Access to this full-text is provided by Taylor & Francis.
Content available from Journal of Freshwater Ecology
This content is subject to copyright. Terms and conditions apply.
RESEARCH ARTICLE
JOURNAL OF FRESHWATER ECOLOGY
2024, VOL. 39, NO. 1, 2419377
Lake regime evaluation based on similarity evaluation
method, taking Poyang Lake as an example
Jutao Liua,b, Xinyuan Liua,b, Chunyun Wena,b and Fang Hua,b
aJiangxi Key Laboratory of Flood and Drought Disaster Defense, Jiangxi Academy of Water Science and
Engineering, Nanchang in Jiangxi Province, China; bJiangxi Provincial Technology Innovation Center for
Ecological Water Engineering in Poyang Lake Basin, Nanchang in Jiangxi Province, China
ABSTRACT
Lake regime shift theory provides effective support for managing
lake eutrophication. However, there are limited studies on identify-
ing lake regime status using mathematical methods. Based on the
similarities in characteristics and evolutionary patterns of lake eco-
systems, and utilizing long-term lake water ecological monitoring
data, this study introduces a method for evaluating lake regime
status using total nitrogen (TN), total phosphorus (TP), and
chlorophyll-a (Chl-a) as evaluation indices. Taking Poyang Lake as
an example, the lake regime status was evaluated. The results
showed that: The weights of total nitrogen (TN), total phosphorus
(TP), and chlorophyll a (Chl-a) in Poyang Lake were 0.207, 0.234
and 0.559, respectively, with Chl-a being the main controlling fac-
tor for evaluating the lake regime. The lake regime of Poyang Lake
is in the Algae-Macrophytes Coexist state. The similarity assess-
ment method has been proven effective for determining a lake
regime using sparse water eco-environmental data.
1. Introduction
Lake regime shift refers to the abrupt changes in the structure and dynamics of a lake eco-
system over a short period. The transformed steady state can maintain a lasting response to
external environmental factors (Mills 2004). Regime shifts can be categorized into three
types: smooth, mutant, and multistable coexistence (Zhang et al. 2022). Among these, the
shallow lake ecosystem is classified as a multistable coexistence type (Scheffer and Jeppesen
2007). Within a certain range of nutrient concentrations, there can be three equilibrium
states for lakes: clear water state, turbidity state, and metastable state (Brookes and Carey
2011; Zhao et al. 2020). When nutrient concentrations are low, submerged plant coverage
increases, and water transparency improves, resulting in a clear water state dominated by
‘grass type’ (Jeppesen et al. 2016). As the concentration of nutrients increases, the coverage
of submerged plants and water transparency either remains stable or gradually decreases until
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
CONTACT Jutao Liu liujutao126@163.com Jiangxi Key Laboratory of Flood and Drought Disaster Defense,
Jiangxi Academy of Water Science and Engineering, Nanchang in Jiangxi Province, 330029, China; Xinyuan Liu
xyliuctgu@outlook.com Jiangxi Key Laboratory of Flood and Drought Disaster Defense, Jiangxi Academy of Water
Science and Engineering, Nanchang in Jiangxi Province, China.
https://doi.org/10.1080/02705060.2024.2419377
KEYWORDS
Regime shift; similarity
assessment; regime
discrimination; shallow
lake; Algae-Macrophytes
Coexist state
ARTICLE HISTORY
Received 22 April 2024
Accepted 16 October
2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://cre-
ativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium,
provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted
Manuscript in a repository by the author(s) or with their consent.
2 J. LIU ETAL.
the nutrient salt concentration reaches a tipping point. At this stage, the lake transitions to
a state dominated by phytoplankton, characterized by low submerged plant coverage and
reduced water transparency, known as the Algae-Dominated Turbid Water state (Ren et al.
2022). A metastable lake represents an unstable condition between a clear water state and a
turbid water state, encompassing both Macrophyte-Algae Coexist and Algae-Macrophyte
Coexist states (Liu et al. 2015). The input of nutrients is the primary factor influencing the
concentrations of total nitrogen (TN) and total phosphorus (TP) in clear water lakes. When
the lake reaches a turbid water state, the nutrient cycling within the sediment may become
a more significant source (Deng et al. 2021). In fact, the transition from a clear water state
to a turbid water state in lakes represents a shift from an oligotrophic state to a eutrophic
state. Consequently, analyzing shifts in lake regimes has become a crucial method for the
early detection of cyanobacterial blooms. (Dakos et al. 2015; Scheffer et al. 2015).
Current studies on lake regime shifts have focused on the multi-stability phenomenon, the
mechanism of regime shifts, key factors, thresholds, and the assessment of lake regime status
(Schindler et al. 2016; Yang et al. 2018; Dong et al. 2021; Xie et al. 2024). The evaluation is
primarily based on the coverage of submerged plants and less frequently on mathematical
methods (Guo et al. 2013). Chen (2009) utilized the random interference model to investigate
the stability and regime shifts of marine and lake ecosystems. The study suggested that changes
in parameters regulating system control and external disturbances were crucial factors leading
to shifts in ecosystem regimes. Through the establishment of lake eutrophication models, Feng
et al. (2010) examined the impact of parameter changes and external random variations on
stability and system regime shifts. Their findings highlighted that the stochastic disturbance in
water’s phosphorus (P) content played a crucial role in driving eutrophication. Wang et al.
(2011) selected four indicators, namely total phosphorus (TP), total nitrogen (TN), transpar-
ency (SD), and phytoplankton cell density, as the criteria for evaluating the lake regime of Erhai
Lake in comparison to shallow lakes in the lower and middle reaches of the Yangtze River.
They employed the fuzzy evaluation method to analyze the regime shift. Liu et al. (2015)
examined the regime shift mutations in Taihu Lake by employing three indicators: total phos-
phorus (TP), total nitrogen (TN), and chlorophyll-a (Chl-a), along with the Mann-Kendall
method. They categorized the lake into three distinct stages: the macrophytes-algae coexist state
and near clear water state spanning from 1981 to 1987, followed by the algae-macrophytes
coexist state observed between 1988 and 1996, ultimately transitioning into the algae-dominated
turbid water state prevailing from 1997 to 2008. PCLake model is one of the most widely used
models in the field of lake water ecosystem research. It is based on the nutrient cycling pro-
cesses in a closed lake and conducts quantitative analyses. Additionally, it has been used to
simulate and predict shifts in lake regimes (Hu et al. 2019)
Eutrophication is currently a significant concern in the lake water environments of China.
