Modelling and mapping soil erosion potential in China

Journal of Integrative Agriculture 2019, 18(2): 251–264 Available online at www.sciencedirect.com

ScienceDirect

RESEARCH ARTICLE

Modelling and mapping soil erosion potential in China TENG Hong-fen1, 2, HU Jie1, ZHOU Yue1, ZHOU Lian-qing1, SHI Zhou1 1 2

College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, P.R.China State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing 210008, P.R.China

Abstract Soil erosion is an important environmental threat in China. However, quantitative estimates of soil erosion in China have rarely been reported in the literature. In this study, soil loss potential in China was estimated by integrating satellite images, field samples, and ground observations based on the Revised Universal Soil Loss Equation (RUSLE). The rainfall erosivity factor was estimated from merged rainfall data using Collocated CoKriging (ColCOK) and downscaled by geographically weighted regression (GWR). The Random Forest (RF) regression approach was used as a tool for understanding and predicting the relationship between the soil erodibility factor and a set of environment factors. Our results show that the average erosion rate in China is 1.44 t ha–1 yr–1. More than 60% of the territory in China is influenced by soil erosion limitedly, with an average potential erosion rate less than 0.1 t ha–1 yr–1. Other unused land and other forested woodlands showed the highest erosion risk. Our estimates are comparable to those of runoff plot studies. Our results provide a useful tool for soil loss assessments and ecological environment protections. Keywords: soil erosion potential, RUSLE, mapping, modelling

1. Introduction Soil erosion by water, hereafter soil erosion, not only causes soil nutrient loss and land degradation, but also degrades water quality and clogs rivers and reservoirs (Zhu et al. 2013; Wang B et al. 2016). The rate of soil erosion is influenced by climate, physical, hydrological, chemical, mineralogical and biological factors, such as rainfall amount and intensity, runoff depth and velocity, and the susceptibility of soil to

Received 8 December, 2017 Accepted 30 May, 2018 TENG Hong-fen, E-mail: [email protected]; Correspondence ZHOU Lian-qing, E-mail: [email protected] © 2019 CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/) doi: 10.1016/S2095-3119(18)62045-3

erosion (Wang et al. 2015). According to the Bulletin of First National Census for Water, China is severely affected by soil erosion; the total area of land affected by water and wind is estimated to be about 3 million km2, which represents approximately 32% of the territory of China (MWR and NBS 2013; Wang B et al. 2016). Precipitation, including extreme events such as heavy precipitation and droughts, will change significantly in China, and these changes might continue in the near future (Jiang et al. 2016), and thus accelerated soil erosion processes. The spatio–temporal variation of soil erosion in China must be analysed quantitatively and rapidly. To calculate erosion risk and identify ways to control soil loss in China, descriptions of erosion have been performed through field surveys (Zhang et al. 2009; Zhu et al. 2013; Guo et al. 2015; Zhao et al. 2017) and laboratory experiments (Li and Zhang 2010; Shi et al. 2010; She et al. 2014) directly. However, these precise predictions are difficult to apply on a large scale, especially for the national

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scale of China, because erosion combines the effects of land surface cover, soil conditions and environmental factors (Park et al. 2011). Guo et al. (2015) reviewed soil erosion based on runoff plot studies in China. Wang X et al. (2016) employed an empirical approach to monitor soil erosion in China and analysed its relationship with land use change. Chen et al. (2017) reviewed the effects of terracing on soil conservation in China. However, these studies failed to estimate and map soil erosion quantitatively in China. Thus, forecasting formulae based on statistical approaches are essential for estimating soil erosion across China. The Revised Universal Soil Loss Equation (RUSLE) is the product of complex factors including climate, soil, terrain, vegetation and human practice (Shi et al. 2004; Teng et al. 2018a), which is used to calculate soil loss rate from hillslopes (Wischmeier and Smith 1978). RUSLE can be used to estimate soil erosion potential over large areas, including on the regional (Park et al. 2011; Zhao et al. 2012; Xu et al. 2013; Teng et al. 2018a), national (Teng et al. 2016; Yue et al. 2016), continental (Lu et al. 2003; Panagos et al. 2015a), and global (Yang et al. 2003; Borrelli et al. 2017) scales. To estimate soil erosion in China, spatially explicit data sets are needed to fulfil the requirements of the RUSLE model. However, the collection of these data sets over periods of time using field experiments are difficult and expensive, so such studies are only available over limited areas. The dearth of suitable data sets creates a need to make soil erosion estimates, which is a challenging prospect over large regions. In this study, soil erosion in China was modelled and estimated based on the RUSLE by integrating satellite images, field samples, ground observations and machine learning techniques.

