Search Results (483)

Accurately assessing dynamic water resource carrying capacity (WRCC) is essential and challenging, particularly in regions like the Gansu sections of the Yellow River Basin (GSYRB), a core water source protection zone in the arid northwest of China, due to its pressing challenge of balancing water resources for socioeconomic needs and ecological security. This study proposes a novel integrated computational assessment framework named SD-VIKOR to address the complexities arising from nonlinear interactions within the “water resources–socioeconomic–ecological environment” (W–S–E) system. The core of this framework is the tight coupling of a system dynamics (SD) simulation model with a VIKOR multi-criteria evaluation module, where indicator weights are objectively–subjectively determined via an Analytic Hierarchy Process (AHP)–entropy weight method. This integrated SD-VIKOR engine enables dynamic, scenario-based WRCC trajectory simulation. To move beyond simulation and enable mechanistic insight, the framework further incorporates a diagnostic suite: a Geodetector module quantifies dominant drivers and their interactions; an obstacle degree model pinpoints key limiting factors; and a coupling coordination degree model evaluates subsystem synergies. Together, they form a closed-loop “dynamic simulation → multi-criteria assessment → driving mechanism analysis and constraint diagnosis → subsystem coordination analysis” workflow. Applied to the GSYRB from 2012 to 2030 under five development scenarios, the framework demonstrated high efficacy. It successfully captured path-dependent WRCC evolution, revealing that the ecological-priority scenario (B2), which shifts system drivers from economic-scale expansion to resource-efficiency and environmental governance, yielded optimal WRCC and the highest system coordination. In contrast, business-as-usual and single-minded economic expansion scenarios underperformed. Six key obstacle factors were quantitatively identified, linking WRCC constraints to natural endowments, economic patterns, and domestic demand. The results reveal pronounced spatial–temporal heterogeneity in WRCC across the GSYRB, with socioeconomic development, water resource use efficiency, and ecological conditions acting as the primary joint drivers of WRCC evolution. Critically, several key indicators are identified as persistent constraints on regional water sustainability. In contrast to conventional static evaluations, the integrated framework captures the complex dynamics and multi-subsystem interactions governing WRCC, offering a more robust diagnostic of resource–environment systems. These insights provide a transferable analytical basis for designing sustainable water management strategies in arid river basins. Full article

The spatiotemporal evolution of landscape patterns represents the most direct manifestation of land use change and remains a pivotal focus within landscape ecology research. Taking Huangshan City—a typical mountainous tourism city—as the study area, this research systematically analyzes the spatiotemporal evolution characteristics and driving mechanisms of landscape patterns over the past three decades. Based on land use data from 1992, 2002, 2012, and 2022, the study employs an integrated methodological framework including land use transition matrices, landscape pattern indices, moving window analysis, and the geographical detector (Geodetector) model, supported by ArcGIS and FRAGSTATS platforms. The results indicate that (1) during the study period, the landscape structure in Huangshan City exhibited a general trend characterized by “a stable foundation of forest land, continuous contraction of cropland, and significant expansion of construction land.” (2) From 1992 to 2012, cropland served as the primary source of transfer, mainly being converted into forest land; conversely, between 2012 and 2022, the reciprocal transformation between cropland and forest land became the dominant transition process. (3) At the landscape level, overall diversity enhanced and spatial distribution tended toward uniformity, whereas landscape fragmentation persisted in localized areas. (4) The driving force analysis revealed that “distance to the urban center” was the primary driving factor shaping landscape pattern changes, with its explanatory power continuously increasing. Furthermore, significant synergistic enhancement effects were observed between natural and socio-economic factors. These findings provide a scientific basis for ecological protection, restoration, and sustainable development strategies in Huangshan City within the context of rapid urbanization and tourism development. Full article

