This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Alpine meadow and grassland ecosystem Superstation from January 1 to October 9 in 2021. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 10m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). 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_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, 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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Subalpine shrub from January 1 to October 13, 2021. The site (100°6'3.62"E, 37°31'15.67") was located in the subalpine shrub ecosystem, near the Gangcha County, Qinghai Province. The elevation is 3495m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5 and 10 m, towards north), wind speed and direction profile (windsonic; 3, 5 and 10 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 2 m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, and Ta_10 m; RH_3 m, RH_5 m, and RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, and Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m and WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_500cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_500cm) (%, 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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient from Janurary 1 to October 13 in 2021. The site (100°14'8.99"E, 37°14'49.00"N) was located in Sanjiaocheng sheep breeding farm, Gangcha County, Qinghai Province. The elevation is 3210m.The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; towards north), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m; RH_3 m, RH_5 m, RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, 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: 2018/8/31 10:30.
Li Xiaoyan
This dataset provides the monitoring data of runoff, precipitation and temperature of the Duodigou Runoff Experimental Station located in the northern suburbs of Lhasa city. Among the dataset, there are two runoff monitoring stations, which provide discharge data from June to December 2019, with a data step of 10 minutes. There are five precipitation monitoring stations, which provide precipitation data from 2018 to 2021, with a data step of 1 day. There are eight air temperature monitoring stations, which provide air temperature data from 2018 to 2021 in 30 minute steps. The discharge, the precipitation and the temperature data are the measured values. The dataset can provide data support for the study of hydrological and meteorological processes in the Tibet Plateau.
LIU Jintao
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Arou Superstation in the Heihe integrated observatory network from January 1 to December 31 in 2021. There were two types of LASs at Arou Superstation: BLS900 and RR-RSS460, produced by Germany and China, respectively. The north tower was set up with the RR-RSS460 receiver and the BLS900 transmitter, and the south tower was equipped with the RR-RSS460 transmitter and the BLS900 receiver. The site (north: 100.471° E, 38.057° N; south: 100.457° E, 38.038° N) was located in Caodaban village of A’rou town in Qilian county, Qinghai Province. The underlying surface between the two towers was alpine meadow. The elevation is 3033 m. The effective height of the LASs was 13.0 m, and the path length was 2390 m. The data were sampled 1 minute at both BLS900 and RR-RSS460. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS900: Cn2>7.25E-14, RR-RSS460: Cn2>7.84E-14). (2) The data were rejected when the demodulation signal was small (BLS900: Average X Intensity<1000; RR-RSS460: Demod>-20mv). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl (1992) and Andreas (1988) were selected for BLS900 and RR-RSS460, respectively. Detailed can refer to Liu et al. (2011, 2013). Several instructions were included with the released data. (1) The data were primarily obtained from BLS900 measurements, and missing flux measurements from the BLS900 instrument were substituted with measurements from the RR-RSS460 instrument. The missing data were denoted by -6999. Due to the problems of storing and wireless transmission. (2) The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) (for data processing) in the Citation section.
LIU Shaomin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei, REN Zhiguo
The multi-scale dataset of environment and element-at-risk for the Qinghai-Tibet Plateau includes geomorphic data, normalized vegetation index data, annual temperature and rainfall data, and disaster bearing value grade data, covering an area of 6.56 million square kilometers. The data set is mainly prepared for disaster and risk assessment. Due to the huge coverage, the geomorphic data adopts 150m spatial resolution and other data adopts 1000m spatial resolution. Geomorphology, vegetation index, temperature and rainfall data are mainly produced by processing open source data, and disaster bearing value grade data are produced by superposition calculation, comprehensively considering population data, night light index, buildings and surface cover types.
TANG Chenxiao
The preparation of this data set is based on the proposed downscaling method of all-weather surface temperature data for the glacier area in Southeast Tibet. By analyzing the relationship between all-weather surface temperature and its spatio-temporal influence factor elevation, surface coverage type, vegetation index, snow cover index, surface reflectance and other data, a downscaling model of all-weather surface temperature is constructed, which increase the spatial resolution of all-weather surface temperature products from 1 km to 250 m. The validation results show that the RMSE of downscaling surface temperature at the site is about 2.25 K and 2.16 K in the daytime and at night, respectively, which is about 0.5 K higher than that of the original 1 km surface temperature product. The results of image quality index show that the downscaling surface temperature not only obtains a lot of detailed thermal information, but also maintains a high consistency with the original 1 km surface temperature in spatial pattern and amplitude. This data set has certain significance for high resolution all-weather surface temperature generation and disaster monitoring in glacier area of Southeast Tibet.
ZHOU Ji, HUANG Zhiming , ZHONG Hailing , TANG Wenbin
The surface meteorological data of tianmogou in Bomi county are collected from the meteorological monitoring points arranged in the middle reaches of tianmogou in PALONG Zangbu basin. The data collection time is 2020. The main content of the data includes the observation data of rainfall and temperature in tianmogou. The rainfall data is collected by hobo rain gauge. Hobo rain gauge is a tipping bucket rain gauge. Every 0.2mm rainfall is recorded as an event, and the number of recorded events is output. The number of events multiplied by 0.2mm is the rainfall value; The air temperature is measured by a built-in 10 bit resolution temperature sensor in the data recorder. The acquisition method is to collect and store once every hour, and the hourly average value of air temperature can be obtained. The data is reliable in quality and high in accuracy. It can be used to reflect the real-time changes of rainfall and temperature in Tianmo gully, monitor the critical conditions of debris flow start-up, and predict the possibility of future debris flow events in this area.
HOU Weipeng
The data were collected from the sample plot of Haibei Alpine Meadow Ecosystem Research Station (101°19′E，37°36′N，3250m above sea level), which is located in the east section of Lenglongling, the North Branch of Qilian Mountain in the northeast corner of Qinghai Tibet Plateau. Alpine meadow is the main vegetation type in this area. The data recorded the light, air temperature and humidity, wind temperature and wind speed above the alpine plant canopy. The radiation intensity above the alpine plant canopy was recorded by LI-190R photosynthetic effective radiation sensor (LI-COR, Lincoln NE, USA) and LR8515 data collector (Hioki E. E. Co., Nagano, Japan), and the recording interval was once per second. S580-EX temperature and humidity recorder (Shenzhen Huatu) and universal anemometer are used (Beijing Tianjianhuayi) record the daily dynamics of air temperature and humidity, wind temperature and wind speed every three seconds. The recording time is from 10:00 on July 13 to 21:00 on August 17, Beijing time. Due to the need to use USB storage time and replace the battery every day, 3-5min of data is missing every day, and the missing time period is not fixed. At present, the data has not been published. Through research on the data The data can further explore the microenvironment of alpine plant leaves and its possible impact on leaf physiological response.
TANG Yanhong, ZHENG Tianyu
1) Data content (including elements and significance): the data includes the daily values of air temperature (℃), precipitation (mm), relative humidity (%), wind speed (M / s) and radiation (w / m2) 2) Data source and processing method; Air temperature, relative humidity, radiation and wind speed are daily mean values, and precipitation is daily cumulative value; Data collection location: 29 ° 39 ′ 25.2 ″ n near the forest line on the east slope of Sejila Mountain; 94°42′25.62″E; 4390m； The underlying surface is natural grassland; Collector model Campbell Co CR1000, acquisition time: 10 minutes. Digital automatic data acquisition. The temperature and relative humidity instrument probe is hmp155a; The wind speed sensor is 05103; The precipitation is te525mm; The radiation is li200x; 3) Data quality description; The original data of air temperature, relative humidity and wind speed are the average value of 10 minutes, and the precipitation is the cumulative value of 10 minutes; The daily average temperature, relative humidity, precipitation and wind speed are obtained by arithmetic average or summation. Due to the limitation of the sensor, the precipitation in winter may have a certain error. 4) Data application achievements and prospects: this data is the update of the existing data "Sejila Mountain meteorological data (2007-2017)" and "basic meteorological data of Sejila east slope forest line of South Tibet station of Chinese Academy of Sciences (2018)". The data time scale span is large, which is convenient for scientists or graduate students in Atmospheric Physics, ecology and atmospheric environment. This data will be updated from time to time every year.
Luo Lun
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Subalpine shrub from Janurary 1to December 31, 2020. The site (100°6'3.62"E, 37°31'15.67") was located in the subalpine shrub ecosystem, near the Gangcha County, Qinghai Province. The elevation is 3495m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5 and 10 m, towards north), wind speed and direction profile (windsonic; 3, 5 and 10 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 2 m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, and Ta_10 m; RH_3 m, RH_5 m, and RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, and Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m and WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_500cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_500cm) (%, 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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
Based on China's daily meteorological elements data set and National Geographic basic data, the extreme precipitation, extreme temperature, drought intensity, drought frequency and other indicators in Hengduan Mountain area were calculated by using rclimdex, nspei and bilinear interpolation methods. The data set includes basic data set of disaster pregnant environment, basic data set of extreme precipitation index, basic data set of extreme temperature index, basic data set of drought intensity and frequency. The data set can provide a basic index system for regional extreme high temperature, precipitation and drought risk assessment.
SUN Peng
This data is the data of automatic weather station (AWS, Campbell company) set up at the top of the mountain in the west slope of Sejila by the comprehensive observation and research station of Southeast Tibet alpine environment of Chinese Academy of Sciences in 2016. The geographical coordinates are 29.5919 n, 94.6102 e, with an altitude of 4640 m, and the underlying surface is alpine grassland. The data include daily arithmetic mean data of air temperature (℃), relative humidity (%), wind speed (M / s) and air pressure (MB) and daily accumulated value of precipitation. The original data is an average of 30 minutes before October 2018, and an average of 10 minutes after that. The temperature and humidity are measured by hmp155a temperature and humidity probe. The rainfall instrument model is rg3-m, the atmospheric pressure sensor probe is ptb210, and the wind speed sensor is 05103. These probes are 2 m above the ground. In terms of data quality: the obvious abnormal values are eliminated, the battery is damaged due to snow in the first half of 2019, and the data is missing. The missing temperature data is corrected by using the temperature fitting regression of 43900 m at nearby stations, and the data is yellow. Please pay attention when using it; the monitoring of precipitation starts from August 2019. The data station is a high altitude meteorological station in Southeast Tibet, which will be updated from time to time. It can be used by scientific researchers studying ecology, climate, hydrology, glaciers, etc.
Luo Lun
This data is the data of the automatic weather station (AWS, Campbell company) set up in Yigong Zangbu basin by the Southeast Tibet alpine environment comprehensive observation and research station of Chinese Academy of Sciences in 2018. The geographic coordinates are 30.1741 n, 94.9334 e, and the altitude is 2282m. The underlying surface is grassland. The data include daily arithmetic mean data of air temperature (℃), relative humidity (%), wind speed (M / s), water vapor pressure (kPa) and air pressure (MB) and daily accumulated value of precipitation. The original data is an average value recorded in 10 minutes. The temperature and humidity are measured by hmp155a temperature and humidity probe. The rainfall instrument is tb4, the atmospheric pressure sensor is ptb210, and the wind speed sensor is 05103. These probes are 2 m above the ground. Data quality: the quality of the original data is better, less missing. The data station is a meteorological station in the lower altitude of the Qinghai Tibet Plateau, which will be updated from time to time in the future. It can be used by researchers studying climate, hydrology, glaciers, etc.
Luo Lun
Estimate of the thermal state of the ocean is of vital importance to understand the process of air-sea interaction and footprint of climate change in the Indian Ocean. However, the insufficiency and poor coverage of subsurface observation brings quite a large challenge to estimate the subsurface temperature changes accurately. Moreover, surface observation from satellite contains well sample and could also reflect the subsurface information partly. We derived the ‘synthetic profiles’ based on the ‘surface-subsurface regression’ method in order to enrich the subsurface profiles. Then the 3-dimentional gridded temperature dataset are established by combining both the in-situ and synthetic profiles through objective analysis technique. Inter-comparison between the previous released datasets, such as IAP, EN4 and Ishii, this dataset could capture main thermal signal among the Indian Ocean and shows more mesoscale signal because of its higher resolution. This dataset provides monthly mean gridded subsurface temperature estimate among 30E-105E, 45S-30N, ranging from 2005 to 2018. We provide with dataset which has horizontal resolution in quarter degree and 42 vertical levels from surface to 2000-m depth in netCDF format.
WANG Gongjie, ZHAO Liang
Precipitation stable isotopes (2H and 18O) are adequately understood on their climate controls in the Tibetan Plateau, especially the north of Himalayas via about 30 years’ studies. However, knowledge of controls on precipitation stable isotopes in Nepal (the south of Himalayas), is still far from sufficient. This study described the intra-seasonal and annual variations of precipitation stable isotopes at Kathmandu, Nepal from 10 May 2016 to 21 September 2018 and analysed the possible controls on precipitation stable isotopes. All samples are located in Kathmandu, the capital of Nepal (27 degrees north latitude, 85 degrees east longitude), with an average altitude of about 1400 m. Combined with the meteorological data from January 1, 2001 to September 21, 2018, the values of precipitation (P), temperature (T) and relative humidity (RH) are given.
GAO Jing
These datasets fill the data gap between GRACE and GRACE-FO, they contain CSR RL06 Mascon and JPL RL06 Mascon. They take China as the study area, and the dataset includes "Decimal_time”, "lat”, "lon”, "time”, "time_bounds”, "TWSA_REC" and "Uncertainty" 7 parameters in total. Among them, "Decimal_time” corresponds to decimal time. There are 191 months from April 2002 to December 2019 (163 months for GRACE data, 17 months for GRACE-FO data, and 11 months for the gap between GRACE and GRACE-FO. We have not filled the missing data of individual months between GRACE or GRACE-FO data). "lat" corresponds to the latitude range of the data; "lon" corresponds to the longitude range of the data; "time" corresponds to the cumulative day of the data from January 1, 2002. And "time_bounds" corresponding to the cumulative day at the start date and end date of each month. “TWSA_REC" represents the monthly terrestrial water storage anomalies from April 2002 to December 2019 in China; "Uncertainty" is the uncertainty between the data and CSR RL06 Mascon products. We use GRACE satellite data from CSR GRACE/GRACE-FO RL06 Mascon solutions (version 02), China Gauge-based Daily Precipitation Analysis (CGDPA, version 1.0) data, and CN05.1 temperature dataset. The precipitation reconstruction model was established, and the seasonal and trend terms of CSR RL06 Mascon products were considered to obtain the dataset of terrestrial water storage anomalies in China. The data quality is good as a whole, and the uncertainty of most regions in China is within 5cm. This dataset complements the nearly one-year data gap between GRACE and GRACE-FO satellites, and provides a full time series for long-term land water storage change analysis in China. As the CSR RL06 Mascon product, the average value between 2004.0000 and 2009.999 is deducted from this dataset. Therefore, the 164-174 months (i.e., July 2017 to May 2018) of this dataset can be directly extracted as the estimation of terrestrial water storage anomalies during the gap period. The reconstruction method for the gap of JPL RL06 Mascon is consistent with that of CSR RL06 Mascon.
ZHONG Yulong, FENG Wei, ZHONG Min, MING Zutao
(1) This data set is the carbon flux data set of Shenzha alpine wetland from 2016 to 2019, including air temperature, soil temperature, precipitation, ecosystem productivity and other parameters. (2) The data set is based on the field measured data of vorticity, and adopts the internationally recognized standard processing method of vorticity related data. The basic process includes: outlier elimination coordinate rotation WPL correction storage item calculation precipitation synchronization data elimination threshold elimination outlier elimination U * correction missing data interpolation flux decomposition and statistics. This data set also contains the model simulation data calibrated based on the vorticity correlation data set. (3) the data set has been under data quality control, and the data missing rate is 37.3%, and the missing data has been supplemented by interpolation. (4) The data set has scientific value for understanding carbon sink function of alpine wetland, and can also be used for correction and verification of mechanism model.
Da Wei
Airborne pollen is mainly produced and disseminated during the process of plant flowering, controlled by plant phenology and climatic conditions. As an important bioindicator of plant behavior, airborne pollen can supply information about reproductive phenology, climate and atmospheric circulations. From 2011 to 2013, airborne pollen samples were collected using a volumetric Burkard pollen trap at the Qomolangma Station for Atmospheric and Environmental Observation and Research, Chinese Academy of Sciences (QOMS, 28.21°N, 86.56°E; 4276 m a.s.l.), on the northern slope of the Himalayas. The sampler is a volumetric air-suction device capable of continuously gathering pollen and spore particles. Air is drawn in at a speed of 10 l/min, and airborne particles are deposited on a sticky tape mounted on a drum that makes one complete rotation per week. The tape is changed weekly after a complete rotation. Then, the tape is removed and cut into seven pieces, with each piece representing one day of sampling. The pieces are mounted on slides using glycerin and safranin. Identification and counting of pollen grains were performed under an Olympus BX41 microscope at 400× magnification; all pollen grains on each slide were counted . Pollen concentration was expressed as the daily pollen grains per cubic meter of air using a constant air intake speed of 10 l/min. The pollen concentration and percentage of each pollen taxon in each year were calculated. The pollen sampling and lab process were followed the standard methods to ensure the authenticity and reliability of the data. The pollen data can provides insights into vegetation response to climate change and has significance for interpreting fossil pollen records.
LÜ Xinmiao
This data includes the daily average water temperature data at different depths of Nam Co Lake in Tibet which is obtained through field monitoring. The data is continuously recorded by deploying the water quality multi-parameter sonde and temperature thermistors in the water with the resolution of 10 minutes and 2 hours, respectively, and the daily average water temperature is calculated based on the original observed data. The instruments and methods used are very mature and data processing is strictly controlled to ensure the authenticity and reliability of the data; the data has been used in the basic research of physical limnology such as the study of water thermal stratification, the study of lake-air heat balance, etc., and to validate the lake water temperature data derived from remote sensing and different lake models studies. The data can be used in physical limnology, hydrology, lake-air interaction, remote sensing data assimilation verification and lake model research.
WANG Junbo
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