction in forest area was the dominant factor causing regional net carbon losses. (3) CS remained relatively stable under SSP1-2.6, representing a sustainable development pathway with low greenhouse gas emissions. In contrast, SSP2-4.5, SSP3-7.0, and SSP5-8.5 exhibited more pronounced declines in carbon storage by 2050, indicating that SSP1-2.6 is the most favorable pathway for maintaining long-term carbon storage stability in China’s coastal regions. (4) The driving mechanism of CS has undergone a profound shift from being dominated by natural ecological baselines to human activities. Land use intensity (LUI) has emerged as the strongest predictor in the model, and the nonlinear impacts of human activities have grown increasingly complex over time. This study highlights the complex impacts of high-intensity human disturbances on the coastal carbon cycle, providing a scientific basis for formulating differentiated carbon management strategies and adaptive spatial land-use planning oriented toward the “Dual Carbon” goals. Full article

Climate change threatens nitrogen cycling in agricultural ecosystems. Optimizing sowing dates and nitrogen management for maize–soybean intercropping is critical for sustainable production in the North China Plain (NCP). Using a calibrated Agricultural Production Systems Simulator (APSIM) model driven by three representative global climate models (GCMs) selected from 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, we evaluated management strategies under two Shared Socioeconomic Pathway scenarios (SSP2-4.5 and SSP5-8.5) across three climatic zones for near-term (2030–2059) and long-term (2070–2099) periods. Under SSP5-8.5, warming was 1.8–2.2 times greater than under SSP2-4.5, nitrate nitrogen (NO₃⁻-N) leaching increased by 12.1%, and nitrate storage in the 100–150 cm soil layer rose by 53.4% in Zone III. Biological nitrogen fixation contributed 20.1–29.1% of soybean nitrogen uptake under low nitrogen and 14.9–23.4% under medium nitrogen. Optimal strategies were identified: sowing on 7 June (S3) with medium nitrogen (220.8 kg N ha⁻¹) under SSP2-4.5, and advancing sowing to 28 May (S2) with medium nitrogen under SSP5-8.5 to alleviate heat stress. This study reveals a climate-driven “earlier supply–shortened demand–concentrated leaching” mismatch, providing adaptive management guidance for maize–soybean intercropping systems in the NCP. Full article

The permafrost in the Tibetan Plateau is extremely sensitive to climate warming, which poses challenges to regional sustainability. Predicting the evolution of permafrost on the Tibetan Plateau in the future could provide a reference for future engineering, construction, and resource management on the Tibetan Plateau. In this study, the Random Forest regression model and the temperature at the top of permafrost (TTOP) model are combined. The Random Forest regression model is used to simulate the long-term series of land surface temperatures. The multiple climate model data sets in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and TTOP model are used to simulate the historical (1979–2018) and predict the future (2019–2100) distribution of permafrost on the Tibetan Plateau. The results show that since 1979, due to climate warming, more than 20% of the permafrost in the Tibetan Plateau has disappeared. The permafrost will degrade at different rates under each of four Shared Socioeconomic Pathways (SSPs), namely SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The degradation rate under SSP1–2.6 is the slowest, indicating that about 20.1% of the permafrost will disappear by 2100. The degradation rate under the SSP5–8.5 is the fastest, predicting that about 82.4% of the permafrost will disappear by 2100. Under SSP2–4.5 and SSP3–7.0, 37.57% and 69.1% of the permafrost will disappear by 2100, respectively. The above results can provide a reference for sustainable engineering construction, infrastructure planning, and climate adaptation strategies on the Tibetan Plateau. Full article

