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

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.

Full text

Citation: 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
Academic Editor: JoséMaria Santos
Received: 18 September 2024
Revised: 2 December 2024
Accepted: 5 December 2024
Published: 10 December 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Review
Global Applications of the CE-QUAL-W2 Model in Reservoir
Eutrophication: A Systematic Review and Perspectives for Brazil
Sarah Haysa Mota Benicio 1, Raviel Eurico Basso 2and Klebber Teodomiro Martins Formiga 2,*
1Program in Environmental Sciences, Federal University of Goiás, Avenida Esperança s/n, Campus Samambaia,
Goiânia 74690-900, GO, Brazil; sarahhaysa@discente.ufg.br
2Postgraduate Program in Environmental Sciences, School of Civil and Environmental Engineering,
Federal University of Goiás, Goiânia 74605-220, GO, Brazil; ravielbasso@ufg.br
*Correspondence: klebberformiga@ufg.br
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 hydrody-
namic 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.
Keywords: eutrophication; reservoir; modeling; CE-QUAL-W2
1. Introduction
Understanding and modeling the reservoir eutrophication process is crucial for effec-
tive 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 deple-
tion, which are crucial in eutrophication [
2
,
3
]. Quality models are essential for assessing
the long-term consequences of eutrophication, enabling preventive measures, and support-
ing 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 inter-
acting 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
Water 2024,16, 3556. https://doi.org/10.3390/w16243556 https://www.mdpi.com/journal/water

Water 2024,16, 3556 2 of 22
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 ap-
plied 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 sim-
ulate 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 manage-
ment 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.

Water 2024,16, 3556 3 of 22
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).
Water 2024, 16, x FOR PEER REVIEW 3 of 22
studies to discuss and compare the applicability of dierent types of water quality models.
Other excluded works were those not directly related to the eutrophication of reservoirs
or lakes and studies investigating the eects of climate change and ow scenarios on ther-
mal 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) Pro-
tocol [20], ensuring transparency, methodological rigor, and standardization of the sys-
tematic review conducted.
Thirty-eight articles, ten from the Scopus database and twenty-eight from Web of Sci-
ence, were deemed relevant to this study (Figure 1).
Figure 1. Flowchart of the methodology adopted in this systematic review of the CE-QUAL-W2
model. This owchart 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.
3. Results and Discussion
In total, 26 dierent journals published on the subject. Among the selected journals,
the Journal of Hydrology published the most, with ve articles, followed by the International
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.
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

Water 2024,16, 3556 4 of 22
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.
Water 2024, 16, x FOR PEER REVIEW 4 of 22
Journal of Environmental Science and Technology, with four. Environmental Engineering Sci-
ence, Environmental Modeling & Assessment, Environmental Monitoring and Assessment, Wa-
ter, and Water Science and Technology each published two articles, while the remaining jour-
nals had only one selected article (Figure 2). The growing acceptance of studies using the
CE-QUAL-W2 model in high-impact scientic journals indicates its global relevance and
role in promoting environmental sustainability, especially in climate change and increas-
ing pressure on water resources.
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 gure categorizes
the journals that published studies on the CE-QUAL-W2 model. It species their thematic areas and
provides quantitative data on citations and publications to highlight the model’s impact in dierent
research elds.
The articles were classied into 12 categories according to the study area, with envi-
ronmental sciences, water resources, and environmental engineering having the highest
number of works. The most signicant production of articles occurred in 2021, with six
publications, followed by 2019 and 2022, with ve 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 ve 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).
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.
The articles were classified into 12 categories according to the study area, with envi-
ronmental 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 cali-
bration and validation with data from different periods, methods, and sources. Typical
objectives of these studies included simulating water quality dynamics in tropical reser-

Water 2024,16, 3556 5 of 22
voirs, 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), am-
monia (NH
3
), nitrate (NO
3−
)/nitrite (NO
2−
), 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.
Water 2024, 16, x FOR PEER REVIEW 5 of 22
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 [12–14,18,21–54].
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 methodol-
ogy (Table 1).
The CE-QUAL-W2 model was widely used in water quality studies, involving cali-
bration and validation with data from dierent periods, methods, and sources. Typical
objectives of these studies included simulating water quality dynamics in tropical reser-
voirs, 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), am-
monia (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, reecting 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 eorts underscore the importance of managing water resources to ensure their sus-
tainability.
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 [12–14,18,21–54].
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 km
2
. These efforts
underscore the importance of managing water resources to ensure their sustainability.

