Next Article in Journal
Hydrogeological, Hydrochemical, and Geophysical Analysis of a Brine-Contaminated Aquifer Addressing Non-Unique Interpretations of Vertical Electrical Sounding Curves
Previous Article in Journal
A Convolutional Graph Neural Network Model for Water Distribution Network Leakage Detection Based on Segment Feature Fusion Strategy
Previous Article in Special Issue
Assessing Zebra Mussels’ Impact on Fishway Efficiency: McNary Lock and Dam Case Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 16
Issue 24
10.3390/w16243556
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Global Applications of the CE-QUAL-W2 Model in Reservoir Eutrophication: A Systematic Review and Perspectives for Brazil

by
Sarah Haysa Mota Benicio
1,
Raviel Eurico Basso
2 and
Klebber Teodomiro Martins Formiga
2,*
1
Program in Environmental Sciences, Federal University of Goiás, Avenida Esperança s/n, Campus Samambaia, Goiânia 74690-900, GO, Brazil
2
Postgraduate Program in Environmental Sciences, School of Civil and Environmental Engineering, Federal University of Goiás, Goiânia 74605-220, GO, Brazil
*
Author to whom correspondence should be addressed.
Water 2024, 16(24), 3556; https://doi.org/10.3390/w16243556
Submission received: 18 September 2024 / Revised: 2 December 2024 / Accepted: 5 December 2024 / Published: 10 December 2024
(This article belongs to the Special Issue Assessment of Hydropower Sustainability in River Habitats and Aquatic Biota)
Browse Figures
Versions Notes

Abstract

:
The CE-QUAL-W2 model is a significant tool extensively used in lentic environments to analyze eutrophication and water quality. This systematic review of the CE-QUAL-W2 hydrodynamic model revealed its widespread application in analyzing reservoir eutrophication. A total of 151 relevant papers were identified, of which 38 were selected after rigorous analysis, showcasing studies in environmental sciences and water resources. In 2021, we saw the highest number of publications, with six papers; 2022 achieved the highest number of citations, with 113. The model has been widely used across countries, with Iran leading in the number of publications, followed by China and Brazil. The standard combination of CE-QUAL-W2 with the SWAT model reflects its effectiveness in complex watershed studies. CE-QUAL-W2 has demonstrated the ability to predict future environmental conditions and diagnose environmental extremes, and it can calculate various hydrodynamic and water quality parameters. Its increasing use in high-impact scientific journals underscores its global relevance and particular promise for Brazilian aquatic environment studies due to its efficiency and accessibility. With its significant potential, this model is poised to enhance the understanding and management of water resources, contributing to environmental sustainability and inspiring optimism for future applications on a global scale.

1. Introduction

Understanding and modeling the reservoir eutrophication process is crucial for effective water resource management. Eutrophication, driven by nutrient enrichment, especially phosphorus and nitrogen, presents significant challenges for aquatic ecosystems’ ecology and water quality [1]. More precise modeling provides a systematic approach to unraveling the complex interactions that govern nutrient dynamics, algal blooms, and oxygen depletion, which are crucial in eutrophication [2,3]. Quality models are essential for assessing the long-term consequences of eutrophication, enabling preventive measures, and supporting sustainable management practices [4]. By integrating observed data and predictive modeling, researchers can gain insights into the underlying mechanisms, facilitating the development of targeted strategies to mitigate the adverse effects of eutrophication in reservoirs [5].
The CE-QUAL-W2 represents a laterally averaged, two-dimensional hydrodynamic model renowned for its computational efficiency compared to three-dimensional systems. Its versatility encompasses various water quality parameters, exceeding 60 variables interacting across different compartments, namely air, water, and sediments. With a decades-long development history, the model’s open-source nature facilitates access to its source code [6]. This model has been widely used in lentic environments to analyze eutrophication and water quality globally, simulating vertical stratification and longitudinal variability in crucial ecosystem properties [7]. Initially developed by [8] and later modified by the U.S. Army Corps of Engineers [9] and Portland State University [10], it was used to simulate hydrodynamics, temperature, and water quality in Lake Simtustus during 1995–1996.
Adapted explicitly for laterally averaged scenarios, the model is predominantly applied in relatively confined aquatic ecosystems such as rivers and reservoirs. It is suitable for simulating long and narrow water bodies as it assumes lateral homogeneity, implying no significant variations in water quality constituents [11]. It has been used in Brazil to simulate the hydrodynamics and evaporation of lakes [12]; to assess residence time and total phosphorus in hypereutrophic lakes [13]; to evaluate the impact of fish farming on water quality [14]; and in other parts of the world, as a management and research tool, mainly to simulate nutrient and sediment dynamics [15], thermal stratification [16], salinity [17], eutrophication [18], and changes in water quality in rivers, reservoirs, dams, lakes, and estuaries. The current model (version 4.5) can simulate suspended solids; nutrient groups and organic matter; residence time; derived variables such as total nitrogen (TN), total Kjeldahl nitrogen (TKN), total organic carbon (TOC), chlorophyll-a, as well as pH; total dissolved gases; and biotic groups such as periphyton, phytoplankton, zooplankton, and macrophyte groups interacting with hydrodynamics.
The CE-QUAL-W2 model incorporates long-term environmental changes, such as climate change and land use alterations, by simulating various environmental variables and external drivers that influence hydrodynamic processes and water quality. It allows for including climate change scenarios by adjusting input variables related to weather, such as air temperature, precipitation, wind, and solar radiation. Additionally, CE-QUAL-W2 integrates these changes by modifying input variables that reflect external and boundary conditions, such as climate and land use, thereby providing a more accurate representation of environmental dynamics over time.
As a model primarily applied to reservoirs, CE-QUAL-W2 has been frequently used to analyze the eutrophication process. It can be employed to predict algal blooms due to hydro-climatic conditions or changes in land use in the basin [19]. With this information, it is possible to reproduce the actual conditions of the studied environment and predict future environmental conditions, thus developing reliable strategies for water resource management and decision-making to ensure future operations and environmental sustainability. This systematic review aimed to elucidate the state of the art regarding the evolution, use, and effectiveness of the CE-QUAL-W2 hydrodynamic model.

2. Materials and Methods

To guide the searches, the keywords used were “Reservoir” and “CE-QUAL-W2” and “Eutrophication” or “algae” or “phytoplankton” or “cyanobacteria” in the databases. The searches focused on two databases, Scopus (Elsevier) and Web of Science, all available on the Periodicals Portal of the Coordination for the Improvement of Higher Education Personnel (CAPES).
The search yielded 151 works, including articles, books, book chapters, and conference papers. Only the “article” and “conference paper” document types were considered for this study. The language selected was English. The period was 20 years (2003–2023), with the last verification conducted on 21 September 2023. Works that were not available in full text, were in languages other than English, or were not articles or conference papers were excluded. The search yielded 127 works that featured the keywords in the title and/or abstract.
After thoroughly analyzing the titles and abstracts, 47 documents were considered of interest for this research due to their significant relevance to the subject matter. Following a complete reading of the 47 selected works, the manuscripts were further excluded if their sole purpose was to verify flow rates using the CE-QUAL-W2 model and review studies to discuss and compare the applicability of different types of water quality models. Other excluded works were those not directly related to the eutrophication of reservoirs or lakes and studies investigating the effects of climate change and flow scenarios on thermal structure without considering eutrophication.
The entire process of selecting and analyzing the articles followed the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Protocol [20], ensuring transparency, methodological rigor, and standardization of the systematic review conducted.
Thirty-eight articles, ten from the Scopus database and twenty-eight from Web of Science, were deemed relevant to this study (Figure 1).

3. Results and Discussion

In total, 26 different journals published on the subject. Among the selected journals, the Journal of Hydrology published the most, with five articles, followed by the International Journal of Environmental Science and Technology, with four. Environmental Engineering Science, Environmental Modeling & Assessment, Environmental Monitoring and Assessment, Water, and Water Science and Technology each published two articles, while the remaining journals had only one selected article (Figure 2). The growing acceptance of studies using the CE-QUAL-W2 model in high-impact scientific journals indicates its global relevance and role in promoting environmental sustainability, especially in climate change and increasing pressure on water resources.
The articles were classified into 12 categories according to the study area, with environmental sciences, water resources, and environmental engineering having the highest number of works. The most significant production of articles occurred in 2021, with six publications, followed by 2019 and 2022, with five publications each; 2020, 2015, and 2023, with three publications each; 2009 and 2016, with two publications each; and 2003, 2006, 2008, 2010, 2012, 2013, 2014, 2017, and 2018, with one publication each. The years 2004, 2005, 2007, and 2011 had no publications (Figure 2).
The highest number of citations occurred in 2022, totaling 113. The article in [21] was the most cited, with 92 citations over the years, and is widely used as a reference.
Of the 38 selected works, studies were conducted in eight countries, with Iran having the highest number of published works, totaling 16 selected articles. Following Iran are China and Brazil, each with five published works, and South Korea and Canada, each with four works. Portugal had two works, while the United States and Turkmenistan each had one work. Among these works, four countries had repeated study locations: Karkheh Lake, Ilam Lake, Seimare Lake, and Behesht-Abad Reservoir in Iran; Yeongsan Lake in South Korea; Diefenbaker Lake in Canada; and Santo Anastácio Lagoon in Brazil (Figure 3).
Twenty-four publications utilized only the CE-QUAL-W2 model. Fourteen used the CE-QUAL-W2 model combined with another type of model or methodology. Five of these fourteen works used the SWAT model, making it the most adopted combined methodology (Table 1).
The CE-QUAL-W2 model was widely used in water quality studies, involving calibration and validation with data from different periods, methods, and sources. Typical objectives of these studies included simulating water quality dynamics in tropical reservoirs, assessing the impact of climate change and human activities on hydrodynamics and water quality, and developing optimization models to protect aquatic ecosystems. The most frequently mentioned quality parameters in the studies were dissolved oxygen (DO), water temperature, total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), ammonia (NH3), nitrate (NO3−)/nitrite (NO2−), total dissolved solids (TDS), and biochemical oxygen demand (BOD). These parameters were essential for understanding and modeling water quality in various contexts, reflecting the importance of monitoring and evaluating their presence and variation in aquatic ecosystems.
The longest period mentioned was 20 years (2000–2019), and the most extensive study area was the Three Gorges Reservoir in China, with a surface area of 1080 km2. These efforts underscore the importance of managing water resources to ensure their sustainability.

