Search Results (2,290)

In the context of low-carbon development targets, urban expansion within the karst ecoregions of southern China has intensified land-use-related carbon-stock losses, which highlights the necessity of scientific and sustainable land-use management strategies. Using multi-temporal land-use datasts spanning six periods from 2000 to 2025, this study integrates carbon-stock accounting and land-use transition-matrix analysis. Multicollinearity testing and spatial autocorrelation analysis are employed to reveal the driving mechanisms behind carbon-stock variations. Based on standardized beta coefficients (β), all driving factors are hierarchically classified into primary dominant factors, secondary constraint factors, and tertiary disturbance factors according to their regulatory intensity, and further grouped into topographic, ecological, and socioeconomic categories. On this basis, three differentiated land-management scenarios are constructed. By coupling the PLUS and InVEST models, this study simulates regional carbon-stock conditions for 2030 and 2035. The results show that regional carbon-stock exhibits a fluctuating pattern: it decreased from 2000 to 2005, rose continuously during 2005–2015 to reach its peak in 2015, and then declined persistently from 2015 to 2025. Spatially, obvious zonal differentiation can be observed: the northwestern mountainous areas maintain high carbon-stock levels, whereas southern coastal towns experience severe carbon-stock degradation. Topographic factors dominate regional carbon-stock dynamics; ecological factors exert critical buffering effects; human-induced land-use activities act as the secondary driver for carbon-stock loss. The conversion of farmland and woodland to construction land represents the major contributor to regional carbon-stock reduction. Under the “2035 Steep-slope Carbon-stock Protection Scenario”, the total regional carbon-stock increases by 1441.69 million tonnes relative to the “2035 Natural Development Scenario”. This scenario achieves the coordinated advancement of rocky desertification control and sustainable land use, facilitates synergistic low-carbon improvement and land-system sustainability, and provides practical references for low-carbon land governance in similar karst regions worldwide. Full article

Accurately characterizing groundwater storage variations and identifying their dominant drivers are essential for regional groundwater assessment and sustainable management. This study focuses on the Heilongjiang (Amur) River Basin and constructs a monthly groundwater storage anomaly (GWSA) series for 2003–2022 using GRACE/GRACE-FO, GLDAS, and multiple environmental datasets. The spatiotemporal evolution of GWSA was analyzed, and groundwater sustainability was further evaluated. On this basis, an XGBoost model with five-fold year-grouped cross-validation was developed, and SHAP and lagged correlation analyses were combined to quantify the model-based importance of the main environmental factors and characterize their nonlinear predictive relationships with GWSA, interaction patterns, and lagged responses. The results showed that basin-averaged GWSA exhibited a significant declining trend during the study period, with groundwater deficits becoming markedly more pronounced after 2017, while substantial spatial heterogeneity was observed among the sub-basins. The groundwater system exhibited relatively low overall sustainability and limited recovery capacity following groundwater deficits. The XGBoost model showed relatively stable performance in characterizing GWSA variations, with a correlation coefficient of 0.660 and an RMSE of 48.54 mm between the pooled predictions from the five test folds and the GRACE-derived GWSA. TreeSHAP analysis showed that precipitation had the highest relative SHAP importance (33.15%), while evapotranspiration, runoff, air temperature, land use, and snowmelt also exhibited varying degrees of SHAP importance and pronounced nonlinear patterns and interaction characteristics. Snowmelt and GWSA exhibited a seasonal timing offset, with a pronounced time–frequency association around the annual scale. GWSA remained elevated after spring thaw, and the annual frozen fraction decreased significantly during 2003–2022. The results provide scientific support for sustainable groundwater management and engineering planning in cold regions. Full article

