Next Issue
Volume 12, December
Previous Issue
Volume 12, October
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.0
3.1
Journals
Hydrology
Volume 12
Issue 11
share announcement

Hydrology, Volume 12, Issue 11 (November 2025) – 36 articles

Cover Story (view full-size image): Stream flow was measured and water samples were collected to determine nitrogen, phosphorus, and E. coli concentrations along several tributaries of Jacks Creek and Runyon Creek in Coastal North Carolina, USA. These watersheds, dominated by agricultural land use (Jacks Creek) and urban land use (Runyon Creek), discharge to the nutrient-sensitive Tar–Pamlico Estuary. Concentrations of nutrients and E. coli from both creeks exceeded US EPA thresholds. Remediation is recommended through the implementation of agricultural best management practices—including controlled drainage and nutrient management—in the Runyon Creek Watershed. In the Jacks Creek watershed, construction of stormwater control measures—such as wetlands, bioretention systems, and rainwater harvesting systems, along with diversion of gutter downspouts to open space—is recommended. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
26 pages, 6770 KB  
Article
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
Viewed by 2082
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 (aitanβ). Applied to the Namatala River catchment in eastern Uganda and calibrated with TI = 15 and aitanβ = 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
►▼ Show Figures

Figure 1

28 pages, 7124 KB  
Article
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
Cited by 2 | Viewed by 2078
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)
►▼ Show Figures

Figure 1

21 pages, 4290 KB  
Article
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
Cited by 3 | Viewed by 1703
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)
►▼ Show Figures

Figure 1

21 pages, 7782 KB  
Article
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
Cited by 2 | Viewed by 2342
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
►▼ Show Figures

Figure 1

16 pages, 2077 KB  
Article
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
Cited by 3 | Viewed by 1715
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
►▼ Show Figures

Figure 1

16 pages, 13612 KB  
Article
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
Cited by 1 | Viewed by 2285
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)
►▼ Show Figures

Figure 1

15 pages, 5222 KB  
Article
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
Cited by 1 | Viewed by 1497
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))
►▼ Show Figures

Figure 1

26 pages, 2546 KB  
Article
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
Viewed by 1492
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)
►▼ Show Figures

Figure 1

18 pages, 4389 KB  
Article
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
Cited by 3 | Viewed by 1099
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)
►▼ Show Figures

Figure 1

18 pages, 13668 KB  
Article
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
Viewed by 1571
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
►▼ Show Figures

Figure 1

13 pages, 6551 KB  
Article
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
Cited by 3 | Viewed by 1565
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
►▼ Show Figures

Figure 1

15 pages, 1533 KB  
Article
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
Cited by 2 | Viewed by 1631
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)
►▼ Show Figures

Figure 1

27 pages, 3207 KB  
Article
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
Cited by 5 | Viewed by 1651
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)
►▼ Show Figures

Figure 1

23 pages, 23725 KB  
Article
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
Viewed by 1747
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
►▼ Show Figures

Figure 1

20 pages, 3186 KB  
Article
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
Cited by 3 | Viewed by 2535
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)
►▼ Show Figures

Figure 1

20 pages, 3920 KB  
Article
Impact Analysis of Climate Change on Hydropower Resource Development in the Vakhsh River Basin of Tajikistan
by Hailong Liu, Aminjon Gulakhmadov and Firdavs Shaimuradov
Hydrology 2025, 12(11), 294; https://doi.org/10.3390/hydrology12110294 - 5 Nov 2025
Cited by 3 | Viewed by 1816
Abstract
With increasing energy demands and environmental pressures, hydropower, as a clean and renewable energy source, has attracted widespread attention for its development and utilization. However, hydropower systems are highly sensitive to climate change, significantly impacting generation, management, and safety. This study addresses the [...] Read more.
With increasing energy demands and environmental pressures, hydropower, as a clean and renewable energy source, has attracted widespread attention for its development and utilization. However, hydropower systems are highly sensitive to climate change, significantly impacting generation, management, and safety. This study addresses the stability of hydropower resources in the Vakhsh River Basin, Tajikistan, using digital analysis, snowmelt runoff simulation, and soil erosion assessment to estimate spatial distribution. Under three climate scenarios (RCP2.6, RCP4.5, and RCP8.5), hydropower trends were simulated, and soil erosion was quantified. Results show annual hydropower potentials: Garm (55.465 billion kWh/a), Rogun (112.737 billion kWh/a), Nurex (78.853 billion kWh/a). Across all scenarios, runoff and hydropower generation increase (162–328,108 kWh/a), with growth rates following RCP4.5 < RCP2.6 < RCP8.5. Soil erosion simulation results indicate that a one millimeter increase in precipitation could lead to sediment deposition of 1.57 × 106 kWh/year in upstream reservoirs. These results demonstrate that climate change has a significant impact on hydropower development in the Vakhsh River Basin. The research provides technical support for hydropower development under climate change. Full article
(This article belongs to the Topic Advances in Hydrological Remote Sensing)
►▼ Show Figures