Around 60% of China’s freshwater lakes are concentrated in the eastern coastal areas and the
Yangtze River region. Most shallow lakes in these areas have experienced or are experiencing
eutrophication (Qin 2002; Qin etal. 2006; Liu etal. 2015), leading to various issues such as
the deterioration of the aquatic environment, ecological degradation, and frequent occur-
rences of blue-green algae blooms, etc. (Zhao et al. 2014). Currently, research on shallow
lakes has primarily focused on heavily polluted ones such as Taihu Lake and Chaohu Lake,
while those with only mild eutrophication have received limited investigation. It is essential
to introduce the theory of system similarity to apply the research findings from severely
eutrophicated lakes to the study of mid eutrophicated lakes. System similarity refers to the
isomorphism and homomorphism of systems, which is evident in the commonalities of sys-
tem structure, modes of existence, and evolutionary processes (Xu et al. 2019). The concept
of system similarity is widely recognized and has predominantly been applied in the fields
of water resources and water environments to study hydrology and flooding. Commonly
JOURNAL OF FRESHWATER ECOLOGY 3
employed methods for assessing similarity include distance functions (Zhang et al. 2007; Li
et al. 2009), clustering models (Liang et al. 1994; Wan et al. 2007), and pattern recognition
models (Liu et al. 2007), all of which have demonstrated favorable practical outcomes. The
quantitative assessment of lake regimes has primarily been conducted on lakes with abun-
dant long-term basic data. In cases where data is limited, the similarity evaluation method
can serve as an effective approach for determining regime shifts in lake ecosystems.
Poyang Lake is a large, shallow lake and the largest freshwater lake in China. The lake
is connected to the Yangtze River. Recently, it was reported that in the northern part of
the main lake area of Poyang Lake (lakeshore areas from Duchang to Zhouxi) and the
Cuoji Lake area, the lake became dominated by phytoplankton communities in the sum-
mer (Wu et al. 2013). Determining the transformation of a partial area of Poyang Lake
into an algae-type lake status merely based on the lake landscape type lacks a scientific
basis for quantitative judgment. Therefore, quantitative analysis of data is necessary to
determine the lake regime characteristics of Poyang Lake and better understand this
recent shift. In this study, the lake regime of Poyang Lake was assessed using environ-
mental monitoring data for the water and the similarity evaluation method based on
three lake regime determining factors, namely TN, TP, and Chl-a, to provide guidance for
the protection and restoration of the aquatic ecology of Poyang Lake
2. Materials and method
2.1. Study area
Poyang Lake (Figure 1) is characterized by a significant seasonal high-low variation in the
water levels, so that ‘when the water line is high, it appears as a lake, and when the water
line is low, it appears as a river’. It has a catchment area of 162,200 km2, accounting for
97.2% of the area of Jiangxi Province and 9% of the catchment area of the Yangtze River,
with a maximum length of 173 km (south-north), a maximum width of 74 km (east-west)
(Jiangxi Water Resources Department (JWRD) 2009), an average width of 18.6 km, and
an average depth of 7.38 m (Jiangxi Water Resources Department (JWRD) 2009). At the
flood level of 21.69 m, the lake area is 2,933 km2 (Wang and Dou 1998), and the water
level at the Hukou Hydrologic Station is 22.59 m (relative to the elevation of the Wusong
Hydrological Station), corresponding to a lake area of 5,100 km2 (Jiangxi Water Resources
Department (JWRD) 2009), including four flood diversion and detention regions, i.e.
Kangshan, Zhuhu, Huanghu, and Fangzhouxietang. When the water level at the Hukou
Hydrologic Station is 5.90 m (the elevation of the Wusong Hydrological Station), the lake
area is only 146 km2 (Jiangxi Water Resources Department (JWRD) 2009). Poyang Lake
is connected to the Yangtze River at Hukou, with an average annual runoff of 152 billion
m3, accounting for 16.3% of the average annual runoff of the Yangtze River basin.
The water quality of Poyang Lake has historically been rather good; before 2001, the
water quality was rated as Grade III and above, with a eutrophication index of 40. The
region has experienced rapid social and economic development and the acceleration of
urbanization in the Poyang Lake basin. With this development the water eco-environment
of Poyang Lake has been under tremendous pressure, with pollution worsening, the water
quality declining, and the lake eutrophication index rising. In 2014, the water area with
a water quality of Grade III or better only accounted for 43.4% of the total water area of
Poyang Lake, with a lake eutrophication index of 49, approaching the lake eutrophication
level, and frequent outbreaks of cyanobacteria blooms. Field survey data during the
high-water period in 2012–2016 observed zonal distributions of cyanobacteria blooms in
the northern Poyang Lake (lakeshore areas from Duchang to Zhouxi) and Cuoji Lake.
4 J. LIU ETAL.
2.2. Data sources
The water level varies significantly between high and low levels in Poyang Lake. The
water level variations are recorded by 44 monitoring sites for the high-flow season and
25 monitoring sites for the normal-flow season, as shown in Figure 1. To avoid the effects
of possible single-year aquatic environmental anomalies, data for Poyang Lake from three
consecutive years (2013–2015) were used to perform the lake regime assessment, and the
annual value of an indicator—the average of the values of the high-flow season and
normal-flow season—was adopted.
In this study, field monitoring of Poyang Lake was conducted during the wet season
(sampling occurred in August) and the dry season (sampling took place in April), respec-
tively. A total of 44 monitoring points were used in Poyang Lake during the wet season,
while 25 monitoring points were used during the dry season (Figure 1). To avoid the
effects of possible single-year aquatic environmental anomalies, data for Poyang Lake
from three consecutive years (2013–2015) were used to perform the lake regime assess-
ment, and the annual value of an indicator—the average of the values of the high-flow
season and normal-flow season—was adopted.
In order to ensure the representativeness and reliability of the samples, a 5 L upright
Plexiglass sampler was used to collect 500 ml of water samples from 0.5 m below the lake
surface and the same amount from 0.5 m above the lake bottom. Then, the surface and
bottom water samples are mixed and stored in a 1 L polyethylene bottle. Finally, the col-
lected water samples were placed in a refrigerator with ice or dry ice and transported
back to the laboratory. The index was determined within 24h.
Figure 1. Sketch of the sampling points in Poyang lake.
JOURNAL OF FRESHWATER ECOLOGY 5
The water samples collected from the field were divided into unfiltered and filtered
samples. Total phosphorus (TP) and total nitrogen (TN) were measured in unfiltered
samples. The detailed operations followed the detection and analysis method for water
and wastewater (fourth edition) (State Environmental Protection Administration Water
and Wastewater Monitoring and Analysis Methods Editorial Committee 2002). The
remaining water samples were filtered using a 0.45 μm mixed fiber filter membrane. The
concentration of chlorophyll a was determined through spectrophotometry after extract-
ing the filter membrane with 95% ethanol, following lake eutrophication survey specifica-
tions (second edition) (Jin and Tu 1990).
