Search Results (478)

Rainfall-induced failures in red clay slopes are common, yet the coupled influence of soil structure degradation and rainfall temporal patterns on slope hydromechanical behavior remains poorly understood. This study advances the understanding by investigating a cut slope failure in Yunnan through integrated field monitoring, laboratory testing, and numerical modeling. Key advancements include: (1) elucidating the coupled effect of structure degradation on both shear strength reduction and hydraulic conductivity alteration; (2) systematically quantifying the impact of rainfall temporal patterns beyond total rainfall; and (3) providing a mechanistic explanation for the critical role of early-peak rainfall. Mechanical and hydrological parameters were obtained from intact and remolded samples, with soil-water retention estimated via pedotransfer functions. A hydro-mechanical finite element model of the slope was constructed and calibrated using recorded rainfall, displacement data and failure surface. Six simulation scenarios were designed by combining three strength conditions (intact at natural water content, intact at saturation, remolded at natural water content) with two hydraulic conductivity values (intact vs. remolded). Additionally, four synthetic rainfall patterns, including uniform, peak-increasing, peak-decaying and bell-shaped rainfall, were simulated to evaluate their influence on pore water pressure development and slope stability. Results show remolding reduced hydraulic conductivity 4.7-fold, slowing wetting front advance and increasing shallow pore water pressure. Intact soil facilitated deeper drainage, elevating pressure near the soil-rock interface. Strength reduction induced by structure degradation (water saturating and remolding) enlarged the slope deformation zone by 1.5 times under same hydraulic conductivity. Simulations using saturated intact strength best matched field observations. The results from this specific slope indicate that strength parameters primarily control stability, while permeability affects deformation depth. Simulations considering different rainfall patterns indicate that slope stability depends more critically on the temporal distribution of rainfall intensity than on the total amount. Overall, peak-decaying rainfall led to the most rapid rise in pore water pressure, earliest instability and lowest failure rainfall threshold, whereas peak-increasing rainfall showed the opposite trends. Our findings outline a practical framework for assessing red clay slope stability during rainfall. This framework recommends using saturated intact strength parameters in stability analysis. It highlights the important influence of rainfall temporal patterns, especially those with an early peak, on failure timing and rainfall threshold. Full article

The pressure of climate change on water resources has made the development of reliable hydrological models increasingly important, especially for data-scarce regions. However, due to the limited availability of ground-based observations, it considerably affects the accuracy of models developed using these inputs. This also limits the ability to investigate future hydrological behavior. Satellite-based data sources have emerged as an alternative to address this challenge and have received significant attention. However, the transferability of these datasets across different model classes has not been widely explored. This paper evaluates the transferability of satellite-derived inputs to eleven types of models, including process-based (SWAT), data-driven methods (XGBoost and WGAN), and hybrid model structures that utilize SWAT outputs with AI models. SHAP has been applied to overcome the black-box limitations of AI models and gain insights into fundamental hydrometeorological processes. In addition, uncertainty analysis was performed for all models, enabling a more comprehensive evaluation of performance. The results indicate that hybrid models using SWAT combined with WGAN can achieve better predictive accuracy than the SWAT model based on ground observation. While the baseline SWAT model achieved satisfactory performance during the validation period (NSE ≈ 0.86, KGE ≈ 0.80), the hybrid SWAT + WGAN framework improved simulation skill, reaching NSE ≈ 0.90 and KGE ≈ 0.89 during validation. Models forced with satellite-derived meteorological inputs additionally performed as well as those forced using station-based observations, validating the feasibility of using satellite products as alternative data sources. The future hydrological status of the basin was assessed based on the best-performing hybrid model and CMIP6 climate projections, showing a clear drought signal in the flows and long-term reductions in average flows reaching up to 58%. Overall, the findings indicate that the proposed framework provides a consistent approach for data-scarce basins. Future applications may benefit from integrating spatio-temporal learning frameworks and ensemble-based uncertainty quantification to enhance robustness under changing climate conditions. Full article

