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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 25196

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Prof. Dr. Karl-Erich Lindenschmidt

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Global Institute for Water Security, School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK S7N 3H5, Canada
Interests: surface water quality modelling; ice-jam flood hazard mapping; ice-jam flood risk assessment; remote sensing of river ice covers; river ice hydraulic modelling
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Dear Colleagues,

Surface water quality modelling has become an important means of better understanding aquatic and riparian ecosystem processes at all scales, from the micro-scale (e.g., bottom sediment dynamics), to the meso-scale (e.g., algal bloom growth) and the macro-scale (e.g., the role of cascading reservoirs on sediment transport). Increasingly, surface water quality models are being coupled to other models (e.g., hydrological models) to determine catchment area impacts on water quality. These impacts include future climate change and land-use developments. Coupling to water resource dynamics models also provides insight into changes in water supply and demand and flow regulation as they relate to surface water quality. Modelling the quality of surface waters under ice-covered conditions has also gained special attention, due to the increased realization that a holistic all-year perspective is required to deepen our understanding of aquatic ecosystem functioning (e.g., impact of lake ice phenology on spring succession of phytoplankton). In this context, I invite you to submit a contribution to this very important topic.

Dr. Karl-Erich Lindenschmidt
Guest Editor

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Keywords

climate change
environmental change
ice covers
lakes
modelling
ponds
rivers
surface water quality
wetlands

Published Papers (11 papers)

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Editorial

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3 pages, 169 KiB

Open AccessEditorial

Surface Water Quality Modelling

by Karl-Erich Lindenschmidt

Water 2023, 15(4), 828; https://0-doi-org.brum.beds.ac.uk/10.3390/w15040828 - 20 Feb 2023

Viewed by 1799

Abstract

Surface water quality modelling has become an important means of better understanding aquatic and riparian ecosystem processes at all scales, from the micro-scale (e [...] Full article

(This article belongs to the Special Issue Surface Water Quality Modelling)

Research

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14 pages, 2686 KiB

Open AccessArticle

Modelling Transport and Fate of Copper and Nickel across the South Saskatchewan River Using WASP—TOXI

by Saurabh Prajapati, Pouya Sabokruhie, Markus Brinkmann and Karl-Erich Lindenschmidt

Water 2023, 15(2), 265; https://0-doi-org.brum.beds.ac.uk/10.3390/w15020265 - 08 Jan 2023

Cited by 3 | Viewed by 1822

Abstract

The South Saskatchewan River (SSR) is one of the most important river systems in Saskatchewan and, arguably, in Canada. Most of the Saskatchewan residents, industries, and powerplants depend on the SSR for their water requirements. An established 1D modelling approach was chosen and coupled with the Hydrologic Engineering Center’s River Analysis System (HEC-RAS). The WASP (Water Quality Analysis Simulation Program) stream transport module, TOXI, is coupled with flow routing for free-flow streams, ponded segments, and backwater reaches and is capable of calculating the flow of water, sediment, and dissolved constituents across branched and ponded segments. Copper and nickel were chosen as two metals with predominantly anthropogenic (agriculture, mining, and municipal and industrial waste management) and geogenic (natural weathering and erosion) sources, respectively. Analysis was carried out at ten different sites along the South Saskatchewan River, both upstream and downstream of the City of Saskatoon, in the years 2020 and 2021. Model performance was evaluated by comparing model predictions with concentrations of copper and nickel measured in a previously published study. The model performed well in estimating the concentrations of copper and nickel in water samples and worked reasonably well for sediment samples. The model underestimated the concentration values at certain segments in both water and sediment samples. In order to calibrate the model more accurately, extra diffusive contaminant loads were added. While several default parameter values had to be used due to the unavailability of primary historical data, our study demonstrates the predictive power of combining WASP—TOXI and HEC-RAS models for the prediction of contaminant loading. Future studies, including those on the impacts of global climate change on water quality on the Canadian prairies, will benefit from this proof-of-concept study. Full article

(This article belongs to the Special Issue Surface Water Quality Modelling)

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14 pages, 2114 KiB

Open AccessArticle

Discrimination of Chemical Oxygen Demand Pollution in Surface Water Based on Visible Near-Infrared Spectroscopy

by Xueqin Han, Xiaoyan Chen, Jinfang Ma, Jiaze Chen, Baiheng Xie, Wenhua Yin, Yanyan Yang, Wenchao Jia, Danping Xie and Furong Huang

Water 2022, 14(19), 3003; https://0-doi-org.brum.beds.ac.uk/10.3390/w14193003 - 23 Sep 2022