The selection criteria for reference lakes are as follows: (1) Reference lakes should
possess a rich history of steady-state transitions; (2) Reference lakes must have compre-
hensive long-term series of water quality monitoring data; (3) The reference lake should
exhibit morphological characteristics similar to those of the evaluated lake.
In this study, Taihu Lake is chosen as the reference lake. This selection is based on sev-
eral factors. Firstly, both Taihu Lake and Poyang Lake are large shallow water bodies that
experience a subtropical monsoon climate. Secondly, Taihu Lake has experienced the regime
shift process of Macrophytes-Algae Coexist, Algae-Macrophytes Coexist and Algae-Dominated
Turbid Water. Finally, Taihu Lake has a comprehensive long-term series of water quality
monitoring data, which serves as a valuable reference for assessing lake regime shift.
Taihu Lake is the third largest freshwater lake in China, situated between 30°56′ and
31°34′ north latitude and 119°54′ and 120°36′ east longitude. It is located in the southern
part of Jiangsu Province, covering an area of 2,338 km2. The lake stretches 69 km in
length, has an average width of 34 km, and an average water depth of 1.89 m. Since the
late 1970s and early 1980s, the continuous development of social and economic activities
has led to a significant influx of pollutants into Taihu Lake, resulting in severe water
pollution and increasingly serious eutrophication (Zhang et al. 2016; Asmaa et al. 2020).
2.3. Index system
A regime shift of a lake ecosystem from the clear water state to the algae-dominated
turbid water state is a typical form of eutrophication (Scheffer et al. 1993; Scheffer and
van Nes 2004). Evaluation methods of lake eutrophication in Organization for Economic
Co-operation and Development (OECD) countries include the indicators N, P, Chl-a, and
SD (Janus and Vollenweider 1981). In China, five indicators are typically used, TN, TP,
Chl-a, chemical oxygen demand (COD), and SD (Jin and Tu 1990). In the study of lake
regime shift, the evaluation indicators of lake homeostasis typically include metrics related
to the growth of submerged plants and phytoplankton, such as total phosphorus (TP),
total nitrogen (TN), and chlorophyll-a (Chl-a) (Wang et al. 2011; Liu et al. 2015). Based
on common indices used for assessing lake eutrophication and studying lake regime shift,
TP, TN, and Chl-a were selected as the evaluation indicators for lake regime shift in this
research. Notably, TN and TP are the prerequisites for the formation of a grass-type eco-
system or an algae-type ecosystem (Qin et al. 2006), whereas the Chl-a content reflects
the level of primary productivity of the water (Cheng et al. 2012)
2.4. Similarity assessment method
Similarity refers to the commonality in feature attributes between two systems (Liang
et al. 1994). In many applications, the similarity between two series is usually measured
using a distance function, in which the degree of similarity is inversely related to the
distance: the shorter the distance, the higher the similarity (Wan et al. 2007)
6 J. LIU ETAL.
Conventional measurements of similarity include the distance measurement and the
similarity coefficient, of which the distance measurement has been a major research direc-
tion in similarity search (Liang etal. 2009). For single-series-feature systems, various mea-
surement methods, e.g. the Manhattan distance, Euclidean distance, time warping distance
(Berndt and Clifford 1994), module distance (Wang and Rong 2004), and slope distance,
have been proposed, in which distance has a natural intuition in spatial imagination as
well as simplicity in calculation and has thus been extensively adopted (Zhang and Zhou
2004). Based on studies of system similarity, Zhang and Zhou (2004) noted the limitations
of the conventional distance coefficient method in measuring similarity and argued that
the impact of each of the feature attributes on the similar systems should not be treated
as a simple weight, as done in the distance coefficient method, and therefore proposed
the non-equal weight distance coefficient method. Wang et al. (2006) adopted this method
to evaluate rainstorm similarity and achieved good results. Therefore, in our study, the
non-equal weight distance measurement method is used to measure the similarity.
2.5. Standardization method
In order to facilitate calculations and analyses, a standardization was needed to remove
the dimensions of the indicators. According to the evolutionary trends in physico-chemical
environmental factors over time, the eco-environment of Poyang Lake showed a deterio-
ration trend. Based on the optimal state of the aquatic eco-environment in historical
observations, the all-time minimum of the TN, TP and Chl-a was taken as the standard,
and the standardization was calculated using Equation (1)
A xx
ij ij imin
=
/ (1)
Where, i represents the ith evaluation index; j represents the jth lake; Aij represents the
jth lake and the standardized value of the ith evaluation index; xij represents the measured
value; and ximin represents the minimum value of the ith evaluation index.
The range of standardized values of evaluation indicators is [0,1]. This ensures compa-
rability among the values of evaluation indicators and guarantees consistency in calcula-
tion results.
2.6. Calculation of indicator weights
Weight coefficients evaluate the membership of each factor in the evaluation field relative
to its importance and can directly affect the outcome of the comprehensive evaluation.
Many methods exist to determine the weight, such as the binomial coefficient method,
fuzzy mathematics, the analytic hierarchy process, rough set theory, the experimental
method, the Delphi method, principal component analysis, the correlation coefficient
method, and the entropy method. After analyzing various weight determination methods,
Zhang and Zhou (2004) argued that because systems show inconsistencies in each of the
feature attributes and a feature attribute with a severe inconsistency has a higher impact
on the similarity evaluation outcome, a feature attribute should be given a greater weight.
Taking these facts into consideration, a new weight determination method was proposed,
as described in the following section.
The standardized value Xij of the i evaluation index of the jth lake is arranged in
descending order from largest to smallest to form a new dataset Xnm. According to
Equation (2), the average difference of each evaluation index is calculated.
JOURNAL OF FRESHWATER ECOLOGY 7
∆
X
XX
m
n
u
m
nu tm
m
nt
=−
=
[]
= +
()
∑∑
1
2
32
2
/
/ (2)
Where,
∆
X
n
represents the average difference of the evaluation index, m represents the
number of rows (where m = j), and n represents the number of columns (where n = i). []
is the integer symbol, u is the number of the preceding term and the new data set Xnm,
and t is the number of the latter term in the new data set Xnm. Xnu represents the new
data set from 1 to [m/2], while Xnt represents the new data set from [(m + 3)/2] to m. The
former sum is the sum of the new data set Xnu from 1 to [m/2], and the latter sum is the
sum of the standardized value of the evaluation index Xnt from [(m + 3)/2] to m
Based on Equation (3), the weight of each evaluation index is obtained.
β
n
n
i
k
n
X
X
=
()
()
=
∑
∆
∆
2
1
2 (3)
Where, βn represents the weight of evaluation index, and k represents the total number
of evaluation index.
