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
Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh
Water 2024, 16(8), 1141; https://doi.org/10.3390/w16081141 - 17 Apr 2024
Abstract
Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world’s largest river delta and is prone to floods that impact social–natural systems through losses of lives
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Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world’s largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta.
Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Machine Learning in Water Resources Management)
Open AccessArticle
Research on the Impact of Using a Combination of Rigid and Flexible Vegetation on Slope Hydrological Properties in Loess Regions
by
Hu Tao, Fucui Wang, Xi Shi, Shilong Bu, Ziming Bao, Dezhi Zhang and Lifeng Xiong
Water 2024, 16(8), 1140; https://doi.org/10.3390/w16081140 - 17 Apr 2024
Abstract
Slope vegetation is a key component of soil erosion control. Rigid vegetation improves slope stability, while flexible vegetation reduces water velocity, and the combination of both improves erosion resistance; however, there are few studies on how the combination of rigid and flexible vegetation
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Slope vegetation is a key component of soil erosion control. Rigid vegetation improves slope stability, while flexible vegetation reduces water velocity, and the combination of both improves erosion resistance; however, there are few studies on how the combination of rigid and flexible vegetation affects the hydraulic characteristics of slope flow. In order to investigate the effect of this combination on the hydraulic characteristics of slopes, a mathematical model of the coefficient of resistance under the cover of rigid–flexible vegetation was established by using theoretical analysis and indoor tests, and the indoor tests were conducted with different rigid–flexible vegetation combinations (single-row interlocking (IS), double-row interlocking (IT), upstream rigid–downstream flexible (RF), and bare slope (BS)). The results showed that the rigid–flexible vegetation combination had a significant effect on the slope water flow. With the increase in flow, the water depth and flow velocity of slope flow showed an increasing trend, the flow velocity of the bare slope was significantly larger than that of the vegetation-covered slope, and the value of the water depth increment of the vegetation-covered slope was 0.086~0.22 times that of the bare slope. The Reynolds number showed a good linear increasing relationship with flow rate, and with the gradual increase in flow rate and slope, the flow pattern gradually changed from slow flow to fast flow. When the slope was 2°, the drag coefficient increased and then decreased. The pattern of erosion reduction capacity was IS > RF > IT > BS. The results of this study provide strong theoretical support for understanding the mechanism of vegetation-controlled erosion and provide scientific guidance for optimizing vegetation design in the Loess Plateau region.
Full article
(This article belongs to the Special Issue Research on Soil and Water Conservation and Vegetation Restoration)
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Open AccessArticle
Influence of Fluvial Discharges and Tides on the Salt Wedge Position of a Microtidal Estuary: Magdalena River
by
Jhonathan R. Cordero-Acosta, Luis J. Otero Díaz and Aldemar E. Higgins Álvarez
Water 2024, 16(8), 1139; https://doi.org/10.3390/w16081139 - 17 Apr 2024
Abstract
The linkage between the salt wedge, tidal patterns, and the Magdalena River discharge is established by assessing the ensuing parameters: stratification (ϵ), buoyancy frequency (β), potential energy anomaly (φ), Richardson number by layers (RL), and
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The linkage between the salt wedge, tidal patterns, and the Magdalena River discharge is established by assessing the ensuing parameters: stratification (ϵ), buoyancy frequency (β), potential energy anomaly (φ), Richardson number by layers (RL), and bottom turbulent energy production (P). The salinity, temperature, density, and water velocity data utilized were derived from MOHID 3D, a previously tailored and validated model for the Magdalena River estuary. To grasp the dynamics of the river, a flow regime analysis was conducted during both the wet and dry climatic seasons of the Colombian Caribbean. The utilization of this model aimed to delineate the estuary’s spatial reach, considering flow rates spanning from 2000 to 6500 m3/s across two tidal cycles. This approach facilitates the prediction of the position, stability, and stratification degree of the salt front. Among the conclusions drawn, it is highlighted that: 1. The river flow serves as the principal conditioning agent for the system, inducing a strong estuary response to weather stations; 2. The extent of wedge intrusion and the river discharge exhibit a non-linear, inversely correlation; 3. Tidal waves cause differences of up to 1000 m in the horizontal extent of the wedge; 4. Widespread channel erosion occurs during the rainy season when the salt intrusion does not exceed 2 km; 5. Flocculation processes intensify during the transition between the dry and wet seasons; 6. The stability of the salt layering and the consolidation of the FSI–TMZ are contingent upon the geometric attributes of the channel.
