Northeast China holds huge wetland soil organic carbon storage: an estimation from 819 soil profiles and random forest algorithm

doi:10.21203/rs.3.rs-1966083/v1

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Research Article

Northeast China holds huge wetland soil organic carbon storage: an estimation from 819 soil profiles and random forest algorithm

https://doi.org/10.21203/rs.3.rs-1966083/v1

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Journal Publication

published 02 Jun, 2023

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Purpose

As a huge natural carbon storage, wetlands play an important role in the global carbon cycle. However, the spatial pattern and storage of soil organic carbon (SOC) in wetland ecosystems remain largely uncertain due to large spatial heterogeneity and insufficient field observations.

Methods

In this study, we predict the spatial pattern of SOC density and estimated SOC storage in wetlands of Northeast China based on 819 field samples and multiple geospatial data using random forest algorithm.

Results

The results showed that climate, vegetation, and topography had notable impacts on the SOC density at different soil depths. The wetland SOC density in the surface layer (0–30 cm) was affected mainly by climatic factors (e.g., temperature, r = 0.54, p < 0.001), while the SOC density in the middle layer (30–60 cm) was mainly influenced by both climatic factors and vegetation factors. Moreover, topography (r = 0.35, p < 0.001) had greater impacts on wetland SOC density in the deeper soil layer (60–100 cm). Our estimation showed that Northeast China holds huge wetland SOC storage in the amount of 3.40 ± 0.13 Pg C. The average wetland SOC density was 44.30 ± 1.72 kg C m^− 2, which decreased gradually from north to south in the study area. The wetland SOC storage in the Greater Khingan Mountains Region was 2461.87 ± 92.21 Tg C, while that in the Liao River Plain was only 26.73 ± 1.33 Tg C.

Conclusion

We used random forest algorithm to achieve high-precision estimation of the wetland SOC storage in Northeast China, avoiding the geographic location-based dependence of traditional interpolation methods. Our results examined the relationship between wetland SOC and environmental factors, which benefits the understanding of the responses of wetland SOC to climate change.

wetland

soil organic carbon

random forest

Northeast China

Wetlands are known as the "kidneys of the earth" and have various ecosystem functions and services, such as carbon sequestration, climate regulation, and biodiversity conservation (Costanza et al. 1998, Wang et al. 2012, Woodward & Wui 2001). As an important carbon pool in the Earth's surface system, wetlands play an important role in the global carbon cycle (Dargie et al. 2017, Gorham 1991, Maltby & Acreman 2011). Although wetland areas only account for 4∼6% of the earth’s land, wetlands hold 20∼30% of the world's soil organic carbon (SOC) storage (Batjes 2014, Mitra et al. 2005). However, global wetland SOC storage estimation has an apparent uncertainty (Mitra et al. 2005, Mitsch et al. 2013), and the largest estimation was more than twice of the smallest, ranging from 202 to 535 Pg C (Adams et al. 1990, Buringh 1984, Gorhaml 1995, Sjörs 1980). The large differences between these estimations may come from the multiplicity of data sources and discrepancies in methodologies (Mitra et al. 2005). Thus, an accurate estimation of the wetland SOC storage is crucial for updating the carbon budget and predicting the carbon-climate feedback (Wiesmeier et al. 2013).

Estimating wetland SOC density and understanding its controlling factors are getting more and more worldwide attention (Banerjee & Mitra 2012, Nahlik & Fennessy 2016, Xiao et al. 2019). There are apparent differences in wetland SOC density with the depth of 0-100 cm in different countries or regions. Carnell et al. (2018) reported that the wetland SOC density in southeastern Australia was 20.4 ± 0.1 kg C m^− 2. Nahlik and Fennessy (2016) estimated the average wetland SOC density in the United States to be 47.8 ± 5.8 kg C m^− 2. The SOC density of China's wetland was estimated to be 46.71 ± 4.32 kg C m^− 2, which was close to the wetland SOC density in the United States (Xiao et al. 2019). However, in Russia, the estimated wetland SOC density reached 81.2 kg C m^− 2 (Stolbovoi 2002). The difference in wetland SOC density was mainly affected by various environmental factors, such as soil, topography, climate, hydrology, vegetation, and human interference (Tuo et al. 2018, Xiao et al. 2019). Soil texture and climate were the main influencing factors of tidal wetland SOC in the United States (Holmquist et al. 2018). Climate and plant biomass were the main factors controlling wetland SOC distribution in China (Xiao et al. 2019). Temperature was a significant control of SOC storage in the northern wetlands (Gorham 1991). Therefore, conducting more regional studies is crucial for wetland SOC reserves at national and global scales.

