Hydrology

22 pages, 3391 KB

Open AccessArticle

Assessing Stream Temperature Interactions with Physical and Environmental Variables Along the Longitudinal Profile of a First- to Fourth-Order Perennial Stream in a Multi-Land Use Watershed in Western Oregon, USA

by Derek C. Godwin and Carlos G. Ochoa

Hydrology 2025, 12(9), 230; https://doi.org/10.3390/hydrology12090230 - 1 Sep 2025

Abstract

Stream temperatures are expected to increase with warming air temperatures, yet the extent and aquatic health impacts vary significantly across heterogeneous landscapes. This study was conducted in a 3360-ha multi-land-use watershed in the Pacific Northwest region of the USA to assess and compare [...] Read more.

Stream temperatures are expected to increase with warming air temperatures, yet the extent and aquatic health impacts vary significantly across heterogeneous landscapes. This study was conducted in a 3360-ha multi-land-use watershed in the Pacific Northwest region of the USA to assess and compare the driving factors for stream temperature heating, cooling, and cool-water refugia along a 12-km mainstem stream longitudinal profile. Study objectives were to (1) determine yearlong stream temperature variability along the entire stream longitudinal profile, and (2) assess stream-environment relationships influencing stream temperature dynamics across forest, agriculture, and urban landscapes within the watershed. Stream and riparian air temperatures, solar radiation, shade, and related stream-riparian characteristics were measured over six years at 21 stations to determine changes, along the longitudinal profile, of thermal sensitivity, maximum and minimum stream temperatures, and correlation between solar radiation and temperature increases, and potential causal factors associated with these changes. Solar radiation was a primary heating factor for an exposed agricultural land use reach with 57% effective shade, while southern stream aspects and incoming tributary conditions were primary factors for forested reaches with greater than 84% effective shade. Potential primary cooling factors were streambank height, groundwater inflows, and hyporheic exchange in an urban reach with moderate effective shade (79%) and forest riparian width (16 m). Combining watershed-scale analysis with on-site stream-environmental data collection helps assess primary temperature heating factors, such as solar radiation and shade, and potential cooling factors, such as groundwater and cool tributary inflows, as conditions change along the longitudinal profile. Full article

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29 pages, 3098 KB

Open AccessArticle

Improving Operational Ensemble Streamflow Forecasting with Conditional Bias-Penalized Post-Processing of Precipitation Forecast and Assimilation of Streamflow Data

by Sunghee Kim and Dong-Jun Seo

Hydrology 2025, 12(9), 229; https://doi.org/10.3390/hydrology12090229 - 31 Aug 2025

Abstract

This work aims at improving the accuracy of ensemble streamflow forecasts at short-to-medium ranges with the conditional bias-penalized regression (CBPR)-aided Meteorological Ensemble Forecast Processor (MEFP) and streamflow data assimilation (DA). To assess the potential impact of the CBPR-aided MEFP and streamflow DA, or [...] Read more.

This work aims at improving the accuracy of ensemble streamflow forecasts at short-to-medium ranges with the conditional bias-penalized regression (CBPR)-aided Meteorological Ensemble Forecast Processor (MEFP) and streamflow data assimilation (DA). To assess the potential impact of the CBPR-aided MEFP and streamflow DA, or CBPR-DA, 20-yr hindcast experiments were carried out using the Global Ensemble Forecast System version 12 reforecast dataset for 46 locations in the service areas of 11 River Forecast Centers of the US NWS. The results show that, relative to the current practice of using the MEFP and no DA, or MEFP-NoDA, CBPR-DA improves the accuracy of ensemble forecasts of 3-day flow over lead times of 0 to 3 days by over 40% for 4 RFCs and by over 20% for 9 of the 11 RFCs. The margin of improvement is larger where the predictability of precipitation is larger and the hydrologic memory is stronger. As the lead time increases, the margin of improvement decreases but still exceeds 10% for the prediction of 14-day flow over lead times of 0 to 14 days for all but 3 RFCs. Full article

(This article belongs to the Special Issue New Perspectives in the Flood Forecasting Chain (Weather Prediction, Rainfall-Runoff Modeling, and Communication with Stakeholders), Second Edition)

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22 pages, 6496 KB

Open AccessArticle

High-Resolution Bathymetric Survey and Updated Morphometric Analysis of Lake Markakol (Kazakhstan)

by Askhat Zhadi, Azamat Madibekov, Serik Zhumatayev, Laura Ismukhanova, Botakoz Sultanbekova, Aidar Zhumalipov, Zhanar Raimbekova, María-Elena Rodrigo-Clavero and Javier Rodrigo-Ilarri

Hydrology 2025, 12(9), 228; https://doi.org/10.3390/hydrology12090228 - 29 Aug 2025

Abstract

Accurate and up-to-date morphometric data on lakes are crucial for hydrological modeling, ecosystem monitoring, and sustainable water resource management. This study presents the first centimeter-scale, high-resolution bathymetric model of Lake Markakol (eastern Kazakhstan), generated using advanced hydroacoustic and geospatial techniques. The primary objective [...] Read more.