Full article
(This article belongs to the Section Oceans and Coastal Zones)
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Open AccessArticle
Impact of Future Climate Scenarios and Bias Correction Methods on the Achibueno River Basin
by
Héctor Moya, Ingrid Althoff, Juan L. Celis-Diez, Carlos Huenchuleo-Pedreros and Paolo Reggiani
Water 2024, 16(8), 1138; https://doi.org/10.3390/w16081138 - 17 Apr 2024
Abstract
Future climate scenarios based on regional climate models (RCMs) have been evaluated widely. However, the use of RCMs without bias correction may increase the uncertainty in the assessment of climate change impacts, especially in mountain areas. Five quantile mapping methods (QMMs) were evaluated
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Future climate scenarios based on regional climate models (RCMs) have been evaluated widely. However, the use of RCMs without bias correction may increase the uncertainty in the assessment of climate change impacts, especially in mountain areas. Five quantile mapping methods (QMMs) were evaluated as bias correction methods for precipitation and temperature in the historical period (1979–2005) of one local climate model and three RCMs at the Achibueno River Basin, southcentral Chile. Additionally, bias-corrected climate scenarios from 2025 to 2050 under two Representative Concentration Pathways (RCPs) were evaluated on the hydrological response of the catchment with the Soil and Water Assessment Tool (SWAT+). The parametric transformation function and robust empirical quantile were the most promising bias correction methods for precipitation and temperature, respectively. Climate scenarios suggest changes in the frequency and amount of precipitation with fluctuations in temperatures. Under RCP 2.6, partial increases in precipitation, water yield, and evapotranspiration are projected, while for RCP 8.5, strong peaks of precipitation and water yield in short periods of time, together with increases in evapotranspiration, are expected. Consequently, flooding events and increasing irrigation demand are changes likely to take place. Therefore, considering adaptation of current and future management practices for the protection of water resources in southcentral Chile is mandatory.
Full article
(This article belongs to the Special Issue Advances in Hydrology: Flow and Velocity Analysis in Rivers)
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Open AccessArticle
Geochemical Assessment of the Evolution of Groundwater under the Impact of Seawater Intrusion in the Mannar District of Sri Lanka
by
Samadhi Athauda, Yunwen Wang, Zhineng Hao, Suresh Indika, Isuru Yapabandara, Sujithra K. Weragoda, Jingfu Liu and Yuansong Wei
Water 2024, 16(8), 1137; https://doi.org/10.3390/w16081137 - 17 Apr 2024
Abstract
Groundwater is an important drinking water resource in the coastal regions of island countries and has suffered from heavy seawater intrusion. However, the areas specifically affected by seawater intrusion and their groundwater hydrogeochemical compositions and evolution processes remain unclear. This study analyzed the
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Groundwater is an important drinking water resource in the coastal regions of island countries and has suffered from heavy seawater intrusion. However, the areas specifically affected by seawater intrusion and their groundwater hydrogeochemical compositions and evolution processes remain unclear. This study analyzed the hydrogeochemical compositions, water quality, and evolution processes of groundwater in the Mannar district, Sri Lanka, during the dry season. A total of 56 samples were collected from shallow wells and tube wells across the region, and about 64.28% of groundwater samples had good quality (WQI < 100). Geochemical compositions and water quality parameters had a high level in the north and south mainland regions, where they severely suffered from seawater intrusion with a high content of Cl− and Na+. The geochemical compositions of groundwater in the Mannar district were predominantly affected by rock weathering and/or evaporation processes. Cl-Na and HCO3-Ca facies were the main hydrochemical types, and the corresponding ions were mainly from silicate and halite dissolution. The reverse cation exchange process mainly occurred in seawater intrusion areas. The study highlights the impacts of seawater intrusion on the hydrogeochemical compositions and evolution processes in Mannar region groundwater, which will enhance the understanding of the local water quality and seawater intrusion situation and aid in protecting drinking water safety by routinely monitoring the groundwater quality and implementing targeted desalination techniques in the key areas.
