Search Results (421)

This study investigates changes in Near-Surface Air Temperature (NSAT) over the South African region using weather station data, reanalysis products, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. It is shown that, based on ERA5 reanalysis, the average NSAT increase in the region (45–10° S, 0–50° E) for the period 1940–2023 was 0.11 ± 0.04 °C. Weak multi-decadal changes in NSAT were observed from 1940 to the mid-1970s, followed by a rapid warming trend starting in the mid-1970s. Weather station data generally confirm these results, although they exhibit considerable inter-station variability. An ensemble of 33 CMIP6 models also reproduces these multi-decadal NSAT change characteristics. Specifically, the average model-simulated NSAT values for the region increased by 0.63 ± 0.12 °C between the periods 1940–1969 and 1994–2023. Based on the results of the comparison between weather station observations, reanalysis, and models, we utilize projections of NSAT changes from the analyzed ensemble of 33 CMIP6 models until the end of the 21st century under various Shared Socioeconomic Pathway (SSP) scenarios. These projections indicate that the average NSAT of the South African region will increase between 1994–2023 and 2070–2099 by 0.92 ± 0.36 °C under the SSP1-2.6 scenario, by 1.73 ± 0.44 °C under SSP2-4.5, by 2.52 ± 0.50 °C under SSP3-7.0, and by 3.17 ± 0.68 °C under SSP5-8.5. Between 1994–2023 and 2025–2054, the increase in average NSAT for the studied region, considering inter-model spread, will be 0.49–1.15 °C, depending on the SSP scenario. Furthermore, climate warming in South Africa, both in the next 30 years and by the end of the 21st century, is projected to occur according to all 33 CMIP6 models under all considered SSP scenarios. The main spatial feature of this warming is a more significant increase in NSAT over the landmass of the studied region compared to its surrounding waters, due to the stabilizing role of the ocean. Full article

As China’s first systematic assessment of high-risk Amaranthaceae invaders, this study addresses a critical knowledge gap identified in the National Invasive Species Inventory, in which four invasive Amaranthaceae species (Dysphania ambrosioides, Celosia argentea, Amaranthus palmeri, and Amaranthus spinosus) are prioritized due to CNY 2.6 billion annual ecosystem damages in China. By coupling multi-species comparative analysis with a parameter-optimized Maximum Entropy (MaxEnt) model integrating climate, soil, and topographical variables in China under Shared Socioeconomic Pathways (SSP) 126/245/585 scenarios, we reveal divergent expansion mechanisms (e.g., 247 km faster northward shift in A. palmeri than D. ambrosioides) that redefine invasion corridors in the North China Plain. Under current conditions, the suitable habitats of these species span from 92° E to 129° E and 18° N to 49° N, with high-risk zones concentrated in central and southern China, including the Yunnan–Guizhou–Sichuan region and the North China Plain. Temperature variables (Bio: Bioclimatic Variables; Bio6, Bio11) were the primary contributors based on permutation importance (e.g., Bio11 explained 56.4% for C. argentea), while altitude (e.g., 27.3% for A. palmeri) and UV-B (e.g., 16.2% for A. palmeri) exerted lower influence. Model validation confirmed high accuracy (mean area under the curve (AUC) > 0.86 and true skill statistic (TSS) > 0.6). By the 2090s, all species showed net habitat expansion overall, although D. ambrosioides exhibited net total contractions during mid-century under the SSP126/245 scenarios, C. argentea experienced reduced total suitability during the 2050s–2070s despite high-suitability growth, and A. palmeri and A. spinosus expanded significantly in both total and highly suitable habitat. All species shifted their distribution centroids northward, aligning with warming trends. Overall, these findings highlight the critical role of temperature in driving range dynamics and underscore the need for latitude-specific monitoring strategies to mitigate invasion risks, providing a scientific basis for adaptive management under global climate change. Full article

