Sensitivity analysis of urban flood model parameters is important for efficient and accurate flood simulation. In order to explore the problems of large sampling parameters and nonlinear correlation between input and output variables, this paper proposed a new correlation analysis approach. The type, strength, and the order of sensitive parameters to the four outputs are analyzed using the proposed approach. The results show that the R values of Manning-N are biggest, its distribution is linear in heat maps, and the Manning-N has a strong linear correlation with Average Depth, Hour of Maximum Flooding, and Time to Peak. For Average Depth, the second sensitive parameter is Conductivity. For Hour of Maximum Flooding, the second and third more sensitive parameters are Conductivity and N-perv; however, there are certain nonlinear correlations from heat maps. For Total Inflow, the R values of each parameter are between 0.021 and 0.534. Most sensitive parameters are none; however, the more sensitive parameters are Conductivity, N-perv, and initial deficit. For Time to Peak, the second and third more sensitive parameters are N-perv and N-Imperv; however, there are certain non-linear correlations from heat maps. The results can provide theoretical guidance for application and parameter calibration of SWMM in airport.

• A new correlation analysis approach was proposed.

• The type, strength, and order of sensitive parameters to the four outputs are analyzed.

• Manning-N has a strong linear correlation with Average Depth, Hour of Maximum Flooding, and Time to Peak.

• The heat maps can be used to determine whether the parameters are linear or nonlinear correlation.

• The Latin hypercube sampling and Python programming were applied.

In recent years, flood safety has become a prominent problem in urban development. The frequent occurrence of sudden torrential rain leads to more and more frequent flood disasters at airports, resulting in greater and greater losses. In the airport, the area of impervious ground, such as buildings, cement concrete, and asphalt concrete floors, accounts for a large proportion. This leads to an increase in the coefficient of rainwater runoff. In the face of sudden heavy rainfall, the drainage capacity of the airport is insufficient, even resulting in water accumulation in the apron, taxiway, and other areas. This seriously affect the take-off, landing, skidding, and docking of aircraft, resulting in large-scale flight delays and other losses (Peng et al. 2020). Many airports in China and other countries are affected by heavy rainfall and water accumulation, which seriously affects the safety and normal operation of aircraft. The Beijing Daxing International Airport of China was affected by heavy rain on July 31, 2023. The apron of the airport was heavily flooded and the depth of the water had submerged half of the car's tire. A large number of inbound flights were diverted and a large area of outbound flights were delayed or canceled. Zhengzhou was affected by a once-in-a-millennium rainstorm disaster on July 20, 2021, and the cumulative rainfall at the Zhengzhou Xinzheng International Airport reached 217.8 mm in 24 h, exceeding the recorded extreme. A total of 422 inbound and outbound flights were canceled and 234 were delayed, including 64 flights delayed for more than 4 h. New Delhi in India was affected by a heavy rain on September 11, 2021, the part of runway and apron and the outside road of the New Delhi International Airport were seriously flooded. As a result, three flights were canceled and five flights scheduled for New Delhi were forced to land at airports in surrounding cities.

Urban flood numerical simulation technology is the core component of flood warning and forecast, and an accurate and efficient forecast result is especially important for urban flood control and disaster reduction (Zhang et al. 2023). In response to the flood problem, scholars in China and other countries have conducted a lot of research and developed a variety of flood models, such as one-dimensional river/pipe network models, two-dimensional surface models, and coupling models (Sidek et al. 2021; Son et al. 2023). The development of new models that combine artificial intelligence (AI) models with flood processes has also become a new research direction (Yan et al. 2023). Urban flood models generally include storm runoff generation module, surface confluence module, and pipeline confluence module. According to the different modes of rainfall–runoff generation and confluence, it can be divided into hydrologic urban flood model and full hydrologic urban flood model. The StormWater Management Model (SWMM) is a dynamic model that can simulate urban stormwater runoff (Ma et al. 2022). After multiple updates, the SWMM has become more robust, with a more user-friendly interface. The SWMM is capable of simulating and analyzing the rainfall–runoff, infiltration, and drainage systems in the study area. Additionally, compared to other models, the SWMM offers advantages such as easier parameter acquisition, simpler usage methods, superior simulation performance, and being a non-commercial model, which contribute to its widespread practical application. Therefore, this paper will utilize the SWMM for the related research (Li et al. 2022; Tansar et al. 2023).

