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Article
Comprehensive Evaluation of Water Resources Carrying
Capacity in the Han River Basin
Lele Deng 1, Jiabo Yin 1,2, Jing Tian 1, Qianxun Li 1and Shenglian Guo 1, *
Citation: Deng, L.; Yin, J.; Tian, J.;
Li, Q.; Guo, S. Comprehensive
Evaluation of Water Resources
Carrying Capacity in the Han River
Basin. Water 2021,13, 249. https://
doi.org/10.3390/w13030249
Received: 1 December 2020
Accepted: 18 January 2021
Published: 20 January 2021
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Licensee MDPI, Basel, Switzerland.
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4.0/).
1State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University,
Wuhan 430072, China; leledeng@whu.edu.cn (L.D.); jboyn@whu.edu.cn (J.Y.); jingtian@whu.edu.cn (J.T.);
qianxunli@whu.edu.cn (Q.L.)
2Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University,
Wuhan 430074, China
*Correspondence: slguo@whu.edu.cn
Abstract:
As one of the most crucial indices of sustainable development and water security, water
resources carrying capacity (WRCC) has been a pivotal and hot-button issue in water resources
planning and management. Quantifying WRCC can provide useful references on optimizing water
resources allocation and guiding sustainable development. In this study, the WRCCs in both current
and future periods were systematically quantified using set pair analysis (SPA), which was formulated
to represent carrying grade and explore carrying mechanism. The Soil and Water Assessment Tool
(SWAT) model, along with water resources development and utilization model, was employed to
project future water resources scenarios. The proposed framework was tested on a case study of
China’s Han River basin. A comprehensive evaluation index system across water resources, social
economy, and ecological environment was established to assess the WRCC. During the current
period, the WRCC first decreased and then increased, and the water resources subsystem performed
best, while the eco-environment subsystem achieved inferior WRCC. The SWAT model projected
that the amount of the total water resources will reach about 56.9 billion m
3
in 2035s, and the water
resources development and utilization model projected a rise of water consumption. The declining
WRCC implies that the water resources are unable to support or satisfy the demand of ecological and
socioeconomic development in 2035s. The study furnishes abundant and valuable information for
guiding water resources planning, and the core idea of this model can be extended for the assessment,
prediction, and regulation of other systems.
Keywords:
water resources carrying capacity; SWAT model; set pair analysis; sustainable develop-
ment; Han River basin
1. Introduction
Water resources are one of the key natural resources, which are vital for feeding human
beings and maintaining socioeconomic development [
1
]. Thanks to the rapid expansion of
urbanization and industrialization, distinctive issues such as water scarcity [
2
] and water
contamination [
3
] have thrown the progress of sustainable development into question in
recent decades.
The concept of carrying capacity was initially proposed in the ecological commu-
nity [
4
] and was adopted to measure the maximum of individuals maintaining a certain
species in a certain area under certain conditions [
5
]. Nowadays, this concept has been
extended into hydrological sciences and is widely used to represent the capacity of the
environment or ecosystem to sustain development and specific activities.
To date, relevant research studies mainly emphasize the concept or theory establish-
ment, and a mass of researchers have evaluated the water resources carrying capacity
(WRCC) using a wide range of methods. Clarke [
6
] defined the WRCC as a concept that
is closely related to population growth and resources consumption. Feng and Huang [
7
]
Water 2021,13, 249. https://doi.org/10.3390/w13030249 https://www.mdpi.com/journal/water
Water 2021,13, 249 2 of 19
reckoned that the WRCC is important to measure the capacity of social development under
well-living conditions. Song et al. [
8
] defined it as the maximum capacity for human activity
in a desirable ecological system with certain socioeconomic and living levels. A host of
studies also reported a variety of definitions of the concept, and a unifying concept has
not yet been established [
9
]. From the perspective of the theory, the majority of research
studies categorized WRCC as theory of sustainable development [
10
], and a number of
new theories were introduced and extended into hydrological fields.
Several methods have been incorporated to assess carrying capacity. For instance, the
ecological footprint theory has been used to evaluate the carrying capacity of resources and
the sustainability of industries [
11
]. Nevertheless, it is arduous to employ the ecological
footprint to calculate the WRCC due to variations in fluidity for water resources [
12
].
Chen et al. [
13
] developed a model based on catastrophe theory to assess the sustainable
utilization of water resources, and the model might highly depend on the subjectivity of
decision-makers. Focusing on the water cycle’s natural and social elements, Zhang et al. [
14
]
constructed an evaluation system that covers the dimensions of water quantity, water
quality, water ecology, and water consumption. The tendency method [
15
], multicriteria
analysis method [
16
], and artificial neural networks model [
17
] have also been utilized
in relevant research studies. Meanwhile, different approaches have also been employed.
Zhang et al. [
18
] used the principal component analysis (PCA) to analyze the river carrying
capacity of the Chang-Zhu-Tan region in China and provided long-run proposals for its
development. Zhao et al. [
19
] applied the system dynamic model to investigate the WRCC
in Kunming City under four different scenarios and found that it would be extremely
enhanced in 2020 under the comprehensive strategy scenario. The analytic hierarchy
process was employed by Lu et al. [
20
] to assess the hydrological cycle in Huai’an City to
achieve a coordinating balance between water resources and society. Fu et al. [
21
] evaluated
the agricultural WRCC using the projection pursuit model in the Sanjiang Plain, China.
Although quantities of studies on the WRCC have been performed, they show de-
ficiency in some aspects. Most current studies focused on investigating the WRCC in
historical conditions. With global warming and anthropogenic changes, precipitation and
runoff may be spatiotemporally redistributed, thus challenging current water resources
management policies [
22
–
24
]. However, existing studies paid less attention to investigat-
ing the WRCC under future conditions. Furthermore, the methods used to evaluate the
carrying capacity did not fully consider the dynamics of the WRCC system, which is highly
impacted by socioeconomic development and changing environments [25].
To conquer the abovementioned deficiencies, a new methodology integrating a hydro-
logical model, climate model, socioeconomic development model, and dynamic evaluation
system is developed to investigate the WRCC in the Han River basin, China. This cou-
pled system evaluates and projects the WRCC within a dynamic and integral framework,
covering the water resources subsystem, socioeconomic subsystem, and eco-environment
subsystem. This study mainly contains the following four parts: (1) establishing an evalua-
tion index system covering three criterion layers across water resources, social economy,
and ecological environment systems; (2) projecting future water resources situation in the
Han River basin under climate change using the Soil and Water Assessment Tool (SWAT)
hydrological model; (3) predicting water utilization situation and constructing water de-
mand and consumption projection in the planning year; and (4) evaluating the WRCC in
the Han River basin under current and future changing environments.
2. Study Area and Data
2.1. Study Area
As the largest tributary of the Yangtze River, the Han River rises in the southern
Qinling Mountain. The mainstream runs through Shaanxi and Hubei provinces and
flows into the Yangtze River, covering a distance of 1577 km and a catchment area of
approximately 159,000 km
2
. The basin has a great many cardinal water systems and
tributaries, involving the six administrative regions of Henan, Hubei, Chongqing, Sichuan,
Water 2021,13, 249 3 of 19
Shaanxi, and Gansu provinces as shown in Figure 1. The Han River basin is situated in the
East Asian subtropical monsoon zone with a seasonal climate, influenced by the Eurasian
cold and high pressure in the winter and the Western Pacific subtropical pressure in the
summer [
26
]. The average annual precipitation in the Han River basin is 904 mm, which
is lower than that in the Yangtze River basin. Recharged by varying precipitation, the
runoff during the year shows symptoms of uneven distribution. The precipitation mainly
concentrates from May to October, accounting for 55%∼65% of the annual amount.
