Data Paper Zone II Versions EN5 Vol 4 (1) 2019
A dataset of ≥10℃ accumulated temperature of field stations in Chinese typical ecosystems from 2001 to 2015
>>
： 2018 - 10 - 15
： 2018 - 10 - 24
： 2019 - 03 - 28
814 5 0
Abstract & Keywords
Abstract: Thermal resources are the important basis for the division of natural areas and natural belts, and the important natural resource for agricultural production. They are usually characterized by the temperature and the accumulated temperature. 10°C is the starting temperature suitable for the growth of thermophilic plants, and it is also the temperature at which the cool crops grow rapidly and the perennial crops begin to accumulate dry matter at a fast rate. The ≥10℃ accumulated temperature and its corresponding duration are used to measure the agro-climatic production potential of a region and to be an important basis for the introduction and cultivation system reform by agricultural production department and researchers. Based on the daily average automatic observation temperature data of Chinese Ecosystem Research Network (CERN) and by using the 5-day moving average method, a dataset of ≥10℃ accumulated temperature in Chinese typical ecosystems from 2001 to 2015 was established, which includes the accumulated temperature, effective accumulated temperature and duration of ≥10℃ for 35 field stations covering major ecosystems of China such as agriculture, forest, grassland, desert, wetland, lake and bay. It will support the research on national or regional temporal and spatial distribution of accumulated temperature.
Keywords: ecosystem; ≥10℃ accumulated temperature; field station; thermal resources
Dataset Profile
1.   Introduction
Thermal resources are the important basis for the division of natural areas and natural belts, and the important natural resource for agricultural production. They are usually characterized by the temperature and the accumulated temperature. The accumulated temperature is the sum of average daily temperature during a certain period of time. It is widely used in the practice of agricultural production, which is significant for agricultural climate zoning, rational allocation of crops, crop phenology prediction, pest and disease prediction, etc..1.2.3.4. In the context of global warming, more and more researchers employ the accumulated temperature as an important indicator to explore the spatial and temporal variation of national or regional agricultural thermal resources, and the impact of thermal resource changes on agricultural production.1.3.5.6.7.8.9.10.
Ecosystems are at the heart of the biosphere, which is most active and closely related to human activities in the earth surface system. Chinese Ecosystem Research Network (CERN) was established in 1988. Currently, it consists of 44 field stations, 5 sub-centers and 1 synthesis center. The stations encompass diverse ecosystems in China, including agriculture, forest, grassland, desert, lake, bay, wetland, karst and urban ecosystems, and distribute in China's major climate zones and economic type regions.11.One of the core missions of CERN is to implement long-term ecological monitoring of ecological processes and their environmental controlling factors of China’s ecosystems. Since 1998, the field stations of CERN continuously measure and record more than 300 monitoring variations in hydrological, pedological, atmospheric and biological elements of ecosystems according to the standard monitoring protocols.12.Each station sets up a meteorological observation site to obtain meteorological data in a place that can reflect the characteristics of meteorological elements of a large range around the station.13.
At present, the researches on accumulated temperature are mainly based on the temperature data from meteorological observing stations of China Meteorological Administration (CMA). The meteorological data of CERN’s field stations not only reflects the meteorological conditions of the stations, but also represents the average meteorological conditions of certain range.14.Therefore, it is an important supplement to the data of CMA’s stations. Based on this, the dataset of accumulated temperature of CERN’s stations has been established to support the research on national or regional temporal and spatial distribution of accumulated temperature.
2.   Data collection and processing
Automatic observation is the main method of ecological observation. Automatic weather stations have been set up in the field stations to continuously measure the meteorological elements. Raw data obtained by the instruments is processed into standardized reports by the data processing software developed by the atmospheric sub-center, and reported to the atmospheric sub-center every year. The sub-center examines the data and submits the data that meets the requirements of the specification to the synthesis center. The synthesis center loads the data into the database and publishes it to the public. In 2005, a manual weather station was set up in each field station, and the observation data is also regularly submitted, reviewed, and released.
This dataset is based on the automatic observation temperature data because of its long time series, and the manual observation data and the meteorological data from the stations of CMA were adopted as the auxiliary data. Detailed processes are shown in Figure 1.