With climate change, the spatial and temporal patterns of precipitation are altered to a certain degree, which potentially affects the grey water footprint (GWF) of total nitrogen (TN) in agriculture, thereby threatening water security in the Yangtze River Basin (YRB), the largest river in China. The current study constructs an assessment framework for climate change impacts on the GWF of agricultural TN by coupling Shared Socioeconomic Pathways (SSPs) with the InVEST model. The framework consists of four components: (i) data collection and processing, (ii) simulating the two critical indicators (L_TN and W) in the GWF model based on the InVEST model, (iii) calculating the GWF and GWF index (GI) of TN, and (iv) calculating climate change impact index on GWF of agricultural TN (CI) under two SSPs. It is applied to the YRB, and the results show the following: (i) GWFs are 959.7 and 961.4 billion m³ under the SSP1-2.6 and SSP5-8.5 climate scenarios in 2030, respectively, which are both lower than that in 2020 (1067.1 billion m³). (ii) The GI values for TN in 2030 under SSP1-2.6 and SSP5-8.5 remain at “High” grade, with the values of 0.95 and 1.03, respectively. Regionally, the water pollution level of Taihu Lake is the highest, while that of Wujiang River is the lowest. (iii) The CI values of the YRB in 2030 under SSP1-2.6 and SSP5-8.5 scenarios are 0.507 and 0.527, respectively. And the CI values of the five regions in the YRB are greater than 0, indicating that the negative effects of climate change on GWFs increase. (iv) Compared with 2020, L_TN and W in YRB in 2030 under the two SSPs decrease, while the GI of TN in YRB rises from SSP1-2.6 to SSP5-8.5. The assessment framework can provide strategic recommendations for sustainable water resource management in the YRB and other regions globally under climate change. Full article

Central Asia (CA) faces growing Water–Energy–Food (WEF) Nexus challenges, due to its complex transboundary water management, legacy Soviet-era water infrastructure, and increasing climate and socio-economic pressures. This study presents the development of a System Dynamics Model (SDM) to evaluate WEF interdependencies across the Aral Sea Basin (ASB), including the Amu Darya and Syr Darya river basins and their sub-basins. Different downscaling strategies based on the area, population, or land use have been applied to process open-access databases at the national level in order to match the scope of the study. Climate and socio-economic assumptions were introduced through the integration of already defined Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The resulting SDM incorporates more than 500 variables interacting through mathematical relationships to generate comprehensive outputs to understand the WEF Nexus concerns. The SDM was successfully calibrated and validated across three key dimensions of the WEF Nexus: final water discharge to the Aral Sea (Mean Absolute Error, MAE, <5%), energy balance (MAE = 4.6%), and agricultural water demand (basin-wide MAE = 1.2%). The results underscore the human-driven variability of inflows to the Aral Sea and highlight the critical importance of transboundary coordination to enhance future resilience. Full article

Atmospheric warming results in increase in temperatures for the mean, the coldest, and the hottest day of the year, season, or month. Global warming leads to a large increase in the atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. Using the GISS-E2.1 climate model, we present the future changes in the coldest and hottest daily temperatures as well as in extreme precipitation indices (under four main Shared Socioeconomic Pathways (SSPs)). The increase in the wet-day precipitation ranges between 6% and 15% per 1 °C global surface temperature warming. Scaling of the 95th percentile versus the total precipitation showed that the sensitivity for the extreme precipitation to the warming is about 10 times stronger than that for the mean total precipitation. For six precipitation extreme indices (Total Precipitation, R95p, RX5day, R10mm, SDII, and CDD), the histograms of probability density functions become flatter, with reduced peaks and increased spread for the global mean compared to the historical period of 1850–2014. The mean values shift to the right end (toward larger precipitation and intensity). The higher the GHG emission of the SSP scenario, the more significant the increase in the index change. We found an intensification of precipitation over the globe but large uncertainties remained regionally and at different scales, especially for extremes. Over land, there is a strong increase in precipitation for the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere. There is an enlargement of the drying patterns in the subtropics including over large regions around Mediterranean, southern Africa, and western Eurasia. For the continental averages, the reduction in total precipitation was found for South America, Europe, Africa, and Australia, and there is an increase in total precipitation over North America, Asia, and the continental Russian Arctic. Over the continental Russian Arctic, there is an increase in all precipitation extremes and a consistent decrease in CDD for all SSP scenarios, with the maximum increase of more than 90% for R95p and R10 mm observed under SSP5–8.5. Full article