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(This article belongs to the Section Oceans and Coastal Zones)
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Open AccessArticle
Evaluation Method of Severe Convective Precipitation Based on Dual-Polarization Radar Data
by
Zhengyang Tang, Xinyu Chang, Xiu Ni, Wenjing Xiao, Huaiyuan Liu and Jun Guo
Water 2024, 16(8), 1136; https://doi.org/10.3390/w16081136 - 17 Apr 2024
Abstract
With global warming and intensified human activities, extreme convective precipitation has become one of the most frequent natural disasters. An accurate and reliable assessment of severe convective precipitation events can support social stability and economic development. In order to investigate the accuracy enhancement
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With global warming and intensified human activities, extreme convective precipitation has become one of the most frequent natural disasters. An accurate and reliable assessment of severe convective precipitation events can support social stability and economic development. In order to investigate the accuracy enhancement methods and data fusion strategies for the assessment of severe convective precipitation events, this study is driven by the horizontal reflectance factor (ZH) and differential reflectance (ZDR) of the dual-polarization radar. This research work utilizes microphysical information of convective storms provided by radar variables to construct the precipitation event assessment model. Considering the problems of high dimensionality of variable data and low computational efficiency, this study proposes a dual-polarization radar echo-data-layering strategy. Combined with the results of mutual information (MI), this study constructs Bayes–Kalman filter (KF) models (RF, SVR, GRU, LSTM) for the assessment of severe convective precipitation events. Finally, this study comparatively analyzes the evaluation effectiveness and computational efficiency of different models. The results show that the data-layering strategy is able to reduce the data dimensions of 256 × 256 × 34,978 to 5 × 2213, which greatly improves the computational efficiency. In addition, the correlation coefficient of interval III–V calibration period is increased to 0.9, and the overall assessment accuracy of the model is good. Among them, the Bayes–KF-LSTM model has the best assessment effect, and the Bayes–KF-RF has the highest computational efficiency. Further, five typical precipitation events are selected for validation in this study. The stratified precipitation dataset agrees well with the near-surface precipitation, and the model’s assessment values are close to the observed values. This study completely utilizes the microphysical information offered by dual-polarized radar ZH and ZDR in precipitation event assessment, which provides a wide range of application possibilities for the assessment of severe convective precipitation events.
Full article
(This article belongs to the Special Issue Application of Digital Twins and Artificial Intelligence Technology in Watershed Flood Disaster Warning and Control)
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Open AccessArticle
Research on Permeability Characteristics and Gradation of Rockfill Material Based on Machine Learning
by
Qigui Yang, Jianqing Zhang, Xing Dai, Zhigang Ye, Chenglong Wang and Shuyang Lu
Water 2024, 16(8), 1135; https://doi.org/10.3390/w16081135 - 16 Apr 2024
Abstract
The density of rockfill material is an important index to evaluate the quality of rockfill dams. It is of great significance to accurately obtain the densities and permeability coefficients of rockfill material dams quickly and accurately by scientific means. However, it takes a
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The density of rockfill material is an important index to evaluate the quality of rockfill dams. It is of great significance to accurately obtain the densities and permeability coefficients of rockfill material dams quickly and accurately by scientific means. However, it takes a long time to measure the permeability coefficient of rockfill material in practice, which means that such measurements cannot fully reflect all the relevant properties. In this paper, using a convolutional neural network (CNN), a machine learning model was established to predict the permeability coefficient of rockfill material with the full scale (d10~d100), pore ratio, Cu, and Cc as the inputs and the permeability coefficient as the output. Through collecting the permeability coefficient and related data in the literature, the set samples were sorted for model training. The prediction results of the trained CNN model are compared with those of the back propagation (BP) model to verify the accuracy of the CNN model. Laboratory constant head penetration experiments were designed to verify the generalization performance of the model. The results show that compared with the BP model, the CNN model has better applicability to the prediction of the permeability coefficient of rockfill material and that the CNN can obtain better accuracy and meet the requirements of the rough estimation of rockfill materials’ permeability in engineering.