Fig.1   Construction flow of accumulated temperature dataset
2.1   Data source
HMP45D temperature and humidity sensor produced by Vaisala Company of Finland is used to measure the air temperature in each field station. This sensor is also used by the meteorological observing network of CMA.15 The raw data is stored in a daily data file in the data logger every hour, and then the daily and monthly average and total value are calculated by the data processing software.13.14.
The temperature data of most field stations began in 1998. Considering the uneven quality of data in the initial stage of observation, the period of the accumulated temperature dataset has been delimited to 2001-2015. The daily average temperature data of automatic observation from 35 field stations (Table 1) with more than 10 years of observation data has been selected as the basic data. This data was downloaded from the long-term monitoring database of CERN data resource service website (http://www.cnern.org.cn) and the manual observation data was also from this website. The daily temperature data of adjacent weather stations of CMA came from the National Meteorological Information Center, and the period is from 2001 to 2004.
2.2   Abnormal data culling
The original data has been subjected to quality control of the atmospheric sub-center each year, including physical extreme value inspection, historical extreme value inspection, internal consistency check, time consistency check, etc.,14.and the apparent error data have been eliminated. In order to correct all errors and extremely suspicious data as much as possible, data quality checks were carried out around long-term sequence comparison and multi-site comparison (see Chapter 4 for details), and the error data detected are removed.
2.3   Data interpolation
Due to instrument failures, human factors, quality inconsistency and other reasons, some temperature data of each station are missing, resulting in incomplete time series of daily temperatures. In order to ensure the integrity and continuity of the accumulated temperature data, the missing temperature data need to be interpolated to obtain a complete data sequence.
A regression equation for the temperature series of the field station and correlative temperature series with good data integrity has been established to interpolate the basic temperature data. From 2005 to 2015, the daily manual observation temperature data of the field stations was selected as the reference data, and from 2001 to 2004, the reference data was the daily temperature data of the nearby meteorological station of CMA. The unary linear regression equation for the daily temperature data of a field station and the reference data sequence was established, and the reference data was used to fill in the missing data. All regression equations have a high degree of fitting which R2 is greater than 0.96 from 2001 to 2004 and above 0.98 from 2005 to 2015, and all pass the significance test. The data interpolation rates of each field station are shown in Table 1.
Table 1   Basic information of the field station and the data interpolation rate
 Stationname Station code Ecological type Location of meteorological observation site Data interpolation rate（%） Longitude（°） latitude（°） Elevation（m） 2001-2004 2005-2015 Hailun HLA Agriculture 126.93 47.45 234 10.95 1.92 Shenyang SYA Agriculture 123.37 41.52 42 37.17 4.63 Luancheng LCA Agriculture 114.69 37.89 50.1 23.07 18.67 Ansai ASA Agriculture 109.33 36.86 1033 31.55 4.21 Yucheng YCA Agriculture 116.57 36.83 22 2.74 5.20 Changwu CWA Agriculture 110.68 35.24 1220 4.93 4.16 Fengqiu FQA Agriculture 114.55 35.02 67.5 9.86 4.63 Changshu CSA Agriculture 120.70 31.55 3.1 7.12 1.57 Yanting YGA Agriculture 105.46 31.27 420 1.23 5.08 Taoyuan TYA Agriculture 111.44 28.93 106 17.11 1.67 Yingtan YTA Agriculture 116.93 28.21 45 19.99 3.31 Lasa LSA Agriculture 91.34 29.68 3688 4.63 Qianyanzhou QYA Agriculture 115.06 26.75 53.5 1.37 1.00 Changbaishan CBF Forest 128.10 42.35 738 7.94 2.99 Beijing BJF Forest 115.43 39.97 1248 24.98 5.00 Maoxian MXF Forest 103.90 31.70 1891 2.54 Gonggashan GGF Forest 102.00 29.58 3000 3.35 8.66 Huitong HTF Forest 109.61 26.85 557 10.13 6.67 Dinghushan DHF