Journal Description
Hydrology
                    Hydrology 
                    is an international, peer-reviewed, open access journal on hydrology published monthly online by MDPI. The American Institute of Hydrology (AIH) and Japanese Society of Physical Hydrology (JSPH) are affiliated with Hydrology and their members receive discounts on the article processing charges.
                - Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), PubAg, GeoRef, and other databases.
- Journal Rank: JCR - Q2 (Water Resources) / CiteScore - Q1 (Oceanography)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Journal Clusters of Water Resources: Water, Journal of Marine Science and Engineering, Hydrology, Resources, Oceans, Limnological Review, Coasts.
                                            Impact Factor: 
                        3.2 (2024);
                        5-Year Impact Factor: 
                        3.0 (2024)
                                    
                
                                
            Latest Articles
        
        
                    
    
        
    
    Remote Sensing-Based Monitoring of Agricultural Drought and Irrigation Adaptation Strategies in the Antalya Basin, Türkiye
                        
    
                
        
                
        Hydrology 2025, 12(11), 288; https://doi.org/10.3390/hydrology12110288 (registering DOI) - 31 Oct 2025
    
                            
    
                    
        
                    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)
            
        
        
    Open AccessArticle
    
    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 (registering DOI) - 31 Oct 2025
    
                            
    
                    
        
                    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
Open AccessArticle
    
    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 (registering DOI) - 31 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
        
        
►▼
             Show Figures
         
            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
    
Graphical abstract
Open AccessArticle
    
    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
    
                            
    
                    
        
                    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 (
    
        
        
                    (This article belongs to the  Special Issue Novel Approaches in Contaminant Hydrology and Groundwater Remediation, 2nd Edition)
            
        
        
►▼
             Show Figures
         
Figure 1
Open AccessArticle
    
    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
    
                            
    
                    
        
                    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
Open AccessArticle
    
    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
    
                            
    
                    
        
                    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
Open AccessArticle
    
    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
    
                            
    
                    
        
                    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
Open AccessArticle
    
    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
    
                            
    
                    
        
                    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
Open AccessArticle
    
    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
    
                            
    
                    
        
                    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
Open AccessArticle
    
    Mapping Soil Erosion and Ecosystem Service Loss: Integrating RUSLE and NDVI Metrics to Support Conservation in El Cajas National Park, Ecuador
                        
            by
                    Diego Portalanza, Javier Del-Cioppo Morstadt, Valeria Polhmann, Gabriel Gallardo, Karla Aguilera, Yoansy Garcia and Fanny Rodriguez-Jarama        
    
                
        
        Hydrology 2025, 12(11), 279; https://doi.org/10.3390/hydrology12110279 - 25 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
    
            Mountain protected areas in the tropical Andes experience localized yet severe soil erosion that threatens erosion-regulating services and downstream water–energy security. We mapped soil loss at 30 m using the Revised Universal Soil Loss Equation (RUSLE) and quantified the erosion-control service in El
             [...] Read more.
        
        
            Mountain protected areas in the tropical Andes experience localized yet severe soil erosion that threatens erosion-regulating services and downstream water–energy security. We mapped soil loss at 30 m using the Revised Universal Soil Loss Equation (RUSLE) and quantified the erosion-control service in El Cajas National Park, Ecuador (28,544 ha) using an NDVI-based index. Replacing categorical land cover C factors with a continuous NDVI surface increased the park-wide soil loss estimate by ∼58%, yielding an area-weighted mean of 5.3 t ha−1 yr−1 and local maxima of 120 t ha−1 yr−1 on steep and sparsely vegetated escarpments. Relative to a bare soil scenario, existing páramo grasslands, shrub mosaics, and scattered Polylepis woodlots avert 95% of potential erosion, quantifying the service supplied by vegetation. Between 2023 and 2024, a ∼60% rise in mean NDVI more than doubled the area delivering moderate-to-high erosion control. A hot-spot analysis further identified ∼30 km2 (≈5% of the park) where high modeled soil loss coincides with low protection; these clusters generate ∼80% of predicted sediment and constitute priority targets for restoration or visitor use regulation. The integrated RUSLE–NDVI–EC approach provides a concise and transferable screening tool for aligning conservation investments with Ecuador’s restoration pledges and for safeguarding critical hydrological services in Andean protected areas.
            Full article
        