Full article
(This article belongs to the Special Issue Research Advances in Hydraulic Structure and Geotechnical Engineering)
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Open AccessArticle
A Multi-Objective Improved Hybrid Butterfly Artificial Gorilla Troop Optimizer for Node Localization in Wireless Sensor Groundwater Monitoring Networks
by
M. BalaAnand and Claudia Cherubini
Water 2024, 16(8), 1134; https://doi.org/10.3390/w16081134 - 16 Apr 2024
Abstract
Wireless sensor networks have gained significant attention in recent years due to their wide range of applications in environmental monitoring, surveillance, and other fields. The design of a groundwater quality and quantity monitoring network is an important aspect in aquifer restoration and the
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Wireless sensor networks have gained significant attention in recent years due to their wide range of applications in environmental monitoring, surveillance, and other fields. The design of a groundwater quality and quantity monitoring network is an important aspect in aquifer restoration and the prevention of groundwater pollution and overexploitation. Moreover, the development of a novel localization strategy project in wireless sensor groundwater networks aims to address the challenge of optimizing sensor location in relation to the monitoring process so as to extract the maximum quantity of information with the minimum cost. In this study, the improved hybrid butterfly artificial gorilla troop optimizer (iHBAGTO) technique is applied to optimize nodes’ position and the analysis of the path loss delay, and the RSS is calculated. The hybrid of Butterfly Artificial Intelligence and an artificial gorilla troop optimizer is used in the multi-functional derivation and the convergence rate to produce the designed data localization. The proposed iHBAGTO algorithm demonstrated the highest convergence rate of 99.6%, and it achieved the lowest average error of 4.8; it consistently had the lowest delay of 13.3 ms for all iteration counts, and it has the highest path loss values of 8.2 dB, with the lowest energy consumption value of 0.01 J, and has the highest received signal strength value of 86% for all iteration counts. Overall, the Proposed iHBAGTO algorithm outperforms other algorithms.
Full article
(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)
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Open AccessArticle
Advancing Digital Image-Based Recognition of Soil Water Content: A Case Study in Bailu Highland, Shaanxi Province, China
by
Yaozhong Zhang, Han Zhang, Hengxing Lan, Yunchuang Li, Honggang Liu, Dexin Sun, Erhao Wang and Zhonghong Dong
Water 2024, 16(8), 1133; https://doi.org/10.3390/w16081133 - 16 Apr 2024
Abstract
Soil water content (SWC) plays a vital role in agricultural management, geotechnical engineering, hydrological modeling, and climate research. Image-based SWC recognition methods show great potential compared to traditional methods. However, their accuracy and efficiency limitations hinder wide application due to their status as
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Soil water content (SWC) plays a vital role in agricultural management, geotechnical engineering, hydrological modeling, and climate research. Image-based SWC recognition methods show great potential compared to traditional methods. However, their accuracy and efficiency limitations hinder wide application due to their status as a nascent approach. To address this, we design the LG-SWC-R3 model based on an attention mechanism to leverage its powerful learning capabilities. To enhance efficiency, we propose a simple yet effective encoder–decoder architecture (PVP-Transformer-ED) designed on the principle of eliminating redundant spatial information from images. This architecture involves masking a high proportion of soil images and predicting the original image from the unmasked area to aid the PVP-Transformer-ED in understanding the spatial information correlation of the soil image. Subsequently, we fine-tune the SWC recognition model on the pre-trained encoder of the PVP-Transformer-ED. Extensive experimental results demonstrate the excellent performance of our designed model (R2 = 0.950, RMSE = 1.351%, MAPE = 0.081, MAE = 1.369%), surpassing traditional models. Although this method involves processing only a small fraction of original image pixels (approximately 25%), which may impact model performance, it significantly reduces training time while maintaining model error within an acceptable range. Our study provides valuable references and insights for the popularization and application of image-based SWC recognition methods.
Full article
(This article belongs to the Special Issue Research on Soil Moisture and Irrigation)
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Open AccessArticle
A Research on Multi-Index Intelligent Integrated Prediction Model of Catchment Pollutant Load under Data Scarcity
by
Donghao Miao, Wenquan Gu, Wenhui Li, Jie Liu, Wentong Hu, Jinping Feng and Dongguo Shao
Water 2024, 16(8), 1132; https://doi.org/10.3390/w16081132 - 16 Apr 2024
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Within a river catchment, the relationship between pollutant load migration and its related factors is nonlinear generally. When neural network models are used to identify the nonlinear relationship, data scarcity and random weight initialization might result in overfitting and instability. In this paper,
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Within a river catchment, the relationship between pollutant load migration and its related factors is nonlinear generally. When neural network models are used to identify the nonlinear relationship, data scarcity and random weight initialization might result in overfitting and instability. In this paper, we propose an averaged weight initialization neural network (AWINN) to realize the multi-index integrated prediction of a pollutant load under data scarcity. The results show that (1) compared with the particle swarm optimization neural network (PSONN) and AdaboostR models that prevent overfitting, AWINN improved simulation accuracy significantly. The R2 in test sets of different pollutant load models reached 0.51–0.80. (2) AWINN is effective in overcoming instability. With more hidden layers, the stability of the models’ outputs was stronger. (3) Sobol sensitivity analysis explained that the main influencing factors of the whole process were the flows of the catchment inlet and outlet, and main factors changed across seasons. The algorithm proposed in this paper can realize stably integrated prediction of pollutant load in the catchment under data scarcity and help to understand the mechanism that influences pollutant load migration.
