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Article

Inconsistent Carbon Budget Estimation Using Dynamic/Static Carbon Density under Land Use and Land Cover Change: A Case Study in Henan Province, China

by
Fan Yang
1,
Shicheng Li
2,
Yang Gao
1,
Meijiao Li
3 and
Pengfei Wu
1,*
1
Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475001, China
2
School of Public Administration, China University of Geosciences, Wuhan 430074, China
3
College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China
*
Author to whom correspondence should be addressed.
Land 2022, 11(12), 2232; https://doi.org/10.3390/land11122232
Submission received: 16 November 2022 / Revised: 30 November 2022 / Accepted: 6 December 2022 / Published: 7 December 2022
(This article belongs to the Section Landscape Ecology)
Browse Figures
Figure 1
<p>Geographical location of Henan Province in China. (<b>a</b>) Four basins and (<b>b</b>) ecogeographical regions.</p> "> Figure 2
<p>Spatial distribution of LUCC in Henan Province in 1980, 2010, and 2015.</p> "> Figure 3
<p>The flow pattern of LUCC in Henan Province from 1980 to 2010.</p> "> Figure 4
<p>The flow pattern of LUCC in Henan Province from 2010 to 2015.</p> "> Figure 5
<p>Henan Province’s terrestrial system carbon stock change driven by land use type conversion between 1980 and 2010 and between 2010 and 2015 (bookkeeping in legend denotes the moderate scenario; the negative sign refers to carbon emission; R1–R4 refer to 4 ecogeographical regions).</p> "> Figure 6
<p>Composition of the terrestrial ecosystem carbon budget.</p> ">
Versions Notes

Abstract

:
The scientific estimation of carbon budgets induced by land use and land cover change (LUCC) can improve the accuracy of carbon budget estimates for terrestrial ecosystems and deepen our understanding of the future carbon sink potential of these ecosystems. Previous studies have typically selected one of many LUCC-induced carbon budget methods and overlooked the differences in the results between the different methods. Taking Henan Province, China, as a case study, we used the IPCC method, the bookkeeping model, and the inventory method to estimate LUCC-induced carbon budgets and compared the differences in these methods. The results showed that LUCC in Henan Province was categorized as a carbon sink from 1980 to 2015 based on the IPCC method and the inventory method. However, the estimates were significantly different, and the total estimates of the IPCC method were 40% less than those of the inventory method. At the regional ecogeographical scale, the two methods were not consistent in assessing the carbon source/sink of LUCC. The bookkeeping model categorized LUCC as a carbon source in Henan Province for the same period, and the carbon storage change trend opposed those of the other two methods. The failure of the IPCC method and the bookkeeping model to include the dynamic changes in carbon density caused by climate and land use management led to the great differences between the three methods. The inventory method is recommended to estimate the carbon budget caused by LUCC for regions where carbon density varies greatly over time. Both the IPCC method and the bookkeeping model may have great uncertainty in estimating changes in LUCC-induced carbon stocks and should be used with caution.

