Drought is an abnormal hydrological phenomenon characterized by high frequency, long duration, and extensive effect, exerting significant effects on both the ecological environment and the socioeconomic system. This study uses the Standardized Precipitation Index (SPI) to identify drought events in the Yellow River Basin from 1961 to 2021. The identified drought events were analyzed from a three-dimensional perspective to examine their spatial-temporal continuity. Furthermore, this study reveals the spatial and temporal evolution of key drought characteristics, including drought duration, drought area, intensity, density, and centroid position (latitude and longitude). The results indicate the following: (1) A total of 77 drought events have occurred in the Yellow River Basin since 1961, with an average duration of 8.7 months. Of these events, 55.8% lasted longer than the average duration. The average drought area was 50 855 km2, and the average drought density was 0.95, indicating a severe overall drought situation. (2) All the drought characteristics showed increasing trends over time (except for drought density). Drought area, drought duration and drought intensity show a strong correlation. Drought events are becoming more severe in the basin. (3) The directions and trajectories of 55 typical drought events in the Yellow River Basin were identified. Overall, they show a reversed migration pattern between the northeastern and southwestern regions of the basin, reflecting a dynamic transition between the Loess Plateau region and Gansu-Ningxia region. This phenomenon is closely associated with variations in El Ni & ntilde;o-Southern Oscillation (ENSO) events.

Land use and land cover (LULC) change significantly affects environmental processes and sustainable land management in river basins. The Upper Blue Nile River Basin, Ethiopia, has experienced significant LULC changes due to population growth, agricultural expansion, and deforestation. This study examines past and future LULC dynamic patterns using an integrated cloud-based framework implemented in Google Earth Engine. Classification and regression tree, random forest (RF), and support vector machine classifiers were used to process multi-temporal Landsat imagery. RF achieved the highest overall accuracy (94.4%) and kappa (0.879) in 2024. The results reveal pronounced agricultural expansion and substantial forest loss over the past two decades. Using the RF-derived LULC maps, a Cellular Automata-Markov model was calibrated and validated with strong agreement (Kappa = 0.886) to project future changes. Projections to 2034 and then 2044 indicate continued expansion of agricultural and built-up areas at the expense of forests and shrub/grasslands. The results support improved land use planning and environmental sustainability in the basin.

Deep learning has greatly improved water quality simulation by integrating with process-based models. Watershed water quality is critically influenced by terrestrial factors. However, many of these influences are incorporated as constant inputs, making it difficult to match the concentration-level boundary conditions required by models. To address this complexity, a hybrid model was developed and applied to the Huangshui River Basin in northwestern China. High seasonal pollution risks are observed for both ammonia nitrogen (NH3−N) and total phosphorus (TP) concentrations at the Xiaoxiaqiao (XXQ), a downstream section of the watershed. For NH3-N, the mean concentration during January and February of 2011–2013 reached 3.38 mg/L. Based on the datasets of hydrology, meteorology, and underlying surface, Nash-Sutcliffe efficiency of hybrid model exceeded 0.80 (runoff), and the Percent Bias were below 4.85% (water quality). Finally, an attribution analysis for output at the unmonitored section, Ledu hydrological station, was conducted. The partial least squares structural equation modeling (PLS-SEM) results showed that terrestrial vegetation had the strongest influence, with path coefficients of −0.537. The hybrid model leverages deep learning as a bridge to integrate terrestrial sources with water quality concentrations, achieving satisfactory simulation performance (R2 > 0.66). This approach breaks away from the conventional methodology of deep learning models that merely mine historical data from water bodies, as well as process-based models that only account for the total pollutant loading patterns of terrestrial sources. It should offer guidance for Huangshui River Basin environment management and planning.