Cited by 3 | Viewed by 2264

Abstract

Chemical oxygen demand (COD) is one of the indicators used to monitor the level of pollution in surface water. To recycle agricultural water resources, it is crucial to monitor, in a timely manner, whether COD in surface water exceeds the agricultural water control standard. A diagnostic model of surface water pollution was developed using visible near-infrared spectroscopy (Vis-NIR) combined with partial least squares discriminant analysis (PLS–DA). A total of 127 surface water samples were collected from Guangzhou, Guangdong, China. The COD content was measured using the potassium dichromate method. The spectra of the surface water samples were recorded using a Vis-NIR spectrometer, and the spectral data were pre-processed using four different methods. To improve the accuracy and simplicity of the model, the synthetic minority oversampling technique (SMOTE) and the competitive adaptive reweighted sampling (CARS) algorithm were used to enhance model performance. The best PLS–DA model achieved an accuracy of 88%, and the SMOTE–PLS–DA model had an accuracy of 94%. The SMOTE algorithm could improve the accuracy of the model despite the sampling imbalance. The CARS–SMOTE–PLS–DA model achieved 97% accuracy, and the CARS band selection technique improved the simplicity and accuracy of the discrimination model. The CARS–SMOTE–PLS–DA model improved the discrimination accuracy by 9% over that of the PLS–DA model. This method can not only save human and material resources but is also a new way for real-time online discrimination of COD in surface water. Full article

(This article belongs to the Special Issue Surface Water Quality Modelling)

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17 pages, 3679 KiB

Open AccessArticle

Marine Environmental Capacity in Sanmen Bay, China

by Yanming Yao, Jiahao Zhu, Li Li, Jiachen Wang and Jinxiong Yuan

Water 2022, 14(13), 2083; https://0-doi-org.brum.beds.ac.uk/10.3390/w14132083 - 29 Jun 2022

Cited by 4 | Viewed by 1688

Abstract

Estuarine environmental capacity is the foundation for coastal biological diversity and self-purification capacity. Hence, studies on the marine environmental capacity (MEC) are the foundation for the total discharge control and water quality improvement of land-based pollutants. In the article, A calibrated two-dimensional hydrodynamic model was used to study the environmental characteristics of Sanmen Bay, including the tides, the residual currents, the tidal prism, and water exchange abilities. The model results were used to estimate the environmental capacity of the bay. Taking the pollution problem in Sanmen Bay as an example, the method of response factor, the sub-unit control method, and the phased control method were used to estimate the environmental capacity, pollutant amounts, and the pollutant reduction in the bay. The concentrations of COD, inorganic nitrogen, and acid salt in Sanmen Bay are spatially varied, with higher values occurring in the western part and in the inner bay. The half exchange time of the whole bay is about 23 days, and the exchange time of 95% water body is about 60 days. The evaluation of MEC cannot only provide technical support for the offshore aquaculture industries but also provide a scientific basis for the total control of terrigenous pollutants in coastal cities in Southern Zhejiang Province. Full article

(This article belongs to the Special Issue Surface Water Quality Modelling)

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12 pages, 3062 KiB

Open AccessArticle

Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam

by Dao Nguyen Khoi, Nguyen Trong Quan, Do Quang Linh, Pham Thi Thao Nhi and Nguyen Thi Diem Thuy

Water 2022, 14(10), 1552; https://0-doi-org.brum.beds.ac.uk/10.3390/w14101552 - 12 May 2022

Cited by 47 | Viewed by 8545

Abstract

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R² and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R² = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management. Full article

(This article belongs to the Special Issue Surface Water Quality Modelling)

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13 pages, 2768 KiB

Open AccessArticle

Analysis of Seasonal Variations in Surface Water Quality over Wet and Dry Regions

by Muhammad Mazhar Iqbal, Lingling Li, Saddam Hussain, Jung Lyul Lee, Faisal Mumtaz, Ahmed Elbeltagi, Muhammad Sohail Waqas and Adil Dilawar

Water 2022, 14(7), 1058; https://0-doi-org.brum.beds.ac.uk/10.3390/w14071058 - 28 Mar 2022

Cited by 14 | Viewed by 2709

Abstract

Water quality is highly affected by riverside vegetation in different regions. To comprehend this research, the study area was parted into wet and dry regions. The WASP8 was applied for the simulations of water quality profile over both Waterways selected from each region. It was found that the Ara Waterway, located in the wet regions, has a higher water quality variation in seasonal scale than that of the Yamuna Waterway, which is in the dry region. The interrelationship between river water quality variables and NDVI produce higher association for water quality variables with Pearson correlation coefficient values of about 0.66, 0.68 and −0.58, respectively, over the annual and seasonal scales in the energy limited regions. This approach will help in monitoring the seasonal variation and effect of the vegetation biomass on water quality for the sustainable water environment. Full article

(This article belongs to the Special Issue Surface Water Quality Modelling)

