Journal Description
Water
Water
is a peer-reviewed, open access journal on water science and technology, including the ecology and management of water resources, and is published semimonthly online by MDPI. Water collaborates with the International Conference on Flood Management (ICFM) and Stockholm International Water Institute (SIWI). In addition, the American Institute of Hydrology (AIH), The Polish Limnological Society (PLS) and Japanese Society of Physical Hydrology (JSPH) are affiliated with Water and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, PubAg, AGRIS, CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Water Resources) / CiteScore - Q1 (Water Science and Technology)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.5 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Water include: GeoHazards and Hydrobiology.
Impact Factor:
3.4 (2022);
5-Year Impact Factor:
3.5 (2022)
Latest Articles
Enhanced Nitrate Nitrogen Removal from Constructed Wetland via Fe3O4/Granular Activated Carbon Anode Microbial Electrolysis Cell under Low C/N Ratio
Water 2024, 16(10), 1377; https://doi.org/10.3390/w16101377 (registering DOI) - 11 May 2024
Abstract
In this study, a constructed wetland–Fe3O4/granular activated carbon anode microbial electrolysis cell (CW-FMEC) was constructed to enhance denitrification in low COD/N ratio wastewater. The introduction of Fe3O4 boosted the expression of functional genes involved in the
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In this study, a constructed wetland–Fe3O4/granular activated carbon anode microbial electrolysis cell (CW-FMEC) was constructed to enhance denitrification in low COD/N ratio wastewater. The introduction of Fe3O4 boosted the expression of functional genes involved in the denitrification pathway, and the abundance of narG, nirS, and nosZ increased by 99.29%, 70.54%, and 132.18%, respectively, compared to CW. In addition, the content of c-type cytochromes (c-Cyts) and EPS were also enhanced in the CW-FMEC. The microbial communities study displayed that Thauera, Dechloromonas, and Arenimonas became the main genera for denitrification. The denitrification performance at different COD/N ratios was investigated in depth. Under optimal working circumstances, the CW-FMEC had an excellent nitrate removal rate (88.9% ± 1.12%) while accumulating nearly no NO2−-N or NH4+-N in the effluent. This study provides a new direction for the development of CW-MEC and accelerates its implementation.
Full article
(This article belongs to the Special Issue Application of Electrochemical Treatment in Water Purification)
Open AccessArticle
Enhancing Soil Moisture Forecasting Accuracy with REDF-LSTM: Integrating Residual En-Decoding and Feature Attention Mechanisms
by
Xiaoning Li, Ziyin Zhang, Qingliang Li and Jinlong Zhu
Water 2024, 16(10), 1376; https://doi.org/10.3390/w16101376 (registering DOI) - 11 May 2024
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This study introduces an innovative deep learning model, Residual-EnDecode-Feedforward Attention Mechanism-Long Short-Term Memory (REDF-LSTM), designed to overcome the high uncertainty challenges faced by traditional soil moisture prediction methods. The REDF-LSTM model, by integrating a residual learning encoder–decoder LSTM layer, enhanced LSTM layers, and
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This study introduces an innovative deep learning model, Residual-EnDecode-Feedforward Attention Mechanism-Long Short-Term Memory (REDF-LSTM), designed to overcome the high uncertainty challenges faced by traditional soil moisture prediction methods. The REDF-LSTM model, by integrating a residual learning encoder–decoder LSTM layer, enhanced LSTM layers, and feedforward attention, not only captures the deep features of time series data but also optimizes the model’s ability to identify key influencing factors, including land surface features, atmospheric conditions, and other static environmental variables. Unlike existing methods, the innovation of this model lies in its first-time combination of the residual learning encoder–decoder and feedforward attention mechanisms in the soil moisture prediction field. It delves into the complex patterns of time series through the encoder–decoder structure and accurately locates key influencing factors through the feedforward attention mechanism, significantly improving predictive performance. The choice to combine the feedforward attention mechanism and encoder–decoder with the LSTM model is to fully leverage their advantages in processing complex data sequences and enhancing the model’s focus on important features, aiming for more accurate soil moisture prediction. After comparison with current advanced models such as EDLSTM, FAMLSTM, and GANBiLSTM, our REDF-LSTM demonstrated the best performance. Compared to traditional LSTM models, it achieved an average improvement of 13.07% in R2, 20.98% in RMSE, 24.86% in BIAS, and 11.1% in KGE key performance indicators, fully proving its superior predictive capability and potential application value in precision agriculture and ecosystem management.