Global climate change and rapid urbanisation exert coupled disturbances on plateau urban agglomerations, where rugged terrain and land-use conversion exacerbate ecological fragmentation and function loss. Most existing research relies on linear analytical frameworks and cannot capture nonlinear ecological shifts, multi-factor coupling feedbacks or stability-transition thresholds for plateau regions; multi-scenario quantitative ecological assessments also remain limited. Taking the Central Yunnan Urban Agglomeration as a case, this study aimed to identify nonlinear ecosystem-service responses and statistically derived ecological breakpoints. Three Shared Socioeconomic Pathway (SSP) scenarios (SSP119, SSP245, SSP585) were integrated into a modelling chain: the Patch-generating Land-Use Simulation (PLUS) model projected 2030 land-use patterns (hindcast: 92.8% overall accuracy (OA), Kappa = 0.8887); the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model quantified four key ecosystem services to compute the Multiple-Ecosystem-Services Landscape Index (MESLI); and eXtreme Gradient Boosting-SHapley Additive exPlanations (XGBoost-SHAP) decoded nonlinear drivers and abrupt thresholds. Built-up land increased from 1.15% (2000) to 2.39% (2020), with 2030 projected values of 2.97%, 3.15% and 3.35% under the three scenarios. MESLI reached maxima in mountain forests and minima in intensively developed intermontane basins. Land-use variables contributed >40% to mean-absolute SHAP importance, followed by precipitation and slope. Precipitation breakpoints differed across scenarios (1008 mm, 929 mm, 1065 mm), and an aridity-index threshold of 1.27 was detected for SSP245. Joint-response surfaces show favourable moisture–terrain conditions amplify forest-related multi-ecosystem-service benefits, which are substantially offset by urban encroachment. Statistical results further indicate hydrothermal thresholds are scenario-dependent instead of static, with divergent land-use-climate coupling strength between mountain and basin landscapes in plateau urban agglomerations. Full article

Urban spatial patterns regulate microclimates, yet those nonlinear interactive actions of 2D landscape patterns and 3D building morphology on land surface temperature (LST) among diverse urban functional zones (UFZs) remain insufficiently quantified. Using Shenyang as the case study, this study coupled Bayesian-optimized LightGBM and GeoShapley to disentangle these driving mechanisms. The models demonstrated high predictive accuracy (R² > 0.84 across all UFZs). Results show high-temperature zone proportions ranking as: commercial (17.93%) > public service (11.40%) > residential (8.70%) > industrial (3.97%). Further factor analysis reveals that while geographic location is the predominant overall driver, after removing this baseline effect, residential, public service, and commercial zones are mainly regulated by MNDWI and NDVI, while industrial zones are dominated by Spatial Congestion Degree (SCD) and Floor Area Ratio (FAR). Morphological metrics exhibit clear ecological thresholds: residential zones show turning points at FAR 1.19 and NDVI 0.13; commercial zones see SCD’s cooling effect reverse to warming beyond 0.27; industrial zones experience sharp deterioration when SCD and FAR exceed 0.09 and 0.51, respectively. These findings demonstrate the necessity of UFZ-based differentiated spatial regulation and provide actionable thresholds for climate-adaptive urban planning. Full article

As a typical turbid-water reef coral, Turbinaria peltata shows strong tolerance to high temperature and sediment disturbance, yet its basin-scale niche drivers and glacial distribution dynamics across the Indo-West Pacific remain unclear. Here we constructed optimized MaxEnt models using 322 independent distribution points and 21 non-collinear marine environmental variables for modern and Last Glacial Maximum (LGM) scenarios. The model average test AUC reached 0.9874, showing excellent predictive performance. Water depth acted as the most influential predictor in the model (contribution = 44.76%), followed by distance to land and warm-season sea temperature; habitat suitability had an extremely weak linear correlation with depth (R² = 0.05), consistent with a strongly nonlinear response along the depth gradient. Modern suitable habitats are mainly concentrated in Southeast Asia and northern Australia, with the South China Sea at the species’ northern margin. Massive shelf exposure during the LGM severely shrank its range, and three potential glacial refugia (northeastern Australia, Madagascar coast, Red Sea–Gulf of Aden) were identified. These refugia may have maintained coral populations through glaciation and contributed to the present-day biogeographic patterns of this species. Our findings provide quantitative spatial evidence for targeted conservation of nearshore stress-resistant corals under climate warming. Full article

