Hydrology

19 pages, 2459 KB

Open AccessArticle

Multivariate RVA Assessment of Hydrological Alterations: Huangshui River, Xining

by Wanqi Wang, Hao Wang, Feng Wang, Xiaohui Lei, Xiaoyan Wei and Kang Li

Hydrology 2025, 12(12), 313; https://doi.org/10.3390/hydrology12120313 - 26 Nov 2025

Indicators of Hydrologic Alteration (IHA) are commonly screened with the Range of Variability Approach (RVA), which captures frequency shifts but can miss changes in central tendency, dispersion, distributional shape, and trend. We propose a Comprehensive Degree (CD) index that integrates RVA with these [...] Read more.

Indicators of Hydrologic Alteration (IHA) are commonly screened with the Range of Variability Approach (RVA), which captures frequency shifts but can miss changes in central tendency, dispersion, distributional shape, and trend. We propose a Comprehensive Degree (CD) index that integrates RVA with these four statistical dimensions and apply it to daily discharge at the Xining station on the Huangshui River (1954–2022). Using conventional RVA, the overall alteration was 61.16% (moderate). After integration, alteration increased by 7.59% to 68.75%, reclassifying the regime as high. Across 32 Indicators, 15 showed larger alteration and 12 moved up one class, whereas 17 decreased and 2 moved down. Distributional shape and trend dominated the signal, revealing strongly altered ecohydrological indicators—most notably low-pulse frequency/duration and 3-day minimum—and, additionally, flagging indicators that RVA downplays (e.g., April–August monthly flows) via high trend and distributional shape shifts. The framework addresses RVA’s frequency-only blind spots, is more robust to short-term or episodic fluctuations, and improves diagnostic precision and ecological interpretability. These results provide a decision-ready basis for adaptive environmental flow management in climatically sensitive, topographically complex plateau basins. Full article

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Graphical abstract

20 pages, 4437 KB

Open AccessArticle

Watershed Runoff Simulation and Prediction Based on BMA Coupled SWAT-LSTM Model

by Wenju Zhao, Yongwei Hao, Yongming Zhang, Haiying Yu and Xing Li

Hydrology 2025, 12(12), 312; https://doi.org/10.3390/hydrology12120312 - 24 Nov 2025

Abstract

In response to the issues of low runoff prediction accuracy and difficulty in parameter determination in regions frequently experiencing extreme hydrological events, this study is based on data such as digital elevation, land use, soil type, and meteorology. The SWAT-LSTM (Long Short-Term Memory) [...] Read more.

In response to the issues of low runoff prediction accuracy and difficulty in parameter determination in regions frequently experiencing extreme hydrological events, this study is based on data such as digital elevation, land use, soil type, and meteorology. The SWAT-LSTM (Long Short-Term Memory) model is coupled based on the Bayesian Model Averaging (BMA) method. The simulation accuracies of the optimized model are, respectively, compared with those of the SWAT (Soil and Water Assessment Tool) model and the SWAT-LSTM model. Taking the Zuli River Basin as an example, the optimal runoff prediction model for this basin is determined. Combining with future meteorological data, runoff predictions for the period from 2025 to 2030 are carried out. The findings indicate that the SWAT-LSTM-BMA coupled model is the optimal runoff prediction model for the Zuli River Basin. Compared with the SWAT model and the SWAT-LSTM model used alone, its simulation accuracy has been systematically improved. During the calibration period, R² increased by 8–12%, NSE increased by 9–13%, and MSE decreased by 14–30%. During the validation period, R² increased by 10–12%, NSE increased by 10–14%, and MSE decreased by 16–31%. Based on the model and the prediction of future climate data under multiple scenarios, the annual runoff of the basin will show a decreasing trend compared with the historical period between 2025 and 2030, with a decrease of 12–15%. The coupling framework proposed in this study effectively improves the accuracy of runoff prediction and provides a reliable theoretical foundation and technological assistance for revealing the evolution law of extreme hydrological events and the management of water resources in the basin. Full article

(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition)

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16 pages, 2253 KB

Open AccessArticle

Coupled Impacts of Bed Erosion and Roughness Variation on Stage-Discharge Relationships: A 1D Hydrodynamic Modeling Analysis of the Regulated Jingjiang Reach

by Yanqing Li, Minglong Dai, Dongdong Zhang and Yingqi Chen

Hydrology 2025, 12(12), 311; https://doi.org/10.3390/hydrology12120311 - 22 Nov 2025

Abstract

The stage-discharge relationship in the Jingjiang Reach of the Yangtze River has undergone significant alterations due to post-Three Gorges Reservoir (TGR) operation effects, notably bed erosion and roughness variation. This study employs a calibrated 1D hydrodynamic model based on Saint-Venant equations. The model [...] Read more.

