Assessment of Rainstorm Waterlogging Disaster Risk in Rapidly Urbanizing Areas Based on Land Use Scenario Simulation: A Case Study of Jiangqiao Town in Shanghai, China

Abstract

The impact of flooding on cities is becoming increasingly significant in the context of climate change and rapid urbanization. Based on the analysis of the land use changes and rainstorm waterlogging inundation scenarios of Jiangqiao Town from 1980 to 2020, a scenario analysis was conducted to simulate and assess the rainstorm waterlogging disaster risk in 2040 under three land use scenarios (a natural development scenario, Scenario ND; an economic growth scenario, Scenario EG; and an ecological development priority scenario, Scenario EP) and three rainstorm scenarios with return periods of 10, 50, and 100 years. The following results were found: (1) Land use change is a significant factor in the risk of urban rainstorm waterlogging disaster caused by surface runoff and inundation depth change. In particular, the resultant increase in impermeable surfaces such as residential land and industrial land and the decrease in farmland during urbanization would lead to an increase in urban rainstorm waterlogging disaster risk. (2) Under three rainstorm scenarios, the future land use exposure elements and losses are consistent in terms of spatial distribution; from 10-year to 100-year return periods, they manifest as an expansion from the south to the surroundings, especially to the central region of the study area. The locations at risk are mainly distributed in the central and southern regions of Jiangqiao Town. (3) The economic losses are different in different land use scenarios and rainstorm scenarios. In the context of rainstorm scenarios with return periods of 10, 50, and 100 years, the total losses in land use scenario ND are CNY 560 million, CNY 890 million, and CNY 1.07 billion; those in land use scenario EG are CNY 630 million, CNY 980 million, and CNY 1.19 billion; and those in land use scenario EP are CNY 480 million, CNY 750 million, and CNY 910 million. The total losses of land use EP are the lowest by comparison. So, the influence of land use change on the rainstorm waterlogging disaster risk shows obvious differences among different rainstorm scenarios. This study has important reference value for decision making on land use management and flood disaster risk management in rapidly urbanizing areas.

Full text

Citation: Xu, H.; Gao, J.; Yu, X.; Qin,
Q.; Du, S.; Wen, J. Assessment of
Rainstorm Waterlogging Disaster Risk
in Rapidly Urbanizing Areas Based on
Land Use Scenario Simulation:
A Case Study of Jiangqiao Town in
Shanghai, China. Land 2024,13, 1088.
https://doi.org/10.3390/
land13071088
Received: 17 June 2024
Revised: 12 July 2024
Accepted: 17 July 2024
Published: 19 July 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
land
Article
Assessment of Rainstorm Waterlogging Disaster Risk in Rapidly
Urbanizing Areas Based on Land Use Scenario Simulation:
A Case Study of Jiangqiao Town in Shanghai, China
Hui Xu 1, 2, *, Junlong Gao 1, Xinchun Yu 1,*, Qianqian Qin 3, Shiqiang Du 1and Jiahong Wen 1,2
1School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
2Key Laboratory of Resilient Cities and Integrated Risk Management, Shanghai Emergency Management
Bureau, Shanghai 200003, China
3Planning School of Architecture Planning and Landscape, Newcastle University,
Newcastle upon Tyne NE1 7RU, UK
*Correspondence: xuhui@shnu.edu.cn (H.X.); yxchappy0602@163.com (X.Y.)
Abstract: The impact of flooding on cities is becoming increasingly significant in the context of
climate change and rapid urbanization. Based on the analysis of the land use changes and rainstorm
waterlogging inundation scenarios of Jiangqiao Town from 1980 to 2020, a scenario analysis was
conducted to simulate and assess the rainstorm waterlogging disaster risk in 2040 under three land
use scenarios (a natural development scenario, Scenario ND; an economic growth scenario, Scenario
EG; and an ecological development priority scenario, Scenario EP) and three rainstorm scenarios
with return periods of 10, 50, and 100 years. The following results were found: (1) Land use change
is a significant factor in the risk of urban rainstorm waterlogging disaster caused by surface runoff
and inundation depth change. In particular, the resultant increase in impermeable surfaces such as
residential land and industrial land and the decrease in farmland during urbanization would lead
to an increase in urban rainstorm waterlogging disaster risk. (2) Under three rainstorm scenarios,
the future land use exposure elements and losses are consistent in terms of spatial distribution; from
10-year to 100-year return periods, they manifest as an expansion from the south to the surroundings,
especially to the central region of the study area. The locations at risk are mainly distributed in the
central and southern regions of Jiangqiao Town. (3) The economic losses are different in different land
use scenarios and rainstorm scenarios. In the context of rainstorm scenarios with return periods of
10, 50, and 100 years, the total losses in land use scenario ND are CNY 560 million,
CNY 890 million
,
and CNY 1.07 billion; those in land use scenario EG are CNY 630 million, CNY 980 million, and
CNY 1.19 billion; and those in land use scenario EP are CNY 480 million, CNY 750 million, and
CNY 910 million
. The total losses of land use EP are the lowest by comparison. So, the influence
of land use change on the rainstorm waterlogging disaster risk shows obvious differences among
different rainstorm scenarios. This study has important reference value for decision making on land
use management and flood disaster risk management in rapidly urbanizing areas.
Keywords: rainstorm waterlogging disaster risk; risk assessment; land use change; scenario analysis;
Shanghai
1. Introduction
Influenced by global climate warming and rapid urbanization, the intensity, frequency,
and extent of flood disasters continue to increase. Consequently, urban areas are at an ele-
vated risk of flood disasters [
1
,
2
]. According to the Emergency Events Database (EM-DAT),
between 2002 and 2021, floods constituted approximately 45% of all disasters annually. The
economic losses attributed to floods accounted for, on average, around 22% of the total
economic losses caused by all disasters each year. Furthermore, the frequency of flood
events exhibited a fluctuating but generally upward trend during this period [
3
]. Existing
Land 2024,13, 1088. https://doi.org/10.3390/land13071088 https://www.mdpi.com/journal/land