China has the largest wetland area in Asia, accounting for approximately 10% of the global wetland area (Mao et al. 2020). As a huge natural carbon storage in China, estimations of wetland SOC storage at depths of 0-100 cm differ among studies due to different the measurement methods and estimated wetland areas (Zheng et al. 2013, Xu et al. 2018, Xiao et al. (2019). For example, Zheng et al. (2013) estimated that wetland SOC storage in China ranged from 5.04 Pg C to 6.19 Pg C based on 7799 soil profile data and inventory approach. Xu et al. (2018) obtained 3.62 ± 0.80 Pg C from a literature synthesis published from 2004 to 2014, while the estimate of Xiao et al. (2019) was 16.82 ± 2.10 Pg C using 370 sites across China and 193 research data sets. With the development of computer technology, machine learning models have been widely used, due to powerful data mining capabilities (Grimm et al. 2008, Ren et al. 2020 Wiesmeier et al. 2011). The random forest method can fully consider the influence of different environmental factors, avoiding the strong spatial autocorrelation of traditional interpolation methods.

Northeast China is located in the mid-high latitudes where wetlands are widely distributed (Mao et al. 2020). The climate in this area is relatively humid and the temperature is low, which is conducive to the accumulation of SOC (Yu et al. 2007). Previous studies have analyzed wetland SOC in parts of Northeast China. For example, Ren et al. (2020) and Kang et al. (2020) used different methods to predict the spatial distribution of wetland SOC in the western Songnen Plain and the Liao River Plain. Man et al. (2019) studied the spatial and vertical variations in wetland SOC concentrations and their controlling factors in the Greater Khingan Mountains Region. However, there is a lack of accurate estimation of wetland SOC storage in Northeast China. Previous studies have evidenced that soil carbon emissions due to climate warming will reduce soil carbon storage (Li et al. 2022). Changes in land cover types caused by human activities can also affect wetland carbon storage. There is still urgent to estimate the storage of wetland SOC accurately and explore the influencing factors of SOC density to facilitate the implementation of national carbon-neutral strategies and understanding of regional responses to human and climatic disturbances.

In this study, we estimated the wetland SOC density and storage in Northeast China based on random forest algorithm and multisource geospatial datasets, as well as a large amount of profile data. It can provide a basis for understanding the regional carbon cycle and provide theoretical guidance for wetland management. Specifically, in this paper, we aimed to 1) examine the wetland spatial pattern of SOC density across the study area; 2) estimate wetland SOC storage in Northeast China; and 3) investigate the relationship of various environmental factors with the SOC density of wetlands at different depths. The generated dataset and related analysis in this study will help update the carbon budget and predict carbon-climate feedback.

Study area

The study area is in Northeast China (115°37′-135°50′ E and 38°43′-53°34′ N), including Liaoning, Jilin and Heilongjiang provinces and eastern Inner Mongolia with an area of 1.24⋅10⁶ km² (Fig. 1). The climate is a north-temperate and semi-humid continental monsoon climate, with annual precipitation varying from 200 to 800 mm. More than 70% of the annual precipitation occurs in June, July, and August. The temperature decreases from south to north, with an average annual value of -8–10°C. The soil types in the study area are dominated by black soil, swampy soil, meadow soil, chernozem and brown earth. The cropland area is approximately 20% of the whole region and is mainly distributed in Sanjiang Plain and Songnen Plain.

Wetland data

Wetlands refering to vegetable wetland, including bog, fen, marsh, and swamp, are widely distributed in Northeast China, with an area of 7.67×10⁴ km² (Mao et al. 2020). To comparative analysis of the spatial variation of wetland SOC density and the differences in carbon storage between different regions, the study area was divided into six geographic regions (Mao et al. 2019): the Greater Khingan Mountains Region, the Sanjiang Plain, the Changbai Mountains Region, the Lesser Khingan Mountains Region, the Liao River Plain, and the Songnen Plain (Fig. 1).