Accurately assessing and predicting vegetation dynamics is of great significance for evaluating regional hydrological and ecological environments. This study focuses on the climate-sensitive Qinghai-Tibetan Plateau (QTP), aiming to reveal the spatiotemporal patterns, underlying driving mechanisms, and future trends of vegetation dynamics. The historical turning points of greening trends were identified using the running slope difference method, and the SHapley Additive exPlanations (SHAP) method was employed to analyze the key driving factors. An Xtreme Gradient Boosting (XGBoost) prediction model was constructed and validated, and then coupled with Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble data to project seasonal vegetation changes under different Shared Socioeconomic Pathways (SSP). The main conclusions are as follows: (1) Vegetation on the QTP showed an overall greening trend with significant spatial heterogeneity. Approximately 47.25% of the area exhibited no trend shift (NS), while 29.42% experienced a shift from greening to browning (GB), with most shifts occurring between 1990 and 2010. (2) Soil moisture and precipitation were the dominant driving factors, with contributions significantly higher than those of temperature, wind speed, and other variables, and they exhibited nonlinear interactive effects with the Normalized Difference Vegetation Index (NDVI). (3) In the future, vegetation is projected to show an overall increasing trend, with stronger responses in spring and autumn. The regional average rate of change is highest in spring, especially under the SSP5-8.5 scenario (17.8% for 2030–2060 and 26.4% for 2061–2100); in autumn, although the regional average rate of change is small, the internal spatial variability is significant. The humid regions in the eastern and southeastern parts of the QTP demonstrated more active greening across all seasons except winter, and high-emission scenarios are expected to exacerbate regional and seasonal differences. This study systematically reveals the adaptive dynamics and future scenarios of vegetation dynamics on the QTP, providing scientific support for the adaptation of alpine ecosystems to global change and the management of regional ecological security barriers. Full article

Climate change poses a significant challenge to food security, as it alters crop productivity, distribution patterns, and the overall food supply. This study modelled the emergence of Amaranthus hybridus L. in bean (Phaseolus vulgaris L.) and maize (Zea mays L.) production systems in the Brazilian state of Minas Gerais, in the cities of Coimbra, Paracatu, São João del-Rei, and Uberaba, under the Coupled Model Intercomparison Project Phase 6 (CMIP6) SSP1-2.6 and SSP5-8.5 scenarios. Using Hydrothermal Time (HTT), computational modelling, and nonlinear Weibull regression, weed emergence was simulated under current and future climate scenarios for 2050 and 2070. Although biological triggers such as temperature and base water potential remain constant, higher average temperatures accelerate HTT accumulation. Thus, this results in earlier and more intense emergence flows. The highest and lowest cumulative emergence were observed in Uberaba and Paracatu, respectively. The SSP5-8.5 scenario projects high emergence windows for 2070. This reduces the time available for management interventions. The root-mean-square error (RMSE) associated with the coefficient of determination (R²) of the models validates HTT as an essential tool in computational agriculture. The integration of these models into decision-support systems is essential to mitigating productivity losses and it will increase control efficiency amid future climate uncertainties. Full article

This study presents the relationship between climate variables and malaria outbreaks and forecasts the future malaria incidence in Arba Minch Town and its surrounding areas. High-resolution gridded climate data (~4 km × 4 km) covering the period 1981 to 2020 was obtained from the Ethiopian Meteorological Institute. Additionally, Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) were used to analyze future climate patterns. Malaria case data were obtained from local health centers located in Arba Minch town and surrounding woredas. Malaria projections were simulated using the Seasonal Autoregressive Integrated Moving Average (SARIMAX) model. Climate projections indicate a significant rise in mean temperature by the end of 21st century, increasing by 2.9 °C under SSP2-4.5 and 3.48 °C under SSP5-8.5. Average monthly rainfall during the baseline period (70.53 mm) is expected to increase to 94.18 mm and 86.09 mm under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. Malaria case distribution during the baseline period (2005–2017) ranged from 79 to 552 cases per month, while future projections suggest that cases will increase by approximately 600 in the near-term and up to more than 1000 cases by the end of the century. The SARIMAX model effectively captured seasonal variations and short-term fluctuations demonstrating a strong forecasting performance. The model generally indicated that wetter conditions and moderate temperatures will favor mosquito breeding and intensify malaria transmission. Full article

