Search Results (2,097)

Soil moisture (SM) is a pivotal state variable of the terrestrial hydrosphere, modulating energy partitioning, agricultural productivity and extreme-event propagation. This study analyzes 43 years (1982–2024) of data to assess soil moisture (SM) dynamics in the Yellow River Basin (YRB). Results indicate a statistically significant basin-wide SM decline across weekly, monthly, and annual scales, with grid-scale slopes ranging from −2.26 × 10⁻⁴ to 8.32 × 10⁻⁵ m³ m⁻³ month⁻¹. Spatially, non-farm areas retain higher SM than cultivated lands, with a distinct upstream-to-downstream variability pattern. While alpine headwaters show moistening, pervasive drying characterizes mid- and lower-catchments. Critically, transitional landscapes are approaching tipping points, risking shifts into persistently wetter or drier stable states where minor perturbations could lock ecosystems into new conditions. This underscores the urgent need for targeted climate-adaptation interventions. Generalized additive modeling identifies surface net solar radiation, soil temperature, and vapor pressure deficit as dominant drivers across multiple temporal scales. Their respective contributions, averaged across the basin, accounted for 29.4%, 25.3%, and 23.0% of the explained variance. Additionally, actual evapotranspiration emerged as a significant driver on the weekly scale, particularly within the center of the basin. These findings enhance process-based understanding of SM variability and provide a scientific foundation for adaptive water-resource management in the YRB. Full article

Activated carbons are usually prepared from natural precursors (e.g., fruit stones or nutshells) by carbonization and activation processes carried out at 400–1000 °C. They exhibit well-developed porosity, and chemical activation introduces hydrophilic functional groups on their surface, providing excellent sorption properties. However, the high temperatures required during thermal treatment increase production costs. In this work, cost-reducing methods for preparing carbon sorbents are proposed. Carbonization of H₃PO₄ activated waste pistachio nutshells was performed using classical pyrolysis (500 or 550 °C, 30 min, N₂ atmosphere) and microwave treatment (power 1000 W, 20 min). The properties of the synthesized carbons were characterized using thermogravimetry and spectroscopic techniques including infrared (ATR), Raman, photoelectron (XPS) spectroscopies, and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). Porous structure parameters were determined using nitrogen adsorption experiments. The efficiency of Pb²⁺ removal from spiked ultrapure, tap and river water was evaluated by batch sorption experiments and inductively coupled plasma–mass spectrometry. The most porous carbons were those prepared at 500 and 550 °C, with specific surface areas of 910 and 256 m²/g, respectively. Surface phosphates increased the Pb²⁺ sorption efficiency to 99% from ultrapure water, at an initial concentration of 300 µg Pb²⁺/L. The material obtained with the microwave method was not fully carbonized and remained nonporous, but it also exhibited 99% Pb²⁺ uptake from ultrapure water due to the presence of oxygen-containing surface groups. The Pb²⁺ removal from spiked tap and river water reached up to 84% and 94%, respectively, at the spiking level of 300 µg Pb²⁺/L. Full article

The middle and lower reaches of the Yangtze River Basin occupy a strategically pivotal position in regional development; yet extreme drought-runoff events pose severe threats to water supply and ecological security. Despite this, systematic research gaps persist, including the lack of a unified definition, standardized identification criteria, and clear understanding of formation mechanisms for extreme drought-runoff. To address these limitations, this study focused on extreme drought-runoff in the basin, utilizing 1956–2024 discharge data from four mainstream hydrological stations and meteorological data from 171 stations. Quantitative discrimination criteria were established via Pearson-III frequency analysis; meteorological characteristics were analyzed using the Meteorological Drought Comprehensive Index; and formation mechanisms were explored through partial correlation analysis and multiple linear regression. This study innovatively proposed a basin-wide three-level quantitative discrimination criterion for drought-runoff based on the June–November flow frequency of key mainstream stations, which is distinguished from single-indicator drought identification methods (SPI/SPEI/SSI) by integrating basin-scale hydrological coherence and seasonal drought characteristics. The results revealed basin-wide extreme drought-runoff in 2006 and 2022, severe drought-runoff in 1972 and 2011, and relatively severe drought-runoff in 1959, 1992, and 2024. Typical extreme drought-runoff events were characterized by sustained low precipitation and high temperatures. Meteorological factors emerged as the primary driver during June–September, while reservoir operation and riverine water intake played secondary roles. Notably, the large-scale reservoir group in the Yangtze River Basin (53 key control reservoirs) helped alleviate drought-runoff impacts from December to May (non-flood season) via water supplementation. These findings provide a robust scientific basis for precise drought-runoff prediction and the development of targeted adaptation strategies in the Yangtze River Basin. Full article

