The Impact of Permafrost Change on Soil Organic Carbon Stocks in Northeast China

Abstract

Climate warming has resulted in significant changes in permafrost in Northeast China, leading to notable alterations in soil organic carbon (SOC) stocks. These changes are crucial for both the global carbon cycle and climate change, as well as directly impacting the sustainable development of ecosystems. In order to examine the SOC dynamics and the impact of permafrost changes on SOC, we investigate the changes of permafrost extent based on a regression model and TTOP (top temperature of permafrost) model and the relationship between land use and land cover (LULC), SOC stocks, and permafrost changes in Northeast China. The results showing a shrinking permafrost area from 37.43 × 10⁴ km² to 16.48 × 10⁴ km² during the period from the 1980s to the 2010s in Northeast China, and the SOC stock decreased by 24.18 Tg C from the 1980s to the 1990s and then rapidly increased by 102.84 Tg C in the 2000s. Permafrost degradation speeds up the succession of LULC, impacting about 90% of the SOC in permafrost regions. The relationship between permafrost changes and SOC in Northeast China shows that permafrost degradation significantly reduces SOC stocks in the short term but increases SOC stocks in the long term, and that LULC play a crucial role in regulating this relationship. The goals of this study are to acquire an understanding of permafrost status and deepening insights into the dynamics of SOC. Simultaneously, the study aims to furnish valuable scientific references for shaping policies on sustainable land use and management in the future, all the while advancing the cause of ecological equilibrium and sustainable development in Northeast China and other areas.

1. Introduction

Soil organic carbon (SOC) is a significant contributor to greenhouse gas emissions and has a considerable impact on the global carbon cycle [1] and represents the largest carbon (C) pool within terrestrial ecosystems [2]. Globally, there is an estimated SOC pool ranging from approximately 700 Pg to 2946 Pg [3]. Against the backdrop of global warming, the relationship between SOC, climate, and the environment has become a focal point of research [4]. Climate and land cover drive SOC [5]. Temperature, precipitation, and humidity, among other factors, impact SOC by affecting plant litter, soil respiration, and microbial decomposition processes [6]. It is worth noting that the cryosphere is a critical component of the Earth’s ecosystem, covering approximately 14% of the land surface [7]. Permafrost, as one of the components of the cryosphere, is also one of the most vulnerable carbon (C) pools [8]. It is estimated that the global permafrost contains approximately 1320 ± 200 Pg of SOC [9,10]. In the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report [11], it was stated with high confidence that permafrost temperatures have risen to record levels, leading to increased release of methane (CH₄) and carbon dioxide (CO₂) from permafrost regions. The rate of permafrost warming, as exemplified by the Qinghai-Tibet Plateau at 0.3 °C per decade, significantly exceeds the global average warming rate of 0.12 °C per decade [12]. During a warming trend, permafrost undergoes significant degradation [13,14,15], which affects the process of SOC decomposition [16,17]. Alterations in the SOC pool within permafrost can have a significant impact on atmospheric CO₂ concentrations, further accelerating the effects of climate change caused by global warming [9].

The relationship between permafrost changes and SOC is intricately linked to the spatial distribution of permafrost. Permafrost simulation models primarily include the Stefan model [18], the frost number model [19], the altitude model [20], and the top temperature of permafrost (TTOP) model [21]. The TTOP model, specifically, has garnered significant attention for its exceptional performance and applicability. It links surface climate with subsurface thermal features and establishes a correlation between compensation effect and climatic factors [22]. With the development of remote sensing technologies such as optical remote sensing, thermal infrared remote sensing, and microwave remote sensing, observation and mapping of perennial permafrost are carried out by developing statistical learning [23], constructing models [24,25], and logical discriminations [26]. The application of TTOP modeling has thus led to many research results [27,28,29,30]. Permafrost degradation is typically marked by a reduction in the permafrost’s extent, an elevation in soil temperature, and an increase in the active layer’s thickness [11,31,32]. This alters surface drainage patterns and the structure of vegetation communities, which impacts the physical properties of soil and ecosystems [33]. The degradation characteristics of permafrost are obvious, but the resulting environmental effects and impacts on soil organic carbon (SOC) unfold over a long period of time. The response mechanisms are multifaceted, creating complex and interconnected relationships. Permafrost degradation can hasten microbial decomposition of organic matter, rendering SOC more vulnerable to exposure and release, which could result in a decrease in SOC [34,35]. In contrast, increased soil temperatures and soil moisture resulting from permafrost degradation promote vegetation growth in the short run [36]. The carbon sequestration from the increased vegetation can offset or even exceed carbon losses [10,37]. Such uncertainty provides a cushion against SOC release, making the study of the impact of permafrost change on SOC dynamics particularly crucial.