Accurate and up-to-date morphometric data on lakes are crucial for hydrological modeling, ecosystem monitoring, and sustainable water resource management. This study presents the first centimeter-scale, high-resolution bathymetric model of Lake Markakol (eastern Kazakhstan), generated using advanced hydroacoustic and geospatial techniques. The primary objective was to reassess key morphometric parameters—surface area, depth, volume, and shoreline configuration—more than six decades after the only existing survey from 1962. High-density depth data were acquired with a Lowrance HDS-12 Live echo sounder, achieving vertical precision of ±0.17 m, and processed using ReefMaster and ArcGIS to produce a three-dimensional, hydrologically correct model of the lake basin. Compared with archival data, results show that while the surface area (455.365 ± 0.005 km²), length (38.304 ± 0.002 km), and width (19.138 ± 0.002 km) have remained stable, the maximum depth is lower (24.14 ± 0.17 m vs. 27 m), and the total water volume is slightly higher (6.667 ± 0.025 km³ vs. 6.37 km³). These differences highlight both the limitations of historical lead-line surveys and the enhanced accuracy of modern hydroacoustic and GIS-based methods. The workflow developed here is transferable to other remote alpine lakes, providing an invaluable baseline for limnological research, ecological assessment, hydrodynamic modeling, and long-term water resource management strategies in data-scarce mountain regions. Full article

(This article belongs to the Special Issue Lakes as Sensitive Indicators of Hydrology, Environment, and Climate)

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16 pages, 4224 KB

Open AccessArticle

Zoning of the Territory of Southern Kazakhstan Based on the Conditions of Groundwater Availability for Watering Pasture Lands

by Vladimir Smolyar, Dinara Adenova, Timur Rakhimov, Rakhmatulla Ayazbayev, Gulnura Nyssanbayeva and Almagul Kerimkulova

Hydrology 2025, 12(9), 227; https://doi.org/10.3390/hydrology12090227 - 28 Aug 2025

Abstract

In the arid and semi-arid climate of Southern Kazakhstan, groundwater is the primary and most resilient source of water for pasture irrigation. This study provides an integrated assessment of the predicted, natural, and operational groundwater resources across five administrative regions—Almaty, Zhetysu, Zhambyl, Kyzylorda, [...] Read more.

In the arid and semi-arid climate of Southern Kazakhstan, groundwater is the primary and most resilient source of water for pasture irrigation. This study provides an integrated assessment of the predicted, natural, and operational groundwater resources across five administrative regions—Almaty, Zhetysu, Zhambyl, Kyzylorda, and Turkestan—considering water quality (total dissolved solids, TDS), potential well yield, and aquifer depth. Hydrogeological maps at 1:200,000 and 1:1,000,000 scales, a regional well inventory, and GIS-based spatial analysis were combined to classify resource availability and identify surplus and deficit zones. Results show that 92.5% of predicted exploitable resources (totaling 1155.2 m³/s) have TDS ≤ 3 g/L, making them suitable for domestic and livestock use. Regional disparities are pronounced: Zhetysu, Almaty, and Zhambyl exhibit resource surpluses, Kyzylorda approaches balance, while Turkestan faces a marked deficit. The developed groundwater availability map integrates mineralization, well productivity, and recommended drilling depth, enabling the design of water intake systems without costly field exploration. This decision-support tool has practical value for optimizing water allocation, reducing operational costs, and improving the sustainability of pasture management under the constraints of limited surface water resources. Full article

(This article belongs to the Section Soil and Hydrology)

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14 pages, 915 KB

Open AccessArticle

Evaluation of Analytical Solutions Based on the Assumption of One-Dimensional Groundwater Flow Using Numerical Solutions for Two-Dimensional Flows

by Konstantinos L. Katsifarakis, Yiannis N. Kontos and Odysseas Keremidis

Hydrology 2025, 12(9), 226; https://doi.org/10.3390/hydrology12090226 - 28 Aug 2025

Abstract

The proper development of groundwater resources is very important in many parts of the world. Its planning requires mathematical simulation of groundwater flows. Simulation can be either analytical or numerical. Analytical tools, when available, require fewer computational resources, but they are usually based [...] Read more.

