In recent decades, warming-induced advances in plant phenology have become widespread across the globe. However, whether early spring greening has beneficial or adverse effects on ecosystem responses to subsequent summer droughts remains unclear. To bridge this gap, this study employs vegetation remote sensing and meteorological data (1982-2022) to construct a Drought Propagation Index (DPI), analyzing the spatiotemporal patterns of how spring greening influences summer drought propagation risk from meteorological to ecological drought in Northern Hemisphere (NH) ecosystems. Research indicates that earlier spring greening exacerbates the overall risk of summer drought propagation in NH ecosystems by significantly increasing drought propagation probability (PP) and shortening propagation time (PT). This adverse effect amplifies synchronously with the intensification of meteorological drought stress. Under extreme drought conditions, greening leads to an average PP increase of 11.4%, resulting in heightened propagation risks across over 60% of the study area. The impacts of greening exhibit spatial heterogeneity. In high-latitude humid and sub-humid regions, the effects of greening are dominated by the Vegetation Structural Overshoot (VSO) pattern, leading to increased DPI in over 75% of grids in boreal forest and significantly heightened summer drought propagation risks. In contrast, grassland and shrub in arid and semi-arid experience relatively minor adverse effects from spring greening, with more than half of the grids showing alleviated summer drought propagation processes. Our results highlight that ecological drought risk planning under climate change, should consider the role of spring greening in promoting drought propagation, especially for ecosystems in humid regions with marked greening trends.

Study region Jialing River Basin of southwest China. Study focus Climate change exacerbates the imbalance in water-resource allocation, while land-surface conditions strongly regulate water resources across different spatial and temporal scales. In this study, the spatiotemporal characteristics of blue and green water resources (BW and GW) were evaluated based on the Soil and Water Assessment Tool (SWAT), and the impacts of climate change (CC) and land-surface change (LSC) on blue-green water resources in the basin were investigated. New hydrological insights for the region First, from 2000 to 2020, both BW and GW showed an increasing trend with 3.61 mm/year and 4.63 mm/year, respectively. The spatial distribution of BW and GW changes is uneven. Second, CC drives the variations in blue-green water within the basin. LSC can locally reverse the dominant influence of climate change. Third, CC not only directly influences the spatiotemporal characteristics of blue-green water, but also alters their dynamic partitioning by modifying the land-surface conditions, notably through vegetation greening. Our results highlight that CC impacts water resources not only directly, but also indirectly by altering vegetation. This implies that water management policies account for these complex climate-vegetation-water feedbacks to ensure sustainability and resilience in the upper Yangtze River.

Rapid global urbanization has made water-quality degradation a core issue in assessing river health and aquatic ecosystem conditions. However, under a warming climate, the coupled relationships among urbanization, drought, and water quality remain unclear. In this study, we used regression analysis together with multiple statistical approaches to evaluate the sensitivity of two key water-quality indicators, specific conductance (SC) and water temperature (WT), to the relative discharge ratio (Q/MQ), which was used as a drought-related low-flow indicator. We also systematically analyzed how SC and WT varied with Q/MQ under different levels of urbanization. The results showed that SC followed a power-law response to Q/MQ and was significantly negatively correlated with the relative discharge ratio. WT showed clear seasonal contrasts in its response to Q/MQ: in the warm season, higher Q/MQ corresponded to lower WT, whereas in the cold season, higher Q/MQ corresponded to higher WT. We distinguished urbanization-related baseline shifts in water quality from drought-related low-flow sensitivity, defined as the response magnitude of SC or WT to changes in Q/MQ. The sensitivity of SC to relative discharge changes tended to be weaker at higher IMP levels. Based on the excess SC response, defined as P0.5 − 1, the SC response in low-urbanization basins was 23.48% and 129.03% higher than that in moderately and highly urbanized basins, respectively. In contrast, the response magnitude of WT to Q/MQ tended to be stronger at higher IMP levels. The WT response magnitude in highly urbanized basins was 47.89% and 70.34% higher than that in moderately and low-urbanization basins, respectively. Overall, urbanization was associated with changes in the sensitivity of water quality to relative discharge changes under drought-related low-flow conditions. This work characterized the response relationships between Q/MQ and SC and WT and documented how their sensitivities varied across urbanization levels. It provides scientific evidence to support watershed water-quality and river-health management for cities with different degrees of urban development.

