Hydrology

20 pages, 1081 KB

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)

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23 pages, 5620 KB

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)

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21 pages, 11986 KB

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

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

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26 pages, 5440 KB

Open AccessArticle

Improved Streamflow Forecasting Through SWE-Augmented Spatio-Temporal Graph Neural Networks

by Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh and Ayman Nassar

Hydrology 2025, 12(10), 268; https://doi.org/10.3390/hydrology12100268 - 11 Oct 2025

Abstract

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph [...] Read more.

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 and Kling–Gupta Efficiency (KGE) of 0.84 in the multivariate setting at the critical downstream node, Lees Ferry. Compared to the univariate setup, SWE-enhanced predictions reduced Root Mean Square Error (RMSE) by 12.8%. Seasonal and spatial analyses showed the greatest improvements at high-elevation and mid-network stations, where snowmelt dynamics dominate runoff. These findings demonstrate that spatio-temporal learning frameworks, especially STGNNs, provide a scalable and physically consistent approach to streamflow forecasting under variable climatic conditions. Full article

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19 pages, 5038 KB

Open AccessArticle

Mechanisms of Soil Aggregate Stability Influencing Slope Erosion in North China

by Ying Yang, Shuai Zhang, Weijie Yuan, Zedong Li, Xiuxiu Deng and Lina Wang

Hydrology 2025, 12(10), 267; https://doi.org/10.3390/hydrology12100267 - 10 Oct 2025

Abstract

Soil aggregate stability plays a central role in mediating slope erosion, a key ecological process in North China. This study aimed to investigate how aggregate structures (reflected by rainfall intensity and vegetation-type differences) influence the erosion process. Using wasteland as the control, we [...] Read more.

Soil aggregate stability plays a central role in mediating slope erosion, a key ecological process in North China. This study aimed to investigate how aggregate structures (reflected by rainfall intensity and vegetation-type differences) influence the erosion process. Using wasteland as the control, we conducted artificial simulated rainfall experiments on soils covered by Quercus variabilis, Platycladus orientalis, and shrubs, with three rainfall intensity gradients. Key findings showed that Platycladus orientalis exhibited the strongest infiltration capacity and longest runoff initiation delay due to its high proportion of stable macroaggregates (>0.25 mm), while barren land readily formed surface crusts, leading to the fastest runoff. Increased rainfall intensity significantly exacerbated runoff and erosion. When the macroaggregate content exceeded 60%, sediment yield rates dropped sharply, with a significant negative exponential relationship between the mean weight diameter (MWD) and sediment yield; barren land (dominated by microaggregates) faced the highest erosion risk and fell into an erosion–fragmentation vicious cycle. Redundancy analysis revealed that microbial communities (e.g., Ascomycota) and fine roots were dominant erosion-controlling factors under heavy rainfall. Ultimately, the synergistic system of the macroaggregate architecture and root-microbial cementation enabled Platycladus orientalis and other tree stands to reduce soil erodibility via maintaining aggregate stability, whereas shrubs and barren land amplified rainfall intensity effects. barren landbarren landmm·h⁻¹ mm·h⁻¹ mm·h⁻¹ barren land. Full article

(This article belongs to the Section Soil and Hydrology)

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36 pages, 12084 KB

Open AccessArticle

Runoff Prediction in the Songhua River Basin Based on WEP Model

by Xinyu Wang, Changlei Dai, Gengwei Liu, Xiao Yang, Jianyu Jing and Qing Ru

Hydrology 2025, 12(10), 266; https://doi.org/10.3390/hydrology12100266 - 9 Oct 2025

Abstract

Songhua River Basin, northeast China, has seen significant changes due to climate change and human activities from 1990 to 2000, when forests were largely reclaimed and agricultural land was taken up to change the terrestrial water cycle drastically. This paper investigates hydrological changes [...] Read more.

