Hydrology

23 pages, 2937 KB

Open AccessArticle

Hydrogeochemical Controls and Explainable Machine Learning for Reliable Prediction of Fluoride Contamination in Groundwater

by Nighat Gulzar, Xin Liao, Zhongyuan Xu and Amir Rehman

Hydrology 2026, 13(6), 144; https://doi.org/10.3390/hydrology13060144 - 29 May 2026

Abstract

Fluoride contamination in groundwater poses a significant public-health concern in most semi-arid areas such as the Punjab alluvial aquifers of Pakistan, with local concentrations exceeding the WHO guideline. Reliable fluoride dynamics prediction and mechanistic interpretation of fluoride is key for targeted monitoring and [...] Read more.

Fluoride contamination in groundwater poses a significant public-health concern in most semi-arid areas such as the Punjab alluvial aquifers of Pakistan, with local concentrations exceeding the WHO guideline. Reliable fluoride dynamics prediction and mechanistic interpretation of fluoride is key for targeted monitoring and risk mitigation. This paper built an integrated hydrogeochemical machine learning model to predict the fluoride concentration and classify exceedance risk in the Rechna Doab aquifer Tehsil Jaranwala, Punjab, Pakistan. Nested cross-validation and independent test evaluation were performed on conventional models (linear regression, random forest, XGBoost) and a deep tabular model (FT-Transformer). Model reliability was evaluated using discrimination and probability-calibration metrics, while Shapley Additive Explanations (SHAP) and permutation importance were applied to identify the main hydrogeochemical controls on fluoride prediction. Moreover, the robustness was tested by noise sensitivity experiments. Fluoride concentrations showed a positive skewed distribution with some local exceedances related to the geogenic and hydrochemical influences. Nonlinear models greatly outperformed the linear baseline; XGBoost showed robust regression performance (test R² = 0.878; RMSE ≈ 0.190 mg/L). The FT-Transformer showed strong exceedance-classification performance, with high sensitivity (recall = 0.875) and good probability calibration (Brier ≈ 0.021). Interpretability analyses identified EC/TDS, Mg²⁺, and Ca²⁺ as important predictors, linking fluoride enrichment to chemically evolved groundwater with reduced calcium activity, sodium enrichment, and alkalinity buffering. The proposed framework provides accurate, interpretable, and risk-oriented support for groundwater fluoride monitoring in alluvial aquifer systems. Full article

18 pages, 3578 KB

Open AccessArticle

Comparative Assessment of Different Satellite-Derived Actual Evapotranspiration Estimates in Northeast Italy

by Marta Chiesi, Sofia Ortenzi, Paulina Bartkowiak, Matteo Camporese, Mariapina Castelli, Jacopo Dari, Luca Fibbi, Beatrice Gatto, Christian Massari, Maurizio Pieri, Silvana Vanucci and Fabio Maselli

Hydrology 2026, 13(6), 143; https://doi.org/10.3390/hydrology13060143 - 29 May 2026

Abstract

Accurate estimation of actual evapotranspiration (ETa) is essential for understanding hydrological processes and managing water resources, especially in regions characterized by intensive agriculture and complex groundwater–surface interactions. This study intercompares three independent satellite-based ETa estimation approaches applied over Northeast Italy. The first two [...] Read more.

Accurate estimation of actual evapotranspiration (ETa) is essential for understanding hydrological processes and managing water resources, especially in regions characterized by intensive agriculture and complex groundwater–surface interactions. This study intercompares three independent satellite-based ETa estimation approaches applied over Northeast Italy. The first two methods correspond to the classical MODIS algorithm (MOD16), which is based on a simplified Penman–Monteith approach, and to the more recent Sen-ET modelling framework, which relies on a surface energy balance principle. The outputs of these methods are compared to those produced by a water balance algorithm, NDVI-Cws, which predicts ETa through the combination of conventional ancillary data and MODIS NDVI imagery. The results obtained show that, while the MODIS algorithm yields ETa estimates which are generally lower than those of Sen-ET and NDVI-Cws, the latter methods produce similar predictions for most cover types examined. The same two methods are potentially capable of providing higher spatial resolution daily ETa estimates depending on the satellite inputs used; out of them, however, only NDVI-Cws can yield spatially complete and temporally continuous datasets. The analysis therefore provides insights into the reliability and usability of different remote sensing approaches for regional-scale water resource monitoring. Full article

(This article belongs to the Special Issue GIS Modelling of Evapotranspiration with Remote Sensing: 2nd Edition)

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25 pages, 18137 KB

Open AccessArticle

Anthropogenic Land Use in Permanent Preservation Areas Within Urban Perimeters as a Determinant of Water Quality: A Case Study in the Peixe River Watershed

by Roger Francisco Ferreira de Campos, Indianara Fernanda Barcaroli, Carolina Fruet de Lima, Cláudia Maté, Rosana Claudio Silva Ogoshi, Cristiane Maria Tonetto Godoy, Cristine Vanz Borges, Levi Hülse, Lincon Bordignon Somensi and Eliana Rezende Adami

Hydrology 2026, 13(6), 142; https://doi.org/10.3390/hydrology13060142 - 28 May 2026

Abstract

Surface water degradation has intensified due to anthropogenic pressures, especially in urban areas, where unplanned land use compromises the integrity of aquatic ecosystems. This study investigated the relationship between water quality and land use in a Permanent Preservation Area (PPA) within an urban [...] Read more.

