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Journal of Geographical Sciences >

2024 , Vol. 34 >Issue 3: 527 - 542

DOI: https://doi.org/10.1007/s11442-024-2216-0

Research Articles

Impact of the Grain-for-Green Programme and climate change on the soil erosion decline in the Yangtze River, China

  • LI Boyan , 1 ,
  • WANG Yunchen 2
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  • 1. School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
  • 2. School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China

Li Boyan, Associate Professor, specialized in ecohydrological responses to climate change. E-mail:

Received date: 2023-05-24

  Accepted date: 2023-11-17

  Online published: 2024-04-24

Supported by

National Natural Science Foundation of China(42101259)

National Natural Science Foundation of China(42371101)

National Natural Science Foundation of China(42301455)

Qin Chuangyuan Cites High-level Innovation or Entrepreneurship Talent Project(QCYRCXM-2023-066)

Fifth Batch Special Funding (Pre-Station) from China Postdoctoral Science Foundation(2023TQ0207)

Fundamental Research Funds for the Central Universities(GK202304024)

Fundamental Research Funds for the Central Universities(1110011297)

Fundamental Research Funds for the Central Universities(1112010355)

Teaching Reform Project of Shaanxi University(23GGYS-JG06)

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Abstract

The area of land that is affected by soil erosion in the Yangtze River has been reduced by 146,000 km2 (accounting for 27% of the entire Yangtze River) since 2000. However, the contributions of different tributaries to this soil erosion decline and the underlying causes have not been determined. Here we quantify the soil erosion decline in the Yangtze River and the impacts of the Grain-for-Green Programme (GFGP) and climate change using the RUSLE model and statistical methods. The results were as follows: (1) After the implementation of the GFGP, soil erosion decreased in ten sub-basins excluding the Dongting Lake catchment; (2) Soil erosion was mainly affected by the GFGP and the rainfall erosivity. Specifically, the contributions of the GFGP and rainfall erosivity to soil erosion decline are estimated to be 70.12% and 29.88%, respectively. (3) “Scenario #9” means the combination of slope (15°-25°) of retired farmland converted to shrub land and slope (>25°) of retired farmland converted to grassland. Considering scenario feasibility and management targets, Scenario #9 was the most appropriate land use scenario for the Yangtze River. This study offers insights into managing and reducing soil erosion in the future.

Key words： soil erosion; RUSLE model; Grain for Green Programme; climate change; Yangtze River

Cite this article

LI Boyan , WANG Yunchen . Impact of the Grain-for-Green Programme and climate change on the soil erosion decline in the Yangtze River, China[J]. Journal of Geographical Sciences, 2024 , 34(3) : 527 -542 . DOI: 10.1007/s11442-024-2216-0