Land 2024,13, 1088 2 of 18
research indicates that rapid urbanization and the resulting significant changes in land
use have a significant impact on hydrological processes such as water evapotranspiration,
underwater infiltration, and surface runoff [
4
–
8
], which increase the risk of rainstorm
waterlogging disasters in urban areas [
9
,
10
]. In addition, the risk of urban flooding disas-
ters is expected to continue increasing in the context of intensified future climate change
and ongoing urbanization [
11
,
12
]. Therefore, urban rainstorm waterlogging disasters, as
high-probability disaster risk events within flooding disaster scenarios, have emerged as a
frontier topic in urban natural disaster risk management research [
13
–
15
]. Currently, the
risk assessment of urban rainstorm waterlogging disasters primarily focuses on hazards,
exposure, and vulnerability [
16
,
17
]. Hazard research can be summarized into three aspects:
simulation analysis based on probability distribution theory [
18
,
19
], inundation simulation
analysis based on runoff modeling [
20
–
22
], and hazard evaluation based on remote sensing
monitoring [
23
,
24
]. Research on exposure mainly focuses on elements exposed to risk,
including populations [
25
], buildings [
26
], agriculture [
27
], etc. Vulnerability research pri-
marily involves establishing damage curves of exposed elements through methods such as
questionnaire surveys or experimental simulations [
28
–
30
] to further analyze the potential
losses caused by hazards. The above studies have formed a relatively integrated framework
for disaster risk assessment.
The research conducted on the mechanisms of land use and flood disaster risk has
yielded relatively rich achievements. Previous studies have mostly focused on disaster loss
assessment through the simulation of different rainstorm waterlogging scenarios
[31,32]
.
However, research on urban rainstorm waterlogging disaster risk combined with multiple
land use scenarios with multiple rainstorm scenarios is relatively scarce [
33
,
34
]. Some
studies have highlighted that land use changes could mitigate or exacerbate urban rain-
storm waterlogging disaster risk [
35
] to some extent by affecting precipitation runoff [
36
,
37
].
Therefore, research related to reducing flood disaster risk by optimizing land use has been
brought to the forefront. In recent years, nature-based solutions (NbS) to land use optimiza-
tion, coping with climate change, and disaster risk reduction have become a new research
hotspot [38].
Shanghai is located in the eastern coastal region of China, where floods occur from
time to time and have become increasingly frequent in recent years. Coupled with the high
population density, extensive infrastructure, and buildings in the region, the impact of
flooding is prominent [
39
–
41
]. Recently, some scholars have researched urban rainstorm
waterlogging disaster risk assessment using Shanghai as a case study [
42
–
45
]. However,
there has been less focus on risk assessment based on different land use scenarios. Given
this, this study focused on Jiangqiao Town in Shanghai, characterized by significant land
use changes. By constructing three future land use scenarios, namely, Scenario ND, Scenario
EG, and Scenario EP, this study aims to analyze the rainstorm waterlogging disaster risk
under multiple rainstorm events and different land use scenarios precipitated by climate
change and urbanization. This research provides decision-making references and valuable
insights into ways to reduce the impact of urban rainstorm waterlogging disaster risk and
enhance urban disaster risk resilience by changing land use patterns.
2. Data and Methods
2.1. Study Area
Jiangqiao Town is located at the intersection of Jiading District, Putuo District, and
Minhang District in Shanghai (Figure 1a), with an area of about 42.47 km
2
and a population
of about 276,000. It is currently a rapidly urbanizing area of Shanghai that offers convenient
transportation. According to the Jiangqiao Town Master Plan and Land Use Master Plan
(
2015–2040
), the urbanization ratio of Jiangqiao Town is 99%. In addition, the overall terrain
of Jiangqiao Town is high in the north and low in the south, with elevations ranging from 0
to 2.87 m (Figure 1b), and its soil types can be classified into three categories (Figure 1c).
According to Shanghai local chronicles like The Chronicle of Jiangqiao Township (1949 to 2020)
and the Jiading District Statistical Yearbook (1988 to 2020), news reports, etc., Jiangqiao Town

Land 2024,13, 1088 3 of 18
has experienced at least 30 rainstorms and waterlogging disasters since 1949, especially
extreme events like Typhoon Fite in 2013. which had a significant impact on the town.
Land 2024, 13, x FOR PEER REVIEW 3 of 18
Plan (2015–2040), the urbanization ratio of Jiangqiao Town is 99%. In addition, the overall
terrain of Jiangqiao Town is high in the north and low in the south, with elevations rang-
ing from 0 to 2.87 m (Figure 1b), and its soil types can be classified into three categories
(Figure 1c). According to Shanghai local chronicles like The Chronicle of Jiangqiao Township
(1949 to 2020) and the Jiading District Statistical Yearbook (1988 to 2020), news reports, etc.,
Jiangqiao Town has experienced at least 30 rainstorms and waterlogging disasters since
1949, especially extreme events like Typhoon Fite in 2013. which had a significant impact
on the town.
Figure 1. (a) Location of Jiangqiao Town in Shanghai. (b) Digital elevation model (DEM) of Jiangqiao
Town. (c) Soil types of Jiangqiao Town.
2.2. Data
Research data used in this article encompass six categories: land use data, elevation
data, soil data, precipitation data, depth–damage functions, and economic data related to
loss assessment.
Land use data were obtained by interpreting six remote sensing images from 1980,
1990, 2000, 2006, 2013, and 2020. The remote sensing images from 1980 and 1990 were
derived from the Geographical Information Monitoring Cloud Platform
(hp://www.dsac.cn/) (accessed on 27 July 2023), and the remote sensing images from
2000, 2006, 2013, and 2020 were derived from Google Earth. In this study, land use types
are classified as transportation land (TL), public building land (PBL), farmland, green land
(GL), residential land (RL), industrial land (IL), water area (WA), and other land (OL),
where PBL refers to government agencies, healthcare, education, cultural tourism, etc.,
while IL mainly includes factories, production workshops, handicraft workshops, etc. The
overall accuracy of land use for six phases exceeded 0.75. The spatial resolution of the land
use data is 10 m by 10 m.
Elevation data were obtained by our group based on a 1:10,000 topographic map
(2010) and Lidar data (2010) provided by Shanghai Surveying and Mapping Institute [46],
with a spatial resolution of 10 m by 10 m.
Soil data were derived from the Shanghai Soil database compiled by the Shanghai Soil
Census Office and the 1:1,000,000 soil map of China published by the Institute of Soil Science,
Chinese Academy of Sciences (hp://www.issas.ac.cn/) (accessed on 10 August 2023). Soil
types in Jiangqiao Town mainly include tidal sandy mud, ditch-dry tidal mud, vegetable
garden grey tidal mud, ditch-dry mud, loess, and hard pavement. In this study, the Soil
Conservation Service (SCS) model was used for surface runoff simulation. According to
Figure 1. (a) Location of Jiangqiao Town in Shanghai. (b) Digital elevation model (DEM) of Jiangqiao
Town. (c) Soil types of Jiangqiao Town.
2.2. Data
Research data used in this article encompass six categories: land use data, elevation
data, soil data, precipitation data, depth–damage functions, and economic data related to
loss assessment.
Land use data were obtained by interpreting six remote sensing images from 1980, 1990,
2000, 2006, 2013, and 2020. The remote sensing images from 1980 and 1990 were derived
from the Geographical Information Monitoring Cloud Platform (http://www.dsac.cn/)
(accessed on 27 July 2023), and the remote sensing images from 2000, 2006, 2013, and 2020
were derived from Google Earth. In this study, land use types are classified as transportation
land (TL), public building land (PBL), farmland, green land (GL), residential land (RL),
industrial land (IL), water area (WA), and other land (OL), where PBL refers to government
agencies, healthcare, education, cultural tourism, etc., while IL mainly includes factories,
production workshops, handicraft workshops, etc. The overall accuracy of land use for six
phases exceeded 0.75. The spatial resolution of the land use data is 10 m by 10 m.
Elevation data were obtained by our group based on a 1:10,000 topographic map (2010)
and Lidar data (2010) provided by Shanghai Surveying and Mapping Institute [
46
], with a
spatial resolution of 10 m by 10 m.
Soil data were derived from the Shanghai Soil database compiled by the Shanghai Soil
Census Office and the 1:1,000,000 soil map of China published by the Institute of Soil Science,
Chinese Academy of Sciences (http://www.issas.ac.cn/) (accessed on 10 August 2023). Soil
types in Jiangqiao Town mainly include tidal sandy mud, ditch-dry tidal mud, vegetable
garden grey tidal mud, ditch-dry mud, loess, and hard pavement. In this study, the Soil
Conservation Service (SCS) model was used for surface runoff simulation. According to the
SCS model, the soil types of Jiangqiao Town can be classified into three distinct categories
(labeled as A, B, and D), where A includes thick sand layers, sandy loam, and so on; B
includes thin loess layers, silty loam, and so on; and D includes sandy clay, hard pavement,
and so on (Figure 1c). The spatial resolution of soil data is 10 m by 10 m.
Precipitation data were calculated using the Standard of Rainstorm Intensity For-
mula and Design Rainstorm Distribution (DB31/T 1043–2017) [
47
] published by Shanghai
Engineering Design Institute:

