减灾研究历史数据集专题 II 区论文(已发表) 版本 EN4 Vol 3 (2) 2018.
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A dataset of agro-meteorological disaster-affected area and grain loss in China (1949 – 2015)
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Abstract & Keywords
Abstract: As a big agricultural country, the People's Republic of China has experienced a series of natural disasters since its founding, such as the 1959 – 1961 Great Famine, the 1998 floods and the 2008 snowstorm. Here we present a dataset summarizing four categories of meteorological disaster-affected area at provincial level in China from 1949 to 2015: mildly-affected area, moderately-affected area, heavily-affected area, and total affected area. Based on crop-planting data and natural disaster data, grain losses are also evaluated by using a grain loss assessment model. The dataset plays an important role in the future prediction, prevention, and reduction of agro-meteorological disasters.
Keywords: natural disasters; grain loss; provincial; China; 1949 – 2015
Dataset Profile
TitleA dataset of agro-meteorological disaster-affected area and grain loss in China (1949 – 2015)
Data corresponding authorsMao Kebiao(maokebiao@caas.cn);
Zhao Yinghui( zhaoyhneau@163.com)
Data authorsGuo Jingpeng, Chen Huiqian, Zhang Xiaorong, Zhao Yinghui, Mao Kebiao, Li Ning, Zhu Liang
Time range1949 – 2015
Geographical scopeChina
Spatial resolutionProvincial
Data volume0.7 MB
Data format*.xlsx
Data service system<http://www.sciencedb.cn/dataSet/handle/540>
Sources of fundingNational Natural Science Foundation of China (No. 41571427); Innovation Group Program of the Chinese Academy of Agricultural Sciences (Grant No. Y2017JC33)
Dataset compositionThis dataset consists of two data files: Natural_disaster.zip stores data of disaster-affected area and Grain_loss.zip stores evaluated data of grain losses.

(1) Natural_disaster.zip is a 0.45 MB disaster data set made up of three files:
● Mildly-affected area.xlsx presents the areas with less than 100% of the yield that would be expected under long-term average temperature and precipitation due to natural disasters;
● Moderately-affected area.xlsx presents the areas with less than 70% of the expected yield due to natural disasters;
● Heavily-affected area.xlsx presents the areas with less than 30% of the expected yield due to natural disasters.

(2) Grain_loss.zip is a 0.25 MB grain loss data set made up of two .xlsx files:
● Grain_loss_amount.xlsx;
● Grain_loss_ratio.xlsx.
1.   Introduction
With a vast territory, diverse climatic types, fragile ecosystems and varied types of disasters, China is a country that suffered from the most serious natural disasters in the world. Agricultural disasters constitute the main natural disasters in China, mainly including flood, drought, low temperature, hail, and typhoon, with flood and drought being the most prominent.13 In June and July 2017, 11 provinces in southern China suffered from floods, which affected more than 11 million people, including 78 people who died or disappeared and 27 thousand houses that collapsed. Direct economic losses reached 25.27 billion yuan, leading to a government supply of up to 1.88 billion yuan for disaster relief. Research on the temporal-spatial laws, driving mechanism, risk assessment, regional regularity, and control measures of natural disasters needs the support of historical disaster datasets.34 Here we sorted, summarized and evaluated the data of major meteorological disasters in China at provincial level from 1949 to 2015, as well as the data of grain losses caused by these disasters.
Firstly, we summarized mildly-, moderately-, and heavily-affected area, as well as total affected area by meteorological disasters from 1949 to 2015 (i.e., flood, drought, low temperature, hail, typhoon). Secondly, based on the crop planting dataset, we estimated grain losses caused by these disasters at provincial level by means of grain loss assessment model. The dataset, which includes information on agro-meteorological disasters and grain losses, can provide a scientific basis for investigating the spatiotemporal pattern of agro-meteorological disasters.
2.   Data acquisition and processing
2.1   Overview
It should be noted that, due to limited data availability, statistics of Hong Kong, Macao, and Taiwan were not included in this dataset. This study covers five main disaster types that took place in 31 provinces/municipalities in China: flood, drought, hail, low temperature, and typhoon. The raw data were collected over the period 1949 2015 via Python scripts (Figure 1) from the website of the Ministry of Agriculture of China (MAC) and China Statistical Yearbooks Database (CSYD), as listed in Table 1. Python scripts 1, 2, and 3 (Figure 1) are Web-based data mining techniques.


