Journal Description
Hydrology
Hydrology
is an international, peer-reviewed, open access journal on hydrology published monthly online by MDPI. The American Institute of Hydrology (AIH) and Japanese Society of Physical Hydrology (JSPH) are affiliated with Hydrology and their members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), PubAg, GeoRef, and other databases.
- Journal Rank: JCR - Q2 (Water Resources) / CiteScore - Q1 (Earth-Surface Processes)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.6 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.1 (2023);
5-Year Impact Factor:
3.0 (2023)
Latest Articles
Urban Flood Vulnerability Assessment in Freetown, Sierra Leone: AHP Approach
Hydrology 2024, 11(10), 158; https://doi.org/10.3390/hydrology11100158 (registering DOI) - 25 Sep 2024
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This study presents a comprehensive flood vulnerability assessment for Freetown, Sierra Leone, spanning the period from 2001 to 2022. The objective of this research was to assess the temporal and spatial changes in the flood vulnerability using Geographic Information System (GIS) tools and
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This study presents a comprehensive flood vulnerability assessment for Freetown, Sierra Leone, spanning the period from 2001 to 2022. The objective of this research was to assess the temporal and spatial changes in the flood vulnerability using Geographic Information System (GIS) tools and AHP-based Multi-Criteria Decision-Making (MCDM) analysis. This study identified the flood-vulnerable zones (FVZs) by integrating critical factors such as the rainfall, NDVI, elevation, slope, drainage density, TWI, distance to road, distance to river, and LULC. The analysis reveals that approximately 60% of the study area is classified as having medium to high vulnerability, with a significant 20% increase in the flood risk observed over the past two decades. In 2001, very-high-vulnerability zones covered about 68.84 km2 (10% of the total area), with high-vulnerability areas encompassing 137.68 km2 (20%). By 2020, very-high-vulnerability zones remained constant at 68.84 km2 (10%), while high-vulnerability areas decreased to 103.26 km2 (15%), and medium-vulnerability zones expanded from 206.51 km2 (30%) in 2001 to 240.93 km2 (35%). The AHP model-derived weights reflect the varied significance of the flood-inducing factors, with rainfall (0.27) being the most critical and elevation (0.04) being the least. A consistency ratio (CR) of 0.068 (< 0.1) confirms the reliability of these weights. The spatial–temporal analysis highlights the east and southeast regions of Freetown as consistently vulnerable over the years, while infrastructure improvements in other areas have contributed to a general decrease in very-high-vulnerability zones. This research highlights the urgent need for resilient urban planning and targeted interventions to mitigate future flood impacts, offering clear insights into the natural and human-induced drivers of the flood risk for effective hazard mitigation and sustainable urban development.
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Open AccessArticle
Effects of Climate Change and Changes in Land Use and Cover on Water Yield in an Equatorial Andean Basin
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Darío Xavier Zhiña, Alex Avilés, Lorena González, Ana Astudillo, José Astudillo and Carlos Matovelle
Hydrology 2024, 11(9), 157; https://doi.org/10.3390/hydrology11090157 - 23 Sep 2024
Abstract
Ecosystem services contribute significantly to human development, with water production being a crucial component. Climate and land use changes can impact water availability within a basin. In this context, researching water-related areas is essential for formulating policies to protect and manage hydrological services.
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Ecosystem services contribute significantly to human development, with water production being a crucial component. Climate and land use changes can impact water availability within a basin. In this context, researching water-related areas is essential for formulating policies to protect and manage hydrological services. The objective of this study was to estimate water yield in the sub-basins of the Tabacay and Aguilán rivers under climate change scenarios in 2030, 2040, and 2050, combined with scenarios of changes in land cover and land use. The InVEST model was employed to analyze water yield. The results show that crop areas were identified as the lowest water yield in future scenarios, and forested areas, particularly the region where the Cubilán Protected Forest is located, contribute the most to water yield in the subbasin. Besides, water yield has increased in the historic period (2016–2018) due to the conservation and reforestation initiatives carried out by the Municipal Public Service Company for Drinking Water, Sewerage, and Environmental Sanitation of the city of Azogues in 2018, the so-called Reciprocal Agreements for Water. Additionally, an increase in water yield is projected for future scenarios. This study can serve as a basis for decision-makers to identify areas that should prioritize protection and conservation.