1 Introduction

With the influences of climate change and human activities, soil erosion, which is the main process of soil erosion export, has attracted widespread attention globally (Loucks et al., 2001; Zhao et al., 2015; Li et al., 2021). In China, many rivers have experienced significant land degradation and environmental crises over the past several decades (Nearing et al., 2005; Teng et al., 2016; Wang et al., 2021). Soil erosion has negative impacts on crop yields (Li and Fang, 2016), dams/reservoirs (Peng et al., 2020; Thapa, 2020), water quality (Hou et al., 2020), hydrological systems (Luo, 2021), ecosystem services (Fu et al., 2011; Xu et al., 2018) and then human well-being (Fu et al., 2011; Zhao et al., 2018). In China, to control soil erosion and mitigate the impacts of the degraded environment, a series of ecological restoration projects were implemented. The Grain-for-Green Programme (GFGP) is one of the biggest programs offering payments for ecosystem services both in China and worldwide in terms of scale, payment, and duration (Liu et al., 2008).
The GFGP began its pilot study in Sichuan province (in the upper reaches of the Yangtze River) in 1999 (Liu et al., 2008; Zhou et al., 2009). It was expanded to 17 provincial-level regions (hereafter province) in 2000, and finally to 25 provinces in 2002. A new round of the GFGP was implemented on very severely desertified irrigated lands, the Three Gorges Reservoir area, the Danjiangkou Reservoir area and the upper reaches of the Yangtze River in 2016 (Wu et al., 2019). After more than 20 years of large-scale vegetation restoration on the Yangtze River, soil erosion caused by unreasonable land use has been curbed (Han et al., 2022). The most notable contribution of the GFGP was an average decrease of 234 Mt yr-1 in the entire basin during 1989-2015 (Yang et al., 2015; Peng et al., 2020).
Soil erosion is affected by many factors, including climate, vegetation cover, topography, root systems, soil properties and land management practices. There are two categories of modeling methods for soil erosion research: physical process models and empirical statistical models. Physical process models use physical concepts, such as the soil erosion and sediment yield balance equation S = f (C, H, G), where S denotes annual soil erosion and C, H, and G represent climatic factors, human factors (i.e., GFGP, vegetation cover, and land management practices), and other factors (i.e., topography, root systems, and soil properties), respectively. Physical process models can simulate soil erosion processes, due to the large amount of input data collected and their complexity (Liu et al., 2019; Coulibaly et al., 2021). Therefore, empirical statistical models are the most widely applied soil erosion models globally. The Revised Universal Soil Loss Equation (RUSLE) is the most famous empirical model of soil loss applied worldwide (Comroe, 1976; Benavidez et al., 2018; Borrelli et al., 2020; Coulibaly et al., 2021; Thakuriah, 2023; Yigez et al., 2023). Several scholars, such as Liu et al. (2020), have developed a series of enhancements to the RUSLE model at different scales and based on various geographical characteristics.
The soil erosion area in the Yangtze River, the longest river in Asia and the fourth in terms of sediment load in the world, has decreased by 146,000 square kilometers since 2000, according to the Ministry of Water Resources of China. There are multiple and complicated causes of soil erosion decline in the Yangtze River (Li and Wang, 2023). Climate change directly affects soil erosion via precipitation. In addition to climate change, intensified human activities (mainly the GFGP) have played an important role in mitigating soil erosion. However, most related studies have primarily concentrated on human activities (especially dam construction in particular), which have been identified as the main cause of the decreasing trend in river sediment loads (Zhang et al., 2019; Peng et al., 2020; Zhou et al., 2020; Li et al., 2021; Li and Wang, 2023). The impacts of climate change and vegetation restoration (caused by the GFGP) on soil erosion decline in different sub-basins of the Yangtze River and the underlying causes have not been determined. Therefore, quantitative assessments of the contributions of the GFGP and climate change are extremely important for increasing our understanding of estuarine and delta evolution (Zhao et al., 2015).
To evaluate the effects of the GFGP and climate change on soil erosion decline in different sub-basins of the Yangtze River and the underlying causes, here, we first quantitatively estimated the soil erosion modulus and extracted the changes in soil erosion before the GFGP (i.e., 1991-1999) and after the GFGP (i.e., 1999-2016), and then we analyzed the spatial distribution of soil erosion during the two periods. The objectives of this study are to (1) detect the trends in soil erosion after the GFGP was implemented in the Yangtze River; (2) analyze the relative contributions of climate factors and human activities (mainly the GFGP) to soil erosion; and (3) recognize land use scenario for retired farmlands proposed by the GFGP according to slope steepness and land use/cover in the basin. This study provides a better understanding of the contributions of the GFGP and climate change to soil erosion.