In the last two decades, the utilization of solar energy has been growing rapidly worldwide, mainly due to the increasing adoption of photovoltaic (PV) systems. Since solar energy is one of the most weather-dependent renewable energy sources, an increasing number of meteorological studies have focused on PV potential (PV_pot) and its projected changes under global warming. GCM outputs disseminated through the Coupled Model Intercomparison Project (CMIP) are often applied in energy-related urban climate studies, as they can be downscaled either statistically or dynamically. It is essential to evaluate raw (not bias-corrected) GCM data, which helps to determine the uncertainties in the GCM simulations before downscaling. Despite their coarse resolution, some studies even rely directly on the GCM grid cell time series to represent individual locations. Accordingly, this study evaluates 10 CMIP Phase 6 (CMIP6) GCMs with respect to some atmospheric variables (air temperature, solar radiation, and wind speed, which are the primary drivers of PV_pot) in four lowland grid cells representing four major urban areas in Central and Southeast Europe: Belgrade (Serbia), Budapest (Hungary), Vienna (Austria), and Prague (Czechia). The use of solar energy has increased significantly in most of these regions in recent years; however, it remains less studied than in Western Europe. ERA5 reanalysis is used as the reference dataset. We analyzed the boreal summer (JJA) days of three overlapping 30-year time periods: 1971–2000, 1981–2010, and 1991–2020. Our main findings are as follows: GCMs tend to overestimate solar radiation and underestimate maximum near-surface air temperature relative to ERA5 in all time periods and in all the four urban areas, which leads to a significant overestimation of the number of JJA days with high PV_pot (PV_pot,90). PV_pot,90 is increasing from 1971–2000 to 1991–2020 in the vast majority of GCMs, in all the four regions. EC-Earth3 and its different configurations (EC-Earth3-Veg, EC-Earth3-CC) are considered the most accurate GCMs relative to ERA5. Full article

Renewables are key pillars of the European Union’s (EU) strategy for green transition and climate neutrality. In particular, wind energy lies at the core of a sustainable framework regarding the energy policy (i.e., European Green Deal and REPowerEU plan) supporting clean, secure, and affordable electricity for a resilient future. In this study, Global Climate Models (GCMs) simulations were used to investigate the efficiency of GCMs to capture and reproduce the spatial and temporal features of Wind Energy Potential (WEP). The GCMs that have been used in this study are available in the context of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The analysis focuses on high-interest regions of the Eastern Mediterranean (EMed) during the summer season (JJA). The ERA5 reanalysis dataset was used as a reference data set. Furthermore, projected changes in WEP were calculated under two Shared Socioeconomic Pathways (the “moderate”, SSP2-4.5 and the “fossil-fueled development”, SSP5-8.5 scenarios), covering the period from 1970 to 2099. The results indicate that most GCMs underestimate mean WEP, with model performance ranging from “poor” to “good” scores based on the Kling–Gupta Efficiency index (−0.45 < KGE < 0.5). Future WEP projections show no consistent spatial patterns among GCMs. By the late 21st century, WEP is projected to decrease (about 10–15%) over the Southeastern Aegean and increase between Crete and Libya (about 10–15%) relative to the baseline historical period (1970–2000) under both SSP scenarios. Finally, findings provide elements for the WEP evolution over the Eastern Mediterranean, contributing to the EU energy policy. Full article

Although Afghanistan’s contribution to global and regional greenhouse gas (GHG) emissions is minimal, it remains among the countries most vulnerable to the impacts of climate change. Rising temperatures and decreasing precipitation have significantly disrupted the country’s natural resources, including water supplies, agriculture, forests, rangelands, and ecosystems, threatening its agrarian economy and socio-economic stability. Simultaneously, Afghanistan has substantial untapped renewable energy potential, especially in hydropower, solar, wind, and biomass. This study analyzes historical (1970–2014) and projected (2015–2099) climate trends across Afghanistan by examining mean annual temperature and precipitation using the Mann–Kendall test and Sen’s Slope estimator. Results indicate a significant warming trend, with a 1.58 °C rise in temperature and a 36 mm decrease in annual precipitation over the past five decades. Future projections based on Shared Socioeconomic Pathways (SSPs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) suggest continued temperature increases, while precipitation trends vary geographically and over time, showing increases, decreases, or little change. The study also evaluates Afghanistan’s GHG emissions, which are negligible on regional and global scales. Despite its abundant renewable energy resources, the country still depends heavily on electricity imports from neighboring nations, leaving much of its domestic potential untapped. Harnessing these renewable resources can provide a practical path toward energy independence, zero-emission energy generation, and sustainable long-term development. This research emphasizes the urgent need for Afghanistan to strategically develop its renewable energy sector to boost climate resilience, enhance energy security, and promote sustainable economic growth. Full article