The dynamic changes in vegetation significantly affect carbon balance and water cycle. However, understanding the changes in vegetation conditions and water stress remains limited. This study proposes a novel methodology for assessing vegetation eco-regime changes, incorporating 18 indicators that comprehensively characterize vegetation conditions. By developing an ecological restoration index and employing interpretable machine learning (IML) approaches, this study systematically evaluated the impacts of both water stress and ecological restoration on vegetation eco-regimes across China. The results showed that the proportion of mutation years in China's kernel NDVI peaked in 2002. The period from 1982 to 2001 was defined as the baseline period (BP), while the period from 2002 to 2022 was designated as the change period (CP). Compared to BP, the degree of change in vegetation eco-regimes across China during CP ranged from 21.5 % to 89.4 %, with a median value of 64.9 %. The proportions of low, medium, and high change categories were 40.7 %, 59.1 %, and 0.2 %, respectively. The IML approach identified precipitation, surface solar radiation, and ecological restoration as the three dominant factors governing vegetation eco-regime dynamics, with a combined mean contribution proportion of 74.8 % during BP and CP. Furthermore, the interpretable results revealed that the transition threshold of vegetation eco-regimes increased by 40 mm for precipitation and by 0.3 hPa for vapor pressure deficit. The methodology provides a transferable approach for global assessments of vegetation-climate-restoration interactions, informing targeted ecological strategies.

Understanding greenhouse gas (GHG) emissions from inland waters in arid and high-altitude regions represents a critical knowledge gap in quantifying the global carbon budget. The Heihe River, originating from glacial headwaters in the northern Qilian Mountains and flowing through arid landscapes of Northwest China, represents a critical yet understudied system in this context. In this study, we conducted seasonal monitoring of GHG emissions along the upstream (including tributaries), midstream, and downstream sections of the Heihe River. We quantified diffusive CO2 and CH4 fluxes, and estimated ebullitive CH4 fluxes to assess their spatial and temporal variability. Results showed that CH4 concentrations and diffusive fluxes peaked in summer, averaging 145.33 f 292.69 nmol L- 1 and 553.91 f 1205.26 mu mol m- 2 d- 1, respectively. In contrast, CO2 concentrations and fluxes were highest in winter, reaching 70.05 f 42.29 mu mol L- 1 and 120.96 f 200.43 mmol m- 2 d- 1. Ebullition accounted for 58.3 f 24.7% of total CH4 fluxes in the upstream alpine region, but contributed less to midstream and downstream areas. Elevated CH4 fluxes at midstream sites may be influenced by nutrient inputs from agricultural irrigation, while enhanced CO2 fluxes at both mainstream and tributary sites were likely driven by geothermal spring-fed groundwater recharge. Annual GHG emissions from riverine waterbodies in the Heihe Basin were estimated at 1.7 & times; 108 kg (0.17 Tg) CO2 and 4.9 & times; 104 kg CH4 for 2023-2024. Although CH4 emissions were relatively minor, CO2 was identified as the dominant carbon species, thereby underscoring the river's pivotal role in mediating regional carbon cycling processes. This study provides integrated assessment of CH4 and CO2 emissions from an arid alpine river system in China. Our findings offer a scientific foundation for improving carbon budget assessments and informing climate mitigation strategies in similar arid alpine watersheds.

Root-zone water storage, the subsurface reservoir supplying water to plant transpiration, is essential for resilience against prolonged droughts. Previous studies have placed excessive emphasis on static root zone water storage capacity, the maximum water storage within the subsurface root zone available for plant transpiration, while neglecting the dynamics of water use (WU, the volume of water depleted from this reservoir for plant transpiration) and their associated environmental effects. This study employs an improved deficit-based approach incorporating groundwater contribution to elucidate seasonal-to-decadal WU dynamics worldwide. Our analysis reveals a widespread increase of WU across diverse biomes, land use/land cover types, and hydroclimatic gradients. Seasonally, WU increases are observed during 52% of drought months. Annually, approximately 56% of the vegetated areas exhibit significantly increasing trends in annual WU, with a trend 0.43 mm year-1 over vegetated areas. Decadal WU significantly increases across 48% of global vegetated areas, with a mean trend of 0.39 mm yr-1. Climates, particularly temperature dominated these WU dynamics, exhibiting a positive correlation, whereas rising water supply and elevation generally reduced trends. Croplands with sustainable blue and green water availability showed prevalent WU changes. However, cropland areas exhibiting unsustainable trends in increased WU account for 34% of global croplands. In addition to water resource, heightened WU trends correlated significantly with increased belowground biomass carbon sequestration. These findings highlight the critical trade-offs between increasing plants water use and water resource availability, carbon storage and agricultural productivity, necessitating integrated management strategies.