1. Introduction

Land use and land cover change (LUCC), one of the major impacts of human activities, has substantially influenced the terrestrial carbon budget by altering the structure and function of the ecosystem [1,2,3,4,5,6]. Globally, LUCC-induced carbon emissions are the largest contributor to the total volume of anthropogenic carbon emitted during the preindustrial period [7,8]. Previous studies show that LUCC-induced carbon emissions account for approximately 33% of total carbon emissions over the last 150 years, 20% of total carbon emissions between the 1980s and 1990s, 12.5% of total emissions from 2000 to 2009 [9], and 14% of total carbon emissions for 2009–2018 [10]. Although the proportion of LUCC-induced carbon emissions continues to decline with the rise in fossil fuel emissions, LUCC is still the second largest carbon emission source and plays an important role in the carbon cycle of terrestrial ecosystems. However, global and regional carbon budget estimates feature high uncertainty regarding LUCC-induced emissions [11,12,13,14,15]. Thus, accurate estimations of LUCC-induced emissions across local to global scales are essential for deepening the understanding of the carbon source or sink of terrestrial ecosystems, balancing regional carbon budgets, and mitigating carbon emissions [7,16,17,18,19,20].
In recent decades, several methods have been developed for estimating LUCC-related carbon budgets at different scales [21,22,23,24,25,26,27,28]. Among them, the IPCC method, bookkeeping model, and inventory method have been widely used to calculate LUCC-related carbon emissions at both global and regional scales due to their simple model structure and minimal requirement for basic data [4,19,24,25,26,27,28]. Unchanged carbon density data are used in the IPCC method and the bookkeeping model. This means that, other than LUCC, factors that affect carbon densities are not included. In contrast, the inventory method is based on direct measurements of the carbon stock of vegetation and soil at the site scale [28]. Thus, this method reflects not only the impact of LUCC on carbon density but also the change in carbon density over time. However, the carbon density inventory is usually performed at intervals of 5 years or longer, and few carbon density observations for different times are available for a given region. Overall, these methods make it possible to estimate carbon budgets in different regions where LUCC and carbon density data are either abundant or relatively scarce. However, their shortcomings are also clear.
Generally, scholars choose a method that matches the available data (LUCC and carbon density) to estimate the regional LUCC-induced carbon budgets. However, little attention has been given to the consistency of different methods in determining the source/sink of carbon in ecosystems and to the differences in the estimated amount of change in the carbon stock. In other words, comparative studies between different carbon budget estimation methods are rare.
Henan Province is China’s largest province in terms of agricultural production, with a high intensity of land use and efficient land use management. The province straddles four important river basins in China and has rich and diverse ecosystem types. Additionally, vegetation and soil carbon density data have been obtained for three points in time (1980, 2010, 2015), which was useful for comparing different methods. Here, we took Henan Province as the study area to explore the differences between three representative and widely adopted methods of LUCC-induced carbon budget estimation, namely, the IPCC method, the bookkeeping model, and the inventory method. The objectives of the present study were to (1) provide a useful reference for the rational selection of LUCC-induced carbon budget estimation methods at the regional scale and (2) deepen our understanding of the regional terrestrial ecosystem carbon budget.

2. Materials and Methods

2.1. Study Area

Henan Province (110.35°–116.65° E, 31.38°–36.37° N) is located in east-central China and has a total area of 16.7 × 104 km2. The landforms within this region are plains, hills, and mountains, and the region demonstrates a great deal of environmental variability; extensive alluvial plains characterize the east, while the western areas are dominated by hills and mountains (Figure 1a). This province has a semihumid and humid monsoonal climate, and the major climatic zones are temperate, with subtropical conditions in the south. Henan Province’s territory spans four river basins, namely, the Yangtze River Basin, Huai River Basin, Yellow River Basin, and Hai River Basin (Figure 1b). There are 98.83 million permanent residents in this region, according to official statistics for 2021. The natural conditions and population resources are very conducive to agricultural development; thus, Henan Province has been traditionally viewed as a cultivated region throughout history [29] and is one of the current 13 major grain-producing areas designated by the Chinese government [30]. Moreover, Henan Province, located in the lower reaches of the Yellow River, plays a significant role in environmental protection as it is the center of a major national strategy for ecological conservation and high-quality development in the Yellow River Basin, which was established in 2019.

2.2. Data Sources

Data used in the study include (1) a map of ecogeographical regions of China; (2) remote-sensing-based LUCC datasets in 1980, 2010, and 2015; and (3) soil organic carbon (SOC) and vegetation carbon (VC) densities in 1980, 2010, and 2015.

2.2.1. Ecogeographical Regions

The map of Chinese ecogeographical regions was downloaded from the Resources and Environmental Sciences Data Center, Chinese Academy of Sciences (http://www.resdc.cn, accessed on 8 July 2022). To explore regional changes in SOC and VC storage, Henan Province’s terrestrial ecosystems were divided into 4 ecogeographical regions (Figure 1b) based on this map, namely, the North China Plain warm temperate semihumid regions (R1); the mountains and hills in the North China warm temperate semihumid regions (R2); the Hanzhong Basin in the north subtropical humid regions (R3); and the southern Huai River Basin and the middle and lower reaches of the Yangtze River in the north subtropical humid regions (R4).

2.2.2. LUCC Dataset

The 30 × 30 m LUCC raster datasets in Henan Province related to 1980, 2010, and 2015 were obtained from the remotely sensed Chinese LUCC dataset (CLUD, available at http://www.resdc.cn, accessed on 15 February 2022) [31]. The CLUD categorizes land use by six primary types (cropland, forestland, grassland, waterbodies, built-up land, and unused land) and 25 secondary land use types in total. This dataset for Henan contains 21 subclasses (see Supplementary Table S1). To better contextualize Henan Province’s land use and to correlate the carbon density data, we used data on primary land use categories to characterize change.

