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
Flood Water Depth Prediction with Convolutional Temporal Attention Networks
Water 2024, 16(9), 1286; https://doi.org/10.3390/w16091286 (registering DOI) - 30 Apr 2024
Abstract
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Robust and accurate flood hazard maps are essential for early warning systems and flood risk management. Although physically based models are effective in estimating pluvial flooding, the computational burden makes them difficult to use for real-time flood prediction. In contrast, data-driven models can
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Robust and accurate flood hazard maps are essential for early warning systems and flood risk management. Although physically based models are effective in estimating pluvial flooding, the computational burden makes them difficult to use for real-time flood prediction. In contrast, data-driven models can provide faster flood predictions if trained offline. While most studies have focused on predicting maximum water depth, in this study, we predict pixel-wise water depth maps for entire catchments at a lead time of 2 h. To that end, we propose a deep learning approach that uses a sequence encoding network with temporal self-attention. We also adapt the popular hydrological performance metric Nash–Sutcliffe efficiency (NSE) as our loss function. We test the effectiveness and generalizability of our method using a new dataset called SwissFlood, which consists of 100 catchments and 1500 rainfall events extracted from real observations in Switzerland. Our method produces 2 m spatial resolution flood maps with absolute error as low as 27 cm for water depth exceeding 1 m.
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Open AccessArticle
A Case Study and Numerical Modeling of Post-Wildfire Debris Flows in Montecito, California
by
Diwakar K. C., Mohammad Wasif Naqvi and Liangbo Hu
Water 2024, 16(9), 1285; https://doi.org/10.3390/w16091285 (registering DOI) - 30 Apr 2024
Abstract
Wildfires and their long-term impacts on the environment have become a major concern in the last few decades, in which climate change and enhanced anthropogenic activities have gradually led to increasingly frequent events of such hazards or disasters. Geological materials appear to become
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Wildfires and their long-term impacts on the environment have become a major concern in the last few decades, in which climate change and enhanced anthropogenic activities have gradually led to increasingly frequent events of such hazards or disasters. Geological materials appear to become more vulnerable to hazards including erosion, floods, landslides and debris flows. In the present study, the well-known 2017 wildfire and subsequent 2018 debris flows in the Montecito area of California are examined. It is found that the post-wildfire debris flows were initiated from erosion and entrainment processes and triggered by intense rainfall. The significant debris deposition in four major creeks in this area is investigated. Numerical modeling of the post-wildfire debris flows is performed by employing a multi-phase mass flow model to simulate the growth in the debris flows and eventual debris deposition. The debris-flow-affected areas estimated from the numerical simulations fairly represent those observed in the field. Overall, the simulated debris deposits are within 7% error of those estimated based on field observations. A similar simulation of the pre-wildfire scenario indicates that the debris would be much less significant. The present study shows that proper numerical simulations can be a promising tool for estimating post-wildfire erosion and the debris-affected areas for hazard assessment and mitigation.
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(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
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Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods
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Yue Zhang, Zimo Zhou, Ying Deng, Daiwei Pan, Jesse Van Griensven Thé, Simon X. Yang and Bahram Gharabaghi
Water 2024, 16(9), 1284; https://doi.org/10.3390/w16091284 - 30 Apr 2024
Abstract
Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and
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Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square ( ) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network.
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(This article belongs to the Special Issue Managing Impacts on Baseflows in Streams and the Associated Impacts on Ecosystems and Water Quality)
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The Use of Boosted Regression Trees to Predict the Occurrence and Quantity of Staphylococcus aureus in Recreational Marine Waterways
by
Bridgette F. Froeschke, Michelle Roux-Osovitz, Margaret L. Baker, Ella G. Hampson, Stella L. Nau and Ashley Thomas
Water 2024, 16(9), 1283; https://doi.org/10.3390/w16091283 - 30 Apr 2024
Abstract
Microbial monitoring in marine recreational waterways often overlooks environmental variables associated with pathogen occurrence. This study employs a predictive boosted regression trees (BRT) model to predict Staphylococcus aureus abundance in the Tampa Bay estuary and identify related environmental variables associated with the microbial
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Microbial monitoring in marine recreational waterways often overlooks environmental variables associated with pathogen occurrence. This study employs a predictive boosted regression trees (BRT) model to predict Staphylococcus aureus abundance in the Tampa Bay estuary and identify related environmental variables associated with the microbial pathogen’s occurrence. We provide evidence that the BRT model’s adaptability and ability to capture complex interactions among predictors make it invaluable for research on microbial indicator research. Over 18 months, water samples from 7 recreational sites underwent microbial quantitation and S. aureus isolation, followed by genetic validation. BRT analysis of S. aureus occurrence and environmental variables revealed month, precipitation, salinity, site, temperature, and year as relevant predictors. In addition, the BRT model accurately predicted S. aureus occurrence, setting a precedent for pathogen–environment research. The approach described here is novel and informs proactive management strategies and community health initiatives in marine recreational waterways.