In Northeast China, permafrost has higher temperatures compared to the permafrost in high-altitude and Arctic regions, with a thinner permafrost thickness, making it more sensitive to climate warming [38,39]. According to a report [40], the mean annual air temperature in Northeast area has risen by 0.31 °C per decade from 1961 to 2017. Climate warming has resulted in a substantial degradation of permafrost, with continuous permafrost being transformed into discontinuous permafrost and the disappearance of many isolated permafrost patches and permafrost islands [14]. Many researchers have conducted extensive research on the extent of permafrost degradation in Northeast China, utilizing multiple sources of data such as meteorological station data, measurement data from boreholes, and remote sensing data (Landsat and MODIS inverted surface temperature data, interferometric synthetic aperture radar data, etc.) [41,42,43]. However, comparing the latest results of permafrost distribution in Northeast China during the same period, it is found that the area of permafrost varies greatly, reaching 12.81 × 10⁴ km², which indicates that further research on the distribution of permafrost in Northeast China is needed. Furthermore, since 2003, Chinese meteorological stations have switched from manual to automated measurements, resulting in significant variations in recorded ground surface temperature (GST) over different periods. This has created a lack of uniformity in GST data after this transition. Undoubtedly, permafrost is mainly concentrated in the northern part of Northeast China, where there is abundant vegetation cover, and an amount of SOC is stored in permafrost [44]. Changes in permafrost could potentially affect SOC density, stock, and succession of land type, thus impacting ecosystem evolution [45]. Land use and land cover (LULC) have a direct influence on the density and stock of SOC. Their capacity to release or sequester SOC differs significantly [46]. However, the precise distribution of permafrost in Northeast China and its impact on SOC remains unexplored, as well as the impact of LULC types on SOC in permafrost degradation zones.

2. Materials and Methods

2.1. Study Area

Northeast China (Figure 1) is located in the southeastern region of the Eurasian. The permafrost in this region represents a transitional type between high-mountain permafrost and high-latitude permafrost, making it a unique form of permafrost known as the ‘Xing’an-Baikal’ permafrost. The permafrost in Northeast China is influenced by both environmental and climatic factors. The Northeast China region covers an area of approximately 14.2 × 10⁵ km². The area is situated in both the temperate and cold temperate zones, and features a continental monsoon climate.

2.2. Data Sources

2.2.1. Meteorological Data

In this study, daily meteorological data including air temperature, ground surface temperature (GST), precipitation, relative humidity, air pressure, and sunshine duration were collected from 263 ground meteorological stations for the period from 1980 to 2020. The data were sourced from the National Meteorological Information Center (https://data.cma.cn, accessed on 20 May 2023). Additionally, daily snow cover data with a spatial resolution of 25 km from 1979 to 2020 were obtained from the China Snow Depth Long-term Time Series dataset available at the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn, accessed on 20 May 2023).

During the period from 2003 to 2005, there was a transition in the monitoring methods of Chinese meteorological stations from manual to automatic measurements. This transition caused several systematic errors between snow-covered and non-snow-covered ground, due to the difference in GST between manual measurements (taken at the surface level of snow cover) and automatic measurements (which measure temperature at a depth of 5–10 cm in the ground) [47].

2.2.2. DEM

The Digital Elevation Model (DEM) data with a spatial resolution of 1 km were extracted from United States Geological Survey (https://earthexplorer.usgs.gov, accessed on 20 May 2023). These data were introduced as covariates when performing spatial interpolation using the Australian National University Spline (ANUSPLIN) software (ANUSPLIN 4.2, Canberra, Australia).

2.2.3. Land Use and Land Cover

Land use and land cover data with a spatial resolution of 1 km from 1980, 1990, 2000, 2010, and 2020 were obtained from the Resource and Environment Science and Data Center (https://www.resdc.cn, accessed on 20 May 2023).

2.2.4. Soil Organic Carbon (SOC)

The global 1 km surface (0–30 cm) SOC pool product for the years 1981 to 2019 was obtained from the National Earth System Science Data Center (http://www.geodata.cn, accessed on 20 May 2023). These data simulate the spatial and temporal distribution of global SOC pools from 1981 to 2019 based on global soil sample point SOC density data, combining static and dynamic covariates, and utilizing integrated machine learning and RothC process models. The production of SOC products at 1 km resolution was realized through the Random Forest Spatial and Temporal Substitution model, and the validation results showed an accuracy of R² = 0.422, RMSE = 27.00, and MRE = 30.9%.

2.3. Data Processing Methods

2.3.1. TTOP Model

The TTOP (top temperature of permafrost) model as described below, developed by Smith and Riseborough in 1996, is an equilibrium equation used to describe the relationship between permafrost and climate:

$$\mathrm{T}\mathrm{T}\mathrm{O}\mathrm{P}=\frac{r_k·\mathrm{D}\mathrm{D}\mathrm{T}-\mathrm{D}\mathrm{D}\mathrm{F}}{\mathrm{P}}$$

(1)

$$\mathrm{D}\mathrm{D}\mathrm{T}={\sum }_{i=1}^{N_T}Ti{, T}_i<0 °\mathrm{C}$$

(2)

$$\mathrm{D}\mathrm{D}\mathrm{F}={\sum }_{i=1}^{N_F}{\left|T_i\right|}_{i, }{T}_i<0 °\mathrm{C}$$

(3)

$$r_k=\frac{{\lambda }_t}{{\lambda }_f}$$

(4)

where DDT is ground surface thawing index (°C·d); DDF is ground surface freezing index (°C·d); T_i is temperature on the ith day (°C); N_F and N_T are the number of days in the year with surface temperature below and above 0 °C, respectively (d); λ_t is the thawing soil thermal conductivity coefficient (W/m·°C); λ_f is the freezing soil thermal conductivity coefficient (W/m·°C); P is total number of days in a year (d); and r_k represents assigned values based on LULC types, as shown in

Table 1. r_k (the ratio of thermal conductivity of soil under thawed to frozen states) value in different LULC types.