Surface water degradation has intensified due to anthropogenic pressures, especially in urban areas, where unplanned land use compromises the integrity of aquatic ecosystems. This study investigated the relationship between water quality and land use in a Permanent Preservation Area (PPA) within an urban perimeter in Caçador, Santa Catarina, Brazil. Monthly sampling was conducted throughout 2024 at 11 points distributed along urban and rural sections of the river and its tributaries. Physicochemical and microbiological parameters were evaluated, and the Water Quality Index (WQI) established by the National Sanitation Foundation (NSF) was calculated in order to associate the results with the sampling points, complemented by Principal Component Analysis (PCA) to identify multivariate patterns of spatial variability in water quality across the study area. In parallel, the PPA within the urban perimeter was delimited according to current environmental legislation, and land use was classified using ArcGIS and Google Earth Pro. The results revealed greater water quality degradation in urban stretches of the river, particularly at sampling point SP7, which recorded the lowest dissolved oxygen concentration (3.10 mg L⁻¹), alongside elevated values of biochemical oxygen demand (5.23 mg L⁻¹), total phosphorus (2.94 mg L⁻¹), nitrate (18.75 mg L⁻¹), and thermotolerant coliforms (2759.20 MPN 100 mL⁻¹). The WQI ranged from 40.18 (SP7: bad category) to 73.57 (SP1: good category), reflecting a pronounced spatial gradient of water quality degradation associated with increasing urbanization along the river course. Mapping of the PPAs revealed that only 43.72% of the total area was covered by native vegetation, while the remaining 56.28% was occupied by anthropogenic land uses, including miscellaneous use (30.32%), agriculture (9.09%), buildings (6.09%), roads (4.64%), and railway infrastructure (5.81%). PCA accounted for 89.06% of the total data variance and indicated that greater interaction of sampling points with urbanized areas was consistently associated with reduced water quality, thereby demonstrating the direct influence of anthropogenic activities on the environmental parameters assessed throughout the study area. These findings demonstrate that land use patterns directly affect water quality and reinforce the need for riparian forest restoration, expanded sanitation infrastructure, and more sustainable urban planning. Full article

(This article belongs to the Topic Water-Soil Pollution Control and Environmental Management)

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31 pages, 9506 KB

Open AccessArticle

Spatio-Temporal Vulnerability Assessment of Coastal Aquifers Using DRASTIC and GALDIT Models with Different Weighting Methods: A Case Study from Iran

by Ali Barzkar, Mohammad Reza Goodarzi and Majid Niazkar

Hydrology 2026, 13(6), 141; https://doi.org/10.3390/hydrology13060141 - 25 May 2026

Abstract

Coastal aquifers are more exposed to pollution and salinity than other hydrogeological systems due to their proximity to the sea, increasing groundwater withdrawals, and climate change. The aim of this study is not only to evaluate and compare the vulnerability of coastal aquifers [...] Read more.

Coastal aquifers are more exposed to pollution and salinity than other hydrogeological systems due to their proximity to the sea, increasing groundwater withdrawals, and climate change. The aim of this study is not only to evaluate and compare the vulnerability of coastal aquifers using the DRASTIC and GALDIT models but also to investigate effects of different weighting methods on the results of vulnerability zoning. The spatio-temporal vulnerability assessment was conducted for coastal aquifers in Hormozgan Province in Iran over a 15-year period (2010–2024). After collecting information layers required for both models, vulnerability maps were calculated for three consecutive five-year periods using three weighting methods: (a) normal weighting, (b) Shannon entropy, and (c) particle swarm optimization (PSO) algorithm. The results indicate that the coastal areas of the western part of the province have the highest vulnerability in both models, and the intensity and extent of high-risk zones have increased in recent periods. Comparison of weighting methods revealed that normal weighting provided a conservative and uniform distribution, while the entropy method, due to its reliance on statistical dispersion of data in some areas, led to a hyperbole of the risk. In contrast, the PSO algorithm provided the most accurate and realistic results compared to classical fixed-weight and entropy-based vulnerability maps, as it was able to identify critical areas with higher spatial concentration and hydrogeological coherence. The combined results of DRASTIC and GALDIT demonstrated that parts of the coastal aquifers of Hormozgan are simultaneously in a critical state in terms of inherent vulnerability and salinity potential. The findings of this study can be used as a scientific basis for sustainable management of groundwater resources, withdrawal control, and protection climate adaptation planning in coastal areas. 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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13 pages, 7203 KB

Open AccessArticle

Short-Term IoT-Enabled Sensor-Based Assessment of Treated Municipal Water and Decentralized Groundwater in Bragança, NE Portugal

by Josean da Silva, Vanessa B. Paula, Cleonilson Protásio de Souza and Ana M. Antão-Geraldes

Hydrology 2026, 13(6), 140; https://doi.org/10.3390/hydrology13060140 - 23 May 2026

Abstract

This study presents a short-term, IoT-enabled sensor-based assessment of treated municipal water and decentralized groundwater in Bragança, northeastern Portugal. Two drinking-water supply contexts were compared: treated surface-water-derived municipal water from the public supply system and groundwater from a decentralized supply system serving part [...] Read more.