The Han River basin, a national strategic water resources security zone and pilot
green development zone, has been confronted with immense problems compounding the
development in this region. The impacts of climate change have exposed the basin to
more varying water resources management problems, such as water supply [
26
] and flood
control [
27
]. Algal blooms have taken place increasingly frequently with longer duration
since 1992 [
28
]. Heavy metal contamination emerges downstream of Ankang City mainly
owing to anthropogenic inputs, which exerts a profound influence on injured aquatic
conditions [
29
]. Thus, policy-makers must be conversant with the WRCC for pursuing
high-quality development. Carrying out research on the WRCC and making a good project
for constructing the ecological–economic belt of the Han River basin are conducive to the
advancement of sustainable and high-quality economic and social development.
Figure 1. Geographic location of the Han River basin and water division projects.
2.2. Water Diversion Project
The Han River basin has affluent natural resources with a strong economic base, occu-
pying a crucial strategic position and playing a prominent role in the economic and social
expansion pattern of the basin. The Danjiangkou Reservoir acts as the water source of the
South-to-North Water Diversion Middle Route Project in China, and it is responsible for the
water supply to Beijing and Tianjin metropolitans and Henan and Hebei provinces [
30
,
31
].
The Yangtze-to-Han River Water Diversion Project is a complementary project of the South-
Water 2021,13, 249 4 of 19
to-North Water Diversion Project, which plays parts in recharging the downstream and
improving conditions for industrial and agricultural water consumption, river navigation,
and ecological water consumption in the lower reaches of the Han River. Besides, the
Han-to-Wei River Water Diversion Project under construction targets to address the water
shortage problems in the Guanzhong Plain in Shanxi province [32].
The large-scale interbasin water diversion projects under construction and in operation
have great impacts on the water resources in this basin, so they will be taken into consid-
eration in this study. Besides, the Middle Line South-to-North Water Diversion Project,
Han-to-Wei River Water Diversion Project, and Yangtze-to-Han River Water Diversion
Project [
33
] also matter. Hence, we set that the scales of the above water diversion projects
in the planning year 2035s will be 11 billion, 1.5 billion, and 3 billion m3, respectively.
2.3. Data Description
Various data (Table 1) are in the running to drive the SWAT model, including digital
elevation model, land use/land cover map, soil map, meteorological data, river discharge,
projected precipitation and temperature, and socioeconomic and environmental data. We
derive the digital elevation model from the United States Geological Survey (USGS), and
the land use/land cover map and soil map are taken from the Resource and Environment
Science and Data Center (RESDC) and Food and Agriculture Organization (FAO), respec-
tively. The meteorological data from 1961 through 2005 can be accessed from the China
Meteorological Administration. The river discharge data are obtained from the Changjiang
Water Resources Commission, Ministry of Water Resources of China. To project future
hydro-climatologic scenarios, the multimodel bias-corrected precipitation and temperature
data under RCP (Representative Concentration Pathway) 4.5 obtained from Shen et al. [
34
]
are forced to drive the SWAT model. The data required for the development and utilization
of water resources mainly come from the China Water Resources Bulletin (2010–2016),
Water Resources Bulletin (2010–2016), and Environmental Bulletin of six metropolitans or
provinces (2010–2016), also listed in Table 1.
Table 1. Data descriptions and sources in this study.
NO. Data Source Relevant Characteristics
1 Digital elevation model USGS 90 ×90 m spatial resolution
2 Land use/land cover map RESDC 1 ×1 km spatial resolution
3 Soil map FAO 1 ×1 km spatial resolution
4 Meteorological data China Meteorological Administration 1961–2005
5 River discharge Changjiang Water Resources Commission,
Ministry of Water Resources of China 1980–2000 and 2010–2016
6Projected precipitation and
temperature 20 global climate models (GCMs) [34] RCP 4.5
7Socioeconomic and environmental
data
China Water Resources Bulletin, Water
Resources Bulletin, and Environmental
Bulletin
Six metropolitans or provinces
involved in this study
3. Methodology
The comprehensive evaluation system is constituted by three portions (i.e., water
resources subsystem, socioeconomic subsystem, and eco-environment subsystem). As the
prediction of the index involved in different subsystems was complex and synthesized,
both the SWAT model and water resources development and utilization model were used
for predicting future scenarios. The SWAT model was used to simulate and project the
runoff and water resources condition based on projected precipitation and temperature
series. The water resources development and utilization model was used to project the
water consumption of socioeconomic development in the planning year, and its inputs
consisted of an economic and social development plan and water consumption level. The
framework of this paper is shown in Figure 2.
Water 2021,13, 249 5 of 19
Figure 2. Framework of assessing and predicting the water resources carrying capacity (WRCC).
3.1. Evaluation Index System
Based on the principles of comprehensiveness, hierarchy, and simplicity, the evalu-
ation index system was built by using the frequency statistical approach and theoretical
analysis method. Additionally, internal and external conditions, such as the spatiotemporal
distribution of water resources and overall plan of socioeconomic development, were con-
sidered. The system consisted of the water resources subsystem, socioeconomic subsystem,
and eco-environment subsystem. Twelve indices were finally selected in consonance with
the nature and connotation of the problems (Table 2).
Table 2. Evaluation index system of water resources carrying capacity in the Han River basin.
Object Hierarchy Rule Hierarchy Index Hierarchy Index Description
Water resources carrying
capacity in the Han River
basin
Water resources X1: Modulus of water resources
production The amount of water resources
subsystem X2: Modulus of water supply The level of water supply capacity
X3: Rate of water resources
exploitation and utilization
The level of water resources
utilization
X4: Water resources per capita The level of water resources per
capita
Socioeconomic X5: GDP per capita
The level of economic development
subsystem X6: Population density The population carrying status
X7: Urbanization rate The level of urbanization
X8: Water consumption per 104
yuan GDP The water consumption
X9: Daily domestic water
consumption per capita
The level of water consumption for
population
X10: Water consumption per 104
yuan of industrial added value
The level of industrial development
Eco-environment
subsystem
X11: Rate of ecological water
consumption
The level of ecological environment
X12: Wastewater discharge per area The pollution status of the water
environment
Water 2021,13, 249 6 of 19
Principal component analysis [
35
,
36
] is a multivariate statistical method that converts
multiple unrelated indices into a few independent comprehensive indices. It possesses
traits that can simplify complicated problems, render the problem analysis easier and
more convenient, get more scientific results, and so forth. Entropy judges the level of
valid information contained in the data by reacting to the disorder of the system [
37
]. The
entropy weight method is characterized by a positive relationship between the degree of
disorder in the performance of an index and the entropy value, and an inverse relationship
with the amount of information it can respond to [
38
]. Hence, PCA and the entropy weight
method were mixed to determine the weight. Figure 3is a chord diagram that shows the
relationship between the selected 12 indices. If these indices belong to one subsystem, they
will be converted into one point. The arc length of each index represents its corresponding
weight. The specific weight value of each index is listed in Table 3. Once the index system
is established, each index needs to be analyzed to establish its reasonable value range and
grading standard. The water carrying capacity of the study area was classified into five
states, and an individual index was divided into five levels correspondingly (Table 3). The
grade classification and reflective meaning are illustrated in Table 4.
Figure 3. Relationship between subsystems and indices with their weights.
Table 3. Estimated evaluation index weights and classification standards of the WRCC in the Han River basin.
Subsystem Index Unit Weighted Value Carrying Grade
Grade I Grade II Grade III Grade IV Grade V
Water resources
subsystem X1104m3/km20.1018 >50 40~50 30~40 20~30 <= 20
X2104m3/km20.1032 <1 1~3 3~10 10~15 >15
X3% 0.0934 <5 5~25 25~35 35~45 >45
X4m3/cap 0.1015 >3000 1700~3000 1000~1700 500~1000 <500
Socioeconomic
subsystem X5104yuan/cap 0.0715 >5 2.5~5 1~2.5 0.4~1 <0.4
X6Population/km20.0768 <110 110~150 150~200 200~250 >250
X7% 0.0715 <15 15~30 30~50 50~60 >60
X8m3/104yuan 0.0529 <90 90~110 110~250 250~600 >600
X9L/d 0.0727 <100 100~150 150~200 200~300 >300
X10 m3/104yuan 0.0546 <8 8~10 10~15 15~20 >20
Eco-environment
subsystem X11 % 0.1024 >4 3~4 2~3 1~2 <1
X12 104tons/km20.0976 <1.5 1.5~2.0 2~2.5 2.5~3.0 >3.0
Water 2021,13, 249 7 of 19
Table 4. Grade classification and state description.