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(This article belongs to the Special Issue Hydrological Modelling for the Sustainable Management of Water Resources in River Basins)
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Enhancing Drought Resilience through Groundwater Engineering by Utilizing GIS and Remote Sensing in Southern Lebanon
by
Nasser Farhat
Hydrology 2024, 11(9), 156; https://doi.org/10.3390/hydrology11090156 - 21 Sep 2024
Abstract
Countries face challenges of excess, scarcity, pollution, and uneven water distribution. This study highlights the benefits of advances in groundwater engineering that improve the understanding of utilizing local geological characteristics due to their crucial role in resisting drought in southern Lebanon. The type
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Countries face challenges of excess, scarcity, pollution, and uneven water distribution. This study highlights the benefits of advances in groundwater engineering that improve the understanding of utilizing local geological characteristics due to their crucial role in resisting drought in southern Lebanon. The type of drought in the region was determined using the Standardized Precipitation Index (SPI), Standardized Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Soil Moisture Anomaly Index (SM). The dry aquifer and its characteristics were analyzed using mathematical equations and established hydrogeological principles, including Darcy’s law. Additionally, a morphometric assessment of the Litani River was performed to evaluate its suitability for artificial recharge, where the optimal placement of the water barrier and recharge tunnels was determined using Spearman’s rank correlation coefficient. This analysis involved excluding certain parameters based on the Shapiro–Wilk test for normality. Accordingly, using the Geographic Information System (GIS), we modeled and simulated the potential water table. The results showed the importance and validity of linking groundwater engineering and morphometric characteristics in combating the drought of groundwater layers. The Eocene layer showed a clearer trend for the possibility of being artificially recharged from the Litani River than any other layer. The results showed that the proposed method can enhance artificial recharge, raise the groundwater level to four levels, and transform it into a large, saturated thickness. On the other hand, it was noted that the groundwater levels near the surface will cover most of the area of the studied region and could potentially store more than one billion cubic meters of water, mitigating the effects of climate change for decades.
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(This article belongs to the Section Surface Waters and Groundwaters)
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RNN-Based Monthly Inflow Prediction for Dez Dam in Iran Considering the Effect of Wavelet Pre-Processing and Uncertainty Analysis
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Arash Adib, Mohammad Pourghasemzadeh and Morteza Lotfirad
Hydrology 2024, 11(9), 155; https://doi.org/10.3390/hydrology11090155 - 19 Sep 2024
Abstract
In recent years, deep learning (DL) methods, such as recurrent neural networks (RNN). have been used for streamflow prediction. In this study, the monthly inflow into the Dez Dam reservoir from 1955 to 2018 in southwestern Iran was simulated using various types of
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In recent years, deep learning (DL) methods, such as recurrent neural networks (RNN). have been used for streamflow prediction. In this study, the monthly inflow into the Dez Dam reservoir from 1955 to 2018 in southwestern Iran was simulated using various types of RNNs, including long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), and stacked long short-term memory (Stacked LSTM). It was observed that considering flow discharge, temperature, and precipitation as inputs to the models yields the best results. Additionally, wavelet transform was employed to enhance the accuracy of the RNNs. Among the RNNs, the GRU model exhibited the best performance in simulating monthly streamflow without using wavelet transform, with RMSE, MAE, NSE, and R2 values of 0.061 m3/s, 0.038 m3/s, 0.556, and 0.642, respectively. Moreover, in the case of using wavelet transform, the Bi-LSTM model with db5 mother wavelet and decomposition level 5 was able to simulate the monthly streamflow with high accuracy, yielding RMSE, MAE, NSE, and R2 values of 0.014 m3/s, 0.008 m3/s, 0.9983, and 0.9981, respectively. Uncertainty analysis was conducted for the two mentioned superior models. To quantify the uncertainty, the concept of the 95 percent prediction uncertainty (95PPU) and the p-factor and r-factor criteria were utilized. For the GRU, the p-factor and r-factor values were 82% and 1.28, respectively. For the Bi-LSTM model, the p-factor and r-factor values were 94% and 1.06, respectively. The obtained p-factor and r-factor values for both models are within the acceptable and reliable range.