3.1. Calibration of the Model

Most studies conducted model calibration, with eight papers utilizing pre-calibrated models and established methodologies, such as those by [24]. Additionally, in two studies by [27,47], these methodologies were updated and extended, extending the calibration period of previous studies. Comparing these studies reveals significant variations in model calibration. Table 2 presents the calibrated values of the parameters used in the different studies.
Ref. [23] reported an R2 of 0.32 for the calibrated parameters (DO, chlorophyll-a, and PO4), indicating a less effective fit. The study in [13] achieved an R2 of 0.70 for total phosphorus, suggesting a good model fit. The study in [40] reported R2 values above 0.9 for water level, temperature, and suspended solids but lower values for chlorophyll-a (0.35). These results illustrate the variability in model performance and parameter calibration, highlighting that higher R2 values indicate a better fit between observed and simulated values.
Data quality is crucial for modeling aquatic systems; studies with precise measurements across various conditions typically yield superior results. For example, systematic data collection at multiple sites in the work of [42] contributed to a more robust model. Furthermore, studies focusing on more direct parameters, such as water level and temperature [26], generally achieved higher R2 values than those addressing more complex parameters, such as dissolved oxygen and chlorophyll-a [23]. These observations are reflected in the analyses of various studies that applied the CE-QUAL-W2 model, revealing significant differences in the quality of methods, which can directly impact the obtained R2 values.
For instance, the study in [22] presents a rigorous approach using the SUFI-2 self-calibration algorithm, resulting in a reduced root mean square error (RMSE) and 76% of the measured data within a 95% confidence interval. This efficiency suggests a high R2. In contrast, the study in [18] adopts a qualitative methodology to simulate oxygen concentration in the Yamchi representation without rigorous forecasting, which led to a lower R2 due to the less precise nature of the analysis. The study in [23], dealing with data scarcity in a tropical reservoir, simplifies the model to include only a few parameters, which, while suitable for predicting seasonal variations, may limit the accuracy of the results, resulting in an acceptable but less reliable R2.
Ref. [12] stands out for its integrated approach, using empirical correlations to relate water quality variables and hydrodynamic modeling, suggesting a relatively high and robust R2 in the results. Finally, the study in [25], which investigates changes in nutrient regimes due to dam construction, presents a complexity that may hinder precise modeling, resulting in a moderate R2. These variations across studies highlight the importance of specific expertise, data quality, and model complexity in successfully applying CE-QUAL-W2, establishing that combining these factors is essential for specific and experimental outcomes in simulating water dynamics and quality.

3.2. Calibration Variability

R2 values vary for each calibrated parameter as different factors and processes influence them. The model tends to achieve high accuracy for parameters such as water level and temperature, which are more direct and less complex. In contrast, the model’s accuracy may be lower for parameters like dissolved oxygen and ammonia nitrogen, which depend on multiple biogeochemical factors and environmental variables. For instance, in the study in [48], the coefficient of determination (R2) was high for water level and surface temperature, with R values of 0.98, indicating excellent accuracy. However, the values were lower for water quality parameters such as dissolved oxygen (DO) and ammonia nitrogen (NH3–N), with R2 values of 0.49 and 0.51, respectively, indicating moderate agreement.
Studies have highlighted the accuracy of the CE-QUAL-W2 model for nutrients and dissolved oxygen. For example, high R2 values for dissolved oxygen were reported, with 0.978 in the upper layer, 0.986 in the middle layer, and 0.913 near the bottom. Some water quality parameters (e.g., ammonium/ammonia, total phosphorus, and total nitrogen) had an R2 of 0.283 [21] and were inadequately replicated by CE-QUAL-W2, attributable to both the quality of the input data and the propagation and compounding of errors, including the assumptions employed to calculate organic matter and its division into four pools. Representing the reservoir’s algal community using only one group (blue-green algae) may adversely affect the chlorophyll simulations and other water quality variables that have causal links with chlorophyll a, utilizing CEQUAL-W2.
One study [51] recorded an R2 of 0.977 for reducing phosphorus loads, showing a good model fit; this reservoir has smooth behavior and good water quality data. Another study [38] found variable R2 values for total phosphorus (0.85 and 0.29) and total nitrogen (0.96 and 0.89) across different seasons; reservoir system-limiting factors caused little correlation between variables like nitrate and eutrophication levels. The Dousti Dam study found that low nitrate concentrations limited phosphorous levels, which were consistently high. In several cases, this mismatch made nitrate a lesser predictor of eutrophication than phosphorus. Eutrophication’s limiting factor often controls nutrient dynamics. When phosphorus is abundant and nitrogen (or its forms like nitrate) is scarce, the system’s productivity and eutrophication are controlled by the scarcer nutrient. Thus, nitrate had a weaker connection with eutrophic states, demonstrating that nitrogen availability is crucial to eutrophication. Additionally, an average R2 of 0.41 for total phosphorus was reported during dry periods and 0.27 for rainy periods, highlighting climatic influences [24]. Further, variations of 0.76 in dry and 0.20 in rainy seasons for total phosphorus were observed, with an average R2 of 0.70 [12]. An average R2 of 0.60, ranging from 0.60 to 0.84, was also reported [13]. The importance of considering environmental and water quality variables in modeling aquatic parameters is evident. Differences in R2 values across studies can be attributed to factors like the sophistication of methodologies, data quality, and research locations. Higher R2 values were observed in studies using the CE-QUAL-W2 model with appropriate calibrations and high-quality data in less complex locations. In contrast, lower R2 values were noted in studies with limited data, conducted in complex locations, or using less established methodologies.
Calibration is necessary to ensure that the CE-QUAL-W2 model is a precise and reliable tool. Model calibration involves adjusting its parameters to replicate the conditions in the aquatic environment under study accurately. Typically, this process includes comparing the model’s simulated outputs with on-site data.
Several steps are required for the precise calibration of CE-QUAL-W2. First, relevant field data must be collected, including hydrological, water quality, and sedimentation information. Next, the model must be configured with the physical characteristics of the water body and defined parameters. The subsequent step involves manually adjusting parameter values to enhance the agreement between the model’s simulations and observed data in an iterative process often assisted by specialized software (version 3.5). Finally, the model is validated using independent data to ensure its capacity to represent the system under different conditions.
Calibration often involves manual adjustments of parameters to improve the correspondence between the model’s simulations and observed data. Although automatic optimization techniques can be applied, the process still requires significant human intervention to ensure accurate and reliable model performance.

3.3. Model Calibration Overview

The CE-QUAL-W2 model accurately replicates reservoir temperature profiles with clear thermal stratification, effectively capturing vertical gradients and seasonal variations, including diurnal patterns. Studies in tropical and Mediterranean climates confirmed precise simulations in regions with stable hydrodynamics, such as Northeast Brazil, Iran, and Taiwan, where atmospheric and inflow data calibration enhanced predictive accuracy [18,23].
For dissolved oxygen (DO), the model performed well in monomictic and tropical reservoirs, accurately reproducing oxygen distribution influenced by seasonal stratification. Upper layers remained oxygenated, while lower levels experienced anoxia during summer. The model excelled under stable temperature conditions, reflecting oxygenation and depletion processes driven by hydrodynamic and atmospheric factors [31,51].
Regarding nutrient concentrations, CE-QUAL-W2 accurately simulated nitrogen and phosphorus dynamics in reservoirs with consistent inflows and stratification. The model performed best in systems with stable nutrient inputs, such as agricultural runoff, and effectively incorporated seasonal variations linked to monsoonal patterns, aligning nutrient cycling with runoff dynamics. This was particularly evident in East Asian reservoirs, where seasonal monsoon-driven inflows were closely tied to nutrient loading [23,42].
The model also showed proficiency in simulating chlorophyll-a and algal concentrations in reservoirs with consistent nutrient loading and light availability. It accurately represented the seasonal and spatial distributions of chlorophyll-a, particularly in monomictic tropical reservoirs with predictable wet and dry seasons. These systems, characterized by nutrient influx during wet periods and stable stratification during dry seasons, fostered algal growth and chlorophyll-a concentration, further validated by observations in East Asian reservoirs [40,42].

