Search Results (2,190)

This study investigates the transformation of river runoff and its sensitivity to changes in large-scale atmospheric circulation in the Zhaiyk–Caspian water management basin during the period of 1951–2023. The analysis is based on hydrometeorological observations data, the Vangengeim–Girs classification of macro-circulation patterns, and the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices. Correlation analysis, the Mann–Kendall test, Sen’s slope estimator, and the Pettitt test were applied to identify trends, structural shifts, and the spatial coherence of hydroclimatic changes. The results show that interannual variability in river runoff is characterized by a degree of spatial coherence, with correlation coefficients between annual streamflow records at most gauging stations reaching up to 0.95. It is demonstrated that the most pronounced changes in the hydrological regime occur during the cold season and are expressed in a statistically significant increase in winter runoff, while no significant long-term trend in annual runoff is observed. Structural shifts in winter runoff are predominantly associated with the late 1990s, whereas changes in the temperature regime are detected earlier and exhibit spatial coherence. The findings indicate that the contemporary transformation of river runoff is primarily driven by rising air temperatures and the associated intra-annual redistribution of flow. 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

In response to the complex erosion environment caused by periodic water level fluctuations, dry–wet cycles, and long-term water flow scouring on the Yangtze River bank, three typical soil-fixing and bank-protecting plants, Cynodon dactylon, Carex breviculmis, and Digitaria sanguinalis, which can adapt to both aquatic and terrestrial conditions, were selected for planting experiments. Tests on root–soil composite shear strength, disintegration, and water flow scouring were conducted to investigate the effects of different bank-protecting plants on bank stabilization. The results show that: 1. The root systems of the three plants significantly enhance the soil shear strength at various soil depths, but the reinforcing effect decreases with increasing soil depth. The cohesion strength of the root–soil composites ranks as Carex breviculmis > Digitaria sanguinalis > Cynodon dactylon, with maximum increases of 54.83 kPa, 20.66 kPa, and 6.5 kPa, respectively, equivalent to 3.16, 1.82, and 1.26 times that of bare soil. 2. Under dry–wet cycling, the water stability of the root–soil composites is significantly higher than that of bare soil. The disintegration residual rate of Cynodon dactylon and Digitaria sanguinalis decreased from 81.76% to 38.23% and from 80.18% to 34.34%, respectively, whereas Carex breviculmis showed only a slight decrease from 80.41% to 75.1%. Carex breviculmis exhibits the strongest stability and is least affected by dry–wet cycles, while the water stability of Cynodon dactylon and Digitaria sanguinalis declines noticeably with increasing cycle numbers. The plants’ ability to improve soil water stability ranks as Carex breviculmis > Cynodon dactylon > Digitaria sanguinalis. 3. The enhancement of bank erosion resistance is mainly attributed to the formation of a root-reinforced network, which strengthens the soil through root–soil interlocking and anchorage, thereby increasing resistance to flow-induced shear stress and reducing particle detachment under hydraulic action. The bank erosion resistance index ranks as Carex breviculmis > Cynodon dactylon > Digitaria sanguinalis, and decreasing with increasing runoff velocity. Compared to bare soil slopes, the maximum enhancement effects on bank erosion resistance are 75.1%, 63.3%, and 54.2% respectively. 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

A continuous integration framework and methodology for hydrological modeling is proposed that integrates model sensitivity analysis with real-time sensor tasking to prioritize data collection in regions and periods of high hydrological variability and drive model refinement. The Cahaba River Watershed in central Alabama serves as a case study to develop this approach. To this end, a benchmark Soil and Water Assessment Tool (SWAT) model (30 m DEM) was refined with high-resolution spatial datasets in QGIS, including 1 m DEMs, NLCD land cover, and SSURGO soil data. The refined model significantly enhanced subbasin delineation, increasing granularity from 8 to 99 subbasins, thereby improving representation of slope, runoff, and storage variability across heterogeneous landscapes. Sensitivity analyses were performed to evaluate the influence of DEM resolution and curve number (CN) perturbations on hydrologic responses, including retention, flow partitioning, and dominant flow direction. High-resolution DEMs (≤5 m) captured microtopographic features that strongly affect infiltration and surface runoff, while coarser DEMs (≥20 m) systematically underestimated retention and smoothed hydrologic gradients. The higher-resolution DEMs can be used to selectively improve the model at certain hotspots/areas of higher sensitivity. Localized flow simulations demonstrated that fine-scale terrain data substantially improve model realism, with up to 58% greater retention captured in 10 m DEMs compared to 30 m DEMs. The results confirm that aligning sensor placement and model refinement with spatially explicit sensitivity zones enhances both predictive accuracy and computational efficiency. The proposed continuous integration approach provides a scalable pathway for coupling high-resolution modeling with adaptive sensing in watershed management and supports future integration of real-time data assimilation for continuous model improvement. Full article