Figure 1

21 pages, 5114 KB  
Article
Exploiting Chloride Conservative Tendencies as Contaminant Surrogates in Groundwater Transport Modeling in a Typical Hydrogeological Environment of Northern New Jersey
by Toritseju Oyen and Duke Ophori
Hydrology 2025, 12(11), 293; https://doi.org/10.3390/hydrology12110293 - 4 Nov 2025
Viewed by 1543
Abstract
This study investigates the transport of chloride, a conservative tracer and surrogate for contaminants, in the fractured Brunswick aquifer of northern New Jersey using a dual-porosity MODFLOW-MT3DMS model. Focusing on the First Watchung Mountain region—a microcosm of northern New Jersey’s hydrogeological environment encompassing [...] Read more.
This study investigates the transport of chloride, a conservative tracer and surrogate for contaminants, in the fractured Brunswick aquifer of northern New Jersey using a dual-porosity MODFLOW-MT3DMS model. Focusing on the First Watchung Mountain region—a microcosm of northern New Jersey’s hydrogeological environment encompassing Montclair State University and adjoining communities, the numerical model simulates groundwater flow and solute transport in a hydrogeologically complex, urbanized setting. Results indicate that chloride migrates through the fractured aquifer via both local flow systems (e.g., Third River) and regional flow systems (Passaic River) within decades. Chloride concentrations exceeded the EPA’s 250 mg/L threshold much faster in local discharge streams (5 years in the Third River) compared to regional base-level rivers (79 years in the Passaic River), demonstrating rapid fracture transport versus delayed matrix diffusion. Over 450 years, chlorides traveled approximately 7000 m, demonstrating potential for widespread salinization and contamination. The study also highlights “salting-out” effects, where elevated salinity enhances contaminant retention and complicates remediation efforts in fractured aquifers. These findings emphasize the need for integrated water management strategies, targeted deicing salt reduction, stormwater management, and recharge-zone protection, to mitigate long-term risks in fractured aquifers. By quantifying dual-domain dynamics previously unaddressed in the Brunswick aquifer, this work provides a framework for contaminant transport modeling and management in similar urbanized fractured systems. Full article
►▼ Show Figures

Figure 1

23 pages, 23857 KB  
Article
Differential Changes in Water and Sediment Transport Under the Influence of Large-Scale Reservoirs Connected End to End in the Upper Yangtze River
by Suiji Wang
Hydrology 2025, 12(11), 292; https://doi.org/10.3390/hydrology12110292 - 3 Nov 2025
Cited by 2 | Viewed by 1461
Abstract
The analysis of changing trends of river runoff and sediment discharge and the exploration of their causes are of great significance for formulating sustainable development measures for river basin systems. Based on methods such as trend test, mutation detection, and regression analysis, this [...] Read more.
The analysis of changing trends of river runoff and sediment discharge and the exploration of their causes are of great significance for formulating sustainable development measures for river basin systems. Based on methods such as trend test, mutation detection, and regression analysis, this study conducts a systematic comparative research on the water–sediment processes in the river reach where large-scale cascaded reservoirs connected end to end are located in the upper Yangtze River, and obtains the following key research progress: For the study reach (between Sanduizi and Xiangjiaba Stations), during the period of 1966–2023, the change rates of annual incoming and outgoing runoff were 2.88 × 108 m3·yr−1 and −0.186 × 108 m3·yr−1, respectively, accounting for 0.017% and 0.013% of the annual average runoff. The changing trends were not significant. During the same period, the change rates of Suspended Sediment Load (SSL) at the inlet and outlet of this river reach were −8.0 × 105 t·yr−1 and −46 × 105 t·yr−1, respectively, accounting for 1.25% and 2.45% of their respective annual average sediment discharge. The SSL showed a significant decreasing trend, which was particularly characterized by a sharp reduction at the outlet. The massive sediment retention and multi-mode operation of cascaded reservoirs are the fundamental reasons for the variation in the water–sediment relationship and the sharp decrease in annual SSL in this reach, and they also lead to an obvious adjustment of water and sediment in the river basin that “cuts peaks and fills valleys” within a year. Climate change and other human activities have reduced the sediment input in the study reach. Looking forward to the next few decades, climate factors will remain the dominant factor affecting the inter-annual variation in runoff in the study area. In contrast, human activities such as reservoir operation will continue to fully control the sediment output of the river reach and also restrict the annual distribution of water and sediment. The results of this study can provide a reference for predicting the changing trends of water and sediment in similar river reaches with cascaded reservoir groups and formulating effective river management measures. Full article
(This article belongs to the Special Issue Advances in Catchments Hydrology and Sediment Dynamics (Second Edition))
►▼ Show Figures