2.2.3. Carbon Density of the Soil and Vegetation

The 1980 SOCdensity data at 0–100 cm in Henan Province were calculated based on soil profile properties (including organic matter content, bulk density, soil depth, and the volumetric percentage of the fraction >2 mm) from the Second National Soil Survey of China in 1979–1985 [32] (Equation (1)).
S O C d e n s i t y = ∑ k = 1 n S O C k × S D k × B D k × 1 − α k × 0.1 ,
where SOCk, SDk, BDk, and αk represent the content of SOC (%), soil depth (SD) (cm), bulk density (BD) (g/cm3), and the volumetric percentage of the fraction >2 mm, respectively, in soil layer k. n represents the number of soil layers across the 0–100 cm depth. For samples with soil depths over 100 cm, only the carbon density at 0–100 cm depth was calculated. For soil samples for which bulk densities were not recorded, the pedo-transfer function was used to estimate bulk density [33].
A dataset for carbon density in Chinese terrestrial ecosystems (2010s) was developed based on the literature featuring carbon density data in Chinese terrestrial ecosystems from 2004 to 2014 and from relevant experimental data for the same period [34]. The SOCdensity data for Henan Province within this dataset are mainly concentrated in the period of 2008–2011. Simultaneously, an additional SOCdensity dataset for 2010 was calculated by utilizing soil profile properties from the Chinese soil series of Henan Province [35] and Equation (1). The present study obtained 2010 SOCdensity data at soil depths of 0–100 cm in Henan Province from these two datasets. The 2015 SOCdensity data at 0–100 cm in Henan Province were collected from Tian et al. [36], Zhao et al. [37], and Zhang et al. [38].
Finally, SOCdensity data of different ecogeographical regions in Henan Province (Table 1) were obtained based on the above collected SOCdensity dataset, soil profile property data related to SOC, and a calculation method for SOCdensity, which included the years 1980, 2010, and 2015.
The 1980 VC density data in Henan Province were collected from Fang et al. [39] and Lai et al. [24]; the 2010 VC density data in Henan Province were obtained from the carbon density dataset in Chinese terrestrial ecosystems (2010s) [34]; and the 2015 VC density data in Henan Province were derived from Tang et al. [1]. The details of the VC density data in Henan Province are given in Table 1.
Although carbon density sources are different, these data were collected from widely used datasets and the published literature, and the sample determination and carbon density calculation were consistent. Moreover, the depth of all SOC samples selected in this study was 100 cm to ensure data consistency. Comparative studies using carbon density data from different periods have also proven feasible [34]. Thus, we believe that Henan’s carbon density data obtained in this way are available and can be used to reflect the change in carbon density over time.

2.3. Methods

The present study took Henan Province as the study area to estimate LUCC-induced carbon budgets using the IPCC method, the bookkeeping model, and the inventory method and compared the differences in these methods. Specifically, the total and subregional land use category areas in 1980, 2010, and 2015 were counted, their spatial distributions were mapped, and the changes between study periods were presented by the land use transfer matrix. Then, SOC storage changes in Henan Province caused by LUCC for 1980–2010 and 2010–2015 were calculated using three methods, namely, the IPCC method [40], the bookkeeping model [9,41], and the inventory method [16,28]. Finally, the values of carbon storage change calculated by different methods were compared in this study.

2.3.1. The IPCC Method

Carbon storage change related to LUCC comes mainly from two sources: VC storage change and SOC storage change in topsoil (0–100 cm in this study). The calculation is as follows [40]:
Δ C s t o r a g e = ∑ R n ,   i V C 2010   a f t e r ,   R n ,   i − V C 2010   b e f o r e ,   R n ,   i × Δ Area R n ,   i + ∑ R n ,   i S O C 2010   100 ,   R n ,   i × F i m p a c t   i × Δ Area R n ,   i
where ΔCstorage denotes all of the carbon storage changes caused by LUCC. Rn denotes the number of ecogeographical regions. i denotes the land use type. VC2010 (after,Rn,i) and VC2010 (before,Rn,i) denote the VC density of land use type i in the Rn region in 2010 after land conversion and before land conversion, respectively. ΔArea(Rn,i) denotes the transformed area of land use type i in the Rn region. SOC2010 (100,Rn,i) denotes the SOCdensity (0–100 cm depth) of land type i in the Rn region in 2010. Fimpact (i) denotes the impact factors of SOC change (see details in Table 2).
Notably, the IPCC method (a static model) assumes that the SOC and VC density of each land use type remain unchanged with time and consists of temporally fixed inventory data while also emphasizing that SOC and VC storage change occurred due to LUCC. Therefore, the 2010 SOC and VC densities with high sampling density are used in this method.