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Open AccessArticle
Integrated Time-Dependent Analysis of a Hydraulic Structure on Soft Foundations during Construction
by
Chao Xu, Liang Ye, Suli Pan and Wen Luo
Water 2024, 16(10), 1375; https://doi.org/10.3390/w16101375 (registering DOI) - 11 May 2024
Abstract
An integrated model that considers multiphysics is necessary to accurately analyze the time-dependent response of hydraulic structures on soft foundations. This study develops an integrated superstructure–foundation–backfills model and investigates the time-dependent displacement and stress of a lock head project on a soft foundation
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An integrated model that considers multiphysics is necessary to accurately analyze the time-dependent response of hydraulic structures on soft foundations. This study develops an integrated superstructure–foundation–backfills model and investigates the time-dependent displacement and stress of a lock head project on a soft foundation during the construction period. Finite element analyses are conducted, incorporating a transient thermal creep model for concrete and an elasto-plastic consolidation model for the soil. The modified Cam-clay model is employed to describe the elasto-plastic behavior of the soil. Subsequently, global sensitivity analyses are conducted to determine the relative importance of the model parameters on the system’s response, using Garson’s and partial derivative algorithms based on the backpropagation (BP) neural network. The results indicate that the integrated system exhibits pronounced time-dependent displacement and stress, with dangerous values appearing during specific periods. These values are easily neglected, highlighting the importance of integrated time-dependent analysis. Construction activities, particularly the backfilling process, could cause a sudden change in stress and significantly impact the stress redistribution of the superstructure. Additionally, the mechanical properties of concrete have a significant impact on the stress on the superstructure, while the mechanical properties of the soil control the settlement of the integrated system.
Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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Open AccessArticle
Study of Steroid Estrogen Loss in Soil after the Application of Composted Manure as a Fertilizer
by
Jimeng Feng, Jian Shen, Yani Li, Lina Chi, Xinze Wang and Jiangping Qiu
Water 2024, 16(10), 1374; https://doi.org/10.3390/w16101374 (registering DOI) - 11 May 2024
Abstract
Steroid estrogens (SEs) play a significant role as endocrine-disrupting substances, and one of their major sources is animal manure. However, there is limited information available regarding the loss of SEs in farmland soil after the application of commercial composted animal manure or fertilizers.
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Steroid estrogens (SEs) play a significant role as endocrine-disrupting substances, and one of their major sources is animal manure. However, there is limited information available regarding the loss of SEs in farmland soil after the application of commercial composted animal manure or fertilizers. To address this gap, our study aimed to simulate rainfall and flood irrigation scenarios and investigate the loss characteristics of SEs, as well as Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP) in runoff from soil–manure mixtures. The results demonstrated that the loss concentrations of SEs (73.1 ng/L of the mean E2β active equivalent factor) presented a potential environmental risk. Additionally, substituting composted manure with commercial organic fertilizers lead to a significant reduction in TP (maximum 56%) and TN (maximum 24%) loss. Consequently, the application of commercial organic fertilizers offers considerable advantages in maintaining nitrogen and phosphorus fertilization efficiency while controlling SEs loss. Furthermore, our study explored the synergistic pollution mechanism among these pollutants and observed significant correlations between SEs and TN, TP, and COD loss concentrations, indicating the simultaneous occurrence and migration of these pollutants in agricultural non-point source pollution. These results provide valuable insights into the environmental risk associated with SEs from agricultural non-point sources.
Full article
(This article belongs to the Special Issue Contaminants of Emerging Concern in Soil and Water Environment)
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Open AccessArticle
An Image Analysis of River-Floating Waste Materials by Using Deep Learning Techniques
by
Maiyatat Nunkhaw and Hitoshi Miyamoto
Water 2024, 16(10), 1373; https://doi.org/10.3390/w16101373 (registering DOI) - 11 May 2024
Abstract
Plastic pollution in the ocean is a severe environmental problem worldwide because rivers carry plastic waste from human activities, harming the ocean’s health, ecosystems, and people. Therefore, monitoring the amount of plastic waste flowing from rivers and streams worldwide is crucial. In response
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Plastic pollution in the ocean is a severe environmental problem worldwide because rivers carry plastic waste from human activities, harming the ocean’s health, ecosystems, and people. Therefore, monitoring the amount of plastic waste flowing from rivers and streams worldwide is crucial. In response to this issue of river-floating waste, our present research aimed to develop an automated waste measurement method tailored for real rivers. To achieve this, we considered three scenarios: clear visibility, partially submerged waste, and collective mass. We proposed the use of object detection and tracking techniques based on deep learning architectures, specifically the You Only Look Once (YOLOv5) and Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT). The types of waste classified in this research included cans, cartons, plastic bottles, foams, glasses, papers, and plastics in laboratory flume experiments. Our results demonstrated that the refined YOLOv5, when applied to river-floating waste images, achieved high classification accuracy, with 88% or more for the mean average precision. The floating waste tracking using DeepSORT also attained F1 scores high enough for accurate waste counting. Furthermore, we evaluated the proposed method across the three different scenarios, each achieving an 80% accuracy rate, suggesting its potential applicability in real river environments. These results strongly support the effectiveness of our proposed method, leveraging the two deep learning architectures for detecting and tracking river-floating waste with high accuracy.