    
        
        
                    (This article belongs to the  Special Issue Hydrological Signatures of a Changing Landscape: Land Degradation Impacts, Monitoring, and Restoration)
            
        
        
►▼
             Show Figures
         
Figure 1
Open AccessArticle
    
    Current Trends and Future Scenarios: Modeling Maximum River Discharge in the Zhaiyk–Caspian Basin (Kazakhstan) Under a Changing Climate
                        
            by
                    Sayat Alimkulov, Lyazzat Makhmudova, Saken Davletgaliev, Elmira Talipova, Daniel Snow, Lyazzat Birimbayeva, Mirlan Dyldaev, Zhanibek Smagulov and Akgulim Sailaubek        
    
                
        
        Hydrology 2025, 12(11), 278; https://doi.org/10.3390/hydrology12110278 - 24 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
    
            In the context of intensifying climate change, it is particularly important to assess the transformation of spring floods as a key phase of the hydrological regime of rivers. This study provides a comprehensive analysis of the characteristics of maximum runoff in the Zhaiyk–Caspian
             [...] Read more.
        
        
            In the context of intensifying climate change, it is particularly important to assess the transformation of spring floods as a key phase of the hydrological regime of rivers. This study provides a comprehensive analysis of the characteristics of maximum runoff in the Zhaiyk–Caspian basin for the modern period and projected changes for 2030, 2040, and 2050 based on CMIP6 climate scenarios (SSP3-7.0 and SSP5-8.5). Analysis of observations at 34 hydrological stations showed a reduction in spring runoff by up to 35%, a decrease in the duration of high water and a reduction in maximum water discharge on some rivers by up to 45%. It has been established that those rising temperatures, more frequent thaws, and reduced autumn moisture lead to lower maximum water discharge and a redistribution of the seasonal flow regime. Scenario projections revealed significant spatial heterogeneity: some rivers are expected to experience an increase in maximum discharge of up to 72%, while others will see a steady decline in maximum discharge of up to 35%. The results obtained indicate the need to transition to an adaptive water management system focused on the regional characteristics of river basins and the sensitivity of small- and medium-sized watercourses to climate change.
            Full article
        
    
        
        
                    (This article belongs to the  Section Water Resources and Risk Management)
            
        
        
►▼
             Show Figures
         
Figure 1
Open AccessArticle
    
    Seasonal Freshwater Inflows in Cochin Backwater Estuary Inferred from Stable Isotopes and Machine Learning
                        
            by
                    Prasanna K., Ravi Rangarajan, Fursan Thabit, Prosenjit Ghosh and Habeeb Rahman        
    
                
        
        Hydrology 2025, 12(11), 277; https://doi.org/10.3390/hydrology12110277 - 24 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
    
            The Cochin Backwater region in Southern India is one of the most dynamic estuaries, strongly influenced by seasonal river runoff and seawater intrusion. This study explores the relationship between monsoonal rains, salinity, and stable isotopic composition (δ18O and δ13C)
             [...] Read more.
        
