Hydrology

21 pages, 11467 KiB

Open AccessArticle

Effects of Climate Change and Changes in Land Use and Cover on Water Yield in an Equatorial Andean Basin

by 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

Viewed by 1067

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. [...] Read more.

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. Full article

(This article belongs to the Special Issue Hydrological Modelling for the Sustainable Management of Water Resources in River Basins)

► Show Figures

Figure 1

47 pages, 19713 KiB

Open AccessArticle

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

Viewed by 1262

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 [...] Read more.

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. Full article

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

► Show Figures

Figure 1

19 pages, 2692 KiB

Open AccessArticle

RNN-Based Monthly Inflow Prediction for Dez Dam in Iran Considering the Effect of Wavelet Pre-Processing and Uncertainty Analysis

by Arash Adib, Mohammad Pourghasemzadeh and Morteza Lotfirad

Hydrology 2024, 11(9), 155; https://doi.org/10.3390/hydrology11090155 - 19 Sep 2024

Viewed by 679

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 [...] Read more.

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)

► Show Figures

Figure 1

22 pages, 3626 KiB

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

by Isaac Dekker, Kristian L. Dubrawski, Pearce Jones and Ryan MacDonald

Hydrology 2024, 11(9), 154; https://doi.org/10.3390/hydrology11090154 - 14 Sep 2024

Viewed by 1074

Abstract

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 [...] Read more.

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 (

τ_{2}

-Precipitation and

τ_{2}

-Discharge: R² = 0.675, p-values < 0.001;

τ_{3}

-Precipitation and

τ_{3}

-Discharge: R² = 0.925, p-value for slope < 0.001, intercept not significant with p = 0.451, assuming

α

= 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. Full article

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

► Show Figures

Figure 1

17 pages, 27193 KiB

Open AccessArticle

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

Viewed by 899

Abstract

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 [...] Read more.

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. Full article

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

► Show Figures

Figure 1

19 pages, 11199 KiB

Open AccessArticle

Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network

by 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

Viewed by 1215

Abstract

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 [...] Read more.

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. Full article

► Show Figures

Figure 1

20 pages, 10622 KiB

Open AccessArticle

Machine Learning Model for River Discharge Forecast: A Case Study of the Ottawa River in Canada

by M. Almetwally Ahmed and S. Samuel Li

Hydrology 2024, 11(9), 151; https://doi.org/10.3390/hydrology11090151 - 12 Sep 2024

Viewed by 779

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 [...] Read more.

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. Full article

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

► Show Figures

Figure 1

28 pages, 4703 KiB

Open AccessArticle

Modeling the Impact of Urban and Industrial Pollution on the Quality of Surface Water in Intermittent Rivers in a Semi-Arid Mediterranean Climate

by Abdelillah Bouriqi, Naaila Ouazzani and Jean-François Deliege

Hydrology 2024, 11(9), 150; https://doi.org/10.3390/hydrology11090150 - 11 Sep 2024

Viewed by 989

Abstract

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. [...] Read more.

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. Full article

► Show Figures

Figure 1

14 pages, 7755 KiB

Open AccessArticle

Turbine-Based Generation in Greenhouse Irrigation Systems

by 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

Cited by 1 | Viewed by 863

Abstract

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 [...] Read more.

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. Full article

► Show Figures

Figure 1

20 pages, 3519 KiB

Open AccessArticle

The Implementation of Multimodal Large Language Models for Hydrological Applications: A Comparative Study of GPT-4 Vision, Gemini, LLaVa, and Multimodal-GPT

by 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

Cited by 1 | Viewed by 1871

Abstract

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 [...] Read more.

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 Full article

► Show Figures

Figure 1

41 pages, 12541 KiB

Open AccessArticle

Adaptive Operating Rules for Flood Control of a Multi-Purpose Reservoir

by 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

Viewed by 1071

Abstract

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 [...] Read more.