Understanding the intricate interrelationships among ecosystem services (ESs) is fundamental to advancing sustainable ecological management. This study focuses on the Taihu Basin and examines five representative ESs, including water yield (WY), carbon sequestration (CS), soil retention (SR), habitat quality (HQ), and crop production (CP), for the years 2000, 2010, and 2020. Spatial distribution characteristics and spatiotemporal dynamics were quantified through the combined application of the InVEST model, a food production model, and ArcGIS. Spearman correlation analysis and K-means clustering were then applied to characterize trade-offs and synergies among ESs and to delineate ecosystem service bundles at multiple spatial scales, including 1 km × 1 km grids, 10 km × 10 km grids, and the county level, while GeoDetector was used to identify the associated driving mechanisms. The results indicated that (1) between 2000 and 2020, the spatial distribution pattern of the ESs in the Taihu Basin underwent significant changes, with WY and SR increasing by 48.97% and 51.89%, respectively, while HQ, CS, and CP decreased by 17.2%, 15.5%, and 47.6%. (2) From an overall perspective of trade-offs and synergies, the interactions among ESs shifted from trade-offs (r < 0) to synergies (r > 0) as the scale increased. From the perspective of the spatial characteristics of trade-offs and synergies, the intensity of these interactions varied significantly with increasing scale, but the trend remained relatively stable. (3) The Taihu Basin can be categorized into six ES bundles (ESBs). ESB 1, ESB 3, ESB 4, and ESB 5 have relatively stable ES structures, whereas ESBs 2 and 6 display significant variations. (4) The primary factors influencing ESs vary significantly across different spatial scales, with land use/land cover (LULC) and the proportions of arable land, forestland, and buildings exhibiting strong explanatory power. This highlights the critical role of coupled natural and anthropogenic processes in shaping the spatial patterns of ESs. This study considers the spatiotemporal variation and scale dependence of ecosystem services, providing management recommendations tailored to different regions and spatial scales, and offering a scientific basis for regional ecological planning and watershed governance. Full article

With China’s urban growthism past its peak, urban development has shifted from incremental expansion to inventory quality improvement. Renovating historical and cultural blocks—a core area for urban quality enhancement—makes exploring their integration with surroundings highly significant. Existing studies on historical district research mainly focus on single-dimensional research such as protection policies, spatial structure analysis, and quality evaluation, lacking a systematic and quantitative evaluation of the spatial integration degree between historical and cultural blocks and their surrounding areas. To improve research on the integrated development of historical and cultural districts and their surrounding areas, this study employs deep learning and machine learning techniques to process street view images from 2721 data points in 2024, investigating the integration of Wuhan Hankou’s historical and cultural districts with their surrounding areas. The spatial integration degree between a historical and cultural district and its surroundings refers to the coordinated development level in terms of history and culture, spatial ecology, and transportation infrastructure. Specifically, the DeepLab v3+ model processes the blocks’ street view images to generate indicator data (Green Visual Index, Sky Visibility Index, Road Area Index, Spatial Enclosure Index, Color Richness (Wheel), Color Richness (Entropy), Spatial Accessibility Index, Vehicle Disturbance Index, Traffic Sign, which is used to quantify the historical culture, spatial ecology, and transportation facilities of historical and cultural blocks and their surrounding areas. The Coupling Coordination Degree model evaluates spatial integration, while the Geodetector Model quantitatively analyzes interactions between spatial integration and driving factors here. The results show that the spatial interaction and dependence between the Hankou Historical and Cultural District and its surrounding areas are relatively high, but spatial coordination is insufficient; the integration remains at a primary stage with structural contradictions. SVI, SEI, and RAI have a significant impact on integration, while Spatial Accessibility Index, Green Visual Index, and CRW have a moderate influence, and CRE, Vehicle Disturbance Index, and Traffic Signs have a relatively weak impact. Among them, SVI exhibits the strongest interactive effect with other indicators and plays a leverage role in improving integration. Full article

The giant panda holds a critical position in global biodiversity conservation, yet the ongoing fragmentation of its habitat poses a severe threat to the long-term viability of its survival. This study focused on the giant panda habitat in the Xiangling region and systematically analyzed the mechanisms through which human socioeconomic activities drive habitat fragmentation. The analysis was based on data from 2000 to 2023, encompassing land use, population density, transportation networks, mining activities, and nighttime light emissions, utilizing a methodology that integrated Principal Component Analysis, the Moving Window method, trend analysis, and the Geodetector model. The findings reveal the following: First, the degree of habitat fragmentation has intensified over time with significant spatial heterogeneity, exhibiting a pattern of “low fragmentation in the core areas and high fragmentation in the periphery,” where areas of very high fragmentation have expanded markedly along the habitat edges. Second, the trend in fragmentation demonstrates an overall improvement in the core zones, particularly within the Giant Panda National Park, where over 70% of the area shows reduced fragmentation; conversely, nearly 30% of the peripheral areas continue to degrade. Third, the driving factors of habitat fragmentation exhibit bi-factor enhancement and nonlinear enhancement effects, with land use identified as the dominant factor. The study recommends enhancing the overall connectivity and ecological functionality of the habitat through measures such as refining land-use planning, constructing ecological corridors, implementing hierarchical management, and promoting community co-management. Full article

