Search Results (82)

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

Amid intensifying global climate anomalies and accelerating urban expansion, land use systems have become increasingly dynamic, complex, and uncertain. Accurately predicting and scientifically evaluating the evolution of land use patterns is essential to advancing territorial spatial governance and achieving ecological security goals. However, most existing land use models emphasize quantity forecasting and spatial allocation, while overlooking the third critical dimension—structural complexity, which is essential for understanding the nonlinear, fragmented evolution of urban systems, thus limiting their ability to fully capture the evolutionary characteristics of urban land systems. To address this gap, this study proposes an integrated SD-PLUS-FD model, which combines System Dynamics, Patch-based Land Use Simulation, and Fractal Dimension analysis to construct a comprehensive three-dimensional framework for simulating and evaluating land use patterns in terms of quantity, spatial distribution, and structural complexity. Wuhan is selected as the case study area, with simulations conducted under three IPCC-aligned climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to project land use changes by 2030. The SD model demonstrates robust predictive performance, with an overall error of less than ±5%, while the PLUS model achieves high spatial accuracy (average Kappa >0.7996; average overall accuracy >0.8856). Fractal dimension analysis further reveals that since 2000, the spatial boundary complexity of all land use types—except forest land—has generally shown an upward trend across multiple scenarios, highlighting the increasingly nonlinear and fragmented nature of urban expansion. The FD values for construction land and cultivated land declined to their historical low in 2005, then gradually increased, reaching their peak under the SSP1-2.6 scenario. Notably, the increase in FD for construction land was significantly greater than that for cultivated land, indicating a stronger dynamic response in spatial structural evolution. In contrast, forest land exhibited pronounced scenario-dependent variations in FD. Its structural complexity remained generally stable under all scenarios except SSP5-8.5, reflecting higher structural resilience and boundary adaptability under diverse socioclimatic conditions. The SD-PLUS-FD model effectively reveals how land systems respond to different socioclimatic drivers in both spatial and structural dimensions. This three-dimensional framework reveals how land systems respond to socioclimatic drivers across temporal, spatial, and structural scales, offering strategic insights for climate-resilient planning and optimized land resource management in rapidly urbanizing regions. Full article

This research assesses four Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) concerning precipitation quantiles across Korea, utilizing the CMIP6 multi-model ensemble comprising 23 General Circulation Models alongside observational data to project future changes. Precipitation quantiles, derived from regional frequency analysis conducted at 615 sites, are calculated as annual averages for the period from 2015 to 2024. Each SSP scenario is evaluated for its spatial distribution through the application of observational data and chi-square tests, with the results indicating that the SSP3-7.0 ensemble most accurately reflects the current quantile estimates derived from observational data. Furthermore, interannual precipitation quantiles are projected to extend to the year 2100 to discern long-term trends within each reproducible period. It is anticipated that precipitation associated with the 100-year reproducible period will increase by over 20% in most regions across the nation by the century’s end, with this increase becoming more pronounced in accordance with the severity of the pathway. These findings underscore significant increases in extreme rainfall events under high-emission scenarios and highlight the critical need for the integration of enhanced flood mitigation, water resource management, and climate adaptation strategies within Korea’s planning framework. Full article

Persistent climate change and anthropogenic activities have caused the degradation of urban ecosystems and the fragmentation of landscapes in the Loess Plateau region, situated in northern China. Ecological networks have been considered an effective measure for reducing urban habitat fragmentation, enhancing landscape connectivity, and identifying priority areas for ecological restoration. However, research on the spatiotemporal dynamics of ecological networks in cities in the Loess Plateau region, especially multi-scenario ecological networks under future climate change scenarios, and the drivers affecting these network elements are still limited. This study analyzed the spatiotemporal dynamic changes in the ecological networks in Shenmu City from 2000 to 2035, and used GeoDetector to explore the driving factors influencing changes in ecological source distribution. The results showed the following: (1) The ecological sources in Shenmu City continued to shrink from 2000 to 2020, while landscape fragmentation increased. By 2035, the results of scenario modeling will differ for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs), with the ecological source area increasing under scenarios SSP119 and SSP245, and continuing to decrease under scenario SSP585. (2) From 2000 to 2020, the α, β, and γ indices increased and then declined, while the ecological networks of the SSP119 and SSP585 scenarios will stabilize. (3) Under the optimal scenario SSP119, 27 ecological pinch points and 40 ecological barrier points will be identified, which are priority areas for the future execution of ecological restoration initiatives. (4) Precipitation is the primary factor that affects the distribution of ecological sources, followed by temperature. This study proposes a new research perspective on ecological networks, and provides a guideline for ecological restoration and conservation in cities (counties) in the Loess Plateau region. Full article