Full article
(This article belongs to the Special Issue Machine-Learning-Based Water Quality Monitoring)
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Open AccessArticle
Enhanced Photocatalytic Degradation of Tetracycline by Magnetically Separable g-C3N4-Doped Magnetite@Titanium Dioxide Heterostructured Photocatalyst
by
Rong Liu, Mingming Li, Jie Chen, Yu Yin, Wei Zhao, Zhanghao Gong, Hua Jin and Zhigang Liu
Water 2024, 16(10), 1372; https://doi.org/10.3390/w16101372 (registering DOI) - 11 May 2024
Abstract
Residual drug pollutants in water environments represent a severe risk to human health, so developing a cheap, environmentally friendly, and effective photocatalyst to deal with them has become a hot topic. Herein, a magnetically separable Fe3O4@TiO2/g-C3
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Residual drug pollutants in water environments represent a severe risk to human health, so developing a cheap, environmentally friendly, and effective photocatalyst to deal with them has become a hot topic. Herein, a magnetically separable Fe3O4@TiO2/g-C3N4 photocatalyst with a special heterojunction structure was fabricated, and its photocatalytic performance was assessed by degrading tetracycline (TC). Compared to Fe3O4@TiO2, the synthesized Fe3O4@TiO2/g-C3N4 exhibited superior TC degradation performance, which was primarily ascribed to the heterojunction formed between TiO2 and g-C3N4 and its ability to enhance the visible light absorption capacity and reduce the photoinduced electron/hole recombination rate. Moreover, a free radical capture experiment further confirmed that·O2− and h+ are the predominant components in the TC degradation reaction. Under UV–Vis irradiation, the TC degradation rate escalated to as high as 98% within 120 min. Moreover, Fe3O4@TiO2/g-C3N4 was demonstrated to be easily recovered by magnetic separation without any notable loss even after five cycles, showing exceptional stability and reusability. These findings indicate that Fe3O4@TiO2/g-C3N4 is a promising photocatalyst for environmental remediation that may provide a sustainable approach to degrading antibiotic pollutants in wastewater.
Full article
(This article belongs to the Special Issue Novel Insights on Wastewater Treatment Processes for Sustainable Removal of Emerging Contaminants)
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Open AccessArticle
The Effects of Climate Change on Streamflow, Nitrogen Loads, and Crop Yields in the Gordes Dam Basin, Turkey
by
Ayfer Özdemir, Martin Volk, Michael Strauch and Felix Witing
Water 2024, 16(10), 1371; https://doi.org/10.3390/w16101371 (registering DOI) - 11 May 2024
Abstract
The Mediterranean region is highly vulnerable to climate change. Longer and more intense heatwaves and droughts are expected. The Gordes Dam in Turkey provides drinking water for Izmir city and irrigation water for a wide range of crops grown in the basin. Using
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The Mediterranean region is highly vulnerable to climate change. Longer and more intense heatwaves and droughts are expected. The Gordes Dam in Turkey provides drinking water for Izmir city and irrigation water for a wide range of crops grown in the basin. Using the Soil and Water Assessment Tool (SWAT), this study examined the effects of projected climate change (RCP 4.5 and RCP 8.5) on the simulated streamflow, nitrogen loads, and crop yields in the basin for the period of 2031–2060. A hierarchical approach to define the hydrological response units (HRUs) of SWAT and the Fast Automatic Calibration Tool (FACT) were used to reduce computational time and improve model performance. The simulations showed that the average annual discharge into the reservoir is projected to increase by between 0.7 m3/s and 4 m3/s under RCP 4.5 and RCP 8.5 climate change scenarios. The steep slopes and changes in precipitation in the study area may lead to higher simulated streamflow. In addition, the rising temperatures predicted in the projections could lead to earlier spring snowmelt. This could also lead to increased streamflow. Projected nitrogen loads increased by between 8.8 and 25.1 t/year. The results for agricultural production were more variable. While the yields of poppy, tobacco, winter barley, and winter wheat will increase to some extent because of climate change, the yields of maize, cucumbers, and potatoes are all predicted to be negatively affected. Non-continuous and limited data on water quality and crop yields lead to uncertainties, so that the accuracy of the model is affected by these limitations and inconsistencies. However, the results of this study provide a basis for developing sustainable water and land management practices at the catchment scale in response to climate change. The changes in water quality and quantity and the ecological balance resulting from changes in land use and management patterns for economic benefit could not be fully demonstrated in this study. To explore the most appropriate management strategies for sustainable crop production, the SWAT model developed in this study should be further used in a multi-criteria land use optimization analysis that considers not only crop yields but also water quantity and quality targets.
