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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
|Title||A dataset of ≥10℃ accumulated temperature at field stations of typical Chinese ecosystems from 2001 to 2015|
|Data corresponding author||Su Wen (email@example.com)|
|Data author||Su Wen|
|Original data producers||CERN station||Observer||Head of Station||Institute|
|Ailaoshan||Wu Chuansheng, Liu Yuhong||Zhang Yiping||Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences|
|Ansai||Jiang Jun||Chen Yunming||Institute of Soil and Water Conservation, Chinese Academy of Sciences|
|Beijing||Bai Fan, Zhou Qinghua||Sang Weiguo||Institute of Botany, Chinese Academy of Sciences|
|Cele||Guo Yongping, Li Xiangyi||Zeng Fanjiang||Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences|
|Changbaishan||Xu Hao, Guan Dexin||Wang Anzhi||Institute of Applied Ecology, Chinese Academy of Sciences|
|Changwu||Zhu Yuanjun, Li Honggang||Liu Wenzhao||Institute of Soil and Water Conservation, Chinese Academy of Sciences|
|Changshu||Lin Jinghui, Wang Shuwei||Yan Xiaoyuan||Institute of Soil Science, Chinese Academy of Sciences|
|Dayawan||Song Xingyu, Pan Jianping||Wang Youshao||South China Sea Institute of Oceanology, Chinese Academy of Sciences|
|Dinghuashan||Meng Ze, Zhang Qianmei||Zhou Guoyi||South China Botanical Garden, Chinese Academy of Sciences|
|Donghu||Han Jun, Guo Longgen||Xie Ping||Institute of Hydrobiology, Chinese Academy of Sciences|
|Ordos||Du Juan, Cui Qinngguo||Huang Zhenying||Institute of Botany, Chinese Academy of Sciences|
|Fengqiu||Li Xiaoli||Ding Weixin||Institute of Soil Science, Chinese Academy of Sciences|
|Fukang||Gao Xinlian, Niu Xinxin||Ma Jian||Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences|
|Gonggashan||Wang Keqin, Liu Mingde||Wang Genxu||Institute of Mountain Hazards and Environment, Chinese Academy of Sciences|
|Haibei||Zhang Fawei||Cao Guangmin||Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences|
|Hailun||Li Meng||Han Xiaozeng||Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences|
|Heshan||Sun Dan, Lin Yongbiao||Shen Weijun||South China Botanical Garden, Chinese Academy of Sciences|
|Huitong||Zhao Xiuyong, Yan Shaokui||Wang Silong||Institute of Applied Ecology, Chinese Academy of Sciences|
|Jiaozhouwan||ZhaoYongfang, Zhu Mingliang||Sun Song||Institute of Oceanology, Chinese Academy of Sciences|
|Lasa||Sun Wei, He Yongtao||Zhang Yangjian||Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences|
|Linze||Du Mingwu, Ji Xibin||Zhao Wenzhi||Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences|
|Luancheng||Cheng Yisong, Hu Chunsheng||Shen Yanjun||Institute of Genetics and Developmental Biology, Chinese Academy of Sciences|
|Maoxian||He Qihua, Zhou Zhiqiong||Bao Weikai||Chengdu Institute of Biology, Chinese Academy of Sciences|
|Naiman||Li Yulin, Feng Jing||Zhao Xueyong||Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences|
|Inner Mongolia||Han Jianmei, Wang Yang||Bai Yongfei||Institute of Botany, Chinese Academy of Sciences|
|Qianyanzhou||Liu Yunfen, Wen Xuefa||Wang Huimin||Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences|
|Sanjiang||Qiao Tianhua, Guo Yuedong||Song Changchun||Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences|
|Shapotou||Gao Yongping, Gao Yanhong||Li Xinrong||Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences|
|Shenyang||Jiang Zhengde, Zheng Lichen||Chen Xin||Institute of Applied Ecology, Chinese Academy of Sciences|
|Taihu||Zhu Guangwei, Yang Hongwei||Qin Boqiang||Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences|
|Taoyuan||Yin Chunmei, Fu Xingan||Wei Wenxue||Institute of Subtropical Agriculture, Chinese Academy of Sciences|
|Xishuangbanna||Liu Wenjie, Dengyun||Cao Min||Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences|
|Yanting||Gao Meirong||Zhu Bo||Institute of Mountain Hazards and Environment, Chinese Academy of Sciences|
|Yingtan||Guan Youjun, Liu Xiaoli||Sun Bo||Institute of Soil Science, Chinese Academy of Sciences|
|Yucheng||Lou Jinyong, Liu Zhenmin||Ouyang Zhu||Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences|
|Time range||2001 – 2015|
|Geographical scope||35 field stations of the Chinese Ecosystem Research Network (CERN), including stations of Hailun, Shenyang, Luancheng, Ansai, Yucheng, Changwu, Fengqiu, Changshu, Yanting, Lasa, Taoyuan, Yingtan, Qianyanzhou, Changbaishan, Beijing, Maoxian, Gonggashan, Huitong, Dinghushan, Heshan, Ailaoshan, Xishuangbanna, Inner Mongolia, Haibei, Fukang, Naiman, Ordos, Linze, Cele, Shapotou, Sanjiang, Taihu, Donghu, Dayawan, Jiaozhouwan.|
|Data volume||497 records|
|Data service system||<http://www.sciencedb.cn/dataSet/handle/664>|
|Sources of funding||National Key R&D Program of China (2017YFC0503803); Science and Technology Service Network Initiative of the Chinese Academy of Sciences (STS Plan, KFJ-SW-STS-168).|
|Dataset composition||The dataset consists of an Excel file which is composed of two sheets. The “Accumulated Temperature Data” sheet stores the accumulated temperature data of the 35 field stations from 2001 to 2015, with a total of 497 records; The “Information of field stations” sheet stores the basic background information of the 35 stations.|
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..18.104.22.168. 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.22.214.171.124.126.96.36.199.
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.
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.
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.
|Station code||Ecological type||Location of meteorological observation site||Data interpolation rate（%）|
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:
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.
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℃.
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.
|Field name||unit||Data type||Sample|
|Station name||character||Ansai Station|
|Location||character||Dun Tan, Ansai County, Yanan City, Shaanxi Province|
|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.|
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.
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.
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.
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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.
How to cite this article
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