        
            The Cochin Backwater region in Southern India is one of the most dynamic estuaries, strongly influenced by seasonal river runoff and seawater intrusion. This study explores the relationship between monsoonal rains, salinity, and stable isotopic composition (δ18O and δ13C) to estimate the contribution of freshwater fluxes at different seasonal intervals for the Cochin Backwater (CBW) estuary. Seasonal variations in oxygen isotopes and salinity revealed distinct trends indicative of freshwater–seawater mixing dynamics. The comparison of Local and Global Meteoric Water Lines highlighted the occurrence of enriched isotope values during the Premonsoon season, showing significant evaporation effects. Carbon (C) isotopic analysis in dissolved inorganic matter (δ13CDIC) at 17 stations during the Premonsoon season revealed spatially distinct carbon dynamics zones, influenced by various sources. These characteristic zones were categorized as Zone 1, dominated by seawater, exhibiting heavier δ13CDIC values; Zone 2, showing significant contributions of lighter terrestrial δ13C; and Zone 3, reflecting inputs from regional and local paddy fields with a distinct C3 isotopic signature (−25‰), modified by estuarine productivity. In addition, different advanced machine learning techniques were tested to improve analysis and prediction of seasonal variations in isotopic composition and salinity. Although the data were sufficiently robust for demonstrating the feasibility and advantages of ML in isotopic hydrology, further expansion of the dataset would be essential for improving the accuracy of models, especially for δ13C. The combination of these advanced machine learning models not only improved the predictive accuracy of seasonal freshwater fluxes but also provided a robust framework for understanding the estuarine ecosystem and could pave the way for better management and conservation strategies of the CBW estuarine system.
            Full article
        
    
        
        
                    (This article belongs to the  Section Marine Environment and Hydrology Interactions)
            
        
        
►▼
             Show Figures
         
Figure 1
Open AccessArticle
    
    Epikarst Flow Dynamics and Contaminant Attenuation: Field and Laboratory Insights from the Suva Planina Karst System
                        
            by
                    Branislav Petrović, Ljiljana Vasić, Saša Milanović and Veljko Marinović        
    
                
        
        Hydrology 2025, 12(11), 276; https://doi.org/10.3390/hydrology12110276 - 23 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
    
            The present research moves the focus from merely describing epikarst flow to quantifying its natural filtration performance and contaminant retention mechanisms through integrating in situ tracer experiments with controlled laboratory modelling—an approach seldom applied in previous studies. Two field experiments at Peč Cave
             [...] Read more.
        
        
            The present research moves the focus from merely describing epikarst flow to quantifying its natural filtration performance and contaminant retention mechanisms through integrating in situ tracer experiments with controlled laboratory modelling—an approach seldom applied in previous studies. Two field experiments at Peč Cave demonstrated that the epikarst exhibits rapid hydraulic connectivity—evidenced by fast tracer breakthrough with virtual flow speeds between 0.0041 and 0.006 m/s—yet simultaneously provides strong attenuation, as shown by the low tracer recovery and near-complete removal of microbial contaminants as well as nitrogen compounds through retention, degradation, and dilution under natural infiltration conditions, including rainfall and snowmelt. Complementary laboratory simulations further confirmed this duality, with nitrate concentrations reduced by 30–50%. Field data and lab results consistently indicated that the epikarst does not merely transmit water but actively adsorbs and transforms pollutants. Overall, the epikarst on Suva Planina functions as an effective natural filtration layer that substantially improves groundwater quality before it reaches major karst springs, acting as a protective yet vulnerable “skin” of the aquifer. These findings highlight the epikarst’s critical role in Suva planina Mt. karst aquifer protection and results support consideration of epikarst in groundwater management strategies, particularly in regions where springs are used for public water supply.
            Full article
        
    
        
        
                    (This article belongs to the  Section Hydrological and Hydrodynamic Processes and Modelling)
            
        
        
►▼
             Show Figures
         
Graphical abstract
Open AccessArticle
    
    Assessing Impacts of Anthropogenic Modification on Surface Soil Moisture Dynamics: A Case Study over Southwest China
                        
            by
                    Chunying Shen, Changrui Qin, Zheng Lu, Dehui Ning, Zhenxiang Zang, Honglei Tang, Feng Pan, Guaimei Cheng, Jimin Hu and Shasha Meng        
    
                
        
        Hydrology 2025, 12(11), 275; https://doi.org/10.3390/hydrology12110275 - 22 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
    
            Anthropogenic activities are profoundly altering the terrestrial water cycle, yet a comprehensive understanding of their impact on surface soil moisture (SSM) at regional scales remains limited. This study investigates the spatiotemporal dynamics of SSM and its relationship with anthropogenic modification (OAM) across Southwest
             [...] Read more.
        