Under global climate change, the Tibetan Plateau, as a sensitive and ecologically vulnerable region, exhibits vegetation dynamics that significantly influence regional ecological security and hydrological cycles. This study aims to project the dynamic changes in vegetation on the Tibetan Plateau under climate change and assess the associated uncertainties in projections. Coupled Model Intercomparison Project Phase 6 (CMIP6) models were used to provide climate change outputs in the future under different greenhouse gas emission scenarios. The vegetation dynamics were described by the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data. By integrating a Random Forest model with the output climate data of CMIP6 models and training the model based on the historical observation data, NDVI changes under future emission scenarios were simulated and evaluated. The key findings of this study are as follows: (1) The multimodel ensemble (MME) performed best in simulating environmental variables, while certain individual models showed significant deviations in simulating specific variables; the Random Forest model demonstrated reliable capability in NDVI simulation and prediction. (2) The future NDVI was projected to increase persistently in the central and eastern plateau but decrease along the northern and southeastern margins, with variability in the trend projections between different models. (3) The MME model indicated an overall NDVI increase in the future, with higher values under SSP245 before the 2060s and stronger increases under SSP585 thereafter; humid basins exhibited more pronounced increases, while arid/semiarid basins showed limited changes. (4) The uncertainty in the NDVI projections showed a sustained increasing trend under both scenarios, with a stronger rise under the SSP585 scenario; spatially, the uncertainty remained low across most of the Tibetan Plateau but was relatively higher in the central–eastern region and major humid basins. These results provide a scientific basis for understanding alpine ecosystem responses to future climate change and for regional ecological risk management. Full article

Exploring future habitat quality changes in the Mid-Section of the Tianshan Mountains (MSTM) is crucial for regional biodiversity conservation. This study utilizes climate projection data from CMIP6 and integrates the SD-PLUS-InVEST analytical framework to simulate future LULC and habitat quality under three distinct future scenarios. Additionally, the XGBoost-SHAP model is applied to identify and interpret the key regulatory factors within the modeling framework that influence habitat quality spatial heterogeneity. The results show the following: (1) the projections under the three 2035 scenarios generally follow the development trend of 2020, with continued spread of dry land and construction land, but general reduction in the ecological land, reflecting an intensifying conflict between land development and ecological preservation. (2) Habitat quality varies significantly across scenarios, generally exhibiting a “U-shaped” distribution pattern characterized by larger areas of high and low quality and smaller areas of moderate quality. Within the SSP5–8.5 scenario, habitat quality is relatively poor, accompanied by pronounced spatial heterogeneity and imbalance. (3) NDVI is identified as the dominant factor influencing habitat quality spatial heterogeneity, followed by GDP, TEM, and DEM. Although the influence of these factors varies slightly across scenarios, their relative importance remains generally consistent, reflecting the structural stability and response coherence of the ecosystem. Full article

Under the context of global warming, accelerated glacier melting poses a severe threat to regional water security, necessitating systematic quantification of the spatiotemporal evolution of glacier mass balance (GMB) and its impacts on runoff. This study employed the Spatial Processes in Hydrology (SPHY) distributed hydrological model, integrated with remote sensing data, meteorological observations, and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios, to reconstruct the spatiotemporal evolution of glacier mass balance in the Manas River Basin on the northern slope of Tianshan Mountains from 2000 to 2014, quantify the coupling relationships between glacier mass balance and climate factors as well as glacier meltwater runoff, and project future trends from 2015 to 2045. Results showed that glaciers in the basin experienced persistent negative mass balance during the study period, with a 15-year mean glacier mass balance of −0.87 $\mathrm{m} \mathrm{w}.\mathrm{e}.·{\mathrm{a}}^{-1}$, cumulative loss of 12.16 $\mathrm{m} \mathrm{w}.\mathrm{e}.$, and glacier area shrinkage of 11.9%. Glacier mass balance exhibited significant spatiotemporal heterogeneity, with the most severe mass loss occurring in steep south-facing slopes, and glacier thickness change displayed a “single-peak” altitudinal dependence with the ablation peak elevation stabilized at approximately 4400 m. Glacier mass balance showed a significant negative correlation with melt-season positive accumulated temperature (r = −0.9, p < 0.01), with a temperature sensitivity coefficient of 55.17 %·°C⁻¹. The contribution rate of glacier meltwater runoff increased from 19.93% to 29.50%, showing a significant negative correlation with glacier mass balance (r = −0.73, p < 0.01), revealing the phenomenon of “compensatory runoff increase”. Under three future scenarios, glacier mass balance loss exhibited an intensifying trend, with the most severe loss in high-altitude areas, and glacier meltwater runoff continued to increase but demonstrated unsustainability. This study provides a scientific basis for predicting “peak water” timing and adaptive water resource management in high mountain glacierized basins under climate change. Full article

