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Article

Simulation of Grassland SOC under Future-Climate Scenarios in Gansu, China

by
Meiling Zhang
1,*,
Xiaojuan Li
1 and
Xiaoni Liu
2,*
1
Center for Quantitative Biology, College of Science, Gansu Agricultural University, Lanzhou 730070, China
2
College of Prataculture, Gansu Agricultural University, Lanzhou 730070, China
*
Authors to whom correspondence should be addressed.
Land 2023, 12(6), 1244; https://doi.org/10.3390/land12061244
Submission received: 21 February 2023 / Revised: 12 June 2023 / Accepted: 15 June 2023 / Published: 17 June 2023
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Abstract

:
The impacts of global warming on the grassland carbon cycle are increasingly severe. To explore the spatiotemporal variation in grassland soil organic carbon (SOC) and its response to climate change in Gansu Province, in this study, we designed five future-climate-scenario simulations (2019–2048), based on the baseline (1989–2018), according to the IPCC Fifth Assessment Report. The CENTURY biogeochemistry model was used to estimate the SOC of Gansu Province. One-way ANOVA and an error analysis were used to verify the model. Meanwhile, a Pearson coefficient diagram was used to analyze the main influencing factors of SOC. The results revealed that there was a good agreement between the observed and predicted SOC. The quarterly and inter-annual SOC trends of the five future-climate-scenario simulations were similar to those of the baseline simulation. The most extensive SOC storage occurred in the central Gannan region, in the simulation B scenario (temperature increase of 2 °C, no change in precipitation, and double the CO2 concentration). Temperature had a significant negative effect on SOC. Precipitation had a weak impact on SOC. The results indicate that SOC was more sensitive to temperature than to precipitation.

1. Introduction

Grassland is one of the world’s largest terrestrial ecosystems, and it plays a vital role in energy and matter circulation, climate change, and carbon balance [1]. Soil is considered to be one of the most significant terrestrial carbon storage sites [2]. Future climate change will have a significant impact on the carbon exchange of soil, vegetation, atmosphere, and grassland carbon storage worldwide [3]. Soil organic carbon (SOC) is a sensitive indicator of climate change, a vital evaluation indicator of grassland ecosystem health, and the largest carbon pool on land [4]. Its content significantly affects the productivity of the grassland ecosystem [5]. Even a small change in the soil organic carbon stock can have a strong impact on the atmospheric CO2 concentration [6]. The study of grassland SOC is of great significance to climate change and the rise of CO2 concentration levels [7]. Some studies have shown that climate change plays an increasingly important role in seasonal or interannual modifications in soil organic carbon, and that increased precipitation reduces SOC, through stimulated soil respiration [8]. Mishra et al. [9] found that warming increases soil respiration, and accelerates the decomposition rate of litter, which may lead to a loss of organic carbon, and affect the carbon-pool balance of alpine grassland ecosystems. Precipitation is another climate factor that is crucial in controlling the carbon cycle of grassland ecosystems [10]. Additionally, previous studies have found that the interaction of multiple climate-change factors with the carbon pool is often additive, rather than antagonistic or synergistic [11]. Some studies have also found that the combination of climate change factors has a more significant impact on carbon dynamics than any single factor, and the change in grassland carbon dynamics will have a far-reaching impact on the global carbon balance [12]. For example, Yue et al. [13] observed the synergistic effects of elevated CO2 and climate warming on grassland SOC storage, and found that the combination of climate change and elevated CO2 affects carbon stocks [14]. Moreover, doubling the CO2 concentration can drastically alter the magnitude of the response of grassland C to climate change, especially rising temperatures [15]. Having knowledge of the individual and interactive effects of elevated CO2 and global climate-change factors on carbon dynamics is vital to addressing future biosphere–climate feedback [16]. Therefore, it is necessary to understand how climate change and elevated CO2 drive grassland carbon dynamics [17].
A change in grassland carbon dynamics would affect the global carbon balance. Because of the complexity of the grassland carbon cycle’s response to the increase in the atmospheric CO2 concentration and climate change [18], it is necessary to use estimation models [19]. The practice of many researchers has proven that ecosystem models are used to quantify the impact of global climate change on grassland, and the emergence of biogeochemical models provides a unique opportunity to calculate the value of grassland organic carbon. In this study, we use the CENTURY [20] model: a process-based carbon cycle model that has been calibrated by using meteorological and soil data from Gansu grassland. It is used to simulate the temporal and spatial dynamics of the SOC of Gansu grassland under the influence of climate change and of a rise in CO2 concentration. The objectives of this study are to predict the quarterly and inter-annual spatial and temporal dynamics of SOC under future-climate scenarios; to investigate correlations between climatic factors, CO2 concentration, and grassland SOC; and to reveal how climate change affects grassland carbon cycles.