Grade State State Description
I Best The situation of the system is optimistic.
II Better The water resources can furnish better guarantee.
III General The system remains in a relatively stable state.
IV Worse
There is a certain degree of guarantee with limited potential.
V Poor The system is severely at risk.
3.2. Set Pair Analysis
A set pair analysis put forward by Zhao [
39
] was primarily adopted to grapple with
uncertainty problems [
40
]. It is well-known for its ease of implementation, qualitative and
quantitative analyses, and uncertainty consideration. Owing to the variability and com-
plexity of the water resources, there is a complicated and uncertain relationship between
each evaluation factor and the carrying capacity level [41].
The main idea is to construct the research into two sets with a certain connection, and
then systematically analyze the characteristics of the two sets in identity, discrepancy, and
contradistinction using the degree of connection for quantitative description. Assuming set
A= (X
1,
X
2,
X
3,
. . . XN)
and set
B= (P
1,
P
2,
P
3,
. . . PN)
construct a set pair
J= (A
,
B)
,
the expression describing the connection degree can be stated as
µ(A∼B) = U
N+V
Ni+W
Nj(1)
where
µ(A−B)
refers to the connection degree of sets
A
and
B
;
N
refers to the total
number of elements;
U
refers to the number of identical elements, in which identity
signifies that sets
A
and
B
are identical;
V
refers to the number of discrepant elements, in
which discrepancy signifies that there are some subtle differences between sets
A
and
B
;
W
refers to the number of contradictory elements, in which contradistinction signifies that
remarkable differences exist in sets
A
and
B
; accordingly,
U/N
,
V/N
, and
W/N
refer to
the identical degree, discrepancy degree, and contrary degree, respectively;
i
refers to the
uncertain coefficients of the discrepancy degree valuing in the range of
−
1 and 1; and
j
refers to the coefficient of the contrary degree and generally takes the value of
−
1, playing
the role of contrary mark sometimes. Setting
a=U/N
,
b=V/N
, and
c=W/N
, then
Equation (1) can be written as
µ(A∼B) = a+bi +cj (2)
where the values of the coefficients satisfy
a+b+c=
1. Equations (1) and (2) are the
connection degrees that are commonly used (i.e., the three-element connection degree).
bi
in Equation (2) can be expanded to
bi =b1i1+b2i2+· · · +bkik
, so a multi-element
connection degree can be attained.
A set pair
H(Al
,
Bk)
can be formed when the values of an index
xl
(l=1, 2, 3,
. . .
,m;m
denotes the number of evaluation indices) in the evaluation are viewed as one set called
Al
and the evaluation criteria for the corresponding index are taken as another set
Bk
(k=1, 2,
3,
. . .
,K;Kdenotes the number of evaluation levels). Based on the principle of SPA, the
K-element connection degree of H(Al,Bk)can be defined as
µ=µAl∼Bk=m
∑
l=1
wlµl
=m
∑
l=1
wlal+m
∑
l=1
wlbl,1i1+m
∑
l=1
wlbl,2i2+· · · +m
∑
l=1
wlbl,K−2iK−2+m
∑
l=1
wlclj
(3)
where
wl
refers to the weight of the lth index, which can be assigned due to its contribution
to µ.
Water 2021,13, 249 8 of 19
Let
S1
,
S2
,
S3
,
S4
,
S5
be the thresholds for each index from Grades I to V, respectively,
and the connection degree
µl
of the sample
xl
with its evaluation criteria at Grade I can
be expressed as the line in Figure 4. Then, the confidence criterion is utilized to judge the
ranks of sample.
hk= ( f1+f2+· · · +fk)>λ,k=1, 2, · · · 5 (4)
in which
f1=
m
∑
l=1
wlal,f2=
m
∑
l=1
wlbl,1,· · · ,fK−1=
m
∑
l=1
wlbl,K−2,fK=
m
∑
l=1
wlcl(5)
where
λ
refers to the confidence level. The greater the value of
λ
is, the more conservative
and safer the evaluation result is.
hk
refers to the sum of the first K-elements in the
connection degree, and
f1
,
f2
,
f3
,
f4
,
f5
refer to the identical degree, partial identical
discrepancy degree, uncertainty discrepancy degree, partial contrary discrepancy degree,
and contrary degree, respectively.
Figure 4. The sketch map of the degree of connection in set pair analysis (SPA).
The set pair potential function of the connection degree is its adjoint function, which
means the relative deterministic state and development trend of the study object at the
macro level. Subtraction set pair potential (SSPP) is used for situation analysis [
42
]. Based
on the identity, discrepancy, and contradistinction of SPA, SSPP can be defined as
Sf(u) = (a−c)(1+b)(6)
where the scope of the value of
Sf(u)
is
[−
1.0, 1.0
]
.a,b, and crefer to the same meaning
in Equations (1) and (2).
Sf(u)
is divided into five classes as inverse potential (
Sf(u)∈
[−
1.0,
−
0.6
)
), partial inverse potential (
Sf(u)∈[
0.6,
−
0.2
)
), symmetrical potential (
Sf(u)∈
[−
0.2, 0.2
]
), partial identical potential (
Sf(u)∈(
0.2, 0.6
]
), and identical potential (
Sf(u)∈
(
0.6, 1.0
]
). The major element degenerating the carrying status is what belongs to the
inverse potential or partial inverse [
43
]. Diagnosed as the vulnerability index, this index
serves as the chief object for the regulation of the carrying capacity.
3.3. Distributed Hydrological Model
The SWAT model is a geographic information system (GIS)-based distributed hydro-
logical model with a clear physical mechanism [
44
]. The model can take advantage of GIS
to extract a digital elevation model to form flow networks in sub-basins for simulating the
hydrological cycle process. The model typically divides watersheds into several sub-basins
with different soil types and land use attributes [
45
]. Water balance is the premise and root
of the hydrological cycle simulation in SWAT, the equation of which can be expressed as
SWt=SW0+
t
∑
i=1
(Rday,i−Qsurf ,i−Ea,i−Wseep,i−Qgw,i)(7)
Water 2021,13, 249 9 of 19
where
SWt
refers to the final soil water content (mm),
SW0
refers to the initial soil water
content (mm),
t
refers to the time with units of days,
Rday,i
refers to the precipitation on
the ith day (mm),
Qsur f ,i
refers to the surface runoff on the ith day (mm),
Ea,i
refers to
the evapotranspiration on the ith day (mm),
Wseep,i
refers to seepage from the soil profile
on the ith day (mm), and
Qgw,i
refers to the underground runoff on the ith day (mm).
Predominantly, the coefficient of the Nash–Sutcliffe efficiency (NSE) and the relative error
(RE) are exploited to evaluate the results of the SWAT simulation.
NSE =1−
n
∑
t=1Qt
0−Qt
s2
n
∑
t=1Qt
0−Q02(8)
RE =
n
∑
t=1Qt
0−Qt
s×100%
n
∑
t=1
Qt
0
(9)
where
Qt
0
represents the observed discharge at time t,
Qt
s
represents the simulated discharge
at time t,
Q0
represents the mean of observed values, and nrepresents the number of
observed data. On the condition that the results satisfy NSE > 0.5 and RE < 15% during
both calibration and validation periods, the SWAT model is applicable for this basin, and
the simulation results are acceptable.
In this paper, the observed discharge data from four hydrological stations, including
Ankang, Baihe, Danjiangkou, and Huangzhuang, were exploited for calibrating and vali-
dating the SWAT model. The Ankang and Baihe hydrological stations are located in the
upper reaches of the basin, while the Danjiangkou hydrological station is in the middle
reaches, and the Huangzhuang station lies in the lower reaches. The calibration began on
01 January 1980 and lasted until 31 December 1993, and validation was performed during
the period 01 January 1994–31 December 2000.
