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Journal of Hydrology 632 (2024) 130912
Available online 15 February 2024
0022-1694/© 2024 Elsevier B.V. All rights reserved.
Research papers
Identifying and optimizing ecological security patterns from the perspective
of the water-energy-food nexus
Tonghui Ding
a
, Junfei Chen
a
,
b
,
c
,
*
, Liping Fang
d
, Juan Ji
a
a
Business School, Hohai University, Nanjing 211100, China
b
Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China
c
Jiangsu Research Base of Yangtze Institute for Conservation and High-Quality Development, Nanjing 210098, China
d
Department of Mechanical and Industrial Engineering, Toronto Metropolitan University, Toronto, Ontario M5B 2K3, Canada
ARTICLE INFO
This manuscript was handled by Marco Borga,
Editor-in-Chief, with the assistance of Matteo
Giuliani, Associate Editor
Keywords:
Ecological security pattern
Ecosystem services
Water-energy-food nexus
Methodological framework
Case application
ABSTRACT
Ecosystem services (ESs) associated with the water-energy-food (WEF) nexus inuence the ecological security of
urban agglomerations. However, how to apply the assessment of ESs relevant to the WEF nexus into the iden-
tication and optimization of ecological security patterns still remains unclear. This study assessed the devel-
opment level of the WEF nexus based on a constructed evaluation index system and the evaluation model
combined with game theory and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)
method. The relevant ESs were selected based on the analysis of stakeholders related to the WEF nexus, and
further quantied by the models. On this basis, the relationship between the WEF nexus and ESs was evaluated
by Spearman rank correlation analysis. Then this study identied ecological security patterns by following the
basic paradigm of “Source identication-Resistance surface establishment-Corridor extraction”. Lastly, this study
optimized ecological security patterns by considering the WEF nexus. Thus, a methodological framework of
identifying and optimizing ecological security patterns from the perspective of ESs related to the WEF nexus was
designed, which was applied to the case of the Yangtze River Delta Urban Agglomeration (YRDUA), China. The
results revealed that the WEF nexus was signicantly negatively correlated with ESs. The sources of water yield
were in the southwest and central areas of the YRDUA, and those of carbon storage and food production were
respectively in the forested and farming areas. The ecological resistance surface displayed a slow declining trend
from east to west, and ecological corridors showed a zigzag distribution from north to south. The total length of
the ecological corridors was 20,891.45 km. A scheme of “two belts, three zones and four groups” was proposed to
optimize the ecological security pattern of the YRDUA. Policy recommendations were therefore put forward in
this study to promote the ecological security of urban agglomerations from different perspectives.
1. Introduction
Ecosystem services (ESs) are dened as a variety of products and
services directly or indirectly provided by ecosystems (Costanza et al.,
1997). Ecosystems can provide various ESs to promote human well-
being, such as water resources, and climate regulation. However,
rapid urbanization and high frequent human activities have signicantly
affected the structure and function of ecosystems, leading to the
continuous degeneration of some ESs (Tilman et al., 2009; Yang et al.,
2023). Additionally, rapid urban expansion has also brought about the
serious fragmentation of ecological patches (Chen et al., 2022; Fulford
et al., 2022). Regional ecological security has been threatened.
Ecological security describes a region with relatively healthy and
unthreatened ecosystems that support local human survival and socio-
economic development (Wu et al., 2020; Ke et al., 2021). Various
ecological elements, their spatial locations and interconnections form
the ecological security pattern, the optimization of which is a vital
means to promote regional ecological security (Yu, 1996). Therefore, it
is worthy of study to identify and optimize ecological security patterns
to ensure ecological security and maintain the sustainable supply of ESs.
There are complicated connections between water resources, energy
and food, which are described as the water-energy-food (WEF) nexus
(Hoff, 2011). The WEF nexus concept is proposed to identify the in-
terrelations and interactions of different resource subsystems with each
* Corresponding author.
E-mail address: chenjunfei@hhu.edu.cn (J. Chen).
Contents lists available at ScienceDirect
Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol
https://doi.org/10.1016/j.jhydrol.2024.130912
Received 18 August 2023; Received in revised form 11 January 2024; Accepted 17 January 2024
Journal of Hydrology 632 (2024) 130912
2
other (Afshar et al., 2022; Molajou et al., 2021). In addition, the sus-
tainable development of the WEF nexus is relevant to the supply of some
ESs (Ding et al., 2023b). For instance, water yield service meets the
demand for water consumption in agricultural irrigation and energy
production (Yu et al., 2021). Carbon storage service provided by carbon
pools such as harvest residues, natural deadwood and soil carbon ab-
sorbs carbon emissions from resource interactions in the WEF nexus
(Withey et al., 2019). However, with explosive population growth and
urbanization, the nexus resources needs have increased at an aston-
ishing rate, putting the supply of relevant ESs under great pressures
(Ding et al., 2023a). The continuous degradation of relevant ESs has also
resulted in increasingly tighter constraints on the sustainable develop-
ment of the WEF nexus (Shi et al., 2022). Therefore, incorporating the
WEF nexus into the assessment of ESs is of great research value for
further identifying and optimizing ecological security patterns to facil-
itate the sustainable development of the WEF nexus and ecosystems.
At present, there have been in-depth studies on identifying ecological
security patterns by evaluating ESs (Jia et al., 2023; Liu et al., 2023c) or
assessing ESs from the WEF nexus perspective (Yin et al., 2022; Yuan
and Lo, 2020). However, combining the WEF nexus and ESs to identify
ecological security patterns still has notable research gaps. Therefore,
this study made a methodological framework to analyze the relationship
between the WEF nexus and ESs, and then to identify and optimize
ecological security patterns. On the one hand, the methodological
framework was used to construct the WEF nexus evaluation index sys-
tem by selecting relevant indicators to reect the interactions of
different subsystems. Meanwhile, the relationship between the WEF
nexus and ESs was evaluated to prove that it was feasible and mean-
ingful to identify and optimize ecological security patterns from the
perspective of ESs related to the WEF nexus. On the other hand,
ecological security patterns were identied in the methodological
framework by following the basic paradigm of “Source identication-
Resistance surface construction-Corridor extraction”, which were
further optimized by considering the WEF nexus perspective. Therefore,
the methodological framework makes a signicant contribution to the
integration of the WEF nexus and ESs to improve regional ecological
security.
Urban agglomeration is the highest spatial organization form when a
city develops to a mature stage (Wei and Liu, 2022). In recent years,
urban agglomerations around the world have not only experienced
large-scale urban expansion through the spread of land development
and occupation of ecological land, but also resulted in serious ecological
destruction and excessive resource consumption. Currently, China has
entered the middle stage of rapid urbanization. The Yangtze River Delta
Urban Agglomeration (YRDUA) is one of the most economically active
and open urban agglomerations in China (Xia and Zhai, 2022). To
develop the economy and accommodate the continuous inux of pop-
ulation, the urban space in the YRDUA has expanded rapidly, and the
demand for resource consumption has increased signicantly. The
YRDUA has faced the dual pressures of ecological destruction and
resource shortage. As China begins to pay attention to ecological pro-
tection, the ecological security of the YRDUA has become a crucial issue
that needs be given importance in the future. Therefore, it is a typical
application case of this methodological framework.
The overall objective of this study is to design a methodological
framework for identifying and optimizing ecological security patterns
from the perspective of ESs related to the WEF nexus, and then to apply
the proposed methodological framework to typical urban agglomera-
tions. On this basis, this study optimizes ecological security patterns
from the WEF nexus perspective. The specic objectives are to: 1)
analyze the correlation between the WEF nexus and the selected ESs; 2)
identify the ecological security pattern from the aspects of ecological
sources, resistance surface and corridors; and 3) optimize the ecological
security pattern on the consideration of the WEF nexus. Therefore, this
study makes important theoretical and practical contributions to
improving the ecological security of urban agglomerations and
promoting the sustainable development of resources and ecosystems.
2. Literature review
Some research progress has been gained in evaluating the WEF nexus
and ESs individually, but less so together (Ding et al., 2021; Wang et al.,
2023b). The exploitation and utilization of the three resources in the
WEF nexus are to some extent dependent on the relevant products or
services provided by ecosystems (Liu et al., 2022; Rasul, 2014; Wang
et al., 2023c). However, existing studies mainly carried out theoretical
or practical research on the WEF nexus or ESs, and so the question of
how to combine the WEF nexus with ESs still remains to be developed
further. Some scholars have begun to conduct relationship assessments
between the sustainable development of the WEF nexus and the supply
of relevant ESs (Hanes et al., 2018). As an example, Ding et al. (2023b)
found that the sustainability of different subsystems in the WEF nexus
had signicant correlation with some ESs. Some scholars have also
carried out the ESs evaluation from the WEF nexus perspective (Ding
et al., 2023a; Zhou et al., 2023). For instance, Yin et al. (2022) quanti-
ed the supply and demand of ESs on the consideration of the WEF
nexus, and analyzed their spatio-temporal evolution characteristics.
