Data set of soil freezing depth in the future scenario of Qinghai Tibet Plateau Based on Stefan equation (2007-2017,2046-2065)

Soil freezing depth (SFD) is necessary to evaluate the balance of water resources, surface energy exchange and biogeochemical cycle change in frozen soil area. It is an important indicator of climate change in the cryosphere and is very important to seasonal frozen soil and permafrost. This data is based on Stefan equation, using the daily temperature prediction data and E-factor data of canems2 (rcp45 and rcp85), gfdl-esm2m (rcp26, rcp45, rcp60 and rcp85), hadgem2-es (rcp26, rcp45 and rcp85), ipsl-cm5a-lr (rcp26, rcp45, rcp60 and rcp85), miroc5 (rcp26, rcp45, rcp60 and rcp85) and noresm1-m (rcp26, rcp45, rcp60 and rcp85), The data set of annual average soil freezing depth in the Qinghai Tibet Plateau with a spatial resolution of 0.25 degrees from 2007 to 2065 was obtained.

0 2022-07-22

Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of Alpine meadow and grassland ecosystem Superstation, 2021)

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.

0 2022-06-29

Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of Subalpine shrub, 2021)

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.

0 2022-06-29

Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of the temperate steppe, 2021

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.

0 2022-06-29

A dataset of rainfall erosivity in the Qinghai-Tibet Plateau (1960-2019)

This dataset is a raster dataset of annual rainfall erosivity on the Qinghai-Tibet Plateau from 1960 to 2019. The rainfall erosivity was calculated using the daily rainfall data of 129 stations in the Qinghai-Tibet Plateau and its surrounding 150km range, of which 74 stations were located inside the Qinghai-Tibet Plateau and 55 stations were located outside. The calculation method is consistent with the algorithm of the first national Water Resources Inventory, using WGS_ 1984 coordinate system and Albers projection (central meridian 105°E, standard parallels 25°N and 47°N), and then Kriging interpolation is carried out year by year to generate grid map with spatial resolution of 250m. Rainfall erosivity is the main dynamic factor of soil erosion, and it is also the basic factor calculated by models such as CSLE and RUSLE. The integrated daily rainfall data of long-time series has high data accuracy, which improves the accuracy of rainfall erosivity estimation, and also helpful to further accurately estimate the amount of soil erosion on the Qinghai Tibet Plateau.

0 2022-06-09

Dataset of digital soil mapping products for the Qinghai-Tibet Plateau (2015-2024)

Based on the "second Qinghai Tibet Plateau comprehensive scientific investigation" and "China's soil series investigation and compilation of China's soil series" "The obtained soil survey profile data, using predictive Digital Soil Mapping paradigm, using geographic information and remote sensing technology for fine description and spatial analysis of the soil forming environment, developed adaptive depth function fitting methods, and integrated advanced ensemble machine learning methods to generate a series of soil attributes (soil organic carbon, pH value, total nitrogen, total phosphorus, total potassium, cation exchange capacity, gravel content (>2mm) in the Qinghai Tibet plateau region." , sand, silt, clay, soil texture type, unit weight, soil thickness, etc.) and quantify the spatial distribution of uncertainty. Compared with the existing soil maps, it better represents the spatial variation characteristics of soil properties in the Qinghai Tibet Plateau. The data set can provide soil information support for the study of soil, ecology, hydrology, environment, climate, biology, etc. in the Qinghai Tibet Plateau.

0 2022-06-03

The dataset of measured soil thickness, soil physical properties and soil hydraulic characteristics in Yarlung Zangbo River Basin

The dataset includes the measured soil thickness data at 148 points in the Yarlung Zangbo River Basin, as well as the physical properties and hydraulic characteristics (such as particle size, saturated water content, organic matter content, saturated hydraulic conductivity, etc.) of soil samples at 40 points. The sampling points are distributed from Zhongba County in the upper reaches of the Yarlung Zangbo River basin to Nyingchi city in the lower reaches. The soil thickness data is obtained through the excavation profile measurement, and other soil data are obtained from the collected ring knife samples according to the standardized experimental process, so the data accuracy is high. The soil data of the Yarlung Zangbo River basin provided by this dataset can provide a reference for large-scale soil mapping on the Qinghai Tibet Plateau and improve the prediction accuracy of relevant studies.

0 2022-06-01

Observation dataset of soil carbon and nitrogen concentarion in western Tibetan Plateau (2019-2020)

This data includes the soil carbon and nitrogen content at 0-10cm, 10-20cm and 20-30cm soil depths of 52 sample points in the west of Qinghai Tibet Plateau. The soil samples were obtained by the research team through soil drilling from 2019 to 2020. After the soil was screened with 2mm aperture, it was air dried and fine roots were removed, and then measured by carbon and nitrogen analyzer in the laboratory. This data can provide a theoretical basis for the study of soil carbon and nitrogen processes at different depths in the western Qinghai Tibet Plateau under the scenario of global climate change in the future, and provide data support for the model to simulate the process of soil carbon and nitrogen cycle, which is conducive to a deeper understanding of the process of soil carbon and nitrogen cycle in the western Qinghai Tibet Plateau.

0 2022-05-30

Seasonally frozen ground soil temperature and moisture data set of alpine meadow site in Qinghai Lake Basin(2018-2021)

This dataset is a high-frequency observation data of soil temperature and humidity in the active layer of seasonal frozen soil observed in the alpine meadow of Qianhuli Small watershed of Qinghai Lake, with a time resolution of half an hour. The data set can provide data support for the rate-dependent soil hydrothermal model and dynamic characterization of soil active layer.

0 2022-05-26

A dataset of soil carbon stock to 3m depth at Tibetan Plateau (2019)

The dataset based on synthesized data from 1114 sites across the Tibetan permafrost region which report that paleoclimate is more important than modern climate in shaping current permafrost carbon distribution.A new estimate of modern soil carbon stock to 3m depth on Tibetan permafrost region was derived by machine learning algorithm, including factors such as climate (paleoclimate and modern climate), vegetation, soil (soil thickness and soil physical and chemical properties, etc.) and topography. This dataset shows that ecosystem models clearly underestimated the Tibetan soil carbon stock, due to the absence of paleoclimate effects in the model. Future modelling of soil carbon cycling should include paleoclimate .

0 2022-05-18