2.3.2. The Bookkeeping Model

The bookkeeping model has been extensively used to calculate the carbon storage change induced by LUCC. This model calculates annual net carbon emissions from land use changes, mostly on regional or global scales [2,9,19,42]. The model is also a static counting model; thus, the 2010 SOC and VC density are used in the bookkeeping model.
To reflect various ecological response processes to human-activity-related disturbances, the model introduces tabulated functions of carbon losses and gains. Tabulated functions of carbon losses and gains suitable for China were collected (see Supplementary Tables S2 and S3) and included two zones (i.e., subtropical climate zone and temperate climate zone) and five land use disturbance scenarios (conversion of forest to cropland, conversion of grassland to cropland, wood harvesting, reforestation and afforestation, and grassland restoration). Each function contained three scenarios (i.e., low, medium, and high) to index the intensity of the carbon storage changes.

2.3.3. The Inventory Method

The inventory method analyzes the ecosystem carbon stock inventory (mainly vegetation and soil) at different periods to estimate the carbon budgets of terrestrial ecosystems [28]. That is, this method considers not only the change in LUCC-induced carbon storage but also the change in the carbon density of each land use type over time (dynamic carbon density). The calculation is as follows:
Δ C s t o r a g e = ∑ R n ,   i V C R n ,   i ,   T a + S O C R n ,   i ,   T b × Area R n ,   i ,   T a − ∑ R n ,   i V C R n ,   i ,   T a + S O C R n ,   i ,   T b × Area R n ,   i ,   T b
where VC(Rn,i,Ta) and VC(Rn,i,Tb) represent the VC density of land type i in the Rn region in years Ta and Tb, respectively. SOC(Rn,i,Ta) and SOC(Rn,i,Tb) represent the SOCdensity of land type i in the Rn region in years Ta and Tb, respectively. Area(Rn,i,Ta) and Area(Rn,i,Tb) represent the area of land type i in the Rn region in years Ta and Tb, respectively.

3. Results

3.1. LUCC in Henan Province between 1980 and 2015

The areas and spatial distribution of LUCC in Henan Province in 1980, 2010, and 2015 are illustrated in Table 3 and Figure 2. Cropland was the largest land use type in Henan Province (over 60% of the total area), followed by forestland (accounting for approximately 16%), while built-up land, grassland, and waterbodies had proportional areas less than 10%, and unused land comprised less than 1% of all land. For example, in 2015, the areas of LUCC were 1041.65 × 104 ha (62.89% of the total area) for cropland, 270.82 × 104 ha (16.35%) for forestland, 211.59 × 104 ha (12.78%) for built-up land, 89.49 × 104 ha (5.40%) for grassland, 42.42 × 104 ha (2.56%) for waterbodies, and 0.22 × 104 ha (0.01%) for unused land. Spatially, most cropland and built-up land were mainly distributed in regions R1 and R4, and forestland and grassland were mainly concentrated in R2 and R3 (Figure 2).
The period of 1980–2015 in Henan Province and its four ecogeographical regions were characterized by the reduction in cropland and the expansion of built-up land. The land use conversion matrix in each region is shown in Table S4. From 1980 to 2010, 9.34% of the land use areas changed in Henan Province. Specifically, the conversion of cropland into built-up land was the most significant land cover change in R1 (Figure 3a) and R2 (Figure 3b) during this period, with a net change of 17.4 × 104 ha in R1 and 5.21 × 104 ha in R2 (Table S4). During the same period, large areas of forestland and grassland were converted to cropland. For example, the conversion of forestland to cropland and grassland to cropland accounted for 2.98 × 104 ha and 4.94 × 104 ha in R1, respectively, while the conversion of grassland to cropland was 3.28 × 104 ha in R2. In R3, the land cover change was not significant, and the largest areas of conversion were from cropland to grassland (Figure 3c), with only 0.61 × 104 ha (Table S4). The conversion of cropland to forestland and built-up land predominated in R4 (Figure 3d), accounting for 4.30 × 104 ha and 2.26 × 104 ha, respectively (Table S4).
From 2010 to 2015, the loss of cropland was mainly caused by the expansion of built-up land in R1, R2, and R4 (Figure 4a,b,d); the net decrease in cropland in the three regions was 14.49 × 104 ha, 1.53 × 104 ha, and 1.65 × 104 ha (Table S4), respectively. In R3, the largest net change was the conversion of forestland to grassland (accounting for 1.18 × 104 ha) (Figure 4c), followed by the conversion of cropland to waterbodies (0.75 × 104 ha) and the conversion of cropland to built-up land (0.57 × 104 ha).

