The North China Plain is among the world’s most severely groundwater-overexploited regions. Since 2018, large-scale ecological water replenishment (EWR) has been applied, yet its long-term impacts on groundwater dynamics and efficient EWR strategies remain poorly understood, particularly in the Shijiazhuang Plain, which hosts the largest shallow groundwater depression cone in the region. In this study, the coupled surface–groundwater model MODCYCLE was applied to investigate these issues in the Shijiazhuang Plain. Results reveal that large-scale EWR has effectively reversed groundwater depletion, recovering the average groundwater table by 1.96 m and storage by 1.76 billion m3 by 2022. Contributions were dominated by EWR from the Hutuo River (82.5%), followed by the Sha-Zhulong River (12%) and other Rivers (5.5%), with significant recovery (> 0.5 m, > 50,000 m3/km2) extending approximately 24.5 km, 12.5 km, and 5.5 km from these rivers. The highest-recovery zones were mainly in the middle and lower reaches of the EWR rivers. Our findings highlight that, due to its favorable infiltration path length and groundwater flow conditions, the Hutuo River should serve as the primary EWR channel, with stable, moderate flows rather than the “greater is better” approach, to achieve both efficient groundwater storage recovery and widespread groundwater table recovery. Optimal flows of 14.64 m3/s during droughts and ≥ 8.4 m3/s under normal precipitation efficiently mitigate and reverse groundwater depletion, respectively. This study provides scientific support for the continued fight against groundwater overexploitation in the North China Plain and offers insights for optimizing EWR strategies to efficiently recover groundwater in globally overexploited aquifers.

The Jiulonggou hillslope in the Ganwuli Watershed, located in the Longmenshan rainstorm region of southwestern China, is an earthquake-affected mountainous area characterized by steep terrain, flash flood susceptibility, and complex hillslope hydrological processes. To investigate hillslope soil water redistribution, this study combined 15-min rainfall observations with soil moisture measurements at six depths (0.10–0.60 m) across upslope, midslope, and downslope positions, and interpreted the observed dynamics using a two-dimensional physics-based hillslope model solving the Richards equation coupled with the van Genuchten–Mualem functions. Soil hydraulic parameters were constrained by measured soil water retention curves and optimized by slope position and soil layer. The model reproduced the observed wetting–drainage processes well during calibration and validation, with RMSE values of 0.002–0.034 and 0.005–0.054 m³ m⁻³, respectively. Scenario experiments were further conducted to evaluate the effects of rainfall intensity structure and antecedent wetness. Results showed that hillslope soil water dynamics exhibited strong spatiotemporal heterogeneity controlled jointly by rainfall temporal structure and topographic position. Rapid near-surface wetting occurred during high-intensity rainfall bursts, followed by delayed subsurface redistribution and drainage that intensified with depth and toward downslope areas. The midslope frequently acted as a redistribution zone linking upslope infiltration and downslope accumulation. Rainfall temporal structure mainly controlled rapid response timing, whereas antecedent wetness regulated storage capacity, response lag, and post-event drainage persistence. These findings improve understanding of hillslope hydrological connectivity and runoff generation processes in flash flood-prone mountain regions.

It remains a major challenge to determine the suitable spatial extent of vegetation restoration within a watershed to balance upstream water conservation (WC) and downstream water supply. To address this trade-off, we propose a novel framework that integrates scenario analysis with multi-objective decision-making methods. The framework primarily consists of three key steps. Firstly, the Water and Energy process in Large River Basin (WEP-L) model is employed to identify the most suitable Land Use/Land Cover (LULC) conversion type to enhance the main WC functions in each sub-basin. Secondly, a multi-objective decision-making approach was developed to simultaneously maximize upstream WC and downstream water supply, incorporating a food security constraint for the study area. Finally, the ideal point method was applied to determine the optimal extent of vegetation restoration that best balances these competing objectives. The framework is implemented in the water conservation area of the Wei River Basin (WCA-WRB), a critical region in the Wei River Basin of China. The results indicated that converting 28% of the study area to the LULC type most suitable for the function of decreasing floods would be the most effective strategy. This corresponds to a vegetation restoration area of approximately 3757 km2, of which approximately 3092 km2 of cropland was converted to vegetation, representing 5% of the total study area. This proposed framework offers a valuable reference for guiding vegetation restoration projects in similar semi-arid and semi-humid regions.