Forest 112.55 23.16 100 33.47 2.36 Heshan HSF Forest 112.90 22.68 75 13.62 3.24 Xishuangbanna BNF Forest 101.26 21.93 565 0.55 1.54 Inner Mongolia NMG Grassland 116.71 43.64 1187 13.69 5.80 Haibei HBG Grassland 101.31 37.61 3200 22.45 4.80 Fukang FKD desert 87.93 44.30 475 18.14 7.64 Naiman NMD desert 120.70 42.93 358 14.99 4.28 Ordos ESD desert 110.19 39.49 1300 9.36 Linze LZD desert 110.13 39.35 1384 1.05 Cele CLD desert 80.43 37.01 1303 3.41 Shapotou SPD desert 105.01 37.47 1340 15.33 5.92 Sanjiang SJM wetland 133.50 47.59 55 12.73 2.94 Taihu THL lake 120.22 31.42 10 20.74 0.77 Donghu DHL lake 114.35 30.55 21 0.57 Dayawan DYB Bay 114.91 22.99 40 2.33 1.24 Jiaozhouwan JZB Bay 120.27 36.05 15 13.07 15.48
2.4   Accumulated temperature calculation
The accumulated temperature commonly used in agricultural production has two types: active accumulated temperature and effective accumulated temperature. The daily average temperature equal to or greater than the biological lower limit temperature is called the active temperature; the sum of the active temperatures in a certain period is the active accumulated temperature. The difference between the activity temperature and the biological lower limit temperature is called the effective temperature, and the sum of the effective temperatures for a certain period of time is the effective accumulated temperature.16.
10℃ is the starting temperature suitable for the growth of thermophilic plants, and it is also the temperature at which the cool crops grow rapidly and the perennial crops begin to accumulate dry matter at a faster rate. The accumulated temperature greater than and equal to 10℃ and its corresponding duration are used to measure the agro-climatic production potential of a region and to be an important basis for the introduction and cultivation system reform by agricultural production department and researchers.3.7.17.Therefore, this dataset sets the threshold temperature for calculating the accumulated temperature as 10℃, and uses the 5-day moving average method to calculate the active accumulated temperature, effective accumulated temperature and duration. The calculation method is as follows.
(1) Determine the beginning and ending date of daily temperature greater than and equal to 10℃
In a year, the longest period in which the 5-day moving average is greater than or equal to 10℃ is selected. In the first 5 days of this period, the first day with a daily average temperature greater than or equal to 10℃ is selected as the beginning date. In the last 5 days, the last day with an average temperature greater than or equal to 10℃ is selected as the ending date.16.18.
(2) Compile a computer program to calculate the accumulated temperature and the duration
The active accumulated temperature is the sum of the daily average temperatures from the beginning date to the ending date calculated with following equation:

 $${\mathrm{T}}_{\mathrm{a}}=\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{T}}_{\mathrm{i}}$$ （1）
Where Ta is the actively accumulated temperature (℃); Ti is the average temperature on day i (℃), when Ti＜10℃, Ti=0; n is the number of days in the calculation period.
Calculate the sum of the difference between the daily average temperature and 10℃ from the beginning date to the ending date according to the following equation, and the effective accumulated temperature is obtained.

 $${T}_{e}=\sum _{i=1}^{n}({T}_{i}-10)$$ （2）
In the equation above, Te is the effective accumulated temperature (℃); Ti is the average temperature on day i (℃), when Ti＜10℃, Ti=10; n is the number of days in the calculation period.
The number of days between the beginning date and the end date is accumulated to obtain the duration of daily temperature greater than and equal to 10℃.
3.   Sample description
The data is stored in an Excel file which is composed of two sheets. The “Accumulated Temperature Data” sheet stores the accumulated temperature data of 35 field stations from 2001 to 2015, with a total of 497 records; The “Information of field stations” sheet stores the basic background information for 35 stations. See Table 2 and Table 3 for the specific field names, types, and examples contained in these two sheets.