LULC Types	rk Value	LCCS Classification System Number
Bare areas	0.95	140, 150, 152, 153, 200, 201, 202
Grasslands and croplands	0.75	10, 11, 12, 20, 130
Shrubs	0.80	120, 121, 122
Deciduous forest	0.95	50, 60, 61, 62, 70, 71, 72, 80, 81, 82
Wetlands	0.55	180
Urban land	0.70	190

.

The permafrost distribution type is determined by the following equation:

$$\left\{\begin{array}{c}\mathrm{TTOP}\le 0, \mathrm{Permafrost}\\ \mathrm{TTOP}>0, \mathrm{Seasonally} \mathrm{frozen} \mathrm{ground}\end{array}\right.$$

(5)

2.3.2. Regression Model [48,49]

The ground freeze-thaw index (DDT/DDF) of the TTOP model is calculated based on GST. Currently, there are two methods for obtaining GST data: remote-sensing-based retrieval and meteorological station measurements. Among them, thermal infrared remote sensing collects and records the thermal infrared information of ground objects by means of on-board or airborne sensors and uses such thermal infrared information to identify the ground objects and invert the ground surface temperature, while the ground meteorological stations obtain the ground surface temperature by means of a temperature meter or temperature sensor observation. However, in Northeast China, which has dense vegetation cover and thick winter snow cover, the retrieval results often represent the temperature of the vegetation canopy or snow surface [50]. Therefore, in this study, GST data from meteorological stations were used as input parameters of the TTOP model. Due to the systematic error in GST measurements before 2003, the GST data before 2003 were used as the basis for training and validating historical data to obtain the feature coefficients in the model. Subsequently, the model was used to output daily GST data from 2003 onwards.

Regression model:

$$y=b_0+b_1{x}_1+b_2{x}_2+\cdots +b_n{x}_n$$

(6)

Here, y represents the target variable (GST), b₀ is the intercept, and b₁ to b_n are the coefficients of the features, while x₁ to x_n represent the feature values.

The regression model is a simple yet effective method, and it is well-suited for establishing relationships between meteorological factors. Utilizing a large amount of data enhances the model’s fitting capability, improves its robustness, and reduces errors. This model was built and calculated using the Sklearn (Scikit-learn) library in Python 3.10. The features used as input include longitude, latitude, elevation, air temperature, precipitation, relative humidity, air pressure, sunshine duration, and snow depth, with GST as the target variable.

2.3.3. Local Thin-Plate Smoothing Spline Interpolation Method

The results of the TTOP were interpolated using the ANUSPLIN software. The model calculations were interpolated utilizing longitude and latitude as independent variables, altitude as a covariate, and TTOP value as the dependent variable. The model expression is as follows:

$$Z_i=f\left(x_i\right)+b^T{y}_i+e_i\left(i=1, 2, 3\cdots , N\right)$$

(7)

where Z_i represents the dependent variable for spatial point i; x_i represents the d-dimensional spline independent variables; f is the unknown smoothing function related to x_i; y_i is the p-dimensional independent covariate; b is the p-dimensional coefficients for y_i; and e_i is the random error associated with the independent variables; T is a symbol for transpose.

The function f and coefficients b are estimated using the least squares method:

$$min:\sum _{i=1}^n\left[\frac{Z_i-f\left(x_i\right)-b^T{y}_i}{w_i}\right]+\rho J_m\left(f\right)$$

(8)

where J_m(f) represents the mth-order partial derivative of the function f and ρ is a positive smoothing parameter; w_i is the known local relative coefficient of variation used as a weight.

3. Results

3.1. Regression Model Adjusted GST

We selected seven representative national meteorological stations in the northern part of Northeast China (Mo’he, Tulihe, Jiagedaqi, Manzhouli, Hailar, Sunwu, and Boketu) to represent the permafrost regions (

Table 2. Basic information on representative national meteorological stations of the permafrost region.

Station	Longitude (°E)	Latitude (°N)	Elevation (m)
Mo’he	122.52	52.97	439.7
Tulihe	121.68	50.48	733.7
Jiagedaqi	124.12	50.40	371.7
Manzhouli	117.32	49.58	661.8
Hailar	119.70	49.25	649.6
Sunwu	127.35	49.43	234.5
Boketu	121.92	48.77	739.7

), and compared the mean annual ground surface temperatures and mean annual air temperatures in China, Northeast China, and permafrost regions. As illustrated in Figure 2a, the mean annual air temperature warming rates from 1980 to 2020 for China, Northeast China, and permafrost regions stand at 0.36 °C/10a, 0.33 °C/10a and 0.37 °C/10a, respectively. During the period from 1980 to 2002, Northeast China and permafrost regions exhibited mean annual ground surface temperature warming rates of 0.61 °C/10a and 0.77 °C/10a, respectively. Notably, the mean annual ground surface temperature warming rates for Northeast China and permafrost regions between 2003 and 2020 are recorded at 1.1 °C/10a and 1.48 °C/10a, respectively.