The stage-discharge relationship in the Jingjiang Reach of the Yangtze River has undergone significant alterations due to post-Three Gorges Reservoir (TGR) operation effects, notably bed erosion and roughness variation. This study employs a calibrated 1D hydrodynamic model based on Saint-Venant equations. The model was validated with high accuracy (Nash-Sutcliffe efficiency >0.94 at key stations) using long-term hydrological data (1996–2022). Four scenarios were simulated: pre-dam conditions, post-dam topography with pre-dam roughness, pre-dam topography with increased roughness, and coupled post-dam changes. A novel scenario-based decomposition framework was developed to isolate individual and coupled factor contributions, advancing beyond traditional descriptive approaches. The results indicate that upstream water level changes are mainly controlled by riverbed erosion (e.g., at the Zhicheng Station: the topographic contribution rate exceeds 80% at a flow rate of 5000 m³/s, resulting in a water level drop of approximately 1.7 m), while downstream, an increase in roughness becomes the dominant factor (e.g., at the Jianli Station: causing a water level rise of about 1.0 m at a flow rate of 13,000 m³/s, with such changes being particularly pronounced under low-flow conditions). Spatially, topographic influence attenuates downstream, whereas roughness sensitivity amplifies in high-sinuosity reaches (bend coefficient: 3.0). Seasonally, the topographic contribution rate remains stable overall during the low-flow period, e.g., within a narrow range of 0.88–0.98 at Zhicheng Station, while roughness effects exhibit negative values in dry periods (November) due to fine sediment deposition. The coupling effect in mid-discharge ranges (15,000–20,000 m³/s) at Jianli partially offsets stage reductions. These findings not only provide critical insights for flood forecasting and navigation management in the Jingjiang Reach but also offer a transferable methodology for quantifying hydro-morphodynamic interactions in global regulated rivers, highlighting the model’s utility in predictive water resource management. Full article

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25 pages, 6426 KB

Open AccessArticle

Dissolved Ion Distribution in a Watershed: A Study Utilizing Ion Chromatography and Non-Parametric Analysis

by Selline Okechi, Keisuke Nakayama and Katsuaki Komai

Hydrology 2025, 12(12), 310; https://doi.org/10.3390/hydrology12120310 - 22 Nov 2025

Abstract

This study presents a unique approach for characterizing ion distribution within the Kushiro River catchment basin, which is characterized by exceptionally high dissolved ion concentrations. principal component analysis, Mann–Whitney U test, and neural network modeling were employed to analyze data from 11 distinct [...] Read more.

This study presents a unique approach for characterizing ion distribution within the Kushiro River catchment basin, which is characterized by exceptionally high dissolved ion concentrations. principal component analysis, Mann–Whitney U test, and neural network modeling were employed to analyze data from 11 distinct locations in two different seasons. The 11 sampling locations were subsequently classified into five distinct groups to facilitate precise analysis of the ion distribution using neural networks. Two principal components were also employed to visualize and interpret our dataset. Compositional similarities and seasonal variations in ion distribution were identified, as well as the key variability patterns, thereby revealing underlying correlations among the dissolved ions. Our findings highlighted that Group 1, encompassing a caldera lake, exhibits the highest dissolved ion concentrations. This observation may be attributed to the geological characteristics of the underlying rock formation. Furthermore, a significant correlation was observed between the major dissolved ions present in the catchment basin, as evidenced by positive correlation coefficients. Conversely, nitrate ions exhibited a negative correlation with F⁻, Cl⁻, and Na⁺ ions. This comprehensive analytical framework offers a robust and insightful tool for determining ion distribution within catchment basins with significant implications for environmental monitoring and sustainable resource management. Full article

(This article belongs to the Section Soil and Hydrology)

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26 pages, 6770 KB

Open AccessArticle

TopEros: An Integrated Hydrology and Multi-Process Erosion Model—A Comparison with MUSLE

by Emmanuel Okiria, Noda Keigo, Shin-ichi Nishimura and Yukimitsu Kobayashi

Hydrology 2025, 12(11), 309; https://doi.org/10.3390/hydrology12110309 - 20 Nov 2025

Abstract

Hydro-erosion is a primary driver of soil degradation worldwide, yet accurate catchment-scale prediction remains challenging because sheet, gully, and raindrop-impact detachment processes operate simultaneously at sub-grid scales. We introduce TopEros, a hydro-erosion model that integrates the hydrological framework of TOPMODEL with three distinct [...] Read more.

Hydro-erosion is a primary driver of soil degradation worldwide, yet accurate catchment-scale prediction remains challenging because sheet, gully, and raindrop-impact detachment processes operate simultaneously at sub-grid scales. We introduce TopEros, a hydro-erosion model that integrates the hydrological framework of TOPMODEL with three distinct erosion modules: sheet erosion, gully erosion, and raindrop-impact detachment. TopEros employs a sub-grid zoning strategy in which each grid cell is partitioned into diffuse-flow (sheet erosion) and concentrated-flow (gully erosion) domains using threshold values of two topographic indices: the topographic index (TI) and the contributing area–slope index (a_itanβ). Applied to the Namatala River catchment in eastern Uganda and calibrated with TI = 15 and a_itanβ = 35, TopEros identified sheet-dominated and gully-prone areas. The simulated specific sediment yields ranged from 95 to 155 Mgha⁻¹yr⁻¹—classified as “high” to “very high”—with gully zones contributing disproportionately large erosion volumes. These results demonstrate the importance of capturing intra-cell heterogeneity: conventional catchment-average approaches can obscure critical erosion hotspots. By explicitly representing multiple soil detachment and transport mechanisms within a unified process-based framework, TopEros has the potential to enhance the realism of catchment-scale erosion estimates and support the precise targeting of soil and water conservation measures. Full article

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28 pages, 7124 KB

Open AccessArticle

Flash Drought Assessment: Insights from a Selection of Mediterranean Islands, Greece

by Chrysoula Katsora, Evangelos Leivadiotis, Nektaria Papadopoulou, Isavela Monioudi, Efthymia Kostopoulou, Petros Gaganis, Aris Psilovikos and Ourania Tzoraki

Hydrology 2025, 12(11), 308; https://doi.org/10.3390/hydrology12110308 - 18 Nov 2025

Abstract

Flash droughts are a significant natural hazard, characterized by rapid onset and potential to cause substantial economic and environmental impacts. This study utilizes ERA5 soil moisture data to identify and define historical flash drought (FD) events in the Northeastern Aegean islands (specifically Chios, [...] Read more.