3.4. Requirements for Good Calibration of CE-QUAL-W2

Precise temperature simulations utilizing CE-QUAL-W2 depend on comprehensive input data, including air temperature, solar radiation, wind velocity, and associated climatic variables. The model is efficient in slender, elongated reservoirs exhibiting longitudinal and vertical temperature gradients, facilitating accurate simulation of vertical thermal stratification. This underscores the need for precisely segmented reservoir inputs and suitably calibrated hydrodynamic parameters [21,51]. Moreover, precise hydrological and climatic data improve the model’s efficacy, allowing it to replicate activities such as gas exchange, photosynthesis, and respiration across time [21].
Simulations of dissolved oxygen (DO) have been successful in aquatic environments characterized by predictable thermal stratification, limited anthropogenic influence, and low sediment or industrial effluent loading [18]. High-quality inflow data and meticulous calibration of nutrient uptake rates and organic matter decomposition are critical for nutrient dynamics modeling [23]. Nutrient distribution in stratified reservoirs is depicted between surface and deeper layers under steady mixing and stratification conditions. The transfer of nutrients between sediments and the water column is vital for nutrient stability, rendering accurate sediment nutrient release data essential for calibration [31]. However, in reservoirs with complex hydrodynamic regimes—such as those affected by rainfall pulses, sporadic agricultural discharges, or highly variable nutrient inflows—precise data on nutrient levels, light availability, and water temperature are essential for simulating chlorophyll-a and algal concentrations. Calibrating phytoplankton growth rates, nutrient uptake dynamics, and light extinction coefficients is vital [23]. Like certain Mediterranean reservoirs in stratified systems, the model effectively replicates chlorophyll-a distribution by capturing resource gradients between nutrient-rich bottom layers and light-penetrable upper layers [31].

3.5. Limitations of CE-QUAL-W2

CE-QUAL-W2 faces challenges in reservoirs with complex hydrodynamic regimes, such as high sediment and nutrient loads, urban pollution, or variable inflows. The model struggles to simulate intricate temperature profiles created by diverse inflows and vertical movement [31,42]. Reliable meteorological input—hourly air temperature, solar radiation, and wind speed—must effectively calibrate thermal parameters. Sensitivity analyses using multiple scenarios improve accuracy in systems with frequent input changes or seasonal variations [31,53].
The model encounters difficulties simulating chlorophyll-a and algal concentrations in shallow reservoirs with significant sediment resuspension or systems with unpredictable nutrient dynamics. Its two-dimensional framework may oversimplify fine-scale variations in algal growth and nutrient distribution, particularly in reservoirs with recurrent disturbances or uniform nutrient profiles [51]. Nutrient simulation depends on carefully calibrated input rates to reflect seasonal variations and inflow quality. High-resolution data enhance the model’s capacity to represent nutrient cycling, particularly in systems influenced by sediment resuspension or pronounced seasonal cycles [40,54].
The model’s limitations extend to DO modeling in systems with substantial variability. DO simulations require precise calibration of organic matter decomposition, oxygen production and consumption, and nutrient fluxes. Frequent nutrient influx episodes and rapid biogeochemical interactions can cause sudden DO variations that are challenging for the model to replicate. The two-dimensional framework struggles to capture hypoxic or anoxic conditions in deeper layers, mainly where sediment oxygen demand is significant [53]. CE-QUAL-W2 cannot fully depict lateral fluxes or spatial variability in reservoirs with multiple contamination sources without a three-dimensional approach.
The model has limitations in representing nutrient dynamics under conditions of rapid environmental variability or high sediment resuspension. Its two-dimensional structure is insufficient for capturing lateral nutrient distributions and complex sediment interactions in heterogeneous aquatic environments. This limitation impacts the accuracy of simulations in shallow reservoirs with nutrient-dense sediments or highly dynamic systems, where nutrient cycling and light attenuation are influenced by external disturbances [51,54]. To improve simulations of chlorophyll-a and algal growth, it is necessary to calibrate growth and nutrient uptake parameters and provide high-resolution, seasonally adjusted data. Scenario-based simulations help evaluate the model’s response to varying nutrient inflows, allowing for a better understanding of chlorophyll-a concentrations under diverse environmental conditions [40].

3.6. CE-QUAL-W2 Associated with Other Methodologies

The CE-QUAL-W2 model effectively simulates water quality in reservoirs and is often combined with the SWAT model for comprehensive watershed analyses. Studies have utilized the SWAT and CE-QUAL-W2 models to analyze and calibrate various parameters related to water quality and hydrodynamics in reservoirs. For instance, the study in [21] achieved a calibration of CE-QUAL-W2, focusing on temperature and dissolved oxygen, with an R of 0.906. In the study in [42], CE-QUAL-W2 was calibrated for temperature, dissolved oxygen, total dissolved solids, total nitrogen, and total phosphorus, with correlation values (R) ranging from 0.62 to 0.95. The study in [31] calibrated SWAT for flow, total nitrogen, and total phosphorus, obtaining coefficients of determination (R2) of 0.71, 0.59, and 0.14, respectively, while CE-QUAL-W2 showed an R2 of 0.92 for water surface elevation. Finally, the study in [26] used SWAT to estimate flow and nutrient loads, achieving an R2 of 0.64, while CE-QUAL-W2 was calibrated for water level, temperature, and dissolved oxygen, with an R2 of 0.92. These studies demonstrate the crucial contribution of SWAT to more comprehensive studies, as it allows for an integrated analysis that encompasses not only reservoirs but also the entire watershed, providing a holistic view of hydrological processes and water quality.

3.7. Modeling Assisting Water Issues in Iran and China

In recent decades, Iran has been experiencing water stress primarily due to the prevalence of arid and semi-arid climates with low rainfall and high evaporation rates. In addition to climatic conditions, other contributing factors include agriculture, urbanization, population growth, land use changes, and an ineffective water management system [32,60]. In this context, modeling becomes a crucial tool and can serve as an ally in developing operational policies that effectively address water availability and quality challenges. An example is the study in [18], which applied the CE-QUAL-W2 model to investigate dissolved oxygen concentration and the level of eutrophication in the Yamchi Dam reservoir. The study demonstrated the model’s effectiveness by finding an R2 value of 0.6781, indicating a significant correlation between air temperature and the inflow water temperature in the reservoir.
China also faces severe water issues, including water scarcity in the northern regions due to high demand and uneven distribution, as well as significant pollution of water bodies caused by industrial, urban, and agricultural waste [61]. Various studies have used the CE-QUAL-W2 model to analyze water quality and eutrophication in reservoirs [62,63,64,65]. The study in [51] investigated the Mingder Reservoir and found an R2 value of 0.977 in the nonlinear relationship between phosphorus load reduction and total phosphorus concentration, indicating a strong correlation between phosphorus reduction and improved water quality. The study in [53] compared the Te-Chi and Tseng-Wen reservoirs, accurately reproducing vertical temperature profiles and concentrations of total phosphorus, ammonia, nitrite/nitrate, chlorophyll-a, and dissolved oxygen. The R2 values for total phosphorus reduction in loads of 1998 and 1999 were 0.9605 and 0.9724, respectively, demonstrating a strong correlation between phosphorus reduction and water quality improvement. Results like these highlight the importance of modeling in understanding and mitigating water availability and quality challenges in different world regions.

3.8. Modeling for Future Scenarios

The CE-QUAL-W2 model can be applied to analyze risks in water quality under future scenarios. The study in [48] used the model to study the impacts of climate change in two future scenarios in the Hsin Shan Reservoir, Taiwan. They identified that increasing temperatures could compromise water quality due to thermal stability and oxygen stratification, resulting in lower dissolved oxygen in deep layers and increased phosphorus release from sediments. The model was effective in assessing these impacts. Subsequent studies, such as those in [26,28,29,62], continued to use CE-QUAL-W2 to investigate future climate conditions and mitigate the effects of climate change on water quality. This integrated approach provides crucial information for water resource management and solutions for stakeholders with different needs.

3.9. The Efficiency of CE-QUAL-W2 for Studies in Brazilian Aquatic Environments

Although the CE-QUAL-W2 model is widely used in various countries, its application in Brazil is still limited despite its abundant water resources. Recent studies highlight the potential and effectiveness of the model in different contexts. For example, the study in [14] evaluated the impact of fish farming in the Tucuruí Reservoir, observing a change in the trophic state due to this practice. The study in [24] modeled the hydrodynamics and evaporation in Lake Santo Anastácio, demonstrating the impact of climatic conditions on water availability. The studies in [12] and [13] investigated the influence of hydrological variability and phosphorus load in the same reservoir. The study in [23] simplified the model to simulate water quality, highlighting the efficiency of this approach. These studies contribute to improving the understanding of processes in water bodies and enhancing the management of these resources in Brazil.

4. Conclusions

The CE-QUAL-W2 model is widely used to analyze eutrophication and water quality in lentic environments. This systematic review revealed its global application, identifying 151 relevant papers, of which 38 were selected after rigorous analysis. The model is effective in complex watershed studies and has demonstrated the ability to predict future environmental conditions and diagnose environmental extremes.
The CE-QUAL-W2 model plays a fundamental role in predicting current and future scenarios and diagnosing extreme environmental conditions, especially in cases where available monitoring data are scarce. Studies using this hydrodynamic model have been growing in recent years, and it has proven to be very efficient for water quality analysis in various aquatic environments, including rivers, lakes, and reservoirs subject to eutrophication. Its ability to calculate a wide range of hydrodynamic and water quality parameters makes it valuable for understanding the health of aquatic ecosystems and identifying potential environmental impacts.
The model is also highly effective when used with other methodologies, especially SWAT, for monitoring watersheds in more complex studies. In the past five years, it has been widely used in studies published in high-impact scientific journals, offering a variety of resources such as technical documents and manuals. Despite its applicability in different parts of the world and various types of reservoirs, from the simplest to the most complex, it could be more thoroughly explored, especially in studies focused on Brazilian aquatic environments. Its efficiency and free availability make it a highly favorable tool for use.
CE-QUAL-W2 replicated temperature profiles in reservoirs with thermal stratification and seasonality. Studies in tropical and subtropical reservoirs show it can imitate vertical temperature gradients and seasonal thermal fluctuations. Reservoirs from Northeast Brazil, Iran, and Taiwan showed predictable stratification and mixing cycles due to atmospheric and inflow data calibration.
The model simulated dissolved oxygen (DO) well in monomictic and tropical stratified water column systems. These habitats allowed oxygen cycles to be simulated since the higher layers stayed oxygenated while the lower layers became anoxic in summer. The model’s two-dimensional architecture made it difficult to model systems with substantial input variability and extreme anoxic conditions in deeper layers.
The CE-QUAL-W2 model simulated nutrients well under steady input conditions such as regulated agricultural or urban runoff. The model accurately simulated stratified reservoir nitrogen and phosphorus changes, matching seasonal nutrient cycles like monsoons. The model simulated chlorophyll-a and algae well in well-stratified settings with consistent fertilizer loading and light availability. It correctly reproduced chlorophyll-a’s seasonal and spatial distribution in tropical and temperate reservoirs, especially when seasonal stratification and separating nutrient-rich bottom and light-penetrable upper layers favored algae growth. The model struggled to capture fine-scale chlorophyll-a concentration variability in shallow or well-mixed reservoirs with more consistent nutrient distribution.
The two-dimensional structure limits the CE-QUAL-W2 model’s ability to describe lateral fluxes and the spatial variability of nutrients, light, and oxygen. Heterogeneous or dynamic aquatic systems with fluctuating nutrient inputs and quick biogeochemical processes demand higher spatial and temporal resolution, making the model unsuitable. To maximize accuracy, use trustworthy, high-resolution input data and run scenario-based simulations to test the model’s responses to different environmental situations. Also required are input parameter sensitivity evaluations and rigorous calibration of essential processes like organic matter breakdown and nutrient uptake.

Author Contributions

S.H.M.B. Conceptualization, Methodology, Validation, Formal Analysis, Writing—Original Draft Preparation, Writing—Review & Editing. K.T.M.F. Validation, Formal Analysis, Writing—Original Draft Preparation, Writing—Review & Editing, Supervision. R.E.B. Writing—Review & Editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Coordination for the Improvement of Higher Education Personnel–Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–CAPES), through the provision of a doctoral scholarship grant to S.H.M.B.

Data Availability Statement

All data supporting the findings of this study were obtained from publicly available academic databases. Specifically, data were sourced from the Web of Science (https://www.webofscience.com, accessed on 21 September 2023) and Scopus (https://www.scopus.com, accessed on 21 September 2023). Access to these databases may require an institutional or individual subscription.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Schindler, D.W.; Hecky, R.E.; Findlay, D.L.; Stainton, M.P.; Parker, B.R.; Paterson, M.J.; Beaty, K.G.; Lyng, M.; Kasian, S.E.M. Eutrophication of Lakes Cannot Be Controlled by Reducing Nitrogen Input: Results of a 37-Year Whole-Ecosystem Experiment. Proc. Natl. Acad. Sci. USA 2008, 32, 11254–11258. [Google Scholar] [CrossRef] [PubMed]
  2. Paerl, H.W.; Gardner, W.S.; Mark, J.; McCarthy, M.J.; Peierls, B.L.; Wilhelm, S.W. Algal Blooms: Noteworthy Nitrogen. Science 2014, 346, 175. [Google Scholar] [CrossRef] [PubMed]
  3. Jeppesen, E.; Søndergaard, M.; Meerhoff, M.; Lauridsen, T.L.; Jensen, J.P. Shallow Lake Restoration by Nutrient Loading Reduction—Some Recent Findings and Challenges Ahead. Hydrobiologia 2007, 584, 239–252. [Google Scholar] [CrossRef]
  4. Conley, D.J.; Paerl, H.W.; Howarth, R.W.; Boesch, D.F.; Seitzinger, S.P.; Havens, K.E.; Lancelot, C.; Likens, G.E. Ecology—Controlling Eutrophication: Nitrogen and Phosphorus. Science 2009, 323, 1014–1015. [Google Scholar] [CrossRef] [PubMed]
  5. Hagy, J.D.; Boynton, W.R.; Keefe, C.W.; Wood, K.V. Hypoxia Relation in Chesapeake Bay, 1950–2001 Long-Term to Nutrient Loading and River Flow. Estuaries 2004, 27, 634–658. [Google Scholar] [CrossRef]
  6. Cole, T.M.; Wells, S.A. CE-QUAL-W2: A Two-Dimensional, Laterally Averaged, Hydro-Dynamic and Water Quality Model; US Army Engineering and Research Development Center: Vicksburg, MS, USA, 2017. [Google Scholar]
  7. Mooij, W.M.; Trolle, D.; Jeppesen, E.; Arhonditsis, G.; Belolipetsky, P.V.; Chitamwebwa, D.B.R.; Degermendzhy, A.G.; DeAngelis, D.L.; De Senerpont Domis, L.N.; Downing, A.S.; et al. Challenges and Opportunities for Integrating Lake Ecosystem Modelling Approaches. Aquat. Ecol. 2010, 44, 633–667. [Google Scholar] [CrossRef]
  8. Edinger, J.E.; Buchak, E. A Hydrodynamic, Two-Dimensional Reservoir Model: The Computational Basis. In Prepared for U.S. Army Engineer Division; Ohio River: Cincinnati, OH, USA, 1975. [Google Scholar]
  9. Cole, T.; Buchak, E. CE-QUAL-W2: A Two-Dimensional, Laterally Averaged, Hydrodynamic and Water Quality Model, Version 2.0 User Manual; U.S. Army Corps of Engineers: Washington, DC, USA, 1995. [Google Scholar]
  10. Cole, T.M.; Wells, S.A. CE-QUAL-W2: A Two-Dimensional, Laterally CE-QUAL-W2: A Two-Dimensional, Laterally Averaged, Hydrodynamic and Water Quality Model, Averaged, Hydrodynamic and Water Quality Model, Version 3.5; Portland State University: Portland, OR, USA, 2006. [Google Scholar]
  11. Mateus, M.; Almeida, C.; Brito, D.; Neves, R. From Eutrophic to Mesotrophic: Modelling Watershed Management Scenarios to Change the Trophic Status of a Reservoir. Int. J. Environ. Res. Public Health 2014, 11, 3015–3031. [Google Scholar] [CrossRef]
  12. Mesquita, J.B.d.F.; Lima Neto, I.E. Coupling Hydrological and Hydrodynamic Models for Assessing the Impact of Water Pollution on Lake Evaporation. Sustainability 2022, 14, 13465. [Google Scholar] [CrossRef]
  13. Rocha, S.M.G.; Rocha, M.d.J.; Araújo, G.M.; Becker, H.; Neto, I.E.L. Seasonal and Interannual Variability of Residence Time and Total Phosphorus in a Small Hypereutrophic Lake in the Brazilian Northeast. Water SA 2022, 48, 278–285. [Google Scholar] [CrossRef]
  14. Deus, R.; Brito, D.; Mateus, M.; Kenov, I.; Fornaro, A.; Neves, R.; Alves, C.N. Impact Evaluation of a Pisciculture in the Tucuruí Reservoir (Pará, Brazil) Using a Two-Dimensional Water Quality Model. J. Hydrol. 2013, 487, 1–12. [Google Scholar] [CrossRef]
  15. Vandenberg, J.A.; Prakash, S.; Buchak, E.M. Sediment Diagenesis Module for CE-QUAL-W2. Part 1: Conceptual Formulation. Environ. Model. Assess. 2015, 20, 239–247. [Google Scholar] [CrossRef]
  16. Zhao, L.; Cheng, S.; Sun, Y.; Zou, R.; Ma, W.; Zhou, Q.; Liu, Y. Thermal Mixing of Lake Erhai (Southwest China) Induced by Bottom Heat Transfer: Evidence Based on Observations and CE-QUAL-W2 Model Simulations. J. Hydrol. 2021, 603, 126973. [Google Scholar] [CrossRef]
  17. Tavoosi, N.; Hooshyaripor, F.; Noori, R.; Farokhnia, A.; Maghrebi, M.; Kløve, B.; Haghighi, A.T. Experimental-Numerical Simulation of Soluble Formations in Reservoirs. Adv. Water Resour. 2022, 160, 104109. [Google Scholar] [CrossRef]
  18. Hanjaniamin, A.E.; Tabrizi, M.S.; Babazadeh, H. Dissolved Oxygen Concentration and Eutrophication Evaluation in Yamchi Dam Reservoir, Ardabil, Iran. Appl. Water Sci. 2023, 13, 9. [Google Scholar] [CrossRef]
  19. Lee, J.; Woo, S.-Y.; Kim, Y.-W.; Kim, S.-J.; Pyo, J.; Cho, K.H. Dynamic Calibration of Phytoplankton Blooms Using the Modified SWAT Model. J. Clean. Prod. 2022, 343, 131005. [Google Scholar] [CrossRef]
  20. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. Rev. Panam. Salud Publica/Pan Am. J. Public Health 2022, 46, e112. [Google Scholar] [CrossRef]
  21. Debele, B.; Srinivasan, R.; Parlange, J.Y. Coupling Upland Watershed and Downstream Waterbody Hydrodynamic and Water Quality Models (SWAT and CE-QUAL-W2) for Better Water Resources Management in Complex River Basins. Environ. Model. Assess. 2008, 13, 135–153. [Google Scholar] [CrossRef]
  22. Masoumi, F.; Masoumzadeh Sayyar, S.; Valizadeh, P.; Nikoo, M.R.; Al-Nuaimi, A.; Sana, A. Developing an Uncertainty-Based Auto-Calibrated Reservoir Eutrophication Model: A Case Study of Karkheh Dam, Iran. Int. J. Environ. Sci. Technol. 2023, 20, 7377–7392. [Google Scholar] [CrossRef]
  23. Neto, I.E.L. Modeling Water Quality in a Tropical Reservoir Using CE-QUAL-W2: Handling Data Scarcity, Urban Pollution and Hydroclimatic Seasonality. Rev. Bras. Recur. Hidr. 2023, 28. [Google Scholar] [CrossRef]
  24. Mesquita, J.B.d.F.; Lima Neto, I.E.; Raabe, A.; de Araújo, J.C. The Influence of Hydroclimatic Conditions and Water Quality on Evaporation Rates of a Tropical Lake. J. Hydrol. 2020, 590, 125456. [Google Scholar] [CrossRef]
  25. Ijaz, U.; Xingxing, Z.; Ayub, H.; Zaiqiang, C.; Siqian, Q.; Niaz, A. Implications of Evolution of Circulation Patterns on Nutrients Regime from 2008–2018, in Xiangxi Bay of the Three Gorges Reservoir, China. Water Supply 2022, 22, 7197–7225. [Google Scholar] [CrossRef]
  26. Almeida, M.; Rebelo, R.; Costa, S.; Rodrigues, A.; Coelho, P. Long-Term Water Quality Modeling of a Shallow Eutrophic Lagoon with Limited Forcing Data. Environ. Model. Assess. 2023, 28, 201–225. [Google Scholar] [CrossRef]
  27. Terry, J.; Davies, J.M.; Lindenschmidt, K.E. Buffalo Pound Lake—Modelling Water Resource Management Scenarios of a Large Multi-Purpose Prairie Reservoir. Water 2022, 14, 584. [Google Scholar] [CrossRef]
  28. Nazari-Sharabian, M.; Taheriyoun, M. Climate Change Impact on Water Quality in the Integrated Mahabad Dam Watershed-Reservoir System. J. Hydro-Environ. Res. 2022, 40, 28–37. [Google Scholar] [CrossRef]
  29. Yosefipoor, P.; Saadatpour, M.; Solis, S.S.; Afshar, A. An Adaptive Surrogate-Based, Multi-Pollutant, and Multi-Objective Optimization for River-Reservoir System Management. Ecol. Eng. 2022, 175, 106487. [Google Scholar] [CrossRef]
  30. Kheirkhah, S.; Saadatpour, M.; Afshar, A. An Adaptive Metamodelling for Multipollutant Waste Load Allocation in a River–Reservoir System; Hydrodynamic Simulation–Optimization. Water Environ. J. 2022, 36, 223–232. [Google Scholar] [CrossRef]
  31. Almeida, C.; Branco, P.; Segurado, P.; Ramos, T.B.; Ferreira, T.; Neves, R.; de Oliveira, R.P. Evaluation of the Trophic Status in a Mediterranean Reservoir under Climate Change: An Integrated Modelling Approach. J. Water Clim. Change 2021, 12, 817–832. [Google Scholar] [CrossRef]
  32. Akomeah, E.; Morales-Marın, L.A.; Carr, M.; Sadeghian, A.; Lindenschmidt, K.E. The Impacts of Changing Climate and Streamflow on Nutrient Speciation in a Large Prairie Reservoir. J. Environ. Manag. 2021, 288, 112262. [Google Scholar] [CrossRef]
  33. Yahyaee, A.R.; Moridi, A.; Sarang, A. A New Optimized Model to Control Eutrophication in Multi-Purpose Reservoirs. Int. J. Environ. Sci. Technol. 2021, 18, 3357–3370. [Google Scholar] [CrossRef]
  34. Morales-Marin, L.A.; Carr, M.; Sadeghian, A.; Lindenschmidt, K.E. Climate Change Effects on the Thermal Stratification of Lake Diefenbaker, a Large Multi-Purpose Reservoir. Can. Water Resour. J. 2021, 46, 1–16. [Google Scholar] [CrossRef]
  35. Hasanzadeh, S.K.; Saadatpour, M.; Afshar, A. A Fuzzy Equilibrium Strategy for Sustainable Water Quality Management in River-Reservoir System. J. Hydrol. 2020, 586, 124892. [Google Scholar] [CrossRef]
  36. Lindenschmidt, K.E.; Carr, M.K.; Sadeghian, A.; Morales-Marin, L. CE-QUAL-W2 Model of Dam Outflow Elevation Impact on Temperature, Dissolved Oxygen and Nutrients in a Reservoir. Sci. Data 2019, 6, 312. [Google Scholar] [CrossRef] [PubMed]
  37. Aghasian, K.; Moridi, A.; Mirbagheri, A.; Abbaspour, M. Selective Withdrawal Optimization in a Multipurpose Water Use Reservoir. Int. J. Environ. Sci. Technol. 2019, 16, 5559–5568. [Google Scholar] [CrossRef]