The optimization of vegetation pattern is of great significance for ensuring ecosystem function and sustainable water resource utilization. This study focused on the Xiliao River Basin, wherein an optimization analysis of vegetation pattern was conducted from the perspective of coordinating and stabilizing hydrological and ecological function stability, aiming to determine the appropriate scale and distribution of vegetation. Firstly, methods such as the InVEST model, morphological spatial pattern analysis, and circuit theory were employed to identify the key ecological restoration areas that support stable ecosystem services. Then, based on groundwater table zoning and terrestrial water storage recovery targets, the scale of cultivated land and grassland required to maintain terrestrial water balance was determined. Finally, considering both ecological and hydrological functions, the spatial distribution and scale of converting cultivated land to grassland in each district and county were determined, obtaining the optimized vegetation pattern for the entire basin. The results indicate that: ① From 1980 to 2020, the average value of habitat quality in the Xiliao River Basin decreased from 0.46 to 0.41, with 95.4% of the area experiencing varying degrees of degradation. To maintain the stability of basin ecosystem services, the key area for ecological restoration was 1 031.78 km2, with cultivated land accounting for nearly 50%. ② Among the 24 districts and counties involved in the basin, 21 of them showed a deficit in terrestrial water storage during 1980-2020. To maintain hydrological function stability, the annual water consumption of the whole basin should be reduced by 427.829 million m3, and 4 278.29 km2 of cultivated land should to be converted to grassland. ③ For Horqin Right Middle Banner, Tongyu County, Zhalot Banner, and Horqin Left Middle Banner, converting cultivated land within the key ecological restoration areas into grassland could ensure hydrological and ecological function stability. For the other districts and counties, converting cultivated land within the key ecological restoration areas into grassland could only achieve the stability of ecological service, but it still cannot meet the demand for hydrological function stability. Therefore, an additional 3 790.60 km2 of cultivated land would still need to be reduced. This study addresses the limitations of a single perspective focused on hydrological or ecological functions. The findings can provide valuable references for formulating vegetation pattern optimization strategies and ensuring the stability of ecosystems and water resources.

Study regions: The Yellow River Water Conservation Area (YRWCA), an ecologically functional water source area of the Yellow River Basin, China. Study focus: This study developed a novel assessment framework based on the dualistic naturalsocietal water cycle paradigm to quantify the Water Conservation Function (WCF) via an ecologically based method, evaluate the impacts of key anthropogenic drivers, and identify effective pathways for WCF enhancement. New hydrological insights for the region: The multi-year average water conservation across the YRWCA was 140.00 mm, with significant spatiotemporal variability. While the upstream Lanzhou region (LYR) showed a slight increase (+0.99 mm/decade), the Nanshan tributary (NWR) and Yiluo River basin (YR) experienced clear declines (-3.84 and -4.25 mm/decade, respectively). Scenario analysis revealed that industrial and domestic water uses exerted suppressive effects on WCF, whereas agricultural irrigation, terracing, and check dam construction contributed positively to its enhancement. Notably, optimizing vegetation structure emerged as the most effective enhancement strategy (+17.08%). However, its efficacy was region-dependent: low-canopy vegetation was most suitable for the LYR, while tree-grass intercropping performed best in the NWR and YR. This study provides a scientific basis and actionable strategies for ecological restoration and water resources management in the Yellow River Basin.