This study presents a short-term, IoT-enabled sensor-based assessment of treated municipal water and decentralized groundwater in Bragança, northeastern Portugal. Two drinking-water supply contexts were compared: treated surface-water-derived municipal water from the public supply system and groundwater from a decentralized supply system serving part of a higher education campus. Five sampling points were monitored during three campaigns between January and March 2026. At each point, pH, electrical conductivity, temperature, oxidation–reduction potential, and total dissolved solids were recorded at 10 s intervals over approximately 10 min monitoring windows using a multiparameter probe integrated into an IoT-enabled data acquisition workflow. Microbiological analyses were performed on groundwater samples as complementary information. Treated municipal water showed lower mineralization, narrower parameter ranges, and higher oxidation–reduction potential, reflecting source-water characteristics, treatment, and operational control. Groundwater showed higher mineralization, lower oxidation–reduction potential, and greater variability among sampling points and campaigns, consistent with stronger local hydrogeochemical and operational influences. The repeated short-interval readings provided more detailed physicochemical profiles than isolated spot measurements, although the short monitoring windows do not represent continuous long-term high-frequency monitoring. Overall, the results support standardized IoT-enabled sensor-based monitoring as a complementary tool for short-term water-quality assessment and indicate the need for longer seasonal datasets and laboratory confirmation. Full article

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17 pages, 26845 KB

Open AccessArticle

Two-Dimensional Modelling to Estimate and Analyse Water Balance in a Shallow Groundwater Wetland in Coastal Australia

by Muhammad Usman, Lloyd H. C. Chua, Kim N. Irvine and Lihoun Teang

Hydrology 2026, 13(6), 139; https://doi.org/10.3390/hydrology13060139 - 22 May 2026

Abstract

Natural ecosystems are facing threats from natural and anthropogenic stressors. Wetlands are among the most delicate natural ecosystems and are particularly vulnerable to the impacts of urbanization. One of the intended purposes of the wetlands is to mitigate the impact of urbanization (e.g., [...] Read more.

Natural ecosystems are facing threats from natural and anthropogenic stressors. Wetlands are among the most delicate natural ecosystems and are particularly vulnerable to the impacts of urbanization. One of the intended purposes of the wetlands is to mitigate the impact of urbanization (e.g., stormwater), but we often lack a comprehensive understanding of their capacity in doing so. Determination of water balance is essential in understanding the efficacy of a wetland when it comes to treating excess stormwater. This study therefore considers the Sparrovale Wetland in Victoria, Australia, to assess its performance in mitigating the impacts of urbanization in the surrounding catchment areas. A 1D model (HYDRUS-1D) was previously developed by the authors based on extensive field and laboratory measurements on one side (north) of the wetland. It was crucial to understand the two-dimensional water balance dynamics in the Sparrovale Wetland to utilize its full potential for managing excessive stormwater. This study therefore employed the HYDRUS-2D model (based on HYDRUS-1D) supported by extended, spatially explicit in situ measurements. The model was run (with additional input of inflow added to the rainfall) on the average Van Genuchten parameters obtained from the previously developed HYDRUS-1D model and the extended determination of the parameters. The model performance in simulating measured water content was good for both the south (average RMSE = 0.013 m³/m³) and the north side (average RMSE = 0.028 m³/m³). The model was also used to simulate surface water levels in the wetland and showed a good agreement (RMSE = 0.1 m AHD and R² = 0.72) with in situ surface water level measurements. This developed model was used to determine the water balance dynamics (infiltration, evapotranspiration, soil water storage, surface and bottom boundary flux) in the Sparrovale Wetland. Our results indicate that evapotranspiration is the major factor controlling the water flux losses in the Sparrovale Wetland, while the role of infiltration was minimal, which might be attributed to the dominant soil type (clay) and shallow groundwater levels in the Sparrovale Wetland. Insights provided by this study might be helpful in optimizing the performance of the Sparrovale Wetland in managing the excess stormwater arising from the surrounding catchments. Full article

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22 pages, 4709 KB

Open AccessArticle

Spatial–Temporal Evapotranspiration Dynamics in the Al-Ahsa Oasis Based on a Remote Sensing Approach for Sustainable Water Management

by Mohamed Elhag, Abdulaziz Alqarawy, Aris Psilovikos, Wei Tian and Imene Benmakhlouf

Hydrology 2026, 13(5), 138; https://doi.org/10.3390/hydrology13050138 - 21 May 2026

Abstract

Accurate evapotranspiration (ET) estimation is critical for sustainable water management in arid environments. This study estimates actual ET over the Al-Hofuf region, Al-Ahsa Oasis, Saudi Arabia, during 2024 using a cloud-based remote sensing approach. Landsat 9 Level-2 imagery was combined with ERA5-Land meteorological [...] Read more.