  38. Moridi, A. Dealing with Reservoir Eutrophication in a Trans-Boundary River. Int. J. Environ. Sci. Technol. 2019, 16, 2951–2960. [Google Scholar] [CrossRef]
  39. Ziaie, R.; Mohammadnezhad, B.; Taheriyoun, M.; Karimi, A.; Amiri, S. Evaluation of Thermal Stratification and Eutrophication in Zayandeh Roud Dam Reservoir Using Two-Dimensional CE-QUAL-W2 Model. J. Environ. Eng. 2019, 145, 05019001. [Google Scholar] [CrossRef]
  40. Kim, D.; Kim, Y.; Kim, B. Simulation of Eutrophication in a Reservoir by CE-QUAL-W2 for the Evaluation of the Importance of Point Sources and Summer Monsoon. Lake Reserv. Manag. 2019, 35, 64–76. [Google Scholar] [CrossRef]
  41. Dehbalaei, F.N.; Javan, M. Assessment of Selective Withdrawal and Inflow Control on the Hydrodynamics and Water Quality of Ilam Reservoir. Water Environ. Res. 2018, 90, 307–321. [Google Scholar] [CrossRef]
  42. Yazdi, J.; Moridi, A. Interactive Reservoir-Watershed Modeling Framework for Integrated Water Quality Management. Water Resour. Manag. 2017, 31, 2105–2125. [Google Scholar] [CrossRef]
  43. Shourian, M.; Moridi, A.; Kaveh, M. Modeling of Eutrophication and Strategies for Improvement of Water Quality in Reservoirs. Water Sci. Technol. 2016, 74, 1376–1385. [Google Scholar] [CrossRef]
  44. Masoumi, F.; Afshar, A.; Palatkaleh, S.T. Selective Withdrawal Optimization in River–Reservoir Systems; Trade-Offs between Maximum Allowable Receiving Waste Load and Water Quality Criteria Enhancement. Environ. Monit. Assess. 2016, 188, 390. [Google Scholar] [CrossRef]
  45. Noori, R.; Yeh, H.D.; Ashrafi, K.; Rezazadeh, N.; Bateni, S.M.; Karbassi, A.; Kachoosangi, F.T.; Moazami, S. A Reduced-Order Based CE-QUAL-W2 Model for Simulation of Nitrate Concentration in Dam Reservoirs. J. Hydrol. 2015, 530, 645–656. [Google Scholar] [CrossRef]
  46. Park, Y.; Pachepsky, Y.A.; Cho, K.H.; Jeon, D.J.; Kim, J.H. Stressor-Response Modeling Using the 2D Water Quality Model and Regression Trees to Predict Chlorophyll-a in a Reservoir System. J. Hydrol. 2015, 529, 805–815. [Google Scholar] [CrossRef]
  47. Park, Y.; Cho, K.H.; Kang, J.-H.; Lee, S.W.; Kim, J.H. Developing a Flow Control Strategy to Reduce Nutrient Load in a Reclaimed Multi-Reservoir System Using a 2D Hydrodynamic and Water Quality Model. Sci. Total Environ. 2014, 466–467, 871–880. [Google Scholar] [CrossRef] [PubMed]
  48. Chang, C.H.; Cai, L.Y.; Lin, T.F.; Chung, C.L.; Van Der Linden, L.; Burch, M. Assessment of the Impacts of Climate Change on the Water Quality of a Small Deep Reservoir in a Humid-Subtropical Climatic Region. Water 2015, 7, 1687–1711. [Google Scholar] [CrossRef]
  49. Afshar, A.; Saadatpour, M.; Marino, M.A. Development of a Complex System Dynamic Eutrophication Model: Application to Karkheh Reservoir. Environ. Eng. Sci. 2012, 29, 373–385. [Google Scholar] [CrossRef]
  50. Lee, J.Y.; Ha, S.R.; Park, I.H.; Lee, S.C.; Cho, J.H. Characteristics of DOC Concentration with Storm Density Flows in a Stratified Dam Reservoir. Water Sci. Technol. 2010, 62, 2467–2476. [Google Scholar] [CrossRef]
  51. Liu, W.C.; Chen, W.B.; Kimura, N. Impact of Phosphorus Load Reduction on Water Quality in a Stratified Reservoir-Eutrophication Modeling Study. Environ. Monit. Assess. 2009, 159, 393–406. [Google Scholar] [CrossRef]
  52. Afshar, A.; Saadatpour, M. Reservoir Eutrophication Modeling, Sensitivity Analysis, and Assessment: Application to Karkheh Reservoir, Iran. Environ. Eng. Sci. 2009, 26, 1227–1238. [Google Scholar] [CrossRef]
  53. Kuo, J.T.; Lung, W.S.; Yang, C.P.; Liu, W.C.; Yang, M.D.; Tang, T.S. Eutrophication Modelling of Reservoirs in Taiwan. Environ. Model. Softw. 2006, 21, 829–844. [Google Scholar] [CrossRef]
  54. Kuo, J.T.; Liu, W.C.; Lin, R.T.; Lung, W.S.; Yang, M.D.; Yang, C.P.; Chu, S.C. Water Quality Modeling for the Feitsui Reservoir in Northern Taiwan. J. Am. Water Resour. Assoc. 2003, 39, 671–687. [Google Scholar] [CrossRef]
  55. Sadeghian, A.; de Boer, D.; Hudson, J.J.; Wheater, H.; Lindenschmidt, K.-E. Lake Diefenbaker Temperature Model. J. Great Lakes Res. 2015, 41, 8–21. [Google Scholar] [CrossRef]
  56. Sadeghian, A. Water Quality Modeling of Lake Diefenbaker; University of Saskatchewan: Saskatoon, Canada, 2017. [Google Scholar]
  57. Sadeghian, A.; Chapra, S.C.; Hudson, J.; Wheater, H.; Lindenschmidt, K.-E. Improving In-Lake Water Quality Modeling Using Variable Chlorophyll a/Algal Biomass Ratios. Environ. Model. Softw. 2018, 101, 73–85. [Google Scholar] [CrossRef]
  58. National Institute of Environmental Research. Han River (Main Stream and North Han River) System Aquatic Ecosystem Water Quality Parameters Research; National Institute of Environmental Research (NIER): Incheon, Republic of Korea, 2007. [Google Scholar]
  59. Afshar, A.; Shojaei, N.; Sagharjooghifarahani, M. Multiobjective Calibration of Reservoir Water Quality Modeling Using Multiobjective Particle Swarm Optimization (MOPSO). Water Resour. Manag. 2013, 27, 1931–1947. [Google Scholar] [CrossRef]
  60. Barati, A.A.; Pour, M.D.; Sardooei, M.A. Water Crisis in Iran: A System Dynamics Approach on Water, Energy, Food, Land and Climate (WEFLC) Nexus. Sci. Total Environ. 2023, 882, 163549. [Google Scholar] [CrossRef] [PubMed]
  61. Jiang, Y. China’s Water Scarcity. J. Environ. Manag. 2009, 90, 3185–3196. [Google Scholar] [CrossRef]
  62. Chuo, M.; Ma, J.; Liu, D.; Yang, Z. Effects of the Impounding Process during the Flood Season on Algal Blooms in Xiangxi Bay in the Three Gorges Reservoir, China. Ecol. Model. 2019, 392, 236–249. [Google Scholar] [CrossRef]
  63. Dutta, R.K.; Das, B. Modeling Curtain Weirs for Controlling Algal Blooms in the Largest Tributary of the Three Gorges Reservoir, China. Alex. Eng. J. 2020, 59, 323–332. [Google Scholar] [CrossRef]
  64. Xu, H.; Yan, M.; Long, L.; Ma, J.; Ji, D.; Liu, D.; Yang, Z. Modeling the Effects of Hydrodynamics on Thermal Stratification and Algal Blooms in the Xiangxi Bay of Three Gorges Reservoir. Front. Ecol. Evol. 2021, 8, 610622. [Google Scholar] [CrossRef]
  65. He, W.; Feng, S.; Bi, Y.; Jiang, A.; Li, Y.; Huang, W.; Zhang, J.; Xu, H.; Liu, C. Influences of Water Level Fluctuation on Water Exchange and Nutrient Distribution in a Bay: Evidence from the Xiangxi Bay, Three Gorges Reservoir. Environ. Res. 2023, 222, 115341. [Google Scholar] [CrossRef]
Water 16 03556 g001
Figure 1. Flowchart of the methodology adopted in this systematic review of the CE-QUAL-W2 model. This flowchart illustrates the steps taken to identify, select, and analyze relevant studies on applying the CE-QUAL-W2 hydrodynamic model, focusing on its use in lentic environments for assessing eutrophication and water quality.
Figure 1. Flowchart of the methodology adopted in this systematic review of the CE-QUAL-W2 model. This flowchart illustrates the steps taken to identify, select, and analyze relevant studies on applying the CE-QUAL-W2 hydrodynamic model, focusing on its use in lentic environments for assessing eutrophication and water quality.
Water 16 03556 g001
Water 16 03556 g002
Figure 2. Distribution of journals and categories based on the area of study of the articles analyzed, including the number of citations and publications for the selected works. This figure categorizes the journals that published studies on the CE-QUAL-W2 model. It specifies their thematic areas and provides quantitative data on citations and publications to highlight the model’s impact in different research fields.
Figure 2. Distribution of journals and categories based on the area of study of the articles analyzed, including the number of citations and publications for the selected works. This figure categorizes the journals that published studies on the CE-QUAL-W2 model. It specifies their thematic areas and provides quantitative data on citations and publications to highlight the model’s impact in different research fields.
Water 16 03556 g002
Water 16 03556 g003
Figure 3. Countries studied in the selected works on the CE-QUAL-W2 model application. This map indicates the geographic distribution of research using the CE-QUAL-W2 model, emphasizing the countries where it has been applied most frequently, including Iran, China, and Brazil. The analysis provides insights into the global adoption of the model in various environmental and water resource studies [12,13,14,18,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54].
Figure 3. Countries studied in the selected works on the CE-QUAL-W2 model application. This map indicates the geographic distribution of research using the CE-QUAL-W2 model, emphasizing the countries where it has been applied most frequently, including Iran, China, and Brazil. The analysis provides insights into the global adoption of the model in various environmental and water resource studies [12,13,14,18,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54].
Water 16 03556 g003
Table 1. Summary of the selected works in the literature review and the methodologies applied in each study. This table lists the 38 studies selected for review, detailing the research objectives, study locations, and specific methodologies used to analyze water quality and eutrophication in reservoirs.
Table 1. Summary of the selected works in the literature review and the methodologies applied in each study. This table lists the 38 studies selected for review, detailing the research objectives, study locations, and specific methodologies used to analyze water quality and eutrophication in reservoirs.
Publication/YearInput Parameters (Nutrients)Model CalibrationAnalysis TimePurpose of the StudyModel UsedStudy AreaSegments
Masoumi et al. 2023
[22]		NO3/ NO2, NH3, TP, Chl-a, and DOAutomatic calibration using the SUFI-2 Algorithm05/2005–12/2005Simulate water quality dynamics in a tropical reservoir
subject to significant urban pollution and hydroclimatic seasonality		CE-QUAL-W2Karkheh Reservoir (Iran)
Surface: 162 km2