Study regions The water conservation area in the Yellow River Basin (YRB WCA) of China, a key ecological function zone with high annual runoff coefficients. Study focus Most water conservation (WC) function assessments in large basins focus on quantity. This study proposes a new framework for evaluating both the quantity and three main functions. A distributed hydrological model calibrated with multi-variable data was used to improve accuracy. New hydrological insights for the regions WC quantity significantly decreased only in Region II (Southern Wei River tributaries). Across all regions, vegetation ecological water use (WC-f1) generally improved, flood reduction (WC-f2) remained stable, while baseflow enhancement (WC-f3) weakened only in Region II. Precipitation and NDVI were primary driving factors of WC quantity, with positive effects except in central Region I (above the Lanzhou area). Temperature, evapotranspiration, and DEM also influenced the three functions. In addition, changes in forest, cropland, and built-up areas since 2000 had strong localized influences on WC-f1 and WC-f2, though their overall impacts were weak. Vegetation restoration strategies should vary by downstream water demand and functional priorities: reduce vegetation cover in central Region I to enhance WC quantity; increase vegetation cover in southeastern Region I and Region III (the Yiluo River Basin) to enhance WC-f2; reduce grassland area in northern Region I and western Region II to enhance WC-f3.

Large-scale groundwater storage (GWS) changes are commonly inferred from either global hydrological/land surface models or Gravity Recovery and Climate Experiment (GRACE) observations, but systematic discrepancies between these approaches remain a major source of uncertainty, particularly in data-scarce regions where ground-truth is not available. Here, we apply a coordinated forward modelling (CoFM) framework to reconcile GWS changes from GRACE observations and global model simulations, to provide observation-constrained and robust estimates in Africa. The Catchment Land Surface Model (CLSM)-GRACE reconciled GWS changes show consistent agreement with groundwater level records from 1,898 monitoring wells, with a regional mean correlation coefficient of 0.72 and positive correlations for 95% of the wells at grid scale, revealing an increasing trend of 1.47 +/- 0.25 mm/yr over Africa during 2003-2020. The reconciling significantly improves GRACE's ability in capturing GWS changes at finer scales, while reduces discrepancies among different model simulations. For example, despite of notable differences in simulated GWS trends of Congo basin from CLSM (-6.21 mm/yr) and WaterGAP Global Hydrological Model (0.21 mm/yr), the CoFM estimates (0.7 mm/yr and 2.7 mm/yr, respectively) remain comparable to Mascon solution (2.4 mm/yr). Thus, given the inherent limitations in GRACE resolution and model uncertainty, this study provides a new observation-constrained framework for robust GWS estimates at fine scales.

Study region: Tianshan Mountain region of China. Study focus: Global warming alters precipitation patterns, with future scenarios predicting variability being greater than merely changes in rainfall amounts. Changes have affected the water cycle and resource stability in the Tianshan Mountain region in China (TM), the primary water source of Central Asia. Based on ensemble precipitation data from the NEX-GDDP-CMIP6 dataset, this study analyzed multiscale changes in precipitation variability in the TM for the near (2030–2065) and far future (2066–2100) using Shannon entropy, offering valuable insights for water resource management and future challenges. New hydrological insights for the region: A negative correlation was observed between precipitation variability and precipitation, with areas experiencing high precipitation usually having low variability. In the near future, the Shared Socioeconomic Pathway (SSP245) scenario indicated higher interannual precipitation variability, whereas the SSP585 scenario showed lower variability. In the far future, the SSP585 scenario showed an increase in interannual and a decrease in intra-annual variability, whereas the SSP245 scenario showed an opposite trend. Seasonal perspectives showed that summer interannual variability decreased in the future, with increases in spring and fall. Owing to changes in precipitation patterns, water resource availability increased in the future, with the SSP585 scenario showing a greater increase than the SSP245 scenario.

Study regions: The Xiliao River Plain. Study focus: This study proposed a technical framework for determining water consumption thresholds in semi-arid regions based on terrestrial water balance. The technical framework includes three parts: adaptability evaluation and selection of remote sensing products, calculation of agricultural and ecological water consumption, and determination of water consumption thresholds. New hydrological insights for the region: This framework could separate agricultural and ecological water consumption, and effectively determining the water consumption thresholds. During the period of 1980-2022, agricultural water consumption increased significantly (p < 0.01) at the rate of 0.67 x 10(8) m(3)/year, but ecological water consumption decreased significantly (p < 0.05) at the rate of 0.20 x 10(8) m(3)/year. According to the type of major water-consuming sectors, the Xiliao River Plain was categorized into three types of regions: regions where agricultural water consumption accounted for the majority (AR), regions where ecological water consumption accounted for the majority (ER), and regions where agricultural and ecological water consumption together accounted for the majority (AER). For all three types of regions, a significant positive linear correlation between precipitation surplus coefficient and terrestrial water storage change was detected. To maintain terrestrial water balance, the proportion of total evapotranspiration consumption to precipitation should be limited to 76.3 similar to 93.5 %. For AR, ER and AER, the proportion of major water-consuming sectors in precipitation should be controlled at 47.3 similar to 63.1 %, 49.9 similar to 62.7 % and 61.8 similar to 80.0 %, respectively.

