Proceedings of the 2023 9th International Conference on Architectural, Civil and Hydraulic Engineering (ICACHE 2023)

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Utilizing an Enhanced Statistical Approach for Accurate Assessment of short-term Probable Maximum Precipitation

Authors
Guangyuan Kan2, 3, 4, 1, *, Xichen Liu1, 2, 3, 4, Xiaodi Fu1, 2, 3, 4, Ke Liang5
1State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing, 100038, China
2China Institute of Water Resources and Hydropower Research, Beijing, 100038, China
3Research Center On Flood & Drought Disaster Prevention and Reduction of the Ministry of Water Resources, Beijing, 100038, China
4Key Laboratory of Water Safety for Beijing-Tianjin-Hebei Region of Ministry of Water Resources, Beijing, 100038, China
5Beijing IWHR Corporation, Beijing, 100048, China
*Corresponding author. Email: kanguangyuan@126.com
Corresponding Author
Guangyuan Kan
Available Online 30 December 2023.
DOI
10.2991/978-94-6463-336-8_57How to use a DOI?
Keywords
probable maximum precipitation; short-term heavy rainfall; enhanced statistical approach; intensity-duration-frequency relationship; urban hydrology
Abstract

Numerous comprehensive studies, both domestically and internationally, have extensively investigated the estimation of Probable Maximum Precipitation (PMP) over durations ranging from 1 to 3 days. However, there remains an unaddressed need for a systematic approach to estimate PMP over short-term. In this study, we present a novel methodology for estimating short-term PMP. This method, grounded in an enhanced statistical estimation approach, is coupled with the intensity-duration-frequency relationship. It offers a solution to the challenge of estimating PMP for short-term when essential storm data is scarce. We apply this approach to estimate short-term PMP for a nuclear power project located in Shandong. Through rigorous comparison and analysis, our method yields estimations that are notably more rational and precise. This improvement holds valuable potential for future general purpose real-world applications.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 9th International Conference on Architectural, Civil and Hydraulic Engineering (ICACHE 2023)
Series
Advances in Engineering Research
Publication Date
30 December 2023
ISBN
978-94-6463-336-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-336-8_57How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Guangyuan Kan
AU  - Xichen Liu
AU  - Xiaodi Fu
AU  - Ke Liang
PY  - 2023
DA  - 2023/12/30
TI  - Utilizing an Enhanced Statistical Approach for Accurate Assessment of short-term Probable Maximum Precipitation
BT  - Proceedings of the 2023 9th International Conference on Architectural, Civil and Hydraulic Engineering (ICACHE 2023)
PB  - Atlantis Press
SP  - 491
EP  - 499
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-336-8_57
DO  - 10.2991/978-94-6463-336-8_57
ID  - Kan2023
ER  -