Soil sampling

Before the field survey, we preset some sample sites based on the wetland map of Northeast China. We fully considered the distribution of wetlands and the accessibility of traffic, and tried our best to make the sampling points evenly distributed in order to represent the wetlands of the whole study area. The field survey was conducted from September to October when the soil was not yet frozen, and the soil moisture content was relatively low. Since the wetland SOC density has a small change in a short period, we don't have a large error in sampling for three consecutive years (Xiao et al. 2019). The depth of 100 cm was the commonly used soil depth for estimating SOC storage (Xiao et al. 2019, Zheng et al. 2013). Therefore, for convenience compared with other studies, we defined the field sampling depth to be 100 cm. In total, we collected 819 samples based on the spatial distribution of wetlands in the study area. (Fig. 1). Among them, 77 sampling points were acquired in 2015, 380 in 2016, and 362 in 2017. The sampling time and depth, and geographic locations were recorded while sampling. The geographic location file of all sample plots was mapped in the ArcGIS (version 10.2, Environmental Systems Research Institute, Redlands, CA, USA) by the longitudes and latitudes. We had three soil profiles within ten meters of the preset point, and the latitude and longitude of the soil profiles in the middle was used as the actual coordinates of the soil point. For each soil point with three soil profiles, the SOC content for each depth range (i.e., 0–30, 30–60, and 60–100 cm) was represented by the average of SOC values of three profiles at the sampling point (Hardisky et al. 1986). Due to the shallow soil layers or sampling difficulties in deep soil, the depth of some sampling points did not reach 60 cm or 100 cm, which resulted in the number difference of observations between various sampling depths. The soil at the center of each depth interval was collected using a standard container with a volume of 100 cm³ for bulk density determination and tools to obtain a soil column for SOC concentration determination. Duplicates of each soil sample were measured for SOC concentration in g C kg^− 1 using a Shimadzu analyzer. (SSM-5000A, Kyoto, Japan). The calculation formulas of SOC density (formula (1)) and SOC storage (formula (2)) are as follows:

$$\text{S}\text{O}\text{C} \text{d}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}=\text{S}\text{O}\text{C} \text{c}\text{o}\text{n}\text{c}\text{e}\text{n}\text{t}\text{r}\text{a}\text{t}\text{i}\text{o}\text{n}\text{H}\text{B}\text{D}$$

$$\text{S}\text{O}\text{C} \text{s}\text{t}\text{o}\text{r}\text{a}\text{g}\text{e}=\text{A}\text{S}\text{O}\text{C} \text{d}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}$$

In the formula, the unit of SOC density is kg C m^− 2, H is the thickness of the soil layer (m), BD is the soil bulk density (g cm^− 3), and A is the area of wetlands (m²).

Datasets

The variability in SOC density could be explained by its relationships with soil-forming factors, such as climate, terrain, and so on (Tuo et al. 2018, Xiao et al. 2019). Based on the data accessibility, we retrieved 17 geospatial data from remote sensing data, climate and terrain (Table 1).

Landsat 8 Operational Land Imager (OLI) images (http://glovis.usgs.gov/) with a moderate resolution (up to 30 m) covering the study area were acquired from July to September 2016. After preprocessing the image, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) were calculated (Whigham & Simpson 1978). Additionally, this study also acquired MODIS products (http://modis.gsfc.nasa.gov/), including leaf area index (LAI) (MOD15A2), land surface temperature (LST) (MOD11A2), and land surface water index (LSWI) (MOD13A3). We used the summer average, including NDVI, EVI, LAI, LST and LSWI, from July to September, as the required data.

Table 1

Geospatial data for SOC density mapping.
Variable Types	Variables	Spatial Resolution	Time	Data Source	Description
Remote sensing factor	NDVI	30m	2016	http://glovis.usgs.gov/ http://modis.gsfc.nasa.gov/	Normalized difference vegetation index
	EVI	30m			Enhanced vegetation index
	LAI	500m			Leaf area index
	LST	500m			Land surface temperature
	LSWI	500m			Land surface water index
Climatic factor	AE	0.1°	2008–2017	http://data.cma.cn/ http://www.ncdc.noaa.gov/	Average evapotranspiration
	MAWS	0.1°			Mean annual wind speed
	MAH	0.1°			Mean annual humidity
	MAT	0.1°			Mean annual temperature
	MAP	0.1°			Mean annual precipitation
Terrain factor	Altitude	30m	2015	http://www.gscloud.cn
	Slope	30m
	SR	30m			Surface roughness
	Curvature	30m
	VC	30m			Vertical curvature
	HC	30m			Horizontal curvature
	TWI	30m			Topographic wetness index

The climatic data including mean annual precipitation (MAP, mm), mean annual temperature (MAT, °C), annual average humidity (MAH, %), mean annual wind speed (MAWS), and average evapotranspiration (AE) from 2008 to 2017, which were calculated from meteorological records were downloaded from the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn/).