The impact of climate change on vegetation ecosystems is a prominent focus in global climate change research. The climate change affects vegetation growth and ecosystem stability in the upper reaches of the Yellow River (UYR). However, the spatiotemporal patterns and driving mechanisms of vegetation growth status (VGS) in the region remain poorly understood. Based on the hydrological model PLS, an innovative WEP‐CHC model was developed by integrating regional environmental and vegetation growth characteristics. Furthermore, combined with the PLS‐SEM model and other methods, this study systematically investigated the spatiotemporal patterns and driving mechanisms of VGS in the UYR. The results indicated that: ① VGS exhibited significant spatiotemporal variation trends within the study area. In the study period of 1970–2020, the GPP onset time was significantly advanced ( p < 0.05) while the GPP peak value was significantly increased. Spatial analysis revealed significant spatial complexity in the GPP onset time and peak values across the region. ② Soil freeze‐thaw conditions significantly influenced VGS ( p < 0.05). The complete thawing time of permafrost was closely coincided with the GPP onset time, with a correlation coefficient exceeding 0.84. After controlling soil freeze‐thaw effects using partial correlation analysis, it was found that better initial soil hydrothermal conditions would lead to better VGS; ③ The model constructed with annual hydrothermal conditions (AHC), soil freeze‐thaw period (SFTP), vegetation growth season (VGS), initial soil hydrothermal conditions (ISHC), and annual solar radiation conditions (ASRC), demonstrated good explanatory power for vegetation growth. The R 2 values of PLS‐SEM were above 0.76 in all five subregions. However, their effects on VGS varied significantly across subregions. Overall, AHC and SFTP were the dominant factors in all subregions. Furthermore, the impacts of ISHC and VGC were statistically insignificant, whereas the effects of ASRC exhibited high complexity. This study not only provides new insights into the current state of hydrological‐ecological coupling in the UYR but also offers a new tool for ecological conservation and sustainable water management in other cold regions and similar watersheds worldwide.

In the context of global climate change, understanding cryosphere degradation and its impact on water resources in alpine regions is crucial for sustainable development. This study investigates the relationship between permafrost degradation and runoff variations in the Source Region of the Yangtze River (SRYR), a critical water tower for sustainable water supply in Asia. We propose a novel method for assessing permafrost sensitivity, which establishes the correlation between cryosphere changes and hydrological responses, contributing to sustainable water resource management. Our research quantifies key uncertainties in runoff change attribution, providing essential data for sustainable decision making. Results show that changes in watershed characteristics account for up to 20% of runoff variation, with permafrost degradation (−0.02 sensitivity) demonstrating a greater influence than NDVI variations. The findings offer critical insights for the development of sustainable adaptation strategies, particularly in maintaining ecosystem services and ensuring long-term water security under changing climate conditions. This study offers a scientific basis for climate-resilient water management policies in high-altitude regions.