Figure 1   Flowchart illustrating the dataset formation
Table 1   Description of the raw data
DatabaseVariableTime spanGeographical scopeSources
Raw data on meteorological disastersMildly-affected area (10*4 ha)1949 – 201531 provinces / municipalities in ChinaWebsite of the Ministry of Agriculture of China (MAC) and China Statistical Yearbooks Database (CSYD)
Moderately-affected area (10*4 ha)1949 – 201531 provinces / municipalities in China
Heavily-affected area (10*4 ha)1949 – 201531 provinces / municipalities in China
Crop planting datasetGrain yield (kg*ha^-1)1949 – 201531 provinces / municipalities in ChinaWebsite of the Ministry of Agriculture of China (MAC)
Crop-sown area (10*4 ha)1949 – 201531 provinces / municipalities in China
Grain-sown area (10*4 ha)1949 – 201531 provinces / municipalities in China
Firstly, we cleaned, sorted and replenished the raw meteorological disaster data drawn from the above two sources. Secondly, the final meteorological disaster dataset and the planting dataset were used to generate grain loss data via grain loss assessment model. A detailed description of the final dataset is given in Table 2.
Table 2   Description of the data of meteorological disaster-affected area and grain loss
DatasetVariableDisaster typeTime spanGeographical scope
Meteorological disaster datasetmildly-affected area (10*4ha )Five major disaster types1949201531 provinces/municipalities in China
moderately-affected area (10*4ha )Five major disaster types1949201531 provinces/municipalities in China
heavily-affected area(10*4ha)Five major disaster types1949201531 provinces/municipalities in China
Grain loss datasetGrain loss amount (t)Five major disaster types1949201531 provinces/municipalities in China
Grain loss rate (%)Five major disaster types1949201531 provinces/municipalities in China
2.2   Methods
Li et al. (2010) constructed a statistical model for estimating the amount of grain loss based on grain yield and disaster-affected area. Researchers (Shi et al., 2014; Guan et al., 2015) in China mainly used the statistical model to estimate the amount of grain loss in recent years.46 This section uses the model to estimate the amount of grain loss based on the following formula:
\[G_{q}= R_{i}\times A_{i1}\times y_{i}\times P_{1}\]
1
\[G_{m}= R_{i}\times A_{i2}\times y_{i}\times P_{2}\]
2
\[G_{z}= R_{i}\times A_{i3}\times y_{i}\times P_{3}\]
3
\[S_{ci}= G_{q}+ G_{m}+ G_{z}\]
4
\[S_{c}= \sum_{i= 1}^{n}S_{ci}\]
5
where Sc is the amount of grain loss in China; n is the number of provinces and municipalities; Sci is the grain loss in Province i; Gq is the grain loss caused by mild disaster; Gm is the grain loss caused by moderate disaster; Gz is the grain loss caused by severe disaster; Ri is the ratio of grain-sown area to crop-sown area; Ai1, Ai2, and Ai3 designate the amount of crop area affected by mild, moderate and severe disasters, respectively (mild disaster: 10 30% grain losses; moderate disaster: 30% 70% grain losses; severe disaster: over 70% grain losses); and yi is the grain yield per ha of Province i in the same year. P1, P2, and P3 assume a value of 20%, 50%, and 85%, respectively, assigned based on the median method in tandem with the definitions of crop affected area and crop failure area.
The ratio of grain losses to total grain output in the same year was defined as the rate of grain losses.8 It is expressed by:
\[R= S_{c}/G_{a}* 100\%\]
6
where R is the ratio of grain loss; Sc is the amount of grain loss; Ga is the amount of total grain output.
3.   Sample description
Moderately-affected area.xls includes six worksheets (tag name: Flood, Drought, Hail, Low_temperature, Typhoon). Each table is a 2 * 2 matrix form; the first column of the worksheets records the name of the province/municipality; time dimension (1949 2015) is given in the first line and matrix values are in units of 104 ha.