Invasive alien plants (IAPs) pose a severe and escalating threat to biodiversity and ecosystem services in China. However, a systematic nationwide assessment that identifies invasion hotspots, quantifies their overlap with protected area networks, and pinpoints critical conservation gaps is still lacking. This hinders the development of spatially targeted management strategies. To address this, we developed an integrated analytical framework coupling the Maximum Entropy (MaxEnt) model with the InVEST habitat quality model. Using a high-resolution, county-level distribution database of 293 IAPs, we mapped potential species richness and habitat degradation across China. The geo-detector model was further employed to identify the primary environmental factors and their interactions. Spatial overlay analysis was conducted to delineate core invasion habitats (areas of high invasion suitability and high degradation) and assess their coverage within China’s national nature reserves. Nighttime light intensity (DMSP, 34.39%), annual precipitation (Bio12, 14.16%), and mean diurnal range (Bio2, 11.82%) were the factors with the highest contribution in the model, highlighting the statistical interaction between anthropogenic pressure and climatic conditions. The core invasion habitat spanned 20.10 × 10⁴ km², predominantly (66.04%) concentrated in high-intensity human disturbance zones. Notably, only 11.18% of this core habitat falls within existing national nature reserves, revealing a vast conservation gap of 17.85 × 10⁴ km². Our results indicate a profound spatial mismatch between invasion hotspots and the current protected area network in China. We prioritize southeastern coastal urban agglomerations-characterized by high anthropogenic pressure (DMSP), high precipitation (Bio12), and low diurnal temperature range (Bio2)-for immediate monitoring and intervention. This integrated assessment provides a national-scale, spatially explicit prediction of invasion risk for 293 plant species in China, and offers an evidence-based decision-support tool for optimizing invasive species management and biodiversity conservation. Full article

As the net gain of carbon by plants after accounting for respiration, vegetation net primary productivity (NPP) plays a central role in the terrestrial carbon cycle. However, a systematic and quantitative analysis of the spatiotemporal evolution and driving mechanisms of vegetation NPP on Hainan Island, a tropical region, is still lacking. Focusing on Hainan Island, this study employs an integrated approach—including the coefficient of variation, Mann–Kendall test, Hurst exponent, geographical detector, and PLS-SEM—to investigate the spatiotemporal dynamics of vegetation NPP and its underlying drivers from 2001 to 2022. The main conclusions as follows: (1) Vegetation NPP on Hainan Island showed a fluctuating upward trend from 2001 to 2022, with a mean annual increase of 3.6 g C·m⁻²·yr⁻¹, and displayed a spatial pattern of decrease from the central-southern mountainous areas toward the coastal regions. (2) NPP changes were generally stable; historically, areas showing an increasing trend exceeded those with a decreasing trend by 30.55%. In the future, the predominant projected trends are “persistent decrease” and “increase to decrease,” which together account for over 80% of the total area. (3) Topography and climate were the dominant drivers of NPP spatial heterogeneity. Elevation had the strongest explanatory power, followed by evapotranspiration and temperature. A significant, nonlinear enhancement effect was observed in the interaction between any two factors. (4) Topographic, climatic, anthropogenic, and vegetation factors all exerted direct positive effects on vegetation NPP. Anthropogenic activities also indirectly promoted NPP by influencing pathways such as vegetation growth. The conclusions of this research provide support for the implementation and evaluation of land-use planning, afforestation projects, and ecological protection and restoration measures on Hainan Island. Full article

Land subsidence (LS) is a major global geo-environmental issue that profoundly affects the suitability and safety of land use planning (LUP). However, existing LUP systems generally neglect the dynamic evolution of LS and lack a systematic framework for assessing conflicts between land use and subsidence. To address this gap, this study develops an integrated evaluation framework that combines SBAS-InSAR, GeoDetector, and a spatial conflict detection model. A total of 166 Sentinel-1A images acquired from 2017 to 2024 were processed using SBAS-InSAR to derive the spatiotemporal characteristics of LS. GeoDetector was subsequently applied to identify the dominant driving factors and their interactions. A sensitivity classification scheme for current land use (CLU) and LUP types with respect to LS hazards was then developed, and a spatial conflict detection model was constructed to delineate conflict zones and quantify conflict intensity. Using Huainan City as a case study, the results show the following: (1) from 2017 to 2024, LS was generally characterized by slight or negligible subsidence, with severe subsidence mainly concentrated in coal mining areas; ongoing and recently suspended mines exhibited pronounced LS, whereas early-closed and unmined areas showed an overall uplift trend. (2) LS in Huainan was primarily driven by soil type, annual rainfall, and mining activities, and two-factor interactions generally exhibited enhancement effects. (3) Compared with CLU, LUP has, to some extent, incorporated LS risk considerations and implemented corresponding mitigation measures, although certain areas still insufficiently account for LS risks. (4) The proposed framework demonstrates strong rationality and applicability in LS monitoring, driving factor identification, and spatial conflict assessment, providing scientific support for LS risk management and land use spatial optimization. Full article

Guaranteeing the ecological security of the Dongting Lake Basin is of paramount importance for national-scale programs, such as the Yangtze River Economic Belt and aquatic conservation projects. Within this framework, carbon storage and its determining drivers act as essential indicators of regional ecological stability. However, the historical trajectory of carbon pools and their response to future multi-scenario land-use transitions remain insufficiently understood. Therefore, this study aims to quantify the spatiotemporal evolution of carbon storage in the Dongting Lake Basin from 2000 to 2020 and project its future dynamics under diverse development pathways. This study, utilizing land use data from 2000 to 2020 and the carbon density database of the Dongting Lake Basin, assessed land use changes over two decades and determined the spatiotemporal distribution of carbon storage. Additionally, using 17 driving factors and various spatial policies, the study projected the land use and land cover changes (LUCC) for 2030 under four scenarios: natural development, ecological protection, economic development, and planned development. The spatiotemporal distribution of carbon storage and its response mechanisms were analyzed for each scenario. The results showed that carbon storage was directly impacted by LUCC, with an overall “decrease-increase-decrease” trend from 2000 to 2020, resulting in a net increase of 3.685 × 10⁶ t. By 2030, the changes in carbon storage under the natural development, ecological protection scenario, economic development, and planned development scenarios were projected to be −1.008 × 10⁷ t, 1.276 × 10⁷ t, 3.292 × 10⁸ t, and −1.200 × 10⁵ t, respectively. Notably, the ecological protection scenario showed a significant positive growth in carbon storage, primarily driven by an increase in forest and wetland areas. Additionally, the spatial distribution of carbon storage exhibited a pattern of “high in the west and low in the east”. These results imply that to achieve the “Dual Carbon Strategy”, future land use planning in the Dongting Lake Basin should prioritize ecological protection and planned development models, including strict control of construction land expansion, increasing ecological land area, and enhancing carbon storage. Full article

Against the backdrop of China’s dual carbon goals, understanding how terrain complexity affects the decoupling linkage between infrastructure investment and carbon emissions is crucial for developing differentiated low-carbon strategies. This study focuses on Sichuan Province, a region characterized by significant topographical heterogeneity, to investigate how terrain constraints influence carbon emission decoupling. We construct a Terrain Constraint Index (TCI) using three indicators (Digital Elevation Model (DEM), Coefficient of Variation of elevation (CV), and Terrain Position Index (TPI)) weighted by a game theory-based combination of entropy and Criteria Importance Through Intercriteria Correlation (CRITIC) methods and employ the Tapio decoupling model combined with group comparison analysis to examine the correlation between terrain complexity and decoupling performance. The key findings are as follows. (1) The TCI exhibits a “high in the west, low in the east” spatial pattern, ranging from 0.151 (Zigong) to 0.591 (Ya’an), with five distinct terrain complexity levels identified. (2) During 2001–2021, good decoupling states (strong + weak decoupling) accounted for 76.8% of all observations, indicating overall improvement in carbon emission efficiency. (3) A monotonic negative association is observed between terrain complexity and decoupling performance: the good decoupling ratio decreases from 82.5% in Low TCI regions to 62.5% in Very High TCI regions, with Mann–Whitney tests showing suggestive differences (raw p < 0.05, though not significant after Bonferroni correction). (4) Average decoupling elasticity increases from 0.182 in Very Low TCI regions to 0.705 in Very High TCI regions, demonstrating that higher terrain complexity is associated with worse decoupling outcomes. (5) Geodetector analysis reveals that infrastructure investment has the highest explanatory power (q = 0.401, p < 0.01), and the interaction between terrain factors and investment shows significant nonlinear enhancement effects (q = 0.544–0.830). These findings suggest that terrain complexity is associated with worse carbon emission decoupling, plausibly through affecting infrastructure investment efficiency, and point to the need for differentiated low-carbon strategies for regions with varying topographical conditions. Full article

