TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-11-2527-2017A new map of permafrost distribution on the Tibetan PlateauZouDefuZhaoLinlinzhao@lzb.ac.cnShengYuChenJiHuGuojieWuTonghuahttps://orcid.org/0000-0002-5084-3570WuJichunXieChangweiWuXiaodongPangQiangqiangWangWuDuErjiLiWangpingLiuGuangyuehttps://orcid.org/0000-0002-7590-0412LiJingQinYanhuiQiaoYongpingWangZhiweiShiJianzongChengGuodonghttps://orcid.org/0000-0002-2758-6211Cryosphere Research Station on Qinghai–Xizang Plateau, State Key
Laboratory of Cryospheric Science, Northwest Institute of Eco–Environment
and Resources (NIEER), Chinese Academy of Sciences (CAS), Lanzhou, 730000,
ChinaState Key Laboratory of Frozen Soil Engineering, NIEER, CAS, Lanzhou,
730000, ChinaLin Zhao (linzhao@lzb.ac.cn)8November20171162527254230July201620October201625September20174October2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/.pdf
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) and validated this
map using various ground-based data sets. The soil thermal properties of five
soil types across the TP were estimated according to an empirical equation
and soil properties (moisture content and bulk density). The temperature at
the top of permafrost (TTOP) model was applied to simulate the permafrost
distribution. Permafrost, seasonally frozen ground, and unfrozen ground
covered areas of 1.06 × 106 km2
(0.97–1.15 × 106 km2, 90 % confidence interval)
(40 %), 1.46 × 106 (56 %), and
0.03 × 106 km2 (1 %), respectively, excluding
glaciers and lakes. Ground-based observations of the permafrost distribution
across the five investigated regions (IRs, located in the transition zones of
the permafrost and seasonally frozen ground) and three highway transects
(across the entire permafrost regions from north to south) were used to
validate the model. Validation results showed that the kappa coefficient
varied from 0.38 to 0.78 with a mean of 0.57 for the five IRs and 0.62 to
0.74 with a mean of 0.68 within the three transects. Compared with earlier
studies, the TTOP modelling results show greater accuracy. The results
provide more detailed information on the permafrost distribution and basic
data for use in future research on the Tibetan Plateau permafrost.
Introduction
Permafrost is a major component of the cryosphere and is sensitive to climate
changes (Wu et al., 2002b; Haeberli and Hohmann, 2008; Li et al., 2008;
Gruber, 2012). Due to its unique and extremely high altitude (mean elevation
over 4000 m) and low mean annual air temperatures (generally lower than
-2 ∘C with an intra-annual amplitude over 20 ∘C in the
permafrost region), the Tibetan Plateau (TP) possesses the largest areas of
permafrost in the mid- and low-latitude regions of the world (Zhou et al.,
2000; Zhao et al., 2004, 2010; Yang et al., 2010). Permafrost and its
dynamics complicate the water and energy exchange between soil and
atmosphere and thereby introduce greater uncertainty into global climate
models (GCMs) that predict climate change (Romanovsky et al., 2002; Smith and
Riseborough, 2002; Cheng and Wu, 2007; Riseborough et al., 2008; Zhao et al.,
2010). To generate better quantitative simulations, a more accurate TP
permafrost distribution is needed. An accurate contemporary permafrost
distribution map is also important as a baseline with which to estimate future
permafrost degradation.
A significant amount of research has been conducted on the permafrost
distribution of the TP, and many permafrost maps have been compiled to evaluate
the distribution and thermal states of permafrost (Shi and Mi, 1988; Li and
Cheng, 1996; Brown et al., 1997, 1998; Qiu et al., 2000; Wang et al., 2006).
These maps have been used to study the responses of permafrost to climate
change (Ran et al., 2012). However, considerable differences in the
permafrost areas and boundaries occur in these maps, from
1.12 × 106 to 1.50 × 106 km2, due to
different data collection periods, data sets, and methods (Yang et al., 2010;
Ran et al., 2012). These maps represent different assessments of the
permafrost distribution on the TP at different times.
Permafrost boundaries in the earlier maps have often been plotted on
topographic maps by hand using conventional cartographic techniques (Tong
and Li, 1983; Shi and Mi, 1988; Li and Cheng, 1996). The standard and most
widely used map is the Map of Permafrost on the Qinghai-Tibetan Plateau (Li and Cheng, 1996). In this map, the permafrost
boundaries were mainly determined using air temperature isotherms combined
with field data, satellite images, and many relevant maps. After 2000, GIS
techniques have been applied to permafrost mapping of the TP. Simple
empirical models with minimal data requirements were established to consider
the permafrost characteristics on the TP. These include the elevation model
(Li and Cheng, 1999) and mean annual ground temperature (MAGT) (Nan et al.,
2002). Meanwhile, some models with simplified physical processes applicable
to high-latitude permafrost were transferred to simulate the permafrost
distribution on the TP. Examples are the frost index (Nelson and Outcalt, 1983)
and the temperature at the top of permafrost (TTOP) (Smith and Riseborough,
1996; Wu et al., 2002a). These models link permafrost temperature with
surface temperature using seasonal surface transfer functions and subsurface
thermal properties, which can provide reasonable assessments of permafrost
distribution when the permafrost upper boundary conditions and regional soil
thermal properties are satisfied. Recently, a global permafrost zonation
index (PZI) was established based on the relationships between the air
temperature and the occurrence of permafrost (Gruber, 2012). The PZI can
represent broad spatial patterns but does not provide the actual extent of
the permafrost.