Land 2024,13, 1088 4 of 18
q=1995.84(p0.3 −0.42)
(t+10 +7lg p)0.82+0.07 lg p(1)
where
q
is the rainstorm intensity (mm/h),
t
is the precipitation duration (min), and
p
is the
stormwater return period (year). In this study, 1 h precipitation durations corresponding
to 10-, 50-, and 100-year return periods are employed. These scenarios correspond to 1 h
precipitation amounts of 65.8 mm, 89.7 mm, and 101 mm, respectively.
Depth–damage functions are used to explore the relationship between the damage ratio
f(x)
(%) and inundation depth
x
(m). The depth–damage functions of buildings are based on
the research conducted by Ke in 2014 [
48
], the depth–damage functions of indoor properties
are based on the research conducted by Quan in 2014 [
49
], and the depth–damage function of
farmland is based on the research conducted by Yin et al. in 2011 [50].
The depth–damage function of buildings on IL and PBL is
f(x)=(−0.004762x2+0.0919x −0.01286×100% (2)
The depth–damage function of buildings on RL is
f(x)=−0.0009524x2+0.06048x −0.001429×100% (3)
The depth–damage function of indoor properties on IL and PBL is
f(x)=(−0.152x3×10−9−0.82x2×10−6+1.479x ×10−3−0.009×100% (4)
The depth–damage function of indoor properties on RL is
f(x)=−0.026x3×10−9−0.049x2×10−6+0.742x ×10−3−0.115×100% (5)
The depth–damage function of farmland is shown in Table 1. Economic data related
to loss assessment are shown in Table 2.
Table 1. Depth–damage function of farmland.
Inundation Depth (m) [0, 0.5) [0.5, 1) [1, 1.5) [1.5, 2.5)
Damage Ratio (%) 55 70 80 95
Table 2. Explanation of economic data related to loss assessment.
Categories Price
(CNY/m2)Data Source
Industrial construction costs
(A standard factory building cost benchmark) 1100
Jiading District Statistical Yearbook (2020),
Shanghai Statistical Yearbook (2020),Reference
Standards for Cost of Various Construction
Projects in Shanghai
Indoor properties of industrial buildings
(Output of industrial enterprises above a designated size) 1460
Construction cost of public buildings
(Cost benchmark for ordinary office buildings, shops, and schools) 1500
Indoor properties of public buildings
(Consistent with indoor properties of residential buildings) 800
Residential construction costs
(A commercial housing cost benchmark) 1400
Indoor properties of residential buildings
(Field research) 800
Farmland properties
(Output of the planting industry in 2020) 5.5

Land 2024,13, 1088 5 of 18
2.3. Methods
Firstly, the SCS model and equal-volume method were used for rainstorm waterlog-
ging inundation simulation under precipitation scenarios with return periods of 10, 50, and
100 years. Secondly, the Future Land Use Simulation (FLUS) model was used to simulate
the spatial distribution of three land use scenarios in 2040. Thirdly, by overlaying the
three precipitation scenarios with the three future land use scenarios, we analyzed the
risk exposure elements related to land use. Fourthly, the economic losses under different
precipitation and land use scenarios were calculated by combining with the depth–damage
functions. (Figure 2). This study aims to analyze the interaction mechanism between differ-
ent land use patterns and the rainstorm waterlogging disaster risk to enlighten individuals
engaged in rainstorm waterlogging disaster risk management and land use management.
Land 2024, 13, x FOR PEER REVIEW 5 of 18
Indoor properties of residential buildings
(Field research) 800
Farmland properties
(Output of the planting industry in 2020) 5.5
2.3. Methods
Firstly, the SCS model and equal-volume method were used for rainstorm waterlog-
ging inundation simulation under precipitation scenarios with return periods of 10, 50,
and 100 years. Secondly, the Future Land Use Simulation (FLUS) model was used to sim-
ulate the spatial distribution of three land use scenarios in 2040. Thirdly, by overlaying
the three precipitation scenarios with the three future land use scenarios, we analyzed the
risk exposure elements related to land use. Fourthly, the economic losses under different
precipitation and land use scenarios were calculated by combining with the depth–dam-
age functions. (Figure 2). This study aims to analyze the interaction mechanism between
different land use paerns and the rainstorm waterlogging disaster risk to enlighten indi-
viduals engaged in rainstorm waterlogging disaster risk management and land use man-
agement.
Figure 2. Methodological framework of this study.
2.3.1. GIS-Based Simulation on Rainstorm Waterlogging Disaster
(1) SCS Model
The SCS model is an empirical hydrological model created by the U.S. Department of
Agriculture’s Soil Conservation Service (USDA-SCS) in the 20th century. It is mainly used
for spatial runoff simulation analysis. This model is characterized by its high accuracy,
minimal parameters, and easy calculations [51]. It is widely used in various spatial scales
[52] and commonly employed in precipitation runoff simulation at different scales in
Shanghai [44,50]. The runoff formula of the SCS model is
Q= 󰇱(𝑃−𝐼𝑎)
𝑃𝑆−𝐼𝑎,𝑃𝐼𝑎
0 ,𝑃 𝐼𝑎 (6)
Figure 2. Methodological framework of this study.
2.3.1. GIS-Based Simulation on Rainstorm Waterlogging Disaster
(1) SCS Model
The SCS model is an empirical hydrological model created by the U.S. Department of
Agriculture’s Soil Conservation Service (USDA-SCS) in the 20th century. It is mainly used for
spatial runoff simulation analysis. This model is characterized by its high accuracy, mini-
mal parameters, and easy calculations [
51
]. It is widely used in various spatial scales [
52
]
and commonly employed in precipitation runoff simulation at different scales in Shang-
hai [44,50]. The runoff formula of the SCS model is
Q=((P−Ia)2
P+S−Ia ,P≥I a
0 , P<Ia (6)
where
Q
is runoff depth (mm),
P
is the total precipitation amount for a single precipitation
event (mm),
Ia
is an initial abstraction (mm), and
S
is the maximum potential retention of
the watershed (mm). However, it is difficult to quantify directly the variable
Ia
, and the
variable
S
exhibits relatively high instability, so the curve number (CN) value is introduced
as follows:
Ia =0.2S (7)
S=25400
CN −254 (8)