Scholars in China and other countries have carried out a lot of research on the sensitivity analysis of parameters. To accurately identify sensitive parameters, both the modified Morris method and mutual information (MI) method were used to conduct sensitivity analysis from local and global perspectives (Liao et al. 2022). A quantitative analysis using the modified Morris screening method and MI method was conducted to assess the sensitivity of model parameters to the peak flow at the drainage outlet and the average runoff coefficient of the basin under design rainstorms with return periods of 1 year, 10 years, and 50 years (Xiang et al. 2020). The impact of the Generalized Likelihood Uncertainty Estimation (GLUE) on the uncertainty analysis of a two-dimensional hydrodynamic model was evaluated in the Daqing River Basin downstream of the Dawen River. They also utilized the MI method to analyze the sensitivity of model parameters and boundary conditions to the prediction results (Sun et al. 2020). An integrated approach was proposed that combines the Morris screening method with the GLUE method, utilizing a Genetic Algorithm for parameter optimization. The results demonstrate the superior applicability of this integrated method across different rainfall intensities and rain-type events (Zhong et al. 2022). Du et al. investigated the feasibility of utilizing the dynamic system response curve (DSRC) method for parameter optimization, offering a solution to the nonlinear challenges encountered by current widely used optimization methods (Du et al. 2023). A reliable framework for setting up and calibrating the SWMM by integrating multiple automated tools was proposed to address some issues (Zhuang et al. 2023).

During the sensitivity analysis of parameters in airport flood models, several issues often arise, such as a large number of sampling parameters, extensive computational requirements, and nonlinear correlations between input parameters and output variables. In order to explore these problems, this paper proposed a new correlation analysis approach combining MI method and heat maps. The Latin hypercube sampling of model parameters applied MATLAB software and the inp files was filed by Python programming, then the multi-group sampling results were obtained. The type, strength, and order of sensitive parameters to the four outputs (Average Depth, Hour of Maximum Flooding, Total Inflow, and Time to Peak) are analyzed using proposed approach in this paper. The study can improve the accuracy of the airport flood simulation model and provide theoretical guidance for the application of SWMM software in airport flood simulation and parameter calibration.

### Location

The case study airport is located in a city in northern China. The airport's flight area is Class 4E. There are two terminals with a total gross leasable area of 3.64 × 105 m2 and cargo warehouses of 7.4 × 104 m2. The airport has two runways, 3,600 and 3,200 m in length. The drainage system of the airport flight area mainly includes trapezoidal open ditch, rectangular open ditch, cover plate ditch, box culvert, and other drainage facilities. There are five stormwater outlets in the flight area, which drain the stormwater from the flight area into nearby reservoirs. In case of heavy rain, when the water level of the reservoir reaches a certain depth, rainwater will be pumped into the river channel around the airport by the pumping station. Taking the east flight area of the case airport as the research object (Figure 1), the SWMM construction and sensitivity analysis of parameters are carried out.
Figure 1

The location and configuration of the case airport.

Figure 1

The location and configuration of the case airport.

Close modal

### Rainfall data

The Chicago rainfall pattern based on storm intensity and peak factor is used to derive short-term precipitation processes for a specified duration and recurrence interval (Liao et al. 2021; Yang et al. 2022). In areas with insufficient rainfall data, design rainfall is usually used in flood simulation model building. In China, rainfall is predominantly of the single-peak type, and the peak coefficient for 2-h rainfall in Tianjin ranges from 0.31 to 0.51, similar to the rainfall pattern in Chicago (Peng et al. 2022). Therefore, this study adopts the Chicago rainfall pattern based on the Tianjin storm formula for rainfall design (Tianjin Urban & Rural Construction Committee 2016). According to the design data, the formula for rainfall intensity in the study area is as follows.
(1)
where q indicates the design storm intensity, L/s·hm2; t refers to the rainfall duration, min; P refers to the design return period, year.

The design of airport's drainage system must take into account numerous factors such as airport safety, operational efficiency, and environmental impact. It is essential to prevent the accumulation of water within internal areas of the airport, including roads, runways, and aprons. Furthermore, measures must be implemented to ensure that the surrounding areas, such as roads and rivers, remain unaffected. In order to study the influence of rainfall beyond design standard on airport safety and operation, we selected the case of a 50-year rainfall recurrence period to study in this paper.

### Correlation analysis approach

In the process of parameter sensitivity analysis of the SWMM, there is often a nonlinear correlation between input parameters and output variables. The linear sensitivity analysis method cannot accurately obtain nonlinear results, but a more satisfactory result can be obtained by using the nonlinear sensitivity analysis method. This study proposed a new correlation analysis approach – a method of analysis combining the MI method and heat maps.

The MI method is a nonlinear sensitivity analysis method, which can be used to analyze whether the input parameters have a nonlinear correlation with the output results and the strength of the nonlinear. The correlation between two random variables is a measure of the interdependence between the variables. Correlation indicates the amount of shared information between two or more variables and is an indicator of the degree of interdependence between random variables in information theory.

The MI method can be regarded as the information amount of one random variable containing another random variable, which is extended to the degree of nonlinear correlation between the two variables. The correlation between two discrete variables X and Y can be defined as:
(2)
where refers to the probability of occurrence when ; refers to the probability of occurrence when ; refers to the probability of simultaneous occurrence of and , that is, the joint probability; refers to the correlation value between two variables.

If the variables X and Y are independent of each other, then , and the correlation has symmetry, namely . When the variables X and Y are completely correlated, then = 0.5[H(x) + H(y)].

The MI method is based on the concept of information theory. Between two random variables X and Y, the mutual information I (X; Y) represents the reduction of the uncertainty measure for x by observing Y. The greater the MI value, the stronger the correlation between Xand Y, both linear independence and nonlinear independence. The value of the MI I does not indicate linear independence or nonlinear independence. The value of I gives the degree of difference between two probability distributions and does not directly reflect the type of relationship between variables. Other statistical methods or indicators, such as heat maps, scatter plots, and regression analyses, are often used to determine linear independence or nonlinear independence between variables. A new method of analysis combining the MI method and heat maps is proposed in this paper. On the basis of MI method analysis, heat maps are used to analyze the linear independence or nonlinear independence between variables and the strength of correlations.

Due to the common use of the R value as a correlation coefficient, it is more intuitive compared to the I value. When the R value is close to 1, it indicates a strong correlation between two variables; when the R value is close to 0, it indicates no correlation between the variables. The intuitive nature of the R value makes it easier for people to understand the degree of correlation between variables. Therefore, this study focuses on exploring the correlation of variables using the R value.

The correlation coefficient indicator R is as follows:
(3)

### Latin hypercube sampling

Latin hypercube sampling is a classic parameter sampling technique used for experimental design or parameter sensitivity analysis. It generates sample points in multi-dimensional space, enhancing sampling efficiency by making full use of the parameter space. The main idea of Latin hypercube sampling is to uniformly divide the possible value range of each parameter into multiple intervals and ensure that only one sample point appears in each interval (Li et al. 2023). This means that the sample points are uniformly distributed on each parameter dimension, allowing for a more even distribution of sample points throughout the entire parameter space. First, determine the possible range of parameter values. Then, divide the value range of each parameter uniformly into an equal number of intervals. Finally, select one sample point from each interval of each parameter, ensuring that each sample point is unique on each parameter dimension. This method reduces the variance of sample points, avoids duplicate points, maintains the independence and randomness of samples, and greatly improves sampling efficiency.

In this paper, Latin hypercube sampling of nine parameters is conducted using MATLAB software programming. In MATLAB software, the ‘rand’ function can generate pseudorandom numbers with a mean of 0.5 and an amplitude between 0 and 1, and the ‘randperm’ function can produce a random sequence uniformly distributed from 1 to n. After obtaining 1,000 sets of sampling data, Python programming is used to generate the corresponding inp files, and 1,000 sets of results are obtained by running the model. The four output results (Average Depth, Hour of Maximum Flooding, Total Inflow, and Time to Peak) are combined with the nine parameters to form a 1,000 × 10 matrix. Finally, MATLAB software programming is performed using the ‘sortrows’, ‘hist’, and ‘zeros’ functions. The flowchart for Latin hypercube sampling of parameters is shown in Figure 2.
Figure 2

The flowchart of Latin hypercube sampling.

Figure 2

The flowchart of Latin hypercube sampling.

Close modal

### Model building

Based on the collected CAD drawings of the airport's drainage pipe network and satellite network maps, the characteristics of the study area's underlying surface and pipe network layout can be generalized. This includes the generalization of sub-catchments, pipe networks, and the setting of basic parameters. The total area of the study area is 251.24 hm2, with impervious area accounting for approximately 55%. In this study, the study area is divided into 164 sub-catchments, 176 nodes, and 176 rainwater pipes as shown in Figure 3.
Figure 3

The sub-catchment divisions of the study area.

Figure 3

The sub-catchment divisions of the study area.

Close modal

### Selection of research parameters

The parameters of the SWMM can be divided into four main types: sub catchment parameters, pipe segment parameters, infiltration parameters, and water quality parameters. These parameters can be divided into two types according to different ways of obtaining them. The first way is through the actual survey, construction drawings, land types, and other design data, such as sub-catchment parameters including area, slope, percentage of impervious area, and pipe segment parameters including pipe type, slope, length, and so on. The second way is to set the parameters empirically, such as impermeability roughness factor, permeability roughness coefficient, impermeable depression water storage, and pipe section roughness factor. In the model manual, these parameters are given a range of values, which will vary according to the land type, climate characteristics, vegetation types, soil types, and so on. There are 14 hydrological and hydraulic parameters in the SWMM, including catchment, slope, and impervious percentage, which can be obtained by the first way, and the parameter setting has greater certainty. Therefore, this paper focuses on the selection of the second way to obtain the parameters. Table 1 shows the selected parameters and ranges of values, which are determined according to the SWMM application manual, related research, and regional actual conditions (Li et al. 2014; Li & Yang 2020).

Table 1

The value range of related parameters

NumberParameterDescriptionCalibration interval
N-Imperv Impermeability roughness factor 0.005–0.04
N-perv Permeability roughness coefficient 0.05–0.5
S-Imperv Impermeable depression water storage (mm) 1–4
S-perv Permeable depression storage (mm) 2–8
Conductivity Hydraulic conductivity (mm/h) 0.01–4.74
Initial deficit Initial loss 1–10
Manning-N Pipe section roughness factor 0.011–0.024
Zero-Imperv Proportion of impervious areas without depressions (%) 10–80
NumberParameterDescriptionCalibration interval
N-Imperv Impermeability roughness factor 0.005–0.04
N-perv Permeability roughness coefficient 0.05–0.5
S-Imperv Impermeable depression water storage (mm) 1–4
S-perv Permeable depression storage (mm) 2–8
Conductivity Hydraulic conductivity (mm/h) 0.01–4.74
Initial deficit Initial loss 1–10
Manning-N Pipe section roughness factor 0.011–0.024
Zero-Imperv Proportion of impervious areas without depressions (%) 10–80

In this paper, the key research parameters of the SWMM are first determined, and the statistical sampling of sample data is carried out by Latin hypercube sampling, and the data extraction and processing are carried out by MATLAB software and Python programming. Finally, a new correlation analysis approach combining MI method and heat maps is applied to the sensitivity analysis of parameters. The sensitivity and ranking of parameters for the four outputs (Average Depth, Hour of Maximum Flooding, Total Inflow, and Time to Peak) are studied, and the detailed findings are shown in Table 2. From Table 2, we can see the value of correlation coefficient R for each parameter clearly. The value of R is between 0 and 1. The closer the value is to 1, the stronger the correlation is. The parameter sensitivity of each output is sorted from high to low. The closer the value of R is to 1, the stronger the correlation between the variables. Similarly, the closer the value of R is to 0, the weaker the correlation between the variables. The parameter sensitivity of each output is sorted from high to low.

Table 2

Results of sensitivity analysis of the MI method

SequenceAverage Depth
Hour of maximum flooding
Total inflow
Time to Peak
ParametersR (x, y)ParametersR (x, y)ParametersR (x, y)ParametersR (x, y)
Manning-N 0.715 Manning-N 0.676 Conductivity 0.534 Manning-N 0.721
Conductivity 0.296 Conductivity 0.312 N-perv 0.418 N-perv 0.316
Initial Deficit 0.233 N-perv 0.286 Initial Deficit 0.399 N-Imperv 0.270
S-perv 0.232 Initial Deficit 0.242 Zero-Imperv 0.202 Conductivity 0.205
N-Imperv 0.220 S-perv 0.216 S-Imperv 0.198 Initial Deficit 0.195
N-perv 0.185 N-Imperv 0.202 Suction Head 0.197 S-perv 0.185
Zero-Imperv 0.182 S-Imperv 0.195 S-perv 0.190 S-Imperv 0.184
S-Imperv 0.171 Zero-Imperv 0.183 Manning-N 0.021 Zero-Imperv 0.180
SequenceAverage Depth
Hour of maximum flooding
Total inflow
Time to Peak
ParametersR (x, y)ParametersR (x, y)ParametersR (x, y)ParametersR (x, y)
Manning-N 0.715 Manning-N 0.676 Conductivity 0.534 Manning-N 0.721
Conductivity 0.296 Conductivity 0.312 N-perv 0.418 N-perv 0.316
Initial Deficit 0.233 N-perv 0.286 Initial Deficit 0.399 N-Imperv 0.270
S-perv 0.232 Initial Deficit 0.242 Zero-Imperv 0.202 Conductivity 0.205
N-Imperv 0.220 S-perv 0.216 S-Imperv 0.198 Initial Deficit 0.195
N-perv 0.185 N-Imperv 0.202 Suction Head 0.197 S-perv 0.185
Zero-Imperv 0.182 S-Imperv 0.195 S-perv 0.190 S-Imperv 0.184
S-Imperv 0.171 Zero-Imperv 0.183 Manning-N 0.021 Zero-Imperv 0.180

Heat map is a valuable tool for intuitively displaying large amounts of data and information on a two-dimensional plane. This paper utilizes a 10 × 10 grid heat map for data analysis. The x-axis divides the subject values into 10 gradients and the y-axis does the same for corresponding parameter values. In heat maps, frequency percentages are used to explore the relevance of the subject to the corresponding parameter, and color changes represent the proportion of a particular value in the entire data set. Blue indicates a low frequency percentage, and red indicates a high frequency percentage. According to the color and shape distribution of the heat map, the correlation between x and y can be intuitively seen.

### Average Depth

From the correlation analysis results of Average Depth in Table 2, it can be seen that the R value of Manning-N is the largest, reached 0.715, and it is the most sensitive parameter. This indicates that there is a strong correlation between the Average Depth and Manning-N. Compared with Manning-N, the R values of other parameters are smaller, ranging from 0.171 to 0.296. They have a weak correlation with Average Depth. It can be seen that Manning-N plays a decisive role in the influence of the Average Depth. From the heat maps of Average Depth in Figure 4, the parameter Manning-N with the largest R value has more red regions and a linear distribution, indicating that Manning-N has a strong linear correlation with the Average Depth. The R values of the other parameters are low, the heat maps distribution are divergent, and there is no significant correlation. Therefore, Manning-N has the greatest influence on Average Depth, and special attention should be paid to the parameter setting of the airport flood model. The second sensitive parameter is Conductivity. The distribution of Conductivity in shown in Figure 4 is divergent, and it has a medium nonlinear correlation with the Average Depth.
Figure 4

The heat maps of Average Depth.

Figure 4

The heat maps of Average Depth.

Close modal

### Hour of Maximum Flooding

From the correlation analysis results of Hour of Maximum Flooding in Table 2, it can be seen that the R value of Manning-N is the largest, reached 0.676, and it is the most sensitive parameter. This indicates that there is a strong correlation between the Hour of Maximum Flooding and Manning-N. The second and third more sensitive parameters are Conductivity and N-perv. The other parameters have smaller R values, ranging from 0.183 to 0.242. It can be seen that Manning-N plays a decisive role in the influence of the Hour of Maximum Flooding, followed by Conductivity and N-perv. From the heat maps of Hour of Maximum Flooding in Figure 5, the parameter Manning-N with the largest R value has more red regions and its arrangement is essentially forming a straight line, indicating that Manning-N has a strong linear correlation with the Hour of Maximum Flooding. The distribution of Conductivity and N-perv in Figure 5 is divergent, and there are certain nonlinear correlations. Therefore, Manning-N has the greatest influence on the Hour of Maximum Flooding, and special attention should be paid to the parameter setting of airport flood model, the selection of Conductivity and N-perv should also be given due attention.
Figure 5

The heat maps of Hour of Maximum Flooding.

Figure 5

The heat maps of Hour of Maximum Flooding.

Close modal

### Total inflow

From the correlation analysis results of Total Inflow in Table 2, it can be seen that the R values of each parameter are between 0.021 and 0.534. The more sensitive parameters are Conductivity, N-perv, and initial deficit, and the R values are 0.534, 0.418, and 0.399, respectively. The sensitivity of Conductivity, N-perv, and initial deficit for Total Inflow is slightly higher than other parameters. From the heat maps of Total Inflow in Figure 6, the arrangement of Conductivity with the R value of 0.534 almost forms a straight line, indicating that Conductivity has a medium linear correlation with Total Inflow. The distribution of N-perv and initial deficit is divergent, and they have a medium nonlinear correlation with Total Inflow. The distribution of other parameters appears to be relatively uniform, and there is no obvious linear distribution. The sensitivity of other parameters to Total Inflow is not obvious.
Figure 6

The heat maps of Total Inflow.

Figure 6

The heat maps of Total Inflow.

Close modal

### Time to Peak

From the correlation analysis results of Time to Peak in Table 2, it can be seen that the R value of Manning-N is the largest, reached 0.721, and it is the most sensitive parameter. This indicates that there is a strong correlation between the Time to Peak and Manning-N. The second and third more sensitive parameters are N-perv and N-Imperv. The other parameters have smaller R values, ranging from 0.180 to 0.205. It can be seen that Manning-N plays a decisive role in the influence of the Time to Peak, followed by N-perv and N-Imperv. From the heat maps of Time to Peak in Figure 7, the arrangement of Manning-N with the largest R value is forming a straight line, indicating that Manning-N has a strong linear correlation with the Time to Peak. The distribution of N-perv and N-Imperv in Figure 7 is divergent, and there are certain nonlinear correlations. Therefore, Manning-N has the greatest influence on the Time to Peak, and special attention should be paid to the parameter setting of airport flood model, and the selection of N-perv and N-Imperv should also be given due attention.
Figure 7

The heat maps of Time to Peak.

Figure 7

The heat maps of Time to Peak.

Close modal

The SWMM has been widely applied for urban rainfall–runoff simulations. Sensitivity analysis of urban flood model parameters is important for efficient and accurate flood simulation. Due to the influence of special land types, functional zoning and use requirements of airports, there are many problems in parameter sensitivity analysis of the airport flood model, such as large sampling parameters, large amount of calculation, and nonlinear correlation between input and output variables. This study proposed a new correlation analysis approach combining the MI method and heat maps. The Latin hypercube sampling of model parameters was applied using MATLAB software and the inp files were filed by Python programming, then the multi-group sampling results were obtained and the sensitivity analysis was carried out using proposed approach. The type, strength, and order of parameter sensitivity to the four outputs (Average Depth, Hour of Maximum Flooding, Total Inflow and Time to Peak) are analyzed in this paper. The detailed results are as follows.

1. For Average Depth, the R value of Manning-N is the largest, reached 0.715, and its distribution shown in Figure 4 is forming a straight line. Manning-N has a strong linear correlation with the Average Depth, and special attention should be paid to the parameter setting of the airport flood model. The second sensitive parameter is Conductivity. The distribution of Conductivity in Figure 4 is divergent, and it has a medium nonlinear correlation with the Average Depth.

2. For Hour of Maximum Flooding, the R value of Manning-N is the largest, reached 0.676, and its distribution shown in Figure 5 is forming a straight line. Manning-N has a strong linear correlation with the Hour of Maximum Flooding. The second and third more sensitive parameters are Conductivity and N-perv, and their distribution in Figure 5 is divergent, and there are certain nonlinear correlations with the Hour of Maximum Flooding. The other parameters have smaller R values, ranging from 0.183 to 0.242, the sensitivity is not obvious.

3. For Total Inflow, the R values of each parameter are between 0.021 and 0.534. There are no most sensitive parameters, the more sensitive parameters are Conductivity, N-perv, and Initial Deficit, and the R values are 0.534, 0.418, and 0.399, respectively. The arrangement of Conductivity almost forms a straight line in Figure 6, and it has a medium linear correlation with Total Inflow. The distribution of N-perv and Initial Deficit is divergent, and they have a medium nonlinear correlation with Total Inflow.

4. For Time to Peak, the R value of Manning-N is the largest, reached 0.721, and it is the most sensitive parameter. The distribution of Manning-N in Figure 7 is forming a straight line, and it has a strong linear correlation with the Average Depth. The second and third more sensitive parameters are N-perv and N-Imperv. The other parameters have smaller R values, ranging from 0.180 to 0.205. The distribution of N-perv and N-Imperv in Figure 7 is divergent, and there are certain nonlinear correlations.

This study quantitatively analyzed the influence of multiple input parameters on the output results in the airport flood model and the correlation between them under the influence of special land types, functional zoning, and usage requirements in the airport airfield area. The sensitive parameters and their ranking are effectively identified, which can greatly improve the accuracy of the airport flood simulation model. Since this paper only analyzes a rainstorm in 50 years, the application of other sensitivity analysis methods under different rainfall return periods should be studied in the future.

The authors wish to thank the anonymous reviewers for their comments and suggestions and people who have supported this study.

This research was funded by the Enterprise Science and Technology Commissioner Project of Tianjin Technology Innovation Fund [grant number: 22YDTPJC00430] and Research Centre for Environment and Sustainable Development of Civil Aviation Administration of China Open Fund [grant number: 2022YB03].

J.P. conceptualized the study; prepared the methodology; did software analysis; supervised the study; wrote, reviewed, and edited the article. H.Z. did data curation; prepared and wrote the original draft; visualized the study; investigated the study; did software analysis. Z.L. validated the study; wrote, reviewed, and edited the article. J.O. guided the study. L.Y. collected resources and did software analysis.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Calculation Standards for Rainwater Runoff in Tianjin: DB/T 29-236-2016
.
Tianjin Urban and Rural Construction Committee. (in Chinese)
.
Du
Y.
,
Li
Q. F.
,
He
P. F.
,
Zou
Z. H.
,
Zhou
Z. M.
,
Xu
S. H.
,
Han
X. Y.
&
Zeng
T. S.
2023
Simultaneous optimization of SWMM parameters by the dynamic system response curve with multi-objective function
.
Water Resources Management
2023
,
1
19
.
doi:10.1007/s11269-023-03595
.
Li
M.
&
Yang
X.
2020
Global sensitivity analysis of SWMM parameters based on Sobol method
.
China Water Wastewater
36
,
95
102
.
Li
C.
,
Wang
W.
,
Xiong
J.
&
Chen
P.
2014
Sensitivity analysis for urban drainage modeling using mutual information
.
Entropy
16
(
11
),
5738
5752
.
https://doi.org/10.3390/e16115738
.
Li
S.
,
Wang
Z.
,
Wu
X.
,
Zeng
Z.
,
Shen
P.
&
Lai
C.
2022
A novel spatial optimization approach for the cost-effectiveness improvement of LID practices based on SWMM-FTC
.
Journal of Environmental Management
307
,
114574
.
https://doi.org/10.1016/j.jenvman.2022.114574
.
Li
D.
,
Ju
Q.
,
Jiang
P.
,
Huang
P.
,
Xu
X.
,
Wang
Q.
,
Hao
Z. C.
&
Zhang
Y. Z.
2023
Sensitivity analysis of hydrological model parameters based on improved Morris method with the double-Latin hypercube sampling
.
Hydrology Research
54
(
2
),
220
232
.
https://doi.org/10.2166/nh.2023.109
.
Liao
D.
,
Zhang
Q.
,
Wang
Y.
,
Zhu
H.
&
Sun
J.
2021
Study of four rainstorm design methods in Chongqing
.
Frontiers in Environmental Science
2021
(
9
),
51
.
https://doi.org/10.3389/fenvs.2021.639931
.
Liao
R. T.
,
Xu
Z. X.
,
Ye
C. L.
,
Zuo
B. B.
,
Xiang
D. F.
&
Shu
X. Y.
2022
Parameter sensitivity analysis methods of storm water management model
.
Journal of Hydroelectric Engineering
41
(
6
),
11
21
.
(in Chinese)
.
Ma
B.
,
Wu
Z.
,
Hu
C.
,
Wang
H.
,
Xu
H.
&
Yan
D.
2022
Process-oriented SWMM real-time correction and urban flood dynamic simulation
.
Journal of Hydrology
2022
(
605
),
127269
.
https://doi.org/10.1016/j.jhydrol.2021.127269
.
Peng
J.
,
Zhong
X.
,
Yu
L.
&
Wang
Q. Q.
2020
Simulating rainfall runoff and assessing low impact development (LID) facilities in sponge airport
.
Water Science and Technology
85
(
5
),
918
926
.
https://doi.org/10.2166/wst.2020.400
.
Peng
J.
,
Wang
Q. Q.
,
Yang
X. S.
,
Yu
L.
&
Zhong
X.
2022
Application and evaluation of LID facilities in sponge airport, China
.
Water Science and Technology
85
(
3
),
756
768
.
https://doi.org/10.2166/wst.2022.026
.
Sidek
L. M.
,
Chua
L. H. C.
,
Azizi
A. S. M.
,
Basri
H.
,
Jaafar
A. S.
&
Moon
W. C.
2021
Application of PCSWMM for the 1-D and 1-D–2-D modeling of urban flooding in Damansara Catchment, Malaysia
.
Applied Sciences
11
(
19
),
9300
.
https://doi.org/10.3390/app11199300
.
Son
Y.
,
Di
L. E.
&
Luo
J.
2023
WRF-Hydro-CUFA: A scalable and adaptable coastal-urban flood model based on the WRF-Hydro and SWMM models
.
Environmental Modelling and Software
167
,
105770
.
https://doi.org/10.1016/j.envsoft.2023.105770
.
Sun
C.
,
Li
C. Q.
,
Zhou
C.
&
Li
J. Z.
2020
Uncertainty study of two-dimensional hydrodynamic model based on GLUE method
.
Water Resources and Power
38
(
05
),
59
62
.
(in Chinese)
.
Tansar
H.
,
Duan
H. F.
&
Mark
O.
2023
Global sensitivity analysis of bioretention cell design for stormwater system: a comparison of VARS framework and Sobol method
.
Journal of Hydrology
2023
(
617
),
128895
.
https://doi.org/10.1016/j.jhydrol.2022.128895
.
Xiang
D. F.
,
Cheng
L.
,
Xue
Z. X.
,
Chen
H.
&
Li
M.
2020
Identification of sensitive parameters of SWMM based on local and global methods
.
Journal of Hydroelectric Engineering
39
(
11
),
71
79
.
(in Chinese)
.
Yan
Y.
,
Zhang
N.
&
Zhang
H.
2023
Applications of advanced technologies in the development of urban flood models
.
Water
15
(
4
).
https://doi.org/10.3390/w15040622
.
Yang
J.
,
Xiang
Y.
,
Xu
X.
&
Sun
J.
2022
Design hyetograph for short-duration rainstorm in Jiangsu
.
Atmosphere
13
(
6
),
899
.
https://doi.org/10.3390/atmos13060899
.
Zhang
X.
,
Qiao
W.
,
Xiao
Y.
&
Lu
Y.
2023
Analysis of regional flooding in the urbanization expansion process based on the SWMM model
.
Natural Hazards
117
(
2
),
1349
1363
.
doi:10.1007/s11069-023-05906-1
.
Zhong
B.
,
Wang
Z.
,
Yang
H.
,
Xu
H.
,
Gao
M.
&
Liang
Q.
2022
Parameter optimization of SWMM model using integrated morris and GLUE methods
.
Water
15
(
1
),
149
.
https://doi.org/10.3390/w15010149
.
Zhuang
Q.
,
Li
M.
&
Lu
Z.
2023
Assessing runoff control of low impact development in Hong Kong's dense community with reliable SWMM setup and calibration
.
Journal of Environmental Management
345
,
118599
.
https://doi.org/10.1016/j.jenvman.2023.118599
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/).