2 Materials and method

2.1 Study area

The Yangtze River basin lies between 24°30'-35°45'N and 90°33'-122°25'E, and covers 1.8 million square kilometers (Figure 1). It mainly contains ten sub-basins (Figure 1). According to the second national soil loss inventory, the size of the Yangtze River's soil loss was approximately 531,000 square kilometers (30% of the total area). The Grain for Green Programme (GFGP) of the Yangtze River in China was implemented in 1999 to control soil and water loss. We divided the Yangtze River into ten sub-basins: the Jinsha River catchment (M1), upper mainstream of Yangtze River (M2), middle mainstream River (M3), lower mainstream of Yangtze River (M4), Minjiang River catchment (T1), Jialing River catchment (T2), Hanjiang River catchment (T3), Wujiang River catchment (T4), Dongting Lake catchment (T5), and Poyang Lake catchment (T6) (Figure 1).
Figure 1 Yangtze River showing major sub-basins and the location of the hydrological stations

2.2 Data collection

Annual sediment load data from ten hydrological stations from 1991 to 2016 were provided by the Yangtze Water Resources Committee. The annual consistent NDVI dataset was downscaled by the ESTARFM algorithm, which was produced by Li et al. (2021). The land use/cover datasets (resolution: 30 m) for 2000, 2010, and 2016 in China, produced by Yang et al. (2021), were downloaded from http://irsip.whu.edu.cn/resources/CLCD.php. Meteorological data from 1991 to 2016 were provided by the National Meteorological Administration of China (http://data.cma.cn). DEM data were obtained from https://earthexplorer. usgs.gov/. The soil data were obtained from http://www.fao.org/soils-portal/soil-survey/. Parameters used to calculate the Revised Universal Soil Loss Equation (RUSLE) model, as the RUSLE model was proven to be applicable in this region (Xu et al., 2018; Hou et al., 2020; Li et al., 2021; Wang and Li, 2022a). The observed sediment load data from ten main gauging stations (1991-2016) from the Yangtze River (Changjiang) Water Resources Commission were used for the validation of the RUSLE model.

2.3 Methodology

2.3.1 Estimates of soil loss via the RUSLE model

The RUSLE model estimates the spatial distribution of soil erosion (Comroe, 1976). It has been validated by the following example of the study area (Yang et al., 2003; Hou et al., 2020; Li et al., 2021):
$A=R \times K \times L \times S \times C \times P$
$T S L=\sum A \times S D R$
where A denotes the amount of soil erosion (t•ha•yr‒1), R stands for the rainfall erosivity factor (MJ•mm•ha•h•yr‒1) (Zhang and Fu, 2003); K denotes the soil erodibility factor (t•ha•h•ha‒1•MJ‒1•mm‒1) (Williams et al., 1983); L stands for the slope length factor (McCool et al. (1987); S denotes slope factor (McCool et al., 1987); C is the vegetation cover factor (Yang et al., 2003; Bakker et al., 2008; Teng et al., 2019); P refers to the soil conservation practice (Lufafa et al., 2003); TSL is the total catchment sediment load (t•yr‒1); and SDR denotes the sediment delivery ratio in catchment (Zhao et al., 2020). Finally, the RUSLE model's estimation of soil erosion was calibrated using observational data. The soil erosion moduli were divided into six levels in accordance with the national unified erosion classification system (Table 1).
Table 1 The criteria for soil erosion intensity
Levels Average soil erosion modulus (t·ha-1·yr-1)
Low <10
Slight 10-25
Moderate 25-50
High 50-80
Severe 80-150
Extreme ≥150

2.3.2 Model validation

The RUSLE model was verified using the observed data from the Yangtze River. A comparison of the simulated annual soil loss and sediment load observed is shown in Table 1. We calculated the sediment load modulus for the entire Yangtze River in 2010 was calculated as 1.12×108 t. The soil erosion modulus for the entire Yangtze River in 2010 calculated using the RUSLE simulation was 0.97×108 t, so the latter was 13.39% lower than the former. Moreover, the accuracy of the simulation of soil erosion during these two periods achieved a relative error of no more than 13.39% (Table 2). This suggests that the soil erosion modulus calculated using the RUSLE model can be used for further calculations and analysis.
Table 2 Comparison of the mean annual soil erosion amount calculated and sediment load observed for the Yangtze River during two periods
Periods Simulated value of soil erosion (×108 t) Observed value of sediment load (×108 t) Relative error (%)
Before the GFGP (2000) 2.15 1.91 12.57
After the GFGP (2010) 0.97 1.12 13.39