Study region The Source Region of Yellow River (SRYR), China. Study focus To further enhance the performance and reliability of runoff prediction models base on signal processing, this study incorporated Frequency Analysis (FA) techniques into the ensemble model. By combining FA with the advantages of Variational Mode Decomposition (VMD) and Convolutional Long Short-Term Memory (ConvLSTM) network in feature extraction and multi-factor prediction, the study constructed an ensemble model capable of uncovering the spatiotemporal contribution of various factors to the runoff process and combining the joint effects of multiple hydrological factors. New hydrological insights for the region Based on the observational data from local hydrological and meteorological stations, along with reanalysis dataset, the VMD-FA-ConvLSTM model reached the best prediction result with NSE and KGE of 0.92 and R2 of 0.93, displaying better performance than many traditional models. This affirms that the proposed ensemble model yields higher precision and greater applicability with hydrological signals. Additionally, the study forecasts the future runoff trends in the study area for the next 20 years based on four Shared Socioeconomic Pathway (SSP) scenarios. The results indicate that the average runoff in SRYR from 2021 to 2040 decreases by about 1.55% under the SSP1–2.6 scenario compared with that from 2001 to 2020, whereas the forecasted runoff increases by about 5.22%, 10.77%, and 6.42% under the SSP2–4.5, SSP3–7.0, and SSP5–8.5 scenarios, respectively.

This study investigates climate- and human-induced hydrological changes in the Zavkhan River–Khyargas Lake Basin, a highly sensitive arid and semi-arid region of Central Asia. Using Mann-Kendall, innovative trend analysis, and Sen’s slope estimation methods, historical climate trends (1980–2100) were analyzed, while land cover changes represented human impacts. Future projections were simulated using the MIROC model with Shared Socioeconomic Pathways (SSPs) and the Tank model. Results show that during the past 40 years, air temperature significantly increased (Z=3.93***), while precipitation (Z=−1.54*) and river flow (Z=−1.73*) both declined. The Khyargas Lake water level dropped markedly (Z=−5.57***). Land cover analysis reveals expanded cropland and impervious areas due to human activity. Under the SSP1.26 scenario, which assumes minimal climate change, air temperature is projected to rise by 2.0°C, precipitation by 21.8 mm, and river discharge by 1.61 m3/s between 2000 and 2100. These findings indicate that both global warming and intensified land use have substantially altered hydrological and climatic processes in the basin, highlighting the vulnerability of western Mongolia’s water resources to combined climatic and anthropogenic influence.

To address the insufficient understanding of centennial-scale runoff-sediment evolutionary characteristics in the Yellow River Basin and the ambiguous identification of driving mechanisms across different temporal scales，the drainage area upstream of the Huayuankou Station is taken as the research object in this study. Based on monthly runoff，sediment transport and climatic datasets from 1919 to 2022，a multi-scale attribution framework coupling the Budyko hydrothermal balance principle and fractal elasticity theory is constructed. The analysis results show that over the centennial timescale，the annual precipitation in the Yellow River Basin exhibits a statistically significant increasing trend (0.31mm/a，p<0.01)，while both runoff and sediment transport decrease markedly (−2.35×108m3/a and −0.16×108t/a，p<0.01)，with the runoff-sediment coupled system exhibiting prominent non-stationary properties. Runoff and sediment evolution feature notable multi-scale asynchronous variations: decadal changes differ greatly in timing，amplitude，and trajectory; at the intra-annual scale，the seasonal allocation of runoff and sediment has transformed from a flood-season-concentrated pattern toward a more uniformized annual distribution since 2000. Human activities act as the predominant driver of runoff-sediment variations at the centennial scale，contributing 73.2% to runoff reduction and 81.4% to sediment transport reduction，and the magnitude of sediment reduction outweighs the runoff reduction. Attribution results display clear temporal scale dependence: human interference dominates runoff-sediment changes during flood seasons and periods with intensive human activities，while climate change exerts a relatively predominant effect in non-flood seasons and certain decadal transition stages. In general，the centennial runoff-sediment system of the Yellow River Basin has completed a regime shift，transitioning from an evolutionary stage mainly driven by natural climate fluctuations to a non-stationary evolutionary stage governed by human regulation.