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. Full article

► Show Figures

Figure 1

18 pages, 6180 KiB

Open AccessArticle

Field Study and Numerical Modeling to Assess the Impact of On-Site Septic Systems on Groundwater Quality of Jeju Island, South Korea

by Mijin Kim, Eun-Hee Koh and Jinkeun Kim

Hydrology 2024, 11(9), 146; https://doi.org/10.3390/hydrology11090146 - 10 Sep 2024

Viewed by 748

Abstract

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 [...] Read more.

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. Full article

► Show Figures

Figure 1

16 pages, 18130 KiB

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

by Hongyuan Zhang, Dongliang Shen, Shaowu Bao and Pietrafesa Len

Hydrology 2024, 11(9), 145; https://doi.org/10.3390/hydrology11090145 - 10 Sep 2024

Viewed by 905

Abstract

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 [...] Read more.

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. Full article

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

► Show Figures

Figure 1

22 pages, 1890 KiB

Open AccessArticle

Development of Statistical Downscaling Model Based on Volterra Series Realization, Principal Components, Climate Classification, and Ridge Regression

by 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

Viewed by 709

Abstract

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 [...] Read more.

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. Full article

► Show Figures

Figure 1

11 pages, 286 KiB

Open AccessArticle

Does Applying Subsampling in Quantile Mapping Affect the Climate Change Signal?

by Philipp Reiter and Markus C. Casper

Hydrology 2024, 11(9), 143; https://doi.org/10.3390/hydrology11090143 - 9 Sep 2024

Viewed by 658

Abstract

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 [...] Read more.

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. Full article

► Show Figures

Figure 1

18 pages, 3266 KiB

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

Cited by 1 | Viewed by 958

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Novel Approaches in Contaminant Hydrology and Groundwater Remediation)

► Show Figures

Graphical abstract

17 pages, 3791 KiB

Open AccessArticle

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

Viewed by 756

Abstract

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 [...] Read more.

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. Full article

► Show Figures

Figure 1

17 pages, 1812 KiB

Open AccessArticle

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

Viewed by 933

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 [...] Read more.

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. Full article

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

► Show Figures

Figure 1

30 pages, 3843 KiB

Open AccessReview

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

Viewed by 1179

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 [...] Read more.

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. Full article

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

► Show Figures

Figure 1

22 pages, 9280 KiB

Open AccessArticle

Concentrations of F⁻, Na⁺, and K⁺ in Groundwater before and after an Earthquake: A Case Study on Tenerife Island, Spain

by Eduardo de Miguel-García and José Francisco Gómez-González

Hydrology 2024, 11(9), 138; https://doi.org/10.3390/hydrology11090138 - 3 Sep 2024

Viewed by 700

Abstract

Freshwater, vital for life and ecosystems, accounts for only 2.5% of Earth’s water, and is primarily located in polar caps, underground reservoirs, and surface water. Its quality varies due to environmental interactions, especially in groundwater. Tenerife, located in the Canary Islands, Spain, relies [...] Read more.

Freshwater, vital for life and ecosystems, accounts for only 2.5% of Earth’s water, and is primarily located in polar caps, underground reservoirs, and surface water. Its quality varies due to environmental interactions, especially in groundwater. Tenerife, located in the Canary Islands, Spain, relies mainly on underground aquifers and tunnels capturing 51.6 cubic hectometers annually. Ensuring safe drinking water is a global challenge due to health risks from poor water quality, including diseases and cancer. Fluoride, sodium, and potassium are essential for health, and are mainly derived from groundwater as fluoride ions (F⁻) and sodium and potassium cations (Na⁺, K⁺). However, excessive F⁻, Na⁺, and K⁺ in drinking water is harmful. The World Health Organization limits F⁻ to 1.5 mg/L, Na⁺ to 8.70 meq/L, and K⁺ to 0.31 meq/L. Geological, climatic, and human factors control the presence and transport of F⁻, Na⁺, and K⁺ in groundwater. Seismic events can impact water quality, with long-term effects linked to aquifer structure and transient effects from gas and fluid expansion during earthquakes. This study was motivated by a 3.8 mbLg earthquake in Tenerife in 2012, which allowed its impact on groundwater quality, specifically F⁻, Na⁺, and K⁻ concentrations, to be examined. Post-earthquake, F⁻ levels alarmingly increased to 8.367 meq/L, while Na⁺ and K⁺ showed no significant changes. This research quantifies the influence of earthquakes on increasing F⁻ levels and evaluates F⁻ reduction during low seismic activity, emphasizing the importance of water management on volcanic islands. Full article