Since 2000, the vegetation cover in the Yellow River Basin (YRB) has significantly increased. However, the responses of carbon and water cycles to large-scale vegetation recovery in the basin and their driving mechanisms remain unclear. This study employs methods such as Sen's slope trend test, partial correlation analysis, residual analysis, and interpretable machine learning models to investigate the variations in gross primary productivity (GPP), evaporation (ET), and water use efficiency (WUE) in the YRB. It aims to reveal the spatial differentiation mechanisms that drive GPP, ET, and WUE. The results indicate the following: (1) From 2001 to 2020, significant increasing trends were observed in GPP, ET, and WUE across the YRB (p < 0.05), with the most pronounced vegetation recovery observed in the middle reaches. (2) GPP, ET, and WUE are most strongly correlated with the Leaf Area Index, with median values of 0.78, 0.30, and 0.70, respectively. (3) On average, climate change contributes spatially 24.8%, 35.6%, and 24.3% to GPP, ET, and WUE, respectively, while human activities contribute, on average, 75.2%, 64.4%, and 75.7%. (4) Regarding their synergistic evolution, GPP changes predominantly drive WUE changes in the YRB relative to ET. (5) The contributions of NDVI changes to WUE, GPP, and ET changes are 60.4%, 73.1%, and 14.9%, respectively. Overall, NDVI changes dominate the changes in GPP and, by extension, in WUE. This research sheds light on the pathways toward ecological restoration and sustainable development in the YRB.

Attributing changes in streamflow processes is crucial for water resource management as well as for understanding and mitigation of flood and drought risks. However, most existing attribution methods lack a unified approach to handle various causal variables, making them unsuitable for comprehensive attribution assessments. Therefore, this study proposed a framework to quantitatively attribute the impacts of natural and anthropogenic climate change, land use and cover change (LUCC), and human water withdrawal on streamflow and its seasonality. The framework consists of three steps: (1) bias correction of Coupled Model Intercomparison Project Phase 6 (CMIP6) data and construction of a dualistic nature-society water cycle model; (2) simulation of streamflow processes and identification of streamflow seasonality under different climate forcing and LUCC scenarios; and (3) quantitative attribution of streamflow evolution characteristics. The Weihe River Basin (WRB) in China has been selected as a case study area for the proposed attribution framework. The quantitative analysis indicates that natural and anthropogenic climate change, LUCC, and human water withdrawal account for 20.8%, 27.9%, 4.6%, and 46.7% of the decreasing trend in streamflow volume and -42.4%, -28.1%, -5.1%, and 175.6% of the weakening trend in streamflow seasonality in the WRB, respectively. These results suggest that human water withdrawal reduces streamflow and weakens its seasonality, while the other three factors contribute to streamflow reduction but enhance its seasonality. Overall, this study effectively distinguishes the impacts of anthropogenic and natural climate change on streamflow processes, thus providing a deep understanding of the influences of human-induced hydro-climate change.