2.7. Calculation of the non-equal weight distance
Two lakes, S1 and S2, with n feature attributes, are assumed to exist. For n feature attri-
butes, when the two systems are identical, their positions will overlap, and the distance is
0. The greater the difference, the greater the distance; i.e. the distance is regarded as the
residue of the similarity. Based on n feature attributes between the two lakes (S1 & S2),
an n-dimension Euclidean space can be defined
Based on the standardized data of the lake evaluation index obtained from the data
standardization Equation (1), the Euclidean distance of the lake evaluation index is cal-
culated as shown in equation (4), specifically:
dXXX
ij ij ij imin
= −
()
()
2
(4)
Where, dij represents the Euclidean distance of the ith evaluation index of the jth lake, and
Ximin represents the minimum standardized value of the ith evaluation index of the jth lake.
The comprehensive distance of lake evaluation indicators is shown in Equation (5), as follows:
zd
ij
i
k
ij
=
=
∑
1
2 (5)
Where, zij represents the comprehensive distance of the ith evaluation index of the jth lake.
3. Results
3.1. Characteristics of the evaluation index for a regime shift in Poyang Lake
We utilized the water trophic status monitoring data from the reference lake (1981–2008)
and the water eco-environmental monitoring data from Poyang Lake (2013–2015). The
8 J. LIU ETAL.
observed trend in the variation of the evaluation factors of the lake regime shift is illus-
trated in Figure 2a-c. The TN and Chl-A values in Poyang Lake were relatively lower,
similar to those in the reference lake in the early 1990s, while the TP value in Poyang
Lake was relatively higher, equivalent to the high content range of the changes in the time
series in the reference lake. Figure 2a shows that the total nitrogen (TN) content in the
reference lake exhibited an increasing trend from 1981 to 2003 and then assumed a
downward trend. The TN contents in Poyang Lake from 2013 to 2015 were comparable
to those of the reference lake in the early 1990s. Figure 2b shows that the total phospho-
rus (TP) content of the reference lake increased from 1981 to 1997 and then decreased
after 1997. The total phosphorus (TP) content of Poyang Lake from 2013 to 2015 was
relatively high, essentially equivalent to that of the reference lake at the beginning of this
century. Figure 2c illustrates that the Chl-a content of the reference lake exhibited an
increasing trend, while that of Poyang Lake in 2013–2015 was relatively lower, essentially
equivalent to the reference lake in the 1990s.
3.2. Calculation of evaluation index weighting for the lake regime
The calculation results according to the weight calculation method are shown in Table 1.
The weights of TN, TP, and Chl-A were 0.207, 0.234, and 0.559, respectively. This indi-
cates that the degree of influence varied for a lake regime shift, with Chl-A playing a
significant role in this transition.
3.3. Lake regime discrimination in Poyang Lake
The reference lake data and Poyang Lake data were standardized according to the data
normalization method (shown in Table 2), and the results using the weighted calcula-
tion method and the non-equal weight distance calculation method are also shown in
Table 2, which shows that the comprehensive distance of the aquatic eco-environmental
features of the reference lake in 1981 was 0, indicating that the value was the historical
minimum, and that this year was the target year of the comprehensive distance evalu-
ation in the time series. Based on the time series data, the distance between each year
and the target year is represented by di (i represents the ordering of the evaluation
time), and the comprehensive distance represents the similarity of the aquatic
eco-environmental features of this year to those of the target year (1981) for the refer-
ence lake; the smaller the comprehensive distance was, the more similar the water
eco-environmental features.
The evaluation results showed that the comprehensive distance values of the water
eco-environmental features in 2013 (d24), 2014 (d25), and 2015 (d26) to the target year
were 9.686, 9.866, and 11.543, respectively, greater than the comprehensive distance of
d 1 to d4, d6, or d7 but smaller than that of each of the other years to the target year.
While accounting for the lake regime continuity and coexisting transitional characteris-
tics, we found that the lake status of Poyang Lake in 2013–2015 ranged from the status
of 1992 and the status of 1993 of the reference lake. Depending on the results of a
previous study in 1981–1987, the reference lake was in a stage of Macrophytes-Algae
Coexist state, close to a Clear Water state, and in 1988–1996, it was in a stage of
Algae-Macrophytes Coexist state. However, in 1997–2008, it was located in a stage of
the Algae-Dominated Turbid Water state. The average comprehensive distance values of
reference lake in the three stages were 0.625, 13.507 and 22.895, respectively. The aver-
age comprehensive distance value of Poyang Lake in 2013–2015 was 10.365, which was
JOURNAL OF FRESHWATER ECOLOGY 9
between the values of the 1stand 2nd stages of the reference lake. In 1992–1993, the
reference lake was located in the 2nd stage (the Algae-Macrophytes Coexist state), while
the lake regime of Poyang Lake was in Algae-Macrophytes Coexist state, close to the
Macrophytes-Algae Coexist state.
The TP concentration and its mean values in Poyang Lake from 2013 to 2015 were
0.110, 0.121, 0.096 and 0.109 mg/L, respectively. It can be seen from Table 3 that Poyang
Lake is in the critical area of the transformation from the Clear Water state to the
Algae-Dominated Turbid Water state
Figure 2. Temporal trend and average values of the different lake regimes for evaluation index.
Table 1. Weights of the regime shift index.
Index Total nitrogen Total phosphorus Chlorophyll A
Weight 0.207 0.234 0.559
10 J. LIU ETAL.
3.4. Key factors of lake steady-state transformation
In this study, the comprehensive distance was used to determine the lake regime and the
correlation between the inclusive distance and TP or Chl-A (Figure 3a & 3b) showed that
the correlation coefficients between the comprehensive distance and TP and between the
Table 2. Comprehensive distance evaluation and the standardization of the eco-environmental data in the reference
lake and Poyang lake.