Land 2024,13, 1088 6 of 18
where the CN value reflects the characteristics of the study area before rainfall, the amount
of which ranges from 0 to 100 and is influenced by a combination of factors such as land use
type, soil type, and the antecedent moisture conditions (AMC) of the soil. The AMC of the
soil are generally divided into three levels: dry (AMCI), normal (AMCII), and wet (AMCIII).
In this study, the AMC were set to AMCII based on a combination of the characteristics of
Jiangqiao Town and previous research [
53
]. Finally, the CN value of Jiangqiao Town was
determined according to the table of CN value parameters of the SCS model (Table 3). In
the SCS model, soil types are categorized based on their infiltration rates into four distinct
categories: A, B, C, and D. Type A soils typically consist of deep, well-drained sands or
gravels. Type B soils are often sandy loam soils with moderate depth and drainage. Type C
soils may have a layer that impedes water movement or be composed of moderately fine
to fine texture materials. Type D soils are usually clay soils with high swelling potential,
shallow soils over nearly impervious material, or soils with a high water table. The soil
types of Jiangqiao Town are classified into three distinct categories (labeled as A, B, and D).
Table 3. Runoff curve numbers of Jiangqiao Town under AMCII.
Land Use
CN Value for Hydrologic Soil Group
A B D
Green land 34 60 80
Farmland 67 76 86
Water area 100 100 100
Industrial land 89 92 95
Other land 69 80 86
Residential land 77 85 92
Transportation land 98 98 98
Public building land 90 93 96
(2) GIS-based simulation of runoff depth
The runoff depth for each grid can be derived by overlaying the land use map and
the soil type map, inputting CN values and precipitation amounts, and applying the
appropriate formulas, i.e., (6) to (8). Because Jiangqiao Town is located in the area outside
the outer ring road of Shanghai, which is self-draining [
50
], the drainage facilities are not
considered in this study. The total waterlogging volume can be calculated as follows:
W=
n
∑
i=1
Qi×Si(9)
where
W
is the total waterlogging volume of the study area (m
3
),
Qi
is the runoff depth of the
ith grid (mm), Siis the catchment area of the igrid (m2), and nis the total number of grids.
In this study, all grids in the DEM of Jiangqiao below the flood elevation height are
recorded as inundation areas. Inundation maps of Jiangqiao Town can be created using
the equal-volume method in ArcGIS. The equal-volume method is characterized by easy
calculations and minimal parameters, which can be used to analyze the range and depth of
rainstorm waterlogging disasters [54].
2.3.2. Land Use Simulation Based on GeoSOS-FLUS Model
Based on the land use data for Jiangqiao Town in 2020, and combined with previous
studies [
55
,
56
] and references to the Jiangqiao Town Master Plan and Land Use Master Plan
(2015–2040), three land use scenarios for 2040 (Scenario ND, Scenario EG, and Scenario EP)
were simulated by using the GeoSOS-FLUS model (Table 4).

Land 2024,13, 1088 7 of 18
Table 4. The design of land use scenarios for 2040.
Land Use Scenarios Explanation
Natural development scenario
(Scenario ND)
This refers to a situation in which land use
change is not significant, there has been no
excessive interference from human society, and
the study area is in a state of natural
development.
Economic growth scenario
(Scenario EG)
This refers to the rapid expansion of urban
areas while strictly adhering to the constraints
of the bottom line of planning, and, in this case,
the study area is in a state of rapid
urbanization.
Ecological development priority scenario
(Scenario EP)
This refers to the continuous expansion of
ecological area, aimed at alleviating ecological
and environmental problems, and, in this case,
the study area is in an ecologically livable state.
In terms of parameters, in the template part of the probabilistic operation, the sampling
parameter was set to 10, the number of hidden layers of the neural network was set to 6,
and the driving factors include the natural factors (DEM, slope, and water area) as well as
the traffic influence factor (roads). In the template part of the cellular automaton (CA), the
number of iterations was set to 200, the neighborhood value was set to 3, the acceleration
factor was set to 0.4, and the river serves as the restriction conversion area. Finally, the cost
matrix was set to three land use scenarios. In addition, the neighborhood weight values
of TL, RL, PBL, OL, farmland, IL, WA, and GL were set to 1, 1, 1, 1, 1, 0.7, 1, 0.5, and 0.5,
respectively.
2.3.3. Landscape Pattern Analysis
Based on the characteristics of Jiangqiao Town, the landscape pattern analysis of future
land use scenarios included the following indices: number of patches (NP), class area (CA),
aggregation index (AI), and landscape shape index (LSI) (Table 5).
Table 5. Indices and explanations of landscape patterns.
Landscape Pattern Index Formula Explanation
Class area
(CA) CA =∑n
j=1aij (1
10000 )
This refers to the total area of a
certain patch type, which is a
component of the landscape and
also the basis for calculating other
indicators.
Number of patches
(NP) NP =n
This reflects the spatial pattern of
the landscape, which can be used to
describe landscape heterogeneity.
Landscape shape index
(LSI) LSI =0.25Cij
√aij
This reflects the degree of
aggregation based on the shape
characteristics of types of patches.
Aggregation index
(AI) AI =gi i
maxgii
This reflects the concentration levels
of certain patches.
Note:
i
is the patch type,
aij
is the patch area (m
2
),
Cij
is the patch circumference (m),
n
is the total number of
patches, and gii is the number of adjacent grid units between patch types.
3. Results
3.1. Land Use Change in Jiangqiao Town from 1980 to 2020
From 1980 to 2020, significant changes occurred in the land use structure of Jiangqiao
Town, characterized by a notable reduction in farmland and a significant increase in
impermeable surfaces (Table 6). In particular, the area of IL increased the most, with
a net increase of 11.25 km
2
, while TL and RL also increased by 6.23 km
2
and 4.59 km
2
,