This data descriptor presents δ-MedBioclim, a newly developed dataset for the Euro-Mediterranean region. This dataset applies the delta-change method by comparing the values of 25 General Circulation Models (GCMs) for the reference period (1981–2010) with their projections for future periods (2026–2050, 2051–2075, and 2076–2100) under the SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5 scenarios. These anomalies are added to two pre-existing datasets, ERA5-Land and CHELSA, yielding resolutions of 0.1° and 0.01°, respectively. Additionally, this manuscript provides a ranking of GCMs for each major river basin within the study area to guide model selection. δ-MedBioclim includes, for all the aforementioned scenarios, monthly mean temperature, total monthly precipitation, and 23 bioclimatic variables, including 9 (biorm1 to biorm9) from the Worldwide Bioclimatic Classification System (WBCS) that are not available in other databases. It also provides two bioclimatic classifications: Köppen–Geiger and WBCS. This dataset is expected to be a valuable resource for modeling the distribution of Mediterranean species and habitats, which are highly affected by climate change. Full article

The assessment of ecosystem service (ES) supply–demand relationships is critical for addressing regional sustainable development challenges, yet systematic studies integrating spatial drivers analysis and multiscenario forecasting in rapidly urbanizing mountainous regions remain scarce. This study focuses on Chongqing as a representative case to investigate spatial patterns, driving mechanisms, and future trajectories of ES supply–demand dynamics. Through spatial quantification of four key ES (food provision, water retention, soil conservation, carbon fixation) and statistical analysis of socioeconomic datasets from 2010 to 2020, geographical weighted regression modeling was employed to identify spatially heterogeneous drivers. Long-term projections (2030–2060) were developed using climate–economy integrated scenarios reflecting different global development pathways. The results demonstrate three principal findings: First, while regional ecosystem quality maintains stable with an improved supply–demand ratio (0.260 to 0.320), persistent deficits in carbon fixation capacity require urgent attention. Second, spatial mismatches exhibit intensifying polarization, with expanding deficit zones concentrated in metropolitan cores and their periurban peripheries. Third, thermal-hydrological factors (aridity index, temperature) coupled with land intensification pressures emerge as dominant constraints on ES supply capacity. Scenario projections suggest coordinated climate mitigation and sustainable development strategies could maintain the supply–demand ratio at 0.189 by 2060, outperforming conventional development pathways by 23.5–41.2%. These findings provide spatial decision support frameworks for balancing ecological security and economic growth in mountainous megacities, with methodological implications for cross-scale ES governance in developing regions. Full article

Climate change is leading to an increase in the frequency and intensity of flooding, making it necessary to consider future changes in flood risk management. In regions where ground-based observations are significantly restricted, the implementation of conventional risk assessment methodologies is always challenging. This study proposes an integrated remote sensing and machine learning approach for flood risk assessment in data-scarce regions. We extracted the historical inundation frequency using Sentinel-1 SAR and Landsat imagery from 2001 to 2023 and predicted flood susceptibility and inundation frequency using XGBoost, Random Forest (RF), and LightGBM models. The risk assessment framework systematically integrates hazard components (flood susceptibility and inundation frequency) with vulnerability factors (population, GDP, and land use) in two SSP-RCP scenarios. The results indicate that in the SSP2-RCP4.5 and SSP5-RCP8.5 scenarios, combined high- and very-high-flood-risk areas in the Ili River Basin in China (IRBC) are projected to reach 29.1% and 29.7% of the basin by 2050, respectively. In the short term, the contribution of inundation frequency to risk is predominant, while vulnerability factors, particularly population, contribute increasingly in the long term. This study demonstrates that integrating open geospatial data with machine learning enables actionable flood risk assessment, quantitatively supporting climate-resilient planning. Full article

