Search Results (4,631)

The source regions of the Yangtze and Yellow Rivers on the Tibetan Plateau provide vital water resources for downstream ecosystems and populations, yet their runoff responses to climate warming remain insufficiently understood. Based on daily runoff data from six hydrological stations in the Yellow and the Yangtze River basins during 1980–2023, this study investigates the evolution and relatively stronger statistical climatic association of runoff in the source regions of the Yangtze and Yellow Rivers. The results indicate that annual runoff exhibited an overall increasing trend, primarily driven by a significant increase in non-flood-season runoff, while flood-season runoff increased insignificantly; Low runoff indices increased significantly at most stations, reflecting enhanced low-flow conditions and increased dry-season water availability, whereas high runoff indices showed no significant trends; wavelet analysis identified dominant periodicities of approximately 13.5 and 4.8 years. Furthermore, it indicates that precipitation remains the relatively stronger partial statistical climatic association for flood-season runoff throughout the study period, with its statistical association continuously strengthening. However, under climate warming, the influence of temperature on runoff is also gradually intensifying. The preceding cold season has exhibited an increased statistical contribution of temperatures, although concurrent warm-season precipitation maintains a stable statistical contribution. These findings carry significant implications for sustainable water resource management and climate adaptation in the Asian Water Tower, underscoring the necessity to incorporate changing climatic associations into long-term hydrological planning to safeguard downstream water security and ecosystem services. Full article

Surface biomass, including litter, organic matter and ground vegetation, plays a very important role in forest ecosystems for regulating water retention and soil erosion. At the global and European temperate forest scale, no bibliometric analysis has previously been conducted, even though the scientific interest has been growing. This bibliometric analysis studied 1189 global and 178 European temperate forest articles retrieved from Scopus from 2005 to 2025 using RStudio (bibliometrix) and VOSviewer. The global output grew at an annual growth rate of almost 20.83%, coinciding with the Paris Agreement 2015 and the European Green Deal 2019 that may have brought scientific attention to climate change, hydrological process, forest ecosystem services and sustainable forest management, leading to the increases, while European research accounted for only 14.9% of global output and was mainly influenced by Mediterranean fire-related research, which limits its direct applicability to central European forest conditions. China and the USA dominated the global research output. Despite being the country with 30% of its area covered with forests, no Polish institution appeared in the top 10 contributors in both the global and European datasets. Thematic analysis showed that infiltration and surface runoff are poorly integrated with forest management and climate change research. Litter and soil organic carbon appeared as an emerging topic in the European Temperate Forest dataset. Three dominant tree species of the central European temperate forest, Scots Pine (Pinus sylvestris L.), Norway Spruce (Picea abies), and European aspen (Populus tremula L.), were each mentioned just once in 178 European publications, indicating the need for species-specific research about water retention and erosion susceptibility and surface runoff. This bibliometric analysis identified the significant species-specific, institutional, and regional knowledge gaps in European temperate forest research, most importantly, for the central European ecosystem and the dominant Polish forest. The study highlighted the limited incorporation of hydrological processes with forest management and climate adaptation strategies. Future research direction should focus on long-term field research analysing biomass changes, water retention and erosion susceptibility in the European temperate forest ecosystem, while promoting stronger international research collaborations. Full article

GRACE and GRACE-FO satellite gravimetry provide unique observations of terrestrial water storage (TWS), but their coarse effective resolution and intermittent temporal gaps limit water-resource applications at subregional and basin scales. This study presents a framework to temporally reconstruct and spatially downscale GRACE TWS anomalies for the transboundary Bug River Basin (Poland–Ukraine–Belarus), a region where in situ monitoring is limited and further disrupted by the 2022 war in Ukraine. First, missing monthly GRACE TWS anomalies (2002–2024) are imputed using a Random Forest model driven only by lagged GRACE values (1–3 months) and seasonal timing, thereby avoiding potential information leakage. Second, the continuous GRACE signal is downscaled to 0.1° using an independent set of hydroclimatic predictors with lagged and rolling features, together with elevation, land type and lithology. Model performance is evaluated under strict spatiotemporal holdouts and cross-validation. The key methodological advance is a bias-aware, block-wise mass-conserving correction that reconciles downscaled fields with the original GRACE water mass at coarse resolution. After downscaling to 0.1°, systematic residual biases between aggregated high-resolution estimates and GRACE observations are quantified monthly and redistributed within spatial blocks using river-runoff-based weights. This procedure enforces exact mass closure while preserving physically meaningful sub-grid variability. Full article

