2.1. Data sources
1) Ground temperature data
The MAGT is an important factor that reflects the thermal state of
permafrost, and is defined as the ground temperature at the zero annual
amplitude depth (ZAA, i.e., the depth at which the annual temperature
variation < 0.1°C) (Qin, 2016). Due to the harsh environment
of the QTP, some boreholes are measured manually using a multimeter once
each year (Qin et al., 2017). Most MAGTs, however, are not easily
accessible from the ZAA. In these cases, the temperature at or closest
to 10 m below the ground surface was used (Nan et al., 2002; Liu et al.,
2017). All disturbed measurement sites (e.g., sites submerged by the
rising waters of a lake) were removed. Ultimately, 84 MAGT
sites
(Figure 1) were selected from both field station observations
(Cryosphere Research Station on the Qinghai-Tibet Plateau, Chinese
Academy of Sciences, available athttp://www.crs.ac.cn/) and the
related literatures (Wu et al., 2012a; Qin et al., 2017;
Wang
et al., 2017). We selected the period from 2000 to 2015 as the reference
period, and all observations obtained were during this period. Some
sites were based on one year of observation, while others were based on
the average of several years, from which we calculated the long-term
average value.
2) Active layer thickness data
In order to better fit the ALT, we attempted to collect a large amount
of observed data from relevant literatures (Wu et al., 2012a; Qin et
al., 2017; Wang et al., 2017). An additional portion of the active layer
data came from field pit detection.
A total of 77 ALT observation
sites (Figure 1) were selected. The time node selection and disturbance
data processing for ALT were the same as those used for the MAGT. Based
on the distribution of MAGT and ALT observation sites, we divided them
into five typical regions, the Wenquan typical region (WQIR), Xikunlun
typical region (XKLIR), Gaize typical region (GZIR), Aerjin typical
region (AEJIR) and Qinghai-Tibet Highway typical region (G109IR), which
represent the permafrost regions of the eastern, western, southern,
northern and central areas of the QTP, respectively.
3) Meteorological data
In order to obtain climate data for the reference periods (2000–2015),
the China Meteorological Forcing Dataset (CMFD) (available athttp://www.tpedatabase.cn/; Yang et al., 2010b; Yang et
al., 2010b; He et al., 2020) with temporal and spatial resolutions of 3
hours and 0.1° × 0.1°, respectively, was utilized in this study. The
time scale of the dataset covered the studying period. According to the
study of He et al. (2020), the CMFD was established by merging Princeton
reanalysis data, GLDAS data, GEWEX-SRB radiation data, and TRMM
precipitation data, as well as the regular meteorological observations
made by the China Meteorological Administration. The accuracy of CMFD is
between the observation data and the remote sensing data (Yang et al.,
2010b), and it has been widely used due to its high reliability (Xue et
al., 2013; Xu et al., 2017a; Wang et al., 2019a).
In the study, we used air temperature and precipitation data from the
CMFD to calculate the two key predictors, including the thawing indices
(thawing degree days, TDD) and the freezing indices (freezing degree
days, FDD), which play essential roles in the studies of the frozen
ground. As useful indicators, they have been widely applied in the
permafrost region to predict the ALT (Zhang et al., 2005; Nelson et al.,
1997; Peng et al., 2018; Shiklomanov and Nelson, 2002) and permafrost
distribution (Nelson and Outcalt, 1987). In addition, we also calculated
the other two predictors, including the solid precipitation (i.e.,
precipitation with a temperature below 0°C, Sol_pre), and liquid
precipitation (i.e., precipitation with a temperature above 0°C,
Liq_pre).
For future conditions, the BCC-CSM 1.1 climate change modeling data was
used (available athttp://www.worldclim.org/). It
was downscaled GCMs data from CMIP5 (IPCC Fifth Assessment). BCC-CSM1.1
is the version 1.1 of the Beijing Climate Center Climate System Model,
the coupling was realized using the flux coupler version 5 developed by
the National Center for Atmosphere Research (NCAR) (Wu et al., 2019). It
was a fully coupled model with ocean, land surface, atmosphere, and
sea-ice components, and was often used to simulate the response of
global climate to rising greenhouse gas concentrations, the performance
is satisfactory in China (Zhang and Wu, 2012b; Xin et al., 2018). In
this study, we chose the monthly average air temperature and
precipitation over the time period 2061–2080 under three Representative
Concentration Pathways (RCPs):
RCP2.6, RCP4.5, and RCP8.5 (Moss et al., 2010; Taylor et al 2012). The
four predictors (TDD, FDD, Sol_pre, and Liq_pre) were recalculated in
the same way for each time period and RCP scenario.
4) Geospatial environmental predictors
The geospatial environmental predictors were mainly derived from
topographic data and regional environmental data. The Shuttle Radar
Topography Mission (SRTM) data for a 1-km spatial resolution digital
elevation model (DEM) (Reuter et al., 2007) were selected to calculate
the predictors of elevation (Ele) and potential incoming solar radiation
(PISR) (McCune and Keon, 2002). Soil organic matter is also an important
factor affecting the ALT of permafrost. Due to the low decomposition
rate of organic matter, high soil organic carbon has been accumulated in
the permafrost regions (Ping et al., 2008). The adiabatic properties of
organic matter relative to minerals will reduce the heat exchange
between ground and air (Mölders and Romanovsky, 2006; Nicolsky et al.,
2007; Paquin and Sushama, 2015). Moreover, the organic matter can also
affect the thermal properties and the amount of unfrozen water of soil
(Romanovsky and Osterkamp, 2000; Nicolsky et al., 2009). In order to
consider the influence of the regional organic matter content (Wu et
al., 2012b), soil organic carbon content information (SOC,
ton·ha-1) from global SoilGrids 1-km data (available
at https://soilgrids.org; Hengl et
al., 2014) was also used in our prediction analysis. Finally, all of the
data layers were resampled to the matching spatial resolution
(0.1°×0.1°) and cropped to the study area
(QTP).
5) Glacier and lake data
The spatial distributions of the glaciers and lakes on the QTP were
collected from the Second Glacier Inventory Dataset of China and the
Chinese Cryosphere Information System provided by the Cold and Arid
Regions Science Data Center
(http://westdc.westgis.ac.cn).