Land 2024,13, 1088 8 of 18
respectively. By 2020, the proportion of these three land use types reached 64%. At the
same time, there was a net loss of 27.31 km
2
of farmland, constituting a dramatic decrease.
In terms of spatial distribution, changes primarily occurred in the western region and
sporadically in the southern and northern parts of the study area (Figure 3). It is worth
noting that IL currently accounts for the largest proportion and is mostly adjacent to RL.
Table 6. Land use change in Jiangqiao Town from 1980 to 2020 (km2).
Land Use 1980 1990 2000 2006 2013 2020 1980 to 2022
Green land 3.56 3.56 0.88 0.88 1.76 4.06 0.50
Farmland 33.55 27.06 23.46 20.24 10.33 6.24 −27.31
Water area 0.27 0.27 2.31 2.24 1.93 2.42 2.15
Industrial land 0.00 5.68 5.42 7.59 10.86 11.25 11.25
Other land 0.00 0.00 0.15 0.65 1.04 2.20 2.20
Residential land 3.64 4.45 7.27 7.34 9.35 8.23 4.59
Transportation land 1.16 1.16 2.67 3.24 6.63 7.39 6.23
Public building land 0.00 0.00 0.10 0.16 0.40 0.48 0.48
Land 2024, 13, x FOR PEER REVIEW 8 of 18
Note: 𝑖 is the patch type, 𝑎 is the patch area (m2), 𝐶 is the patch circumference (m), 𝑛 is the
total number of patches, and 𝑔 is the number of adjacent grid units between patch types.
3. Results
3.1. Land Use Change in Jiangqiao Town from 1980 to 2020
From 1980 to 2020, significant changes occurred in the land use structure of Jiangqiao
Town, characterized by a notable reduction in farmland and a significant increase in im-
permeable surfaces (Table 6). In particular, the area of IL increased the most, with a net
increase of 11.25 km2, while TL and RL also increased by 6.23 km2 and 4.59 km2, respec-
tively. By 2020, the proportion of these three land use types reached 64%. At the same
time, there was a net loss of 27.31 km2 of farmland, constituting a dramatic decrease. In
terms of spatial distribution, changes primarily occurred in the western region and spo-
radically in the southern and northern parts of the study area (Figure 3). It is worth noting
that IL currently accounts for the largest proportion and is mostly adjacent to RL.
Table 6. Land use change in Jiangqiao Town from 1980 to 2020 (km2).
Land Use 1980 1990 2000 2006 2013 2020 1980 to 2022
Green land 3.56 3.56 0.88 0.88 1.76 4.06 0.50
Farmland 33.55 27.06 23.46 20.24 10.33 6.24 −27.31
Wat er a rea 0.27 0.27 2.31 2.24 1.93 2.42 2.15
Industrial land 0.00 5.68 5.42 7.59 10.86 11.25 11.25
Other land 0.00 0.00 0.15 0.65 1.04 2.20 2.20
Residential land 3.64 4.45 7.27 7.34 9.35 8.23 4.59
Transportation land 1.16 1.16 2.67 3.24 6.63 7.39 6.23
Public building land 0.00 0.00 0.10 0.16 0.40 0.48 0.48
Figure 3. Spatial distribution of Jiangqiao land use from 1980 to 2020.
3.2. Rainstorm Waterlogging Simulation
To obtain the average surface runoff depth for six phases of land use in Jiangqiao
Town based on three precipitation scenarios with return periods of 10, 50, and 100 years,
the SCS model was run in the GIS environment (Table 7). As depicted in Table 7, under
the same land use scenario, the average surface runoff depth is positively correlated with
Figure 3. Spatial distribution of Jiangqiao land use from 1980 to 2020.
3.2. Rainstorm Waterlogging Simulation
To obtain the average surface runoff depth for six phases of land use in Jiangqiao Town
based on three precipitation scenarios with return periods of 10, 50, and 100 years, the SCS
model was run in the GIS environment (Table 7). As depicted in Table 7, under the same
land use scenario, the average surface runoff depth is positively correlated with the return
period of rainstorms. For instance, considering the average surface runoff depth in 1980 as
an example, the precipitation scenario with a 50-year return period exhibited an increase in
rainfall by 1.6 cm compared to the precipitation scenario with a 10-year return period, and
the precipitation scenario with a 100-year return period exhibited an increase in rainfall by
0.8 cm compared to the precipitation scenario with a 50-year return period. Moreover, under
the same precipitation scenario, the average surface runoff depth is positively correlated
with urbanization level. For example, in the context of the precipitation scenario with a
10-year return period, the average surface runoff depth was 2.1 cm in 1980, while it was
3.8 cm in 2020.

Land 2024,13, 1088 9 of 18
Table 7. Average surface runoff depth in different rainstorm scenarios (cm).
Year
Return Period
10-Year 50-Year 100-Year
1980 2.1 3.7 4.5
1990 2.4 4.0 4.9
2000 2.9 4.7 5.6
2006 3.1 5.0 5.9
2013 3.7 5.7 6.7
2020 3.8 5.7 6.7
On this basis, further analysis was conducted on the inundation characteristics of
land use types under different precipitation scenarios based on the equal-volume method
(
Figure 4
). It can be observed that under the same precipitation scenario, the total waterlog-
ging amount, inundation area, and maximum inundation depth increase year by year with
the increase in the urbanization level of Jiangqiao Town. Specially, these characteristics are
more significant in comparison to the two stages of 1990 to 2000 and 2006 to 2013. In addi-
tion, the inundation depth under the three precipitation scenarios is mainly concentrated
in the range of 0 cm to 15 cm. From the perspective of spatial characteristics, the maximum
inundation depth occurs in the southern area of Jiangqiao Town, where the terrain is lower
and the impermeable surfaces are more concentrated.
Land 2024, 13, x FOR PEER REVIEW 9 of 18
the return period of rainstorms. For instance, considering the average surface runoff depth
in 1980 as an example, the precipitation scenario with a 50-year return period exhibited
an increase in rainfall by 1.6 cm compared to the precipitation scenario with a 10-year
return period, and the precipitation scenario with a 100-year return period exhibited an
increase in rainfall by 0.8 cm compared to the precipitation scenario with a 50-year return
period. Moreover, under the same precipitation scenario, the average surface runoff depth
is positively correlated with urbanization level. For example, in the context of the precip-
itation scenario with a 10-year return period, the average surface runoff depth was 2.1 cm
in 1980, while it was 3.8 cm in 2020.
On this basis, further analysis was conducted on the inundation characteristics of
land use types under different precipitation scenarios based on the equal-volume method
(Figure 4). It can be observed that under the same precipitation scenario, the total water-
logging amount, inundation area, and maximum inundation depth increase year by year
with the increase in the urbanization level of Jiangqiao Town. Specially, these characteris-
tics are more significant in comparison to the two stages of 1990 to 2000 and 2006 to 2013.
In addition, the inundation depth under the three precipitation scenarios is mainly con-
centrated in the range of 0 cm to 15 cm. From the perspective of spatial characteristics, the
maximum inundation depth occurs in the southern area of Jiangqiao Town, where the
terrain is lower and the impermeable surfaces are more concentrated.
Figure 4. Spatial distribution of inundation depth in different rainstorm scenarios.
Table 7. Average surface runoff depth in different rainstorm scenarios (cm).
Year Return Period
10-Year 50-Year 100-Year
1980 2.1 3.7 4.5
Figure 4. Spatial distribution of inundation depth in different rainstorm scenarios.

