In the present changing climate, a dichotomy exists between climate-driven sediment generation and sediment trapping by dam at basin scale, affecting ecological health of river system. The role of dams on climate-driven changes in fluvial sediment transport remains uncertain, which poses a risk to the suitability of river habitats. Thus, this study reconstructs daily fluvial sediment series in the Jinsha River Basin (JRB) based on sediment availability transport models to reflect the impact of dams on climate-driven changes in fluvial sediment transport. Furthermore, the study has sought to investigate the sediment sensitivity and transport patterns under dam impact, as well as to analyze the underlying main control of fluvial sediment change. As expected, climate change has led to an increase in fluvial sediment across the entire catchment. However, river damming dampens climate-driven changes in sediment at the all stations except for Shigu station. Additionally, dams weaken or even completely alter the scale effect of sediment sensitivity to climate change, e.g. the operation of downstream cascade dams significantly reduces the sensitivity of fluvial sediments to precipitation. From upstream to downstream, the hysteresis effect in historical monthly SSC-Q relationships is intensified by dams. In particular, extreme sediment transport pattern shift from transport-limited to supply-limited patterns in the downstream dam-impacted reaches. The attribution analysis reveals that the climate change directly contributes to the increase in sediment, while vegetation shows a bidirectional effect on fluvial sediment. Nonetheless, the adverse effects of dams lead to a reduction in climate-driven sediment, a phenomenon evident in cascade reservoirs. Taken together, these findings highlight that river damming strongly reverses the change pattern of climate-driven sediment, effectively dampening downstream sediment delivery. The disruption of the natural state of sediment signifies that river ecosystems are undergoing significant impact, necessitating broader attention and comprehensive research.

The combined pressures associated with environmental contamination and energy demand, driven by intensified anthropogenic activities, significantly hinder the long-term sustainability of urban systems. This scenario imposes new demands on the coordinated management of carbon emissions and pollution in urban areas, particularly against the backdrop of climate change. To advance the sustainable development of energy and the environment, this study proposes coupling a system dynamics (SD) model with a variable fuzzy model to assess the environment and energy carrying capacity (EECC). The 19 key indicators covering five subsystems (including population, economic, water resources, water environment, and energy subsystems) in the SD model were utilized in the development of the EECC assessment index system. The variable fuzzy evaluation model was employed to determine the sample membership degree, reflecting the dynamic continuous change of the indicators. The evaluation results of the samples were quantified by comparing the advantages and disadvantages among the indicators. This overcame the limitations of binary comparison of indicator evaluation and the lack of standardization in evaluation. The results reveal an increase in EECC from 0.10 in 2005 to 0.77 in 2021, reflecting concurrent changes in water environmental carrying capacity (WECC) and resource and environmental carrying capacity (RECC). This positive trend is attributed to strengthened environmental governance and improved energy efficiency. However, water resource scarcity and declining forest coverage have limited the increase in EECC. Scenario analysis indicates that EECC improvement will remain less optimistic under the current development scenario. Comprehensive measures, including water conservation, energy efficiency improvement, and emission reduction, are recommended to further elevate the EECC to 0.92 by 2025. This study offers valuable implications for urban sustainable development and advocates for the exploration of evaluation standards in future research.

Understanding compound dynamics of runoff and sediment is essential for managing river basins affected by cascade reservoirs. This study proposes a bivariate copula-wavelet risk analysis (BCWRA) approach to assess synchronous and asynchronous runoff-sediment risks. This approach integrates trend detection, periodicity analysis, copula-based joint probability modeling, and joint return period estimation. BCWRA is then applied to four hydrological stations in the Upper Yellow River Basin (UYRB) to demonstrate its advantages. The results show that cascade reservoir operations reduce synchronous runoff-sediment events by an average of 17.74%, while increasing asynchronous combinations by 18.25%. Particularly, midstream stations exhibit increased risks of sediment retention and channel erosion. The BCWRA approach reveals that reservoir regulation reduces runoff variability and significantly decreases sediment transport, resulting in shorter return periods under the OR condition, in which at least one of the two variables exceeds the defined threshold. We propose adaptive management strategies, including real-time monitoring and coordinated sediment flushing, to mitigate these risks. This study focuses on the two principal regulating reservoirs, Longyangxia and Liujiaxia, and therefore primarily captures their dominant regulation signals. Consequently, the presented dependence and risk changes may underestimate additional interactions arising from the broader cascade system in the upper Yellow River. This study advances risk analysis methodologies for regulated rivers, offering generalizable tools for managing compound hydrological hazards.