Full article
(This article belongs to the Special Issue Big Data and Machine Learning in Hydrology: Recent Advances and Trends)
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Open AccessArticle
Estimating Non-Stationary Extreme-Value Probability Distribution Shifts and Their Parameters Under Climate Change Using L-Moments and L-Moment Ratio Diagrams: A Case Study of Hydrologic Drought in the Goat River Near Creston, British Columbia
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Isaac Dekker, Kristian L. Dubrawski, Pearce Jones and Ryan MacDonald
Hydrology 2024, 11(9), 154; https://doi.org/10.3390/hydrology11090154 - 14 Sep 2024
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Here, we investigate the use of rolling-windowed L-moments (RWLMs) and L-moment ratio diagrams (LMRDs) combined with a Multiple Linear Regression (MLR) machine learning algorithm to model non-stationary low-flow hydrological extremes with the potential to simultaneously understand time-variant shape, scale, location, and probability distribution
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Here, we investigate the use of rolling-windowed L-moments (RWLMs) and L-moment ratio diagrams (LMRDs) combined with a Multiple Linear Regression (MLR) machine learning algorithm to model non-stationary low-flow hydrological extremes with the potential to simultaneously understand time-variant shape, scale, location, and probability distribution (PD) shifts under climate change. By employing LMRDs, we analyse changes in PDs and their parameters over time, identifying key environmental predictors such as lagged precipitation for September 5-day low-flows. Our findings indicate a significant relationship between total August precipitation L-moment ratios (LMRs) and September 5-day low-flow LMRs ( -Precipitation and -Discharge: R2 = 0.675, p-values < 0.001; -Precipitation and -Discharge: R2 = 0.925, p-value for slope < 0.001, intercept not significant with p = 0.451, assuming = 0.05 and a 31-year RWLM), which we later refine and use for prediction within our MLR algorithm. The methodology, applied to the Goat River near Creston, British Columbia, aids in understanding the implications of climate change on water resources, particularly for the yaqan nuʔkiy First Nation. We find that future low-flows under climate change will be outside the Natural Range of Variability (NROV) simulated from historical records (assuming a constant PD). This study provides insights that may help in adaptive water management strategies necessary to help preserve Indigenous cultural rights and practices and to help sustain fish and fish habitat into the future.
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(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)
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A Machine Learning Approach to Map the Vulnerability of Groundwater Resources to Agricultural Contamination
by
Victor Gómez-Escalonilla and Pedro Martínez-Santos
Hydrology 2024, 11(9), 153; https://doi.org/10.3390/hydrology11090153 - 13 Sep 2024
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Groundwater contamination poses a major challenge to water supplies around the world. Assessing groundwater vulnerability is crucial to protecting human livelihoods and the environment. This research explores a machine learning-based variation of the classic DRASTIC method to map groundwater vulnerability. Our approach is
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Groundwater contamination poses a major challenge to water supplies around the world. Assessing groundwater vulnerability is crucial to protecting human livelihoods and the environment. This research explores a machine learning-based variation of the classic DRASTIC method to map groundwater vulnerability. Our approach is based on the application of a large number of tree-based machine learning algorithms to optimize DRASTIC’s parameter weights. This contributes to overcoming two major issues that are frequently encountered in the literature. First, we provide an evidence-based alternative to DRASTIC’s aprioristic approach, which relies on static ratings and coefficients. Second, the use of machine learning approaches to compute DRASTIC vulnerability maps takes into account the spatial distribution of groundwater contaminants, which is expected to improve the spatial outcomes. Despite offering moderate results in terms of machine learning metrics, the machine learning approach was more accurate in this case than a traditional DRASTIC application if appraised as per the actual distribution of nitrate data. The method based on supervised classification algorithms was able to produce a mapping in which about 45% of the points with high nitrate concentrations were located in areas predicted as high vulnerability, compared to 6% shown by the original DRASTIC method. The main difference between using one method or the other thus lies in the availability of sufficient nitrate data to train the models. It is concluded that artificial intelligence can lead to more robust results if enough data are available.
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(This article belongs to the Section Surface Waters and Groundwaters)
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Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network
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Leon S. Besseling, Anouk Bomers and Suzanne J. M. H. Hulscher
Hydrology 2024, 11(9), 152; https://doi.org/10.3390/hydrology11090152 - 12 Sep 2024
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Hydrodynamic models are often used to obtain insights into potential dike breaches, because dike breaches can have severe consequences. However, their high computational cost makes them unsuitable for real-time flood forecasting. Machine learning models are a promising alternative, as they offer reasonable accuracy
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Hydrodynamic models are often used to obtain insights into potential dike breaches, because dike breaches can have severe consequences. However, their high computational cost makes them unsuitable for real-time flood forecasting. Machine learning models are a promising alternative, as they offer reasonable accuracy at a significant reduction in computation time. In this study, we explore the effectiveness of a Long Short-Term Memory (LSTM) neural network in fast flood modelling for a dike breach in the Netherlands, using training data from a 1D–2D hydrodynamic model. The LSTM uses the outflow hydrograph of the dike breach as input and produces water depths on all grid cells in the hinterland for all time steps as output. The results show that the LSTM accurately reflects the behaviour of overland flow: from fast rising and high water depths near the breach to slowly rising and lower water depths further away. The water depth prediction is very accurate (MAE = 0.045 m, RMSE = 0.13 m), and the inundation extent closely matches that of the hydrodynamic model throughout the flood event (Critical Success Index = 94%). We conclude that machine learning techniques are suitable for fast modelling of the complex dynamics of dike breach floods.