Land 2024,13, 1088 10 of 18
3.3. Impact of Land Use Change on Rainstorm Waterlogging
In this section, the precipitation scenario with a 100-year return period was selected.
This study analyzed the impact of land use changes on rainstorm waterlogging in the
context of rapid urbanization by comparing the surface runoff and inundation depths
between two land use scenarios in Jiangqiao Town in 1980 and 2020.
3.3.1. Impact of Land Use Change on Surface Runoff Depth
As shown in Figure 5, in 1980, the surface runoff depths of both TL and RL exceeded
60 mm, with relatively scattered distribution characters, while the runoff depth of farmland
was mainly concentrated in the range of 20.2 mm to 40.4 mm. During this period, farmland
was the main land use, so there were fewer areas with high runoff depth in Jiangqiao Town
as a whole in 1980. As of 2020, the land use of Jiangqiao Town has changed significantly,
with a rapid expansion of construction land, including TL, PBL, IL, and RL, and a significant
reduction in farmland, resulting in significant changes in surface runoff depth. In 2020, areas
of high runoff depth significantly increased, mainly distributed in the eastern, southern,
central, northern, and northwestern regions of Jiangqiao Town, similar to the distribution
pattern of impermeable surfaces. Meanwhile, areas with low runoff depth have significantly
decreased and are distributed in the western, northwestern, and southwestern regions of
the study area, which are also relatively concentrated areas of farmland.
Land 2024, 13, x FOR PEER REVIEW 10 of 18
1990 2.4 4.0 4.9
2000 2.9 4.7 5.6
2006 3.1 5.0 5.9
2013 3.7 5.7 6.7
2020 3.8 5.7 6.7
3.3. Impact of Land Use Change on Rainstorm Waterlogging
In this section, the precipitation scenario with a 100-year return period was selected.
This study analyzed the impact of land use changes on rainstorm waterlogging in the
context of rapid urbanization by comparing the surface runoff and inundation depths be-
tween two land use scenarios in Jiangqiao Town in 1980 and 2020.
3.3.1. Impact of Land Use Change on Surface Runoff Depth
As shown in Figure 5, in 1980, the surface runoff depths of both TL and RL exceeded
60 mm, with relatively scaered distribution characters, while the runoff depth of farm-
land was mainly concentrated in the range of 20.2 mm to 40.4 mm. During this period,
farmland was the main land use, so there were fewer areas with high runoff depth in
Jiangqiao Town as a whole in 1980. As of 2020, the land use of Jiangqiao Town has changed
significantly, with a rapid expansion of construction land, including TL, PBL, IL, and RL,
and a significant reduction in farmland, resulting in significant changes in surface runoff
depth. In 2020, areas of high runoff depth significantly increased, mainly distributed in
the eastern, southern, central, northern, and northwestern regions of Jiangqiao Town, sim-
ilar to the distribution paern of impermeable surfaces. Meanwhile, areas with low runoff
depth have significantly decreased and are distributed in the western, northwestern, and
southwestern regions of the study area, which are also relatively concentrated areas of
farmland.
Figure 5. Comparison of surface runoff depth and land use change in 1980 and 2020.
Figure 5. Comparison of surface runoff depth and land use change in 1980 and 2020.
3.3.2. Impact of Land Use Change on Inundation Depth
In this study, four significantly changed land use types (TL, RL, IL, and farmland)
were selected to analyze the impact of land use changes on inundation depth (Figure 6).
Due to the smaller scale and less significant changes of land use types such as PBL, WA,
GL, and OL, they are not discussed here.
On the whole, the expansion of TL, RL, and IL could lead to an increase in inundation
depth, while farmland could effectively mitigate the degree of inundation to a certain extent.
By comparing Figure 6a,c, it can be found that the increased TL in the southwest region of
Jiangqiao Town has led to an increase in inundation depth from 0.36 m to 0.45 m. In 1980,
RL was less affected by rainstorm waterlogging. However, after urbanization expanded
from north to south, the inundation depth of RL was distributed among all districts in

Land 2024,13, 1088 11 of 18
2020, especially in the southern region, where the inundation depth of some areas of RL
has exceeded 0.36 m. In addition, IL has continuously expanded to the northwestern,
western, central, and southeastern regions of Jiangqiao Town since 1980, and the above
areas are significantly affected by rainstorm waterlogging, especially in the central and
southeast regions, with a maximum inundation depth of more than 0.18 m. Meanwhile,
the inundation depth of this area was less than 0.09 m in 1980. As for farmland, it was the
main land use pattern in Jiangqiao Town in 1980. During this period, flooded areas were
concentrated in southern Jiangqiao Town, where the terrain is lower, with an inundation
depth of less than 0.09 m. By comparing Figure 6c,d, it can be observed that the scale of
farmland in Jiangqiao Town has significantly decreased in the past 40 years. Currently,
farmland is mainly distributed in the western, northwestern, and southwestern regions of
the study area, where the inundation depth remains less than 0.09 m.
Land 2024, 13, x FOR PEER REVIEW 11 of 18
3.3.2. Impact of Land Use Change on Inundation Depth
In this study, four significantly changed land use types (TL, RL, IL, and farmland)
were selected to analyze the impact of land use changes on inundation depth (Figure 6).
Due to the smaller scale and less significant changes of land use types such as PBL, WA,
GL, and OL, they are not discussed here.
On the whole, the expansion of TL, RL, and IL could lead to an increase in inundation
depth, while farmland could effectively mitigate the degree of inundation to a certain ex-
tent. By comparing Figure 6a,c, it can be found that the increased TL in the southwest
region of Jiangqiao Town has led to an increase in inundation depth from 0.36 m to 0.45
m. In 1980, RL was less affected by rainstorm waterlogging. However, after urbanization
expanded from north to south, the inundation depth of RL was distributed among all dis-
tricts in 2020, especially in the southern region, where the inundation depth of some areas
of RL has exceeded 0.36 m. In addition, IL has continuously expanded to the northwestern,
western, central, and southeastern regions of Jiangqiao Town since 1980, and the above
areas are significantly affected by rainstorm waterlogging, especially in the central and
southeast regions, with a maximum inundation depth of more than 0.18 m. Meanwhile,
the inundation depth of this area was less than 0.09 m in 1980. As for farmland, it was the
main land use paern in Jiangqiao Town in 1980. During this period, flooded areas were
concentrated in southern Jiangqiao Town, where the terrain is lower, with an inundation
depth of less than 0.09 m. By comparing Figure 6c,d, it can be observed that the scale of
farmland in Jiangqiao Town has significantly decreased in the past 40 years. Currently,
farmland is mainly distributed in the western, northwestern, and southwestern regions of
the study area, where the inundation depth remains less than 0.09 m.
Figure 6. Comparison of inundation depth and land use change in 1980 and 2020.
3.4. Rainstorm Waterlogging Disaster Risk Assessment regarding Future Land Use
3.4.1. Land Use Scenario Simulations for 2040
This study took the 2000 and 2020 land use status data for Jiangqiao Town as the
initial-year data and the validation data, respectively. Then, the driving factors, including
the natural factors as well as the traffic influence factors, were selected to simulate land
use in 2020. The simulation results were tested using the actual land uses in 2020 by con-
sidering the overall accuracy, the Kappa coefficient, and the Fom index. For the accuracy
Figure 6. Comparison of inundation depth and land use change in 1980 and 2020.
3.4. Rainstorm Waterlogging Disaster Risk Assessment regarding Future Land Use
3.4.1. Land Use Scenario Simulations for 2040
This study took the 2000 and 2020 land use status data for Jiangqiao Town as the
initial-year data and the validation data, respectively. Then, the driving factors, including
the natural factors as well as the traffic influence factors, were selected to simulate land use
in 2020. The simulation results were tested using the actual land uses in 2020 by considering
the overall accuracy, the Kappa coefficient, and the Fom index. For the accuracy assessment
results, all three accuracy indexes meet the research needs (the overall accuracy is 0.903,
the Kappa coefficient is 0.782, and the Fom index is 15.83%). Based on this, the conversion
probability was adjusted using different land use scenarios to derive the three land use
scenarios in 2040. The spatial distribution of the three land use scenarios in Jiangqiao Town
in 2040 is illustrated in Figure 7. It can be found that the three land use scenarios have
spatial similarities. This is because the urbanization ratio of Jiangqiao Town reached 99%
as early as 2014. Therefore, according to the Jiangqiao Town Master Plan and Land Use Master
Plan (2015–2040), all land use types are developed en masse and limited by the master plan.
Therefore, to better understand the inherent differences among the three land use scenarios,
landscape pattern analysis of the three future land use scenarios was conducted using the
landscape pattern index introduced in this study.