Research on the coupling coordination relationship between land–use carbon emissions (LUCE) and ecosystem service value (ESV) is vital for achieving China’s “dual carbon” goals and advancing ecological civilization. Applying the carbon emission coefficient method, equivalent factor method, coupling coordination degree model, and Geographically and Temporally Weighted Regression (GTWR) model, this study examined spatiotemporal patterns of land–use changes, carbon emissions per unit area (ACE), and ecosystem service value per unit area (AESV) across China (excluding Xizang, Hong Kong, Macao, and Taiwan) from 2000 to 2020, along with their coupling relationship and driving forces. Results showed that grassland, forest, and cropland accounted for more than 70% of China’s land area, with a total land–use conversion area of 1.42 × 106 km². ACE increased while AESV generally decreased, both displaying strong spatial heterogeneity and agglomeration. The average Dagum Gini coefficients were approximately 0.70 for ACE and 0.58 for AESV. Coupling coordination degree mostly fluctuated around 0.20, rising initially and then falling, with distinct spatial variations in coordinated development types. Population density, Normalized Difference Vegetation Index (NDVI), and average elevation were negatively correlated with the synergistic effect between LUCE and ESV, whereas GDP density, annual precipitation, and average slope showed positive correlations. These findings provided theoretical support for formulating scientific land–use policies, optimizing carbon emission management, and implementing effective ecological protection measures. Full article

Wetlands regulate hydrological processes, maintain biodiversity, and moderate local climate. However, long-term evidence on hydroclimatic variability and anthropogenic pressure in West African coastal wetlands remains limited. This study assesses rainfall, land surface temperature (LST), and land use/land cover (LULC) change in the Keta Lagoon Complex (KLC) and Muni-Pomadze Ramsar Site (MPRS) from 2000 to 2024. Mann–Kendall tests and Sen’s slope estimators were applied to annual and seasonal rainfall, while Landsat observations were used to evaluate seasonal LST and classify LULC. The indicators were compared across sites and through time to assess their correspondence without attributing causality. Annual rainfall trends were weak and not statistically significant, although minor rainy-season rainfall increased significantly at KLC (p = 0.018). MPRS showed greater thermal variability, with some major rainy season LST values exceeding 44 °C. Built-up cover increased by 9.89 percentage points at KLC and 8.34 percentage points at MPRS, while natural vegetation at MPRS declined from 27.56% to 7.25%. These concurrent patterns indicate stronger landscape modification and lower apparent buffering capacity at MPRS. The findings support site-specific controls on land conversion, restoration of vegetation buffers, improved waste management, and protection of hydrological connectivity. Because the study is comparative rather than causal, the results identify co-variation among indicators but do not quantify the relative contributions of climatic and anthropogenic drivers. Full article

Land-use change is the primary direct driver of terrestrial biodiversity loss. Around Cerro Castillo National Park (PNCC) in Chilean Patagonia, rural land subdivision increased by 400% between 2011 and 2023, generating ecological, economic, and social pressures on the park and surrounding communities. Protected-area conservation requires integrating social and cultural dimensions that shape how territories are inhabited and transformed. This exploratory study analyzes how relational valuation contributes to understanding people–nature relationships by comparing two socio-environmental profiling models of landowners adjacent to PNCC. Based on 129 surveys, relational values and disvalues were coded from an open-ended question. Models were compared using Multiple Correspondence Analysis, Hierarchical Clustering on Principal Components, and a Chi-square test. Although exploratory and non-generalizable, both models identified three profiles, with Model B offering a complementary characterization that emphasized relational orientations and pro-environmental preferences. These findings suggest that relational valuation can complement sociodemographic and territorial characterization by revealing differentiated ways in which landowners in this sample relate to nature and territory, including identity- and stewardship-oriented relationships, as well as relationships shaped by socio-environmental tensions. These exploratory insights provide a basis for further research and participatory validation in protected-area contexts and may inform differentiated conservation and land-use management strategies under rural subdivision pressures. Full article