        
            Anthropogenic activities are profoundly altering the terrestrial water cycle, yet a comprehensive understanding of their impact on surface soil moisture (SSM) at regional scales remains limited. This study investigates the spatiotemporal dynamics of SSM and its relationship with anthropogenic modification (OAM) across Southwest China from 2000 to 2017. We employed multi-year geospatial and statistical analyses, including kernel density estimation and boxplots, to examine the impacts of human activities on regional soil moisture patterns. The results revealed that SSM exhibited a slight long-term declining trend (Sen’s slope = −0.0009 m3/m3/year) but showed a notable recovery after 2011, while overall anthropogenic modification (OAM) intensified until 2010 before declining sharply by 2015. A statistically significant and systematic relationship was observed, with increasing OAM intensity corresponding to higher median SSM and reduced spatial variability, indicating a homogenizing effect of human activities. Critically, the impacts of detailed anthropogenic stressors were highly divergent: agricultural modification correlated with elevated SSM, whereas transportation infrastructure and energy-related activities exhibited a suppressive effect. These findings highlight the necessity of integrating high-resolution SSM and anthropogenic data into land-use planning and implementing stressor-specific management strategies, such as improving irrigation efficiency and developing infrastructure designs that minimize SSM suppression, to achieve sustainable water resource management in rapidly developing regions.
            Full article
        
    
        
        
                    (This article belongs to the  Section Soil and Hydrology)
            
        
        
►▼
             Show Figures
         
Figure 1
Open AccessArticle
    
    Flood Frequency Analysis Using the Bivariate Logistic Model with Non-Stationary Gumbel and GEV Marginals
                        
            by
                    Laura Berbesi-Prieto and Carlos Escalante-Sandoval        
    
                
        
        Hydrology 2025, 12(11), 274; https://doi.org/10.3390/hydrology12110274 - 22 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
        
        
►▼
             Show Figures
         
            Flood frequency analysis is essential for designing resilient hydraulic infrastructure, but traditional stationary models fail to capture the influence of climate variability and land-use change. This study applies a bivariate logistic model with non-stationary marginals to eight gauging stations in Sinaloa, Mexico, each
             [...] Read more.
        
        
            Flood frequency analysis is essential for designing resilient hydraulic infrastructure, but traditional stationary models fail to capture the influence of climate variability and land-use change. This study applies a bivariate logistic model with non-stationary marginals to eight gauging stations in Sinaloa, Mexico, each with over 30 years of maximum discharge records. We compared stationary and non-stationary Gumbel and Generalized Extreme Value (GEV) distributions, along with their bivariate combinations. Results show that the non-stationary bivariate GEV–Gumbel distribution provided the best overall performance according to AIC. Importantly, GEV and Gumbel marginals captured site-specific differences: GEV was most suitable for sites with highly variable extremes, while Gumbel offered a robust fit for more regular records. At station 10086, where a significant increasing trend was detected by the Mann–Kendall and Spearman tests, the stationary GEV estimated a 50-year return flow of 772.66 m3/s, while the non-stationary model projected 861.00 m3/s for 2075. Under stationary assumptions, this discharge would be underestimated, occurring every ~30 years by 2075. These findings demonstrate that ignoring non-stationarity leads to systematic underestimation of design floods, while non-stationary bivariate models provide more reliable, policy-relevant estimates for climate adaptation and infrastructure safety.
            Full article
        
    
Figure 1
Open AccessArticle
    
    Improving Soil Water Simulation in Semi-Arid Agriculture: A Comparative Evaluation of Water Retention Curves and Inverse Modeling Using HYDRUS-1D
                        
            by
                    Ali Rasoulzadeh, Mohammad Reza Kohan, Arash Amirzadeh, Mahsa Heydari, Javanshir Azizi Mobaser, Majid Raoof, Javad Ramezani Moghadam and Jesús Fernández-Gálvez        
    
                
        
        Hydrology 2025, 12(10), 273; https://doi.org/10.3390/hydrology12100273 - 21 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
        
        
►▼
             Show Figures
         
            Water scarcity in semi-arid regions necessitates accurate soil water modeling to optimize irrigation management. This study compares three HYDRUS-1D parameterization approaches—based on the drying-branch soil water retention curve (SWRC), wetting-branch SWRC (using Shani’s drip method), and inverse modeling—to simulating soil water content at
             [...] Read more.
        
