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)%.
Sentine-1 SAR data were used to monitor the permafrost of Biuniugou in Heihe River Basin of Qinghai-Tibet Plateau. Based on the Sentine-1 SAR image of Bison Valley from 2014 to 2018, the active layer thickness in the study area was estimated by using the small baseline set time series InSAR (DSs-SBAS) frozen soil deformation monitoring method based on distributed radar target, combined with SAR backscattering coefficient, MODIS surface temperature and Stefan model. The results show that the thickness of active layer is between 0.8 m and 6.6 m, with an average of about 3.3 M. It is of great significance to carry out large-scale and high-resolution monitoring.
The near-surface freeze-thaw affects the water and energy exchanges mode and efficiency between the land and atmosphere. The transition of the freeze/thaw state affects the pattern of runoff concentration, which has an important impact on regional and global water cycle. Based on the remote sensing data of AMSR-E/2 passive microwave sensors and MODIS optical sensor, this data set uses the discriminant function algorithm and its downscaling method to produce a global mapping of near-surface freeze-thaw states with higher spatial resolution. This product covers the time period from 2002 to 2018 (daily), and spatial coverage is global scale (spatial resolution of 0.05°). It can be used to analyze the start/end time of global near-surface freeze/thaw states, the duration of freezing/thawing and their changing trends, and provide data support for studying the mechanism of water cycle and energy exchanges in the context of global change.
This data is a simulated output data set of 5km monthly hydrological data obtained by establishing the WEB-DHM distributed hydrological model of the source regions of Yangtze River and Yellow River, using temperature, precipitation and pressure as input data, and GAME-TIBET data as verification data. The dataset includes grid runoff and evaporation (if the evaporation is less than 0, it means deposition; if the runoff is less than 0, it means that the precipitation in the month is less than evaporation). This data is a model based on the WEB-DHM distributed hydrological model, and established by using temperature, and precipitation (from itp-forcing and CMA) as input data, GLASS, MODIA, AVHRR as vegetation data, and SOILGRID and FAO as soil parameters. And by the calibration and verification of runoff，soil temperature and soil humidity, the 5 km monthly grid runoff and evaporation in the source regions of Yangtze River and Yellow River from 1998 to 2017 was obtained. If asc can't open normally in arcmap, please delete the blacks space of the top 5 lines of the asc file.
The Map of Permafrost on the Qinghai-Tibet Plateau (1:3,000,000) (Shude Li and Guodong Cheng, 1996) was made by the State Key Laboratory of Frozen Soil Engineering, LIGG, CAS (currently called the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences). It was based on first-hand information from the study of frozen soil and previous research papers and literature. By detailed study and consultation of aerial photographs, satellite images, the Permafrost Map along the Qinghai-Tibet Highway (1:600,000) (Boliang Tong, et al., 1983), Geomorphological Map of the Qilian Mountains (1:1,000,000) (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 1985), Natural Landscape Map of Qinghai-Tibetan Plateau (1:3,000,000) (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 1990), Quaternary Glacial Distribution Map of the Qinghai-Tibetan Plateau (1:3,000,000) (Bingyuan Li and Jijun Li, 1991), Frozen Soil Remote Sensing Map of the Western Channel Project of the South-North Water Diversion in the Region of the Tongtian-Yalong Rivers (1:500,000) (Lanzhou Institute of Glaciology and Cryopedology, Chinese Academy of Sciences, 1995), and Map of Snow, Ice, Frozen Ground in China (1:4,000,000) (Yafeng Shi and Desheng Mi, 1988), with editing on 1,000,000 aerial survey topographic maps, and the 1:3,000,000 Map of Permafrost on the Qinghai-Tibetan Plateau was then generated. It was later digitized by Zhuotong Nan of the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences. The data include: 1) Digitized distribution map of frozen soil on the Qinghai-Tibetan Plateau 2) Scanned map of frozen soil map on the Qinghai-Tibetan Plateau The types of frozen soil in the digitized frozen soil map include: 0. Seasonally frozen ground; seasonal frozen soil 1. Permafrost 2. Island permafrost; 3. Continuous permafrost;
The Tibetan Plateau (TP) has the largest areas of permafrost terrain in the mid- and low-latitude regions of the world. Some permafrost distribution maps have been compiled but, due to limited data sources, ambiguous criteria, inadequate validation, and deficiency of high-quality spatial data sets, there is high uncertainty in the mapping of the permafrost distribution on the TP. We generated a new permafrost map based on freezing and thawing indices from modified Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperatures (LSTs)、The temperature at the top of permafrost (TTOP) model was applied to simulate the permafrost distribution ， validated this map using various ground-based data sets. The properties of frozen soil include: Seasonally frozen ground、Permafrost、Unfrozen ground. The results provide more detailed information on the permafrost distribution and basic data for use in future research on the Tibetan Plateau permafrost.
Based on the existing natural hole data of 15 active layer depth monitoring sites in the Qinghai-Tibet Engineering Corridor, the active layer depth distribution map of the Qinghai-Tibet Engineering Corridor was simulated using the GIPL2.0 frozen soil model. The model required synthesis of a temperature data set of time series. The temperature data were divided into two phases according to the time spans, which were 1980-2009 and 2010-2015. The data of the first phase were from the Chinese meteorological driving data set (http://dam. Itpcas.ac.cn/rs/?q=data#CMFD_0.1), and the data of the second phase was the application of MODIS surface temperature products (MOD11A1/A2 and MYD11A1/A2) with a spatial resolution of 1 km. In addition, the soil type data required by the model came from the China Soil Database (V1.1) and have a resolution of 1 km. At the same time, the topography was also considered. The research area was classified into 88 types based on the measured soil thermophysical parameters and land cover types, and then the simulation was performed. The simulation results were compared with the field measured data. The results showed that they were highly consistent, and the correlation coefficient reached 0.75. In alpine areas, the average depth of the active layer is below 2.0 m. However, in the river valleys, the average depth of the active layer is above 4.0 m. In the high plain area, the depth of the active layer is usually between 3.0 m and 4.0 m.
The High Asia region is an area sensitive to global changes in mid-latitude regions and is a hotspot for research. The lakes in the territory are scattered, and the lake freeze-thaw process is one of the key factors sensitive to global change. Due to the large difference in the dielectric constant between ice and water, satellite-borne passive microwave remote sensing is weather insensitive and has a high revisiting rate; thus, it can achieve rapid monitoring of the freeze-thaw state of lakes. According to the area ratio of the lake and the land surface in the sub-pixels of passive microwave radiometer data, this data set represents the lake brightness temperature information of the pixel (sub-pixel level) by applying the hybrid pixel decomposition method in order to monitor the lake freeze-thaw process in the High Asia region. Thus, by adopting a variety of passive microwave data, time series of lake brightness temperature and freeze-thaw status were obtained for a total of 51 medium to large lakes from 2002 to 2016 in the High Asia region. Using cloudless MODIS optical products as validation data, three lakes of different sizes in different regions of High Asia, i.e., Hoh Xil Lake, Dagze Co Lake, and Kusai Lake, were selected for freeze-thaw detection validation. The results indicated that the lake freeze-thaw parameters obtained by microwave and optical remote sensing were highly consistent, and the correlation coefficients reached 0.968 and 0.987. This data set contained the time series brightness temperature of lakes and the freeze-thaw parameters of lake ice, which could be used to further invert the characteristic parameters of lakes and enhance the understanding of lake ice freezing and thawing in the High Asia region. This database will be useful in the assessment of climatic and environmental changes in the High Asia region and in global climatic change response models. The data set consists of two parts: the passive microwave remote sensing brightness temperature data set of 51 lakes in the High Asia region from 2002 to 2016, with an observation interval of 1 to 2 days, and the lake ice freeze-thaw data set obtained by estimation of the lake brightness temperature. The files are the lake brightness temperature data via the nearest neighbour method and pixel decomposition in the form of a .zip file (12 MB) and the lake freeze-thaw data set for 51 lakes in the High Asia region from 2002 to 2016 in the form of an .xls file (0.1 MB).