Accurate evapotranspiration (ET) estimation is critical for sustainable water management in arid environments. This study estimates actual ET over the Al-Hofuf region, Al-Ahsa Oasis, Saudi Arabia, during 2024 using a cloud-based remote sensing approach. Landsat 9 Level-2 imagery was combined with ERA5-Land meteorological data to quantify spatial and temporal ET variations across a 25 km buffer. Vegetation dynamics were characterized using the Normalized Difference Vegetation Index (NDVI) to derive crop coefficients (Kc) within a Kc–ET₀ framework, where reference ET (ET₀) was obtained from ERA5-Land potential evaporation. All processing utilized Python (Version 3.14) on Google Colab and Google Earth Engine for scalable computation. Eighty-eight cloud-free Landsat 9 scenes were processed following cloud and shadow masking. Mean NDVI, Kc, and daily ET values were compiled into a comprehensive time-series dataset. Model performance was evaluated through cross-validation with MODIS MOD16A2 and internal consistency checks, demonstrating strong statistical agreement (R² = 0.82, NSE = 0.71, PBIAS = +8.3%). Results revealed pronounced seasonal variability closely linked to vegetation activity and atmospheric demand, with peak ET occurring during summer months (June–July: 7.2–7.5 mm day⁻¹) and minima in winter (January–February: 2.0–2.6 mm day⁻¹). Findings demonstrate that cloud-based techniques provide reliable, cost-effective ET monitoring in data-scarce, groundwater-dependent regions. Validation confirms Kc-ET₀ estimates reliably capture spatial and temporal patterns, supporting practical irrigation management applications. This approach aids precision irrigation and long-term water sustainability planning in Al-Hofuf, contributing significantly to national water conservation objectives under Saudi Arabia’s Vision 2030 and National Water Strategy. Full article

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18 pages, 1589 KB

Open AccessArticle

Teleconnection-Based Long-Term Precipitation Forecasting Using Functional Data Analysis and Regressive Models: Application to North-Eastern Tunisia

by Farah Ben Souissi, Pierre Masselot, Taha B. M. J. Ouarda and Emna Gargouri-Ellouze

Hydrology 2026, 13(5), 137; https://doi.org/10.3390/hydrology13050137 - 16 May 2026

Abstract

Tunisia is characterized by high precipitation variability, which results in frequent extreme floods and droughts. This study aims to develop long-term forecasting models for total and daily maximum annual precipitation by incorporating information related to climate variability. These models use low-frequency climate oscillation [...] Read more.

Tunisia is characterized by high precipitation variability, which results in frequent extreme floods and droughts. This study aims to develop long-term forecasting models for total and daily maximum annual precipitation by incorporating information related to climate variability. These models use low-frequency climate oscillation indices as predictors. A linear functional model for scalar response is developed for this purpose. The model based on functional data analysis is also compared to a linear regression model. The station under study is located in north-eastern Tunisia. The association between precipitation and four climate indices is evaluated: the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), the Mediterranean Oscillation (MO) and the Western Mediterranean Oscillation (WeMO) climate indices. The results show that both linear and functional regression provide good and comparable results, likely due to the limited length of the data series. NAO, PDO and MO are the best indices to forecast total annual precipitation with an RMSE between 3.564% and 4.151% of the average precipitation, while MO seems to be the best index to forecast daily maximum annual precipitation achieving slightly higher RMSE between 11.174% and 11.916% of the average maximum precipitation. These results suggest that total precipitation at the study station is controlled by large-scale climatic processes operating over the Atlantic, Pacific, and Mediterranean regions, whereas the few most extreme precipitation events are primarily driven by regional climatic phenomena occurring at the Mediterranean scale. The results may have practical applications to improve disaster response preparedness and water resource management. 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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25 pages, 5827 KB

Open AccessArticle

Transient Responses of Freshwater Lens Development and Seawater Intrusion Mitigation to Freshwater Injection in Unconfined Island Aquifers

by Weijiang Yu and Yipeng Zhang

Hydrology 2026, 13(5), 136; https://doi.org/10.3390/hydrology13050136 - 14 May 2026

Abstract

Subsurface freshwater in oceanic islands is typically shaped like a thin lens due to limited land area and recharge, often the primary freshwater source for local communities and highly vulnerable to seawater intrusion (SWI). Freshwater injection (FI) is considered as a feasible strategy [...] Read more.

Subsurface freshwater in oceanic islands is typically shaped like a thin lens due to limited land area and recharge, often the primary freshwater source for local communities and highly vulnerable to seawater intrusion (SWI). Freshwater injection (FI) is considered as a feasible strategy for mitigating SWI in coastal aquifers. However, its transient effectiveness for freshwater lens (FWL) development and SWI mitigation in island aquifers and how the design parameters like FI depth, intensity, duration and injectant concentration affect its performance remain poorly understood. To address this, this study employs a two-dimensional, variable-density island groundwater model to simulate the transient responses of FWL development and SWI mitigation to various FI patterns. Five indicators are developed for comprehensive evaluation, including (1) freshwater recovery efficiency (FRE), and the relative changes in (2) average water table elevation (WTE), (3) FWL depth, (4) FWL volume, and (5) total aquifer salt mass. Results reveal FI universally raises average WTE, expands FWL dimensions, and promotes aquifer desalinization. Injection intensity is the primary driver of WTE rises and salt mass reduction, with higher intensities consistently yielding greater WTE rises and salt mass reductions. Deeper injection within the mixing zone increases FWL depth, but reduces the net gain in FWL volume. Moreover, early-stage FI is highly efficient for expanding FWL volume, often yielding FRE values above 100%, but FRE converges toward zero over time as the system moves toward a new hydrodynamic equilibrium, returning diminishing marginal benefits for long-term FI. Full article

