4.1. Comparisons with previous results
The most likely permafrost area on the QTP is 1.04 ×
106 km2 (the region where MAGT
< 0°C, Figure 4), or about 45.4% of the total QTP land
surface area. Our results were compared with the permafrost distribution
map of the QTP for the period 2003–2012 based on the TTOP model, which
was basically consistent with the new permafrost area (1.06 ×
106 km2, Zou et al., 2017). The two
results showed substantial consistency, with a kappa coefficient of 0.63
(Table 3). However, there were still certain spatial differences (Figure
9). These differences mainly occurred at the southern margin of the
continuous permafrost and the islands of permafrost in the south eastern
QTP.
For the results of MAGT and ALT, a similar study showed relatively large
deviations at the hemispheric scale (the RMSEs of MAGT and ALT were
1.6°C and 0.89 m, respectively; Aalto et al., 2018). In their study, an
interesting discovery was mentioned, for both MAGT and ALT: after
considering the area north of 60°N, the uncertainty was greatly reduced.
This is primarily due to the fact that the permafrost on the QTP is
quite different from that of the pan-Arctic region. The QTP is the
dominant by the high-altitude permafrost, while the pan-Arctic is mainly
the high-latitude permafrost. Compared with the pan-Arctic region, the
active layer on the QTP is thicker, the ground temperature is higher,
and the spatial heterogeneity is greater (Nicolsky et al., 2017; Cao et
al., 2017; Qin et al., 2017). Therefore, combining the QTP permafrost
and the pan-Arctic permafrost hemispherically will inevitably reduce the
accuracy of the results.
We further compared the simulated results of MAGT and ALT with previous
studies on the QTP. Table 4 summarizes the error statistics among
different types of permafrost models (i.e., equilibrium model, transient
model and statistical model). We can find that for the R-value, our
method combined of the statistical and ML has the similar accuracy with
the transient model. Although the RMSE of ALT in our study is the
largest among all models, the differences are not significant. Moreover,
the RMSE of MAGT in our study shows relatively smaller error. Meanwhile,
from the overall spatial distribution of the ALT, although there are
some differences in the spatial details, the distribution pattern of our
result is comparable with the presented recently (Zhao and Wu, 2019;
Wang et al., 2020b). In generally, our model can obtain a relatively
higher simulation accuracy.
We qualitatively analyzed the main reasons for these differences as
follows. Firstly, there are differences in accuracy among different
types of models, such as the equilibrium models and mechanistic
transient models. Secondly, there is a slight gap between the research
period and the data used for verification. Permafrost is often viewed as
a product of long-term climate change, which is slowly changing (Zhang
et al., 2007); this may also lead to differences between the results.
Finally, the 0.1° resolution of our model can’t capture all of regional
information on climate change, which may limit the model’s ability to
capture detailed changes in the permafrost to some extent, especially in
the boundary of the permafrost region
(Etzelmüller,
2013; Guo and Wang, 2016). Therefore, the ability to capture the
permafrost edge information should be further improvement. Overall, by
comparing with previous studies on the QTP, that our method is
relatively simple and effective, and thus could be a useful tool to
evaluate the permafrost conditions with a high accuracy on the QTP.