Hydrology

20 pages, 6052 KB

Open AccessArticle

Distributed Estimation of the Curve Number (CN) in Continental Ecuador Using Machine Learning, Official Geo-Pedological Data, and Field-Based Hydrological Validation

by Carlos Andrés Maldonado Chávez, Benito Guillermo Mendoza Trujillo, Andrés Santiago Cisneros Barahona, Guido Patricio Santillán Lima, Nelson Bravo Yumi, Tamia Samai Nuñez Cruz and María Rafaela Viteri Uzcategui

Hydrology 2026, 13(7), 177; https://doi.org/10.3390/hydrology13070177 - 3 Jul 2026

Abstract

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed [...] Read more.

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed CN approaches is the absence of independent hydrological validation; most machine learning models are trained and evaluated against the same SCS-USDA lookup values used to construct the training target, a circular scheme that measures statistical agreement rather than physical credibility. This study develops a reproducible geospatial workflow for distributed CN estimation across continental Ecuador, combining official MAG land use, soil surface texture natural drainage, and topographic slope layers at 1:25,000 scale with a Random Forest regression model at 10 m spatial resolution. The CN reference raster was derived from official geo-pedological layers and independently validated, not against tabulated assumptions, but against observed hydrological behaviour. Field hydraulic characterization across four dominant land cover classes in the Guamote microwatershed (Chimborazo Province), combined with HEC-HMS (US Army Corps of Engineers, Davis, CA, USA) rainfall-runoff modelling over 41 years (1981–2021), confirmed a mean annual discharge of 0.1568 m³ s⁻¹ consistent with the tabulated CN assignments. To our knowledge, this is the first nationally distributed CN map with field-anchored hydrological benchmarking for an Andean country. The Random Forest model achieved an RMSE = 10.4, an R² = 0.42, and an NSE = 0.41, a performance consistent with published field-based CN estimation studies and expected given the inherent scatter of the SCS-USDA method under real-world conditions. Zonal CN comparisons confirmed a mean absolute error below 5 CN units across the Andean highland and Amazon watersheds; the Guamote watershed showed a mean ∆CN below 4 units against the field-calibrated model. Land use and surface texture emerged as the dominant CN predictors, with natural drainage providing critical discrimination in volcanic and poorly drained soil environments. The resulting 10 m national CN map offers a physically grounded, spatially explicit parameterization layer for distributed hydrological modeling and water resources planning across data-scarce Andean and tropical territories, with direct relevance for flood risk screening, irrigation planning, watershed conservation, and climate adaptation under SDG 6, SDG 11, SDG 13 and SDG 15. Full article

(This article belongs to the Special Issue Advancing Flood Detection, Monitoring & Simulation: Integrating Machine Learning, Remote Sensing & Hydrodynamic Model)

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16 pages, 2625 KB

Open AccessArticle

ArticleWater Availability and Precipitation Indicators in the Muriaé River Basin, Southeast Brazil

by Eduardo Cochrane Novo, Monica de Aquino Galeano Massera da Hora and José Paulo Soares de Azevedo

Hydrology 2026, 13(7), 176; https://doi.org/10.3390/hydrology13070176 - 2 Jul 2026

Abstract

This study investigated the relationship between precipitation indicators and water availability in the Muriaé River Basin (MRB), Southeast Brazil, using rainfall and streamflow series from 1961 to 2020. Monthly mean precipitation (MMP), the total annual precipitation (PRCPTOT), the Rainfall Anomaly Index (RAI), and [...] Read more.

This study investigated the relationship between precipitation indicators and water availability in the Muriaé River Basin (MRB), Southeast Brazil, using rainfall and streamflow series from 1961 to 2020. Monthly mean precipitation (MMP), the total annual precipitation (PRCPTOT), the Rainfall Anomaly Index (RAI), and the Q95 low flow parameter were analyzed to evaluate hydrological variability and drought conditions. Trend analyses were performed using the Mann–Kendall test and Sen’s slope estimator, and Pearson correlation analysis was applied to assess the relationship between precipitation and low flow availability. The results showed marked temporal variability in precipitation and hydrological conditions throughout the basin. Although statistically significant increasing trends in annual precipitation were identified at Carangola and Patrocínio do Muriaé, no generalized long-term reduction in precipitation was observed in the MRB. In contrast, Q95 exhibited reductions at all monitored stations, with decadal decreases ranging from approximately 31% at Carangola to 56% at Itaperuna. The RAI analysis indicated predominance of very dry and extremely dry events during the most recent decade, coinciding with reduced low flow availability. The results indicate that changes in water availability are linked to the temporal distribution and persistence of dry anomalies. These findings can influence decisions in hydrological monitoring and water resource management strategies in the basin. 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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21 pages, 9451 KB

Open AccessArticle

Hydrogeochemical Processes Controlling Groundwater Quality and Water-Use Constraints in Semi-Arid Central Iraq

by Zainab Salah Abd Alameer, Amer A. Mohammed, Ali A. Al Maliki, Ahmed Gad, Muhammad Aufaristama and Alaa Ahmed

Hydrology 2026, 13(7), 175; https://doi.org/10.3390/hydrology13070175 - 27 Jun 2026

Abstract

Groundwater quality in arid and semi-arid regions is increasingly affected by salinization, evaporation, abstraction, and agricultural return flow. This study evaluates the hydrochemical evolution, isotopic characteristics, ²²²Rn activity, and water-use suitability of groundwater and associated waters in Karbala Governorate, central Iraq. Seventeen [...] Read more.