Recently, differentiable modeling techniques have emerged as a promising approach to bidirectionally integrating neural networks and hydrologic models, achieving performance levels close to deep learning models while preserving the ability to output physical states and fluxes. However, there remains a lack of systematic exploration into the performance and physical interpretability of hybrid models that use neural networks to replace the runoff generation and routing processes in regionalized modeling. This research developed 12 regionalized hybrid models based on a differentiable parameter learning (DPL) framework, utilizing the Hydrologiska Byr & aring;ns Vattenbalansavdelning (HBV) model as the foundational backbone. These hybrid models incorporate neural networks to replace the various physical processes within the runoff generation and routing modules. The publicly available CAMELS dataset is employed to evaluate the performance and interpretability of these hybrid models. The results show that while the median Nash-Sutcliffe efficiency (NSE) and Kling-Gupta efficiency (KGE) coefficients for all hybrid models are lower than those of the purely data-driven regionalized long short-term memory neural network (LSTM) model (median NSE: 0.742, median KGE: 0.762), the best- performing hybrid model (median NSE: 0.731, median KGE: 0.761) approaches the LSTM model and has better physical interpretability. Embedding neural networks does not inherently guarantee improved performance and may, in some cases, even result in reduced performance. The degree of performance enhancement is not significantly correlated with the number of embedded neural networks. Compared to replacing the runoff generation process, substituting the routing process with neural networks yields more substantial performance improvements and enables the learning of different routing patterns based on the catchment's static attributes. This study underscores the importance of reasonably balancing the location, complexity, and quantity of embedded neural networks to achieve a trade-off between model performance and interpretability in hybrid modeling. These insights contribute to advancing regionalized hybrid modeling development.

Root zone storage capacity (Sr) represents the maximum subsurface storage accessible to plant roots. It is primarily influenced by water availability and water demand, thus exhibiting temporal change in response to climate variations. Previous studies have primarily focused on the spatial patterns of Sr across local to global scales; however, there remains a limited understanding of its temporal patterns, particularly in relation to seasonal changes. This work explores the seasonal behavior of Sr for wet and dry seasons and the hydrological regulation of seasonal Sr. We propose a seasonal modeling framework based on apportionment entropy, which considers the phase difference between water and energy. Within this framework, the PDM-FLEX hydrological model, an integration of the probability distributed model (PDM) with the FLEX lumped model, was employed to calculate catchment-scale Sr for each season across 671 catchments in the contiguous United States. Results show that: i) this framework can effectively capture seasonal Sr, with wet season Sr (an average of 564 mm) generally being smaller than dry season Sr (an average of 820 mm) for most catchments. In the west, plants routinely access deep water, leading to comparable Sr for both wet and dry seasons. Incorporating seasonal Sr into the hydrological model can improve simulation performance across time scales; ii) dry season Sr is more responsive to hydroclimatic control compared to wet season Sr, as plants in arid climates are more sensitive to water accessibility; and iii) during the wet season, low Sr relative to precipitation leads to an unresponsive hydrological reaction. However, during the dry season, a routine correlation between Sr and precipitation produces responsive hydrological behavior. These findings indicate that plants seasonally adapt their root systems and that these seasonal variations in Sr would have significant hydrological implications.

Inter-basin water transfers (IBWT) are vital for regional and basin water security. However, climate change, characterized by reduced runoff and frequent extreme droughts, poses significant challenges to these projects. This study aims to explore the compound drought risks of past and future IBWT Projects (IBWTP) in Yellow River Basin(YeRB), Huai River Basin(HuRB), Hai River Basin(HaRB), and Yangtze River Basin(YaRB) from 1965 to 2100. Using the latest CMIP6 data and hydrological models, we predicted future runoff changes and calculated the standardized runoff index (SRI). We then evaluated the compound drought risks using two-dimensional and three-dimensional copula methods. This study assesses the compound hydrological drought risks of IBWTPs under different climate scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) using CMIP6 projections. Historical drought risks ranged from 5.29 % to 25.64 %, with YeRB and HaRB facing the highest frequency and intensity. Future projections show significant increases in drought intensity, especially in HuRB and YaRB under SSP1-2.6, with a rise of up to 28.42 %. Under SSP3-7.0 and SSP5-8.5, drought intensity continues to increase, with the Far-Future showing up to a 26.4 % increase.For constructed IBWTPs, compound drought risks increase across all scenarios. In SSP1-2.6, risks range from 7.46 % to 29.75 %, decreasing in the Far-Future (4.43 %-26.31 %). In SSP3-7.0 and SSP5-8.5, risks increase, with SSP5-8.5 reaching 6.17 %-25.34 % in the Far-Future. Planned IBWTPs show moderate increases, highlighting the need for projects to be designed with higher baseline risks for resilience. These findings emphasize the importance of adaptive water resource management and IBWTP design to address future drought risks under climate change.