6600 MCM capacity		66 longitudinal and 55 lateral layers, distributed with an equal length of 1 km by 66 km, with a thickness of each layer between 1.5 and 4 m
Neto 2023
[23]		Water temperature, DO, Chl-a, and PO4The model was calibrated using data from 20132013Simulate water quality dynamics in a tropical reservoir in Fortaleza, Ceará, BrazilCE-QUAL-W2Santo Anastácio Lake
(Brazil)

900 m long and 185 m wide		31 segments
Hanjaniamin et al. 2023
[18]		DOThe model was calibrated in this study05/2015–04/2016Identify water quality, thermal stratification, dissolved oxygen concentration, and eutrophication conditions in the reservoirCE-QUAL-W2Yamchi Dam on the Balkhlichai River (Iran)

82 MCM capacity		Twenty-eight longitudinal segments, each 200 m long; the depth of the reservoir was also divided into 32 elements with a depth of 2 m
Mesquita et al. 2022
[12]		TP and BODThe model was calibrated and validated in a previous study [24]Time series 2013, 2018, and 2019Evaluate the impact of hydrological characteristics on hydrodynamics, considering water quality and its impact on evaporation ratesSWMM
CE-QUAL-W2		Santo Anastácio Lake
(Brazil)

900 m long and 185 m wide		32 longitudinal segments, 29 m long, and in vertical layers with a distance of 0.2 m per layer
Ijaz et al. 2022
[25]		TN and TPThe CE-QUAL-W2 was calibrated and validated in this study01/01/2008–12/31/2018Simulate reflective current density patterns in collaboration with variables and water qualityCE-QUAL-W2Three Gorges Reservoir (China)

Surface: 1080 km2
Capacity: 3.93 × 10 10 L		64 longitudinal segments ranging from 500 to 1000 m in length
Almeida et al. (2022)
[26]		Water temperature, DO, PO4, TP, NO3/NO2, NH3, TN, BOD, TDS, pH, algal biomass (six groups), and Chl-aCE-QUAL-W2 and SWAT were calibrated in this study2000–2019Simulate long-term water qualityCE-QUAL-W2
SWAT		Lagoa das Furnas–São Miguel Island/Azores Archipelago
(Portugal)

Surface of 1.87 km2
Volume of 14.6 hm3		8 segments and 24 layers with a thickness of 0.5 m
Rocha et al. 2022
[13]		TPThe model was calibrated and validated in a previous study [24]01/2009–01/2018Evaluate residence time and total phosphorusCE-QUAL-W2Lake Santo Anastácio (Brazil)

The average surface area is 16 ha, and the maximum depth is 5 m		Uninformed
Terry et al. 2022
[27]		TP, COD, TDS, TN, and Chl-aThis research updates the pre-existing calibrated W2 model, extending the calibration period by including an additional 6.5 years (between April 2013 and December 2019)2013–2019Assess the impact of water diversion between basins after the dammed lake received high flows of local runoffCE-QUAL-W2Buffalo Pound Lake (Canada)

Average depth of 3.8 m
Surface area: 30 km		100 longitudinal segments around 300 m and up to 28 vertical layers 0.25 m deep
Nazari-Sharabian et al. 2022
[28]		Water temperature, TP, and DOThe model was calibrated for 2011–2012 and validated for 20132011–2013Investigate the effects of climate change on hydrological parameters, catchment yields, and reservoir water quality; investigate the impact of future climate conditions on catchment runoff, total phosphorus (TP) load, and water quality statusCanESM2
SWAT
CE-QUAL-W2		Mahabad Dam Reservoir (Iran)

