Water

Research

25 pages, 5465 KiB

Open AccessArticle

An Integrated Water Quality Model to Support Multiscale Decisions in a Highly Altered Catchment

by Tania F. Santos Santos and Luis A. Camacho

Water 2022, 14(3), 374; https://doi.org/10.3390/w14030374 - 26 Jan 2022

Cited by 6 | Viewed by 2721

Decision-making in highly altered catchments occurs at different temporal and spatial scales, requiring integration of various datasets and models. This paper introduces two of the components of an environmental multiscale decision support system (EMDSS) for highly altered catchments, designed to make decisions at [...] Read more.

Decision-making in highly altered catchments occurs at different temporal and spatial scales, requiring integration of various datasets and models. This paper introduces two of the components of an environmental multiscale decision support system (EMDSS) for highly altered catchments, designed to make decisions at different time scales. First, an integrated dynamic flow and water quality model is proposed to analyze the river system, including wastewater discharges and water intakes. This integrated model is capable of representing unsteady flow conditions, allowing analysis at different time scales. Second, three postprocessing tools are presented to support short- (hours to days), medium- (days to months), and long- (years to decades) term operational, management, and planning decisions. The water quality component of the model can represent conventional and toxic determinands to simultaneously analyze domestic and industrial pollution throughout a river system. The first postprocessing tool of the EMDSS is useful in defining concentration limits for wastewater discharges for different water users downstream. The second tool allows the assessment of river water quantity and quality to determine water availability for intake extensions and medium-term wastewater flow augmentation. The third makes it possible to simulate and perform effective operational reservoir releases to improve water quality in the river during short-term pollution incidents. The proposed integrated model and postprocessing tools are applied in the upper Bogotá River stretch in Colombia, one of the most altered catchments and polluted rivers in the world. The results obtained illustrate the utility of the proposed EMDSS for river management and decision making regarding water quality at different time scales. Full article

(This article belongs to the Special Issue Water-Quality Modeling)

► Show Figures

Graphical abstract

31 pages, 6823 KiB

Open AccessArticle

Open Lake Phosphorus Forcing of Cladophora Growth: Modeling the Dual Challenge in Great Lakes Trophic State Management

by Xing Zhou, Martin T. Auer and Pengfei Xue

Water 2021, 13(19), 2680; https://doi.org/10.3390/w13192680 - 28 Sep 2021

Cited by 2 | Viewed by 2382

Abstract

Stimulated by excess levels of phosphorus, the attached, filamentous green alga Cladophora grows to nuisance proportions in Lake Michigan, one of the Laurentian Great Lakes. While nearshore waters impacted by local sources of the nutrient continue to support nuisance conditions, offshore waters have [...] Read more.

Stimulated by excess levels of phosphorus, the attached, filamentous green alga Cladophora grows to nuisance proportions in Lake Michigan, one of the Laurentian Great Lakes. While nearshore waters impacted by local sources of the nutrient continue to support nuisance conditions, offshore waters have undergone oligotrophication in response to reductions in phosphorus loading and benthification of phosphorus cycling by invasive dreissenid mussels. A concept termed the Dual Challenge recognizes that implementation of more stringent phosphorus-loading objectives (to control Cladophora in the nearshore) stands in conflict with a foreseen need to mitigate oligotrophication in the offshore (to sustain a healthy fishery). Attention to this nearshore–offshore dynamic calls into play the role of cross-margin phosphorus transport in mediating both endmembers of the conflict. We applied a biophysical model simulating soluble reactive (SRP) and particulate (PP) phosphorus, mussel biokinetics, and cross-margin mass transport in addressing the Dual Challenge. Pre- and post-dreissenid monitoring results suggest that a reduction in offshore PP levels (food web nutrition) in excess of 40% (2.4 to 1.4 mgP·m⁻³) has driven oligotrophication and attendant food web dysfunction. Yet, in the absence of local sources, model-predicted nearshore SRP levels remain at or below those required to prevent nuisance growth. These findings indicate that there is a margin of ~1 mgP·m⁻³ over which offshore PP levels could be increased (to the benefit of the food web and the fishery) without hindering efforts to reduce nuisance algal growth through local source control. Full article

(This article belongs to the Special Issue Water-Quality Modeling)

► Show Figures

Figure 1

20 pages, 3709 KiB

Open AccessFeature PaperArticle

Impact of Global Warming on Dissolved Oxygen and BOD Assimilative Capacity of the World’s Rivers: Modeling Analysis

by Steven C. Chapra, Luis A. Camacho and Graham B. McBride

Water 2021, 13(17), 2408; https://doi.org/10.3390/w13172408 - 1 Sep 2021

Cited by 31 | Viewed by 10961

Abstract

For rivers and streams, the impact of rising water temperature on biochemical oxygen demand (BOD) assimilative capacity depends on the interplay of two independent factors: the waterbody’s dissolved oxygen (DO) saturation and its self-purification rate (i.e., the balance between BOD oxidation and reaeration). [...] Read more.