Water 2024,16, 3556 6 of 22
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/Year Input Parameters
(Nutrients) Model Calibration Analysis Time Purpose of the Study Model Used Study Area Segments
Masoumi et al. 2023
[22]
NO3/ NO2, NH3, TP,
Chl-a, and DO
Automatic calibration
using the
SUFI-2 Algorithm
05/2005–12/2005
Simulate water quality
dynamics in a tropical
reservoir
subject to significant
urban pollution and
hydroclimatic
seasonality
CE-QUAL-W2
Karkheh 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 PO4
The model was
calibrated using data
from 2013
2013
Simulate water quality
dynamics in a tropical
reservoir in Fortaleza,
Ceará, Brazil
CE-QUAL-W2
Santo Anastácio Lake
(Brazil)
900 m long and 185 m wide
31 segments
Hanjaniamin et al. 2023
[18]DO The model was
calibrated in this study 05/2015–04/2016
Identify water quality,
thermal stratification,
dissolved oxygen
concentration, and
eutrophication
conditions in
the reservoir
CE-QUAL-W2
Yamchi 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 BOD
The model was
calibrated and validated
in a previous study [24]
Time series 2013, 2018,
and 2019
Evaluate the impact of
hydrological
characteristics on
hydrodynamics,
considering water
quality and its impact
on evaporation rates
SWMM
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 TP
The CE-QUAL-W2 was
calibrated and validated
in this study
01/01/2008–12/31/2018
Simulate reflective
current density patterns
in collaboration with
variables and
water quality
CE-QUAL-W2
Three 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-a
CE-QUAL-W2 and
SWAT were calibrated
in this study
2000–2019 Simulate long-term
water quality
CE-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

Water 2024,16, 3556 7 of 22
Table 1. Cont.
Publication/Year Input Parameters
(Nutrients) Model Calibration Analysis Time Purpose of the Study Model Used Study Area Segments
Rocha et al. 2022
[13]TP
The model was
calibrated and validated
in a previous study [24]
01/2009–01/2018 Evaluate residence time
and total phosphorus CE-QUAL-W2
Lake 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-a
This 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)
2013–2019
Assess the impact of
water diversion
between basins after the
dammed lake received
high flows of
local runoff
CE-QUAL-W2
Buffalo 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 DO
The model was
calibrated for 2011–2012
and validated for 2013
2011–2013
Investigate 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 status
CanESM2
SWAT
CE-QUAL-W2
Mahabad Dam
Reservoir (Iran)
200 mm3capacity
28 segments of
variable lengths
Yosefipoor et al. 2022
[29]
DO, NO3, PO4, Fe,
and BOD
The model was
calibrated and validated
in this study
2008–2009
Propose 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 management
WQSM
CE-QUAL-W2
Ilam Reservoir (Iran)
16.8 MCM capacity
42 longitudinal
segments, 500 m long

Water 2024,16, 3556 8 of 22
Table 1. Cont.
Publication/Year Input Parameters
(Nutrients) Model Calibration Analysis Time Purpose of the Study Model Used Study Area Segments
Kheirkhah et al. 2022
[30]
TP, Chl-a, DO, NO3,
NH3, PO4, and BOD
Calibration was
performed using
significant water-quality
calibration coefficients
133 monthly periods
Determine 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 study
2005–2014
Assess the present and
future trophic state
of a reservoir
SWAT
CE-QUAL-W2
Montargil
Sorraia River (Portugal)
164 hm3capacity
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-a
Model previously
calibrated by [55,56]2011–2013
Evaluate how future
hydrological and
meteorological
conditions may affect
nutrient regimes and
water chemistry
in the Lake
Diefenbaker Reservoir
SPARROW
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 DO
The model was
calibrated based on data
collected over one year
02/2011–02/2013
Evaluate the impact of
water release from the
lower layers of the
reservoir on
water quality
CE-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 temperature
The model was
previously calibrated
and validated by [
55
,
57
]
2001 to 2010
Investigate the effects of
climate change and flow
scenarios on the thermal
structure of
Lake Diefenbaker
CE-QUAL-W2
Lake 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