3.4. Water Resources Development and Utilization Model
To capture the dynamic properties of the water resources subsystem, socioeconomic
subsystem, and eco-environment subsystem, a compiled model was used to predict the
values of evaluation indices scientifically in the planning year.
Water resources
Modulus of water resources production refers to annual water resources amount per
unit area [
46
]. The total water resources amount of the study area can be obtained according
to the simulation result of the SWAT model. The corresponding formula for computing the
entire amount of water resources is as follows:
W=Rs+Pr=R+Pr−Rg(10)
where
W
denotes the entire amount of water resources (m
3
),
Rs
denotes the surface runoff
(i.e., the difference between streamflow and baseflow, m
3
),
Pr
denotes the precipitation
infiltration quantity (m
3
),
R
denotes the streamflow (i.e., the surface water resources
amount, m3), and Rgdenotes the baseflow (m3).
Water supply and consumption
Water consumption refers to the sum of the water used by all types of off-stream water
users, including losses from water transmission [
47
]. The quota method was applied to
assess the water consumption, the explicit steps of which are as follows: Step 1: explore the
trends of the main factors affecting water consumption and determine water consumption
indices and quotas. Step 2: compute the amount of water consumption in the planning year
in terms of the indices and quotas derived from Step 1, such as population and industrial
production. In the light of the classification of water users, the water consumption of each
Water 2021,13, 249 10 of 19
sector can be estimated based on the influence factors and quotas. The gross amount of
water consumption refers to the sum of water consumption of each sector. Meanwhile,
the estimated amount of water supply is equal to water consumption. The formula for
estimating water consumption is as follows:
WD =WQ ·WA/(1−LR)(11)
where
WD
refers to the water consumption of one sector,
WQ
refers to the water quota
per unit of one sector, and
WA
and
LR
refer to water use per activity level and the water
transferring loss ratio of the sector, correspondingly. For example, when we estimate the
domestic water consumption,
WD
refers to the amount of domestic water consumption
(m
3
),
WQ
refers to the water consumption per person (m
3
/person),
WA
refers to the
total population (number of persons), and
LR
refers to the water transferring loss ratio
of domesticity. As for the water consumption of an industry, agriculture, and ecological
environment, the parameters represent the corresponding water consumption indices and
quotas.
Water pollution and water environment
The rate of ecological water consumption is calculated by the outputs from the water
consumption in the water resources development and utilization model. The wastewater
discharge is calculated based on the water consumption of a domesticity and industry with
the corresponding pollution discharging coefficients.
X11 =WDeco/WDto tal (12)
X12 =α·WDdom +β·WDind (13)
where
WDeco
refers to the water consumption of an ecology, and
WDtotal
refers to the sum
of water consumption of all sectors.
WDdom
and
WDind
refer to the water consumption of a
domesticity and industry, respectively.
α
and
β
refer to the pollution discharging coefficient
of a domesticity and industry, respectively. Historical data from the Statistical Bulletin of
the National Economic and Social Development and Environmental State Bulletin of the
cities in the Han River basin are allowed for this study. Besides, the industrial structures,
development trends, and environmental protection requirements are considered to predict
the value comprehensively.
4. Results
4.1. Evaluation of the Current Water Resources Carrying Capacity
The WRCC during the period 2010–2016 is shown in Figure 5. The calculation of
the connection degree of each index is based on Figure 4. The connection degree of the
whole system and three subsystems can be attained by combining with the weight of each
index. The WRCC was maintained at Grade IV when
λ
was set to 0.75. Meanwhile, the
condition was detrimental to facilitating the expansion of the industry. Further analysis
of five individual elements illustrated that the value of aslid from 0.21 to 0.10 and then
returned up to 0.19, while the value of cremained unchanged. The value of b
1
was on the
wane, but b
2
and b
3
showed a growing trend, which implied that the WRCC condition was
getting worse.
Table 5shows that the SSPP belonged to a symmetrical potential from 2010 to 2016. A
higher SSPP implies a better carrying condition. The pressure of the WRCC bottomed out
in 2010 with a corresponding SSPP value of 0.067; on the contrary, it reached its peak in
2013 with a corresponding value of
−
0.069. The WRCC in the current years could support
the socioeconomic development in this basin. Although the WRCC in 2010–2016 pertained
to a symmetrical potential from the perspective of situation analysis, the result of the SSPP
fluctuated to a certain extent.
Water 2021,13, 249 11 of 19
Figure 5. The five-element connection degree of the WRCC.
Table 5. WRCC connection and subtraction set pair potential.
Year
Five-Element Connection Degree
SSPP Potential
a b1b2b3c
Water
resources
carrying
capacity
2010 0.21 0.23 0.27 0.13 0.16 0.067 Symmetrical
2011 0.12 0.29 0.29 0.14 0.16 −0.041 Symmetrical
2012 0.12 0.13 0.37 0.23 0.16 −0.047 Symmetrical
2013 0.10 0.17 0.20 0.36 0.16 −0.069 Symmetrical
2014 0.11 0.18 0.31 0.25 0.16 −0.060 Symmetrical
2015 0.12 0.16 0.33 0.23 0.16 −0.040 Symmetrical
2016 0.19 0.11 0.35 0.20 0.16 0.036 Symmetrical
mean 0.14 0.18 0.30 0.22 0.16 −0.022 Symmetrical
2035s 0.22 0.03 0.18 0.26 0.31 −0.100 Symmetrical
Water
resources
subsystem
2010 0.20 0.44 0.24 0.12 0 0.250 Partial identical
2011 0 0.47 0.39 0.14 0 0 Symmetrical
2012 0 0 0.66 0.34 0 0 Symmetrical
2013 0 0 0.33 0.67 0 0 Symmetrical
2014 0 0 0.64 0.36 0 0 Symmetrical
2015 0 0 0.70 0.30 0 0 Symmetrical
2016 0 0.04 0.77 0.19 0 0 Symmetrical
mean 0.03 0.14 0.53 0.30 0 0.036 Symmetrical
2035s 0 0 0.35 0.42 0.23 −0.291 Partial inverse
Socioeconomic
subsystem
2010 0.09 0.14 0.45 0.19 0.14 −0.059 Symmetrical
2011 0.07 0.24 0.35 0.21 0.14 −0.090 Symmetrical
2012 0.05 0.33 0.26 0.23 0.14 −0.106 Symmetrical
2013 0.01 0.43 0.17 0.25 0.14 −0.162 Symmetrical
2014 0.03 0.45 0.13 0.26 0.14 −0.140 Symmetrical
2015 0.07 0.40 0.12 0.28 0.14 −0.088 Symmetrical
2016 0.22 0.22 0.11 0.30 0.14 0.103 Symmetrical
mean 0.08 0.32 0.23 0.24 0.14 −0.077 Symmetrical
2035s 0.31 0.08 0.10 0.03 0.48 −0.180 Symmetrical
Eco-
environment
subsystem
2010 0.49 0 0 0 0.51 −0.024 Symmetrical
2011 0.49 0 0 0 0.51 −0.024 Symmetrical
2012 0.49 0 0 0 0.51 −0.024 Symmetrical
2013 0.49 0 0 0 0.51 −0.024 Symmetrical
2014 0.49 0 0 0 0.51 −0.024 Symmetrical
2015 0.49 0 0 0 0.51 −0.024 Symmetrical
2016 0.49 0 0 0 0.51 −0.024 Symmetrical
mean 0.49 0 0 0 0.51 −0.024 Symmetrical
2035s 0.49 0 0 0.39 0.12 0.412 Partial identical
Note: SSPP refers to subtraction set pair potential; “mean” refers to the mean values in 2010~2016.