Land 2024,13, 1088 12 of 18
Land 2024, 13, x FOR PEER REVIEW 12 of 18
assessment results, all three accuracy indexes meet the research needs (the overall accu-
racy is 0.903, the Kappa coefficient is 0.782, and the Fom index is 15.83%). Based on this,
the conversion probability was adjusted using different land use scenarios to derive the
three land use scenarios in 2040. The spatial distribution of the three land use scenarios in
Jiangqiao Town in 2040 is illustrated in Figure 7. It can be found that the three land use
scenarios have spatial similarities. This is because the urbanization ratio of Jiangqiao
Town reached 99% as early as 2014. Therefore, according to the Jiangqiao Town Master Plan
and Land Use Master Plan (2015-2040), all land use types are developed en masse and lim-
ited by the master plan. Therefore, to beer understand the inherent differences among
the three land use scenarios, landscape paern analysis of the three future land use sce-
narios was conducted using the landscape paern index introduced in this study.
As illustrated in Figure 7, at the landscape scale, Scenario EG exhibits the highest
degree of land use fragmentation, while Scenario EP shows the lowest fragmentation. The
number of patches in Scenario ND is 3239, that in Scenario EG is 3768, and that in Scenario
EP is 1337. At the patch-type scale, under Scenario ND and Scenario EG, RL, IL, and farm-
land patches dominate in terms of both area and quantity. Under Scenario EP, GL, RL,
and IL patches dominate in terms of both area and quantity. It can be observed that the
LSI and AI show similarities across the three land use scenarios, while there are significant
differences in NP and CA.
Figure 7. Spatial distribution of land use and landscape paern in 2040.
3.4.2. Rainstorm Waterlogging Simulation in Different Land Use Scenarios
To further explore rainstorm waterlogging inundation under different land use sce-
narios, we generated rainstorm waterlogging inundation maps for three future land use
scenarios under the precipitation scenarios with return periods of 10, 50, and 100 years
based on the SCS model and the equal-volume method. These maps are presented in Fig-
ure 8.
Figure 7. Spatial distribution of land use and landscape pattern in 2040.
As illustrated in Figure 7, at the landscape scale, Scenario EG exhibits the highest
degree of land use fragmentation, while Scenario EP shows the lowest fragmentation. The
number of patches in Scenario ND is 3239, that in Scenario EG is 3768, and that in Scenario
EP is 1337. At the patch-type scale, under Scenario ND and Scenario EG, RL, IL, and
farmland patches dominate in terms of both area and quantity. Under Scenario EP, GL, RL,
and IL patches dominate in terms of both area and quantity. It can be observed that the
LSI and AI show similarities across the three land use scenarios, while there are significant
differences in NP and CA.
3.4.2. Rainstorm Waterlogging Simulation in Different Land Use Scenarios
To further explore rainstorm waterlogging inundation under different land use scenarios,
we generated rainstorm waterlogging inundation maps for three future land use scenarios
under the precipitation scenarios with return periods of 10, 50, and 100 years based on the
SCS model and the equal-volume method. These maps are presented in Figure 8.
As shown in Figure 8, due to the relatively large area of GL and farmland, lesser extent
of built-up area, and well-planned land use spatial layout in Scenario EP, the mitigation
effect of rainstorm waterlogging risk is greater compared to such effects in Scenario ND and
Scenario EG under the same precipitation scenario. For example, under the precipitation
scenario with a 100-year return period, only the inundation area in Scenario EP is less than
50% of the study area, while in Scenario ND and Scenario EG, the inundation areas are
both greater than 50% of Jiangqiao Town. In addition, as the return period of precipitation
scenarios increases, the rainstorm waterlogging disaster risk mitigation effect of land use in
Scenario EP gradually becomes less significant but remains better than that for Scenario

Land 2024,13, 1088 13 of 18
ND and Scenario EG. Meanwhile, the inundation area in Scenario EP is consistently the
smallest across all three precipitation scenarios. However, with the increase in the return
period of precipitation events, the change in inundation area in Scenario EP compared to
Scenario ND and Scenario EG shows a growing trend but with a gradually decreasing rate
of increase. For instance, under precipitation scenarios with return periods of 10, 50, and
100 years, the inundation area in Scenario EP decreased by 0.5 km
2
, 1.2 km
2
, and 1.2 km
2
,
respectively, compared to Scenario ND. Compared to Scenario EG, the inundation area in
Scenario EP decreased by approximately 0.9 km2, 1.8 km2, and 1.9 km2, respectively.
Land 2024, 13, x FOR PEER REVIEW 13 of 18
Figure 8. Spatial distribution of inundation area in different land use scenarios.
As shown in Figure 8, due to the relatively large area of GL and farmland, lesser
extent of built-up area, and well-planned land use spatial layout in Scenario EP, the miti-
gation effect of rainstorm waterlogging risk is greater compared to such effects in Scenario
ND and Scenario EG under the same precipitation scenario. For example, under the pre-
cipitation scenario with a 100-year return period, only the inundation area in Scenario EP
is less than 50% of the study area, while in Scenario ND and Scenario EG, the inundation
areas are both greater than 50% of Jiangqiao Town. In addition, as the return period of
precipitation scenarios increases, the rainstorm waterlogging disaster risk mitigation ef-
fect of land use in Scenario EP gradually becomes less significant but remains beer than
that for Scenario ND and Scenario EG. Meanwhile, the inundation area in Scenario EP is
consistently the smallest across all three precipitation scenarios. However, with the in-
crease in the return period of precipitation events, the change in inundation area in Sce-
nario EP compared to Scenario ND and Scenario EG shows a growing trend but with a
gradually decreasing rate of increase. For instance, under precipitation scenarios with re-
turn periods of 10, 50, and 100 years, the inundation area in Scenario EP decreased by 0.5
km2, 1.2 km2, and 1.2 km2, respectively, compared to Scenario ND. Compared to Scenario
EG, the inundation area in Scenario EP decreased by approximately 0.9 km2, 1.8 km2, and
1.9 km2, respectively.
3.4.3. Rainstorm Waterlogging Disaster Risk Assessment in Different Land Use Scenarios
To further explore the relationship between different land use scenarios and losses
from rainstorm waterlogging disasters, spatial distribution maps of inundation losses in
different land use scenarios in Jiangqiao Town were generated (Figure 9) based on formu-
las (2), (3), (4), and (5) and related economic data.
Figure 8. Spatial distribution of inundation area in different land use scenarios.
3.4.3. Rainstorm Waterlogging Disaster Risk Assessment in Different Land Use Scenarios
To further explore the relationship between different land use scenarios and losses
from rainstorm waterlogging disasters, spatial distribution maps of inundation losses
in different land use scenarios in Jiangqiao Town were generated (Figure 9) based on
Formulas (2)–(5) and related economic data.
As depicted in Figure 9, under precipitation scenarios with return periods of 10, 50, and
100 years, the total economic losses in Scenario ND are CNY 560 million, CNY 890 million,
and CNY 1.07 billion, respectively. In Scenario EG, the total economic losses are CNY
630 million, CNY 980 million, and CNY 1.19 billion, respectively. In Scenario EP, the total
economic losses are CNY 480 million, CNY 750 million, and CNY 910 million, respectively.
The total economic loss in Scenario EP is the lowest under the same precipitation scenario.
In addition, the total loss in Scenario EP under the precipitation scenario with a 100-year
return period is CNY 910 million, which is even lower than the total loss in Scenario EG
under the precipitation scenario with a 50-year return period. These findings highlight