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Open AccessArticle
Machine Learning Model for River Discharge Forecast: A Case Study of the Ottawa River in Canada
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M. Almetwally Ahmed and S. Samuel Li
Hydrology 2024, 11(9), 151; https://doi.org/10.3390/hydrology11090151 - 12 Sep 2024
Abstract
River discharge is an essential input to hydrosystem projects. This paper aimed to modify the group method of data handling (GMDH) to create a new artificial intelligent forecast model (abbreviated as MGMDH) for predicting discharges at river cross-sections (CSs). The basic idea was
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River discharge is an essential input to hydrosystem projects. This paper aimed to modify the group method of data handling (GMDH) to create a new artificial intelligent forecast model (abbreviated as MGMDH) for predicting discharges at river cross-sections (CSs). The basic idea was to optimise the weights for selected hydrometric and meteorological predictors. One novelty of this study was that MGMDH could take the discharge observed from a neighbouring CS as a predictor when observations from the CS of interest had ceased. Another novelty was that MGMDH could include meteorological parameters as extra predictors. The model was validated using data from natural rivers. For given lead times, MGMDH automatically determined the best forecast equations, consistent with physical river hydraulics laws. This automation minimised computing time while improving accuracy. The model gave reliable forecasts, with a coefficient of determination greater than 0.978. For lead times close to the advection time from upstream to the CS of interest, the forecast had the highest reliability. MGMDH results compared well with some other machine learning models, like neural networks and the adaptive structure of the group method of data handling. It has potential applications for efficiently forecasting discharge and offers a tool to support flood management.
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(This article belongs to the Section Water Resources and Risk Management)
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Modeling the Impact of Urban and Industrial Pollution on the Quality of Surface Water in Intermittent Rivers in a Semi-Arid Mediterranean Climate
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Abdelillah Bouriqi, Naaila Ouazzani and Jean-François Deliege
Hydrology 2024, 11(9), 150; https://doi.org/10.3390/hydrology11090150 - 11 Sep 2024
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Ensuring the protection of the aquatic environment and addressing the water scarcity and degradation of water quality in the Mediterranean region pose significant challenges. This study specifically aims to assess the impact of urban and industrial pollution on the ZAT River water quality.
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Ensuring the protection of the aquatic environment and addressing the water scarcity and degradation of water quality in the Mediterranean region pose significant challenges. This study specifically aims to assess the impact of urban and industrial pollution on the ZAT River water quality. The study exploits a combination of field measurements and mathematical simulations using the PEGASE model. The objective is to evaluate how water quality changes throughout the different seasons and to determine whether olive oil factories discharge industrial wastewater into the river. The study reveals that the river water quality remains relatively stable along its course, up to km 64 in winter and km 71.77 in summer, where poor water quality is recorded. This degradation can be attributed to multiple factors. One of these factors is the discharge of industrial wastewater, which accounts for 47% of the COD pollution load. This industrial wastewater is released into the river without treatment during the production period (January–February) and inactivity period (March–May). The combined impact of urban and industrial wastewater is also associated with the decrease in water flow resulting from water withdrawals due to irrigation canals and groundwater recharge, which both contribute to the observed changes in river water quality. Importantly, field measurements combined with results obtained from the calibrated model provide compelling evidence of unauthorized wastewater discharges from the olive oil factories into the river. These results emphasize the need for stricter regulation, such as developing water quality monitoring strategies based on the use of modeling methodologies. They also emphasize the importance of improving wastewater management practices, such as setting up treatment plants for different sources of pollution or developing a co-treatment plant to mitigate the adverse impact of industrial pollution on river water quality.
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Open AccessArticle
Turbine-Based Generation in Greenhouse Irrigation Systems
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Ángel M. Rodríguez-Pérez, Antonio García-Chica, Julio J. Caparros-Mancera and César A. Rodríguez
Hydrology 2024, 11(9), 149; https://doi.org/10.3390/hydrology11090149 - 11 Sep 2024
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This study addresses the need for sustainable and energy-efficient agricultural practices by integrating turbine systems into greenhouse irrigation setups that utilize water from storage basins or ponds. The purpose is to harness excess pressure to generate electricity, enhancing overall system efficiency. This study
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This study addresses the need for sustainable and energy-efficient agricultural practices by integrating turbine systems into greenhouse irrigation setups that utilize water from storage basins or ponds. The purpose is to harness excess pressure to generate electricity, enhancing overall system efficiency. This study involves designing a scalable turbine system that adapts to different greenhouse sizes and water pressure conditions. Key methods include a novel 3D design and implementation of a turbine outlet, using CAD modeling and high-precision 3D printing, and the experimental characterization of the system’s power–pressure relationship and pressure losses. Results demonstrate that a single Banki-type turbine generates nearly 12 W at a maximum pressure of 1.4 bar, 0.98 m3/h of flow, pressure 92% loss performance, and 32% efficiency. Scalability tests in the study case reveal that up to eight turbines can be installed in series without dropping below the critical pressure threshold, that is, above 0.6–0.7 bar, the minimum pressure expected for adequate irrigation, and the turbines collectively produce around 60 W, considering the pressure losses with respect to production. These findings confirm the system’s potential to enhance sustainability and energy efficiency in greenhouse operations. This study lays a foundation for future research to optimize 3D-printed components, integrate renewable energy sources, and conduct long-term performance studies, aiming to further improve the system’s applicability and performance in agricultural settings.