Tropical cyclones (TCs) strongly influence autumn ozone pollution in the Pearl River Delta (PRD), yet the relationship between ozone concentration changes and TC intensity evolution remains unclear. Here, we investigate how ozone in the PRD responds to the intensity evolution of westward, landfalling, and northward TCs during autumn by using observational and reanalysis data in 2014-2024, and quantify the contributions of key meteorological factors with a LightGBM-SHAP analysis. Results reveal that ozone concentration does not peak when TCs reach their maximum intensity. Westward TCs exhibit relatively high ozone only two days before maximum intensity when 1000-2000 km from the PRD. Ozone decreases markedly (-4.97 mu g m(-3) day(-1)) as landfalling TCs intensify and approach, peaking three days before maximum intensity. In contrast, ozone gradually increases (2.22 mu g m(-3) day(-1)) even as northward TCs remain 2000-2500 km away and peaks two days after maximum intensity. During intensity evolution of all three TC types, vertical velocity, surface solar radiation downwards (SSRD), total and low cloud cover (TCC and LCC), and total precipitation all significantly influence ozone changes. LightGBM-SHAP analysis further quantifies the relative contributions of meteorological variables within the model, identifying low LCC (similar to 40 mu g m(-3)), combined low LCC and high SSRD (similar to 15 and similar to 20 mu g m(-3)), and high SSRD (similar to 40 mu g m(-3)) as the leading factors to high ozone concentration for westward, landfalling, and northward TCs, respectively. Moreover, meteorological mechanism indicates that westward TCs primarily affect ozone in the PRD through their internal dynamical and thermal effects. Landfalling TCs initially enhance ozone via ridge strengthening and upper-level easterly wind anomalous, then ozone declines as westerly jet retreats eastward and subtropical high expands westward. Northward TCs promote ozone buildup north of the PRD under anomalous upper-level easterly wind and geopotential height anomalies, with ozone then transported southward by peripheral airflow.

Research on how cascade reservoirs modulate carbon cycling in climate-sensitive alpine rivers remains limited. This study investigates greenhouse gas (GHG) emissions and dissolved organic matter (DOM) transport regulated by three cascade reservoirs in the upper Yellow River: Longyangxia (LYX), Lijiaxia (LJX), and Liujiaxia (UJX). Field measurements during spring thaw revealed that reservoirs significantly enhance CO2 and CH4 diffusion fluxes (FCO2 and FCH4) compared to natural river sections. FCO2 showed a decreasing trend along the flow direction (LYX: 14.6 ± 1.11 > LJX: 10.0 ± 0.51 > UJX: 9.06 ± 0.66 mmol m-2·d-1), while FCH4 exhibited an increasing trend (UJX: 0.31 ± 0.04 > LJX: 0.26 ± 0.01 > LYX: 0.12 ± 0.03 mmol m-2·d-1). Optical spectroscopy showed that DOM composition transitioned from terrestrial, glacial signatures upstream to more humified, anthropogenic characteristics downstream. The vertical distribution of DOM was closely linked to reservoir depth. Shallow reservoirs (≤40 m) exhibit high DOM, the humic acid content, and humification levels in subsurface layers (peaking at 7-10 m depth); deep reservoirs (≥70 m) show these traits in benthic layers (70-100 m). Critically, correlation analysis linked CO2 emissions to microbial degradation of labile DOM components, whereas CH4 emissions were associated with both fresh autochthonous and recalcitrant terrestrial humic substances, differing between reservoirs. Our findings demonstrate that cascade reservoirs can function as interconnected biogeochemical processors during the thaw period, with potential to enhance regulation of carbon emissions and DOM transport, thereby playing a critical role in understanding the carbon cycle in reservoirs across the fragile Qinghai-Tibet Plateau region.