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Open AccessArticle
Irrigation Distribution Network Design Parameters and Their Influence on Sustainability Management
by
Melvin Alfonso Garcia-Espinal, Modesto Pérez-Sánchez, Francisco-Javier Sánchez-Romero and P. Amparo López-Jiménez
Water 2024, 16(8), 1131; https://doi.org/10.3390/w16081131 - 16 Apr 2024
Abstract
In 2030, the world population will exceed 8.5 billion, increasing the challenges to satisfy basic needs for food, shelter, water, and/or energy. Irrigation plays a vital role in productive and sustainable agriculture. In the current context, it is determined not only by water
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In 2030, the world population will exceed 8.5 billion, increasing the challenges to satisfy basic needs for food, shelter, water, and/or energy. Irrigation plays a vital role in productive and sustainable agriculture. In the current context, it is determined not only by water availability but also by optimal management. Several authors have attempted to measure the performance of irrigation networks through various approaches in terms of technical indicators. To improve the sustainability in the pipe sizing of the pressurised irrigation networks, 25 different models were evaluated to discuss the advantages and disadvantages to consider in future methodologies to size water systems, which guarantee the network operation but contribute to improving the sustainability. They enable water managers to use them as tools to reduce a complex evaluation of the performance of a system, and focusing on better management of resources and sustainability indicators for agricultural ecosystems are clear and objective values.
Full article
(This article belongs to the Special Issue Improved Irrigation Management Practices in Crop Production)
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Open AccessArticle
Diversity of Macrophytes and Macroinvertebrates in Different Types of Standing Waters in the Drava Field
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Mateja Germ, Žiga Tertinek and Igor Zelnik
Water 2024, 16(8), 1130; https://doi.org/10.3390/w16081130 - 16 Apr 2024
Abstract
The diversity of macrophytes and macroinvertebrates in small standing waters of different origins and characteristics was investigated. This survey covered 19 ponds in the Drava field in northeastern Slovenia. The influence of the macrophytes on the macroinvertebrates was investigated and the main environmental
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The diversity of macrophytes and macroinvertebrates in small standing waters of different origins and characteristics was investigated. This survey covered 19 ponds in the Drava field in northeastern Slovenia. The influence of the macrophytes on the macroinvertebrates was investigated and the main environmental factors that had the most significant influence on the composition of the two communities were identified. Sixty-seven taxa of macrophytes and seventy-three families of macroinvertebrates were identified. We found that a diverse macrophyte community has a positive effect on the macroinvertebrate community. In contrast, the dominance of a single macrophyte species has a strong negative influence on the richness of the macroinvertebrate community. The taxonomic richness and abundance of the macroinvertebrate community in the natural ponds was statistically significantly higher than that in artificial ponds. The significant differences in the environmental characteristics between the natural and artificial ponds, such as the macrophyte cover, conductivity, and riparian zone width, may account for these differences. Our study suggests that a greater diversity of macrophyte and macroinvertebrate communities in natural ponds is enabled by abundant but diverse macrophyte cover, low phosphorus content, and wide riparian zones, which require appropriate management of ponds and their catchments.