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Open AccessArticle
The Implementation of Multimodal Large Language Models for Hydrological Applications: A Comparative Study of GPT-4 Vision, Gemini, LLaVa, and Multimodal-GPT
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Likith Anoop Kadiyala, Omer Mermer, Dinesh Jackson Samuel, Yusuf Sermet and Ibrahim Demir
Hydrology 2024, 11(9), 148; https://doi.org/10.3390/hydrology11090148 - 11 Sep 2024
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Large Language Models (LLMs) combined with visual foundation models have demonstrated significant advancements, achieving intelligence levels comparable to human capabilities. This study analyzes the latest Multimodal LLMs (MLLMs), including Multimodal-GPT, GPT-4 Vision, Gemini, and LLaVa, with a focus on hydrological applications such as
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Large Language Models (LLMs) combined with visual foundation models have demonstrated significant advancements, achieving intelligence levels comparable to human capabilities. This study analyzes the latest Multimodal LLMs (MLLMs), including Multimodal-GPT, GPT-4 Vision, Gemini, and LLaVa, with a focus on hydrological applications such as flood management, water level monitoring, agricultural water discharge, and water pollution management. We evaluated these MLLMs on hydrology-specific tasks, testing their response generation and real-time suitability in complex real-world scenarios. Prompts were designed to enhance the models’ visual inference capabilities and contextual comprehension from images. Our findings reveal that GPT-4 Vision demonstrated exceptional proficiency in interpreting visual data, providing accurate assessments of flood severity and water quality. Additionally, MLLMs showed potential in various hydrological applications, including drought prediction, streamflow forecasting, groundwater management, and wetland conservation. These models can optimize water resource management by predicting rainfall, evaporation rates, and soil moisture levels, thereby promoting sustainable agricultural practices. This research provides valuable insights into the potential applications of advanced AI models in addressing complex hydrological challenges and improving real-time decision-making in water resource management
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Open AccessArticle
Adaptive Operating Rules for Flood Control of a Multi-Purpose Reservoir
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Radu Drobot, Aurelian Florentin Draghia, Cristian Dinu, Nicolai Sîrbu, Viorel Chendeș and Petrișor Mazilu
Hydrology 2024, 11(9), 147; https://doi.org/10.3390/hydrology11090147 - 10 Sep 2024
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Almost all multipurpose reservoirs in Romania were put into operation 30–50 years ago or even earlier. Meanwhile, a large volume of hydrologic data has been collected, and the initial design flood should be reconsidered. Consequently, the operating rules of flow control structures (bottom
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Almost all multipurpose reservoirs in Romania were put into operation 30–50 years ago or even earlier. Meanwhile, a large volume of hydrologic data has been collected, and the initial design flood should be reconsidered. Consequently, the operating rules of flow control structures (bottom gates and weir gates) should be re-examined, mainly for medium and low-frequency floods. The design flood is not unique, being characterized by different shapes and time to peak, which has consequences for flood mitigation rules. Identifying the critical design flood is an important preliminary step, although it is usually neglected in flood management. Simulating the operation of the Stânca–Costești reservoir on the Prut River, it was found that the design flood corresponding to the maximum value of the compactness coefficient is the most difficult to mitigate considering the specific conditions of the dam and the reservoir: the prescribed conservation level in the reservoir, and the design flood volume of medium and rare floods that far exceeds the flood control volume. These conditions can jeopardize both dam safety and downstream flood protection. The main steps of the proposed approach are as follows: (1) developing the hydraulic model; (2) statistical processing of the registered floods and defining critical design floods for different AEPs (Annual Exceedance Probabilities); (3) deriving optimal operation rules based on a simulation-optimization model; (4) implementing real-time adaptive operation of the mechanical outlets; and (5) critically assessing the operating rules after the event. Based on the hydrological forecast, if necessary, new outlets are put into operation while keeping the ones already activated. Based on the hydrological forecast and properly operated, the safety of the Stânca–Costești dam is guaranteed even in the event of a 0.1% CC (Climate Change) flood. However, for floods greater than 1% magnitude, the carrying capacity of the downstream riverbed is exceeded. The main gaps addressed in this paper are the following: (1) the establishment of critical design floods, and (2) the adaptive operating rules of outlet devices aimed at optimizing flood control results, using short-term flood forecasts.