2. Materials and methods Using an approach with climate, soil, terrain, vegetation and human practice factors as inputs, the simplicity of RUSLE facilitates the analysis of large data sets and calculations over large scale assessments (Kinnell 2010). A=R×K×C×LS×P (1) –1 –1 Where, A is the annual soil erosion (t ha yr ); R is the rainfall erosivity factor (MJ mm ha–1 h–1 yr–1); K is the soil erodibility factor (t ha h ha–1 MJ–1 mm–1); C is the cover management factor; LS is the slope length and steepness factor; and P is the control practice factor.

2.1. Rainfall erosivity factor (R) The R factor is an indicator which combines the effects of the duration, magnitude, and intensity of rainfall invents (Teng et al. 2017). Daily precipitation data that provided

by the National Climate Centre of the China Meteorological Administration (CMA) was used in this study. For the fifteen-year period from 2002–2016, daily rain gauge data from 650 stations across China were selected in this study (Fig. 1-A). Rainfall estimates from the 3-hourly tropical rainfall measuring mission (TRMM) provided by algorithm 3B42 Version 7 (Huffman et al. 2007; Teng et al. 2014; Ma et al. 2017), with a spatial resolution of 0.25°×0.25°, were also used in the calculation of the R factor in this study. TRMM 3B42 rainfall products were accumulated over 24 h to construct daily rainfall estimates. In this study, the R factor was modeled following the steps described by Teng et al. (2017): first, rain gauge and TRMM measurements were merged by Collocated CoKriging (ColCOK) and thus to provide a new daily rainfall product with less error and high spatial resolution. Then, a power function model, which was developed by Zhang et al. (2002) and widely used in China (Sun et al. 2014; Gu et al. 2016; Qin et al. 2016), was applied to calculate R factor using the merged daily rainfall data. Zhang et al. (2002) estimated R based on this model and verified the result by EI30. The results indicated that the daily model showed a better performance than other models, with an average R2 of 0.718. This method estimates half-month R value: j

Rk=m∑ (dij)n

(2)

i=1

Where, Rk is the R value of the i half-month (MJ mm ha h−1); j is the days in the k half-month; and dij is the effective precipitation (≥12 mm) for day i of the k half-month. Otherwise, dij is equal to zero. The parameters m and n are defined as: m=21.586n–7.1891 (3) 18.114 24.455 n=0.8363+ + (4) d12 y12 −1

Where, d12 is the average daily rainfall (≥12 mm), y12 is the yearly average rainfall for days with rainfall no less than 12 mm. Monthly and annual R value can be obtained by aggregating R values in each half-month. In addition, geographically weighted regression (GWR) (Xu et al. 2015; Ma et al. 2017) was applied to downscale R factor to 1-km spatial resolution. Some key steps were described in Ma et al. (2017) and Teng et al. (2017). The environmental variables, including terrain attributes, climatic variables, vegetation and soil (Table 1), were chosen in the downscaling procedure. Before merging daily rainfall data by ColCOK, the 650 rain gauges were split into a training set (433) and a test set (217) randomly (Fig. 1-A), following the approach presented in Teng et al. (2017). The 217 testing gauges were used to validate the performance of the model independently. The predicted R value was estimated by the root mean square

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Map inspection number: GS(2018)4313

Fig. 1 Maps of rain gauges (A), soil samples (B), soil types (C) and land use types (D) used in this study.

Table 1 The environmental predictors used in the machine learning methodologies Factor Terrain Climate

Variables1) DEM, slope, aspect, curvature, roughness index, topographic wetness index, MrVBF Mean annual rainfall, mean annual temperature Mean annual solar radiation Mean annual evapotranspiration, land surface temperature (day and night) Prescott index NDVI, NPP Soil type, land use type Sand, silt, clay, SOC

Vegetation Soil/Land

Resolution 90 m 1 km 1 km 1 km 90 m 1 km 1 km 1 km

Source2) SRTM CMA RESDC MODIS SRTM MODIS RESDC HWSD

1)

DEM, digital elevation model; MrVBF, multi resolution index of valley bottom flatness; NDVI, the normalized difference vegetation index; NPP, net primary productivity; SOC, soil organic carbon. 2) SRTM, Shuttle Radar Topography Mission; CMA, China Meteorological Administration; RESDC, Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences; MODIS, moderate resolution imaging spectroradiometer; HWSD, Harmonized World Soil Database.

error (RMSE) and coefficient of determination (R2) based on the test set: RMSE=

1 n

n

∑ (Rm–Rt)2 i=1

(5)

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∑(Rm–Rm)(Rt–Rt) ∑(Rm–Rm)2∑(Rt–Rt)2

(6)

Where, n is the gauges used in the analysis, Rt is the R

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factor value estimated by training set, and Rm is the R factor value estimated by test set.