Climate change presents considerable challenges to hydrological stability by modifying precipitation patterns and exacerbating the frequency and intensity of extreme rainfall events. This research evaluates the prospective alterations in rainfall quantiles in South Korea by employing a multi-model ensemble (MME) derived from 23 Global Climate Models (GCMs) associated with the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Historical rainfall data from simulations (1985–2014) and future projections (2015–2044, 2043–2072, and 2071–2100) were analyzed across a total of 615 sites. Statistical Quantile Mapping (SQM) bias correction significantly enhanced the accuracy of projections (RMSE reduction of 63.0–85.3%, Pbias reduction of 93.6%, and R² increase of 0.73). An uncertainty analysis revealed model uncertainty to be the dominant factor (approximately 71.87–70.49%) in the near- to mid-term periods, and scenario uncertainty increased notably (up to 5.94%) by the end of the century. The results indicate substantial temporal and spatial changes, notably including increased precipitation in central inland and eastern coastal regions, with peak monthly increases exceeding 40 mm under high-emission scenarios. Under the SSP2-4.5 and SSP5-8.5 scenarios, the 100-year rainfall quantile is projected to increase by over 40% across significant portions of the country, emphasizing growing challenges for water resource management and infrastructure planning. These findings provide critical insights for water resource management, disaster mitigation, and climate adaptation strategies in South Korea. Full article

Equilibrating the supply and demand for ecosystem services (ESs) is essential for sustainable development. Nonetheless, elements like policy modifications, land utilization, and climate change are profoundly transforming the dynamics of ecosystem service supply and demand (ESSD). As a result, there is an imperative necessity to methodically evaluate and predict these alterations by including both social and environmental elements. This study utilized the Henan region of the Yellow River Basin (HYRB) as a case study to forecast alterations in the supply and demand for three ESs—water production (WY), carbon storage (CS), and food production (FP)—under three scenarios for 2030 and 2050, grounded in the SSP-RCP framework. We further evaluated the supply–demand equilibrium at both grid and county degrees. The results indicate the following key findings: (1) From 2020 to 2050, there are significant spatial differences in the supply and demand of these services. While the supply of CS and FP exceeds demand, the supply of WY falls short. (2) The supply–demand ratios for WY and CS are projected to decline under all scenarios, whereas FP is expected to continue growing. Surplus areas for WY and CS are aggregated in the northwest, southwest, and central areas, while FP surpluses are found in the eastern and northern plains. Deficits for all three services are primarily located in urban areas. (3) The dominant spatial patterns of supply–demand matching also vary. WY and CS exhibit high–low agglomeration patterns, particularly in the northwest and southwest mountain regions, while FP shows low–low agglomeration, mainly in the southwest and northwest mountain areas. These findings enhance comprehension of the dynamics of ESSD, serving as a foundation for environmental preservation and sustainable advancement in the Yellow River Basin, China. Full article

Bangladesh, one of the most vulnerable countries to climate change, has been experiencing significant climate change-induced risks. Particularly, the northwest region of the country has been severely affected by climate extremes, including droughts and heat waves. Therefore, proper understanding and assessment of future climate change scenarios is crucial for the adaptive management of water resources. The current study used the statistical downscaling model (SDSM) to downscale and analyze climate change-induced future changes in temperature and precipitation based on multiple global climate models (GCMs), including HadCM3, CanESM2, and CanESM5. A quantitative approach was adopted for both calibration and validation, showing that the SDSM is well-suited for downscaling mean temperature and precipitation. Furthermore, bias correction was applied to enhance the accuracy of the downscaled climate variables. The downscaled projections revealed an upward trend in mean annual temperatures, while precipitation exhibited a declining trend up to the end of the century for all scenarios. The observed data periods for the CanESM5, CanESM2, and HadCM3 GCMs used in SDSM were 1985–2014, 1975–2005, and 1975–2001, respectively. Based on the aforementioned periods, the projections for the next century indicate that under the CanESM5 (SSP5-8.5 scenario), temperature is projected to increase by 0.98 °C, with a 12.4% decrease in precipitation. For CanESM2 (RCP8.5 scenario), temperature is expected to rise by 0.94 °C, and precipitation is projected to decrease by 10.3%. Similarly, under HadCM3 (A2 scenario), temperature is projected to increase by 0.67 °C, with a 7.0% decrease in precipitation. These downscaled pathways provide a strong basis for assessing the potential impacts of future climate change across the northwestern region of Bangladesh. Full article