Accurately assessing water quality and eutrophication remains a critical challenge in global lake pollution management. Traditional models, such as the Water Quality Index (WQI) and Trophic Level Index (TLI), often rely on expert judgment and outdated guidelines, limiting their adaptability to evolving pollution conditions. This study proposes a novel, reproducible, and comprehensive evaluation framework by integrating Principal Component Analysis (PCA) and Factor Analysis (FA) for linear weighting of parameters, using Random Forest (RF) for non-linear validation of weight rankings, and performing consistency checks through exceedance rate and algal density (AD) for management association. West Lake Taihu (WTH), the largest natural pretreatment reservoir of Lake Taihu, was selected as the case study for assessing long-term (2008-2023) pollution dynamics. Results showed that total phosphorus (TP) and total nitrogen (TN) were the dominant factors influencing both water quality and eutrophication, replacing traditional dissolved oxygen (DO) and chlorophyll-a (Chl-a). RF and exceedance rate analyses confirmed TP (22.71%) and TN (18.11%) as the primary contributors to WQI, with exceedance rates >80%, while DO showed little influence (<9%) and an exceedance rate below 5%. For TLI, TN and TP contributed 27.02% and 22.67%, while Chl-a exhibited lower contribution (22.28%) and a distinct temporal mismatch. From 2008 to 2023, WQI increased by 27.25% and TLI decreased by 22.70%, indicating substantial improvements in both water quality and eutrophication. However, persistent seasonal and spatial differences disrupted WQI-TLI consistency in spatiotemporal trends. The enhanced WQI model provided more realistic assessments by reducing the influence of stable parameters and emphasizing frequently exceeding ones. The modified TLI model improved the identification of eutrophic zones and periods, enhancing the model's targeting ability and correlating more strongly with AD. Both models also showed higher consistency with external RF outputs, improving their reliability and management applicability. This study demonstrates that the integrated framework, combining data-driven PCA/FA, RF validation and management-related indicators check, significantly outperforms traditional approaches by enhancing accuracy, management relevance, and adaptability for contemporary lake pollution management. It proposes a robust, transferable framework for dynamic freshwater ecosystem management.

Estuarine wetlands are important blue-carbon ecosystems that contribute substantially to carbon sequestration and climate regulation. Hydrological processes exert strong controls on carbon emissions in these systems, yet their effects across hydrological zonation, water-level fluctuation regimes, and water-level thresholds remain poorly constrained. In this study, we integrated field-based fixed-point observations, DNDC model simulations, and Marsh Organ experiments to examine the spatiotemporal patterns of CH4 and CO2 fluxes and their responses to multidimensional hydrological processes in the Yellow River Estuary. The results showed that: (i) Both CH4 and CO2 fluxes decreased significantly from groundwater-controlled to tidal-controlled zones. CH4 flux was primarily regulated by water level, soil moisture, and salinity, whereas CO2 flux was more closely associated with temperature and soil aeration. (ii) Longer-duration and higher-frequency water-level fluctuations enhanced both CH4 and CO2 fluxes, with CH4 exhibiting markedly greater sensitivity. And (iii) along the water-level gradient, CH4 flux exhibited a unimodal response, peaking at an estimated threshold of -35.00 cm, whereas CO2 flux decreased continuously with increasing water level, with a more rapid decline beyond an estimated threshold of -29.15 cm. These findings indicate that carbon emissions from estuarine wetlands are controlled not only by mean water level, but also by the combined effects of hydrological zonation, fluctuation disturbance regimes, and vegetation-associated threshold responses. This study provides a process-based basis for predicting and managing carbon emissions from estuarine wetlands under future sea-level rise and hydrological change.