Altitude was derived from the DEM data, downloaded from the Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn). At the same time, we calculated factors such as Slope, SR (Surface roughness), Curvature, VC (Vertical curvature), HC (Horizontal curvature) and TWI (Topographic wetness index) based on Altitude.

All the above related geospatial information was unified into the Albers equal-area conic WGS84 coordinate system. The attribute values of all grids were extracted for all sampling points and used as inputs for the spatial algorithms.

Algorithm development and predictions

The random forest algorithm is an integrated machine learning algorithm (Breiman 2001). The algorithm generates the final results by averaging the class allocation probabilities of all produced trees. The trees are created by replacing a subset of training samples and randomly selecting variables in the R environment (v. 4.0.2). The random forest algorithm is one of the more accurate machine learning algorithms. It can estimate the importance of predictor variables and run efficiently on large data bases (Akpa et al. 2016). The mean decrease in accuracy (%IncMSE) was used to present the relative importance of each variable. The value of the factor indicated the contribution of the factor in the algorithm. The greater the contribution value is, the greater the impact on the SOC density in the algorithm. When constructing the random forest model, we adjust the parameters of the model (mtry = 4, ntree = 500) to achieve the best model accuracy. The standard deviations of our estimates during different depth intervals were quantified of the ten-fold cross-validation simulations. General data processing and analysis followed the flow chart depicted in Fig. 2.

Accuracy verification

Tenfold cross-validation is a common test method used to test the accuracy of the algorithm. It divides the dataset into ten parts and takes turns using n parts as training data and one part as test data for experimentation. It can obtain a good error estimate through many tests.

Tenfold cross-validation was used for testing and comparing the performance of the random forest algorithms based on the root mean squared error (RMSE, formula (3)), mean absolute error (MAE, formula (4)), mean absolute percentage error (MAPE, formula (5)), and correlation coefficient (r) between the measured and predicted values. The calculation equations for RMSE, ME, and MAPE are as follows (Platt 1999):

$$\text{R}\text{M}\text{S}\text{E} =\sqrt{\frac{1}{n}\sum _{i=1}^{n}{({y}_{i}-{{y}_{i}}^{{\prime }})}^{2}}$$

$$\text{M}\text{E} =\frac{1}{n}\sum _{i=1}^{n}|{y}_{i}-{{y}_{i}}^{{\prime }}|$$

$$\text{M}\text{A}\text{P}\text{E} =MAE/Mean$$

where ${y}^{{\prime }}$ is the predicted value of the SOC density (kg C m^− 2), $y$ is the measured value of the SOC density (kg C m^− 2), and ME is a measure of the predictive lack of bias.

Descriptive statistics of wetland SOC density

The descriptive statistical values of wetland SOC density are listed in Fig. 3. The average SOC density at a depth of 0-100 cm was 33.01 kg C m^− 2. The SOC density decreased with increasing depth with a mean SOC density value of 15.57 kg C m^− 2 for 0–30 cm (n = 891), 9.81 kg C m^− 2 for 30–60 cm (n = 763), and 7.63 kg C m^− 2 for 60–100 cm (n = 692). The standard deviation (SD) gradually decreased with the depth increase. The SDs of the SOC density at depths of 0–30 cm, 30–60 cm, and 60–100 cm were 16.98 kg C m^− 2, 14.11 kg C m^− 2, and 11.02 kg C m^− 2, respectively, indicating that the SOC density values at the sampling points were quite different.