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Open AccessArticle
Field Study and Numerical Modeling to Assess the Impact of On-Site Septic Systems on Groundwater Quality of Jeju Island, South Korea
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Mijin Kim, Eun-Hee Koh and Jinkeun Kim
Hydrology 2024, 11(9), 146; https://doi.org/10.3390/hydrology11090146 - 10 Sep 2024
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Septic-derived nitrogen (N) sources have harmful effects on water resources, humans, and ecosystems in several countries. On Jeju Island, South Korea, the rapid increase in personal sewage treatment facilities (PSTFs, also known as on-site septic systems) raises concerns regarding the deterioration of groundwater
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Septic-derived nitrogen (N) sources have harmful effects on water resources, humans, and ecosystems in several countries. On Jeju Island, South Korea, the rapid increase in personal sewage treatment facilities (PSTFs, also known as on-site septic systems) raises concerns regarding the deterioration of groundwater quality, as groundwater is the sole water resource on the island. Therefore, this study employed a field study and numerical modeling to assess the impact of PSTF effluents on groundwater quality in the Jocheon area of northeastern Jeju. Water quality analysis revealed that the total nitrogen (T-N) concentrations in the effluent exceeded the effluent standards (75–92% PSTFs). The numerical model simulated the transport of N species, showing limited NH4+ and NO2− plume migration near the surface due to nitrification and adsorption. However, NO3− concentrations increased and stabilized over time, leaching on the water table with higher levels in lowland areas and clustered PSTFs. The predictive model estimated a 79% reduction in NO3− leaching when the effluents followed standards, indicating the necessity of effective PSTF management. This study highlights the importance of managing improperly operated septic systems to mitigate groundwater contamination based on an understanding of the behavior of N species in subsurface hydrologic systems.
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Open AccessArticle
Two-Way Coupling of the National Water Model (NWM) and Semi-Implicit Cross-Scale Hydroscience Integrated System Model (SCHISM) for Enhanced Coastal Discharge Predictions
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Hongyuan Zhang, Dongliang Shen, Shaowu Bao and Pietrafesa Len
Hydrology 2024, 11(9), 145; https://doi.org/10.3390/hydrology11090145 - 10 Sep 2024
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This study addresses the limitations of and the common challenges faced by one-dimensional river-routing methods in hydrological models, including the National Water Model (NWM), in accurately representing coastal regions. We developed a two-way coupling between the NWM and the Semi-implicit Cross-scale Hydroscience Integrated
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This study addresses the limitations of and the common challenges faced by one-dimensional river-routing methods in hydrological models, including the National Water Model (NWM), in accurately representing coastal regions. We developed a two-way coupling between the NWM and the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM). The approach demonstrated improvements in modeling coastal river dynamics, particularly during extreme events like Hurricane Matthew. The coupled model successfully captured tidal influences, storm surge effects, and complex river–river interactions that the standalone NWM missed. The approach revealed more accurate representations of peak discharge timing and magnitude as well as water storage and release in coastal floodplains. However, we also identified challenges in reconciling variable representations between hydrological and hydraulic models. This work not only enhances the understanding of coastal–riverine interactions but also provides valuable insights for the development of next-generation hydrological models. The improved modeling capabilities have implications for flood forecasting, coastal management, and climate change adaptation in vulnerable coastal areas.
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(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)
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Development of Statistical Downscaling Model Based on Volterra Series Realization, Principal Components, Climate Classification, and Ridge Regression
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Pooja Singh, Asaad Y. Shamseldin, Bruce W. Melville and Liam Wotherspoon
Hydrology 2024, 11(9), 144; https://doi.org/10.3390/hydrology11090144 - 10 Sep 2024
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This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model
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This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin.
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Open AccessArticle
Does Applying Subsampling in Quantile Mapping Affect the Climate Change Signal?
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Philipp Reiter and Markus C. Casper
Hydrology 2024, 11(9), 143; https://doi.org/10.3390/hydrology11090143 - 9 Sep 2024
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Bias in regional climate model (RCM) data makes bias correction (BC) a necessary pre-processing step in climate change impact studies. Among a variety of different BC methods, quantile mapping (QM) is a popular and powerful BC method. Studies have shown that QM may
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Bias in regional climate model (RCM) data makes bias correction (BC) a necessary pre-processing step in climate change impact studies. Among a variety of different BC methods, quantile mapping (QM) is a popular and powerful BC method. Studies have shown that QM may be vulnerable to reductions in calibration sample size. The question is whether this also affects the climate change signal (CCS) of the RCM data. We applied four different QM methods without subsampling and with three different subsampling timescales to an ensemble of seven climate projections. BC generally improved the RCM data relative to observations. However, the CCS was significantly modified by the BC for certain combinations of QM method and subsampling timescale. In conclusion, QM improves the RCM data that are fundamental for climate change impact studies, but the optimal subsampling timescale strongly depends on the chosen QM method.