With climate change and rapid urbanization, urban flooding is rising. High-resolution rainfall input is essential for improving the accuracy of hydrological models (SWMM for example), which are the core tools for urban flooding prediction. This study proposes a short-duration hyetograph construction framework that integrates Gauss-Markov process with spatial interpolation to timing and intensity through its temporal envelope, ensuring realistic representation of rainfall evolution. It further exhibits stochastic adaptability and parameter transferability, allowing calibration to local climate and varying station densities for broader hydrological applicability. In particular, the integration of multi-method spatial interpolation enables optimal reconstruction of hyetograph fields under different station densities and spatial uniformity. Case study results show that Gauss-Markov hyetographs effectively depict early-middle peaks rainfall structures, reducing errors by 22.8% compared with Chicago hyetographs. Inverse distance weighting (IDW) with seven evenly distributed stations achieve the most consistent results (RMSE = 0.0169, R2 = 0.6939), suggesting that moderate-density, well-distributed station networks with IDW are suitable for local weak spatial autocorrelation. In contrast, Kriging-type has relatively poor stability under strong spatial variability. SWMM simulation results demonstrate for moderate-intensity, short-duration rainfall, interpolated Gauss-Markov hyetographs significantly outperform traditional methods: its SWMM flow simulation reduces RSR by 32.9% and 57.6% compared to Chicago and Huff hyetographs. The short-duration hyetographs construction framework offers a novel methodological approach to addressing the challenge of rainfall characterization in sparsely observed urban areas, enhancing the spatio-temporal resolution of rainfall input for flooding forecasting.

Landscape restoration projects (LRPs), including ecological water replenishment and ecological emigration, have been implemented globally as effective ways to relieve water scarcity and land competition, thereby enhancing sustainable wetland ecosystem health. There is growing concern about complex net-benefit relationships from economic, social, and environmental aspects in LRP optimization, with the most crucial issue being competitive demands of multi-stakeholders for ecological benefits in wetland systems. This study proposed a new framework for integrating in-depth analysis of synergies and trade-offs among multi-stakeholders, thereby enabling decision-makers to coordinate crucial relationships. The framework consisted of 2 key modules. Specifically, in the net-benefit assessment module, the patterns of landscape and normalized difference vegetation index (NDVI) were simulated. Based on the biophysical relationships between NDVI and ecosystem services, ecological benefits and management costs of the government, residents, and ecological sectors were accurately evaluated to assess their net benefits. In the multi-stakeholder relationship analysis module, a novel vector synthesis method was introduced to evaluate the resultant benefits of an integrated wetland system (IWS) and to analyze synergy and trade-off dynamics among stakeholder–stakeholder and stakeholder–IWS interactions. The results showed that optimal LRPs were to maintain water levels at 8.0, 8.5, 8.8, and 7.3 m under SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, and SSP 5-8.5, respectively, along with a 60% emigration ratio. These could achieve synergies only 7.41% below the optimum and maximize the net benefits of most stakeholders. This study provides a novel research perspective and practical optimization strategies for improving equity and effectiveness in LRP optimization.

Climate change introduces severe non-stationarity and high-frequency noise into hydro-meteorological data. This noise degrades the predictive accuracy of traditional data-driven streamflow models. We propose a physics-inspired data enhancement framework coupling the CEEMDAN-based Hilbert-Huang Transform (HHT) with Stochastic Resonance (SR). We applied this framework to the Lanzhou section of the upper Yellow River. HHT isolates the dominant characteristic frequency of the basin's streamflow system at 0.0026 cycles/day. Using this frequency as a target, we constructed a Bayesian-optimized SR system. The system converts the energy of high-frequency meteorological noise into low-frequency periodic components, facilitating frequency alignment between the meteorological inputs and the hydrological response. We evaluated the SR-enhanced meteorological inputs across three machine learning architectures: Random Forest, XGBoost, and LSTM. All algorithms demonstrated an improved performance. The SR-LSTM model achieved a Nash-Sutcliffe Efficiency (NSE) of 0.91 +/- 0.03. This represents a 19% improvement over the baseline LSTM score of 0.79 +/- 0.02. The SR-LSTM demonstrated robust accuracy during extreme hydrological events; it achieved a high-flow NSE of 0.89 and effectively mitigated the common peak-underestimation issue by constraining relative peak magnitude errors to approximately -5.08%. Overall, this study presents a practical data enhancement approach for streamflow forecasting under complex climatic conditions.