2.3.3 Land use scenarios

Our approach to developing representations of regional land use/cover for each of the ten scenarios (Scenarios #1-#9) was based on an initial regional land cover product (henceforth referred to as the 2016 Land Use Base Map or Base_2016 for short) and slope. First, the slopes of the Yangtze River were calculated from a digital elevation model and classified as moderate slope (15°-25°), or steep slope (>25°) according to the GFGP policy. Second, the GFGP retired cropland areas with moderate slope steepness and steep slope steepness were converted to forest, shrub, and grassland. Third, we assumed that the human disturbances in vegetation, such as those caused by the ecological restoration program before 1999, were relatively small (Shi et al., 2022). The soil conservation capacity was sorted in the following order: Forest, shrub, grassland, and cropland. We were then able to recombine the new land cover extents into one regional coverage map for each scenario. For detailed information, please refer to the literature (Li and Wang, 2023). We followed the same procedures to produce the other ten scenarios (Table 3). For example, “Scenario #9” refers to the combination of slope (15°-25°) of retired farmland converted to shrub land and the slope (>25°) of retired farmland converted to grassland.
Table 3 The potential land use scenario settings used in this study
Scenario sets 15° < Slope < 25° Slope ≥ 25°
Scenario 1 Cropland Cropland
Scenario 2 Cropland Forest
Scenario 3 Cropland Shrub
Scenario 4 Cropland Grassland
Scenario 5 Forest Forest
Scenario 6 Forest Shrub
Scenario 7 Forest Grassland
Scenario 8 Shrub Shrub
Scenario 9 Shrub Grassland
Scenario 10 Grassland Grassland

2.3.4 Attribution of the soil erosion changes

Soil erosion changes (Ai) are influenced by climate factors (rainfall erosivity factor) and human activities (mainly the GFGP), and various other factors (li). We can express this as follows:
$A_{i}=f\left(R E, E E, \lambda_{1}, \lambda_{2}, \lambda_{3} \cdots \lambda_{i}\right)$
We first divided the data into two periods of before the GFGP and after the GFGP to determine the effects of the GFGP, and rainfall erosivity on soil erosion change.
Second, the RUSLE model was divided into three parts: characterizing the rainfall variability (i.e., rainfall erosivity), characterizing the GFGP (e.g., vegetation cover factor), and characterizing the other factor (α0).
Third, we set the following experiments to separate the contributions of rainfall erosivity and GFGP:
(i) Case 1: RUSLE forced by both rainfall erosivity and the GFGP during two periods;
$\overline{A_{\mathrm{P} 1}}=R E_{\mathrm{P} 1} \cdot E E_{\mathrm{P} 1} \cdot \alpha_{0}$
$\overline{A_{\mathrm{P} 2}}=R E_{\mathrm{P} 2} \cdot E E_{\mathrm{P} 2} \cdot \alpha_{0}$
(ii) Case 2: RUSLE forced by rainfall erosivity in period 2 and the GFGP in period 1.
$\overline{A_{\mathrm{P} 2}}=R E_{\mathrm{P} 2} \cdot E E_{\mathrm{P} 1} \cdot \alpha_{0}$
where REP1 and REP2 denote the changes in rainfall erosivity before the GFGP and after the GFGP, respectively; REP1 and REP2 stand for the vegetation cover factor and soil conservation practices before the GFGP and after the GFGP, respectively.
The total change in soil erosion from Case 1 run between the two periods (ΔAtotal) reflects the combined impact of rainfall erosivity and the GFGP.
$\Delta A_{\text {total }}=\overline{A_{\mathrm{P} 2}}-\overline{A_{\mathrm{P} 1}}$
where $\overline{A_{\mathrm{P} 1}}$ and $\overline{A_{\mathrm{P} 2}}$ stand for the mean annual changes in soil erosion between the two periods.
The change in soil erosion in period 2 run Case 1 minus Case 2 can indicate the impact of the GFGP (ΔAEE).
$\Delta A_{E E}=\overline{A_{\mathrm{P} 2}}-\overline{A_{\mathrm{P} 2}}$
Thus, the contributions of rainfall erosivity (CRE) and the GFGP (CEE) to the change in soil erosion can be expressed as follows:
$C_{E E}=\Delta A_{E E} / \Delta A_{\text {total }} \times 100 \%$
$C_{R E}=1-C_{E E}$