Water 2024,16, 3556 9 of 22
Table 1. Cont.
Publication/Year Input Parameters
(Nutrients) Model Calibration Analysis Time Purpose of the Study Model Used Study Area Segments
Mesquita et al. 2020
[24]TP
The model was
calibrated and validated
in this study
2009–2019
Investigate the influence
of hydroclimatic forcing
and water quality on
the evaporation process
of a shallow
tropical lake
CE-QUAL-W2
Santo 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 flows
The model was
calibrated and validated
in this study 15 days
Reduce the potential for
eutrophication in a
river-reservoir system
with discharges from
aquaculture industries
MPWLA
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 NH4
The model was
calibrated using the
same methodology
described in [55]
2011–2013
Investigate the impacts
of various withdrawal
elevations on the water
chemistry and nutrients
of the Lake
Diefenbaker Reservoir
CE-QUAL-W2
Lake Diefenbaker
(Canada)
Surface of 394 km2
Capacity of 9.03 km3
87 horizontal segments
and in 60 water depths
Aghasian et al. 2019
[37]TDS The model was
calibrated in this study 2014–2016
Determine the amount
of water released from
various outlets to
discharge brine from
the hypolimnion layer
considering
downstream water
quality limitations
MOPSO
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 DO The model was
calibrated in this study 2000–2002
Develop an
optimization model to
improve reservoir water
quality and protect
downstream
water quality
CE-QUAL-W2
Dousti Reservoir
Harirud River
(Iran/Turkmenistan)
Capacity of 3 billion m
3
.
Uninformed
Ziaie et al. 2019
[39]
Water temperature, TP,
DO, NO3−, and PO4
Water quality
calibration was carried
out in October,
November,
December, and March
of 2013 and in April and
July of 2014
10/2013–01/2015
Investigate thermal
stratification and
eutrophication in the
Zayandeh Roud
dam reservoir
CE-QUAL-W2
Lake 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

Water 2024,16, 3556 10 of 22
Table 1. Cont.
Publication/Year Input Parameters
(Nutrients) Model Calibration Analysis Time Purpose of the Study Model Used Study Area Segments
Kim et al. 2019
[40]
SS, TP, TN, Chlorophyll,
and COD
The model was
calibrated based on
research conducted by
the National Institute of
Environmental
Research
(NIER 2007) [58]
2005–2009
and 2013–2014
Evaluate how nutrient
reduction influences
water quality
CE-QUAL-W2
Lake 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-a
Model calibration and
validation periods were
selected from July 2009
to December 2009 and
from March 2010 to
May 2010, respectively
2009–2010
Investigate the effects of
selective withdrawal
and inflow control on
thermal stratification
and water quality
CE-QUAL-W2
Ilam 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, NO
3−
, TN, and TP
The model was
calibrated in this study 2011–2013
Develop a methodology
to mitigate and control
the current and future
eutrophication conditions
SWAT
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 DO
The model was
calibrated and validated
in this study
2006–2008
Survey the thermal
regime and
eutrophication states in
the Ilam reservoir
CE-QUAL-W2
Ilam 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]TP
The model calibration
was performed using
the method presented
by [59]
180 months
Present 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 system
CE-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 NO3
The model was
calibrated using data
from 05/2005 to
04/2006 and validated
from 05/2006
to 08/2006
2005–2006
Provide a reduced-order
model to condense
simulated results
POD
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

Water 2024,16, 3556 11 of 22
Table 1. Cont.
Publication/Year Input Parameters
(Nutrients) Model Calibration Analysis Time Purpose of the Study Model Used Study Area Segments
Park et al. 2015
[46]
TDS, SS, PO43--, NH4+,
NO3−, BOD, algal
biomass, DO, and COD
Model previously
calibrated by [47]2007–2012
Evaluate the efficiency
of regression trees in
developing a
stressor–response
model for
chlorophyll-a (Chl-a)
CE-QUAL-W2
Yeongsan Reservoir
(South Korea)
Surface: 34.6 km2
Annual flow:
2.19 ×109m3
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-a
The model was
calibrated using data
from 2004 to 2008 and
validated from 2009
to 2012
2004–2012
Assess 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 future
CE-QUAL-W2
Hsin 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 DTP
The model was
calibrated using
two new methods: a
sensitivity analysis to
determine significant
model parameters and a
pattern search to
optimize the parameters
2007–2008
Predict the pollutant
load released from each
reservoir in response to
different flow scenarios
for the
interconnection channel
CE-QUAL-W2
Reservoirs: 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.

Water 2024,16, 3556 12 of 22
Table 1. Cont.
Publication/Year Input Parameters
(Nutrients) Model Calibration Analysis Time Purpose of the Study Model Used Study Area Segments
Deus et al. 2013
[14]
Water temperature,
NO
3−
, NH3, P, SST, DO,
and chlorophyll-a
The model was
calibrated with data
from 2007 to 2011
2007–2011
Quantify mass
transport, thermal
stratification, and
changes in water
quality due to the
possible expansion of
fish farming activities in
the reservoir
CE-QUAL-W2
TucuruíReservoir
(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 DO
The model was
calibrated with data
from 2005 to 2006 and
validated from
2003 to 2004
2003–2006
Simulate the main
temporal patterns of
epilimnion, thermocline,
and hypolimnion
S.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 pH
The model was
calibrated and validated
using data from 2003
and 2005
2003–2005
Identify the effect of
diffuse pollution from
allochthonous organic
matter on the temporal
and spatial characteristics
of autochthonous organic
matter in a stratified
dam reservoir
CE-QUAL-W2
Daecheong Reservoir
(South Korea)
Hydrographic basin:
4166 km2
72 km2storage
Extension: 86 km
Maximum depth: 78 m
Uninformed
Liu et al. 2009
[51]
Water temperature, DO,
chlorophyll-a, TP, NH4,
and NO3/NO2
The model was
calibrated with data
from 2003 to 2004 2003–2004
Quantify mass
transport, thermal
stratification, and
variations in
water quality
CE-QUALW2
Mingder 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 algae
The model was
calibrated with data
from May 2005 to
December 2005 and
validated from
December 2005
to July 2006
2005–2006
Predict the formation of
the eutrophication
process in Karkheh
Reservoir under
different
management strategies
CE-QUAL-W2
Karkheh Reservoir
(Iran)
Surface: 162 km2
Capacity: 5109 m3
Extension: 64 km
66 longitudinal
segments, 1 km long,
with up to
55 vertical layers

Water 2024,16, 3556 13 of 22
Table 1. Cont.
Publication/Year Input Parameters
(Nutrients) Model Calibration Analysis Time Purpose of the Study Model Used Study Area Segments
Debele et al. 2008
[21]
TSS, PO4, NO3/ NO2,
NH4/NH3, LDOM,
RDOM, LPOM, RPOM,
CBOD, a species of
blue-green algae,
and DO
The calibrated results of
the SWAT model were
used as input for the
CE-QUAL-W2 model
1997–2001
Understand water
processes and their
constituents’
movements, interactions,
and transformations in
the dryland watershed
and the downstream
water body
SWAT
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 study 1998–2000
Quantify 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-W2
Reservoirs: Te-Chi and
Tseng-Wen
(China)
Te-Chi: total watershed
area of 592 km2
183 ×106 m3storage
Tseng-Wen: watershed
area of 481 km2
659 ×106 m3storage
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 1997
1996–1997
Formulate water quality
management strategies
for Feitsui Reservoir to
achieve
oligotrophic condition
CE-QUAL-W2
Feitsui 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.