As an important region underpinning ecological security and water-resource balance in Northern China，the Loess Plateau’s ecohydrological regulation plays a pivotal and strategic role in ensuring water security across the Yellow River Basin. However，in the wake of large-scale ecological restoration，the region has been confronted with a complex combination of both emerging and historical challenges，such as vegetation degradation，progressive soil desiccation，and abrupt declines in runoff and sediment yield. The conventional single-objective regulation model centered on “soil conservation and greening” is now inadequate for the realities of multi-objective and coordinated management. This study demonstrated the necessity of a paradigm shift from a “soil conservation and greening” focus toward a system-equilibrium framework that integrates water，ecological，economic，and social dimensions. The core content of this paradigm innovation was proposed across five dimensions (objectives，concepts，scales，assessment，and governance)，and an enabling pathway was established based on an intelligent，closed-loop technological system encompassing “sensing-cognition-prediction-regulation.” Building on this framework，five priority frontiers in fundamental science requiring breakthrough advances were identified: deep vadose-zone water cycling，vegetation water-use adaptability，rebalancing of water-sediment regimes，coupled water-carbon-nitrogen processes，and socio-ecological system modeling. This study provides a scientific foundation and decision-making reference for integrating ecological conservation and water security on the Loess Plateau. Future progress will depend on the coordinated advancement of science and technology，policy instruments，and engineering interventions to support high-quality regional development and the long-term goal of harmonious human-water relations.

Accurately assessing grassland health necessitates quantifying changes in plant community diversity and soil physicochemical properties during the successional processes of vegetation degradation and restoration. This study classified degradation into three stages (light, moderate, and heavy) and restoration into two stages (enclosure <= 5 years and enclosure >5 years). We conducted a quantitative assessment of vegetation characteristics and the dynamics of soil carbon, nitrogen, and phosphorus across different degradation and restoration stages, utilizing 30 pairs of grazed and exclosure control plots across the watershed. Our results revealed significant variations in vegetation cover and species richness along the degradation and restoration gradients. Aboveground biomass and vegetation cover declined progressively with increasing degradation intensity. Soil nutrients, including SOC, TN, and TP, exhibited a pronounced "surface accumulation" pattern, with the highest concentrations in the topsoil (0-10 cm). Significant differences in SOC, TN, and TP were observed among degradation stages in surface soils, while bulk density (BD) and pH values showed relatively little variation. Soil water content and TP differed significantly in the topsoil among restoration stages. Enclosure enhanced aboveground biomass and species richness, but species diversity declined when enclosure duration exceeded five years. Although soil nutrient contents changed after enclosure, the relative rates of change in soil stoichiometry remained largely unchanged. These findings highlight the importance of integrating vegetation and soil parameters for a comprehensive assessment of grassland health and restoration effectiveness.

Extreme precipitation，severe flooding，widespread droughts，and compound disasters occur more frequently as the "non-stationary" features of the global water cycle become more obvious. When confronted with "low-probability，high-impact" natural disasters，the traditional approach of "defense based on historical patterns," which assumes climate stationarity，suffers from a lack of adaptation. By investigating the conceptual chain of "flood-drought-disaster-impact-prevention-resilience," this study points out a systemic bias in the present approach. This bias emphasizes structural prevention over adaptive resilience and disaster control over damage mitigation. We establish a new paradigm of "intelligent adaptation to uncertain extreme" in response，along with its practical applications. According to the study，a modernized flood and drought defense system should: apply the National Water Network as a strategic carrier to improve spatiotemporal water reallocation capacity; use smart water management as the main driver for establishing a closed-loop system that combines "perception-forecasting-simulation-decision making"; with ecological measures as a resilience foundation to promote "grey-green synergy" in systemic governance. Additionally，the perspectives of energy dynamics and social psychology expand the theoretical bounds of disaster comprehension and defense evaluation. The aim of this study is to offer a theoretical foundation and workable solutions to establish a new-generation flood and drought mitigation system that will withstand unpredictable conditions in the future.