Songhua River Basin, northeast China, has seen significant changes due to climate change and human activities from 1990 to 2000, when forests were largely reclaimed and agricultural land was taken up to change the terrestrial water cycle drastically. This paper investigates hydrological changes in three basins: the main stream basin of the Songhua River, the Second Songhua River Basin, and the Nenjiang River Basin. Machine learning and signal processing techniques have been applied to reconstruct historical river records with high accuracy, achieving determination coefficients exceeding 0.97. The physically based WEP model effectively simulates both natural hydrological patterns and human-induced hydrological processes in the northern Nenjiang region. Climate projections indicate clear temperature increases across all scenarios. The most significant warming is observed under the SSP5-8.5 scenario, where runoff increases by 8.52% to 12.02%t, with precipitation driving 62% to 78% of the changes. Summer runoff shows the most significant increase, while autumn runoff decreases, particularly in the Nenjiang Basin, where permafrost loss alters spring melt patterns. This change elevates flood risk in summer, with the rate of increase strongly dependent on the scenario. Water resources show strong scenario dependence, with the average growth rate of SSP5-8.5 being 4 times that of SSP1-2.6. A critical threshold is reached at a 2.5 °C increase in temperature, triggering system instability. These results emphasize the need for adaptation to spatial differences to address emerging water security challenges in rapidly changing northern regions, including nonlinear hydroclimatic responses, infrastructure resilience to flow changes, and cross-basin coordination. Full article

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46 pages, 2458 KB

Open AccessReview

Global Insights into Micro- and Nanoplastic Pollution in Surface Water: A Review

by Aujeeta Shehrin Razzaque and Assefa M. Melesse

Hydrology 2025, 12(10), 265; https://doi.org/10.3390/hydrology12100265 - 9 Oct 2025

Abstract

Microplastics (<5 mm) and nanoplastics (~100 nm), which are invisible to the naked eye, originate primarily from fragmentation and breakdown larger plastic debris are increasingly pervasive in the environment. Once released, they can disperse widely in the environment, pollute them adversely and ultimately [...] Read more.

Microplastics (<5 mm) and nanoplastics (~100 nm), which are invisible to the naked eye, originate primarily from fragmentation and breakdown larger plastic debris are increasingly pervasive in the environment. Once released, they can disperse widely in the environment, pollute them adversely and ultimately be taken up by living organisms, including humans, through multiple exposure pathways. Their distribution in aquatic systems is influenced by their physiochemical properties including density, hydrophobicity, and chemical stability, along with environmental conditions and biological activities. To better understand the dynamics of micro- and nanoplastics in surface water, this study conducted a comprehensive review of 194 published articles and scientific reports covering marine, freshwater, and wastewater systems. We assessed the abundance, spatial distribution and the factors that govern their behavior in aquatic systems and analyzed the sampling techniques, pretreatment process, and detection and removal techniques to understand the ongoing scenario of these pollutants in surface water and to identify the ecological risks and potential toxicological effects on living biota via direct and indirect exposure pathways. Full article

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

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24 pages, 7261 KB

Open AccessArticle

Coupling Rainfall Intensity and Satellite-Derived Soil Moisture for Time of Concentration Prediction: A Data-Driven Hydrological Approach to Enhance Climate Responsiveness

by Kasun Bandara, Kavini Pabasara, Luminda Gunawardhana, Janaka Bamunawala, Jeewanthi Sirisena and Lalith Rajapakse

Hydrology 2025, 12(10), 264; https://doi.org/10.3390/hydrology12100264 - 6 Oct 2025

Abstract

Accurately estimating the time of concentration (Tc) is critical for hydrological modelling, flood forecasting, and hydraulic infrastructure design. However, conventional methods often overlook the combined effects of rainfall intensity and antecedent soil moisture, thereby limiting their applicability under changing climates. This [...] Read more.