2. Materials and Methods

2.1. Study Area

Gansu Province (between 32°20′ and 42°50′ North latitude and between 92°13′ and 108°46′ East longitude) is in the west of China (Figure 1). It has a typical continental climate, with a mean annual temperature of 16.4 °C (6–19 °C), and a mean annual precipitation of 322.4 mm (36.6–734.9 mm). Rainfall is mainly concentrated from June to August. Generally, the southeast part of Gansu belongs to the humid region, while the northwest part belongs to the dry zone. In addition, Gansu Province is at the intersection of the Qinghai–Tibet Plateau, Loess Plateau, and Inner Mongolia Plateau, with a complex terrain, crisscrossed mountains, and significant differences in altitude. The general elevation of Gansu is between 1500 m and 3000 m.
As one of the five major pastoral areas in China, Gansu has a wide range of grassland vegetation types, with a natural grassland area of 1.79 × 105 km2, accounting for 39.4% of the land area, and ranking sixth in China, following Xinjiang, Inner Mongolia, Qinghai, Tibet, and Sichuan.

2.2. Model Description

In this study, CENTURY 4.0 models were used to estimate the SOC values from 1989 to 2018. The CENTURY model is a generalized plant–soil ecosystem model [20]; it can simulate ecosystem dynamics for all major ecosystems, and has been used for cropland and agroecosystems. The model requires several initial input data items, including latitude, longitude, soil texture, soil pH, soil depth, monthly precipitation, and monthly maximum and minimum temperatures. The major structural components of the CENTURY model are the plant production, soil organic matter (SOM), soil water and temperature. In the SOM sub-model, CENTURY 4.0 divides the SOM pool into three fractions: an active pool, a slow pool, and a recalcitrant or passive pool. The primary running process of the CENTURY model is shown in Figure S1, and the model input data and parameters are listed in Table S1.

2.3. Data Description

The meteorological data (1989–2018) that were derived from the new global climate dataset (WORLDCLIM) have a spatial resolution of 1 km.
Grassland-type data were obtained from the China Ecosystem Assessment and Ecological Security Database (http://www.ecosystem.csdb.cn/), accessed on 12 August 2019, and soil-type data are based on the Harmonized World Soil Database Version 1.2 (HWSD V 1.2), with a spatial resolution of 1 km.
The SOC measurement was extracted from the Resources and Environment Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/), accessed on 12 August 2019, using the geographical coordinates of 77 study sites.

2.4. Climate Scenario Design

We designed five simulation scenarios (2019–2048) to assess the rising atmospheric CO2 concentration, temperature, and precipitation change, according to the IPCC Fifth Assessment Report [21], using the actual climate change (Baseline 1989–2018) as a reference (Table 1). The doubling of the CO2 concentration is a parameter set by the CENTURY model itself [22], and a temperature increase of 2 °C, and precipitation increase of 12% are based on the range of climate change projected in the IPCC Fifth Assessment Report. Finally, the future-climate-scenario data (2019–2048) were obtained by adding future climate change to temperature and precipitation for 1989–2018 [23], as shown in Table 1.

