Search Results (197)

Identifying the dominant mechanisms of water and soil salinization in arid and semi-arid endorheic basins is fundamental for our understanding of basin-scale water–salt balance and supports water resources management. In many inland basins, mineral dissolution, evaporation, and transpiration govern salinization, but disentangling these processes remains difficult. Using the Barkol Basin in northwestern China as a representative endorheic system, we sampled waters and soils along a transect from the mountain front through alluvial fan springs and rivers to the terminal lake. We integrated δ¹⁸O–δ²H with hydrochemical analyses, employing deuterium excess (d-excess) to partition salinity sources and quantify contributions. The results showed that mineral dissolution predominated, contributing 65.8–81.8% of groundwater salinity in alluvial fan settings and ~99.7% in the terminal lake, whereas direct evapoconcentration was minor (springs and rivers ≤ 4%; lake ≤ 0.2%). Water chemistry types evolved from Ca-HCO₃ in mountainous runoff, to Ca·Na-HCO₃·SO₄ in groundwater and groundwater-fed rivers, and finally to Na-SO₄·Cl in the terminal lake. The soil profiles showed that groundwater flow and vadose-zone water–salt transport control spatial patterns: surface salinity rises from basin margins (<1 mg/g) to the lakeshore and is extremely high near the lake (23.85–244.77 mg/g). In spring discharge belts and downstream wetlands, the sustained evapotranspiration of groundwater-supported soil moisture drives surface salt accumulation, making lakeshores and wetlands into terminal sinks. The d-excess-based method can robustly separate the salinization processes despite its initial isotopic variability. Full article

Hydrological runoff prediction serves as the core technological foundation for water resource management and flood/drought mitigation. However, the nonlinear, non-stationary, and multi-temporal scale characteristics of runoff data result in insufficient accuracy of traditional prediction methods. To address the challenges of single decomposition methods’ inability to effectively separate multi-scale components and single deep learning models’ limitations in capturing long-range dependencies or extracting local features, this study proposes an Informer-GRU runoff prediction model based on STL-CEEMDAN secondary decomposition and Gorilla Troops Optimizer (GTO). The model extracts trend, seasonal, and residual components through STL decomposition, then performs fine decomposition of the residual components using CEEMDAN to achieve effective separation of multi-scale features. By combining Informer’s ProbSparse attention mechanism with GRU’s temporal memory capability, the model captures both global dependencies and local features. GTO is introduced to optimize model architecture and training hyperparameters, while a multi-objective loss function is designed to ensure the physical reasonableness of predictions. Using daily runoff data from the Liyuan Basin in Yunnan Province (2015–2023) as a case study, the results show that the model achieves a coefficient of determination (R²) and Nash-Sutcliffe efficiency coefficient (NSE) of 0.9469 on the test set, with a Kling-Gupta efficiency coefficient (KGE) of 0.9582, significantly outperforming comparison models such as LSTM, GRU, and Transformer. Ablation experiments demonstrate that components such as STL-CEEMDAN secondary decomposition and GTO optimization enhance model performance by 31.72% compared to the baseline. SHAP analysis reveals that seasonal components and local precipitation station data are the core driving factors for prediction. This study demonstrates exceptional performance in practical applications within the Liyuan Basin, providing valuable insights for water resource management and prediction research in this region. Full article

Surface subsidence induced by underground mining in mining areas significantly alters surface topography and hydrogeological conditions, forming depressions and fissures, thereby affecting regional runoff-ponding processes and groundwater infiltration patterns. Accurate assessment of infiltration volumes in subsidence zones under heavy rainfall is crucial for designing underground drainage systems and evaluating water-inrush risks in open-pit to underground transition mines. Taking the surface subsidence area of the Dahongshan Iron Mine as a case study, this paper proposes a rainfall infiltration calculation method based on the precise delineation of surface ponding-infiltration zones. By numerically simulating the subsidence range, the study divides the area into two distinct infiltration characteristic zones under different mining states: the caved zone and the water-conducting fracture zone. The rainfall infiltration volume under storm conditions was calculated separately for each zone. The results indicate that high-intensity mining-induced subsidence leads to a nonlinear surge in stormwater infiltration, primarily due to the significant expansion of the highly permeable caved zone. The core mechanism lies in the area expansion of the caved zone as a rapid infiltration channel, which dominates the overall infiltration capacity multiplication. These findings provide a scientific basis for the design of mine drainage systems and the prevention of water-inrush disasters. Full article