3 Results

3.1 Changes of soil erosion

Figure 2 shows the spatial pattern of the Yangtze River's soil erosion intensity in 2000, 2010, and 2016. The majority of the sub-basins experienced low or slight soil erosion level, only a few areas experienced intensive to severe and extreme soil erosion levels (Figures 2a-2c). Figure 2d shows the comparison of the proportional change rate of the soil erosion intensity in the Yangtze River in 2000, 2010 and 2016. The Jinsha River catchment (M1) and Minjiang River catchment (T1) experienced obvious soil erosion in 2000 and 2010 (Table 4).
Figure 2 Spatial patterns of the soil erosion intensity in 2000 (a), 2010 (b), and 2016 (c) and comparison of the proportional change rate of the soil erosion intensity in the Yangtze River in 2000, 2010 and 2016 (d)
Table 4 Changes in the soil erosion moduli of different regions of the Yangtze River between 2000 and 2010 and between 2010 and 2016
Sub-basins Mean soil erosion modulus (t·ha-1) Change in soil erosion modulus (%)
2000 2010 2016 2000-2010 2010-2016
M1 6.75 3.24 0.83 -52.00 -74.38
M2 2.63 0.78 0.30 -70.34 -61.54
M3 1.76 0.81 0.46 -53.98 -43.21
M4 9.77 1.03 0.69 -89.46 -33.01
T1 2.63 2.16 1.04 -17.87 -51.85
T2 2.63 1.73 1.59 -34.22 -8.09
T3 0.56 0.55 0.23 -1.79 -58.18
T4 2.66 1.26 0.43 -52.63 -65.87
T5 0.76 0.55 0.75 -27.63 36.36
T6 1.53 0.65 0.58 -57.52 -10.77
The whole Yangtze River 3.01 1.62 0.75 -46.18 -53.70
Retired cropland 2.41 1.31 0.65 -45.64 -50.38
Non-retired cropland 3.23 1.76 0.80 -45.51 -54.55
In 2000, the total soil erosion in the Yangtze River was 3.01 t·ha-1, with erosion in retired cropland and non-retired cropland totaling 2.41 t·ha-1 and 3.23 t·ha-1, respectively. In 2010, the total soil erosion in the Yangtze River was 1.62 t·ha-1, with erosion in retired cropland and non-retired cropland totaling 1.31 t·ha-1 and 1.76 t·ha-1, respectively. In 2016, total soil erosion in the Yangtze River was 0.75 t·ha-1, with erosion in retired cropland and non-retired cropland totaling 0.65 t·ha-1 and 0.80 t·ha-1, respectively. Due to the large-scale implementation of the Grain for Green Project, soil erosion in the Yangtze River decreased significantly by 46.18% during 2000-2010 and 53.70% during 2010-2016, respectively. In particular, soil erosion in M4 decreased by 89.46% during 2000-2010 and that in M1 decreased by 74.38% during 2010-2016. The soil erosion in the Yangtze River showed a decreasing trend from 2000 to 2016, indicating that the GFGP has improved throughout most of the Yangtze River.