For rivers and streams, the impact of rising water temperature on biochemical oxygen demand (BOD) assimilative capacity depends on the interplay of two independent factors: the waterbody’s dissolved oxygen (DO) saturation and its self-purification rate (i.e., the balance between BOD oxidation and reaeration). Although both processes increase with rising water temperatures, oxygen depletion due to BOD oxidation increases faster than reaeration. The net result is that rising temperatures will decrease the ability of the world’s natural waters to assimilate oxygen-demanding wastes beyond the damage due to reduced saturation alone. This effect should be worse for nitrogenous BOD than for carbonaceous BOD because of the former’s higher sensitivity to rising water temperatures. Focusing on streams and rivers, the classic Streeter–Phelps model was used to determine the magnitude of the maximum or “critical” DO deficit that can be calculated analytically as a function of the mixing-point BOD concentration, DO saturation, and the self-purification rate. The results indicate that high-velocity streams will be the most sensitive to rising temperatures. This is significant because such systems typically occur in mountainous regions where they are also subject to lower oxygen saturation due to decreased oxygen partial pressure. Further, they are dominated by salmonids and other cold-water fish that require higher oxygen levels than warm-water species. Due to their high reaeration rates, such systems typically exhibit high self-purification constants and consequently have higher assimilation capacities than slower moving lowland rivers. For slow-moving rivers, the total sustainable mixing-point concentration for CBOD is primarily dictated by saturation reductions. For faster flowing streams, the sensitivity of the total sustainable load is more equally dependent on temperature-induced reductions in both saturation and self-purification. Full article

(This article belongs to the Special Issue Water-Quality Modeling)

► Show Figures

Figure 1

35 pages, 8924 KiB

Open AccessFeature PaperArticle

Management of the Phosphorus–Cladophora Dynamic at a Site on Lake Ontario Using a Multi-Module Bioavailable P Model

by Martin T. Auer, Cory P. McDonald, Anika Kuczynski, Chenfu Huang and Pengfei Xue

Water 2021, 13(3), 375; https://doi.org/10.3390/w13030375 - 31 Jan 2021

Cited by 8 | Viewed by 3740

Abstract

The filamentous green alga Cladophora grows to nuisance proportions in Lake Ontario. Stimulated by high phosphorus concentrations, nuisance growth results in the degradation of beaches and clogging of industrial water intakes with attendant loss of beneficial uses. We develop a multi-module bioavailable phosphorus [...] Read more.

The filamentous green alga Cladophora grows to nuisance proportions in Lake Ontario. Stimulated by high phosphorus concentrations, nuisance growth results in the degradation of beaches and clogging of industrial water intakes with attendant loss of beneficial uses. We develop a multi-module bioavailable phosphorus model to examine the efficacy of phosphorus management strategies in mitigating nuisance algal growth. The model platform includes modules simulating hydrodynamics (FVCOM), phosphorus-phytoplankton dynamics (GEM) and Cladophora growth (GLCMv3). The model is applied along a 25 km stretch of the Lake Ontario nearshore, extending east from Toronto, ON and receiving effluent from three wastewater treatment plants. Simulation results identify the Duffin Creek wastewater treatment plant effluent as a driving force for nuisance conditions of Cladophora growth, as reflected in effluent bioavailable phosphorus concentrations and the dimensions of the plant’s phosphorus footprint. Simulation results demonstrate that phosphorus removal by chemically enhanced secondary treatment is insufficient to provide relief from nuisance conditions. Tertiary treatment (chemically enhanced secondary treatment with ballasted flocculation) is shown to eliminate phosphorus-saturated conditions associated with the Duffin Creek wastewater treatment plant effluent, providing local relief from nuisance conditions. Management guidance presented here has wider application at sites along the highly urbanized Canadian nearshore of Lake Ontario. Full article