As the global energy transition accelerates, clean energy development has surged. However, accurately modeling correlations and uncertainties of hydro, wind, and photovoltaic energy remains challenging in long-term scheduling for energy complementarity. This study employs Latin hypercube sampling and Cholesky decomposition to capture the temporal correlations of water runoff, wind, and photovoltaic resources. It generates numerous scenarios for uncertainty simulation. The scenario set is reduced based on probability distance while maintaining a high-fidelity approximation. A stochastic dual-objective model is proposed for long-term multi-energy complementary system scheduling (LMCS), aiming to maximize expected revenue considering carbon emission costs while ensuring minimum power output guarantees. An evolutionary algorithm—namely, an orthogonal multi-population evolutionary (OMPE) algorithm based on orthogonal design and a multi-population search framework—is introduced, along with constraint-handling strategies. Three annual-regulation hydropower stations in the Hongshui River Basin serve as a case study. The experimental results indicate that generated scenarios capture temporal characteristics with high accuracy. The proposed algorithm efficiently solves the LMCS problem, achieving average increases of 5.46% and 3.89% in revenue and minimal output compared to benchmarks. The validation results demonstrate that orthogonalization-based initialization, recombination operators, and dominance rules significantly enhance OMPE performance. Sensitivity analysis indicates that economic efficiency and risk trade-offs can be adjusted by varying scenario numbers. Full article

Characterizing surface runoff and the transport process of non-point source pollutants (NSPs) carried by this runoff is crucial for identifying key source areas, estimating pollution loads entering water bodies, and implementing pollution control, which is particularly important in regions dominated by smallholder farming in China. Currently, most of the commonly used NSP models originated from international countries and have shortcomings such as high data requirements, high generalization degrees, and requiring the calibration of numerous parameters in the application process. Therefore, a distributed non-point source pollution model based on the time-varying gain and stormwater runoff response was constructed, designed for application at the watershed scale. This study describes the construction of the model, introducing its principles and structure through three key modules: a rainfall–runoff module, a soil erosion module, and a pollutant migration and transformation module. The proposed model was used to simulate the rainfall–runoff, soil erosion, and nutrient migration and transformation processes at different spatiotemporal scales. Although it achieved the best performance at the monthly and annual scales, its simulation results at the daily and hourly scales still met the relevant requirements, with relative errors within 20% and Nash–Sutcliffe Efficiency (NSE) coefficients of approximately 0.7. The annual sediment delivery ratios for the Yangliu Small Watershed and the basin above the Ankang section in 2022 were determined to be 0.445 and 0.36, respectively. The pollutant processes corresponding to different runoff events in the Yangliu Small Watershed were simulated, and the average NSE for total nitrogen (TN), ammonia nitrogen (NH₃-N), nitrate nitrogen (NO₃-N), total phosphorus (TP), and soluble reactive phosphorus (SRP) were determined to be 0.69, 0.74, 0.79, 0.71, and 0.71, respectively. For the basin above the Ankang section, the NSE coefficients for the simulation of NH₃-N and TP pollutant processes were 0.78 and 0.83, respectively. The model demonstrated robust applicability across various spatial (ranging from small to large watersheds) and temporal (hourly−daily−monthly−annual) scales, and exhibited stability across different basins in a semi-humid region of China. The model is characterized by a parsimonious parameter set, ease of calibration, and strong spatiotemporal versatility, thus providing an efficient and reliable tool for non-point source pollution simulation. Full article

Flood susceptibility mapping in high-altitude ungauged basins faces a structural dichotomy: physically based models often suffer from systematic biases due to uncertain satellite precipitation, whereas data-driven models are prone to overfitting and lack physical consistency in data-scarce regions. To resolve this, this study proposes a Physically constrained Particle Swarm Optimization–Random Forest (P-PDRF) framework, validated in the Lhasa River Basin. The core innovation lies in coupling a hydrological model with statistical learning by utilizing the maximum daily runoff depth as a “Relative Hydraulic Intensity Index.” This approach leverages the topological correctness of physical simulations to circumvent absolute forcing errors. Furthermore, a Physiographically Constrained Negative Sampling (PCNS) strategy and a PSO-optimized “Shallow Tree” configuration are introduced to enforce structural regularization against stochastic noise. Empirical results demonstrate that P-PDRF achieves superior generalization (AUC = 0.942), significantly outperforming standard Random Forest, Support Vector Machine, and Analytic Hierarchy Process models. Ablation studies confirm that the dynamic index outweighs the static Topographic Wetness Index in feature importance, effectively correcting topographic artifacts where static models misclassify arid depressions as high-risk zones. This study offers a scalable Physics-Informed Machine Learning solution for the global “Prediction in Ungauged Basins” initiative. Full article

Global freshwater scarcity is a pressing environmental challenge, particularly in Egypt, which depends entirely on the Nile River and its tributaries. Rapid population growth, domestic wastes, agricultural runoff, and rapid industrial expansion exert highly anthropogenic stress on aquatic ecosystems, including Bahr Yusuf and Ibrahimia Canals in Upper Egypt. This study aimed to evaluate the ecological health and sustainability status of the two canals using an integrated multi-metric framework combining physicochemical variables, microbiological indicators, and community structures of zooplankton and benthic fauna. Multivariate statistical analyses (PCA, CCA), and ecological indices, including the water quality index (WQI), microbial assessment index (MAI), Rotifer-Based Index (TSI_Rot) and Hilsenhoff Biotic Index, were applied to determine pollution gradients. The results revealed that Bahr Yusuf suffers from higher pollution levels than the Ibrahimia Canal. Canonical correspondence analysis (CCA) showed that nutrient enrichment and elevated organic load are responsible for over 72% of the variance in zooplankton and benthic invertebrate assemblage in both water bodies. The dominance of pollution-tolerant species, Philodina roseola and B. calyciflorus of zooplankton and Limnodrilus udekemianus, Chironomidae larvae, Melanoides tuberculate and Cleopatra bulimoides of benthic taxa, further indicates a direct increase in organic loading and nutrient enrichment from agricultural and domestic sources. According to the Integrated Water Quality–Biotic Health Index (IWQ-BHI), the downstream stations of Bahr Yusuf are critical risk zones, with scores below 50.0, while the upstream stations of Ibrahimia Canal fell within the “good” category, with scores exceeding 70.0. Overall, both waterbodies are approaching a critical threshold of ecological instability and require urgent, integrated and sustainable management to restore and preserve these vital freshwater ecosystems. Full article

Water dynamics and nutrients are widely recognized as the main triggers of phytoplankton blooms. These factors may control the stability of marine ecosystems. Penzhina Bay and the Shelikhov Gulf are famous for their high tidal dynamics in comparison with the basins of the World Ocean and for being the feeding places of Bowhead whales. Here, we study the dynamics and thermohaline structure of water; nutrients; isotopic signatures of δ¹⁵N–$\mathrm{N}{\mathrm{O}}_3^{-}$ and δ¹⁸O–$\mathrm{N}{\mathrm{O}}_3^{-}$; as well as chlorophyll a in Penzhina Bay, the Shelikhov Gulf, and the Penzhina River to understand the features of an ecosystem with intense tidal dynamics in the subpolar region. This work is based on data obtained in three cruises of the R/V “Akademik Oparin” in the period from 2023 to 2025, with speed boat observations in the Penzhina River from May to October, including the flooding peak in June. The observations covered cases with tides from 7 to 13.4 m in height. The interaction between tides and river runoff was observed to supply dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) from the sea and dissolved silicate (DSi) from the river. The “white nights” in July, combined with the increased supply of nutrients, are good conditions for phytoplankton blooms, and as a result, the concentration of chlorophyll a in the study area was observed to be up to 39 µg/L. High primary production supports the food chain, and this is probably the main reason why Bowhead whales come to feed in the summer. The DIN/DIP ratio indicates DIN as a limiting factor in most of Penzhina Bay and throughout the Shelikhov Gulf. At the same time, the DSi/DIP ratio at a significant distance from the mouth of the Penzhina River is close to 0, indicating unfavorable conditions for diatoms. The DSi limit can cause the blooming of dinoflagellates, which sometimes occurs in this region. Full article