3.2 Changes in soil erosion of retired cropland during 2000-2016

Our study focused on the differences in soil erosion in croplands inside retired croplands before and after the GFGP implementation as a way to assess the effectiveness of the GFGP. The soil erosion in areas where the GFGP was implemented decreased by 993.64×104 t between 2000 and 2010, and the soil erosion in these areas with retired cropland was reduced by an average of 46.86%, equivalent to a reduction in soil erosion of 17.7% during 2000-2010 (Figure 3a and Table 5). In particular, soil erosion in retired cropland areas decreased by 548.46×104 t between 2010 and 2016, and soil erosion in these areas with retired cropland was reduced by an average of 57.23%, equivalent to a reduction in soil erosion of 1.8% during 2010-2016 (Figure 3b and Table 5).
Figure 3 Changes in the soil erosion moduli of retired cropland in the Yangtze River between 2000 and 2010 (a) and between 2010 and 2016 (b)
Table 5 Changes in soil erosion resulting from retiring cropland in the Yangtze River between 2000 and 2010 and between 2010 and 2016
Type of change 2000-2010 2010-2016
Soil erosion (104 t) Proportion1 (%) Soil erosion (104 t) Proportion1 (%)
Cropland→Forest -688.72 -52.78 -351.77 -56.63
Cropland→Shrub -77.47 -55.29 -30.32 -72.35
Cropland→Grassland -169.48 -46.20 -106.13 -56.70
Cropland→Water body -19.10 -45.12 -50.03 -70.79
Cropland→Barren 0.00 -37.98 -0.01 -57.27
Cropland→Impervious -38.87 -43.77 -10.20 -29.63
Total -993.64 -548.46
Mean -46.86 -57.23

Note: 1 Ratio of soil erosion before to after the change in land use.

The transformation of retired cropland to forest, shrub, and grassland accounted for the majority of the reduction in soil erosion. Specifically, the conversion of cropland into forest led to a 688.72×104 t decrease in soil erosion, equivalent to 52.78% of soil erosion before the GFGP, while the conversion of cropland into grassland and shrubland resulted in a decrease of 169.48×104 t and 77.47×104 t, respectively, equivalent to 46.20% and 55.29% of soil erosion before the GFGP. Moreover, retired cropland for impervious land led to a decrease of 38.87×104 t in soil erosion, which was the largest driver of the decline in soil erosion except for the GFGP (Table 5).
Between 2010 and 2016, the first phase (1999-2010) of the GFGP was completed, and the primary focus was on consolidating the achievements of the GFGP, with little cropland still being retired first. Compared with the period 1999-2010, the area affected by soil erosion was slightly lower (only 1.8%). The conversion of cropland into forest decreased by 351.77×104 t, equivalent to 56.63% of soil erosion prior to the GFGP, while the conversion of cropland into grassland decreased by 106.13×104 t, equivalent to 56.70% of soil erosion before the programme.

3.3 Assessment of land use scenarios

The ten land use scenarios we established were based on the 2016 land cover product (hereafter referred to as 2016 land use/cover) (Figure 4a). The land use scenarios of GFGP retired cropland settings according to slope steepness and land use/cover are shown in Table 3. The current soil erosion intensity was subsequently produced (Figure 4b) and the variations in the percentage of soil erosion intensity are depicted in Figure 4c.
Figure 4 Comparison of the changes in the proportion of the soil erosion intensity between 2016 and the ten potential scenarios. (a) Land use/cover in 2016 on the Yangtze River; (b) The degree of soil erosion of the Yangtze River in 2016; (c) Conversion of retired cropland under the ten potential scenarios
“Scenario #5” refers to the combination of slope (15°-25°) of retired farmland converted to forest and slope (>25°) of retired farmland converted to forest (Table 3). Under Scenario #5, the slight, moderate, high, and severe soil erosion decreased by 0.25%, 0.06%, 0.01%, and 0.01%, respectively, whereas low soil erosion increased by 0.33%. The results were similar for Scenario #9, Scenario #7, Scenario #8, and Scenario #6. Thus, improvements in the slight, moderate, high, and severe soil erosion came at the expense of low soil erosion (Figure 4c). In conclusion, the land use scenarios were assigned a ranking of Scenario #9, Scenario #7, Scenario #8, Scenario #6, and Scenario #5 in an ascending order of difficulty.
Table 6 shows the areas of various land use/cover types in 2016 and under the various land use scenarios (Figure 4c). Scenario #9 has the lowest cost of the land use scenarios, which means that land use transformation under this scenario is more feasible owing to its lower cost. However, Scenario #5 is the most expensive of the land use scenarios, implying that land use transformation under this scenario is more difficult to achieve due to its higher cost. Therefore, Scenario #9 is the most suitable land use scenario in the Yangtze River.
Table 6 Land use/cover in 2016 and five potential land use scenarios (km2)
Land use types 2016 Scenario #5 Scenario #6 Scenario #7 Scenario #8 Scenario #9 Scenario #10
Cropland 516965 510382 510382 510382 510382 510382 510382
Forest 825109 858023 849618 849618 749320 749320 749320
Shrub 9721 8296 16701 8296 116999 108594 8296
Grassland 331352 308941 308941 317346 308941 317346 417644
Water 37829 36981 36981 36981 36981 36981 36981
Snow/Ice 3074 2557 2557 2557 2557 2557 2557
Barren 15061 14154 14154 14154 14154 14154 14154
Impervious 40033 39809 39809 39809 39809 39809 39809