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21 pages, 2407 KB

Open AccessReview

GRACE Downscaling and Machine Learning Models for Groundwater Prediction: A Systematic Review

by Mohammed S. Al Nadabi, Mohammed El-Diasty, Talal Etri and Mohammad Reza Nikoo

Hydrology 2026, 13(5), 135; https://doi.org/10.3390/hydrology13050135 - 14 May 2026

Abstract

Gravity Recovery and Climate Experiment (GRACE) satellites primarily monitor changes in land water storage, including groundwater, soil moisture, lake and river surface water, and canopy and snow water. However, its coarse spatial resolution of 0.25 degrees limits its ability to observe smaller basins. [...] Read more.

Gravity Recovery and Climate Experiment (GRACE) satellites primarily monitor changes in land water storage, including groundwater, soil moisture, lake and river surface water, and canopy and snow water. However, its coarse spatial resolution of 0.25 degrees limits its ability to observe smaller basins. To assess aquifer depletion and evaluate a long-term water resource management framework, GRACE data are crucial. It remains rare for GRACE-focused studies to be conducted in great depth. A comprehensive review of 80 articles published between 2011 and 2025 was conducted using the Scopus and Web of Science databases. These articles focused on downscaling GRACE data using machine learning (ML) methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were used in this review. This study highlights the attributes of ML models, the input variables used, the evaluation metrics, and the output resolution. Based on the analysis of the articles, random forest (RF) methods were used in the majority of the papers. Gradient boosting (GB), artificial neural networks (ANN), support vector machines (SVM), support vector regression (SVR), and long short-term memory (LSTM) were the most widely used ML methods. As input variables, rainfall (Pr), soil moisture (SM), and runoff (Qs) are essential. In 2011, there were very few journal articles; since 2021, the number has increased. The number of published studies from China was the highest (24), followed by the USA (12) and Iran (9). A total of 38 journals published reviewed articles. In terms of articles, Remote Sensing generates 19%, Journal of Hydrology has 10%, and Journal of Hydrology: Regional Studies has 8%. The paper also discusses limitations, challenges, recommendations, and potential future directions for improving the accuracy of the GWS change prediction model. Full article

(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)

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16 pages, 3269 KB

Open AccessArticle

Leveraging Artificial Intelligence in Hydrology to Process Citizen Science Photos of Water Levels

by Abhinna Manandhar and Christopher S. Lowry

Hydrology 2026, 13(5), 134; https://doi.org/10.3390/hydrology13050134 - 14 May 2026

Abstract

Emerging Large Language Model capabilities create opportunities for applying AI reasoning across various domains with minimal technical complexity. Motivated by the development of citizen scientists submitting photos of water levels on staff gauges and the increasing need for hydrologic data in ungauged watersheds, [...] Read more.

Emerging Large Language Model capabilities create opportunities for applying AI reasoning across various domains with minimal technical complexity. Motivated by the development of citizen scientists submitting photos of water levels on staff gauges and the increasing need for hydrologic data in ungauged watersheds, this research develops an artificial intelligence approach to measuring stream stage across an existing citizen science monitoring network. To lower the barrier to entry for professional scientists, this research develops a methodology leveraging a Large Language Model (LLM) to extract water levels from images submitted by citizen scientists, and then follows a human-in-the-loop workflow for validating the final results, leaving space for correcting reasoning errors and hallucinations. Various techniques, such as labeling the input image, are also explored in this research to extract maximum accuracy from the LLM. Full article

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29 pages, 37348 KB

Open AccessArticle

Transient Responses of Freshwater Lens Development and Seawater Intrusion Mitigation to Saltwater Abstraction in Unconfined Island Aquifers

by Weijiang Yu, Yipeng Zhang and Wenqi Liu

Hydrology 2026, 13(5), 133; https://doi.org/10.3390/hydrology13050133 - 14 May 2026

Abstract

Subsurface freshwater in oceanic islands is typically shaped like a thin lens due to limited land area and recharge, often the primary freshwater source for local communities and highly vulnerable to seawater intrusion (SWI). Saltwater abstraction (SA) is considered as a feasible strategy [...] Read more.

Subsurface freshwater in oceanic islands is typically shaped like a thin lens due to limited land area and recharge, often the primary freshwater source for local communities and highly vulnerable to seawater intrusion (SWI). Saltwater abstraction (SA) is considered as a feasible strategy for mitigating SWI. However, its transient effectiveness for freshwater lens (FWL) development and SWI mitigation in island aquifers, and how the design parameters like SA depth, intensity, and duration affect its performance, remain poorly understood. Therefore, this study employs a two-dimensional, variable-density island groundwater model to simulate the transient responses of FWL development and SWI mitigation to various SA patterns. Six indicators are developed for comprehensive evaluation, including: (1) freshwater recovery efficiency, and the relative changes in (2) average water table elevation (WTE), (3) WTE at the SA well, (4) FWL depth, (5) fresh groundwater volume, and (6) total aquifer salt mass. Simulation results highlight SA depth as the primary determinant of its effectiveness, characterized by critical thresholds that dictate whether SA imposes net positive or negative effects on FWL depth, volume, and aquifer desalinization, with SA intensity and duration serving as scaling factors that amplify the magnitude of these responses. Moreover, while SA can effectively expand FWL volume and shift it toward a more favorable hydrodynamic equilibrium, the diminishing marginal benefits over time cause the FRE to approach zero, indicating SA is a potent short-term restoration strategy rather than a long-term solution from a cost–benefit perspective. Full article