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(This article belongs to the Special Issue Biodiversity of Freshwater Ecosystems: Monitoring and Conservation)
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Open AccessArticle
Decoupling Agricultural Grey Water Footprint from Economic Growth in the Yellow River Basin
by
Xiaoyan Zhang, Yunan Xiao, Thomas Stephen Ramsey, Songpu Li and Qingling Peng
Water 2024, 16(8), 1129; https://doi.org/10.3390/w16081129 - 16 Apr 2024
Abstract
Decoupling agricultural economic growth from agricultural water pollution is of great importance to regional sustainable development. It is necessary to further explore the decoupling state and key driving factors connecting agricultural water pollution and agricultural economic growth on the basis of accurate measurement
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Decoupling agricultural economic growth from agricultural water pollution is of great importance to regional sustainable development. It is necessary to further explore the decoupling state and key driving factors connecting agricultural water pollution and agricultural economic growth on the basis of accurate measurement of agricultural water pollution. Accordingly, taking the Yellow River Basin (YRB) as the research object, this study combined the water footprint theory, the Logarithmic Mean Divisia Index (LMDI) model and the Tapio decoupling model (TDM) to conduct an in-depth decoupling analysis of the connection between the agricultural grey water footprint (AGWF) and agricultural economic growth in the YRB. Specifically, this study first calculated the AGWF of the YRB during 2016–2021 and objectively evaluated the water resource utilization in this region based on the AGWF. Then, the LMDI model was used to explore the driving factors of the AGWF in the YRB. Finally, the decoupling states between the AGWF and its driving factors with agricultural GDP (AGDP) were studied using the TDM. The main results are as follows: (1) The overall AGWF in the YRB showed a decreasing trend and a slow increase, decreasing by 5.39% in 2021 compared to 2016. (2) The primary promoting factor and inhibiting factor of AGWF reduction are the efficiency effect and agricultural economic effect, respectively. (3) The decoupling states of the AGWF and AGDP presented strong decoupling (SD) and then weak decoupling (WD) in the YRB during the research period. The decoupling states between the agricultural grey water footprint intensity (AGWFI) and AGDP changed from expansive negative decoupling (END) to SD. The decoupling state of population and AGDP remained SD. This study will contribute to alleviating agricultural water pollution in the YRB and help policymakers in water-stressed countries to formulate agricultural water management policies.
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(This article belongs to the Special Issue Water Sustainability and High-Quality Economic Development)
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Open AccessArticle
p-Phenylenediamine Derivatives in Tap Water: Implications for Human Exposure
by
Jianqiang Zhu, Ruyue Guo, Fangfang Ren, Shengtao Jiang and Hangbiao Jin
Water 2024, 16(8), 1128; https://doi.org/10.3390/w16081128 - 16 Apr 2024
Abstract
Human exposure to p-phenylenediamine derivatives (PPDs) may induce hepatotoxicity and altered glycolipid metabolism. Recent studies have demonstrated the wide presence of PPDs in environmental matrixes. However, until now, the occurrence of PPDs in tap water has not been well known. This study
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Human exposure to p-phenylenediamine derivatives (PPDs) may induce hepatotoxicity and altered glycolipid metabolism. Recent studies have demonstrated the wide presence of PPDs in environmental matrixes. However, until now, the occurrence of PPDs in tap water has not been well known. This study analyzed nine PPDs in tap water collected from Hangzhou and Taizhou, China. The results showed that seven PPDs were detected in tap water samples from Hangzhou (n = 131), with the concentration of total detected PPDs ranging from 0.29 to 7.9 ng/L (mean: 1.6 ng/L). N-(1, 3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD; mean: 0.79 ng/L, <LOD−5.7 ng/L) was the predominant PPD in tap water from Hangzhou, followed by N, N′-di-2-butyl-p-phenylenediamine (44PD; 0.39 ng/L, <LOD−2.2 ng/L) and N-isopropyl-N′-phenyl-1, 4-phenylenediamine (IPPD; 0.31 ng/L, <LOD−1.4 ng/L). Five PPDs were detected in tap water collected from Taizhou (n = 30). N-phenyl-N′-cyclohexyl-p-phenylenediamine (CPPD; mean: 1.0 ng/L, <LOD−4.2 ng/L) was the predominant PPD in tap water from Taizhou, followed by 6PPD (0.93 ng/L, <LOD−2.6 ng/L) and 44PD (0.78 ng/L, <LOD−1.8 ng/L). The mean daily intake (DI) of PPDs for adults and children in Hangzhou was estimated to be 4.9–24 and 6.4–32 pg/kg bw/day, respectively. Meanwhile, the mean DI of PPDs for adults and children living in Taizhou was 11–31 and 14–40 pg/kg bw/day, respectively. To our knowledge, this study provides the first data on the occurrence of PPDs in tap water, which is vital for human exposure risk assessment.
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(This article belongs to the Special Issue Research on Water Quality, Sanitation and Human Health)
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Open AccessCorrection
Correction: Dehkordi et al. An Empirical Relation for Estimating Sediment Particle Size in Meandering Gravel-Bed Rivers. Water 2024, 16, 444
by
Arman Nejat Dehkordi, Ahmad Sharafati, Mojtaba Mehraein and Seyed Abbas Hosseini
Water 2024, 16(8), 1127; https://doi.org/10.3390/w16081127 - 16 Apr 2024
Abstract
There were errors in the original publication [...]