After adjusting the GST data, as shown in Figure 2b, the mean annual ground surface temperature warming rates for Northeast China and permafrost regions from 1980 to 2020 are 0.53 °C/10a and 0.63 °C/10a, respectively.

3.2. TTOP and Spatial Distribution of Permafrost

Using the ANUSPLIN software for spatial interpolation of TTOP, Figure 3a shows the TTOP results obtained for Northeast China. Upon analysis of the spatial distribution of TTOP and classification of permafrost types, the following observations were made: The highest temperature in Northeast China rose from 9.306 °C to 10.164 °C, indicating a warming trend of 0.858 °C or an increase of 9.22%. The lowest temperature increased from −6.972 °C to −3.886 °C, showing a warming of 3.086 °C or an increase of 44.26%. Seasonally frozen ground areas were defined as regions with TTOP ≥ 0 °C, while permafrost areas were defined as regions with TTOP < 0 °C, Figure 3b. The permafrost areas in the 1980s, 1990s, 2000s, and 2010s were about 37.43 × 10⁴ km², 24.87 × 10⁴ km², 18.93 × 10⁴ km², and 16.48 × 10⁴ km², respectively. The largest stage of permafrost degradation happened between the 1980s and the 1990s, resulting in a decrease in the area of 12.57 × 10⁴ km². Over the span of four decades, from the 1980s to the 2010s, the extent of permafrost decreased by about 20.95 × 10⁴ km², accounting for a 55.98% decline. In contrast, the proportion of seasonally frozen ground areas increased considerably as the permafrost area percentage fell from 25.90% to 11.41%. Presently, the Da Xing’anling Mountains host a significant proportion of the remaining permafrost, raising alarm about its preservation.

3.3. Changes in Soil Organic Carbon

As shown in Figure 4a, the regions in Northeast China that have the highest SOC density and SOC stocks are concentrated in the major forestland and grassland areas of the Da and Xiao Xing’anling Mountains, as well as the Songnen and Sanjiang Plains.

From the SOC data presented in Figure 4b, it can be observed that the overall trend of SOC density and SOC stocks in Northeast China experienced a rapid decrease followed by an increase. SOC density and stock increased at rates of 0.036 kg C/m² a and 5 Tg C/a, respectively. SOC density decreased from 7.22 kg C/m² in the 1980s to 7.20 kg C/m² in the 1990s, before rising to 7.28 kg C/m² in the 2000s and 7.32 kg C/m² in the 2010s. The SOC stock started at 10.15 Pg C in the 1980s, experienced a rapid decline of 24.18 Tg C to 10.13 Pg C in the 1990s, and then substantially rose to 10.23 Pg C in the 2000s, finally reaching 10.28 Pg C in the 2010s. The trend in SOC stocks corresponds to that of SOC density. According to

Table 4. Proportion of SOC stocks (SOCS) corresponding to permafrost and different LULC types.

Year	LULC												Permafrost
	Arable Land		Forestland		Grassland		Waters		Construction Land		Unused Land
	SOCS (Pg C)	Proportion (%)	SOCS (Pg C)	Proportion (%)	SOCS (Pg C)	Proportion (%)	SOCS (Pg C)	Proportion (%)	SOCS (Pg C)	Proportion (%)	SOCS (Pg C)	Proportion (%)	SOCS (Pg C)	Proportion (%)
1980s	2.32	22.86	4.14	40.86	2.75	27.12	0.13	1.30	0.17	1.65	0.63	6.22	3.18	31.34
1990s	2.59	25.56	4.01	39.67	2.64	26.06	0.12	1.19	0.17	1.68	0.59	5.85	2.15	21.26
2000s	2.65	25.89	4.05	39.64	2.64	25.82	0.12	1.17	0.18	1.74	0.59	5.74	1.66	16.23
2010s	2.74	26.72	3.96	38.59	2.52	24.59	0.11	1.09	0.23	2.21	0.70	6.81	1.46	14.17

, the corresponding total SOC stocks and proportions are displayed for the four periods based on various LULC types and the extent of permafrost.

The area of arable land has steadily increased between 1980 and 2020, showing a total growth of 5.8 × 10⁴ km². However, during the same period, the area covered by forestland and grasslands has consistently decreased, with a cumulative reduction of 7.0 × 10⁴ km². Some of these lands have been converted into construction land, while others have become unused land.

Table 5. Dynamics of LULC change in areas affected by permafrost degradation across various time periods (%).