Soil degradation exerts profound impacts on soil ecological functions, global food security, and human development, making the development of effective technologies to mitigate degradation a critical research focus. Microorganisms play a leading role in rehabilitating degraded land, improving soil hydraulic properties, and enhancing soil structural stability. Mosses contribute to soil particle fixation through their unique rhizoid structures; however, the mechanisms underlying their interactions in mixed inoculation remain unclear. Therefore, this study addresses soil and water loss caused by rainfall erosion in the cold black soil region. We conducted controlled laboratory experiments cultivating Bacillus subtilis and cold-adapted moss species, evaluating the erosion mitigation effects of different biological treatments under gradient slopes (3°, 6°, 9°) and rainfall intensities (70 mm h⁻¹, 120 mm h⁻¹), and elucidating their carbon-based structural reinforcement mechanism. The results indicated that compared to the control group, Treatment C significantly increased the mean weight diameter (MWD) and geometric mean diameter (GMD) of soil aggregates by 121.6% and 76.75%, respectively. In separate simulated rainfall events at 70 mm h⁻¹ and 120 mm h⁻¹, Treatment C reduced soil loss by 95.70% and 96.75% and decreased runoff by 38.31% and 67.21%, respectively. Crucially, the dissolved organic carbon (DOC) loss rate in Treatment C was only 21.98%, significantly lower than that in Treatment A (32.32%), Treatment B (22.22%), and the control group (51.07%)—representing a 59.41% reduction compared to the control. This demonstrates the following: (1) Bacillus subtilis enhances microbial metabolism, driving carbon conversion into stable pools, while mosses reduce carbon leaching via physical barriers, synergistically forming a dual “carbon protection–structural reinforcement” barrier. (2) The combined inoculation optimizes soil structure by increasing the proportion of large soil particles and enhancing aggregate stability, effectively suppressing soil loss even under extreme rainfall erosion. This study elucidates, for the first time, the biological pathway through which microbe–moss interactions achieve synergistic carbon sequestration and erosion resistance by regulating aggregate formation and pore water dynamics. It provides a scalable “carbon–structure”-optimized biotechnology system (co-inoculation of Bacillus subtilis and moss) for the ecological restoration of the cold black soil region. Full article

In the context of global warming and the intensification of human activities, the change in runoff is also increasing. It is very important to determine the change in runoff for the rational utilization of water resources. In order to determine the influencing factors of runoff change in Muling River, the SWAT model was used in this study to separate different coupling factors and calculate the contribution rate of a single factor to runoff change at the annual scale and quarterly scale, respectively. In the process of calibration, different single rate times were used to analyze the influence of different rate times on the calibration results. The results show that the runoff in the Muling River basin shows a downward trend, the quarterly temperature factor has the greatest influence on the runoff change, which is 50–60%, the annual precipitation has the greatest influence on the runoff change, which is 68%, and the maximum change in the runoff from the reservoir is 42.5% under the change in human activities. In the SWAT-CUP software, the optimal number of calibration for this basin is 500. This research provides a scientific basis for the flow analysis of the Muling River basin. Full article

Accurately estimating storm hydrographs for various return periods is crucial for planning and designing hydrological infrastructure, such as dams and drainage systems. A key aspect of this estimation is the separation of baseflow from storm runoff. This study proposes a method for deriving storm hydrographs for different return periods based on hydrological station records. The proposed approach uses three baseflow separation methods: constant, linear, and master recession curve. A significant advantage of the proposed method over traditional rainfall–runoff approaches is its minimal parameter requirements during calibration. The methodology is tested on records from the Lengupá River watershed in Colombia, using data from the Páez hydrological station, which has a drainage area of 1090 km². The results indicate that the linear method yields the most accurate hydrograph estimates, as demonstrated by its lower root mean square error (RMSE) of 0.35%, compared to the other baseflow separation techniques, the values of which range from 2.92 to 3.02%. A frequency analysis of hydrological data was conducted using Pearson Type III and Generalized Extreme Value distributions to identify the most suitable statistical models for estimating extreme events regarding peak flow and maximum storm hydrograph volume. The findings demonstrate that the proposed methods effectively reproduce storm hydrographs for return periods ranging from 5 to 200 years, providing valuable insights for hydrological design, which can be employed using the data from stream gauging stations in rivers. Full article