To address severe soil erosion and ecological restoration difficulties on abandoned karst sloping farmland, this study proposed and tested an innovative micro-topography modification strategy—film covering of rock outcrops. Field experiments were conducted on typical abandoned karst slopes with 20 standard runoff plots (10 covered, 10 uncovered). Compared with non-covered plots, film covering significantly improved soil properties: bulk density decreased by 6.6%, total porosity increased by 6.0%, non-capillary porosity by 29.6%, macro-aggregates (>2 mm) by 11.6%, soil organic carbon by 14.5%, and volumetric water content by 26.0%. Vegetation also benefited: aboveground biomass increased by 39.9%, root biomass by 45.3%, vegetation coverage by 12.2%, and root vertical pullout resistance by 29.7%. Consequently, surface runoff depth was reduced by 23.9% and sediment concentration by 24.8%. Correlation analysis revealed significant correlations between soil–vegetation indicators and runoff/sediment yield. Specifically, runoff depth was closely linked to bulk density and total porosity, while sediment concentration correlated strongly with total porosity, root pullout resistance, aboveground/root biomass, capillary porosity, and bulk density. This study confirms that film covering of rock outcrops improves soil–vegetation conditions at the microscale and effectively reduces runoff and sediment generation. Full article

Across the Mediterranean, most olive orchards are still rainfed and have traditionally been planted at low density on the assumption that wide spacing buffers the risk of crop failure in dry years. We tested this assumption with the process-based model OliveCan, simulating three planting densities (100, 204 and 408 trees ha⁻¹) at three contrasting sites (Córdoba, Izmir and Pisa) on shallow and deep soils under a baseline climate and two perturbations (+2 °C and −10% rainfall). Increasing density raised both yield and water productivity (WP_y, the yield-to-evapotranspiration ratio) in all studied scenarios. As density increases, soil evaporation (E_s) and runoff are reduced, and more water can be used for transpiration (E_p). Our analysis suggests that yield gains overcome E_p rises when tree density increases, resulting in a better WP_y. Deep soils out-yielded shallow ones and buffered climatic stress. Yields were robust to a moderate, evenly distributed rainfall reduction but were eroded by warming. A stability analysis showed the highest density to be the most productive across the studied site × climate scenario × simulation year × soil depth combinations, while the lowest planting density was more stable. The present work explores new possibilities for rainfed olive farmers. Fieldwork would help test the results presented here and tailor new tree densities for rainfed olive orchards. Full article

Understanding how climate change may alter river discharge in semi-arid regions is essential for sustainable water-resource management. This study assesses future streamflow in the Zat River Basin (High Atlas Mountains, Morocco) using a hybrid framework that combines machine-learning rainfall–runoff modeling, CMIP6 multi-model climate forcing, monthly quantile-mapping post-processing of simulated discharge, and an exploratory temperature-sensitivity assessment. Monthly hydroclimatic observations of precipitation, air temperature, reference evapotranspiration, and discharge were compiled from February 1962 to August 2024. The period 1962–2005 was used for model development, the 2006–2014 window for chronological validation, and the more recent observations for supplementary evaluation of climate-driven simulations. Four algorithms were compared: Gradient Boosting Regressor (GBR), Histogram-based Gradient Boosting Regressor (HGBR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Performance was assessed using NSE, KGE, RMSE, MAE, and R². GBR provided the best validation performance (NSE = 0.71, KGE = 0.80, and R² = 0.72). The selected model was then forced with CMIP6 projections under SSP2-4.5 and SSP5-8.5 to simulate streamflow to 2100. Quantile mapping was applied to the simulated discharge, rather than separately to precipitation, temperature, and reference evapotranspiration. The multi-model ensemble indicates a persistent drying tendency: relative to the historical baseline and without an additional temperature-sensitivity adjustment, mean annual discharge is projected to decline by approximately 12.1% under SSP2-4.5 and 27.3% under SSP5-8.5 by 2081–2100. Under an exploratory sensitivity case using a runoff-temperature-sensitivity coefficient of 0.04 °C⁻¹, the projected declines increase to approximately 23.3% and 44.1%, respectively. Episodic high-flow events nevertheless remain possible, suggesting a shift toward lower mean flows combined with persistent hydrological extremes. Full article