Urbanisation is a major driver of environmental contamination, yet its physiological consequences for amphibians in rapidly developing regions remain poorly understood. This study investigated spatial variation in heavy-metal bioaccumulation and associated physiological stress biomarkers in Bufo regularis along an urban–rural gradient in Southwestern Nigeria. Adult toads were sampled from Osogbo, Ile-Ife, and Modakeke sites, and concentrations of selected heavy metals (including Pb, Cd, Zn, Cu, and Cr) were quantified in liver and muscle tissues. To assess sublethal biological effects, oxidative stress biomarkers superoxide dismutase (SOD), catalase (CAT), glutathione-S-transferase (GST), reduced glutathione (GSH), and lipid peroxidation (malondialdehyde, MDA) were analysed alongside body condition indices. Heavy-metal concentrations were significantly higher in individuals from Osogbo sites than in those from Ile-Ife and Modakeke locations, with Pb and Cd showing the strongest spatial gradients. Correspondingly, Osogbo populations exhibited elevated antioxidant enzyme activities and MDA levels, indicating heightened oxidative stress, while GSH levels and body condition indices were reduced. These findings demonstrate that B. regularis effectively reflects spatial patterns of metal pollution and associated biological effects across land-use gradients. The study highlights the utility of amphibians as bioindicators of urban environmental health and underscores the potential long-term ecological consequences of metal contamination in tropical urbanising landscapes. Full article

Vegetation change is regarded as a key indicator of environmental change and ecosystem functional evolution. In mountainous regions, vegetation dynamics arise from complex and non-linear interactions among climate, topography, land use, and human activities, yet the mechanisms governing these interactions remain poorly understood. Clarifying long-term vegetation trajectories and their interacting controls is essential for understanding ecosystem structure and function. In this study, we integrated machine learning and causal modeling by combining random forest (RF) and structural equation modeling (SEM) to quantify both the relative importance and the direct and indirect pathways of natural and anthropogenic drivers of vegetation change in the Longnan region from 2000 to 2020. The RF results showed that the selected driving factors explained 82.62% and 72.39% of the spatial variation in vegetation cover in 2000 and 2020, respectively, with climatic and anthropogenic factors ranking as the most important drivers. Although ecological restoration activities contributed to an overall improvement in vegetation conditions, land-use heterogeneity and ecological constraints imposed by high elevation jointly produced contrasting local responses, resulting in vegetation degradation in surrounding areas. SEM revealed that the net influence of anthropogenic activities shifted from positive to negative over time, mainly due to land-use change, indicating a reorganization of human–vegetation interactions. Climate effects remained positive, with precipitation having a stronger influence than temperature. Topography moderated vegetation responses, as slopes below 40° favored vegetation growth. Soil effects shifted from positive to negative, likely associated with changes in soil organic matter. By jointly applying RF and SEM, this study captures both non-linear responses and causal pathways, providing a system-level perspective on the complex mechanisms underlying vegetation change. Full article

Agrivoltaic systems (AVSs) present a promising solution to land-use conflicts between solar photovoltaics and agriculture. Nevertheless, their electricity-side cost performance is strongly influenced by site-specific conditions and agricultural constraints, and a comprehensive nationwide geospatial assessment of optimal designs under agronomic light constraints remains lacking. To address this gap, we developed a GIS-based techno-economic framework for Japan to identify site-specific optimal designs by minimizing the Levelized Cost of Electricity (LCOE) while satisfying crop light requirements represented by target Daily Light Integral (DLI) values. The analysis indicates a national average LCOE of 0.1021 EUR kWh⁻¹ for optimized AVSs, with lower electricity-side costs in central to southwestern regions characterized by high solar irradiance. The optimal design, particularly the projected ground coverage ratio (average: 30.38%), varied considerably across the country to balance power generation and crop light availability. Scenario and sensitivity analyses revealed that LCOE is highly sensitive to crop light requirements, whereas PV system efficiency and capital expenditure are the most influential cost drivers. This study provides the first high-resolution, nation-scale LCOE map for AVS in Japan derived through grid-level design optimization under explicit DLI-based constraints, offering a quantitative screening-level foundation for region-specific deployment planning. Full article