Global climate change and changes in land use structures during rapid urbanization have profoundly impacted ecosystem carbon storage. Previous studies have not combined different climate scenarios and land use patterns to predict carbon storage. Using scenarios from both the InVEST-PLUS model and SSP-RCP, combined with multi-source remote sensing data, this study takes the Lijiang River Basin as the study area to explore the dynamic changes in land use and carbon storage under different climate scenarios. The findings are as follows: (1) From 2000 to 2020, cultivated and construction land increased, while forest land significantly decreased, lowering from 4331.404 km² to 4111.936 km². This land use change mainly manifests in the significant transformation of forest land into cultivated and construction lands. Under different climate scenarios, the cultivated and construction lands will continue to expand, the forest land will decrease, and the grassland area will increase. (2) Total carbon storage decreased significantly from 2000 to 2020, with forest carbon storage changing the most significantly, for a total reduction of 5,540,612.13 tons, followed by grassland and water area. Regardless of the future scenario, the total carbon storage in the Lijiang River Basin will experience a decreasing trend; the decline in carbon reserves is most significant in the SSP585 scenario and smallest in the SSP126 scenario, with slight increases even appearing in some regions. (3) From the perspective of land use change, the large-scale expansion of construction land in the process of rapid urbanization has occupied a large amount of ecological land, such as forests and grasslands, and this is the main reason for the reduction in total carbon storage in the basin. From the perspective of climate change scenarios, a global temperature increase caused by a high-emission scenario (SSP585) may exceed the optimal growth temperature for some plants, inhibit the carbon absorption capacity of vegetation, and thus reduce the carbon fixation capacity of forest land and grassland. Therefore, to maintain long-term climate goals and sustainable development, the SSP126 scenario should be prioritized to strengthen the protection of forest resources in the northern and central regions of the Lijiang River Basin, balance the relationship between ecological protection and urbanization, avoid the occupation of ecological land by excessive urbanization, and improve the carbon sink potential of the basin. These research results can provide a scientific basis for the optimization of land spatial patterns, ecological restoration and protection, and the enhancement of carbon sink potential in the Lijiang River Basin under the “double carbon” goal. Full article

Assessing water scarcity risks under climate change has become an important research topic for sustainable development. Regional water scarcity is driven not only by direct local water deficits but also by indirect effects from upstream supply chains. Despite their significance, existing studies seldom integrate both local water scarcity and indirect water scarcity comprehensively. This study utilizes multi-regional input–output tables (MRIO) to quantify virtual water flows among eight provinces in the Yellow River Basin, elucidating the extent of local (WSI) and indirect water scarcity (IWS) from 2007 to 2017. Leveraging Representative Concentration Pathway (RCP) projections and Shared Socioeconomic Pathway (SSP) scenarios, the research further projects future virtual water flow patterns and associated water scarcity risks in the Yellow River Basin from the 2020s to the 2090s. Findings reveal that downstream provinces (Shandong, Henan, Shanxi) experience more severe water scarcity—both locally and indirectly—than upstream regions (Inner Mongolia, Gansu). Local water scarcity surpasses indirect scarcity, with the agricultural sector predominantly driving IWS, accounting for 76.1% to 91.3%. Additionally, downstream provinces facing severe water scarcity not only exhibit high local water use but also rely on imports from middle and upper regions grappling with water shortages. Under SSP1-RCP2.6 and SSP5-RCP8.5 scenarios, water scarcity risks in the Yellow River Basin are projected to intensify, with the overall WSI potentially reaching 0.59 and IWS attaining severe levels of 0.42 by the 2050s. This study enhances the understanding of water scarcity risks in arid and semi-arid regions, providing valuable insights for policymakers to develop more climate-resilient water-resource management strategies. Full article