The Yellow River (YR), China's second-longest river, remains understudied regarding its greenhouse gases (GHGs) emissions, particularly the impacts of urban drainage ditches and wastewater treatment facilities on regional GHGs dynamics. This study investigated methane (CH4) and carbon dioxide (CO2) concentrations, fluxes and stable carbon isotopes (513C-CH4 and 513C-CO2) across six main stream, three ditches, and one wastewater treatment site along the upper Lanzhou section of the YR, spanning from the urban entrance (36.176 degrees N, 103.449 degrees E) to the exit of Lanzhou city (36.056 degrees N, 104.020 degrees E). Measured CH4 diffusion fluxes in mainstem sites ranged from 0.01 to 2.58 mmol m-2 d-1 (mean: 0.36 mmol m-2 d-1), while ebullitive fluxes (gas bubbles) ranged from 0.01 to 18.89 mmol m-2 d-1 (mean: 0.90 mmol m-2 d-1). CO2 diffusion fluxes varied between 9.16-92.80 mmol m-2 d-1 (averaged: 39.11 mmol m-2 d-1) at these locations. Ebullition (bubble) fluxes accounted for 53.1% +/- 22.4% (range: 9.0% to 98.4%) to total CH4 emissions (diffusion plus ebullition), with peak fluxes occurring during summer, indicating its significance as a CH4 transport mechanism. Notably, both diffusion CH4 and CO2 fluxes and ebullitive CH4 rates at ditch sites substantially exceeded those in mainstream reaches. The lowest CH4 and highest CO2 concentrations were observed at a wastewater treatment site, likely resulting from the removal of high organic loads. Acetoclastic methanogenesis-the process converting acetate-derived methyl groups to CH4-was identified as the dominant production pathway in both mainstream and ditch environments. CH4 and CO2 flux magnitudes in the upper YR (Lanzhou section) were comparable to those observed in subtropical Yangtze River tributaries. These results demonstrate that anthropogenic influences significantly enhance CO2/CH4 emissions, and the lateral exports of dissolved carbon (DIC and DOC) in the main stream site was quantified., which cannot be overlooked. The findings emphasize the critical need to account for pronounced spatiotemporal variations in arid-region GHG fluxes to improve basin-scale estimates for the YR. (c) 2025 China University of Geosciences (Beijing) and Peking University. Published by Elsevier B.V. on behalf of China University of Geosciences (Beijing). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Plant-available groundwater water is well-documented. However, the impact of groundwater flux on root zone storage capacity (Sr), the maximum water storage within the subsurface root zone available for plant transpiration, remains poorly understood. In this study, we present a more conservative, lower-bound global estimate of Sr, incorporating groundwater for the first time into the deficit-based calculation of Sr using a novel conceptual method that accounts for groundwater contribution fraction. Our findings reveal widespread plant reliance on groundwater, with a mean use of at least 20 mm, equivalent to a water volume of 1700 km3. In western United States, this use can exceed 100 mm. Distinct spatial patterns in Sr emerge globally, with higher values in mountainous forests and lower values in boreal grasslands. Comparisons with observed rooting depths confirm that the deficit-based method, when incorporating groundwater, effectively predicts underground root traits. Biotic and abiotic factors critically influence Sr values, with irrigation and topographic convergence exacerbating this reduction. Groundwater-dependent ecosystems rely heavily on root-zone water storage, utilizing an average of 2151 km3 of water. Despite inherent uncertainties in input data, our study provides the first systematic evaluation of groundwater's role in shaping Sr estimates, offering key insights for water resource management and ecosystem sustainability.

Utilization of wind and solar energy is an effective approach to achieving the goal of carbon neutrality. The Gobi Desert region of China has installed and will construct massive wind and solar bases. Despite studies on the projection of wind and solar energy in the future, however, how to account for the influence of climate disasters on wind and solar energy potential, to provide references for wind and solar energy development, is the concerned issue for the both Chinese government and society. This study investigates the influences of extreme climate events, including snowfall, sand dust, cold waves, and extreme high and low temperatures on the efficiency loss of wind and solar energy over the Gobi Desert region of China under different future climate scenarios. Results suggest that the annual mean efficiency loss of wind and solar energy caused by future extreme climate events would be 15