This study investigates the relationship between key circulation factors and ensemble forecast uncertainty during an atypical extreme heavy-rainfall event under weak synoptic forcing that affected the Guangzhou–Foshan megacity cluster in the Pearl River Delta (PRD) on 8 September 2022. Here, “atypical” refers to a localized extreme event occurring over the low-elevation urban river network without strong synoptic-scale drivers. Framed as an event-specific retrospective diagnostic analysis, the study used the China Land Multi-source Precipitation Analysis System version 2.1 (CMPAS-V2.1), ERA5 reanalysis, and 3-km (R3) and 9-km (R9) ensemble forecasts from the CMA Tropical Regional Atmospheric Model Ensemble Prediction System (CMA-TRAMS EPS). Spearman rank correlation and Monte Carlo field-significance tests were first applied to identify environmental variables closely associated with hourly precipitation variations, pinpointing the 700-hPa U-wind, 500-hPa V-wind, and 850-hPa V-wind as the most significant circulation predictors. Spatial anomaly fields of these key predictors were then subjected to hierarchical clustering, with clustering robustness evaluated using the cophenetic correlation coefficient (CCC) and bootstrap resampling. Because the clustering structure of the 925-hPa V-wind was comparatively weak, it was excluded from the final member-selection procedure. Finally, circulation-consistent ensemble members were selected using a multi-predictor consensus criterion (retaining 13 R3 members and 7 R9 members), and their 24 h accumulated precipitation was averaged to obtain a circulation-conditioned subset mean. The results show that persistent high temperatures, abundant moisture in the middle and lower troposphere, and a favorable multilayer circulation configuration provided suitable conditions for convective instability accumulation and localized heavy rainfall development. The precipitation forecasts exhibited substantial member-to-member variability. Under a consistent evaluation threshold, the 9-km configuration demonstrated superior overall ensemble-mean spatial skill compared to the 3-km configuration, indicating that increasing horizontal resolution does not necessarily improve forecast skill for weakly forced extreme rainfall. The resulting circulation-conditioned subset means successfully shifted the predicted heavy-rainfall center toward the observed Guangzhou–Foshan region and reduced the overestimated heavy-rainfall magnitudes over northern Guangzhou. Rather than serving as a purely objective score-maximizing post-processing algorithm, this approach extracts physically indicative spatial scenarios from ensemble spread. These findings provide a practical diagnostic framework for conditional forecast correction of localized heavy rainfall under weak synoptic forcing, though validation across multiple independent cases and consistent quantitative verification are necessary to assess its operational generalizability. Full article

Long-term groundwater exploitation causes water-level fluctuations, which are a major driver of land subsidence and infrastructure deformation in sedimentary basins. To investigate the stress-history-dependent compression–rebound behavior of deep cohesive soils, soil samples along the Beijing–Tianjin High-Speed Railway were examined using high-pressure consolidation tests, cyclic loading–unloading tests, SEM, and XRD. Prescribed cyclic effective-stress paths were applied to simulate groundwater-level decline and recovery at different burial depths. The results showed that irreversible deformation was highly concentrated in the early loading stage. Under the groundwater-level decline paths, the first loading cycle alone accounted for approximately 77% and 94.8% of the total cumulative compression in the shallow and deep soils, respectively. During repeated loading–unloading between 0 and P₀, the plastic deformation decreased significantly with cycle number, whereas the elastic deformation remained relatively stable. Under the groundwater-level rise paths, the cumulative rebound ratios were only 9.8% for the shallow soils and 17% for the deep soils, indicating that unloading recovered only a small fraction of the preceding compression. In terms of microstructure, the shallow soils had relatively loose and pore-rich fabrics, while the deeper soils exhibited denser particle packing and stronger interparticle contacts. These findings demonstrate pronounced mechanical irreversibility. Together with the measured low permeability, they support a mechanistic interpretation in which slow pore-pressure and effective-stress adjustments may further contribute to delayed deformation. Overall, this study provides a quantitative experimental characterization of the evolution of irreversible compression and incomplete recovery of deep cohesive soils under cyclic effective-stress changes induced by groundwater-level variations. Full article