Water 2021,13, 249 12 of 19
In terms of the water resources subsystem, the carrying capacity stood at the top in
2010 and gradually declined until 2013. The SSPP belonged to a partial identical potential,
which was beneficial to the subsystem in 2010; however, it remained 0 in the following years.
Although it belonged to a symmetrical potential, it did not achieve a better performance
than the historical period. Specific analysis of the five elements in the connection degree
pointed out that the value of adecreased from 0.20 to 0. The value of b
2
subordinate to
Grade III followed a fluctuating ascending trend, reaching its peak at 0.77 in 2016. The
value of b
3
climbed from 2010 to 2013 and then declined. It demonstrated that the situation
of the water resources subsystem was not optimistic, coincident with the fact that the rate
of water resources exploitation and utilization remained relatively high, but the water
resources per capita stayed relatively poor.
In terms of the socioeconomic and eco-environment subsystems, the SSPP of the
socioeconomic subsystem dropped from
−
0.059 to
−
0.162 and then bounced back to
0.103. X
5
and X
8
were the two main factors influencing the socioeconomic subsystem. The
classification of X
5
and X
8
rose from Grade III to II. However, X
6
and X
7
continued to rise,
and X
10
dropped steadily but they remained relatively high, giving rise to the fluctuation
trend. The result of the eco-environment subsystem remained unchanged during this
period, which had to do with the fixed level of each index in this subsystem intimately.
4.2. Performance of the SWAT Model
The parameter calibration is used to find out the simulated value performing best in
tone with the observed value. In this study, the Sequential Uncertainty Fitting programme
algorithm (SUFI-2) was harnessed to calibrate the hydrological model on the basis of
the monthly discharge data from four hydrological stations. The SWAT-CUP software
developed by U.S. Department of Agriculture-Agricultural Research Service exported 12
selected parameters for calibration, and the optimal values are listed in Table 6.
Table 6. Description of runoff calibration parameters.
No. Parameter Description Fitted Value
1 Alpha_Bf Baseflow recession constant 0.5
2 Ch_K2 Effective hydraulic conductivity of channel (mm/hr) 70.06
3 Ch_N2 Manning’s “n” value for the main channel 0.06
4 Cn2 Moisture condition II curve number −0.27
5 Gw_Delay Delay time for aquifer recharge (days) 184.35
6 Gw_Revap Revap coefficient 0.1
7 Gwqmn
Threshold water level in the shallow aquifer for baseflow (mm)
0.46
8 Esco Soil evaporation compensation factor 1.06
9 Smtmp Threshold temperature for snow melt (°C) −3.39
10 Sol_BD Bulk density of the layer (mg/m3)0.26
11 Sol_Awc Available water capacity 0.38
12 Sol_K Saturated hydraulic conductivity (mm/hr) −0.43
The simulation results of each hydrological station are shown in Figure 6, and the
evaluation criteria are concluded in Table 7.
Figure 6shows that the calibration resulted in at least satisfactory model performances
in simulating river runoff. The SWAT model shows a fine performance, but the deficiency
of simulating maximum discharge and peak values was exposed. Nonetheless, it exhibited
quite a good performance of average and minimum discharge rates. Taking the Ankang
station as an example, the NSEs were 0.93 and 0.83 in the calibration and validation periods,
respectively. Meanwhile, the REs were 2.4% and 8.1% in the calibration and validation
periods, respectively. The NSE was greater than 0.8, and the RE was lower than 15% for
all the stations, implying a good performance of the SWAT model. Nevertheless, due to
the damage of watershed hydrological cycle processes triggered by tremendous impacts
of human activities, the simulation results were not conspicuously well produced for all.
Still and all, the average NSE and the average absolute value of RE equaled 0.90, 2.8% and
Water 2021,13, 249 13 of 19
0.76, 6.2% for the calibration and validation periods, respectively. Those indices indicated a
good SWAT model performance for streamflow simulation in the Han River basin.
1
(a) Ankang
(b) Danjiangkou
(c) Baihe
(d) Huangzhuang
Figure 6.
The observed and SWAT-simulated monthly streamflow for the calibration period (1980–1993) and for the
validation period (1994–2000) in the Han River basin, China.
Table 7. The calibration and validation results of the SWAT model.
No. Hydrological
Station
Calibration Period
(1980–1993)
Validation Period
(1994–2000)
NSE RE/% NSE RE/%
1 Ankang 0.93 2.4 0.83 8.1
2 Baihe 0.91 −0.3 0.78 −1.9
3 Danjiangkou 0.92 6.9 0.75 7.5
4
Huangzhuang
0.82 −1.4 0.66 7.1
Average absolute value 0.90 2.8 0.76 6.2
In addition, projected temperature and precipitation have been utilized to analyze
climate change impact on hydrological cycle processes under RCP 4.5. As the global
climate models’ (GCMs) outputs are usually too coarse and biased to represent the spatial
heterogeneity of the climatic condition at a basin scale, the daily bias correction method
was utilized to correct the GCMs’ raw outputs. The bias-corrected temperature and
precipitation projections from 20 GCMs were used to force the SWAT model. The ensemble
mean streamflow projections from the SWAT is utilized to represent the water resources
condition in the future. The water resources during the period 2030–2040 in the study area
were acquired from the SWAT model. Compared with the simulated annual mean runoff
of 1743 m
3
/s in the base period, the runoff in 2035s will reach 1829 m
3
/s. The results
demonstrated that the water resources of the whole study area tended to increase under
RCP 4.5.
4.3. Socioeconomic Projection in Future Periods
To predict the state of water resources development and utilization in the planning
year reasonably, the relevant economic indices and water use level indices need to be
determined by comprehensive prediction through different methods.
The statistical analysis of the resident population of the Han River basin illustrated
that the average annual growth rate was around 1.82
‰
from 2010 to 2016. The growth
Water 2021,13, 249 14 of 19
rate further expanded to 4.7
‰
in 2016. Eventually, the population of each city in 2035s
was predicted according to its population planning, contributing to 38 million people in
the basin in total. The prediction of the urbanization rate was primarily based on the
current urbanization quality and level and strategic development plans. Consequently,
the urbanization rate of the Han River basin was predicted to be 66% in 2035s. The Han
River basin achieved an average annual GDP growth of 12.44% between 2010 and 2016.
With the forward movements of the economic restructuring and the economy’s gain in
size, economic growth would slow down. Therefore, a target growth rate of approximately
6.5% was set. The added value of the primary industry, secondary industry, and tertiary
industry could be acquired in the light of GDP growth rate and the industry structure of
each city based on the economic development plans. Factors including cultivated land and
water-saving reconstruction were considered. An irrigated area of 1.65 million hectares
was predicted for the Han River basin in 2035s.
The projected water resources development and utilization in the planning year is
shown in Table 8. The domestic water demands for urban and rural areas are 1.66 billion
and 0.48 billion m
3
, respectively. The water demand for the industry is estimated to be
7.62 billion m
3
in total. Meanwhile, the water demands for agriculture and the off-stream
eco-environment are 10.52 billion and 0.28 billion m
3
, respectively. Large-scale water
diversion projects will reduce the available water resources enormously. This demonstrates
that there will be a water shortage of 2.49 billion m
3
in 2035s. Hence, rational utilization of
water resources stands out.
Table 8. Water resources and water demands in 2010~2016 and in the planning year (billion m3).
Year
Water Resources Water Demands
Water Deficit
Local Water
Resources
Available Water
Resources Domesticity Industry Agriculture Ecology
2010 75.95 46.61 1.26 5.59 7.65 0.05 0
2011 67.05 37.71 1.27 5.60 8.12 0.10 0
2012 46.29 16.95 1.29 5.66 8.07 0.07 0
2013 38.92 9.58 1.30 4.58 8.83 0.07 5.19
2014 44.66 15.32 1.31 4.37 8.68 0.08 0
2015 46.72 17.38 1.37 4.50 8.82 0.09 0
2016 49.64 20.30 1.43 4.25 8.38 0.13 0
mean 52.75 23.41 1.32 4.94 8.36 0.08 0.74
2035s 56.90 18.06 2.14 7.62 10.52 0.28 2.49
Note: “mean” refers to the mean values in 2010~2016.