Groundwater quality in arid and semi-arid regions is increasingly affected by salinization, evaporation, abstraction, and agricultural return flow. This study evaluates the hydrochemical evolution, isotopic characteristics, ²²²Rn activity, and water-use suitability of groundwater and associated waters in Karbala Governorate, central Iraq. Seventeen groundwater, lake water, and municipal supply water samples were analyzed for physicochemical parameters, major ions, δ¹⁸O, δ²H, and ²²²Rn. Hydrochemical, isotopic, and water-quality assessment methods were applied to evaluate groundwater evolution, salinization, and suitability for drinking and irrigation. The waters are near-neutral, with pH values of 6.18–7.35, but are strongly mineralized. Electrical conductivity ranges from 1440 to 16,305 µS/cm, and total dissolved solids (TDS) range from 592 to 10,191 mg/L. Most samples belong to a Ca–Mg–SO₄–Cl facies, indicating sulfate- and chloride-rich hard water evolution. The highest mineralization occurs near Karbala proper and lake-influenced sites. Ion ratios and chloro-alkaline indices indicate that evaporite dissolution, gypsum/anhydrite dissolution, carbonate interaction, evaporation, and local ion exchange jointly control groundwater chemistry. Stable isotopes indicate meteoric origin with variable evaporative enrichment; however, highly saline but isotopically depleted water, particularly W8, shows that evaporation alone cannot explain salinization. ²²²Rn activities range from below detection to 11.28 Bq/L and mainly reflect local aquifer contact and degassing. High TDS, sulfate, chloride, and very high hardness limit suitability for drinking-water use. For irrigation, the sodium hazard is low, but salinity, hardness, magnesium hazard, and permeability constraints make most samples unsuitable or restricted. Management should prioritize salinity and hardness control, treatment or blending before domestic use, restricted irrigation of the least saline wells under drainage and soil-salinity monitoring, protection of less mineralized recharge zones, and long-term monitoring of lake-adjacent and agriculturally influenced wells. Full article

(This article belongs to the Special Issue Geochemical Signatures for Groundwater Resource Sustainability)

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19 pages, 3038 KB

Open AccessArticle

³H/³He Dating of Anthropogenic Tritium in a Shallow Alluvial Aquifer at Paks Nuclear Power Plant, Hungary

by László Palcsu, Andor Hajnal, István Csige, Árpád Csámer, Krisztián Baranyi, Danny Vargas and Marianna Túri

Hydrology 2026, 13(7), 174; https://doi.org/10.3390/hydrology13070174 - 26 Jun 2026

Abstract

The tritium–helium-3 (³H/³He) dating method was applied to quantify groundwater apparent ages and estimate the migration of anthropogenic tritium in the shallow alluvial aquifer surrounding the Paks Nuclear Power Plant (Hungary). Groundwater samples were collected from monitoring wells between [...] Read more.

The tritium–helium-3 (³H/³He) dating method was applied to quantify groundwater apparent ages and estimate the migration of anthropogenic tritium in the shallow alluvial aquifer surrounding the Paks Nuclear Power Plant (Hungary). Groundwater samples were collected from monitoring wells between 2013 and 2016 and analyzed for tritium and dissolved noble gases. The investigated aquifer consists mainly of highly permeable sand and gravel deposits hydraulically connected to the Danube River. Reference wells indicate apparent groundwater ages between 26 and 43 years, with an average apparent ³H/³He age of approximately 37 years. Wells located within the operational area of the power plant show apparent ³H/³He ages ranging from 1.3 to 14.1 years, reflecting the transport of tritium released during leakage events associated with damaged sewer pipelines between 2005 and 2007. The spatial distribution of apparent ages reveals heterogeneous groundwater flow paths, and highlights the influence of well-screen sampling on age interpretation. The paper demonstrates that anthropogenic tritium released from nuclear infrastructure can serve as an effective age dating method and improve conceptual models of flow dynamics in shallow alluvial aquifers. Full article

(This article belongs to the Special Issue Geochemical Signatures for Groundwater Resource Sustainability)

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18 pages, 3862 KB

Open AccessArticle

Missing Data Imputation for Reservoir Inflow Flood Discharge of Dams Based on Improved Singular Value Decomposition

by Yongjiang Chen, Kui Wang, Mingjie Zhao, Gang Liu and Jianfeng Liu

Hydrology 2026, 13(7), 173; https://doi.org/10.3390/hydrology13070173 - 26 Jun 2026

Abstract

Missing values commonly exist in dam inflow flood discharge monitoring data, which hinders flood analysis, risk assessment and reservoir scheduling. Aiming at the problems of insufficient imputation accuracy and the difficulty in adaptive threshold selection of traditional Singular Value Decomposition (SVD) in flood [...] Read more.