Figure 1

26 pages, 7289 KB  
Article
Spatiotemporal Modeling of Surface Water–Groundwater Interactions via Multi-Task Transformer-Based Learning
by Hao Jing, Yong Tian and Chunmiao Zheng
Hydrology 2025, 12(11), 291; https://doi.org/10.3390/hydrology12110291 - 2 Nov 2025
Cited by 1 | Viewed by 2639
Abstract
A spatiotemporal, multi-task learning (MTL) model for simulating surface water–groundwater (SW-GW) dynamics is developed and applied to the Heihe River Basin, Northwest China. The Transformer-based model (MT-TFT) jointly forecasts surface runoff and groundwater levels, outperforming MTL models built on gated recurrent unit (GRU) [...] Read more.
A spatiotemporal, multi-task learning (MTL) model for simulating surface water–groundwater (SW-GW) dynamics is developed and applied to the Heihe River Basin, Northwest China. The Transformer-based model (MT-TFT) jointly forecasts surface runoff and groundwater levels, outperforming MTL models built on gated recurrent unit (GRU) and long short-term memory (LSTM) architectures. Compared with single-task learning, adding a coupled groundwater-level task markedly improves surface runoff prediction, achieving a Nash–Sutcliffe efficiency (NSE) of 0.73 and a coefficient of determination (R2) of 0.75. Attention-based interpretability shows that the model assigns the highest weights to time steps with elevated precipitation; as lead time shortens, attention further concentrates on these periods, improving the accuracy of near-term, multi-step forecasts. These results highlight the value of inductive transfer across hydrologic targets and demonstrate that MT-TFT provides an effective, interpretable framework for SW–GW coupling. Full article
(This article belongs to the Topic Advances in Groundwater Science and Engineering)
►▼ Show Figures

Figure 1

31 pages, 5778 KB  
Article
A Novel Water Quality Index (Novel WQI) for the Assessment of Water Body Pollution in a Semi-Arid Gold Mining Area (Bam Province, Burkina Faso)
by Sidkeita Aissa Nacanabo, Youssouf Koussoube, Nadjibou Abdoulaye Hama, Mohamed Tahar Ammami and Tariq Ouahbi
Hydrology 2025, 12(11), 290; https://doi.org/10.3390/hydrology12110290 - 2 Nov 2025
Cited by 1 | Viewed by 2014
Abstract
Since the 2000s, Burkina Faso has experienced a rapid mining expansion with more than one hundred sites established, leading to increased waste generation often discharged untreated into the environment. Assessing water quality in these areas is therefore critical to mitigate environmental degradation and [...] Read more.
Since the 2000s, Burkina Faso has experienced a rapid mining expansion with more than one hundred sites established, leading to increased waste generation often discharged untreated into the environment. Assessing water quality in these areas is therefore critical to mitigate environmental degradation and public health risks. This study develops a site-specific water quality index (WQI) for a gold mining area in Bam Province, Burkina Faso, with the objective of improving pollution monitoring and management in relation to tailing dams. Surface and groundwater samples were collected between 2021 and 2024. Physico-chemical and bacteriological analyses of groundwater sources including wells, piezometers and boreholes revealed that several parameters such as pH, turbidity, nitrates, sulphates, total iron, aluminium, arsenic, cadmium, cyanide and total and faecal coliforms exceeded international drinking water standards. Geospatial techniques were employed to identify the main contamination sources: domestic wastewater, industrial and artisanal mining and agricultural runoff. The evolution of these parameters in relation to the dynamics of soil occupation and the influence of geological structure has enabled the distinction of key parameters associated with discharges. Although individual contaminant levels were mostly moderate, their combined effects pose a significant long-term risk to ecosystems and human health. The tailored WQI is suitable for both surface water and groundwater. It provides an integrated tool for classifying and monitoring water quality in mining environments, supporting evidence-based decision making in the management of tailing dams, environmental protection and public health. Full article
(This article belongs to the Special Issue Novel Approaches in Contaminant Hydrology and Groundwater Remediation, 2nd Edition)
►▼ Show Figures