4.4. Evaluation Result in the Planning Year
The WRCC in the planning year can be estimated using SPA (shown in Table 5). The
five elements of the connection degrees will be 0.22, 0.03, 0.18, 0.26, and 0.31. The WRCC
will belong to Grade IV if
λ
is set to 0.75, which means that it will take a toll on urban
sprawl or economic growth under overloading circumstances. Compared with the status of
the WRCC in the current years, the changes of the five elements indicate that the WRCC has
undergone a development towards being overloaded. Although there is a slight increase
in avalue, the value of b
1
(partial identical discrepancy degree) will fall sharply, and the
value of c(contrary degree) will rise drastically. Table 5shows that the SSPP of the WRCC
will be subordinate to the symmetrical potential in 2035s, whose value is the minimum. In
comparison with the evaluation results in 2010–2016, the pressure of the WRCC will be
further increased in 2035s with a corresponding SSPP of −0.1.
Regarding the water resources subsystem in the planning year, Table 5shows that it
will perform worst during all the evaluation periods. With all of the water diversion projects
to be completed and to operate, water diversion inflow and outflow will dramatically
change the available water resources. The total water resources will reach 47.4 billion m
3
after considering the water diversion, lower than that in 2010–2016. With the population
growth and development of the economy, water consumption of various types will increase
Water 2021,13, 249 15 of 19
to a large extent, translating to an increase in water supply. Particularly, the mean of X
2
is equal to 9.46 10
4
m
3
/km
2
in the current years, but it will rise by nearly 40%. X
2
will
climb to 13.22 10
4
m
3
/km
2
, which is prejudicial to the subsystem. As for the restrictive
index, this alteration not only is devastating to the subsystem but also exposes the water
resources to more pressure. In respect of the socioeconomic subsystem, the SSPP will
decrease compared with that in 2016. It will decrease from
−
0.077 in the current years
to –0.18 in 2035s. Specifically, the level of X
7
will metamorphose from Grade III to V, and
X
9
will change from Grade II to III. For the eco-environment subsystem, it will improve
a lot compared with the current period. X
11
will reach Grade IV in 2035s, while it had
been at Grade V in 2010–2016; X
12
will be at Grade I with a further optimized value of
10,500 tons/km2. The indices will work together to improve the carrying capacity.
Figure 7shows the SSPP of each index in the current years and in the planning year.
There were two, one, and three identical potential indices in 2010, 2011–2015, and 2016.
Meanwhile, there were two inverse potential indices in 2010–2016. This was consistent with
the distribution of the carrying status in these years. Likewise, the numbers of identical and
inverse potential indices were consistent with their temporal distributions. Consequently,
the number of identical and inverse potential indices could mirror the carrying status
basically. In the current years, the SSPP values of X
10
and X
11
are less than 0.2, so these two
indices should be paid attention to. In 2035s, there will be four inverse potential indices,
the number of which will be greater than that of the current years. A higher number of
inverse potential indices implies a worse carrying capacity condition. Therefore, the water
resources in the Han River basin will face more pressure and challenges in the planning
year. In the planning year, X
5
, X
8
, and X
12
will perform best with the same value of 1,
indicating that it will have a strong carrying capacity and can well support harmonious
development. X
1
, X
4
, and X
9
will be just average belonging to the symmetrical potential,
geared towards the WRCC at the planning stage. The poor performers will be X
2
and X
11
belonging to the partial inverse potential. In contrast, X
3
, X
6
, X
7
, and X
10
will perform
worst and belong to the inverse potential. Therefore, X
2
, X
3
, X
6
, X
7
, X
10
, and X
11
will be
the vulnerable indices, causing the weak regional water resource carrying capacity at the
planning level. Furthermore, X
7
and X
10
will be the most prioritized regulation objects for
improving the regional WRCC.
Figure 7. Subtraction set pair potential of each index in the current years and in the planning year.
Water 2021,13, 249 16 of 19
5. Discussion
The WRCC is a dramatically essential index measuring coordination between water
resources, human beings, and the sustainability of regional economic development. There
are numerous collisions between humans and nature over water resources in the Han
River basin [
33
]. To cater to water resources planning, protection, and rational utilization,
there is a compelling need to evaluate the WRCC for the national strategic water resources
security zone. Previous research studies mainly focused on historical conditions but rarely
considered future scenarios under climate changes and social development. Apart from
that, the dynamics of the water resources carrying capacity system have not been paid
great attention to. This study develops a methodology integrating a hydrological model,
projected socioeconomic development model, and evaluation system for assessing and
predicting the WRCC of the Han River basin. This methodology not only encompasses
three subsystems with features of varying indices but also provides a comprehensive
evaluation result considering dynamics. Its application to the Han River basin has proved
its superior performance (Tables 5–8and Figures 5–7). The constructed evaluation index
system adheres to the requirements of simplicity, operability, and comprehensiveness and
hierarchy. A water resources subsystem, socioeconomic subsystem, and eco-environment
subsystem were all included in the evaluation index system. Embedded in the GIS platform
in the form of a toolbar, the SWAT model features a convenient operation, user-friendly
interface, high degree of structure, ease of use, and so forth. This study divided the total
water consumption into four main types (i.e., water consumption for domesticity, industry,
agriculture, and ecology). The quota method was employed to accomplish the prediction
with a thorough view of the growth in population and economy and water consumption
level and condition. The comprehensiveness of SPA was shown in practical experience that
it not only considers the identity of two mathematical sets but also mirrors the discrepancy
and contradistinction between the sets.
Even though the superior performance of the proposed methodology has been demon-
strated in its application to the Han River basin in China, some work still requires to be
further explored. The WRCC is a complex system that is affected and interacted by the
three major systems of water resources, social economy, and ecological environment. This
paper fully considers the influencing factors of the water resources system and the social
and economic systems. Due to the obstacle to collecting data, the consideration of the
ecological environment system is not remarkably comprehensive, especially in the aspects
of water ecology and water environment. With increasing attention being paid to the spells
of ecology and the environment by the nation and social public [
3
], it is bound to be a key
issue for future research to comprehensively consider the water environment and water
ecology carrying capacity while studying the WRCC.
The SWAT model involves a multitude of parameters that are intricate and arduous
to be calibrated. Therefore, the hydrological model needs to be improved, combining
specific natural conditions to obtain more desired simulation results. We can call the
existing modules and add the required modules according to the conditions, which gains
more applicability, prosperity, and reliability to brace for complex hydrological cycle
issues [
48
,
49
]. Besides, this paper solely puts emphasis on the simulation and projection
of discharge in the Han River basin under the RCP 4.5 scenario. Other representative
concentration pathways [
50
] can also be included to simulate discharge under future
climate changes to provide more diverse results. The evaluation and diagnostic model
based on the connection degree proposed in this paper has good application value and
universality in the WRCC. Alongside this, we can try to apply the evaluation and diagnostic
model and research ideas established in this paper to land carrying capacity [
51
], water
environmental carrying capacity [
52
], ecological carrying capacity [
53
], and other systems.
6. Conclusions
A comprehensive methodology is developed to assess the WRCC, and is applied to
one of the most essential basins in China. A comprehensive evaluation system coupling
Water 2021,13, 249 17 of 19
different models is established for the assessment of the WRCC. This system with com-
prehensiveness and rationality is quite conducive to perceiving water resources condition.
The findings of the research are of high significance to advance high-quality development
between water resources, social economy, and ecological environment subsystems. This
study gives policy-makers a window to digest important information on water resources
exploitation and regulation strategies. The main conclusions are as follows:
(1)
An evaluation index system including three criterion layers, such as water resources,
social economy, and ecological environment, was set up to evaluate the WRCC in
the Han River basin, and weight analysis demonstrated that X
2
(modulus of water
supply) weighed the most with a weight of 0.103, while X
8
(water consumption per
10
4
yuan GDP) accounted for the lightest weight. The SPA manifested that the WRCC
in the Han River basin first decreased and then increased. The water resources sub-
system operated best with the lowest pressure, while the eco-environment subsystem
performed worst in 2010–2016.