Missing values commonly exist in dam inflow flood discharge monitoring data, which hinders flood analysis, risk assessment and reservoir scheduling. Aiming at the problems of insufficient imputation accuracy and the difficulty in adaptive threshold selection of traditional Singular Value Decomposition (SVD) in flood discharge data with strong fluctuations and high noise, this study introduces a method for filling in missing dam inflow flood discharge based on Dam Monitoring Data Reconstruction Model (DSVD). The method constructs a non-repeating sequence monitoring matrix, introduces a hard singular value threshold for adaptive denoising, and completes time series data imputation combined with a weight optimization model, which effectively improves the imputation accuracy of strongly fluctuating flood discharge data. Taking the measured inflow flood discharge data of Jinjiaba Reservoir in Chongqing as the research object, this study systematically analyzes the influence of column-to-row ratio (R_a) and data missing rate on imputation performance, and conducts a comparative verification against other models. Experimental results indicate that the optimal R_a value is 6. The coefficient of determination (R²) stays above 0.830 within a missing rate range of 5–40%, showing strong robustness against data loss. Compared with other benchmark models, the method has the highest R² (0.875) and the lowest Root Mean Square Error (RMSE, 7.771), exhibiting stronger adaptability to mountainous flood discharge data with steep rise and fall characteristics. The research findings provide a new method for the high-precision recovery of missing dam inflow flood discharge data and reliable data support for reservoir flood risk analysis and safe operation. Full article

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25 pages, 6740 KB

Open AccessArticle

A Novel Data-Driven Attribution Analysis of Long-Term Streamflow Changes in the Heavily Regulated, Data-Scarce Middle Reach of the Minjiang River

by Minghao Chen, Cong Li and Taihua Wang

Hydrology 2026, 13(7), 172; https://doi.org/10.3390/hydrology13070172 - 25 Jun 2026

Abstract

Streamflow variations in the Middle Minjiang River Basin (MMR) are vital for the flood mitigation and water resources management of the Chengdu metropolitan area which is important for the development of Southwest China. However, how climate change, Chengdu metropolitan area and Zipingpu Reservoir [...] Read more.

Streamflow variations in the Middle Minjiang River Basin (MMR) are vital for the flood mitigation and water resources management of the Chengdu metropolitan area which is important for the development of Southwest China. However, how climate change, Chengdu metropolitan area and Zipingpu Reservoir influence streamflow in the MMR remains unclear. Hence, we coupled the Geomorphology-Based Ecohydrological Model (GBEHM), the Physic-aware Hybrid Learning (PaHL) model and the Extreme Gradient Boosting (XGBoost) model to reproduce streamflow variations at Pengshan station—the outlet cross section of MMR—from 1980 to 2019, subsequently performing attribution analysis. Annual streamflow at Pengshan station exhibits a decreasing trend from 1980 to 2019. Coupled simulations effectively reproduce daily streamflow at Pengshan station during 35 years, with values of NSE, R² and KGE exceeding 0.96. The dominant influence of anthropogenic disturbance on daily streamflow decrease is generally steady at Pengshan station, explaining 62.3% and 430.8% of it before and after the impoundment of Zipingpu Reservoir (in 2006), respectively. Majority of the climate change’s influence is notably concentrated from June to September, suggesting a potential temporal imbalance in water resources and a threat of extreme hydrological events. Our study contributes to flood mitigation and water resources management in the MMR. Full article

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22 pages, 14702 KB

Open AccessArticle

Blending Precipitation Records and SEAS5 Forecasts for SPI12-Based Drought Prediction in the Lima River Basin

by Kenny Pabón Cevallos, Luis Angel Espinosa, Miguel Costa and João Pedro Pêgo

Hydrology 2026, 13(7), 171; https://doi.org/10.3390/hydrology13070171 - 25 Jun 2026

Abstract

Recurrent meteorological droughts, projected to intensify under climate change, affect the cross-border Lima River Basin shared between Portugal and Spain, highlighting the need for robust early warning systems to support proactive water management. Within the EU-funded RISC_PLUS project—aimed at strengthening resilience to hydro-climatic [...] Read more.

Recurrent meteorological droughts, projected to intensify under climate change, affect the cross-border Lima River Basin shared between Portugal and Spain, highlighting the need for robust early warning systems to support proactive water management. Within the EU-funded RISC_PLUS project—aimed at strengthening resilience to hydro-climatic risks in the cross-border Minho–Lima River Basins—this study develops a regionalised forecasting framework to evaluate meteorological drought forecast skill using precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecasting System 5 (SEAS5) for the Portuguese section of the Lima River Basin. A precipitation-only 12-month Standardized Precipitation Index (SPI12) is employed to isolate the contribution of seasonal precipitation forecasts. SPI12 is computed from hybrid 12-month accumulations combining observed monthly precipitation (October 1979 to February 2025) and SEAS5 forecasts (October 2018 to February 2025). Four hybrid configurations (1 to 6 months lead time) are evaluated: 11 obs + 1 fcst, 10 obs + 2 fcsts, 9 obs + 3 fcsts, and 6 obs + 6 fcsts. Forecast performance is assessed from October 2018 to February 2025. Deterministic SPI12 forecasts and categorical drought classifications are evaluated using regression-based metrics (e.g., Pearson correlation and RMSE) and contingency-table metrics (e.g., FAR and F1-score), across SEAS5 ensemble members, percentiles, and spread-based indicators. The 11 obs + 1 fcst configuration, particularly when using the Dry Spread (SpD; Q10 + Q25 percentiles) and the Q75 percentile, exhibits the highest skill, achieving a Pearson correlation coefficient of

r = 0.97

and an RMSE of approximately 0.17, alongside near-perfect categorical performance (POD = 1.00; FAR = 0.00), although these scores are partly conditioned by the shared observed accumulation window. Conversely, longer lead-time configurations exhibit degraded performance, with the 6 obs + 6 fcsts configuration showing weak or negative skill relative to climatology, indicating that 6-month lead forecasts should be interpreted with caution. These results demonstrate that SEAS5 precipitation forecasts can provide skilful drought predictions at lead times of several months in the Lima River Basin within the SPI12 framework. The proposed blending methodology provides a transparent benchmark and a technical basis for the early-warning system being developed under the RISC_PLUS project to support drought risk management in the Minho–Lima region and complement data-driven drought forecasting approaches. Full article

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

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38 pages, 25309 KB

Open AccessArticle

Integrated Flood Susceptibility and Multi-Temporal Flood Risk Prioritization in Pakistan Using Hydro-Climatic and Geospatial Indicators

by Mehjabeen Khan, Ruishan Chen and Sheheryar Khan

Hydrology 2026, 13(7), 170; https://doi.org/10.3390/hydrology13070170 - 25 Jun 2026

Abstract

Flood susceptibility in Pakistan is strongly influenced by hydro-climatic variability, land-surface conditions, topography, and recurrent floodplain exposure; however, national-scale studies often lack a comprehensive assessment that captures both spatial patterns and temporal flood-risk dynamics within a single framework. This study is one of [...] Read more.