200 mm3 capacity		28 segments of variable lengths
Yosefipoor et al. 2022
[29]		DO, NO3, PO4, Fe, and BODThe model was calibrated and validated in this study2008–2009Propose an optimization algorithm based on modular support vector regression (SVR) in which several small sub-SVR modules are trained through an efficient adaptive procedure cooperate to solve a large-scale problem related to integrated river–reservoir quality and quantity managementWQSM
CE-QUAL-W2		Ilam Reservoir (Iran)

16.8 MCM capacity		42 longitudinal segments, 500 m long
Kheirkhah et al. 2022
[30]		TP, Chl-a, DO, NO3, NH3, PO4, and BODCalibration was performed using significant water-quality calibration coefficients133 monthly periodsDetermine the necessary treatment levels of pollutants released into a river–reservoir system to minimize the total cost of wastewater treatment, maximize profit from fish production, and improve the water quality index;
the CE-QUAL-W2 model is used to address the relationships between pollutant loads and the responses of water bodies		WQSM
CE-QUAL-W2
WQSM-ANN		Behesht-Abad River and Reservoir
(Will)

Surface: 34 km2
Capacity: 1800 MCM		56 longitudinal segments with up to 72 vertical layers
Almeida et al. 2021
[31]		Water temperature, DO, TP, TN, TSSD, and Chl-a (6 main parameters)The model was calibrated and validated in this study2005–2014Assess the present and future trophic state of a reservoirSWAT
CE-QUAL-W2		Montargil
Sorraia River (Portugal)

164 hm3 capacity		13 segments with lengths of 360–2700 m and widths of 500–3000 m
Akomeah et al. 2021
[32]		Water temperature, DO, PO4, TP, NO3/NO2, NH3, TN, BOD, TDS, pH, algal biomass, and Chl-aModel previously calibrated by [55,56]2011–2013Evaluate how future hydrological and meteorological conditions may affect nutrient regimes and water chemistry in the Lake Diefenbaker ReservoirSPARROW
CE-QUAL-W2		Lake Diefenbaker (Canada)

Surface of 394 km2
Capacity of 9.03 km3		Variable horizontal targeting
Yahyaee et al. 2021
[33]		Water temperature and DOThe model was calibrated based on data collected over one year02/2011–02/2013Evaluate the impact of water release from the lower layers of the reservoir on water qualityCE-QUAL-W2
NSGA-II		Seimare Reservoir (Iran)

60 km long
Total storage volume of 3200		Twenty-eight longitudinal segments with a distance of 1000 m between segments and with a depth of 2 to 4 m and in 32 layers
Morales-Marin et al. 2021
[34]		Water temperatureThe model was previously calibrated and validated by [55,57]2001 to 2010Investigate the effects of climate change and flow scenarios on the thermal structure of Lake DiefenbakerCE-QUAL-W2Lake Diefenbaker (Canada)

Surface of 394 km2
Capacity of 9.03 km3		515 horizontal segments and vertical layers of one meter with a maximum of 60 layers at the deepest point
Mesquita et al. 2020
[24]		TPThe model was calibrated and validated in this study2009–2019Investigate the influence of hydroclimatic forcing and water quality on the evaporation process of a shallow tropical lakeCE-QUAL-W2Santo Anastácio Lagoon
Fortaleza Brazil)

The water surface is 16.00 ± 2.60 ha, and depth is 4.79 ± 0.56 m		32 longitudinal segments of 29 m each and in vertical layers with a layer thickness of 0.2 m
Hasanzadeh et al. 2020
[35]		NO3, NH4, PO4, BOD, DO, and thermal input flowsThe model was calibrated and validated in this study
15 daysReduce the potential for eutrophication in a river-reservoir system with discharges from aquaculture industriesMPWLA
ANN WQSMs
CE-QUAL-W2		Behesht-Abad and Kaj Reservoir (Iran)

Surface: 3860 km2
Capacity: 1070 MCM		56 longitudinal segments and up to 72 deep layers
Lindenschmidt et al. 2019
[36]		Water temperature, DO, TP, P, LP, TN, NO3, LN, and NH4The model was calibrated using the same methodology described in [55]2011–2013Investigate the impacts of various withdrawal elevations on the water chemistry and nutrients of the Lake Diefenbaker ReservoirCE-QUAL-W2Lake Diefenbaker
(Canada)

Surface of 394 km2
Capacity of 9.03 km3		87 horizontal segments and in 60 water depths
Aghasian et al. 2019
[37]		TDSThe model was calibrated in this study2014–2016Determine the amount of water released from various outlets to discharge brine from the hypolimnion layer considering downstream water quality limitationsMOPSO
CE-QUAL-W2		Gotvand Reservoir (Iran)

Capacity of 4.5 billion m3
Height: 182 m		60 horizontal sections, horizontal cell length varies between 800 and 1800 m, vertical cell length 2.5 m
Moridi 2019
[38]		NO3−, P, and DOThe model was calibrated in this study2000–2002Develop an optimization model to improve reservoir water quality and protect downstream water qualityCE-QUAL-W2Dousti Reservoir
Harirud River (Iran/Turkmenistan)

Capacity of 3 billion m3.		Uninformed
Ziaie et al. 2019
[39]		Water temperature, TP, DO, NO3−, and PO4Water quality calibration was carried out in October, November,
December, and March of 2013 and in April and July of 2014		10/2013–01/2015Investigate thermal stratification and eutrophication in the Zayandeh Roud dam reservoirCE-QUAL-W2Lake Zayandeh Roud (Iran)

Area of 54 km2
Total volume of 1470 mm3
Maximum depth of 75 m		Forty-six longitudinal segments 235 to 1600 m long; the deepest part of the reservoir consists of at least the majority of 77 layers in 1 m depth increments
Kim et al. 2019
[40]		SS, TP, TN, Chlorophyll, and CODThe model was calibrated based on research conducted by the National Institute of Environmental Research (NIER 2007) [58]2005–2009 and 2013–2014Evaluate how nutrient reduction influences water qualityCE-QUAL-W2Lake Uiam (South Korea)

Capacity of 80 million m3
Average annual flow of 206 m3/s
Average depth of 5 m		56 segments
Dehbalaei et al. 2018 [41]NH4, NO3, PO4, DO, Si, and Chl-aModel calibration and validation periods were selected from July 2009 to December 2009 and from March 2010 to May 2010, respectively2009–2010Investigate the effects of selective withdrawal and inflow control on thermal stratification and water qualityCE-QUAL-W2Ilam Reservoir (Iran)

Capacity of 71 million m3		16 segments with a length of 500 m to 700 m and layers with a depth of 1 m
Yazdi et al. 2017
[42]		Water temperature, DO, TDS, NO3−, TN, and TPThe model was calibrated in this study2011–2013Develop a methodology to mitigate and control the current and future eutrophication conditionsSWAT
CE-QUAL-W2		Seimare river basin and reservoir (Iran)

Seimare River, 417 km long		Uninformed
Shourian et al. 2016
[43]		TN, TP, Chl-a, and DOThe model was calibrated and validated in this study2006–2008Survey the thermal regime and eutrophication states in the Ilam reservoirCE-QUAL-W2Ilam Reservoir (Iran)

Capacity: 71 MCM		16 sectors 500 and 700 m long, and with the depth segmented into 43 layers, 1 m deep
Masoumi et al. 2016
[44]		TPThe model calibration was performed using the method presented by [59]180 monthsPresent an efficient methodology for the optimal operation of a river–reservoir system to control the quality and quantity of water downstream while maximizing the total daily load to the systemCE-QUAL-W2
PSO
ANN		Karkheh Reservoir (Iran)

Surface of 162 km2
Capacity of 6.6 billion m3		Upstream river bodies and reservoirs include 14 and 28 longitudinal segments with 5 vertical layers
Noori et al. 2015
[45]		Water temperature and NO3The model was calibrated using data from 05/2005 to 04/2006 and validated from 05/2006 to 08/20062005–2006Provide a reduced-order model to condense simulated resultsPOD
CE-QUAL-W2
ROM		Karkheh Reservoir (Iran)

Capacity of 5.9 billion m3		65 longitudinal segments 1000 m long; each segment is divided vertically into 2 m thick layers
Park et al. 2015
[46]		TDS, SS, PO43--, NH4+, NO3−, BOD, algal biomass, DO, and CODModel previously calibrated by [47]2007–2012Evaluate the efficiency of regression trees in developing a stressor–response model for chlorophyll-a (Chl-a)CE-QUAL-W2Yeongsan Reservoir (South Korea)

Surface: 34.6 km2
Annual flow: 2.19 × 109 m3
Average depth: 10.1 m Maximum depth: 21.9 m
39 longitudinal and 23 vertical segments
Chang et al. 2015
[48]		Water temperature, DO, NO3, TN, NH3, PO43−-, TP, and Chl-aThe model was calibrated using data from 2004 to 2008 and validated from 2009 to 20122004–2012Assess the impacts of climate change on water quality and investigate risks to water quality in scenarios A1B and A2 for the short- and long-term futureCE-QUAL-W2Hsin Shan Reservoir (China)

Capacity: 9.7 × 10 6 m3		11 longitudinal segments, 80 to 220 m long, and 25 to 38 vertical segments, one meter thick.
Park et al. 2014
[47]		Water temperature, TDS, pH, DO, BOD, COD, SS, TC, TN, TP, transparency, Chl-a, EC, NO3, N, NH4, FC, PO4, DTN, and DTPThe model was calibrated using two new methods: a sensitivity analysis to determine significant model parameters and a pattern search to optimize the parameters2007–2008Predict the pollutant load released from each reservoir in response to different flow scenarios for the interconnection channelCE-QUAL-W2Reservoirs: Yeongsan, Yeongam and Kumho
(South Korea)