Reconciling hydropower development with aquatic biodiversity conservation is a central challenge for sustainable river management worldwide. Cascade dam configurations, in which multiple impoundments are arranged in series along a single channel, impose longitudinal environmental gradients that restructure biological communities across trophic levels. Whether the resulting multi-trophic responses are independently driven by shared abiotic gradients (environmental filtering) or mechanistically coupled through direct food-web interactions (trophic cascading) remains unresolved. We surveyed phytoplankton, zooplankton, and benthic macroinvertebrates simultaneously at seven stations along a 430 km gradient downstream of Danjiangkou Dam in the Hanjiang River, the largest tributary of the Yangtze River and the source of China’s South-to-North Water Diversion Middle Route, over eight seasonal campaigns (2015–2017). Variance partitioning, piecewise structural equation modeling, Mantel tests, and co-occurrence network analysis were applied to partition environmental and trophic pathways. Environmental filtering dominated community restructuring at all three trophic levels, while the biotic proxy for direct trophic interactions explained less than 0.4% of community variation, consistent with weak detectable trophic coupling at seasonal resolution. Distance from Danjiangkou Dam shaped downstream transparency and turbidity gradients that mediated trophic-level-specific responses along distinct environmental axes (pH and water temperature for phytoplankton, conductivity for zooplankton, and transparency for benthic macroinvertebrates). Benthic macroinvertebrates were systematically decoupled from the pelagic analytical framework, absent from the cross-trophic co-occurrence network and structured more by spatial configuration than by water-column variables. Hub species in the network were associated with downstream mineralized conditions, confirming that network architecture reflects shared environmental preferences rather than biotic interactions. These findings support a management shift from single-dam mitigation toward cascade-scale coordination of environmental flow regimes, sediment connectivity, and substrate restoration as integrated strategies for sustaining multi-trophic biodiversity in regulated rivers. Full article

As large-scale underground storage infrastructure, deep tunnels exhibit distinct water quality dynamics driven by ground temperature gradients. Currently, there is limited investigation into water quality modeling for deep tunnel systems. Unraveling the correlation between temperature–depth gradients and water quality evolution is crucial for the operation and management of such systems. In this study, field experiments were carried out in the Qianhai–Nanshan Deep Tunnel to investigate complex water quality behavior, leading to the development of chemical oxygen demand (COD) and ammonia nitrogen (NH₃–N) models that incorporate temporal variation, temperature, and burial depth. Results indicate that temperature is the dominant factor influencing water quality in deep tunnel storage. Increased ground temperature promotes the degradation and mass transport of pollutants within the tunnel system. Owing to temperature–depth effects, the deeply buried Qianhai tunnel significantly reduces river discharge pollution after water storage, with COD and NH₃–N removal rates reaching 74.9% and 26.8%, respectively. Temperature-controlled experiments showed that COD and NH₃–N reduction rates varied between 60–94% and 10–30% across a temperature range of 20–34 °C. The proposed model was validated against experimental data, achieving Nash–Sutcliffe efficiency coefficients of 0.7–0.8. This study provides a methodological foundation for simulating complex aquatic environments and offers a decision-support tool for optimizing the operational strategies of deep tunnel systems. However, the model’s current generalization capability is constrained by the limited experimental conditions (20–34 °C, 12 days) and the lack of experimental replicates, which should be systematically addressed in future studies. Full article

The Juncal Norte Glacier (33°00′ S, 70°06′ W) is in the Dry Andes of central Chile within the Juncal Basin, a headwater watershed of the Aconcagua River, a semi-arid region experiencing an ongoing megadrought since 2010 and a 37% reduction in streamflow relative to pre-1990 baselines. This study provides the first glacier-specific annual melt and runoff estimate for Juncal Norte during mature megadrought conditions. Mass balance was estimated using a temperature index (positive degree day, PDD) model calibrated with automatic weather station (AWS) air temperature data and glacier hypsometry, assuming limited snow accumulation given that 2018–2019 precipitation and snow water equivalent (SWE) were extremely low relative to the long-term mean. Basin runoff was evaluated using a closure method comparing proglacial sub-basin-integrated discharge with modeled glacier melt volumes. Modeled glacier melt for 2018–2019 was equivalent to approximately 30% of observed annual discharge at the proglacial sub-basin, a disproportionate contribution given the glacier covers only 2.7% of the total basin area. The lower ablation zone (2900–4000 m), comprising 30% of glacier area, produced 90% of total melt volume. A + 1 °C temperature perturbation increased glacier-wide melt by 21.4%, confirming high climatic sensitivity. These results underscore the glacier’s critical but increasingly vulnerable buffering role for downstream water availability in the Dry Andes. Full article

River water quality assessment has traditionally been conducted using univariate or threshold-based approaches; however, the exploration of extremes assessment under bivariate water quality variables has been limited by many studies. Understanding the compound extremes of low river discharge (Q) and elevated river water temperatures (RWTs) resulting from climatic variability is essential for effective water quality management and protection of the river. This study investigates the joint behaviour of RWTs and Q in six Indian rivers: Kaveri, Mahi, Sabarmati, Vardha, Bhadra, and Yamuna. The Weibull-3P and Generalised Extreme Value (GEV-3P) distributions best fit for Q and RWTs, respectively. The adequacy of eighteen different parametric copula classes was evaluated. The Gaussian copula provided the best fit for the Vardha River, the Frank copula for Bhadra, and the BB8 copula for the Yamuna River. The evaluation of joint return periods (RPs) and conditional distributions has identified notable spatial variability in compound hydrological and thermal extreme hazards. The semi-arid Vardha River showed the shortest RPs for simultaneous low Q and high RWTs, indicating a greater likelihood of combined extremes. Conversely, the monsoon-fed Bhadra River displayed moderate hazard levels, while the Himalayan-fed Yamuna River had the longest joint RPs and the lowest conditional probabilities. This suggests that simultaneous extreme drought and heat events are less likely in the Yamuna basin, although significant risks remain for less severe thresholds. Full article

In 2013, plants tentatively identified as Potamogeton nodosus Poir. were discovered in the Biebrza River (NE Poland). In this study, the authors confirm the presence of P. nodosus by collecting new specimens at the original location and analyzing their microscopic characteristics, an essential step due to significant overlap in macromorphological traits with the closely related P. × fluitans complex. Additionally, new occurrences of the species within Biebrza National Park are reported, and the possibility that its spread is linked to rising river water temperatures is discussed. The authors provide evidence of an increasing average water temperature in the Biebrza River and of a northbound expansion of P. nodosus in Europe. Given similar trends observed elsewhere in Northern Europe, it is likely that P. nodosus will continue to expand its range northward in response to ongoing climate change. Full article