Full article
(This article belongs to the Topic Hydrology and Water Resources Management)
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Open AccessArticle
Study on Seismic Source Parameter Characteristics of Baihetan Reservoir Area in the Lower Reaches of the Jinsha River
by
Jing Shi, Cuiping Zhao, Zhousheng Yang and Lisheng Xu
Water 2024, 16(10), 1370; https://doi.org/10.3390/w16101370 (registering DOI) - 11 May 2024
Abstract
The source parameters of earthquakes (stress drop, corner frequency, seismic moment, source size, radiant energy, etc.) provide important information about the source features, the state of stress, and the mechanism of earthquake rupture dynamics. Using the digital observation data obtained from a high-density
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The source parameters of earthquakes (stress drop, corner frequency, seismic moment, source size, radiant energy, etc.) provide important information about the source features, the state of stress, and the mechanism of earthquake rupture dynamics. Using the digital observation data obtained from a high-density seismic monitoring network deployed in the Baihetan reservoir area of the lower Jinsha River, we obtained Brune source parameters of the 459 earthquakes ranging in magnitude ML 1.50~4.70. The results revealed seismic moments M0 within the range of 2.03 × 1012~1.45 × 1016 N·m, corner frequencies between 2.00 and 10.00 Hz, and source dimensions varying from 130.00 to 480.00 m, with stress drops spanning from 0.12 to 61.24 MPa. It is noteworthy that the majority of the earthquakes had stress drops less than 10.00 MPa, with as much as 73.30% of these events having stress drops within the range of 0.10 to 2.00 MPa. We found that stress drop, corner frequency, and source size in the study area exhibited positive correlations with earthquake magnitude. Earthquakes occurring at shallower depths for the same magnitude tended to have smaller stress drops and corner frequencies, but larger rupture scales. During the first 2 years of impoundment with significant water level fluctuation, earthquakes beneath or near the reservoir released higher stress drops relative to pre-reservoir conditions, with average stress drops significantly elevated from 5.52 to 13.562 Mpa for events above ML3 since the impoundment. The radiated energy released by earthquakes with magnitudes below ML3.0 are significantly more than before impoundment, indicating that earthquakes of similar magnitudes in the reservoir area may produce greater intensity and perceptibility following the impoundment. According to our result, the triggered seismicity will continue to be active under annual regulation changes in the water level of the Baihetan Dam at high elevations in future years.
Full article
(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)
Open AccessArticle
Assessment of Microplastic Pollution in River Ecosystems: Effect of Land Use and Biotic Indices
by
David Gutiérrez-Rial, Iria Villar, Romina Álvarez-Troncoso, Benedicto Soto, Salustiano Mato and Josefina Garrido
Water 2024, 16(10), 1369; https://doi.org/10.3390/w16101369 (registering DOI) - 11 May 2024
Abstract
The proximity of freshwater ecosystems to anthropogenic activities makes them one of the most threatened environments by plastic pollution in the form of microplastics (MPs). Therefore, it is crucial to identify the primary drivers of MP dynamics in rivers to enhance their management.
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The proximity of freshwater ecosystems to anthropogenic activities makes them one of the most threatened environments by plastic pollution in the form of microplastics (MPs). Therefore, it is crucial to identify the primary drivers of MP dynamics in rivers to enhance their management. This work analyzed the concentration of MPs in water and sediments and evaluated the influence of land use and its relationship with the main biotic indices employed to assess the water quality of rivers. This research was carried out in four different catchments, with three sampling points established in each river basin. The results revealed that MPs were ubiquitous across all locations, with concentrations ranging from 0.10 to 35.22 items m−3 in waters and from 26 to 643 items Kg−1 in sediments. The highest concentration of MPs both in water and sediments were found in the Lagares River (35.22 items m−3 and 643 items Kg−1), while the lowest concentrations were found in the Miñor River for water (0.10 items m−3) and Tea River for sediments (138 items Kg−1). Urbanization degree was identified as the primary driver of MP pollution in water, whereas population density correlated with sediment pollution levels. These findings explain the elevated MPs abundance in the more urbanized and populated Gafos and Lagares rivers compared to the relatively pristine Miñor and Tea rivers. Furthermore, the presence of MPs in sediments was found to negatively impact the most sensitive benthic macroinvertebrate taxa, as evidenced by lower values of the IASPT and EPT indices at sampling points with higher sediment MPs concentrations (Gafos and Lagares).