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(This article belongs to the Section Oceans and Coastal Zones)
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Open AccessArticle
Proper Orthogonal Decomposition Based Response Analysis of Inlet Distortion on a Waterjet Pump
by
Puyu Cao, Rui Yue, Jinfeng Zhang, Xinrui Liu, Gang Wu and Rui Zhu
Water 2024, 16(9), 1282; https://doi.org/10.3390/w16091282 - 29 Apr 2024
Abstract
This study addresses the challenge of performance degradation in waterjet pumps due to non-uniform suction flow. Utilizing the Proper Orthogonal Decomposition (POD) method, it decomposes and reconstructs the flow features within a waterjet pump under non-uniform inflow into a series of modes ranked
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This study addresses the challenge of performance degradation in waterjet pumps due to non-uniform suction flow. Utilizing the Proper Orthogonal Decomposition (POD) method, it decomposes and reconstructs the flow features within a waterjet pump under non-uniform inflow into a series of modes ranked in descending order of energy. By analyzing the modes with dominant energy, which contain complex information about the flow field, it is revealed that modes 1 and 2 predominantly represent the formation of a concentrated vortex, whereas modes 3 and 4 illustrate its spatial offset. Notably, in the hub section, mode 3 exhibits a delayed flow separation caused by the reduction of circumferential vortex (CV), with a consequent lift in blade loading at the leading edge and a higher head compared to mode 1. In the shroud section, the delayed flow separation in mode 3 suppressed reverse flow and the concentrated separation vortex (CSV) and then increased the blade loading, ultimately enhancing the pump head. The findings provide significant insights into optimizing waterjet pump performance by detailing the interactions between various flow structures and pump components, effectively filling a knowledge gap in applying dimensionality reduction techniques within the distorted flow fields of water jet pumps.
Full article
(This article belongs to the Special Issue Design and Optimization of Fluid Machinery)
Open AccessArticle
Effective Degradation of 1,4-Dioxane by UV-Activated Persulfate: Mechanisms, Parameters and Environmental Impacts
by
Xiuneng Zhu, Jie Qiu, Yexing Wang, Yulin Tang and Yongji Zhang
Water 2024, 16(9), 1281; https://doi.org/10.3390/w16091281 - 29 Apr 2024
Abstract
There is more and more research focusing on the removal of dioxane by advanced oxidation technology at this stage, and this study investigated the efficacy of an advanced oxidation system with UV-activated persulfate (UV/PDS). This method had the advantages of fast reaction rate,
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There is more and more research focusing on the removal of dioxane by advanced oxidation technology at this stage, and this study investigated the efficacy of an advanced oxidation system with UV-activated persulfate (UV/PDS). This method had the advantages of fast reaction rate, simple equipment and convenient operation. Free radical quenching test and electron paramagnetic resonance (EPR) analysis showed that the main active radicals in the reaction system were and ·OH. This study also investigated that the optimal parameters were the initial PDS dosage of 3 mM, the UV intensity of 0.190 mM/cm2, the pH between 5 and 7 and the initial dioxane concentration of 50 mg/L. Additionally, after a reaction time of 150 min, the total organic carbon (TOC) content still remained at 83.53%, which revealed that the mineralization degree of organic matter was not fully achieved through UV/PDS treatment. The concentration of in the reaction system was 74.69 mg·L−1, which complied with the standard concentration specified. Furthermore, the cytotoxicity of the system exhibited an initial increase followed by a subsequent decrease, under the influence of the intermediates. It showed that the technology could efficiently degrade organic pollutants.
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(This article belongs to the Section Water Quality and Contamination)
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Characterization of a Contaminated Site Using Hydro-Geophysical Methods: From Large-Scale ERT Surface Investigations to Detailed ERT and GPR Cross-Hole Monitoring
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Mirko Pavoni, Jacopo Boaga, Luca Peruzzo, Ilaria Barone, Benjamin Mary and Giorgio Cassiani
Water 2024, 16(9), 1280; https://doi.org/10.3390/w16091280 - 29 Apr 2024
Abstract
This work presents the results of an advanced geophysical characterization of a contaminated site, where a correct understanding of the dynamics in the unsaturated zone is fundamental to evaluate the effective management of the remediation strategies. Large-scale surface electrical resistivity tomography (ERT) was
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This work presents the results of an advanced geophysical characterization of a contaminated site, where a correct understanding of the dynamics in the unsaturated zone is fundamental to evaluate the effective management of the remediation strategies. Large-scale surface electrical resistivity tomography (ERT) was used to perform a preliminary assessment of the structure in a thick unsaturated zone and to detect the presence of a thin layer of clay supporting an overlying thin perched aquifer. Discontinuities in this clay layer have an enormous impact on the infiltration processes of both water and solutes, including contaminants. In the case here presented, the technical strategy is to interrupt the continuity of the clay layer upstream of the investigated site in order to prevent most of the subsurface water flow from reaching the contaminated area. Therefore, a deep trench was dug upstream of the site and, in order to evaluate the effectiveness of this approach in facilitating water infiltration into the underlying aquifer, a forced infiltration experiment was carried out and monitored using ERT and ground-penetrating radar (GPR) measurements in a cross-hole time-lapse configuration. The results of the forced infiltration experiment are presented here, with a particular emphasis on the contribution of hydro-geophysical methods to the general understanding of the subsurface water dynamics at this complex site.