Global warming leads to uncertainties in the hydrological response to greening. In this study, we employ Eagleson's ecological optimality framework to quantitatively assess vegetation response and its hydrological effects in the upper Yangtze River, China. The results showed that: (i) Vegetation cover increased due to the global trend in warming and wetting and eventually reached a new equilibrium state. Compared with the pre-period, this equilibrium was marked by a lower proportion of vegetation in the equilibrium state and a higher proportion in the recoverable state. (ii) Unexpectedly, when vegetation deviates from its equilibrium state, the ecosystem tends to maintain higher streamflow and evapotranspiration. In contrast, the equilibrium state tends to maintain higher water storage (e.g. soil moisture) by reducing streamflow and evapotranspiration. (iii) Sediment decreased in association with increased vegetation, as vegetation greening reduced streamflow and consequently decreased sediment transport. In the equilibrium state, the enhanced soil structural stability further suppressed soil erosion. This study contributes to understanding the hydrological regulation induced by vegetation changes under a warming climate.

Natural hydrological regime creates and sustains the diverse habitats necessary for completing fish lifecycle, yet dams fragment and degrade these habitats by disrupting the flow conditions, thereby threatening fish stocks. This study herein aimed to assess the impact of dam operations on suitable habitat of the migratory fish Coreius guichenoti (C. guichenoti) in the Jinsha River using a novel hydrological framework. A two-dimensional hydrodynamic model was coupled with habitat suitability index to reveal the habitat condition before and after dam construction. The results showed that dam operations have reduced free-flowing river habitats by decreasing flow velocity. During the May-July spawning season, the peak spawning habitat window shifted from May under pre-dam conditions to June under post-dam conditions, indicating a temporal mismatch between optimal habitat and the historical spawning window. During the spawning season, suitable habitat decreased by 44-79%, with the spawning habitat centre of gravity shifting markedly upstream. These changes suggest that dam operation has caused a function loss of spawning habitat for C. guichenoti. Management should therefore focus on maintaining hydraulic conditions during spawning season, synchronizing reservoir releases with the spawning window, and protecting unregulated tributaries as free-flowing compensatory rivers.

Study region: The Upper Yellow River Basin (UYRB), China. Study focus: In this study, a variety of mathematical statistical methods, the Indicators of Hydrologic Alteration—Range of Variability (IHA-RVA) method, and the newly proposed Flow Surplus-Deficit (QS-QD) method were integrated to analyze the impact of cascade dam development on hydrological changes over the past 70 years in the UYRB. Additionally, the Double Mass Curve (DMC) method was utilized to evaluate changes in annual sediment transport, quantifying the influences of precipitation and human activities. New hydrological insights for the region: Long-term statistical analysis revealed significant declining trends in both the annual runoff and sediment load following dam construction. Abrupt changes in runoff and sediment were identified during the study period in 1969 and 1987. Dam operations have altered the relationship between water and sediment, resulting in intensified summer flow deficits and winter-spring flow surpluses, with significant increases in flow deficit during July. The operation of the Longyangxia Reservoir and Liujiaxia Reservoir cascade systems exhibits cumulative effects over time and space. The proposed QS-QD method quantitatively estimates monthly flow variations and effectively addresses the limitations of RVA variation based on frequency. Furthermore, sediment transport at hydrological stations indicated a sequential downstream decrease, with human activities contributing between 95.93 % and 116.51 % to these changes.