Accurately estimating the time of concentration (Tc) is critical for hydrological modelling, flood forecasting, and hydraulic infrastructure design. However, conventional methods often overlook the combined effects of rainfall intensity and antecedent soil moisture, thereby limiting their applicability under changing climates. This study presents a novel approach that integrates data-driven techniques with remote sensing data to improve Tc estimation. This method was successfully applied in the Kalu River Basin, Sri Lanka, demonstrating its performance in a tropical catchment. While an overall inverse relationship between rainfall intensity and Tc was observed, deviations in several events underscored the influence of initial soil moisture conditions on catchment response times. To address this, a modified kinematic wave-based equation incorporating both rainfall intensity and soil moisture was developed and calibrated, achieving high predictive accuracy (calibration: R² = 0.97, RMSE = 1.1 h; validation: R² = 0.96, RMSE = 0.01 h). A hydrological model was developed to assess the impacts of Tc uncertainties on design hydrographs. Results revealed that underestimating Tc led to substantially shorter lag times and significantly increased peak flows, highlighting the sensitivity of flood simulations to Tc variability. This study highlights the need for improved

T_{C}

estimation and presents a robust, transferable methodology for enhancing hydrological predictions and climate-resilient infrastructure planning. Full article

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17 pages, 7625 KB

Open AccessArticle

Outdoor Ice Rinks in Ontario, Canada—An Oversimplified Model for Ice Water Equivalent and Operational Duration to Evaluate Changing Climate

by Huaxia Yao and Steven R. Fassnacht

Hydrology 2025, 12(10), 263; https://doi.org/10.3390/hydrology12100263 - 5 Oct 2025

Abstract

Outdoor ice rinks have long been a staple for inexpensive exercise and entertainment in cold environments. However, the possible deterioration of or impact on outdoor ice rinks from a changing climate is poorly understood due to no or little monitoring of data of [...] Read more.

Outdoor ice rinks have long been a staple for inexpensive exercise and entertainment in cold environments. However, the possible deterioration of or impact on outdoor ice rinks from a changing climate is poorly understood due to no or little monitoring of data of such facilities. To investigate long-term changes in ice rinks over recent decades, an energy-balance-based ice rink model (with three versions considering precipitation and melt) was applied to a simulated ice rink for two representative area—Dorset of south-central Ontario and the Experimental Lakes Area (ELA) of northwestern Ontario, Canada. The model was calibrated and tested using four-year ice rink data (since limited data are available) and applied to a 40-year period starting in 1978 to reproduce the dates of rink-on and rink-off, rink duration in a season, and ice water equivalent under daily climate inputs, and to illustrate any changing trend in these variables, i.e., the ice rink responses to changed climate. Results showed no clear trend in any ice rink features over four decades, attributed to winter temperature that did not increase substantially (a weak driver), no change in events of rain-on-ice and snowfall-on-rink, and reduced wind speed (possibly slowing ice melting). This is the first trial of a physically based rink model to evaluate outdoor ice rinks. More in situ monitoring and in-depth modelling are necessary, and this model can help guide the monitoring. Full article

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Graphical abstract

23 pages, 8881 KB

Open AccessArticle

Stable Water Isotopes and Machine Learning Approaches to Investigate Seawater Intrusion in the Magra River Estuary (Italy)

by Marco Sabattini, Francesco Ronchetti, Gianpiero Brozzo and Diego Arosio

Hydrology 2025, 12(10), 262; https://doi.org/10.3390/hydrology12100262 - 3 Oct 2025

Abstract

Seawater intrusion into coastal river systems poses increasing challenges for freshwater availability and estuarine ecosystem integrity, especially under evolving climatic and anthropogenic pressures. This study presents a multidisciplinary investigation of marine intrusion dynamics within the Magra River estuary (Northwest Italy), integrating field monitoring, [...] Read more.

Seawater intrusion into coastal river systems poses increasing challenges for freshwater availability and estuarine ecosystem integrity, especially under evolving climatic and anthropogenic pressures. This study presents a multidisciplinary investigation of marine intrusion dynamics within the Magra River estuary (Northwest Italy), integrating field monitoring, isotopic tracing (δ¹⁸O; δD), and multivariate statistical modeling. Over an 18-month period, 11 fixed stations were monitored across six seasonal campaigns, yielding a comprehensive dataset of water electrical conductivity (EC) and stable isotope measurements from fresh water to salty water. EC and oxygen isotopic ratios displayed strong spatial and temporal coherence (R² = 0.99), confirming their combined effectiveness in identifying intrusion patterns. The mass-balance model based on δ¹⁸O revealed that marine water fractions exceeded 50% in the lower estuary for up to eight months annually, reaching as far as 8.5 km inland during dry periods. Complementary δD measurements provided additional insight into water origin and fractionation processes, revealing a slight excess relative to the local meteoric water line (LMWL), indicative of evaporative enrichment during anomalously warm periods. Multivariate regression models (PLS, Ridge, LASSO, and Elastic Net) identified river discharge as the primary limiting factor of intrusion, while wind intensity emerged as a key promoting variable, particularly when aligned with the valley axis. Tidal effects were marginal under standard conditions, except during anomalous events such as tidal surges. The results demonstrate that marine intrusion is governed by complex and interacting environmental drivers. Combined isotopic and machine learning approaches can offer high-resolution insights for environmental monitoring, early-warning systems, and adaptive resource management under climate-change scenarios. Full article

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

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26 pages, 6665 KB

Open AccessArticle

Using Entity-Aware LSTM to Enhance Streamflow Predictions in Transboundary and Large Lake Basins

by Yunsu Park, Xiaofeng Liu, Yuyue Zhu and Yi Hong

Hydrology 2025, 12(10), 261; https://doi.org/10.3390/hydrology12100261 - 2 Oct 2025

Abstract

Hydrological simulation of large, transboundary water systems like the Laurentian Great Lakes remains challenging. Although deep learning has advanced hydrologic forecasting, prior efforts are fragmented, lacking a unified basin-wide model for daily streamflow. We address this gap by developing a single Entity-Aware Long [...] Read more.

Hydrological simulation of large, transboundary water systems like the Laurentian Great Lakes remains challenging. Although deep learning has advanced hydrologic forecasting, prior efforts are fragmented, lacking a unified basin-wide model for daily streamflow. We address this gap by developing a single Entity-Aware Long Short-Term Memory (EA-LSTM) model, an architecture that distinctly processes static catchment attributes and dynamic meteorological forcings, trained without basin-specific calibration. We compile a cross-border dataset integrating daily meteorological forcings, static catchment attributes, and observed streamflow for 975 sub-basins across the United States and Canada (1980–2023). With a temporal training/testing split, the unified EA-LSTM attains a median Nash–Sutcliffe Efficiency (NSE) of 0.685 and a median Kling–Gupta Efficiency (KGE) of 0.678 in validation, substantially exceeding a standard LSTM (median NSE 0.567, KGE 0.555) and the operational NOAA National Water Model (median NSE 0.209, KGE 0.440). Although skill is reduced in the smallest basins (median NSE 0.554) and during high-flow events (median PBIAS −29.6%), the performance is robust across diverse hydroclimatic settings. These results demonstrate that a single, calibration-free deep learning model can provide accurate, scalable streamflow prediction across an international basin, offering a practical path toward unified forecasting for the Great Lakes and a transferable framework for other large, data-sparse watersheds. Full article

(This article belongs to the Special Issue Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications)

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31 pages, 11924 KB

Open AccessArticle

Enhanced 3D Turbulence Models Sensitivity Assessment Under Real Extreme Conditions: Case Study, Santa Catarina River, Mexico

by Mauricio De la Cruz-Ávila and Rosanna Bonasia

Hydrology 2025, 12(10), 260; https://doi.org/10.3390/hydrology12100260 - 2 Oct 2025

Abstract

This study compares enhanced turbulence models in a natural river channel 3D simulation under extreme hydrometeorological conditions. Using ANSYS Fluent 2024 R1 and the Volume of Fluid scheme, five RANS closures were evaluated: realizable k–ε, Renormalization-Group k–ε, Shear Stress Transport k–ω, Generalized k–ω, [...] Read more.