Hydrological behavior plays a critical role in seasonally dry forest ecosystems, as it underpins water availability for multiple productive activities, including forestry, agriculture, grazing, and urban supply. This study evaluated the hydrological effects of thinning treatments in a semi-arid oak–pine forest of Chihuahua, Mexico, using a Before–After–Control–Impact (BACI) design. Three Micro-catchments (MC) with initially comparable tree density and canopy cover were monitored during the rainy seasons of 2018 (pre-thinning) and 2019 (post-thinning). Thinning treatments were applied at 20% and 60% canopy cover in two MC, while a third remained unthinned as a 100% control. Precipitation and surface runoff were recorded at the event scale, and data were analyzed using Weibull probability models with a log link to capture the frequency and magnitude of runoff events. Precipitation patterns were broadly comparable across years, although 2018 included an extreme storm event (59 mm). In contrast, runoff volumes in 2019 were lower despite marginally higher seasonal rainfall, reflecting the absence of large storms. Statistical modeling indicated that for each additional millimeter of precipitation, mean runoff increased by approximately 12%, although thinning significantly altered baseline conditions. Relative to 2018, mean runoff ratios were 0.087 in the 100% canopy catchment, 0.296 in the 60% treatment, and 0.348 in the 20% treatment, suggesting that reduced canopy cover retained proportionally more runoff than the control. BACI contrasts confirmed that thinned catchments maintained higher proportions of runoff than the unthinned control, although statistical significance was marginal for the 20% canopy treatment. Overall, the study provides ecohydrological insights relevant to the management of semi-arid forest ecosystems. Full article

The accurate estimation of outflow discharge from Piano Key Weirs (PKWs) under unsteady flow conditions is critical for effective flood management and the safety of dams. While extensive research exists on PKWs under steady flow, their hydraulic behavior during unsteady flow remains poorly understood. This study addresses this gap by investigating a Type-A PKW using combined physical and numerical modeling. A total of eight steady-flow and fifty-three unsteady-flow experiments were conducted. The steady flow experiments covered a range of Q = 5.13–40.76 L/s (H = 1.29–10.45 cm), while the unsteady experiments employed hydrographs with peak discharges up to ~68 L/s. Outflow was estimated via the Modified Puls method (hydrological routing) and a validated 3D numerical model (hydraulic routing). The results revealed significant discrepancies between steady and unsteady stage-discharge relationships, with a mean relative error of up to 41.37% and instantaneous errors exceeding 150% during the rising limbs of hydrographs with high rates of change in discharge, associated with intensified unsteady flow effects. A validated looped stage-discharge curve was observed under unsteady conditions, showing lower discharge on the rising limb for the same head. The Modified Puls method exhibited high accuracy, with relative errors below 5% when compared to hydraulic routing results. Additionally, three comparative indices were proposed and used to evaluate the performance of outflow estimation methods. The findings underscore the importance of incorporating unsteady flow conditions in the design and analysis of PKWs, particularly in the context of climate change and increasing flood uncertainties. Full article

Seabed topography exerts a profound influence on underwater acoustic propagation, and the coupling effect between bottom acoustic properties and the source–receiver geometric configuration remains insufficiently quantified, particularly in seamount shielding scenarios. To address this gap, in this study, the BELLHOP ray model was integrated with Earth topography 1 (ETOPO1) topographic data and Hybrid Coordinate Ocean Model (HYCOM) hydrological data for seamounts east of Taiwan. Transmission loss (TL) of 300 Hz sound waves was simulated across four typical bottom types (rock, coarse sand, silt, and clay) under varying source depths (50–1000 m) and receiver depths (50–500 m). The maximum sonar detection range was delineated using an 80 dB TL threshold as the criterion for effective detection. The key findings reveal that the bottom properties are the primary factors that reduce the detection range: the maximum detection range over rock bottom exceeds that over clay by more than 8-fold. Notably, a shallow source–shallow receiver configuration mitigates the acoustic shadow effect induced by seamounts, whereas deep receiver deployment (≥500 m) diminishes the discriminative impact of bottom types on the propagation behavior. Furthermore, a segmented empirical prediction formula was established, which reconciles both the physical mechanisms (e.g., bottom reflection-absorption and seamount shielding) and engineering applicability. This formula provides a robust theoretical basis for evaluating sonar performance in complex seabed topography settings, thereby facilitating optimized underwater detection strategies in seamount-dominated marine environments. Full article