Study region: The Yellow River Basin, China. Study focus: Extreme weather events occur frequently under global change, and the assumption of stationary for hydrological series may no longer be valid. Therefore, we proposed a new framework based on a nonstationary statistical model that incorporates machine learning for detecting spatiotemporal variations of extreme events. New hydrological insights for the region: The series of these events at most stations show nonstationary characteristics during both the base period and the change period. By optimizing and evaluating different types of nonstationary models based on the Generalized Additive Model for Location, Scale and Shape (GAMLSS), the model with the climate index (CI) and the human-induced index (HI) as covariates demonstrates superior applicability compared to the model using the CI and the reservoir index (RI). Furthermore, the higher probability of extreme flood and low flow were observed at Tangnaihai, while the lower probability of extreme low flow was identified at Huaxian. Extreme flood in the YRB show weak inter-station correlations with high spatial heterogeneity, especially between Tangnaihai and Huayuankou, while extreme low flow is generally well correlated except between Lanzhou and its downstream stations (Toudaoguai and Longmen) due to water withdrawals from irrigation districts. The results provide scientific basis for reservoir flood control, river ecological health and the safety and stability of power systems.

To quantitatively deconstruct the impacts of multiple factors,such as climate change,human activities,and vegetation dynamics,on the evolution of watershed water cycles,a decoupling framework for multi-factor influences on the water cycle in the Wei River Basin was constructed.Partial least squares structural equation moeling(PLS-SEM),Pearson correlation analysis,and trend analysis methods were used to deconstruct the direct and indirect effects of climate change,human activities,and vegetation dynamics on runoff changes in the Wei River Basin from 1982 to 2020.The results indicated that from 1982 to 2001,the combined effects of climate change and human activities on runoff in the Wei River Basin exhibited a balanced situation,with the comprehensive effect value of climate change and human activities being 0.313 and-0.315,respectively.From 2002 to 2020,the comprehensive effect value of human activities(-0.667)significantly surpassed that of climate change(0.319),becoming the dominant driving factor of runoff evolution in the basin.From 1982 to 2001,the mechanism characteristics of runoff reduction are precipitation-dominated and multi-factor collaborative,with the direct effect of reduced precipitation and the combined effects of temperature increase,vegetation restoration,and human activities jointly driving the process of runoff attenuation.During the period from 2002 to 2020,the mechanism characteristics of runoff increase are precipitation-dominated and engineering-reduction,with the significant increase in precipitation being the dominant factor driving the rise in runoff.However,the direct effect of engineering regulation suppressed the rising trend of runoff,resulting in only a slight recovery trend in runoff.

Anthropogenic pressure and climate change have severely impacted coastal ecosystems. Coastal environmental issues have become a major concern worldwide. The ecological stability of coastal waters requires the establishment of a scientific and systematic conservation program. This paper explores the relationships between riverine inputs and the nutrient structure of the coastal waters of Dalian. It also analyzes the anthropogenic activities from the watershed on riverine systems, utilizing the data collected in Dalian between 2021 and 2023. The results showed that the Bohai Sea part of Dalian coastal had the highest average DIN in spring at 0.114 mg·L-1 and highest PO4-P in autumn at 0.008 mg·L-1, while the Yellow Sea part had the highest average DIN in spring at 0.095 mg·L-1 and highest PO4-P in autumn at 0.005 mg·L-1 in 2023. In the two sea areas, significant phosphorus limitation was observed, with a notable proportion of over 74 % of monitoring stations recording the DIN/DIP exceeding 16 across the entire coastal water in autumn. The coastal waters of Dalian are heavily influenced by rivers in different seasons. There are higher concentrations of nutrients and more severe phosphorus limitation in estuaries and bays, which respond well to high nitrogen-to-phosphorus ratio nutrient flux inputs from rivers. The nutrient structure of coastal waters is also influenced by physical and biogeochemical processes and current transport. The nutrients from the rivers in the watershed are closely related to anthropogenic activities, and agriculture is an important source. The contribution of nutrient inputs from rivers in different watersheds varies significantly, with the Fuzhou River and Biliu River having the largest input fluxes of TN and TP, amounting to 145.18 and 2.10 t/month and 246.36 and 4.42 t/month in 2023, respectively. It is recommended to adhere to the "land-sea cooperation" and organically integrate the development of watershed nutrient reduction policies with the stabilization of the marine ecosystem.