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(This article belongs to the Special Issue Water Quality Assessment of River Basins)
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Open AccessArticle
Repeatability of Hydroacoustic Results versus Uncertainty in Assessing Changes in Ecological Status Based on Fish: A Case Study of Lake Widryńskie (Poland)
by
Andrej Hutorowicz
Water 2024, 16(10), 1368; https://doi.org/10.3390/w16101368 (registering DOI) - 11 May 2024
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Assessments of changes in the ecological state of aquatic ecosystems are always burdened with uncertainty, which results from environmental reasons and poor repeatability of measurement results of elements enabling the assessment. This study determines the uncertainty related to the elements of the assessment
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Assessments of changes in the ecological state of aquatic ecosystems are always burdened with uncertainty, which results from environmental reasons and poor repeatability of measurement results of elements enabling the assessment. This study determines the uncertainty related to the elements of the assessment of the hydroacoustic structure of fish communities’ (1) vertical target strength distribution (TS) in two-meter layers of water and (2) changes in the area where fish were recorded (which was determined on the basis of maps of their distribution in 2 m deep water layers). The object of this study was a lake (depth: 27 m) in which at the end of June 2016 the O2 concentration was <1.4 mg L−1 below 8 m depth, which resulted in the accumulation of fish to a depth of 6 m. Hydroacoustic acquisition was carried out along transects arranged in the east–west (WE), north–south (NS), and zigzag (ZZ) directions in three repetitions. It was shown that the empirical probability of obtaining statistically different results was 2/9 when (1) Kendall’s τ coefficient, used to determine the similarity of the TS distribution, was less than 0.7—moderate correlation—and (2) fish occurrence areas in two cases (WE and ZZ on the third day of research) in layers 2–4 m and 4–6 m differed statistically significantly from the average area for all repetitions by 10–14% and 56–66% (p < 0.05), respectively. The obtained results indicate quite good repeatability of acoustic measurements; however, in order to reduce the uncertainty, it is recommended that tests be conducted in this type of lake in three series of measurements.
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Open AccessArticle
Do Water Transfer Projects Promote Water Use Efficiency? Case Study of South-to-North Water Transfer Project in Yellow River Basin of China
by
Li Ma and Qi Wang
Water 2024, 16(10), 1367; https://doi.org/10.3390/w16101367 (registering DOI) - 11 May 2024
Abstract
With a huge capital and labor input influx, inter-basin water transfer (IBWT) projects have been shown to effectively mitigate water stress and ensure the water demand for social and economic development in the receiving area. Whether they have promoted the improvement of regional
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With a huge capital and labor input influx, inter-basin water transfer (IBWT) projects have been shown to effectively mitigate water stress and ensure the water demand for social and economic development in the receiving area. Whether they have promoted the improvement of regional water use efficiency (WUE) is crucial for sustainable management of regional water resources. Targeting the South-to-North Water Transfer Project (SNWTP), the largest and most ambitious inter-basin water transfer project in China, this study establishes quantitatively econometric models to analyze the impact of different water diversion projects, specifically the eastern route of the SNWTP (ER-SNWTP), middle route of the SNWTP (MR-SNWTP), and diversion from the main stream of the Yellow River (DYR), on the regional water consumption per unit of GDP; regional water stress, water use structure, economic structure, and urbanization level are used as control variables in different types of cities in the Yellow River Basin, and some intriguing results are found. While the overall water transfer project demonstrates a positive impact on water use efficiency, the effects of the three water transfer measures vary significantly. The ER-SNWTP does not exhibit a notable positive effect on regional water use efficiency, whereas the MR-SNWTP demonstrates a significant positive impact. Interestingly, the DYR has a notable negative influence on water use efficiency in developed cities. The water use structure, shaped by the pricing, scale, and policies of different projects, emerges as a pivotal factor in explaining these differences. Finally, this paper suggests that the impact of water transfer projects on the improvement of regional water use efficiency be viewed from a more comprehensive and developmental perspective.
