Water Quality Modeling and Monitoring II

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Quality and Contamination".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 7178

Special Issue Editors


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Guest Editor
Department of Civil and Environmental Engineering, Auburn University, Auburn, AL 36849, USA
Interests: water quality modeling in aquatic systems; lakes; water quality monitoring; climate change impacts; ecological modeling; fish habitat modeling; eutrophication; surface hydrology; hydrological modeling and analysis; stormwater management
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Guest Editor

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Guest Editor
Department of Civil and Environmental Engineering, Youngstown State University, Youngstown, OH 44555, USA
Interests: watershed modeling; water quality modeling; hydrologic investigation; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water quality in watersheds and waterbodies is a critical issue due to its direct influence on public health, the biological integrity of natural resources, and the economy. Anthropogenic influences on climate change and variability, land use, and land cover change at the watershed scale can have various impacts on the hydrological, biological, and chemical processes within freshwater watersheds, bringing about significant changes in the water quality of rivers, lakes, and reservoirs. Water quality issues in watersheds and inland waterbodies eventually affect water quality in estuaries and oceans. The Earth has a tremendous variety of waterbodies, from small ponds to Lake Superior, and from humanmade reservoirs to natural lakes. Even though waterbodies are only a small part of our planet, they play a critical and important role in the Earth’s biosphere. Understanding the impacts of changes from upstream or surrounding watersheds on water quality is important to people who live or visit the waterbody and is also fundamental for providing better ecological and environmental strategies and mitigation methods to protect our ecosystems.

Dissolved oxygen and other water quality constituents have implications for the growth, reproduction, and survival of freshwater organisms such as phytoplankton, zooplankton, benthic organisms, and fish. Climate variations (seasonal or inter-annual) and global climate warming directly affect the heat budget of an aquatic system through surface heat exchange between the water and the atmosphere and then influence water quality characteristics. Climate warming will alter water temperature, ice/snow cover, and water quality characteristics in aquatic systems. Nutrients and other chemicals in aquatic systems affect freshwater organism populations and biodiversity. Monitoring and modeling approaches have been used by citizen volunteers, biologists, water resources managers, engineers, and scientists to understand and study water quality issues in watersheds and waterbodies. Different monitoring techniques and modern monitoring devices/sensors allow us to obtain more in-depth information that has not been obtained before. Advanced models or modeling methods also allow us to better understand water quality dynamics and spatial distributions in watersheds and waterbodies that discrete data collections or monitoring cannot reveal. Over the last few decades, significant improvements in watershed models have been achieved to explore water quality modeling in waterbodies due to various anthropogenic interventions. Monitoring data are necessary for model calibration and validation before the model can be used for scenario studies, sensitivity analysis, and future projections based on certain changes in watersheds.

In this Special Issue, we would like to invite journal articles related to watershed- and waterbody-scale studies on water quality, providing innovative approaches to address emerging environmental problems. This Special Issue covers recent advanced modeling and monitoring studies related to water quality and includes but is not limited to the following:

  • Modeling and monitoring of point sources from urban areas and non-point source pollution from agricultural land at the watershed scale;
  • Development of new tools or the improvement of existing models to investigate and evaluate the consequences of point and non-point source pollution on the downstream water quality;
  • Application of watershed models for small- to large-scale watershed studies to simulate the water quality both at the surface and at the subsurface level to incorporate the environmental impact of land use development, and land management strategies;
  • Studies of nutrient losses from agricultural land, instream nutrient transport processes, and in-stream water quality processes from rivers;
  • Innovative research to potentially improve the watershed models to address the complexity of watersheds in terms of the better representation of land use change, forest management, agricultural best management practices, and various scenarios of real-world systems in watershed modeling to improve water quality;
  • Modeling and monitoring the impact of land development and agricultural practices in the water quality in field-, watershed- and basin-scale studies;
  • Identifying the knowledge gap in watershed and waterbody modeling processes;
  • The improvement of the watershed and waterbody models to integrate advanced processes and new sciences for appropriate representation of land development and agricultural growth processes at the spatial and temporal scales within a watershed;
  • Pollutant build-ups at the watershed scale and their impact on downstream water bodies;
  • Development of models to incorporate nutrient transport and various transformation processes of several forms of nutrients (nitrogen, phosphorus, etc.), plant growth, pesticides, bacteria, micro/nano plastics, and sediments at the watershed scale or inside waterbodies;
  • Modeling and monitoring of emerging contaminants in the water cycle including, but not limited to, per- and polyfluoroalkyl substances (PFAS), perfluorooctanoic acid (PFOA), and perfluorooctanesulfonic acid (PFOS), which act as so-called endocrine disruptors (EDCs);
  • Watershed- and basin-scale water quality assessment/projection under both existing and project land use and land management conditions;
  • Development of management scenarios for water quality improvement in waterbodies using various best management practices;
  • Research incorporating complex water quality processes at the watershed scale and developing scientific support tools for decision-making systems;
  • Uncertainty in model application, sensitivity analysis of watershed and waterbody model parameters.

Both reviews as well as new research papers are welcome.