This study compares enhanced turbulence models in a natural river channel 3D simulation under extreme hydrometeorological conditions. Using ANSYS Fluent 2024 R1 and the Volume of Fluid scheme, five RANS closures were evaluated: realizable k–ε, Renormalization-Group k–ε, Shear Stress Transport k–ω, Generalized k–ω, and Baseline-Explicit Algebraic Reynolds Stress model. A segment of the Santa Catarina River in Monterrey, Mexico, defined the computational domain, which produced high-energy, non-repeatable real-world flow conditions where hydrometric data were not yet available. Empirical validation was conducted using surface velocity estimations obtained through high-resolution video analysis. Systematic bias was minimized through mesh-independent validation (<1% error) and a benchmarked reference closure, ensuring a fair basis for inter-model comparison. All models were realized on a validated polyhedral mesh with consistent boundary conditions, evaluating performance in terms of mean velocity, turbulent viscosity, strain rate, and vorticity. Mean velocity predictions matched the empirical value of 4.43 [m/s]. The Baseline model offered the highest overall fidelity in turbulent viscosity structure (up to 43 [kg/m·s]) and anisotropy representation. Simulation runtimes ranged from 10 to 16 h, reflecting a computational cost that increases with model complexity but justified by improved flow anisotropy representation. Results show that all models yielded similar mean flow predictions within a narrow error margin. However, they differed notably in resolving low-velocity zones, turbulence intensity, and anisotropy within a purely hydrodynamic framework that does not include sediment transport. 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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23 pages, 7845 KB

Open AccessArticle

Projected Runoff Changes and Their Effects on Water Levels in the Lake Qinghai Basin Under Climate Change Scenarios

by Pengfei Hou, Jun Du, Shike Qiu, Jingxu Wang, Chao Wang, Zheng Wang, Xiang Jia and Hucai Zhang

Hydrology 2025, 12(10), 259; https://doi.org/10.3390/hydrology12100259 - 2 Oct 2025

Abstract

Lake Qinghai, the largest closed-basin lake on the Qinghai–Tibet Plateau, plays a crucial role in maintaining regional ecological stability through its hydrological functions. In recent decades, the lake has exhibited a continuous rise in water level and lake area expansion, sparking growing interest [...] Read more.

Lake Qinghai, the largest closed-basin lake on the Qinghai–Tibet Plateau, plays a crucial role in maintaining regional ecological stability through its hydrological functions. In recent decades, the lake has exhibited a continuous rise in water level and lake area expansion, sparking growing interest in the mechanisms driving these changes and their future evolution. This study integrates the Soil and Water Assessment Tool (SWAT), simulations under future Shared Socioeconomic Pathways (SSPs) and statistical analysis methods, to assess runoff dynamics and lake level responses in the Lake Qinghai Basin over the next 30 years. The model was developed using a combination of meteorological, hydrological, topographic, land use, soil, and socio-economic datasets, and was calibrated with the sequential uncertainty fitting Ver-2 (SUFI-2) algorithm within the SWAT calibration and uncertainty procedure (SWAT–CUP) platform. Sensitivity and uncertainty analyses confirmed robust model performance, with monthly R² values of 0.78 and 0.79. Correlation analysis revealed that runoff variability is more closely associated with precipitation than temperature in the basin. Under SSP 1-2.6, SSP 3-7.0, and SSP 5-8.5 scenarios, projected annual precipitation increases by 14.4%, 18.9%, and 11.1%, respectively, accompanied by temperature rises varying with emissions scenario. Model simulations indicate a significant increase in runoff in the Buha River Basin, peaking around 2047. These findings provide scientific insight into the hydrological response of plateau lakes to future climate change and offer a valuable reference for regional water resource management and ecological conservation strategies. Full article

(This article belongs to the Special Issue Runoff Modelling under Climate Change)

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20 pages, 3818 KB

Open AccessArticle

Seasonal Design Floods Estimated by Stationary and Nonstationary Flood Frequency Analysis Methods for Three Gorges Reservoir

by Bokai Sun, Shenglian Guo, Sirui Zhong, Xiaoya Wang and Na Li

Hydrology 2025, 12(10), 258; https://doi.org/10.3390/hydrology12100258 - 30 Sep 2025

Abstract

Seasonal design floods and operational water levels are critical for high-efficient water resource utilization. In this study, statistical and rational analyses methods were applied to divide the flood season based on seasonal rainfall patterns. The Mann–Kendall test and Theil–Sen analysis were used to [...] Read more.

Seasonal design floods and operational water levels are critical for high-efficient water resource utilization. In this study, statistical and rational analyses methods were applied to divide the flood season based on seasonal rainfall patterns. The Mann–Kendall test and Theil–Sen analysis were used to detect trend changes in the observed flow series. Both stationary and nonstationary flood frequency analysis methods were conducted to estimate seasonal design floods. The Three Gorges Reservoir (TGR) in the Yangtze River, China, was selected as the case study. Results show that the TGR flood season could be divided into four periods: the reservoir drawdown period (1 May–20 June), the Meiyu flood period (21 June–31 July), the transition period (1 August–10 September), and the Autumn Rain refill period (11 September–31 October). Trend analyses indicate that the flow series at the TGR dam site exhibited a decreasing trend in recent decades. Upstream reservoir regulation has significantly reduced inflow discharges of TGR, and the nonstationary seasonal 1000-year design floods in the transition period are decreased by about 20%, and the flood control water level could rise from 145 m to 157 m, which can generate 2.288 billion kW h more hydropower (16.57% increase) while maintaining unchanged flood prevention standards. This study provides valuable insights into the TGR operational water level in the flood season and highlights the necessity of considering the regulation impact of upstream reservoirs for design floods and reservoir operational water levels. Full article

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23 pages, 10418 KB

Open AccessArticle

Daily Water Mapping and Spatiotemporal Dynamics Analysis over the Tibetan Plateau

by Qi Feng, Kai Yu and Luyan Ji

Hydrology 2025, 12(10), 257; https://doi.org/10.3390/hydrology12100257 - 30 Sep 2025

Abstract

The Tibetan Plateau, known as the “Asian Water Tower”, contains thousands of lakes that are sensitive to climate variability and human activities. To investigate their long-term and short-term dynamics, we developed a daily surface-water mapping dataset covering the period from 2000 to 2024 [...] Read more.

The Tibetan Plateau, known as the “Asian Water Tower”, contains thousands of lakes that are sensitive to climate variability and human activities. To investigate their long-term and short-term dynamics, we developed a daily surface-water mapping dataset covering the period from 2000 to 2024 based on MODIS daily reflectance time series (MOD09GQ/MYD09GQ and MOD09GA/MYD09GA). A hybrid methodology combining per-pixel spectral indices, superpixel segmentation, and fusion of Terra and Aqua results was applied, followed by temporal interpolation to produce cloud-free daily water maps. Validation against Landsat classifications and the 30 m global water dataset indicates an overall accuracy of 96.89% and a mean relative error below 9.1%, confirming the robustness of our dataset. Based on this dataset, we analyzed the spatiotemporal evolution of 1293 lakes (no less than 5 km²). Results show that approximately 87.7% of lakes expanded, with the fastest growth reaching +43.18 km²/y, whereas 12.3% shrank, with the largest decrease being −5.91 km²/y. Seasonal patterns reveal that most lakes reach maximum extent in October and minimum extent in January. This study provides a long-term, cloud-free daily water mapping product for the Tibetan Plateau, which can serve as a valuable resource for future research on regional hydrology, ecosystem vulnerability, and climate–water interactions in high-altitude regions. Full article

(This article belongs to the Special Issue Advances in Cold Regions' Hydrology and Hydrogeology)

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13 pages, 2001 KB

Open AccessArticle

Effect of Geothermal Heating on Deep-Water Temperature in Lake Baikal

by Bair O. Tsydenov

Hydrology 2025, 12(10), 256; https://doi.org/10.3390/hydrology12100256 - 30 Sep 2025

Abstract

Geothermal heating that emanates from the interior of the Earth, including the Baikal Rift Zone, produces potential energy for water movement. The basic concept behind the mechanism of deep-water renewal in Lake Baikal is conditional instability, which is a consequence of the joint [...] Read more.

Geothermal heating that emanates from the interior of the Earth, including the Baikal Rift Zone, produces potential energy for water movement. The basic concept behind the mechanism of deep-water renewal in Lake Baikal is conditional instability, which is a consequence of the joint effects of temperature and pressure on water density. However, an exact trigger of this instability is unknown. In this study, based on a non-hydrostatic 2.5D numerical model taking into account the intraday variability of atmospheric conditions, it was shown that, due to geothermal heating, the water column near the lake bed becomes slightly warmer (0.1–0.2 °C) than ambient waters, which can lead to instability. Simulated temperature distributions showed that 3.4 °C waters gradually shifted along the bed slope to ~650 m on day 1, ~750 m on day 3, ~830 m on day 5, and >1200 m on day 10 in the presence of geothermal heat flux; however, in its absence these waters remained at the level of ~600 m. In view of these findings, a conceptual model of deep convection and a map with potential zones of high ventilation processes in Lake Baikal are proposed. According to the map developed, deep-water renewal is expected to be the most intense at the eastern shore of Lake Baikal because of abnormally high heat release. Full article

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

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19 pages, 7615 KB

Open AccessArticle

GMesh: A Flexible Voronoi-Based Mesh Generator with Local Refinement for Watershed Hydrological Modeling

by Nicolás Velásquez, Miguel Díaz and Antonio Arenas

Hydrology 2025, 12(10), 255; https://doi.org/10.3390/hydrology12100255 - 30 Sep 2025

Abstract

Partial Differential Equation (PDE)-based hydrologic models demand extensive preprocessing, creating a bottleneck and slowing down the model setup process. Mesh generation typically lacks integration with hydrological features like river networks. We present GHOST Mesh (GMesh), an automated, watershed-oriented mesh generator built within the [...] Read more.

Partial Differential Equation (PDE)-based hydrologic models demand extensive preprocessing, creating a bottleneck and slowing down the model setup process. Mesh generation typically lacks integration with hydrological features like river networks. We present GHOST Mesh (GMesh), an automated, watershed-oriented mesh generator built within the Watershed Modeling Framework (WMF), to address this. While primarily designed for the GHOST hydrological model, GMesh’s functionalities can be adapted for other models. GMesh enables rapid mesh generation in Python by incorporating Digital Elevation Models (DEMs), flow direction maps, network topology, and online services. The software creates Voronoi polygons that maintain connectivity between river segments and surrounding hillslopes, ensuring accurate surface–subsurface interaction representation. Key features include customizable mesh generation and variable refinement to target specific watershed areas. We applied GMesh to Iowa’s Bear Creek watershed, generating meshes from 10,000 to 30,000 elements and analyzing their effects on simulated stream flows. Results show that higher mesh resolutions enhance peak flow predictions and reduce response time discrepancies, while local refinements improve model performance with minimal additional computation. GMesh’s open-source nature streamlines mesh generation, offering researchers an efficient solution for hydrological analysis and model configuration testing. Full article

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

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29 pages, 3422 KB

Open AccessArticle

Unveiling Asymptotic Behavior in Precipitation Time Series: A GARCH-Based Second Order Semi-Parametric Autocorrelation Framework for Drought Monitoring in the Semi-Arid Region of India

by Namit Choudhari, Benjamin G. Jacob, Yasin Elshorbany and Jennifer Collins

Hydrology 2025, 12(10), 254; https://doi.org/10.3390/hydrology12100254 - 28 Sep 2025

Abstract

This study evaluated ten drought indices focusing on their ability to monitor drought events in Marathwada, a semi-arid region of India. High-resolution gridded monthly total precipitation data for 75 years (1950–2024) from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to [...] Read more.

This study evaluated ten drought indices focusing on their ability to monitor drought events in Marathwada, a semi-arid region of India. High-resolution gridded monthly total precipitation data for 75 years (1950–2024) from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to evaluate the drought indices. These indices were computed across six timescales: 1, 3, 4, 6, 9, and 12 months. A Generalized Autoregressive Conditional Heteroscedastic (GARCH) model was employed to detect temporal volatility in precipitation, followed by a second-order geospatial autocorrelation eigenfunction eigendecomposition using Global Moran’s Index statistics to geolocate both aggregated and non-aggregated precipitation locations. The performance of drought indices was assessed using non-parametric Spearman’s correlation to identify the strength, direction, and similarity of regional-specific drought events. The temporal lag interdependence between meteorological and agricultural droughts was assessed using a non-parametric Spearman’s cross correlation function (SCCF). The findings revealed that the GARCH model with a skewed Student’s t distribution effectively captured conditional temporal volatility and asymptotic behavior in the precipitation series. The model’s sensitivity enabled the incorporation of temporal fluctuations related to droughts and extreme meteorological events. The Bhalme and Mooley Drought Index (BMDI-6) and Z-Score Index (ZSI-6) were the most applicable indices for drought monitoring. Spearman’s cross-correlation analysis revealed that meteorological droughts influenced agricultural droughts with a time lag of up to 4 months. Full article

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19 pages, 2329 KB

Open AccessArticle

Forecasting the Athabasca River Flow Using HEC-HMS as Hydrologic Model for Cold Weather Applications

by Chiara Belvederesi, Gopal Achari and Quazi K. Hassan

Hydrology 2025, 12(10), 253; https://doi.org/10.3390/hydrology12100253 - 28 Sep 2025

Abstract

The Athabasca River flows through the Lower Athabasca Region (LAR) in Alberta, Canada, which is characterized by variable inter-annual weather, long winters and short summers. LAR is important for the extraction of energy resources and industrial activities that lead to environmental concerns, including [...] Read more.

The Athabasca River flows through the Lower Athabasca Region (LAR) in Alberta, Canada, which is characterized by variable inter-annual weather, long winters and short summers. LAR is important for the extraction of energy resources and industrial activities that lead to environmental concerns, including river pollution and exploitation. This study attempts to forecast the Athabasca River at Fort McMurray and understand the suitability of HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) in cold weather regions, characterized by poorly gauged streams. Daily temperature and precipitation records (1971–2014) were employed in two calibration–validation schemes: (1) a temporally dependent partition (1971–2000 for calibration; 2001–2014 for validation) and (2) a temporally independent partition (alternating years assigned to calibration and validation). The temporally independent approach achieved superior performance, with a Nash–Sutcliffe efficiency of 0.88, outperforming previously developed regional models. HEC-HMS successfully reproduced hydrologic dynamics and peak discharge events under conditions of sparse hydroclimatic data and limited computational inputs, underscoring its robustness for operational forecasting in data-scarce, cold-climate catchments. However, long-term projections may be subject to uncertainty due to the exclusion of anticipated changes in land use and climate forcing. These results substantiate the applicability of HEC-HMS as a cost-effective and reliable tool for hydrological modeling and flow forecasting in support of water resource management, particularly in regions subject to industrial pressures and associated environmental impacts. Full article

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