Lake Year
Total
nitrogen
Total
phosphorus Chlorophyll A
Comprehensive
distance(d) Lake regime
Taihu Lake 1981 1.000 1.000 1.000 d10.000 Macrophytes-algae
coexist state
Taihu 1987 1.503 1.381 1.750 d21.251 Macrophytes-algae
coexist state
Taihu 1988 2.812 2.619 3.000 d35.301 Algae-macrophytes
coexist state
Taihu 1989 2.518 3.381 1.500 d45.034 Algae-macrophytes
coexist state
Taihu 1990 2.386 2.762 10.750 d532.173 Algae -macrophytes
coexist state
Taihu 1991 1.919 2.952 3.250 d65.415 Algae-macrophytes
coexist state
Taihu 1992 2.914 3.381 3.000 d76.468 Algae-macrophytes
coexist state
Taihu 1993 2.386 3.810 7.500 d818.749 Algae-macrophytes
coexist state
Taihu 1994 2.883 6.190 3.250 d913.909 Algae-macrophytes
coexist state
Taihu 1995 3.188 6.333 4.750 d10 16.193 Algae-macrophytes
coexist state
Taihu 1996 3.340 5.238 6.750 d11 18.318 Algae-macrophytes
coexist state
Taihu 1997 3.706 8.333 11.500 d12 41.751 Algae -dominated
turbid water state
Taihu 1998 3.036 4.048 5.250 d13 12.042 Algae-dominated
turbid water state
Taihu 1999 3.198 5.000 5.500 d14 14.381 Algae-dominated
turbid water state
Taihu 2000 3.249 5.762 6.250 d15 17.870 Algae-dominated
turbid water state
Taihu 2001 3.401 6.048 9.750 d16 30.334 Algae-dominated
turbid water state
Taihu 2002 3.909 5.429 8.250 d17 23.941 Algae-dominated
turbid water state
Taihu 2003 4.142 4.238 5.000 d18 12.858 Algae-dominated
turbid water state
Taihu 2004 2.824 3.652 8.078 d19 20.970 Algae-dominated
turbid water state
Taihu 2005 2.902 3.664 8.069 d20 20.972 Algae-dominated
turbid water state
Taihu 2006 3.215 3.965 11.346 d21 35.567 Algae -dominated
turbid water state
Taihu 2007 2.597 4.509 10.125 d22 30.087 Algae -Dominated
Turbid Water State
Taihu 2008 2.680 4.045 6.000 d23 13.967 Algae-dominated
turbid water state
Poyang
Lake
2013 2.112 5.242 1.498 d24 9.866 Algae-macrophytes
coexist state
Poyang
Lake
2014 1.421 5.739 2.329 d25 11.543 Algae-macrophytes
coexist state
Poyang
Lake
2015 2.386 4.553 3.866 d26 9.686 Algae-macrophytes
coexist state
Note: The assessment results of the lake regime in reference lake were taken from the literature (Liu etal. 2015).
JOURNAL OF FRESHWATER ECOLOGY 11
ample distance and Chl-A were 0.301 and 0.911, respectively. The correlation between the
comprehensive distance and Chl-A content was high, indicating that Chl-A was a key
factor, which is useful for evaluating a lake regime shift.
4. Discussion
The extinction of submerged macrophytes is a key regime shift feature of shallow lakes
from the grass-type state to the algae-type steady state. The growth and extinction of
submerged macrophytes are affected by various environmental and biological factors, e.g.
nutrients, light, temperature, and algae (Zhao et al. 2014), of which N and P are major
limiting nutrients of the ecosystem (Conley et al. 2009a, 2009b) and can fundamentally
change the resilience of the ecosystem and create the necessary conditions for the lake
ecosystem regime shift (Zhao etal. 2014). Lake ecosystem regime shifts induced by nutri-
ent stress have been widely reported (González Sagrario et al. 2005; Chen et al. 2006;
Ibelings et al. 2007), of which the transformation from a grass-type state to an algae-type
state due to overloading of N and P in some areas of Dianchi Lake, Chaohu Lake, and
Taihu Lake in China were typical cases (Zhao et al. 2014), and TN and TP are important
factors for use in evaluating these regime shifts
In shallow lakes, light is the main factor affecting the growth of aquatic plants (Scheffer
2004), and the higher the content of suspended matters is, the poorer the SD. The excessive
input of N and P results in algal overgrowth, which in turn affects the light transmittance
of lake water (Bachmann etal. 2002) and ultimately reduces the photosynthesis and growth
Table 3. The regime shift threshold and comprehensive evaluation of Poyang lake.
Lake regime
Total nitrogen
(mg/L)
Total phosphorus
(mg/L)
Chlorophyll A
(mg/L)
Comprehensive
evaluation References
Clear water state-A <0.5 <0.03 <0.005 —— Wang et al.
2011; Liu
et al. 2015
Macrophytes-algae coexist
state-B
0.5 ~ 2.5 0.03 ~ 0.10 0.002 ~ 0.010 ——
Algae-macrophytes coexist
state-C
0.5 ~ 2.5 0.03 ~ 0.10 0.005 ~ 0.020 ——
Algae-dominated turbid
water state-D
>5 >0.1 >0.02 ——
Black-odor state-E P/R<1, Heterotrophic bacteria dominated ——
Reference lake(1988–1996) 2.66 C-D 0.086 B-C 0.019 C C
Poyang Lake(2013) 2.080 B-C 0.110 D 0.006 B-C C
Poyang Lake(2014) 1.400 B-C 0.121 D 0.009 B-C C
Poyang Lake(2015) 2.350 B-C 0.096 B-C 0.015 C C
Poyang Lake(2013–2015) 1.944 B-C 0.109 D 0.010 B-C C
Figure 3. Correlation between the comprehensive distance and evaluation index
12 J. LIU ETAL.
of aquatic plants. This phenomenon prompts the rapid death of aquatic plants and further
decreases the SD, leading to a lake ecosystem fully transformed from the Clear Water state
to the Algae-Dominated Turbid Water state (Nian et al. 2006). Blindow et al. (2002) inves-
tigated the Clear Water state and found that with increasing Chla content, the SD decreased.
Meanwhile, some studies noted that the attenuation of light intensity was mainly caused by
suspended matter and Chl-A (Zhang et al. 2006). Poyang Lake connects to the Yangtze
River and has active shipping logistics as well as extensive sand mining activities, resulting
in a higher content of suspended matter and a lower SD, while making it difficult to use
the SD indicator to assess lake eutrophication and the lake regime. Chl-A content represents
the primary productivity of a lake and indirectly affects SD; therefore, Chl-A can be
employed as an important factor in evaluating the lake regime of Poyang Lake.
The weight calculation results in this study indicated that the weight of the Chl-A
content in the lake regime similarity evaluation was 0.559, greater than the sum of the
weights of TN and TP; thus, the Chl-A content played an important role in the lake
regime shift. Previous studies on lake regime shifts showed that TN, TP, and Chl-A were
key factors, and changes in the TP/Chl-A ratio (Blindow et al. 2002; Martin and Katrin
2003) or the threshold value of TP (Yang et al. 2005; Chen et al. 2006) were often used
to assess the lake regime shift. This study also found that Chl-A is the key factor to
evaluate the steady-state transformation of lakes (Figure 3).