Figure 1

22 pages, 3019 KB  
Article
Probabilistic Forecast for Real-Time Control of Rainwater Pollutant Loads in Urban Environments
by Annalaura Gabriele, Federico Di Palma, Ezio Todini and Rudy Gargano
Hydrology 2025, 12(11), 289; https://doi.org/10.3390/hydrology12110289 - 1 Nov 2025
Cited by 2 | Viewed by 1263
Abstract
Advanced wastewater management systems are necessary to effectively direct severely contaminated initial rainwater runoff to the treatment facility only when pollutant concentrations are elevated during the initial flush event, thereby reducing the risk of water pollution caused by urban drainage systems. This necessitates [...] Read more.
Advanced wastewater management systems are necessary to effectively direct severely contaminated initial rainwater runoff to the treatment facility only when pollutant concentrations are elevated during the initial flush event, thereby reducing the risk of water pollution caused by urban drainage systems. This necessitates the implementation of intelligent decision-making systems, forecasting, and monitoring. However, conventional “deterministic” forecasts are inadequate for making informed decisions in the presence of uncertainty regarding future values, despite the fact that a variety of modeling techniques have been employed to predict total suspended solids at specific locations. The literature contains a number of “probabilistic” forecasting approaches that take into account uncertainty. Among them, this paper proposes the Model Conditional Processor (MCP), which is well-known in hydrological, hydraulic, and climatological fields, to forecast the predictive probability density of total suspended solids based on one or more deterministic predictions. This is intended to address the issue. The decision to divert the first flush is subsequently guided by the predictive density and probabilistic thresholds. The effective implementation of the MCP approach is demonstrated in a real case study that is part of the USGS’s extensive and long-term stormwater monitoring initiative, based on observations of a real stormwater drainage system. The results obtained confirm that probabilistic approaches are suitable instruments for enhancing decision-making. Full article
(This article belongs to the Special Issue Understanding, Forecasting and Control of Flooding and Pollution in the Urban Environment: The 10th Anniversary of Hydrology)
►▼ Show Figures

Figure 1

24 pages, 3756 KB  
Article
Remote Sensing-Based Monitoring of Agricultural Drought and Irrigation Adaptation Strategies in the Antalya Basin, Türkiye
by Venkataraman Lakshmi, Elif Gulen Kir, Alperen Kir and Bin Fang
Hydrology 2025, 12(11), 288; https://doi.org/10.3390/hydrology12110288 - 31 Oct 2025
Cited by 3 | Viewed by 3823
Abstract
Drought is a critical hazard to agricultural productivity in semi-arid regions such as the Antalya Agricultural Basin of Türkiye. This study assessed agricultural drought from 2001 to 2023 using multiple remote sensing-based indices processed in Google Earth Engine (GEE). Vegetation indicators (Normalized Difference [...] Read more.
Drought is a critical hazard to agricultural productivity in semi-arid regions such as the Antalya Agricultural Basin of Türkiye. This study assessed agricultural drought from 2001 to 2023 using multiple remote sensing-based indices processed in Google Earth Engine (GEE). Vegetation indicators (Normalized Difference Vegetation Index, Normalized Difference Water Index, Normalized Difference Drought Index, Vegetation Condition Index, Temperature Condition Index, and Vegetation Health Index) were derived from MODIS datasets, while the Precipitation Condition Index was calculated from CHIRPS precipitation data. Composite indicators included the Scaled Drought Composite Index, integrating vegetation, temperature, and precipitation factors, and the Soil Moisture Condition Index derived from reanalysis soil moisture data. Results revealed recurrent moderate drought with strong seasonal and interannual variability, with 2008 identified as the driest year and 2009 and 2012 as wet years. Summer was the most drought-prone season, with precipitation averaging 5.5 mm, PCI 1.1, SDCI 15.6, and SMCI 38.4, while winter exhibited recharge conditions (precipitation 197 mm, PCI 40.9, SDCI 57.3, SMCI 89.6). Interannual extremes were detected in 2008 (severe drought) and wetter conditions in 2009 and 2012. Vegetation stress was also notable in 2016 and 2018. The integration of multi-source datasets ensured consistency and robustness across indices. Overall, the findings improve understanding of agricultural drought dynamics and provide practical insights for irrigation modernization, efficient water allocation, and drought-resilient planning in line with Türkiye’s National Water Efficiency Strategy (2023–2033). Full article
(This article belongs to the Section Soil and Hydrology)
►▼ Show Figures