Flood susceptibility in Pakistan is strongly influenced by hydro-climatic variability, land-surface conditions, topography, and recurrent floodplain exposure; however, national-scale studies often lack a comprehensive assessment that captures both spatial patterns and temporal flood-risk dynamics within a single framework. This study is one of Pakistan’s first national efforts to address the gap between flood risk assessment and prioritization through a unified geospatial assessment. This study assesses flood susceptibility across Pakistan for 2002, 2012, and 2022 using a GIS-based AHP approach by integrating climatic, environmental, topographic, hydrological, soil, LULC, and anthropogenic indicators. The study results were further analyzed through district-level assessments, risk change analysis, persistence mapping, LULC exposure assessments, and the Comprehensive Flood Risk Priority Index (FRPI). The results show that high and very high flood susceptibility zones are primarily concentrated along the Indus River corridor, lower floodplains, and coastal Sindh, accounting for more than 7% of the total land area of Pakistan. Persistent flood hotspots are identified in Rann of Kutch (66.6%), Jacobabad (65.0%), and Jafarabad (61.1%), indicating strong temporal stability of flood-prone conditions. LULC exposure analysis reveals that cropland is the dominant exposed class, with the highest district-level exposure observed in Badin (17.1%) and Larkana (10.1%). The FRPI further identifies priority flood-risk zones where susceptibility, persistence, risk change, and exposure converge, with the highest FRPI values observed in Jacobabad (0.742), Rann of Kutch (0.738), and Badin (0.711). Model validation demonstrates strong predictive performance, with susceptibility ROC-AUC values ranging from 0.85 to 0.87 and FRPI AUC reaching 0.85. The proposed framework provides a robust decision-support tool for targeted flood-risk management and climate-resilient land-use planning in Pakistan. Full article

(This article belongs to the Special Issue Advances in Urban Flood Modeling, Forecasting and Early Warning)

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14 pages, 11919 KB

Open AccessArticle

Improving Daily Runoff Forecasting with VMD-VPPSO-LSTM

by Yunyi Wang, Wei Wu, Chengjun Yang, Xiaoyu Liu, Linxuan Li, Yuyue Chen and Yang Liu

Hydrology 2026, 13(7), 169; https://doi.org/10.3390/hydrology13070169 - 25 Jun 2026

Abstract

To further improve prediction accuracy, a VMD-VPPSO-LSTM model is proposed in this study, which combines Variational Mode Decomposition (VMD) for signal decomposition, Velocity-Pause Particle Swarm Optimization (VPPSO) for parameter optimization, and Long Short-Term Memory (LSTM) for runoff prediction. The model was evaluated at [...] Read more.

To further improve prediction accuracy, a VMD-VPPSO-LSTM model is proposed in this study, which combines Variational Mode Decomposition (VMD) for signal decomposition, Velocity-Pause Particle Swarm Optimization (VPPSO) for parameter optimization, and Long Short-Term Memory (LSTM) for runoff prediction. The model was evaluated at Huangtaiqiao station in the Xiaoqing River Basin, Dawenkou station in the Dawen River Basin, and Tangnaihai station in the source region of the Yellow River Basin. The proposed model achieved the best overall performance among all comparison models, with Nash–Sutcliffe Efficiency (NSE) values of 0.970, 0.962, and 0.994 and Root Mean Square Error (RMSE) values of 1.357, 0.989, and 46.804 at the three stations, respectively. Compared with VMD-LSTM, VPPSO further reduced the RMSE at all stations and maintained training-test NSE gaps below 0.006, indicating strong generalization performance. The model also achieved the lowest Peak Percent Standard Deviation (PPSD) values for high-flow events, reaching 9.03%, 14.42%, and 3.88% at the three stations, respectively. These results demonstrate that VMD-VPPSO-LSTM is a reliable and effective model for daily runoff prediction. Full article

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46 pages, 4743 KB

Open AccessArticle

Hydrographic Stratification and Pollutant Retention at Constanța Port Roadstead, NW Black Sea: Five-Layer Dissolved Oxygen Structure and a CTD-Derived Retention Index from a Single-Station Profile

by Andra-Teodora Nedelcu, Tiberiu Pazara and Manuela Rossemary Apetroaei

Hydrology 2026, 13(7), 168; https://doi.org/10.3390/hydrology13070168 - 24 Jun 2026

Abstract

High-resolution CTD profiles, with SVP cross-validation of the sound speed field, were recorded at a single station in the outer roadstead of the Port of Constanța (northwest Black Sea; 44°07′41″ N, 28°53′15″ E; depth ≈ 25 m; June 2024), revealing a strongly stratified, [...] Read more.