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36 pages, 28484 KB

Open AccessArticle

The Spectral Illusion of Crop Health: Evaluating the Groundwater Cost of Agricultural Maladaptation in the Souss-Massa Basin (Morocco)

by Maryame El-Yazidi, Mohammed Benabdelhadi, Brahim Benzougagh, Yasmine Boukhlouf, Malika El-Hamdouny, Manal El Garouani, Mohammed Mouad Mliyeh, Hassan Tabyaoui, Zineb El Attar Soufi, Soukaina El Aissaoui, Khaled Mohamed Khedher and Abderrahim Lahrach

Hydrology 2026, 13(5), 132; https://doi.org/10.3390/hydrology13050132 - 13 May 2026

Abstract

The Souss-Massa basin, one of Morocco’s major agricultural regions, is increasingly affected by water scarcity and climatic stress. However, the long-term interactions between hydro-climatic change and farmers’ cropping system adjustments remain insufficiently documented. This study analyzes hydro-climatic trends and agricultural transformations over the [...] Read more.

The Souss-Massa basin, one of Morocco’s major agricultural regions, is increasingly affected by water scarcity and climatic stress. However, the long-term interactions between hydro-climatic change and farmers’ cropping system adjustments remain insufficiently documented. This study analyzes hydro-climatic trends and agricultural transformations over the period 1995–2021. The methodology combines statistical trend analysis of meteorological data (Mann–Kendall test and Sen’s slope estimator), diachronic land use/land cover mapping using Google Earth Engine, Crop Water Stress Index (CWSI) assessment, and groundwater piezometric analysis. Results reveal declining and highly variable precipitation, together with a significant warming trend reaching +0.116 °C/year. In parallel, cultivated cereal areas (rainfed and irrigated) declined, while irrigated forage crops expanded, particularly Berseem/Maize. Despite increasing aridity, CWSI results indicate maintained crop vigor in irrigated areas, suggesting growing dependence on groundwater extraction. These findings highlight an ongoing agricultural transition that increases pressure on already vulnerable water resources and underscores the need for integrated climate adaptation and groundwater management strategies in the basin. Full article

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25 pages, 15660 KB

Open AccessArticle

Multi-Scale Analysis of Meteorological and Hydrological Droughts in the Yujiang River Basin of Southern China: Response Mechanisms and Influencing Factors

by Yanbing Huang, Xiaoli Yang, Xungui Li, Jian Sun, Qiyong Yang, Xu Dong and Yongjun Huang

Hydrology 2026, 13(5), 131; https://doi.org/10.3390/hydrology13050131 - 13 May 2026

Abstract

Drought exhibits a complex coupling response to regional meteorological factors, hydrological characteristics, land cover, and large-scale teleconnection climate indices, while their direct and indirect influences on multi-scale meteorological and hydrological droughts remain insufficiently understood, particularly in karst basins. This study investigated drought dynamics [...] Read more.

Drought exhibits a complex coupling response to regional meteorological factors, hydrological characteristics, land cover, and large-scale teleconnection climate indices, while their direct and indirect influences on multi-scale meteorological and hydrological droughts remain insufficiently understood, particularly in karst basins. This study investigated drought dynamics in China’s Yujiang River Basin using an integrated framework combining run theory, drought propagation analysis, and the partial least squares–structural equation model (PLS-SEM). We analyzed the 1-, 3-, 6-, and 12-month standardized precipitation index (SPI) and standardized streamflow index (SSI) at four hydrological stations during 1984–2014, together with meteorological factors, land cover indices, large-scale climate indices, areal precipitation, and naturalized streamflow. The results show that precipitation and streamflow exhibited slight declining tendencies with marked seasonal variability, and that drought durations of all severity levels generally decreased with increasing time scales. At the same time scale, SSI was more stable than SPI, and both indices tended to become more stable as the time scale increased. SPI-3 and SSI-1 were identified as the optimal time scales for monitoring meteorological and hydrological drought, respectively, providing a practical basis for drought identification and early warning in karst basins. Hydrological drought lagged meteorological drought by 1–3 months, indicating a measurable propagation time that is valuable for improving drought preparedness and water resources regulation. PLS-SEM further revealed that precipitation and streamflow were the dominant direct drivers of drought development, while land cover exerted a persistent negative effect, and climate-related factors mainly influenced drought indirectly. These findings enhance the understanding of drought propagation and multi-factor coupling mechanisms in karst basins and provide scientific support for regional drought monitoring and water resources management. Full article

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

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11 pages, 1867 KB

Open AccessArticle

HYDROPOT: A Reproducible Geospatial Framework for Hydrological Descriptor Extraction and Regional Hydropower Screening in Ungauged Basins: A Case Study in the Lazio Region (Italy)

by Andrea Petroselli

Hydrology 2026, 13(5), 130; https://doi.org/10.3390/hydrology13050130 - 12 May 2026

Abstract

Assessing hydropower potential in ungauged basins requires consistent derivation of key hydrological variables from heterogeneous geospatial and climatic data. Conventional GIS-based approaches often rely on fragmented, user-dependent workflows, limiting reproducibility and comparability. This study presents HYDROPOT, a web-based geospatial framework for the automated [...] Read more.