Full article
(This article belongs to the Topic Research on River Engineering)
Open AccessArticle
Identifying and Interpreting Hydrological Model Structural Nonstationarity Using the Bayesian Model Averaging Method
by
Ziling Gui, Feng Zhang, Kedong Yue, Xiaorong Lu, Lin Chen and Hao Wang
Water 2024, 16(8), 1126; https://doi.org/10.3390/w16081126 - 16 Apr 2024
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Understanding hydrological nonstationarity under climate change is important for runoff prediction and it enables more robust decisions. Regarding the multiple structural hypotheses, this study aims to identify and interpret hydrological structural nonstationarity using the Bayesian Model Averaging (BMA) method by (i) constructing a
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Understanding hydrological nonstationarity under climate change is important for runoff prediction and it enables more robust decisions. Regarding the multiple structural hypotheses, this study aims to identify and interpret hydrological structural nonstationarity using the Bayesian Model Averaging (BMA) method by (i) constructing a nonstationary model through the Bayesian weighted averaging of two lumped conceptual rainfall–runoff (RR) models (the Xinanjiang and GR4J model) with time-varying weights; and (ii) detecting the temporal variation in the optimized Bayesian weights under climate change conditions. By combining the BMA method with period partition and time sliding windows, the efficacy of adopting time-varying model structures is investigated over three basins located in the U.S. and Australia. The results show that (i) the nonstationary ensemble-averaged model with time-varying weights surpasses both individual models and the ensemble-averaged model with time-invariant weights, improving from 0.04 to 0.15; (ii) the optimized weights of Xinanjiang model increase and that of GR4J declines with larger precipitation, and vice versa; (iii) the change in the optimized weights is proportional to that of precipitation under monotonic climate change, as otherwise the mechanism changes significantly. Overall, it is recommended to adopt nonstationary structures in hydrological modeling.
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Open AccessArticle
The Forecast of Streamflow through Göksu Stream Using Machine Learning and Statistical Methods
by
Mirac Nur Ciner, Mustafa Güler, Ersin Namlı, Mesut Samastı, Mesut Ulu, İsmail Bilal Peker and Sezar Gülbaz
Water 2024, 16(8), 1125; https://doi.org/10.3390/w16081125 - 15 Apr 2024
Abstract
Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability
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Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system.
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(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
Occurrence and Risk Assessment of Antibiotics in Urban River–Wetland–Lake Systems in Southwest China
by
Yanbo Zeng, Lizeng Duan, Tianbao Xu, Pengfei Hou, Jing Xu, Huayu Li and Hucai Zhang
Water 2024, 16(8), 1124; https://doi.org/10.3390/w16081124 - 15 Apr 2024
Abstract
Antibiotics in the aquatic environment are of great concern as novel contaminants. In this study, we investigated the occurrence, distribution, potential sources, and risk assessment of antibiotics in an interconnected river–wetland–lake system. Thirty-three target antibiotics, including sulfonamides (SAs), macrolides (MLs), fluoroquinolones (FQs), tetracyclines
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Antibiotics in the aquatic environment are of great concern as novel contaminants. In this study, we investigated the occurrence, distribution, potential sources, and risk assessment of antibiotics in an interconnected river–wetland–lake system. Thirty-three target antibiotics, including sulfonamides (SAs), macrolides (MLs), fluoroquinolones (FQs), tetracyclines (TCs), and chloramphenicol (CLs) belong to five common groups of antibiotics, were tested from water samples collected in the Panlong River, Xinghai Wetland, and Lake Dian (or Dianchi). Mass spectrophotometry was used to detect the target antibiotics, and the water quality parameters were measured in situ. We found four antibiotics, lincomycin (LIN), trimethoprim (TMP), sulfamethoxazole (SMX), and ofloxacin (OFL), with relatively low concentrations at the ng/L level, and detection rates among sample sites ranged from 42.3% to 76.9%, with maximum concentrations of 0.71 ng/L~5.53 ng/L. TMP was not detected in the Panlong River but appeared in the wetlands and Lake Dian. Midstream urban areas of the Panlong River showed the highest pollution among sites. Antibiotic concentrations were positively correlated with total nitrogen (TN) (p < 0.05) and showed some negative correlation with pH, salinity, and DO. According to the risk assessment, antibiotics in water do not pose a threat to human health and aquatic ecosystems, but a potentially harmful combined effect cannot be excluded. Our research offers a geographical summary of the distribution of antibiotics in urban river, wetland, and lake ecosystems in the plateau (PWL), which is important for predicting the distribution characteristics of antibiotics in the plateau water environment and establishing a standardized antibiotic monitoring and management system for the government.