The proper development of groundwater resources is very important in many parts of the world. Its planning requires mathematical simulation of groundwater flows. Simulation can be either analytical or numerical. Analytical tools, when available, require fewer computational resources, but they are usually based on more assumptions, at the conceptual level, which restrict their applicability. In this paper, we aim to check the applicability of one-dimensional analytical solutions for groundwater flows through non-homogeneous aquifers, which are bound by two constant head and two impermeable boundaries and bear many zones of different transmissivities. These solutions are based on the stepwise inclusion of neighboring zones to larger ones, with equivalent transmissivity coefficients. We compare analytical results with numerical ones, obtained from a two-dimensional numerical model. We have selected the boundary element method (BEM) for this task. BEM is very versatile in solving steady-state groundwater flow problems, since discretization is restricted to external and internal field boundaries only. This feature fits perfectly with our research, which requires flow velocities at the boundaries only. Our research shows that analytical results can serve as upper and lower limits of total inflow. If the differences between the transmissivities of adjacent zones are small, they can be used in preliminary calculations too. Full article

(This article belongs to the Section Surface Waters and Groundwaters)

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19 pages, 2239 KB

Open AccessArticle

Assessment of Satellite Precipitation Products in an Andean Catchment: Ambato River Basin, Ecuador

by Pablo Arechúa-Mazón, César Cisneros-Vaca, Julia Calahorrano-González and Mery Manzano-Cepeda

Hydrology 2025, 12(9), 225; https://doi.org/10.3390/hydrology12090225 - 28 Aug 2025

Abstract

Accurate precipitation data are essential for hydrological planning in mountainous regions with sparse opportunities for observation, such as the Ambato River basin in Ecuador. In this study, CHIRPS and IMERG satellite precipitation products were compared against six automatic rain gauges from 2014 to [...] Read more.

Accurate precipitation data are essential for hydrological planning in mountainous regions with sparse opportunities for observation, such as the Ambato River basin in Ecuador. In this study, CHIRPS and IMERG satellite precipitation products were compared against six automatic rain gauges from 2014 to 2023, using both categorical metrics (to assess daily rainfall detection skill) and continuous validation (to evaluate rainfall amount), complemented by bias decomposition and spatiotemporal analysis. Our results show that IMERG demonstrated higher skill in detecting daily rainfall, while CHIRPS delivered a more stable performance during dry conditions, with fewer false alarms. Both products capture the main seasonal precipitation patterns but differ in bias behavior: CHIRPS tends to under-estimate daily rainfall less, whereas IMERG provides more reliable volumetric estimates overall. These findings suggest that IMERG may be best suited for flood risk and hydrological modelling, while CHIRPS could be preferred for drought monitoring and climatological studies in Andean catchments. Full article

(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations)

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23 pages, 4474 KB

Open AccessArticle

Anthropogenic River Segmentation Case Study: Bahlui River from Romania

by Nicolae Marcoie, Ionuț Ovidiu Toma, Șerban Chihaia, Tomi Alexandrel Hrăniciuc, Daniel Toma, Cătălin Dumitrel Balan, Elena Niculina Drăgoi and Mircea-Teodor Nechita

Hydrology 2025, 12(9), 224; https://doi.org/10.3390/hydrology12090224 - 25 Aug 2025

Abstract

This manuscript introduces a river segmentation method and explores the impact of human interventions through a long-term study of total nitrogen, total phosphorus, chemical oxygen demand, and biochemical oxygen demand. An indicator linking parameter concentrations to the river’s flow rate was used to [...] Read more.

This manuscript introduces a river segmentation method and explores the impact of human interventions through a long-term study of total nitrogen, total phosphorus, chemical oxygen demand, and biochemical oxygen demand. An indicator linking parameter concentrations to the river’s flow rate was used to assess the development of the examined parameters. The analysis spanned from 2011 to 2022, considering both seasonal and yearly variations. Normal probability plots served as statistical tools to evaluate whether the data followed normal distributions and identify outliers. The proposed segmentation divided the Bahlui River into four segments, each defined by anthropogenic stressors. It was found that, due to human activity, each river segment could be viewed as an “independent” river. This supports the idea that river segments can be analyzed separately as distinct components. The proposed segmentation approach represents an alternative approach in river water quality research, moving from traditional continuous system models to fragmented system analysis, which better reflects the reality of heavily modified river systems. The study’s findings are important for understanding how anthropogenic modifications affect river ecosystem functioning in the long term. Full article

(This article belongs to the Topic Climate Change and Human Impact on Freshwater Water Resources: Rivers and Lakes)

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28 pages, 2147 KB

Open AccessArticle

Generalized Methodology for Two-Dimensional Flood Depth Prediction Using ML-Based Models

by Mohamed Soliman, Mohamed M. Morsy and Hany G. Radwan

Hydrology 2025, 12(9), 223; https://doi.org/10.3390/hydrology12090223 - 24 Aug 2025

Abstract

Floods are among the most devastating natural disasters; predicting their depth and extent remains a global challenge. Machine Learning (ML) models have demonstrated improved accuracy over traditional probabilistic flood mapping approaches. While previous studies have developed ML-based models for specific local regions, this [...] Read more.