Flood occurrence in tropical regions is intensifying due to climate variability and land-use change, increasing the need for reliable flood response time estimation. Accurate prediction of flood lag time (TL)—the interval between the centroid of excess rainfall and peak runoff—is critical for flood early warning and water resource planning. However, TL estimation remains challenging in data-scarce regions because of complex interactions among watershed morphology, rainfall characteristics, and runoff generation processes. This study evaluates four machine learning (ML) algorithms—Random Forest (RF), Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—for predicting TL across twenty gauged watersheds in the Blue Nile Basin of Ethiopia. Fourteen physiographic and hydro-climatic watershed characteristics were used as predictors. Among the tested models, XGBoost achieved the highest training performance (R² = 0.98, NSE = 0.96), while RF showed better generalization in the test dataset (R² = 0.77, NSE = 0.70, KGE = 0.71). SVM produced the lowest prediction errors (MAE = 0.95; RMSE = 2.25) but had lower explanatory power (R² = 0.49). To enhance interpretability and practical applicability, ML-based feature importance was used to develop a parsimonious empirical model: TL = 0.8 + 0.011A − 0.023RI, where A is watershed area and RI is rainfall intensity. This model explained 51% of TL variability and retained much of the predictive skill of more complex ML models. The proposed hybrid ML–empirical framework provides a transparent and operational approach for flood response time estimation in tropical highland watersheds. Its broader applicability remains subject to additional watershed-level validation and regional calibration. Full article

Water scarcity and uneven spatiotemporal water distribution trigger severe supply–demand conflicts in arid irrigation districts, making rational water allocation critical to safeguarding food security and improving water use efficiency. This study selects the Jiamu Town Irrigation District in the Tailan River Basin of Xinjiang as the research case. By incorporating hydrological year types, ten-day runoff sequences, exploitable groundwater volumes, and crop irrigation priorities, a multi-objective optimization model was constructed. The two optimization objectives are minimizing total groundwater extraction and the weighted irrigation water deficit. Herein the weighted deficit equals the physical water shortage of each crop-growth-stage irrigation task multiplied by its priority coefficient, which drives the optimization iteration to prioritize water-shortage relief for high-priority irrigation demands. The key findings are summarized as follows: (1) Relative to the actual water consumption in 2023, the optimized scheme raises surface water utilization efficiency from 73.1% to 81.3% and cuts groundwater exploitation by 2.05 million m³ (from 11.09 million m³ to 9.04 milion m³). (2) Under a fixed crop planting structure, irrigation water demands across all ten-day intervals can be largely satisfied. (3) Crop irrigation satisfaction rates exceed 97% across all hydrological scenarios, with water deficits predominantly occurring in drip-irrigated pepper and orchards. The proposed optimal allocation model can effectively curb groundwater overexploitation and minimized water use conflicts among diverse crops and their critical growth stages, offering technical references for refined water resource management in arid canal–well irrigation districts. Full article

River sediments act not only as important sinks for heavy metal pollution in watersheds, but also as potential secondary sources under changing environmental conditions. Heavy metals can enter river systems through industrial wastewater discharge, agricultural non-point runoff, urban stormwater and sewage inputs, mining and smelting activities, and atmospheric deposition. During adsorption onto suspended particles, sedimentation, and resuspension, metals such as Cd, Pb, Cr, Cu, Zn, Ni, As, and Hg progressively accumulate in sediments. Because heavy metals are persistent, non-degradable, and bioaccumulative, contaminated sediments can record historical watershed pollution while also releasing metals back into overlying water under hydrodynamic disturbance, pH and redox fluctuations, organic matter mineralization, benthic bioturbation, and dredging activities, thereby threatening aquatic ecosystem stability and human health. Using a global methodological framework with particular emphasis on Chinese river basins, this review systematically summarizes key issues in the study of heavy metal pollution in river sediments, including spatial–temporal distribution and operationally defined fractionation, pollution levels and ecological risk assessment, source apportionment, and remediation and management technologies. Current evidence indicates that heavy metal contamination in river sediments exhibits pronounced spatial heterogeneity and watershed-specific characteristics. Its distribution is jointly controlled by geological background, land use patterns, source input intensity, hydrodynamic conditions, sediment particle size composition, and organic matter content. Methodologically, the field has evolved from single total concentration monitoring and exceedance-based evaluation toward integrated assessment systems that combine total concentrations, operationally defined fractionation, bioavailability, ecological risk, health risk, and source contribution. The joint use of BCR sequential extraction, the geoaccumulation index (Igeo), the pollution load index (PLI), the potential ecological risk index (RI), the risk assessment code (RAC), sediment quality guidelines (SQGs), receptor models, isotope tracing, and machine learning has substantially improved pollution identification, risk zoning, and source apportionment. Overall, research on heavy metal pollution in river sediments has shifted from descriptive judgments of whether contamination exists toward mechanistic and management-oriented questions concerning pollution sources, risk evolution, and remediation strategies. However, important gaps remain in compound pollution transformation mechanisms, regional background values and evaluation benchmarks, uncertainty in model parameters, long-term dynamic monitoring, and engineering-scale verification of remediation technologies. Future studies should strengthen multi-media, multi-scale, and long-term monitoring and further integrate fractionation analysis, toxicological effects, source apportionment models, and remediation technologies to provide a scientific basis for watershed ecological security and precision management of contaminated sediments. Full article