        
            Water scarcity in semi-arid regions necessitates accurate soil water modeling to optimize irrigation management. This study compares three HYDRUS-1D parameterization approaches—based on the drying-branch soil water retention curve (SWRC), wetting-branch SWRC (using Shani’s drip method), and inverse modeling—to simulating soil water content at 15 cm and 45 cm depths under center-pivot irrigation in a semi-arid region. Field experiments in three maize fields provided daily soil water, soil hydraulic, and meteorological data. Inverse modeling achieved the highest accuracy (NRMSE: 2.29–7.40%; RMSE: 0.006–0.023 cm3 cm−3), particularly at 15 cm depth, by calibrating van Genuchten parameters against observed water content. The wetting-branch approach outperformed the drying branch at the same depth, capturing irrigation-induced wetting processes more effectively. Statistical validation confirmed the robustness of inverse modeling in reproducing temporal patterns, while wetting-branch data improved deep-layer accuracy. The results demonstrate that inverse modeling is a reliable approach for soil water simulation and irrigation management, whereas the wetting-branch parameterization offers a practical, field-adaptable alternative. This study provides one of the first side-by-side evaluations of these three modeling approaches under real-world semi-arid conditions.
            Full article
        
    
Figure 1
Open AccessArticle
    
    A Geospatial Assessment Toolbox for Spatial Allocation of Large-Scale Nature-Based Solutions for Hydrometeorological Risk Reduction
                        
            by
                    Adam Mubeen, Vishal Balaji Devanand, Laddaporn Ruangpan, Zoran Vojinovic, Arlex Sanchez Torres, Jasna Plavšić, Natasa Manojlovic, Guido Paliaga, Ahmad Fikri Abdullah, João P. Leitão, Agnieszka Wojcieszak, Marzena Rutkowska-Filipczak, Katarzyna Izydorczyk, Tamara Sudar, Božidar Deduš, Draženka Kvesić, Lyudmil Ikonomov and Valery Penchev        
    
                
        
        Hydrology 2025, 12(10), 272; https://doi.org/10.3390/hydrology12100272 - 17 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
    
            The compounding effects of hydrometeorological hazards are being driven by climate change. As urban areas expand, this leads to degradation of the surrounding environment and exposes more people to hazards. Growing losses show that conventional approaches to addressing these issues can compound these
             [...] Read more.
        
        
            The compounding effects of hydrometeorological hazards are being driven by climate change. As urban areas expand, this leads to degradation of the surrounding environment and exposes more people to hazards. Growing losses show that conventional approaches to addressing these issues can compound these problems. Over the last few decades, nature-based solutions (NBSs) have become an increasingly popular alternative. These measures, inspired by natural processes, have shown potential for reducing hazards by complementing traditional approaches and providing co-benefits in the form of eco-system services. With the adoption of NBSs becoming a more mainstream approach, there is a need for tools that support the planning and implementation of interventions. Geospatial suitability assessment is a part of this planning process. Existing tools are limited in their application for large-scale measures. This paper intends to improve this by building upon a multi-criteria analysis (MCA)-based approach that incorporates biophysical and land use criteria and conditions for mapping the suitability of large-scale NBSs. The methodology was developed and tested on six sites to assess the suitability of floodplain restoration, retention or detention, afforestation, and forest buffer strips. The resulting suitability maps also show potential for combining two or more measures for greater risk reduction.
            Full article
        
    
        
        
                    (This article belongs to the  Special Issue Advances in Nature-Based Solutions for Hydrometeorological Risk Reduction)
            
        
        
►▼
             Show Figures
         
Figure 1
Open AccessReview
    
    A Review of the Key Impacts of Deforestation and Wildfires on Water Resources with Regard to the Production of Drinking Water
                        
            by
                    Olivier Banton, Sylvie St-Pierre, Guillaume Banton, Nicolas Laures and Anne Triganon        
    
                
        
        Hydrology 2025, 12(10), 271; https://doi.org/10.3390/hydrology12100271 - 12 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
    
            Deforestation and wildfires drastically impact vegetation cover, consequently affecting water dynamics. These hazards alter the different components of the water cycle, including evapotranspiration, runoff, infiltration, and groundwater recharge. Overall, runoff increases while infiltration and groundwater recharge decrease. Furthermore, these hazards significantly alter the
             [...] Read more.
        