Year	LULC
	Arable Land	Forestland	Grassland	Waters	Construction Land	Unused Land
1980–2000	45.23	−7.91	−1.76	19.82	17.33	−3.26
1990–2010	50.68	−1.25	−0.24	−8.55	27.45	−0.51
2000–2020	10.84	−1.86	−9.33	−8.47	72.97	184.07

displays the dynamics of LULC change in areas affected by permafrost degradation across various time periods. During the period from the 1980s to the 1990s, the increase in SOC stocks in arable land was the most significant in Northeast China, amounting to 267.94 Tg C. Conversely, forestland experienced the greatest loss in SOC stocks, totaling 129.71 Tg C, followed by grasslands. The region where permafrost is located witnessed a decrease in SOC stocks by 1026.97 Tg C. In the 1990s to 2000s, arable land contributed the most to the increase in SOC stocks, with an addition of 60.38 Tg C, followed by forestland, which had transitioned from losses to gains at this point, with an increase of 37.32 Tg C. Unused land experienced the greatest reduction in SOC stocks, amounting to 4.77 Tg C. In the region with permafrost, SOC stocks decreased by 494.52 Tg C. Between the 2000s and 2010s, unused land reversed its trend, registering the highest increase in soil SOC stocks, with an addition of 112.14 Tg C. Arable land followed closely behind. Both forestland and grasslands switched from gains to losses, with grasslands experiencing the most significant reduction in SOC stocks at 115.01 Tg C. The region with permafrost witnessed a decrease in SOC stocks of 202.45 Tg C.

3.4. Relationship between LULC and SOC Changes in Permafrost Degradation Areas

Permafrost degradation can cause alterations in hydrogeological conditions, which subsequently impact the succession of LULC types.

During the period from the 1980s to the 2000s, the largest increase in arable land area occurred, totaling 5201 square kilometers and resulting in a dynamic change rate of 5.23%. The highest reduction in land area during this time period occurred in forestland areas, decreasing by 4436 square kilometers and resulting in a dynamic change rate of −7.91%. Additionally, grassland also experienced a decrease of 834 square kilometers, resulting in a dynamic change rate of −1.76%. Permafrost degradation was identified as the most significant contributing factor to the decrease in forestland and grassland areas, ultimately making the land more suitable for cultivation. The changes in SOC are significantly impacted by the combined effect of permafrost degradation and human activity.

SOC changes can be divided into two parts: SOC increase (density ≥ 0) and SOC decrease (density < 0). Comparing the area occupied by various LULC types in the permafrost degradation zone with their corresponding contributions to SOC alterations, the following trends can be observed. The outcomes are displayed in Figure 5:

The data suggest that ongoing permafrost degradation leads to a significant increase in the proportion of forestland and grassland within the affected area, accounting for over 75% during each succession period. Additionally, their respective contributions to SOC increase or decrease show the most notable changes. Forestland dominates the increase in SOC, with a contribution rate that is consistent with its proportion of the total area. The contribution rates for forestland are 50%, 75%, and 72%, for the periods of 1980s–1990s, 1990s–2000s, and 2000s–2010s, respectively. A decrease in SOC mainly occurs in grasslands, with contribution rates of 50%, 83%, and 60% for the same periods. This relationship remains unchanged, even when the area of forestland exceeds that of grassland. Unlike the situation of SOC increase, the ratio of forestland area to grassland area displays a fluctuation in strength when associated with SOC decrease. This trend may be related to the different carbon sequestration capacities and sensitivities of the two land types.

3.5. Relationship between Permafrost Change and SOC Change

When comparing the permafrost change status with the corresponding regional SOC stocks, the following observations can be made (

Table 6. Types of permafrost changes and corresponding types and proportions of SOC alterations.

Year	Subject	Areas of Permafrost Degradation	Northeast China	Proportion (%)	Subject	Areas of Permafrost Degradation	Northeast China	Proportion (%)
1980s–1990s	Areas of increased SOC (km2)	3.77 × 104	61.66 × 104	6.11	Areas of reduced SOC (km2)	8.75 × 104	78.60 × 104	11.13
	Total increase in SOC stocks (Tg C)	2.05	44.33	4.63	Total reduction in SOC stocks (Tg C)	8.14	68.52	11.88
	Total area (km2)	12.52 × 104	140.26 × 104	8.92	The total amount of change in SOC stocks (Tg C)	−6.09	−24.18	25.17
1990s–2000s	Areas of increased SOC (km2)	3.90 × 104	106.22 × 104	3.66	Areas of reduced SOC (km2)	1.99 × 104	34.04 × 104	5.84
	Total increase in SOC stocks (Tg C)	3.91	121.10	3.23	Total reduction in SOC stocks (Tg C)	1.93	18.26	10.57
	Total area (km2)	5.88 × 104	140.26 × 104	4.19	The total amount of change in SOC stocks (Tg C)	1.98	102.84	1.93
2000s–2010s	Areas of increased SOC (km2)	2.50 × 104	97.36 × 104	2.57	Areas of reduced SOC (km2)	1.17 × 104	42.90 × 104	2.72
	Total increase in SOC stocks (Tg C)	1.42	75.81	1.88	Total reduction in SOC stocks (Tg C)	0.66	21.42	3.07
	Total area (km2)	3.67 × 104	140.26 × 104	2.62	The total amount of change in SOC stocks (Tg C)	0.77	54.39	1.41
	Subject	Permafrost Growth Areas	Northeast China	Proportion (%)	Subject	Permafrost Growth Areas	Northeast China	Proportion (%)
2000s–2010s	Areas of increased SOC (km2)	0.55 × 104	97.36 × 104	0.56	Areas of reduced SOC (km2)	0.71 × 104	42.90 × 104	1.66
	Total increase in SOC stocks (Tg C)	0.24	75.81	0.32	Total reduction in SOC stocks (Tg C)	0.47	21.42	2.20
	Total area (km2)	1.26 × 104	140.26 × 104	0.90	The total amount of change in SOC stocks (Tg C)	−0.23	54.39	−0.42

).