Extreme precipitation events have grown more common as a result of global climate change, and conventional static hazard assessments find it difficult to account for the dynamic progression of flood disasters. This study considers extreme precipitation factors for different return times and creates different extreme precipitation scenarios based on multiyear historical precipitation data and actual storm events. The study proposes a method for the dynamic assessment of regional flood hazard that takes extreme rainfall scenarios into account by simulating the dynamic flood inundation processes under each scenario using the Accumulated Runoff and Flood Estimation Model (AccRo v.1.0), iterative flow accumulation, and hydrological calculations. A dynamic assessment and zoning of flood hazards was carried out in Laiyuan County, Hebei Province. The results reveal that high-hazard zones coincide with the distribution of historically badly damaged townships, concentrated in the river valley plains along the Juma River. The results show that spatial patterns are simultaneously influenced by precipitation, terrain, and the river network. In the temporal dimension, under Scenario 3, the superimposition of the 50-year return period daily maximum rainfall at the 12th hour increased the high-hazard area by approximately 110% compared with that at the 11th hour. In addition, the non-uniform multi-peak rainfall pattern in Scenario 4 represented the rise, peak, and recession stages of the flood process. A combined assessment of water depth and flow velocity can effectively distinguish between two disaster-causing modes—deep water with low flow velocity and shallow water with high flow velocity—thereby addressing the underestimation of hazard in transition zones associated with the use of water depth as a single indicator. Full article

The Arctic is warming approximately four times faster than the global mean, accelerating retreat of marine-terminating glaciers. Changes in glacier extent are linked to environmental factors, and their accurate quantification depends on the measurement methods used. This study compares five terminus change quantification methods applied to Austre Torellbreen, analyses terminus position changes of four outlet glaciers of the Amundsenisen Glacial System—Paierlbreen, Austre Torellbreen, Vestre Torellbreen, and Recherchebreen—in SW Svalbard, over 1975–2022, and assesses environmental controls on glacier retreat. Curvilinear box and GTT emerge as the most broadly applicable methods. Multi-centreline, Rectangle box, and Curvilinear box methods form the most internally consistent group, while GTT diverges moderately from this group. The Centreline method deviates most strongly from all others and is unsuitable for short-term analysis. A ~15° change in fjord orientation caused the Rectangle box to underestimate cumulative recession by ~330 m relative to the Curvilinear box, confirming that rectilinear approaches are limited to glaciers with low fjord sinuosity. Fjord depth and surge phase are likely key modulators of the environmental signal: deep-water, marine-terminating fronts show the strongest associations with sea surface temperature and runoff, whereas shallow fjords and restricted near-terminus water circulation weaken the oceanic imprint. Land-terminating sections of glaciers retreat approximately 3.4 times more slowly than marine counterparts and show no significant annual correlations with environmental variables. The terminus record constrains the timing and magnitude of surge-related frontal advance at Paierlbreen (1993–1995, ~280 m), Vestre Torellbreen (2008–2013, ~170 m), and Recherchebreen (2018–2020, ~660 m). Full article

This study investigates the influence of rainfall variability on runoff generation in the Upper Ing River Basin and inflow to the Mae Tum Reservoir in northern Thailand using the physically based Soil and Water Assessment Tool (SWAT) version 2012. In small mountainous watersheds, water supply reliability for irrigation and domestic use—particularly for unmonitored royal initiated projects like the Mae Tum Reservoir—has become a critical concern due to shifting climatic extremes. A SWAT model was developed using detailed spatial data on topography, land use, and soil characteristics together with long-term daily climate and streamflow records. The model performance at Station I.17 was evaluated through calibration and validation using the R², Nash–Sutcliffe Efficiency (NSE), and percent bias indices. Rainfall regimes were classified into dry, normal, and wet years based on the mean and standard deviation of 25-year gauge records to drive scenario simulations. The calibrated model reproduced seasonal runoff patterns satisfactorily (monthly NSE up to 0.685 and R² up to 0.712). The simulations demonstrated the strong sensitivity of both the runoff at Station I.17 and reservoir inflow to interannual rainfall differences, with the annual runoff ranging from 71.5 to 379.7 million m³ and the annual inflow to Mae Tum Reservoir ranging from 28.84 to 48.33 million m³. These findings demonstrate that physically based spatial modeling can effectively replace traditional empirical operating rules, providing a highly transferable framework for runoff forecasting, reservoir inflow assessment, and climate responsive water resources planning in data-scarce tropical mountainous basins. Full article