Prof. Dr. Xing Fang
Prof. Dr. Jiangyong Hu
Dr. Suresh Sharma
Guest Editors

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Keywords

  • watershed modeling of water quality
  • water quality modeling in waterbodies
  • water quality monitoring in watersheds and waterbodies
  • anthropogenic influence
  • climate change impact on water quality
  • land use change impact

Published Papers (4 papers)

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Research

13 pages, 2226 KiB  
Article
Managing the Taste and Odor Compound 2-MIB in a River-Reservoir System, South Korea
by Miri Kang, Deok-Woo Kim, Minji Park, Kyunghyun Kim and Joong-Hyuk Min
Water 2023, 15(23), 4107; https://doi.org/10.3390/w15234107 - 27 Nov 2023
Viewed by 1032
Abstract
High concentrations of 2-methylisoborneol (2-MIB) were reported during winter in the Paldang reservoir and North Han River, South Korea. The causes of the unusual taste and odor problems in the regulated river-reservoir system were not understood; however, a short-term solution is to flush [...] Read more.
High concentrations of 2-methylisoborneol (2-MIB) were reported during winter in the Paldang reservoir and North Han River, South Korea. The causes of the unusual taste and odor problems in the regulated river-reservoir system were not understood; however, a short-term solution is to flush out 2-MIB-rich water to secure water sources for over 20 million people. Approximately 150 million tons of water was released from upstream dams for 12 days (late November to early December 2018) to reduce the elevated levels of 2-MIB. Simultaneously, the spatio-temporal variations of the measured concentration of sample 2-MIB from five sites were simulated using a multi-dimensional hydrodynamics-based solute transport model to monitor the flushing effect. A modified environmental fluid dynamics code (EFDC) was adopted as the primary model framework. Five scenarios on the kinetic constants related to the characteristics of 2-MIB transport and behavior, such as conservative, net decay, and net production, were applied, and the results were compared. We found that the simulation errors on the elapsed times to satisfy the Korean drinking water monitoring standard (≤20 ngL−1) were smallest with the conservative dye transport option, indicating that the physical and biochemical characteristics of 2-MIB may not play an essential role. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring II)
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18 pages, 3473 KiB  
Article
A Machine Learning-Based Framework for Water Quality Index Estimation in the Southern Bug River
by Adil Masood, Majid Niazkar, Mohammad Zakwan and Reza Piraei
Water 2023, 15(20), 3543; https://doi.org/10.3390/w15203543 - 11 Oct 2023
Cited by 11 | Viewed by 2453
Abstract
River water quality is of utmost importance because the river is not only one of the key water resources but also a natural habitat serving its surrounding environment. In a bid to address whether it has a qualified quality, various analytics are required [...] Read more.
River water quality is of utmost importance because the river is not only one of the key water resources but also a natural habitat serving its surrounding environment. In a bid to address whether it has a qualified quality, various analytics are required to be considered, but it is challenging to measure all of them frequently along a river reach. Therefore, estimating water quality index (WQI) incorporating several weighted analytics is a useful approach to assess water quality in rivers. This study explored applications of ten machine learning (ML) models to estimate WQI for the Southern Bug River, which is the second-longest river in Ukraine. The ML methods considered in this study include artificial neural networks (ANNs), Support Vector Regressor (SVR), Extreme Learning Machine, Decision Tree Regressor, random forest, AdaBoost (AB), Gradient Boosting Regressor, XGBoost Regressor (XGBR), Gaussian process (GP), and K-nearest neighbors (KNN). Each data measurement consists of nine analytics (NH4, BOD5, suspended solids, DO, NO3, NO2, SO4, PO4, Cl), while the quantity of data is more than 2700 data points. The results indicated that all ML models demonstrate satisfactory performance in predicting WQI. However, GP outperformed the other models, followed by XGBR, SVR, and KNN. Furthermore, ANN and AB demonstrated relatively weaker performance. Moreover, a reliability assessment conducted on both training and testing datasets also confirmed the results of the comparative analysis. Overall, the results enhance the assertion that ML models can sufficiently predict WQI, thereby enhancing water quality management. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring II)
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25 pages, 27050 KiB  
Article
Monitoring, Modeling and Planning Best Management Practices (BMPs) in the Atwood and Tappan Lake Watersheds with Stakeholders Engagements
by Suresh Sharma, Shuvra Bijukshe and Sai Sree Puppala
Water 2023, 15(17), 3028; https://doi.org/10.3390/w15173028 - 23 Aug 2023
Cited by 2 | Viewed by 1097
Abstract
This study was conducted in the Atwood and Tappan Lakes watersheds of the Tuscarawas basin of Ohio. The flow, total nitrogen (TN), and total phosphorus (TP) loadings were monitored with the help of local stakeholders for a few years at various locations of [...] Read more.