The area of permafrost degradation between the 1980s and the 1990s was 12.52 × 10⁴ km². Of this area, 3.77 × 10⁴ km² was an area of SOC increase, with a total increase of SOC stocks of 2.05 Tg C, and 8.75 × 10⁴ km² was an area of SOC decrease, with a total decrease of SOC stocks of 8.14 Tg C. Surprisingly, although the area of permafrost degradation in this period accounted for only 8.92% of the area of Northeast China, the total amount of the change in SOC stocks was 25.17% of the total change in SOC stocks in Northeast China, which is inconsistent with the correspondence between area and SOC and positively illustrates that the effect of permafrost degradation on SOC is significant and is especially pronounced when permafrost degradation are evident.

In the four periods of the 1980s, 1990s, 2000s, and 2010s, there was a region of permafrost between the Da Xing’anling Mountains, Genhe City, and Arctic Village with an area of 15.12 × 10⁴ km². The SOC stocks in this region were 1.359 Pg C, 1.346 Pg C, 1.347 Pg C, and 1.349 Pg C for the respective periods. In the 1980s and 1990s, there was a significant increase in temperature, a rapid decrease in permafrost area, and a rapid decrease in SOC stocks. During this period, the longitudinal SOC stocks in this region also decreased rapidly. From the 1990s to the 2010s, there was a gradual increase, indicating that permafrost degradation not only affects SOC through area reduction, but also influences SOC changes in the active layer of permafrost.

In the permafrost growth area, the growth area is 12,610 km², the increase in SOC stocks is 0.24 Tg, the decrease is 0.47 Tg, and the total amount is a decrease of 0.23 Tg. Among them, the SOC growth area is 5,486 km², which accounts for 0.56% of SOC growth, and the SOC increment accounts for 0.32%; the SOC decrease area is 7,124 km², which accounts for 1.66%, and the SOC decrease accounts for 2.20%. The total change in SOC stocks had an impact of −0.42% on SOC stocks in Northeast China.

3.6. Model Evaluation

The regression model is a machine learning technique that exhibits efficient and stable advantages when dealing with large datasets. The trend in MAGST from 1980 to 2002 is generally consistent with the 2003–2020 MAGST for the Northeast and permafrost regions, which has been adjusted by regression modeling. The model assesses the error outcomes (Figure 6) of a day-to-day surface air temperature simulation (N = 4748) from 1990 to 2002 with an RMSE of 1.289 °C, an MAE of 1.028 °C, and an R² of 0.99. Thus, the model is deemed to have an accurate fit.

In this paper, 50 borehole data (MAGT) were collected from permafrost regions of Northeast China between 2000 and 2020 [51,52,53,54,55,56]. The data were divided into 25 groups each from the 2000s and 2010s and compared with the TTOP of the same period in the present study. The results are presented in

Table 7. The average annual ground temperature (MAGT) measured by boreholes and the top temperature of permafrost (TTOP)simulated in this study. (When the borehole-measured MAGT is greater than 0 °C, that temperature represents the average temperature of the borehole and there is no permafrost at that borehole location).