Riparian land use and land cover (LULC) exerts scale- and season-dependent controls on surface water quality, yet its influence in regulated agricultural–urban rivers is poorly characterized. We combined seasonal t-tests, one-way ANOVA, and redundancy analysis (RDA) at three riparian buffer scales (500, 1000, and 2000 m) to examine discharge, dissolved oxygen (DO), temperature, turbidity, and pH at four USGS stations along the Kansas River mainstem (2019–2026). DO and temperature showed the strongest seasonal contrasts: DO was 3.1–3.7 mg/L higher in the dry season and temperature 13–15 °C higher in the wet season, a coupling central to aquatic habitat suitability. Turbidity rose significantly in the wet season, consistent with agricultural runoff and sediment mobilization, whereas discharge showed no significant seasonal difference at three of four stations, reflecting upstream reservoir regulation. Spatial ANOVA detected station-level differences only for wet-season DO ($F_{3,28}=4.91$, $p=0.007$), which was lowest at the downstream urbanized station. RDA linked agricultural cover to turbidity and urban cover to reduced wet-season DO, although permutation tests were non-significant ($p\ge 0.42$) at $n=4$ replicates. Seasonality and riparian LULC jointly shape water quality along this regulated river, and the 500 m buffer is the most spatially discriminating scale for land-cover assessment. Full article

Climate change may alter both basin-outlet water availability and runoff responses within large semi-arid tributaries. We evaluated climate-driven runoff changes at four gauged catchments in the Jinghe River Basin using a 16-gauge, multi-gauge EA-LSTM. Thirty-one NEX-GDDP-CMIP6 model–grid configurations were evaluated against 1960–2014 observations from 66 stations, and four models with strong historical statistical agreement and complete scenario records formed the screened ensemble. Native NEX-GDDP-CMIP6 forcing was used directly. At the Zhangjiashan outlet, test-period NSE and KGE were 0.823 and 0.615, respectively, while PBIAS was −31.6%. Under late-century SSP585, annual runoff decreased at Jingheyuan and Zhanghe but increased at Jingcun and Zhangjiashan, ranging from −16.7% at Zhanghe to +16.8% at Jingcun; flood-season responses showed a similar upstream–downstream contrast. At Zhangjiashan, screened-ensemble means were 0.2–11.5 percentage points lower than the corresponding expanded-ensemble means across the scenario–period combinations. Within the present input set and learned relation, precipitation and runoff changes were associated (Pearson r = 0.918), although this does not establish physical attribution. Full article

Soil erosion is a critical global challenge and presents particularly alarming characteristics in the Amazon–Cerrado transition in Mato Grosso, a leading agricultural state in Brazil. Therefore, soil and water losses were evaluated under three soil cover conditions for corn and pasture cultivation (with vegetation cover, without vegetation cover, and without vegetation cover with soil scarified to a depth of 0.10 m) and four precipitation patterns (Advanced, Intermediate, Delayed, and Constant). The results showed that both soil cover and rainfall patterns directly influence the erosion processes. Soil loss increased up to sixfold under the Intermediate compared to the Constant rainfall, highlighting the strong influence of rainfall temporal distribution on erosion dynamics. The highest maximum runoff rates (MRR) and soil losses (SL) were recorded in tilled plots under maize cultivation, reaching 98.57 mm h⁻¹ and 5.90 g m⁻², respectively, under the intermediate pattern. In pasture areas, SL followed a similar pattern to the maize area, with maximum values of 6.96 g m⁻², but the MRR was recorded under the advanced pattern and in plots with cover (89.71 mm h⁻¹). This may be attributed to soil management conditions in pasture areas. Advanced and Intermediate patterns resulted in greater soil losses (3.78 and 2.04 g m², respectively), highlighting the impact of peak intensity timing on soil erosion. Greater soil losses were observed at the pasture experimental site than at the maize experimental site. Because the experiments were conducted at different locations with contrasting soil and management conditions, these differences should not be interpreted as being caused exclusively by crop type. The current study reinforces the need for erosion control and management strategies that account for natural variations in rainfall and soil cover to mitigate the negative impacts of land degradation on agricultural production and environmental sustainability. Full article