Building damage is the primary component of economic damage resulting from flood disasters. Understanding flood building damage enables effective disaster risk reduction strategies and community resilience planning. In this study, a comprehensive framework for quantifying floodinduced damage to individual building properties (structural and content) is developed. This methodology combines geospatial data with machine learning and hydrodynamic modeling, as demonstrated through the 2023 flood event in the Dongdian flood storage and detention area (FSDA), Hebei Province, China. The main findings are as follows: (1) building-type classification using random forest algorithms achieved 98.4% accuracy in distinguishing residential, commercial, and industrial structures; (2) two-dimensional hydrodynamic simulations revealed maximum inundation depths predominantly ranging from 1.5 to 2.5 m, with structural damage ratios of 0.2-0.3 and interior property damage ratios of 0.9-1.0; (3) total direct economic damage to building properties in the Dongdian FSDA reached CNY 10.00-11.91 billion (approximately USD 1.42-1.69 billion), with industrial buildings accounting for 68.74% of damage, representing the dominant damage category. This framework delivers a precise flood damage assessment of building properties, transcends traditional survey limitations and offers a globally transferable approach for enhancing disaster resilience and reducing property risks in flood-vulnerable regions, subject to appropriate data availability and parameter adaptation.

Mountain rivers serve as critical links between terrestrial eco-hydrological processes and aquatic ecological environments，playing a pivotal role in global hydrological cycles，material transport，and ecosystem maintenance. Given the heightened disaster risks and poorly defined characteristics of mountain rivers under climate change，this study systematically reviews research on mountain rivers to establish a clear scientific definition. Through comparative analysis of the geomorphological，hydrological，sedimentological，and ecological characteristics of mountain versus lowland rivers，this study identifies the defining features of mountain rivers as: surface relief ≥200 m/km2，longitudinal channel gradient ≥2‰，gravel-bedrock dominated channel beds，and relatively narrow valleys. Based on an interdisciplinary perspective integrating geomorphology，hydrology，and ecology，a classification system and spatial identification framework for mountain rivers are proposed. Future research priorities should include refined identification of mountain rivers，delineation of management and control zones，identification of flood-obstructing elements，and quantitative assessment of flood conveyance risks. The research findings can provide technical support for the classified management and systematic governance of mountain rivers.

Under the climate change paradigm, urban waterlogging triggered by extreme rainfall is intensifying. Precise, dynamic waterlogging forecasting is vital for urban management and residents to cope with disasters. Current studies often use near-real-time rainfall forecasts to drive hydro-hydraulic models for short-term urban waterlogging prediction, with static inputs and parameters, termed static forecasting. Absent a correction mechanism, static forecasts' accuracy often falls short for effective waterlogging control. This study takes the SWMM model as an example, introduces a real-time calibration mechanism for model parameters, and proposes a method for dynamic forecasting of urban waterlogging processes. A case application was carried out in Zhengzhou, China. The results show that compared with static forecasting, dynamic forecasting can improve the forecasting accuracy in stages during the process, with the average Nash-Sutcliffe Efficiency (NSE) coefficient increased by 0.29, and all nodes achieving a forecasting level above Class B. This method balances accuracy and timeliness, and can be effectively applied to urban waterlogging emergency management.