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24 pages, 5974 KiB

Open AccessArticle

Application of DPSIR and Tobit Models in Assessing Freshwater Ecosystems: The Case of Lake Malombe, Malawi

by Ishmael Bobby Mphangwe Kosamu, Rodgers Makwinja, Chikumbusko Chiziwa Kaonga, Seyoum Mengistou, Emmanuel Kaunda, Tena Alamirew and Friday Njaya

Water 2022, 14(4), 619; https://0-doi-org.brum.beds.ac.uk/10.3390/w14040619 - 17 Feb 2022

Cited by 10 | Viewed by 3734

Abstract

Inland freshwater shallow lake ecosystem degradation is indistinctly intertwined with human-induced factors and climate variability. Changes in climate and human-induced factors significantly influence the state of lake ecosystems. This study provides evidence of the driver, pressure, state, impact, and response (DPSIR) indicators for freshwater lake ecosystem dynamics, taking Lake Malombe in Malawi as a case study. We used the DPSIR framework and Tobit model to achieve the study’s objectives. The study’s findings indicate that top-down processes gradually erode Lake Malombe’s ecosystem state. The lake resilience is falling away from its natural state due to increasing rates of drivers, pressures, and impacts, indicating the lake ecosystem’s deterioration. The study shows that demographic, socio–economic, climatic drivers, pressures, state, and responses significantly (p < 0.05) influenced the lake ecosystem’s resilience. The study suggests that substantial freshwater ecosystem management under the current scenario requires a long-term, robust, and sustainable management plan. The findings from this study provide a roadmap for short-term and long-term practical policy-focused responses, particularly in implementing a freshwater ecosystem restoration programs in Malawi and Africa more broadly. Full article

(This article belongs to the Special Issue Surface Water Quality Modelling)

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19 pages, 3546 KiB

Open AccessArticle

Buffalo Pound Lake—Modelling Water Resource Management Scenarios of a Large Multi-Purpose Prairie Reservoir

by Julie Terry, John-Mark Davies and Karl-Erich Lindenschmidt

Water 2022, 14(4), 584; https://0-doi-org.brum.beds.ac.uk/10.3390/w14040584 - 15 Feb 2022

Cited by 7 | Viewed by 2144

Abstract

Water quality models are an emerging tool in water management to understand and inform decisions related to eutrophication. This study tested flow scenario effects on the water quality of Buffalo Pound Lake—a eutrophic reservoir supplying water for approximately 25% of Saskatchewan’s population. The model CE-QUAL-W2 was applied to assess the impact of inter-basin water diversion after the impounded lake received high inflows from local runoff. Three water diversion scenarios were tested: continuous flow, immediate release after nutrient loading increased, and a timed release initiated when water levels returned to normal operating range. Each scenario was tested at three different transfer flow rates. The transfers had a dilution effect but did not affect the timing of the nutrient peaks in the upstream portion of the lake. In the lake’s downstream section, nutrients peaked at similar concentrations as the base model, but peaks arrived earlier in the season and attenuated rapidly. Results showed greater variation among scenarios in wet years compared to dry years. Dependent on the timing and quantity of water transferred, some but not all water quality parameters are predicted to improve along with the water diversion flows over the period tested. The results suggest that it is optimal to transfer water while local watershed runoff is minimal. Full article

(This article belongs to the Special Issue Surface Water Quality Modelling)

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18 pages, 4827 KiB

Open AccessArticle

Ammonium Nitrogen Streamflow Transport Modelling and Spatial Analysis in Two Chinese Basins

by Jingchen Yin, Haitao Chen, Yuqiu Wang, Lifeng Guo, Guoguang Li and Puzhou Wang

Water 2022, 14(2), 209; https://0-doi-org.brum.beds.ac.uk/10.3390/w14020209 - 11 Jan 2022

Cited by 3 | Viewed by 1642

Abstract

Ammonium nitrogen (NH₄⁺-N), which naturally arises from the decomposition of organic substances through ammonification, has a tremendous influence on local water quality. Therefore, it is vital for water quality protection to assess the amount, sources, and streamflow transport of NH₄⁺-N. SPAtially Referenced Regressions on Watershed attributes (SPARROW), which is a hybrid empirical and mechanistic modeling technique based on a regression approach, can be used to conduct studies of different spatial scales on nutrient streamflow transport. In this paper, the load and delivery of NH₄⁺-N in Poyang Lake Basin (PLB) and Haihe River Basin (HRB) were estimated using SPARROW. In PLB, NH₄⁺-N load streamflow transport originating from point sources and farmland accounted for 41.83% and 32.84%, respectively. In HRB, NH₄⁺-N load streamflow transport originating from residential land and farmland accounted for 40.16% and 36.75%, respectively. Hence, the following measures should be taken: In PLB, it is important to enhance the management of the point sources, such as municipal and industrial wastewater. In HRB, feasible measures include controlling the domestic pollution and reducing the usage of chemical fertilizers. In addition, increasing the vegetation coverage of both basins may be beneficial to their nutrient management. The SPARROW models built for PLB and HRB can serve as references for future uses for different basins with various conditions, extending this model’s scope and adaptability. Full article

