Author:
Ma Wenchao,Ishitsuka Yuta,Takeshima Akira,Hibino Kenshi,Yamazaki Dai,Yamamoto Kosuke,Kachi Misako,Oki Riko,Oki Taikan,Yoshimura Kei
Abstract
AbstractFloods can be devastating in densely populated regions along rivers, so attaining a longer forecast lead time with high accuracy is essential for protecting people and property. Although many techniques are used to forecast floods, sufficient validation of the use of a forecast system for operational alert purposes is lacking. In this study, we validated the flooding locations and times of dike breaking that had occurred during Typhoon Hagibis, which caused severe flooding in Japan in 2019. To achieve the goal of the study, we combined a hydrodynamic model with statistical analysis under forcing by a 39-h prediction of the Japan Meteorological Agency's Meso-scale model Grid Point Value (MSM-GPV) and obtained dike-break times for all flooded locations for validation. The results showed that this method was accurate in predicting floods at 130 locations, approximately 91.6% of the total of 142 flooded locations, with a lead time of approximately 32.75 h. In terms of precision, these successfully predicted locations accounted for 24.0% of the total of 542 locations under a flood warning, and on average, the predicted flood time was approximately 8.53 h earlier than a given dike-break time. More warnings were issued for major rivers with severe flooding, indicating that the system is sensitive to extreme flood events and can issue warnings for rivers subject to high risk of flooding.
Publisher
Springer Science and Business Media LLC
Reference68 articles.
1. Hirabayashi, Y. et al. Global flood risk under climate change. Nat. Clim. Chang. 3, 816–821 (2013).
2. Chang, L. et al. flood forecasts up to two days in advance. Nat. Commun. https://doi.org/10.1038/s41467-020-15734-7 (2020).
3. Paprotny, D., Sebastian, A., Morales-Nápoles, O. & Jonkman, S. N. Trends in flood losses in Europe over the past 150 years. Nat. Commun. 9, (2018).
4. Takemi, T. & Unuma, T. Environmental factors for the development of heavy rainfall in the eastern part of Japan during Typhoon Hagibis (2019). Sci. Online Lett. Atmos. 16, 30–36 (2020).
5. Sayama, T., Yamada, M., Sugawara, Y. & Yamazaki, D. Ensemble Flash Flood Predictions Using a High-Resolution Nationwide Distributed Rainfall-Runoff Model: Case Study of the Heavy Rain Event of July 2018 and Typhoon Hagibis in 2019. (2020) https://doi.org/10.21203/rs.3.rs-40714/v1.
Cited by
13 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献