Figure 1

35 pages, 12090 KB  
Article
Multidimensional Copula-Based Assessment, Propagation, and Prediction of Drought in the Lower Songhua River Basin
by Yusu Zhao, Tao Liu, Zijun Wang, Xihao Huang, Yingna Sun and Changlei Dai
Hydrology 2025, 12(11), 287; https://doi.org/10.3390/hydrology12110287 - 31 Oct 2025
Viewed by 1425
Abstract
As global climate change intensifies, understanding drought mechanisms is crucial for managing water resources and agriculture. This study employs the Standardized Precipitation–Actual Evapotranspiration Index (SPAEI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSMI) to analyze meteorological, hydrological, and agricultural droughts in [...] Read more.
As global climate change intensifies, understanding drought mechanisms is crucial for managing water resources and agriculture. This study employs the Standardized Precipitation–Actual Evapotranspiration Index (SPAEI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSMI) to analyze meteorological, hydrological, and agricultural droughts in the lower Songhua River basin. The PLUS model was used to predict future land types, with model accuracy validated using four evaluation metrics. The projected land cover was integrated with CMIP6 data into the SWAT model to simulate future runoff, which was used to calculate future SRI. Drought events were extracted using run theory, while drought occurrence probability and return period were calculated via a Copula-based joint distribution model. Bayesian conditional probability was employed to explore propagation mechanisms. The results indicate a significant increase in multidimensional drought risk, particularly when the cumulative frequency of univariate droughts reaches 25%, 50%, or 75%. Although increased duration and intensity enhance the likelihood of combined droughts, extremely high values cause a decline in joint probability under “OR” and “AND” conditions. Under different climate scenarios, the recurrence intervals of meteorological, hydrological, and agricultural droughts in the lower reaches of the Songhua River exhibit increased sensitivity with severity, demonstrating consistent propagation patterns across the meteorological–hydrological–agricultural system. Meteorological drought was found to propagate to hydrological and agricultural drought within ~6.00 months and ~3.67 months, respectively, with severity amplifying this effect. Propagation thresholds between drought types decreased with increasing intensity. This study combined SWAT and CMIP6 models with PLUS-based land-use scenarios, highlighting that land-use changes significantly influence spatiotemporal drought patterns. Model validation (Kappa = 0.83, OA = 0.92) confirmed robust predictive accuracy. Overall, this study proposes a multidimensional drought risk model integrating Copula and Bayesian networks, offering valuable insights for drought management under climate change. Full article
(This article belongs to the Special Issue Water Resources Management Under Uncertainty and Climate Change (Second Edition))
►▼ Show Figures

Figure 1

25 pages, 3760 KB  
Article
Estimating Reservoir Evaporation Under Mediterranean Climate Using Indirect Methods: A Case Study in Southern Portugal
by Carlos Miranda Rodrigues, Rita Cabral Guimarães and Madalena Moreira
Hydrology 2025, 12(11), 286; https://doi.org/10.3390/hydrology12110286 - 31 Oct 2025
Cited by 2 | Viewed by 1978 | Correction
Abstract
This study focuses on the Alentejo and Algarve regions of southern Portugal, which is characterized by a typical Mediteranean climate. In the Mediterranean region, evaporation plays a significant role in reservoir water budgets. Therefore, estimating water surface evaporation is essential for efficient reservoir [...] Read more.
This study focuses on the Alentejo and Algarve regions of southern Portugal, which is characterized by a typical Mediteranean climate. In the Mediterranean region, evaporation plays a significant role in reservoir water budgets. Therefore, estimating water surface evaporation is essential for efficient reservoir water management. This study aims to (i) assess the reservoir evaporation pattern in southern Portugal from meteorological offshore measures, (ii) benchmark various indirect methods for evaluating reservoir evaporation at a monthly scale, and (iii) provide recommendations on the most suitable indirect method to apply in operational practices. This study presents meteorological data collected from floating weather stations on instrumented platforms across nine reservoirs in Alentejo and Algarve. This is the first time that so many offshore local measurements have been made available in a Mediterranean climate region. The reservoir evaporation was estimated by the Energy Budget (Bowen Ratio) method, having concluded that monthly evaporation rates across the nine reservoirs ranged from 0.8 mm d­−1 in winter to 4.6 mm d­−1 in summer, with an annual average of 2.7 mm d­−1. Annual evaporation values ranged from 750 to 1230 mm, showing a positive gradient from the northern Alentejo region to the southwest Algarve region. To evaluate the performance of five empirical and semi-empirical evaporation indirect methods, a benchmarking analysis was conducted. The indirect methods studied are Mass Transfer (MT), Penman (PEN), Priestley and Taylor (PT), Thornthwaite (THOR), and Pan Evaporation (PE). Regarding the MT method, an N function of a reservoir superficial area is presented for the Mediterranean climate regions. In the Pan Evaporation method, the pan coefficient was considered equal to one. The benchmarking analysis revealed that all studied methods produced estimates that had good correlation with the Energy Budget method’s results across all reservoirs. All the methods showed small biases at the monthly scale, particularly in the dry semester. The estimates’ evaporation variability depended on the reservoir. Overall, the evaluation of evaporation methods concluded that (i) the stakeholders should considerer having an evaporation pan offshore; (ii) to manage the water balance of the studied reservoirs, the manager must apply the method with the best performance, depending on the data available; (iii) to manage other reservoirs located in the Mediterranean climate region, the manager must compare reservoir characteristics and the data available in order to choose the most suitable method to apply. Full article
►▼ Show Figures