3.4 Attribution of the soil erosion reduction

Figure 5 shows the temporal dynamics of the GFGP and rainfall erosivity factor and their effects on changes in soil erosion from 2000 to 2010 (Figure 5a) and from 2010 to 2016 (Figure 5b), respectively. With the exception of the M3 and T3 sub-basins, the GFGP significantly contributed to the decrease in soil erosion during the period 2000-2010. Similarly, the GFGP significantly contributed to the decrease in the soil erosion moduli in sub-basins except for the M4 and T6 during 2010-2016.
Figure 5 Changes in the GFGP and rainfall erosivity factor and their relative contributions to the change in soil erosion moduli in the ten sub-basins of the Yangtze River during the periods 2000-2010 (a), and 2010-2016 (b)
The relative contributions of the GFGP and rainfall erosivity factor to the changes in the soil erosion moduli in the Yangtze River between 2000 and 2016 were 70.12% and 29.88%, respectively (Figure 5). During 2000-2010, approximately 61.99%, and 38.01% of the decreases were attributable to a mean increase in the GFGP and a decrease in the rainfall erosivity factor, respectively (Figure 5a). During 2010-2016, approximately 78.26% of the decline was affected by the GFGP, and the remaining 21.74% decreased due to a decline in rainfall erosivity factor (Figure 5b). These results emphasize that the decrease in the soil erosion moduli is mostly the result of human activities, especially the GFGP. Taking the M1 as an example, the rainfall erosivity factor contributed only 28.82% and 27.04% of the decline in the soil erosion moduli during 2000-2010 and 2010-2016, respectively. Thus, the GFGP is the key driver of the decline in soil erosion moduli in the Yangtze River and its ten sub-basins, while the contribution of the rainfall erosivity factor to soil erosion is weak.

4 Discussion

4.1 Comparison of the results with those of other studies

Previous studies have attempted to explain the reasons for the soil erosion decline in the Yangtze River (Yang et al., 2015; Li and Wang, 2023); however, this study provides a comprehensive evaluation of the causes of soil erosion decline including climate change and the GFGP, and identifies land use scenarios for retired farmland by assessing soil erosion changes in set scenarios. Compared with the studies of Yang et al. (2015), Hou et al. (2020), and Zhou et al. (2009), this study reveals the spatial heterogeneity of soil erosion decline and the contributions to this soil erosion decline from different tributaries and the underlying causes of the Yangtze River. According to our results, the area of soil erosion has decreased by 14.62×104 km2, a decrease of 27.45% compared with that in 2013. Intensified human activities (mainly the GFGP) were the major influencing factor on the decrease in soil erosion (accounting for 70.12%) in the Yangtze River, These findings are the second national remote sensing monitoring of soil and water loss data in 2002 despite the differences in the study period, and previous studies by Zhao et al. (2020) despite the differences in the study area. For instance, Zhao et al. (2015, 2017) showed that the influence of human activities on sediment load exceeded that of climate change from 1970 to 2010, and thus played a key role in soil erosion change.