Baseflow is a crucial component of river flow in alpine inland basins, playing an essential role in watershed ecological health and water resource management. In high-latitude cold regions, seasonal freeze-thaw processes make baseflow formation mechanisms particularly complex. However, the dominant factors affecting baseflow and their relative contributions remain unclear, limiting the accuracy of flow estimation and effective water resource management. This study employed baseflow separation techniques and statistical methods, including the Mann-Kendall test, to investigate temporal trends and abrupt changes in baseflow and the baseflow index (BFI) at multiple time scales (annual, seasonal, and monthly) from 2005 to 2012. Additionally, the timing of snowmelt and its impact on baseflow were examined. Key findings include the following: (1) Baseflow and BFI showed distinct temporal variability with non-significant upward trends across all time scales. Annual BFI ranged from 0.48 to 0.61, contributing approximately 50% of total runoff. (2) At the seasonal scale, baseflow remained relatively stable in spring, increased in autumn, and showed non-significant decreases in summer and winter. Monthly baseflow exhibited an increasing trend. (3) The snowmelt period occurred between April and May, with baseflow during this period strongly correlated with climatic factors in the following order: winter precipitation > positive accumulated temperature > winter air temperature > negative accumulated temperature. The strongest positive correlation was observed between baseflow and winter precipitation (R = 0.724), while negative correlations were found with accumulated temperatures and winter air temperature. These findings offer valuable insights for predicting water resource availability and managing flood and ice-jam risks in cold regions. Full article

The generalized proportionality hypothesis (GPH) highlights the competitive relationships among hydrological components as precipitation (P) transforms into runoff (Q) and evapotranspiration (E), providing a novel perspective on E partitioning that differs from the traditional physical source-based approach. To achieve sequential partitioning of E into initial (E_i) and continuing (E_c) evapotranspiration under the GPH, a P-Q relationship-based E_i estimation method was proposed for the Model Parameter Estimation Experiment (MOPEX) catchments. On this basis, we analyzed the relationship between the GPH-based E components and the physical source-based ones separated by the Penman-Monteith-Mu algorithm. Additionally, we explored the differences between the calculated and inverse Budyko-WT model parameter (E_i/E) and discussed the implications for the Budyko framework. The results showed the following: (1) A significant linear P-Q relationship (p < 0.05) prevailed in the MOPEX catchments, providing a robust data foundation for E_i estimation. Across the MOPEX catchments, E_i and E_c contributed 73% and 27% of total E, respectively. (2) The combined proportion of evaporation from canopy interception and wet soil averaged about 25%, and it was much lower than that of E_i, indicating that it was difficult to establish a connection between E_i and the physical source-based E components. (3) The potential evapotranspiration (E_P) satisfying the Budyko-WT model was strictly constrained by the GPH, while the inappropriate E_P estimation method largely explained the discrepancy between the calculated and inverse E_i/E. This study deepens the knowledge of the sequential partitioning of E components, uncovers the discrepancies between different E partitioning frameworks, and provides new insights into the characterization of key variables in Budyko models. Full article

With accelerating climate change, droughts have increased in frequency and exerted a substantial influence on socioeconomic factors. Under conditions of insufficient precipitation and high temperatures, meteorological droughts have the potential to develop into more intense hydrological droughts, and the independent impact of temperature factors on drought propagation has not been considered separately. This study constructed a Standardized Temperature Index (STI) and, combined with time-series datasets of standardized indices of precipitation and runoff (SPI and SRI), based on Bayesian network principles, analyzed the probabilistic characteristics of drought propagation from meteorology to hydrology due to the influence of single or dual factors in the Yiluo River Basin (1961–2020). It also explored the transmission mechanisms of temperature and precipitation that drive and affect meteorological and hydrological drought. The results showed that propagation of meteorological to hydrological droughts increased with rising temperatures, and the propagation probability to severe and extreme hydrological drought increased by approximately 5%. Under the most adverse circumstances (high temperature and precipitation shortage scenarios), the likelihood of meteorological droughts progressing into intense hydrological drought events rose to 80%. Increasing temperature is expected to lead to more severe hydrological droughts. This study offers a theoretical foundation for drought prevention and mitigation. Full article