(2)
For the sake of the prediction of the WRCC in the future, the hydrological model
and water resources development and utilization model were coupled to predict the
value of each index in the planning year. The SWAT model revealed that the total
water resources will reach 47.4 billion m
3
after considering the quantity of transferred
water in 2035s, which presented lower than that in 2010–2016. The water resources
development and utilization model showed that there will be 38 million people with
an urbanization rate of 66% in the Han River basin in 2035s, resulting in the water
consumption climbing to 20.5 billion m3in total.
(3)
The pressure of the WRCC will further increase, and the system will be confronted
with more challenges in 2035s in reference to the current years. The results of the
SSPP show that half of the indices will be vulnerability indices weakening the WRCC
at the 2035s planning level. The water distribution projects and optimal water re-
sources allocation system should be promoted for construction and implementation
to alleviate the grave condition. The methodology integrated the natural water cycle,
and water resources management could be utilized to assess and predict the WRCC
dynamically and efficiently. Furthermore, the core idea of this paper provided a new
tool to evaluate and predict other systems.
Author Contributions:
Conceptualization and software, L.D. and J.T.; data curation, J.Y. and Q.L.;
formal analysis, L.D., J.Y., and J.T.; writing—Original draft preparation, L.D.; writing—Review and
editing, J.Y. and S.G. All authors have read and agreed to the published version of the manuscript.
Funding:
This study was funded by the National Natural Science Foundation of China (Grant
Nos. U20A20317, 51879192, and 52009091). It is also partly supported by the Natural Science
Foundation of Hubei Province (No. 2020CFB239) and the China Postdoctoral Science Foundation
(No. 2020M682478).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to the raw/processed data required
to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing
study.
Acknowledgments:
The authors would like to express their gratitude to anonymous reviewers for
their insightful and constructive comments.
Conflicts of Interest: The authors declare no conflict of interest.
Water 2021,13, 249 18 of 19
References
1.
Liu, D.; Guo, S.; Liu, P.; Xiong, L.; Zou, H.; Tian, J.; Zeng, Y.; Shen, Y.; Zhang, J. Optimisation of water-energy nexus based on its
diagram in cascade reservoir system. J. Hydrol. 2019,569, 347–358. [CrossRef]
2. Yao, J.; Wang, G.; Xue, B.; Xie, G.; Peng, Y. Identification of regional water security issues in China, using a novel water security
comprehensive evaluation model. Hydrol. Res. 2020,51, 854–866. [CrossRef]
3.
Strickling, H.; DiCarlo, M.F.; Shafiee, M.E.; Berglund, E. Simulation of containment and wireless emergency alerts within targeted
pressure zones for water contamination management. Sustain. Cities Soc. 2020,52, 101820. [CrossRef]
4.
Plumb, G.E.; White, P.J.; Coughenour, M.B.; Wallen, R.L. Carrying capacity, migration, and dispersal in Yellowstone bison. Biol.
Conserv. 2009,142, 2377–2387. [CrossRef]
5.
Kessler, J.J. Usefulness of the human carrying-capacity concept in assessing ecological sustainability of land-use in semiarid
regions. Agric. Ecosyst. Environ. 1994,48, 273–284. [CrossRef]
6. Clarke, A.L. Assessing the carrying capacity of the Florida Keys. Popul. Environ. 2002,23, 405–418. [CrossRef]
7.
Feng, L.H.; Huang, C.F. A risk assessment model of water shortage based on information diffusion technology and its application
in analyzing carrying capacity of water resources. Water Resour. Manag. 2008,22, 621–633. [CrossRef]
8.
Song, X.; Kong, F.; Zhan, C. Assessment of water resources carrying capacity in Tianjin City of China. Water Resour. Manag.
2011
,
25, 857–873. [CrossRef]
9.
Fang, H.; Gan, S.; Xue, C. Evaluation of regional water resources carrying capacity based on binary index method and reduction
index method. Water Sci. Eng. 2019,12, 263–273. [CrossRef]
10.
Ofoezie, I.E. Human health and sustainable water resources development in Nigeria: Schistosomiasis in artificial lakes. Nat.
Resour. Forum 2002,26, 150–160. [CrossRef]
11.
Li, H.; Hou, L. Evaluation on sustainable development of scenic zone based on tourism ecological footprint: Case study of
Yellow Crane Tower in Hubei Province, China. In Proceedings of the 2010 International Conference on Energy, Environment and
Development, Kuala Lumpur, Malaysia, 8–9 December 2010; Zhang, W., Ed.; Elsevier B.V.: Amsterdam, The Netherlands, 2011;
Volume 5, pp. 145–151.
12.
Wang, S.; Yang, F.; Xu, L.; Du, J. Multi-scale analysis of the water resources carrying capacity of the Liaohe basin based on
ecological footprints. J. Clean. Prod. 2013,53, 158–166. [CrossRef]
13.
Chen, Y.; Zhang, S.; Zhang, Y.; Xu, L.; Qu, Z.; Song, G.; Zhang, J. Comprehensive assessment and hierarchical management of
the sustainable utilization of urban water resources based on catastrophe theory. J. Taiwan Inst. Chem. Eng.
2016
,60, 430–437.
[CrossRef]
14.
Zhang, S.; Xiang, M.; Yang, J.; Fan, W.; Yi, Y. Distributed hierarchical evaluation and carrying capacity models for water resources
based on optimal water cycle theory. Ecol. Indic. 2019,101, 432–443. [CrossRef]
15.
Liu, H. Comprehensive carrying capacity of the urban agglomeration in the Yangtze River Delta, China. Habitat Int.
2012
,36,
462–470. [CrossRef]
16.
Kumar, T.; Gautam, A.K.; Jhariya, D.C. Multi-criteria decision analysis for planning and management of groundwater resources
in Balod District, India. Environ. Earth. Sci. 2016,75, 649. [CrossRef]
17.
Maier, H.R.; Dandy, G.C. Neural networks for the prediction and forecasting of water resources variables: A review of modelling
issues and applications. Environ. Modell. Softw. 2000,15, 101–124. [CrossRef]
18.
Zhang, Y.; Yin, L.; Li, X. A decision study on river carrying capacity of Changsha-Zhu-Tan region. In Engineering and Risk
Management; Wu, D.D., Ed.; Elsevier: Amsterdam, The Netherlands, 2011; Volume 1, pp. 422–431.
19.
Zhao, X.; Rao, H.; Yi, Q.; He, C.; Yang, H. Scenarios simulation on carrying capacity of water resources in Kunming City. In
Proceedings of the 2012 International Conference on Structural Computation and Geotechnical Mechanics, Kunming, China,
24–25 March 2012; Yang, P., Jiang, X., Eds.; Elsevier B.V.: Amsterdam, The Netherlands, 2012; Volume 5, pp. 107–112.
20.
Lu, Y.; Xu, H.; Wang, Y.; Yang, Y. Evaluation of water environmental carrying capacity of city in Huaihe River basin based on the
AHP method: A case in Huai’an City. Water Resour. Ind. 2017,18, 71–77. [CrossRef]
21.
Fu, Q.; Jiang, Q.; Wang, Z. Comprehensive evaluation of regional agricultural water and land resources carrying capacity based on
DPSIR concept framework and PP model. In Computer and Computing Technologies in Agriculture V, Proceedings of the International
Conference on Computer and Computing Technologies in Agriculture, Zhangjiajie, China, 19–21 October 2012; Li, D.L., Chen, Y.Y., Eds.;
Springer: Berlin/Heidelberg, Germany, 2012; Volume 370, pp. 391–398.
22.
Yang, X.; Magnusson, J.; Xu, C.-Y. Transferability of regionalization methods under changing climate. J. Hydrol.
2019
,568, 67–81.