Flash droughts are a significant natural hazard, characterized by rapid onset and potential to cause substantial economic and environmental impacts. This study utilizes ERA5 soil moisture data to identify and define historical flash drought (FD) events in the Northeastern Aegean islands (specifically Chios, Lemnos, Lesvos and Samos). Hourly soil moisture data, spanning from 1990 to the present, covering three soil layers (0–7 cm, 7–28 cm and 28–100 cm), were analyzed and mapped onto a 0.1° × 0.1° grid with a native resolution of approximately 9 km. Additionally, the Standardized Precipitation Evapotranspiration Index (SPEI) was applied to the island of Lesvos, using precipitation and average temperature data from the local meteorological stations. The number and characteristics of these events—including frequency, duration, decline rate, magnitude, intensity, recovery rate and recovery duration—were produced to construct a regional overview of FD risk across the Northeastern Aegean Islands. These results reveal a considerable variability in the spatial, seasonal and temporal distribution of past FD events. Furthermore, this study highlights the value of using satellite-derived soil moisture data for identifying FD events and demonstrates that analyzing this data with field temperature and precipitation measurements enables a more localized and accurate interpretation of past events. This approach facilitates the definition of FD “hotspot” areas, which, when combined with further investigation, can lead to the development of a predictive FD model. Full article

(This article belongs to the Section Hydrology–Climate Interactions)

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21 pages, 4290 KB

Open AccessArticle

Robust and Fast Sensing of Urban Flood Depth with Social Media Images Using Pre-Trained Large Models and Simple Edge Training

by Lin Lin, Zhenli Zeng, Chaoqing Tang, Yilin Xie and Qiuhua Liang

Hydrology 2025, 12(11), 307; https://doi.org/10.3390/hydrology12110307 - 17 Nov 2025

Abstract

Accurately estimating urban floodwater depth is a critical step in enhancing urban resilience and strengthening disaster prevention and mitigation capabilities. Traditional methods relying on hydrological monitoring stations and numerical simulations suffer from limitations such as sparse spatial coverage, insufficient validation data, limited accuracy, [...] Read more.

Accurately estimating urban floodwater depth is a critical step in enhancing urban resilience and strengthening disaster prevention and mitigation capabilities. Traditional methods relying on hydrological monitoring stations and numerical simulations suffer from limitations such as sparse spatial coverage, insufficient validation data, limited accuracy, and delayed fast performance. In contrast, social media data—characterized by its vast volume and fast availability, can effectively compensate for these shortcomings. When processed using artificial intelligence (AI) algorithms, such data can significantly improve credibility, disaster perception speed, and water depth estimation accuracy. To address these challenges, this paper proposes a robust and widely applicable method for rapid urban flood depth perception. The approach integrates AI technology and social media data to construct an AI framework capable of perceiving urban physical parameters through multimodal big data fusion without costly model training. By leveraging the near real-time and widespread nature of social media, an automated web crawler collects flood images and their textual descriptions (including reference objects), eliminating the need for additional hardware investments. The framework uses predefined prompts and pre-trained models to automatically perform relevance verification, duplicate filtering, object detection, and feature extraction, requiring no manual data annotation or model training. With only a minimal amount of water depth annotated data and compressed cross-modal feature vectors as training input, a lightweight Multilayer Perceptron (MLP) achieves high-precision depth estimation based on reference objects. This method avoids the need for large-scale model fine-tuning, allowing rapid training even on devices without GPUs. Experiments demonstrate that the proposed method reduces the Mean Square Error (MSE) by over 80%, processes each image in less than 0.5 s (more than 20 times faster than existing large-model approaches), and exhibits strong robustness to changes in perspective and image quality. The solution is fully compatible with existing infrastructure such as surveillance cameras, offering an efficient and reliable approach for fast flood monitoring in urban hydrology and water engineering applications. Full article

(This article belongs to the Special Issue Advances in Urban Hydrology and Stormwater Management)

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21 pages, 7782 KB

Open AccessArticle

Groundwater Recharge Estimation Based on Environmental Isotopes, Chloride Mass Balance and SWAT Model in Arid Lands, Southwestern Saudi Arabia

by Milad Masoud, Maged El Osta, Jalal Basahi, Burhan Niyazi, Nassir Al-Amri, Michael Schneider, Abdulaziz Alqarawy and Riyadh Halawani

Hydrology 2025, 12(11), 306; https://doi.org/10.3390/hydrology12110306 - 16 Nov 2025

Abstract

Estimated groundwater recharge is considered the essential factor for groundwater management and sustainability, especially in arid lands such as the Kingdom of Saudi Arabia (KSA). Consequently, assessing groundwater recharge is a key process for forecasting groundwater accessibility to sustain safe withdrawal. So, this [...] Read more.