Against the backdrop of global climate change, drought-flood abrupt alternation (DFAA) events have become increasingly frequent, yet the research on their driving mechanisms remains in the exploratory stage. To address the limitation of existing studies that focus primarily on the linear effects of climate change and atmospheric circulation, this study incorporated multiple factors, including surface energy fluxes, to conduct a multidimensional analysis. Using a revised DFAA index (R-SDFAI), we systematically analyzed the linear time-lag effects and nonlinear interactions of these factors on global DFAA across different lag times, employing Pearson correlation coefficients, multiple linear regression, and interpretable machine learning models. The study found that DFAA events were most frequent and intense in continental climate zones, whereas overall risk was relatively low in tropical climate zones. After accounting for time-lag effects, the explanatory power of multiple factors on DFAA increased from 33.03% to 70.05%, revealing clear spatial heterogeneity. For instance, in tropical climate zones, DFAA was bidirectionally influenced by vapor pressure deficit, whereas in arid climate zones, net radiation exhibited bidirectional associations with DFAA. After removing intra-annual seasonal signals, the dominant relationship converged on the moisture component. The study further revealed the key nonlinear threshold regulation of multiple factors, including the negative impacts of high net radiation and low rainfall in tropical climate zones, and the synergistic reversal driven by low heat flux and solar-induced chlorophyll fluorescence in arid climate zones. These findings transcended conventional linear frameworks for DFAA analysis, laying a scientific foundation for accurate prediction and disaster prevention.

The Tibetan Plateau (TP) has experienced pronounced climate change over recent decades, yet the coupled interactions and trade-offs between vegetation dynamics and water yield (WY) remain insufficiently quantified. In this study, we employed the Lund-Potsdam-Jena (LPJ) model to simulate the spatiotemporal evolution of net primary productivity (NPP) and WY across the TP from 1981 to 2060, and applied the Geodetector method to identify the dominant drivers of vegetation dynamics. The results showed that: (1) during 1981-2020, both NPP and WY generally increased across the TP but exhibited distinct spatial patterns, with NPP showing more widespread and pronounced increases than WY; (2) sensitivity experiments revealed that a 2 degrees C warming substantially increased NPP (+48.79%) but suppressed WY (-17.96%), whereas a 25% increase in precipitation resulted in only a modest rise in NPP (+5.72%) but a sharp increase in WY (+46.72%); (3) the driving factor analysis showed that precipitation, temperature, and WY were the primary controls on NPP, while interaction analysis revealed that their combined effects explained NPP variability more effectively than individual factors; (4) under the Shared Socioeconomic Pathways (SSPs), vegetation-water interactions were projected to shift, with continued greening intensifying water depletion in arid regions, while humid regions were more capable of meeting increased water demand. These findings enhance understanding of vegetation-water coupling across the TP and provide a scientific basis for evaluating future ecohydrological risks under climate change.

Drought-flood abrupt alternation (DFAA) events are an important manifestation of instability in the global climate system and pose substantial risks to society and ecosystems. However, the risks that DFAA events impose on affected systems have not yet been systematically quantified at the global scale. Based on the three key components of hazard, exposure, and vulnerability, this study applies a multiplicative model to assess global DFAA risk, develops a "double-mean, double-threshold, and double-driver" analytical framework, quantifies the effective risks of DFAA events to social and ecological systems, and identifies their major influencing factors. The results showed that, although the global mean DFAA hazard increased slightly, regional differences are pronounced. The social system exhibited a pattern of increasing exposure and decreasing vulnerability, whereas the ecosystem showed simultaneous increases in both exposure and vulnerability. Although the spatial extent of flood-to-drought risk was smaller than that of drought-to-flood risk, it was more destructive in some regions. The overall centroid of social system risk shifted toward the southeast and southwest, whereas that of ecosystem risk shifted toward the northeast. The mechanisms driving risk also showed pronounced asymmetry, with socioecological system factors dominating during the drought-to-flood stage and climatic factors playing a leading role during the flood-to-drought stage. Overall, this study reveals the differences in DFAA risk and the asymmetric driving characteristics affecting social and ecological systems at the global scale, providing a scientific basis for risk warning, adaptive management, and sustainable development decision-making related to DFAA events.