3.2. Carbon Storage Change Caused by LUCC

The changes in LUCC-induced carbon storage in Henan Province during 1980–2010 and 2010–2015 were calculated based on the proposed three methods. Overall, considerable differences exist among the results of these methods (Table 4). Based on the IPCC and inventory methods, the total carbon storage increased from 1980 to 2015, and the net carbon sink accounted for 4.89 Tg C and 8.15 Tg C, respectively. However, for the same period, the decrease in total LUCC-induced carbon storage ranged from 0.00 to 4.59 Tg C according to the proposed bookkeeping model; the moderate estimate was 0.79 Tg C.
The changes in LUCC-induced carbon storage in four ecogeographical regions between 1980 and 2010 and between 2010 and 2015 are illustrated in Figure 5. The carbon storage change results of these regions, as calculated by the IPCC method, bookkeeping model, and inventory method, show disparate trends in these two periods. For example, during the period of 1980–2010, the inventory and IPCC methods characterized the increase in carbon storage in R1, while the bookkeeping model indicated a decrease in carbon storage in R1 (Figure 5a); during the period of 2010–2015, the inventory method and bookkeeping model suggested that R1 was a carbon sink, while the IPCC method categorized R1 as a carbon source (Figure 5b). Furthermore, R4 was categorized as a significant carbon sink in 1980–2010 because this region was dominated by the conversion of cropland to forestland; R3 was characterized as a significant carbon source in 2010–2015 due to the expansion of built-up land. Nonetheless, based on the three proposed methods, R4 in 1980–2010 and R3 in 2010–2015 show large differences in values for carbon storage change.

4. Discussion

4.1. Differences among the IPCC Method, Bookkeeping Model, and Inventory Method

Taking Henan Province as the study area, we used three different methods to measure the carbon budget of terrestrial ecosystems. Among the proposed methods, the inventory method estimates LUCC-induced carbon budgets by comparing the ecosystem carbon stock inventories (including vegetation and soil) of different periods [28]. As a dynamic carbon storage calculation method, this method reflects not only the impact of LUCC on carbon storage but also the impact of land use management and climate on carbon storage (Figure 6). Therefore, the results based on the inventory method most accurately reflected LUCC-induced carbon storage change.
Both the IPCC method and the bookkeeping model assume that the SOC and VC densities remain constant [4,25]. That is, these methods calculate the change in carbon storage caused by LUCC without considering the possible impact of land use management on carbon storage in the LUCC process (Figure 6). This is mainly due to the universality of these methods. Generally, remote-sensing-based LUCC data are available; however, obtaining SOC and VC densities over multiple years is often challenging. Thus, we consider these as static estimations of carbon storage change. As a result, the principles behind the proposed three methods differ.
Moreover, the results presented in Section 3.2 show both disparate trends and large differences in values. Although the IPCC method and bookkeeping model introduce SOC impact factors for change in land use conversion (Table 2) and tabulated functions of carbon losses and gains in vegetation and soil (Tables S2 and S3), compared with the inventory method these two methods feature high uncertainties regarding LUCC-induced carbon budgets. They may mislead us in assessing carbon budgets for terrestrial ecosystems in a region and should be used with caution. Therefore, at present, the IPCC method and bookkeeping model are simple ways of estimating the carbon budget in the absence of carbon density data at different points in time. Additionally, these methods cannot be used to accurately measure carbon sources/sinks at the regional scale. Other methods, such as the InVEST model, that take carbon density data as a constant value to estimate the impact of LUCC on carbon storage in different periods may have problems similar to those of the IPCC method and bookkeeping model.
Furthermore, unlike the IPCC method, the bookkeeping model calculates annual net carbon emissions from land use changes and introduces tabulated functions of carbon losses and gains to reflect various ecological responses to disturbances related to human activity [41,42,43]. This method may be more suitable for estimating the historical carbon budget [42,43], as the intensity of human LUCC in the historical period was substantial and had a large impact on carbon storage, while historical land use management, limited by technology at that time, had a small impact on carbon budgets. The current situation differs from the historical period. That is, land use has intensified, and modern land management practices have been greatly improved.