Estimated groundwater recharge is considered the essential factor for groundwater management and sustainability, especially in arid lands such as the Kingdom of Saudi Arabia (KSA). Consequently, assessing groundwater recharge is a key process for forecasting groundwater accessibility to sustain safe withdrawal. So, this study focused on environmental isotopes, the chloride mass balance (CMB) method, and a SWAT model by integrating GIS with hydrological and hydrochemical techniques to detect the origin of coastal aquifer groundwater and to compute the recharging rate in the study area. This study is based on the results of chemical analysis of 78 groundwater samples and environmentally stable isotopes, including deuterium (²H) and oxygen-¹⁸O, in 29 representative samples. The results revealed that the origin of groundwater recharge comes through precipitation, where the ranges of δ¹⁸O and δ²H isotopes in the analyzed groundwater were from −1.10‰ to +1.03‰ and from −0.63‰ to 11.63‰, respectively. The CMB finding for estimating the average recharge is 3.57% of rainfall, which agrees with a previous study conducted in the wadi Qanunah basin (north of the study area), where the estimated average value of recharge was 4.25% of rainfall. Meanwhile, the estimated annual recharge using a SWAT model ranged between 1 mm and 16.5 mm/year at an average value of approximately 8.75 mm/year. The results obtained by the two techniques are different due to some reasons such as the presence of additional chloride sources, as well as evaporation. Outputs of this study will be valuable for the local community, officials, and decision-makers who are concerned with groundwater resources. Full article

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16 pages, 2077 KB

Open AccessArticle

Snowmelt Volume from Rain-on-Snow Events Under Controlled Temperature and Rainfall: A Laboratory Experimental Study

by Wenjun Liu, Gulimire Hanati, Keke Hu, Sulitan Danierhan and Lei Jin

Hydrology 2025, 12(11), 305; https://doi.org/10.3390/hydrology12110305 - 16 Nov 2025

Abstract

Rain-on-snow (ROS) events profoundly influence mixed rain–snow flooding and the water resource cycle. However, current research regarding ROS events remains predominantly reliant on existing datasets, lacking detailed controlled experiments under variable conditions. This study employed control variables and an orthogonal experimental design to [...] Read more.

Rain-on-snow (ROS) events profoundly influence mixed rain–snow flooding and the water resource cycle. However, current research regarding ROS events remains predominantly reliant on existing datasets, lacking detailed controlled experiments under variable conditions. This study employed control variables and an orthogonal experimental design to conduct laboratory-controlled experiments simulating ROS events with different temperatures, rainfall intensities, and rainfall durations. Observations and analyses were performed on the snowmelt volumes during and after events. The results indicate that ROS events significantly accelerate snowmelt rates and increase total snowmelt volume. Under low-intensity ROS, snowmelt volume exhibits greater sensitivity to temperature changes. A temperature threshold exists between 2 °C and 6 °C; beyond this threshold, the melting rate accelerates and ablation volume increases. Under high-intensity ROS, rainwater becomes the dominant factor driving snowpack ablation. When rainfall intensity exceeds 60 mm·h⁻¹, it triggers a sharp increase in snowmelt volume. Concurrently, following an ROS event, snowpacks subjected to low-intensity rainfall exhibit a stronger rainwater retention capacity, an effect that becomes more pronounced at lower temperatures. Additionally, snowmelt volume increases with prolonged rainfall duration, with the increment in snowmelt volume attributable to extended rainfall time being greater under weaker rainfall intensities. These findings provide a scientific reference for better understanding ROS-related disasters mechanisms. Full article

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16 pages, 13612 KB

Open AccessArticle

Integrated Multi-Scale Hydrogeophysical Characterisation of a Coastal Phreatic Dune Aquifer: The Belvedere–San Marco Case Study (NE Italy)

by Benedetta Surian, Emanuele Forte and Luca Zini

Hydrology 2025, 12(11), 304; https://doi.org/10.3390/hydrology12110304 - 15 Nov 2025

Abstract

Low-lying coastal plains are increasingly threatened by saltwater intrusion, yet the extent of the phenomenon and the role of coastal dune systems remain unevenly assessed. In the northern Adriatic Sea (NE Italy), salinisation has been documented, but systematic, spatially resolved studies are lacking. [...] Read more.