Watershed resilience under compounded climate variability and intensifying human disturbances has become a critical prerequisite for sustainable development in semi-arid inland basins. This study developed a spatially explicit watershed health resilience (WHR) framework that integrates three resilience dimensions (resistance, restoration, and adaptability) within the water resources, ecosystem, and Socioeconomic subsystems. Using the upper Xilin River Basin (XRB) as a case study, this study constructed a grid-based indicator system and quantified WHR dynamics during 2010-2023 based on multi-source hydro-climatic, remote sensing, and Socioeconomic datasets. The obstacle degree model and GeoDetector were employed to diagnose key constraints and attribute spatial heterogeneity, while KDE-based marginal estimation and copula models are used to characterize dependence structures and joint and conditional low-resilience risks among subsystems. WHR exhibits persistent spatial heterogeneity, with stable low-resilience clusters along the main river course, tributary confluences, and the reservoir-affected reach, whereas upstream areas remain consistently higher. Basin-averaged WHR increases from 0.41 (2010) to a peak of 0.59 (2021). Obstacle analysis reveals that the water resources subsystem constitutes the primary bottleneck, mainly constrained by the surface runoff restoration coefficient and the seasonal rebound amplitude of groundwater level. GeoDetector demonstrates that vegetation-water coupling factors are the main drivers of WHR spatial heterogeneity, led by seasonal rebound amplitude of NDVI and water use efficiency. Copula-based analysis indicates the strongest lower-tail dependence and the highest joint low-resilience risk for the water resources-ecosystem pair, suggesting a higher likelihood of co-failure under adverse states. This framework supports resilience-oriented hotspot targeting and cross-subsystem risk-informed watershed management.

Actinomycetes possess strong ecological resilience, enabling them to withstand drought while sustaining metabolic functionality. This adaptability allows them to play a crucial role in mitigating drought-induced impairments on crop growth. Yet, the mechanisms underlying Actinomycete community responses to drought remain poorly understood. To address this gap, we conducted a rain-shelter field experiment with graded water-deficit treatments, using arable soil collected from a conventional maize field. We examined the effects of drought on soil bacterial (non-actinomycete, non-act.) communities and the actinomycete communities, combining co-occurrence network analysis (CoNA) with squares structural equation models (SEM) to assess how drought shapes community structure and assembly. Our findings show that Actinomycete communities were more drought-resilient and maintained higher structural stability than bacteria (non-act.) communities. Both groups exhibited pronounced compositional changes from the jointing stage to filling stage of summer maize under drought conditions. CoNA showed that drought stress induced distinct network reorganization patterns in bacterial (non-act.) and actinobacterial communities. The bacterial (non-act.) network showed moderate increases in connectivity (edges: +5.62%, average degree: +14.11%) but minimal changes in modularity (-4.56%). In contrast, the actinomycete network exhibited more pronounced changes, with a 30.15% increase in edges and a 16.67% increase in graph density, indicating a more densely connected and complex network. This superior stability was further corroborated by robustness analysis, where actinobacteria exhibited stronger resistance to perturbations, characterized by a flatter robustness slope (-0.16 vs.-0.23) and lower vulnerability (0.0066 vs. 0.0112) compared to bacteria (non-act.), highlighting their critical role in maintaining ecological stability under water-limited conditions. Furthermore, SEM analysis identified Available Potassium (AK) as the primary positive driver for both bacterial (non-act.) (lambda=0.836) and actinobacterial (lambda=0.446) diversity. The model explained 53.8% of the variance in bacterial(non-act.) diversity, indicating high sensitivity to environmental fluctuations (e.g., AK and SWC). In contrast, the actinobacterial community (explained variance: 13.4%) exhibited attenuated responses to SWC deficits, providing compelling evidence of its superior ecological stability and drought resistance compared to bacteria (non-act.). Together, these findings reveal distinct drought-response strategies between (non-act.) and Actinomycete communities and provide new insights into the environmental controls governing microbial community assembly under drought in agricultural ecosystems.