4.2. The Effects of Land Use Change and Land Use Management on Carbon Storage

From 1980 to 2010, the area of land use change in Henan Province accounted for 9.34% of the total land, and the corresponding carbon storage increased by 5.38 Tg C according to the inventory method (Table 5). For the same period, 90.66% of the land use areas were unchanged in this region, and with land use management improvements, the carbon storage of this part of the land increased, up to 197.62 Tg C. From 2010 to 2015, the changed land use area was only 1.59% of the total land area, and the carbon storage caused by LUCC became slightly negative, indicating carbon emissions (−0.49 Tg C). The carbon storage increase due to land use management in the same period was as high as 204.14 Tg C. As a result, when the areas of changed land use are far less than those of unchanged land use, carbon storage change in terrestrial ecosystems is dominated by land use management. That is, land use management induces the shift of terrestrial ecosystems in Henan Province from a carbon source or slight carbon sink to a strong carbon sink.
For eastern China, where the land use structure and development processes are similar, the effect of land use management on the increase in terrestrial ecosystem carbon storage is widespread. While western China has a high proportion of natural forestland and grassland, eastern China is dominated by cropland [31]. The management measures for croplands in these provinces, including pesticide spraying, chemical fertilization, straw returning, no-tillage, and highly efficient irrigation, maintain and promote high agricultural yields and enhance carbon stocks [44]. However, these regions face problems such as agricultural nonpoint source pollution and water resource shortages. Thus, trade-offs between agricultural output, carbon sequestration services, water resources, and ecosystems are crucial.
Forestry management, including greening national land, implementing afforestation, and protecting natural forests, is a significant way to increase terrestrial ecosystem carbon storage. For example, greening is very obvious in R4 (Figure 2); the area in this region where cropland was converted to forestland accounted for 4.30 × 104 ha between 1980 and 2010 (Figure 3 and Table S4), and the corresponding carbon storage increased by 3.26 Tg C. Moreover, by overlaying the spatial distribution data of landform types and LUCC data for 2020 at 1 km resolution (http://www.resdc.cn, accessed on 25 July 2022), we see a 67.37% coverage rate for forests in the mountainous areas of Henan Province, which has a great potential to increase. Overall, agriculture and forestry management have a positive impact on terrestrial carbon storage.
Over the past 40 years, a large number of studies and experiments have been conducted in China on the carbon density of terrestrial ecosystems. Overall, these studies are mainly focused on natural ecosystems, including cropland, forestland, and grassland [1,34]. However, experimental data on the carbon density of built-up land and waterbodies are limited [45,46,47]. Thus, previous studies first obtained carbon densities of soil types based on the experimental carbon density sample data. Then, these data were overlaid with remote-sensing-based LUCC data at spatial scales to obtain the corresponding carbon density of each land use type [2,24]. However, according to the Second National Soil Survey of China, the land use types where experimental carbon density samples were collected in eastern China are mostly cropland. As a result, based on the method of “sample to soil type and then to land use”, the derived carbon density of each land use type features high uncertainty. In recent years, some progress has been made regarding improvements in quantifying the carbon density of Chinese urban ecosystems, such as the Urumqi [48], Guangzhou [49], and Xi’an ecosystems [50]. The results indicate that some land types in urban areas, such as urban forests and lawns, have higher carbon densities than their rural counterparts. Thus, previous studies simplified the SOCdensity of built-up land to 0, 1, or 0.33 kg C/m2 [51,52], which overestimated carbon emissions caused by urbanization. For Henan Province, few studies have focused on urban SOC and VC densities. Therefore, carbon budgets caused by the conversion of only three land use types, cropland, forestland, and grassland, were calculated in the present study. However, urban ecosystems undoubtedly have considerable carbon sink potential.

4.3. Uncertainties

The selection of the LUCC data years (1980, 2010, and 2015), analysis periods (1980–2010 and 2010–2015), and geographic units (four ecogeographical regions) in this study was mainly based on matching the collected carbon density data. Although LUCC data have detailed land use information (30 m resolution and 21 subcategories), we needed to merge LUCC data into the primary land types (upscaling) and calculate LUCC-induced carbon budgets at a subregional scale. Due to the insufficient SOC (100 cm depth or more) and VC density data, the spatiotemporal heterogeneity of carbon density at the grid scale was not considered. For example, the carbon densities of paddy fields and dry farmland are not the same, although these two land use types are both croplands. Modeling grid-scale carbon density based on large amounts of sample data and machine learning (e.g., the random forest algorithm) can reduce uncertainty in carbon budget estimation [53]. However, at present, only the 2010 sample data meet the grid-scale carbon density simulation (Figure S1). The upscaled LUCC data and insufficient carbon density data result in the large uncertainty in LUCC-related carbon budgets. Furthermore, the records of the data points are not paired for SOC and VC, and the potential impact of unpaired data points on the estimation of this study needs further study.