Water 2024,16, 3556 14 of 22
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 2presents the calibrated values of the parameters used in the
different studies.
Ref. [
23
] reported an R
2
of 0.32 for the calibrated parameters (DO, chlorophyll-a, and
PO4), indicating a less effective fit. The study in [
13
] achieved an R
2
of 0.70 for total
phosphorus, suggesting a good model fit. The study in [
40
] reported R
2
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 R
2
values indicate a better fit between observed and
simulated values.
Data quality is crucial for modeling aquatic systems; studies with precise measure-
ments 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 tem-
perature [
26
], generally achieved higher R
2
values than those addressing more complex
parameters, such as dissolved oxygen and chlorophyll-a [
23
]. These observations are re-
flected 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
R2values.
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
R
2
. 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 R
2
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 R
2
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 R
2
. 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.
Table 2. Values of the coefficient of determination (R
2
) obtained in different studies for various water
quality parameters using the CE-QUAL-W2 model. This table presents a comparative analysis of
the R
2
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.
R2Value Calibrated Parameters Publication
0.32 DO, Chlorophyll-a, PO4 [23]
0.6781 DO [18]
0.92 Water Level, Temperature, DO [26]
0.70 Total Phosphorus (TP) [13]
0.76 Total Phosphorus (TP) [12]

Water 2024,16, 3556 15 of 22
Table 2. Cont.
R2Value Calibrated Parameters Publication
0.92 Flow, TN, TP [31]
0.41 Total Phosphorus (Dry Period) [24]
>0.9 Water Level, Temperature, and Suspended Solids (SS) [40]
0.62–0.95 DO, Temperature, TDS, TN, TP [42]
0.977 Total Phosphorus (TP) [51]
0.906 DO, Temperature [21]
0.9605 and 0.9724 TP, Ammonia, NO2/NO3, Chlorophyll-a, DO [53]
3.2. Calibration Variability
R
2
values vary for each calibrated parameter as different factors and processes in-
fluence 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 (R
2
) 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 R2values 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 R
2
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 R
2
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 R
2
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 R
2
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 R
2
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 R
2
of 0.70 [
12
]. An average R
2
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 R
2
values across studies can be
attributed to factors like the sophistication of methodologies, data quality, and research
locations. Higher R
2
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
R
2
values were noted in studies with limited data, conducted in complex locations, or using
less established methodologies.

Water 2024,16, 3556 16 of 22
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 cor-
respondence between the model’s simulations and observed data. Although automatic
optimization techniques can be applied, the process still requires significant human inter-
vention 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 varia-
tions, including diurnal patterns. Studies in tropical and Mediterranean climates con-
firmed 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 sum-
mer. 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 concen-
trations in reservoirs with consistent nutrient loading and light availability. It accurately
represented the seasonal and spatial distributions of chlorophyll-a, particularly in monomic-
tic 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

Water 2024,16, 3556 17 of 22
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 varia-
tions 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 pro-
duction 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

Water 2024,16, 3556 18 of 22
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 correla-
tion 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 (R
2
) of 0.71, 0.59,
and 0.14, respectively, while CE-QUAL-W2 showed an R
2
of 0.92 for water surface elevation.
Finally, the study in [
26
] used SWAT to estimate flow and nutrient loads, achieving an R
2
of 0.64, while CE-QUAL-W2 was calibrated for water level, temperature, and dissolved
oxygen, with an R
2
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 preva-
lence 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 dis-
solved oxygen concentration and the level of eutrophication in the Yamchi Dam reservoir.
The study demonstrated the model’s effectiveness by finding an R
2
value of 0.6781, indicat-
ing 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 bod-
ies 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
–
65
]. The
study in [
51
] investigated the Mingder Reservoir and found an R
2
value of 0.977 in the
nonlinear relationship between phosphorus load reduction and total phosphorus concen-
tration, indicating a strong correlation between phosphorus reduction and improved water
quality. The study in [
53
] compared the Te-Chi and Tseng-Wen reservoirs, accurately re-
producing vertical temperature profiles and concentrations of total phosphorus, ammonia,
nitrite/nitrate, chlorophyll-a, and dissolved oxygen. The R
2
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 stratifi-
cation, 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.

Water 2024,16, 3556 19 of 22
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 TucuruíReservoir, 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 eutrophica-
tion. 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 reser-
voir 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 stratifica-
tion 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.

Water 2024,16, 3556 20 of 22
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 Analy-
sis, 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.
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