Floods are among the most devastating natural disasters; predicting their depth and extent remains a global challenge. Machine Learning (ML) models have demonstrated improved accuracy over traditional probabilistic flood mapping approaches. While previous studies have developed ML-based models for specific local regions, this study aims to establish a methodology for estimating flood depth on a global scale using ML algorithms and freely available datasets—a challenging yet critical task. To support model generalization, 45 catchments from diverse geographic regions were selected based on elevation, land use, land cover, and soil type variations. The datasets were meticulously preprocessed, ensuring normality, eliminating outliers, and scaling. These preprocessed data were then split into subgroups: 75% for training and 25% for testing, with six additional unseen catchments from the USA reserved for validation. A sensitivity analysis was performed across several ML models (ANN, CNN, RNN, LSTM, Random Forest, XGBoost), leading to the selection of the Random Forest (RF) algorithm for both flood inundation classification and flood depth regression models. Three regression models were assessed for flood depth prediction. The pixel-based regression model achieved an R² of 91% for training and 69% for testing. Introducing a pixel clustering regression model improved the testing R² to 75%, with an overall validation (for unseen catchments) R² of 64%. The catchment-based clustering regression model yielded the most robust performance, with an R² of 83% for testing and 82% for validation. The developed ML model demonstrates breakthrough computational efficiency, generating complete flood depth predictions in just 6 min—a 225× speed improvement (90–95% time reduction) over conventional HEC-RAS 6.3 simulations. This rapid processing enables the practical implementation of flood early warning systems. Despite the dramatic speed gains, the solution maintains high predictive accuracy, evidenced by statistically robust 95% confidence intervals and strong spatial agreement with HEC-RAS benchmark maps. These findings highlight the critical role of the spatial variability of dependencies in enhancing model accuracy, representing a meaningful approach forward in scalable modeling frameworks with potential for global generalization of flood depth. Full article

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22 pages, 4204 KB

Open AccessArticle

Integrative Runoff Infiltration Modeling of Mountainous Urban Karstic Terrain

by Yaakov Anker, Nitzan Ne’eman, Alexander Gimburg and Itzhak Benenson

Hydrology 2025, 12(9), 222; https://doi.org/10.3390/hydrology12090222 - 22 Aug 2025

Abstract

Global climate change, combined with the construction of impermeable urban elements, tends to increase runoff, which might cause flooding and reduce groundwater recharge. Moreover, the first flash of these areas might accumulate pollutants that might deteriorate groundwater quality. A digital elevation model (DEM) [...] Read more.

Global climate change, combined with the construction of impermeable urban elements, tends to increase runoff, which might cause flooding and reduce groundwater recharge. Moreover, the first flash of these areas might accumulate pollutants that might deteriorate groundwater quality. A digital elevation model (DEM) describes urban landscapes by representing the watershed relief at any given location. While, in concept, finer DEMs and land use classification (LUC) are yielding better hydrological models, it is suggested that over-accuracy overestimates minor tributaries that might be redundant. Optimal DEM resolution with integrated spectral and feature-based LUC was found to reflect the hydrological network’s significant tributaries. To cope with the karstic urban watershed complexity, ModClark Transform and SCS Curve Number methods were integrated over a GIS-HEC-HMS platform to a nominal urban watershed sub-basin analysis procedure, allowing for detailed urban runoff modeling. This precise urban karstic terrain modeling procedure can predict runoff volume and discharge in urban, mountainous karstic watersheds, and may be used for water-sensitive design or in such cities to control runoff and prevent its negative impacts. Full article

(This article belongs to the Special Issue The Influence of Landscape Disturbance on Catchment Processes)

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12 pages, 2068 KB

Open AccessArticle

Effect of Storm Event Duration on the Indices of Concentration Discharge Hysteresis

by Samer Majdalani

Hydrology 2025, 12(8), 221; https://doi.org/10.3390/hydrology12080221 - 20 Aug 2025

Abstract

The relationship between concentration and discharge (C/Q) is widely studied to understand the behavior of solute transport in complex natural media during storm events. The causes of C/Q hysteresis are due to the delay between the signals of [...] Read more.

The relationship between concentration and discharge (C/Q) is widely studied to understand the behavior of solute transport in complex natural media during storm events. The causes of C/Q hysteresis are due to the delay between the signals of C and Q at a given observation point. Many indices are used to characterize the C/Q hysteresis curve, like the hysteresis index (HI) and the flushing index (FI). The limitation of relating C/Q hysteresis relationships or indices to storm event parameters is because, in real-world situations, we ignore and do not control storm event parameters. This paper is the first attempt to study the variability of C/Q relationships under a well-known storm event on a controlled experimental channel. We tested nine scenarios where the storm event consisted of a triangular input signal with a constant peak and a variable duration. The main parameter of this study is the storm event duration. We calculated known indices, like the hysteresis index (HI) and the flushing index (FI), and we introduced the following two new indices: the saturation index (SI) and the bisector index (BI). Then we related all calculated indices to the storm duration parameter. The importance of our study is that it presents, for the first time, a quantitative description of how the magnitude of the hysteresis indices varies with the storm duration parameter. We found that the most popular HI index does not follow a monotonic behavior for increasing storm duration. Conversely, the FI index and the two newly introduced indices (SI and BI) follow a monotonic behavior for increasing storm duration according to a Fermi-type function. The SI varies between 0.11 and 0.93, while the BI varies between 1 and 0.32 for an increasing storm event duration. Full article

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33 pages, 12539 KB

Open AccessArticle

A Flood Forecasting Method in the Francolí River Basin (Spain) Using a Distributed Hydrological Model and an Analog-Based Precipitation Forecast

by Daniel Carril-Rojas, Carlo Guzzon, Luis Mediero, Javier Fernández-Fidalgo, Luis Garrote, Maria Carmen Llasat and Raul Marcos-Matamoros

Hydrology 2025, 12(8), 220; https://doi.org/10.3390/hydrology12080220 - 19 Aug 2025

Abstract

Recent flooding events in Spain have highlighted the need to develop real-time flood forecasts to estimate streamflows over the next few hours and days. Therefore, a meteorological forecast that provides possible precipitation for the upcoming hours combined with a hydrological model to simulate [...] Read more.

Recent flooding events in Spain have highlighted the need to develop real-time flood forecasts to estimate streamflows over the next few hours and days. Therefore, a meteorological forecast that provides possible precipitation for the upcoming hours combined with a hydrological model to simulate the rainfall-runoff processes in the basin and its flood response are needed. In this paper, a probabilistic flood forecasting tool is proposed for the Francolí river basin, located in Catalonia (Spain). For this purpose, the Real-time Interactive Basin Simulator (RIBS) distributed hydrological model was calibrated in this basin for a set of flood events. Then, a series of rainfall field forecasts based on the analog method have been used as input data in the hydrological model, obtaining a set of hydrographs for given flood events as output. Finally, a probabilistic forecast that supplies the probability distribution of the possible response flows of the Francolí river is provided for a set of episodes. Full article

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

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19 pages, 1347 KB

Open AccessArticle

Enhancing MUSIC’s Capability for Performance Evaluation and Optimization of Established Urban Constructed Wetlands

by Fujia Yang, Shirley Gato-Trinidad and Iqbal Hossain

Hydrology 2025, 12(8), 219; https://doi.org/10.3390/hydrology12080219 - 18 Aug 2025

Abstract

The Model for Urban Stormwater Improvement Conceptualization (MUSIC) serves as a key hydrological tool for simulating urban stormwater runoff pollution and evaluating the treatment performance in Water-Sensitive Urban Designs like constructed wetlands (CWs). However, a significant limitation exists in MUSIC’s current inability to [...] Read more.

The Model for Urban Stormwater Improvement Conceptualization (MUSIC) serves as a key hydrological tool for simulating urban stormwater runoff pollution and evaluating the treatment performance in Water-Sensitive Urban Designs like constructed wetlands (CWs). However, a significant limitation exists in MUSIC’s current inability to model heavy metal contaminants, even though they are commonly found in urban stormwater and pose significant environmental risks. This eventually affects the model’s utility during critical planning phases for urban developments. Thus, there is a need to address this limitation. Field investigations were conducted across established CWs in residential and industrial catchments throughout Greater Melbourne, Australia. Through systematic monitoring and calibration, an approach was developed to extend MUSIC’s predictive capabilities to include several prevalent heavy metals. The results indicate that the enhanced model can generate plausible estimates for targeted metals while differentiating catchment-specific pollutant generation and treatment patterns. This advancement enhances MUSIC’s functionality as a planning support tool, enabling the preliminary assessment of heavy metal dynamics alongside conventional pollutants during both design and operational stages. The findings underscore the value of incorporating metal-specific parameters into stormwater models, offering improved support for urban water management decisions and long-term water quality protection. Full article

(This article belongs to the Special Issue Advances in Urban Hydrology and Stormwater Management)

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20 pages, 4874 KB

Open AccessArticle

Evaluation and Bias Correction of ECMWF Extended-Range Precipitation Forecasts over the Confluence of Asian Monsoons and Westerlies Using the Linear Scaling Method

by Mahmut Tudaji, Fuqiang Tian, Keer Zhang and Haoyang Lyu

Hydrology 2025, 12(8), 218; https://doi.org/10.3390/hydrology12080218 - 18 Aug 2025

Abstract

This study evaluates and corrects ECMWF precipitation forecasts (Set VI-ENS extended) over the confluence of Asian monsoons and westerlies, deriving a time series of correction factors for medium- and long-term hydrological forecasting. Based on a 15-year dataset (2008–2023), a dominant spatial and temporal [...] Read more.

This study evaluates and corrects ECMWF precipitation forecasts (Set VI-ENS extended) over the confluence of Asian monsoons and westerlies, deriving a time series of correction factors for medium- and long-term hydrological forecasting. Based on a 15-year dataset (2008–2023), a dominant spatial and temporal bias pattern was identified: ~50% of the study area—in particular, the entire Tibetan Plateau—experienced overestimated precipitation, with larger relative errors in dry seasons than in wet seasons. Daily correction factors were derived using the linear scaling method and applied to distributed hydrological models for the Mekong, Salween, and Brahmaputra river basins. The results demonstrated substantial efficacy in correcting streamflow forecasts, particularly in the Brahmaputra basin at the Nuxia station, where the relative error in the total water volume over a 32-day period was reduced from 25% to 10% during the calibration period (2008–2020) and from 20% to 9% in the validation period (2021–2023). Furthermore, over 90% (calibration) and 85% (validation) of hydrological forecast events were successfully corrected at Nuxia. Comparable improvements were observed in key stations across the Salween and Mekong basins, with the combined success rates exceeding 70% and 65%, demonstrating the method’s regional robustness. Challenges remain in areas with weak linear relationships between forecasted and observed data, highlighting the need for further investigation. Full article

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

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17 pages, 2644 KB

Open AccessArticle

Intelligent Decoupling of Hydrological Effects in Han River Cascade Dam System: Spatial Heterogeneity Mechanisms via an LSTM-Attention-SHAP Interpretable Framework

by Shuo Ouyang, Changjiang Xu, Weifeng Xu, Mingyuan Zhou, Junhong Zhang, Guiying Zhang and Zixuan Pan

Hydrology 2025, 12(8), 217; https://doi.org/10.3390/hydrology12080217 - 16 Aug 2025

Abstract

The construction of cascade dam systems profoundly reshapes river hydrological processes, yet the analysis of their spatial heterogeneity effects has long been constrained by the mechanistic deficiencies and interpretability limitations of traditional mechanistic models. Focusing on the middle-lower Han River (a 652 km [...] Read more.

The construction of cascade dam systems profoundly reshapes river hydrological processes, yet the analysis of their spatial heterogeneity effects has long been constrained by the mechanistic deficiencies and interpretability limitations of traditional mechanistic models. Focusing on the middle-lower Han River (a 652 km reach regulated by seven dams) as a representative case, this study develops an LSTM-Attention-SHAP interpretable framework to achieve, for the first time, intelligent decoupling of dam-induced hydrological effects and mechanistic analysis of spatial differentiation. Key findings include the following: (1) The LSTM model demonstrates exceptional predictive performance of water level and flow rate in intensively regulated reaches (average Nash–Sutcliffe Efficiency, NSE = 0.935 at Xiangyang, Huangzhuang, and Xiantao stations; R² = 0.988 for discharge at Xiantao Station), while the attention mechanism effectively captures sensitive factors such as the abrupt threshold (>560 m³/s) in the Tangbai River tributary; (2) Shapley Additive exPlanations (SHAP) values reveal spatial heterogeneous dam contributions: the Cuijiaying Dam increases discharge at Xiangyang station (mean SHAP +0.22) but suppresses water level at Xiantao station (mean SHAP −0.15), whereas the Wangfuzhou Dam shows a stable negative correlation with Xiangyang water levels (mean SHAP −0.18); (3) dam operations induce cascade effects through altered channel storage capacity. These findings provide spatially adaptive strategies for flood risk zoning and ecological operations in globally intensively regulated rivers such as the Yangtze and Mekong. Full article

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19 pages, 2326 KB

Open AccessArticle

Effectiveness of Wetlands for Improving Different Water Quality Parameters in Various Climatic Conditions

by Aruna Shrestha, Rohan Benjankar, Ajay Kalra and Amrit Bhusal

Hydrology 2025, 12(8), 216; https://doi.org/10.3390/hydrology12080216 - 15 Aug 2025

Abstract

Engineered wetland has been used as a Best Management Practice (BMP) to remove pollutants and maintain water quality in watersheds. This study is focused on developing models to analyze the impacts of discharges on the efficiency of wetlands to improve water quality downstream. [...] Read more.