Graphical abstract

25 pages, 3502 KB  
Article
Developing a Groundwater Quality Assessment in Mexico: A GWQI-Machine Learning Model
by Hector Ivan Bedolla-Rivera and Mónica del Carmen González-Rosillo
Hydrology 2025, 12(11), 285; https://doi.org/10.3390/hydrology12110285 - 30 Oct 2025
Viewed by 1295
Abstract
Groundwater represents a critical global resource, increasingly threatened by overexploitation and pollution from contaminants such as arsenic (As), fluoride (F), nitrates (NO3−), and heavy metals in arid to semi-arid regions like Mexico. Traditional Water Quality Indices ( [...] Read more.
Groundwater represents a critical global resource, increasingly threatened by overexploitation and pollution from contaminants such as arsenic (As), fluoride (F), nitrates (NO3−), and heavy metals in arid to semi-arid regions like Mexico. Traditional Water Quality Indices (WQIs), while useful, suffer from subjectivity in assigning weights, which can lead to misinterpretations. This study addresses these limitations by developing a novel, objective Groundwater Quality Index (GWQI) through the seamless integration of Machine Learning (ML) models. Utilizing a database of 775 wells from the Mexican National Water Commission (CONAGUA), Principal Component Analysis (PCA) was applied to achieve significant dimensionality reduction. We successfully reduced the required monitoring parameters from 13 to only three key indicators: total dissolved solids (TDSs), chromium (Cr), and manganese (Mn). This reduction allows for an 87% decrease in the number of indicators, maximizing efficiency and generating potential savings in monitoring resources without compromising water quality prediction accuracy. Six WQI methods and six ML models were evaluated for quality prediction. The Unified Water Quality Index (WQIu) demonstrated the best performance among the WQIs evaluated and exhibited the highest correlation (R2 = 0.85) with the traditional WQI based on WHO criteria. Furthermore, the ML Support Vector Machine with polynomial kernel (svmPoly) model achieved the maximum predictive accuracy for WQIu (R2 = 0.822). This robust GWQI-ML approach establishes an accurate, objective, and efficient tool for large-scale groundwater quality monitoring across Mexico, facilitating informed decision-making for sustainable water management and enhanced public health protection. Full article
(This article belongs to the Special Issue Novel Approaches in Contaminant Hydrology and Groundwater Remediation, 2nd Edition)
►▼ Show Figures

Figure 1

18 pages, 5442 KB  
Article
Tail-Aware Forecasting of Precipitation Extremes Using STL-GEV and LSTM Neural Networks
by Haoyu Niu, Samantha Murray, Fouad Jaber, Bardia Heidari and Nick Duffield
Hydrology 2025, 12(11), 284; https://doi.org/10.3390/hydrology12110284 - 30 Oct 2025
Cited by 3 | Viewed by 2204
Abstract
Accurate prediction of extreme precipitation events remains a critical challenge in hydrological forecasting due to their rare occurrence and complex statistical behavior. These extreme events are becoming more frequent and intense under the influence of climate change. Their unpredictability not only hampers water [...] Read more.
Accurate prediction of extreme precipitation events remains a critical challenge in hydrological forecasting due to their rare occurrence and complex statistical behavior. These extreme events are becoming more frequent and intense under the influence of climate change. Their unpredictability not only hampers water resource management and disaster preparedness but also leads to disproportionate impacts on vulnerable communities and critical infrastructure. Therefore, in this article, we introduce a hybrid modeling framework that combines Generalized Extreme Value (GEV) distribution fitting with deep learning models to forecast monthly maximum precipitation extremes. Long Short-term Memory models (LSTMs) are proposed to predict the cumulative distribution (CDF) values of the GEV-fitted remainder series. This approach transforms the forecasting problem into a bounded probabilistic learning task, improving model stability and interpretability. Crucially, a tail-weighted loss function is designed to emphasize rare but high-impact events in the training process, addressing the inherent class imbalance in extreme precipitation predictions. Results demonstrate strong predictive performance in both the CDF and residual domains, with the proposed model accurately identifying anomalously high precipitation months. This hybrid GEV–deep learning approach offers a promising solution for early warning systems and long-term climate resilience planning in hydrologically sensitive regions. Full article
(This article belongs to the Special Issue Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications)
►▼ Show Figures