However, there is still a gap in the research on identifying and opti-
mizing ecological security patterns by assessing ESs related to the WEF
nexus.
Ecological security patterns, originating from landscape ecological
planning, are the basis and prerequisites for regional ecological spatial
planning and governance (Liang et al., 2018). At present, an analytical
paradigm of “Source-Resistance Surface-Corridor” has a wide applica-
tion in the eld of ecological security pattern identication (Peng et al.,
2018a; Huang et al., 2021a; Mu et al., 2022). How to scientically
identify ecological sources, resistance surfaces and corridors has become
a hot issue for academics within and beyond China (Zhang et al., 2022).
The ecological source is the core foundation that improves the structural
and functional integrity of ecosystems (Gou et al., 2022). Determining
ecological sources is the rst step to identifying ecological security
patterns (Zeng et al., 2023; Liu et al., 2023a). There has been some
research on ecological source identication based on nature reserve
selection (Mu et al., 2022) and ecological indicator assessments such as
landscape connectivity, ecological importance and ecological sensitivity
(Zhou et al., 2021; Shen et al., 2022; Jia et al., 2023). However, few
studies assess the importance of ESs associated with the WEF nexus, and
further identify ecological sources accordingly.
Ecological resistance represents the degree to which species are
prevented from migrating through different landscape units (Adriaensen
et al., 2003). The ecological resistance surface is the embodiment of the
difculty of species migration (Peng et al., 2018b). Previous studies
assigned different resistance values to different land use types to identify
the ecological resistance surface (Xiao et al., 2020). However, this
method lacked comprehensive consideration of other resistance factors,
such as vegetation coverage and nighttime light data, resulting in
inaccurate establishment of ecological resistance surfaces. The ecolog-
ical corridor refers to the minimum resistance pathway among different
ecological sources, the identication of which is a critical point in
identifying ecological security patterns (Peng et al., 2018b; Zhou et al.,
2021). Some scholars have used the minimum cumulative resistance
(MCR) model or circuit theory to extract ecological corridors (Peng
et al., 2018b; Li et al., 2023a), where the MCR model mainly adopts the
“source-sink” theory to determine the minimum resistance pathway in
species migration as the ecological corridor (Dai et al., 2021). In addi-
tion, most of the existing studies mainly identied ecological security
patterns in nature reserve areas (Liu et al., 2021; Sun et al., 2022b),
rapidly urbanized areas (Huang et al., 2021b; Ding et al., 2022), and
ecologically fragile areas (Zhang et al., 2023). However, as for the na-
tional strategic core areas, the identication and optimization of
ecological security patterns in urban agglomerations has had a lack of
attention.
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
3
Based on a review of the literature, a methodological framework was
proposed in this study to identify and optimize ecological security pat-
terns from the ESs related to the WEF nexus perspective. Using the
YRDUA for instance, the relationship between the WEF nexus and ESs
was rst evaluated by Spearman rank correlation analysis. Here, the
developed status of the WEF nexus was assessed by constructing the
evaluation index system and evaluation model combined with game
theory and the Technique for Order Preference by Similarity to an Ideal
Solution (TOPSIS) method. The ESs associated with the WEF nexus were
selected by relevant stakeholder analysis and quantied by the Inte-
grated Valuation of Ecosystem Services and Tradeoffs (InVEST) model.
Then the basic paradigm of “Source identication-Resistance surface
construction-Corridor extraction” was applied to identify ecological se-
curity patterns. Finally, a scheme from the WEF nexus perspective was
put forward to optimize ecological security patterns of urban
agglomerations.
3. Framework and methods
3.1. Methodological framework
In this paper, a methodological framework was constructed to
identify and optimize ecological security patterns by evaluating ESs
related to the WEF nexus (Fig. 1). The framework consisted of four steps.
In the rst step, this study selected three types of ESs based on the
analysis of stakeholders relevant to the WEF nexus. In the second step,
the relationship between the WEF nexus and the selected ESs was
analyzed. To be specic, the evaluation model, combined with game
theory and the TOPSIS method, was established to assess the develop-
ment status of the WEF nexus based on the constructed evaluation index
system. Then the InVEST model and normalized difference vegetation
index (NDVI) were utilized to quantify the selected ESs. Next, Spearman
rank correlation analysis was applied to carry out a relationship analysis
between the WEF nexus and ESs. In the third step, the ecological security
Fig. 1. The methodological framework of this study.
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
4
pattern was constructed by identifying ecological sources, resistance
surfaces and corridors, which was then further optimized on the
consideration of the WEF nexus. Specically, the comprehensive
ecosystem service was obtained by superimposing ESs with equal
weight, which was further used to identify the ecological sources. Then
the resistance surface was constructed by weighted superposition of
each resistance factor. Next, the ecological corridors were extracted by
the MCR model. Lastly, the ecological spatial pattern was optimized
from the WEF nexus perspective. In the fourth step, some management
policies were formulated from different perspectives.
3.2. Selecting ESs related to the WEF nexus
ESs are dened as benets that humans derive from ecosystems
(Costanza et al., 1997), which were classied into four types in inter-
national classications, namely provisioning services (materials and
resources provided by ecosystems, like food and water resources),
regulating services (the control of ecosystems over natural processes,
such as hydrologic regulation), cultural services (the non-material
contributions of ecosystems to human well-being, like recreation) and
supporting services (habitats that support life on Earth, such as the
nutrient cycle) (MEA, 2005). Therefore, according to Maslow’s hierar-
chy of needs, human preference for ESs is rst provisioning services,
followed by regulating services, and then cultural and supporting ser-
vices. As different stakeholders have different selection preferences for
ESs, this study identied relevant stakeholders related to the WEF nexus,
namely, government, enterprises, farmers and residents.
The governments associated with the WEF nexus are mainly involved
in the water, industrial, and agricultural sectors. These governments not
only ensure the provision of these three resources to satisfy the needs of
residents, industries and agriculture, but also maintain the health of
local ecosystems. Enterprises are mainly related to industrial enterprises
through energy consumption. These enterprises are more concerned
with the continuous supply of water resources and energy to improve
their economic benets, and also with consideration for reducing carbon
emissions. Farmers mainly focus on irrigation water and food produc-
tion per unit area. Residents are more concerned with access to clean
water and safe food that are closely related to their physical and mental
health. Since water resources and food are provisioning services, and the
absorption of carbon emissions caused by the production and con-
sumption of energy is a regulating service, three types of ESs (water
yield, carbon storage and food production) associated with the WEF
nexus were selected in this study.
3.3. Assessing the relationship between ESs and the WEF nexus
3.3.1. Constructing the WEF nexus evaluation index system
The WEF nexus is a complicated open system consisting of the water,
energy and food subsystems, which interact with and restrict each other.
The improvement or reduction of the performance of any subsystem will
cause changes in the performance of other subsystems. A better under-
standing of the interdependencies and interactions among different
resource subsystems in the WEF nexus will help develop some effective
recommendations for managing the WEF nexus (Soleimanian et al.,
2023). Additionally, a quantitative assessment of the development level
of the WEF nexus can help in better reviewing its sustainable develop-
ment status, while relevant evaluation models can be used to carry out
quantitative research (Wang et al., 2021). Fifteen indicators were
selected to establish a WEF nexus evaluation index system by referring
to existing research (Arthur et al., 2019; Qi et al., 2022; Ding et al.,
2023a), as shown in Table A.1 in Part 1 of Supplementary Information
(SI). Moreover, the collection and sorting of statistical data based on
these indicators can reect the interactions between different
subsystems.
3.3.2. Constructing the evaluation model based on game theory and the
TOPSIS method
This study combined game theory and the TOPSIS method to
establish the evaluation model to assess the development level of the
WEF nexus. Here, the entropy weight method and coefcient of varia-
tion were used to obtain basic weights of indicators, which were further
utilized to obtain a combined weight by the game theory combination
weighting method. Then the TOPSIS method was used for calculating
the comprehensive evaluation values of the WEF nexus. These two
methods are described below.
3.3.2.1. Game theory combination weighting method. The indicator
weight can reect the degree of inuence of the selected indicators on
the development status of the WEF nexus. The current most commonly
used weighting methods are subjective and objective weighting
methods, respectively. In addition, some combination weighting
methods, such as addition and multiplication weighting methods, have
been used to synthesize weights obtained by different methods, but
these methods do not consider the relationship between different
weights (Sun et al., 2022a). Game theory can seek the equilibrium
behind decisions by assessing the relationship among different behav-
iors (Li et al., 2023b). Therefore, this study constructed a game theory
combination weighting method to obtain combined weights. According
to game theory, the players in the game are considered to be different
weighting methods. The weight values of indicators are assigned to the
player’s interests in various dimensions. Therefore, the matter of
addressing combined weights is converted to an optimization problem
that balances different players’ interests. Thus, the interests represented
by indicator weights are moderated among different players, thus
minimizing the deviation between combined weights and basic weights
(Gao et al., 2022). Therefore, this study rst standardized the original
data. Then, the entropy weight method and coefcient of variation were
both utilized to obtain the indicators’ basic weights. Lastly, the game
theory combination weighting method was applied to measure the in-
dicators’ combined weights. The specic weight calculation process and
results are shown in Section 1.2 and Table A.1 in Part 1 of SI.