High-resolution CTD profiles, with SVP cross-validation of the sound speed field, were recorded at a single station in the outer roadstead of the Port of Constanța (northwest Black Sea; 44°07′41″ N, 28°53′15″ E; depth ≈ 25 m; June 2024), revealing a strongly stratified, five-layer water column driven by three combined forcing mechanisms: seasonal thermal stratification with an abnormally shallow Cold Intermediate Water layer (7.3–15.6 m), Danube-sourced freshwater input, and anthropogenic disturbances consistent with port and anchorage activity. A contextual hypothesis is proposed that conflict-related marine traffic intensification may contribute to observed signals, but physical measurements cannot establish causation. At the main pycnocline (7.31–15.62 m), a density difference of Δρ = 4.02 kg m⁻³ yields a maximum Brunt–Väisälä frequency of N² = 2.37 × 10⁻³ s⁻², reducing vertical eddy diffusivity by two orders of magnitude (Kz ≈ 10⁻⁶ m² s⁻¹). Physical conditions—a shallow mixed layer (~0.7–1.2 m) and strong pycnocline—support the theoretical expectation of surface-layer contaminant accumulation; however, no chemical measurements were carried out to confirm contaminant presence. All contamination inferences rely exclusively on physical proxies (turbidity, dissolved oxygen, and density gradients), and contaminant retention remains untested for lack of direct chemical evidence. A dimensional Stratification-Controlled Retention Index (SCRI = N²/Kz; units: m⁻² s⁻¹) is introduced, and its consistency with the observed hydrographic structure is demonstrated. Full article

(This article belongs to the Topic Global Water and Environmental Challenges)

34 pages, 9950 KB

Open AccessArticle

Multi-Scale Variability and Linkages Between Runoff and Meteorological Factors in the Songhua River Basin

by Ruinan Zhao, Changlei Dai, Xinyu Wang, Xiao Yang and Wenzhao Xu

Hydrology 2026, 13(7), 167; https://doi.org/10.3390/hydrology13070167 - 24 Jun 2026

Abstract

Understanding the spatiotemporal evolution of runoff and its driving mechanisms is of great significance for water resources development, utilization, and sustainable management in mid- to high-latitude river basins under climate change. However, runoff variability is jointly influenced by multiple meteorological factors, and a [...] Read more.

Understanding the spatiotemporal evolution of runoff and its driving mechanisms is of great significance for water resources development, utilization, and sustainable management in mid- to high-latitude river basins under climate change. However, runoff variability is jointly influenced by multiple meteorological factors, and a comprehensive understanding of its multi-scale response characteristics and the relative contributions of different drivers remains limited. In this study, runoff data from three hydrological stations in the Songhua River Basin during 1980–2022 were analyzed. A set of statistical and time-series methods, including the Mann–Kendall test, Pettitt change-point test, Hurst exponent, wavelet analysis, and wavelet coherence, was applied, and a random forest model was used to quantify the influence of key climatic factors such as precipitation, air temperature, and evapotranspiration. The results show that air temperature exhibits significant increasing trends in all four seasons, with the strongest warming occurring in spring (Sen’s slope ≈ 0.06 °C a⁻¹). Precipitation displays pronounced spatial heterogeneity and interannual variability, while evapotranspiration shows an overall increasing trend. Both runoff and major meteorological variables exhibit significant spatial heterogeneity across the basin. Hydro-meteorological variables also show distinct periodic variations among seasons, with temperature, precipitation, and evapotranspiration exhibiting stronger seasonal fluctuations during summer. Wavelet coherence analysis indicates that short-term runoff variability is mainly driven by temperature and precipitation. Temperature exhibits significant coherence with runoff across multiple time scales ranging from approximately 2 to 20 years, whereas precipitation shows stronger coherence at medium- to long-term scales (approximately 10–35 years), with evident seasonal differences. Random forest results indicate that evapotranspiration is the most important contributor to runoff variability at all three stations, accounting for 33.5%, 28.6%, and 26.2% of the total importance at Jiamusi, Fuyu, and Jiangqiao stations, respectively. Temperature and sunshine duration rank second, while precipitation and relative humidity contribute comparatively less. These findings indicate that evapotranspiration plays a key regulatory role in long-term water balance. In addition, runoff exhibits multi-scale variability and a transition from gradual changes to stage-like abrupt shifts. The findings provide a scientific basis for water resources management, flood mitigation, and climate change adaptation in the Songhua River Basin. Full article

24 pages, 5266 KB

Open AccessArticle

Prediction of Groundwater-Level Fluctuations Under Climate Change Conditions in the Berrechid Plain (Morocco) Using a Hybrid Physical–Machine Learning Approach

by Adil Zerouali, Mohamed Jalal El Hamidi, Abdelkader Larabi, Mohamed Faouzi and Omar Chafik

Hydrology 2026, 13(7), 166; https://doi.org/10.3390/hydrology13070166 - 24 Jun 2026

Abstract

The issue of water resources in a semi-arid country such as Morocco has been present for many years and is becoming increasingly critical. The droughts experienced over recent decades have demonstrated the country’s extreme vulnerability to any water deficit. In this context, the [...] Read more.

The issue of water resources in a semi-arid country such as Morocco has been present for many years and is becoming increasingly critical. The droughts experienced over recent decades have demonstrated the country’s extreme vulnerability to any water deficit. In this context, the Berrechid plain represents a relevant case study illustrating both the practical and theoretical challenges of groundwater governance. The aquifer is heavily exploited to satisfy agricultural, industrial, and domestic needs. This study develops a hybrid “grey-box” modeling approach for predicting groundwater depth (GWD) fluctuations under climate change (CC). Unlike conventional black-box machine learning models, our framework combines a deterministic physical engine with a stochastic machine learning corrector. The physical component simulates aquifer mass balance using the Hargreaves method for evapotranspiration, linear drainage, climate memory via exponential decay, and an anthropogenic trend parameter (xi). The machine learning component—XGBoost with quantile regression—is trained exclusively on physical model residuals and predicts the 5th, 50th, and 95th percentiles, providing explicit 90% confidence intervals. Hydrological states (dry, normal, wet) are identified via K-means clustering for context-aware correction. The model is calibrated using historical data (1972–2019) and validated using blocked time-series cross-validation. Climate projections under the RCP 4.5 and RCP 8.5 scenarios were used to forecast GWD up to 2100. At piezometer 3933/20, the best performance was achieved, with an RMSE of 0.347 m and a KGE of 0.742 during the validation period. The proposed approach is suitable for seasonal GWD forecasting and offers practical value for water managers and decision-makers in the Berrechid region. Full article

(This article belongs to the Special Issue Machine Learning Applications in Soil Water and Groundwater Assessment)

26 pages, 7583 KB

Open AccessArticle

Assessment of Seawater Intrusion Vulnerability in the Keta Strip Aquifer, Ghana, Using the GALDIT Model

by Delaiah Antwi Nyarko and Larry Pax Chegbeleh

Hydrology 2026, 13(7), 165; https://doi.org/10.3390/hydrology13070165 - 23 Jun 2026

Abstract

Seawater intrusion presents a significant risk to coastal aquifers, particularly in low-lying locations where groundwater resources are intensively exploited. This study assesses the vulnerability of the Keta Strip aquifer in Southeastern Ghana to seawater intrusion using the GALDIT model; a widely applied index-based [...] Read more.

Seawater intrusion presents a significant risk to coastal aquifers, particularly in low-lying locations where groundwater resources are intensively exploited. This study assesses the vulnerability of the Keta Strip aquifer in Southeastern Ghana to seawater intrusion using the GALDIT model; a widely applied index-based approach that evaluates seawater intrusion risk based on six key hydrogeological indicators: groundwater occurrence (G), aquifer hydraulic conductivity (A), groundwater level above sea level (L), distance from the shoreline (D), impact of existing intrusion (I), and aquifer thickness (T). These parameters were analyzed using data from 105 monitoring wells within a Geographic Information System (GIS) environment. The resulting vulnerability index was spatially grouped into four categories: low, moderate, high, and very high vulnerability. Results indicate that very high and high vulnerability regions are predominantly clustered along the coastal margins and central portions of the study area, driven mainly by low hydraulic gradients, proximity to the shoreline, and high hydraulic conductivity. Moderate vulnerability zones dominate inland areas, while low vulnerability zones are limited and confined to northern sections. Sensitivity analysis reveals that hydraulic head (L) and distance from shoreline (D) are the most influential parameters, whereas TDS exhibits relatively low contribution to overall vulnerability. The findings highlight the critical role of hydrogeological controls and anthropogenic pressures in shaping seawater intrusion risk and provide a scientific basis for sustainable groundwater management in the Keta Strip and similar coastal environments. Full article

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

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20 pages, 7625 KB

Open AccessReview

Exploring Nutrient Stoichiometry in Inland Waters: A Bibliometric and Ecological Review of C:N:P Ratios in Freshwater Ecosystems

by Jehangir Ijaz, Marko Šrajbek, Muhammad Azaan Irshad and Takai Eddine Yahi

Hydrology 2026, 13(7), 164; https://doi.org/10.3390/hydrology13070164 - 23 Jun 2026

Abstract

Nutrient stoichiometry, particularly the balance of carbon (C), nitrogen (N), and phosphorus (P), plays a fundamental role in regulating freshwater ecosystem dynamics, primary production, and biogeochemical cycling. This study presents one of the first dedicated reviews to combine bibliometric mapping with ecological synthesis [...] Read more.

Nutrient stoichiometry, particularly the balance of carbon (C), nitrogen (N), and phosphorus (P), plays a fundamental role in regulating freshwater ecosystem dynamics, primary production, and biogeochemical cycling. This study presents one of the first dedicated reviews to combine bibliometric mapping with ecological synthesis of C:N:P ratios in inland waters, drawing on 1004 publications indexed in the Web of Science Core Collection (2000–2025), comprising peer-reviewed articles and review articles refined by document type, language, and research area. Bibliometric mapping using VOSviewer (version 1.6.20) identified exponential growth in publications after 2010, with phosphorus dynamics and eutrophication emerging as the most-cited themes, while recent years have shown increasing attention to C:P ratios as reliable ecological indicators. Four dominant thematic clusters were identified: Nutrient Cycling and Biogeochemistry; Phytoplankton and Food Web Dynamics; Eutrophication and Water Quality; and Climate Change and Ecosystem Responses. Ecological synthesis demonstrated substantial deviations from the canonical Redfield ratio (106C:16N:1P), with pronounced stoichiometric variability across trophic states, latitudes, and ecosystem types. Case comparisons revealed high C:P ratios in Arctic and alpine lakes linked to dissolved organic carbon inputs, low N:P ratios in tropical waters that promote cyanobacterial dominance, and stable, low phosphorus concentrations in deep African lakes. These findings emphasize the significance of flexible stoichiometry in predicting ecosystem tipping points, managing harmful algal blooms (HABs), and guiding nutrient restoration strategies. By integrating bibliometric and ecological evidence, this study identifies C:P ratios as a promising candidate indicator that merits further field validation for freshwater management, while underscoring persistent research gaps in microbial stoichiometry, cross-scalar modeling, and policy uptake in the Global South. Full article