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

► Show Figures

Figure 1

21 pages, 2114 KiB

Open AccessArticle

Livelihood Vulnerability from Drought among Smallholder Livestock Farmers in South Africa

by Yonas T. Bahta and Stephen Aniseth Nyaki

Hydrology 2024, 11(9), 137; https://doi.org/10.3390/hydrology11090137 - 1 Sep 2024

Viewed by 682

Abstract

The impacts of drought and climate change on agriculture have become increasingly apparent, and affect smallholder livestock farmers. Farmers’ livelihoods rely on agriculture; thus, they are vulnerable to the primary and secondary impacts of climate change. In South Africa, policies for increasing the [...] Read more.

The impacts of drought and climate change on agriculture have become increasingly apparent, and affect smallholder livestock farmers. Farmers’ livelihoods rely on agriculture; thus, they are vulnerable to the primary and secondary impacts of climate change. In South Africa, policies for increasing the resilience of smallholder livestock farmers who have suffered from agricultural drought have not paid sufficient attention to the level of livelihood vulnerability. This study assessed the level of livelihood vulnerability of smallholder livestock farmers in the drought-stricken Frances Baard District Municipality in the Northern Cape Province of South Africa. The livelihood vulnerability of 217 randomly selected farmers from the municipality were determined using the Livelihood Vulnerability Index (LVI) and Livelihood Vulnerability Index of the Intergovernmental Panel on Climate Change (LVI-IPCC), which includes seven components and 34 subcomponents addressing livelihood. A high level of livelihood vulnerability, with an LVI score of 0.436, was determined and attributed to high-risk livelihood strategies, food, social networks, health, water, sociodemographics, natural disasters, and climate change. The LVI-IPCC of 0.04 also showed moderate vulnerability due to high exposure, high sensitivity, and low adaptive capacity, especially for the Phokwane, Dikgatlong, and Magareng districts in the Frances Baard municipality. Given continued drought recurrences, it is crucial for the government and other stakeholders to implement strategic and targeted sustainable interventions. The resilience of smallholder livestock farmers should be enhanced by increasing their adaptive capacity through diversified livelihood options while decreasing exposure and sensitivity to agricultural drought risks. Full article

► Show Figures

Figure 1

23 pages, 2574 KiB

Open AccessArticle

Geo-Referenced Databases and SWOT Analysis for Assessing Flood Protection Structures, Measures, and Works at a River Basin Scale

by Eleni Tzanou and Charalampos Skoulikaris

Hydrology 2024, 11(9), 136; https://doi.org/10.3390/hydrology11090136 - 27 Aug 2024

Viewed by 1211

Abstract

This research aims to evaluate the operational effectiveness of current flood protection infrastructure and measures in a flood-prone area using geo-referenced information systems and SWOT analysis. To achieve this, all existing flood protection measures and works in the case study basin, namely Strymonas [...] Read more.

This research aims to evaluate the operational effectiveness of current flood protection infrastructure and measures in a flood-prone area using geo-referenced information systems and SWOT analysis. To achieve this, all existing flood protection measures and works in the case study basin, namely Strymonas River basin in Greece, were mapped and recorded. These data, along with water-related spatial information, were stored in a geo-referenced database created within an open-source GIS environment. Additionally, the system was populated with the basin’s recorded historic floods, derived from the European Union’s Floods Directive implementation process. The outputs of the research, which include a spatial comparison of flood protection measures and works with flood event occurrences as well as analyses of the figures, density, and locations of flood protection works, were evaluated as an integrated system and further processed using SWOT analysis. The latter was informed by questionnaire results, and the identified strengths and weaknesses of the flood protection infrastructure were used to explore potential opportunities and threats, which could respectively reinforce or jeopardize the basin’s capacity to effectively respond to future floods. The research framework can be applied to any river basin and could provide important assets in flood protection planning at a basin scale. Full article