3.3.2.2. TOPSIS method. Hwang and Yoon (1981) rst introduced the
TOPSIS method, which was used to calculate the Euclidean distance
between assessment objects and the optimal and worst projects, and take
the proximity between assessment objects and the optimal projects as
the basis of the overall ranking. Therefore, as a classical multi-attribute
decision evaluation method, this method has been widely used for the
evaluation in multiple elds (Chenhong and Guofang, 2022; Wątr´
obski
et al., 2022). To better reect the development level of the WEF nexus,
the comprehensive evaluation values of the WEF nexus were calculated
by the TOPSIS method, the specic formulas of which are presented in
Section 1.3 in Part 1 of SI.
3.3.3. Quantifying ESs
At present, the InVEST model can not only accurately quantify
multiple ESs, but also can display ESs in a spatial form (Bai et al., 2020;
Cong et al., 2020). Thus, this study adopted the InVEST model to esti-
mate the water yield and carbon storage, and the specic computational
process is demonstrated in Part 2 of SI. Furthermore, the accuracy of the
InVEST model results was veried by observed results of an open-source
spatial dataset and other literature. Therefore, the water supply values
from the Spatial Distribution Dataset of Terrestrial Ecosystem Service
Value in China in 2020 (https://www.resdc.cn/DOI/doi.aspx?DOIid =
48) were used for validation accuracy of the simulated results of the
water yield model. The results of other literature were utilized for
validation accuracy of the simulated results of the carbon storage model.
This study rst created 100 random points by ArcGIS 10.2, and then
the simulated results and the water supply values of random points were
extracted. Finally, a scatter plot between simulated results and observed
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
5
values was drawn to obtain the R-squared (R
2
) value. The results
revealed that the R
2
value between the simulated results of water yield
and water supply values was 0.85, indicating that there was a good
linear regression between the two (as shown in Fig. A.1). For the carbon
storage model, Tang et al. (2016) collected research results on soil
carbon density in China (as presented in Table A.6), which showed that
carbon density kept at about 100 ton/ha. In this paper, the carbon
density of the YRDUA was 132.03 ton/ha, consistent with the results of
most other studies. Therefore, the simulated results of the InVEST model
were reasonable.
Food production, as an essential provisioning service, is important
for maintaining human survival and promoting sustainable develop-
ment. Besides, a dramatically linear relationship between food produc-
tion and NDVI was found in previous studies (Cao et al., 2020). The
supply capacity of food production in a region can be reected by the
NDVI data (Xiang et al., 2022). Based on this, the ratio of the grid NDVI
value to the total NDVI value of cultivated land was rst calculated, and
then the total food production in a region was allocated on a grid scale.
The calculation formula is shown below:
FPi=Gsum ×NDVIi
NDVIsum
(1)
where FPi is the food production on the grid i, Gsum denotes the total food
production in a region, NDVIi denotes the NDVI value on the grid i, and
NDVIsum is the total NDVI value of cultivated land.
3.3.4. Analyzing the correlation by the correlation analysis
Based on the comprehensive evaluation values of the WEF nexus and
the supply of ESs, Spearman rank correlation analysis was used to
calculate the correlation coefcients between the WEF nexus and ESs
(Huang and Wu, 2023). The equations are shown as follows:
rs=1−6d2
j
n(n2−1)(2)
dj=rg(Xj) − rg(Yj)(3)
where rs is the Spearman correlation coefcient, which ranged from −1
to 1, Xj represents the comprehensive evaluation value of the WEF nexus
of city j, Yj represents the total amount of ESs of city j, dj denotes the rank
difference, and n represents the number of cities. When rs>0, this in-
dicates that there is a positive correlation. When rs<0, this indicates
that there exists a negative correlation. When rs=0, this indicates that
there exists no correlation. Additionally, the Spearman correlation co-
efcient was also applied to test the signicance (P) between the two
variables. When P is lower than 0.05, there is a signicant correlation.
When P is lower than 0.01, there is a remarkably signicant correlation.
3.4. Identifying ecological security patterns
According to the correlation coefcients between the WEF nexus and
ESs, this paper took relevant data of the YRDUA in 2020 as an instance to
identify its ecological security pattern. The identication process is
described below.
3.4.1. Identifying ecological sources
Considering that the selected ESs related to the WEF nexus are
equally important for ensuring regional ecological security, equal
weight was assigned to each ecosystem service. At present, importance
classication methods mainly include the threshold setting method and
natural breakpoints method, among which the natural breakpoints
method is more reliable and has wide application (Wu et al., 2013; Li
et al., 2023a). Therefore, this study divided ESs into ve levels through
the natural breakpoints method. The higher the level, the more impor-
tance it has. Then ve levels of comprehensive ecosystem service were
also obtained by superimposing each ecosystem service with equal
weight. Finally, the patches with the highest level of comprehensive
ecosystem service and an area of >100 km
2
were extracted as ecological
sources.
3.4.2. Constructing the comprehensive ecological resistance surfaces
Identifying ecological corridors is the premise for establishing
resistance surface (Fu et al., 2020). Among them, land use type is the
most signicant resistance to species migration (Graviola et al., 2022).
For example, species migrate most smoothly in natural landscapes, but
are blocked in areas with dense articial landscapes (Yang et al., 2016;
Jia et al., 2023). Therefore, land use type, digital evaluation model
(DEM), slope, vegetation coverage and nighttime light data were
selected as resistance factors in this paper. In addition, each resistance
factor was assigned by a ve-level system. Following the principle that a
higher level means a greater resistance value, this study obtained the
resistance values of ve levels of each resistance factor, as shown in
Table 1. Then the analytic hierarchy process was applied to calculate the
factors’ weights (as shown in Table 1). The test coefcient of the judg-
ment matrix was 0.0000, indicating that the factors’ weights were set
reasonably. Finally, ve resistance factors were weighted and super-
imposed to construct the comprehensive ecological resistance surface.
3.4.3. Extracting ecological corridors
Ecological nodes are landscape components scattered over ecosys-
tems, which connect adjoining ecological sources and have an essential
role in the process of species migration (Yuan et al., 2022). Therefore,
this study perceived the geometric center points of ecological sources as
nodes. Then the minimum cumulative resistance (MCR) model was used
to extract ecological corridors (Peng et al., 2018b), which take n
ecological nodes as source points and the remaining n−1 ecological
nodes as target points (Santos et al., 2018). The calculation formula of
this model is shown as follows:
MCR =fmin
i=m
j=n
(Dij ×Ri)(4)
where MCR denotes the minimum cumulative resistance, f is the positive
correlation function between MCR and the ecological process, Dij de-
notes the spatial distance of species migrated from source point j to
target point i, and Ri denotes the diffusion resistance coefcient of target
point i.
4. Study area and data sources
4.1. Study area
The YRDUA, located in the lower reaches of the Yangtze River along
the east coast of China, is mainly composed of cities in Anhui, Jiangsu
and Zhejiang provinces as well as Shanghai, with a total of 27 cities
(Fig. 2). The YRDUA totals about 229,000.00 km
2
, covering 2.30 % of
China’s land area (Jiang et al., 2023). In 2020, the urbanization rate of
the YRDUA was 71.97 %, higher than the national average of 63.89 %,
and its GDP reached 21.20 trillion Yuan (NBS, 2020a, 2020b). Addi-
tionally, the YRDUA displays a topographic trend of being higher in the
west and south, lower in the east and north. Among the YRDUA’s
provinces, Anhui province is dominated by plains and hills, Jiangsu
province by plains, Shanghai by hills and Zhejiang province by hills and
mountains. In the past few decades, due to the excessive pursuit of rapid
socio-economic development, a large amount of ecological land in the
YRUDA has been eroded (Qiao and Huang, 2022).