Full article
(This article belongs to the Special Issue Socio-Economics of Water Resources Management)
Open AccessArticle
Vulnerability Assessment of Groundwater Influenced Ecosystems in the Northeastern United States
by
Shawn D. Snyder, Cynthia S. Loftin and Andrew S. Reeve
Water 2024, 16(10), 1366; https://doi.org/10.3390/w16101366 (registering DOI) - 11 May 2024
Abstract
Groundwater-influenced ecosystems (GIEs) are increasingly vulnerable due to groundwater extraction, land-use practices, and climate change. These ecosystems receive groundwater inflow as a portion of their baseflow or water budget, which can maintain water levels, water temperature, and chemistry necessary to sustain the biodiversity
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Groundwater-influenced ecosystems (GIEs) are increasingly vulnerable due to groundwater extraction, land-use practices, and climate change. These ecosystems receive groundwater inflow as a portion of their baseflow or water budget, which can maintain water levels, water temperature, and chemistry necessary to sustain the biodiversity that they support. In some systems (e.g., springs, seeps, fens), this connection with groundwater is central to the system’s integrity and persistence. Groundwater management decisions for human use often do not consider the ecological effects of those actions on GIEs. This disparity can be attributed, in part, to a lack of information regarding the physical relationships these systems have with the surrounding landscape and climate, which may influence the environmental conditions and associated biodiversity. We estimate the vulnerability of areas predicted to be highly suitable for the presence of GIEs based on watershed (U.S. Geological Survey Hydrologic Unit Code 12 watersheds: 24–100 km2) and pixel (30 m × 30 m pixels) resolution in the Atlantic Highlands and Mixed Wood Plains EPA Level II Ecoregions in the northeastern United States. We represent vulnerability with variables describing adaptive capacity (topographic wetness index, hydric soil, physiographic diversity), exposure (climatic niche), and sensitivity (aquatic barriers, proportion urbanized or agriculture). Vulnerability scores indicate that ~26% of GIEs were within 30 m of areas with moderate vulnerability. Within these GIEs, climate exposure is an important contributor to vulnerability of 40% of the areas, followed by land use (19%, agriculture or urbanized). There are few areas predicted to be suitable for GIEs that are also predicted to be highly vulnerable, and of those, climate exposure is the most important contributor to their vulnerability. Persistence of GIEs in the northeastern United States may be challenged as changes in the amount and timing of precipitation and increasing air temperatures attributed to climate change affect the groundwater that sustains these systems.
Full article
(This article belongs to the Special Issue Aquatic Ecosystems Health Assessment Using Biological and Geospatial Analyses)
Open AccessArticle
Remote Measurement of Tide and Surge Using a Deep Learning System with Surveillance Camera Images
by
Gaetano Sabato, Giovanni Scardino, Alok Kushabaha, Giulia Casagrande, Marco Chirivì, Giorgio Fontolan, Saverio Fracaros, Antonio Luparelli, Sebastian Spadotto and Giovanni Scicchitano
Water 2024, 16(10), 1365; https://doi.org/10.3390/w16101365 (registering DOI) - 11 May 2024
Abstract
The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for
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The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments, providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions.
Full article
(This article belongs to the Section Oceans and Coastal Zones)
Open AccessArticle
Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020
by
Chaoxu Pu, Shuaibo Zhou, Peijun Sun, Yunchuan Luo, Siyi Li and Zhangli Sun
Water 2024, 16(10), 1364; https://doi.org/10.3390/w16101364 (registering DOI) - 11 May 2024
Abstract
The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020
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The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.
Full article
Open AccessArticle
Sorption-Based Removal Techniques for Microplastic Contamination of Tap Water
by
Natalya S. Salikova, Almagul R. Kerimkulova, Javier Rodrigo-Ilarri, Kulyash K. Alimova, María-Elena Rodrigo-Clavero and Gulzhanat A. Kapbassova
Water 2024, 16(10), 1363; https://doi.org/10.3390/w16101363 (registering DOI) - 11 May 2024
Abstract
This study investigates the presence of microplastics in tap drinking water and evaluates the efficacy of various sorbents for their removal in the context of Kazakhstan’s water treatment system. Water samples taken in the cities of Kokshetau and Krasny Yar (Akmola region) were
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This study investigates the presence of microplastics in tap drinking water and evaluates the efficacy of various sorbents for their removal in the context of Kazakhstan’s water treatment system. Water samples taken in the cities of Kokshetau and Krasny Yar (Akmola region) were analyzed. Microplastics were detected in all samples, with concentrations ranging from 2.0 × 10−2 to 6.0 × 10−2 particles/dm3, predominantly in fiber form (74.1%). Outdated technologies and non-compliance with treatment regimens contribute to poor water quality, including high turbidity (87% of samples), color deviations (40% of samples), and acidity issues (20% of samples). To address these challenges, the study examined the sorption efficiency of different sorbents, with results indicating high retention rates (82.7–97.8%) for microplastic particles. Notably, aliphatic structures like PE and PP exhibited higher retention than PET. Among the sorbents tested, the synthesized carbon sorption material (CSM) demonstrated the highest efficiency in both microplastic retention and improvement in water quality parameters, making it a promising option for water treatment facilities and household filters.