Full article
(This article belongs to the Special Issue Application of Geophysical Methods for Hydrogeology)
Open AccessArticle
Prediction of Diffuse Attenuation Coefficient Based on Informer: A Case Study of Hangzhou Bay and Beibu Gulf
by
Rongyang Cai, Miao Hu, Xiulin Geng, Mohammed Khalil Ibrahim and Chunhui Wang
Water 2024, 16(9), 1279; https://doi.org/10.3390/w16091279 (registering DOI) - 29 Apr 2024
Abstract
Marine water quality significantly impacts human livelihoods and production such as fisheries, aquaculture, and tourism. Satellite remote sensing facilitates the predictions of large-area marine water quality without the need for frequent field work and sampling. Prediction of diffuse attenuation coefficient (Kd), which describes
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Marine water quality significantly impacts human livelihoods and production such as fisheries, aquaculture, and tourism. Satellite remote sensing facilitates the predictions of large-area marine water quality without the need for frequent field work and sampling. Prediction of diffuse attenuation coefficient (Kd), which describes the speed at which light decays as it travels through water, obtained from satellite-derived ocean color products can reflect the overall water quality trends. However, current models inadequately explore the complex nonlinear features of Kd, and there are difficulties in achieving accurate long-term predictions and optimal computational efficiency. This study innovatively proposes a model called Remote Sensing-Informer-based Kd Prediction (RSIKP). The proposed RSIKP is characterized by a distinctive Multi-head ProbSparse self-attention mechanism and generative decoding structure. It is designed to comprehensively and accurately capture the long-term variation characteristics of Kd in complex water environments while avoiding error accumulation, which has a significant advantage in multi-dataset experiments due to its high efficiency in long-term prediction. A multi-dataset experiment is conducted at different prediction steps, using 70 datasets corresponding to 70 study areas in Hangzhou Bay and Beibu Gulf. The results show that RSIKP outperforms the five prediction models based on Artificial Neural Networks (ANN, Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Long Short-Term Memory Networks (LSTM)). RSIKP captures the complex influences on Kd more effectively to achieve higher prediction accuracy compared to other models. It shows a mean improvement of 20.6%, 31.1%, and 22.9% on Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Particularly notable is its outstanding performance in the long time-series predictions of 60 days. This study develops a cost-effective and accurate method of marine water quality prediction, providing an effective prediction tool for marine water quality management.
Full article
(This article belongs to the Special Issue Application of Satellite Remote Sensing in Water Quality Monitoring)
Open AccessArticle
Gas Fracturing Simulation of Shale-Gas Reservoirs Considering Damage Effects and Fluid–Solid Coupling
by
Enze Qi, Fei Xiong, Yun Zhang, Linchao Wang, Yi Xue and Yingpeng Fu
Water 2024, 16(9), 1278; https://doi.org/10.3390/w16091278 - 29 Apr 2024
Abstract
With the increasing demand for energy and the depletion of traditional resources, the development of alternative energy sources has become a critical issue. Shale gas, as an abundant and widely distributed resource, has great potential as a substitute for conventional natural gas. However,
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With the increasing demand for energy and the depletion of traditional resources, the development of alternative energy sources has become a critical issue. Shale gas, as an abundant and widely distributed resource, has great potential as a substitute for conventional natural gas. However, due to the low permeability of shale-gas reservoirs, efficient extraction poses significant challenges. The application of hydraulic fracturing technology has been proven to effectively enhance rock permeability, but the influence of environmental factors on its efficiency remains unclear. In this study, we investigate the impact of gas fracturing on shale-gas extraction efficiency under varying environmental conditions using numerical simulations. Our simulations provide a comprehensive analysis of the physical changes that occur during the fracturing process, allowing us to evaluate the effects of gas fracturing on rock mechanics and permeability. We find that gas fracturing can effectively induce internal fractures within the rock, and the magnitude of tensile stress decreases gradually during the process. The boundary pressure of the rock mass is an important factor affecting the effectiveness of gas fracturing, as it exhibits an inverse relationship with the gas content present within the rock specimen. Furthermore, the VL constant demonstrates a direct correlation with gas content, while the permeability and PL constant exhibit an inverse relationship with it. Our simulation results provide insights into the optimization of gas fracturing technology under different geological parameter conditions, offering significant guidance for its practical applications.