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Open AccessArticle
Hydro Geochemical Characteristics and Mineralization Process of Groundwater in the Phosphatic Basin of Gafsa, Southwestern Tunisia
by
Nada Nasri, Fouad Souissi, Takoua Ben Attia, Amina Ismailia, Olfa Smida, Dhouha Tangour, Eduardo Alberto López Maldonado and Radhia Souissi
Hydrology 2024, 11(9), 142; https://doi.org/10.3390/hydrology11090142 - 6 Sep 2024
Abstract
The present study examines the water quality in the Quaternary Mio-Plio-Quaternary aquifer of the mining basin of Gafsa using a hydrochemical approach and multivariate statistical methods, to assess groundwater mineralization processes. Results from the analysis of groundwater quality collected during the winter (January
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The present study examines the water quality in the Quaternary Mio-Plio-Quaternary aquifer of the mining basin of Gafsa using a hydrochemical approach and multivariate statistical methods, to assess groundwater mineralization processes. Results from the analysis of groundwater quality collected during the winter (January 2020) and summer (June 2021) seasons reveal a pronounced stability in geochemical parameters, emphasizing a noteworthy consistency in water composition between the two seasons, with the dominance of the Na-Ca-Mg-SO4-Cl facies, in addition to the fact that all year round these concentrations are beyond their respective WHO limits. Despite the intensive extractive and transformation phosphate industry, the prolonged interaction of water with geological formations is the primary factor controlling their high mineralization. This results from the dissolution of carbonates (calcite, dolomite), gypsum, and halite. The results of the PCA represent two correlation classes. Class 1 comprises major elements sulfate, chloride, sodium, magnesium, and calcium strongly correlated with electrical conductivity (EC) and total dissolved solids (TDS). This correlation is indicative of the water mineralization process. Class 2 includes major elements nitrate and potassium weakly correlated with (TDS) and (EC) As regards heavy metals, their concentrations fall consistently below their respective potability standards established by the WHO across all water sampling points. Meanwhile, fluoride (F-) concentrations exhibited values ranging from (1.6 mg·L−1 to 2.9 mg·L−1) in the winter of January 2020 and (1 to 2.9 mg·L−1) in the summer of June 2021, surpassing its WHO limit (1.5 mg·L−1) in almost all water samples. These findings allow us to conclude that the high mineralization of these waters is acquired due to the dissolution of carbonates (calcite, dolomite), gypsum, and halite due to their prolonged interaction with the geological formations. The deterioration of groundwater quality in the Gafsa mining basin associated with phosphate extraction and processing activities appears to be primarily due to the intensive exploitation of deep-water resources.
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(This article belongs to the Special Issue Novel Approaches in Contaminant Hydrology and Groundwater Remediation)
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Transport and Deposition of Microplastics at the Water–Sediment Interface: A Case Study of the White River near Muncie, Indiana
by
Blessing Yaw Adjornor, Bangshuai Han, Elsayed M. Zahran, John Pichtel and Rebecca Wood
Hydrology 2024, 11(9), 141; https://doi.org/10.3390/hydrology11090141 - 6 Sep 2024
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Microplastics, plastic particles smaller than 5 mm, pose a significant environmental threat due to their persistence and distribution in aquatic ecosystems. Research on the dynamics of microplastics within freshwater systems, particularly concerning their transport and deposition along river corridors, remains insufficient. This study
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Microplastics, plastic particles smaller than 5 mm, pose a significant environmental threat due to their persistence and distribution in aquatic ecosystems. Research on the dynamics of microplastics within freshwater systems, particularly concerning their transport and deposition along river corridors, remains insufficient. This study investigated the occurrence and deposition of microplastics at the water–sediment interface of the White River near Muncie, Indiana. Sediment samples were collected from three sites: White River Woods (upstream), Westside Park (midstream), and Morrow’s Meadow (downstream). The microplastic concentrations varied significantly, with the highest concentration recorded upstream, indicating a strong influence from agricultural runoff. The types of microplastics identified were predominantly fragments (43.1%), fibers (29.6%), and films (27.3%), with fragments being consistently the most abundant at all sampling sites. A polymer analysis with selected particles using Fourier-transform infrared (FTIR) spectroscopy revealed that the most common polymers were polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET). The hydrodynamic conditions played a crucial role in the deposition and transport of microplastics. The statistical analysis demonstrated a strong positive correlation between the microplastic concentration and flow velocity at the downstream site, suggesting that lower flow velocities contribute to the accumulation of finer sediments and microplastics. Conversely, the upstream and midstream sites exhibited weaker correlations, indicating that other environmental and anthropogenic factors, such as land use and the sediment texture, may influence microplastic retention and transport. This study provides valuable insights into the complex interactions between river dynamics, sediment characteristics, and microplastic deposition in freshwater systems. These findings contribute to the growing body of knowledge on freshwater microplastic pollution and can help guide mitigation strategies aimed at reducing microplastic contamination in riverine ecosystems.