Groundwater in alpine source regions plays a crucial role in sustaining regional water cycles and safeguarding downstream ecological security. However, a systematic understanding of its dynamic response to climate change and associated hydrological functions remains limited. In this study, we employed a distributed hydrological model that integrates ground observations and CMIP6 climate projections under four SSP scenarios (SSP126, SSP245, SSP370, and SSP585) to simulate groundwater-depth evolution and quantify its runoff contribution in the Nu-Salween River headwaters (Qinghai-Tibet Plateau) for 1960-2100. Results reveal that groundwater depth deepened overall during the historical period, with pronounced spatial heterogeneity. Future projections exhibit distinct nonlinear behaviour: widespread deepening is expected before 2060, followed by regionally divergent trends thereafter. This nonlinear groundwater response appears to be driven not only by climate change but also by the degradation of permafrost. In the composition of runoff, the long-term mean groundwater contribution remains stable at roughly 10 %, while its seasonal regulatory effect intensifies. Specifically, the groundwater share of runoff in winter and spring is projected to increase by 2.0-2.6 %, highlighting its critical role in sustaining dry-season flows in cold plateau environments. These findings reveal the nonlinear evolution of groundwater depth under climate change and its regulatory influence on runoff, providing a solid scientific basis for advancing research on alpine water cycle mechanisms and for guiding adaptive water resource management in cold plateau regions.

Warming and humidification in alpine regions are altering ecohydrological processes in frozen ground areas, influencing ecosystem services (ESs) as well as the interrelationships among them. Taking the source regions of the Yangtze River and the Yellow River(SRYY) as examples, this study analyzed the spatial-temporal evolution characteristics and their interrelationships of five key ESs: water yield (WY), water conservation (WC), soil conservation (SC), wind prevention and sand fixation (SF), and habitat quality (HQ), based on the InVEST, RUSLE, and RWEQ models. Additionally, the SFN model was used to simulate the change of frozen ground (permafrost, seasonally frozen ground) to reveal variations in ESs across different types of frozen ground from 1990 to 2020. The results show that from 1990 to 2020, the overall trends for WY, WC, SC, SFand HQ were positive, with average five-year increases of 10.35 mm, 1.75 mm, 0.14 t/ha, 0.07 t/km2, and 0.004, respectively. The southeastern region of the Yellow River source region (YRSR) mostly showing high-high or low-high clustering patterns, while the Yangtze River source region (YZRSR) predominantly exhibited low-low clustering patterns. In seasonally frozen ground areas, HQ-WC, HQ-WY, and HQ-SF exhibited synergistic relationships, while in permafrost areas, they display trade-off strategies. From an overall perspective, significant synergies were found between WY-WC and HQ-SC, whereas SF-WC and SF-WY showed significant trade-offs. Seasonally frozen ground zone had better performance in WY, WC, SC, and HQ to various extents than permafrost, whereas permafrost areas were high-value zones for SF. This study deepens the understanding of ESs in alpine regions.

Climate warming has impacted the sustainability of freshwater supply in the global water tower unit (WTU) zone. The rainfall infiltration process, a key component of WTUs supply, is affected by freeze-thaw cycles, yet it remains uncertain whether it has undergone corresponding changes. We propose a temperature-mediated infiltration model considering changes in soil water holding, water potential, and hydraulic conductivity due to varying degrees of freezing under negative temperature. Using this model, we calculate the infiltration of 78 WTUs globally from 1980 to 2023. Our results indicate that global WTUs have a multi-year average infiltration of 26 similar to 2359 mm/year. Notably, WTUs in the key latitudinal zone (24 degrees S-42 degrees N) contribute 54 % of the total infiltration volume, showing expanding differences in infiltration characteristics compared to other regions. While rainfall primarily influences infiltration and infiltration capacity, soil temperature and initial soil water content also significantly impact these characteristics. Enhanced infiltration capacity promotes vegetation growth, though the relationship is not linear. Variations in infiltration characteristics threaten the water resource buffering and the stability of downstream living ecological water supply of WTUs. This study provides crucial references for the integrated management of water resources and ecological conservation amid changing infiltration characteristics.