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(This article belongs to the Section Hydraulics and Hydrodynamics)
Open AccessArticle
Denitrification Performance and Microbiological Mechanisms Using Polyglycolic Acid as a Carbon Source
by
Zhichao Wang, Chenxi Li, Wenhuan Yang, Yuxia Wei and Weiping Li
Water 2024, 16(9), 1277; https://doi.org/10.3390/w16091277 - 29 Apr 2024
Abstract
When treating municipal wastewater, nitrogen removal is often limited due to low C/N, which needs to be compensated for by additional carbon source injections. This study investigated the feasibility of using industrial-waste polyglycolic acid (PGA) as a carbon source for denitrification in an
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When treating municipal wastewater, nitrogen removal is often limited due to low C/N, which needs to be compensated for by additional carbon source injections. This study investigated the feasibility of using industrial-waste polyglycolic acid (PGA) as a carbon source for denitrification in an SBR to obtain an economical carbon source. The results revealed that an optimal denitrification performance in a methanol-fed activated sludge system was achieved with a PGA dosage of 1.2 mL/L, a pH of 7–8, and a dissolved-oxygen (DO) concentration of 3 ± 0.5 mg/L. Under these conditions, all quality parameters for effluent water met the required criteria [COD < 50 mg/L; TN < 15 mg/L; NH4+-N < 5(8) mg/L]. PGA enhanced the variety and richness of microbial communities, thereby markedly increasing the relative abundance of major phyla such as Proteobacteria and Bacteroidota and major genera such as Paracoccus and Dechloromonas. Furthermore, PGA upregulated the expression of nitrogen-metabolism-related genera, including amo, hao, nar, and nor, which improved the denitrification performance of the system. This study provides a reference for applying PGA as a carbon source for low-C/N-wastewater treatment and solid-waste utilization.
Full article
(This article belongs to the Special Issue Biological Wastewater Treatment Process and Nutrient Recovery)
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Open AccessArticle
HYPOSO Map Viewer: A Web-Based Atlas of Small-Scale Hydropower for Selected African and Latin American Countries
by
Petras Punys, Linas Jurevičius and Andrius Balčiūnas
Water 2024, 16(9), 1276; https://doi.org/10.3390/w16091276 - 29 Apr 2024
Abstract
In many countries, the advancement of hydropower resources has been hindered by economic factors and insufficient data on topography, streamflow, environmental sensitivity, power grid, and, most importantly, the location of potential hydropower sites. This challenge is particularly pronounced in certain African and Latin
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In many countries, the advancement of hydropower resources has been hindered by economic factors and insufficient data on topography, streamflow, environmental sensitivity, power grid, and, most importantly, the location of potential hydropower sites. This challenge is particularly pronounced in certain African and Latin American river systems. Developing web-based maps of hydropower resources based on geographic information systems and advanced mapping technologies can facilitate the initial assessment of hydropower sites. This is especially relevant for developing sites in remote areas and data-scarce regions. The available geospatial datasets, remote sensing technologies, and advanced GIS modelling techniques can be used to identify potential hydropower sites and assess their preliminary characteristics. This paper reviews web-based hydropower atlases in African and Latin American countries. Their main features are represented and compared with the recently launched HYPOSO map viewer covering two African countries (Cameroon and Uganda) and three Latin American countries (Bolivia, Colombia, and Ecuador). This hydropower atlas consists of 20 spatial layers. Its particular focus is to present a geospatial dataset of new hydropower sites with concise information for potential investors. These so-called virtual hydropower atlases can be only one type of discovery at the early project stage, automatically identifying sites worthy of further investigation. A formal validation of the web-based atlases, including the HYPOSO hydropower atlas, is briefly considered. Creating open-access hydropower map viewers is anticipated to significantly enhance the hydropower development database in these nations, offering valuable insights for small and medium-scale projects.
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(This article belongs to the Section Water-Energy Nexus)
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Open AccessArticle
Comparison between Hyperspectral and Multispectral Retrievals of Suspended Sediment Concentration in Rivers
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Sung Hyun Jung, Siyoon Kwon, Il Won Seo and Jun Song Kim
Water 2024, 16(9), 1275; https://doi.org/10.3390/w16091275 - 29 Apr 2024
Abstract
Remote sensing (RS) is often employed to estimate suspended sediment concentration (SSC) in rivers, and the availability of hyperspectral imagery enhances the effectiveness of RS-based water quality monitoring due to its high spectral resolution. Yet, the necessity of hyperspectral imagery for SSC estimation
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Remote sensing (RS) is often employed to estimate suspended sediment concentration (SSC) in rivers, and the availability of hyperspectral imagery enhances the effectiveness of RS-based water quality monitoring due to its high spectral resolution. Yet, the necessity of hyperspectral imagery for SSC estimation in rivers has not been fully validated. This study thus compares the performance of hyperspectral RS with that of multispectral RS by conducting field-scale experiments in shallow rivers. In the field experiments, we measured radiance from a water body mixed with suspended sediments using a drone-mounted hyperspectral sensor, with the sediment and riverbed types considered as controlling factors. We retrieved the SSC from UAV imagery using an optimal band ratio analysis, which successfully estimated SSC distributions in the sand bed conditions with both multispectral and hyperspectral data. In the vegetated bed conditions, meanwhile, the prediction accuracy decreased significantly due to the temporally varying bottom reflectance associated with the random movement of vegetation caused by near-bed turbulence. This is because temporally inhomogeneous bottom reflectance distorts the relationship between the SSC and total reflectance. Nevertheless, the hyperspectral imaging exhibited better prediction accuracy than the multispectral imaging, effectively extracting optimal spectral bands sensitive to back-scattered reflectance from sediments while constraining the bottom reflectance caused by the vegetation-covered bed.
Full article
(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)
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Open AccessArticle
Preparation of CeO2 Supported on Graphite Catalyst and Its Catalytic Performance for Diethyl Phthalate Degradation during Ozonation
by
Xin-Yi Tao, Yu-Hong Cui and Zheng-Qian Liu
Water 2024, 16(9), 1274; https://doi.org/10.3390/w16091274 - 29 Apr 2024
Abstract
Catalysts for the efficient catalytic decomposition of ozone to generate reactive free radicals to oxidize pollutants are needed. The graphite-supported CeO2 catalyst was optimally prepared, and its activity in ozonation was evaluated using the degradation of diethyl phthalate (DEP) as an index.
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Catalysts for the efficient catalytic decomposition of ozone to generate reactive free radicals to oxidize pollutants are needed. The graphite-supported CeO2 catalyst was optimally prepared, and its activity in ozonation was evaluated using the degradation of diethyl phthalate (DEP) as an index. The stability of CeO2/graphite catalyst and the influence of operating conditions on its catalytic activity were investigated, and the mechanism of CeO2/graphite catalytic ozonation was analyzed. CeO2/graphite had the highest catalytic activity at a Ce load of 3.5% and a pyrolysis temperature of 400 °C with the DEP degradation efficiency of 75.0% and the total organic carbon (TOC) removal efficiency of 48.3%. No dissolution of active components was found during the repeated use of CeO2/graphite catalyst. The ozone dosage, catalyst dosage, initial pH, and reaction temperature have positive effects on the DEP degradation by CeO2/graphite catalytic ozonation. The presence of tert-butanol significantly inhibits the degradation of DEP at an initial pH of 3.0, 5.8, or 9.0, and the experimental results of the •OH probe compound pCBA indicate that the CeO2/graphite catalyst can efficiently convert ozone into •OH in solution. The DEP degradation in the CeO2/graphite catalytic ozonation mainly depends on the •OH in the bulk solution formed by ozone decomposition.