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(This article belongs to the Topic Microplastics Pollution)
Open AccessArticle
An Efficient Water Quality Prediction and Assessment Method Based on the Improved Deep Belief Network—Long Short-Term Memory Model
by
Zhiyao Zhao, Bing Fan and Yuqin Zhou
Water 2024, 16(10), 1362; https://doi.org/10.3390/w16101362 (registering DOI) - 11 May 2024
Abstract
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The accuracy of water quality prediction and assessment has always been the focus of environmental departments. However, due to the high complexity of water systems, existing methods struggle to capture the future internal dynamic changes in water quality based on current data. In
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The accuracy of water quality prediction and assessment has always been the focus of environmental departments. However, due to the high complexity of water systems, existing methods struggle to capture the future internal dynamic changes in water quality based on current data. In view of this, this paper proposes a data-driven approach to combine an improved deep belief network (DBN) and long short-term memory (LSTM) network model for water quality prediction and assessment, avoiding the complexity of constructing a model of the internal mechanism of water quality. Firstly, using Gaussian Restricted Boltzmann Machines (GRBMs) to construct a DBN, the model has a better ability to extract continuous data features compared to classical DBN. Secondly, the extracted time-series data features are input into the LSTM network to improve predicting accuracy. Finally, due to prediction errors, noise that randomly follows the Gaussian distribution is added to the assessment results based on the predicted values, and the probability of being at the current water quality level in the future is calculated through multiple evolutionary computations to complete the water quality assessment. Numerical experiments have shown that our proposed algorithm has a greater accuracy compared to classical algorithms in challenging scenarios.
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Open AccessReview
The Role of Ferrate (VI) in the Pretreatment of Algal Cells and Algal Organic Matters: A Review
by
Saige Wang, Shuyi Yang, Huan Chen and Qiufeng Lin
Water 2024, 16(10), 1361; https://doi.org/10.3390/w16101361 (registering DOI) - 11 May 2024
Abstract
Algal blooms are caused by excessive levels of nitrogen, phosphorus, and other plant nutrients in water. Algae and algal organic matter (AOM) pose a great threat to the quality of drinking water. This manuscript offers a systematic review of algal removal by ferrate
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Algal blooms are caused by excessive levels of nitrogen, phosphorus, and other plant nutrients in water. Algae and algal organic matter (AOM) pose a great threat to the quality of drinking water. This manuscript offers a systematic review of algal removal by ferrate (Fe(VI)) oxidation, including the conditions for the removal of different algae by Fe(VI) and the factors affecting the removal efficiency. On this basis, the oxidation and coagulation mechanisms of algae removal by Fe(VI) are discussed. Then, the review introduces the process combining Fe(VI) pre-oxidation with aluminum sulfate action. The addition of aluminum sulfate can further enhance the coagulation effect and reduce the formation of disinfection byproducts (DBPs) in the subsequent chlorination process by effectively removing AOM, which is recognized as a precursor of DBPs. In addition, recent studies on the combined application of Fe(VI) and Fe(II) are also reviewed. In a reasonable dose range, the synergistic effect of Fe(VI) and Fe(II) can significantly improve the removal of algae and algal toxins. Finally, this review provides a comprehensive evaluation of the applicability of Fe(VI) in removing algal material, offers guidance for the harmless treatment of algae with Fe(VI), and identifies future research questions.
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(This article belongs to the Special Issue Removal of Trace Organic Pollutants in Water Using Advanced Oxidation Technology)
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Open AccessArticle
Polystyrene Plastic Particles Result in Adverse Outcomes for Hyalella azteca When Exposed at Elevated Temperatures
by
Felix Biefel, Susanne M. Brander, Richard E. Connon and Juergen Geist
Water 2024, 16(10), 1360; https://doi.org/10.3390/w16101360 (registering DOI) - 10 May 2024
Abstract
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Micro- and nano-plastics are pervasive pollutants in global ecosystems, yet their interactions with aquatic wildlife and abiotic factors are poorly understood. These particles are recognized to cause subtle detrimental effects, underscoring the necessity for sensitive endpoints in ecotoxicological exposure studies. We investigated the
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Micro- and nano-plastics are pervasive pollutants in global ecosystems, yet their interactions with aquatic wildlife and abiotic factors are poorly understood. These particles are recognized to cause subtle detrimental effects, underscoring the necessity for sensitive endpoints in ecotoxicological exposure studies. We investigated the effects of particle uptake, size, and temperature on Hyalella azteca. Organisms were exposed to blue fluorescent polystyrene beads (500 nm and 1000 nm in diameter) at 0.43 mg/L for 96 h at temperatures mirroring climate predictions (21 °C, 24 °C, 27 °C). Besides survival and growth, particle uptake, visualized via confocal microscopy, and swimming behavior were analyzed. Mortality rates increased at 27 °C, and particle presence and temperature affected organism growth. Particle treatments influenced various behaviors (thigmotaxis, cruising, movement, acceleration, meander, zone alternation, and turn angle), with hypoactivity observed with 1000 nm particles and hypo- as well as hyper-activity responses with 500 nm particles. Particle uptake quantities were variable and increased with temperature in 500 nm treatments, but no migration beyond the gut was observed. Particle size correlated with uptake, and relationships with behavior were evident. Elevated temperatures exacerbated particle effects, highlighting the urgency of addressing plastic pollution in light of climate change for aquatic organism welfare and ecosystem health.