【Objective】Stratifying soil with sand is an engineering technique commonly used to improve the drainage and aeration of fine-textured soils. This study investigates its effects on the growth, transpiration, and photosynthesis of alfalfa.【Method】A field experiment was conducted to compare intact soil (FUS) and soil treated with a sand-stratification technique (ALS). In each treatment, physiological traits, photosynthetic traits, transpiration, and water consumption of alfalfa were measured. The hydrothermal balance of each soil type was analyzed using the soil-heat-water model.【Result】Under identical conditions, ALS significantly influenced the physiological and ecological traits of alfalfa. Specifically, compared to FUS, ALS increased average plant height by 8.5%, stem diameter by 10.4%, stem-to-leaf ratio by 18.2%, and dry mass by 7.1%. Under natural conditions, ALS increased the daily average net photosynthetic rate by 8.69% to 109.39%, with maximum increases ranging from 9.27% to 64.23%. Moreover, ALS significantly enhanced transpiration and stomatal conductance compared to FUS. The total evapotranspiration of alfalfa was 576.85 mm under FUS and 381.45 mm under ALS. Additionally, ALS reduced soil-surface evaporation by 51.22% during the peak water-demand period of the growing season.【Conclusion】Stratifying fine-textured soil with sand improved water infiltration, reduced soil surface evaporation, and enhanced the water use efficiency of alfalfa, thereby increasing its yield.

Reservoir construction has profoundly altered natural runoff evolution in river basins. Dynamic conflicts among multi-objective operational strategies—such as flood control, water supply, and ecological compensation—across varying temporal scales exacerbate uncertainties in runoff prediction, primarily due to the complex interplay between hydrological rhythm variations and anthropogenic regulation. To address these challenges, this study proposes a hierarchical multi-scale coupling framework. Long short-term memory (LSTM) networks are employed to extract implicit operational patterns from long-term reservoir records at monthly and weekly scales, while short-term decision dynamics are captured through deviations from these established long-term rules. The proposed framework is validated in the Dongjiang River Basin, a key water source for the Guangdong–Hong Kong–Macao Greater Bay Area. Compared to single-scale models, the hierarchical approach improves prediction accuracy with an average Nash–Sutcliffe Efficiency (NSE) increase of 9.4% and reductions in the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) of 13.2% and 9.6%, respectively. When coupled with a hydrological model, the framework enhances simulation accuracy in reservoir-regulated basins by up to 37.8%. By integrating multi-source decision variables, the framework captures the feedback mechanisms between natural flow variability and human interventions across temporal scales, providing a transferable strategy to reconcile operational conflicts with ecological flow requirements. Its flexibility supports optimized water allocation in regulated river basins, contributing to enhanced water security for downstream urban agglomerations.

Low-temperature waste heat from industrial devices is a valuable heat resource that can be effectively utilized. Therefore, a novel Combined Cooling, Heating, and Power (CCHP) system is proposed to recover waste heat. A mechanistic model for the selection of working fluid is established, based on multidimensional parameters including thermal efficiency, safety, and environmental impact. Subsequently, the proposed system is optimized by a multi-objective approach utilizing the genetic algorithm NSGA-III, focusing on exergy efficiency, CO2 emission reduction (CER), and return on investment (ROI). The solutions are then evaluated using the TOPSIS method, and the performance of the optimized system is assessed. The results showed that R1233zd(E) exhibited superior performance in the organic Rankine cycle (ORC) system across various heat source temperatures. The exergy efficiency of the CCHP system improved from 0.78 to 0.80. The operating cost experienced a marginal increase to $29,470.82, while the equipment cost was substantially reduced to $467,039.89. Additionally, the ROI increased from 0.49 to 0.54 prior to optimization, marking a 7.80 % rise. The CER of the system improved to 1958.31 tons/year, an increase of 6.42 %. The refined design enhanced the system's economic benefits and promoted its environmental sustainability through judicious equipment selection and process optimization.

Waste heat generation,upgrading,and refrigeration are the fundamental ways to recover and utilize waste heat.Rationalizing the use of refrigerants also contributes to creating energy savings and mini-mizing carbon emissions.This study evaluates the thermodynamics,economics,and environment of different refrigerants in three waste heat recovery schemes:generate electricity,heat pump,and refrigeration.Based on this,the entropy weight and technique for order preference by similarity to an ideal solution are combined to assess the overall performance of the refrigerants.A thorough analysis reveals that R1234ze(E)could replace R245fa and R123 in the organic Rankine cycle.The best refrigerant for vapor compression refrigeration and high-temperature heat pump systems is R1243zf.In addition,the multi-objective decision analysis shows that the performance difference among the nine selected refrigerants is the total cost,followed by the environmental impact.The approach successfully recognizes the variations between different refrigerants in the same waste heat recovery scheme and gives a thorough evaluation.It sets instructions for the future use of eco-friendly refrigerants and their appli-cation of waste heat recovery schemes.