Based on the change in the values of the aquatic eco-environmental features of the
reference lake and Poyang Lake in 2013–2015 and the average values in various stages
(Figure 2a-c, Table 3), coupled with the division of lake regime shift stages and the
threshold values (Wang et al. 2011; Liu et al. 2015), we evaluated three fundamental fac-
tors of Poyang Lake: TN, TP, and Chl-A. In recent years, the TN and Chl-A contents of
Poyang Lake were still in the Macrophytes-Algae Coexist state or Algae-Macrophytes
Coexist state, while the TP content was essentially in the Algae-Dominated Turbid Water
state. In 2013–2015, the annual average TN content was 1.944 mg/L, which was higher
than the average of the 1st stage of the reference lake (1.233 mg/L) but lower than that
of the 2nd stage of the reference lake (2.664 mg/L), belonging to the Macrophytes-Algae
Coexist state or Algae-Macrophytes Coexist state. The TP content was 0.109 mg/L, which
was higher than the average of each of the first three stages (0.025, 0.086, and 0.096 mg/L)
and slightly higher than the threshold value of the Macrophytes-Algae Coexist state or the
Algae-Macrophytes Coexist state (0.010 mg/L). The Chl-A content was 0.010 mg/L, which
was higher than the average of the 1st stage of the reference lake (0.006 mg/L) but lower
than that of the 2nd stage of the reference lake (0.019 mg/L) and at the threshold value
of the Macrophytes-Algae Coexist state (0.010 mg/L). Taking the three indicators together,
we found that Poyang Lake was still in the stage of Algae-Macrophytes Coexist state but
was nearer the Macrophytes-Algae Coexist state, which is consistent with the results
found when the similarity evaluation method was used in this study. Meanwhile, existing
studies and field investigations showed that cyanobacteria blooms only occurred in cer-
tain areas of Poyang Lake at specified times (Wu et al. 2013; Xu 2013; Wu et al. 2014)
and did not extend to large areas, without forming ecological disasters.
Previous studies on the key factors of the lake regime shift in shallow lakes have focused
on the contents of nutrients (TN, TP), and TP was believed to be the main nutrient driv-
ing factor of lake regime shift (Richardson et al. 2007; Chang 2008; Richardson etal. 2008;
Wang 2009; Zimmer et al. 2009; Wang et al. 2011;). When using the single factor of TP
as the evaluation basis for assessing the lake regime, the TP content of Poyang Lake was
0.109 mg/L, which was higher than that of the reference lake before 2008, and the lake
regime was in the Algae-Macrophytes Coexist state or the Algae-Dominated Turbid Water
state. According to the evaluation criteria of the lake regime (Table 3), the regime of
JOURNAL OF FRESHWATER ECOLOGY 13
Poyang Lake belongs to the Algae-Dominated Turbid Water state, and when using the
comprehensive evaluation criteria and comparison with the reference lake, Poyang Lake is
already in the Algae-Dominated Turbid Water state. The eutrophication index of Poyang
Lake was measured at 49, indicating a level close to eutrophication. In certain areas, the
water quality was classified into IV categories, and the frequency of cyanobacterial blooms
increased (Wang et al. 2024). However, the single factor of TP alone could not accurately
represent the overall condition of Poyang Lake. At the same time, when changes occurred
in the TP content as the threshold range of the lake regime transformed from a grass-type
lake to an algae-type lake, the lower limit of the range was approximately 0.07–0.08 mg/L,
and the upper limit fluctuated in the range of approximately 0.1–0.15 mg/L (Wang et al.
2014; Wang and Wang 2014). This value is consistent with the conclusion of this study
that Poyang Lake is in a stage of Algae-Macrophytes Coexist state (Table 3)
5. Conclusion
Based on the long-term monitoring data of the reference lake, the similarity assessment
method was used to determine the lake regime of Poyang Lake (2013–2015). The results
indicate that TN, TP, and Chl-A can be used as evaluation factors for assessing shifts in
the lake regime. Notably, Chl-A plays a significant role in this evaluation. Furthermore,
the lake regime of Poyang Lake is in the Algae-Macrophytes Coexist state. The results of
this study validate the feasibility and effectiveness of the similarity evaluation method for
determining lake status using water quality monitoring data.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
Thanks are extended to Jiangxi Provincial Key Laboratory of Water Resources and Environment of Poyang
Lake for providing the foundation for the experiment. This study was financially supported by the
National Key Research and Development Program of China [Nos. 2023YFC3209000-05,
2023YFC3208700-05], National Natural Science Foundation of China [Nos. 42161016, 32160305], Jiangxi
Province science and technology major project [No. 20213AAG01012], Jiangxi Province Poyang Lake
basin ecological water conservancy technology innovation center [No. 20212BCD43002], Open Fund for
the Jiangxi Key Laboratory of Flood and Drought Disaster Defense [2021SKSH05]. We are very grateful
to all staff members of our team for their assistance during field work.
Data availability statement
The datasets used and/or analysed during the current study available from the corresponding author on
reasonable request.
References
Asmaa Q, Li YP, Wei J, Jalil A, Bu MS, Gao XM, Du W, Wang WC, Wang JW. 2020. Contribution of
spring and summer hydrodynamic conditions in the eutrophication process at Lake Taihu, China. J
Ecol Nat Environ. 12(2):85–94. doi: 10.5897/JENE2020.0820.
Bachmann RW, Horsburgh CA, Hoyer MV, Mataraza LK, Canfield DE. 2002. Relations between trophic state
indicators and plant biomass in Florida lakes. Hydrobiologia. 470(1/3):219–234. doi: 10.1023/A:1015660922494.
Berndt DJ, Clifford J. 1994. Using dynamic time warping to find patterns in time series. In Proceedings
of the KDD Workshop; Seattle, WA. p. 359–370.
14 J. LIU ETAL.
Blindow I, Hargeby A, Andersson G. 2002. Seasonal changes of mechanisms maintaining clear water in a shal-
low lake with abundant Chara vegetation. Aquat Bot. 72(3–4):315–334. doi: 10.1016/S0304-3770(01)00208-X.
Brookes JD, Carey CC. 2011. Resilience to blooms. Science. 334(6052):46–47. doi: 10.1126/science.1207349.
Chang YF. 2008. Study on the steady and regime shift of the grass- algae in shallow lake ecosystem.
Wuhan: Institute of Hydrobiology, Chinese Academy of Sciences.
Chen KN, Bao XM, Shi LX, Chen WM, Lan CJ, Xu H, Hu HY. 2006. Ecological restoration engineering
in Lake Wuli, Lake Taihu: a large enclose experiment. J Lake Sci. 18:139–149.
Chen W. 2009. Stability and regime shifts of ocean and lake ecosystems under stochastic fluctuations.
Tianjin: Tianjin University. p. 16–36.
Cheng X, Li XP, Chen XH. 2012. Statistical characteristics of eutrophication process in Dianshan Lake.
