Pan-third-polar environmental change and green silk road construction

Brief Introduction: Pan-third-polar environmental change and green silk road construction

Number of Datasets: 1099

  • High resolution land cover data sets of Dhaka, Bangladesh, 2010 and 2020

    High resolution land cover data sets of Dhaka, Bangladesh, 2010 and 2020

    The data set is the land cover data set of 2010 and 2020. The spatial range is Dhaka City, Bangladesh. The spatial resolution is 30m and the temporal resolution is year. The data comes from globeland30 (Global geographic information public goods, http://www.globallandcover.com/ ), acquired after mosaic and reorganization. The data accuracy evaluation of the source data is led by Tongji University and Institute of aerospace information innovation, Chinese Academy of Sciences. The overall accuracy of the data is more than 83.50%. The data set can provide high-precision basic geographic information for related research, and has important applications in resource and environment bearing state identification, natural disaster risk assessment, disaster prevention and mitigation, etc.

    2021-01-13 707 16

  • Data set of heat wave risk assessment in Dhaka, Bangladesh, 2015

    Data set of heat wave risk assessment in Dhaka, Bangladesh, 2015

    The data set is a 2015 heat wave risk data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of year. Heat wave risk refers to the probability or loss possibility of harmful consequences caused by the interaction between heat wave hazard (possible heat wave events in the future), heat wave exposure (total population, livelihood and assets in the area where heat wave events may occur) and heat wave vulnerability (the tendency of the disaster bearing body to suffer adverse effects when affected by heat wave events) . The risk assessment method of heat wave is "hazard-exposure-vulnerability". The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.

    2021-01-13 782 16

  • Data set of key factors of heat wave risk in Dhaka, Bangladesh, 2015

    Data set of key factors of heat wave risk in Dhaka, Bangladesh, 2015

    The data set is a 2015 heat wave hazard, exposure and vulnerability data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of yearly. Heat wave hazard is an index to measure the severity of heat wave event, which is expressed by surface temperature; heat wave exposure refers to the degree that human, livelihood and economy may be adversely affected, which is expressed by nighttime lighting data, and population density. The population older than 65 and younger than 5 years old constitute vulnerable groups; heat wave vulnerability is a measure of increased / reduced risk in the environment. The distance from road / hospital and ambulance station / water body, NDVI, impervious layer and slum area are used to represent the vulnerability of high temperature heat wave. The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.

    2021-01-13 760 9

  • Division map of agricultural development in the Tibetan Plateau (2020)

    Division map of agricultural development in the Tibetan Plateau (2020)

    Taking villages and towns as the basic division unit, the division map of agricultural development in the Tibetan Plateau comprehensively considers climate, topography, vegetation type and coverage, land use type and proportion, distribution of nature reserves, key points of ecological protection and direction of agricultural development, puts forward the zoning scheme of agricultural and animal husbandry regulation for ecological protection in Qinghai Tibet Plateau, and divides the Qinghai Tibet Plateau into 8 areas (3 areas are based on ecological protection) The protection areas are the key limited control areas of agriculture and animal husbandry, 5 moderate development areas of agriculture and 23 small areas, and the zoning is named by the way of protection + development direction of agriculture and animal husbandry. The purpose of the zoning map is to develop agriculture and animal husbandry moderately on the basis of effective ecological protection, which can provide reference information for the protection of ecological security barrier function and sustainable management.

    2021-01-11 874 30

  • Bird data along Elevation Gradients in Gangrigabu Mountains, 2020

    Bird data along Elevation Gradients in Gangrigabu Mountains, 2020

    The birds along elevation gradients in Gangrigabu Mountains were investigated by point count method. With a 400-meter elevational gradient, elevation zones were set up in the survey area. Five elevation zones were built in the north slope from TongMai Town to Galong Temple in Bome County, and 8 elevation zones were built in the south slope from Jiefang Bridge to Galongla in Medog County. So that we can make clear about the pattern and maintenance mechanism of bird diversity along elevation gradients in this region. The data of bird diversity and distribution will be used to further explore the key scientific issues such as the impact of climate change on bird diversity and adaptation strategies, and the response and protection strategies of bird species diversity under the global climate change.

    2021-01-11 804 11

  • Data list of the field sampling on mammal diversity at Yarlung Zangbo Grand Canyon National Nature Reserve in 2020

    Data list of the field sampling on mammal diversity at Yarlung Zangbo Grand Canyon National Nature Reserve in 2020

    From October to November 2020, we used both live traps and camera traps to collect mammal diversity and distributions along the elevational gradients at the Yarlung Zangbo Grand Canyon National Nature Reserve. We set trap lines for small mammals inventory, with a total of 8000 live trap nights. We collected 526 individuals and1052 tissue samples of small mammals during the field sampling. We also retrived images of 130 camera traps placed between May 2020 and October 2020. We obtained 4218 pictures of wild animals,25 species of large and medium mammals were recorded.. The camera traps were reset in the same locations after renew batteries and memory cards. Small mammal data consist of richness, abundance, traits, environmental gradients etc, and could be used to model relationship between environmental gradients and traits concatenated by richness matrix. Camera trap data could inventory endangered species in the region, and provide information to identify biodiversity hotspots and conservation priorities.