Site	Longitude (°E)	Latitude (°N)	Borehole Measured MAGT (°C)	TTOP (°C)	TTOP (°C) by Obu, J. et al. (2019) [7]
CW1	123.98	53.33	−1.63	−2.11	0.74
CW3	124.58	52.99	−0.1	−1.87	1.86
CW4	124.51	52.74	1.9	−1.8	1.32
CW5	124.58	52.54	−1.8	−1.75	1.24
CW6	124.66	52.43	−0.8	−1.59	1.75
CW7	124.66	52.06	−0.7	−1.19	1.58
CW8	124.53	51.81	−0.5	−1.1	0.21
CW9	124.39	51.70	−1.3	−1.28	0.21
CW10	124.27	51.47	−2.75	−1.26	0.74
CW12	124.21	51.21	−1.3	−0.91	0.72
CW13	124.31	50.70	1.9	0.12	0.61
CW14	124.21	50.47	3	0.17	0.85
CW15	124.65	49.77	2.3	1.12	3.03
XL1	124.39	51.69	−1.13	−1.13	0.83
XL2	124.39	51.69	−1.71	−1.13	0.83
XL3	124.39	51.69	−1.43	−1.13	0.83
GH1	121.51	50.94	−0.24	−2.43	−0.03
GH2	121.51	50.94	−0.8	−2.43	−0.03
GH3	121.50	50.93	−3.87	−2.33	−0.20
GH4	121.50	50.93	−2.84	−2.33	−0.20
GH5	121.53	50.80	−0.59	−1.95	−0.72
GH6	121.50	50.80	−1.92	−2.19	0.01
GH9	121.51	50.94	−3.3	−2.43	−0.03
YT1	121.55	50.63	−2.18	−1.78	−0.22
YT2	121.55	50.63	−1.92	−1.78	−0.22
DW01	124.19	53.21	0.2	−0.57	1.38
DW02	124.20	53.19	−1.2	−0.56	1.95
DW04	124.42	53.10	0.5	−0.43	1.61
DW05	124.57	53.00	−1.4	−0.37	1.49
DW06	124.49	52.69	−0.5	−0.77	0.51
DW07	124.69	52.23	−0.6	0.51	1.40
DW08	124.66	52.03	−1.4	−0.64	1.53
DW09	124.34	51.59	−1.7	−0.58	0.40
DW10	124.33	50.90	0.9	0.8	0.16
DW11	124.34	50.74	1	1.14	0.19
DW12	124.29	50.60	1.9	1.28	1.15
MH1	122.02	53.03	−2.9	−2.35	0.28
MH2	122.02	53.02	−1.8	−2.36	0.83
NE2	125.14	51.13	−1.94	0.72	0.97
NE3	125.14	51.13	0.92	0.72	0.97
NE4	125.14	51.12	2.16	0.71	1.84
NE5	125.15	51.15	−1.32	0.69	1.91
YTLH1	121.55	50.63	−2.18	−1.71	−0.22
YTLH2	121.55	50.63	−1.93	−1.71	−0.22
MG	122.28	52.28	−2.31	−2.74	−0.70
ALS	121.90	51.89	−3.63	−2.61	−0.15
P1	125.14	51.13	−0.52	0.72	0.97
P2	125.14	51.13	−1.19	0.72	0.97
P3	125.14	51.13	0.17	0.72	0.97
P4	125.14	51.12	1.65	0.71	1.84
Average Value			−0.81	−0.89	0.75

:

The average temperature for the 50 boreholes’ data during both periods was −0.81 °C. In this study, the average TTOP was −0.89 °C, resulting in a difference of 0.09 °C. The root mean square error (RMSE) was 1.23 °C and the mean absolute error (MAE) was 0.94 °C. This indicates that the map of the distribution of permafrost in Northeast China developed through this study has a high degree of confidence.

As shown in Figure 7, the correlation (R) between the model results obtained in this study and the borehole data was 0.65 (p < 0.001), which is higher than that reported by Obu et al. [7] (R = 0.50, N = 50). Therefore, we believe that after adjusting the GST for the years 2003–2020 through the regression model, using the TTOP model to simulate the distribution of permafrost in Northeast China region becomes more reliable.

4. Discussion

4.1. Distribution and Degradation of Permafrost

Under the influence of global climate change, the degradation of permafrost in the northern hemisphere is an undeniable fact [57,58,59,60]. Different researchers have used different methods and data to assess the distribution of permafrost. In this study, the primary data source for permafrost distribution comes from ground meteorological station data. The data from 1980 to 2002 are consistent with the data used by other researchers. Therefore, in delineating permafrost regions, the areas for the 1980s (37.43 × 10⁴ km²) and 1990s (24.87 × 10⁴ km²) are basically consistent with the results of other studies [61,62]. Concerning the daily GST data, which were adjusted using regression models during the period of 2003–2020, it is crucial to note the substantial dissimilarities in GST measurements before and after a specific point. The discrepancy was observed in almost half of the data between 1980 and 2020, having a significant influence on the analysis of permafrost changes. Consequently, data adjustment is requisite to alleviate the irregularity in the information [63]. The study’s findings align with Ran et al.’s permafrost distribution map for the Northern Hemisphere during 2000–2016(Figure 8), available at https://data.tpdc.ac.cn (accessed on 22 August 2023), with the exception of a slightly lower total permafrost area. The discrepancy could be attributed to the use of different time intervals for the simulation [64].

4.2. The Relationship between SOC and TTOP

The classification of permafrost is based on mean annual ground temperature at the top temperature of permafrost (TTOP), so changes in permafrost are fundamentally related to changes in TTOP. A comparison between TTOP and SOC in Northeast China from 1981 to 2019 is shown in Figure 9a:

Both TTOP and SOC stocks show an overall increasing trend, but TTOP shows a fluctuating pattern with significant variations every few years, which is influenced by the climatic conditions at that time. In contrast, SOC stocks showed a rapid decline from 1981 to 1985, followed by small fluctuations. Starting in 1998, SOC stocks began to increase rapidly and have maintained a stable upward trend since then. In the early period, the increase in TTOP led to the degradation and release of a significant amount of organic matter, resulting in a decrease in SOC. In the middle period, as TTOP continued to increase, it promoted vegetation growth and increased the carbon sequestration capacity of both vegetation and soil, which slowed and stabilized SOC changes. In the later period, both the carbon sequestration capacity and the carbon content of vegetation and soil began to increase, indicating that Northeast China was continuously adding carbon to SOC during this period. This not only compensated for earlier losses, but also contributed to further increases in SOC, resulting in a stable upward trend.