Acta Ecologica Sinica. 32:1355–1362.
Conley DJ, Paerl HW, Howarth RW, Boesch DF, Seitzinger SP, Havens KE, Lancelot C, Likens GE. 2009a.
Controlling eutrophication: nitrogen and phosphorus. Science. 323(5917):1014–1015. doi: 10.1126/sci-
ence.1167755.
Conley DJ, Paerl HW, Howarth RW, Boesch DF, Seitzinger SP, Havens KE, Lancelot C, Likens GE. 2009b.
Eutrophication: time to adjust expectations response. Science. 324(5928):724–725. doi: 10.1126/science.324_724.
Dakos V, Carpenter SR, van Nes EH, Scheffer M. 2015. Resilience indicators: prospects and limitations
for early warnings of regime shifts. Phil Trans R Soc B. 370(1659):20130263. doi: 10.1098/rstb.2013.0263.
Deng WW, Wang R, Liu ZW, Zheng WX, Zhang CX. 2021. The influencing factors of critical transition
in shallow lakes revealed by model. Adv Earth Sci. 36(01):83–94.
Dong YF, Zheng WX, Zhang CX, Xu M, Wang R, Wu XH, Li Y. 2021. Temporal and spatial differences
of lake ecosystem regime shift in China. J Lake Sci. 33(4):992–1003.
Feng JF, Tan JG, Chen W, Wang HL. 2010. Stability and regime shifts of lake ecosystems under stochas-
tic fluctuations. Ocean Technol. 29:72–75.
González Sagrario MA, Jeppesen E, Gomà J, Søndergaard M, Jensen JP, Lauridsen T, Landkildehus F.
2005. Does high nitrogen loading prevent clear water conditions in shallow lakes at moderately high
phosphorous concentrations? Freshwater Biol. 50(1):27–41. doi: 10.1111/j.1365-2427.2004.01290.x.
Hu W, Wang J, Li CH, Ye C, Wei WW. 2019. The application and review of shallow lake model: PCLake.
J Ecol Rural Environ. 35(06):681–688.
Ibelings BW, Portielje R, Lammens EH, Noordhuis R, Berg MS, Joosse W, Meijer ML. 2007. Resilience of
alternative stable states during the recovery of shallow lakes from eutrophication: lake Veluwe as a case
study. Ecosystems. 10(1):4–16. doi: 10.1007/s10021-006-9009-4.
Janus LL, Vollenweider RA. 1981. Summary report: the CECO cooperative programme on
eutrophication-Canadian contribution scientific series No. 131 and 131-S. Burlington, Ontario, Canada:
Canada Centre for Inland Waters.
Jeppesen E, Trolle D, Davidson TA, Bjerring R, Søndergaard M, Johansson LS, Lauridsen TL, Nielsen A,
Larsen SE, Meerhoff M. 2016. Major changes in CO2 efflux when shallow lakes shift from a turbid to
a clear water state. Hydrobiologia. 778(1):33–44. doi: 10.1007/s10750-015-2469-9.
Jiangxi Water Resources Department (JWRD). 2009. Jiangxi rivers and lakes encompassing. Wuhan:
Yangtze River Press. p. 419–482.
Jin XC, Tu QY. 1990. Specification for eutrophication investigation of lakes. second edition. Beijing: China
Environmental Science Press.
Li SJ, Zhu YL, Zhang XH, Wan DS. 2009. BORDA counting method based similarity analysis of multi-
variate hydrological time series. J Hydraul Eng. 40:378–394.
Liang JH, Zhang JY, Yang F, Pan Q. 2009. Study of distance-measuring method in time series based on
slope departure. Comput Eng Appl. 45:4–6.
Liang YL, Cai QH, Wang J, Li ZS. 1994. Comprehensive environmental analyses and similarity of fresh
water ecosystems around the Chinese Zhongshan station, Antarctica. AHS. 18(1):92–94. doi: 10.3724/
issn1000-3207-1994-1-92-m.
Liu JT, Fang SW, Feng Q, Wu ZD, Han L, Huang JC, Bai XL. 2015. Analysis of regime shift in Taihu
Lake based on Mann-Kendall method. China Environ Sci. 35:3707–3713.
Liu WL, Dong ZC, Liang ZM, Chen B. 2007. Similarity analysis in the storm-flood and its application.
China Rural Water Hydropower. 148(2):132–135.
Martin TD, Katrin T. 2003. Eutrophication and restoration of shallow lakes: the concept of stable equi-
libria revisited. Hydrobiologia. 506–509(1–3):29–35.
Mills E. 2004. Regime shifts in lake ecosystems: pattern and variation. Trans Am Fish Soc. 133(6):1540–
1540. doi: 10.1577/1548-8659(2004)133<1540:BR>2.0.CO;2.
Nian YG, Song YW, Li YJ, Sun YN, Nie ZD. 2006. Regime shift theory and ecological restoration discus-
sion in eutrophic shallow lakes. Res Environ Sci. 19:67–70.
JOURNAL OF FRESHWATER ECOLOGY 15
Qin BQ, Song YZ, Gao G. 2006. The role of attached organisms in the process of the transformation
of algal to grass regime ecosystem in a shallow eutrophic lake. Sci China Ser C Life Sci. 36:283–288.
Qin BQ. 2002. Approaches to mechanisms and control of eutrophication of shallow lakes in the middle
and lower reaches of the Yangze River. J Lake Sci. 14:193–201.
Ren WJ, Wen ZH, Cao Y, Wang H, Yuan CB, Zhang XL, Ni LY, Xie P, Cao T, Li KY, et al. 2022.
Cascading effects of benthic fish impede reinstatement of clear water conditions in lakes: a mesocosm
study. J Environ Manage. 301:113898–113898. doi: 10.1016/j.jenvman.2021.113898.
Richardson CJ, King RS, Qian SS, Vaithiyanathan P, Qualls RG, Stow C. 2007. Estimating ecological
thresholds for phosphorus in the Everglades. Environ Sci Technol. 41(23):8084–8091. doi: 10.1021/
es062624w.
Richardson CJ, King RS, Qian SS, Vaithiyanathan P, Qualls RG, Stow C. 2008. An ecological basis for
establishment of a phosphorus threshold for the everglades ecosystem. In: Richardson CJ. Everglades
experiments. New York,Heidelberg, Springer; p. 595–619.
Scheffer M, Bakema AH, Wortelboer FG. 1993. MEGAPLANT-a simulation model for the dynamics of
submerged plants. Aquat Bot. 45(4):341–356. doi: 10.1016/0304-3770(93)90033-S.
Scheffer M, Carpenter SR, Dakos V, van Nes EH. 2015. Generic indicators of ecological resilience: infer-
ring the chance of a critical transition. Annu Rev Ecol Evol Syst. 46(1):145–167. doi: 10.1146/annurev-
ecolsys-112414-054242.