        
            Deforestation and wildfires drastically impact vegetation cover, consequently affecting water dynamics. These hazards alter the different components of the water cycle, including evapotranspiration, runoff, infiltration, and groundwater recharge. Overall, runoff increases while infiltration and groundwater recharge decrease. Furthermore, these hazards significantly alter the chemistry of both surface water and groundwater. The main changes to water quality relate to turbidity, bacterial load, mineralization and nutrients. Forest fires can also release contaminants such as heavy metals, polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs). Other contaminants can be introduced by products used in firefighting, such as retardants and perfluoroalkyl substances (PFAS). This paper reviews the impact of deforestation and wildfires on water resources, especially with a view to their use as raw water for drinking water production. The paper identifies the magnitude of the changes induced in water quantity and quality. Even if the results are climate- and site-specific, they provide an indication of the possible magnitude of these impacts. Finally, the various changes brought about by these hazards are ranked according to their potential impact on drinking water production.
            Full article
        
    
        
        
                    (This article belongs to the  Special Issue Novel Procedures and Methodologies for Surface and Underground Water Quality Analysis: Theory and Application)
            
        
        
►▼
             Show Figures
         
Figure 1
Open AccessArticle
    
    Long-Term Hydrodynamic Modeling of Low-Flow Conditions with Groundwater–River Interaction: Case Study of the Rur River
                        
            by
                    You Wu, Daniel Bachmann and Holger Schüttrumpf        
    
                
        
        Hydrology 2025, 12(10), 270; https://doi.org/10.3390/hydrology12100270 - 11 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
    
            Groundwater plays a critical role in maintaining streamflow during low-flow periods. However, accurately quantifying groundwater flow still remains a modeling challenge. Prolonged low-flow or drought conditions necessitate long-term simulations, further increasing the complexity of achieving reliable results. To address these issues, a novel
             [...] Read more.
        
        
            Groundwater plays a critical role in maintaining streamflow during low-flow periods. However, accurately quantifying groundwater flow still remains a modeling challenge. Prolonged low-flow or drought conditions necessitate long-term simulations, further increasing the complexity of achieving reliable results. To address these issues, a novel modeling framework (HYD module in LoFloDes) that integrates a one-dimensional (1D) river module with two-dimensional (2D) groundwater module via bidirectional coupling, enabling robust and accurate simulations of both groundwater and river dynamics throughout their interactions, especially over extended periods, was developed. The HYD module was applied to the Rur River, calibrated using gridded groundwater data, groundwater and river gauge data from 2002 to 2005 and validated from 1991 to 2020. During validation periods, the simulated river and groundwater levels generally reproduced observed trends, although suboptimal performance at certain gauges is attributed to unmodeled local anthropogenic influences. Comparative simulations demonstrated that the incorporation of groundwater–river interactions markedly enhanced model performance, especially at the downstream Stah gauge, where the coefficient of determination (R2) increased from 0.83 without interaction to 0.9 with interaction. Consistent with spatio-temporal patterns of this interaction, simulated groundwater contributions increased from upstream to downstream and were elevated during low-flow months. These findings underscore the important role of groundwater contributions in local river dynamics along the Rur River reach. The successful application of the HYD module demonstrates its capacity for long-term simulations of coupled groundwater–surface water systems and underscores its potential as a valuable tool for integrated river and groundwater resources management.
            Full article
        
    
        
        
                    (This article belongs to the  Special Issue Integrated Surface Water and Groundwater Resource Management, 2nd Edition)
            
        
        
