Water

Open AccessFeature PaperEditor’s ChoiceReview

A Review of the Application of the Soil and Water Assessment Tool (SWAT) in Karst Watersheds

by

Ibrahim Al Khoury

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Laurie Boithias

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David Labat

Water 2023, 15(5), 954; https://doi.org/10.3390/w15050954 - 01 Mar 2023

Cited by 1 | Viewed by 2684

Abstract

Karst water resources represent a primary source of freshwater supply, accounting for nearly 25% of the global population water needs. Karst aquifers have complex recharge characteristics, storage patterns, and flow dynamics. They also face a looming stress of depletion and quality degradation due [...] Read more.

Karst water resources represent a primary source of freshwater supply, accounting for nearly 25% of the global population water needs. Karst aquifers have complex recharge characteristics, storage patterns, and flow dynamics. They also face a looming stress of depletion and quality degradation due to natural and anthropogenic pressures. This prompted hydrogeologists to apply innovative numerical approaches to better understand the functioning of karst watersheds and support karst water resources management. The Soil and Water Assessment Tool (SWAT) is a semi-distributed hydrological model that has been used to simulate flow and water pollutant transport, among other applications, in basins including karst watersheds. Its source code has also been modified by adding distinctive karst features and subsurface hydrology models to more accurately represent the karst aquifer discharge components. This review summarizes and discusses the findings of 75 SWAT-based studies in watersheds that are at least partially characterized by karst geology, with a primary focus on the hydrological assessment in modified SWAT models. Different karst processes were successfully implemented in SWAT, including the recharge in the epikarst, flows of the conduit and matrix systems, interbasin groundwater flow, and allogenic recharge from sinkholes and sinking streams. Nonetheless, additional improvements to the existing SWAT codes are still needed to better reproduce the heterogeneity and non-linearity of karst flow and storage mechanisms in future research. Full article

(This article belongs to the Special Issue Hydrogeology and Geochemistry of Karst Aquifers)

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Open AccessFeature PaperEditor’s ChoiceArticle

Investigation of Hillslope Vineyard Soil Water Dynamics Using Field Measurements and Numerical Modeling

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Jasmina Defterdarović

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Water 2023, 15(4), 820; https://doi.org/10.3390/w15040820 - 20 Feb 2023

Cited by 2 | Viewed by 1067

Abstract

Soil heterogeneities can impact hillslope hydropedological processes (e.g., portioning between infiltration and runoff), creating a need for in-depth knowledge of processes governing water dynamics and redistribution. The presented study was conducted at the SUPREHILL Critical Zone Observatory (CZO) (hillslope vineyard) in 2021. A [...] Read more.

Soil heterogeneities can impact hillslope hydropedological processes (e.g., portioning between infiltration and runoff), creating a need for in-depth knowledge of processes governing water dynamics and redistribution. The presented study was conducted at the SUPREHILL Critical Zone Observatory (CZO) (hillslope vineyard) in 2021. A combination of field investigation (soil sampling and monitoring campaign) and numerical modeling with hydrological simulator HYDRUS-1D was used to explore the water dynamics in conjunction with data from a sensor network (soil water content (SWC) and soil-water potential (SWP) sensors), along the hillslope (hilltop, backslope, and footslope). Soil hydraulic properties (SHP) were estimated based on (i) pedotransfer functions (PTFs), (ii) undisturbed soil cores, and (iii) sensor network data, and tested in HYDRUS. Additionally, a model ensemble mean from HYDRUS simulations was calculated with PTFs. The highest agreement of simulated with observed SWC for 40 cm soil depth was found with the combination of laboratory and field data, with the lowest average MAE, RMSE and MAPE (0.02, 0.02, and 5.34%, respectively), and highest average R² (0.93), while at 80 cm soil depth, PTF model ensemble performed better (MAE = 0.03, RMSE = 0.03, MAPE = 7.55%, R² = 0.81) than other datasets. Field observations indicated that heterogeneity and spatial variability regarding soil parameters were present at the site. Over the hillslope, SWC acted in a heterogeneous manner, which was most pronounced during soil rewetting. Model results suggested that the incorporation of field data expands model performance and that the PTF model ensemble is a feasible option in the absence of laboratory data. Full article

(This article belongs to the Special Issue Identification and Quantification of Water Flow and Solute Transport Processes in Agricultural, Natural and Artificial Hillslopes)

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Open AccessFeature PaperEditor’s ChoiceArticle

The Influence of Circular Economy and 4IR Technologies on the Climate–Water–Energy–Food Nexus and the SDGs

by

Mohamed Sameer Hoosain

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Babu Sena Paul

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Wesley Doorsamy

and

Seeram Ramakrishna

Water 2023, 15(4), 787; https://doi.org/10.3390/w15040787 - 17 Feb 2023

Cited by 1 | Viewed by 1672

Abstract

The United Nations Member States created a common roadmap for sustainability and development in 2015. The UN-SDGs are included in the 2030 Plan as an immediate call to action from all nations in the form of global partnerships. To date, a handful of [...] Read more.

The United Nations Member States created a common roadmap for sustainability and development in 2015. The UN-SDGs are included in the 2030 Plan as an immediate call to action from all nations in the form of global partnerships. To date, a handful of countries have achieved substantial progress toward the targets. The climate–water–energy–food nexus is being advocated as a conceptual method for achieving sustainable development. According to research, frameworks for adopting nexus thinking have not been the best solution to clearly or sufficiently include thoughts on sustainability. Therefore, there is much room for other solutions; these are in the form of newer Fourth Industrial Revolution digital technologies, as well as transitioning from a linear economy to a circular economy. In this paper, we come to understand these two models and their linkages between climate, water, energy, and food; their application and challenges, and, finally, the effects on the UN-SDGs. It was found that both circular economy and newer Fourth Industrial Revolution digital technologies can positively support the nexus as well as directly address the UN-SDGs, specifically SDGs 7, 8, 9, 11, 12, and 13. Full article

(This article belongs to the Special Issue Understanding Circular Economy Complexities and the Climate–Water–Food–Energy Nexus in an SDG Framework: Interactions and Alternatives for Decision Making)

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Open AccessFeature PaperEditor’s ChoiceArticle

Effect of Sand Co-Presence on Cr^VI Removal in Fe⁰-H₂O System

by

Marius Gheju

and

Ionel Balcu

Water 2023, 15(4), 777; https://doi.org/10.3390/w15040777 - 16 Feb 2023

Cited by 3 | Viewed by 944

Abstract

The aim of the present study was to provide new knowledge regarding the effect of non-expansive inert material addition on anionic pollutant removal efficiency in Fe⁰-H₂O system. Non-disturbed batch experiments and continuous-flow-through column tests were conducted using Cr^VI [...] Read more.

The aim of the present study was to provide new knowledge regarding the effect of non-expansive inert material addition on anionic pollutant removal efficiency in Fe⁰-H₂O system. Non-disturbed batch experiments and continuous-flow-through column tests were conducted using Cr^VI as a redox–active contaminant in three different systems: “Fe⁰ + sand”, “Fe⁰ only” and ”sand only”. Both experimental procedures have the advantage that formation of (hydr)oxide layers on Fe⁰ is not altered, which makes them appropriate proxies for real Fe⁰-based filter technologies. Batch experiments carried out at pH 6.5 showed a slight improvement of Cr^VI removal in a 20% Fe⁰ system, compared to 50, 80 and 100% Fe⁰ systems. Column tests conducted at pH 6.5 supported results of batch experiments, revealing highest Cr^VI removal efficiencies for “Fe⁰ + sand” systems with lowest Fe⁰ ratio. However, the positive effect of sand co-presence decreases with increasing pH from 6.5 to 7.1. Scanning electron microscopy—energy dispersive angle X-ray spectrometry and X-ray diffraction spectroscopy employed for the characterization of Fe⁰ before and after experiments indicated that the higher the volumetric ratio of sand in “Fe⁰ + sand” system, the more intense the corrosion processes affecting the Fe⁰ grains. Results presented herein indicate the capacity of sand at sustaining the efficiency of Cr^VI removal in Fe⁰-H₂O system. The outcomes of the present study suggest that a volumetric ratio Fe⁰:sand = 1:3 could assure not only the long-term permeability of Fe⁰-based filters, but also enhanced removal efficiency of Cr^VI from contaminated water. Full article

(This article belongs to the Special Issue Sustainable Remediation Using Metallic Iron: Quo Vadis?)

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Open AccessFeature PaperEditor’s ChoiceArticle

Used Filter Cartridges as Potential Adsorbents of Organic Pollutants

by

Martyna Szymańska

and

Piotr Nowicki

Water 2023, 15(4), 714; https://doi.org/10.3390/w15040714 - 11 Feb 2023

Cited by 1 | Viewed by 1067

Abstract

The main objective of this study was to assess the usefulness of exhausted activated carbon-based filter cartridges for the removal of organic pollutants from aqueous solutions using the example of two model pollutants: synthetic dyes with different particle sizes, i.e., methylene blue (MB) [...] Read more.

The main objective of this study was to assess the usefulness of exhausted activated carbon-based filter cartridges for the removal of organic pollutants from aqueous solutions using the example of two model pollutants: synthetic dyes with different particle sizes, i.e., methylene blue (MB) and malachite green (MG). In order to determine the organic dyes’ adsorption mechanism, the effects of phase contact time, initial dye concentration, pH, and temperature of the system were investigated. Langmuir and Freundlich isotherm models were employed to analyze the experimental data. Additionally, all adsorbents were characterized in terms of the ash content, type of porous structure, presence of surface functional groups, pH value, and iodine adsorption number—which is one of the quality control parameters of activated carbons. Adsorption tests have shown that carbonaceous materials from bottle filters and filter jugs can be successfully used for the removal of organic dyes from the liquid phase. The maximum sorption capacity of this type of adsorbent towards methylene blue was 333.06 mg/g, while in the case of malachite green it was 308.75 mg/g. For all carbonaceous materials, a better fit to the experimental data was achieved with a Langmuir isotherm than a Freundlich one. It has also been shown that the efficiency of MB and MG adsorption from aqueous solutions decreases with increasing temperature of the system—the best results were obtained at 25 °C. A better fit of the kinetics data was achieved using the pseudo-second order model. Full article

(This article belongs to the Special Issue Sorption Procesess in Wastewater Treatment: Current State of Knowledge and Future Opportunities)

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Open AccessFeature PaperEditor’s ChoiceReview

Full-Scale Sewage Sludge Reduction Technologies: A Review with a Focus on Energy Consumption

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Water 2023, 15(4), 615; https://doi.org/10.3390/w15040615 - 04 Feb 2023

Cited by 1 | Viewed by 1747

Abstract

In recent years, increasing attention has been paid to the problem of sewage sludge management and the relevant energy consumption, which represent the main cost items in wastewater treatment plants. Therefore, implementation of technologies that can reduce sludge production and ensure a positive [...] Read more.