    2021-01-11 827 25

  • Collection data of fish collection in Qinghai province of Qinghai Tibet Plateau, in 2020

    Collection data of fish collection in Qinghai province of Qinghai Tibet Plateau, in 2020

    In November 2020, we made a collection in Qinghai Tibet Plateau were collected by net and electric capture methods, and the sampling area included the main water systems in Qinghai Province. A total of 30 sampling points were collected, and 685 fish specimens were collected in 12 points, including Schizothorax of loach.This work is a part of the project of “Building Methods for Detection of Aquatic Organisms in the Lake System of the Qinghai-Tibet Plateau”, using traditional fish survey data to generate a list of species in the lake system, which will then be used to combine multiple lakes in the plateau. High-throughput molecular data acquired from the system's environmental water samples and tested for visual parameters (lake size, isolation, geographic location, and spectral characteristics) that can be used to predict aquatic biodiversity.

    2021-01-11 733 12

  • Vegetation Coverage Data of the China-Mongolia-Russia Economic Corridor with a Resolution of 1km from 2000 to 2020 (Version 1.0)

    Vegetation Coverage Data of the China-Mongolia-Russia Economic Corridor with a Resolution of 1km from 2000 to 2020 (Version 1.0)

    Based on the MODIS satellite remote sensing data, the overall vegetation coverage (VC) of the China-Mongolia-Russia Economic Corridor was calculated. The traditional VC formula selects the normalized difference vegetation index (NDVI) as a variable. For the reduction of deviation caused by soil background and the impacts of the atmosphere, the enhanced vegetation index (EVI) instead of NDVI is adopted in the calculation process of VC data set. The original data is the enhanced vegetation index data in the Terra MODIS Vegetation Index Data Version 6 (MOD13A3) with the resolution of 1 km. The MOD13A3 dataset is of higher quality than the source data because it filters the outliers or missing measurements of the MODIS satellite data. The China-Mongolia-Russia Economic Corridor is an area with high risk of desertification. At present, the development of desertification in the corridor extends along the main road between China and Mongolia, and the desertification is the most serious in densely populated urban areas. The regional desertification information can be extracted effectively from the vegetation coverage data, which will provide ecological and environmental data support for the disaster risk prevention and safe operation of transportation and pipelines.

    2021-01-11 174 3

  • Dataset of climate background and changes in China-Mongolia-Russia Economic Corridor from 1981 to 2019 (Version 1.0)

    Dataset of climate background and changes in China-Mongolia-Russia Economic Corridor from 1981 to 2019 (Version 1.0)

    The China-Mongolia-Russia Economic Corridor is confronted with security problems related with global warming, mostly including the increasingly serious of degradation of permafrost and land desertification. On one hand, frozen soil degradation has caused frequent disasters such as debris flow, flood, ice and snow damage along the China-Mongolia-Russia transportation and pipeline, which will cause water and soil erosion followed by exposed pipes in frozen soil, in particular in summer. On the other hand, desertification will drive the ecological environment more vulnerable with the compound hazards of soil erosion and sandstorms occurring frequently. Therefore, this dataset will hopefully provide basic climate data for the research on the climate change and its impacts on permafrost and desertification for the China-Mongolia-Russia Economic Corridor. The original data is extracted from ERA5- Land surface climate reanalysis data (ERA5 – Land) (source: https://cds.climate.copernicus.eu). We adopted the inverse distance weight (IDW) method to interpolate the original data with the spatial resolution of 10 km. Based on this dataset, the spatial and temporal distribution pattern of climatic factors are outlined over the past 40 years for the corridor.