[CrossRef]
23.
Yin, J.; Gentine, P.; Zhou, S.; Sullivan, S.C.; Wang, R.; Zhang, Y.; Guo, S.L. Large increase in global storm runoff extremes driven
by climate and anthropogenic changes. Nat. Commun. 2018,9, 10. [CrossRef]
24.
Yin, J.; Guo, S.; Gu, L.; Zeng, Z.; Liu, D.; Chen, J.; Shen, Y.; Xu, C.-Y. Blending multi-satellite, atmospheric reanalysis and gauge
precipitation products to facilitate hydrological modelling. J. Hydrol. 2021,593, 125878. [CrossRef]
25.
Liu, D.; Guo, S.; Shao, Q.; Liu, P.; Xiong, L.; Wang, L.; Hong, X.; Xu, Y.; Wang, Z. Assessing the effects of adaptation measures on
optimal water resources allocation under varied water availability conditions. J. Hydrol. 2018,556, 759–774. [CrossRef]
26.
Yang, G.; Guo, S.; Li, L.; Hong, X.; Wang, L. Multi-Objective Operating Rules for Danjiangkou Reservoir under Climate Change.
Water Resour. Manag. 2016,30, 1183–1202. [CrossRef]
Water 2021,13, 249 19 of 19
27.
Yin, J.; Guo, S.; Wu, X.; Yang, G.; Xiong, F.; Zhou, Y. A meta-heuristic approach for multivariate design flood quantile estimation
incorporating historical information. Hydrol. Res. 2018,50, 526–544. [CrossRef]
28.
Xin, X.; Zhang, H.; Lei, P.; Tang, W.; Yin, W.; Li, J.; Zhong, H.; Li, K. Algal blooms in the middle and lower Han River:
Characteristics, early warning and prevention. Sci. Total Environ. 2020,706, 135293. [CrossRef]
29.
Guo, Y.; Huang, C.; Pang, J.; Zha, X.; Li, X.; Zhang, Y. Concentration of heavy metals in the modern flood slackwater deposits
along the upper Hanjiang River valley, China. Catena 2014,116, 123–131. [CrossRef]
30.
Zhou, Y.; Guo, S.; Hong, X.; Chang, F.-J. Systematic impact assessment on inter-basin water transfer projects of the Hanjiang River
basin in China. J. Hydrol. 2017,553, 584–595. [CrossRef]
31.
Tian, F.; Li, Y.; Zhao, T.; Hu, H.; Pappenberger, F.; Jiang, Y.; Lu, H. Evaluation of the ECMWF System 4 climate forecasts for
streamflow forecasting in the Upper Hanjiang River Basin. Hydrol. Res. 2018,49, 1864–1879. [CrossRef]
32.
Tian, J.; Liu, D.; Guo, S.; Pan, Z.; Hong, X. Impacts of Inter-Basin Water Transfer Projects on Optimal Water Resources Allocation
in the Hanjiang River Basin, China. Sustainability 2019,11, 2044. [CrossRef]
33.
Yu, M.; Wang, C.; Liu, Y.; Olsson, G.; Wang, C. Sustainability of mega water diversion projects: Experience and lessons from
China. Sci. Total Environ. 2018,619, 721–731. [CrossRef]
34.
Shen, M.; Chen, J.; Zhuan, M.; Chen, H.; Xu, C.-Y.; Xiong, L. Estimating uncertainty and its temporal variation related to global
climate models in quantifying climate change impacts on hydrology. J. Hydrol. 2018,556, 10–24. [CrossRef]
35.
Pearson, K. On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci
1901
,2, 559–572.
[CrossRef]
36. Hotelling, H. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 1933,24, 417. [CrossRef]
37.
Zou, Z.; Yun, Y.; Sun, J. Entropy method for determination of weight of evaluating indices in fuzzy synthetic evaluation for water
quality assessment. J. Environ. Sci. 2006,18, 1020–1023. [CrossRef]
38.
Wang, B.; Teng, Y.; Wang, H.; Zuo, R.; Zhai, Y.; Yue, W.; Yang, J. Entropy weight method coupled with an improved DRASTIC
model to evaluate the special vulnerability of groundwater in Songnen Plain, Northeastern China. Hydrol. Res.
2020
,51, 1184–1200.
[CrossRef]
39.
Zhao, K. Set pair and set pair analysis—A new concept and systematicanalysis method. In Proceedings of the National Conference on
System Theory and Regional Planning, Baotou, China; ACM, Inc.: New York, NY, USA, 1989; pp. 87–91.
40.
Jin, J.; Wu, K.; Wei, Y. Connection number based assessment model for watershed water security. J. Hydraul. Eng.
2008
,39,
401–409.
41.
Zeng, J.; Huang, G. Set pair analysis for karst waterlogging risk assessment based on AHP and entropy weight. Hydrol. Res.
2017
,
49, 1143–1155. [CrossRef]
42.
Chong, T.; Yi, S.; Heng, C. Application of set pair analysis method on occupational hazard of coal mining. Saf. Sci.
2017
,92, 10–16.
[CrossRef]
43.
Cui, Y.; Feng, P.; Jin, J.; Liu, L. Water resources carrying capacity evaluation and diagnosis based on set pair analysis and improved
the entropy weight method. Entropy 2018,20, 359. [CrossRef]
44.
Bhatta, B.; Shrestha, S.; Shrestha, P.K.; Talchabhadel, R. Evaluation and application of a SWAT model to assess the climate change
impact on the hydrology of the Himalayan River basin. Catena 2019,181. [CrossRef]
45.
Liu, T.; Chen, Y.; Li, B.; Hu, Y.; Qiu, H.; Liang, Z. Long-term streamflow forecasting for the Cascade Reservoir System of Han
River using SWAT with CFS output. Hydrol. Res. 2018,50, 655–671. [CrossRef]
46.
Wu, C.; Zhou, L.; Jin, J.; Ning, S.; Zhang, Z.; Bai, L. Regional water resource carrying capacity evaluation based on multi-
dimensional precondition cloud and risk matrix coupling model. Sci. Total Environ. 2020,710, 136324. [CrossRef] [PubMed]
47.
Heravi, G.; Abdolvand, M.M. Assessment of water11virtual water. consumption during production of material and construction
phases of residential building projects. Sustain. Cities Soc. 2019,51, 101785. [CrossRef]
48.
Lei, F.; Huang, C.; Shen, H.; Li, X. Improving the estimation of hydrological states in the SWAT model via the ensemble Kalman
smoother: Synthetic experiments for the Heihe River basin in northwest China. Adv. Water Resour. 2014,67, 32–45. [CrossRef]
49.
Yin, J.; Guo, S.; Gu, L.; He, S.; Ba, H.; Tian, J.; Li, Q.; Chen, J. Projected changes of bivariate flood quantiles and estimation
uncertainty based on multi-model ensembles over China. J. Hydrol. 2020,585, 124760. [CrossRef]
50.
He, S.; Guo, S.; Yang, G.; Chen, K.; Liu, D.; Zhou, Y. Optimizing operation rules of cascade reservoirs for adapting climate change.
Water Resour. Manag. 2020,34, 101–120. [CrossRef]
51.
Sun, M.; Wang, J.; He, K. Analysis on the urban land resources carrying capacity during urbanization—A case study of Chinese
YRD. Appl. Geogr. 2020,116, 102170. [CrossRef]
52.
Bai, H.; Gao, W.; Wang, D.; Chen, Y.; Zhang, H.; Zhao, Y.; Zhao, K.; Sun, Y.; Sun, Z. Allocating total emission pollutant control
based on water environmental carrying capacity: Model establishment and case study. Water Policy
2019
,21, 1175–1192. [CrossRef]
53.
Peng, B.; Li, Y.; Elahi, E.; Wei, G. Dynamic evolution of ecological carrying capacity based on the ecological footprint theory: A
case study of Jiangsu province. Ecol. Indic. 2019,99, 19–26. [CrossRef]