Low-lying coastal plains are increasingly threatened by saltwater intrusion, yet the extent of the phenomenon and the role of coastal dune systems remain unevenly assessed. In the northern Adriatic Sea (NE Italy), salinisation has been documented, but systematic, spatially resolved studies are lacking. This work investigates the Belvedere–San Marco relict dune system to assess its hydrogeological function and vulnerability to seawater intrusion. An integrated methodology combining borehole and core stratigraphy, in situ water electrical conductivity (EC) measurements, and multi-method geophysical surveys (FDEM, ERT, GPR, active seismics) was tested. Results reveal a consistent stratigraphy of permeable aeolian sands overlying clay-rich units, with groundwater EC values in the dune sector always remaining well below thresholds for brackish or saline conditions. Geophysical imaging reveals that the dunes are low-conductive bodies contrasting sharply with the conductive surrounding lowlands, thus indicating the persistence of a freshwater lens sustained by local recharge within the dunes. The Belvedere–San Marco dunes therefore act as both freshwater reservoirs and natural hydraulic barriers, buffering shallow aquifers against salinisation. This study demonstrated the applicability of integrated geophysical methods to extensively investigate shallow phreatic aquifers lying a few metres below the surface, and establishes a baseline for monitoring future changes under rising sea levels, subsidence, and increased groundwater exploitation. Full article

(This article belongs to the Special Issue Characterization and Monitoring of Coastal Hydrological Environment for Assessing the Impact of Seawater Intrusion on Coastal Aquifers)

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15 pages, 5222 KB

Open AccessArticle

Multi-Objective Optimization of the Physical Design of a Horizontal Flow Subsurface Wetland

by Jhonatan Mendez-Valencia, Carlos Sánchez-López, Eneida Reyes-Pérez, Rocío Ochoa-Montiel, Lucila Marquez-Pallares, Juan Aguila-Muñoz, Fredy Montalvo-Galicia, Miguel Angel Carrasco-Aguilar, Jorge Alberto Sánchez-Martínez and Jorge Arellano-Hernández

Hydrology 2025, 12(11), 303; https://doi.org/10.3390/hydrology12110303 - 14 Nov 2025

Abstract

Decontamination of wastewater, industrial effluents, stormwater, and graywater can be carried out through the use of natural or constructed wetlands. In either case, the natural functions of soil, vegetation, and organisms are widely applied for the treatment of contaminated water. In particular, in [...] Read more.

Decontamination of wastewater, industrial effluents, stormwater, and graywater can be carried out through the use of natural or constructed wetlands. In either case, the natural functions of soil, vegetation, and organisms are widely applied for the treatment of contaminated water. In particular, in the physical design of a constructed wetland, several operational factors must be adjusted with the aim of reducing pollution levels. Although various fully customized design methodologies have been developed and reported in the literature, they often fail to meet the required decontamination objectives. In this context, the application of the NSGA-II evolutionary algorithm is adequate to optimize the physical design of a horizontal subsurface flow wetland for graywater treatment, focusing specifically on the removal of biodegradable organic matter (BOD⁵). Four competing objectives are considered: minimizing physical volume and total design cost, while maximizing contaminant removal efficiency and graywater flow rate. Five constraint functions are also incorporated: removal efficiency greater than 95%, physical volume below 1000 m³, flow rate above 10 m³/d, a limit on total construction cost of MXN 1,000,000, and maintaining a length-to-width ratio greater than or equal to 2 but less than or equal to 4. The proposed methodology generates a wide set of non-dominated solutions, visualized through Pareto surfaces, which highlight the trade-offs among different objectives. This approach offers the possibility of selecting optimal designs under specific conditions, which underscores the limitations of conventional single-solution models. The results show that the methodology consistently achieved removal efficiencies above 95%, with construction costs within budget and physical volumes below the established limit, offering a more versatile and cost-effective alternative. This work demonstrates that the integration of NSGA-II into wetland design is an effective and adaptable strategy, capable of providing sustainable alternatives for graywater treatment and constituting a valuable decision-making tool. Full article

(This article belongs to the Special Issue First Papers by Young Investigators in the Hydrological Sciences (2025-2026))

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26 pages, 2546 KB

Open AccessArticle

Characterization of Hyetograms and Rainfall Patterns in Southern Amazonia

by Brenda Buose, Daniela Roberta Borella, Frederico Terra de Almeida and Adilson Pacheco de Souza

Hydrology 2025, 12(11), 302; https://doi.org/10.3390/hydrology12110302 - 14 Nov 2025

Abstract

The variability of rainfall, mainly convective, in the southern Amazon remains poorly understood due to the limited number of studies examining the relationships between the intensities and durations of rainfall events in this region. This study aimed to characterize the intensity patterns—hyetograms (advanced, [...] Read more.

The variability of rainfall, mainly convective, in the southern Amazon remains poorly understood due to the limited number of studies examining the relationships between the intensities and durations of rainfall events in this region. This study aimed to characterize the intensity patterns—hyetograms (advanced, intermediate, delayed, and constant, as well as observations of new patterns)—in the northern state of Mato Grosso (southern Amazon). Generally, most research in Brazil on this topic has focused on other regions of the country or used simulations or data disaggregation processes, limiting the representation of the regional reality. Historical data series from five conventional stations (with pluviograms) and ten automatic stations with data obtained by tipping rain gauges were analyzed. The analysis involved classifying 6187 events into four main patterns: Advanced (53.52%), Intermediate (31.74%), Delayed (14.58%), and Constant (less than 1%), with 93 events unclassified. The hourly distribution of rainfall revealed greater occurrence in the afternoon and evening periods, suggesting a predominance of thermal convection in regional dynamics. The results offer valuable insights for water planning, agricultural security, and adaptive infrastructure, in addition to promoting integration between science, engineering, and public policies aimed at environmental management and risk prevention. Full article

(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables: 2nd Edition)

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18 pages, 4389 KB

Open AccessArticle

A Pressure-Impact Approach to Assess Contamination and Risk in Surface Water Bodies

by Siwar Aydi, Javier Paredes-Arquiola, Rafael J. Bergillos, Abel Solera and Joaquín Andreu

Hydrology 2025, 12(11), 301; https://doi.org/10.3390/hydrology12110301 - 14 Nov 2025

Abstract

This study assesses the chemical state of surface water bodies (SWBs) in the Júcar River Basin District (Spain), specifically focusing on contaminants such as nickel, lead, imazalil, and thiabendazole. To identify risky zones, the RREA model was combined with a Python-based subroutine to [...] Read more.