Southeast Asia relies heavily on hydropower from dams and reservoir projects, but this dependence comes at the cost of ecological damage and increased vulnerability to extreme events. This dilemma necessitates a choice between continued dam development and adopting alternative renewable options. Concerns over these environmental impacts have already led to halts in dam construction across the region. This study assesses the potential of run-of-river hydropower plants (RHPs) across 199 hydrometric stations in Mainland Southeast Asia (MSEA). The assessment utilizes power duration curves for the historical period and projections from the HBV hydrological model, which is driven by an ensemble of 31 climate models for future scenarios. Energy production was analyzed at four levels (minimum, maximum, balanced, and optimal) for both historical and future periods under varying Shared Socioeconomic Pathways (SSPs). To promote sustainable development, environmental flow constraints and carbon dioxide (CO₂) emissions were evaluated for both historical and projected periods. The results indicate that the aggregate energy production potential during the historical period ranges from 111.15 to 229.62 MW (Malaysia), 582.78 to 3615.36 MW (Myanmar), 555.47 to 3142.46 MW (Thailand), 1067.05 to 6401.25 MW (Laos), 28.07 to 189.77 MW (Vietnam), and 566.13 to 2803.75 MW (Cambodia). The impact of climate change on power production varies significantly across countries, depending on the level and scenarios. At the optimal level, an average production change of −9.2–5.9% is projected for the near future, increasing to 15.3–19% in the far future. Additionally, RHP development in MSEA is estimated to avoid 32.5 Mt of CO₂ emissions at the optimal level. The analysis further shows avoidance change of 8.3–25.3% and −8.6–25.3% under SSP245 and SSP585, respectively. Full article

This study projects future Residential Water Use (RWU) under climate change scenarios using a Bayesian Neural Network (BNN) model that quantifies the relationship between observed temperatures and RWU. Eighteen Global Climate Models (GCMs) under the Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) scenario were used to assess the uncertainties across these models. The findings indicate that RWU in Republic of Korea (ROK) is closely linked to temperature changes, with significant increases projected in the distant future (F3), especially during summer. Under the SSP5–8.5 scenario, RWU is expected to increase by up to 10.3% by the late 21st century (2081–2100) compared to the historical baseline. The model achieved a root mean square error (RMSE) of 11,400 m³/month, demonstrating reliable predictive performance. Unlike conventional deep learning models, the BNN provides probabilistic forecasts with uncertainty bounds, enhancing its suitability for climate-sensitive resource planning. This study also projects inflows to the Paldang Dam, revealing an overall increase in future water availability. However, winter water security may decline due to decreased inflow and minimal changes in RWU. This study suggests enhancing summer precipitation storage while considering downstream flood risks. Demand management strategies are recommended for addressing future winter water security challenges. This research highlights the importance of projecting RWU under climate change scenarios and emphasizes the need for strategic water resource management in ROK. Full article

