Hourly meteorological forcing & land surface state dataset of Tibet Plateau with 10 km spatial resolution (2000-2010)

The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.

0 2022-05-17

Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Liancheng Station, 2021)

The data set contains the phenological camera observation data of Sidalong station of Lanzhou University cold and arid area scientific observation network in Heihe River Basin from February 1, 2021 to September 15, 2021. The longitude and latitude of the observation points are 99.926e, 38.428n and 3146m above sea level. The data is processed using the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way. The resolution of shooting data is 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenological period, it is necessary to calculate the relative greenness index according to the region of interest (GCC, green chromatographic coordinate formula is GCC = g / (R + G + b), and R, G and B are the pixel values of red, green and blue channels of the image), then fill in the invalid values and filter and smooth them, and finally determine the key phenological period parameters according to the growth curve fitting, such as the start date, peak and end date of the growth season; For the coverage, data preprocessing is carried out first, the image with less strong illumination is selected, and then the image is divided into vegetation and soil. The proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user. The filtered result is the final time series coverage. This data set includes the relative greenness index (GCC).

0 2022-05-17

Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Suganhu Station, 2021)

The data set contains the phenological camera observation data of suganhu station of Lanzhou University cold and arid area scientific observation network in halteng River Basin of Qaidam Basin from January 1, 2021 to December 31, 2021. The longitude and latitude of the observation points are 94.125 ° e, 38.992n and 2798m above sea level. The data is processed using the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way. The resolution of shooting data is 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenological period, it is necessary to calculate the relative greenness index according to the region of interest (GCC, green chromatographic coordinate formula is GCC = g / (R + G + b), and R, G and B are the pixel values of red, green and blue channels of the image), then fill in the invalid values and filter and smooth them, and finally determine the key phenological period parameters according to the growth curve fitting, such as the start date, peak and end date of the growth season; For the coverage, data preprocessing is carried out first, the image with less strong illumination is selected, and then the image is divided into vegetation and soil. The proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user. The filtered result is the final time series coverage. This data set includes the relative greenness index (GCC). The phenological camera of the site adjusted the shooting angle on August 12, 2021.

0 2022-05-17

Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Liancheng Station, 2021)

The data set contains the phenological camera observation data of Minqin station of Lanzhou University cold and arid area scientific observation network in Shiyang River Basin from March 1, 2021 to December 31, 2021. The longitude and latitude of the observation points are 103.668e, 39.208n and 1020m above sea level. The data is processed using the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way. The resolution of shooting data is 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenological period, it is necessary to calculate the relative greenness index according to the region of interest (GCC, green chromatographic coordinate formula is GCC = g / (R + G + b), and R, G and B are the pixel values of red, green and blue channels of the image), then fill in the invalid values and filter and smooth them, and finally determine the key phenological period parameters according to the growth curve fitting, such as the start date, peak and end date of the growth season; For the coverage, data preprocessing is carried out first, the image with less strong illumination is selected, and then the image is divided into vegetation and soil. The proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user. The filtered result is the final time series coverage. This data set includes the relative greenness index (GCC).

0 2022-05-17

Cold and Arid Research Network of Lanzhou university (phenology camera observation data set of Liancheng Station,2021)

The data set contains the phenological camera observation data of Liancheng station of Lanzhou University cold and arid area scientific observation network in Datong River Basin from January 1, 2021 to December 31, 2021. The longitude and latitude of the observation points are 102.737e, 36.692n and 2903m above sea level. The data is processed using the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way. The resolution of shooting data is 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenological period, it is necessary to calculate the relative greenness index according to the region of interest (GCC, green chromatographic coordinate formula is GCC = g / (R + G + b), and R, G and B are the pixel values of red, green and blue channels of the image), then fill in the invalid values and filter and smooth them, and finally determine the key phenological period parameters according to the growth curve fitting, such as the start date, peak and end date of the growth season; For the coverage, data preprocessing is carried out first, the image with less strong illumination is selected, and then the image is divided into vegetation and soil. The proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user. The filtered result is the final time series coverage. This data set includes the relative greenness index (GCC).

0 2022-05-17

Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Xiyinghe Station, 2021)

The data set contains the phenological camera observation data of xiyinghe station of Lanzhou University cold and arid area scientific observation network in Shiyang River Basin from January 1, 2021 to December 31, 2021. The longitude and latitude of the observation points are 101.855e, 37.561n and the altitude is 3616m. The data is processed using the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way. The resolution of shooting data is 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenological period, it is necessary to calculate the relative greenness index according to the region of interest (GCC, green chromatographic coordinate formula is GCC = g / (R + G + b), and R, G and B are the pixel values of red, green and blue channels of the image), then fill in the invalid values and filter and smooth them, and finally determine the key phenological period parameters according to the growth curve fitting, such as the start date, peak and end date of the growth season; For the coverage, data preprocessing is carried out first, the image with less strong illumination is selected, and then the image is divided into vegetation and soil. The proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user. The filtered result is the final time series coverage. This data set includes the relative greenness index (GCC).

0 2022-05-17

Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Guazhou Station, 2021)

The data set contains the phenological camera observation data of Guazhou station of Lanzhou University cold and arid area scientific observation network in Shule River Basin from January 9, 2021 to December 31, 2021. The longitude and latitude of the observation points are 95.673e, 41.405n and the altitude is 2014m. The data is processed using the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way. The resolution of shooting data is 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenological period, it is necessary to calculate the relative greenness index according to the region of interest (GCC, green chromatographic coordinate formula is GCC = g / (R + G + b), and R, G and B are the pixel values of red, green and blue channels of the image), then fill in the invalid values and filter and smooth them, and finally determine the key phenological period parameters according to the growth curve fitting, such as the start date, peak and end date of the growth season; For the coverage, data preprocessing is carried out first, the image with less strong illumination is selected, and then the image is divided into vegetation and soil. The proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user. The filtered result is the final time series coverage. This data set includes the relative greenness index (GCC). The camera was adjusted on October 2021.

0 2022-05-17

1 km resolution water conservation data set of Qinghai Tibet Plateau (2000-2020)

Water conservation service is an important ecosystem service, which directly affects the overall level of regional water resources and has an important impact on regional ecosystem, agriculture, industry, human consumption, hydropower, fishery and recreational activities. It is of great significance to maintain ecosystem stability and improve human well-being. Aiming at the production of water conservation products, based on the principle of water balance, coupled with the data of rainfall, evapotranspiration, solar radiation, temperature and vegetation type, the modeling of water conservation of ecosystem in national barrier area is studied. The water conservation service is calculated by the invest model based on the principle of water balance. The invest model has the advantages of less input data, large amount of export data and quantitative analysis of abstract ecosystem service functions. It is an important means of water conservation service evaluation at present. This method considers that the water conservation service is precipitation minus evapotranspiration, and the calculated indexes include annual precipitation and annual evapotranspiration. The precipitation data is based on the meteorological station data, the daily meteorological data is accumulated to the annual scale, and then interpolated to the space by ArcGIS spatial interpolation method; The calculation of evapotranspiration is realized by Zhang model. Taking multi-source data as the input variable of the invest model, the estimation of water conservation services in the Qinghai Tibet Plateau with a resolution of 1km from 2000 to 2020 is realized based on the parametric model.

0 2022-04-25

Data set of soil freezing depth in the future scenario of Qinghai Tibet Plateau Based on Stefan equation (2007-20172046-2065)

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 2022-04-22

Landslides and debris flows in Central and West Asia Economic Corridor (2018-2021)

The Central Asia West Asia economic corridor is dominated by deserts, mountains and plateaus, with an average altitude of about 1000m. The climate is extremely arid, the desert distribution area is large, the ecology is fragile, the dry and hot season lasts for a long time, up to 7 months, and the annual average rainfall is only 150mm at most. There are great differences in natural environment and complex geological conditions in the area. Under the compound driving action of regional differentiated structure, earthquake, meteorology, hydrology and ecology, debris flow and landslide are widely distributed in the corridor. Based on remote sensing images, the landslide and debris flow disasters in China Central Asia West Asia economic corridor are interpreted. Statistics show that 303 landslides and 2159 debris flow disasters are developed in China Central Asia West Asia economic corridor. Debris flows mainly include freeze-thaw debris flow, ice water debris flow and rainstorm debris flow.

0 2022-04-21