Figure 2   Moderately-affected area at provincial level
4.   Quality control and assessment
Quality control involved detailed inspection and exploration of patterns to detect any typographical errors or other data errors. The data provided herein were re-processed from the raw data supplied by MAC and CSYD, and as such depended on the quality and reliability of the data sources. Zhao et al. (2017) used the above data to analyze the tempo-spatial characteristics of natural disasters and grain losses in China from 1949 to 2015, and found that flood and typhoon were concentrated in, and greatly limited to, central China, eastern China and the southeast coast of China, unlike other disasters which were randomly distributed. Their findings were hence consistent with the findings of Guan et al. (2015) and Du et al.(2015).2–5
5.   Usage notes
The dataset is released via Science Data Bank (http://www.sciencedb.cn/dataSet/handle/540), and the Resource Service System of the CERN Data Center (http://www.cnern.org.cn), where users can log into the system, click data resources and select data to enter the download page. There are no copyright or proprietary restrictions on the dataset.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No.41571427), and the Innovation Group Program of the Chinese Academy of Agricultural Sciences (Grant No. Y2017JC33).
1.
Liu Y, Yu Y & Li L. Major natural disasters and their spatio-temporal variation in the history of China. Journal of Geographical Sciences 22 (2012): 963 – 976.
2.
Du X, Jin X, Yang X et al. Spatial-temporal pattern changes of main agriculture natural disasters in China during 1990 – 2011.Journal of Geographical Sciences 25 (2015): 387 – 398.
3.
Gu X, Zhang Q & Zhang S. Spatio-temporal properties of flood/drought hazards and possible causes and impacts in 1961 – 2010. Scientia Geographica Sinica 36 (2016): 439 – 447.
4.
Zhao Y, Guo J, Mao K et al. Spatio-temporal distribution of typical natural disasters and grain disaster losses in China from 1949 to 2015. Acta Geographica Sinica 27 (2017): 1261 – 1276.
5.
Guan Y, Zheng F, Zhang P et al. Spatial and temporal changes of meteorological disasters in China during 1950 – 2013. Natural Hazards 75 (2015): 2607 – 2623.
6.
Shi W & Tao F, Spatio-temporal distributions of climate disasters and the response of wheat yields in China from 1983 to 2008. Natural Hazards 74 (2014): 569 – 583.
7.
Yang J, Huo Z, Wu L et al. Indicator-based evaluation of spatiotemporal characteristics of rice flood in Southwest China. Agriculture, Ecosystems and Environment 230 (2016): 221 – 230.
8.
Li W, Qin Z & Lin L. Quantitative analysis of agro-drought impact on food security in China. Journal of Natural Disasters19 (2010): 111 – 118.
Data citation
1. Guo J, Chen H, Zhang X et al. A dataset of agro-meteorological disaster-affected area and grain loss in China (1949 – 2015). Science Data Bank. DOI: 10.11922/sciencedb.537
稿件与作者信息
How to cite this article
Guo J, Chen H, Zhang X et al. A dataset of agro-meteorological disaster-affected area and grain loss in China (1949 – 2015). China Scientific Data 3 (2018), DOI: 10.11922/csdata.2017.0006.en
Guo Jingpeng
motivation of study, data analysis, writing.
Master's student; research area: remote sensing monitoring of agricultural disasters.
Chen Huiqian
manuscript writing.
Master's student; research area: climate change, agricultural economic assessment.
Zhang Xiaorong
data collection.
Master's student; research area: prevention and control of social disasters.
Zhao Yinghui
project supervision and modification.
zhaoyhneau@163.com
PhD, Associate Professor; research area: environmental changes in urban and rural areas.
Mao Kebiao
motivation of study.
maokebiao@caas.cn
PhD, Professor; research area: remote sensing of environment, landscape effects.
Li Ning
data analysis.
Master's student; research area: remote sensing monitoring of agricultural disasters.
Zhu Liang
data collection.
Master's student; research area: regional agricultural economy.
National Natural Science Foundation of China (No. 41571427); Innovation Group Program of the Chinese Academy of Agricultural Sciences (Grant No. Y2017JC33)
出版历史
II区出版时间:2018年4月9日 ( 版本EN4
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