2.5. Model Validation

One-way ANOVA mainly tests the significance of the difference between the means of two or more samples, and is also known as the coefficient of variation analysis. Under the normal distribution of all data, one-way ANOVA was used to test whether there was a significant difference between the measured value of SOC, and the predicted value. If the difference was not significant, it indicated good consistency between the measured value and the predicted value.
In this study, the model was evaluated by the fitting degree of the SOC simulation value and measured data. The root mean square error (RMSE) and normalized root mean square error (NRMSE) were used for evaluation.
To illustrate the sensitivity of grassland SOC to climate factors, Pearson’s correlation coefficients were used to calculate SOC with precipitation, temperature, longitude, latitude, and altitude, for each climate scenario. Excel (Version 2016) was used to analyze the data, and RStudio (Version 1.1.1106) was used to plot the correlation coefficients.

3. Results

3.1. Accuracy Verification of the CENTURY Model

The field observations of SOC were used to validate the modeling result. There is good agreement between the observed and predicted SOC (r = 0.718, NRMSE = 23.51, p < 0.01). No significant difference is shown between the measured and predicted SOC, and the simulation effect is good. The results are shown in Figure 2 and Table S2.

3.2. Temporal Changes Trend of the Quarterly and Inter-Annual Grassland SOC

The simulation results revealed that the inter-annual variation in SOC decreased year by year under different scenarios. There were significant differences among them compared with baseline simulation, which indicated that the carbon sequestration capacity of Gansu grassland was decreasing year by year (Figure 3a,b). The future scenarios could significantly decrease SOC, which showed as 370.08 gC·m−2, 476.01 gC·m−2, and 348.73 gC·m−2 lower than that between 1989–2018 for simulations B, D, and E, respectively. However, SOC increased in simulation A and simulation C, relative to the baseline. In addition, the results of simulation A are very close to simulation C, and the results of simulation B are very close to simulation D. The quarterly values of SOC show a U-shaped curve in each scenario (Figure 3c,d). The highest SOC was in the first quarter, whereas the lowest SOC was found in the third quarter, under all simulations. Among them, the SOC values of simulation A and simulation C were all higher in the third quarter, at 252 gC·m−2 and 108 gC·m−2, compared to the baseline. Meanwhile, the SOC values of simulations B, D, and E in the third quarter (−90 g C·m−2, 197 gC·m−2, and 129 gC·m−2) were all lower than the baseline simulation. This is mainly because the increase in high temperature and precipitation enhances the biological activity of the soil, and accelerates the decomposition of SOC.

3.3. SOC Variation at Spatial Scales

3.3.1. Spatial Distribution of Quarterly Grassland SOC

The spatial distribution of quarterly grassland SOC under different climate simulations is presented in Figure S2. Overall, the SOC value of each region in the study area was high in the first quarter and fourth quarter; in contrast, it then gradually decreased in the second and third quarters. In the baseline simulation (Figure S2a), the sensitive areas of SOC value change were the northwest, central, and eastern regions, and the Gannan region had the most significant SOC value. Compared with the baseline simulation, double CO2 concentration and constant precipitation and temperature increased SOC in the study area, with the most significant change in the central region in the third quarter (Figure S2b). This may be due to the increased CO2 concentration, which enhanced plant photosynthesis, and increased vegetation productivity, leading to an increase in SOC.
The SOC values of different areas in the study area were significantly different, due to there being no change in precipitation, increasing temperature, and elevated CO2 concentration. SOC decreased with the increase in temperature in most regions compared with simulation A, showing an overall downward trend except in the eastern part, where the SOC value increased significantly. However, there was little change in the northwest region (Figure S2c), which indicated that temperature had a significant adverse effect on SOC in most parts of the study area. At the same time, the impact was small in the arid region of the northwest; however, with no change in temperature, the effect of precipitation fluctuation had little influence on the overall change in SOC in the study area. Precipitation significantly reduced SOC in the southern and Gannan regions, and slightly increased SOC in the east, but the difference was not significant in the other areas (Figure S2d).
SOC in the study area significantly decreased with the increase in temperature and precipitation when the CO2 concentration remained unchanged, compared with the baseline simulation (Figure S2e). The benefits of increased temperature and precipitation to SOC would differ across seasons. In each quarter, the minimum value of SOC appeared in the third quarter, and the maximum value appeared in the first quarter. The maximum value was in the central region of the study area. This indicates that the increase in temperature and precipitation was significantly negatively correlated with SOC, and SOC decreased with the rise in temperature and precipitation. The additive effects of elevated CO2 and climate change increased SOC in most areas (Figure S2f). Among them, the most sensitive region was the northwest region in the third quarter, where the decrease in the SOC value was most apparent. This suggests that higher CO2 concentrations helped mitigate the loss of SOC caused by climate change. An elevated CO2 concentration had a positive effect, but temperature and precipitation had adverse effects, on SOC.

3.3.2. Spatial Distribution of Inter-Annual Grassland SOC

The spatial change in annual grassland SOC appears to show a significant decreasing trend in the future-climate simulations, compared with the baseline, under the increase in precipitation and warming (Figure 4c,e,f). The spatial variation trend of SOC under six climate simulations is presented in Figure 4. Its distribution varied over time, and from region to region, under different climate simulations. The magnitude of SOC in each region varied with climate change and an elevated CO2 concentration. Throughout the study area, the most significant increment of SOC mainly occurred in the central and Gannan regions of simulation A (Figure 4b), compared with the baseline (Figure 4a). The increase in CO2 concentrations would promote the accumulation of SOC (Figure 4b), but raised CO2 concentrations would also promote a rise in temperature, and result in the expansion of the soil respiration rate and a decrease in SOC, so that the increase in SOC would not be obvious (Figure 4c). An increased CO2 concentration, and increasing precipitation alone (Figure 4d) led to a decrease in SOC in most areas, especially in the northwest and central parts of Gansu. By contrast, an increase in temperature and precipitation (Figure 4e) led to a slightly more significant decrease in most areas, because soil microbial activity is weakened in cold and dry places. This result indicated that SOC was more sensitive to temperature than precipitation, and temperature had a significant negative effect on SOC.

3.4. Patterns of Regional Change in SOC under Five Future-Climate Simulations

Because of the different constraints of precipitation, temperature, and carbon dioxide, the distribution of grassland SOC in Gansu province is preferentially different from the rest of China. SOC decreased in the Gansu province of China as temperatures increased from low to high, and from south to north (Figure 5). SOC in the study area decreased from the middle to the north and south. The most extensive SOC storage mainly occurred in the central region of Gannan, and the lowest in the northernmost and southernmost areas. Among the five future-climate simulations, simulation A showed the most significant SOC, and simulation D showed the smallest SOC. The low temperature would limit the decomposition process of the soil, and there would be more carbon storage over the years, with accumulation. Furthermore, although SOC exhibited a decreasing trend in Gansu province, the change rate differed with a significant spatial distribution pattern.

3.5. Analysis of the Main Influencing Factors of SOC

The correlation matrix between SOC and the five model variables (mean annual temperature, mean annual precipitation, latitude, longitude, and altitude) is shown in Figure 6. Among them, the ALT exhibited a correlation coefficient of 0.69 with the SOC, suggesting that the ALT provided a suitable parameter of grassland SOC. Secondly, there was a significant negative correlation between SOC and temperature (r = −0.74); a temperature decrease induced an SOC increase, and a temperature increase could decrease SOC, which indicates that SOC significantly reduced with the rise in temperature. SOC also exhibited a negative correlation with LON, with a coefficient of −0.54. This shows that SOC also varied with the longitude of each station. Meanwhile, the AMP attained a low negative correlation with SOC (r = −0.18), indicating that precipitation had a negligible effect on grassland SOC in the study area.