Changes in land use and land cover (LULC) and climate increasingly influence flood occurrences in the Gumara watershed, located in the Upper Blue Nile (UBN) basin of Ethiopia. This study assesses how these factors impact return period-based peak floods, flood source areas, and future high-flow extremes. Merged rainfall data (1981–2019) and ensemble means of four CMIP5 and four CMIP6 models were used for historical (1981–2005), near-future (2031–2055), and far-future (2056–2080) periods under representative concentration pathways (RCP4.5 and RCP8.5) and shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). Historical LULC data for the years 1985, 2000, 2010, and 2019 and projected LULC data under business-as-usual (BAU) and governance (GOV) scenarios for the years 2035 and 2065 were used along with rainfall data to analyze flood peaks. Flood simulation was performed using a calibrated Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) model. The unit flood response (UFR) approach ranked eight subwatersheds (W1–W8) by their contribution to peak flood magnitude at the main outlet, while flow duration curves (FDCs) of annual maximum (AM) flow series were used to analyze changes in high-flow extremes. For the observation period, maximum peak flood values of 211.7, 278.5, 359.5, 416.7, and 452.7 m³/s were estimated for 5-, 10-, 25-, 50-, and 100-year return periods, respectively, under the 2019 LULC condition. During this period, subwatersheds W4 and W6 were identified as major flood contributors with high flood index values. These findings highlight the need to prioritize these subwatersheds for targeted interventions to mitigate downstream flooding. In the future period, the highest flow is expected under the SSP5-8.5 (2056–2080) climate scenario combined with the BAU-2065 land use scenario. These findings underscore the importance of strategic land management and climate adaptation measures to reduce future flood risks. The methodology developed in this study, particularly the application of RF-MERGE data in flood studies, offers valuable insights into the existing knowledge base on flood modeling. Full article

Coastal zones are facing intensive ecological pressures and challenges, which could vary over a wide range of spatiotemporal scales. Our limited capability to understand and especially predict this variability can lead to the misinterpretation of coastal ecological resilience. Therefore, the assessment and prediction of ecological resilience are particularly important. In this study, a new approach based on the Pressure–State–Response model is developed to assess and predict pixel-scale multi-year ecological resilience (ER) and then applied to investigate the spatiotemporal variations of ER in the China’s coastal zone (CCZ) in the past few decades and predict future ER trend under various scenarios. The results show that ER in the CCZ displayed a general spatial distribution pattern of “higher in the southern half and lower in the northern half” from 1995 to 2020. Over the 25-year period, ER exhibited a declining trend. Specifically, the eastern provinces experiencing the most significant decline. The ER levels across scenarios ranked from high to low as follows: SSP1-2.6 > SSP4-3.4 > SSP2-4.5 > SSP3-7.0 > SSP5-8.5. The assessment and prediction methods designed can be applied to ER studies in other coastal zones, making it a valuable approach for broader applications. Full article

Traditional coastal flood risk prediction often overlooks critical geographic features, underscoring the need for accurate risk prediction in coastal cities to ensure resilience. This study enhances the prediction of coastal flood occurrence by utilizing the Geospatial Artificial Intelligence (GeoAI) approach. This approach employed models—random forest (RF), k-nearest neighbor (kNN), and artificial neural networks (ANN)—and compared them to the IPCC risk framework. This study used El Salvador as a demonstration case. The models incorporated seven input variables: extreme sea level, coastline proximity, elevation, slope, mangrove distance, population, and settlement type. With a recall score of 0.67 and precision of 0.86, the RF model outperformed the other models and the IPCC approach, which could avoid imbalanced datasets and standard scaler issues. The RF model improved the reliability of flood risk assessments by reducing false negatives. Based on the RF model output, scenario analysis predicted a significant increase in flood occurrences by 2100, mainly under RCP8.5 with SSP5. The study also highlights that the continuous mangrove along the coastline will reduce coastal flood occurrences. The GeoAI approach results suggest its potential for coastal flood risk management, emphasizing the need to integrate natural defenses, such as mangroves, for coastal resilience. Full article