Assessing hydropower potential in ungauged basins requires consistent derivation of key hydrological variables from heterogeneous geospatial and climatic data. Conventional GIS-based approaches often rely on fragmented, user-dependent workflows, limiting reproducibility and comparability. This study presents HYDROPOT, a web-based geospatial framework for the automated and reproducible extraction of hydrologically relevant basin descriptors for regional-scale hydropower screening. The platform integrates centralized datasets with server-side geoprocessing to delineate upstream catchments and compute quantitative basin descriptors, including drainage area (2–400 km²), Curve Number (CN), concentration time, and spatially aggregated monthly thermo-pluviometric variables derived from 95 stations over the 2004–2022 period. These descriptors provide essential inputs for rainfall–runoff modeling and preliminary discharge estimation, thereby supporting (although not directly performing) the assessment of water availability in ungauged basins. By eliminating manual preprocessing, HYDROPOT ensures consistent and reproducible analyses, reducing user-induced variability and improving comparability across applications, without implying increased predictive accuracy. The framework, applied to the Lazio Region (Central Italy) over the 2004–2022 period, enables rapid and transparent screening of river reaches, offering a scalable decision-support tool for preliminary, input-based screening in early-stage small hydropower planning. Full article

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27 pages, 4365 KB

Open AccessArticle

Integrated Geospatial Assessment of Soil Erosion, Water Quality, and Sediment Fertility for Sustainable Hill Reservoir Management in Arid Catchments: A Case Study of the Es-Sabba Watershed, Naama Province, Southwestern Algeria

by Mohammed Khelifi, Abdessamed Derdour, Tayeb Nouri, Tayyib Moussaoui, Said Bouarfa, Sanliana, Wan Abd Al Qadr Imad Wan-Mohtar, Bilel Zerouali and Yong Jie Wong

Hydrology 2026, 13(5), 129; https://doi.org/10.3390/hydrology13050129 - 11 May 2026

Abstract

Small hill reservoirs in arid North Africa face accelerating threats from soil erosion and siltation, yet integrated assessments linking erosion dynamics, water quality, and soil fertility remain scarce. This study presents a multi-component geospatial assessment of the 345 km² Es-Sabba watershed in [...] Read more.

Small hill reservoirs in arid North Africa face accelerating threats from soil erosion and siltation, yet integrated assessments linking erosion dynamics, water quality, and soil fertility remain scarce. This study presents a multi-component geospatial assessment of the 345 km² Es-Sabba watershed in the Saharan Atlas of southwestern Algeria. Soil loss was quantified using the revised universal soil loss equation (RUSLE) integrated with Sentinel-2 imagery, a 30 m digital elevation model (DEM), and GIS analysis for 2016–2025. The mean annual soil loss reached 26.3 t/ha/yr, with 68.4% of the watershed under high-to-severe erosion; topography and vegetation cover were the dominant controls. Estimated sediment delivery to the reservoir is 135,300 t/yr, projecting a functional lifespan of 11–15 years without intervention. Hydrochemical analysis classified reservoir water as alkaline- and sulfate-rich, yet suitable for irrigation with very low sodicity risk (sodium adsorption ratio, SAR = 0.08) and an excellent Irrigation Water Quality Index (IWQI = 91.75). Soils exhibited low-to-moderate fertility (mean soil fertility index, SFI = 0.416), with widespread nitrogen deficiency constraining vegetation-based erosion control. The integrated framework identifies circular-economy opportunities through nutrient-rich sediment reuse and provides actionable guidance for climate-resilient reservoir management in arid catchments. Full article

(This article belongs to the Special Issue Integrated Surface Water and Groundwater Resource Management, 2nd Edition)

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24 pages, 7760 KB

Open AccessArticle

Enhancing GEOGLOWS River Forecast System with a High-Resolution Pre-Processing Approach for Runoff Bias Correction

by Juseth E. Chancay, Jorge Luis Sánchez-Lozano, Bryan G. Valencia, Mario Germán Trujillo-Vela, E. James Nelson, Riley C. Hales and Angélica L. Gutiérrez

Hydrology 2026, 13(5), 128; https://doi.org/10.3390/hydrology13050128 - 10 May 2026

Abstract

Accurate streamflow information is critical for early flood and drought warning. However, global hydrological forecasting systems are affected by residual errors in meteorological forcing, model structure, and routing, which propagate into simulated streamflow. Within the GEOGLOWS River Forecast System (RFS), ERA5 runoff biases [...] Read more.

Accurate streamflow information is critical for early flood and drought warning. However, global hydrological forecasting systems are affected by residual errors in meteorological forcing, model structure, and routing, which propagate into simulated streamflow. Within the GEOGLOWS River Forecast System (RFS), ERA5 runoff biases are routed into streamflow simulations. The most effective operational bias-correction method, MFDC-QM, requires local discharge observations and cannot be applied consistently in ungauged basins. This study evaluates a pre-routing, grid-scale runoff bias-correction framework that adjusts ERA5 runoff before routing by combining Flow Duration Curve (FDC) mapping and Sparse Cumulative Distribution Function (CDF) matching, using GSCD as a spatially distributed reference runoff data. Baseline GEOGLOWS RFS, pre-routing correction, and MFDC-QM were compared for 1980–2025 using 16,517 gauging stations, Kling–Gupta Efficiency (KGE), and paired significance tests. Globally, the median KGE increased modestly from 0.16 to 0.22, compared with 0.48 for MFDC-QM. Results demonstrate a clear regional dependence: pre-routing correction produced statistically significant gains in South America and Africa (p < 0.05), where ERA5 runoff exhibits stronger residual biases, but had limited effects in Europe and North America, where dense hydrometeorological networks likely impose stronger observational constraints on the underlying reanalysis. These patterns show that pre-routing correction is most valuable where residual forcing bias is large and observational constraints are limited, complementing observation-based post-processing in ungauged, data-limited regions. Full article

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17 pages, 4433 KB

Open AccessArticle

Regionalization of Short-Duration Storm Temporal Patterns Using Huff Curves in a Coastal Tropical Region

by Valeria Hernández Zambrano, Luis Simancas Martínez, Andrés Hatum Pontón and John J. Ramirez-Avila

Hydrology 2026, 13(5), 127; https://doi.org/10.3390/hydrology13050127 - 8 May 2026

Abstract

Tropical coastal regions exhibit pronounced spatial and temporal variability in rainfall driven by seasonal atmospheric circulation and coastal–orographic interactions. Accurate representation of the temporal distribution of rainfall is essential for hydrologic modeling and infrastructure design. This study develops regionalized Huff curves for the [...] Read more.

Tropical coastal regions exhibit pronounced spatial and temporal variability in rainfall driven by seasonal atmospheric circulation and coastal–orographic interactions. Accurate representation of the temporal distribution of rainfall is essential for hydrologic modeling and infrastructure design. This study develops regionalized Huff curves for the Department of Magdalena, Colombia, addressing a critical gap in the characterization of rainfall temporal patterns in tropical coastal regions. A total of 270 short-duration (5–6 h) rainfall events from automatic stations were converted into normalized cumulative mass curves. The resulting curves were grouped into homogeneous temporal patterns using clustering algorithms. Three dominant storm types were identified: early-peak (Curve 1), intermediate (Curve 2), and uniform (Curve 3), reflecting the region’s coastal, lowland, and orographic influences. Probability envelopes and representative design hyetographs were derived to quantify intra-event variability. Rainfall–runoff simulations for a 100-km² watershed showed peak-flow differences of up to 132% between storm types, highlighting the sensitivity of hydrologic response to rainfall temporal distributions. The resulting regionalized Huff curves provide a practical and transferable framework for hydrologic modeling, flood-risk assessment, and infrastructure planning in tropical regions with limited high-resolution rainfall data. Full article

(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)

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30 pages, 5473 KB

Open AccessArticle

Attribute Analysis and Quantitative Estimation of Runoff Reduction in the Upper Yangtze River Basin Under Changing Environment

by Xiaoya Wang, Shenglian Guo, Hua Chen, Bokai Sun and Xin Xiang

Hydrology 2026, 13(5), 126; https://doi.org/10.3390/hydrology13050126 - 8 May 2026

Abstract

Under the influence of climate change and human activities, hydrologic regime and runoff in the upper Yangtze River basin (UYRB) have exhibited significant alterations. This study aims to address the primary drivers of runoff change and the destination of runoff reduction. Based on [...] Read more.

Under the influence of climate change and human activities, hydrologic regime and runoff in the upper Yangtze River basin (UYRB) have exhibited significant alterations. This study aims to address the primary drivers of runoff change and the destination of runoff reduction. Based on hydro-meteorological data from 1980 to 2022 and other related datasets, the temporal trend in hydro-meteorological variables was analyzed, and the impacts of climate change and human activities on runoff were quantified using the SWAT model. The destination of runoff reduction was also addressed based on the water balance equation. The SWAT model was calibrated using a top-down sequential strategy at five hydrological stations. The results show that despite a slight increase in precipitation and a pronounced rise in potential evapotranspiration, the annual average runoff at Yichang station is decreased by 22.3 billion m³. The SWAT model can simulate the monthly runoff hydrograph well with the NSE exceeding 0.85 during calibration and validation periods in the UYRB. Attribution analysis reveals that the contribution rate of climate change and human activities on runoff are 36.21% and 63.79% at the Yichang station, respectively. The annual average runoff change can be attributed to four pathways: (1) actual evapotranspiration increases due to land use and land cover (LULC) change and basin greening (−12.85 billion m³); (2) water intake and consumption increase (−2.94 billion m³); (3) reservoir dead storage impoundment (−3.34 billion m³); and (4) ground water storage variations (−3.21 billion m³). These findings highlight the impact of human water abstraction and land use change on runoff, providing a scientific basis for water resource management in the UYRB. Full article

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