Accurate prediction of soil texture is essential for effective soil management, precision agriculture, and hydrological modeling. This study proposes a novel, data-driven approach for estimating soil texture without the need for laboratory-based analysis. High-frequency in situ soil moisture measurements from EnviroSCAN (Sentek Technologies, Stepney, Australia) sensors and satellite-derived vegetation indices (NDVI) from Sentinel-2 were collected across 25 sites in Hungary. Temporal soil moisture dynamics were encoded using a Long Short-Term Memory (LSTM) neural network, designed to capture soil-specific hydrological response behavior from time-series data. The resulting latent embeddings were subsequently used within an ordinal regression framework to predict ordered soil texture classes, explicitly enforcing physical consistency between classes. Model performance was evaluated using leave-one-soil-out cross-validation, achieving an overall classification accuracy of 0.54 and a mean absolute error (MAE) of 0.50, indicating predominantly adjacent-class errors. The proposed approach demonstrates that soil texture can be inferred from dynamic environmental responses alone, offering a transferable alternative to fraction-based regression models and supporting scalable sensor calibration and digital soil mapping in data-scarce regions. Full article

As a sink for microplastics (MPs) in the aquatic environment, sediments have garnered considerable attention. However, the occurrence characteristics of MPs in sediments of different water seasons are not clear, especially for reservoir sediment cores. This study aimed to elucidate the occurrence, spatial and vertical distribution, fragmentation and pollution risk of MPs in the sediment cores of the Xiangxi River, Three Gorges Reservoir (TGR) during different seasons. In sediment cores, the average abundance of MPs was 8.57 × 10³ ± 5.65 × 10³ items/kg DW in the wet season (WS) and 7.98 × 10³ ± 4.00 × 10³ items/kg DW in the dry season (DS), respectively. The abundance of MPs in surface sediments and sediment cores exhibited spatial heterogeneity, reflecting seasonally contrasting hydrodynamic conditions between sites S1 and S3. However, the abundance of MPs in the river estuary was the highest, both in surface sediments and sediment cores. Interestingly, the occurrence characteristics of MPs in surface sediments indicated that in addition to anthropogenic activity, hydrological conditions of the river can also have an impact on the spatial distribution of MP abundance in surface sediments. Polypropylene (PP), polyethylene (PE), polystyrene (PS), and polyethylene-propylene copolymer (EPM) were identified as the dominant polymer types (57–99%), with small-sized microplastics (SMPs, 0–300 μm) being the most prevalent. Water seasons influenced the size distribution of MPs in surface sediments. Using a conditional fragmentation model, MP sources were inferred by comparing fragmentation parameters (λ and α) in sediments with those reported for atmospheric deposition, reservoir water, and water-level fluctuation zone soils. Furthermore, the pollution load index (PLI) exceeded 1, indicating MP accumulation in the sediments. The pollution risk index (PRI) values indicated a considerable (300 < PRI < 1000) pollution risk in two water seasons, primarily due to the presence of polyvinyl chloride (PVC). This study enhances the understanding of MP behavior and associated environmental risks in reservoir sediments, offering valuable insights for future research and pollution mitigation efforts. Full article

Urban catchments are increasingly vulnerable to hydrologic extremes driven by land-use change and climate variability, challenging the traditional assumption of stationarity. This study develops a computational framework to assess the nonstationary behavior of peak flow, volume, and duration in an urban catchment in the Philippines using 39 years of daily flow records (June 1984–November 2022). Missing observations (~8% of the series) were reconstructed using multiple linear regression (MLR) and artificial neural networks (ANNs) with four predictors: daily rainfall, antecedent rainfall, antecedent flow, and built-up area index. MLR with all predictors yielded the most accurate reconstructions. Nonstationarity was detected using the Mann–Kendall test, Sen slope estimator, Pettitt test, and variance change test. Flood events were extracted using block maxima (BM) and peak-over-threshold (POT) methods. BM-based results showed stationary peak flow and volume, while duration increased by 1.78 h/year. POT analyses revealed nonstationarity across all variables, without significant shifts in variance. These findings demonstrate that methodological choices strongly influence nonstationary detection. The framework underscores the importance of reliable data reconstruction and robust statistical testing for nonstationary analysis of flood events. POT-based approaches more effectively capture evolving trends in peak flow, volume, and duration. These can be used in designing resilient infrastructure and flood risk management in urbanizing catchments. Full article