(This article belongs to the Special Issue Surface Water Quality Modelling)

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19 pages, 6867 KiB

Open AccessArticle

Proof-of-Concept of a Quasi-2D Water-Quality Modelling Approach to Simulate Transverse Mixing in Rivers

by Pouya Sabokruhie, Eric Akomeah, Tammy Rosner and Karl-Erich Lindenschmidt

Water 2021, 13(21), 3071; https://0-doi-org.brum.beds.ac.uk/10.3390/w13213071 - 02 Nov 2021

Cited by 3 | Viewed by 2270

Abstract

A quasi-two-dimensional (quasi-2D) modelling approach is introduced to mimic transverse mixing of an inflow into a river from one of its banks, either an industrial outfall or a tributary. The concentrations of determinands in the inflow vary greatly from those in the river, leading to very long mixing lengths in the river downstream of the inflow location. Ideally, a two-dimensional (2D) model would be used on a small scale to capture the mixing of the two flow streams. However, for large-scale applications of several hundreds of kilometres of river length, such an approach demands too many computational resources and too much computational time, especially if the application will at some point require ensemble input from climate-change scenario data. However, a one-dimensional (1D) model with variables varying in the longitudinal flow direction but averaged across the cross-sections is too simple of an approach to capture the lateral mixing between different flow streams within the river. Hence, a quasi-2D method is proposed in which a simplified 1D solver is still applied but the discretisation of the model setup can be carried out in such a way as to enable a 2D representation of the model domain. The quasi-2D model setup also allows secondary channels and side lakes in floodplains to be incorporated into the discretisation. To show proof-of-concept, the approach has been tested on a stretch of the lower Athabasca River in Canada flowing through the oil sands region between Fort McMurray and Fort MacKay. A dye tracer and suspended sediments are the constituents modelled in this test case. Full article

(This article belongs to the Special Issue Surface Water Quality Modelling)

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23 pages, 11776 KiB

Open AccessCommentary

Assessing and Mitigating Ice-Jam Flood Hazards and Risks: A European Perspective

by Karl-Erich Lindenschmidt, Knut Alfredsen, Dirk Carstensen, Adam Choryński, David Gustafsson, Michał Halicki, Bernd Hentschel, Niina Karjalainen, Michael Kögel, Tomasz Kolerski, Marika Kornaś-Dynia, Michał Kubicki, Zbigniew W. Kundzewicz, Cornelia Lauschke, Albert Malinger, Włodzimierz Marszelewski, Fabian Möldner, Barbro Näslund-Landenmark, Tomasz Niedzielski, Antti Parjanne, Bogusław Pawłowski, Iwona Pińskwar, Joanna Remisz, Maik Renner, Michael Roers, Maksymilian Rybacki, Ewelina Szałkiewicz, Michał Szydłowski, Grzegorz Walusiak, Matylda Witek, Mateusz Zagata and Maciej Zdralewicz Show full author list Hide full author list

Water 2023, 15(1), 76; https://0-doi-org.brum.beds.ac.uk/10.3390/w15010076 - 26 Dec 2022

Cited by 7 | Viewed by 3503

Abstract

The assessment and mapping of riverine flood hazards and risks is recognized by many countries as an important tool for characterizing floods and developing flood management plans. Often, however, these management plans give attention primarily to open-water floods, with ice-jam floods being mostly an afterthought once these plans have been drafted. In some Nordic regions, ice-jam floods can be more severe than open-water floods, with floodwater levels of ice-jam floods often exceeding levels of open-water floods for the same return periods. Hence, it is imperative that flooding due to river ice processes be considered in flood management plans. This also pertains to European member states who are required to submit renewed flood management plans every six years to the European governance authorities. On 19 and 20 October 2022, a workshop entitled “Assessing and mitigating ice-jam flood hazard and risk” was hosted in Poznań, Poland to explore the necessity of incorporating ice-jam flood hazard and risk assessments in the European Union’s Flood Directive. The presentations given at the workshop provided a good overview of flood risk assessments in Europe and how they may change due to the climate in the future. Perspectives from Norway, Sweden, Finland, Germany, and Poland were presented. Mitigation measures, particularly the artificial breakage of river ice covers and ice-jam flood forecasting, were shared. Advances in ice processes were also presented at the workshop, including state-of-the-art developments in tracking ice-floe velocities using particle tracking velocimetry, characterizing hanging dam ice, designing new ice-control structures, detecting, and monitoring river ice covers using composite imagery from both radar and optical satellite sensors, and calculating ice-jam flood hazards using a stochastic modelling approach. Full article

(This article belongs to the Special Issue Surface Water Quality Modelling)

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