(This article belongs to the Special Issue Water-Quality Modeling)

► Show Figures

Graphical abstract

18 pages, 6576 KiB

Open AccessArticle

Modeling the Effectiveness of Cooling Trenches for Stormwater Temperature Mitigation

by Scott A. Wells

Water 2021, 13(3), 373; https://doi.org/10.3390/w13030373 - 31 Jan 2021

Viewed by 2429

Abstract

Due to elevated runoff stormwater temperatures from impervious areas, one management strategy to reduce stormwater temperature is the use of underground flow through rock media termed a cooling trench. This paper examines the governing equations for the liquid phase and media phases for [...] Read more.

Due to elevated runoff stormwater temperatures from impervious areas, one management strategy to reduce stormwater temperature is the use of underground flow through rock media termed a cooling trench. This paper examines the governing equations for the liquid phase and media phases for modeling the temperature leaving a cooling trench assuming that changes in temperature occurred longitudinally through the cooling trench. This model is dependent on parameters such as the media type, porosity, media initial temperature, inflow rate, and inflow temperature. Several approaches were explored mathematically for evaluating the change in temperature of the water and the cooling trench media. Typical soil–water heat transfer coefficients were summarized. Examples of predictions of outflow temperatures were shown for different modeling assumptions, such as well-mixed conditions, batch mixing and subsequent release, and steady-state and dynamic conditions. Several of these examples evaluated how long rock media would cool following a stormwater event and how the cooling trench would respond to multiple stormwater events. Full article

(This article belongs to the Special Issue Water-Quality Modeling)

► Show Figures

Figure 1

13 pages, 2450 KiB

Open AccessEditor’s ChoiceArticle

Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach

by Sang-Soo Baek, Jongcheol Pyo and Jong Ahn Chun

Water 2020, 12(12), 3399; https://doi.org/10.3390/w12123399 - 3 Dec 2020

Cited by 119 | Viewed by 9368

Abstract

A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a deep learning approach was created by combining CNN and LSTM networks simulated water quality including total nitrogen, total phosphorous, and total organic carbon. Water level and water quality data in the Nakdong [...] Read more.

A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a deep learning approach was created by combining CNN and LSTM networks simulated water quality including total nitrogen, total phosphorous, and total organic carbon. Water level and water quality data in the Nakdong river basin were collected from the Water Resources Management Information System (WAMIS) and the Real-Time Water Quality Information, respectively. The rainfall radar image and operation information of estuary barrage were also collected from the Korea Meteorological Administration. In this study, CNN was used to simulate the water level and LSTM used for water quality. The entire simulation period was 1 January 2016–16 November 2017 and divided into two parts: (1) calibration (1 January 2016–1 March 2017); and (2) validation (2 March 2017–16 November 2017). This study revealed that the performances of both of the CNN and LSTM models were in the “very good” range with above the Nash–Sutcliffe efficiency value of 0.75 and that those models well represented the temporal variations of the pollutants in Nakdong river basin (NRB). It is concluded that the proposed approach in this study can be useful to accurately simulate the water level and water quality. Full article

(This article belongs to the Special Issue Water-Quality Modeling)

► Show Figures

Figure 1

12 pages, 1650 KiB

Open AccessArticle

Fuzzy Optimization Model for Waste Load Allocation in a River with Total Maximum Daily Load (TMDL) Planning

by Jae Heon Cho and Jong Ho Lee

Water 2020, 12(9), 2618; https://doi.org/10.3390/w12092618 - 18 Sep 2020

Cited by 3 | Viewed by 2019

Abstract

In traditional waste load allocation (WLA) decision making, water quality-related constraints must be satisfied. Fuzzy models, however, can be useful for policy makers to make the most reasonable decisions in an ambiguous environment, considering various surrounding environments. We developed a fuzzy WLA model [...] Read more.

