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2019 年台风“海贝思”全国洪水预报系统的适用性。

Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019.

机构信息

IIS, The University of Tokyo, Tokyo, Japan.

University of Massachusetts, Amherst, USA.

出版信息

Sci Rep. 2021 May 13;11(1):10213. doi: 10.1038/s41598-021-89522-8.

DOI:10.1038/s41598-021-89522-8
PMID:33986352
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8119424/
Abstract

Floods 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.

摘要

洪水在河流沿岸人口密集地区可能造成毁灭性影响,因此,获得具有高精度的更长预报提前期对于保护人员和财产至关重要。尽管有许多技术用于洪水预报,但对于将预报系统用于业务警报目的的充分验证却缺乏。在这项研究中,我们验证了 2019 年在日本造成严重洪灾的台风“海贝思”期间决堤的洪水位置和时间。为了实现研究目标,我们结合了一个水动力模型和统计分析,根据日本气象厅中尺度模型格点值(MSM-GPV)的 39 小时预测进行强迫,获得了所有洪水泛滥地点的决堤时间进行验证。结果表明,这种方法在预测 130 个地点的洪水方面非常准确,大约占 142 个洪水泛滥地点的 91.6%,提前期约为 32.75 小时。在精度方面,这些成功预测的地点占 542 个发出洪水警报地点的 24.0%,平均而言,预测的洪水时间比给定的决堤时间早约 8.53 小时。更多的警报是针对严重洪水泛滥的主要河流发出的,这表明该系统对极端洪水事件敏感,并可以对存在高洪水风险的河流发出警报。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/a6f920e22fb7/41598_2021_89522_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/22c6b12e25a8/41598_2021_89522_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/b65ebf49ba2b/41598_2021_89522_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/931cc362b3a0/41598_2021_89522_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/356c953c9a3d/41598_2021_89522_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/8fb95d318fe7/41598_2021_89522_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/7bc488389a4a/41598_2021_89522_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/a6f920e22fb7/41598_2021_89522_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/22c6b12e25a8/41598_2021_89522_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/b65ebf49ba2b/41598_2021_89522_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/931cc362b3a0/41598_2021_89522_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/356c953c9a3d/41598_2021_89522_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/8fb95d318fe7/41598_2021_89522_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/7bc488389a4a/41598_2021_89522_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0d4/8119424/a6f920e22fb7/41598_2021_89522_Fig7_HTML.jpg

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