    2021-01-11 663 12

  • Traffic and pipeline data sets of traffic and China-Mongolia-Russia Economic Corridor in 1990- 2015(Arcgis 10.2)

    Traffic and pipeline data sets of traffic and China-Mongolia-Russia Economic Corridor in 1990- 2015(Arcgis 10.2)

    Main railway lines of China-Mongolia-Russia Economic Corridor: Manzhouli-Chita; Hohhot-Erlian-Ulaanbaatar; Suifenhe-Vladivostok/Khabarovsk; Erlian-Zamen Uud; Dalian-Harbin; Harbin-Manzhouli; Jining-Erlian; Changchun-Huichun; Zamen Udda-Ulaanbaatar-Sukhbaatar; Zabaikalsk-Chita; Novosibirsk-Ulan-Ude; Ulan-Ude-Chaktu-Darhan-Bayan Gol-Ulaanbaatar-Bayantar-Gobi Sumber-Joy Er-Sinshanda-Zamyn-Uud-Erenhot-Jining-Yanggao-Zhangjiakou-Langfang-Tianjin Port; Inner Mongolia-Erenhot-Zamyn-Uud-Joyel-Ulaanbaatar-Dalkhan-A Letan Bragg-Chaktu-Ulan-Ude; Naushki-Ulan-Ude; Changchun-Hunchun; Sino-Russian oil pipeline: The first and second lines of the Sino-Russian crude oil pipeline (Linyuan-Daqing-Lindian-Nehe-Nenjiang-Dayangshu-Uerqi-Jagedaqi-Mohe-Songling-Jingsong-Xinlin-Tahe-Walagan- 22nd Station-Xing'an Town-Skovorodino (Siberia-Pacific Crude Oil Pipeline System) East Siberia-Pacific Pipeline ((Daqing-Taishe 1, 2) Taishet-Skovorodino-Magdagazi-Khabarovsk-Perevoznaya-Kozimino) Sino-Russian crude oil pipeline (Taishet-Lensk-Olyekminsk-Ardan-Tenda-Skovorodino-Mohe-Qiqihar-Daqing) Sino-Russian Far East pipeline (Tashet-Lensk-Olyekminsk-Ardan-Tenda-Khabarovsk-Vladivostok)

    2021-01-11 229 11

  • Aerosol assimilation data set in Pan third polar region (2015-2017)

    Aerosol assimilation data set in Pan third polar region (2015-2017)

    1) The optical depth, vertical mass concentration and extinction coefficient of dust, sulfate, organic carbon, black carbon and sea salt aerosols and total aerosols were measured; 2) Data source: numerical simulation, processing method: Based on CALIPSO satellite vertical observation and global aerosol model, it is generated by four-dimensional local ensemble transformation Kalman filter assimilation method; 3) The data quality is good; 4) It can also be used to study the spatiotemporal distribution of aerosols and their spatial and temporal characteristics of precipitation and their assimilation.

    2021-01-08 123 4

  • Vulnerability forecast scenarios dataset of the water resources, agriculture, and ecosystem of the Manasi River Basin (Version 1.0) (2010-2050)

    Vulnerability forecast scenarios dataset of the water resources, agriculture, and ecosystem of the Manasi River Basin (Version 1.0) (2010-2050)

    By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, on which basis the vulnerability of the water resources system of the basin was evaluated. The IPAT equation was used to set a future water resource demand scenario, setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption to establish the scenario. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were predicted. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations for the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.

    2021-01-08 1935 19

  • Vulnerability forecast scenarios dataset of water resources, agriculture, and ecosystem of the Urmuqi River Basin (Version 1.0) ( 2010-2050)

    Vulnerability forecast scenarios dataset of water resources, agriculture, and ecosystem of the Urmuqi River Basin (Version 1.0) ( 2010-2050)

    By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, based on which the vulnerability of the water resources system of the basin was evaluated. The IPAT equation was used to set a future water resource demand scenario, which was to establish the scenario by setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were predicted. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations of the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.

    2021-01-08 2819 16

  • The elevation data set of hambantota port area with 5-meter resolution (2018-2019)

    The elevation data set of hambantota port area with 5-meter resolution (2018-2019)

    The elevation data set of Hambantota port area with 5-meter resolution is obtained from the stereo image pair data of ZY-3 satellite. ZY-3 carried four optical cameras, including an emmetropic panchromatic TDI CCD camera with a ground resolution of 2.1m, a forward and backward panchromatic TDI CCD camera with a ground resolution of 3.5m, and an emmetropic multispectral camera with a ground resolution of 5.8m. Among them, the three line array stereo image pairs formed by push broom imaging of forward looking and back looking panchromatic cameras can be used for DEM extraction. Through the retrieval of the transit information and data of ZY-3 from 2018 to 2019, the cloudless stereo images of hambantota area are selected for DEM extraction. The steps including defining ground control points, connection points, setting DEM extraction parameters and editing results.

    2021-01-08 281 1

  • Meter resolution image data set of Hanbantota port area (2018-2019)

    Meter resolution image data set of Hanbantota port area (2018-2019)

    The meter resolution remote sensing image data of hanbantota area is composed of data fusion and splicing of different satellites. Multispectral remote sensing images with resolution between 0.5 m and 1 m from 2018 to 2019 are selected, and cloud free data with similar time are selected, and the result data set is formed by cutting and splicing according to the research area. The spatial resolution of the data is about 0.6 meters. The data is mainly used to study the high-precision extraction of disaster bearing body elements, such as port facilities, roads and so on. The extracted thematic elements will be used as the basic data of storm surge exposure and vulnerability analysis.

    2021-01-08 731 1

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin(Huailai station-automatic weather station-10m tower, 2019)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin(Huailai station-automatic weather station-10m tower, 2019)

    This dataset includes data obtained from the automatic weather station (AWS) at the observation system of Meteorological elements of Huailai station between January 1 and December 31, 2019. The site (115.7880° E, 40.3491° N) was located on a maize surface, which is near Donghuayuan Town of Huailai city in Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (5 m, north), wind speed and direction profile (10 m, north), air pressure (in the box), rain gauge (10 m), four-component radiometer (5 m, south), two infrared temperature sensors (5 m, south, vertically downward), soil heat flux (-0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and a TCAV averaging soil thermocouple probe (-0.02, -0.04 m). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), and average soil temperature (TCAV, ℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.

    2021-01-08 906 22

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-large aperture scintillometer, 2019)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-large aperture scintillometer, 2019)

    This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station. There were two types of LASs: German BLS450 and zzLAS. The observation periods were from January 1 to December 31, 2019. The site ( (north: 115.7825° E, 40.3522° N; south: 115.7880° E, 40.3491° N) was located in the Donghuahuan town of Huailai city, Hebei Province. The elevation is 480 m. The underlying surface between the two towers contains mainly maize. The effective height of the LASs was 14 m; the path length was 1870 m. Data were sampled at 1 min intervals. Raw data acquired at 1 min intervals were processed and quality-controlled. The data were subsequently averaged over 30 min periods. The main quality control steps were as follows. (1) The data were rejected when Cn2 was beyond the saturated criterion. (2) Data were rejected when the demodulation signal was small. (3) Data were rejected within 1 h of precipitation. (4) Data were rejected at night when weak turbulence occurred (u* was less than 0.1 m/s). The sensible heat flux was iteratively calculated by combining with meteorological data and based on Monin-Obukhov similarity theory. There were several instructions for the released data. (1) The data were primarily obtained from BLS450 measurements; missing flux measurements from the BLS450 were filled with measurements from the zzLAS. Missing data were denoted by -6999. (2) The dataset contained the following variables: data/time (yyyy-mm-dd hh:mm:ss), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). (3) In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.

    2021-01-08 905 6

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin(Huailai station-automatic weather station-10m tower, 2018)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin(Huailai station-automatic weather station-10m tower, 2018)

    This dataset includes data obtained from the automatic weather station (AWS) at the observation system of Meteorological elements of Huailai station between January 1 and December 31, 2017. The site (115.7880° E, 40.3491° N) was located on a maize surface, which is near Donghuayuan Town of Huailai city in Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (5 m, north), wind speed and direction profile (10 m, north), air pressure (in the box), rain gauge (10 m), four-component radiometer (5 m, south), two infrared temperature sensors (5 m, south, vertically downward), soil heat flux (-0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and a TCAV averaging soil thermocouple probe (-0.02, -0.04 m). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), and average soil temperature (TCAV, ℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2017-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Yang et al. (2015) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.

    2021-01-08 866 24

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin(Huailai station-automatic weather station-40m tower, 2019)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin(Huailai station-automatic weather station-40m tower, 2019)

    This dataset obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2019. The site (115.7923° E, 40.3574° N) was located on a cropland (maize surface) which is near Donghuayuan town of Huailai city, Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (in the box), rain gauge (3 m, south of tower), four-component radiometer (4 m, south of tower), two infrared temperature sensors (4 m, south of tower, vertically downward), photosynthetically active radiation (4 m, south of tower, vertically upward), soil heat flux (3 duplicates, -0.06 m), a TCAV averaging soil thermocouple probe (-0.02, -0.04 m), soil temperature profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2019-6-10 10:30. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.

    2021-01-08 946 19

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin(Huailai station-automatic weather station-40m tower, 2018)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin(Huailai station-automatic weather station-40m tower, 2018)

    This dataset obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2018. The site (115.7923° E, 40.3574° N) was located on a cropland (maize surface) which is near Donghuayuan town of Huailai city, Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (in the box), rain gauge (3 m, south of tower), four-component radiometer (4 m, south of tower), two infrared temperature sensors (4 m, south of tower, vertically downward), photosynthetically active radiation (4 m, south of tower, vertically upward), soil heat flux (3 duplicates, -0.06 m), a TCAV averaging soil thermocouple probe (-0.02, -0.04 m), soil temperature profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.

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