Full article
(This article belongs to the Special Issue Advanced Treatment and Disinfection Technologies for Water and Wastewater)
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Open AccessArticle
Monitoring the Landscape Pattern Dynamics and Driving Forces in Dongting Lake Wetland in China Based on Landsat Images
by
Mengshen Guo, Nianqing Zhou, Yi Cai, Wengang Zhao, Shuaishuai Lu and Kehao Liu
Water 2024, 16(9), 1273; https://doi.org/10.3390/w16091273 - 29 Apr 2024
Abstract
Dongting Lake wetland is a typical lake wetland in the Middle and Lower Yangtze River Plain in China. Due to the influence of natural and human activities, the landscape pattern has changed significantly. This study used 12 Landsat images from 1991 to 2022
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Dongting Lake wetland is a typical lake wetland in the Middle and Lower Yangtze River Plain in China. Due to the influence of natural and human activities, the landscape pattern has changed significantly. This study used 12 Landsat images from 1991 to 2022 and applied three common classification methods (support vector machine, maximum likelihood, and CART decision tree) to extract and classify the landscape information, with the latter having a superior annual accuracy of over 90%. Based on the CART decision tree classification results, the dynamic characteristics of wetland spatial patterns were analyzed through the landscape pattern index, dynamic degree model, and transition matrix model. Redundancy and grey correlation analysis were employed to investigate the driving factors. The results showed increased landscape fragmentation, reduced heterogeneity, and increased complexity from 1991 to 2022. The water and mudflat areas exhibited three distinct stages: gradual decline until 2001 (−3.06 km2/a); sharp decrease until 2014 (−19.44 km2/a); and steady increase (22.93 km2/a). Vegetation conversion, particularly between sedge and reed, dominated the change in landscape pattern. Reed area initially increased (18.88 km2/a), then decreased (−35.89 km2/a), while sedge showed the opposite trend. Woodland area fluctuated, peaking in 2016 and declined by 2022. The construction of the Three Gorges Dam significantly altered landscape dynamics through water level changes, reflected by a 4.03% comprehensive dynamic degree during 2001–2004. Potential evaporation also emerged as a significant natural factor, exhibiting a negative correlation with the landscape index. During 1991–2001 and 2004–2022, the comprehensive explanatory rates of temperature, precipitation, potential evaporation, and water level on landscape pattern dynamics were 88.56% and 52.44%, respectively. Other factors like policies and socio-economic factors played a crucial role in wetland change. These findings offer valuable insights into the dynamic evolution and driving mechanisms of Dongting Lake wetland.
Full article
(This article belongs to the Special Issue Ecohydrological Processes, Environmental Effects, and Integrated Regulation of Wetland Ecosystems, Volume II)
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Open AccessArticle
Sensitivity of Runoff to Climatic Factors and the Attribution of Runoff Variation in the Upper Shule River, North-West China
by
Ling Jia, Zuirong Niu, Rui Zhang and Yali Ma
Water 2024, 16(9), 1272; https://doi.org/10.3390/w16091272 - 29 Apr 2024
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Climate change and human activities exert significant impact on the mechanism of runoff generation and confluence. Comprehending the reasons of runoff change is crucial for the sustainable development of water resources. Taking the Upper Shule River as the research area, the M-K test
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Climate change and human activities exert significant impact on the mechanism of runoff generation and confluence. Comprehending the reasons of runoff change is crucial for the sustainable development of water resources. Taking the Upper Shule River as the research area, the M-K test and the moving t test were used to diagnose the runoff mutation time. Furthermore, the slope changing ratio of cumulative quantity method (SCRCQ), climate elasticity method, and Budyko equation were utilized to quantitatively evaluate the impacts and contribution rates of climate change and human activities. The following results were obtained: (1) The Upper Shule River experienced a significant increase in runoff from 1972 to 2021, with 1998 marking the year of abrupt change. (2) The runoff sensitivity showed a downward trend from 1972 to 2021. The main factor affecting the decrease in runoff sensitivity was the characteristic parameters of underlying surface (n), followed by precipitation (P), while the influence of potential evapotranspiration (ET0) was the weakest. (3) The response of runoff changes to runoff sensitivity and influencing factors were 90.32% and 9.68%, respectively. (4) The results of three attribution methods indicated that climate change was the primary factor causing the alteration of runoff in the Upper Shule River. The research results supplement the hydrological change mechanisms of the Upper Shule River and provide a scientific basis for future water resources management and flood control measures.
Full article
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Open AccessArticle
Human-Altered Water and Carbon Cycles in the Lake Yangzong Basin since the Yuan Dynasty
by
Huayong Li, Yuxue Jing, Hucai Zhang, Xuanxuan Shang, Lizeng Duan, Huayu Li, Donglin Li and Zhuohan Li
Water 2024, 16(9), 1271; https://doi.org/10.3390/w16091271 - 29 Apr 2024
Abstract
Due to the dual influence of climate change and human activities, the water cycle patterns in the lakesheds of the Yunnan karst plateau are undergoing significant changes, leading to increasingly prominent ecological issues. In the history of Lake Yangzong, an artificial water-diversion channel
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Due to the dual influence of climate change and human activities, the water cycle patterns in the lakesheds of the Yunnan karst plateau are undergoing significant changes, leading to increasingly prominent ecological issues. In the history of Lake Yangzong, an artificial water-diversion channel was excavated, altering the lake basin structure. Human activities have intensified, posing severe challenges to water resource supply and water security in recent decades. To investigate the significant increase in human activities, the temporal and phase changes, and the resulting transformation of the water and carbon cycles in the Lake Yangzong basin, we applied X-ray fluorescence spectroscopy (XRF) to scan elements continuously in a 10.2 m sediment core from this lake. By combining correlation analysis, principal component analysis (PCA), core chronology, and total organic carbon (TOC) content, we reconstructed the historical sequence of geochemical element contents in the Lake Yangzong catchment over the past 13,000 years. The results show that PC1 and PC2 contribute 78.4% and 10.3%, respectively, suggesting that erosion intensity is the main factor influencing the lake sedimentation process. From 13,400 to 680 cal a BP (calibrated years before the present), the sedimentation process in Lake Yangzong was mainly controlled by climatic conditions, with vegetation degradation during cold periods and relatively high erosion intensity in the watershed. During the Yuan dynasty, a province was established by the central government in Yunnan, promoting settlement and attracting a large number of immigrants from other provinces to Yunnan. Human activities in the Lake Yangzong basin began to intensify, surpassing natural changes and becoming the dominant force influencing the sedimentation process. In the Ming and Qing dynasties, the population and cultivated land area in Yunnan further increased, resulting in the significant exacerbation of erosion and soil loss in the watershed due to vegetation destruction. In the year 1388, the Tangchi Canal was excavated, transforming Lake Yangzong to an outflow lake, causing Ca2+ to be lost through the Tangchi Canal and preventing the formation of precipitation due to oversaturation. The research results indicate that human activities in the Lake Yangzong area have intensified since the Yuan dynasty, leading to increased erosion intensity. The excavation of the outflow canal transformed Lake Yangzong from an inland lake basin into an outflow state, simultaneously generating a significant transformation in the water and carbon cycling patterns in the watershed.