Control factors of wetland SOC density

The relationships between wetland SOC density and major environmental factors, such as MAT, MAP, MAH, and altitude, are shown in Fig. 4. The wetland SOC density at all three soil depths of 0–30, 30–60, and 60–100 cm decreased with the increase of MAT (Fig. 4a1, a2, and a3). The most notable relationship between wetland SOC density and MAP was detected for the topsoil of 0–30 cm (P < 0.001, r = 0.54, Fig. 4a1). The wetland SOC density at all depths was positively correlated with MAP and MAH (P < 0.001, Fig. 4b1, b2, b3, c1, c2, and c3); that is, with the increase in MAP and MAH, the wetland SOC density tended to increase. As shown in Fig. 4d1, d2, and d3, wetland SOC density at all soil depths increased linearly with increasing elevation (P < 0.001).

The random forest algorithm was used to predict the SOC density of wetlands at different depths. The results of the accuracy verification are shown in Table 2. The RMSEs at the 0–30 cm, 30–60 cm, and 60–100 cm soil depths were 6.54 kg C m^− 2, 3.57 kg C m^− 2, and 3.52 kg C m^− 2 respectively. The MAPE at the 0–30 cm, 30–60 cm, and 60–100 cm soil depths were 10.79%, 12.33%, and 23.2%, while the correlation coefficients (r) at different depths were 0.86, 0.77 and 0.73, respectively. These validations suggested that the trained RF models are capable to predict the spatial distribution of SOC density at the different soil depths.

Table 2

Random forest algorithm accuracy at different soil depths
Soil Depth (cm)	RMSE (kg C m^− 2)	ME (kg C m^− 2)	MAPE	r
0–30	6.54	1.68	10.79%	0.86
30–60	3.57	1.21	12.33%	0.77
60–100	3.52	1.78	23.32%	0.73

Figure 5 presents the contribution of each variable to the random forest model, which was calculated by the mean decrease in accuracy. At the 0–30 cm soil depth, MAT had the largest impacts on the wetland SOC density, followed by LST, MAH, altitude and LAI. At the 30–60 cm soil depth, remote sensing factors (e.g., EVI and NDVI) and MAT had the greatest influences on SOC density, followed by altitude and LST. At 60–100 cm intervals, altitude played a dominant role in affecting SOC density, followed by MAT, MAH and NDVI. Terrain factor (e.g., TWI) was also important factors influencing wetland SOC density at this soil depth.

Spatial patterns of wetland SOC density and storage in Northeast China

Figure 6 presents the spatial patterns of wetland SOC density at different soil depths over Northeast China estimated by the random forest algorithm. At the 0–30 cm soil depth, the average SOC density was 24.09 ± 0.92 kg C m^− 2. In terms of space, the SOC density in the northwest was significantly higher than that in other regions. At the 30–60 cm soil depth, the average wetland SOC density was 10.53 ± 0.39 kg C m^− 2. At the 60–100 cm soil depth, the average wetland SOC density was 9.68 ± 0.41 kg C m^− 2. The wetland SOC density at a 0–30 cm depth was significantly higher than that at the 30–60 and 60–100 cm soil depths. As the soil depth increased, the wetland SOC density decreased correspondingly. The average SOC density of the wetland of 0-100 cm was 44.30 ± 1.72 kg C m^− 2. The SOC density at different depths was spatially lower in the south than in the north, showing a trend of gradual decrease from north to south.

Figure 7 shows the statistical SOC density of different regions. The SOC density of different regions was significantly different. At different depths, the SOC density was the highest in the Greater Khingan Mountains Region and the lowest in the Liao River Plain. At the 0-100 cm soil depth, the SOC density in the Greater Khingan Mountains Region was 56.87 ± 2.13 kg C m^− 2, which was much higher than the average value in Northeast China. In different regions, the SOC density of the Lesser Khingan Mountains Region, the Changbai Mountain, the Sanjiang Plain, and the Songnen Plain gradually decreased at depths of 0-100 cm. The SOC density in the Liao River Plain was the smallest, only 13.85 ± 0.69 kg C m^− 2.