Urbanization, driven by socio-economic development, has significantly impacted river ecosystems, particularly in plain city regions, leading to disruptions in river network structure and function. These changes have exacerbated hydrological fluctuations and ecological degradation. This study focuses on the central urban area of Changzhou using a MIKE11 model to assess the effects of four hydrological connectivity strategies—water diversion scheduling, river connectivity, river dredging, and sluice connectivity—across 13 different scenarios. The results show that water diversion, river dredging, and sluice connectivity scenarios provide the greatest improvements in water environmental capacity, with maximum increases of 54.76%, 41.97%, and 25.62%, respectively. The spatial distribution of improvements reveals significant regional variation, with some areas, particularly in Tianning and Zhonglou districts, experiencing declines in environmental capacity under sluice diversion and river-connectivity scenarios. In addition, the Lao Zaogang River is identified as crucial for improving the overall water quality in the network. Based on a multi-objective evaluation, combining environmental and economic factors, the study recommends optimizing water diversion scheduling at sluices (Weicun, Zaogang, and Xiaohe) with flow rates between 20–40 m3/s, enhancing connectivity at key river hubs, and focusing management efforts on the Lao Zaogang and Xinmeng rivers to strengthen hydrological and water quality linkages within the network.

Study region: Baiyangdian Basin, and Jinsha River Basin, China. Study focus: Variations in river hydrological connectivity influence carbon cycling, yet the regulatory mechanisms remain unclear. This study integrates field experiment, carbon flux simulation, and model construction to assess how hydrological connectivity affects riverine carbon emissions. New hydrological insights for the region: (i) Disruption of longitudinal hydrological connectivity increased CO2 emissions from dry channels compared to flowing channels, whereas CH4 emissions showed the opposite trend. (ii) Floodplain released more CO2 and CH4 than riverbed. (iii) Enhanced vertical groundwater recharge stimulated carbon emissions, with CO2 and CH4 fluxes being greater in high baseflow areas. (iv) Reservoir CO2 emissions were higher during the dry season than those during the wet season, particularly in shallow areas, while CH4 emissions showed an inverse pattern. (v) Under actual scenarios, the expansion of dry channels resulted in higher CO2 emissions but lower CH4 emissions than natural scenarios. And (vi) discharge was positively correlated with CO2 emissions, with the Jinsha River Basin exhibiting greater sensitivity of CO2 emissions to discharge variations than the Baiyangdian Basin. This study offers insights for watershed management and emission mitigation.

Climate change has emerged as a global issue, and the emission levels of greenhouse gases (GHGs) significantly influence global climate change. The patterns and controls of GHG emissions in urban rivers remain unclear. GHG fluxes in different types of urban rivers in Changzhou City, China, were calculated via the floating static chamber method and boundary layer equation method. Ultraviolet-visible (UV-vis) absorption spectroscopy and three-dimensional excitation-emission matrix fluorescence spectroscopy (3D-EEM) were employed to explore the sources and characteristics of the dissolved organic matter (DOM) in the rivers. The physical and chemical indicators of the rivers and sediment were monitored on-site and analyzed in the laboratory. Additionally, the species and quantity of bacteria in the sediment were determined. Spearman correlation analysis was used to identify the key factors influencing GHG emissions. The results showed (1) that the intensity of sunlight had an impact on the activity of pseudomonas, and thus affected N2O flux and that (2) the CO2 flux measured by the two above-mentioned methods significantly differed and were negatively correlated (p < 0.05), possibly because low wind speed influences the robustness of the boundary layer model. Therefore, using only the boundary layer equation method cannot accurately measure the GHG emissions of rivers in urban areas with low wind speeds. (3) The CO2 flux exhibited a strong positive correlation with both total phosphorus and ammonia nitrogen in the water, as indicated by a Spearman correlation coefficient with a significance level of p < 0.01. Thus, pollution control and input control play crucial roles in reducing GHG emissions. (4) DOM in the urban rivers was derived mainly from autochthonous sources, which are protein-like substances related to the metabolism of phytoplankton. Studies indicate that GHG emissions are negatively correlated with autochthonous parameters, suggesting that reduced human interference leads to lower GHG emissions.