Table 2   The content of “Accumulated Temperature Data” sheet
 Field name Unit Data type sample Station code character ASA Year Number 2001 Active accumulated temperature ℃ Number 3110.1 Effective accumulated temperature ℃ Number 1530.1 Beginning date character 2001-4-27 Ending date character 2001-10-6 Duration day Number 163
Table 3   The content of “Information of field stations” sheet
 Field name unit Data type Sample Station name character Ansai Station Station code character ASA Location character Dun Tan, Ansai County, Yanan City, Shaanxi Province Ecosystem type character Agriculture Longitude of meteorological observation site ° Number 109.33 Latitude of meteorological observation site ° Number 36.86 Elevation of meteorological observation site m Number 1033 Brief description of regional representativeness character Ansai station is located in the typical loess hilly and gully region in the middle of the Loess plateau. It is in the interlaced zone of loess and sandy loess, in the transition region from semi-humid to semi-arid of warm temperate zone, in the forest-steppe ecotone from warm temperate deciduous broad-leaved forest to dry steppe, and it is also a typical serious soil erosion area affected by human activities. It is typical for carrying out scientific research and experimental demonstration of soil and water conservation and ecological environment construction for the various land types and the abundant resources.

Fig. 2   Changes in ≥10℃ accumulated temperature of HaiLun station and Yingtan station for 2001-2015
Figure 2 shows the variation of active accumulated temperature and effective accumulated temperature greater than and equal to 10℃ of Hailun station and Yingtan station from 2001 to 2015.
4.   Quality control
In order to improve the accuracy, authenticity and reliability of the data, the basic data has been checked by means of “partner test”, which compares the meteorological elements of one or more nearby stations that are similar in climate.19.
The 2001-2004 temperature data was checked by comparing the daily temperature of the field station with a nearby meteorological station of CMA. The temperature anomalies of the tow stations were compared to eliminate the inherent gap of the data between the two stations, and the data with the difference between the two stations exceeding 2.5℃ was regarded as the erroneous data.19.
The temperature data for 2005-2015 was compared with the manual observation data. The data of the field station with an anomaly difference of more than 2.5℃ was regarded as suspicious data, and then the data of the adjacent meteorological station of CMA was used to determine whether it was erroneous data.
5.   Usage notes
This dataset collects the accumulated temperature greater than and equal to 10℃ for 35 field stations of CERN from 2001 to 2015, covering major ecosystems of China such as agriculture, forest, grassland, desert, wetland, lake and bay. It provides a new data source for relevant researchers to study on regional or national spatio-temporal pattern and variation characteristics of accumulated temperature greater than and equal to 10℃, and further to support a more comprehensive disclosure of the pattern of thermal resource allocation, planning and adjustment of planting structure and crop layout, and formulation of agricultural sustainable development planning.
Authors and contributions
Su Wen , bachelor, senior engineer, area: Ecoinformatics. Contribution: data processing and paper writing.
In addition, some other staffs of CERN stations also contributing for data observation and quality control are as follows:
Bai Fan, Bai Yongfei, Bao Weikai, Cao Min, Chen Xin, Chen Yunming, Cheng Yisong, Cui Qingguo, Deng Yun, Ding Weixin, Du Juan, Du Mingwu, Feng Jing, Fu Xingan, Gao Meirong, Gao Xinlian, Gao Yanhong, Gao Yongping, Guan Dexin, Guan Youjun, Guo Yongping, Guo Yuedong, Guo Longgen, Han Jianmei, Han Jun, Han Xiaozeng, He Qihua, He Yongtao, Hu Chunsheng, Huang Zhenying, Ji Xibin, Jiang Jun, Jiang Zhengde, Li Honggang, Li Meng, Li Xiangyi, Li Xiaoli, Li Xinrong, Li Yulin, Lin Jinghui, Lin Yongbiao, Liu Mingde, Liu Wenjie, Liu Wenzhao, Liu Xiaoli, Liu Yuhong, Liu Yunfen, Liu Zhenmin, Lou Jinyong, Ma Jian, Meng Ze, Niu Xinxin, Ouyang Zhu, Pan Jianping, Qiao Tianhua, Qin Boqiang, Sang Weiguo, Shen Weijun, Shen Yanjun, Song Xingyu, Song Changchun, Sun Bo, Sun Dan, Sun Song, Sun Wei, Wang Silong, Wang Anzhi, Wang Genxu, Wang Huimin, Wang Keqin, Wang Shuwei, Wang Yang, Wang Youshao, Wei Wenxue, Wen Xuefa, Wu Chuansheng, Xie Ping, Xu Hao, Yan Shaokui, Yan Xiaoyuan, Yang Hongwei, Yin Chunmei, Zeng Fanjiang, Zhang Fawei, Zhang Qianmei, Zhang Xiuyong, Zhang Yangjian, Zhang Yiping, Zhao Wenzhi, Zhao Xueyong, Zhao Yongfang, Zheng Lichen, Zhou Guoyi, Zhou Qinghua, Zhou Zhiqiong, Zhu Bo, Zhu Guangwei, Zhu Mingliang, Zhu Yuanjun.