According to calculations, the wetland SOC storage at a 0-100 cm soil depth in Northeast China was approximately 3.40 ± 0.13 Pg C (Table 3). Among them, the 0–30 cm, 30–60 cm, and 60–100 cm soil depths of SOC storage were 1.85 ± 0.07 Pg C, 0.81 ± 0.03 Pg C, 0.74 ± 0.03 Pg C respectively. The SOC storage of 0–30 cm accounted for the main part, which was approximately 54.49%. The SOC storage of different regions was significantly different (Fig. 7). At the 0-100 cm soil depth, the SOC storage in the Greater Khingan Mountains Region was 2461.87 ± 92.21Tg C, which was much higher than other regions. In different regions, the SOC storage of the Lesser Khingan Mountains Region, Songnen Plain, Sanjiang Plain and Changbai Mountain gradually decreased at depths of 0-100 cm. The SOC storage in the Liao River Plain was the smallest, only 26.73 ± 1.33 Tg C.

Table 3

Statistics of SOC density and storage
Soil Depth (cm)	Mean (kg C m^− 2)	Total (Pg C)	Percent (%)
0–30	24.09 ± 0.92	1.85 ± 0.07	54.41%
30–60	10.52 ± 0.39	0.81 ± 0.13	23.82%
60–100	9.68 ± 0.41	0.74 ± 0.05	21.76%
0-100	44.30 ± 1.72	3.40 ± 0.13	100.00%
Note: 1 Pg C = 1⋅10⁹ t C

Prediction method of wetland SOC density

We generated spatial estimates of wetland SOC density at a 30 m resolution in Northeast China by combining random forest algorithm with multiple geospatial data. Using a new database with 819 field sampling sites, we quantified the spatial distribution of SOC storage in Northeast China and determined the differences in different regions (Fig. 6 and Fig. 7). At the same time, we identified significant environmental predictors of SOC storage at depth intervals.

The Data source and method was two differences of wetland SOC storage estimation between this study and the previous studies focusing on wetland SOC storage estimation. The first was the. Our sampling time was more concentrated, and the number of sampling points was more than that in previous studies on wetland SOC storage estimation (Xiao et al. 2019, Zheng et al. 2013). All field sampling data were obtained through a unified processing method, avoiding uncertainty from different data sources. Our wetland distribution data were derived from the up-to-date wetland distribution dataset of China (CAS_Wetland) with high classification accuracy (Mao et al. 2020). Moreover, we integrated climate, topography, and remote sensing variables characterize different features to predict the spatial pattern of the wetland SOC density in Northeast China. The random forest algorithm was used for mapping SOC density which fully considered the different influences of environmental variables (Song et al. 2017). All these improvements enhanced the reliability and accuracy of the wetland SOC storage estimation in Northeast China, which was the novelty of this study compared to previous researches.

Spatial heterogeneity and influencing factors of wetland SOC density

We found that the wetland SOC density in Northeast China decreased with soil depth. This result is consistent with the report in other regions (Banerjee & Mitra 2012, Xiao et al. 2019). The wetland SOC in Northeast China was concentrated in the surface layer (0–30 cm), accounting for approximately 54% of the storage in total depth of 1 m. This finding is also consistent with the estimation of global wetland SOC storage (Balesdent et al. 2018, Banerjee & Mitra 2012, Berg & Reich 1993). In this study, the mean wetland SOC density was 44.30 ± 1.72 kg C m^-2 in the upper 1 m soil, while that of global wetlands is approximately 43.27-102.88 kg C m^-2 (Mitra et al. 2005). The wetland SOC density in Northeast China is at a relatively low level worldwide. However, Xiao et al (2019) estimated China's wetland SOC density to be 31.17 kg C m^-2, which suggests a larger SOC density value of Northeast China than the national average. Ma et al (2015) reported that the average wetland SOC density in the top 1 m soil layer in Northeast China was 35.80 ± 9.39 kg C m^-2 based on published literature and zonal statistics. We argue that the SOC density predicted using the random forest and the uniform soil samples in this study was more accurate and reliable because previous studies lack adequate soil samples and most the used samples were acquired from publications in different years.