Urban and regional ecosystems are increasingly challenged by the compounded effects of climate change and intensive land use. In this study, a predictive assessment framework for ecosystem resilience in the Beijing–Tianjin–Hebei region was developed by integrating multi-source remote sensing data, with the aim of quantitatively evaluating the coupled effects of climate change and land use change on future ecosystem resilience. In the first stage of the study, the SD-PLUS coupled modeling framework was employed to simulate land use patterns for the years 2030 and 2060 under three representative combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Building upon these simulations, ecosystem resilience was comprehensively evaluated and predicted on the basis of three key attributes: resistance, adaptability, and recovery. This enabled a quantitative investigation of the spatio-temporal dynamics of ecosystem resilience under each scenario. The results reveal the following: (1) Temporally, ecosystem resilience exhibited a staged pattern of change. From 2020 to 2030, an increasing trend was observed only under the SSP1-2.6 scenario, whereas, from 2030 to 2060, resilience generally increased in all scenarios. (2) In terms of scenario comparison, ecosystem resilience typically followed a gradient pattern of SSP1-2.6 > SSP2-4.5 > SSP5-8.5. However, in 2060, a notable reversal occurred, with the highest resilience recorded under the SSP5-8.5 scenario. (3) Spatially, areas with high ecosystem resilience were primarily distributed in mountainous regions, while the southeastern plains and coastal zones consistently exhibited lower resilience levels. The results indicate that climate and land use changes jointly influence ecosystem resilience. Rainfall and temperature, as key climate drivers, not only affect land use dynamics but also play a crucial role in regulating ecosystem services and ecological processes. Under extreme scenarios such as SSP5-8.5, these factors may trigger nonlinear responses in ecosystem resilience. Meanwhile, land use restructuring further shapes resilience patterns by altering landscape configurations and recovery mechanisms. Our findings highlight the role of climate and land use in reshaping ecological structure, function, and services. This study offers scientific support for assessing and managing regional ecosystem resilience and informs adaptive urban governance in the face of future climate and land use uncertainty, promotes the sustainable development of ecosystems, and expands the applicability of remote sensing in dynamic ecological monitoring and predictive analysis. Full article

The distribution of Itea yunnanensis, a shrub species in the genus Itea of the family Iteaceae, is primarily concentrated in the Hengduan Mountains region of China, where it faces severe threats from global climate change. However, systematic research on the species’ distribution patterns, climatic response mechanisms, and future suitable habitat dynamics remains insufficient. This study aims to assess the spatiotemporal evolution and driving mechanisms of I. yunnanensis-suitable habitats under current and future climate change scenarios to reveal the migration patterns of its distribution centroid and ecological thresholds, and to enhance the reliability and interpretability of predictions through model optimization. For MaxEnt modeling, we utilized 142 georeferenced occurrence records of I. yunnanensis alongside environmental data under current conditions and three future Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, SSP5-8.5). Model parameter optimization (Regularization Multiplier, Feature Combination) was performed using the R (v4.2.1) package ‘ENMeval’. The optimized model (RM = 3.0, FC = QHPT) significantly reduced overfitting risk (ΔAICc = 0) and achieved high prediction accuracy (AUC = 0.968). Under current climate conditions, the total area of potential high-suitability habitats for I. yunnanensis is approximately 94.88 × 10⁴ km², accounting for 9.88% of China’s land area, with core areas located around the Hengduan Mountains. Under future climate change, the suitable habitats show significant divergence, area fluctuation and contraction under the SSP1-2.6 scenario, and continuous expansion under the SSP5-8.5 scenario. Meanwhile, the species’ distribution centroid exhibits an overall trend of northwestward migration. This study not only provides key spatial decision-making support for the in situ and ex situ conservation of I. yunnanensis, but also offers an important methodological reference for the adaptive research on other ecologically vulnerable species facing climate change through its optimized modeling framework. Full article