This study assesses the chemical state of surface water bodies (SWBs) in the Júcar River Basin District (Spain), specifically focusing on contaminants such as nickel, lead, imazalil, and thiabendazole. To identify risky zones, the RREA model was combined with a Python-based subroutine to estimate the minimum non-compliance load (MNCL). The results show that many SWBs fail to meet water quality criteria due to point source pollution. The RREA (Rapid Response to Environmental status) model improves monitoring capacities by confirming SWB chemical statuses and detecting locations that have not been monitored or assessed thoroughly. The study also analyzes confidence levels by comparing MNCL to the current accumulated load (CAL), allowing for the identification and prioritization of important non-compliant SWBs and locations that require additional examination. This methodology not only enhances the accuracy of compliance evaluations but also serves as a useful tool for targeted water quality management initiatives. The results of this paper highlight the potential of the proposed pressure-impact approach to assess the chemical state of SWBs. This approach is useful to support sustainable management measures that mitigate water quality issues and preserve the environmental status of SWBs. Full article

(This article belongs to the Section Water Resources and Risk Management)

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18 pages, 13668 KB

Open AccessArticle

Mudflow Hazard on Rivers in the Khamar-Daban Mountains (East Siberia): Hydroclimatic and Geomorphological Prerequisites

by Natalia V. Kichigina, Marina Y. Opekunova, Artem A. Rybchenko and Anton A. Yuriev

Hydrology 2025, 12(11), 300; https://doi.org/10.3390/hydrology12110300 - 12 Nov 2025

Abstract

Hydroclimatic and geomorphological prerequisites for mudflow hazard were studied using data on several of the largest flood events in the Khamar-Daban mountain area (Lake Baikal, East Siberia) for the period from 1966 to 2022. The data include flood-forming precipitation and atmospheric circulation patterns, [...] Read more.

Hydroclimatic and geomorphological prerequisites for mudflow hazard were studied using data on several of the largest flood events in the Khamar-Daban mountain area (Lake Baikal, East Siberia) for the period from 1966 to 2022. The data include flood-forming precipitation and atmospheric circulation patterns, the amount of related suspended sediment discharge in the years of high floods, as well as terrain features favorable for the formation of catastrophic floods and mudflows. Floods and mudflows in the area can arise under conditions of extremely high daily precipitation (up to 200 mm or more) after the territory becomes moistened by prolonged rainfall under meridional air transport. The maximum water discharge correlates with a multifold increase in the suspended sediment discharge and turbidity. The increase in sediment discharge associated with maximum water discharge (floods) of ≤10% probability is apparently due to 4–9 times higher flow rates. On the other hand, the formation of the solid runoff component in the area is controlled geomorphologically by slope processes depending on slope steepness, elevation contrasts, and the thickness of soft sediments subject to denudation and transport. The geomorphological conditions are most favorable for the development of mudflows and catastrophic floods in the catchments of the Bezymyannaya, Slyudyanka, Khara-Murin, and Utulik rivers. Floods and mudflows are especially hazardous on the southern shore of Lake Baikal, encircled by the Khamar-Daban Range, where active mudflow processes pose risks to the towns of Slyudyanka and Baikalsk, as well as to the sludge storage facilities of the abandoned Baikal Pulp and Paper Mill. Full article

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13 pages, 6551 KB

Open AccessArticle

Research on Remote Sensing Inversion of Total Phosphorus in East Juyan Lake Based on Machine Learning

by Yi Zhou, Weilong Yang, Ming Hu, Junnan Li and Xiaotong Liu

Hydrology 2025, 12(11), 299; https://doi.org/10.3390/hydrology12110299 - 11 Nov 2025

Abstract

Timely and accurate monitoring of lakes’ water quality is crucial for assessing regional ecological health and implementing targeted conservation activities. Compared with traditional in situ water quality measurement methods, satellite remote sensing technology is more cost-effective and convenient, and also enables long-term time-series [...] Read more.

Timely and accurate monitoring of lakes’ water quality is crucial for assessing regional ecological health and implementing targeted conservation activities. Compared with traditional in situ water quality measurement methods, satellite remote sensing technology is more cost-effective and convenient, and also enables long-term time-series monitoring. This study utilizes Sentinel-2 multispectral imagery, selects East Juyan Lake as the study area, and employs measured water quality data from 30 in situ sampling points as training and testing samples. Using the correlation coefficient, root mean square error, and mean absolute error as evaluation metrics, a Grid Search-based XGBoost machine-learning method is applied to invert the concentration of total phosphorus (TP), a key parameter for water quality assessment. The experiments demonstrate that: (1) The XGBoost model, after parameter tuning via Grid Search, achieved the highest inversion accuracy, with R², RMSE, and MRE values of 0.856, 0.017, and 7.20%, respectively; The average TP concentration retrieved for the lake was 0.231 mg/L. This method requires minimal manual setting of numerous training parameters, reducing human intervention. (2) The spatial distribution shows that TP is primarily enriched in the deeper central and eastern parts of the lake, while concentrations are relatively lower in the near-shore vegetation zones and the western shallow water areas. The findings provide a significant reference for remote sensing monitoring of lake water quality and can be used to predict and regulate salinity, eutrophication, and similar conditions in comparable lakes. Full article

