Based on a recently developed inventory of permafrost presence or absence from 1475 in situ observations, we developed and trained a statistical model and used it to compile a high‐resolution (30 arc‐ seconds) permafrost zonation index (PZI) map. The PZI model captures the high spatial variability of permafrost distribution over the QTP because it considers multi- ple controlling variables, including near‐surface air temperature downscaled from re‐ analysis, snow cover days and vegetation cover derived from remote sensing. Our results showed the new PZI map achieved the best performance compared to avail- able existing PZI and traditional categorical maps. Based on more than 1000 in situ measurements, the Cohen's kappa coefficient and overall classification accuracy were 0.62 and 82.5%, respectively. Excluding glaciers and lakes, the area of permafrost regions over the QTP is approximately 1.54 (1.35–1.66) ×106 km2, or 60.7 (54.5– 65.2)% of the exposed land, while area underlain by permafrost is about 1.17 (0.95–1.35) ×106 km2, or 46 (37.3–53.0)%.
The continuous snow cover area in time and space is one of key elements to study of land surface energy and water exhange, mountain hydrology, land surface model, numerical weather forecast and climate change. However, the large number of clouds causes data gaps in the snow cover area from optical remote sensing. The MODIS observations of Terra and aqua, FY-2E and FY-2F VISSR are used to obtain fractional snow cover (subpixel snow cover) which is less affected by the cloud, and the snow cover of the remaining cloud pixels is supplemented according to the time series information. Finally the cloudless daily snow fraction is obtained. This data set includes the daily fractional snow cover at 5 km spatial resolution in the Tibetan Plateau and China.
Lakes on the Tibetan Plateau (TP) are an indicator and sentinel of climatic changes. We extended lake area changes on the TP from 2010 to 2018, and provided a long and dense lake observations between the 1970s and 2018. We found that the number of lakes, with area larger than 1 km2, has increased to ~1400 in 2018 from ~1000 in the 1970s. The total area of these lakes decreased between the 1970s and ~1995, and then showed a robust increase, with the exception of a slight decrease in 2015. This expansion of the lakes on the highest plateau in the world is a response to a hydrological cycle intensified by recent climate changes.
This is the daily temperature observation data set of 6 points in Xiaodong Kemadi, 4 points in Yangbajing, and 4 points in Hariqin during 2012-2015.
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
This data set includes the temperature, precipitation, relative humidity, wind speed, wind direction and other daily values in the observation point of Kunsha Glacier. The data is observed from October 3, 2015 to September 19, 2017. It is measured by automatic meteorological station (Onset Company) and a piece of data is recorded every 2 hours. The original data forms a continuous time series after quality control, and the daily mean index data is obtained through calculation. The original data meets the accuracy requirements of China Meteorological Administration (CMA) and the World Meteorological Organization (WMO) for meteorological observation. Quality control includes eliminating the systematic error caused by the missing point data and sensor failure. The data is stored as an excel file.
This data set includes the temperature, relative humidity, and other daily values at the end of the observation point of the terminus of Naimona’nyi Glacier The data is observed from July 3, 2011 to September 15, 2017. It is measured by automatic meteorological station (Onset Company) and a piece of data is recorded every 60minutes. The original data forms a continuous time series after quality control, and the daily mean index data is obtained through calculation. The original data meets the accuracy requirements of China Meteorological Administration (CMA) and the World Meteorological Organization (WMO) for meteorological observation. Quality control includes eliminating the systematic error caused by the missing point data and sensor failure. The data is stored as an excel file.
The dataset contains: I. document Dataset description file 二. Grid The Urumqi River Basin in Tianshan is divided into two sub regions: the upper reaches and the No. 1 glacier area. The data scale of the upstream area is 1:50000, and the grid size of the two kinds of precision digital elevation model is 2000 × 2000m and 100 × 100m respectively; the data scale of the source area is 1:5000, and the grid size of the digital elevation model is 5*5m. Digital elevation model of glacier in Headwater Area Digital elevation model of No.1 glacier in 1973、1980 and 1986; digital elevation model of No.2 glacier in 1963、1968、1973、1980 and 1986. 三, Map Thumbnails of various data types 四. rsimage TM, ETM,remote sensing image 五. Vector includes: (1) soil type map (Soil): geocode soil type (2) land resource evaluation map (Landeval): geocode, land type , subclass (3) land type map(Landtype): geocode, category, subclass (4) landuse map(Landuse): geocode, category, subclass (5) current situation of water resources utilization (wateruse): geocode, category, subclass (6) human activity(activity): geocode , category 2、 glaicer: No.1 glacier map (73, 80, 86 years), No.2 glacier map (62, 64, 73, 80years), including glacier, glacier boundary, contour data 3、 upstream sub area UP: (1) boundary (2) Subregional drainage system（River）(3) soil type map (Soil) (4) land resource evaluation map (Landeval) (5) land type map(Landtype) (6)landuse map(Landuse) (7) current situation of water resources utilization (wateruse) (8) human activity(activity) (9) Glacier distribution map(Glacier) Data projection: Project: reverse & Mercator False_easting: 500000.000000 False_northing: 0.000000 Central_meridian: 87.000000 Scale factor: 1.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) Geographic Coordinate System: GCS_Krasovsky_1940
There are many lakes in the Qinghai Tibet Plateau. The glacial phenology and duration of lakes in this region are very sensitive to regional and global climate change, so they are used as the key indicators of climate change research, especially the comparative study of the three polar environmental changes of the earth. However, due to its poor natural environment and sparse population, there is a lack of conventional field measurement of lake ice phenology. The lake ice was monitored with a resolution of 500 meters by using the normalized difference snow index (NDSI) data of MODIS. The traditional snow map algorithm is used to detect the lake daily ice amount and coverage under the condition of sunny days, and the lake daily ice amount and coverage under the condition of cloud cover are re determined through a series of steps based on the spatiotemporal continuity of the lake surface conditions. Through time series analysis, 308 lakes larger than 3km2 are identified as effective records of lake ice range and coverage, forming a daily lake ice range and coverage data set, including 216 lakes.
River lake ice phenology is sensitive to climate change and is an important indicator of climate change. 308 excel file names correspond to Lake numbers. Each excel file contains six columns, including daily ice coverage information of corresponding lakes from July 2002 to June 2018. The attributes of each column are: date, lake water coverage, lake water ice coverage, cloud coverage, lake water coverage and lake ice coverage after cloud treatment. Generally, the ice cover area ratio of 0.1 and 0.9 is used as the basis to distinguish the lake ice phenology. The excel file contained in the data set can further obtain four lake ice phenological parameters: Fus, fue, bus, bue, and 92 lakes. Two parameters, Fus and bue, can be obtained.