►▼
             Show Figures
         
Figure 1
Open AccessArticle
    
    Advancing Water Resources Management Through Reservoir Release Optimization: A Study Case in Piracicaba River Basin in Brazil
                        
            by
                    Raphael Ferreira Perez, João Rafael Bergamaschi Tercini, Dário Hachisu Hossoda, Veronica Lima Gonsalez Rabioglio and Joaquin Ignacio Bonnecarrère        
    
                
        
        Hydrology 2025, 12(10), 269; https://doi.org/10.3390/hydrology12100269 - 11 Oct 2025
    
                            
    
                    
        
                    Abstract 
            
            
                        
        
        
►▼
             Show Figures
         
            Given significant water scarcity events in the recent past, water resources management in the Piracicaba River Basin, São Paulo, Brazil, has intensified the adoption of complex measures to meet the population’s water supply demands. This study presents a methodology to optimize reservoir water
             [...] Read more.
        
        
            Given significant water scarcity events in the recent past, water resources management in the Piracicaba River Basin, São Paulo, Brazil, has intensified the adoption of complex measures to meet the population’s water supply demands. This study presents a methodology to optimize reservoir water release while adhering to restrictive rules, aiming to also conserve water. A rainfall–runoff model was utilized alongside a hydrological routing model, incorporating meteorological forecasts for simulation over ten consecutive years. The results demonstrated significant water savings when comparing the optimization scenario with the actual reservoir operation during the same period. The applied methodology reduced water releases up to 66% in comparison to the observed scenario. Overall, the study introduces tools to improve reservoir operation with computational techniques, enriching local water resources management, water security, and decision-making processes, ensuring water security for the São Paulo Metropolitan Region, the most populous region in Brazil.
            Full article
        
    
Figure 1
 
            
            
            Journal Menu
► ▼ Journal Menu- 
                        - Hydrology Home
- Aims & Scope
- Editorial Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Editorial Office
 
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
            Topic in 
            Geosciences, Hydrology, Remote Sensing, Sustainability, Water
        Advances in Hydrogeological Research
    Topic Editors: Karl Auerswald, Jordan ClarkDeadline: 30 November 2025
            Topic in 
            Agriculture, Remote Sensing, Sustainability, Water, Hydrology, Limnological Review, Earth
        Water Management in the Age of Climate Change
    Topic Editors: Yun Yang, Chong Chen, Hao SunDeadline: 31 January 2026
            Topic in 
            Environments, Geosciences, Hydrology, Water, Biosphere, Limnological Review
        Geological Processes: A Key to Understand Water Quality Issues
    Topic Editors: Weiying Feng, Hao WangDeadline: 20 March 2026
            Topic in 
            Agriculture, Hydrology, Land, Sustainability, Toxics, Water, Limnological Review
        Water-Soil Pollution Control and Environmental Management
    Topic Editors: Yunhui Zhang, Xubo Gao, Hong Liu, Qili Hu, Liting Hao, Antonije Onjia, Md Galal UddinDeadline: 31 March 2026
 
                    Special Issues
                            Special Issue in 
                    Hydrology
        Lakes as Sensitive Indicators of Hydrology, Environment, and Climate
    Guest Editors: E. Troy Rasbury, Guleed Ali, John LuczajDeadline: 31 October 2025
                            Special Issue in 
                    Hydrology
        GRACE Observations for Global Groundwater Storage Analysis
    Guest Editors: Norman L. Jones, Gustavious Paul WilliamsDeadline: 31 October 2025
                            Special Issue in 
                    Hydrology
        Geographic Information Systems (GIS) Techniques and Applications for Sustainable Water Resources Management in Agriculture
    Guest Editors: Iolanda Borzì, Beatrice Monteleone, Hailong YinDeadline: 22 November 2025
                            Special Issue in 
                    Hydrology
        Advances in Urban Hydrology and Stormwater Management
    Guest Editors: Shirley Gato-Trinidad, Upaka Rathnayake, Kuok KuokDeadline: 30 November 2025
Topical Collections
                            Topical Collection in 
                    Hydrology
        Feature Papers of Hydrology
    Collection Editors: Ezio Todini, Tammo Steenhuis 
                         
                        