5. Conclusions

Terrestrial ecosystems play an important role in the global carbon cycle, but their carbon budget estimates feature high uncertainty. In this study, the carbon budgets related to LUCC in Henan Province from 1980 to 2010 and 2010 to 2015 were calculated using three methods, including the IPCC method, the bookkeeping model, and the inventory method. Cropland was the largest land use type in Henan Province, followed by forestland and built-up land. Henan Province experienced a reduction in cropland and the expansion of built-up land from 1980 to 2015. The total LUCC-induced carbon storage increased from 1980 to 2015, and the net carbon sink accounted for 4.88 Tg C using the inventory method and 8.15 Tg C using the IPCC method. However, according to the bookkeeping model, carbon storage decreased from 0.00 to 4.59 Tg C during the same period. The IPCC method and the inventory method were consistent in their overall estimation of the carbon budget related to LUCC but disagreed with the bookkeeping model. LUCC-induced carbon storage in the four ecogeographical regions between 1980 and 2010 and between 2010 and 2015 had large differences in trend and storage quantity.
The inventory method used the dynamic carbon density data. In addition to calculating LUCC-induced carbon budgets, this method also considers the effect of climate and land use management on carbon stocks. The results were close to the LUCC-induced carbon storage change. The IPCC method and the bookkeeping model used static carbon density data (which remain unchanged with time) and emphasized that carbon storage change occurred due to LUCC. Thus, the last two methods did not include the effects of climate and land use management on carbon density, resulting in the differences among the three methods. The IPCC method and bookkeeping model should be used with caution in calculating carbon budgets, especially for regions where carbon density varies greatly over time.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land11122232/s1, Figure S1: The sampling point of the study area in 2010; Table S1: Land use categories in Henan Province; Table S2: Tabulated functions of carbon losses and gains in vegetation and soil during four land use disturbance scenarios (conversion of forest to cropland, conversion of grassland to cropland, reforestation and afforestation, and grassland restoration); Table S3: Tabulated functions for carbon losses and gains in vegetation and soil during harvesting of wood; Table S4: Transition matrix of LUCC in Henan Province between 1980 and 2010 and between 2010 and 2015 (unit: 104 ha).

Author Contributions

Conceptualization, F.Y. and P.W.; methodology, F.Y. and S.L.; software, F.Y.; validation, Y.G., M.L. and P.W.; formal analysis, F.Y. and S.L.; data curation, Y.G. and P.W.; writing—original draft preparation, F.Y.; writing—review and editing, Y.G.; visualization, M.L.; supervision, P.W.; funding acquisition, F.Y. 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, grant number 42201263, and the Major Program of Key Research Base of Humanities and Social Sciences of the Ministry of Education of China (Study on the Evolution of Ecosystem Pattern, Problem Diagnosis, and Governance Model of the Yellow River Basin; Study on Evolution of Man–land Relationship and Its Eco-environmental Effects in Floodplain of the Lower Yellow River).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data is available on request from the corresponding author. The data is not publicly available.

Conflicts of Interest

The authors declare no conflict of interest.