Engineered wetland has been used as a Best Management Practice (BMP) to remove pollutants and maintain water quality in watersheds. This study is focused on developing models to analyze the impacts of discharges on the efficiency of wetlands to improve water quality downstream. The watershed hydrological Soil & Water Assessment Tool (SWAT) and wetland (Personal Computer Storm Water Management Model—PCSWMM) models were developed to analyze the efficiency of engineered wetlands to remove the pollutants for different basins under three different climatic conditions (i.e., dry, average and wet year). The SWAT was calibrated and validated to simulate discharge and water quality parameters. The wetland model was developed using inflow hydrographs and concentrations of the water quality parameters biochemical oxygen demand (BOD), total suspended solids (TSSs), total nitrogen (TN) and total phosphorous (TP), simulated from a Soil & Water Assessment Tool (SWAT) model. A PCSWMM (wetland) was developed based on the physical and first order decay process within the wetland system for three basins in Prairie du Pont watershed in Illinois, USA. The results showed that pollutant removal efficiencies decreased from low to high discharges (dry to wet climatic conditions) for all watersheds and pollutants (except for BOD) based on trendline analysis. Nevertheless, the efficiencies were highly variable, specifically during low discharges. Furthermore, the sensitivity of the k-parameter (areal rate constant) was pollutant dependent. Overall, this study is helpful to understand the efficacy of wetlands’ pollutant removal as a function of discharge. The approach can be used in watersheds located in other geographic regions for the preliminary design of engineered wetlands to remove non-point source pollution and treat stormwater runoff. Full article

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14 pages, 1449 KB

Open AccessArticle

Effects of Climate Variables and Human Activities on Groundwater Level Fluctuations in Unconsolidated Sedimentary Aquifers: A Data-Driven Approach

by Liu Yang, Ming Gao, Jiameng Chen, Wenqing Shi, Changhong Hou, Zichun Liu, Cheng Luo, Jiahui Yu, Xiangyu Yang and Jie Dong

Hydrology 2025, 12(8), 215; https://doi.org/10.3390/hydrology12080215 - 15 Aug 2025

Abstract

Groundwater level (GWL) in unconfined aquifers is highly susceptible to climate variables and human activities, exhibiting nonlinear fluctuations; these can further contribute to or exacerbate environmental hazards, such as land subsidence. Understanding the relationship between GWL changes and external conditions is essential for [...] Read more.

Groundwater level (GWL) in unconfined aquifers is highly susceptible to climate variables and human activities, exhibiting nonlinear fluctuations; these can further contribute to or exacerbate environmental hazards, such as land subsidence. Understanding the relationship between GWL changes and external conditions is essential for effective groundwater resource management and ecological protection. However, this relationship remains unclear and variable. This study systematically analyzes the correlations between climate and human factors and GWLs, using data from monitoring stations in the unconsolidated sedimentary aquifers of Beijing, China. It evaluates the importance of influencing factors on GWL simulation accuracy and tests how different inputs affect simulation performance. The results indicate that human factors are more strongly correlated with GWLs, yet climate factors hold higher importance scores. In GWL simulations, different input variables yield varying accuracy, with the inclusion of precipitation notably decreasing simulation precision because of its lagged or indirect effects on groundwater levels. The variation in accuracy across monitoring stations further suggests that the primary differences may stem from the GWL data itself. These findings underscore the need for high-resolution, localized data and tailored input selection to improve GWL projections and inform adaptive water-resource strategies under changing climatic and anthropogenic pressures. Full article

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23 pages, 13439 KB

Open AccessArticle

Precision Identification of Irrigated Areas in Semi-Arid Regions Using Optical-Radar Time-Series Features and Ensemble Machine Learning

by Weifeng Li, Changlai Xiao, Xiujuan Liang, Weifei Yang, Jiang Zhang, Rongkun Dai, Yuhan La, Le Kang and Deyu Zhao

Hydrology 2025, 12(8), 214; https://doi.org/10.3390/hydrology12080214 - 14 Aug 2025

Abstract

Addressing limitations in remote sensing irrigation monitoring (insufficient resolution, single-source constraints, poor terrain adaptability), this study developed a high-precision identification framework for Jianping County, China, a semi-arid region. We integrated Sentinel-1 SAR (VV/VH), Sentinel-2 multispectral, and MOD11A1 land surface temperature data. Savitzky–Golay (S-G) [...] Read more.