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15 pages, 1533 KB

Open AccessArticle

Chemical and Physical Characterisation of Microplastics Present on Beaches of the Cantabrian Coast, Bay of Biscay (Spain)

by Uxue Uribe-Martinez, Thomas Maupas, Aritz Lapazaran, Ruben Rodriguez, Olivia Gómez-Laserna, María Ángeles Olazabal, Juan F. Ayala-Cabrera and Alberto de Diego

Hydrology 2025, 12(11), 298; https://doi.org/10.3390/hydrology12110298 - 10 Nov 2025

Abstract

We investigated the presence, chemical/morphological characteristics, and distribution of microplastics (MPs, 1–5 mm) in three beaches located at the southeast of the Bay of Biscay, an area where this kind of study is scarce. Sampling was carried out in March 2022/2023 and October [...] Read more.

We investigated the presence, chemical/morphological characteristics, and distribution of microplastics (MPs, 1–5 mm) in three beaches located at the southeast of the Bay of Biscay, an area where this kind of study is scarce. Sampling was carried out in March 2022/2023 and October 2023/2024. The microplastics found were chemically characterised by Raman spectroscopy and morphologically described (size, shape, and colour) by visual observation. A total of 836 MPs were found, with Atxabiribil beach showing the highest mean concentrations (15 MPs kg⁻¹), followed by Sonabia (10 MPs kg⁻¹) and Gorliz (3 MPs kg⁻¹). The highest concentrations were recorded in March 2023 and the lowest ones in March 2024, with no clear seasonal trend. Foam, fragments, and pellets were dominant, although filaments, films, and fibres were also found. White MPs were the most abundant, followed by blue and black items. Polyethylene, polypropylene, and polystyrene, in this order, were the most common polymers. In conclusion, we report here valuable information about the abundance and characteristics of MPs in beaches located in an area poorly investigated previously. The results obtained underline the importance of the implementation of regular monitoring campaigns to estimate the impact and consequences that plastic pollution has in our coastal environments. Full article

(This article belongs to the Special Issue Recent Research Advances in Microplastics in Water and the Environment)

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27 pages, 3207 KB

Open AccessArticle

Interpolation and Machine Learning Methods for Sub-Hourly Missing Rainfall Data Imputation in a Data-Scarce Environment: One- and Two-Step Approaches

by Mohamed Boukdire, Çağrı Alperen İnan, Giada Varra, Renata Della Morte and Luca Cozzolino

Hydrology 2025, 12(11), 297; https://doi.org/10.3390/hydrology12110297 - 10 Nov 2025

Abstract

Complete sub-hourly rainfall datasets are critical for accurate flood modeling, real-time forecasting, and understanding of short-duration rainfall extremes. However, these datasets often contain missing values due to sensor or transmission failures. Recovering missing values (or filling these data gaps) at high temporal resolution [...] Read more.

Complete sub-hourly rainfall datasets are critical for accurate flood modeling, real-time forecasting, and understanding of short-duration rainfall extremes. However, these datasets often contain missing values due to sensor or transmission failures. Recovering missing values (or filling these data gaps) at high temporal resolution is challenging due to the imbalance between rain and no-rain periods. In this study, we developed and tested two approaches for the imputation of missing 10-min rainfall data by means of machine learning (Multilayer Perceptron and Random Forest) and interpolation methods (Inverse Distance Weighting and Ordinary Kriging). The (a) direct approach operates on raw data to directly feed the imputation models, while the (b) two-step approach first classifies time steps as rain or no-rain with a Random Forest classifier and subsequently applies an imputation model to predicted rainfall depth instances classified as rain. Each approach was tested under three spatial scenarios: using all nearby stations, using stations within the same cluster, and using the three most highly correlated stations. An additional test involved the comparison of the results obtained using data from the imputed time interval only and data from a time window containing several time intervals before and after the imputed time interval. The methods were evaluated with reference to two different environments, mountainous and coastal, in Campania region (Southern Italy), under data-scarce conditions where rainfall depth is the only available variable. With reference to the application of the two-step approach, the Random Forest classifier shows a good performance both in the mountainous and in the coastal area, with an average weighted F1 score of 0.961 and 0.957, and an average Accuracy of 0.928 and 0.946, respectively. The highest performance in the regression step is obtained by the Random Forest in the mountainous area with an R² of 0.541 and an RMSE of 0.109 mm, considering a spatial configuration including all stations. The comparison with the direct approach results shows that the two-step approach consistently improves accuracy across all scenarios, highlighting the benefits gained from breaking the data imputation process in stages where different physical conditions (in this case, rain and no-rain) are separately managed. Another important finding is that the use of time windows containing data lagged with respect to the imputed time interval allows capturing the atmospheric dynamics by connecting rainfall instances at different time levels and distant stations. Finally, the study confirms that machine learning models outperform spatial interpolation methods, thanks to their ability to manage data with complicated internal structure. Full article