Growing urbanization has increased impermeable surfaces, raising and polluting stormwater runoff, and exacerbating the risk of urban flooding. Effective stormwater management is essential to curb sedimentation, minimize pollution, and mitigate urban flooding. This systematic literature review from the Web of Science and Scopus between January 2000 and June 2024 presents hydrodynamic separation (HDS) technologies. It sheds light on the significant issues that urban water management faces. HDS is classified into four categories: screening, filtration, settling, and flotation, based on the treatment mechanisms. The results show a shift from traditional standalone physical separations to multi-stage hybrid treatment processes with nature-based solutions. The great advantage of these approaches is that they combine different separation mechanisms and integrate ecological sustainability to manage urban stormwater better. The findings showed that future research will examine hybrid AI-assisted separation technologies, biochar-enhanced filtration, and green infrastructure systems. When adopting an integrated approach, the treatment system will perform like natural processes to remove pollutants effectively with better monitoring and controls. These technologies are intended to fill existing research voids, especially in removing biological contaminants and new pollutants (e.g., microplastics and pharmaceutical substances). In the long term, these technologies will help to enforce Sustainable Development Goals (SDGs) and orient urban areas in developing countries towards meeting the circular economy objective. Full article

As an important drinking water source for Beijing, the capital of China, the water inflow of Miyun Reservoir has been decreasing year by year, which has affected the urban water supply security. To understand the variation trend of the inflow and analyze the main factors influencing the runoff change, this research focused on the watershed of Miyun Reservoir as the target. Based on the runoff data from 1984 to 2020 at the outlet of the basin, as well as the precipitation, potential evaporation intensity, NDVI (normalized difference vegetation index), population, and GDP (Gross Domestic Product) data, combined with correlation analysis methods, empirical statistical methods, the SCRCQ (Slope Change Ratio of Cumulative Quantity) method, and the GIS, the interannual variation characteristics of various elements in the basin were analyzed, the correlation between runoff and other factors was studied, and the influencing degrees of precipitation, water surface evaporation intensity, human activities, and other factors on the runoff change in the basin were quantitatively separated. The research results showed that the runoff exhibited a distinct decreasing trend, and there were two mutation points in the basin runoff from 1984 to 2020, which were 1995 and 2014, respectively. The runoff change was divided into three stages: 1984–1995 (upward trend in T1), 1995–2014 (downward trend in T2), and 2014–2020 (stable trend in T3). Runoff was significantly correlated with four indicators: the summer leaf area index of the Chaohe River and Baihe River, the regional GDP and population, among which the correlation of the summer leaf area index was the largest. Compared with the period T1, the contribution rates of climate change to the runoff reduction in T2 and T3 were 6.38% and 5.73%, and the contribution rates of human activities to the runoff reduction were 93.62% and 94.27%, respectively. Therefore, the change in annual runoff in the Miyun Reservoir watershed is mainly affected by human activities, and the contribution of climate change to the runoff attenuation is weak. This study is significant in the maintenance and enhancement of runoff in typical watershed. Full article

Quantifying the water budget in the Great Salt Lake (GSL) basin is a nontrivial task, especially under a changing climate that contributes to increasing temperatures and a shift towards more rainfall and less snowfall. This study examines the potential impacts of temperature thresholds on the water budget in the GSL, emphasizing the influence on snowmelt, evapotranspiration (ET), and runoff under varying climate warming scenarios. Current hydrological models such as the Variable Infiltration Capacity (VIC) model use a universal temperature threshold to partition snowfall and rainfall across different regions. Previous studies have argued that there is a wide range of thresholds for partitioning rainfall and snowfall across the globe. However, there is a clear knowledge gap in quantifying water budget components in the Great Salt Lake (GSL) basin corresponding to varying temperature thresholds for separating rainfall and snowfall under the present and future climates. To address this gap, the study applied temperature thresholds derived from observation-based data available from National Center for Environmental Prediction (NCEP) to the VIC model. We also performed a suite of hydrological experiments to quantify the water budget of the Great Salt Lake basin by perturbing temperature thresholds and climate forcing. The results indicate that higher temperature thresholds contribute to earlier snowmelt, reduced snowpack, and lower peak runoff values in the early spring that are likely due to increased ET before peak runoff periods. The results show that the GSL undergoes higher snow water equivalent (SWE) values during cold seasons due to snow accumulation and lower values during warm seasons as increased temperatures intensify ET. Projected climate warming may result in further reductions in SWE (~71%), increased atmospheric water demand, and significant impacts on water availability (i.e., runoff reduced by ~20%) in the GSL basin. These findings underscore the potential challenges that rising temperatures pose to regional water availability. Full article