Analysis of the hydroclimatic variations in complex topographic and climatic regimes is important in determining the freshwater availability and its response. Although several previous studies have assessed the changing patterns of hydroclimatic variables in South Asian River basins, most of them have considered traditional statistical methods, which may inadequately reflect potential non-linear hydroclimatic trends. This study determines long-term variations in precipitation, temperature, and streamflow in the Soan River Basin of Pakistan, using three decades of in situ records (1991–2020). A non-parametric (Mann–Kendall) trend test along with an artificial neural network (ANN) approach was used to check the linear and non-linear trends. The results exhibited that the basin was getting warmer at a consistent rate, although the amount of precipitation varied significantly with location and season. The annual average amount of precipitation over the entire basin was decreasing at the rate of −7.33 mm/year. As compared to the westerly season, the trend of monsoon precipitation was less certain. Changes in streamflow patterns generally demonstrated the consequences of changing precipitation and rising temperature patterns. The annual average streamflow was decreasing at the rate of −0.47 (−1.30) m³/year, as per the results of MK (ANN). A moderate positive correlation between precipitation and streamflow indicates that precipitation mainly governed the flows in the basin. The results of the MK test and the machine-learning approach demonstrated the similar decreasing tendencies of hydroclimatic variables. However, the ANN approach more precisely demonstrates the non-linear behavior of hydroclimatic variables. It was concluded that the streamflow patterns were considerably responsive to the warming of the Soan River Basin, as well as to the changing behavior of precipitation. These findings emphasized the significance of integrating statistical and machine-learning approaches to enhance the comprehension of hydroclimatic trends. Results of this research could be applicable in sustainable management and planning of the water resources within the basin. Full article

Coal-fired power plants can adversely affect aquatic ecosystems through wastewater discharge, waste landfills, and the atmospheric deposition of toxic substances released during coal combustion. These processes degrade the water quality of nearby surface and underground water bodies. The study presents the impact of the coal-fired power plant Contour Global Maritza East 3 on the ecological status of the Sokolitsa River, reflected by changes in the composition and structure of the sensitive phytobenthos and macrozoobenthos communities and supporting environmental variables, including water temperature, pH, dissolved oxygen, conductivity, nutrients, sulfates, calcium, and calcium carbonate hardness. Methods for monitoring and assessing the ecological status of surface water bodies compliant with European and national legislation were applied to the studied biological quality elements and key physicochemical variables. Historical monitoring data from a ten-year period, 2013–2022, together with data collected during the study in 2023 and 2024 were analyzed and evaluated. The results indicated a significant increase in most physicochemical variables downstream of the CFPP compared with the upstream site, including water temperature, conductivity, calcium carbonate hardness, calcium, sulfates and nitrogen (N) nutrients (ammonium N, nitrite N, nitrate N, total N). The ecological status of the river deteriorated, as indicated by the negatively affected aquatic habitats and the changes in the taxonomic richness and abundance of the studied organism groups. Full article

As key headwater regions of the upper Yangtze River, the Yalong and Dadu River basins are expected to experience highly uncertain hydrological responses under climate warming. However, the nonlinear and spatially heterogeneous evolution of streamflow across multiple time-frequency scales remains insufficiently understood. In this study, a SWAT model driven by CMIP6 climate projections under four shared socioeconomic pathways (SSP1-2.6 to SSP5-8.5) was coupled with multivariate wavelet coherence, spatial wavelet transform, and change-point detection methods to investigate the spatiotemporal evolution of streamflow and extreme risks during 2017–2100. Results indicate that precipitation is the primary driver of streamflow variability, with streamflow responding rapidly, while air temperature mainly regulates seasonal intensity via snowmelt. Streamflow seasonal intensity exhibits a northwest-southeast gradient, with low variability upstream and high sensitivity downstream, reflecting precipitation-concentrated, forested canyons where rapid lateral flow and dry-season evapotranspiration amplify flow contrasts. Moreover, hydrological nonstationarity and extreme risks are projected to intensify, with structural regime shifts emerging in the 2040s–2050s and extreme high-flow magnitudes doubling under SSP5-8.5, accompanied by more frequent drought-flood alternations. These findings highlight an upstream buffering-downstream sensitivity pattern, emphasizing the need for spatially differentiated water resources management under nonstationary climate conditions. Full article