Land 2024,13, 1088 14 of 18
that land use under Scenario EP significantly reduces the rainstorm waterlogging losses
compared to those in Scenario ND and Scenario EG.
Land 2024, 13, x FOR PEER REVIEW 14 of 18
As depicted in Figure 9, under precipitation scenarios with return periods of 10, 50,
and 100 years, the total economic losses in Scenario ND are CNY 560 million, CNY 890
million, and CNY 1.07 billion, respectively. In Scenario EG, the total economic losses are
CNY 630 million, CNY 980 million, and CNY 1.19 billion, respectively. In Scenario EP, the
total economic losses are CNY 480 million, CNY 750 million, and CNY 910 million, respec-
tively. The total economic loss in Scenario EP is the lowest under the same precipitation
scenario. In addition, the total loss in Scenario EP under the precipitation scenario with a
100-year return period is CNY 910 million, which is even lower than the total loss in Sce-
nario EG under the precipitation scenario with a 50-year return period. These findings
highlight that land use under Scenario EP significantly reduces the rainstorm waterlog-
ging losses compared to those in Scenario ND and Scenario EG.
Figure 9. Spatial distribution of inundation losses in different land use scenarios.
4. Conclusions
Based on the scenario analysis method, the rainstorm waterlogging disaster risk was
analyzed in multiple interactional scenarios of rainstorm events in and land use paern
models of Jiangqiao Town. The main findings are as follows.
Precipitation intensity is a significant factor influencing the risk of rainstorm water-
logging disaster. Under the same land use paern, the increase in precipitation intensity
will exacerbate the risk of urban rainstorm waterlogging disasters, which is consistent
with existing research [57]. From the precipitation scenario with the return period ranging
from 10 to 100 years, the total losses of Scenario ND are CNY 560 million, CNY 890 million,
and CNY 1.07 billion, respectively, and those of Scenario EG are CNY 630 million, CNY
980 million, and CNY 1.19 billion, respectively, while those of Scenario EP are (in order)
CNY 480 million, CNY 750 million, and CNY 910 million.
Figure 9. Spatial distribution of inundation losses in different land use scenarios.
4. Conclusions
Based on the scenario analysis method, the rainstorm waterlogging disaster risk was
analyzed in multiple interactional scenarios of rainstorm events in and land use pattern
models of Jiangqiao Town. The main findings are as follows.
Precipitation intensity is a significant factor influencing the risk of rainstorm wa-
terlogging disaster. Under the same land use pattern, the increase in precipitation in-
tensity will exacerbate the risk of urban rainstorm waterlogging disasters, which is con-
sistent with existing research [
57
]. From the precipitation scenario with the return pe-
riod ranging from 10 to 100 years, the total losses of Scenario ND are CNY 560 mil-
lion,
CNY 890 million
, and CNY 1.07 billion, respectively, and those of Scenario EG are
CNY 630 million
,
CNY 980 million
, and CNY 1.19 billion, respectively, while those of Sce-
nario EP are (in order) CNY 480 million, CNY 750 million, and CNY 910 million.
Different land use patterns exhibit varying mitigation effects on urban rainstorm
waterlogging disaster risk. The increase in impermeable surfaces, such as residential
land, and the decrease in farmland driven by urbanization have resulted in an annual
increase in surface runoff, inundation area, and inundation depth in Jiangqiao Town.
Previous studies have demonstrated that under the same level of rainstorm hazard, the
areas of medium- and high-risk patches exhibit an obvious increasing trend with the
expansion of construction land [
58
]. This finding is consistent with the results of this
study. Under the same precipitation intensity, the total losses in land use scenario EP are
the lowest compared to those in land use scenarios ND and EG, which are much more
pronounced under precipitation scenarios with return periods of 10 years and 50 years.

Land 2024,13, 1088 15 of 18
The ecological development prioritization land use pattern significantly mitigates the risk
of rainstorm waterlogging disaster, which provides a theoretical foundation for the use
of ecological measures in flood control. Based on the results of the analysis, it can be
conducted that, given a fixed urban scale, increasing ecological spaces and regulating
the expansion of construction land can significantly reduce the risk of urban rainstorm
waterlogging disasters. Specific measures, such as the implementation of concave green
spaces [59] and the development of rain gardens [60], should be considered.
This study also found that the land use pattern under Scenario EP exhibits limited
effectiveness in mitigating waterlogging risk under the precipitation scenario with the
100-year return period. This study confirms that changing land use patterns can mitigate
rainstorm waterlogging disaster risk by altering the exposure states of various land use
types to such risk. However, disaster risk is jointly affected by hazards, exposure, and
vulnerability [
61
]. It is important to note that this approach represents only one aspect of
broader waterlogging disaster risk management, and additional measures may be necessary
to address the full spectrum of risks, especially under extreme weather conditions. It is
essential to integrate grey infrastructure, green infrastructure, and institutional policies into
land use management decision-making processes [
62
,
63
]. It is important to acknowledge
that with urban construction and development, the drainage infrastructure in Jiangqiao
Town has gradually improved, yet the characteristics of self-drainage remain distinctly
evident because the town is located outside the outer ring road of Shanghai. In addition, the
existing drainage network for Jiangqiao Town is classified as confidential, which results in
a lack of available drainage data and means that drainage effects are not considered in this
study. To better enhance Jiangqiao’s adaptability to rainstorm waterlogging disaster risk,
future research should combine land use changes with drainage facility improvements.
This study aims to analyze the interaction mechanism between different land use
patterns and the rainstorm waterlogging disaster risk, providing insights for rainstorm
waterlogging disaster risk management. However, it is necessary to further elucidate
risk thresholds to determine when land use strategies begin to significantly reduce flood
risk, especially in the context of specific projects. Building on this, more practical and
feasible recommendations can be developed for land use management decision making. In
addition, due to the use of a unified cost benchmark for building costs, it is challenging
to estimate the indoor characteristics of industrial and public buildings. As a result, the
indoor characteristics of buildings have been used as substitutes. To improve the accuracy
of loss assessments, future research should aim to refine the economic data related to loss
assessment to ensure that evaluations are more reflective of actual conditions.
Author Contributions: H.X. conceived the research’s framework, developed research objectives,
and revised the manuscript; J.G. conducted the data analysis and wrote, translated, and revised the
manuscript; X.Y. collected the data and wrote the manuscript; Q.Q. revised the manuscript and edited
and refined its English. S.D. and J.W. made suggestions for revising and improving the research. All
authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the Projects of Humanities and Social Science Youth Foun-
dation of the Ministry of Education in China, grant number 21YJC630146; National Natural Science
Funds of China, grant number 71603168; and China Scholarship Council and National Natural Science
Funds of China, grant number 42171080.
Data Availability Statement: Data sharing is not applicable to this article.
Conflicts of Interest: The authors declare no conflict of interest.
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