The total amount of water resources of the YRDUA has risen from
117.32 billion m
3
in 2000 to 205.69 billion m
3
in 2020 (NBS, 2020a,
2020b). However, due to accelerated industrialization and urbanization,
water resources consumed by the industrial, agricultural and domestic
sectors has increased signicantly, putting tremendous pressure on
available water resource supplies. The YRDUA is also the main
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
6
industrial-intensive area in eastern China, with extensive industrial
development and rapid expansion of heavy industry. Over the years, the
total energy consumption in the YRDUA has continued to grow, domi-
nated by fossil fuel consumption, which accounted for 85.00 % of the
total energy consumption (NBS, 2020b). However, a large amount of
fossil fuel consumption has caused a substantial rise of carbon dioxide
emissions (Liang et al., 2022). In addition, the disorderly urban expan-
sion has also caused the encroachment of cultivated land into non-
cultivated land. As a result, the sown area of grain crops has
decreased from 71,564.10 km
2
in 2000 to 63,170.00 km
2
in 2020,
resulting in a total food production drop from 43.99 million tons to
40.60 million tons (NBS, 2020a, 2020b). Therefore, it is urgent for the
YRUDA to identify and optimize the ecological security pattern based on
the assessment of ESs related to the WEF nexus.
4.2. Data sources
In this paper, land use/land cover maps were collected from the
website of GlobeLand30 for three periods, namely 2000, 2010, and
2020. Then land use/land cover in the YRDUA was divided into eight
types according to the national classication standard, and the
description of each land use type is given in Table A.2 of SI. This paper
obtained the NDVI data from the Resources and Environmental Sciences
and Data Center. This paper also collected some statictical data from the
Table 1
Assignment table of each resistance factor.
Resistance factor Resistance value Weight
1 10 50 75 100
Land use type Forest Grassland, Cultivated land and Shrubland Water body, Wetland Unused land Developed land 0.3441
DEM 0–200 200–400 400–800 800–1000 >1000 0.1077
Slope 0–7 7–15 15–25 25–30 >30 0.1287
Vegetation coverage >0.8 0.6–0.8 0.4–0.6 0.2–0.4 0–0.2 0.1695
Nighttime light data 0–15 15–30 30–45 45–60 >60 0.2500
Fig. 2. The study area.
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
7
China Statistical Yearbook, China Energy Statistical Yearbook, China Urban
Statistical Yearbook, and Water Resources Bulletin. In addition, a detailed
description of the data required for the InVEST model is presented in
Table A.3 of SI.
5. Results
5.1. Relationship evaluation between the WEF nexus and ESs
5.1.1. Temporal evolution of the development level of the WEF nexus
The WEF nexus comprehensive evaluation values of 27 cities in the
YRDUA are presented in Table A.7 in SI, from which a bar plot was
drawn (Fig. 3). From the YRDUA as a whole, during the period of 2000
to 2020, the comprehensive evaluation values of the WEF nexus in the
YRDUA rose from 0.60 to 0.67. This indicates that the development level
of the WEF nexus in the YRDUA displayed an improving trend from 2000
to 2020. As for the different provinces, Jiangsu had the best-performing
development level, with the evaluation values of the WEF nexus
reaching above 0.70 in 2020. However, those in Zhejiang and Anhui did
not perform well. The comprehensive evaluation values of Zhejiang
showed an increasing trend, while those of Anhui demonstrated a
decreasing trend. In terms of the different cities, the development level
in Shanghai was the best, followed by Suzhou, Wuxi, Zhenjiang, and
Tongling, while those in Chizhou, Chuzhou, and Xuancheng were poor,
with the evaluation values of the WEF nexus all less than 0.50.
5.1.2. Spatio-temporal evolution of the selected ESs
The total amount of three types of ESs in the YRDUA in the periods of
2000, 2010, and 2020 is given in Table 2. This shows two stages of
upward trends for the water yield during the study period. In the rst
stage (from 2000 to 2010), the water yield presented a signicant up-
ward trend and grew by nearly 4.47 billion mm. However, in the second
stage (from 2010 to 2020), the upward trend of the water yield slowed
down dramatically, and only went up by 1.02 billion mm. As for carbon
storage from 2000 to 2020, it showed a slow decline of −0.05 billion
tons. Food production rose at rst and then declined rapidly. From 2000
to 2010, the total food production rose by nearly 0.34 million tons, but it
decreased to 40.60 million tons in 2020.
In terms of spatial distribution, ESs presented obvious spatial dis-
tribution characteristics from 2000 to 2020, but the distribution pattern
of each ecosystem service remained relative stable during this period
(Fig. 4). As for water yield, it showed an apparent spatial distribution of
being higher in the southwest and lower in the northeast. Affected by
this changing trend of the spatial distribution of precipitation, areas with
high water yield presented a trend of shifting to the center of the
YRDUA. In 2000 and 2010, the water yield in the southwest was rela-
tively high compared to other areas. However, in 2020, almost all areas
had higher water yield. Except for Ningbo, Taizhou, and Wenzhou in
Zhejiang province, water yield in other cities was all on the rise over the
past 20 years.
Carbon storage was spatially distributed with the characteristic of
being higher in the south and lower in the north. There was excellent
supply of carbon storage in the southwest areas with abundant forest,
especially Chizhou, and Xuancheng of Anhui province and Hangzhou,
Jinhua, Taizhou, and Wenzhou of Zhejiang province. However, there
was poor performance in the northern plains area. With regard to
change, the decreased areas of carbon storage were more obvious than
the increased areas. In addition, the reduction in carbon storage
occurred mainly in the peripheral areas of urban center areas. This was
mainly due to the destruction of vegetation land induced by urban
expansion. The increased areas mainly occurred in the forested and
northern areas of Yancheng.
In terms of food production, it presented distribution characteristics
of being lower in the south and higher in the north. The high-value
accumulation areas of food production were mainly in the plains areas
of the northern Anhui and Jiangsu. However, the distribution areas of
low-values were in the mountainous and hilly areas of southwest Zhe-
jiang. The increased areas of food production mainly occurred in
northern Jiangsu province (Nantong, Taizhou, Yancheng and Yangz-
hou), and western Anhui province (Chuzhou, Hefei, Maanshan, Ton-
gling and Wuhu). The decreased areas were mainly in southern Jiangsu
Fig. 3. Comprehensive evaluation values of the water-energy-food nexus in the study area.
Table 2
The total amount of ecosystem services in the periods of 2000, 2010 and 2020.
Year Water yield
(billion mm)
Carbon storage
(billion tons)
Food production
(million tons)
2000 9.86 1.29 43.99
2010 14.33 1.27 44.33
2020 15.35 1.24 40.60
2000–2020 5.49 −0.05 −3.39
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
8
province and most cities in Zhejiang province.
5.1.3. Correlation analysis between the WEF nexus and ESs
The correlation coefcients between the WEF nexus and ESs in the
YRDUA in the periods of 2000, 2010, and 2020 are given in Table 3. It
can be shown that the correlation coefcients between the two were all
negative. To be specic, the WEF nexus presented a signicantly nega-
tive correlation with water yield (p less than at least 0.05), and a
remarkably signicantly negative correlation with carbon storage (p less
than 0.01). A signicantly negative correlation between the WEF nexus
and food production was found only in 2020 (p less than 0.1), but not in
other years. However, the intensity of correlation coefcients between
the WEF nexus and food production gradually increased from 2000 to
2020.
5.2. Identication of the ecological security pattern
5.2.1. Ecological sources
The ecological sources of the selected ESs were identied, which are
shown in Fig. 5a. This indicates that obvious spatial heterogeneities exist
in the spatial distribution. Specically, the sources of water yield were
primarily scattered over the southwest and central areas, showing a
patchy distribution. On the one hand, these sources had abundant pre-
cipitation. On the other hand, the increase of impervious surfaces in
these areas reduced the evaporation and inltration of water resources.
The sources of carbon storage are concentrated in the forested areas, and
those of food production were gathered in the farming areas. The
Fig. 4. Spatial dustribution and change of each ecosystem service in the periods of 2000, 2010, and 2020.
Table 3
Correlation coefcients between the water-energy-food nexus and ecosystem
services in the periods of 2000, 2010, and 2020.
Ecosystem services Water-energy-food nexus
2000 2010 2020
Water yield −0.47
**
−0.69
***
−0.46
**
Carbon storage −0.56
***
−0.78
***
−0.54
***
Food production −0.12 −0.14 −0.33*
Note: *p <0.1;
**
p <0.05;
***
p <0.01.
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
9
ecological sources of the YRDUA are presented in Fig. 5b, which were
mainly composed of large patches. There were 38 patches of the YRDUA.
The total area of these patches reached 66,125.61 km
2
, which accounted
for 29.80% of the YRDUA. In addition, the ecological sources were
mainly spread over the northern plains areas (Nantong, Taizhou, Yan-
cheng, and Yangzhou) and southwestern hilly areas (Anqing, Chizhou,
Xuancheng, Hangzhou, Jinhua, and Huzhou).
The tabulate area tool in ArcGIS 10.2 was utilized to obtain the areas
of the different land use types of ecological sources, and then to further
obtain the compositions of the land use types of ecological sources in the
YRDUA (Fig. 5c–d). These indicate that the primary land use type of
each ecological source was forest (41,815.90 km
2
), followed by culti-
vated land (22,130.15 km
2
), and the sum of the proportion of forest and
cultivated land was as high as 96.71% (Fig. 5c). For the other areas, the
main land use type was cultivated land (81,868.17 km
2
), followed by
developed land (28,992.21 km
2
), and then forest (19,904.45 km
2
) and
water body (19,372.95 km
2
), the total proportion of which reached
96.40%.