In traditional waste load allocation (WLA) decision making, water quality-related constraints must be satisfied. Fuzzy models, however, can be useful for policy makers to make the most reasonable decisions in an ambiguous environment, considering various surrounding environments. We developed a fuzzy WLA model that optimizes the satisfaction level by using fuzzy membership functions and minimizes the water quality management cost for policy decision makers considering given environmental and socioeconomic conditions. The fuzzy optimization problem was formulated using a max–min operator. The fuzzy WLA model was applied to the Yeongsan River basin, which is located in the southwestern part of the Korean Peninsula and Korean TMDLs were applied. The results of the fuzzy model show that the pollutant load reduction should be increased in the Gwangju 1 and Gwangju 2 wastewater treatment plants (WWTPs) and in subcatchments with high pollutant load. In particular, it is necessary to perform advanced wastewater treatment to decrease the load of 932 kg ultimate biochemical oxygen demand (BODu)/day in the large-capacity Gwangju 1 WWTP and reduce the BODu emission concentration from 4.3 to 2.7 mg/L during the low-flow season. The satisfaction level of the fuzzy model is a relatively high at 0.81. Full article

(This article belongs to the Special Issue Water-Quality Modeling)

► Show Figures

Figure 1

21 pages, 4940 KiB

Open AccessArticle

Modeling Daily and Monthly Water Quality Indicators in a Canal Using a Hybrid Wavelet-Based Support Vector Regression Structure

by Yuxin Wang, Yuan Yuan, Ye Pan and Zhengqiu Fan

Water 2020, 12(5), 1476; https://doi.org/10.3390/w12051476 - 21 May 2020

Cited by 19 | Viewed by 3095

Abstract

Accurate prediction of water quality indicators plays an important role in the effective management of water resources. The models which studied limited water quality indicators in natural rivers may give inadequate guidance for managing a canal being used for water diversion. In this [...] Read more.

Accurate prediction of water quality indicators plays an important role in the effective management of water resources. The models which studied limited water quality indicators in natural rivers may give inadequate guidance for managing a canal being used for water diversion. In this study, a hybrid structure (WA-PSO-SVR) based on wavelet analysis (WA) coupled with support vector regression (SVR) and particle swarm optimization (PSO) algorithms was developed to model three water quality indicators, chemical oxygen demand determined by KMnO₄ (COD_Mn), ammonia nitrogen (NH₃-N), and dissolved oxygen (DO), in water from the Grand Canal from Beijing to Hangzhou. Modeling was independently conducted over daily and monthly time scales. The results demonstrated that the hybrid WA-PSO-SVR model was able to effectively predict non-linear stationary and non-stationary time series and outperformed two other models (PSO-SVR and a standalone SVR), especially for extreme values prediction. Daily predictions were more accurate than monthly predictions, indicating that the hybrid model was more suitable for short-term predictions in this case. It also demonstrated that using the autocorrelation and partial autocorrelation of time series enabled the construction of appropriate models for water quality prediction. The results contribute to water quality monitoring and better management for water diversion. Full article

(This article belongs to the Special Issue Water-Quality Modeling)

► Show Figures

Figure 1

33 pages, 12276 KiB

Open AccessEditor’s ChoiceArticle

WASP 8: The Next Generation in the 50-year Evolution of USEPA’s Water Quality Model

by Tim Wool, Robert B. Ambrose, Jr., James L. Martin and Alex Comer

Water 2020, 12(5), 1398; https://doi.org/10.3390/w12051398 - 14 May 2020

Cited by 57 | Viewed by 9020

Abstract

The Water Quality Analysis Simulation Program (WASP) helps users interpret and predict water quality responses to natural phenomena and manmade pollution for various pollution management decisions. WASP is a dynamic compartment-modeling program for aquatic systems, including both the water column and the underlying [...] Read more.

The Water Quality Analysis Simulation Program (WASP) helps users interpret and predict water quality responses to natural phenomena and manmade pollution for various pollution management decisions. WASP is a dynamic compartment-modeling program for aquatic systems, including both the water column and the underlying benthos. WASP allows the user to investigate 1, 2 and 3 dimensional systems and a variety of pollutant types—including both conventional pollutants (e.g., dissolved oxygen, nutrients, phytoplankton, etc.) and toxic materials. WASP has capabilities of linking with hydrodynamic and watershed models which allows for multi-year analyses under varying meteorological and environmental conditions. WASP was originally developed by HydroScience, Inc. in 1970 and was later adapted by the US Environmental Protection Agency’s Large Lakes Research Station (LLRS) for applications to the Great Lakes. The LLRS first publicly released the model in 1981. WASP has undergone continuous development since that time and this year will mark its 50th anniversary. This paper follows the development of WASP from its origin to the latest release of the model in 2020, documenting its evolution and present structure and capabilities. Full article

(This article belongs to the Special Issue Water-Quality Modeling)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Water-Quality Modeling

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI