1. Introduction
Frozen soil is a temperature-sensitive and variable geological body that is highly correlated with air temperature (Daanen et al. 2011; Liu et al. 2021; Messan et al. 2020). Climate is one of the main driving forces of environmental change. In recent years, research on climate change has shown that the global temperature has risen by 0.15°–0.20°C every 10 years since the late 1970s (Hansen et al. 2010), especially in high-elevation and high-latitude areas, where the increase is more significant. The temperature of the continuous permafrost zone in the Arctic increased by 0.39° ± 0.15°C in the decade 2007–16, and during the same period, the discontinuous frozen soil temperature increased by 0.20° ± 0.10°C (Biskaborn et al. 2019). Continuously increasing temperature will act on the existing frozen soil environment, resulting in the degradation of frozen soil characterized by a northward shift of the boundary, reduction in area, decreasing thickness, etc. (Gao et al. 2020). These changes will also cause a series of environmental problems, such as the degradation of vegetation types and a more fragile frozen soil environment (Z. Zhang et al. 2021), which will increase the vulnerability of the ecosystem and induce disasters such as frost heave and thawing that will endanger existing engineering facilities (Ni et al. 2021b). In the context of global warming, the temperature has risen significantly by 0.38°C (10 yr)−1 in China (Liu et al. 2013). The northeastern area is sensitive to climate change (Zhao et al. 2007). In recent years, the annual average temperature of Heilongjiang Province in China has shown a significant upward trend (X. W. Zhang et al. 2018) that is greater than those in other regions of the country during the same period. Frozen soil is widely scattered in Heilongjiang Province. Studying the characteristics of seasonal frozen soil in Heilongjiang Province on a regional scale holds great importance for winter engineering construction and prevention of frost damage to hydraulic structures, such as dams, sluices, channels, pipelines, and revetment works, in cold areas.
The increasing greenhouse effect is deteriorating the global permafrost environment, and changes in temperature have caused the permafrost in Eurasia to degenerate from continuous to discontinuous, discontinuous to sporadic and sporadic to isolated (Ford and Frauenfeld 2016). During the 30 years from 1981 to 2010, the annual average ground temperature of the Qinghai–Tibet Plateau increased significantly by 0.37°C (Xu et al. 2017), while the permafrost near the surface of the Qinghai–Tibet Plateau decreased significantly at a rate of 0.46 × 106 km2 (10 yr)−1 (Guo et al. 2020) and the active layer thickened at a rate of 19.5 cm (10 yr)−1 (Zhao et al. 2020). The degradation of frozen soil is responsible for the drying of the surface soil and the decrease in the bioavailability of soil nutrients (Jin et al. 2020). The active layer of frozen soil in the Tianshan Mountains increased by 45 cm from 1992 to 2009 (Liu et al. 2017), which may affect plant growth, competition, and community composition, which then influence the ecosystem (Colombo et al. 2018; Mekonnen et al. 2021). The percentage of permafrost in the northeast of China has dropped from 29% in the early twenty-first century to the current 22.5% (Gao et al. 2020). According to the current frozen soil temperature, the frozen soil environment is forecast 50 years and 100 years into the future, and the results show that frozen soil will decrease from the current 2.57 × 105 km2 to 1.84 × 105 km2 in 50 years and to 1.29 × 105 km2 in 100 years (Wei et al. 2011). Current research on the characteristics of frozen soil changes mostly focuses on permafrost, and most studies combine annual climatic characteristics, precipitation, elevation, latitude, and vegetation; however, research that combines these data with winter half-year temperature is relatively scarce. Modeling simulations have greater uncertainty than data analysis through site monitoring (Zhao et al. 2015).
Given the above factors, this study collected freezing depth data from 20 frozen soil observation sites and daily temperature data from 34 temperature observation sites in Heilongjiang Province. The aim is to analyze the temporal and spatial characteristics of seasonal frozen soil and the average temperature in the winter half-year in Heilongjiang Province during recent years and the responses of seasonal frozen soil to temperature changes in the winter half-year. This research can provide a reference for engineering construction, regional development, and agricultural planning under the conditions of climate warming in Heilongjiang Province.
2. Materials and methods
a. Overview of the study area
Heilongjiang Province is located in northeastern China, at latitude 43°26′–53°33′N, longitude 121°11′–135°05′E; this province is approximately 1120 km long from north to south and 930 km wide from east to west, with an area of 47.3 × 104 km2. It has a temperate continental climate that is severely cold and dry with strong winds in winter; summers are warm and humid with low winds and abundant rainfall; the annual precipitation can reach 500–600 mm. The annual average temperature is 2.4°C, the highest temperature can reach 35°C, and the lowest temperature is as low as −36°C. The severe winter conditions make the soil susceptible to frost heave and existing engineering structures vulnerable to frost damage. Hydraulic concrete structures in cold regions are easily damaged by freeze–thaw action, which affects the fracture performance of concrete structures (Zhu et al. 2022). Freeze–thaw cycles will not only destroy the interfacial transition zone, but also damage the cement matrix and aggregates (Luo et al. 2018). Once ice forms in highly saturated concrete capillaries, internal tensile stresses are created and material damage occurs (Gong et al. 2015). Material deterioration caused by freeze–thaw cycles is one of the main sources of damage to cement-based materials and structures in cold regions (Chen and Qiao 2015).
b. Data source and processing
The research data used in this paper include freezing depth data from 20 frozen soil observation stations and temperature data from 34 meteorological stations in Heilongjiang Province. The time scale of the freezing depth data is 1972–2016, with daily observations; the time scale of the temperature data is 1972–2020, with temperature observations for the winter half-year, that is, the daily temperature data from November of one year to April of the following year.
Temperature monitoring at all meteorological stations across China was carried out on a daily basis by trained professional technicians. The ground temperature was measured with a mercury ball thermometer. The measurement standard states that half of the thermometer sensors should be buried in the ground and the other half exposed to the air, in practice, sensors are often painted white to reduce solar heating. When the ground was covered with snow, the thermometer was moved to the snow surface. Ground temperature was measured 4 times per day (0200, 0800, 1400, and 2000 Beijing time) and averaged as a daily average (Wang et al. 2015). Thermometers at research stations are accurate to 0.1°C and should be calibrated at least annually (Ma and Bin 2007). During the study period, no thermometers were replaced. The vast majority of meteorological stations remained geographically stable during the study period (Ma et al. 2009).
The observation of frozen soil was in accordance with the National Meteorological Observation Standard. Once the surface temperature reaches or falls below 0°C, the observation of permafrost begins (Wang et al. 2020). From the position at which the water column in the soil is frozen, the corresponding scale numbers at the upper and lower ends of the ice are read from the scale line of the pipe wall; these values are recorded as the upper and lower limit depths, respectively, of the frozen layer. In case of two or more frozen layers, the upper and lower limit depths of each frozen layer are measured separately and recorded in the order of the layers from bottom to top in the frozen soil depth column of the observation record.
The following principles are applied for site selection. 1) The sequence is moderately long, and the dates are relatively complete; 2) The geographic location and spatial uniformity of the observation stations are considered. The data are obtained from the China Meteorological Administration (http://data.cma.cn/). The specific site distribution is shown in Fig. 1.
Study area and station distribution map.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0195.1
Long-term monitoring at most stations inevitably results in missing data. In the process of data collection, we screened the sites and retained sites with less than 1% missing data. According to the method used by (Liu et al. 2004), we used the data of two adjacent days to interpolate for a single day of missing data and we used simple linear regression to interpolate for continuous missing data; the method we used to impute missing data did not affect the analysis results. For all variables examined, missing frozen soil data accounted for less than 0.54% of the total records from 1972 to 2016, and missing air temperature data accounted for less than 0.42% of the total records from 1972 to 2020. We compared the dataset containing interpolated data with the original dataset (with missing values) and found no statistically significant difference in mean and trend between datasets (p = 0.05).
c. Methods
The frozen soil data studied in this paper include average freezing depth and maximum freezing depth. The temperature data are the averages for the winter half-year. The methods used in this research are linear regression, kriging interpolation, and the Mann–Kendall test.
1) Linear regression analysis
Linear regression is a commonly used method to study the trend of temperature and freezing depth. Linear regression is also used to analyze trends in other climate variables, including thawing index, maximum snow depth, annual precipitation (Luo et al. 2017).
The linear equation slope a is defined as the climate tendency rate, which represents the change trend of the time series. The magnitude of the climate tendency rate characterizes the speed of change; a value of a greater than 0 indicates an increase, whereas a value of a less than 0 indicates a decrease; b is the regression coefficient.
2) Kriging interpolation
The kriging interpolation method, also known as the spatial autocovariance optimal interpolation approach, is a geostatistical gridding technique (Meng et al. 2013). This paper uses the kriging method to analyze the spatial distributions of frozen soil and temperature in Heilongjiang Province.
3) Mann–Kendall test
3. Results and analysis
a. Time change in seasonal frozen soil and its response to the change in average temperature during the winter half-year
Graphs plotting the trends of the average freezing depth, the maximum freezing depth, and the average temperature in the winter half-year over time in the seasonal frozen soil area of Heilongjiang Province are shown in Fig. 2. Figure 2 illustrates the following features. 1) The time changes in the average and the maximum freezing depths of seasonal frozen soil in Heilongjiang Province were highly consistent, and both showed a gradual decrease from 1972 to 2016. Among them, the average freezing depth decreased at a rate of 4.8 cm (10 yr)−1 and passed the significance test of α = 0.01 (R = 0.71 > R0.01 = 0.38); the rate of decrease in the maximum freezing depth was 10.1 cm (10 yr)−1, which also passed the significance level test of α = 0.01 (R = 0.68 > R0.01 = 0.38). 2) The maximum value of the average freezing depth was 84 cm in 1976, and the maximum freezing depth of 224 cm also appeared in 1976. The minimum value of the average freezing depth was only 46 cm in 2011, and the minimum value of the maximum freezing depth, which was 135 cm, also occurred in 2011. 3) The average temperature in the winter half-year showed a fluctuating upward trend overall, with a rate of increase of 0.3°C (10 yr)−1, passing the α = 0.01 significance level test (R = 0.38 > R0.01 = 0.36). From 1972 to 2020, the maximum average temperature in the winter half-year appeared in 2007 with a value of −8.79°C, and the minimum occurred in 2000 with a value of −13.09°C.
Linear trends of freezing depth and temperature.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0195.1
From the above analysis, correlation diagrams of the average and maximum freezing depths with the average temperature during the winter half-year are drawn, as shown in Fig. 3. Figure 3 illustrates that the average and maximum freezing depths have good linear relationships with the average temperature during the winter half-year. According to the Pearson correlation coefficient (Schober et al. 2018), the correlations between the average and maximum freezing depths and the winter half-year average temperature are −0.53 and −0.49, respectively; both correlations are negative and pass the α = 0.01 significance level test (R = 0.53, R = 0.49 > R0.01 = 0.38). The degrees of correlation are relatively close. With the increase in the winter half-year average temperature, the average and maximum freezing depths decrease. Specifically, for every 1°C increase in the winter half-year average temperature, the average freezing depth decreases by 3.85 cm and the maximum freezing depth decreases by 7.84 cm.
Correlations between freezing depths and average temperature in the winter half-year.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0195.1
b. Mutation detection analysis of the average and maximum freezing depth sequences and the average temperature in the winter half-year
Using the Mann–Kendall test, mutation detection was performed on the average freezing depth sequence, the maximum freezing depth sequence and the winter half-year average temperature sequence, and the results are shown in Fig. 4. This figure reveals that within the confidence interval, the average freezing depth sequence changes suddenly in 1987, and the maximum freezing depth sequence changes abruptly in 1988, lagging behind the change in average freezing depth; the winter half-year average temperature shows multiple mutations from 1972 to 2020, and the mutation times are 1982, 1984, 1986, 2013, 2015, and 2017. Given this analysis, the times of abrupt changes in the average and maximum freezing depths may be delayed responses to the change in the average temperature in the winter half-year of 1986.
Mann–Kendall mutation detection graphs.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0195.1
c. Spatial changes in seasonal frozen soil and its response to the average temperature change in the winter half-year
Before the interpolation analysis of the spatial distribution of climate indicators, we used three interpolation models for analysis, including synergy kriging, the inverse-distance weighting (IDW) method and the radial basis function. The model performance metrics are shown in Table 1.
Interpolation model accuracy analysis. Note that the interpolation of the average temperature in the winter half-year is taken as an example.
Through the comparative analysis of the three interpolation methods, cross-validation is used to compare the measured and predicted values of each model to evaluate the accuracy of the model. The model prediction evaluation index R2, root-mean-square error (RMSE), and mean absolute error (MAE) were calculated to evaluate the accuracy of the model. It can be seen from the table that the three indicators are all optimal with collaborative kriging interpolation, indicating that the simulation accuracy of this approach was the best.
The spatial distributions of the average freezing depth, maximum freezing depth, winter half-year average temperature in Heilongjiang Province and their rates of change are plotted, as shown in Fig. 5. 1) As shown in Figs. 5a and 5b, both the average freezing depth and the maximum freezing depth show a beltlike distribution with latitude. As the latitude increases, the values of the average and maximum freezing depths also increase. The maximum values of the average and maximum freezing depths are located in the northernmost Xing’an Mountains, while the minimum values are located at the southernmost point. 2) The average temperature in the winter half-year shows a significant change with latitude as shown in Fig. 5c. The temperature continues to decrease with increasing latitude from the southern to northern region (Z. Q. Zhang et al. 2018). The high-value area is located in the eastern and southern parts of Heilongjiang Province, and the average temperature range is from −8.62° to −7.43°C. The area with low values of the winter half-year average temperature is still located in the Xing’an Mountains, and these values range from −20.47° to −15.57°C. From the temperature values of these two locations, it can be seen that the temperature varies greatly among different regions. 3) In Fig. 5d, the average temperature in the winter half-year shows an upward trend throughout the territory. In this province, a small part of the Xing’an Mountains and the central part have the fastest rate of increase, which is 0.47°–0.79°C (10 yr)−1. The second fastest rate is in the southern and western parts and the Xing’an Mountains, with a value of 0.4°C (10 yr)−1. From the central to the eastern part, the rate of increase in the average winter half-year temperature gradually decreases and reaches a minimum value in the east of only 0.1°C (10 yr)−1. 4) In Fig. 5e, the average freezing depth of seasonal frozen soil shows a decreasing trend across Heilongjiang Province. Among the regions, the western part and eastern part have the fastest reduction rate, and the reduction rate at 0.8–1.22 cm yr−1. 5) In Fig. 5f, the maximum freezing depth also shows a decreasing trend across the province. The change trend in maximum freezing depth is geographically consistent with the change trend in average freezing depth. The reduction rate of maximum freezing depth is greatest in the western and eastern region, with a value of 1.68–2.99 cm yr−1.
Spatial distribution map: (a) average freezing depth, (b) maximum freezing depth, (c) average temperature in the winter half-year, (d) tendency rate in average temperature in the winter half-year [°C (10 yr)−1], (e) tendency rate in average freezing depth (cm yr−1), and (f) tendency rate in maximum freezing depth (cm yr−1).
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0195.1
4. Discussion
The research in this paper proves that the freezing depth in Heilongjiang Province shows a thinning trend; the rate of reduction in the average freezing depth is 4.8 cm (10 yr)−1, and that in the maximum freezing depth is 10.1 cm (10 yr)−1. In general, there is a clear trend of degradation, which is consistent with the current global permafrost degradation environment. Between 1950 and 2010, the area of frozen soil shrank by 60% on the Alaska Plateau (Jones et al. 2016). From 1967 to 2012, the frozen soil depth in China decreased significantly at a rate of 0.18 ± 0.03 cm yr−1, and the net reduction was 8.05 ± 1.5 cm (Peng et al. 2017). According to the surface frost number model, Lu et al. (Lu et al. 2017) simulated the future distribution characteristics of permafrost on the Qinghai–Tibet Plateau under the four scenarios of representative concentration pathways (RCPs): RCP2.6, RCP4.5, RCP6.0, and RCP8.5; this study showed that from 2011 to 2040, the area of permafrost will shrink by 17.17%, 18.07%, 12.95%, and 15.66%, respectively. Unstable permafrost is mainly distributed on the edge of the permafrost area, and the rate of reduction in permafrost is accelerating; nearly half of permafrost will be in danger of disappearing in the future (Cheng et al. 2019; Ni et al. 2021a; Wang et al. 2019). The distribution area of permafrost in two river basins in the northern Tianshan Mountains has decreased by approximately 18% (Marchenko et al. 2006). The permafrost in the Xing’an Mountains has also degraded. The degradation trend is from low to high elevation and from continuous to discontinuous permafrost. The permafrost was significantly degraded from the 1980s to the 1990s, and the rate of degradation has slowed since the 2000s (Z. Q. Zhang et al. 2019).
The degradation of frozen soil in Heilongjiang Province is mainly due to rising temperatures in winter, the increase in warm and humid airflow during winter (Zhang et al. 2020) and the increase in precipitation, which increases the latent heat flux in the soil and accelerates the degradation of frozen soil. An increase in rainfall will change the latent heat flux of the soil, affect the surface energy balance, strengthen the heating effect on the active layer and make the active layer thicker (Jan and Painter 2020; Li et al. 2019). Because of differences in elevation, latitude, vegetation coverage, human disturbance, etc. across the province, the rate of decrease in the eastern region is greater than that in the western region (Wei et al. 2011).
The results show that the average temperature in the winter half-year displays a comprehensive upward trend in Heilongjiang Province. The Pacific decadal oscillation (PDO) and the Arctic Oscillation (AO) may be key factors affecting temperature changes in Heilongjiang Province (Zhou et al. 2020), and the spatial distribution of the rate of increase does not show obvious zonal changes with latitude. The central, western, and northwestern regions may be more affected by global warming (Zhou et al. 2020). Forest coverage is another factor that affects temperature changes, and the total forest area of Heilongjiang Province decreased by 28% from the twentieth century to the 2010s, with the most obvious decreases in the west, southwest, and northeast (Zhang et al. 2017).
This article focuses on the winter half-year temperature indicators related to the degradation of frozen soil. The results show that the correlations between the average temperature in the winter half-year and the average and maximum freezing depths are −0.53 and −0.49, respectively, which are strong correlations. The increase in the average winter half-year temperature will have a great impact on the permafrost environment and aggravate the thermal degradation of permafrost (G. F. Zhang et al. 2019). In addition to the strong influence of the average temperature in the winter half-year on the permafrost environment, many other factors have impacts on permafrost. The soil freezing depth has strong correlations with the freezing index and vegetation growth (Peng et al. 2017). For the change in frozen soil, snow cover is an important factor (Gądek and Leszkiewicz 2009). Snow cover is a common and relatively stable land cover condition in cold winter regions, and snow cover has significant effects on water and heat transfer (Fu et al. 2010). The increase in snow depth on the Qinghai–Tibet Plateau and the temperature in winter seem to be the most important factors controlling climate warming during the 10-year period (Isaksen et al. 2011). Snow cover plays a role in insulating or cooling the active layer of frozen soil in different months (Zhao et al. 2018). Snow cover is a major driver of interannual variability in seasonal frozen ground conditions (W. Zhang et al. 2021), and the low thermal conductivity and high heat capacity of snow impair heat transfer between soil and the environment (Calonne et al. 2011). In Heilongjiang Province, maximum snow depth is significantly correlated with the soil freezing depth and the date of first thaw, and the change of snow depth also offsets the effects of temperature rise on the soil freezing depth and the date of first thaw to a certain extent (Xu et al. 2022). During the freezing period, the influence of external factors on the soil is hindered by the snow cover (Fu et al. 2018). The insulating effect of snow cover reduces the rate of soil freezing, and this effect is more pronounced as snow cover increases in thickness and density (Fu et al. 2017). Thick snow cover impedes the transfer of temperature from air to soil, causes a time lag between changes in air and changes in soil temperature, reduces changes in soil temperature and soil moisture content, and reduces soil freeze depth (Xiang et al. 2013).
Abrupt climate change can be defined as a sudden change in climate from one stable state (or stable and continuing trend) to another stable state (or stable and continuing trend). These changes are related to changes in the statistical characteristics of climate variables over time and space (Zhao et al. 2016). Temperature in northeast China is, overall, strongly correlated with the annual greenhouse gases (RFAGG) and the Atlantic multidecadal oscillation (AMO) (Liang et al. 2020). From the late 1970s to the 1990s, with the continuous increase in RFAGG and AMO, the temperature indicators suddenly changed (Liang et al. 2020). For most stations in China, in the late 1980s and early 1990s, abrupt changes in three temperature indicators (mean minimum temperature, mean temperature, mean maximum temperature) began to occur on a large scale (Huang et al. 2020). This pattern is consistent with the mutation results in this paper. There is a clear correlation between frozen soil and air temperature, and an abrupt change in frozen soil lags behind an abrupt change in temperature. The abrupt change in temperature indicators in the north is mainly affected by the AO, and the AO has risen rapidly since 1980 and was in a positive phase from 1980 to 1995 (Huang et al. 2021).
In this paper, only the average temperature is used to analyze the impact on the frozen soil, and the impact of the daily minimum temperature on the frozen soil environment is ignored. Furthermore, the freeze–thaw state of soil is not determined solely by temperature but is the result of a combination of factors (including snow cover, vegetation, etc.).
5. Conclusions
Through studying the characteristics of freezing depth changes in seasonal frozen soil regions and the average winter half-year temperature in Heilongjiang Province, the following four conclusions are drawn:
The time variation characteristics of the average freezing depth and the maximum freezing depth are highly consistent. Both show a gradual decreasing trend from 1972 to 2016. The average freezing depth decreases at a rate of 4.8 cm (10 yr)−1, and the rate of decrease in the maximum freezing depth is 10.1 cm (10 yr)−1; the winter half-year average temperature shows a fluctuating upward trend overall, and the rate of increase is 0.3°C (10 yr)−1 in Heilongjiang Province.
The correlations between the average and maximum freezing depths and the winter half-year average temperature are −0.53 and −0.49, respectively. The average freezing depth will decrease by 3.85 cm for every 1°C increase in the average temperature in the winter half-year, and the maximum freezing depth will be reduced by 7.84 cm.
The average freezing depth sequence changes abruptly in 1987, and the maximum freezing depth sequence changes in 1988, lagging the average freezing depth; meanwhile, the winter half-year average temperature undergoes multiple mutations from 1972 to 2020. The mutation times are 1982, 1984, 1986, 2013, 2015, and 2017.
The average temperature in the winter half-year shows a rising trend across the province. A small part of the Xing’an Mountains and the central part of Heilongjiang Province have the fastest rate of increase at 0.47°–0.79°C (10 yr)−1, and the eastern region has the smallest rate of increase at 0.1°C (10 yr)−1. The reduction rates of the average freezing depth and maximum freezing depth in the western and eastern parts of the province are the largest, with rates of decrease of 0.8–1.22 and 1.68–2.99 cm yr−1, respectively.
Acknowledgments.
This research has been supported by the Basic Scientific Research Fund of Heilongjiang Provincial Universities (2020-KYYWF-1044) and the National Nature Science Foundation of China Youth Fund (Grant 52109055).
Data availability statement.
Datasets analyzed during the current study are available in the Daily Value Dataset of China’s Surface Climate Data (http://www.nmic.cn/). These datasets were derived from the following public domain resource: China Meteorological Data Service Center (https://data.cma.cn/).
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