5.2.2. Comprehensive ecological resistance surface
After the construction of the comprehensive ecological resistance
surface, this study identied the ecological security pattern of the
YRDUA (Fig. 6a). The resistance value of comprehensive ecological
resistance surface displayed a downward trend from east to west. To be
specic, there were higher resistance values in the urban central areas of
cities in the YRDUA, especially Hangzhou, Ningbo, Shanghai, Suzhou,
and Wuxi in the eastern YRDUA, as well as Hefei and Nanjing in the
central YRDUA. This was because the high-resistance areas usually had a
higher rate of urbanization, lacked green space, and were more inu-
enced by human activities. Therefore, these cities showed high
Fig. 5. Spatial distribution and land use type composition of ecological sources and other areas: (a) Sources of each ecosystem service; (b) Spatial distribution of
ecological sources and other areas; (c) Area of land use types of ecological sources and other areas; and (d) Proportions of land use types of ecological sources and
other areas.
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
10
resistance spatial distribution characteristics, and their species migra-
tions were more easily hindered. The northern plains and southern hilly
areas were mainly low-resistance areas. The northern plains areas were
dominated by cultivated land, while the southern hilly areas were
widely covered with forest. Therefore, these areas were to some extent
undisturbed by human activities, which allowed species to spread and
migrate more smoothly.
5.2.3. Important ecological corridors
The important ecological corridors of the YRDUA were extracted in
this study, as described in Fig. 6b. It can be discerned that the southern
hilly areas with better vegetation coverage and the northern plains areas
with large cultivated land were the main gathering areas of ecological
corridors. A total of 59 ecological corridors appeared in the YRDUA. The
total length of the ecological corridors was 20,891.45 km. The ecological
corridors presented a zigzag distribution from the north to the south,
which effectively connected different ecological sources and provided a
structural foundation for species migration and ESs ow. This was
because the ecological corridor avoided areas with strong human ac-
tivities, thus it was conducive to species migration and ESs ow among
various ecological sources.
6. Discussion
6.1. Methodological advantages and research signicance
At present, few studies implemented relationship analysis between
the WEF nexus and ecosystems by the WEF nexus assessment method
and ecosystem service spatial quantication method. Moreover, there
was also a lack of research on the identication and optimization of
ecological security patterns by considering the concept of the WEF
nexus. It has been found that the development status of the WEF nexus is
affected by various socio-economic factors (Wang et al., 2021), and the
supply capacity of ESs is also inuenced by many factors such as natural
conditions (Ding et al., 2021). Therefore, both the WEF nexus evaluation
and the ESs quantication are complex research issues, and the rela-
tionship between the two is also complicated (Ding et al., 2023b).
Therefore, this study used Spearman rank correlation analysis to eval-
uate the correlation between the WEF nexus and ESs.
As an effective application tool, the ecological security pattern can
lessen the negative impacts of regional socio-economic development on
ecosystems and strengthen ecological protection to the greatest extent
(Wei et al., 2022). However, most of the existing research identied
ecological security patterns by assessing natural factors (Li et al., 2023c;
Zhang et al., 2023), but lacked consideration of socio-economic factors
such as the WEF nexus. Based on this, this study designed a methodo-
logical framework by (i) analyzing the relationship between the WEF
nexus and ESs, and (ii) identifying ecological security patterns based on
the ESs related to the WEF nexus, making the optimization of ecological
security patterns more targeted and more conducive. Therefore, future
studies can utilize the methodological framework proposed here to
develop regional spatial management of ecological security from the
WEF nexus perspective.
In this study, it was found that the WEF nexus was signicantly
negatively correlated with the selected ESs. This suggests that the supply
capacity of relevant ESs was to some extent affected by the development
status of the WEF nexus. The WEF nexus evaluation index system was
established by selecting consumption indicators of water and energy
subsystems, and productivity indicators of the food subsystem. This was
due to the fact that excessive water consumption had an impact on
ecological functions, such as the hydrological cycle (Xie et al., 2022).
The large consumption of energy was the main source of carbon emis-
sions, which led to serious air pollution (Wang et al., 2023a). In addi-
tion, measures to increase food production such as large-scale
reclamation of cultivated land, agricultural planting activities, and the
application of fertilizers and pesticides exacerbated ecological degra-
dation, resulting in severe soil erosion, water pollution and agricultural
carbon emissions (Liu et al., 2023b). Therefore, it was further indicated
that the greater the demand for water and energy and the supply of food
in the WEF nexus, the stronger the damage to local ecosystems.
Fig. 6. The comprehensive resistance surface and the ecological security pattern.
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
11
To sum up, there existed a complex relationship between the WEF
nexus and ESs. When the development status of the WEF nexus was
unsustainable, it had a negative effect on local ecosystems. Meanwhile,
the excessive consumption of ESs also put great pressures on local eco-
systems. Both the WEF nexus and ESs affected regional ecological se-
curity. Therefore, the proposed methodological framework improves the
theoretical signicance for identifying and optimizing ecological secu-
rity patterns by considering ESs relevant to the WEF nexus. Additionally,
the case study of the YRDUA further validates the practical signicance
of this framework. This study was not only benecial for providing a
reference for improving the sustainable development of the WEF nexus
and local ecosystems in urban agglomerations, but also can help to
propose management policies from different perspectives to ensure
ecological security.
6.2. Optimizing the ecological security pattern from the WEF nexus
perspective
To further optimize the ecological security pattern, a scheme of “two
belts, three zones and four groups” was formulated on the basis of the
spatial distribution characteristics of sources of ESs related to the WEF
nexus, ecological corridors, and socio-economic development of the
YRDUA (Ding et al., 2022), as shown in Fig. 7.
(1) Two belts
The ecological sources of the YRDUA presented two concentrated
areas, namely the food producing areas of northern Jiangsu and the
carbon pool areas of southern Anhui and southern Zhejiang. Ecological
corridors presented a development trend from the north to the south.
Therefore, this study proposed two “carbon-food” transport belts,
namely Northern Jiangsu-Southern Anhui and Northern Jiangsu-
Southern Zhejiang ecological corridor belts, to facilitate the circula-
tion of food production and carbon storage from the north to the south.
(2) Three zones
The three zones were mainly composed of the ecological buffer zone,
the important watershed zone, and the ecological cultivation zone.
Specically, the ecological buffer zone refers to the peripheral areas of
ecological sources, which play the role of habitat protection and
disturbance buffer. This study took ecological sources as the center and
set different width buffer zones as the ecological source radiation zone.
It was found that when the buffer width was 10 km, the sum of
ecological sources area and buffer zone area reached more than 50% of
the YRDUA. Therefore, this study designated the areas within 10 km
near ecological sources as the ecological buffer zone.
The important watershed zone is described as the areas where
important river basins and lakes are located, which contribute to the
hydrological cycle and also facilitate nutrient cycling and energy ow
between different ecosystems (Palmer and Ruhi, 2019; Jiang et al.,
2021) This zone undertakes many water ecological functions, such as
water resource conservation, water supply reliability, hydrological
regulation, and water purication, playing a crucial role in promoting
the sustainable development of the WEF nexus (Behboudian et al.,
2021). Thus, it can be seen that the proposed framework has certain
advantages in protecting water resources and promoting the hydrolog-
ical cycle by optimizing ecological security patterns. On this basis, this
study delimited the important rivers and lakes in the YRDUA as the
important watershed zone, including the Yangtze River basin across the
east and west, the Huaihe River basin in northern Jiangsu, the Chaohu
Lake basin in the center of Anhui, as well as the Taihu Lake basin at the
border of Jiangsu and Zhejiang.
The ecological cultivation zone is dened as the areas with weak
Fig. 7. Optimization of the ecological security pattern in the study area.
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
12
environmental carrying capacity and poor ecological background. By
analyzing the identied ecological security pattern, it can be seen that
Chuzhou and southern Hefei in Anhui province lacked water coverage
areas and ecological sources. Therefore, these areas were classied as
the ecological cultivation zone of the YRDUA. This zone should actively
cultivate public welfare forests, implement ecological conservation, and
reclaim food farming areas to improve the supply of ESs.
(3) Four groups
The four groups were the town development group, the northern
“water-food” group, the southern Anhui “water-energy” group and the
southern Zhejiang “water-energy” group, respectively. In terms of the
town development group, this study divided the urban central areas of
seven rst-tier cities in the YRDUA (Shanghai, Suzhou, Wuxi, Nanjing,
Hefei, Hangzhou, and Ningbo) into ve town development groups ac-
cording to geographical location. Each town development group had
dense population, developed socio-economic development levels, sound
infrastructure, and serious ecological degradation. Additionally, the
rapid urbanization in these groups also provoked the encroachment of
ecological space into the living and production spaces. In terms of the
northern “water-food” group, the terrain of this group was mainly plains
with vast cultivated land. This group has the major food producing areas
of the YRDUA. In terms of the “water-energy” groups in Southern Anhui
and Southern Zhejiang, these two groups, as the important ecological
barriers of the YRDUA, had rich vegetation cover areas, which can
provide sufcient carbon storage service for other areas.
6.3. Management implications
6.3.1. Improving ESs from the WEF nexus perspective
At present, some issues of water shortage, high carbon emissions, and
food security in the YRDUA are prominent. One of the challenges in the
YRDUA is to provide enough drinking water, adequate energy and safe
food for the rapidly growing population without triggering a resource
crisis (Liang et al., 2022; Ding et al., 2023b). Therefore, some policy
recommendations were put forward for different resource departments
and inter-departmental cooperation to boost the capacity of the YRDUA
to provide ESs related to the WEF nexus. First, the water department
should establish a water resources monitoring and evaluation system to
achieve regular monitoring and evaluation of the quantity, quality, and
utilization of water resources. The department should also take mea-
sures to prevent water pollution, such as strengthening sewage treat-
ment in industrial and agricultural production, so as to protect water
sources such as reservoirs, lakes, and rivers from damage. Second, the
energy department should adjust the industrial structure to reduce its
dependence on high carbon emission energy (Ju et al., 2023). This
department should also establish a management system to monitor,
count, and manage the carbon emission from energy production and
consumption. Third, the agricultural department should guide farmers
to adopt efcient farming techniques, water-saving irrigation tech-
niques, and seedling cultivation techniques to increase food production
(Zhang et al., 2021). Meanwhile, this department should also establish
the circulation and reserve system of agricultural products (Zhu et al.,
2022). Lastly, the inter-departmental cooperation mechanism between
three departments should be actively established to heighten the
comprehensive utilization efciency of resources.
6.3.2. Implementing ecological protection policies based on the ecological
security pattern
According to the distribution characteristics of ecological sources
and corridors in the identied ecological security pattern, some
ecological protection policies should be formulated and implemented to
enhance regional ecological security (Nie et al., 2021; Zhang et al.,
2022). First, the spatial distribution of ecological sources should be
optimized and adjusted actively. The cultivation of public welfare forest
areas and water source protection areas in the center should be actively
implemented. Second, the governments in the YRDUA should avoid
encroaching on the ecological sources and corridors when developing
production land, construction land, residential land, and infrastructure.
In addition, some policies should be adopted to limit the development
and destruction of ecological corridors, and to take full advantage of the
diffusion and connectivity of ecological corridors to resource elements
in the WEF nexus. Third, the governments should protect and restore key
ecological sources to establish a solid ecological security barrier for the
YRDUA.
6.3.3. Adopting differentiated management policies for different zones and
groups
In this study, the scheme of “two belts, three zones and four groups”
was proposed to optimize the ecological security pattern of the YRDUA.
On this basis, this study proposed differentiated management policies
for different zones and groups. Specically, for the ecological buffer
zone, it should actively construct the ecological infrastructure to mini-
mize the ecological destruction caused by human activities. For the
important watershed zone, water sources protection should be
strengthened, and human activities such as reservoir construction,
human water withdrawal, and agricultural irrigation should be properly
managed to reduce the impacts on river hydrological health. For the
ecological cultivation zone, it should actively expand the restoration of
land use types (forest, grassland, and water body) to speed up the
restoration of local ecosystems, so as to promote its ecological structure
and function.
As for the town development group, it has scarce ecological resources
and difculty in satisfying the growing ecological demands of local
residents. Therefore, in the future, this group should invest more
ecological funds, accelerate green parks development, and avoid disor-
derly urban space expansion. For the northern “water-food” group, it
should vigorously develop agriculture. Meanwhile, this group should
also establish a food trade mechanism with the town development group
and the southern hilly areas. In addition, through the green space con-
struction based on the “two belts”, the “water-energy” and “water-food”
groups could be connected effectively, so as to gradually penetrate
ecological benets into the town development group.
6.4. Limitations and further prospects
In this paper, some limitations still exist and are worth exploring in
further research. First, considering the limitation in the availability of
statistical data at the urban scale, this study only selected 15 indicators
from three resource subsystems to establish the WEF nexus evaluation
index system. However, the current evaluation index system still lacks
some linkage indicators that can reect the concept of the nexus.
Therefore, in the future, some indicators that fully reect the in-
teractions between resources in the WEF nexus, such as agricultural and
industrial water consumption should be considered in the evaluation
index system. Second, the relationship between the WEF nexus and ESs
was evaluated in this study, but the mechanism by which the WEF nexus
affected relevant ESs was still unexplored. Thus, in future research, the
mechanisms of inuence between the WEF nexus and relevant ESs
should be further studied. Finally, this study only selected three types of
ESs based on the analysis of stakeholders associated with the WEF nexus.
However, when considering more relevant stakeholders (such as tour-
ists) or different perspectives (such as the sustainable development
needs of resource subsystems), there are still some ESs that could be
further selected, such as recreation and hydrological regulation.
Therefore, in future studies, some other feasible alternatives should be
taken to carry out relevant research and further compare with the results
of this study.
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
13
7. Conclusions
This paper proposed a methodological framework for identifying and
optimizing ecological security patterns by assessing ESs related to the
WEF nexus. Then the YRDUA was selected as a typical case for appli-
cation. The results showed that the WEF nexus had signicant negative
correlations with ESs, especially with water yield and carbon storage.
For identifying the ecological security pattern, the sources of water yield
spread over the southwest and central areas of the YRDUA, and those of
carbon storage and food production were mainly concentrated in the
forested and farming areas, respectively. The YRDUA had 38 ecological
sources, the total area of which reached 66,125.61 km
2
. Besides, the
ecological sources were mainly distributed in the northern plains areas
(Nantong, Taizhou, Yancheng, and Yangzhou) and the southwestern
hilly areas (Anqing, Chizhou, Xuancheng, Hangzhou, Jinhua, and Huz-
hou). The comprehensive ecological resistance surface displayed a
downward trend from east to west. The urban central areas usually had
higher resistance values, while the northern plains and southern hilly
areas had relatively lower resistance values. Ecological corridors pre-
sented a zigzag distribution from north to south. Based on this, the
optimization scheme of “two belts, three zones and four groups” was
proposed by considering the WEF nexus. Therefore, some management
policies were formulated to promote regional ecological security from
different perspectives: the WEF nexus, the ecological security pattern,
and different zones and groups.
CRediT authorship contribution statement
Tonghui Ding: Conceptualization, Data curation, Formal analysis,
Methodology, Writing – original draft, Writing – review & editing,
Funding acquisition. Junfei Chen: Funding acquisition, Investigation,
Methodology, Writing – review & editing, Supervision. Liping Fang:
Conceptualization, Methodology, Writing – review & editing, Supervi-
sion. Juan Ji: Methodology, Writing – review & editing.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgements
This work is nancially supported by the National Natural Science
Foundation of China (Grant No. 42071278), the Fundamental Research
Funds for the Central Universities (Grant Nos. B230207002,
B230205041), the Postgraduate Research & Practice Innovation Pro-
gram of Jiangsu Province (Grant No. KYCX23_0646), and Key Projects of
Jiangsu Social Science Foundation (Grant No. 21GLA006).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jhydrol.2024.130912.
References
Adriaensen, F., Chardon, J., De Blust, G., et al., 2003. The application of ‘least-cost’
modelling as a functional landscape model. Landsc. Urban Plan. 64, 233–247.
Afshar, A., Soleimanian, E., Akbari Variani, H., Vahabzadeh, M., Molajou, A., 2022. The
conceptual framework to determine interrelations and interactions for holistic
Water. Energy, and Food Nexus. Environ. Dev. Sustain. 24, 10119–10140.
Arthur, M., Liu, G., Hao, Y., et al., 2019. Urban food-energy-water nexus indicators: A
review. Resour. Conserv. Recycl. 151, 104481.
Bai, Y., Chen, Y., Alatalo, J., Yang, Z., Jiang, B., 2020. Scale effects on the relationships
between land characteristics and ecosystem services-a case study in Taihu Lake
Basin. China. Sci. Total Environ. 716, 137083.
Behboudian, M., Kerachian, R., Motlaghzadeh, K., Ashra, S., 2021. Evaluating water
resources management scenarios considering the hierarchical structure of decision-
makers and ecosystem services-based criteria. Sci. Total Environ. 751, 141759.
Cao, Y., Cao, Y., Li, G., et al., 2020. Linking ecosystem services trade-offs, bundles and
hotspot identication with cropland management in the coastal Hangzhou Bay area
of China. Land Use Policy 97, 104689.