Debris flows are among the severe gravity-driven mass phenomena that pose a significant threat to the environment and communities. Recent events and studies in the Chalk Cliffs basin in Colorado suggest that it is very susceptible to debris flow incidents that initiate from surface run-off, which involves significant entrainment of material along the hill slope and channel sediments. The entrainment of material along the flow makes these events destructive, with large travel distances s well as high velocity, flow pressure, kinetic energy, etc. This paper presents a case study of a debris flow event on 15 September 2009 based on a multi-phase flow model. The model provides the ability to investigate the effect of fluid and solid phases individually. Three sensitivity analyses are presented investigating the effect of bed roughness on solid and fluid phases separately, and also the effect of the entrainment of bed material. The findings demonstrate that the numerical model effectively replicates the observed field data, with the simulated peak discharge and runout distance closely aligning with the observed measurements. The analysis reveals that lower bed roughness promotes higher flow mobility and longer runout distances, while entrainment significantly influences flow height, velocity, and deposition pattern. Furthermore, the analysis highlights the dominant role of entrainment in debris flow evolution and emphasizes its importance in determining deposition and erosion patterns. These findings provide critical insights into the key processes of debris flows and could contribute to the development of accurate numerical models for debris flow events. Full article

This study highlights the importance of distinguishing the mechanisms driving spatial and temporal variances in groundwater database analyses, with a particular focus on bacteriological contamination processes. Groundwater quality data from the Bourgogne-Franche-Comté region of France forms the basis of this investigation. Specifically, the SISE-EAUX database includes 3569 groundwater samples collected over various dates from 989 monitoring points. The methodology involves structuring the data into three distinct sets: (1) A spatio-temporal dataset without any conditioning; (2) A spatial dataset that assigns the mean values of each parameter to each sampling point; (3) A temporal dataset that captures deviations from the mean for each sampling point and parameter. These datasets enable a separate analysis of spatial and temporal variances. Principal component analysis (PCA) and parameter hierarchical clustering were used to compare the results, yielding valuable insights into the underlying processes. This analysis helps distinguish between factors related to geological or pedological spatial distributions and those associated with climatic events, such as intense rainfall episodes exhibiting seasonal patterns. Such differentiation enhances the understanding of fecal contamination vectors and nitrate pollution, which are often linked to surface and subsurface runoff in vulnerable catchment areas. While conceptually clear, the practical separation of spatial and temporal variability presents challenges. For example, catchments sensitive to surface water inflows during rainfall events are unevenly distributed across the region, correlating with specific natural environments. As a result, areas of high temporal variability are also well-structured spatially, underscoring the interdependence of these two types of variability. This complexity is exemplified by the behavior of iron, which varies in association with surface and subsurface parameters depending on spatial and temporal contexts. Additionally, asynchronous sampling and varying frequencies across sites lead to discrepancies in the average temporal data acquisition between points. These disparities can influence spatial variability calculations, as temporal variability effects are not entirely removed. Despite these challenges, the distinction between spatial and temporal components is essential for a deeper understanding of groundwater quality mechanisms. This refined approach improves diagnostic precision and supports more targeted and effective water resource management strategies. Full article

Water pollution, resulting from industrial effluents, agricultural runoff, and pharmaceutical residues, poses serious threats to ecosystems and human health, highlighting the need for innovative approaches to effective remediation, particularly for non-biodegradable emerging pollutants. This research work explores the influence of shape-controlled nanocrystalline titanium dioxide (TiO₂ NC), synthesized by a simple hydrothermal method, on the photodegradation efficiency of three different classes of emerging environmental pollutants: phenol, pesticides (methomyl), and drugs (sodium diclofenac). Experiments were conducted to assess the influence of the water matrix on treatment efficiency by using ultrapure water and stormwater (basic) collected from an urban drainage system as matrices. The size and shape of the nano-cuboids were accurately controlled during synthesis to assess their impact on photoactivity and selectivity. Regarding total organic carbon removal using TiO₂ nano-cuboids in basic environments, the results were particularly remarkable. TiO₂ nano-cuboids and truncated bipyramids synthesized in the 200–250 °C temperature range showed an enhanced photocatalytic efficiency when compared to alternative formulations. Diclofenac, methomyl, and phenol were fully mineralized from ultrapure water and basic stormwater. The TiO₂ nano-cuboids/nano-bipyramids demonstrated better selectivity and photoactivity in comparison to irregular TiO₂ nanoparticles. The differences in photoactivity and selectivity are explained in terms of charge carrier separation and trapping on the different crystal facets. Their performance demonstrates their potential as sustainable materials for the photodegradation of emerging pollutants in various water matrices. Full article