Most temperature fields that have previously been used in these models were
generated from spatially interpolated air temperature (Pang et al., 2011) or
coarser-resolution (e.g. 0.125∘× 0.125∘)
atmospheric reanalysis data (Gruber, 2012; Qin et al., 2015). Although air
temperature produces inaccurate and low-resolution estimates of ground
surface temperature (GST, defined as the surface or near-surface temperature
of the ground (bedrock or surficial deposit) and measured in the uppermost
centimetres of the ground), it was still widely used in practical
applications because of limited GST observations. In these studies, the N
factor has been the most effective way to transform the air temperature to
the GST (Lunardini, 1978; Klene et al., 2001). With the recent development of
infrared remote sensing technology, an increasing number of land surface
temperature (LST, defined as the average temperature of an element of the
exact surface of the Earth (e.g. surface of ground, vegetation canopy, or snow
cover) calculated from measured radiance; Gillespie, 2014) products derived
from different satellite images have been applied to global and regional
permafrost distribution research (Kääb, 2008; Hachem et al., 2009;
Nguyen et al., 2009; Langer et al., 2010; Westermann et al., 2012, 2015).
These products are effective alternatives to GST, especially for regions with
limited in situ observations, such as the TP. However, the LSTs observed by
satellite sensors are instantaneous values at the passing times and must be
transformed into mean daily temperature to serve as the thermal state of each
day before use. Wang et al. (2011) averaged the twice-daily LST observations
of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on board
the Terra satellite to drive the TTOP model. Their results show a systematic
bias with the ground observations because of different observation times
(Wang et al., 2011). In addition, the limited availability of soil thermal
property spatial data sets creates another problem when modelling permafrost
distribution. Most soil surveys have been carried out in seasonally frozen
ground or permafrost along the Qinghai–Tibet Highway (Li et al., 2014,
2015a) rather than in permafrost regions in the plateau hinterland due to the
harsh climate and inconvenient access. Therefore, soil thermal properties
have generally been estimated via soil types generated from a limited number
of plateau geologic classification studies (Wang et al., 2011; Li et al.,
2015b). Overall, there are an insufficient number of field investigations to use for
modelling and validating the accuracy of the maps.
Field survey data sets have recently been obtained based on the project
“Investigation of Permafrost and Its Environment over The Qinghai–Xizang
(Tibet) Plateau” conducted by the Cryosphere Research Station on
Qinghai–Xizang Plateau, Chinese Academy of Sciences (CAS). These data could
provide validation of permafrost distribution maps. In addition, new
information on remote sensing LST applications and spatial soil
characteristics on the TP have been studied. An empirical model of daily mean
LST was established and performed well in continuous permafrost regions of
the central TP (Zou et al., 2014). Li et al. (2015b) studied the
relationships between environmental factors and soil types in the permafrost
region of the TP, and they used a decision-making tree to spatialize the soil
types. The results exhibited good reliability and could be used to realize
the spatialization of soil thermal properties.
This study aims to generate a new permafrost distribution map on the TP
using remote sensing LST products and investigated soil properties. A
multiple linear regression model based on MODIS LST was established, and
ground-based LST observations were employed to calibrate the results. Soil
thermal conductivities of each soil type on the TP were calculated via in
situ observed soil moisture content and bulk density. The TTOP model was
used to simulate the permafrost distribution, and the results were validated
by the observed permafrost distributions of boreholes, five investigated
regions (IRs, located in the transition zones of the permafrost and
seasonally frozen ground), and three transects (across the entire permafrost
regions from north to south). The TTOP modelling result was also compared
with two recent benchmark maps (made in 1996 and 2006).
Spatial distribution of the field survey regions of the Tibetan
Plateau (based on the permafrost distribution map made in 1996).
Materials and methodsField survey data sets
The investigation of permafrost and its environments on the TP was conducted
from 2009 to 2014. We studied five IRs – WenQuan (WQ)
(Zhang et al., 2011, 2012) and Budongquan–Qingshuihe (B–Q) in the eastern TP,
AErJin (AEJ) in the north-eastern TP, GaiZe (GZ) (Chen et al., 2016) in the
southern TP, and XiKunLun (XKL) (Li et al., 2012) in the western TP (Fig. 1),
which are located in the transition zones between permafrost and seasonally
frozen ground with different climatic and geographic conditions. Ground-based
observations, mechanical excavation, geophysical exploration (ground
penetrating radar, GPR; time-domain electromagnetic, TEM), and borehole
drilling were used, and comprehensive surveys of the permafrost distribution
boundary, soil, vegetation, climate, and landform were carried out in all
five IRs. The data sets of ground temperature profiles, spatial distribution
of vegetation (Wang et al., 2016), and soil types (Li et al., 2014, 2015a)
were obtained and a long-term permafrost monitoring network was established,
including automatic weather station and borehole records.
Field survey sample statistics in five investigated regions.
Investigated region (IR) WQB–QAEJGZXKLTotalBoreholes2140132328125Soil pits7455–1951199Boreholes and soil pits
Field survey data sets including ground temperature, soil moisture content,
and bulk density were obtained. The ground temperature, measured by
temperature probes at different depths (generally set at 0.5 m intervals
from 0 to 5 m, 1 m from 5 to 20 m, 2 m from 20 to 40 m, 5 m from 40 to
60 m, and 10 m greater than 60 m) in boreholes were used to determine the
existence of permafrost. The soil samples were collected according to depth
increments at each pit. The field bulk density (weight of the soil per unit
volume) was measured by the clod method. Samples for moisture determination
were stored in aluminum sampling boxes and carefully sealed to prevent
moisture changes. The soil moisture content was expressed by weight as the
ratio of the mass of water present to the dry weight of the soil sample (Wu
et al., 2016). The sampling period was concentrated from July to October, so
the weighted mean moisture content by depth was used to denote the mean state
of each pit. The soil moisture content and bulk density were used to
calculate the soil thermal conductivity. There was a total of 125 boreholes
and 199 soil pits in five IRs of the field survey (Table 1).
Permafrost maps of five investigated regions
The permafrost maps of the five IRs were used as the validation data in this
study. In a local region, the elevation and terrain factors have greater
influence on permafrost occurrence than longitude and latitude, especially on
mountainous permafrost (Riseborough et al., 2008). The lower limit of
permafrost (LLP) was determined based on the linear regression relationship
between MAGT and borehole elevation. The elevation where MAGT equals
0 ∘C was regarded as the LLP (Li et al., 2012; Zhang et al., 2012;
Chen et al., 2016). In view of the influence of aspects on LLPs, the
boreholes were classified into three types: north-facing, south-facing, and
east–west facing and the LLP of each type was determined. The
permafrost distribution was generated based on the LLPs of different aspects
and the digital elevation model (DEM) data, and a portion of the observed
results of boreholes and geophysical methods (GPR and TEM) was reserved to
validate the maps (Li et al., 2012; Zhang et al., 2012). For example, the
results of GZ IR showed that the LLP was about 4950 m for north-facing,
5000 m for east–west-facing, and 5100 m for south-facing slopes (Chen et
al., 2016).
Permafrost distribution of three highway transects
Three highway transects were established as follows (Fig. 1): National
Highway 214 (Qinghai–Yunnan Highway, hereafter G214) from the northern Ela
Mountain to Qingshuihe town, National Highway 109 (Qinghai–Xizang Highway,
hereafter G109) from Xidatan to Nagqu, and National Highway 219
(Xinjiang–Xizang Highway, hereafter G219) from Kudi to Shiquanhe town. The
overall transect lengths of G214, G109, and G219 were approximately 400, 750,
and 900 km, respectively. Three transects across the entire permafrost
regions from north to south in the eastern, central, and western TP were
established. Many permafrost geological conditions were discovered in the
process of the construction and renovation of the three highways, and many
permafrost roadbed monitoring sections were subsequently set along the
highways (Jin et al., 2008; Sheng et al., 2015). Based on these background
data and our investigation results (geophysical and drilling exploration),
the permafrost distribution limits and geothermal features of the three
transects were generated and used as the validation data sets.
Spatial data setsExisting two benchmark permafrost maps
One of the most widely used permafrost distribution benchmark maps is the
Map of Permafrost on the Qinghai–Tibetan Plateau, compiled by the
Lanzhou Institute of Glaciology and Geocryology, Chinese Academy of Sciences
(hereafter TP-1996) to support basic research on cryospheric dynamics in
China (Li and Cheng, 1996). TP-1996 synthesizes field data, literature,
aerial photographs, satellite images, and many relevant maps and shows that
the area of permafrost is 1.41 × 106 km2. The other is
the Map of the Glaciers, Frozen Ground and Deserts in China,
compiled by Cold and Arid Regions Environmental and Engineering Research
Institute, Chinese Academy of Sciences (hereafter TP-2006) (Wang et al.,
2006). In this map, the permafrost distribution was generated using a
0.5 ∘C MAGT isotherm as a threshold, which shows that the area of
permafrost is 1.12 × 106 km2. The MAGT was interpolated
based on the relationship between elevation/latitude and the MAGT observation
from all 76 boreholes along the Qinghai–Xizang Highway (Nan et al., 2002).
MODIS LST products
The MODIS LST data used in this study were the 1 km gridded clear-sky
MOD11A2 (Terra MODIS) and MYD11A2 (Aqua MODIS) products (reprocessing version
5), which span from 2003 to 2012. The results of the radiance-based and
temperature-based validation indicated that the accuracy of the global MODIS
LST product is better than 1 ∘C in most cases, including lakes,
homogeneous vegetation, and soils under clear-sky conditions (Wan et al.,
2002, 2004; Coll et al., 2005; Wan and Li, 2008; Wan, 2008). Langer et
al. (2010) and Westermann et al. (2011) focused on weekly averages and
demonstrated an agreement generally better than 2 ∘C for MODIS LST
in the summer season, at permafrost sites in Siberia and Svalbard, Norway,
respectively.
Both MOD11A2 and MYD11A2 provide two observations (daytime and night-time),
which means that there are four LST observations per day for the same pixel.
The temporal resolution of MOD11A2/MYD11A2 was 8 days. The LST values represent the 8-day average LST values
(the missing values were ignored in the calculation) (Wan, 2009; Wan and
Dozier, 1996), and there are theoretically 46 groups of LST values per year.
While the 8-day MODIS LST products have more reliable data than the daily products,
they still have numerous missing values when establishing the mean daily LST
empirical models due to clouds or other factors (Prince et al., 1998). In
this study, the Harmonic Analysis Time-Series (HANTS) algorithm, developed to deal with time series of irregularly
spaced observations and to identify and fill in the missing values (Roerink et
al., 2000), was applied to smoothen and reconstructing MODIS LST series on
a per-pixel basis for the entire study area. The parameters set for the HANTS
analysis were described in detail by Xu et al. (2013).
The full coverage of the entire TP requires a total of 13 swathes (h23v04,
h23v05, h24v04, h24v05, h24v06, h25v04, h25v05, h25v06, h26v04, h26v05,
h26v06, h27v05, and h27v06) of the MOD11A2/MYD11A2 products. The Terra
overpass time is around 10:30(local time) in its descending mode and
22:30 in its ascending mode. The Aqua overpass time is around 13:30
in its ascending mode and 01:30 in its descending mode
(https://modis.gsfc.nasa.gov/). The MODIS LSTs represent instantaneous
observation values, and the overpass times of the satellites do not
accurately correspond to standard meteorological observation times (Beijing
time: 02:00, 08:00, 14:00, and 20:00) (China Meteorological Administration,
2003). Therefore, an arithmetic mean of the four LST observations with the
same weights will produce a large deviation from the mean daily LST (Wang et
al., 2011). We used a multiple linear regression to distribute different
weights to each MODIS LST observation to establish the mean daily LST
empirical model. The details of processing were described by Zou et
al. (2014). The model validation at three permafrost sites showed that the
determination, mean error, mean absolute error, and root mean squared error
of mean daily LST were 0.91 to 0.93, -0.21 to 1, 2.28 to 2.42, and 2.96 to
3.05 ∘C, respectively. In this study, the empirical formula is as
follows:
LSTdaily=0.18×Terraday+0.269×Terranight+0.143×Aquaday+0.435×Aquanight+0.896,
where LSTdaily is the mean daily LST, Terraday is
the daytime LST observation of MOD11A2, Terranight is the night-time LST
observation of MOD11A2, Aquaday is the daytime LST observation of
MYD11A2, and Aquanightis the night-time LST observation of MYD11A2.
The calculations of the thawing indices (thawing degree days, TDD) and
freezing indices (freezing degree days, FDD) were based on the 8-day average LST
calculated from the previous processing. The procedures were realized using
the IDL programming language, and the FDD and TDD from 2003 to 2012 were
obtained and averaged as the model inputs.
Soil thermal properties
Soil thermal characteristics were modelled according to parameters measured
from soil types encountered in the field. The classification of soil types
was performed using the Decision Tree See 5.0 software and the Soil Land
Inference Model (SoLIM) in conjunction with soil type and environment factor
data (Li et al., 2014, 2015b). According to the USDA soil taxonomy system, there
are five soil orders on the TP as follows: Gelisols, Aridisols, Mollisols,
Inceptisols, and Entisols. Considering the availability of soil sample
parameters, the characteristics of sampling regions, and model applicability,
the empirical model of soil thermal conductivity proposed by Kersten (1949)
was adopted in this study. The equation of thawed soil thermal conductivity
is
kt=0.1442×(0.7×logω+0.4)×10(0.6243×γd).
Furthermore, the equation of frozen soil thermal conductivity is
kf=0.01096×10(0.8116×γd)+0.00461×10(0.9115×γd)×ω,
where kt/kf is the thermal conductivity
(W m-1 K-1) of thawed/frozen soil, ω is the soil moisture
content (%), and γd is the soil bulk density
(kg m-3). Both ω and γd were measured via soil
samples collected in the field survey. The soil samples were classified
according to soil orders; moisture content and bulk density values were
averaged within soil orders to eliminate abnormal values (Table 2, the values
show the mean with standard deviation of soil thermal parameters of each
type).
Flow diagram of the modelling scheme.
Soil thermal parameters of each type on the Tibetan Plateau.
The spatial distribution and data on areas of glaciers and lakes on the TP
were from the Second Glacier Inventory Dataset of China (Guo et al., 2014)
and the Chinese Cryosphere Information System (Li, 1998) provided by the Cold and Arid
Regions Science Data Center
(http://westdc.westgis.ac.cn).
TTOP model
Considering the model's usefulness and sophistication, spatial scales, and
available data sets (Riseborough et al., 2008), we selected the temperature at the top of permafrost (TTOP) model (Smith and Riseborough, 1996) to
simulate the permafrost distribution on the TP.
The TTOP model can be expressed as follows:
TTOP=kt/kf×TDD-FDDP=(rk×nt×It)-(nf×If)P,
where P is the annual period (365 days). TDD
(nt×It) is the ground surface thawing
index, and FDD (nf×If) is the ground
surface freezing index. nt and nf are n factors of
the thawing and freezing seasons, and Itand If are
the air temperature thawing and freezing indices.
rk=kt/kf is defined as the ratio of the
thermal conductivity coefficient when soil is thawing and freezing. We used
the modified MODIS LST data processed in Sect. 2.2.2 as the GST to derive
the TDD and FDD, and the rk was calculated from the soil
properties derived from processes in Sect. 2.2.3.
From Eq. (4), if the FDD is greater than
TDD ×kt/kf(nf×If > rk×nt×It),
then TTOP will be below 0 ∘C and permafrost exists. This
processing was realized in the ArcGIS software programme with the following
expression:
D=1,TTOP≤0permafrost0,TTOP>0seasonally
frozen ground
The glacier and lake regions were excluded from the permafrost distribution
modelling of the TTOP model. In addition to permafrost and seasonally frozen
ground, unfrozen ground was also identified in this study. The unfrozen
ground was defined as the region where the extreme minimum LST ≥ 0 ∘C. The night Aqua MODIS LST (observation time approximately
03:00) was employed as input data for the determination of unfrozen
ground area. The uncertainty analysis of total permafrost area was conducted
with R statistical software (version 3.3.1, www.r-project.org) using
the percentile method, and we used a 90 % confidence interval to
determine the range of the total permafrost area. The modelling scheme in
this study is shown in Fig. 2.
Accuracy evaluation
The permafrost distribution of the borehole locations, five IRs, and three
transects were used to estimate the accuracies of the three maps (TP-1996,
TP-2006, and TP-2016). The spatial distribution of borehole temperature data
across a permafrost domain or seasonally frozen ground area was used as the
criterion of advantages and disadvantages of results for the three time
snapshots of 1996, 2006, and 2016. The permafrost distribution across the
five IRs and three transects were selected as the real values with which to validate
the three maps.
To evaluate the agreement of the simulated permafrost distribution and the
observed results, the kappa coefficient (K) (Cohen, 1960), which measures
the degree of agreement, was selected for accuracy evaluation.
K=s/n-(a1b1+a0b0)/n21-(a1b1+a0b0)/n2,
where the total number of pixels is n, and s is the number of pixels in
which the simulation and investigated results agree. The number of
investigated result pixels with permafrost is a1, and those without are
a0, and the simulated map pixel numbers are b1 and b0.
Empirically and statistically arbitrary quality values for K have been
proposed. Cohen (1960) suggested that K≥0.8 signifies excellent
agreement, 0.6≤K<0.8 represents substantial agreement, 0.4≤K<0.6 represents moderate agreement, 0.2≤K<0.4 represents fair agreement, and a lack of agreement corresponds to K<0.2.
Spatial distribution of permafrost with the derived TTOP on the
Tibetan Plateau.
ResultsPermafrost distribution modelling of TTOP
Figure 3 shows the simulated permafrost distribution of the TTOP model on the TP
(TP-2016). The distribution areas of permafrost and seasonally frozen ground
were 1.06 × 106 km2 with a 90 % confidence interval
of 0.97–1.15 × 106 km2, and
1.46 × 106 km2. This estimate excluded glaciers and
lakes, which account for 40 and 56 % of the total TP area, respectively.
The result shows that the permafrost distribution was centred in southern
Qinghai and northern Tibet. The northern Qiangtang Plateau and the Kunlun
Mountains were the regions with the most permafrost, which
extends west and north-west to the Karakoram mountains. The permafrost continuity
decreases gradually as the elevation decreases and the ground temperature
increases with increasing distance from the central region. The geographic
northern and southern boundaries of permafrost were Xidatan and Anduo from the mark
sites of the Qinghai–Xizang Highway. There were a few areas of permafrost in the
high mountains from Anduo to the southern Tibet Valley. Due to the existence
of the Bayan Har Mountains and Anemaqen Mountain, the elevations of which are above
5000 m, there is permafrost occurrence in the eastern TP. Some unfrozen
ground exists in the south-eastern margin of the TP, and the size of this area is
approximately 0.03 × 106 km2 (account for 1 % of the
total TP area).
Spatial distribution of boreholes in five IRs of three maps.
Validation with borehole observations
Boreholes can determine whether permafrost exists or not. Figure 4 shows the
spatial distribution of borehole locations at permafrost or seasonally frozen
ground in five IRs of three maps. Different combinations were set up to
analyse the differences between the three results. Columns a, b, and c show the
results of TP-1996, TP-2006, and TP-2016, and rows 1, 2, 3, 4, and 5 show the
results of XKL, GZ, AEJ, B–Q, and WQ IRs, respectively. The results show that
TP-1996 is insensitive to the geographical boundaries across all five IRs,
and there are many erroneous interpretations of both permafrost and
seasonally frozen ground. TP-2006 had higher sensitivity to the boundaries,
especially in WQ IR; however the recognition of the other four IRs is
inadequate and the areas of permafrost distribution were overestimated.
Compared to TP-1996 and TP-2006, TP-2016 performed better at identifying the
geographic boundary of permafrost distribution, identifying almost all the
boundaries of the five IRs correctly, especially for the seasonally frozen
ground in the valley of the north-western XKL IR (Fig. 4c1) and that around
the lakes of the eastern AEJ IR (Fig. 4c3). TP-2016 had some errors that were
mainly affected by local terrain factors. These included the seasonally
frozen ground distributed in valleys and a few permafrost spots at the
margin, such as the two seasonally frozen ground boreholes in the northern
AEJ IR (Fig. 4c3) and three permafrost boreholes at the south-western limit of
GZ IR (Fig. 4c2).
Comparison of the three maps in and around the AErJin investigated
region (a TP-1996, b TP-2006, c TP-2016).
Validation with five investigated regions (IRs)
The permafrost distributions of the five IRs were employed as true values to
validate the modelling results of the three maps in order to analyse their performance
in geographical boundary recognition ability. TP-1996 was the worst at
recognizing the boundaries of permafrost in the five IRs. It misidentified
all boundaries, with a low kappa coefficient (K<0.2), due to
greater misjudgment or overestimation of permafrost pixels. TP-2006 also
performed poorly in the XKL, GZ, and AEJ IRs (K<0.2) but
performed better in the B–Q and WQ IRs, with a kappa coefficient reaching
0.63 and 0.77. TP-2016 had poor performance in the AEJ IR. The kappa
coefficient was only 0.38, which is a slight improvement over estimates of the
former two. In addition, it represents moderate agreement with the XKL and GZ
IRs and substantial agreement with the B–Q and WQ IRs, which have kappa
coefficients of 0.54, 0.48, 0.68, and 0.78, respectively. The average
accuracies of TP-1996, TP-2006, and TP-2016 were 0.06, 0.35, and 0.57. TP-2016 performed best in the validation with the investigated
permafrost distribution from both the individual and mean accuracies of the
five IRs (Table 3). The TP-2016 performed better at identifying the
permafrost boundary in the regions with complex terrain because of sharp
changes in the LST within short distances, such as the WQ, B–Q, and XKL IRs.
For GZ and AEJ IRs, where surface relief is much lower, the TP-2016 does not
perform as well as the other three IRs. The worst performance in AEJ IR might also
be due to no soil pits in the investigation and the soil thermal properties
are inferred completely from the relationship between the environmental factors
and the soil samples of the other four IRs.
Kappa coefficient statistics in five investigated regions of three
maps.
The results of the AEJ IR and surrounding area were selected to compare the
differences among the three maps (Fig. 5). In the AEJ IR, the investigated
result shows that the seasonally frozen ground is mainly distributed at the
northern valley and the eastern Ayakekumu Lake surrounding areas with
permafrost. TP-2006 shows all judgements for permafrost in the AEJ IR, and the
permafrost area is clearly overestimated. TP-1996 shows some seasonally
frozen ground in the north-western AEJ IR, but the locations were misjudged.
TP-2016 judged approximately 30 % seasonally frozen ground in the
northern and eastern AEJ IR. Although there were few correct pixels, the
locations in the eastern part were at the geographic boundary of permafrost.
The observed MAGT of the borehole closest to Ayakekumu Lake was
3 ∘C, which indicates the existence of seasonally frozen ground
there. TP-2016 accurately modelled this phenomenon. In the regions around the
AEJ IR, TP-2016 simulated the seasonally frozen ground around Aqikekule Lake
(area approximately 350 km2) and its source river, and this was not
found in the other two maps. Most lakes on the TP are formed due to
tectogenesis. The major axis basically remains consistent with the main
structure directions and the secondary level fracture direction, and there
generally exists penetrative or non-penetrative taliks under and around
tectonic lakes (Zhou et al., 2000). TP-2016 also shows seasonally frozen
ground in the mountainous region in proximity to the Pitileke River, while the
other two maps did not identify this area. TP-2016 performed better at
identifying the seasonally frozen ground formed by the surface water.
The permafrost distribution of TP-1996 and TP-2006 was modelled according to
the relationship between temperature and 3-dimensional zonalities
(longitude, latitude, and elevation) (Cheng, 1984). The higher weight given
to elevation from the regression equation determined that it has a greater
influence than that of longitude and latitude when interpolating temperature
(air temperature or MAGT). The high continuity and low variability of the
elevation difference in permafrost regions lead to results that appear more
continuous. However, the temperature differences caused by local factors
(e.g. lakes or rivers) are largely masked and this results in an excessive
occurrence of the lower extrapolated temperature. This may explain the
overestimated area of permafrost distribution in the previous TP-1996 and
TP-2006. The use of remote sensing data can better reveal the spatial
heterogeneity of LST. Relative to the two benchmark maps, the TP-2016 result
driven by the processed MODIS LST in this paper was very sensitive to
seasonally frozen ground formed by surface water, and the results show that
there are many seasonally frozen ground areas surrounding lakes and major
rivers that correspond to the previous studies (Lin et al., 2011; Niu et
al., 2011).
Comparison of permafrost distribution of three maps along the G109
transect with investigated results (P: permafrost, SFG: seasonally frozen
ground; XDT: Xidatan, KLSYK: Kunlun Mountain Peak, WDL: Wudaoliang, BLH:
Beilu River, FHSYK: Fenghuo Mountain Peak, WL: Wuli, TTH: Tuotuo River, KXL:
Kaixin Mountain Ridge, TTH': Tongtian River, YSP: Yanshiping town, TGL:
Tangula Mountain Peak, AD: Anduo town, LDH: Liangdaohe, NQ: Nagqu town,
G109-IR: investigated results of permafrost distribution in the G109 transect).
Validation with three transects
The permafrost distribution of the three transects (G214, G109, and G219) of
three maps were extracted for comparison with the investigated results to
comprehensively evaluate their performance on the mainly permafrost
developed regions of the TP. The accuracy statistics of the three maps for the
three transects are listed in Table 4. TP-1996 had the worst accuracy of the
three maps with an average kappa coefficient of 0.34. The accuracy of
TP-2006 was higher than that of TP-1996 with an average kappa coefficient of
0.50. It performed well, especially for transect G109. TP-2016 has the
highest accuracy. The kappa coefficients were 0.62, 0.69, and 0.74 for G214,
G109, and G219, respectively, with an average of 0.68. TP-2016 performed
best in the validation with the investigated permafrost distribution from
both the individual and averaged accuracies of the three transects. In the
three transects across all permafrost regions from north to south in the
eastern, central, and western TP, which include most permafrost distribution
characteristics in TP, the validation results should be a synthetic
evaluation of the three maps.
Kappa coefficient statistics in three transects of three maps.
Figure 6 shows the distributions of permafrost and seasonally frozen ground
along the G109 transect of the three maps and the investigated result. The
elevation and mark sites were also added for analysis. For convenient
comparison, the G109 transect was divided into five segments according to the
investigated result as follows: two continuous permafrost regions (from XDT
to southern FHSYK, and southern YSP to northern AD), one region of seasonally
frozen ground only (from southern LDH to NQ) and two regions in which
permafrost and seasonally frozen ground coexist (from WL to YSP, and AD to
LDH). The comparison shows that the three maps performed well in two
continuous permafrost regions. Almost all permafrost is identified correctly
except for several seasonally frozen ground areas in the CMEH and BLH of
TP-1996. In the region of seasonally frozen ground only, TP-1996 judged
permafrost from AD to NQ, which is different from the investigated result and
overestimated the permafrost area in this region. TP-2006 and TP-2016
identified that only seasonally frozen ground exists in this region, which is
consistent with the investigated result. In two regions where permafrost and
seasonally frozen ground coexist, a large difference was seen between the
three maps and the investigated result. TP-2006 shows that continuous
permafrost exists from XDT to northern AD, performed poorly in the
recognition of the seasonally frozen ground, and overestimated the area of
permafrost in the G109 transect. TP-1996 performed better than TP-2006
and
recognized some of the seasonally frozen ground in TTH, TTH', YSP, and AD.
However, the widths and locations reveal bias from that of the investigated
result. TP-2016 identified almost all locations of seasonally frozen ground
correctly with a smaller width difference, and was more consistent with the
investigated result than the former two. Both TP-2006 and TP-2016 identified
the sporadic permafrost in LDH, which was generally expected to be the
southern limit of permafrost in previous studies.
In the G109 transect, seasonally frozen ground mainly exists due to the
surface water effects, regional geologic structure/geothermal effects, and
penetration/radiation effects, which cause a discontinuity in the plane and
depth of the continuous distribution of permafrost (Zhou et al., 2000). Due
to the large streamflow and high water temperature of TTH,
TTH' and Buqu (flow through YSP)
rivers, the penetrative taliks not only developed on the riverbed and high
floodplain, but also expanded to the first or second terrace (the width
generally reached 5–10 km). Additionally, a bare ground, gravel layer
exists, and a higher mean annual air temperature was beneficial to
precipitation infiltration, which created active thermal transfer conditions.
Therefore, the seasonally frozen grounds existing in TTH and YSP were also
affected by penetration/radiation effects. However, for the rivers with less
streamflow and at higher latitudes, such as the CMEH and BHL rivers, the
non-penetrative taliks are much smaller (generally < 100 m) and thus
almost impossible to identify. The seasonally frozen ground in northern WL
was mainly affected by regional geologic structure/geothermal effects, which
has been validated by the results of engineering geologic surveys of the
Qinghai–Xizang Highway and Railway (Jin et al., 2008). From the kappa
coefficients of the three maps and investigated result (Table 4) along the
G109 transect, TP-2016 can better identify the seasonally frozen ground that
is several kilometres wide and caused by local factors (surface water, geothermal,
and permeation/radiation effects).
Spatial difference among the three maps (96: TP-1996, 06: TP-2006,
16: TP-2016; SFG: seasonally frozen ground, P: permafrost).
Spatial difference among the three maps
The kappa coefficients of each pair among the three maps were calculated
(Table 5) to analyse the spatial difference. TP-1996 had low consistency with
both TP-2006 and TP-2016. The kappa coefficients were 0.56 and 0.53,
respectively, which indicates a large difference. TP-2006 had substantial
agreement with TP-2016 and the kappa coefficient reached 0.71. The spatial
differences between each pair among the three maps were compared (Fig. 7).
Compared with TP-2006 and TP-2016, TP-1996 overestimated the permafrost area,
which was mainly distributed in the south-eastern TP, south margin of
continuous permafrost, and predominantly continuous and island permafrost in
the Southern TP. In addition, TP-1996 misidentified some seasonally frozen
ground on the continuous permafrost edge and the interior TP. The permafrost
distribution area of TP-2006 was similar to that of TP-2016. Differences
mainly existed in the interior TP regions, southern margin of continuous
permafrost, and the regions surrounding the Bayan Har Mountains and eastern
Nyainqêntanglha Mountains.
Annual average snow depth (a edited after Che et al., 2008)
and vegetation types of the permafrost region (b edited after Wang
et al., 2016) on the Tibetan Plateau.
Discussion
TTOP was formulated with the modified MODIS LST, rather than ground surface
temperature (GST) in this study. It is well known that MODIS LST observes a
mixture of the vegetation canopy, snow cover, and ground surface, depending
on the region and seasons. The snow cover and vegetation might have a
significant influence on the relationship between the GST and MODIS LST,
depending on the snow depth and duration (Zhang, 2005), and vegetation height
and coverage. The snow cover distribution is spatially variable over the TP
(Fig. 8a), with the most persistently snow-covered areas occurring in the
south-eastern and western edge of the TP and in some alpine regions with
elevations higher than 6000 m (Qin et al., 2006; Pu et al., 2007). Overall,
the snow cover is rare, thin (< 3 cm) and has a short duration
(mostly existing less than 1 day for a single snow event) due to the strong
solar radiation and wind in the vast interior and the northern TP (Che et
al., 2008; Huang et al., 2017), where the permafrost is most developed.
Therefore, although thin snow cover might have a cooling effect on GST due to
the high albedo of fresh snow and a rapid process of snowmelt (Zhang, 2005),
the cooling effect may be of short duration and have very little effect on
our study. The vegetation types in the alpine ecosystem of the permafrost
region on the TP (Fig. 8b) are all composed of grassland and characterized by
dwarf and sparsely distributed plants (Wang et al., 2016). The vegetation
cover across most of the permafrost region was less than 30 % (Lehnert et
al., 2015) and even less than 10 % in the middle and western TP. In view
of the conditions of both snow cover and vegetation on the TP, there are only
slight differences between the GST and MODIS LST on average, and even smaller
differences in FDD and TDD in our study area. In addition, the HANTS
algorithm might cause some bias under cloudy-sky conditions. Further
evaluation of the algorithm was not performed in this study, because it has
been proved to be an effective approach for filling in gaps in the MODIS LST data
for the TP where clear-sky conditions dominated (Xu et al., 2013).
The data set used in the earliest maps (compiled in the 1980s and 1990s)
included air temperature, field data, aerial photographs, satellite images,
and many relevant maps (Tong and Li, 1983; Shi and Mi, 1988; Li and Cheng,
1996). The permafrost boundary was mainly based on a threshold of air
temperature isotherms and modified in several regions (such as
Qinghai–Xizang Highway, Qinghai–Yunnan Highway, and the Hengduan Mountains)
using field data based on the authors' knowledge. The threshold was determined
by the empirical statistical relationship between permafrost occurrence and
meteorological observations in the eastern TP (Li and Cheng, 1996), while
the universality of the threshold is questionable in the western TP due to
insufficient data. In addition, high uncertainty exists in the use of air
temperature interpolation because of the scarce, unevenly distributed
monitoring sites. There were more sites in the eastern TP and fewer in the
western TP, more sites at lower elevations and fewer at higher elevations,
and very few sites in permafrost regions. This resulted in the low accuracy of
extrapolated air temperature on the TP (Lin et al., 2002; Li et al., 2003),
especially in the permafrost region. The permafrost maps were compiled with
conventional cartographic techniques that plotted the permafrost boundaries
on the topographic maps by hand (Tong and Li, 1983; Shi and Mi, 1988; Li and
Cheng, 1996). The artefactual errors were very difficult to control and
depended on the knowledge and skill of the mapper. These factors led to
significant uncertainties in the maps. These maps place much emphasis on the
broad concept of “possible” permafrost regions and this overestimated the
actual permafrost areas (Wang et al., 2016). The permafrost mapping of
TP-2006 was based on the MAGT that considered the characteristics of high-altitude permafrost. The regional MAGT was interpolated based on the
relationship between elevation/latitude and the borehole observations along
the Qinghai–Xizang Highway (Nan et al., 2002). The MAGT model performed
more accurately in the central TP than in the eastern and western TP,
which was demonstrated in the validation of the three transects. In view of
the medium spatio-temporal resolution and sensitivity to spatial temperature
heterogeneity of the MODIS LST data used in the mapping of TP-2016, it can
accurately represent the spatial pattern of LST on the TP. In addition, the
MODIS LST data were calibrated using ground-based LST observations obtained
from automatic weather stations in typical permafrost regions (Zou et al.,
2014), which correspond to actual climate conditions of the permafrost
region. Moreover, the subsurface thermal properties derived from soil
investigation data were also considered in the TTOP model. The improvement
in upper boundary conditions of the permafrost model and the use of large
quantities of reliable in situ observed data sets led to higher modelling
accuracy.
In the earliest maps, only observational data from the field sites along
Qinghai–Xizang Highway were used for map evaluation (Tong and Li, 1983; Shi
and Mi, 1988; Li and Cheng, 1996). For TP-2006, the threshold of 0.5 ∘C MAGT
was determined by a sensitivity analysis of comparison with
the TP-1996, without independent validation (Nan et al., 2002). The
validation in this study showed that the TP-2006 accuracy was higher than
that of TP-1996. However, TP-2006 highlights the excessive elevation effects
in the MAGT interpolation and masks the effects of local factors to some
degree. The better performance of TP-2006 in the B–Q and WQ IRs might be
explained by geomorphology similar to the Qinghai–Xizang Highway, because
these two IRs were closer to the highway than the other three IRs. This
suggests that the MAGT model could reflect the permafrost distribution when
there are sufficient borehole ground temperature observations, and this is
why we used it to model the permafrost distribution of five IRs. The
validation results of the five IRs emphasized the performance when recognizing
permafrost boundaries and that of the three transects emphasized the overall
evaluation of the three maps. Overall, the validation results of both the
five IRs and three transects suggested that TP-2016 performed the best and
achieved the highest accuracy among the three maps. The results provide a
standard permafrost distribution map on the TP based on current climate
conditions.
The ground temperatures of permafrost on the TP have increased during the
past several decades (Wu and Liu, 2004; Wu and Zhang, 2008; Zhao et al.,
2010). This means a disequilibrium of permafrost under ongoing climate
warming, and thereby any map based on a contemporary climate forcing is
likely to underestimate the extent of permafrost. However, permafrost
bodies have a long response time to atmospheric conditions (Riseborough,
2007; Romanovsky et al., 2010; Smith et al., 2010). The increasing rates of
ground temperature were much lower in the TP than that in the circumpolar
regions and much lower for the warm permafrost (Wu and Zhang, 2008; Smith
et al., 2005; Zhao et al., 2010), which is mostly distributed near the
permafrost boundaries. Moreover, the degradation of permafrost in these
regions was characterized by a deepening of the active layer, rather than
the disappearance of permafrost. The changes of the permafrost distribution
on the TP might be very limited in the past several decades. Therefore, the
spatial difference among the three maps might be mainly induced by the
differences in methods and data sources. The TP-2016 could be used as the
benchmark map for permafrost distribution on the TP, although more work is
needed to improve the accuracy of surface forcing and the soil parameters.
In addition, although the approach based on the relationship between current
climate and permafrost occurrence is useful for mapping the distribution of
the TP permafrost, it should be cautious when applying the
transient responses of permafrost to climate change to modelling.
Conclusions
This study exploited the advantages of the medium spatio-temporal resolution
of MODIS LST products to construct a database of mean daily LST of the TP.
The permafrost distribution is simulated by the TTOP model combined with
ground observations and soil investigated data sets. The model was validated
against the permafrost distribution obtained from the borehole temperature
data, five IRs and three transects and compared to two recent benchmark maps.
From the validation with borehole temperature data, the suggested method of
permafrost boundary identification shows a better result than the two maps,
especially for the seasonally frozen ground in valleys and around lakes. The
accuracy of the method validation shows that the TP-2016 case has the highest
kappa coefficients for the five IRs and three transects. The average
coefficients were 0.57 and 0.68. The modelling estimation shows
that 1.06 × 106 km2 of permafrost
(0.97 × 106–1.15 × 106 km2, 90 %
confidence interval), 1.46 × 106 km2 of seasonally frozen
ground, and 0.03 × 106 km2 of unfrozen ground could be on
the TP. Compared with two recent benchmark maps, the TTOP model is superior
in recognizing the boundary of permafrost, especially in the seasonally
frozen ground areas caused by local factors. The new permafrost distribution
map represents a basic data set for future permafrost research.
The data of permafrost distribution on the Tibetan Plateau generated in this paper is provided in the supplement.
The Supplement related to this article is available online at https://doi.org/10.5194/tc-11-2527-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank the Atmosphere Archive and Distribution
System for providing MODIS land products, and Cold and Arid Regions Science
Data Center for data on glacier, lake and snow depth on the Tibetan Plateau.
This research was financially supported by the National Major Scientific
Project of China “Cryospheric Change and Impacts Research” (2013CBA01803),
the Creative Research Groups of National Natural Science Foundation of China
(no. 41421061), the key project of the Chinese Academy of Sciences
(KJZD-EW-G03-02). We also acknowledge Jerry Brown, Stephan Gruber,
and Sergey Marchenko for their constructive suggestions.
Edited by: Tobias Bolch
Reviewed by: two anonymous referees
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