River discharge scaling is fundamental to the global hydrological cycle and to water resource assessment. This study investigates the existence of multiple scaling regimes and introduces a novel framework for clustering river discharge using multiscale fractal characteristics. We analyzed daily discharge data from 38 stations across continental Canada over an 80-year period. Multifractal characterization was performed at decadal and long-term scales using three key parameters: the singularity exponent (${\alpha }_0$), multifractal strength ($\alpha$), and asymmetry index (r). K-means clustering in the $\alpha -r$, ${\alpha }_0-r$, and $\alpha -{\alpha }_0$ planes revealed distinct clusters, with the asymmetric parameter (r) emerging as the strongest distinguishing factor. These clusters represent groups of rivers with similar dynamical structures: the $\alpha -r$ clusters categorize discharge based on scaling strength and fluctuation influence. Analysis of the generalized Hurst exponent revealed anti-persistent behavior at most stations, with exceptions at five specific locations. This multifractal clustering approach provides a powerful method for classifying river regimes based on intrinsic characteristics and identifying the physical drivers of discharge fluctuations. Full article

This study uses high-frequency monitoring across a river–barrier lake–reservoir continuum in the upper Minjiang River, southwestern China, to quantify the spatiotemporal dynamics and drivers of aquatic CO₂ partial pressure (pCO₂) and to identify the dominant controls under contrasting lotic and lentic conditions. River reaches were CO₂-supersaturated throughout the year, with higher pCO₂ in the wet season (mean 521 ppm) than in the dry season (421 ppm), indicating persistent CO₂ evasion to the atmosphere. In contrast, the downstream canyon-type reservoir showed a pronounced seasonal reversal. During the wet season, surface-water pCO₂ averaged 395 ppm, about 24% lower than that of the river and below atmospheric levels (~419 ppm); more than 55% of observations were undersaturated, with minima as low as 141–185 ppm, indicating temporary CO₂-sink behavior. In the dry season, mean pCO₂ increased to 563 ppm, exceeding both riverine and atmospheric levels and returning the reservoir to a CO₂ source. The reservoir pCO₂ variability was governed by the interaction of hydrology and metabolism: rising water levels and longer residence times likely enhanced CO₂ accumulation from the decomposition of inundated organic matter, while warm temperatures, high light and monsoon-driven nutrient inputs promoted phytoplankton growth that removed dissolved CO₂ and elevated dissolved oxygen, producing temporary sink behavior. In the river, short residence time and strong turbulence limited in-stream biological regulation, and pCO₂ variability was mainly driven by catchment-scale carbon inputs along the elevation gradient. Overall, our results demonstrate that dam construction and impoundment can substantially modify carbon cycling in high-mountain rivers. Under specific conditions (warm water, sufficient nutrients, high algal biomass), lentic environments may strengthen photosynthetic CO₂ uptake and temporarily transform typical riverine CO₂ sources into sinks, with important implications for carbon-budget assessments and reservoir management in mountainous basins. Full article

Hydrological droughts represent a growing challenge for northern watersheds in Thailand, where climate change is projected to intensify seasonal water stress and destabilize agricultural productivity and water resource management. This study employed the Composite Hydrological Drought Index (CHDI) to evaluate the spatiotemporal characteristics of future droughts under representative concentration pathway (RCP) scenarios. The findings revealed a pronounced seasonal contrast: under RCP8.5, the CHDI values indicated more severe drought conditions during the dry season and greater flood potential during the wet season. Consequently, the region faces dual hydrological threats: prolonged water deficits and increased flood exposure within the same annual cycle. Drought persistence is expected to intensify, with maximum consecutive drought runs extending up to 10–11 months in future projections. The underlying mechanisms include increased actual evapotranspiration, which accelerates soil moisture depletion, enhanced rainfall variability, which drives the sequencing of floods and droughts, and catchment storage properties, which govern hydrological resilience. These interconnected processes alter the timing and clustering of drought events, concentrating hydrological stress during periods that are sensitive to agriculture. Overall, drought behavior in northern Thailand is projected to intensify in a spatially heterogeneous pattern, emphasizing the need for localized, integrated adaptation measures and flexible water management strategies to mitigate future risks of drought. Full article

In this work, we introduce a new four-parameter distribution, called the integrated linear–Weibull (ILW) model, constructed by embedding a dynamic linear component within the Weibull framework. The ILW distribution is capable of capturing a wide variety of symmetric and asymmetric density shapes and accommodates diverse failure-rate behaviors. We derive several of its key mathematical and statistical properties, including moments, extropy, cumulative residual entropy, order statistics, and their asymptotic forms. The mean residual life function and its reciprocal relationship with the failure rate are also obtained. An algorithm for generating random samples from the ILW distribution is provided, and model identifiability is examined numerically through the Kullback–Leibler divergence. Parameter estimation is carried out via maximum likelihood, and a simulation study is conducted to assess the accuracy of the estimators; the results show improvements in estimator performance as sample size increases. Finally, three real datasets involving failure-time observations and one describing hydrological and epidemiological data are analyzed to demonstrate the practical usefulness of the ILW model. In these applications, the proposed model exhibits competitive or superior performance relative to several existing lifetime distributions based on standard model selection criteria and goodness-of-fit measures. Full article