Wetland hydrological connectivity, which is altered by water level fluctuations, can dramatically result in a series of changes in wetland structure and function. However, the responses of wetland habitats to changes in hydrological connectivity and its mechanisms remain unclear. This study provides a new concept of effective hydrological connectivity that considers wetland water level fluctuations. Wetland InSAR technology was used to investigate the structural connectivity of 14 wetland patches in combination with hydrological barriers and relative water levels in the Momoge National Nature Reserve, China. The 14 wetland patches were categorized into three types as follows: wetland patches with near-natural hydrological processes, wetland patches with hydrological processes under anthropogenic regulation and wetland patches with hydrological processes regulated by river flooding. Water level fluctuations further validated the consistent changes in hydrological regimes within three types of wetland patches to varying extent. Specifically, the coefficients of variation (CVs) of the water levels were 0.75 <= CV <= 0.90, CV < 0.75, and CV > 0.90 for the three types of wetland patches (from I to III), which induced differences in effective hydrological connectivity. Subsequently deviations in effective hydrological connectivity induced different ecological responses, i.e., the ecological resilience is accordingly -1.38, -0.78, and -0.76. The differences in water level thresholds for the intensity of hydrological connectivity provide further evidence of differences in the mechanisms of hydrological action that maintain different wetland habitats. Taken together, these results indicate that the formation and maintenance of wetland habitats depends on the existence of distinct hydrological conditions.

The allocation of river assimilative capacity (RAC) remains a complex challenge due to the trade-offs between environmental fairness and efficiency. To address these issues, a novel integrated framework for the optimal allocation of RAC was proposed. The Luan River Basin in Chengde City, China, was selected as a case study. Hydrodynamic and advection-dispersion modules from the MIKE 11 model were implemented and then integrated into the RAC model. An orthogonal experimental method was used to identify significant factors influencing RAC, and three regulatory scenarios were designed. The environmental Gini coefficient (EGC) was modified using a probability distribution function to evaluate fairness-based allocation of RAC, and the environmental benefits were used to quantitatively measure efficiency-based allocation. Subsequently, a modified optimization model was developed to determine the optimal RAC allocation under three regulatory scenarios based on environmental fairness and efficiency trade-offs. Results showed that the initial state was the least favorable, with the lowest RAC and highest modified EGC. Under high regulation, the average modified EGC decreased by 68.97 %, 53.49 %, and 18.22 % compared to the initial state, low, and moderate regulation, respectively. High regulation was ideal for environmental fairness. However, when considering the trade-offs between fairness and efficiency, moderate regulation achieved the optimal allocation, minimizing the objective function by 68.64 % compared to high regulation. The study provides new insights into targeted RAC allocation strategies to promote fairness and maintain efficiency.

The contributions of climate change and human activities to runoff variation have been examined in multiple individual catchments, but upstream-downstream interaction relationships remain underexplored. Here, we propose a three-tiered attribution framework to investigate the spatiotemporal patterns, underlying drivers, and upstream-downstream relationships of runoff variations in the Yellow River Basin (1952-2021). The results indicate that the longitudinal cumulative effects of climatic (ranging from -8.6% upstream to 7.1% downstream relative to the whole basin) and anthropogenic disturbance (15.2%-92.9%) factors lead to higher risks of runoff variations in downstream regions, with more pronounced seasonal fluctuations (particularly in reservoir-regulated reaches). Glaciers, lakes, and reservoirs' storage capacities cause lagged effects of climate change and human activities on runoff variations, while simultaneously serving as critical drivers for inter-basin water resource regulation. Integrating comprehensive understanding of multi-scale hydrological variability drivers and spatial interaction mechanisms is essential for advancing adaptive river basin management and sustainable water resources allocation.