Watersheds provide fundamental hydrological ecosystem services for human well-being and the environment, such as water provisioning, hydrological cycle regulation, and erosion control; however, these services face increasing anthropogenic and climatic pressures. This study assessed individual and combined impacts on the hydrological functionality of the Piuray–Ccorimarca watershed (Cusco, Peru) using a calibrated Soil and Water Assessment Tool (SWAT) model, analyzing water yield, soil water storage, and sediment transport across 20 scenarios. An ensemble of 10 Coupled Model Intercomparison Project Phase 6 (CMIP6) models with bias correction was implemented, integrated with land transformation projections contemplating urban expansion associated with airport development and forest recovery through Payment for Ecosystem Services mechanisms. The results reveal climate change as the dominant driver, generating water yield increases and soil water content improvements primarily due to evapotranspiration decoupling that increases the runoff coefficient. In contrast, land use change produces substantially smaller hydrological effects but critically intensifies sediment yield. Spatial vulnerability analysis identified eight persistently critical sub-basins (20.5% of area) where soil water content emerged as the dominant limiting factor. These findings establish a clear management hierarchy prioritizing climate adaptation over land use interventions, with differentiated strategies required for critical zones demanding structural interventions versus non-critical areas amenable to flexible conservation approaches. Full article

The Lake Sevan basin is particularly sensitive to climate change due to its continental climate and mountainous terrain, which collectively amplify climatic impacts. This study aimed to assess the influence of climate change on the thermal dynamics of the basin by analyzing both historical and projected temperature variations. Over the past three decades, the region has experienced a marked rise in air temperatures. Seasonal variability revealed distinct contrasts between winter and summer, with winter exhibiting greater fluctuations, ranging from 1.67 to 2.41 °C, compared to the more stable summer range of 0.81 to 1.41 °C. An analysis of heat inflow and outflow patterns demonstrated a moderating effect of Lake Sevan on temperature extremes. Stations, located near the lake, recorded lower levels of heat inflow and outflow, indicating that the lake’s thermal inertia helps buffer seasonal temperature extremes. In contrast, stations situated farther from the lake exhibited more pronounced fluctuations, reflecting the absence of this stabilizing influence. These results underscore the lake’s critical role in modulating the local climate by dampening extreme thermal variations. Additionally, comparative analysis of air and water temperature trends revealed that, while both exhibit warming, air temperatures show greater interannual variability. In contrast, water temperatures remained more stable, particularly during winter, due to the lake’s thermal inertia. Future climate projections for the Lake Sevan region, based on CMIP6 (Coupled Model Intercomparison Project phase 6) ensemble outputs under four Shared Socioeconomic Pathways (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), suggest a persistent warming trend throughout the 21st century. We project that the most significant increases are expected during summer months, with an anticipated mean annual temperature rise of up to 6 °C by the end of the century under the high-emission scenario (SSP5–8.5). Full article

Based on precipitation extremes calculated from high-resolution daily observational data in East China during 1961–2014, the performance of 34 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) are assessed in terms of climatology and interannual variability. Four extreme precipitation indices, including the total precipitation (Prcptot), the total precipitation for events exceeding the 95th percentile (R95p), and the maximum of 1-day (Rx1day) and 5-day (Rx5day) precipitation, are analyzed. Results show that the CMIP6 models demonstrate good performances in reproducing the climatological spatial distribution and interannual variability of precipitation extremes, with the best from Prcptot. Based on an integrated assessment of the above two factors, the models that perform relatively well for all four extreme precipitation indices are GFDL-CM4, MIROC6, EC-Earth3-Veg, EC-Earth3, and EC-Earth3-CC. Furthermore, the optimal multi-model ensemble (A-MME) constructed from a selection of the most skillful models shows improved behavior compared to the all-model ensemble. The wet (dry) biases over the northern (southern) region of East China are all decreased. This may benefit from the improvement that A-MME can reproduce well the characteristics of moisture and vertical velocity. Full article