Zeolite is a popular soil amendment capable of improving physical and chemical properties of soils. This study investigates how zeolite concentration affects the hydrological connectivity of sandy loam soil. Soil samples with different zeolite concentrations C_z (0, 1, 1.5, 2.5, 5, 10, 15, and 30%) were analyzed for changes in water dynamics through Fast Field Cycling Nuclear Magnetic Resonance (FFC-NMR) relaxometry. FFC-NMR data revealed that the investigated zeolite can modify the pore size distribution in a wide range (1–15%) of C_z, as the zeolite particle size distribution has a percentage of coarse particles (56%) appreciably higher than that of the original soil (37%). Moreover, a concentration of 1% produces a more relevant increase in the soil’s meso- and macropores, while for C_z > 1.5%, the change in pore size distribution is damped by the increase in water retention that occurs upon increasing zeolite concentration. The analysis also demonstrated that C_z = 1% is sufficient to achieve the highest values of both structural and functional connectivity indexes. In conclusion, for sandy loam soil, adding a zeolite concentration of 1% is sufficient to improve the soil’s physical characteristics, with significant effects on soil hydrological behavior, and can be considered a valid practice to manage the addition of a water resource to the soil. Full article

Shallow lakes are highly sensitive to hydrological changes and human activities; however, the effect of hydrological extremes on water quality dynamics remains unclear. In this study, we investigated hydroclimatic and water quality changes in Datong Lake (a typical shallow lake within the Yangtze River Basin) over the period 2021–2024, with the objective of detecting the dynamic response of lake water quality to its driving factors during extreme hydrological years. Our analysis suggested that precipitation, water level, and temperature of Datong Lake all fluctuated during the study period. Total nitrogen (TN) concentrations increased to 1.25 mg/L, 1.42 mg/L, and 1.05 mg/L in the lake, inlets, and outlet, respectively, driven largely by external nutrient inputs from agricultural and aquacultural activities. Precipitation and water level were significantly higher in the wet year (1051.15 mm and 27.26 m, respectively) than in the dry year (805.05 mm and 27.05 m, respectively). TN and total phosphorus (TP) concentrations at the river inlet were higher in wet years than in dry years, whereas TN and TP in the lake showed the opposite trend. Notably, both TN and TP were positively correlated with temperature, water level, and turbidity, and negatively correlated with dissolved oxygen and electrical conductivity. Among these drivers, turbidity emerged as key influential variable (R² ranging from 0.18 to 0.41) in modulating lake water quality during extreme hydrological years, followed by temperature (R² ranging from 0.11 to 0.17) and water level (R² ranging from 0.12 to 0.13). These findings reveal that extreme hydrological shifts drive changes in lake water quality, underscoring the necessity of integrated management strategies to alleviate climate change impacts on shallow lake ecosystems. Full article

The accurate mapping of soil texture, a key determinant of soil’s hydrological and nutritional behavior, is essential for agricultural drought assessment, yet the application of multi-temporal satellite data for this purpose remains largely unexplored. In this study, we first identified the optimal prediction period by evaluating the performance of single-date imagery (satellite images captured on individual observation dates). Subsequently, dual-phase imagery (DPI) was developed to increase mapping accuracy. Finally, these refined predictions quantified soil texture’s response to drought and its corresponding thresholds. Results demonstrated that: (1) the bare soil period in April provided peak prediction accuracy for all texture fractions (Sand: R² = 0.617, RMSE = 10.21%; Silt: R² = 0.606, RMSE = 8.648%; Clay: R² = 0.604, RMSE = 1.945%); (2) Significant accuracy gain from DPI using April-August imagery fusion (Sand: R² = 0.677, RMSE = 9.386%; Silt: R² = 0.660, RMSE = 8.034%; Clay: R² = 0.658, RMSE = 1.807%); (3) sand content was the most critical factor influencing crop drought stress, with a threshold of 31%. By integrating multi-temporal satellite observations with quantitative drought evaluation for high-resolution soil texture mapping and precision agricultural management in Northeast China’s black soil region. Full article