4.2 Contribution of driving factors

Soil erosion is affected by climate change, and the GFGP, land use, topography, root systems, soil properties and land management practices have been observed in previous studies (Comroe, 1976; Fu et al., 2010; Borrelli et al., 2020; Zhou et al., 2020). To determine the extent to which climate (especially precipitation) and human activities (mainly the GFGP) contributed to the variations in soil erosion, we performed two model experiments (Case 1 and Case 2) to separate the contributions of climate and human activities to soil erosion changes (Figure 5). In the Yangtze River, the annual average soil erosion during the post-GFGP period declined significantly compared with that in the pre-GFGP period, and the GFGP effectively reduced soil erosion between 2000 and 2016. In general, the relative contribution ratios of the GFGP were high (from 61.99% to 78.26%) in the Yangtze River, while the contributions of the GFGP for the ten tributaries ranged from 1.37% to 144%. The most significant human impact on soil erosion was also experienced by the Jinsha River catchment (from 119% to 144%). These results suggest that the impact of the GFGP on soil erosion exceeds that of climate change, and thus plays a more substantial role in soil erosion change. We did not consider the effects of the construction of check dams and reservoirs on soil erosion, which might lead to some uncertainty. Despite the above limitations, this study combined observations to comprehensively provide a comprehensive picture of the soil erosion response to the GFGP, and climate change in different sub-basins of the Yangtze River.

4.3 Policy implications

In the Yangtze River, forestland and grassland have significantly curbed soil erosion, and the GFGP has improved the local eco-environment. The development of mega-cities (e.g., Shanghai, Wuhan, Chengdu, and Chongqing) had negative effects on the ecosystem and increased the pressure on the ecological environment. Thus, a priority should be given to protect the natural ecology of the Yangtze River for future development. The GFGP could continue to be implemented in key areas as part of broader advocacy for green development (Fang et al., 2021; Gao et al., 2022; Li and Wang, 2023). Environmental protection should emphasize sustainable and coordinative development between nature and the social economy (Wang et al., 2022; Wang and Li, 2022b).

4.4 Limitations

Our study has certain limitations. For instance, the input data, parameters, verification data, and other physical processes that we have not taken into account might all contribute to the uncertainty in the RUSLE model (Qi et al., 2023). However, the RUSLE model has been widely used in different regions of the world (Ma et al., 2003; Yang et al., 2003; Teng et al., 2016; Hou et al., 2020; Chen et al., 2022). Some input parameters of the RUSLE model were extracted from previous studies. We employ hydrological station observation data with high-quality for verification. Therefore, the RUSLE model we created can satisfy the needs of this research.

5 Conclusions

We evaluated the spatiotemporal distribution characteristics of soil erosion before and after the GFGP and developed ten land use scenarios based on slope steepness (>25° and 15°-25°) and a 2016 land use/cover map. We also quantified the contributions of the GFGP and rainfall erosivity factor to changes in soil erosion in the Yangtze River. The main conclusions are as follows: (1) The soil erosion intensity in the Yangtze River showed a decreasing trend during 2000-2016, indicating that the GFGP has improved in the majority of the Yangtze River's areas. (2) Soil erosion in areas with retired cropland was reduced by an average of 46.86%, corresponding to a decline in soil erosion of 17.7% during 2000-2010, and soil erosion in these areas with retired cropland was reduced by an average of 57.23%, equivalent to a reduction in soil erosion of 1.8% during 2010-2016. (3) “Scenario #9” refers to the combination of slope (15°-25°) of retired farmland converted to shrub land and slope (> 25°) of retired farmland converted to grassland, while “Scenario #5” refers to the combination of slope (15°-25°) of retired farmland converted to forest and slope (>25°) of retired farmland converted to forest. In the Yangtze River, Scenario #9 is the most suitable land use scenario, and Scenario #5 is more difficult to achieve due to higher cost. (4) The impacts of the GFGP and rainfall erosivity factor explained 70.12%, and 29.88%, respectively, of the soil erosion moduli decrease in the Yangtze River. Our analysis provides a simple yet effective approach for determining the causes of soil erosion changes and a reference for large-scale restoration programs in large river basins such as the Yangtze River.

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