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

► Show Figures

Figure 1

20 pages, 7624 KiB

Open AccessArticle

An Analysis of the Spatiotemporal Variability of Key Water Quality Parameters in China

by Kexin Li, Qichun Yang and Xia Li

Hydrology 2024, 11(9), 135; https://doi.org/10.3390/hydrology11090135 - 26 Aug 2024

Viewed by 850

Abstract

Intensifying anthropogenic disturbances have caused water pollution in China in recent decades. China has a vast territory with diverse climate conditions, land use types, and human activities, leading to significant water quality variability. However, few studies have investigated nationwide spatiotemporal patterns of key [...] Read more.

Intensifying anthropogenic disturbances have caused water pollution in China in recent decades. China has a vast territory with diverse climate conditions, land use types, and human activities, leading to significant water quality variability. However, few studies have investigated nationwide spatiotemporal patterns of key water quality parameters. In this study, we analyze monthly water quality observations from 3647 gauge stations to understand how water quality changes over time and space in China. We group the stations by water resource regions and adopt Python and SPSS to analyze the spatiotemporal variability and intercorrelations of eight water quality parameters. Results indicate that the concentrations of biochemical oxygen demand of 5 days (BOD5), chemical oxygen demand (COD), dissolved oxygen (DO), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) show similar spatial patterns, with higher concentrations in the northern parts than the southern regions of China. The concentrations of COD and TP are higher in the rainy season than in the dry season, while DO, NH3-N, and TN show the opposite seasonal patterns. Strong positive correlations were found between BOD and COD, NH3-N and TP. The annual cumulative distribution figures demonstrate that all parameters showed slightly lower concentrations in 2022 and 2023 than in 2021, except for DO and TN. The TN/TP ratios across different water resource regions in China are significantly higher than 16, indicating that phosphorus is the limiting factor of eutrophication. This investigation provides a comprehensive understanding of the spatiotemporal variability of water quality parameters across China. The results of this study are highly valuable for investigating mechanisms regulating water quality across large spatial scales, thus providing valuable implications for improving water quality and mitigating water pollution. Full article

(This article belongs to the Special Issue Novel Approaches in Contaminant Hydrology and Groundwater Remediation)

► Show Figures

Figure 1

14 pages, 18123 KiB

Open AccessArticle

A Monte Carlo Model for WWTP Effluent Flow Treatment through Enhanced Willow Evapotranspiration

by Aristoteles Tegos

Hydrology 2024, 11(9), 134; https://doi.org/10.3390/hydrology11090134 - 26 Aug 2024

Viewed by 1440

Abstract

The effectiveness of using enhanced evapotranspiration rates of willow plantation is a modern environmentally friendly practice for advanced treatment of effluent WWTP flow. The key idea is that through advanced willow evapotranspiration rates, a significant proportion of the effluent flow can be transferred [...] Read more.

The effectiveness of using enhanced evapotranspiration rates of willow plantation is a modern environmentally friendly practice for advanced treatment of effluent WWTP flow. The key idea is that through advanced willow evapotranspiration rates, a significant proportion of the effluent flow can be transferred into the atmosphere through the physical process of evapotranspiration. This study further discusses the concept in a real-world problem using a wide dataset consisting of a recent PET monthly remote dataset namely RASPOTION, monthly recorded rainfall gauge, and experimental willow evapotranspiration surveys across Ireland, to identify the monthly cropping pattern. A Monte Carlo water balance model has been developed for the period 2003–2016. The model was applied in an existing willow plantation at Donard WWTP co. Wicklow, Ireland to identify the exceedance probability of willow plantation runoff against estimated low flows (i.e., Q₉₅, Q₉₉) at the adjacent small tributary. In this case study, any failure which can lead to river quality deterioration was not assessed. The overall framework aims to provide new insights considering the multiple sources of uncertainty (i.e., monthly willow cropping pattern and WWTP effluent flow) in associated environmental engineering problems. Full article

(This article belongs to the Special Issue Forest Hydrometeorology)

► Show Figures

Figure 1

21 pages, 11032 KiB

Open AccessArticle

Evaporation from Porous Rock: Deciphering the Importance of Measuring the Evaporation Front Depth

by Martin Slavík and Martin Lanzendörfer

Hydrology 2024, 11(9), 133; https://doi.org/10.3390/hydrology11090133 - 23 Aug 2024

Viewed by 711

Abstract

The study is concerned with the rate of evaporation from porous rock, including the second stage of evaporation characterised by the existence of a dry surface layer separated from the wet capillary zone by a sharp evaporation front. The main objective is to [...] Read more.

The study is concerned with the rate of evaporation from porous rock, including the second stage of evaporation characterised by the existence of a dry surface layer separated from the wet capillary zone by a sharp evaporation front. The main objective is to investigate the relationship between the depth of evaporation front and the rate of evaporation as the drying process progresses, and to compare measured evaporation rate with the corresponding calculated values. Sandstone core samples saturated with water were allowed to dry naturally under room conditions, while the changes in the evaporation rate and the depth of evaporation front, among other quantities, were measured. We demonstrate that the evaporation rate can be very accurately determined from the depth of the evaporation front and the ambient air temperature and relative humidity using Fick’s law for water-vapor diffusion. During the second stage of evaporation, the diffusion flux through the dry surface layer is computed using the water-vapor diffusion coefficient of the rock, determined from a separate wet cup experiment. In order to cover the first stage of evaporation, an additional parameter characterising the diffusion layer of air above the surface is required, either determined by the best fit to the measured evaporation rates, or adopted from previous studies. The calculated evaporation rate was in good agreement with measurements, with Pearson correlation coefficient 0.98 and relative error of the calculations averaging 15% over the evaporation front depths ranging from 0 to 29 mm. A workflow for determining the evaporation rate from sandstone outcrops is suggested, along with possible applications in sandstone weathering research. Full article

► Show Figures

Figure 1

30 pages, 6101 KiB

Open AccessArticle

Exploring the Added Value of Sub-Daily Bias Correction of High-Resolution Gridded Rainfall Datasets for Rainfall Erosivity Estimation

by Roland Yonaba, Lawani Adjadi Mounirou, Amadou Keïta, Tazen Fowé, Cheick Oumar Zouré, Axel Belemtougri, Moussa Bruno Kafando, Mahamadou Koïta, Harouna Karambiri and Hamma Yacouba

Hydrology 2024, 11(9), 132; https://doi.org/10.3390/hydrology11090132 - 23 Aug 2024

Viewed by 1092

Abstract

This study evaluates the impact of sub-daily bias correction of gridded rainfall products (RPs) on the estimation rainfall erosivity in Burkina Faso (West African Sahel). Selected RPs, offering half-hourly to hourly rainfall, are assessed against 10 synoptic stations over the period 2001–2020 to [...] Read more.

This study evaluates the impact of sub-daily bias correction of gridded rainfall products (RPs) on the estimation rainfall erosivity in Burkina Faso (West African Sahel). Selected RPs, offering half-hourly to hourly rainfall, are assessed against 10 synoptic stations over the period 2001–2020 to appraise their accuracy. The optimal product (the integrated multi-satellite retrievals for GPM, IMERG) is further used as a reference for bias correction, to adjust the rainfall distribution in the remaining RPs. RPs-derived rainfall erosivity is compared to the global rainfall erosivity database (GloREDa) estimates. The findings indicate that bias correction improves the rainfall accuracy estimation for all RPs, in terms of quantitative, categorial metrics and spatial patterns. It also improved the distributions of rainfall event intensities and duration across all products, which further significantly improved the annual rainfall erosivity estimates at various timescales along with spatial patterns across the country, as compared to raw RPs. The study also highlights that bias correction is effective at aligning annual trends in rainfall with those in rainfall erosivity derived from RPs. The study therefore underscores the added value of bias correction as a practice for improving the rainfall representation in high-resolution RPs before long-term rainfall erosivity assessment, particularly in data-scarce regions vulnerable to land degradation. Full article

► Show Figures

Figure 1

17 pages, 5499 KiB

Open AccessArticle

An Experimental Investigation of Tsunami Bore Impact on Coastal Structures

by Kutsi S. Erduran, Yahya E. Akansu, Uğur Ünal and Olusola O. Adekoya

Hydrology 2024, 11(9), 131; https://doi.org/10.3390/hydrology11090131 - 23 Aug 2024

Viewed by 919

Abstract

This experimental study focused on the measurement and analysis of the impact force caused by a tsunami bore on a coastal structure. The bore wave was produced by a dam break mechanism. The water depth in the reservoir and the location of the [...] Read more.