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Open AccessArticle
Application of Oversampling Techniques for Enhanced Transverse Dispersion Coefficient Estimation Performance Using Machine Learning Regression
by
Sunmi Lee and Inhwan Park
Water 2024, 16(10), 1359; https://doi.org/10.3390/w16101359 (registering DOI) - 10 May 2024
Abstract
The advection–dispersion equation has been widely used to analyze the intermediate field mixing of pollutants in natural streams. The dispersion coefficient, manipulating the dispersion term of the advection–dispersion equation, is a crucial parameter in predicting the transport distance and contaminated area in the
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The advection–dispersion equation has been widely used to analyze the intermediate field mixing of pollutants in natural streams. The dispersion coefficient, manipulating the dispersion term of the advection–dispersion equation, is a crucial parameter in predicting the transport distance and contaminated area in the water body. In this study, the transverse dispersion coefficient was estimated using machine learning regression methods applied to oversampled datasets. Previous research datasets used for this estimation were biased toward width-to-depth ratio (W/H) values ≤ 50, potentially leading to inaccuracies in estimating the transverse dispersion coefficient for datasets with W/H > 50. To address this issue, four oversampling techniques were employed to augment the dataset with W/H > 50, thereby mitigating the dataset’s imbalance. The estimation results obtained from data resampling with nonlinear regression method demonstrated improved prediction accuracy compared to the pre-oversampling results. Notably, the combination of adaptive synthetic sampling (ADASYN) and eXtreme Gradient Boosting regression (XGBoost) exhibited improved accuracy compared to other combinations of oversampling techniques and nonlinear regression methods. Through the combined ADASYN–XGBoost approach, it is possible to enhance the transverse dispersion coefficient estimation performance using only two variables, W/H and bed friction effects (U/U*), without adding channel sinuosity; this represents the effects of secondary currents.
Full article
(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)
Open AccessArticle
Connecting Water Quality and Ecosystem Services for Valuation and Assessment of a Groundwater Reserve Area in South-East Mexico
by
Myrna L. López-Monzalvo, Eduardo Batllori-Sampedro, Jairo A. Ayala-Godoy, Eugenio Guerrero-Ruiz and Laura M. Hernández-Terrones
Water 2024, 16(10), 1358; https://doi.org/10.3390/w16101358 (registering DOI) - 10 May 2024
Abstract
Even though the role of ecosystem services is known, the identification and assessment of water-related services is usually absent or often less represented as an ecosystem service. Progress in water quality indicator definition and compliance with regulations has been made; however, the relationship
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Even though the role of ecosystem services is known, the identification and assessment of water-related services is usually absent or often less represented as an ecosystem service. Progress in water quality indicator definition and compliance with regulations has been made; however, the relationship between water quality degradation and benefits to individuals and ecosystems remains little recognized. Here, we present an assessment of water quality and identification of ecosystem services in south-east Mexico. This study was performed within the geohydrological reserve zone of the Ring of Sinkholes, Yucatán Peninsula. Thirteen ecosystem services provided by the aquifer were identified. Water quality was evaluated in sinkholes based on national and international norms, considering different sinkhole uses. Results show a dynamic system, without saltwater intrusion and good to excellent water quality. The research demonstrates the relationship between ecosystem services and water quality, showing pressure in services related to uses for aquatic life protection and to a lesser extent those related to consumption. Current productive activities showed no pressure at this time. Principal Component Analysis (PCA) and Analysis of Variance (ANOVA) exhibited a significant difference in parameters and campaigns, but not between sinkholes. A long-lasting monitoring program for water quality is necessary to accurately evaluate the status of ecosystem services provided by the aquifer. Moreover, it is necessary to assess aquifers as ecosystems with economic, ecologic and socio-cultural importance. Effective water governance requires a balance of interests between all parties, within a legal and institutional framework.
Full article
(This article belongs to the Section Water Quality and Contamination)
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