(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations: 2nd Edition)

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23 pages, 23725 KB

Open AccessArticle

Assessment of Nutrient and Bacteria Contributions from Watersheds with Predominantly Agricultural and Urban Land Uses in Coastal North Carolina

by Charles Humphrey, Guy Iverson, Jude Dilioha and Anna Smith

Hydrology 2025, 12(11), 296; https://doi.org/10.3390/hydrology12110296 - 8 Nov 2025

Abstract

Excess concentrations of fecal indicator bacteria, nitrogen, and phosphorus have caused closure of shellfish growing waters, swimming advisories, eutrophication, and impairment of aquatic habitat in the Tar–Pamlico Estuary, North Carolina. Regulatory requirements to reduce nutrient and bacteria loading to the estuary were enacted [...] Read more.

Excess concentrations of fecal indicator bacteria, nitrogen, and phosphorus have caused closure of shellfish growing waters, swimming advisories, eutrophication, and impairment of aquatic habitat in the Tar–Pamlico Estuary, North Carolina. Regulatory requirements to reduce nutrient and bacteria loading to the estuary were enacted more than two decades ago, but water quality problems persist. The goals of this study were to (1) assess the nutrient and bacteria concentrations and exports from Jacks Creek and Runyon Creek to the Tar–Pamlico Estuary in Coastal North Carolina, USA, and (2) recommend watershed-specific practices to reduce pollutant loadings and improve estuarine water quality. Stream water samples were collected for nutrient, bacteria, and physicochemical property (flow, pH, temperature, turbidity, and dissolved oxygen) analyses from five segments of Jacks Creek and six segments of Runyon Creek. Samples were collected between 8 and 10 times over a two-and-a-half-year period (2021–2024). Mean concentrations of total dissolved nitrogen, total dissolved phosphorus, and E. coli for Jacks Creek (1.55 mg/L, 0.10 mg/L, 502 MPN/100 mL) and Runyon Creek (1.70 mg/L, 0.07 mg/L, 262 MPN/100 mL) exceeded reference conditions or thresholds established by the US EPA (0.69 mg/L, 0.036 mg/L, 126 MPN/100 mL). Therefore, both watersheds have been contributing to the nutrient and bacteria problems of the estuary. Implementation of stormwater control measures in the urbanized Jacks Creek Watershed and agricultural best management practices in the Runyon Creek Watershed is encouraged. Some of the suggested practices have been installed, but additional remediation efforts are needed. Full article

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20 pages, 3186 KB

Open AccessArticle

The Effect of Urbanization on the Groundwater Availability in the Masingini–Mwanyanya Catchment Forest, Unguja Island, Zanzibar (Tanzania)

by Said Suleiman Bakari, Suleyman Majaliwa Kyonda, Kombo Hamad Kai, Federica Giaccio, Giuseppe Sappa and Francesco Maria De Filippi

Hydrology 2025, 12(11), 295; https://doi.org/10.3390/hydrology12110295 - 6 Nov 2025

Abstract

The Island of Unguja in Zanzibar (Tanzania) has experienced an accelerated urban development growth since the 1990s due to a rapidly increasing population. These rapid land demands put additional stress on the country’s ability to plan urban centers, cities, and the management of [...] Read more.

The Island of Unguja in Zanzibar (Tanzania) has experienced an accelerated urban development growth since the 1990s due to a rapidly increasing population. These rapid land demands put additional stress on the country’s ability to plan urban centers, cities, and the management of natural resources. The study aimed to determine the impact of urbanization on groundwater availability in the catchment area of the Masingini–Mwanyanya forest reserves from 1992 to 2022. The study used a detection approach to determine the Land Use Land Cover (LULC) changes for three decades, starting from 1992 to 2022. Landsat remote sensed images of 1992, 2002, 2012, and 2022 were used. Additionally, a paired t-test was conducted to determine the significant changes in mean population growth, urbanization, and humidity. The aquifer recharge evolution analysis was conducted using the QGIS software (3.34.8 released version). Obtained results revealed that for these three decades, the forest areas decreased by 14.5% (i.e., from 8.3 km² in 1992 to 7.1 km² in 2022), while built-up area increased from 0 km² in 1992 to 1.7 km² in 2022. Moreover, the evolution of undesirable Land Use Land Cover (LULC) changes, particularly the persistent conversion of forested areas into built-up zones, has been detected. This trend poses a significant threat to the sustainable management of water resources and catchment forest reserves. The study also indicated a decline in the recharge of the coastal aquifer supplying Zanzibar City, which decreased from 15.5 Mm³ to 11.1 Mm³. These findings highlight that the Masingini Forest Reserve is increasingly encroached by rapid urbanization, which is a phenomenon that may jeopardize the availability and sustainability of groundwater resources in the catchment without proper urban planning. Based on these results, the study recommends further research and upscaling of the existing findings, as well as collaboration with relevant authorities to redefine the Masingini–Mwanyanya forest catchment area to ensure the sustainable use of groundwater resources. Full article

(This article belongs to the Special Issue Impact of Climate Change on Groundwater Resources in Coastal Aquifers: Qualitative and Quantitative Assessments)

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