Chen, J., Wang, S., Zou, Y., 2022. Construction of an ecological security pattern based on
ecosystem sensitivity and the importance of ecological services: A case study of the
Guanzhong Plain urban agglomeration. China. Ecol. Indic. 136, 108688.
Chenhong, X., Guofang, Z., 2022. The spatiotemporal evolution pattern of urban
resilience in the Yangtze River Delta urban agglomeration based on TOPSIS-PSO-
ELM. Sustain. Cities and Society 87, 104223.
Cong, W., Sun, X., Guo, H., Shan, R., 2020. Comparison of the SWAT and InVEST models
to determine hydrological ecosystem service spatial patterns, priorities and trade-
offs in a complex basin. Ecol. Indic. 112, 106089.
Costanza, R., d’Arge, R., de Groot, R., et al., 1997. The value of the world’s ecosystem
services and natural capital. Nature 387, 253–260.
Dai, L., Liu, Y., Luo, X., 2021. Integrating the MCR and DOI models to construct an
ecological security network for the urban agglomeration around Poyang Lake. China.
Sci. Total Environ. 754, 141868.
Ding, T., Chen, J., Fang, Z., Chen, J., 2021. Assessment of coordinative relationship
between comprehensive ecosystem service and urbanization: A case study of Yangtze
River Delta urban Agglomerations. China. Ecol. Indic. 133, 108454.
Ding, T., Chen, J., Fang, L., Ji, J., Fang, Z., 2023a. Urban ecosystem services supply-
demand assessment from the perspective of the water-energy-food nexus. Sustain.
Cities Soc. 90, 104401.
Ding, T., Fang, L., Chen, J., Ji, J., Fang, Z., 2023b. Exploring the relationship between
water-energy-food nexus sustainability and multiple ecosystem services at the urban
agglomeration scale. Sustain. Prod. Consum. 35, 184–200.
Ding, M., Liu, W., Xiao, L., et al., 2022. Construction and optimization strategy of
ecological security pattern in a rapidly urbanizing region: A case study in central-
south China. Ecol. Indic. 136, 108604.
Fu, Y., Shi, X., He, J., Yuan, Y., Qu, L., 2020. Identication and optimization strategy of
county ecological security pattern: A case study in the Loess Plateau. China. Ecol.
Indic. 112, 106030.
Fulford, R., Russell, M., Myers, M., Malish, M., Delmaine, A., 2022. Models help set
ecosystem service baselines for restoration assessment. J. Environ. Manag. 317,
115411.
Gao, J., Wang, Z., Wang, Z., et al., 2022. Macro-site selection and obstacle factor
extraction of biomass cogeneration based on comprehensive weight method of Game
theory. Energy Rep. 8, 14416–14427.
Gou, M., Li, L., Ouyang, S., et al., 2022. Integrating ecosystem service trade-offs and
rocky desertication into ecological security pattern construction in the Daning river
basin of southwest China. Ecol. Indic. 138, 108845.
Graviola, G., Ribeiro, M., Pena, J., 2022. Reconciling humans and birds when designing
ecological corridors and parks within urban landscapes. Ambio 51, 253–268.
Hanes, R., Gopalakrishnan, V., Bakshi, B., 2018. Including nature in the food-energy-
water nexus can improve sustainability across multiple ecosystem services. Resour.
Conserv. Recycl. 137, 214–228.
Hoff, H., 2011. Understanding the Nexus: Background Paper for the Bonn2011
Conference: The Water, Energy and Food Security Nexus.
Huang, L., Wang, J., Fang, Y., Zhai, T., Cheng, H., 2021a. An integrated approach
towards spatial identication of restored and conserved priority areas of ecological
network for implementation planning in metropolitan region. Sustain. Cities Soc. 69,
102865.
Huang, X., Wang, H., Shan, L., Xiao, F., 2021b. Constructing and optimizing urban
ecological network in the context of rapid urbanization for improving landscape
connectivity. Ecol. Indic. 132, 108319.
Huang, Y., Wu, J., 2023. Spatial and temporal driving mechanisms of ecosystem service
trade-off/synergy in national key urban agglomerations: A case study of the Yangtze
River Delta urban agglomeration in China. Ecol. Indic. 154, 110800.
Hwang, C., Yoon, K., 1981. Multiple attribute decision making: Methods and
applications. Springer-Verlag.
Jia, Q., Jiao, L., Lian, X., Wang, W., 2023. Linking supply-demand balance of ecosystem
services to identify ecological security patterns in urban agglomerations. Sustain.
Cities and Society 92, 104497.
Jiang, Y., Lin, W., Xu, D., et al., 2023. Spatio-temporal variation of the relationship
between air pollutants and land surface temperature in the Yangtze River Delta
Urban Agglomeration. China. Sustain. Cities and Society 91, 104429.
Jiang, S., Zhou, L., Ren, L., et al., 2021. Development of a comprehensive framework for
quantifying the impacts of climate change and human activities on river hydrological
health variation. J. Hydrol. 600, 126566.
Ju, K., Wang, J., Wei, X., Li, H., Xu, S., 2023. A comprehensive evaluation of the security
of the water-energy-food systems in China. Sustain. Prod. Consum. 39, 145–161.
Ke, X., Wang, X., Guo, H., Yang, C., Zhou, Q., Mougharbel, A., 2021. Urban ecological
security evaluation and spatial correlation research-based on data analysis of 16
cities in Hubei Province of China. J. Clean. Prod. 311, 127613.
Li, L., Huang, X., Wu, D., Yang, H., 2023a. Construction of ecological security pattern
adapting to future land use change in Pearl River Delta. China. Appl. Geogr. 154,
102946.
T. Ding et al.
Journal of Hydrology 632 (2024) 130912
14
Li, Q., Liu, Z., Yang, Y., Han, Y., Wang, X., 2023b. Evaluation of water resources carrying
capacity in Tarim River Basin under game theory combination weights. Ecol. Indic.
154, 110609.
Li, Y., Liu, W., Feng, Q., et al., 2023c. The role of land use change in affecting ecosystem
services and the ecological security pattern of the Hexi Regions. Northwest China.
Sci. Total Environ. 855, 158940.
Liang, J., He, X., Zeng, G., et al., 2018. Integrating priority areas and ecological corridors
into national network for conservation planning in China. Sci. Total Environ. 626,
22–29.
Liang, M., Huang, G., Chen, J., Li, Y., 2022. Energy-water-carbon nexus system planning:
A case study of Yangtze River Delta urban agglomeration. China. Appl. Energy 308,
118144.
Liu, X., Su, Y., Li, Z., Zhang, S., 2023c. Constructing ecological security patterns based on
ecosystem services trade-offs and ecological sensitivity: A case study of Shenzhen
metropolitan area. China. Ecol. Indic. 154, 110626.
Liu, H., Wang, Z., Zhang, L., Tang, F., Wang, G., Li, M., 2023a. Construction of an
ecological security network in the Fenhe River Basin and its temporal and spatial
evolution characteristics. J. Clean. Prod. 417, 137961.
Liu, L., Wang, X., Meng, X., Cai, Y., 2023b. The coupling and coordination between food
production security and agricultural ecological protection in main food-producing
areas of China. Ecol. Indic. 154, 110785.
Liu, W., Zhan, J., Zhao, F., et al., 2022. The tradeoffs between food supply and demand
from the perspective of ecosystem service ows: A case study in the Pearl River
Delta. China. J. Environ. Manag. 301, 113814.
Liu, Y., Zhao, C., Liu, X., et al., 2021. The multi-dimensional perspective of ecological
security evaluation and drive mechanism for Baishuijiang National Nature Reserve.
China. Ecol. Indic. 132, 108295.
Millennium Ecosystem Assessment (MEA), 2005. Ecosystems and human Well-Being:
Synthesis. Island Press.
Molajou, A., Afshar, A., Khosravi, M., et al., 2021. A new paradigm of water, food, and
energy nexus. Environ. Sci. Pollut. Res.
Mu, H., Li, X., Ma, H., et al., 2022. Evaluation of the policy-driven ecological network in
the Three-North Shelterbelt region of China. Landsc. Urban Plan. 218, 104305.
NBS, 2020a. China City Statistical Yearbook 2020. National Bureau of Statistics, Beijing.
NBS, 2020b. China Energy Statistical Yearbook 2020. National Bureau of Statistics,
Beijing.
Nie, W., Shi, Y., Siaw, M., et al., 2021. Constructing and optimizing ecological network at
county and town Scale: The case of Anji County. China. Ecol. Indic. 132, 108294.
Palmer, M., Ruhi, A., 2019. Linkages between ow regime, biota, and ecosystem
processes: Implications for river restoration. Science 365, eaaw2087.
Peng, J., Pan, Y., Liu, Y., Zhao, H., Wang, Y., 2018a. Linking ecological degradation risk
to identify ecological security patterns in a rapidly urbanizing landscape. Habitat
Intern. 71, 110–124.