Land-use change is a primary driver of changes in ecosystem services, and different development scenarios imply markedly different ecological responses, with consequences for regional economic development and ecological protection. However, existing studies have mostly focused on historical and current conditions, and quantitative assessments of future ecosystem-service changes under multiple land-use scenarios remain limited. In this study, the patch-generating land-use simulation (PLUS) model was used to simulate land-use patterns of Hubei Province under business-as-usual (BAU), economic development (ED), and ecological conservation (EC) scenarios for 2030 and 2040; five ecosystem services—habitat quality (HQ), soil conservation (SC), grain productivity (GP), water yield (WY), and water purification (WP, proxied by nitrogen export)—were then assessed for 2000–2020 and the future scenarios at a 30 m resolution using InVEST-based modules; Spearman correlation analysis was used to identify trade-offs and spatial associations among services, and the geographical detector model (GDM) was applied at 5 km and 10 km grid scales to quantify the explanatory power of driving factors. The results show that from 2000 to 2020, cultivated land, forest land, grassland, and unused land in Hubei Province decreased by 273,146.76 hm², 61,111.08 hm², 8712.99 hm², and 5392.26 hm², respectively, while construction land and water areas expanded. Over the same period, GP, SC, and WY increased, nitrogen export grew (indicating a weakening of water purification), and HQ declined continuously from 0.545 to 0.527; the comprehensive ecosystem-service index (CESI) followed a U-shaped trajectory (0.268 in 2000, 0.243 in 2010, and 0.267 in 2020). Under the future scenarios, GP increases markedly under BAU and ED, while HQ and SC deteriorate and nitrogen export rises; under EC, HQ and SC improve significantly, GP first decreases and then increases, and both WY and nitrogen export decline. Among the driving factors, elevation, slope, and NDVI dominate the spatial heterogeneity of ecosystem services, and their explanatory power varies between the 5 km and 10 km scales. These findings provide a reference for optimizing land-resource allocation and for reconciling economic development, food security, and ecological protection in Hubei Province. Full article

Compound heat and drought events (CHDEs) are occurring more frequently due to climate change. However, the role of urbanization as a key driver of climate change in modulating these events remains poorly quantified. Hence, this study evaluates the impacts of urbanization on CHDE frequency, duration, and severity using a daily scale analytical framework, and further quantifies the key influencing factors contributing to these impacts using explainable machine learning. The results revealed that: (1) the urban expansion rates ranged from 0.06% to 22.92% per decade in the Beijing–Tianjin–Hebei (BTH) region. Concurrently, the frequency, duration, and severity of CHDEs exhibited overall upward trends, with rates of 0.11, 1.45, and 0.06 per decade, respectively. (2) Urban stations exhibited more apparent upward trends in the frequency, duration, and severity of CHDEs than rural stations. Urbanization contributed to these increases in CHDEs, with its greatest contribution to duration (44.0%), followed by severity (39.1%) and frequency (33.6%). (3) The explainable machine learning analysis revealed that the attributes of CHDEs are driven by multiple urban and climatic factors under urbanization. While enhanced Tmean and UHI consistently intensified CHDEs, changes in urban underlying surfaces manifest complex non-linear relationships with CHDE attributes. Notably, BV and UrbF amplified the duration and severity of CHDEs more strongly than frequency. These findings suggest that urbanization may primarily amplify the duration and severity of CHDEs through enhanced thermal conditions and surface modification, highlighting the need to prioritize heat mitigation and urban land-use regulation in climate adaptation planning. Full article