The thermal history of petroliferous basins controls the thermal evolution of source rocks and the diagenetic evolution of reservoirs. However, although various thermal events are common in such basins, previous studies have largely focused on the outcomes of thermal anomalies rather than systematically evaluating the spatiotemporal extent of their thermal effects. This oversight has impeded accurate assessment of source rock maturation and the timing of hydrocarbon accumulation. This study takes the Baiyun Deep Water Area in the Pearl River Mouth Basin as a case study, aiming to identify types of thermal events and systematically evaluate the extent of their impacts using geologic thermometers, numerical simulations, and measured data. Magmatic activity and hydrocarbon charging are two widely distributed types of thermal events in this area. Apatite fission track (AFT) data reveal two magmatic underplating events in the southern part of the area at 20 Ma and 10 Ma, which led to a rapid increase in vitrinite reflectance (R_o) in the overlying strata. COMSOL Multiphysics 6.2 simulations of the B6-1 diapir show that its thermal impact extends laterally up to 10 km, with the Wenchang Formation source rocks within 2 km of the diapir rapidly heating to 310 °C and reaching over-maturity. Abnormally high homogenization temperatures recorded by saline inclusions associated with hydrocarbon inclusions provide evidence of thermal anomalies induced by hydrocarbon charging. By reconstructing the trapping depths of these inclusions, the timing of their formation was determined. Comparison with normal burial-thermal histories indicates that their homogenization temperatures are 20–30 °C higher than the ambient formation temperatures. Current thermal anomalies in the Enping Formation reservoir of Well K18-1, caused by ongoing hydrocarbon charging, were simulated using COMSOL. The results show that hydrocarbon charging only causes mild thermal anomalies confined to the reservoir and adjacent strata, with a temperature increase of about 29 °C. Present-day measured vitrinite reflectance data further confirm that hydrocarbon charging does not lead to an increase in R_o. Clarifying the types and effects of thermal events is essential for accurately reconstructing the thermal evolution of source rocks and the history of hydrocarbon accumulation. This study provides a new methodology for geothermal field research in petroliferous basins. By integrating AFT, R_o, and fluid inclusion analyses, we reveal past thermal events, and through numerical simulation, quantify the spatiotemporal influence of magmatic activity and hydrocarbon charging on the geothermal field. Full article

Vegetation restoration in water-limited regions typically increases evapotranspiration (ET) while reducing runoff. Over the past four decades, Daihai Lake in China’s northwest inland river basin has experienced significant shrinkage. Previous studies attribute this primarily to climate change and water resource exploitation, yet the impact of vegetation dynamics remains insufficiently examined. This study analyzed changes in the water budget across different vegetation types in the Daihai Lake Basin, based on remote sensing-derived precipitation and ET data, and employed correlation analysis to examine the relationships between environmental factors (such as climate change, afforestation projects, and water-saving irrigation) and lake shrinkage. Our findings revealed that afforestation has expanded forest cover by 69.42 km² since 2000, accounting for 73.95% of the total forest area. Notably, forest ET demonstrated the strongest negative correlation (r = −0.89, p < 0.001) with lake area among all vegetation types. Grasslands emerged as the primary water-surplus vegetation, contributing 81.34% to the basin’s total water surplus. The synergistic effects of precipitation reduction, temperature increase, and enhanced ET from forest expansion drove the shrinkage of the lake. These results highlight the need for science-based vegetation management in arid and semi-arid regions, where we recommend adopting shrub-grass combined restoration approaches to enhance the sustainability of ecological restoration. Full article

Lakes on the Inner Mongolia–Xinjiang Plateau (IMXP) are increasingly vulnerable to eutrophication under climate change and human pressure, yet long-term monitoring remains limited by sparse field sampling. Here, we reconstruct multi-decadal trophic dynamics across the IMXP using Landsat time series and temporally transferable machine-learning models and further quantify the underlying natural and anthropogenic drivers. We compiled monthly in situ water-quality observations (chlorophyll-a, Chl-a; total phosphorus, TP; total nitrogen, TN; Secchi depth, SD; and permanganate index, COD_Mn;) and calculated the trophic level index (TLI). After rigorous quality control and monthly aggregation, we compiled a dataset of 1345 matched lake–month samples spanning 2000–2024, and divided it into a training set (n = 1076; ≤2019) and an independent test set (n = 269; 2020–2024) to evaluate temporal transferability. We utilized Google Earth Engine to generate monthly surface reflectance composites from Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2. Four supervised regression algorithms—ridge regression (RR), support vector regression (SVR), random forest (RF), and eXtreme Gradient Boosting (XGBoost)—were trained to estimate TLI. On the independent test period, XGBoost performed best (R² = 0.780, RMSE = 3.290, MAE = 1.779), followed by RF (R² = 0.770, RMSE = 3.364), SVR (R² = 0.700, RMSE = 3.842), and RR (R² = 0.630, RMSE = 4.267); we then used XGBoost to reconstruct monthly and yearly TLI for 610 perennial grassland lakes from 2000 to 2024. From 2000 to 2024, the annual mean TLI (48–49) across the IMXP exhibited a statistically significant upward trend (slope = 0.0158 TLI yr⁻¹; 95% confidence interval (CI) = 0.0050–0.0267; p = 0.006). Meanwhile, spatial heterogeneity was distinct (TLI: 41.51–59.70). High values concentrated in endorheic and desert–oasis basins (e.g., Eastern Inner Mongolia Plateau, >51), whereas lower values characterized high-altitude regions (e.g., Yarkant River, <45). Overall, trends ranged from −0.49 to 0.51 yr⁻¹, increasing in 54% of lakes (15.6% significantly) and decreasing in 46% (15.4% significantly). Attribution analyses identified NDVI (33.92%) and temperature (21.67%) as dominant drivers (55.59% combined), followed by precipitation (13.99%) and human proxies (30.42% combined: population 10.66%, grazing 10.31%, built-up 9.45%). Across 53 sub-basins, NDVI was the primary driver in 28, followed by temperature (11), population (7), precipitation (3), grazing (3), and built-up land (1); notably, the top two drivers explained 56.6–87.1% of variations. TWFE estimates revealed bidirectional NDVI effects (significant in 31/53): positive associations in 22 basins were linked to nutrient retention, contrasting with negative effects in nine basins associated with agricultural return flows. Temperature effects were significant in 15 basins and predominantly negative (14/15), except for the Qiangtang Plateau. Overall, eutrophication risk across the IMXP lake region reflects the combined influences of climatic conditions, vegetation conditions, and human activities, with their relative contributions varying among basins. Full article

Climate change is altering the frequency, duration, and seasonality of low flows, which are critical for water availability, ecosystem functioning, and river management. Low-flow characteristics, defining the minimum, often seasonal, flow levels in rivers or streams primarily fed by groundwater, snow or glacier melt, or lake drainage, are essential for assessing hydrological droughts and water resource vulnerability. In the Upper Vistula River Basin, variable precipitation and rising air temperatures increase the risk of droughts, impacting both natural systems and human water use. This study analyzed long-term trends in annual low flows and associated parameters, including drought frequency, duration, and deficit volume, across 41 small- and medium-sized catchments. Two datasets were considered: 25 stations with 58-year daily discharge records (1961–2019) and 41 stations with 38-year records (1981–2019). Low flows were identified using the threshold level method (TLM) at 70% and 90% exceedance (FDC70 and FDC90). Trends were assessed with the Mann–Kendall test, and spatial drought patterns were mapped to evaluate regional variability. Deep and shallow low flows occurred at all analyzed cross-sections. For the period 1961–2019, deep low flows (FDC90) occurred almost annually in 18 of the 25 cross-sections since 2012. Statistically significant increasing trends in deep low-flow parameters were detected in five cross-sections for 1961–2019 and in seven cross-sections for 1981–2019. Shallow low flows (FDC70) occurred in all sections; four rivers exhibited annual shallow droughts during 1961–2019, whereas 12 rivers showed annual events in 1981–2019. Summer droughts predominated over winter events, reflecting enhanced evapotranspiration and higher seasonal water demand. These findings highlight the relevance of analyzing low-flow parameters for understanding hydrological droughts. Such information can support water resource management, planning, and ecosystem protection under variable climatic conditions. Full article