Our study focused on identifying the evolution of soil erosion and its key drivers under multiple future scenarios in the Yiluo River Basin. Integrating the Universal Soil Loss Equation (USLE), future land use and vegetation cover simulation methods, and the Geodetector model, we analyzed historical soil erosion trends (2000–2020), projected future soil erosion risks under multiple Shared Socioeconomic Pathways (SSPs), and quantified the interactive effects of key driving factors. The results showed that soil erosion within the basin exhibited moderate intensity. Over the past 20 years, soil erosion decreased by 28.78%, with 76.29% of the area experiencing reduced erosion intensity. Future projections indicated an overall declining trend in soil erosion, showing reductions of 4.93–35.95% compared to baseline levels. However, heterogeneous patterns emerged across various scenarios, with the highest risk observed under SSP585. Land use type was identified as the core driving factor behind soil erosion (explanatory capacity q-value > 5%). Under diverse future climate scenarios, interactions between land use type and precipitation and temperature exhibited high sensitivity, highlighting the critical regulatory role of climate change in regulating erosion processes. This research provides a scientific foundation for the precise prevention and adaptive management of soil erosion in the Loess Plateau region. Full article

This study aimed to predict the potential habitats of Luciola unmunsana using a species distribution model (SDM). Luciola unmunsana is an endemic species that lives only in South Korea, and because its females do not have genus wings and are less fluid, it is difficult to collect, so research related to its distribution and restoration is relatively understudied. Therefore, this study predicted the potential habitats of Luciola unmunsana across South Korea using the single model Maximum Entropy (MaxEnt) and a multi-model ensemble model to prepare basic data necessary for a conservation and habitat restoration plan for the species. A total of 39 points of occurrence were built based on public data and prior research from the Jeonbuk Green Environment Support Center (JGESC), the Global Biodiversity Information Facility (GBIF), and the National Institute of Biological Resources (NIBR). Among the input variables, climate variables were based on the shared socioeconomic pathway (SSP) scenario-based ecological climate index, while nonclimate variables were based on topography, land cover maps, and the Enhanced Vegetation Index (EVI). The main findings of this study are summarized below. First, in predicting Luciola unmunsana potential habitats, the EVI, water network analysis, land cover, and annual precipitation (Bio12) were identified as good predictors in both models. Accordingly, areas with high vegetation activity in their forests, adjacent to water resources, and stable humidity were predicted as potential habitats. Second, by overlaying the predicted potential habitats and highly significant variables, we found that areas with high vegetation vigor within their forests, proximity to water systems, and relatively high annual precipitation, which can maintain stable humidity, are potential habitats for Luciola unmunsana. Third, literature surveys used to predict potential habitat sites, including Geumsan-gun, Chungcheongnam-do, Yeongam-gun, Jeollabuk-do, Mudeungsan Mountain, Gwangju-si, Korea, and Gijang-gun, Busan-si, Korea, confirmed the occurrence of Luciola unmunsana. This study is significant in that it is the first to develop a regional SDM for Luciola unmunsana, whose population is declining due to urbanization. In addition, by applying various environmental variables that reflect ecological characteristics, it contributes to more accurate predictions of the potential habitats of this species. The predicted results can be used as basic data for the future conservation of Luciola unmunsana and the establishment of habitat restoration strategies. Full article

Catalpa bungei C. A. Mey, an economically and ecologically important tree species endemic to China, exhibits notable drought resistance; however, the spatial dynamics of its habitat under future climate change have not been thoroughly investigated. We employed a parameter-optimized MaxEnt modeling framework to project current and future suitable habitats for C. bungei under two Shared Socioeconomic Pathway scenarios, SSP126 (low-emission) and SSP585 (high-emission), based on CMIP6 climate data. We incorporated 126 spatially rarefied occurrence records and 22 environmental variables into a rigorous modeling workflow that included multicollinearity assessment and systematic variable screening. Parameter optimization was performed using the kuenm package in R version 4.2.3, and the best-performing model configuration was selected (Regularization Multiplier = 2.5; Feature Combination = LQT) based on the AICc, omission rate, and evaluation metrics (AUC, TSS, and Kappa). Model validation demonstrated robust predictive accuracy. Four primary environmental predictors obtained from WorldClim version 2.1—the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), maximum temperature of the warmest month (Bio5), and elevation—collectively explained over 90% of habitat suitability. Currently, the optimal habitats are concentrated in central and eastern China. By the 2090s, the total suitable habitats are projected to increase by approximately 4.25% under SSP126 and 18.92% under SSP585, coupled with a significant northwestward shift in the habitat centroid. Conversely, extremely suitable habitats are expected to markedly decline, particularly in southern China, due to escalating climatic stress. These findings highlight the need for adaptive afforestation planning and targeted conservation strategies to enhance the climate resilience of C. bungei under future climate change. Full article

Desertification presents a significant ecological challenge in arid and semi-arid regions, posing a severe threat to regional ecological security and sustainable development. This study introduces an integrated framework for desertification vulnerability assessment, combining the MEDALUS model with the XGBoost algorithm, to evaluate desertification dynamics across the Mongolian Plateau from 2000 to 2020 and project future trends under four Shared Socioeconomic Pathways (SSPs) for 2030. The findings are as follows: (1) Between 2000 and 2020, desertification vulnerability was most pronounced in the southern and western regions of the plateau, with lower vulnerability observed in the northern and eastern areas. High-vulnerability zones expanded over time, highlighting the need for targeted and prioritized management efforts. (2) Climate factors—particularly temperature, wind speed, and precipitation—emerged as the dominant drivers of desertification, followed by soil characteristics and vegetation (NDVI). The influence of human activities on desertification became increasingly significant, stressing the need for improved land management and sustainable practices. (3) Future risks show that desertification vulnerability in the Mongolian Plateau will intensify under high-emission scenarios (SSP3-7.0, SSP5-8.5), with significant expansion of high vulnerability areas. Lower-emission scenarios (SSP1-2.6, SSP2-4.5) may reduce some impacts, but high vulnerability will persist, highlighting the need for urgent climate mitigation and adaptation efforts. Full article

This study presents a comprehensive coastal vulnerability assessment framework by integrating a range of physical, environmental, and climatic parameters. Key criteria include shoreline changes, coastal geomorphology, slope, elevation, bathymetry, tidal range, wave height, shoreline change rates, population density, land use and land cover (LULC), temperature, precipitation, and coastal inundation factors. By synthesizing these parameters with real-time coastal monitoring data, the framework enhances the accuracy of regional risk evaluations. The study employs Multi-Criteria Spatial Analysis (MCSA) to systematically assess and prioritize vulnerability indicators, enabling a data-driven and objective approach to coastal zone management. The findings aim to support coastal planners, policymakers, and stakeholders in designing effective, sustainable adaptation and mitigation strategies for regions most at risk. This integrative approach not only strengthens the scientific understanding of coastal vulnerabilities but also serves as a valuable tool for informed decision-making under changing climate and socioeconomic conditions. Full article

The relict tree Cercidiphyllum japonicum, a Tertiary paleoendemic with significant ecological and timber value, prefers warm–cool humid climates and acidic soils. Using MaxEnt and ArcGIS, we modeled its distribution under current and future climate scenarios (SSP, Shared Socioeconomic Pathways). High-suitability areas (>0.6 probability) under current conditions are mainly concentrated in the Sichuan Basin and the Yellow–Yangtze transition zones. By 2050, projections show northwestward expansions (14.32–18.76% increase in area) and eastward movement toward Central China under both SSP1-2.6 and SSP5-8.5 scenarios. However, by 2090, habitat loss could exceed 22% under SSP5-8.5. The main environmental drivers of its distribution are minimum coldest-month temperature (bio6, 38.7%), annual precipitation (bio12, 29.1%), and temperature range (bio7, 18.5%). Precipitation seasonality and thermal extremes are expected to become more significant constraints in the future. Conservation strategies should focus on the following: (1) protecting refugia in the Daba–Wushan mountains, (2) facilitating assisted migration to northwestern high-latitude regions, and (3) preserving microclimates. This study offers a framework for evidence-based conservation of paleoendemic species under climate change. Full article