(This article belongs to the Special Issue Novel Procedures and Methodologies for Surface and Underground Water Quality Analysis: Theory and Application)

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35 pages, 15939 KB

Open AccessArticle

Flood Susceptibility Assessment in Two Eastern Mediterranean Catchments Using a Multi-Indicator Approach

by Despina Giannadaki, Antonis Bezes, Vassiliki Kotroni, Kostas Lagouvardos, Katerina Papagiannaki, Christina Oikonomou and Haris Haralambous

Hydrology 2026, 13(6), 163; https://doi.org/10.3390/hydrology13060163 - 22 Jun 2026

Abstract

Flooding triggered by intense precipitation is a significant natural hazard affecting Mediterranean regions, where complex terrain, rapid hydrological response and increasing urbanization can amplify flood impacts. This study assesses flood susceptibility in two representative Mediterranean River catchments: the Koiliaris in Crete, Greece, and [...] Read more.

Flooding triggered by intense precipitation is a significant natural hazard affecting Mediterranean regions, where complex terrain, rapid hydrological response and increasing urbanization can amplify flood impacts. This study assesses flood susceptibility in two representative Mediterranean River catchments: the Koiliaris in Crete, Greece, and the Pediaios in Cyprus. A compact Flood Hazard Index (FHI) was developed by integrating the Topographic Wetness Index (TWI), Curve Number (CN), and R20 heavy rain frequency index, representing the principal geomorphological, hydrological and climatological controls of flood generation. Spatial datasets including EU-DEM elevation data, CORINE land cover, European soil databases, and Copernicus CERRA precipitation reanalysis were combined within a GIS-based multi-criteria framework using Analytic Hierarchy Process weighting. The resulting FHI maps identify high flood susceptibility along river corridors, low-lying accumulation zones, and urbanized areas. In the Koiliaris basin, 34% of the area fell within the high and very high susceptibility classes, mainly in downstream alluvial zones, whereas in the Pediaios basin, 29% of the area fell within the high and very high susceptibility classes, concentrated around the urbanized Nicosia corridor. The analysis of historical flood events provided a qualitative consistency assessment of the FHI patterns, acknowledging that the absence of spatially explicit flood-inundation footprints limits quantitative validation. Full article

(This article belongs to the Special Issue Advances in Urban Flood Modeling, Forecasting and Early Warning)

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20 pages, 4905 KB

Open AccessArticle

Seasonal Changes in Mire Surface Oscillation as an Indicator of Water Storage Capacity—A Case Study of the Great Vasyugan Mire, Western Siberia

by Yulia Kharanzhevskaya

Hydrology 2026, 13(6), 162; https://doi.org/10.3390/hydrology13060162 - 22 Jun 2026

Abstract

Surface oscillation is an important mechanism for the hydrological self-regulation of mires: it prevents the attenuation of flooding by storing water during high precipitation events and snowmelt. To investigate the spatial and temporal variability in surface oscillation, we conducted monthly measurements of the [...] Read more.

Surface oscillation is an important mechanism for the hydrological self-regulation of mires: it prevents the attenuation of flooding by storing water during high precipitation events and snowmelt. To investigate the spatial and temporal variability in surface oscillation, we conducted monthly measurements of the surface elevation and water level at three monitoring sites in the Great Vasyugan Mire (GVM), Western Siberia, over a nine-year period (2017–2025). Surface oscillation in the GVM varied from 14 to 25 cm in winter and early spring as a result of frost heaving, and from 2 to 16 cm in the frost-free period. Surface oscillation depends on the water table level variation, which is disturbed when the water level rises above the surface during freezing–thawing periods and due to released biogenic gases. Our data showed that within large mire systems, such as the Great Vasyugan Mire, the spatial variability in surface oscillation is influenced by several key factors: the type of plant community, peat properties, and the location relative to water flow pathways. Surface oscillation increased along a transect extending from the sedge–Sphagnum community to the pine–dwarf shrub–Sphagnum community, which runs parallel to the slope toward the marginal area. Long-term records demonstrate an increasing trend in surface elevation in the central part of the GVM, while showing a decrease at the mire boundary. Full article

(This article belongs to the Section Ecohydrology)

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28 pages, 13372 KB

Open AccessArticle

Modeling of Climate-Driven Socioeconomic Landslide Risk in a Tropical Andean Region

by Daniel Camilo Ortiz-Hernández, Carlos Alfonso Zafra-Mejía and Amed Bonilla Pérez

Hydrology 2026, 13(6), 161; https://doi.org/10.3390/hydrology13060161 - 18 Jun 2026

Abstract

Landslides constitute one of the most lethal and costly hydrometeorological hazards at the global scale. There is a growing trend associated with the increase in extreme precipitation and the expansion of urban development on unstable slopes. In the tropical Andes, this problem is [...] Read more.