1.
Zhang H, Zhang Y. Preliminary discussion on the response of active accumulated temperature of China to climate warming. Acta Geographica Sinica 1(1994): 27-36.
2.
Lioa S, Li Z. Study on methodology for rasterizing accumulated temperature data. Geographical Research 23 (2004): 633-640.
3.
Dai S, Li H, Luo H et al. The spatio-temporal change of active accumulated temperature≥10℃ in Southern China from 1960 to 2011. Acta Geographica Sinica 69(2014):650-660.
4.
Liao N. Talk about accumulated temperature. Meteorological Monthly (06)(1978):29.
5.
Miao Q, Ding Y, Wang Y et al. Impact of climate warming on the distribution of China' s thermal resources. Journal of natural resources 24(2009): 934-944.
6.
Hu Q, Pan X, Shao C et al. Distribution and variation of China agricultural heat resources in 1961－2010. Chinese Journal of Agrometeorology 35(2014): 119-127.
7.
Meng L, Yin S, Yang F et al. Spatial and temporal distribution of accumulated temperature above 10℃ in Shanxi-Shaanxi-Inner Mongolia region. Chinese Journal of Agrometeorology 37(2016):615-622.
8.
Qiu X, Wang Z, Zeng Y et al. Temporal and spatial variation characteristics of accumulated temperature ≥10℃ and its leading factors in China from 1960 to 2013. Jiangsu Agricultural Sciences 45(2017):220-225.
9.
Liao B, Yan J, Li H. Changes of the accumulated temperature above 10℃ in east China. Chinese Journal of Agrometeorology 36(2015):674-682.
10.
Wang T, Shen W, Lin N et al. Changes in ≥0℃ accumulated temperature and agricultural adaptative strategies in the Yangtze river Basin in recent 50 Years. Journal of Ecology and Rural Environment 31(2015):22-29.
11.
Yu G, Yu X. Chinese Ecosystem Research Network (CERN) and natural ecosystem protection. Bulletin of Chinese Academy of Sciences 28(2013):275-283.
12.
Huang T, Niu D. Chinese Ecosystem Research Network (CERN)—Basic information, achievements and perspectives. Advance in Earth Sciences 20(2005):895-902.
13.
Chinese Ecosystem Research Network Science Committee. Protocols for standard atmosphere environmental observation and measurement in terrestrial ecosystems. Beijing: China Environmental Science Press, 2007.
14.
Hu B, Liu G, Wang Y. Quality assurance and quality control for meteorology and solar radiation monitoring in terrestrial ecosystems. Beijing: China Environmental Science Press, 2012.
15．Hua J, Zhang S, Liu L. Application of HMP45D temperature and humidity sensor in automatic weather station. Technology Wind (04)( 2012):88+90.
16.
Editorial Department. Accumulated temperature and calculation method. Southern Horticulture 15(2004):53-55.
17.
Jin J. An identification method of heat index. Meteorological Monthly (03) (1982):24-25.
18.
Wang S. A statistical method for determining the beginning and ending date of daily average temperature through the boundary temperatures at all levels. Meteorological Monthly (06) (1982): 29-30.
19.
Yang X. Quality inspections of meteorological data for special climate applications database. Meteorological Monthly (12) (1998): 34-37.
Data citation
Chinese Ecosystem Research Network . A dataset of ≥10℃ accumulated temperature of field stations in Chinese typical ecosystems from 2001 to 2015. Science Data Bank, 2018. DOI: 10.11922/sciencedb.664.