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Land 11 02232 g001 550
Figure 1. Geographical location of Henan Province in China. (a) Four basins and (b) ecogeographical regions.
Figure 1. Geographical location of Henan Province in China. (a) Four basins and (b) ecogeographical regions.
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Figure 2. Spatial distribution of LUCC in Henan Province in 1980, 2010, and 2015.
Figure 2. Spatial distribution of LUCC in Henan Province in 1980, 2010, and 2015.
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Figure 3. The flow pattern of LUCC in Henan Province from 1980 to 2010.
Figure 3. The flow pattern of LUCC in Henan Province from 1980 to 2010.
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Figure 4. The flow pattern of LUCC in Henan Province from 2010 to 2015.
Figure 4. The flow pattern of LUCC in Henan Province from 2010 to 2015.
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Figure 5. Henan Province’s terrestrial system carbon stock change driven by land use type conversion between 1980 and 2010 and between 2010 and 2015 (bookkeeping in legend denotes the moderate scenario; the negative sign refers to carbon emission; R1–R4 refer to 4 ecogeographical regions).
Figure 5. Henan Province’s terrestrial system carbon stock change driven by land use type conversion between 1980 and 2010 and between 2010 and 2015 (bookkeeping in legend denotes the moderate scenario; the negative sign refers to carbon emission; R1–R4 refer to 4 ecogeographical regions).
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Figure 6. Composition of the terrestrial ecosystem carbon budget.
Figure 6. Composition of the terrestrial ecosystem carbon budget.
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Table
Table 1. Values of SOC and VC in Henan Province in 1980, 2010, and 2015 (unit: kg/m2).
Table 1. Values of SOC and VC in Henan Province in 1980, 2010, and 2015 (unit: kg/m2).
RegionItemsLand Use Type198020102015
R1SOCCropland4.556.007.89
Forestland7.858.089.16
Grassland4.345.775.81
VCForestland4.326.136.16
Grassland0.380.380.69
R2SOCCropland6.386.807.19
Forestland7.858.088.71
Grassland4.345.775.81
VCForestland4.326.136.16
Grassland0.380.380.69
R3SOCCropland7.477.177.58
Forestland7.858.0811.71
Grassland4.345.775.81
VCForestland4.326.136.16
Grassland0.380.380.69
R4SOCCropland6.628.719.20
Forestland7.858.0811.42
Grassland4.344.424.49
VCForestland4.326.136.16
Grassland0.380.380.69
SOC: soil organic carbon, VC: vegetation carbon.
Table
Table 2. SOC impact factors for change in land use conversion.
Table 2. SOC impact factors for change in land use conversion.
ItemsForestlandGrasslandCropland
Forestland 0.100.27
Grassland−0.90 0.20
Cropland−0.80−1.00
SOC: soil organic carbon.
Table
Table 3. The area of land use types in the total and ecogeographical regions in Henan Province (unit: 104 ha).
Table 3. The area of land use types in the total and ecogeographical regions in Henan Province (unit: 104 ha).
RegionYearCroplandForestlandBuilt-Up LandGrasslandWaterbodyUnused Land
Total19801078.16270.09162.99102.0541.061.84
20101061.65272.25192.7188.7940.530.27
20151041.65270.82211.5989.4942.420.22
R11980727.9840.89130.1943.6622.601.66
2010721.1739.67150.7934.5920.650.10
2015705.9439.58165.6834.4121.340.04
R21980131.14113.2310.6841.765.900.14
2010129.07113.0316.7637.216.660.11
2015127.32112.9918.4137.176.840.13
R3198052.5570.114.6813.305.300.03
201051.7769.885.1313.815.350.03
201550.3868.695.7714.756.340.03
R41980165.1745.0417.363.136.710.00
2010158.1648.8419.912.987.480.02
2015156.5448.7521.622.967.500.02
Table
Table 4. Carbon stock change caused by land use type conversion from 1980 to 2015 in Henan Province (unit Tg C).
Table 4. Carbon stock change caused by land use type conversion from 1980 to 2015 in Henan Province (unit Tg C).
Items1980–20102010–20151980–2015
IPCC8.630.488.14
Inventory5.380.494.89
BookkeepingLMHLMHLMH
0.490.294.020.490.500.570.00−0.79−4.59
The negative sign refers to carbon emission; L, M, and H denote the low, moderate, and high scenario, respectively.
Table
Table 5. The proportion of land use change and land use unchanged in total land and their corresponding carbon storage change.
Table 5. The proportion of land use change and land use unchanged in total land and their corresponding carbon storage change.
PeriodLand Use ChangeLand Use Unchanged
Proportion (%)Carbon Storage Change (Tg C)Proportion (%)Carbon Storage Change (Tg C)
1980–20109.345.3890.66197.62
2010–20151.590.4998.41204.14
The negative sign refers to carbon emission.
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Yang, F.; Li, S.; Gao, Y.; Li, M.; Wu, P. Inconsistent Carbon Budget Estimation Using Dynamic/Static Carbon Density under Land Use and Land Cover Change: A Case Study in Henan Province, China. Land 2022, 11, 2232. https://doi.org/10.3390/land11122232

AMA Style

Yang F, Li S, Gao Y, Li M, Wu P. Inconsistent Carbon Budget Estimation Using Dynamic/Static Carbon Density under Land Use and Land Cover Change: A Case Study in Henan Province, China. Land. 2022; 11(12):2232. https://doi.org/10.3390/land11122232

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Yang, Fan, Shicheng Li, Yang Gao, Meijiao Li, and Pengfei Wu. 2022. "Inconsistent Carbon Budget Estimation Using Dynamic/Static Carbon Density under Land Use and Land Cover Change: A Case Study in Henan Province, China" Land 11, no. 12: 2232. https://doi.org/10.3390/land11122232

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