Peng, J., Yang, Y., Liu, Y., et al., 2018b. Linking ecosystem services and circuit theory to
identify ecological security patterns. Sci. Total Environ. 644, 781–790.
Qi, Y., Farnoosh, A., Lin, L., Liu, H., 2022. Coupling coordination analysis of China’s
provincial water-energy-food nexus. Environ. Sci. Pollut. Res. 29, 23303–23313.
Qiao, W., Huang, X., 2022. The impact of land urbanization on ecosystem health in the
Yangtze River Delta urban agglomerations. China. Cities 130, 103981.
Rasul, G., 2014. Food, water, and energy security in South Asia: A nexus perspective from
the Hindu Kush Himalayan region☆. Environ. Sci. Policy 39, 35–48.
Santos, J., Leite, C., Viana, J., et al., 2018. Delimitation of ecological corridors in the
Brazilian Atlantic Forest. Ecol. Indic. 88, 414–424.
Shen, Z., Wu, W., Tian, S., Wang, J., 2022. A multi-scale analysis framework of different
methods used in establishing ecological networks. Landsc. Urban Plan. 228, 104579.
Shi, X., Matsui, T., Machimura, T., Haga, C., Hu, A., Gan, X., 2022. Impact of
urbanization on the food-water-land-ecosystem nexus: A study of Shenzhen. China.
Sci. Total Environ. 808, 152138.
Soleimanian, E., Afshar, A., Molajou, A., Ghasemi, M., 2023. Development of a
comprehensive aater simulation model for water, food, and energy nexus analysis in
Basin Scale. Water Resour. Manag. 37, 4589–4621.
Sun, L., Niu, D., Yu, M., et al., 2022a. Integrated assessment of the sustainable water-
energy-food nexus in China: Case studies on multi-regional sustainability and multi-
sectoral synergy. J. Clean. Prod. 334, 130235.
Sun, Q., Sun, J., Baidurela, A., et al., 2022b. Ecological landscape pattern changes and
security from 1990 to 2021 in Ebinur Lake Wetland Reserve. China. Ecol. Indic. 145,
109648.
Tang, X., Xie, C., Huang, F., Miu, Y., Wu, J., Jiang, Z., 2016. Review of forest soil/
vegetation carbon storage and its spatial distribution characteristics. J. Anhui
Agriculture 44, 146–149. In Chinese.
Tilman, D., Socolow, R., Foley, J., et al., 2009. Benecial biofuels-The food, energy, and
environment trilemma. Sci. 325, 270–271.
Wang, Y., Xie, Y., Qi, L., He, Y., Bo, H., 2021. Synergies evaluation and inuencing
factors analysis of the water-energy-food nexus from symbiosis perspective: A case
study in the Beijing-Tianjin-Hebei region. Sci.Total Environ. 151731.
Wang, M., Wang, Y., Teng, F., Ji, Y., 2023a. The spatiotemporal evolution and impact
mechanism of energy consumption carbon emissions in China from 2010 to 2020 by
integrating multisource remote sensing data. J. Environ. Manag. 346, 119054.
Wang, Y., Wang, H., Zhang, J., et al., 2023c. Exploring interactions in water-related
ecosystem services nexus in Loess Plateau. J. Environ. Manag. 336, 117550.
Wang, S., Yang, J., Wang, A., et al., 2023b. Coordinated analysis and evaluation of water-
energy-food coupling: A case study of the Yellow River basin in Shandong Province.
China. Ecol. Indic. 148, 110138.
Wątr´
obski, J., Bączkiewicz, A., Ziemba, E., Sałabun, W., 2022. Sustainable cities and
communities assessment using the DARIA-TOPSIS method. Sustain. Cities Soc. 83,
103926.
Wei, Q., Halike, A., Yao, K., Chen, L., Balati, M., 2022. Construction and optimization of
ecological security pattern in Ebinur Lake Basin based on MSPA-MCR models. Ecol.
Indic. 138, 108857.
Wei, L., Liu, Z., 2022. Spatial heterogeneity of demographic structure effects on urban
carbon emissions. Environ. Impact Assess. Review 95, 106790.
Withey, P., Johnston, C., Guo, J., 2019. Quantifying the global warming potential of
carbon dioxide emissions from bioenergy with carbon capture and storage.
Renewable and Sustain. Energy Reviews 115, 109408.
Wu, J., Zhang, L., Peng, J., Liu, H., He, S., 2013. The integrated recognition of the source
area of the urban ecological security pattern in Shenzhen. Acta Ecol. Sin. 33,
4125–4133.
Wu, Y., Zhang, T., Zhang, H., et al., 2020. Factors inuencing the ecological security of
island cities: A neighborhood-scale study of Zhoushan Island. China. Sustain. Cities
and Society 55, 102029.
Xia, C., Zhai, G., 2022. The spatiotemporal evolution pattern of urban resilience in the
Yangtze River Delta urban agglomeration based on TOPSIS-PSO-ELM. Sustain. Cities
and Society 87, 104223.
Xiang, H., Zhang, J., Mao, D., Wang, Z., Qiu, Z., Yan, H., 2022. Identifying spatial
similarities and mismatches between supply and demand of ecosystem services for
sustainable Northeast China. Ecol. Indic. 134, 108501.
Xiao, S., Wu, W., Guo, J., Ou, M., Pueppke, S.G., Ou, W., Tao, Y., 2020. An evaluation
framework for designing ecological security patterns and prioritizing ecological
corridors: application in Jiangsu Province. China. Landscape Ecol. 35, 2517–2534.
Xie, X., Zhang, J., Lian, Y., et al., 2022. Cloud model combined with multiple weighting
methods to evaluate hydrological alteration and its contributing factors. J. Hydrol.
610, 127794.
Yang, M., Gao, X., Siddique, K., Wu, P., Zhao, X., 2023. Spatiotemporal exploration of
ecosystem service, urbanization, and their interactive coercing relationship in the
Yellow River Basin over the past 40 years. Sci. Total Environ. 858, 159757.
Yang, S., Zou, C., Weishou, S., Runping, S., Xu, D., 2016. Construction of ecological
security patterns based on ecological red line: A case study of Jiangxi Province.
Chinese J. Ecology 35, 250–258. In Chinese.
Yin, D., Yu, H., Shi, Y., et al., 2022. Matching supply and demand for ecosystem services
in the Yellow River Basin, China: A perspective of the water-energy-food nexus.
J. Clean. Prod. 135469.
Yu, K., 1996. Security patterns and surface model in landscape ecological planning.
Landsc. Urban Plan. 36, 1–17.
Yu, H., Xie, W., Sun, L., Wang, Y., 2021. Identifying the regional disparities of ecosystem
services from a supply-demand perspective. Resour. Conserv. Recycl. 169, 105557.
Yuan, Y., Bai, Z., Zhang, J., Xu, C., 2022. Increasing urban ecological resilience based on
ecological security pattern: A case study in a resource-based city. Ecol. Engineering
175, 106486.
Yuan, M., Lo, S., 2020. Ecosystem services and sustainable development: Perspectives
from the food-energy-water Nexus. Ecosyst. Serv. 46, 101217.
Zeng, W., Tang, H., Liang, X., et al., 2023. Using ecological security pattern to identify
priority protected areas: A case study in the Wuhan Metropolitan Area. China. Ecol.
Indic. 148, 110121.
Zhang, Z., Peng, J., Xu, Z., Wang, X., Meersmans, J., 2021. Ecosystem services supply and
demand response to urbanization: A case study of the Pearl River Delta. China.
Ecosyst. Serv. 49, 101274.
Zhang, Z., Hu, B., Jiang, W., Qiu, H., 2023. Construction of ecological security pattern
based on ecological carrying capacity assessment 1990–2040: A case study of the
Southwest Guangxi Karst - Beibu Gulf. Ecol. Modelling 479, 110322.
Zhang, Y., Zhao, Z., Fu, B., et al., 2022. Identifying ecological security patterns based on
the supply, demand and sensitivity of ecosystem service: A case study in the Yellow
River Basin. China. J. Environ. Manag. 315, 115158.
Zhou, S., Li, W., Lu, Z., Yue, R., 2023. An analysis of multiple ecosystem services in a
large-scale urbanized area of northern China based on the food-energy-water
integrative framework. Environ. Impact Assess. Review 98, 106913.
Zhou, D., Lin, Z., Ma, S., Qi, J., Yan, T., 2021. Assessing an ecological security network
for a rapid urbanization region in Eastern China. Land Degrad. Dev. 32, 2642–2660.
Zhu, Y., Zhang, C., Fang, J., Miao, Y., 2022. Paths and strategies for a resilient megacity
based on the water-energy-food nexus. Sustain. Cities Soc. 82, 103892.
T. Ding et al.