Accurately identifying the spatial differentiation mechanisms of rural homesteads in ecologically fragile mountainous areas is essential for implementing United Nations Sustainable Development Goal 11—building inclusive, safe, resilient, and sustainable human settlements—and for advancing differentiated rural revitalization strategies. Taking Zhaotong City in Yunnan Province as a case study, this study innovatively couples the binary logistic regression model with the geographic detector model to systematically analyze the spatial patterns and driving mechanisms of rural homesteads from the dual perspectives of “occurrence probability” and “agglomeration intensity.” The results show that: (1) Spatial pattern analysis reveals a macro-level distribution characterized by “higher density in the east than the west, and higher elevation in the south than the north.” At the local level, high-density small-scale clusters coexist with low-density large-scale clusters. The landscape is highly fragmented and morphologically complex, and can be classified into two regional types: “regular-dense” and “complex-expansive.” (2) The driving mechanism analysis reveals that the spatial differentiation of rural homesteads is closely linked to rigid topographic constraints, elastic responses to accessibility for both production and daily life, and adaptive adjustments to climatic conditions. Geodetector analysis further identifies widespread nonlinearly enhanced interactions among these factors, reflecting the synergistic interplay between natural and human elements. Building on these findings, this study proposes a three-tiered analytical framework—“rigid constraints–elastic responses–coupled amplification”—to characterize the multidimensional driving logic underlying homestead spatial differentiation in mountainous regions. This framework advances empirical understanding of mountain settlement dynamics in ecologically fragile developing countries and yields actionable governance insights: in areas exhibiting high landscape fragmentation, complex morphological patterns, and low agglomeration intensity, priority should be given to remediation strategies such as voluntary homestead withdrawal and ecological relocation. The findings provide a scientific basis for revitalizing existing homestead land, spatial restructuring, and refined governance in Southwest China’s mountainous regions. Furthermore, this research offers a transferable analytical framework and practical reference for sustainable human settlement development in similar contexts, including the Qinba and Hengduan Mountains, as well as ecologically fragile zones globally. Full article

Rapid urbanization has heightened concerns over ecological degradation. This study analyzes spatiotemporal dynamics of land use in Jiangsu Province from 2000 to 2020 and integrates the intPLUS model to simulate and project land-use patterns for 2030–2050. Habitat quality was assessed with the InVEST model, and key driving factors were identified using the GeoDetector method. The results show that (1) from 2000 to 2020, cultivated land, forest, and grassland decreased markedly by 10.50%, 4.38%, and 35.55%, respectively, whereas built-up land and water bodies increased by 46.70% and 8.92%. (2) Projections for 2030–2050 indicate that land-use change will generally follow the 2000–2020 trajectory but with reduced land-use intensity, a slower expansion of built-up land, and relatively minor changes in ecological land. (3) Habitat quality declined overall during 2000–2020: areas of high habitat quality decreased by 1024.29 km², while low-quality areas increased by 6386.78 km². Spatially, habitat quality exhibited a pattern of “higher in the central region and lower in the south and north,” with relatively low values in southern and northern Jiangsu and higher values in central Jiangsu. By 2050, habitat quality is expected to improve gradually. (4) Nighttime light intensity and elevation exerted strong effects on habitat quality changes, with vegetation cover identified as the dominant driver. Among factor interactions, the interaction between nighttime light intensity and elevation showed the greatest explanatory power. Full article

This study applies an integrated analytical framework combining GeoDetector and multiscale geographically weighted regression (MGWR) to examine how the spatial distribution of cultural heritage values in the Chungcheong region of South Korea (Chungcheongnam-do and Chungcheongbuk-do) relates to regional socio-spatial contexts. Using the Korea Heritage Service’s heritage basic survey data (coordinates, attributes, and value assessments), we aggregated heritage value scores to a 1 km grid and modeled six value dimensions—historical, artistic, academic, social, rarity, and conservation—as separate dependent variables. We then integrated socio-spatial indicators derived from statistical grid maps published by the National Geographic Information Institute (official land price, building density, green space, road accessibility, total population, working-age population share, and aging rate). GeoDetector was first used to identify key determinants and interaction effects by value dimension, and MGWR was then used to estimate local effect heterogeneity and variable-specific operating scales. Results show that heritage values are better explained by multi-factor configurations—urbanization, land value, green space, accessibility, and demographic structure—whose importance varies by value dimension, and that the same factor can exert different directions and strengths across local contexts. By linking “what matters” (key determinants) with “where and at what scale it matters” (local effects and bandwidths), this study provides quantitative evidence to support place-based conservation and utilization strategies. The proposed GeoDetector–MGWR framework is transferable to other regions where spatial heritage inventories and comparable socio-spatial indicators are available. Full article

Against the backdrop of the ongoing advancement of China’s dual-carbon goals and the coordinated strategy for ecological protection and high-quality development in the Yellow River Basin (YRB), it is important to clarify the spatiotemporal dynamics of air pollution in the densely populated urban agglomerations of the mid–lower YRB. Using station-based daily observations from 2015 to 2024, this study examines six major air pollutants (PM_2.5, PM₁₀, CO, NO₂, O₃ and SO₂) across the Shandong Peninsula, Central Plains, and Guanzhong Plain urban agglomerations. Sen’s slope estimator and the Mann–Kendall test are applied to quantify long-term trends, while partial correlation analysis and the GeoDetector model are used to diagnose pollutant co-variations and the drivers of spatial heterogeneity. Results indicate that while PM_2.5, PM₁₀, NO₂, SO₂, and CO concentrations significantly decreased, O₃ exhibited a statistically significant upward trend (Z = 2.32, p = 0.02), particularly with pronounced summer maxima. PM_2.5 shows clear seasonal variation, with elevated levels during winter and reduced levels during summer. Marked spatial contrasts are also observed: elevated particulate matter and CO are concentrated in the northern part of the Central Plains, while higher O₃ levels are more evident in coastal areas, particularly within the Shandong Peninsula urban agglomeration. In terms of inter-pollutant relationships, particulate matter and CO are positively associated with SO₂, whereas O₃ is negatively correlated with NO₂. GeoDetector results further suggest that air temperature, wind speed, and topography are the key factors associated with the spatial differentiation of pollutant levels; notably, the interaction between wind speed and temperature provides the greatest explanatory power, with effects that vary seasonally. These findings provide a scientific basis for region-specific air-pollution control and for advancing the co-benefits of carbon reduction and pollution mitigation in the YRB. Full article

As a major water conservation region and ecological security barrier in China, the Three-River Source Region (TRSR) of the Qinghai–Tibet Plateau (QTP) is underlain by extensive permafrost. However, how permafrost degradation alters regional water conservation, particularly the existence of critical thresholds and time-lagged responses, remains insufficiently understood. To clarify these issues, spatiotemporal variations in water conservation (1990–2020) were quantified, and their nonlinear, lagged, and spatially heterogeneous responses to active layer thickness (ALT) were assessed. Using multi-source remote sensing and in situ observations from 1990 to 2020, spatiotemporal variations in water conservation were quantified with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and responses to permafrost degradation were examined by integrating Sen’s slope, GeoDetector, geographically weighted regression (GWR), and structural equation modeling (SEM) methods. The results showed that water conservation increased overall during 1990–2020 and exhibited a pronounced southeast–northwest gradient (higher in the southeast and lower in the northwest); the rates of change in the Lancang, Yellow, and Yangtze headwaters were 63.5, 56.5, and 31.0 mm a⁻¹, respectively. GeoDetector results indicate that precipitation was the dominant control on the spatial heterogeneity of water conservation (q = 0.704), and its interaction with active layer thickness (ALT) further increased explanatory power (q = 0.736). ALT also interacted with vegetation (q = 0.224) and topography (q = 0.157), suggesting that permafrost effects are modulated by vegetation condition and topographic setting in addition to water inputs. Piecewise regression identified a potential threshold at ALT = 1.77 m, indicating a shift in the ALT–water conservation relationship across this threshold. A 5–7-year lag in the response of water conservation to ALT was also detected, particularly apparent in continuous permafrost zones. Overall, water conservation exhibits a clear southeast–northwest gradient and a delayed response to ALT changes. In addition, the response exhibits clear spatial clustering, with the strongest sensitivity observed in areas with ice-rich permafrost overlain by alpine meadow, and a potential ALT breakpoint further suggests nonlinear permafrost–water conservation coupling. Full article