4. Discussion

Understanding the spatial and temporal changes in grassland SOC, as well as their responses to climate change, can improve our understanding of the grassland carbon cycle, and provide the scientific basis for grassland management and utilization. Our study found that the temporal distribution of SOC decreased over time under different climate scenarios; this is consistent with the results of Zhang et al. [24]. Precipitation and warming are the main driving factors of SOC. Our study found that climate warming had a significant negative effect (r = −0.74) on SOC. In contrast, precipitation had a weak negative impact (r = −0.18) on SOC in the study area, implying that grassland SOC decreased with the increase in temperature and precipitation. This is mainly due to warming, which increases soil respiration, and causes significant loss of SOC [25]; it is consistent with the study of Volk et al. [26]. This study points out that enhanced precipitation fluctuation in the study area significantly decreases SOC, but the decrease is limited, which is explained by the study of Schoenbach et al. [27]. They believe that with the increase in precipitation, soil decomposition is enhanced, and leads to additional soil carbon loss. However, the impacts of climate change on SOC vary in different regions. Zhang et al. [28] found that the increase in precipitation led to a rise in grassland SOC on the Tibetan Plateau. However, the effects of climate change on grassland SOC are very complex, and require further study.
In this study, we assumed the same grassland type in the study area, since the community structure of different grassland types would respond differently to climate change and CO2 concentration rise. Thus, taking the same grassland types in other regions might lead to errors in the research results, and generate uncertainty in the simulation results.
In the CENTURY model, the SOM sub-model is required to set management events and crop-growth control at a specific time, and generate ASCII output files, allowing users to set various crop-production activities, according to the actual grassland activities between months and years. There are significant differences in farmland production activities in different regions, leading to significant subjectivity and flexibility in this part of the simulation. Therefore, such subjectivity may give rise to uncertainty in the simulation results.
Most researchers use representative concentration pathways (RCPs) and future-climate-scenario data from the Fifth Assessment Report of the Intergovernmental Panel [29], which combines climate, air, carbon cycle, and socio-economic factors. In this study, we assumed that temperature and precipitation increases would be constant under future-climate scenarios. We simplified this complex process, by calculating data based on the IPCC Fifth Assessment Report results through simple linear increases. This could also lead to uncertainty about the outcome.
The spatiotemporal variation in soil organic carbon is mainly affected by climate, parent material, and topography factors such as biological and land-use patterns and management measures [30]. In this study, the timing of organic carbon spatial variability was determined by structural factors, such as climate, altitude, soil texture, and grazing. Moreover, other random factors of human activities, including chronic soil organic carbon, are more susceptible to random factors such as human activity; these results are consistent with Pringle’s [31]. The influence of grazing management on total soil organic carbon in tropical grasslands is an issue of considerable ecological and economic interest. This study only applied simple regulations of human activities during parameterization, such as animal grazing, fertilization, and irrigation activities, as other human activities were not considered. In fact, the impact of human activities on soil organic carbon storage is a more complex process.

5. Conclusions

In this study, we used the CENTURY model to study the spatiotemporal distribution of SOC under different climate changes. There is good agreement between the observed and predicted SOC (r = 0.718, NRMSE = 23.51, p < 0.01), and no significant difference, indicating that the CENTURY model has a good simulation result. The inter-annual variation in SOC decreased year by year under different scenarios, and there were substantial differences among them, compared with the baseline simulation (1989–2018). The future-climate simulation decreased SOC for simulations B, D, and E significantly, respectively. However, it increased in simulations A and C. First quarter and fourth quarter temperature and rainfall stimulated SOC, and the third quarter suppressed SOC. We found significant differences in the spatial distribution of SOC trends under different climate scenarios. On a regional scale, SOC showed a decreasing trend in Gansu province, but its change rate increased in the central region. SOC were more sensitive to temperature than precipitation. Although the SOC exhibited a decreasing trend in Gansu province, the change rate of the spatial distribution pattern differed significantly. Temperature was the main factor affecting the change in SOC, and precipitation had a weak effect on SOC. In addition, longitude and altitude also played an important role in SOC; SOC fell with the increase in longitude, but increased with the rise in altitude. In the study of soil organic carbon, there is not yet sufficient research on the impact of human activities on land use change (reclamation, grazing). The impact of human activities on soil organic carbon will be a key and difficult point in our follow-up research.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land12061244/s1, Figure S1: CENTURY model structure diagram [32]; Figure S2: Spatial distribution of quarter grassland SOC under different climate simulations. (a), (b), (c), (d), (e), and (f) represents the baseline, simulation A, B, C, D, and E, respectively. Each row represents a different climate simulation, and each column represents the different quarter.; Table S1: Main parameters of CENTURY model; Table S2: Result of One-way ANOVA and error analysis.