In recent years, increasing attention has been paid to the problem of sewage sludge management and the relevant energy consumption, which represent the main cost items in wastewater treatment plants. Therefore, implementation of technologies that can reduce sludge production and ensure a positive impact on the energy of the entire sewage treatment plant has gained considerable importance in the scientific and technical community. The objective of this study was thus to screen full-scale sludge reduction technologies integrated into both the water line and the sludge line of a municipal sewage treatment plant with a sustainable impact on the overall balance of the plant. The results showed that, within the water line, ultrasound in the recirculation line of the activated sludge allowed for greater reductions in sludge production than the Cannibal and UTN systems, despite the higher energy consumption. CAMBI^TM, BioThelys^TM, Exelys^TM and TurboTec^® enabled the greatest reductions in sludge production among the technologies integrated into the sludge line, and although they required a large amount of energy, this was partially offset by energy recovery in terms of additional biogas production. Full article

(This article belongs to the Special Issue Sewage Sludge: Treatment and Recovery)

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Open AccessFeature PaperEditor’s ChoiceArticle

Euryhalinity and Geographical Origin Aid Global Alien Crayfish Invasions

by

Aldona Dobrzycka-Krahel

and

Maria Leonor Fidalgo

Water 2023, 15(3), 569; https://doi.org/10.3390/w15030569 - 01 Feb 2023

Cited by 1 | Viewed by 1097

Abstract

Salinity tolerance is a determinant of a narrow or wide distribution range of organisms. Crayfishes are important key species in many aquatic environments so require a better understanding of their ability to live in different saline regimes. We identified all alien crayfish and [...] Read more.

Salinity tolerance is a determinant of a narrow or wide distribution range of organisms. Crayfishes are important key species in many aquatic environments so require a better understanding of their ability to live in different saline regimes. We identified all alien crayfish and examined their habitats (freshwater and/or saline) and origins to test whether these factors predict their dispersal. We used contingency tables populated with raw frequency data with χ2—tests and assessed statistical significance at α of 0.05. We identified 21 alien crayfishes and we found that alien crayfish species were disproportionately freshwater (71%), with significantly lower proportions of euryhaline crayfishes inhabiting freshwater to saline environments (29%). Alien crayfishes also significantly disproportionally originate from America (67% of these taxa) when compared to all ‘other’ grouped regions (33%). In total, 36% of American crayfishes represent euryhaline species inhabiting freshwater to saline habitats against only 14% of crayfishes from all “other” grouped regions. This suggests that binomial euryhalinity/origin can help understand the potential of spread. We discussed obtained results with known experimental data on salinity tolerance, osmoregulation, growth, and reproduction of American alien crayfish. The paper will help in the management of crayfish spread. Full article

(This article belongs to the Special Issue Aquatic Ecosystem: Problems and Benefits)

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Open AccessFeature PaperEditor’s ChoiceArticle

Contaminant Back Diffusion from Low-Conductivity Matrices: Case Studies of Remedial Strategies

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Water 2023, 15(3), 570; https://doi.org/10.3390/w15030570 - 01 Feb 2023

Cited by 1 | Viewed by 1397

Abstract

Recalcitrant groundwater contamination is a common problem at hazardous waste sites worldwide. Groundwater contamination persists despite decades of remediation efforts at many sites because contaminants sorbed or dissolved within low-conductivity zones can back diffuse into high-conductivity zones, and therefore act as a continuing [...] Read more.

Recalcitrant groundwater contamination is a common problem at hazardous waste sites worldwide. Groundwater contamination persists despite decades of remediation efforts at many sites because contaminants sorbed or dissolved within low-conductivity zones can back diffuse into high-conductivity zones, and therefore act as a continuing source of contamination to flowing groundwater. A review of the available literature on remediation of plume persistence due to back diffusion was conducted, and four sites were selected as case studies. Remediation at the sites included pump and treat, enhanced bioremediation, and thermal treatment. Our review highlights that a relatively small number of sites have been studied in sufficient detail to fully evaluate remediation of back diffusion; however, three general conclusions can be made based on the review. First, it is difficult to assess the significance of back diffusion without sufficient data to distinguish between multiple factors contributing to contaminant rebound and plume persistence. Second, high-resolution vertical samples are decidedly valuable for back diffusion assessment but are generally lacking in post-treatment assessments. Third, complete contaminant mass removal from back diffusion sources may not always be possible. Partial contaminant mass removal may nonetheless have potential benefits, similar to partial mass removal from primary DNAPL source zones. Full article

(This article belongs to the Special Issue Diffusion Processes in Water Pollution and Remediation)

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Open AccessFeature PaperEditor’s ChoiceArticle

Investigating Climate Change Effects on Evapotranspiration and Groundwater Recharge of the Nile Delta Aquifer, Egypt

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Mohamed Galal Eltarabily

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Water 2023, 15(3), 572; https://doi.org/10.3390/w15030572 - 01 Feb 2023

Cited by 1 | Viewed by 1690

Abstract

Climate change (CC) directly affects crops’ growth stages or level of maturity, solar radiation, humidity, temperature, and wind speed, and thus crop evapotranspiration (ET_c). Increased crop ET_c shifts the fraction of discharge from groundwater aquifers, while long-term shifts in [...] Read more.

Climate change (CC) directly affects crops’ growth stages or level of maturity, solar radiation, humidity, temperature, and wind speed, and thus crop evapotranspiration (ET_c). Increased crop ET_c shifts the fraction of discharge from groundwater aquifers, while long-term shifts in discharge can change the groundwater level and, subsequently, aquifer storage. The long-term effect of CC on the groundwater flow under different values of ET_c was assessed for the Nile Delta aquifer (NDA) in Egypt. To quantify such impacts, numerical modeling using MODFLOW was set up to simulate the groundwater flow and differences in groundwater levels in the long term in the years 2030, 2050, and 2070. The model was initially calibrated against the hydraulic conductivity of the aquifer layers of the groundwater levels in the year 2008 from 60 observation wells throughout the study area. Then, it was validated with the current groundwater levels using an independent set of data (23 points), obtaining a very good agreement between the calculated and observed heads. The results showed that the combination of solar radiation, vapor pressure deficit, and humidity (H) are the best variables for predicting ET_c in Nile Delta zones (north, middle, and south). ET_c among the whole Nile Delta will increase by 11.2, 15.0, and 19.0% for the years 2030, 2050, and 2070, respectively. Zone budget analysis revealed that the increase of ET_c will decrease the inflow and the groundwater head difference (GWHD). Recharge of the aquifer will be decreased by 19.74, 27.16, and 36.84% in 2030, 2050, and 2070, respectively. The GWHD will record 0.95 m, 1.05 m, and 1.40 m in 2030, 2050, and 2070, respectively when considering the increase of ET_c. This reduction will lead to a slight decline in the storage of the Nile Delta groundwater aquifer. Our findings support the decision of the designers and the policymakers to guarantee a long-term sustainable management plan of the groundwater for the NDA and deltas with similar climate conditions. Full article

(This article belongs to the Special Issue Assessment and Management of Hydrological Risks Due to Climate Change)

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Open AccessFeature PaperEditor’s ChoiceReview

A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling

by

Fazlul Karim

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Mohammed Ali Armin

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David Ahmedt-Aristizabal

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Lachlan Tychsen-Smith

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Lars Petersson

Water 2023, 15(3), 566; https://doi.org/10.3390/w15030566 - 01 Feb 2023

Cited by 2 | Viewed by 1626

Abstract

Machine learning (also called data-driven) methods have become popular in modeling flood inundations across river basins. Among data-driven methods, traditional machine learning (ML) approaches are widely used to model flood events, and recently deep learning (DL) approaches have gained more attention across the [...] Read more.

Machine learning (also called data-driven) methods have become popular in modeling flood inundations across river basins. Among data-driven methods, traditional machine learning (ML) approaches are widely used to model flood events, and recently deep learning (DL) approaches have gained more attention across the world. In this paper, we reviewed recently published literature on ML and DL applications for flood modeling for various hydrologic and catchment characteristics. Our extensive literature review shows that DL models produce better accuracy compared to traditional approaches. Unlike physically based models, ML/DL models suffer from the lack of using expert knowledge in modeling flood events. Apart from challenges in implementing a uniform modeling approach across river basins, the lack of benchmark data to evaluate model performance is a limiting factor for developing efficient ML/DL models for flood inundation modeling. Full article

(This article belongs to the Special Issue Advances in Flood Frequency and Inundation Modeling: Application of Statistical, Hydrodynamic, Remote Sensing, and Machine Learning Tools)

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Open AccessEditor’s ChoiceArticle

Data-Driven Community Flood Resilience Prediction

by

Moustafa Naiem Abdel-Mooty

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Wael El-Dakhakhni

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Paulin Coulibaly

Water 2022, 14(13), 2120; https://doi.org/10.3390/w14132120 - 02 Jul 2022

Cited by 2 | Viewed by 2295

Abstract

Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery [...] Read more.

Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towards developing comprehensive, proactive flood disaster management planning to further ensure functioning urban centers and mitigate the risk of future catastrophic flood events. In this framework, resilience indices are synthesized considering resilience goals (i.e., robustness and rapidity) using unsupervised ML, coupled with climate information, to develop a supervised ML prediction algorithm. To showcase the utility of the framework, it was applied on historical flood disaster records collected by the US National Weather Services. These disaster records were subsequently used to develop the resilience indices, which were then coupled with the associated historical climate data, resulting in high-accuracy predictions and, thus, utility in flood resilience management studies. To further demonstrate the utilization of the framework, a spatial analysis was developed to quantify communities’ flood resilience and vulnerability across the selected spatial domain. The framework presented in this study is employable in climate studies and patio-temporal vulnerability identification. Such a framework can also empower decision makers to develop effective data-driven climate resilience strategies. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Open AccessFeature PaperEditor’s ChoiceArticle

Sluice Gate Design and Calibration: Simplified Models to Distinguish Flow Conditions and Estimate Discharge Coefficient and Flow Rate

by

Arash Yoosefdoost

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William David Lubitz

Water 2022, 14(8), 1215; https://doi.org/10.3390/w14081215 - 10 Apr 2022

Cited by 3 | Viewed by 8201

Abstract

Sluice gates are common hydraulic structures for controlling and regulating flow in open channels. This study investigates five models’ performance in distinguishing conditions of flow regimes, estimating the discharge coefficient (

C_{d}

) and flow rate. Experiments were conducted for different gate [...] Read more.

Sluice gates are common hydraulic structures for controlling and regulating flow in open channels. This study investigates five models’ performance in distinguishing conditions of flow regimes, estimating the discharge coefficient (

C_{d}

) and flow rate. Experiments were conducted for different gate openings, flow rates, upstream and downstream conditions. New equation forms and methods are proposed to determine

C_{d}

for energy–momentum considering losses (EML) and HEC-RAS models. For distinguishing the flow regimes, results indicated a reasonable performance for energy–momentum (EM), EML, and Swamee’s models. For flow rate and discharge coefficient performance of EM, EML, and Henry’s models in free flow and for EM and EML in submerged flow were reasonable. The effects of physical scale on models were investigated. There were concerns about the generality and accuracy of Swamee’s model. Scaling effects were observed on loss factor k in EML. A new equation and method were proposed to calibrate k that improved the EML model’s accuracy. This study facilitates the application and analysis of the studied models for the design or calibration of sluice gates and where the flow in open channels needs to be controlled or measured using sluice gates such as irrigation channels or water delivery channels of small run-of-river hydropower plants. Full article

(This article belongs to the Special Issue Hydraulic Transient of Hydropower Station and Pump Station)

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Open AccessEditor’s ChoiceArticle

Data-Driven Flood Alert System (FAS) Using Extreme Gradient Boosting (XGBoost) to Forecast Flood Stages

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Water 2022, 14(5), 747; https://doi.org/10.3390/w14050747 - 26 Feb 2022

Cited by 9 | Viewed by 4271

Abstract

Heavy rainfall leads to severe flooding problems with catastrophic socio-economic impacts worldwide. Hydrologic forecasting models have been applied to provide alerts of extreme flood events and reduce damage, yet they are still subject to many uncertainties due to the complexity of hydrologic processes [...] Read more.

Heavy rainfall leads to severe flooding problems with catastrophic socio-economic impacts worldwide. Hydrologic forecasting models have been applied to provide alerts of extreme flood events and reduce damage, yet they are still subject to many uncertainties due to the complexity of hydrologic processes and errors in forecasted timing and intensity of the floods. This study demonstrates the efficacy of using eXtreme Gradient Boosting (XGBoost) as a state-of-the-art machine learning (ML) model to forecast gauge stage levels at a 5-min interval with various look-out time windows. A flood alert system (FAS) built upon the XGBoost models is evaluated by two historical flooding events for a flood-prone watershed in Houston, Texas. The predicted stage values from the FAS are compared with observed values with demonstrating good performance by statistical metrics (RMSE and KGE). This study further compares the performance from two scenarios with different input data settings of the FAS: (1) using the data from the gauges within the study area only and (2) including the data from additional gauges outside of the study area. The results suggest that models that use the gauge information within the study area only (Scenario 1) are sufficient and advantageous in terms of their accuracy in predicting the arrival times of the floods. One of the benefits of the FAS outlined in this study is that the XGBoost-based FAS can run in a continuous mode to automatically detect floods without requiring an external starting trigger to switch on as usually required by the conventional event-based FAS systems. This paper illustrates a data-driven FAS framework as a prototype that stakeholders can utilize solely based on their gauging information for local flood warning and mitigation practices. Full article

(This article belongs to the Special Issue Advances in Flood Forecasting and Hydrological Modeling)

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Open AccessEditor’s ChoiceArticle

Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture

by

Veerachamy Ramachandran

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Ramar Ramalakshmi

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Balasubramanian Prabhu Kavin

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Irshad Hussain

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Abdulrazak H. Almaliki

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Abdulrhman A. Almaliki

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Ashraf Y. Elnaggar

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Enas E. Hussein

Water 2022, 14(5), 719; https://doi.org/10.3390/w14050719 - 24 Feb 2022

Cited by 25 | Viewed by 4449

Abstract

The increase in population growth and demand is rapidly depleting natural resources. Irrigation plays a vital role in the productivity and growth of agriculture, consuming no less than 75% of fresh water utilization globally. Irrigation, being the largest consumer of water across the [...] Read more.

The increase in population growth and demand is rapidly depleting natural resources. Irrigation plays a vital role in the productivity and growth of agriculture, consuming no less than 75% of fresh water utilization globally. Irrigation, being the largest consumer of water across the globe, needs refinements in its process, and because it is implemented by individuals (farmers), the use of water for irrigation is not effective. To enhance irrigation management, farmers need to keep track of information such as soil type, climatic conditions, available water resources, soil pH, soil nutrients, and soil moisture to make decisions that resolve or prevent agricultural complexity. Irrigation, a data-driven technology, requires the integration of emerging technologies and modern methodologies to provide solutions to the complex problems faced by agriculture. The paper is an overview of IoT-enabled modern technologies through which irrigation management can be elevated. This paper presents the evolution of irrigation and IoT, factors to be considered for effective irrigation, the need for effective irrigation optimization, and how dynamic irrigation optimization would help reduce water use. The paper also discusses the different IoT architecture and deployment models, sensors, and controllers used in the agriculture field, available cloud platforms for IoT, prominent tools or software used for irrigation scheduling and water need prediction, and machine learning and neural network models for irrigation. Convergence of the tools, technologies and approaches helps in the development of better irrigation management applications. Access to real-time data, such as weather, plant and soil data, must be enhanced for the development of effective irrigation management applications. Full article

(This article belongs to the Special Issue Advanced Research on Sustainable Water Resources Management and Planning under Climate Change)

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Open AccessFeature PaperEditor’s ChoiceArticle

Assessing the Groundwater Reserves of the Udaipur District, Aravalli Range, India, Using Geospatial Techniques

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Water 2022, 14(4), 648; https://doi.org/10.3390/w14040648 - 19 Feb 2022

Cited by 10 | Viewed by 3805

Abstract

Population increase has placed ever-increasing demands on the available groundwater (GW) resources, particularly for intensive agricultural activities. In India, groundwater is the backbone of agriculture and drinking purposes. In the present study, an assessment of groundwater reserves was carried out in the Udaipur [...] Read more.

Population increase has placed ever-increasing demands on the available groundwater (GW) resources, particularly for intensive agricultural activities. In India, groundwater is the backbone of agriculture and drinking purposes. In the present study, an assessment of groundwater reserves was carried out in the Udaipur district, Aravalli range, India. It was observed that the principal aquifer for the availability of groundwater in the studied area is quartzite, phyllite, gneisses, schist, and dolomitic marble, which occur in unconfined to semi-confined zones. Furthermore, all primary chemical ingredients were found within the permissible limit, including granum. We also found that the average annual rainfall days in a year in the study area was 30 from 1957 to 2020, and it has been found that there are chances to receive surplus rainfall once in every five deficit rainfall years. Using integrated remote sensing, GIS, and a field-based spatial modeling approach, it was found that the dynamic GW reserves of the area are 637.42 mcm/annum, and the total groundwater draft is 639.67 mcm/annum. The deficit GW reserves are 2.25 mcm/annum from an average rainfall of 627 mm, hence the stage of groundwater development is 100.67% and categorized as over-exploited. However, as per the relationship between reserves and rainfall events, surplus reserves are available when rainfall exceeds 700 mm. We conclude that enough static GW reserves are available in the studied area to sustain the requirements of the drought period. For the long-term sustainability of groundwater use, controlling groundwater abstraction by optimizing its use, managing it properly through techniques such as sprinkler and drip irrigation, and achieving more crop-per-drop schemes, will go a long way to conserving this essential reserve, and create maximum groundwater recharge structures. Full article

(This article belongs to the Special Issue Water Resource Management through the Lens of Planetary Health Approach)

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Open AccessEditor’s ChoiceArticle

A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory

by

Junhao Wu

and

Zhaocai Wang

Water 2022, 14(4), 610; https://doi.org/10.3390/w14040610 - 17 Feb 2022

Cited by 33 | Viewed by 3082

Abstract

Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality conditions has a significant social and economic value. In this study, a model based [...] Read more.

Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality conditions has a significant social and economic value. In this study, a model based on an artificial neural network (ANN), discrete wavelet transform (DWT), and long short-term memory (LSTM) was constructed to predict the water quality of the Jinjiang River. Firstly, a multi-layer perceptron neural network was used to process the missing values based on the time series in the water quality dataset used in this research. Secondly, the Daubechies 5 (Db5) wavelet was used to divide the water quality data into low-frequency signals and high-frequency signals. Then, the signals were used as the input of LSTM, and LSTM was used for training, testing, and prediction. Finally, the prediction results were compared with the nonlinear auto regression (NAR) neural network model, the ANN-LSTM model, the ARIMA model, multi-layer perceptron neural networks, the LSTM model, and the CNN-LSTM model. The outcome indicated that the ANN-WT-LSTM model proposed in this study performed better than previous models in many evaluation indices. Therefore, the research methods of this study can provide technical support and practical reference for water quality monitoring and the management of the Jinjiang River and other basins. Full article

(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)

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Open AccessEditor’s ChoiceArticle

Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

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Mohammed Sarfaraz Gani Adnan

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Ashraf Dewan

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Rashedur M. Rahman

Water 2022, 14(4), 612; https://doi.org/10.3390/w14040612 - 17 Feb 2022

Cited by 10 | Viewed by 2886

Abstract

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood [...] Read more.

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere. Full article

(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)

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Open AccessEditor’s ChoiceArticle

Wavelet Analysis of Dam Injection and Discharge in Three Gorges Dam and Reservoir with Precipitation and River Discharge

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Water 2022, 14(4), 567; https://doi.org/10.3390/w14040567 - 13 Feb 2022

Cited by 46 | Viewed by 2187

Abstract

The Yangtze River has been the primary support of the resources and transportation of China. The Three Gorges Dam and Reservoir on the Yangtze River is one of the world’s largest dams. The influence caused by the dam and reservoir on the river [...] Read more.

The Yangtze River has been the primary support of the resources and transportation of China. The Three Gorges Dam and Reservoir on the Yangtze River is one of the world’s largest dams. The influence caused by the dam and reservoir on the river system has been overwhelming and destructive. For better water resource use and flood-prevention planning, more understanding is needed regarding the dam’s impact on river discharge, regional precipitation, and frequency of extreme rainfall events. This study aims to analyze the changes in river discharge and regional precipitation records before and after the construction of the Three Gorges Dam. This research examines temporal correlations among these data by collecting daily dam injection and dam discharge records, the precipitation from ground stations, and river discharge. The time series are analyzed with the wavelet analysis. The precipitation datasets decrease in wavelet magnitude after 1998 when the dam was built in the wavelet analysis. The annual cycle, shown as a bright year line through the time range, still exists in the analysis result after 1998, but the magnitude of the annual cycle has reduced. The river discharge shows a decrease of wavelet magnitude at the three downstream locations. The possible explanation of this pattern could be the human-controlled dam discharge. The constant water level maintained in the reservoir by human control would slow down the flow speed and stabilize it. Full article

(This article belongs to the Special Issue Research of the Relationship between Climate Change and Runoff in Watershed)

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Open AccessFeature PaperEditor’s ChoiceArticle

Surface Water Change Detection via Water Indices and Predictive Modeling Using Remote Sensing Imagery: A Case Study of Nuntasi-Tuzla Lake, Romania

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Cristina Șerban

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Carmen Maftei

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Gabriel Dobrică

Water 2022, 14(4), 556; https://doi.org/10.3390/w14040556 - 12 Feb 2022

Cited by 11 | Viewed by 2267

Abstract

Water body feature extraction using a remote sensing technique represents an important tool in the investigation of water resources and hydrological drought assessment. Nuntasi-Tuzla Lake, a component of the Danube Delta Natural Reserve, is located on the Romanian Black Sea littoral. On account [...] Read more.

Water body feature extraction using a remote sensing technique represents an important tool in the investigation of water resources and hydrological drought assessment. Nuntasi-Tuzla Lake, a component of the Danube Delta Natural Reserve, is located on the Romanian Black Sea littoral. On account of an event in summer 2020, when the lake surface water decreased significantly, this study aims to identify the variation of the Nuntasi-Tuzla Lake surface water over a long-term period in correlation with human intervention and climate change. To this end, it provides an analysis in the period 1965–2021 via hydrological drought indices and data mining classification. The latter approach is based on several water indices derived from Landsat TM/ETM+/OLI and MODIS full-time series datasets: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (NDVI), Modified NDWI (MNDWI), Weighted Normalized Difference Water Index (WNDWI), and Water Ratio Index (WRI). The experimental results indicate that the proposed classification methods can extract relevant features from waterbodies using remote sensing imagery with a high accuracy. Moreover, the study shows a similarity in the evolution of surface water cover identified with the data mining classification and the drought periods detected in the flow data series for the Nuntasi and Sacele Rivers that supply the Nuntasi-Tuzla Lake. Overall, the results of our investigation show that human intervention and hydrological drought had an extensive impact on the long-term changes in surface water of the Nuntasi-Tuzla Lake. Full article

(This article belongs to the Special Issue Assessing Hydrological Drought in a Climate Change: Methods and Measures)

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Open AccessFeature PaperEditor’s ChoiceArticle

A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

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Water 2022, 14(4), 514; https://doi.org/10.3390/w14040514 - 09 Feb 2022

Cited by 9 | Viewed by 2966

Abstract

Water distribution system monitoring is currently carried out using advanced real-time control technologies to achieve a higher operational efficiency. Data analysis techniques can be implemented for condition estimation, which are crucial tools for managing, developing, and operating water networks using the monitored flow [...] Read more.

Water distribution system monitoring is currently carried out using advanced real-time control technologies to achieve a higher operational efficiency. Data analysis techniques can be implemented for condition estimation, which are crucial tools for managing, developing, and operating water networks using the monitored flow rate and pressure data at some network pipes and nodes. This work proposes a state estimation methodology that enables one to infer the hydraulic state of the operating speed of pumping systems from these pressure and flow measurements. The presented approach suggests using graph convolutional neural network theory linked to hydraulic models for generating a digital twin of the water system. It is validated on two benchmark hydraulic networks: the Patios-Villa del Rosario, Colombia, and the C-Town networks. The results show that the proposed model effectively predicts the state estimation in the two hydraulic networks used. The results of the evaluation metrics indicate low values of mean squared error and mean absolute error and high values of the coefficient of determination, reflecting high predictive ability and that the prediction results adequately represent the real data. Full article

(This article belongs to the Special Issue Urban Water Networks Modelling and Monitoring)

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Open AccessEditor’s ChoiceArticle

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

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Water 2022, 14(3), 492; https://doi.org/10.3390/w14030492 - 07 Feb 2022

Cited by 13 | Viewed by 2172

Abstract

The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning [...] Read more.

The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning model that synthesizes the advantages of different ML models deserves more attention. In this study, an ensemble learning model based on stacking approach was proposed. Four prevalent ML models, namely k-nearest neighbors (KNN), extreme gradient boosting (XGB), support vector regression (SVR), and artificial neural networks (ANN) are taken as base models. To combine the outputs from the base models, the weighting algorithm is used as second-layer learner to generate predictions. Large-scale climate indices, large-scale atmospheric variables, and local meteorological variables were used as predictors. R², RMSE and MAE, were used as evaluation metrics. The results show that the performance of base models varied among the nine stations in the Taihu Basin, while the stacking approach generally performed better than the four base models. The stacking model showed better performance in spring and winter than in summer and autumn. During wet months, the accuracy of model prediction varied more significantly. On the whole, based on performance evaluation measures, it is concluded that the proposed stacking ensemble multi-ML model can provide a flexible and reasonable prediction framework applicable to other regions. Full article

(This article belongs to the Special Issue Statistics in Hydrology)

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Open AccessEditor’s ChoiceArticle

Development of Deep Learning Models to Improve the Accuracy of Water Levels Time Series Prediction through Multivariate Hydrological Data

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Water 2022, 14(3), 469; https://doi.org/10.3390/w14030469 - 04 Feb 2022

Cited by 12 | Viewed by 1653

Abstract

Since predicting rapidly fluctuating water levels is very important in water resource engineering, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to evaluate water-level-prediction accuracy at Hangang Bridge Station in Han River, South Korea, where seasonal fluctuations were large and [...] Read more.

Since predicting rapidly fluctuating water levels is very important in water resource engineering, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to evaluate water-level-prediction accuracy at Hangang Bridge Station in Han River, South Korea, where seasonal fluctuations were large and rapidly changing water levels were observed. The hydrological data input to each model were collected from the Water Resources Management Information System (WAMIS) at the Hangang Bridge Station, and the meteorological data were provided by the Seoul Observatory of the Meteorological Administration. For high-accuracy high-water-level prediction, the correlation between water level and collected hydrological and meteorological data was analyzed and input into the models to determine the priority of the data to be trained. Multivariate input data were created by combining daily flow rate (DFR), daily vapor pressure (DVP), daily dew-point temperature (DDPT), and 1-hour-max precipitation (1HP) data, which are highly correlated with the water level. It was possible to predict improved high water levels through the training of multivariate input data of LSTM and GRU. In the prediction of water-level data with rapid temporal fluctuations in the Hangang Bridge Station, the accuracy of GRU’s predicted water-level data was much better in most multivariate training than that of LSTM. When multivariate training data with a large correlation with the water level were used by the GRU, the prediction results with higher accuracy (

R^{2} = 0.7480 - 0.8318

;

N S E = 0.7524 - 0.7965

;

M R P E = 0.0807 - 0.0895

) were obtained than those of water-level prediction results by univariate training. Full article

(This article belongs to the Section Hydrology)

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Open AccessEditor’s ChoiceArticle

Remote Analysis of the Chlorophyll-a Concentration Using Sentinel-2 MSI Images in a Semiarid Environment in Northeastern Brazil

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Thaís R. Benevides T. Aranha

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Jean-Michel Martinez

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Enio P. Souza

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Mário U. G. Barros

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Eduardo Sávio P. R. Martins

Water 2022, 14(3), 451; https://doi.org/10.3390/w14030451 - 02 Feb 2022

Cited by 8 | Viewed by 1689

Abstract

In this paper, the authors use remote-sensing images to monitor the water quality of reservoirs located in the semiarid region of Northeast Brazil. Sentinel-2 MSI TOA Level 1C reflectance images were used to remotely estimate the concentration of chlorophyll-a (chl-a), the main indicator [...] Read more.

In this paper, the authors use remote-sensing images to monitor the water quality of reservoirs located in the semiarid region of Northeast Brazil. Sentinel-2 MSI TOA Level 1C reflectance images were used to remotely estimate the concentration of chlorophyll-a (chl-a), the main indicator of the trophic state of aquatic environments, in five reservoirs in the state of Ceará, Brazil. A three-spectral band retrieval model was calibrated using 171 water samples, collected from November 2015 through July 2018 in 5 reservoirs. For validation, 71 additional samples, collected from August 2018 through December 2019, were used to ensure a robust accuracy assessment. The TOA Level 1C products performed very well, achieving a relative RMSE of 28% and R² = 0.80. Data on wind direction and speed, solar radiation and reservoir volume were used to generate a conceptual model to analyze the behavior of chl-a in the surface waters of the Castanhão reservoir. During 2019, the reservoir water quality showed strong variation, with concentration fluctuating from 30 to 95 µg/L We showed that the end of the dry season is marked by strong eutrophic conditions corresponding to very low water inflows into the reservoir. During the rainy season there is a large decrease in the chl-a concentration following the increase of the lake water storage. During the following dry season, satellite data show a progressive improvement of the trophic state controlled by wind intensity that promotes a better mixing of the reservoir waters and inhibiting the development of most phytoplankton. Full article

(This article belongs to the Special Issue Monitoring Lakes Water Based on Multisource Remote Sensing and Novel Modeling Techniques)

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Open AccessEditor’s ChoiceArticle

Evaluation of Machine Learning Techniques for Hydrological Drought Modeling: A Case Study of the Wadi Ouahrane Basin in Algeria

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Water 2022, 14(3), 431; https://doi.org/10.3390/w14030431 - 30 Jan 2022

Cited by 11 | Viewed by 3013

Abstract

Forecasting meteorological and hydrological drought using standardized metrics of rainfall and runoff (SPI/SRI) is critical for the long-term planning and management of water resources at the global and regional levels. In this study, various machine learning (ML) techniques including four methods (i.e., ANN, [...] Read more.

Forecasting meteorological and hydrological drought using standardized metrics of rainfall and runoff (SPI/SRI) is critical for the long-term planning and management of water resources at the global and regional levels. In this study, various machine learning (ML) techniques including four methods (i.e., ANN, ANFIS, SVM, and DT) were utilized to construct hydrological drought forecasting models in the Wadi Ouahrane basin in the northern part of Algeria. The performance of ML models was assessed using evaluation criteria, including RMSE, MAE, NSE, and R². The results showed that all the ML models accurately predicted hydrological drought, while the SVM model outperformed the other ML models, with the average RMSE = 0.28, MAE = 0.19, NSE = 0.86, and R² = 0.90. The coefficient of determination of SVM was 0.95 for predicting SRI at the 12-months timescale; as the timescale moves from higher to lower (12 months to 3 months), R² starts decreasing. Full article

(This article belongs to the Special Issue Assessing and Managing Risk of Flood and Drought in a Changing World)

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Open AccessEditor’s ChoiceArticle

A Model-Based Approach for Improving Surface Water Quality Management in Aquaculture Using MIKE 11: A Case of the Long Xuyen Quadangle, Mekong Delta, Vietnam

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Water 2022, 14(3), 412; https://doi.org/10.3390/w14030412 - 29 Jan 2022

Cited by 9 | Viewed by 2879

Abstract

This study utilized MIKE 11 to quantify the spatio-temporal dynamics of water quality parameters (Biochemical Oxygen Demand (BOD₅), Dissolved Oxygen (DO) and temperature) in the Long Xuyen Quadrangle area of the Vietnamese Mekong Delta. Calibrated for the year of 2019 and [...] Read more.

This study utilized MIKE 11 to quantify the spatio-temporal dynamics of water quality parameters (Biochemical Oxygen Demand (BOD₅), Dissolved Oxygen (DO) and temperature) in the Long Xuyen Quadrangle area of the Vietnamese Mekong Delta. Calibrated for the year of 2019 and validated for the year of 2020, the developed model showed a significant agreement between the observed and simulated values of water quality parameters. Locations near to cage culture areas exhibited higher BOD₅ values than sites close to pond/lagoon culture areas due to the effects of numerous point sources of pollution, including upstream wastewater and out-fluxes from residential and tourism activities in the surrounding areas, all of which had a direct impact on the quality of the surface water used for aquaculture. Moreover, as aquacultural effluents have intensified and dispersed over time, water quality in the surrounding water bodies has degraded. The findings suggest that the effective planning, assessment and management of rapidly expanding aquaculture sites should be improved, including more rigorous water quality monitoring, to ensure the long-term sustainable expansion and development of the aquacultural sector in the Long Xuyen Quadrangle in particular, and the Vietnamese Mekong Delta as a whole. Full article

(This article belongs to the Special Issue Water Resource Management through the Lens of Planetary Health Approach)

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Open AccessFeature PaperEditor’s ChoiceReview

Permafrost Degradation and Its Hydrogeological Impacts

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Water 2022, 14(3), 372; https://doi.org/10.3390/w14030372 - 26 Jan 2022

Cited by 18 | Viewed by 3961

Abstract

Under a warming climate, permafrost degradation has resulted in profound hydrogeological consequences. Here, we mainly review 240 recent relevant papers. Permafrost degradation has boosted groundwater storage and discharge to surface runoffs through improving hydraulic connectivity and reactivation of groundwater flow systems, resulting in [...] Read more.

Under a warming climate, permafrost degradation has resulted in profound hydrogeological consequences. Here, we mainly review 240 recent relevant papers. Permafrost degradation has boosted groundwater storage and discharge to surface runoffs through improving hydraulic connectivity and reactivation of groundwater flow systems, resulting in reduced summer peaks, delayed autumn flow peaks, flattened annual hydrographs, and deepening and elongating flow paths. As a result of permafrost degradation, lowlands underlain by more continuous, colder, and thicker permafrost are getting wetter and uplands and mountain slopes, drier. However, additional contribution of melting ground ice to groundwater and stream-flows seems limited in most permafrost basins. As a result of permafrost degradation, the permafrost table and supra-permafrost water table are lowering; subaerial supra-permafrost taliks are forming; taliks are connecting and expanding; thermokarst activities are intensifying. These processes may profoundly impact on ecosystem structures and functions, terrestrial processes, surface and subsurface coupled flow systems, engineered infrastructures, and socioeconomic development. During the last 20 years, substantial and rapid progress has been made in many aspects in cryo-hydrogeology. However, these studies are still inadequate in desired spatiotemporal resolutions, multi-source data assimilation and integration, as well as cryo-hydrogeological modeling, particularly over rugged terrains in ice-rich, warm (>−1 °C) permafrost zones. Future research should be prioritized to the following aspects. First, we should better understand the concordant changes in processes, mechanisms, and trends for terrestrial processes, hydrometeorology, geocryology, hydrogeology, and ecohydrology in warm and thin permafrost regions. Second, we should aim towards revealing the physical and chemical mechanisms for the coupled processes of heat transfer and moisture migration in the vadose zone and expanding supra-permafrost taliks, towards the coupling of the hydrothermal dynamics of supra-, intra- and sub-permafrost waters, as well as that of water-resource changes and of hydrochemical and biogeochemical mechanisms for the coupled movements of solutes and pollutants in surface and subsurface waters as induced by warming and thawing permafrost. Third, we urgently need to establish and improve coupled predictive distributed cryo-hydrogeology models with optimized parameterization. In addition, we should also emphasize automatically, intelligently, and systematically monitoring, predicting, evaluating, and adapting to hydrogeological impacts from degrading permafrost at desired spatiotemporal scales. Systematic, in-depth, and predictive studies on and abilities for the hydrogeological impacts from degrading permafrost can greatly advance geocryology, cryo-hydrogeology, and cryo-ecohydrology and help better manage water, ecosystems, and land resources in permafrost regions in an adaptive and sustainable manner. Full article

(This article belongs to the Special Issue Hydrological Impacts of Degrading Permafrost and Changing Climate)

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Open AccessFeature PaperEditor’s ChoiceArticle

Estimating Phosphorus and COD Concentrations Using a Hybrid Soft Sensor: A Case Study in a Norwegian Municipal Wastewater Treatment Plant

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Abhilash Nair

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Aleksander Hykkerud

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Harsha Ratnaweera

Water 2022, 14(3), 332; https://doi.org/10.3390/w14030332 - 24 Jan 2022

Cited by 8 | Viewed by 2875

Abstract

Online monitoring of wastewater quality parameters is vital for an efficient and stable operation of wastewater treatment plants (WWTP). Several WWTPs rely on daily/weekly analysis of water samples rather than online automated wet-analyzers due to their high capital and maintenance costs. Soft-sensors are [...] Read more.

Online monitoring of wastewater quality parameters is vital for an efficient and stable operation of wastewater treatment plants (WWTP). Several WWTPs rely on daily/weekly analysis of water samples rather than online automated wet-analyzers due to their high capital and maintenance costs. Soft-sensors are emerging as a viable alternative for real-time monitoring of parameters that either lack a reliable measuring principle or are measured using expensive online sensors. This paper presents the development, implementation, and validation of a hybrid soft sensor used to estimate Total Phosphorus (TP) and Chemical Oxygen Demand (COD) in the influent and effluent streams of a full-scale WWTP. A systematic method for cleaning and processing sensor data, identifying statistically significant correlations, and developing a mathematical model, is discussed. A non-intrusive Industrial Internet of Things (IIoT) infrastructure for soft-sensor deployment and a web-based GUI for data visualization are also presented in this work. The values of TP and COD estimated by the soft sensor are validated by comparing the estimated values to the daily average of their corresponding lab measurements. The data validation results demonstrate the potential of soft sensors in providing real-time values of essential wastewater quality parameters with an acceptable degree of accuracy. Full article

(This article belongs to the Special Issue Water Quality Monitoring and Modeling Research)

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Open AccessFeature PaperEditor’s ChoiceArticle

Applications of Computational and Statistical Models for Optimizing the Electrochemical Removal of Cephalexin Antibiotic from Water

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Maliheh Arab

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Mahdieh Ghiyasi Faramarz

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Khalid Hashim

Water 2022, 14(3), 344; https://doi.org/10.3390/w14030344 - 24 Jan 2022

Cited by 17 | Viewed by 2415

Abstract

One of the most serious effects of micropollutants in the environment is biological magnification, which causes adverse effects on humans and the ecosystem. Among all of the micro-pollutants, antibiotics are commonly present in the aquatic environment due to their wide use in treating [...] Read more.

One of the most serious effects of micropollutants in the environment is biological magnification, which causes adverse effects on humans and the ecosystem. Among all of the micro-pollutants, antibiotics are commonly present in the aquatic environment due to their wide use in treating or preventing various diseases and infections for humans, plants, and animals. Therefore, an aluminum-based electrocoagulation unit has been used in this study to remove cephalexin antibiotics, as a model of the antibiotics, from water. Computational and statistical models were used to optimize the effects of key parameters on the electrochemical removal of cephalexin, including the initial cephalexin concentration (15–55 mg/L), initial pH (3–11), electrolysis time (20–40 min), and electrode type (insulated and non-insulated). The response surface methodology-central composite design (RSM-CCD) was used to investigate the dependency of the studied variables, while the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods were applied for predicting the experimental training data. The results showed that the best experimental and predicted removals of cephalexin (CEX) were 88.21% and 93.87%, respectively, which were obtained at a pH of 6.14 and electrolysis time of 34.26 min. The results also showed that the ANFIS model predicts and interprets the experimental results better than the ANN and RSM-CCD models. Sensitivity analysis using the Garson method showed the comparative significance of the variables as follows: pH (30%) > electrode type (27%) > initial CEX concentration (24%) > electrolysis time (19%). Full article

(This article belongs to the Special Issue Advanced Applications of Electrocoagulation and Water Quality Monitoring Techniques)

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Open AccessEditor’s ChoiceArticle

Recharge and Geochemical Evolution of Groundwater in Fractured Basement Aquifers (NW India): Insights from Environmental Isotopes (δ¹⁸O, δ²H, and ³H) and Hydrogeochemical Studies

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Water 2022, 14(3), 315; https://doi.org/10.3390/w14030315 - 21 Jan 2022

Cited by 10 | Viewed by 3419

Abstract

Considering water as a limiting factor for socio-economic development, especially in arid/semi-arid regions, both scientific communities and policymakers are interested in groundwater recharge-related data. India is fast moving toward a crisis of groundwater due to intense abstraction and contamination. There is a lack [...] Read more.

Considering water as a limiting factor for socio-economic development, especially in arid/semi-arid regions, both scientific communities and policymakers are interested in groundwater recharge-related data. India is fast moving toward a crisis of groundwater due to intense abstraction and contamination. There is a lack of understanding regarding the occurrence, movement, and behaviors of groundwater in a fractured basement terrane. Therefore, integrated environmental isotopes (δ¹⁸O, δ²H, and ³H) and hydrogeochemical studies have been used to understand the recharge processes and geochemical evolution of groundwater in the fractured basement terranes of Gujarat, NW India. Our results show that the relative abundance of major cations and anions in the study basin are Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ and HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻, respectively. This suggests that the chemical weathering of silicate minerals influences the groundwater chemistry in the aquifer system. A change in hydrochemical facies from Ca-HCO₃ to Na-Mg-Ca-Cl. HCO₃ has been identified from the recharge to discharge areas. Along the groundwater flow direction, the presence of chemical constituents with different concentrations demonstrates that the various geochemical mechanisms are responsible for this geochemical evolution. Furthermore, the chemical composition of groundwater also reflects that the groundwater has interacted with distinct rock types (granites/granulites). The stable isotopes (δ¹⁸O and δ²H) of groundwater reveal that the local precipitation is the main source of recharge. However, the groundwater recharge is affected by the evaporation process due to different geological conditions irrespective of topographical differences in the study area. The tritium (³H) content of groundwater suggests that the aquifer is mainly recharged by modern rainfall events. Thus, in semi-arid regions, the geology, weathering, and geologic structures have a significant role in bringing chemical changes in groundwater and smoothening the recharge process. The findings of this study will prove vital for the decision-makers or policymakers to take appropriate measures to design water budgets as well as water management plans more sustainably. Full article

(This article belongs to the Special Issue Water Resource Management through the Lens of Planetary Health Approach)

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Open AccessFeature PaperEditor’s ChoiceReview

IoT-Based Solutions to Monitor Water Level, Leakage, and Motor Control for Smart Water Tanks

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Water 2022, 14(3), 309; https://doi.org/10.3390/w14030309 - 20 Jan 2022

Cited by 14 | Viewed by 14397

Abstract

Today, a large portion of the human population around the globe has no access to freshwater for drinking, cooking, and other domestic applications. Water resources in numerous countries are becoming scarce due to over urbanization, rapid industrial growth, and current global warming. Water [...] Read more.

Today, a large portion of the human population around the globe has no access to freshwater for drinking, cooking, and other domestic applications. Water resources in numerous countries are becoming scarce due to over urbanization, rapid industrial growth, and current global warming. Water is often stored in the aboveground or underground tanks. In developing countries, these tanks are maintained manually, and in some cases, water is wasted due to human negligence. In addition, water could also leak out from tanks and supply pipes due to the decayed infrastructure. To address these issues, researchers worldwide turned to the Internet-of-Things (IoT) technology to efficiently monitor water levels, detect leakage, and auto refill tanks whenever needed. Notably, this technology can also supply real-time feedback to end-users and other experts through a webpage or a smartphone. Literature reveals a plethora of review articles on smart water monitoring, including water quality, supply pipes leakage, and water waste recycling. However, none of the reviews focus on the IoT-based solution to monitor water level, detect water leakage, and auto control water pumps, especially at the induvial level that form a vast proportion of water consumers worldwide. To fill this gap in the literature, this study presents a review of IoT-controlled water storage tanks (IoT-WST). Some important contributions of our work include surveying contemporary work on IoT-WST, elaborating current techniques and technologies in IoT-WST, targeting proper hardware, and selecting a secure IoT cloud server. Full article

(This article belongs to the Special Issue Water Quality Engineering and Wastewater Treatment Ⅱ)

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Open AccessEditor’s ChoiceArticle

Method Development for Low-Concentration PAHs Analysis in Seawater to Evaluate the Impact of Ship Scrubber Washwater Effluents

by

Chih-Feng Chen

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Yee Cheng Lim

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Yun-Ru Ju

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Frank Paolo Jay B. Albarico

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Jia-Wei Cheng

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Chiu-Wen Chen

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Cheng-Di Dong

Water 2022, 14(3), 287; https://doi.org/10.3390/w14030287 - 19 Jan 2022

Cited by 11 | Viewed by 1547

Abstract

A naval ship’s exhaust gas scrubber may discharge polycyclic aromatic hydrocarbons (PAHs) into seawater. Due to the high lipophilicity and low water solubility of PAHs, their concentrations in seawater are extremely low, making them difficult to detect or accurately determine. To accurately assess [...] Read more.

A naval ship’s exhaust gas scrubber may discharge polycyclic aromatic hydrocarbons (PAHs) into seawater. Due to the high lipophilicity and low water solubility of PAHs, their concentrations in seawater are extremely low, making them difficult to detect or accurately determine. To accurately assess the impact of scrubber washwater effluent on the PAHs concentration of seawater, appropriate analysis methods must be established. In this study, a large-volume pre-concentration water sampler was used onboard to concentrate PAHs in surface seawater (100 L) from four sites offshore of southern Taiwan. The quantitative and qualitative analysis of dissolved PAHs in seawater and quality control samples were implemented using a GC/MS system with the aid of internal and surrogate standards. Results showed that the field and equipment blank samples of quality control samples were lower than twice the detection limit. The detection limit of individual PAHs is between 0.001 (naphthalene, NA) and 0.014 ng/L (dibenzo[a,h]anthracene, DBA), which meets the requirements for evaluating PAHs in seawater (that is, less than the maximum permissible concentrations (MPCs)). The concentration of total PAHs (TPAHs) in the four seawater samples ranged from 2.297 to 4.001 ng/L and had an average concentration of 3.056 ± 0.727 ng/L. The concentrations of 16 PAHs were determined in each seawater sample, indicating that the analytical method in this study is suitable for the determination of low-concentration PAHs in seawater. Phenanthrene (PHE) is the most dominant compound in seawater samples accounting for 59.6 ± 12.6% of TPAHs, followed by fluorine (FL) accounting for 8.5 ± 3.7%. The contribution of high-ring PAHs to TPAHs is not high (0.5–9.2%), but the observed concentrations can cause a higher risk to aquatic organisms than low-ring PAHs. The diagnostic ratio showed that the sources of PAHs in the seawater collected offshore of southern Taiwan may include mixed sources such as petrogenic, petroleum combustion, and biomass combustion. The results can be used for regular monitoring, which contributes to pollution prevention and management of the marine environment. Full article

(This article belongs to the Special Issue The Relationship between Ships and Marine Environment)

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Open AccessEditor’s ChoiceArticle

Geographical Preference for Installation of Solar Still Water Desalination Technologies in Iran: An Analytical Hierarchy Process (AHP)-Based Answer

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Water 2022, 14(2), 265; https://doi.org/10.3390/w14020265 - 17 Jan 2022

Cited by 11 | Viewed by 1542

Abstract

Water shortage is one of the most crucial challenges worldwide, especially in the Middle Eastern countries, with high population and low freshwater resources. Considering this point and the increasing popularity of solar stills desalination systems, as the contribution, this study aims at finding [...] Read more.

Water shortage is one of the most crucial challenges worldwide, especially in the Middle Eastern countries, with high population and low freshwater resources. Considering this point and the increasing popularity of solar stills desalination systems, as the contribution, this study aims at finding the geographical preference for installation of those technologies in Iran, which is one of the biggest and most populated countries in the Middle East. For this purpose, from each climatic zone of Iran, one representative city is chosen, and analytical hierarchy process (AHP), as one of the most powerful tools for systematic decision-making, is applied. Annual fresh water production (AFWP) from the technical aspect, energy payback period (EPBP) from the energy perspective, and investment payback period from the economic point of view are selected as the decision criteria. Obtaining the three indicated indicators is done using artificial neural networks (ANNs) for yield and water temperature in the basin, which are developed by means of the recorded experimental data. The results indicate that hot arid cities with high received solar radiation, or the ones that have a higher water tariff compared to the others, are the preferred places for installation of solar stills. The example of the first category is Ahvaz, while Tehran is representative of the cities from the second category. AHP demonstrates that they are the first and second priorities for solar still installation, with scores of 26.9 and 22.7, respectively. Ahvaz has AWFP, EPBP, and IPP of 2706.5 L, 0.58 years, and 4.01 years; while the corresponding values for Tehran are 2115.3 L, 0.87 years, and 2.86 years. This study belongs to three classifications in the mathematical problems: 1. experimental work (code: 76–05), 2. Neural networks (code: 92B20), 3. and decision problems, (code: 20F10). Full article

(This article belongs to the Special Issue Renewable Energy Systems Flexibility for Water Desalination)

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Open AccessFeature PaperEditor’s ChoiceArticle

Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery

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Water 2022, 14(2), 244; https://doi.org/10.3390/w14020244 - 15 Jan 2022

Cited by 9 | Viewed by 2678

Abstract

Mangroves, as unique coastal wetlands with numerous benefits, are endangered mainly due to the coupled effects of anthropogenic activities and climate change. Therefore, acquiring reliable and up-to-date information about these ecosystems is vital for their conservation and sustainable blue carbon development. In this [...] Read more.

Mangroves, as unique coastal wetlands with numerous benefits, are endangered mainly due to the coupled effects of anthropogenic activities and climate change. Therefore, acquiring reliable and up-to-date information about these ecosystems is vital for their conservation and sustainable blue carbon development. In this regard, the joint use of remote sensing data and machine learning algorithms can assist in producing accurate mangrove ecosystem maps. This study investigated the potential of artificial neural networks (ANNs) with different topologies and specifications for mangrove classification in Iran. To this end, multi-temporal synthetic aperture radar (SAR) and multi-spectral remote sensing data from Sentinel-1 and Sentinel-2 were processed in the Google Earth Engine (GEE) cloud computing platform. Afterward, the ANN topologies and specifications considering the number of layers and neurons, learning algorithm, type of activation function, and learning rate were examined for mangrove ecosystem mapping. The results indicated that an ANN model with four hidden layers, 36 neurons in each layer, adaptive moment estimation (Adam) learning algorithm, rectified linear unit (Relu) activation function, and the learning rate of 0.001 produced the most accurate mangrove ecosystem map (F-score = 0.97). Further analysis revealed that although ANN models were subjected to accuracy decline when a limited number of training samples were used, they still resulted in satisfactory results. Additionally, it was observed that ANN models had a high resistance when training samples included wrong labels, and only the ANN model with the Adam learning algorithm produced an accurate mangrove ecosystem map when no data standardization was performed. Moreover, further investigations showed the higher potential of multi-temporal and multi-source remote sensing data compared to single-source and mono-temporal (e.g., single season) for accurate mangrove ecosystem mapping. Overall, the high potential of the proposed method, along with utilizing open-access satellite images and big-geo data processing platforms (i.e., GEE, Google Colab, and scikit-learn), made the proposed approach efficient and applicable over other study areas for all interested users. Full article

(This article belongs to the Special Issue Mapping and Monitoring of Wetlands)

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Open AccessEditor’s ChoiceArticle

Evaluation of Soil Water Content Using SWAT for Southern Saskatchewan, Canada

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Mohammad Zare

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Shahid Azam

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David Sauchyn

Water 2022, 14(2), 249; https://doi.org/10.3390/w14020249 - 15 Jan 2022

Cited by 8 | Viewed by 1848

Abstract

Soil water content (SWC) is one of the most important hydrologic variables; it plays a decisive role in the control of various land surface processes. We simulated SWC using a Soil and Water Assessment Tool (SWAT) model in southern Saskatchewan. SWC was calibrated [...] Read more.

Soil water content (SWC) is one of the most important hydrologic variables; it plays a decisive role in the control of various land surface processes. We simulated SWC using a Soil and Water Assessment Tool (SWAT) model in southern Saskatchewan. SWC was calibrated using measured data and Soil Moisture Active Passive (SMAP) Level-4 for the surface (0–5 cm) SWC for hydrological response units (HRU) at daily and monthly (warm season) intervals for the years 2015 to 2020. We used the SUFI-2 technique in SWAT-CUP, and observed daily instrumented streamflow records, for calibration (1995 to 2004) and validation (2005–2010). The results reveal that the SWAT model performs well with a monthly PBIAS < 10% and Nash–Sutcliffe efficiency (NS) and R² ≥ 0.8 for calibration and validation. The correlation coefficient between ground measurement with SMAP and SWAT products are 0.698 and 0.633, respectively. Moreover, SMAP data of surface SWC coincides well with measurements in terms of both amount and trend compared with the SWAT product. The highest r value occurred in July when the mean r value in SWAT and SMAP were 0.87 to 0.84, and then in June for r value of 0.75. In contrast, the lowest values were in April and May (0.07 and 0.04, respectively) at the beginning of the growing season in southern Saskatchewan. Furthermore, calibration in the SWAT model is based on a batch form whereby parameters are adjusted to corresponding input by modifying simulations with observations. SWAT underestimates the abrupt increase in streamflow during the snowmelt months (April and May). This study achieved the objective of developing a SWAT model that simulates SWC in a prairie watershed, and, therefore, can be used in a subsequent phase of research to estimate future soil moisture conditions under projected climate changes. Full article

(This article belongs to the Section Soil and Water)

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Open AccessEditor’s ChoiceArticle

The Effect of Trophic Modes on Biomass and Lipid Production of Five Microalgal Strains

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Water 2022, 14(2), 240; https://doi.org/10.3390/w14020240 - 14 Jan 2022

Cited by 10 | Viewed by 1638

Abstract

Five microalgae strains, namely Isochrysis galbana, Microchloropsis gaditana, Scenedesmus obliquus, Nannochloropsis oculata and Tetraselmis suecica, were selected as potential candidates for polyunsaturated fatty acids’ production, evaluating biomass productivity and their capacity to accumulate high lipid contents under different trophic [...] Read more.

Five microalgae strains, namely Isochrysis galbana, Microchloropsis gaditana, Scenedesmus obliquus, Nannochloropsis oculata and Tetraselmis suecica, were selected as potential candidates for polyunsaturated fatty acids’ production, evaluating biomass productivity and their capacity to accumulate high lipid contents under different trophic modes. Microalgae strains were cultivated in the presence of 1% glucose using mixotrophic and heterotrophic conditions, while autotrophic cultures served as control experiments. The results demonstrate that S. obliquus performed the highest biomass productivity that reached 0.13 and 0.14 g L⁻¹ d⁻¹ under mixotrophic and heterotrophic conditions, respectively. I. galbana and S. obliquus utilized elevated contents of glucose in mixotrophy, removing 55.9% and 95.6% of the initial concentration of the carbohydrate, respectively, while glucose consumption by the aforementioned strains also remained high under heterotrophic cultivation. The production of lipids was maximal for I. galbana in mixotrophy and S. obliquus in heterotrophy, performing lipid productivities of 24.85 and 22.77 mg L⁻¹ d⁻¹, respectively. The most abundant saturated acid detected for all microalgae strains evaluated was palmitic acid (C16:0), while oleic and linolenic acids (C18:1n9c/C18:3n3) comprised the most abundant unsaturated fatty acids. I. galbana performed the highest linoleic acid (C18:2n6c) content under heterotrophic nutrition, which reached 87.9 mg g⁻¹ of ash-free dry weight. Among the microalgae strains compared, the biomass and lipid production monitored for I. galbana and S. obliquus confirm that both strains could serve as efficient bioproducers for application in algal biorefineries. Full article

(This article belongs to the Special Issue Nutrient Recovery from Wastewaters Using Microalgae-Based Systems and Potential Applications of the Produced Biomass)

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Open AccessFeature PaperEditor’s ChoiceReview

Review on Methylene Blue: Its Properties, Uses, Toxicity and Photodegradation

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Water 2022, 14(2), 242; https://doi.org/10.3390/w14020242 - 14 Jan 2022

Cited by 196 | Viewed by 23071

Abstract

The unavailability of clean drinking water is one of the significant health issues in modern times. Industrial dyes are one of the dominant chemicals that make water unfit for drinking. Among these dyes, methylene blue (MB) is toxic, carcinogenic, and non-biodegradable and can [...] Read more.

The unavailability of clean drinking water is one of the significant health issues in modern times. Industrial dyes are one of the dominant chemicals that make water unfit for drinking. Among these dyes, methylene blue (MB) is toxic, carcinogenic, and non-biodegradable and can cause a severe threat to human health and environmental safety. It is usually released in natural water sources, which becomes a health threat to human beings and living organisms. Hence, there is a need to develop an environmentally friendly, efficient technology for removing MB from wastewater. Photodegradation is an advanced oxidation process widely used for MB removal. It has the advantages of complete mineralization of dye into simple and nontoxic species with the potential to decrease the processing cost. This review provides a tutorial basis for the readers working in the dye degradation research area. We not only covered the basic principles of the process but also provided a wide range of previously published work on advanced photocatalytic systems (single-component and multi-component photocatalysts). Our study has focused on critical parameters that can affect the photodegradation rate of MB, such as photocatalyst type and loading, irradiation reaction time, pH of reaction media, initial concentration of dye, radical scavengers and oxidising agents. The photodegradation mechanism, reaction pathways, intermediate products, and final products of MB are also summarized. An overview of the future perspectives to utilize MB at an industrial scale is also provided. This paper identifies strategies for the development of effective MB photodegradation systems. Full article

(This article belongs to the Special Issue Efficient Catalytic and Microbial Treatment of Water Pollutants)

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Open AccessEditor’s ChoiceArticle

Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network

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Teh Hong Khai

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Siti Norul Huda Sheikh Abdullah

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Mohammad Kamrul Hasan

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Ahmad Tarmizi

Water 2022, 14(2), 222; https://doi.org/10.3390/w14020222 - 12 Jan 2022

Cited by 10 | Viewed by 3073

Abstract

Fish production has become a roadblock to the development of fish farming, and one of the issues encountered throughout the hatching process is the counting procedure. Previous research has mainly depended on the use of non-machine learning-based and machine learning-based counting methods and [...] Read more.

Fish production has become a roadblock to the development of fish farming, and one of the issues encountered throughout the hatching process is the counting procedure. Previous research has mainly depended on the use of non-machine learning-based and machine learning-based counting methods and so was unable to provide precise results. In this work, we used a robotic eye camera to capture shrimp photos on a shrimp farm to train the model. The image data were classified into three categories based on the density of shrimps: low density, medium density, and high density. We used the parameter calibration strategy to discover the appropriate parameters and provided an improved Mask Regional Convolutional Neural Network (Mask R-CNN) model. As a result, the enhanced Mask R-CNN model can reach an accuracy rate of up to 97.48%. Full article

(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)

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Open AccessEditor’s ChoiceArticle

Research on the Non-Point Source Pollution Characteristics of Important Drinking Water Sources

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Water 2022, 14(2), 211; https://doi.org/10.3390/w14020211 - 12 Jan 2022

Cited by 15 | Viewed by 2333

Abstract

In recent years, freshwater resource contamination by non-point source pollution has become particularly prominent in China. To control non-point source (NPS) pollution, it is important to estimate NPS pollution exports, identify sources of pollution, and analyze the pollution characteristics. As such, in this [...] Read more.

In recent years, freshwater resource contamination by non-point source pollution has become particularly prominent in China. To control non-point source (NPS) pollution, it is important to estimate NPS pollution exports, identify sources of pollution, and analyze the pollution characteristics. As such, in this study, we established the modified export coefficient model based on rainfall and terrain to investigate the pollution sources and characteristics of non-point source total nitrogen (TN) and total phosphorus (TP) throughout the Huangqian Reservoir watershed—which serves as an important potable water source for the main tributary of the lower Yellow River. The results showed that: (1) In 2018, the non-point source total nitrogen (TN) and total phosphorus (TP) loads in the Huangqian Reservoir basin were 707.09 t and 114.42 t, respectively. The contribution ratios to TN export were, from high to low, rural life (33.58%), farmland (32.68%), other land use types (20.08%), and livestock and poultry breeding (13.67%). The contribution ratios to TP export were, from high to low, rural life (61.19%), livestock and poultry breeding (21.65%), farmland (12.79%), and other land use types (4.38%). The non-point source pollution primarily originated from the rural life of the water source protection zone. (2) Non-point source TN and TP pollution loads and load intensities showed significantly different spatial distribution patterns throughout the water source protection area. Specifically, their load intensities and loads were the largest in the second-class protected zone, which is the key source area of non-point source pollution. (3) When considering whether to invest in agricultural land fertilizer control or rural domestic sewage, waste, and livestock manure pollution control, the latter is demonstrably more effective. Thus, in addition to putting low-grade control on agricultural fertilizer loss, to rapidly and effectively improve potable water quality, non-point source pollution should, to a larger extent, also be controlled through measures such as establishing household biogas digesters, introducing village sewage treatment plants, and improving the recovery rate of rural domestic garbage. The research results discussed herein provide a theoretical basis for formulating a reasonable and effective protection plan for the Huangqian Reservoir water source and can potentially be used to do the same for other similar freshwater resources. Full article

(This article belongs to the Special Issue Water and Soil Resources Management in Agricultural Areas)

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Open AccessEditor’s ChoiceArticle

Comparison of Rainfall-Runoff Simulation between Support Vector Regression and HEC-HMS for a Rural Watershed in Taiwan

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Shen Chiang

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Chih-Hsin Chang

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Wei-Bo Chen

Water 2022, 14(2), 191; https://doi.org/10.3390/w14020191 - 11 Jan 2022

Cited by 14 | Viewed by 1738

Abstract

To better understand the effect and constraint of different data lengths on the data-driven model training for the rainfall-runoff simulation, the support vector regression (SVR) approach was applied to the data-driven model as the core algorithm in the present study. Various features selection [...] Read more.

To better understand the effect and constraint of different data lengths on the data-driven model training for the rainfall-runoff simulation, the support vector regression (SVR) approach was applied to the data-driven model as the core algorithm in the present study. Various features selection strategies and different data lengths were employed in the training phase of the model. The validated results of the SVR were compared with the rainfall-runoff simulation derived from a physically based hydrologic model, the Hydrologic Modeling System (HEC-HMS). The HEC-HMS was considered a conventional approach and was also calibrated with a dataset period identical to the SVR. Our results showed that the SVR and HEC-HMS models could be adopted for short and long periods of rainfall-runoff simulation. However, the SVR model estimated the rainfall-runoff relationship reasonably well even if the observational data of one year or one typhoon event was used. In contrast, the HEC-HMS model needed more parameter optimization and inference processes to achieve the same performance level as the SVR model. Overall, the SVR model was superior to the HEC-HMS model in the performance of the rainfall-runoff simulation. Full article

(This article belongs to the Special Issue Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art)

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Open AccessEditor’s ChoiceArticle

Mind the Gap! Reconciling Environmental Water Requirements with Scarcity in the Murray–Darling Basin, Australia

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Matthew J. Colloff

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Jamie Pittock

Water 2022, 14(2), 208; https://doi.org/10.3390/w14020208 - 11 Jan 2022

Cited by 10 | Viewed by 2632

Abstract

The Murray–Darling Basin Plan is a $AU 13 billion program to return water from irrigation use to the environment. Central to the success of the Plan, commenced in 2012, is the implementation of an Environmentally Sustainable Level of Take (ESLT) and a Sustainable [...] Read more.

The Murray–Darling Basin Plan is a $AU 13 billion program to return water from irrigation use to the environment. Central to the success of the Plan, commenced in 2012, is the implementation of an Environmentally Sustainable Level of Take (ESLT) and a Sustainable Diversion Limit (SDL) on the volume of water that can be taken for consumptive use. Under the enabling legislation, the Water Act (2007), the ESLT and SDL must be set by the “best available science.” In 2009, the volume of water to maintain wetlands and rivers of the Basin was estimated at 3000–7600 GL per year. Since then, there has been a steady step-down in this volume to 2075 GL year due to repeated policy adjustments, including “supply measures projects,” building of infrastructure to obtain the same environmental outcomes with less water. Since implementation of the Plan, return of water to the environment is falling far short of targets. The gap between the volume required to maintain wetlands and rivers and what is available is increasing with climate change and other risks, but the Plan makes no direct allowance for climate change. We present policy options that address the need to adapt to less water and re-frame the decision context from contestation between water for irrigation versus the environment. Options include best use of water for adaptation and structural adjustment packages for irrigation communities integrated with environmental triage of those wetlands likely to transition to dryland ecosystems under climate change. Full article

(This article belongs to the Special Issue Advances in Water Scarcity and Conservation)

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Open AccessEditor’s ChoiceArticle

Development and Application of an Urban Flood Forecasting and Warning Process to Reduce Urban Flood Damage: A Case Study of Dorim River Basin, Seoul

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Water 2022, 14(2), 187; https://doi.org/10.3390/w14020187 - 10 Jan 2022

Cited by 9 | Viewed by 1734

Abstract

Early and accurate flood forecasting and warning for urban flood risk areas is an essential factor to reduce flood damage. This paper presents the urban flood forecasting and warning process to reduce damage in the main flood risk area of South Korea. This [...] Read more.

Early and accurate flood forecasting and warning for urban flood risk areas is an essential factor to reduce flood damage. This paper presents the urban flood forecasting and warning process to reduce damage in the main flood risk area of South Korea. This process is developed based on the rainfall-runoff model and deep learning model. A model-driven method was devised to construct the accurate physical model with combined inland-river and flood control facilities, such as pump stations and underground storages. To calibrate the rainfall-runoff model, data of gauging stations and pump stations of an urban stream in August 2020 were used, and the model result was presented as an R² value of 0.63~0.79. Accurate flood warning criteria of the urban stream were analyzed according to the various rainfall scenarios from the model-driven method. As flood forecasting and warning in the urban stream, deep learning models, vanilla ANN, Long Short-Term Memory (LSTM), Stack-LSTM, and Bidirectional LSTM were constructed. Deep learning models using 10-min hydrological time-series data from gauging stations were trained to warn of expected flood risks based on the water level in the urban stream. A forecasting and warning method that applied the bidirectional LSTM showed an R² value of 0.9 for the water level forecast with 30 min lead time, indicating the possibility of effective flood forecasting and warning. This case study aims to contribute to the reduction of casualties and flood damage in urban streams and accurate flood warnings in typical urban flood risk areas of South Korea. The developed urban flood forecasting and warning process can be applied effectively as a non-structural measure to mitigate urban flood damage and can be extended considering watershed characteristics. Full article

(This article belongs to the Special Issue Urban and River Flooding: Theory, Experimental and Numerical Models, and Applications in Hydraulic Engineering)

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Open AccessEditor’s ChoiceArticle

Development of a Distributed Mathematical Model and Control System for Reducing Pollution Risk in Mineral Water Aquifer Systems

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Alexander V. Martirosyan

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Yury V. Ilyushin

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Olga V. Afanaseva

Water 2022, 14(2), 151; https://doi.org/10.3390/w14020151 - 07 Jan 2022

Cited by 14 | Viewed by 1590

Abstract

The article is devoted to the problem of the growing need of the mineral water fields’ exploitation process automation. The implementation of control systems and mathematical modeling methods can significantly reduce the fields’ structural integrity violation and pollution of aquifers risks. This research [...] Read more.

The article is devoted to the problem of the growing need of the mineral water fields’ exploitation process automation. The implementation of control systems and mathematical modeling methods can significantly reduce the fields’ structural integrity violation and pollution of aquifers risks. This research is especially relevant for the fields with difficult conditions of mineral waters occurrence, since the insufficient accuracy of determining the fields’ operating mode parameters can lead to a severe incident. The article describes a distributed mathematical model developed from the geo-filtration equation. Based on this model, a new method for assessing the mutual influence of the fields, the production of which is carried out from one aquifer, is presented. For a more detailed study of the operating mode parameters influence on the object a physical model of the reservoir was developed. The using of Arduino sensors and the developed software allows us to construct a 3D graph of the input action and its response at the different points of the object as temperature distribution. The simulation results make it possible to use the proposed model for the automatic control system synthesis. Full article

(This article belongs to the Special Issue Groundwater Flow and Transport Models)

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Open AccessFeature PaperEditor’s ChoiceReview

Application of Natural Coagulants for Pharmaceutical Removal from Water and Wastewater: A Review

by

Motasem Y. D. Alazaiza

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Ahmed Albahnasawi

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Gomaa A. M. Ali

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Mohammed J. K. Bashir

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Dia Eddin Nassani

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Tahra Al Maskari

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Salem S. Abu Amr

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Mohammed Shadi S. Abujazar

Water 2022, 14(2), 140; https://doi.org/10.3390/w14020140 - 06 Jan 2022

Cited by 30 | Viewed by 8599

Abstract

Pharmaceutical contamination threatens both humans and the environment, and several technologies have been adapted for the removal of pharmaceuticals. The coagulation-flocculation process demonstrates a feasible solution for pharmaceutical removal. However, the chemical coagulation process has its drawbacks, such as excessive and toxic sludge [...] Read more.