Scheffer M, Jeppesen E. 2007. Regime shifts in shallow lakes. Ecosystems. 10(1):1–3. doi: 10.1007/
s10021-006-9002-y.
Scheffer M, van Nes EH. 2004. Mechanisms for marine regime shifts: can we use lakes as microcosms
for oceans? Prog Oceanogr. 60(2–4):303–319. doi: 10.1016/j.pocean.2004.02.008.
Scheffer M. 2004. Ecology of shallow lake. The Netherlands: R12A, Lelystad.
Schindler DW, Carpenter SR, Chapra SC, Hecky RE, Orihel DM. 2016. Reducing phosphorus to curb lake
eutrophication is a success. Environ Sci Technol. 50(17):8923–8929. doi: 10.1021/acs.est.6b02204.
State Environmental Protection Administration Water and Wastewater Monitoring and Analysis Methods
Editorial Committee. 2002. Detection and analysis method for water and wastewater. Fourth edition.
Beijing: China Environmental Science Press.
Wan XY, Bao WM, Jing YD, Yi JJ. 2007. Study on flood similarity based on principal components anal-
ysis. Water Resources and Power. 25:36–39.
Wang D, Rong G. 2004. Pattern distance of time series. J Zhejiang Univ. 38:795–798.
Wang HC, Dong ZC, Liang ZM, Liu WL, Li W. 2006. A study on the index system for storm-flood re-
semblance analyzing. J China Hydrol. 26:13–17.
Wang HJ, Wang HZ, Liang XM, Wu SK. 2014. Total phosphorus thresholds for regime shifts are nearly
equal in subtropical and temperate shallow lakes with moderate depths and areas. Freshwater Biol.
59(8):1659–1671. doi: 10.1111/fwb.12372.
Wang HJ, Wang HZ. 2014. Macrophyte effects on algal turbidity in subtropical versus temperate lakes: a
reply to Dolman. Freshwater Biol. 59(12):2659–2660. doi: 10.1111/fwb.12486.
Wang HJ. 2007. Predicitve limnological researches on small- to medium-sized lakes along the mid-lower
Yangtze River. Wuhan: Institute of hydrobiology. Chinese Academy of Science; p. 10–35.
Wang YC. 2009. Study on the regime shift and the regime steady division of lake ecosystem. Wuhan:
Institute of Hydrobiology, Chinese Academy of Sciences.
Wang YD, Jia JJ, Lu Y, Tian LL, Gao Y. 2024. Nitrogen and phosphorus budget and eutrophication im-
pacts in Poyang Lake from 2010 to 2019. Acta Scientiae Circumstantiae. 44(06):151–160.
Wang Z, Li GB, Wang GH, Shen YW, Liu YD. 2011. Application of fuzzy synthetic evaluation on the
regime shift phases of Lake Erhai. J Hydroecol. 3:53–58.
Wu ZS, Cai YJ, Liu X, Xu CP, Chen YW, Zhang L. 2013. Temporal and spatial variability of phytoplank-
ton in Lake Poyang: the largest freshwater lake in China. J Great Lakes Res. 39(3):476–483. doi:
10.1016/j.jglr.2013.06.008.
Wu ZS, He H, Cai YJ, Zhang L, Chen YW. 2014. Spatial distribution of chlorophyll a and its relationship
with the environment during summer in Lake Poyang: a Yangtze-connected lake. Hydrobiologia.
732(1):61–70. doi: 10.1007/s10750-014-1844-2.
Xie GH, Chen YF, Zhou Y, Dong Y, Sun YX. 2024. Regime shift of phytoplankton community structure
and its influencing factors in the Honghu Lake. Wetland Sci. 22(2):264–272.
Xu CP. 2013. Study on the characteristics of the phytoplankton community structure in Poyang Lake.
Beijing: University of Chinese Academy of Sciences.
Xu W, Dong ZC, Ren L, Ren J, Guan XK, Zhong DY. 2019. Using an improved interval technique for
order preference by similarity to ideal solution to assess river ecosystem health. J Hydroinformat.
21(4):624–637. doi: 10.2166/hydro.2019.133.
16 J. LIU ETAL.
Yang Y, Yin XN, Yang ZF, Sun T, Xu C. 2018. Detection of regime shifts in a shallow lake ecosystem
based on multi-proxy paleolimnological indicators. Ecol Indic. 92:312–321. doi: 10.1016/j.ecolind.
2017.05.059.
Yang XD, Shen J, Dong XH, Liu EF. 2005. Eutrophic state evoluation and ecological response of the
historical periods in the shallow lakes in the Yangtze River—case study in Longgan Lake and Taibai
Lake. Sci China Ser D Earth Sci. 35(Suppl II):45–54.
Zhang JY, Pan Q, Zhang P, Liang JH. 2007. Similarity measuring method in time series based on slope.
Pattern Recog Artif Intell. 20:271–274.
Zhang S, Ma FK, Xu WP, Wang J, Zhu JY, Li YX, Wu ZB. 2022. Study progress of steady—state trans-
formation theory of phytoplankton and macrophyte in shallow lakes. Express Water Resour Hydropower
Inform. 43(12):121–127.
Zhang SB, Zhou ML. 2004. The method of eneven weight distance coefficient in the measurement of
system similarity. J Hefei Univ Technol. 27:903–906.
Zhang YL, Qin BQ, Hu WP, Wang SM, Chen YW, Chen WM. 2006. Temporal and spatial variation of
the true light layer depth and its ecological significance in typical lake area of Taihu Lake. Sci China
Ser D Earth Sci. 36:287–296.
Zhang Z, Peng GQ, Guo F, Hu D, Huang CC, Song ZY. 2016. The key technologies for eutrophication
simulation and algal bloom prediction in Lake Taihu, China. Environ Earth Sci. 75(18):1295. doi:
10.1007/s12665-016-6106-3.
Zhao L, Liu Y, Li YZ, Zhu X, Zou R. 2014. Survey on theory and driving factors of regime shifts on lake
ecosystems. Ecol Environ Sci. 23:1697–1707.
Zhao L, Wang MG, Liang ZY, Zhou QC. 2020. Identification of regime shifts and their potential drivers in the
shallow eutrophic Lake Yilong, Southwest China. Sustainability. 12(9):3704. doi: 10.3390/su12093704.
Zimmer KD, Hanson MA, Herwig BR, Konsti ML. 2009. Thresholds and stability of alternative regimes
in shallow Prairie-Parkland Lakes of central North America. Ecosystems. 12(5):843–852. doi: 10.1007/
s10021-009-9262-4.