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(This article belongs to the Special Issue Plateau Lake Water Quality and Biodiversity: Impacts of Human Activity and Trans-regional Water Diversion)
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Spatial and Temporal Distribution Characteristics and Potential Sources of Microplastic Pollution in China’s Freshwater Environments
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Hualong He, Sulin Cai, Siyuan Chen, Qiang Li, Pengwei Wan, Rumeng Ye, Xiaoyi Zeng, Bei Yao, Yanli Ji, Tingting Cao, Yunchao Luo, Han Jiang, Run Liu, Qi Chen, You Fang, Lu Pang, Yunru Chen, Weihua He, Yueting Pan, Gaozhong Pu, Jiaqin Zeng and Xingjun Tianadd
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Water 2024, 16(9), 1270; https://doi.org/10.3390/w16091270 - 29 Apr 2024
Abstract
Microplastic pollution is a research hotspot around the world. This study investigated the characteristics of microplastic pollution in the freshwater environments of 21 major cities across China. Through indoor and outdoor experimental analysis, we have identified the spatial and temporal distribution characteristics of
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Microplastic pollution is a research hotspot around the world. This study investigated the characteristics of microplastic pollution in the freshwater environments of 21 major cities across China. Through indoor and outdoor experimental analysis, we have identified the spatial and temporal distribution characteristics of microplastic pollution in China’s freshwater environments. Our findings indicate that the average concentration of microplastics in China’s freshwater environments is 3502.6 n/m3. The majority of these microplastics are fibrous (42.5%), predominantly smaller than 3 mm (28.1%), and mostly colored (64.7%). The primary chemical components of these microplastics are polyethylene (PE, 33.6%), polyvinyl chloride (PVC, 21.5%), polypropylene (PP, 16.8%), and polystyrene (PS, 15.6%). The abundance of microplastics in China’s freshwater environments generally tends to increase from west to east and from south to north, with the lowest concentration found in Xining, Qinghai (1737.5 n/m3), and the highest in Jiamusi, Heilongjiang (5650.0 n/m3). The distribution characteristics of microplastics are directly related to land use types, primarily concentrated in areas of intense human activity, including agricultural, transport, and urban land. Seasonal changes affect the abundance of microplastics, peaking in summer, followed by spring and autumn, mainly due to variations in rainfall, showing a positive correlation.
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(This article belongs to the Topic Microplastics Pollution)
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Constituents of Coliform Species Contained in the Permeate of Microfiltration Membranes in Wastewater Treatment
by
Shuai Zhou, Taro Urase and Saki Goto
Water 2024, 16(9), 1269; https://doi.org/10.3390/w16091269 - 28 Apr 2024
Abstract
MBRs (Membrane bioreactors) have been increasingly employed for municipal and industrial wastewater treatment in the last decades for their small footprint and excellent effluent quality. However, microorganisms are often detected in the permeates of microfiltration (MF) membranes even with small pore sizes. Coliform
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MBRs (Membrane bioreactors) have been increasingly employed for municipal and industrial wastewater treatment in the last decades for their small footprint and excellent effluent quality. However, microorganisms are often detected in the permeates of microfiltration (MF) membranes even with small pore sizes. Coliform bacteria are known for indicating the potential presence of pathogenic bacteria that cause infectious disease such as bacteremia, respiratory tract infections, and urinary tract infections. Thus, the retention of coliform bacteria by membrane processes is important when the membrane process is utilized in water reclamation. In this study, a microbial community of coliform bacteria in the permeates of MF membranes with different pore sizes (0.2, 0.4, and 0.8 µm) was identified. The results showed that the dominant coliform bacteria changed from Enterobacter spp. and Citrobacter spp. in the activated sludge to Enterobacter spp. and Klebsiella spp. in the permeate of MF membranes, while some pieces of membranes showed complete retention. The bacterial regrowth on the surface of the piping system on the permeate side could be a significant factor contributing to the frequent and exclusive detection of Enterobacter spp. and Klebsiella spp. in the case of membranes with small pore size (0.2 and 0.4 µm) after a long continuous filtration time. To indicate the public health-related risk of treated wastewater by MF, Escherichia coli may not be a suitable indicator species because E. coli is relatively retentive in MF compared to other coliforms.