Pharmaceutical contamination threatens both humans and the environment, and several technologies have been adapted for the removal of pharmaceuticals. The coagulation-flocculation process demonstrates a feasible solution for pharmaceutical removal. However, the chemical coagulation process has its drawbacks, such as excessive and toxic sludge production and high production cost. To overcome these shortcomings, the feasibility of natural-based coagulants, due to their biodegradability, safety, and availability, has been investigated by several researchers. This review presented the recent advances of using natural coagulants for pharmaceutical compound removal from aqueous solutions. The main mechanisms of natural coagulants for pharmaceutical removal from water and wastewater are charge neutralization and polymer bridges. Natural coagulants extracted from plants are more commonly investigated than those extracted from animals due to their affordability. Natural coagulants are competitive in terms of their performance and environmental sustainability. Developing a reliable extraction method is required, and therefore further investigation is essential to obtain a complete insight regarding the performance and the effect of environmental factors during pharmaceutical removal by natural coagulants. Finally, the indirect application of natural coagulants is an essential step for implementing green water and wastewater treatment technologies. Full article

(This article belongs to the Special Issue Wastewater Treatment: Current and Future Techniques)

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Open AccessEditor’s ChoiceArticle

Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing

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Water 2022, 14(1), 128; https://doi.org/10.3390/w14010128 - 05 Jan 2022

Cited by 9 | Viewed by 2174

Abstract

The water components affecting turbidity are complex and changeable, and the spectral response mechanism of each water quality parameter is different. Therefore, this study mainly aimed at the turbidity monitoring by unmanned aerial vehicle (UAV) hyperspectral technology, and establishes a set of turbidity [...] Read more.

The water components affecting turbidity are complex and changeable, and the spectral response mechanism of each water quality parameter is different. Therefore, this study mainly aimed at the turbidity monitoring by unmanned aerial vehicle (UAV) hyperspectral technology, and establishes a set of turbidity retrieval models through the artificial control experiment, and verifies the model’s accuracy through UAV flight and water sample data in the same period. The results of this experiment can also be extended to different inland waters for turbidity retrieval. Retrieval of turbidity values of small inland water bodies can provide support for the study of the degree of water pollution. We collected the images and data of aquaculture ponds and irrigation ditches in Dawa District, Panjin City, Liaoning Province. Twenty-nine standard turbidity solutions with different concentration gradients (concentration from 0 to 360 NTU—the abbreviation of Nephelometric Turbidity Unit, which stands for scattered turbidity.) were established through manual control and we simultaneously collected hyperspectral data from the spectral values of standard solutions. The sensitive band to turbidity was obtained after analyzing the spectral information. We established four kinds of retrieval, including the single band, band ratio, normalized ratio, and the partial least squares (PLS) models. We selected the two models with the highest R² for accuracy verification. The band ratio model and PLS model had the highest accuracy, and R² was, respectively, 0.65 and 0.72. The hyperspectral image data obtained by UAV were combined with the PLS model, which had the highest R² to estimate the spatial distribution of water turbidity. The turbidity of the water areas in the study area was 5–300 NTU, and most of which are 5–80 NTU. It shows that the PLS models can retrieve the turbidity with high accuracy of aquaculture ponds, irrigation canals, and reservoirs in Dawa District of Panjin City, Liaoning Province. The experimental results are consistent with the conclusions of the field investigation. Full article

(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)

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Open AccessEditor’s ChoiceArticle

Degradation of Azo Dyes with Different Functional Groups in Simulated Wastewater by Electrocoagulation

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Water 2022, 14(1), 123; https://doi.org/10.3390/w14010123 - 05 Jan 2022

Cited by 18 | Viewed by 2710

Abstract

Increasing attention has been paid to the widespread contamination of azo dyes in water bodies globally. These chemicals can present high toxicity, possibly causing severe irritation of the respiratory tract and even carcinogenic effects. The present study focuses on the periodically reverse electrocoagulation [...] Read more.

Increasing attention has been paid to the widespread contamination of azo dyes in water bodies globally. These chemicals can present high toxicity, possibly causing severe irritation of the respiratory tract and even carcinogenic effects. The present study focuses on the periodically reverse electrocoagulation (PREC) treatment of two typical azo dyes with different functional groups, involving methyl orange (MO) and alizarin yellow (AY), using Fe-Fe electrodes. Based upon the comparative analysis of three main parameters, including current intensity, pH, and electrolyte, the optimal color removal rates for MO and AY could be achieved at a rate of up to 98.7% and 98.6%, respectively, when the current intensity is set to 0.6 A, the pH is set at 6.0, and the electrolyte is selected as NaCl. An accurate predicted method of response surface methodology (RSM) was established to optimize the PREC process involving the three parameters above. The reaction time was the main influence for both azo dyes, while the condition of PREC treatment for AY simulated wastewater was time-saving and energy conserving. According to the further UV–Vis spectrophotometry analysis throughout the procedure of the PREC process, the removal efficiency for AY was better than that of MO, potentially because hydroxyl groups might donate electrons to iron flocs or electrolyze out hydroxyl free radicals. The present study revealed that the functional groups might pose a vital influence on the removal efficiencies of the PREC treatment for those two azo dyes. Full article

(This article belongs to the Special Issue Innovative Technologies for Wastewater and Water Treatment)

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Open AccessFeature PaperEditor’s ChoiceArticle

Water Economics: An In-Depth Analysis of the Connection of Blue Water with Some Primary Level Aspects of Economic Theory I

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Kalomoira Zisopoulou

,

Dimitris Zisopoulos

and

Dionysia Panagoulia

Water 2022, 14(1), 103; https://doi.org/10.3390/w14010103 - 04 Jan 2022

Cited by 8 | Viewed by 2839

Abstract

An analysis of the following aspects of water economics was undertaken: Water as an Economic and Social Good, Modes of Government Intervention, Water Scarcity in Economic Theory and Agricultural Water Management Changes, with the support of over 300 sources. Emphasis was placed on [...] Read more.

An analysis of the following aspects of water economics was undertaken: Water as an Economic and Social Good, Modes of Government Intervention, Water Scarcity in Economic Theory and Agricultural Water Management Changes, with the support of over 300 sources. Emphasis was placed on the connection with primary aspects of economics, in contrast to the usual applicative expositions found in water economics literature. This is a novel approach comparing international bodies’ definitions with economic theory at primary level which leads, upon occasion, to serious contradictions which were exhibited in broad lines. Furthermore, it compares the global implications of these definitions to the existing reality at country level, and a lack of bilateral consistency is exhibited. The uniform picture presented at global level is shown to become a non-uniform one at country level, where sharp variations in resources and availability form a competitive market between nations, and water-rich countries already possessing a competitive advantage are shown to attain a water-based comparative advantage as well. It is shown that although at country level water has a quasi-public good character with minimal private good market existence, this is achieved with the existence of a private goods market at international level via international trade in virtual water. A novel approach to management problems stemming from authority levels starting at global level and ending at farm level is analyzed and redressed by employing reality gap theory. Full article

(This article belongs to the Special Issue The Economic Role of Water under Scarcity and Sustainability Constraints)

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Open AccessFeature PaperEditor’s ChoiceArticle

A Hybrid Model for Streamflow Forecasting in the Basin of Euphrates

by

Huseyin Cagan Kilinc

and

Bulent Haznedar

Water 2022, 14(1), 80; https://doi.org/10.3390/w14010080 - 03 Jan 2022

Cited by 25 | Viewed by 2168

Abstract

River flow modeling plays a crucial role in water resource management and ensuring its sustainability. Therefore, in recent years, in addition to the prediction of hydrological processes through modeling, applicable and highly reliable methods have also been used to analyze the sustainability of [...] Read more.

River flow modeling plays a crucial role in water resource management and ensuring its sustainability. Therefore, in recent years, in addition to the prediction of hydrological processes through modeling, applicable and highly reliable methods have also been used to analyze the sustainability of water resources. Artificial neural networks and deep learning-based hybrid models have been used by scientists in river flow predictions. Therefore, in this study, we propose a hybrid approach, integrating long-short-term memory (LSTM) networks and a genetic algorithm (GA) for streamflow forecasting. The performance of the hybrid model and the benchmark model was taken into account using daily flow data. For this purpose, the daily river flow time series of the Beyderesi-Kılayak flow measurement station (FMS) from September 2000 to June 2019 and the data from Yazıköy from December 2000 to June 2018 were used for flow measurements on the Euphrates River in Turkey. To validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the two FMSs. Statistical methods such as linear regression was used during the comparison process to assess the proposed method’s performance and to demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, STD and R² statistical metrics. The comparison of daily streamflow predictions results revealed that the LSTM-GA model provided promising accuracy results and mainly presented higher performance than the benchmark model and the linear regression model. Full article

(This article belongs to the Special Issue Advances in Water Use Efficiency in a Changing Environment)

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Open AccessFeature PaperEditor’s ChoiceArticle

Sensitivity, Hazard, and Vulnerability of Farmlands to Saltwater Intrusion in Low-Lying Coastal Areas of Venice, Italy

by

Luigi Tosi

,

Cristina Da Lio

,

Alessandro Bergamasco

,

,

,

,

,

and

Water 2022, 14(1), 64; https://doi.org/10.3390/w14010064 - 30 Dec 2021

Cited by 6 | Viewed by 1843

Abstract

Saltwater intrusion is a growing threat for coastal aquifers and agricultural practices in low-lying plains. Most of the farmlands located between the margin of the Southern Venice lagoon and the Northern Po delta, Italy, lie a few meters below mean sea level and [...] Read more.

Saltwater intrusion is a growing threat for coastal aquifers and agricultural practices in low-lying plains. Most of the farmlands located between the margin of the Southern Venice lagoon and the Northern Po delta, Italy, lie a few meters below mean sea level and are drained by a large network of artificial channels and hydraulic infrastructures to avoid frequent flooding and allow agricultural practices. This work proposes an assessment of the vulnerability to saltwater intrusion, following a new concept of the hazard status, resulting in combining the depth of the freshwater/saltwater interface and the electrical resistivity of the shallow subsoil. The sensitivity of the farmland system was assessed by using ground elevation, distance from freshwater and saltwater sources, permeability, potential runoff, land subsidence, and sea-level rise indicators. Relative weights were assigned by a pairwise comparison following the Analytic Hierarchy Process approach. The computed vulnerability map highlights that about 30% of the farmlands is under strong and extreme conditions, 28% between marginal and moderate, and 40% under negligible conditions. Results from previous vulnerability assessments are discussed in order to explain their differences in terms of hazard status conceptualization and sensitivity characterization of farmland system. Full article

(This article belongs to the Special Issue Salinization of Water Resources: Ongoing and Future Trends)

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