Freshwater accumulation is one of the most striking observations in the Beaufort Gyre (BG) region in the Arctic Ocean. A 39-year simulation, using the validated high-resolution, geometrical-fitting, unstructured grid Finite-Volume Community Ocean Model for the Arctic Ocean, aimed to investigate the contributions of coastal currents and their interannual variability to this phenomenon. The model reasonably reproduced the interannual variability of freshwater content (FWC) in the BG region. Analysis revealed the constructive role of Ekman pumping in supplying FWC, while the lateral flux generally acts to remove FWC from the region. The disparity between Ekman pumping and lateral flux drives the interannual variability of total FWC, with accumulation occurring when the downward Ekman FWC flux surpasses the net outflow-induced lateral FWC flux. Since 2007, there has been a significant increase in downward Ekman pumping, accompanied by a rise in net outflow lateral flux, indicating heightened variability of FWC in the BG region. The model results suggested that the coastal flow over the Arctic continental shelf underwent dramatic changes, especially during summer, and these changes were partially due to increased freshwater and sea ice melting. Increased lateral FWC flux during summer has become a competitive source for unprecedented seasonal freshwater accumulation in the BG region. Flow intensification over the North American coast is influenced by increased freshwater runoff, including the Firth, Kobuk, and Mackenzie Rivers. Interannual FWC variation in the Beaufort Sea could be influenced by the changes in slope flow, with the water originating in part from the Barents and Kara Seas. 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

Nutrient inputs from human activities, such as agriculture and sewage discharge, influence algal blooms in water bodies. In Ecuador, the Daule River receives wastewater discharges. In addition, poor agricultural practices, including the unsuitable use of fertilisers in combination with soil erosion and surface runoff processes, increase the nutrient load to the river. Considering this, the objective of this study was to evaluate environmental and biological variables using statistical analysis to identify the parameters that influence algal blooms in the main stem of the Daule River. The methodology consisted of two phases: (i) data collection, including water sampling and laboratory work for the analysis of nutrients and phytoplankton, and (ii) statistical analysis, which includes univariate, bivariate, inferential and multivariate analysis (STATICO technique). The results showed that pH and dissolved oxygen were the main drivers of diatoms (Polymyxus coronalis and Aulacoseira granulate) and the charophyte Mougeotia sp. Similarly, ammonium-N was the main driver of the diatom Ulnaria ulna and the cyanobacteria Planktothrix cf. agardhii. The outcomes of this study identified the main environmental variables driving blooms of the five most abundant species, providing a basis for the development of ecological models in the context of land use and climate change. Full article

Frequent flash floods threaten human well-being, hydropower infrastructure, and ecosystems. However, the long-term evolution of flash flood patterns over recent decades remains insufficiently understood, particularly in data-scarce high-altitude regions. Using multi-source remote sensing data integrated with historical disaster records and field investigations, this study examined the spatiotemporal evolution and driving factors of flash floods across the Qinghai–Tibet Plateau (QTP). The results indicate that flash floods have increased exponentially, which may be influenced by disaster management policies, with peaks in July–August and frequent occurrences from April to September. The seasonal trajectory of the center of gravity of flash floods from April to September exhibited a clear directional pattern. Regions with the highest disaster density were concentrated in the headwaters of five major rivers, including the Yarlung Zangbo, Jinsha, Nu, Lancang, and Yellow Rivers. Shapley Additive Explanation (SHAP) and Random Forest analyses reveal that soil moisture, anthropogenic intensity, and seasonal runoff variability are the dominant driving factors. With ongoing socioeconomic development, intensified human activities have become a key contributor to the increasing frequency of flash floods. These findings highlight the value of remote sensing-based assessments for flash flood monitoring and early warning and provide scientific support for risk mitigation, loss reduction, and the advancement of water-related targets under the United Nations’ Sustainable Development Goals. 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