The wetland SOC density in Northeast China has clear spatial heterogeneity, with a general increase trend with increasing latitude. The SOC density of the Greater Khingan Mountains Region was the highest, while that of the Liao River Plain was the lowest. Many studies have reported that climatic factors, in particular temperature and precipitation, are the most important determinants of SOC distribution at large scales (Jobbagy & Jackson 2000, Wiesmeier et al. 2013). Our study showed that the main influencing factors of wetland SOC density at different depths were varied. The wetland SOC density in the surface soil layer (0–30 cm) was affected mostly by climate (such as MAP and MAT). The low temperature was conducive to the storage of SOC, while the high temperature caused the decomposition of SOC (Crowther et al. 2016). The temperature in the study area tended to decrease gradually with increasing latitude, which could clarify the difference in the spatial distribution of wetland SOC density in our study. The Greater Khingan Mountains Region had large areas of peatlands where had low-intensive human activities and lower temperatures. Therefore, higher SOC density was observed in the soil profile (Wang et al. 2021). The effect of climate on SOC density gradually decreases with increasing depth. Vegetation residues were conducive to the formation of humus and the enrichment of organic carbon, and the long-term deposition gradually increased the SOC density of the middle soil layer (30–60 cm). Therefore, NDVI and EVI, characterizing vegetation conditions, had an important influence on SOC density in the middle soil layer (30–60 cm). The deeper layer (60–100 cm) was buried deep in the soil, and climate change and vegetation index had little impact on SOC density. Compared with low-altitude wetlands, wetlands in middle- or high-altitude areas such as the Greater Khingan Mountains Region still have higher SOC density in deeper soil layers (60–100 cm), so the importance of topography for the density in the deeper layers (60–100 cm) gradually increases.

Potential response of wetland SOC storage to human and climatic interference

Our study indicated that the relationship between wetland SOC density and climatic factors gradually decreases with increasing soil depth. This means that the surface SOC may be more sensitive to global climate change. High latitude regions have the fastest rate of temperature increase. Therefore, carbon loss caused by warming may occur in the wetlands of Northeast China. Northeast China is located in the middle and high latitudes, and there are large areas of permafrost. Global warming could put the total SOC storage at risk of instability. Cao et al. (1998) found that global warming may produce higher methane emissions, and increasing temperatures could melt permafrost soils and subsequently emit methane hydrates entrapped by these wetlands (Meng et al. 2017). In addition, climate warming and human activities will have an important impact on wetlands. Xue et al. (2021) study showed that 4.5–23.8% of the high-latitude wetlands in the Xing'anling Mountains would be lost following widespread thawing of permafrost under different climate warming scenarios by the end of this century. Mao et al. (2018, 2021) study showed that the impact of human activities led to the disappearance and degradation of wetlands. If wetlands are not preserved properly, these could switch from being net sinks of carbon to becoming sources of greenhouse gas sources that accelerate climate change. From the perspective of wetland carbon storage, there is a corresponding relationship between wetland ecosystems and climate change and human activities. Therefore, it is important to correctly understand the carbon sink function of wetland ecosystems.

We achieved an accurate estimation of wetland SOC storage in Northeast China based on 819 soil profiles and random forest algorithm. The average SOC density of the wetlands in Northeast China with a depth of 1 meter was 44.30 ± 1.72 kg C m^− 2. SOC storage was abundant, with total storage reaching 3.40 ± 0.13 Pg C. Through spatial analysis, it was found that the SOC density generally showed a trend of more in the north and less in the south. By establishing independent algorithm at different depths, we concluded that the SOC density of wetlands at different depths was affected differently by environmental factors. The SOC density of the surface layer (0–30 cm) was more susceptible to climatic change. Given that this region contributes a substantial amount of SOC sequestration, proper wetland management may be very important for the accumulation of SOC substances.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yongxing Ren, Xiaoyan Li and Dehua Mao. The first draft of the manuscript was written by Yongxing Ren and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (No. 41771383 and 41730643) the Science and Technology Development Program of Jilin Province (20200301014RQ), and the funding from Youth Innovation Promotion Association of CAS (2017277, 2021227). We are very grateful to those who participated in the field soil surveys and thank the National Earth System Science Data Center of China for supporting geographic data (www.geodata.cn)

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Northeast China holds huge wetland soil organic carbon storage: an estimation from 819 soil profiles and random forest algorithm

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Abstract

Purpose

Methods

Results

Conclusion

Figures

Introduction

Materials And Methods

Study area

Wetland data

Soil sampling

Datasets

Algorithm development and predictions

Accuracy verification

Results

Control factors of wetland SOC density

Spatial patterns of wetland SOC density and storage in Northeast China

Discussion

Prediction method of wetland SOC density

Spatial heterogeneity and influencing factors of wetland SOC density

Potential response of wetland SOC storage to human and climatic interference

Conclusion

Declarations

References

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