Article and author information
Su W, Bai F, Cheng Y S, et al. A dataset of ≥10℃ accumulated temperature of field stations in Chinese typical ecosystems from 2001 to 2015. China Scientific Data 4(2019). DOI: 10.11922/csdata.2018.0065.zh
Su Wen
Contribution: data processing and paper writing.
suw@igsnrr.ac.cn
bachelor, senior engineer, area: Ecoinformatics.
Bai Fan
Main responsibilities: Data monitoring and quality control of Beijing Station.
Cheng Yisong
Main responsibilities: Data monitoring and quality control of Luancheng Station.
Du Juan
Main responsibilities: Data monitoring and quality control of Ordos Station.
Du Mingwu
Main responsibilities: Data monitoring and quality control of Linze Station.
Gao Meirong
Main responsibilities: Data monitoring and quality control of Yanting Station.
Gao Xinlian
Main responsibilities: Data monitoring and quality control of Fukang Station.
Gao Yongping
Main responsibilities: Data monitoring and quality control of Shapotou Station.
Guan Youjun
Main responsibilities: Data monitoring and quality control of Yingtan Station.
Guo Yongping
Main responsibilities: Data monitoring and quality control of Cele Station.
Han Jianmei
Main responsibilities: Data monitoring and quality control of Inner Mongolia Station.
Han Jun
Main responsibilities: Data monitoring and quality control of Donghu Station.
He Qihua
Main responsibilities: Data monitoring and quality control of Maoxian Station.
Jiang Jun
Main responsibilities: Data monitoring and quality control of Ansai Station.
Jiang Zhengde
Main responsibilities: Data monitoring and quality control of Shenyang Station.
Li Meng
Main responsibilities: Data monitoring and quality control of Hailun Station.
Li Xiaoli
Main responsibilities: Data monitoring and quality control of Fengqiu Station.
Li Yulin
Main responsibilities: Data monitoring and quality control of Naiman Station.
Lin Jinghui
Main responsibilities: Data monitoring and quality control of Changshu Station.
Liu Wenjie
Main responsibilities: Data monitoring and quality control of Xishuangbanna Station.
Liu Yunfen
Main responsibilities: Data monitoring and quality control of Qianyanzhou Station.
Lou Jinyong
Main responsibilities: Data monitoring and quality control of Yucheng Station.
Meng Ze
Main responsibilities: Data monitoring and quality control of Dinghuashan Station.
Qiao Tianhua
Main responsibilities: Data monitoring and quality control of Sanjiang Station.
Song Xingyu
Main responsibilities: Data monitoring and quality control of Dayawan Station.
Sun Dan
Main responsibilities: Data monitoring and quality control of Heshan Station.
Sun Wei
Main responsibilities: Data monitoring and quality control of Lasa Station.
Wang Keqin
Main responsibilities: Data monitoring and quality control of Gonggashan Station.
Wu Chuansheng
Main responsibilities: Data monitoring and quality control of Ailaoshan Station.
Xu Hao
Main responsibilities: Data monitoring and quality control of Changbaishan Station.
Yin Chunmei
Main responsibilities: Data monitoring and quality control of Taoyuan Station.
Zhang Fawei
Main responsibilities: Data monitoring and quality control of Haibei Station.
Zhang Xiuyong
Main responsibilities: Data monitoring and quality control of Huitong Station.
Zhao Yongfang
Main responsibilities: Data monitoring and quality control of Jiaozhouwan Station.
Zhu Guangwei
Main responsibilities: Data monitoring and quality control of Taihu Station.
Zhu Yuanjun
Main responsibilities: Data monitoring and quality control of Changwu Station.
National Key R&D Program of China（2017YFC0503803）；Science and technology service network Initiative of Chinese Academy of Sciences（STS Plan，KFJ-SW-STS-168）
Publication records
Published: March 28, 2019 （ VersionsEN4
Updated: March 28, 2019 （ VersionsEN5
Released: Oct. 24, 2018 （ VersionsZH2
Published: March 28, 2019 （ VersionsZH3
Updated: March 28, 2019 （ VersionsZH5
References

csdata