Addressing limitations in remote sensing irrigation monitoring (insufficient resolution, single-source constraints, poor terrain adaptability), this study developed a high-precision identification framework for Jianping County, China, a semi-arid region. We integrated Sentinel-1 SAR (VV/VH), Sentinel-2 multispectral, and MOD11A1 land surface temperature data. Savitzky–Golay (S-G) filtering reconstructed time-series datasets for NDVI, SAVI, TVDI, and VV/VH backscatter coefficients. Irrigation mapping employed random forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. Key results demonstrate the following. (1) RF achieved superior performance with overall accuracies of 91.00% (2022), 88.33% (2023), and 87.78% (2024), and Kappa coefficients of 86.37%, 80.96%, and 80.40%, showing minimal deviation (0.66–3.44%) from statistical data; (2) SAVI and VH exhibited high irrigation sensitivity, with peak differences between irrigated/non-irrigated areas reaching 0.48 units (SAVI, July–August) and 2.78 dB (VH); (3) cropland extraction accuracy showed <3% discrepancy versus governmental statistics. The “Multi-temporal Feature Fusion + S-G Filtering + RF Optimization” framework provides an effective solution for precision irrigation monitoring in complex semi-arid environments. Full article

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24 pages, 6056 KB

Open AccessArticle

Intermittency as an Environmental Filter: Diatom Traits and Water Quality Indicators in a Hydrodynamic Context

by Alexander G. Rusanov, Zsuzsa Trábert, Keve T. Kiss, János L. Korponai, Mikhail Y. Kolobov, Tibor Bíró, Edit Vadkerti and Éva Ács

Hydrology 2025, 12(8), 213; https://doi.org/10.3390/hydrology12080213 - 13 Aug 2025

Abstract

Global climate changes have led to dramatic increases in drought durations in previously permanent streams, impacting the biodiversity and functioning of river ecosystems. However, the response of benthic diatom communities to hydrological intermittency remains poorly understood. In this study, we compared the taxonomic [...] Read more.

Global climate changes have led to dramatic increases in drought durations in previously permanent streams, impacting the biodiversity and functioning of river ecosystems. However, the response of benthic diatom communities to hydrological intermittency remains poorly understood. In this study, we compared the taxonomic and functional compositions of the diatom communities between permanent and intermittent sections in two hilly stream systems in southwestern Hungary. Our results showed that both the taxonomic and functional compositions of diatom communities were significantly affected by changes in the hydrological regime, leading to a decline in species richness and diversity and functional richness in intermittent sections. Functional richness and dispersion decreased significantly with declining taxonomic richness, likely as a consequence of species loss driven by flow intermittency. Aquatic–subaerial diatoms with moderate oxygen requirements were indicative of intermittent sections, while large, occasionally aerophilic and oxybiontic diatoms characterized permanent sections. The relative abundance of low-profile diatoms increased in intermittent sections, indicating that the natural successional process of communities was disrupted due to streambed drying. Furthermore, intermittent sections were marked by elevated abundances of α-mesosaprobous and α-meso-polysaprobous diatoms, indicating a reduced self-purification capacity under intermittent-flow conditions. These findings provide detailed insight into the responses of diatom communities to drought and water scarcity in intermittent streams, which are becoming increasingly common in warm temperate regions. Full article

(This article belongs to the Special Issue Hydrodynamics and Water Quality of Rivers and Lakes)

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29 pages, 3879 KB

Open AccessArticle

Challenges and Limitations of Using Monitoring Data in Catchment-Based Models—A Case Study of Rivers Taw and Torridge, UK

by Richard Heal, Wayne Rostant and Paulette Posen

Hydrology 2025, 12(8), 212; https://doi.org/10.3390/hydrology12080212 - 12 Aug 2025

Abstract

Water quality monitoring is a key requirement for fulfilling various national environmental policies, but with many competing needs and limited resources, data collected can suffer from both spatial and temporal deficiencies. Modelling offers the potential to substitute estimated values into observational gaps, but [...] Read more.

Water quality monitoring is a key requirement for fulfilling various national environmental policies, but with many competing needs and limited resources, data collected can suffer from both spatial and temporal deficiencies. Modelling offers the potential to substitute estimated values into observational gaps, but model validation often requires the very data that are lacking. In this paper we present the results of a pilot study to investigate spatial and temporal issues around the monitoring of faecal indicator bacteria (Escherichia coli) in rivers of the Taw and Torridge catchments in the UK. Statistical analysis of in situ measurements versus simulated data from the catchment models reveals similar seasonal associations between riverine bacterial counts and rainfall patterns. Furthermore, spatial apportionment of livestock to better reflect land use was found to be important in the models, especially in upstream reaches of the catchments. In conclusion, successful monitoring of faecal bacteria levels in UK rivers requires risk-based monitoring (sufficient to identify possible seasonal trends) and informed spatial consideration of sampling sites. Catchment models can be useful aids for directing and augmenting such monitoring programmes, but these models should undergo rigorous validation, particularly in upper catchment areas, to ensure correct model response to changes in land use and/or climate. Full article

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