Alpine reservoirs represent critical but poorly quantified sources of greenhouse gas (GHG) emissions from inland waters. This study examines spatiotemporal variations and drivers of methane (CH4) and carbon dioxide (CO2) fluxes in the Liujiaxia (LJX) Reservoir (1735 m a.s.l.), a high-altitude hydropower system in the upper Yellow River, through multi-season field campaigns (2021-2023). High-frequency flux measurements, stable isotope analyses (S13C-CH4 and S13C-CO2), and statistical modeling identified contrasting emission patterns between central and nearshore zones. Ebullition dominated CH4 emissions (54-86 % of total fluxes), with summer total fluxes surpassing spring and winter levels by factors of 2.8-5.7. Isotopic evidence revealed hydrogenotrophic methanogenesis as the principal pathway in the central zone (alpha c= 1.053 f 0.01; S13C-CH4 =-50.19 f 4.09 %o), contrasting with acetoclastic dominance in nearshore regions (alpha c= 1.028 f 0.01; S13C-CH4 =-65.04 f 6.38 %o), implicating sediment-driven methane production. CO2 fluxes exhibited pronounced seasonality, linked to organic carbon mineralization in nearshore sediments. Hydrological regulation amplified upstream GHG emissions, where CH4 and CO2 fluxes exceeded downstream values by 3.7-and 1.8-fold, respectively. A random forest model, informed by high-resolution spatiotemporal flux data, estimated annual emissions of 0.23 Gg CH4 and 20.18 Gg CO2. These findings underscore the significant influence of hydrologic management and biogeochemical processes on GHG budgets in alpine reservoirs, providing essential data to improve global models of inland water emissions.

Study Region Yellow River Basin (YRB), China. Study focus The ecohydrological variables of the YRB vary significantly under a changing environment. Therefore, it is crucial to explore that how climate change and human activities alter the evolution of ecological drought. In this study, change-point and trend-analysis were employed to identify the non-stationary characteristics of hydro-meteorological variables in the YRB. Then, the natural streamflow series was recreated using variable infiltration capacity (VIC) model and the most suitable ecological streamflow (MSES) was calculated through the non-parametric kernel density estimation (KDE) function. Finally, the impacts of climate change and human activity to the evolution on ecological drought in river were quantified. New Hydrological Insights for the Region Most of change points of streamflow series across the YRB appeared in 1985 and 2002. Then, baseline (1961-1985) and two change periods (1986-2002 and 2003-2020) were defined according to the change points. Spatially, climate change mitigated upstream ecological drought while human activities mainly affected those at downstream. The contribution of human influence on the ecological drought in river roughly increased gradually from upstream to downstream. In change period II, from Toudaoguai to Huayuankou, the rates are 87 %, 88 %, 99 %, 102 %, respectively. Overall, this study explored the driving mechanisms of ecological drought in river from the perspective of climate change and human activities, thus providing a theoretical basis for watershed ecological sustainability.

To address the limitations of process-driven models in characterizing physical mechanisms and the interpretability challenges of data-driven models in flood forecasting, this study proposes a distributed hybrid flood modeling (DHFM) framework that integrates physical mechanisms with deep learning. Differentiable diffusion wave (DW) and convolutional neural network (CNN) routing methods are introduced, which can be seamlessly integrated into the DHFM framework. A differentiable Muskingum (MK) routing method is also implemented as a benchmark. The Mishui Basin in China is selected as a case study to systematically evaluate the performance and interpretability of these three routing methods under both gauged and ungauged scenarios. Results show that the DHFM framework can effectively achieve physical parameterization across different sub-basins. Compared to the lumped Xin'anjiang hydrological model, it achieve s higher accuracy in both daily streamflow and flood simulations, while also demonstrating favorable interpretability of the embedded neural network. Under gauged scenarios, the differentiable CNN method slightly outperforms DW in terms of performance and efficiency, and significantly surpasses MK. As the number of training stations increases, model performance tends to stabilize or decline. In ungauged scenarios, CNN performs well with sufficient training data (>2 stations) but is sensitive to station selection, exhibiting a substantial performance drop with only one station. In contrast, DW and MK show greater stability. The differentiable CNN method shows potential for adaptively learning unit hydrographs based on channel attributes. The proposed DHFM framework not only enhances flood simulation accuracy but also provides novel perspectives for understanding the physical mechanisms underlying flood processes.