The Tabu River Basin (TRB) is one of the most ecologically fragile areas in the arid regions of northern China; it is a key component of the desert steppe north of the Yinshan Mountains. The fractional vegetation coverage (FVC) represents a vital indicator of ecological health in the TRB. In this study, we explored the impacts of climate change and human activities on vegetation growth and utilized Landsat data (30 m) from the Google Earth Engine to generate a long-term FVC dataset (1986–2023) in the TRB. Furthermore, we established a framework for quantitatively identifying the effects of climate change and anthropogenic activities on the FVC in desert steppe regions. The results revealed that: (1) the FVC exhibits considerable spatial heterogeneity, with higher values observed in the southeastern and southwestern areas and lower values in the northern part; (2) over the past 38 years, the annual average FVC has shown fluctuations, with a slight declining trend, while the Hurst exponent indicates a reverse persistence pattern in the FVC across the TRB; and (3) the correlation between the FVC and the temperature is marginally stronger than that with precipitation, and the influence of climate change on promoting the FVC outweighs the role of human activities. These results offer valuable insights for ecological restoration and sustainable development efforts and provide scientific support for monitoring vegetation in the region.

Composite drought indices, which integrate multiple drought drivers, hold significant implications for agricultural water management in the context of climate change. In this study, we systemically evaluated the Copula-based Multivariate Standardized Drought Index (CMSDI) in characterizing meteorological, hydrological and agricultural droughts across global land areas. Six two-, three-, and four-dimensional CMSDIs were developed using bivariate and vine copulas by integrating precipitation, potential evapotranspiration, runoff, and soil moisture. The fitting performance, correlation, sensitivity, and time series trend of the CMSDIs were assessed. The results demonstrate the applicability and reliability of CMSDIs in monitoring and detecting diverse composite drought conditions. The vine copula models exhibit superior effectiveness in modeling the dependence structure of high-dimensional drought indices, confirming the propagation pathways from meteorological to hydrological, and then to agricultural droughts. However, their fitting performances exhibit significant spatial and seasonal heterogeneity, which are closely related to the correlations between the constructed marginal univariate drought indices. The CMSDIs that do not consider potential evapotranspiration show significant drying trends in eastern Asia and central Africa, along with significant wetting trends in central Asia, western Australia, and North America. The CMSDIs integrated with potential evapotranspiration exhibit a consistent global drying trend due to the increased atmospheric evaporative demand, particularly in eastern Asia and Africa. The findings contribute to improving composite drought monitoring and early warning systems, which are closely associated with key aspects of agricultural water management, including irrigation scheduling and water allocation planning.

The source area of the Yangtze River (SAYR), part of the Tibetan Plateau, is an ecologically fragile alpine region sensitive to climate change. Current research has predominantly examined hydrological and ecological responses as isolated systems, failing to address the coupled mechanisms through which permafrost degradation mediates water-carbon interactions‌. In this study, we used a fully coupled eco-hydrological model that integrates permafrost processes, along with multi-source remote sensing data, experimental monitoring, and machine learning, to quantify the water retention and carbon sequestration capacity over the past 20 years. The region was categorized into three risk zones based on changes in soil moisture, net ecosystem productivity (NEP), and dissolved organic carbon (DOC) fluxes in streams. We evaluated eight factors, including precipitation, temperature, vegetation phenology and cover, and their contributions to changes of water retention and carbon sequestration using an interpretable machine learning approach. Results show that the central and eastern regions of the study area face the highest risk of declining water retention and carbon sequestration capacity. The changes of temperatures and precipitation have led to depletion of soil water and carbon reserves. This depletion raises concerns about the potential shift from a carbon sink to a carbon source considering land-to-river carbon loss. Our study provides critical insights into the water and carbon flux dynamics and offers valuable guidance for water resource and ecological management in alpine river systems.