Author Contributions

Conceptualization, writing, review and editing: M.Z. Writing—original draft, data curation, and formal analysis: X.L. (Xiaojuan Li). X.L. (Xiaoni Liu) mainly modified and embellished the linguistic structure of the article. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (32260353); the Key Research and Development Program of Gansu province, China (21YF5WA096); Ministry of Science and Technology of China High-end Foreign Expert Introduction Program (G2022042009L); the Natural Science Foundation of Gansu province, China (1606RJZA077 and 1308RJZA262).

Data Availability Statement

Data will be made available on request.

Acknowledgments

We would like to thank Teddy Nkrumah for checking the language of this study.

Conflicts of Interest

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

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Figure 1. Spatial distribution of meteorological stations.
Figure 1. Spatial distribution of meteorological stations.
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Figure 2. Comparison of the observed SOC with the predicted SOC. The blue dot mainly tests whether the predicted SOC is closely related to the measured SOC.
Figure 2. Comparison of the observed SOC with the predicted SOC. The blue dot mainly tests whether the predicted SOC is closely related to the measured SOC.
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Figure 3. Temporal changes of quarterly and inter-annual grassland SOC under different climate simulations. (a,c) represent SOC values in the baseline simulation; (b,d) represent SOC values under five future-climate simulations.
Figure 3. Temporal changes of quarterly and inter-annual grassland SOC under different climate simulations. (a,c) represent SOC values in the baseline simulation; (b,d) represent SOC values under five future-climate simulations.
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Figure 4. Spatial change trend of annual average grassland SOC under different climate scenarios. (a–f) represents the baseline and simulations A, B, C, D, and E, respectively.
Figure 4. Spatial change trend of annual average grassland SOC under different climate scenarios. (a–f) represents the baseline and simulations A, B, C, D, and E, respectively.
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Figure 5. Response of SOC to climate change on a regional scale, under different climate scenarios. Light blue is simulation A, pink is simulation B, dark blue is simulation C, yellow is simulation D, and red is simulation E.
Figure 5. Response of SOC to climate change on a regional scale, under different climate scenarios. Light blue is simulation A, pink is simulation B, dark blue is simulation C, yellow is simulation D, and red is simulation E.
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Figure 6. Correlation matrix between SOC and the model variables. AMT represents the average annual temperature, AMP represents the average yearly precipitation, LAT represents the latitude, LON represents the longitude, and ALT represents the altitude. The color line on the right represents the correlation coefficient between variables. The darker the blue, the more significant the positive correlation. The darker the red, the more significant the negative correlation. The greater the fan-shaped proportion of each color, the more significant the positive or negative correlation; the smaller the proportion, the less significant the positive or negative correlation.
Figure 6. Correlation matrix between SOC and the model variables. AMT represents the average annual temperature, AMP represents the average yearly precipitation, LAT represents the latitude, LON represents the longitude, and ALT represents the altitude. The color line on the right represents the correlation coefficient between variables. The darker the blue, the more significant the positive correlation. The darker the red, the more significant the negative correlation. The greater the fan-shaped proportion of each color, the more significant the positive or negative correlation; the smaller the proportion, the less significant the positive or negative correlation.
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Table
Table 1. Five future-climate simulations.
Table 1. Five future-climate simulations.
Simulation ScenariosTemperature (°C)Precipitation (mm)CO2 (ppm)
ANo changeNo changeDouble
B+2No changeDouble
CNo change+12%Double
D+2+12%No change
E+2+12%Double
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Zhang, M.; Li, X.; Liu, X. Simulation of Grassland SOC under Future-Climate Scenarios in Gansu, China. Land 2023, 12, 1244. https://doi.org/10.3390/land12061244

AMA Style

Zhang M, Li X, Liu X. Simulation of Grassland SOC under Future-Climate Scenarios in Gansu, China. Land. 2023; 12(6):1244. https://doi.org/10.3390/land12061244

Chicago/Turabian Style

Zhang, Meiling, Xiaojuan Li, and Xiaoni Liu. 2023. "Simulation of Grassland SOC under Future-Climate Scenarios in Gansu, China" Land 12, no. 6: 1244. https://doi.org/10.3390/land12061244

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