This study was conducted in the Atwood and Tappan Lakes watersheds of the Tuscarawas basin of Ohio. The flow, total nitrogen (TN), and total phosphorus (TP) loadings were monitored with the help of local stakeholders for a few years at various locations of the watershed to develop the Soil and Water Assessment Tool (SWAT). The multi-site SWAT model calibration and validation were accomplished with a reasonable model performance. In the next step, the scenario analysis was conducted in the SWAT model using various BMPs, including vegetative filter strips, grass waterways, fertilizer reduction, crop rotation, and cover crops to evaluate their performance in reducing TN and TP from the watershed. While BMPS in many studies are decided based on researchers’ intuition, these BMPs were selected based on active consultation with the local stakeholders, who were engaged in the reduction of TN and TP loadings from the watersheds. Since the SWAT model calibration for TN and TP was not as good as the hydrologic model calibration, various scenarios of TN and TP reduction using BMPs were investigated for several years using both calibrated and uncalibrated SWAT models. We examined all the BMPs in 12 sub-watersheds of the Atwood and 10 sub-watersheds of the Tappan Lake watershed. The analysis indicated that the management practices of cover crops (rye) in combination with grass waterways with a 10% fertilizer reduction could minimize the TN and TP loading by as much as 88%, without significantly compromising the agricultural yield. However, a 10% fertilizer reduction without any BMPs could reduce TN and TP by just 9%. The cover crop (rye) including 10% fertilizer reduction with grass waterways seemed to be the most effective in reducing TN and TP, whereas the implementation of a filter strip led to a 70% reduction and was the next effective BMPs in reducing TN and TP loadings. In general, TN losses were reduced by 8% to 53%, while TP losses were reduced by 7% to 88%, depending on the BMPs used. By and large, the TN and TP reduction achieved through the calibrated model was not significantly different from the uncalibrated model, even though the reduction using the calibrated model was slightly higher for all scenarios than that of the uncalibrated model. The TN and TP loadings were highly sensitive to cattle grazing. When just 50% of the cattle were permitted to graze, the model predicted that there would be a 40% increase in total nitrogen and a 70% increase in total phosphorus in both watersheds. Our investigation revealed that monitoring the watershed at a small sub-watershed scale and calibrating the SWAT model for nitrogen and phosphorus is delicate. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring II)
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21 pages, 12435 KiB  
Article
Salinity Modeling Using Deep Learning with Data Augmentation and Transfer Learning
by Siyu Qi, Minxue He, Raymond Hoang, Yu Zhou, Peyman Namadi, Bradley Tom, Prabhjot Sandhu, Zhaojun Bai, Francis Chung, Zhi Ding, Jamie Anderson, Dong Min Roh and Vincent Huynh
Water 2023, 15(13), 2482; https://doi.org/10.3390/w15132482 - 6 Jul 2023
Cited by 2 | Viewed by 2021
Abstract
Salinity management in estuarine systems is crucial for developing effective water-management strategies to maintain compliance and understand the impact of salt intrusion on water quality and availability. Understanding the temporal and spatial variations of salinity is a keystone of salinity-management practices. Process-based numerical [...] Read more.
Salinity management in estuarine systems is crucial for developing effective water-management strategies to maintain compliance and understand the impact of salt intrusion on water quality and availability. Understanding the temporal and spatial variations of salinity is a keystone of salinity-management practices. Process-based numerical models have been traditionally used to estimate the variations in salinity in estuarine environments. Advances in data-driven models (e.g., deep learning models) make them effective and efficient alternatives to process-based models. However, a discernible research gap exists in applying these advanced techniques to salinity modeling. The current study seeks to address this gap by exploring the innovative use of deep learning with data augmentation and transfer learning in salinity modeling, exemplified at 23 key salinity locations in the Sacramento–San Joaquin Delta which is the hub of the water-supply system of California. Historical, simulated (via a hydrodynamics and water quality model), and perturbed (to create a range of hydroclimatic and operational scenarios for data-augmentation purposes) flow, and salinity data are used to train a baseline multi-layer perceptron (MLP) and a deep learning Residual Long-Short-Term Memory (Res-LSTM) network. Four other deep learning models including LSTM, Residual Network (ResNet), Gated Recurrent Unit (GRU), and Residual GRU (Res-GRU) are also examined. Results indicate that models pre-trained using augmented data demonstrate improved performance over models trained from scratch using only historical data (e.g., median Nash–Sutcliffe efficiency increased from around 0.5 to above 0.9). Moreover, the five deep learning models further boost the salinity estimation performance in comparison with the baseline MLP model, though the performance of the latter is acceptable. The models trained using augmented data are then (a) used to develop a web-based Salinity Dashboard (Dashboard) tool that allows the users (including those with no machine learning background) to quickly screen multiple management scenarios by altering inputs and visualizing the resulting salinity simulations interactively, and (b) transferred and adapted to estimate observed salinity. The study shows that transfer learning results more accurately replicate the observations compared to their counterparts from models trained from scratch without knowledge learned and transferred from augmented data (e.g., median Nash–Sutcliffe efficiency increased from around 0.4 to above 0.9). Overall, the study illustrates that deep learning models, particularly when pre-trained using augmented data, are promising supplements to existing process-based models in estuarine salinity modeling, while the Dashboard enables user engagement with those pre-trained models to inform decision-making efficiently and effectively. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring II)
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