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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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Open AccessArticle
Spatio-Temporal Dynamics of crAssphage and Bacterial Communities in an Algerian Watershed Impacted by Fecal Pollution
by
Dalal Boulainine, Aziz Benhamrouche, Elisenda Ballesté, Samia Mezaache-Aichour and Cristina García-Aljaro
Water 2024, 16(8), 1123; https://doi.org/10.3390/w16081123 - 15 Apr 2024
Abstract
This study investigates the influence of urban pollution and climate dynamics on water quality and the bacterial communities in an Argelian watershed. Twenty-one sampling campaigns were conducted over two years at six sites along the Oued Boussellam, a river impacted by the effluent
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This study investigates the influence of urban pollution and climate dynamics on water quality and the bacterial communities in an Argelian watershed. Twenty-one sampling campaigns were conducted over two years at six sites along the Oued Boussellam, a river impacted by the effluent of a sewage treatment plant, from a low-polluted site to a water reservoir within a 50 km distance. Fecal indicators and the human fecal marker crAssphage were monitored. Illumina 16S rRNA amplicon sequencing was used to assess water microbial populations’ changes. Urban sewage discharge had an impact on the river quality and microbial ecosystem, which was attenuated along the river course. Significant reductions (>4 log10 for E. coli and somatic coliphages, >3 log10 for crAssphage) occurred, particularly during high-temperature periods. crAssphage correlated strongly with somatic coliphages downstream the river. Seasonal differences were observed in the diversity of the bacterial communities, with higher values during the high-temperature period. The genus-level community structure was similar at highly polluted river sites, also displaying seasonal differences. Despite high pollution levels, natural processes reduced fecal indicators to acceptable levels in the reservoir as well as shaped the bacterial communities along the river, highlighting the importance of understanding indicator persistence and microbial community resilience for effective water quality management within the context of the global warming scenario.
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(This article belongs to the Section Water Quality and Contamination)
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Open AccessArticle
A Numerical Expedition through the Mathematical Representation of Complex Braided Morphometry—A Case Study of Brahmaputra River in India
by
Mohammad Parwez Akhtar, Chandra Shekhar Prasad Ojha, Nayan Sharma, Prathap Somu and Shweta Kodihal
Water 2024, 16(8), 1122; https://doi.org/10.3390/w16081122 - 15 Apr 2024
Abstract
The present work explores the process of mathematical representation for the complex geometry of a wide alluvial river with high braiding intensities. It primarily focuses on an approach to developing a numerical solution algorithm for representing the complex channel geometry of the braided
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The present work explores the process of mathematical representation for the complex geometry of a wide alluvial river with high braiding intensities. It primarily focuses on an approach to developing a numerical solution algorithm for representing the complex channel geometry of the braided Brahmaputra River. Traditional elliptic PDEs with boundary-fitted coordinate transformation were deployed, converting the non-uniform physical plane into a transformed uniform orthogonal computational plane. This study was conducted for the river channel reach with upstream and downstream nodes at Pandu and Jogighopa (reach length ~100 km), respectively, within the Assam flood plain in India, with fourteen measured river cross-sections for the year of 1997. The geo-referenced image covering the river stretch in 1997 was delineated using a ArcGIS software 9.0 tool by digitizing the bank lines. Stream bed interpolation was conducted by interpolating bed elevation from a bathymetrical database onto code-generated mesh nodes. Discretization of the domain was performed through the developed computer code, and the bed-level matrix was generated by the IDW method as well as the MATLAB tool using the nearest neighborhood technique. A mathematical representation of a digital terrain model was thus developed. This generated model was employed as a geometrical data input to simulate secondary flow utilizing 2D depth-averaged equations with the flow dispersion stress tensor as an extra source component, coming from curvilinear flow patterns caused by severe river braiding. The developed model may further be useful in mathematically representing the geometrical complexities of braided rivers with a relatively realistic assessment of the various parameters involved if deployed with improved river modeling with morphometric evolution.
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(This article belongs to the Section Hydraulics and Hydrodynamics)
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