Landslides constitute one of the most lethal and costly hydrometeorological hazards at the global scale. There is a growing trend associated with the increase in extreme precipitation and the expansion of urban development on unstable slopes. In the tropical Andes, this problem is intensified under climate change scenarios. The objective of this study is to develop a logistic regression model to analyze socioeconomic risk due to landslides in the Bogotá Savannah (Colombia). An integrated risk model was developed using binary logistic regression and a socioeconomic vulnerability index. A total of 12 physical–biotic variables and SSP climate projections (2021–2040) were used. A GIS-based environment was implemented to generate prospective spatial risk scenarios. The model demonstrated high robustness and predictive capability, with an improvement in statistical goodness-of-fit of 8.2% (AIC: 2574–2367), adequate probabilistic calibration (Pseudo-R²: 0.675; Brier Score: 0.084), and excellent predictive performance (AUC: 0.935; sensitivity: 84.7%; specificity: 90.0%). Simulations estimated maximum risk probabilities close to 0.600 (scale between 0 and 1), concentrated in geomorphologically critical sectors. Simulations under SSP scenarios showed a progressive increase in risk toward 2040 (up to 0.673), associated with precipitation increases between 10 and 30%. Integrated modeling constitutes a reliable technical tool for land-use planning, climate adaptation, and prospective landslide risk management in urbanized Andean regions. Full article

(This article belongs to the Special Issue Mitigating Hydrologically Induced Slope Failures Through Nature-Based Solutions)

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26 pages, 5189 KB

Open AccessArticle

Hydrological Forcing of Anthropogenic Pulses of Trace Metal Mass Loading in the Santiago River, Mexico

by Aida Alejandra Guerrero de León, Valerie Natalia Salazar-Zepeda, Virgilio Zúñiga-Grajeda, Hasbleidy Palacios-Hinestroza, Walter Ramírez Meda and Jesús Barrera-Rojas

Hydrology 2026, 13(6), 160; https://doi.org/10.3390/hydrology13060160 - 18 Jun 2026

Abstract

The Santiago River is a highly anthropogenically impaired lotic system globally, yet the mechanisms governing its contaminant transport remain poorly understood under static monitoring paradigms. This study evaluates how hydrological forcing dictates the mobilization and bioavailability of trace metals by integrating a 15-year [...] Read more.

The Santiago River is a highly anthropogenically impaired lotic system globally, yet the mechanisms governing its contaminant transport remain poorly understood under static monitoring paradigms. This study evaluates how hydrological forcing dictates the mobilization and bioavailability of trace metals by integrating a 15-year public hydrochemical database from 10 monitoring nodes with SAR-derived discharge estimates and thermodynamic metal modeling (PHREEQC). To validate the structural integrity of the mass load estimates against hydrometric uncertainties, a deterministic boundary-sensitivity analysis was conducted. Results empirically refute the classical dilution paradigm, introducing the “Anthropogenic Pulse” to describe the non-linear acceleration of pollutant export during high-flow events (discharge Q surging from 36.62 to 286.13 m³/s). While climate-driven parameters follow seasonal cycles, industrial stressors (COD, Pb, Cd) remain in a chronic steady state, decoupling from volumetric dilution. Based on coupled × CQ × C (discharge × concentration) estimates, this dynamic induces a synchronized flushing of toxic burdens, exporting monthly peak loads exceeding 51,000 kg of Zinc, 6500 kg of Lead, and 3100 kg of Cadmium. Thermodynamic simulations reveal that this hydrological flushing functions as a chemical activator; the seasonal dilution of natural Alkalinity and Hardness suppresses the river’s theoretical buffered pH (from 8.5 to 7.0), maintaining metals in their uncomplexed free-ion states (Me²⁺). Modeling indicates that nearly 90% of the exported Cadmium remains in this highly labile, toxic form due to a dual coupling with both river Discharge (r_s = 0.87) and pH (r_s = 0.79). The identification of stochastic arsenic peaks 100 times above regulatory limits at Paso de Guadalupe (RS-08) underscores the failure of concentration-based monitoring. Our findings suggest that restoration strategies should shift toward mass-loading-based regulatory frameworks and targeted sediment management at critical nodes to mitigate the chronic export of bioavailable industrial waste. Full article

(This article belongs to the Topic Recent Advances in Hydrological and Hydraulic Engineering: A Contemporary Perspective)

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16 pages, 2306 KB

Open AccessArticle

Land Use and Land Cover Changes and Their Impacts on Hydrological Sustainability in a Tropical Watershed, Brazil

by Rogerio Gonçalves Lacerda de Gouveia

Hydrology 2026, 13(6), 159; https://doi.org/10.3390/hydrology13060159 - 17 Jun 2026

Abstract

Land use and land cover change (LULCC) is increasingly recognized as a dominant driver of hydrological alteration in tropical watersheds, often exceeding the influence of climatic variability. This study evaluates the spatiotemporal dynamics of LULCC and their implications for hydrological sustainability in the [...] Read more.

Land use and land cover change (LULCC) is increasingly recognized as a dominant driver of hydrological alteration in tropical watersheds, often exceeding the influence of climatic variability. This study evaluates the spatiotemporal dynamics of LULCC and their implications for hydrological sustainability in the Uberabinha River Basin, southeastern Brazil, between 1990 and 2020. Utilizing MapBiomas data and statistical analysis, the results reveal a marked expansion of mechanized agriculture, particularly soybean cultivation, which grew from 3426 ha to 54,162 ha, and urban areas, which expanded by approximately 89.4%. Conversely, natural vegetation and pasturelands decreased continuously, with pastures showing the sharpest absolute reduction, from 72,248 ha to 34,535 ha. Despite a 10.76% increase in annual precipitation between 1990 and 2020, the hydrological response exhibited a severe decline in streamflow, characterized by a 76.35% drop in minimum flow. Furthermore, the runoff index decreased from 0.0574 in 1990 to 0.0211 in 2020, indicating a critical loss in the basin’s capacity to convert rainfall into streamflow. These findings demonstrate a clear decoupling between precipitation and streamflow driven by LULCC, posing a severe threat to regional water security and highlighting the urgent need for integrated land–water management. Full article

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