Figure 1

26 pages, 12574 KB  
Article
Impact of Urbanization on Flooding and Risk Based on Hydrologic–Hydraulic Modeling and Analytic Hierarchy Process: A Case of Kathmandu Valley of Nepal
by Badri Bhakta Shrestha, Mohamed Rasmy, Katsunori Tamakawa, Sauhardra Joshi and Daisuke Kuribayashi
Hydrology 2025, 12(11), 283; https://doi.org/10.3390/hydrology12110283 - 30 Oct 2025
Viewed by 3751
Abstract
Understanding urbanization and its impact on flooding and flood risk is crucial to better manage flood risk in the future. This study analyzed land use/land cover changes and how urbanization would impact flooding and flood risk in Kathmandu Valley of Nepal, and assessed [...] Read more.
Understanding urbanization and its impact on flooding and flood risk is crucial to better manage flood risk in the future. This study analyzed land use/land cover changes and how urbanization would impact flooding and flood risk in Kathmandu Valley of Nepal, and assessed flood risk by integrating flood hazards based on hydrologic–hydraulic modeling with the Analytic Hierarchy Process-based Multi-Criteria Decision Analysis (AHP-MCDA) approach. Land cover maps for past years were generated using Landsat satellite images, and land use/land cover maps for future years were projected based on machine learning techniques. Flood simulations were conducted using a rainfall runoff inundation model with land cover maps for different flood scales to analyze the impact of urbanization and land cover changes on flood runoff, flood inundation extent, and flood inundation volume. Then, we comprehensively assessed flood risk by integrating hazard conditions simulated under different land cover conditions using a hydrologic–hydraulic model and the AHP-MCDA approach. The results showed that the flood inundation extent and the peak inundation volume for a 200-year flood may increase in the future by 10.66% and 15.04%, respectively, as a result of urbanization. The results also highlighted that urbanization may lead to an expansion of high-risk and very-high-risk areas in the future by 3.2% and 9.4%, respectively, indicating an increase in the valley’s flood vulnerability and greater severity of flood hazards. Full article
(This article belongs to the Special Issue The Influence of Landscape Disturbance on Catchment Processes)
►▼ Show Figures

Figure 1

21 pages, 6079 KB  
Article
Machine Learning Models for Groundwater Level Prediction and Uncertainty Analysis in Ruataniwha Basin, New Zealand
by Dawit Kanito, Mohammed Benaafi and Husam Musa Baalousha
Hydrology 2025, 12(11), 282; https://doi.org/10.3390/hydrology12110282 - 29 Oct 2025
Cited by 3 | Viewed by 3100
Abstract
Groundwater level predictive monitoring is necessary to address accelerated aquifer depletion and ensure sustainable management under increasing climatic and anthropogenic pressures. This study uses machine learning approaches to model groundwater level (GWL) dynamics in six observation wells in the Ruataniwha Basin, New Zealand. [...] Read more.
Groundwater level predictive monitoring is necessary to address accelerated aquifer depletion and ensure sustainable management under increasing climatic and anthropogenic pressures. This study uses machine learning approaches to model groundwater level (GWL) dynamics in six observation wells in the Ruataniwha Basin, New Zealand. These models are enhanced with seasonal decomposition techniques. This study uses both static properties and dynamic variables to capture hydrogeological heterogeneity. Random Forest (RF) and Support Vector Machine (SVM), with seasonal decomposition preprocessing, were developed for GWL modelling. The models were trained on 80% of the dataset and tested using the remaining 20% of the data. Model accuracy was assessed using five statistical metrics: mean absolute error (MAE), root mean square error (RMSE), the coefficient of determination (R2), mean absolute percent error (MAPE), and percent bias (PBIAS). Model uncertainty was analyzed using Bayesian Model Averaging combined with the p-factor and d-factor at the 95% confidence level. The results demonstrate that both models delivered strong predictive performance across training, testing, and full period evaluations. However, the RF model demonstrated a marginally superior predictive accuracy by achieving lower errors (MAE: 0.013–0.174; RMSE: 0.04–0.283), better bias control (PBIAS ≈ 0%), and slightly tighter error bounds in most wells. Uncertainty quantification revealed that models provided a minimum p-factor of 0.878, capturing more than 87% of the observed GWL data within the uncertainty bounds. Comparing the results of both models, the RF model has higher p-factor values ranging from 0.878 to 0.976 with precise interval widths (d-factor: 0.436–0.769), indicating its reliability for adaptive groundwater management. Full article
(This article belongs to the Special Issue Water Resources Management Under Uncertainty and Climate Change (Second Edition))
►▼ Show Figures

Figure 1

35 pages, 18392 KB  
Article
Assessing the Impacts of Land Cover and Climate Changes on Streamflow Dynamics in the Río Negro Basin (Colombia) Under Present and Future Scenarios
by Blanca A. Botero, Juan C. Parra, Juan M. Benavides, César A. Olmos-Severiche, Rubén D. Vásquez-Salazar, Juan Valdés-Quintero, Sandra Mateus, Jean P. Díaz-Paz, Lorena Díez, Andrés F. García and Oscar E. Cossio
Hydrology 2025, 12(11), 281; https://doi.org/10.3390/hydrology12110281 - 28 Oct 2025
Cited by 2 | Viewed by 3381
Abstract
Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation [...] Read more.
Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation over recent decades, profoundly altering its hydrological dynamics. This study integrates advanced satellite image processing, AI-based land cover modeling, climate change projections, and distributed hydrological simulation to assess future streamflow responses. Multi-sensor satellite data (Landsat, Sentinel-1, Sentinel-2, ALOS) were processed using Random Forest classifiers, intelligent multisensor fusion, and probabilistic neural networks to generate high-resolution land cover maps and scenarios for 2060 (optimistic, trend, and pessimistic), with strict area constraints for urban growth and forest conservation. Future precipitation was derived from MPI-ESM CMIP6 outputs (SSP2-4.5, SSP3-7.0, SSP5-8.5) and statistically downscaled using Empirical Quantile Mapping (EQM) to match the basin scale and precipitation records from the national hydrometeorological service of the Colombia IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales, Colombia). The TETIS hydrological model was calibrated and validated using observed streamflow records (1998–2023) and subsequently used to simulate hydrological responses under combined land cover and climate scenarios. Results indicate that urban expansion and forest loss significantly increase peak flows (Q90, Q95) and flood risk while decreasing baseflows (Q10, Q30), compromising water availability during dry seasons. Conversely, conservation-oriented scenarios mitigate these effects by enhancing flow regulation and groundwater recharge. The findings highlight that targeted land management can partially offset the negative impacts of climate change, underscoring the importance of integrated land–water planning in the Andes. This work provides a replicable framework for modeling hydrological futures in data-scarce mountainous basins, offering actionable insights for regional authorities, environmental agencies, and national institutions responsible for water security and disaster risk management. Full article
(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations: 2nd Edition)
►▼ Show Figures

Figure 1

21 pages, 13671 KB  
Article
Refined Simulation of Old Urban Inundation and Assessment of Stormwater Storage Capacity Based on Surface–Pipe Network–Box Culvert–River Coupled Modeling
by Ning Li, Liping Ma, Jingming Hou, Jun Wang, Xuan Li, Donglai Li, Xinxin Pan, Ruijun Cui, Yue Ren and Yangshuo Cheng
Hydrology 2025, 12(11), 280; https://doi.org/10.3390/hydrology12110280 - 28 Oct 2025
Cited by 2 | Viewed by 2204
Abstract
Old urban districts, characterized by complex drainage networks, heterogeneous surfaces, and high imperviousness, are particularly susceptible to flooding during extreme rainfall. In this study, the moat drainage district of Xi’an was selected as the research area. A refined hydrologic–hydrodynamic simulation and an assessment [...] Read more.
Old urban districts, characterized by complex drainage networks, heterogeneous surfaces, and high imperviousness, are particularly susceptible to flooding during extreme rainfall. In this study, the moat drainage district of Xi’an was selected as the research area. A refined hydrologic–hydrodynamic simulation and an assessment of drainage and flood-retention capacities were conducted based on the coupled GAST–SWMM model. Results show that the model can accurately capture the rainfall–surface–pipe–river interactions and reproduce system responses under different rainfall intensities. The box culvert’s effective regulation capacity is limited to 1- to 2-year return periods, beyond which overflow rises sharply, with overflow nodes exceeding 80% during a 2-year event. The moat’s available storage capacity is 17.20 × 104 m3, sufficient for rainfall events with 5- to 10-year return periods. In a 10-year return period event, the box culvert overflow volume (12.56 × 104 m3) approaches the upper limit, resulting in overtopping. These findings provide a scientific basis for evaluating drainage efficiency and guiding flood control management in old urban districts. Full article
(This article belongs to the Special Issue Advancing Flood Detection, Monitoring & Simulation: Integrating Machine Learning, Remote Sensing & Hydrodynamic Model)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-36
Previous Issue
Next Issue
Back to TopTop