3.1. Reliability assessment of MAGT and ALT
The simulation results were compared with the in situ observation
data using cross-validation. A comparison of the five results (Figure 2)
reveals that there was no significant bias between the simulated values
and the available borehole data on the QTP, but the RMSE and
R2 of the ensemble method imply that it was more
reliable than the other four methods. The consistency between the
measured and simulated MAGT at most sites for the five models was less
than 1°C. Among these models, the ensemble method performed optimally,
with a simulation accuracy for 80 sites of < 1°C, which
account for 95% of the total sites. It exhibited a strong positive
correlation between the simulated and observed MAGT
(R2 = 0.73, p < 0.001).
Overall, the ensemble method (Figure 2(e)) displayed the highest
accuracy among the models in forecasting the MAGT. For this reason, the
ensemble model was selected to simulate the present MAGT and future
trends.
Similarly, the simulated ALT results were compared with the insitu observation data using the same statistical method. For ALT,
the best fitting result was RF (Figure 3(d)), which exhibited the
highest R2 and the lowest RMSE values of 0.51
and 0.69 m, respectively. Although the GLM method exhibited a smaller
bias, the difference between the two methods was not large. Overall, the
validations for the five results did not differ significantly. Based on
further comparison of Figures 2 and 3, it can be seen that the fitting
accuracy of MAGT was better than that of ALT, withR2 values of the corresponding optimal fitting
results of 0.73 and 0.51, respectively. This is due to the fact that the
spatial heterogeneity of the ALT is larger than that of the MAGT on the
QTP, and the ALT will fluctuate greatly during climate change within a
short period (Cao et al., 2017).
We calculated the error distribution for five typical regions separately
(Table 1). Overall, the distribution of RMSE and bias on the QTP was
relatively uniform, with the exception of the RMSE in the AEJIR. The
reason for this may be that there are relatively few observation sites
in the northern part of the whole investigated regions, and the
simulating accuracy has high sensitivity to single points and poor
regional representation. In addition, permafrost along the G109 Highway
is greatly affected by human activities, and there are more observation
sites in this region. Compared with the error statistics of the entire
QTP, the RMSE of MAGT in the G109IR was relatively small, while the RMSE
of ALT was relatively large. Thus, we may conclude that MAGT is
relatively less affected by human activities, while ALT is more affected
by disturbance and displays great spatial heterogeneity. In terms of
bias, the region with the largest bias was GZIR. The reason is that GZIR
located in the transition zone between permafrost and seasonally frozen
ground, and the accuracy of the results would be affected to some
extent.