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(This article belongs to the Topic Membrane Separation Technology Research)
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Spatial Distribution and Seasonal Variation of Antibiotic-Resistant Bacteria in an Urban River in Northeast China
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Qingshan Xiao, Xin Wang, Chongxin Xu, Wei Chen, Qianchi Huang and Xin Wang
Water 2024, 16(9), 1268; https://doi.org/10.3390/w16091268 - 28 Apr 2024
Abstract
As the largest freshwater river flowing through Harbin, the Songhua River is a standby water source. It is very important to know the species and distribution of antibiotic-resistant bacteria (ARB) in the river. In this study, five antibiotics were selected to screen and
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As the largest freshwater river flowing through Harbin, the Songhua River is a standby water source. It is very important to know the species and distribution of antibiotic-resistant bacteria (ARB) in the river. In this study, five antibiotics were selected to screen and identify ARB in spring and autumn. The results showed that the concentration of cefotaxime-resistant bacteria was the highest, and the maximum concentration at S6 in spring was up to 1.40 × 104 CFU/mL. In spring and autumn, bacteria resistant to three antibiotics were screened at S1 of the Songhua River, and bacteria resistant to five antibiotics were screened at S6. No multiple antibiotic-resistant bacteria (MARB) were screened in the other four sites in autumn, while MARB were screened in the other three samples except S2 in spring. In all sample areas in spring and autumn, the probability of screening MARB at S1 and S6 was the highest, reaching 100%. The identification results of 16S rDNA polymerase chain reaction (PCR) products of ARB showed that a total of 51 ARB strains from 15 bacterial genera were screened in the Songhua River, of which 20 ARB strains were from Pseudomonas. Among the 15 bacterial genera, bacteria from 8 bacterial genera have pathogenicity. The results of this study revealed the concentration, spatial distribution, and seasonal variation of culturable ARB in the Songhua River, providing data support for the remediation of antibiotic resistance gene (ARG) pollution in the river.
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(This article belongs to the Topic Climate Change and Human Impact on Freshwater Water Resources: Rivers and Lakes)
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Prediction of Inland Excess Water Inundations Using Machine Learning Algorithms
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Balázs Kajári, Zalán Tobak, Norbert Túri, Csaba Bozán and Boudewijn Van Leeuwen
Water 2024, 16(9), 1267; https://doi.org/10.3390/w16091267 - 28 Apr 2024
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Regularly, large parts of the agricultural areas of the Great Hungarian Plain are inundated due to excessive rainfall and insufficient evaporation and infiltration. Climate change is expected to lead to increasingly extreme weather conditions, which may even increase the frequency and extent of
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Regularly, large parts of the agricultural areas of the Great Hungarian Plain are inundated due to excessive rainfall and insufficient evaporation and infiltration. Climate change is expected to lead to increasingly extreme weather conditions, which may even increase the frequency and extent of these inundations. Shallow “floods”, also defined as inland excess water, are phenomena that occur due to a complex set of interrelated factors. Our research presents a workflow based on active and passive satellite data from Sentinel-1 and -2, combined with a large auxiliary data set to detect and predict these floods. The workflow uses convolutional neural networks to classify water bodies based on Sentinel-1 and Sentinel-2 satellite data. The inundation data were complimented with meteorological, soil, land use, and GIS data to form 24 features that were used to train an XGBoost model and a deep neural network to predict future inundations, with a daily interval. The best prediction was reached with the XGBoost model, with an overall accuracy of 86%, a Kappa value of 0.71, and an F1 score of 0.86. The SHAP explainable AI method showed that the most important input features were the amount of water detected in the satellite imagery during the week before the forecast and during the period two weeks earlier, the number of water pixels in the surroundings on the day before the forecast, and the potential evapotranspiration on the day of the forecast. The resulting inland excess water inundation time series can be used for operational action, planning, and prevention.
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