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Assessments of Heavy Metal Contaminants in the Drenica River and Bioremediation by Typha angustifolia
by
Osman Fetoshi, Romina Koto, Fatbardh Sallaku, Hazir Çadraku, Smajl Rizani, Pajtim Bytyçi, Demokrat Nuha, Bojan Đurin, Berat Durmishi, Veton Haziri, Fidan Feka, Shkendije Sefa Haziri, Upaka Rathnayake and Dragana Dogančić
Hydrology 2024, 11(9), 140; https://doi.org/10.3390/hydrology11090140 - 5 Sep 2024
Abstract
The concentrations of cadmium, copper, lead, zinc, nickel, and chromium in samples of sediment, water, and Typha angustifolia plants in the stream of the Drenica River were determined to assess the level of pollution. According to sediment analysis results from seven locations, the
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The concentrations of cadmium, copper, lead, zinc, nickel, and chromium in samples of sediment, water, and Typha angustifolia plants in the stream of the Drenica River were determined to assess the level of pollution. According to sediment analysis results from seven locations, the concentrations of Cu, Ni, Zn, and Cr exceeded the permitted limits according to WHO standards from 1996. In the plant samples, the concentrations of Cd and Pb were above the allowed limits according to GD161 and ECE standards, and according the WHO standard, the water quality in the Drenica River is classified into the first, second, and third quality categories. The results of this study show the bioaccumulation coefficient in Typha angustifolia plants, and it was found that the most bioaccumulated of the metals is Cd, with a bioaccumulation coefficient (BAF) greater than 1. The pollution load index (PLI), enrichment factor (EF index), Geoaccumulation index (Igeo), potential ecological risk factor (Eif), and potential ecological risk index (RI) were used in combination to assess the degree of pollution and the environmental risk presented to the freshwater ecosystem of the Drenica River. The results show that the Drenica River is mainly polluted by Ni, Cu, and Cr, reflecting substantial impacts of anthropogenic activities, including sizeable industrial effects, the development of urbanism, agricultural activities, and the deposition of waste from a ferronickel factory in the area.
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(This article belongs to the Section Surface Waters and Groundwaters)
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Bibliometric Analysis of River Erosion Control Measures: Examination of Practices and Barriers in Colombia
by
Nelson Javier Cely Calixto, Alberto Galvis Castaño and Jefferson E. Contreras-Ropero
Hydrology 2024, 11(9), 139; https://doi.org/10.3390/hydrology11090139 - 4 Sep 2024
Abstract
This study presents a comprehensive bibliometric analysis of research on bank erosion and control measures, utilizing the Scopus database and VOSviewer software. Key terms such as “bank”, “erosion”, “control”, and “protection” frequently appear in the literature, underscoring their importance in studies on riverbank
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This study presents a comprehensive bibliometric analysis of research on bank erosion and control measures, utilizing the Scopus database and VOSviewer software. Key terms such as “bank”, “erosion”, “control”, and “protection” frequently appear in the literature, underscoring their importance in studies on riverbank erosion. Since 2000, scientific production has steadily increased, particularly in disciplines such as Environmental Sciences and Earth and Planetary Sciences, driven by growing concerns about climate change and sustainable water resource management. Countries with substantial research resources, such as the United States and China, lead in the production of studies, reflecting their commitment to addressing this global issue. In parallel, the evaluation of erosion mitigation practices in Colombia revealed that, although effective techniques such as gabion walls and riparian vegetation exist, 40% of respondents do not implement specific measures. This lack of implementation is attributed to insufficient knowledge, limited resources, and misconceptions about the effectiveness of these techniques. The findings highlight the need to promote proven practices and enhance professional training. Future research should focus on developing more accurate predictive models, integrating interdisciplinary approaches, and assessing the impacts of climate change on bank erosion. Addressing barriers to applying effective techniques at the local level and improving access to resources and knowledge are critical steps to reducing bank erosion and ensuring sustainable water management.
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(This article belongs to the Section Water Resources and Risk Management)
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