This experimental study focused on the measurement and analysis of the impact force caused by a tsunami bore on a coastal structure. The bore wave was produced by a dam break mechanism. The water depth in the reservoir and the location of the coastal structures were varied to simulate different impact scenarios. The time history of the force resulting from the impact of the bore wave on the coastal structure was measured. The propagation of the bore wave along the flume was recorded and the video recordings were converted into digital data using an image-processing technique in order to determine the flow depth variations with time. The hydrostatic forces and the corresponding depth and time-averaged hydrodynamic forces as well as the maximum hydrodynamic forces were acquired for each scenario. The ratio of hydrodynamic to hydrostatic forces were obtained, and it was observed that the calculated averaged ratio was within the recommended design ratio. The results indicate that an increase in the reservoir level caused an increase in the magnitude and intensity of the impact forces, however, the relationship was non-linear. Moreover, it was found that the location of the structure did not play a significant role on the intensity of the impact forces. Full article

(This article belongs to the Special Issue Climate Change Effects on Coastal Management)

► Show Figures

Figure 1

18 pages, 3713 KiB

Open AccessArticle

Water Level Temporal Variability of Lake Mégantic during the Period 1920–2020 and Its Impacts on the Frequency of Heavy Flooding of the Chaudière River (Quebec, Canada)

by Samuel Goulet, Ali Arkamose Assani and Alexandre Roy

Hydrology 2024, 11(9), 130; https://doi.org/10.3390/hydrology11090130 - 23 Aug 2024

Viewed by 707

Abstract

The objective of this study is to analyze the temporal variability in water levels of Lake Mégantic (27.4 km²) during the period 1920–2020 in relation to anthropogenic and natural factors on the one hand, and its impact on the intensity and [...] Read more.

The objective of this study is to analyze the temporal variability in water levels of Lake Mégantic (27.4 km²) during the period 1920–2020 in relation to anthropogenic and natural factors on the one hand, and its impact on the intensity and frequency of heavy flooding (recurring floods ≥ 10 years) of the Chaudière River of which it is the source, on the other hand. The application of four different Mann–Kendall tests showed a significant decrease in lake water levels during this period. The Lombard test revealed two breaks in the average daily maximum and average water levels, but only one break in the average daily minimum water levels. The first shift, which was smoothed, occurred between 1957 and 1963. It was caused by the demolition in 1956 of the first dam built in 1893 and the significant storage of water in the dams built upstream of the lake between 1956 and 1975. The second shift, which was rather abrupt, occurred between 1990 and 1993. It was caused by the voluntary and controlled lowering of the lake’s water levels in 1993 to increase the surface area of the beaches for recreational purposes. However, despite this influence of anthropogenic factors on this drop in water levels, they are negatively correlated with the global warming climate index. It is therefore a covariation, due to anthropogenic factors whose impacts are exerted at different spatial scales, without a physical causal link. However, the winter daily minimum water levels, whose temporal variability has not been influenced by anthropogenic activities, are positively correlated with the NAO and AO indices, but negatively with PDO. Finally, since the transformation of Lake Mégantic into a reservoir following the construction of the Mégantic dam in 1893 and 1973 to control heavy flooding in the Chaudière River, all recurrent floods ≥ 10 years have completely disappeared in the section of this river located downstream of Lake Mégantic. However, the disappearance of these floods and the drop in water levels of Lake Mégantic have not significantly impacted the stationarity in the flow series of the Chaudière River since 1920. Full article

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Hydrology, Volume 11, Issue 9 (September 2024) – 28 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI