Reconstructed county-level dataset of crop areas affected by typhoons in China’s coastal regions (1980–2022)

The scarcity of long-term, high-resolution typhoon disaster data, particularly for agricultural metrics, poses significant challenges for stable typhoon agricultural disaster risk modeling, limiting predictive accuracy. To address this critical issue, we reconstructed a county-level dataset of typhoon-affected crop areas across China's coastal regions (Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi and Hainan) from 1980 to 2022. Data used in this study included meteorological data from 530 county-level weather stations and 1,845 original disaster records (2004-2013; 75 typhoons were included) that could be matched to local weather stations (398 stations are covered). After error revisions, we obtained a dataset covering 530 stations and 514 typhoons from 1980 to 2022. To increase the applicability of the dataset, we categorized the disasters into four classes, light, moderate, severe, and extremely severe, regarding to single station and typhoon cases, respectively. Validation through comparative analyses confirmed the strong reliability of the reconstructed dataset. The reconstructed dataset can be used to advance typhoon disaster risk research, improve forecasting and early warning systems, and support related decision-making efforts.

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Publication Details

Journal
Scientific Data
Published
2025-09-29
DOI
https://doi.org/10.1038/s41597-025-05834-8
Primary Topic
Tropical and Extratropical Cyclones Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Reconstructed county-level dataset of crop areas affected by typhoons in China’s coastal regions (1980–2022)

Caiming Wu, Fumin Ren, Wenjing Wang
Scientific Data
Tropical and Extratropical Cyclones Research
article

Reconstructed county-level dataset of crop areas affected by typhoons in China’s coastal regions (1980–2022)

Caiming Wu, Fumin Ren, Wenjing Wang
article en

Abstract

The scarcity of long-term, high-resolution typhoon disaster data, particularly for agricultural metrics, poses significant challenges for stable typhoon agricultural disaster risk modeling, limiting predictive accuracy. To address this critical issue, we reconstructed a county-level dataset of typhoon-affected crop areas across China's coastal regions (Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi and Hainan) from 1980 to 2022. Data used in this study included meteorological data from 530 county-level weather stations and 1,845 original disaster records (2004-2013; 75 typhoons were included) that could be matched to local weather stations (398 stations are covered). After error revisions, we obtained a dataset covering 530 stations and 514 typhoons from 1980 to 2022. To increase the applicability of the dataset, we categorized the disasters into four classes, light, moderate, severe, and extremely severe, regarding to single station and typhoon cases, respectively. Validation through comparative analyses confirmed the strong reliability of the reconstructed dataset. The reconstructed dataset can be used to advance typhoon disaster risk research, improve forecasting and early warning systems, and support related decision-making efforts.

Scientific DataVol. 12(1)
Nanjing University of Information Science and Technology (CN), Fudan University (CN), Chinese Academy of Meteorological Sciences (CN)
Jiangsu Collaborative Innovation Center for Climate Change
Openalex Percentile: Top 68%
Tropical and Extratropical Cyclones Research
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