The average annual outflows during the 2 years 2007 to 2008 were
YSR 1650 million m3/year,
YAR 252 million m3/year, and KMR and 202 million m3/year		Depths were measured at 480 locations in the YSR, 140 in the YAR, and 140 in the KMR. In the model, the physical domain of the YSR consists of 2 branches, totaling 39 active segments with a length between 700 and 800 m each in the longitudinal direction and 23 maximum layers in the vertical direction. One branch represents the main body of the YSR, while the other is the connected waterway that supplies freshwater from the YSR to the YAR. YAR and KMR have 2 branches, 45 active segments, and 26 maximum layers for YAR and three branches, 56 active segments, and 24 maximum layers for KMR.
Deus et al. 2013
[14]		Water temperature, NO3−, NH3, P, SST, DO, and chlorophyll-aThe model was calibrated with data from 2007 to 20112007–2011Quantify mass transport, thermal stratification, and changes in water quality due to the possible expansion of fish farming activities in the reservoirCE-QUAL-W2Tucuruí Reservoir
(Brazil)

Area of 2430 km2
Average flow: 11,000 m3/s
Maximum depth: 72 m		The reservoir has three main branches: the first has 12 segments from 2100 to 46,600 m, totaling 145,800 m; the second has six segments from 5200 to 7700 m, totaling 38,700 m; and the third has two segments of 4100 to 4700 m, totaling 8800 m. Each segment has up to 36 2 m thick vertical layers.
Afshar et al. 2012
[49]		Rates of change of phytoplankton, herbivorous zooplankton, carnivorous zooplankton, POM, DOM, NH4, N, P, TSS, and DOThe model was calibrated with data from 2005 to 2006 and validated from 2003 to 20042003–2006Simulate the main temporal patterns of epilimnion, thermocline, and hypolimnionS.D.
CE-QUAL-W2		Karkheh Reservoir (Iran)

Surface of 162 km2
Extension: 64 km
Capacity: 5×10 9 m3		Series of 30 differential equations for 10 variables and three segmented layers
Lee et al. 2010
[50]		LPOC, RPOC, LDOC, RDOC. DO, BOD, COD, TSS, TP, TN, and pHThe model was calibrated and validated using data from 2003 and 20052003–2005Identify the effect of diffuse pollution from allochthonous organic matter on the temporal and spatial characteristics of autochthonous organic matter in a stratified dam reservoirCE-QUAL-W2Daecheong Reservoir (South Korea)

Hydrographic basin: 4166 km2
72 km2 storage
Extension: 86 km
Maximum depth: 78 m		Uninformed
Liu et al. 2009
[51]		Water temperature, DO, chlorophyll-a, TP, NH4, and NO3/NO2The model was calibrated with data from 2003 to 2004
2003–2004Quantify mass transport, thermal stratification, and variations in water qualityCE-QUALW2Mingder Reservoir (China)
Hydrographic basin: 61 km2
Storage: 1.65 × 108 m3		22 longitudinal segments, 200 m long, segments divided into 1 m layers, with a total of 278 cells
Afshar et al. 2009
[52]		Water temperature, TP, NO3, NH3, Cl, and DO; simulated constituents: COD, POD, SD, SS, BOD, pH, and algaeThe model was calibrated with data from May 2005 to December 2005 and validated from December 2005 to July 20062005–2006Predict the formation of the eutrophication process in Karkheh Reservoir under different management strategiesCE-QUAL-W2Karkheh Reservoir (Iran)

Surface: 162 km2
Capacity: 5109 m3
Extension: 64 km		66 longitudinal segments, 1 km long, with up to 55 vertical layers
Debele et al. 2008
[21]		TSS, PO4, NO3/ NO2, NH4/NH3, LDOM, RDOM, LPOM, RPOM, CBOD, a species of blue-green algae, and DOThe calibrated results of the SWAT model were used as input for the CE-QUAL-W2 model1997–2001Understand water processes and their constituents’ movements, interactions, and transformations in the dryland watershed and the downstream water bodySWAT
CE-QUAL-W2		Cedar Creek Watershed and Reservoir (USA)

Basin area: 5244 km2
Reservoir: 13,880.8 ha
Capacity: 6.98 × 10 8 m3		Eight branches with 37 segments, totaling 925 segments; each segment can have a maximum of 25 vertical layers, resulting in a total division of 925 layers—segments with vertical layers 0.74 m thick
Kuo et al. 2006
[53]		Water temperature, DO, chlorophyll-a, TP, NH4, and NO3−/NO2−The model was calibrated in this study1998–2000Quantify variations in mass transport, thermal stratification, and water quality in the Te-Chi reservoir (temperate climate) and the Tseng-Wen reservoir (subtropical climate)CE-QUAL-W2Reservoirs: Te-Chi and Tseng-Wen
(China)

Te-Chi: total watershed area of 592 km2
183 × 106 m3 storage

Tseng-Wen: watershed area of 481 km2
659 × 106 m3 storage		The Te-Chi Reservoir has 15 longitudinal segments ranging from 900 to 1080 m long; each segment is divided into 2 m layers in the water column, resulting in 990 segments

The Tseng-Wen Reservoir: 17 longitudinal segments, 1000 m long; each segment is divided into 2 m layers in the water column, resulting in 356 segments
Kuo et al. 2003
[54]		Water temperature, DO, chlorophyll-a, TP, NH4, and NO3−/NO2−The model was calibrated and verified using data from 1996 and 19971996–1997Formulate water quality management strategies for Feitsui Reservoir to achieve oligotrophic conditionCE-QUAL-W2Feitsui Reservoir (China)

Surface: 10.24 km2
Average depth: 39.68 m
Maximum depth: 113.5 m		33 longitudinal segments, 600 m long, and 26 vertical layers, 4 m thick
Notes: Ammonia (NH3). Ammonium (NH4+). Biological Oxygen Demand (BOD). Carbonaceous Biochemical Oxygen Demand (CBOD). Chemical Oxygen Demand (COD). Chlorophyll-a (chl-a). Dissolved Organic Matter (DOM). Dissolved Oxygen (DO). Iron (Fe). Labile Dissolved Organic Matter (LDOM). Labile-Specific Organic Carbon (LPOC). Nitrate (NO3). Nitrite (NO2). Nitrogen (N). Orthophosphate (PO43-). Particulate Organic Matter Labile (LPOM). Particulate Organic Matter Refractory (RPOM). Phosphate (PO4). Phosphorus (P). Refractory Dissolved Organic Matter (RDOM). Refractory Organic Carbon (RPOC). Silica (Si). Total Coliforms (TC). Total Dissolved Phosphorus (DTP). Total Dissolved Solids (TDS). Total Nitrogen (TN). Total Phosphorus (TP). Total Suspended Solids (TSS). Transparency. Water Temperature.
Table 2. Values of the coefficient of determination (R2) obtained in different studies for various water quality parameters using the CE-QUAL-W2 model. This table presents a comparative analysis of the R2 values reported in the reviewed studies, highlighting the model’s performance and reliability in simulating key water quality parameters, such as temperature, dissolved oxygen, and nutrient concentrations, across different environments.
Table 2. Values of the coefficient of determination (R2) obtained in different studies for various water quality parameters using the CE-QUAL-W2 model. This table presents a comparative analysis of the R2 values reported in the reviewed studies, highlighting the model’s performance and reliability in simulating key water quality parameters, such as temperature, dissolved oxygen, and nutrient concentrations, across different environments.
R2 ValueCalibrated ParametersPublication
0.32DO, Chlorophyll-a, PO4[23]
0.6781DO[18]
0.92Water Level, Temperature, DO[26]
0.70Total Phosphorus (TP)[13]
0.76Total Phosphorus (TP)[12]
0.92Flow, TN, TP[31]
0.41Total Phosphorus (Dry Period)[24]
>0.9Water Level, Temperature, and Suspended Solids (SS)[40]
0.62–0.95DO, Temperature, TDS, TN, TP[42]
0.977Total Phosphorus (TP)[51]
0.906DO, Temperature[21]
0.9605 and 0.9724TP, Ammonia, NO2/NO3, Chlorophyll-a, DO[53]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Benicio, S.H.M.; Basso, R.E.; Formiga, K.T.M. Global Applications of the CE-QUAL-W2 Model in Reservoir Eutrophication: A Systematic Review and Perspectives for Brazil. Water 2024, 16, 3556. https://doi.org/10.3390/w16243556

AMA Style

Benicio SHM, Basso RE, Formiga KTM. Global Applications of the CE-QUAL-W2 Model in Reservoir Eutrophication: A Systematic Review and Perspectives for Brazil. Water. 2024; 16(24):3556. https://doi.org/10.3390/w16243556

Chicago/Turabian Style

Benicio, Sarah Haysa Mota, Raviel Eurico Basso, and Klebber Teodomiro Martins Formiga. 2024. "Global Applications of the CE-QUAL-W2 Model in Reservoir Eutrophication: A Systematic Review and Perspectives for Brazil" Water 16, no. 24: 3556. https://doi.org/10.3390/w16243556

APA Style

Benicio, S. H. M., Basso, R. E., & Formiga, K. T. M. (2024). Global Applications of the CE-QUAL-W2 Model in Reservoir Eutrophication: A Systematic Review and Perspectives for Brazil. Water, 16(24), 3556. https://doi.org/10.3390/w16243556

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop