Key Points:
- The
combined statistical method with machine learning is efficient to
obtain the thermal regime of permafrost on the QTP.
- The present permafrost area on the QTP is ~1.04 ×
106 km2, and the average MAGT and
ALT are -1.35 ± 0.42°C and 2.3 ± 0.60 m, respectively.
- The
future changes of permafrost are projected to be pronounced due to
climate change, but region-specific.
Abstract
The
comprehensive understanding of the occurred changes of permafrost,
including the changes of mean annual ground temperature (MAGT) and
active layer thickness (ALT), on the Qinghai-Tibet Plateau (QTP) is
critical to project permafrost changes due to climate change. Here, we
use statistical and machine learning (ML) modeling approaches to
simulate the present and future changes of MAGT and ALT in the
permafrost regions of the QTP. The results show that the combination of
statistical and ML method is reliable to simulate the MAGT and ALT, with
the root-mean-square error of 0.53°C and 0.69 m for the MAGT and ALT,
respectively.
The
results show that the present
(2000−2015)
permafrost area on the QTP is 1.04 × 106km2 (0.80−1.28 × 106km2), and the average MAGT and ALT are -1.35 ± 0.42°C
and 2.3 ± 0.60 m, respectively. According to the classification system
of permafrost stability, 37.3% of the QTP permafrost is suffering from
the risk of disappearance. In the future (2061−2080), the near-surface
permafrost area will shrink significantly under different Representative
Concentration Pathway scenarios (RCPs). It is predicted that the
permafrost area will be reduced to 42% of the present area under
RCP8.5. Overall, the future changes of MAGT and ALT are pronounced and
region-specific. As a result, the combined statistical method with ML
requires less parameters and input variables for simulation permafrost
thermal regimes and could present an efficient way to figure out the
response of permafrost to climatic changes on the QTP.
Keywords: permafrost; mean annual ground temperature;
active
layer; climate change;
Qinghai-Tibet
Plateau
1. Introduction
Frozen ground is an important component of the cryosphere, which exerts
strong influences on regional ecology, hydrology and infrastructure
engineering (Westermann et al., 2015; Wang et al., 2018a). The
Qinghai-Tibet Plateau (QTP) is
underlain by typical high-altitude permafrost region, which is
undergoing more dramatic climatic warming than its surrounding regions
(Wang et al., 2019a). A growing number of studies have reported the
present status and predicted degradation of permafrost under various
global warming scenarios (Pang et al., 2010, 2012;
Zhang and Wu, 2012a; Guo and Wang,
2017; Xu et al., 2017a; Wang et al., 2018a). The degradation of
permafrost may trigger the release of organic carbon into the atmosphere
(Cheng and Wu 2007; Wu et al., 2017a; Chang et al., 2018; Wang et al.,
2018b; Ran et al., 2018). It is also a potential threat to engineering
construction and maintenance. However, most of these studies are based
on linear statistical models and equilibrium models, and mainly focused
on identifying the extent of permafrost, while researches on the present
and future change of ground thermal regimes (including: the mean annual
ground temperature, MAGT, and the active layer thickness, ALT) are
relatively rare (Zhang et al., 2012a; Wang et al., 2019a). The changes
of MAGT and ALT could affect the ecosystem of the QTP by altering
the
ground ice evolution, hydrological processes, vegetation dynamics and
carbon cycling, etc. (Yang et al., 2010a; Wu et al., 2016; Niu et al.,
2019; Hu et al., 2020). Therefore, it is of great importance to
investigate present and future changes of the MAGT and ALT in the
permafrost region (Qin et al., 2017; Zhang et al., 2018).
Permafrost is a thermally-defined subsurface phenomenon (Westermann et
al 2015). Satellite sensors could obtain limited surface information,
and only portion of the microwave remote sensing could penetrate several
centimetres underground (Zhao et al., 2011; Michaelides et al., 2018; Qu
et al., 2019). In general, it is difficult to use remote sensing to
directly obtain information on changes in the physical state of
permafrost (Yang et al., 2019). The current research on permafrost
thermal regime is mostly focus on either in situ observing or
modeling using atmospheric circulation models (Westermann et al., 2015).
Most of the existing modeling frameworks require ground-based
measurements as model inputs, while the in situ observations of
permafrost are relatively sparse and highly non-uniform in cold regions.
The long-term and continuous in situ observation sites for
permafrost on the QTP are mostly located along the Qinghai-Tibet Highway
and Railway, and other regions are less well distributed (Hu et al.,
2015; Qin et al., 2017; Zheng et
al., 2019). The absence of observation data would greatly weakens the
accuracy of simulation results. Therefore, it is challenging to select
reliable modeling approaches with limited data to obtain the occurrence
of permafrost and its projection due to climate change.
At
present, the simulation studies on the ALT and soil thermal state of the
QTP fall into two categories, including equilibrium models and
mechanistic transient models. (Riseborough et al., 2008; Qin et al.,
2017; Aalto et al., 2018). The
most commonly used equilibrium models include Stefan formula
(Zhang and Wu 2012a; Xu et al.,
2017a), Kudryavtsev formula (Pang et al., 2009; Wang et al., 2020a), the
N factor (Nan et al., 2012), and the Temperature at the Top of the
Permafrost model (TTOP) (Zou et al., 2017). The form of the equilibrium
model is relatively simple and requires fewer driving data for input
(Riseborough et al., 2008; Pang et al., 2009).
However, this type of model tend to
show poor portability. In contrast, mechanistic transient models
consider more details of the hydrothermal exchange processes between the
atmosphere and ground. Examples of this model include the Community Land
Model (CLM; Oleson et al., 2010; Fang et al., 2016; Chen et al., 2017),
Noah (Gao et al., 2015; Chen et al., 2015),
the
Geomorphology-based Eco-hydrological Model (GBEHM; Zheng et al., 2019),
the SHAW model (Guo et al., 2011; Liu et al., 2013), and the CoupModel
(Zhang et al., 2012b; Hu et al., 2013). Nevertheless, the processes of
these models are complex and often insufficiently account for the
hydrothermal dynamics, with the understanding of the soil physical
mechanisms increase, the parameterization processes will become more
complex (Harris et al., 2009; Hu et al., 2015; Guo and Wang, 2016).
In
addition to the transient models mentioned above, in recent years, the
fine-scale tightly coupled hydro-thermal modeling of permafrost has also
made great progress (e.g., models like ATS, Jafarov et al., 2018; and
SUTRA, Walvoord et al., 2019, etc.), These models are typically based on
a multidimensional solution to address fully coupled surface/subsurface
permafrost thermal hydrology, which have played an important role to
study the permafrost of local scale and microtopography (Painter et al.,
2016).
Physics-based mechanistic models are currently the popular methods to
study the permafrost, and the simulation results can show high accuracy.
However, even with significant improvements in computer technology and
algorithm simulation (Westermann et al., 2016), the current modeling
still exists a trade-off between modeling resolution and size of the
geographical domain (Etzelmüller, 2013). Especially in the case of lack
of data and insufficient computing resources, the extensive application
of physics-based mechanistic models would be limited. Whereas, the
combined statistical method with machine learning (ML) can make up these
deficiencies. In recent years, their great power in permafrost modeling
has been confirmed (Xu et al., 2017b; Chadburn et al., 2017; Aalto et
al., 2018). The main purpose of statistical and ML model
is
to identify the relationship between a dependent variable and one or
more explanatory variables (Wheeler et al., 2013). They can easily
explain environmental conditions related to topography and land
cover, whereas these factors may
be difficult to express with physical parameters (Etzelmüller, 2013).
Due to the good coupling between air temperature (often characterized by
mean annual air temperature or cumulative temperature sums) and ground
thermal regime (Chadburn et al., 2017; Aalto et al., 2018), the
subsurface (<10−20 m)
soil thermal conditions respond well to climate change at the decadal
scale (Aalto et al., 2018). In addition, precipitation type (e.g., snow,
rain and sleet) and local environmental predictors (e.g., topography,
underlying surface condition and soil texture condition) have great
impacts on soil hydrothermal dynamics and the surface radiation budget
(Lee et al., 2013; Zhu et al.,
2019).
Therefore, in this study, we employed statistical and ML methods to
investigate the MAGT and ALT across the QTP. The objective is to verify
the applicability of the combined method on the QTP and quantitatively
assess the present and future status of QTP permafrost. Firstly, we
identified the critical factors which determining the occurrence of
permafrost. Secondly, we used the combined modeling approaches
integrated with field observation data, meteorological data and
geospatial environmental predictors to calculate the present MAGT and
ALT. Thirdly, the present results
were benchmarked against in situ measurements of ALT and
ground
temperatures. Finally, the optimal modeling framework was used to
predict future MAGT and ALT forced by different RCPs. The projection of
the MAGT and ALT can serve as a useful reference and provide important
information for the study of climate change, hydrology, ecology, and
geohazards resulted from permafrost degradation on the QTP.
2. Data and Methods