The spatial correlation analysis between TTOP and SOC density over about 40 years is shown in Figure 9b. The average absolute correlation coefficient between SOC density and TTOP in Northeast China over about 40 years is 0.51. In the southeastern part of the region, TTOP and SOC density mostly show a positive correlation, indicating that higher TTOP is associated with higher SOC density levels. In the northwestern part of the region, TTOP and SOC density are mostly negatively correlated, indicating that lower ground surface temperatures are associated with higher SOC density. As ground surface temperatures continue to rise, the positive correlation is expected to increase. The permafrost area of Northeast China may become an enhanced carbon sink, serving as one of the buffer zones against global warming.

4.3. SOC in Different LULC and Response to Changes in Permafrost

Permafrost degradation can impact hydrogeological conditions [65], leading to changes in land use rotation and succession. These modifications, in turn, affect the rates of input and decomposition of SOC [66]. The surface soil layer (0–30 cm) is the most biologically active part, requiring careful attention [67]. As a result, changes in LULC types under the influence of permafrost degradation can have a significant impact on SOC [68]. The change in land area corresponds directly to the change in SOC within different LULC types in areas undergoing permafrost degradation. Northeast China has the highest land areas devoted to forestland, grasslands, and arable lands, resulting in the highest SOC content. Forestland and grasslands are the predominant land types in areas with concentrated permafrost distribution. Additionally, as one travels further northward and up to higher altitudes, there is a higher proportion of forestland and grasslands. This trend aligns with the degradation of permafrost.

Vegetation has a beneficial impact on safeguarding permafrost, leading to slower rates of permafrost degradation and subsequently, a deceleration in SOC change [69]. Within the region of permafrost degradation, forestlands are the primary contributors to an increase in SOC, while grasslands are a factor in SOC reduction. This can be attributed to several factors. On one hand, the extensive coverage of forestland in the area leads to an increase in SOC. Forestland produce greater amounts of plant litter and organic matter due to their vast size. This organic matter, combined with tree growth, improves carbon sequestration in the soil, increasing the land’s capacity for carbon storage and facilitating the growth of SOC. In contrast, grasslands have a weaker ability to sequester carbon compared to forestland. In large grassland areas, the release of SOC into the atmosphere through decomposition processes tends to exceed the rate of carbon fixation, resulting in a net decrease in SOC levels in regions dominated by grasslands. On the other hand, in the part of SOC growth, the contribution of forestland and grasslands to SOC growth is positively correlated with the ratio of their respective areas, with a ratio of approximately 1. In the part of SOC reduction, the loss of SOC from grasslands is intensified, with a contribution-to-area ratio ranging from 1.22 to 2.02. Conversely, the reduction in SOC from forestland is mitigated, with a contribution-to-area ratio ranging from 0.4 to 0.8. Even when forestland areas exceed those of grasslands, grasslands remain the primary contributor to SOC reduction. When there is a positive net change in SOC (1990s–2000s and 2000s–2010s), the ratio of the percentage of SOC growth to the percentage of multi-year permafrost area is approximately 0.46–0.53. However, when there is a negative net change in SOC (1980s–1990s), the ratio of the percentage of SOC decrease to the percentage of multi-year permafrost area is about 2.82. At the time of severe degradation of permafrost, the reduction in SOC was exacerbated by the enhancing effect of grassland compared to the inhibiting effect of forestland. This suggests that the decrease in SOC was greater, despite the fact that the area was dominated by forestland. Therefore, although the total area of permafrost in Northeast China is relatively small, permafrost degradation has a significant effect on the reduction in SOC.

The permafrost degradation trend in Northeast China undergoes an initial substantial decrease, followed by a gradual decline, resembling the SOC changes. The primary difference is that SOC exhibits a slow increase in the later stages. Rising air temperatures mainly contribute to permafrost degradation [70,71] which, combined with its degradation, induces significant SOC decomposition and release. Even small amounts of permafrost degradation can cause significant changes in SOC. Permafrost degradation upsets the equilibrium between the input of SOC and its decomposition, leading to a continuous adjustment of the environment to this dynamic. While SOC will initially decrease and then increase in the long term due to permafrost degradation, the prolonged impact on interrelationships between these factors under complex ecological circumstances may pose further complications.

5. Conclusions

In this study, we utilized a regression model and TTOP model to simulate the distribution of permafrost in Northeast China from 1980 to 2020. At the same time, we analyzed the changes in SOC over a forty-year period and examined the impacts of alterations in both permafrost and LULC on SOC changes. The study results demonstrate that climate warming has caused ongoing permafrost degradation in Northeast China, and the outlook for permafrost retention is not optimistic. SOC in Northeast China experienced a sharp decline, but has now recovered and is growing over the long term. In the short term, permafrost degradation has a negative impact on SOC growth, but over the long term, it has a positive effect. In the future, there is a need to enhance monitoring methods in Northeast China’s permafrost regions to provide more accurate observations of permafrost conditions. Additionally, further research efforts should be directed towards refining models that describe the nuanced response of SOC to permafrost degradation, taking into account different carbon fractions. This holistic approach will contribute to a more nuanced comprehension of the impacts of permafrost thaw on SOC dynamics in the region.