Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling

Yi Lu; Jie Yin; Dandan Wang; Yuhan Yang; Hui Yu; Peiyan Chen; Shuai Zhang

doi:10.1007/s13753-022-00446-x

Volume 13 Issue 6

Feb. 2023

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Article Navigation > International Journal of Disaster Risk Science > 2022 > 13(6): 974-986

Yi Lu, Jie Yin, Dandan Wang, Yuhan Yang, Hui Yu, Peiyan Chen, Shuai Zhang. Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling[J]. International Journal of Disaster Risk Science, 2022, 13(6): 974-986. doi: 10.1007/s13753-022-00446-x

Citation:

Yi Lu, Jie Yin, Dandan Wang, Yuhan Yang, Hui Yu, Peiyan Chen, Shuai Zhang. Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling[J]. International Journal of Disaster Risk Science, 2022, 13(6): 974-986. doi: 10.1007/s13753-022-00446-x

Citation:

Yi Lu, Jie Yin, Dandan Wang, Yuhan Yang, Hui Yu, Peiyan Chen, Shuai Zhang. Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling[J]. International Journal of Disaster Risk Science, 2022, 13(6): 974-986. doi: 10.1007/s13753-022-00446-x

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Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling

doi: 10.1007/s13753-022-00446-x

Yi Lu^1,2,3,
Jie Yin^{1,4,5
,
,},
Dandan Wang⁶,
Yuhan Yang⁷,
Hui Yu^2,3,
Peiyan Chen^2,3,
Shuai Zhang^2,3

1. Key Laboratory of Geographic Information Science(Ministry of Education), East China Normal University, Shanghai, 200241, China;
2. Shanghai Typhoon Institute, China Meteorological Administration, Shanghai, 200030, China;
3. Key Laboratory of Numerical Modeling for Tropical Cyclone of China Meteorological Administration, Shanghai, 20030, China;
4. Institute of Eco-Chongming, East China Normal University, Shanghai, 202162, China;
5. Research Center for China Administrative Division, East China Normal University, Shanghai, 202162, China;
6. National Disaster Reduction Center of China, Beijing, 100124, China;
7. State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China

Funds:

This study was sponsored by the National Natural Science Foundation of China (Grant Nos. 41871164, 41806046), the Shanghai Sailing Program (Grant No. 21YF1456900), the Shanghai Philosophy and Social Science Planning Program (Grant No. 2021XRM005), and the Fundamental Research Funds for the Central Universities (Grant No. 2022ECNU-XWK-XK001).

Received Date: 2022-09-12
Accepted Date: 2022-09-12

Available Online: 2023-02-09

Publish Date: 2022-10-10

Abstract

Abstract

Based on station precipitation observations, radar quantitative precipitation estimates (QPE), and radar fusion data during Typhoon Fitow (2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China, as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows:(1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%. (2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details. (3) One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result-all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems.
- City and neighborhood scale,
- Flood validation,
- Multisource precipitation data,
- Pluvial flood modeling,
- Shanghai

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References(42)

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