2.2. Soil erodibility factor (K)

Li, j=

The K factor was calculated following recommendations of Sharpley and Williams (1990). Kepic=0.1317

(

)(

Sil Cla+Sil 0.7SN1

Sil [ –0.0256San (1– )] 100 0.2+0.3e

)

(7) 0.25TOC 1– TOC+e(3.72–2.95*TOC) SN1+e(22.9*SN1–5.51) Where, San is the sand content (0.05–2 mm); Sil is the silt content (0.002–0.05 mm); Cla is the clay content (<0.002 mm); TOC is the soil total organic carbon (TOC) content; and SN1=1–San/100. All these contents are presented in %. Zhang et al. (2008) evaluated the accuracy of the environmental policy integrated climate (EPIC) model for estimating the K factor and developed an improved model to estimate K value. The improved model was built based on the relationship between K values that were modelled by EPIC and those calculated using soil loss ratio from natural experiments in China. Their results showed that the estimated K value using the improved model was much improved compared to the EPIC estimates. In this study, the final K values were calculated following the recommendations of Zhang et al. (2008). (8) K=–0.01383+0.5158Kepic Data for the soil texture and organic carbon contents were collected from the 3 758 soil profiles (see Fig. 1-B) analysed during the Second National Soil Survey (NSSO 1993, 1994a, b, 1995a, b, 1996, 1998). After calculation, digital soil mapping (DSM) methodology (McBratney et al. 2003; Zhang et al. 2017) was applied to map K factor over China: K(u; t)=f(s[u; t], c[u; t], v[u; t]r[u; t], p[u; t]) (9) Where, the factors describe the interactions between soil (s), climate (c), vegetation (v), terrain (r), and geology (p) in space (u={u1; u2}), and in time (t). It should be noted that sand, silt, clay and TOC have been used in the calculation of K values (eq. (7)), and thus these variables in the Table 1 were not included in the K mapping procedure. Random Forest (RF) was used in the K mapping procedure in this study. Its theoretical details can be found in the literature (Viscarra Rossel and Behrens 2010; He et al. 2016; Teng et al. 2018b). From the 3 758 data, 2 506 data were randomly selected and used for training the model by 10-fold cross validation. The remaining 1 252 data were used for validation. The accuracy of the model was shown using R2 and RMSE of the predictions. 1–

(approximately 90 m) grid Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) following the methodology presented by Desmet and Govers (1996).

2.3. Slope length and steepness factor (LS) The LS factor was calculated using the 3 arc–second

(Ai, j–in+D2)m+1–Ai,m+1 j–in

(10)

Dm+2xmi, j 22.13m

β m= (1+β) β= S=

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(sinθ/0.0896) (12)

[3(sinθ)0.8+0.56] 10.8sinθ+0.03,

θ<9%

16.8sinθ–0.5,

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Where, Li, j is the L factor from the cell (i, j); Ai, j–in is the contributing area at the inlet of the grid cell measured in m2; D is the cell size; xi, j=(sinαi, j+cosαi, j); αi, j is the aspect direction for the cell (i, j); θ is the slope angle.

2.4. Cover management factor (C) and control practice factor (P) As a factor that is influenced by the vegetation, land surface roughness, land use type and mulch cover (Mhangara et al. 2012), the C factor is known to be difficult to estimate at the national scale and also vary considerably during the seasons. The P factor can be estimated according to the land cover type (Teng et al. 2018a). In this study, Land-Use/Cover Data set (Liu et al. 2010) in China with a resolution of 1 km, which is publicly shared by the Data Centre for Resources and Environmental Sciences at the Chinese Academy of Sciences (RESDC) (http://www.resdc. cn), is used to derive the C and P values according to the classification criteria that listed in the Table 2 in Teng et al. (2018a).

3. Results 3.1. Erosion factors in China Maps of the erosion factors are shown in Fig. 2. It should be noted that areas of cities, rocks, salt crusts, water bodies and permanent glaciers, which are commonly deemed to be soil-free areas, are excluded from consideration. The spatial pattern of the predicted annual R factor in China during the period of 2002–2016 is represented in Fig. 2-A. For the downscaled R factor, the validation results of GWR model showed that R2 and RMSE were 0.86 and 804.36 MJ mm ha–1 h–1 yr–1, respectively. R value less than 100 MJ mm ha–1 h–1 yr–1 mainly existed in the northwestern China. The largest R values (>10 000 MJ mm ha–1 h–1 yr–1) were observed in southern China, in accordance with the local climate. Given 217 testing rain gauges data, the results

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Table 2 Conversion from erosion rate to erosion grade, with corresponding areas of each erosion grade and its proportion in China Soil loss rates (t ha–1 yr–1)

Erosion grade

<0.1 0.1–1 1–2 2–11 11–20 20–30 >30

A

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Proportion of erosion areas of each loss module (%) 63.37 22.09 4.57 6.93 1.37 0.70 0.98

Area (×104 km2) 543.78 189.57 39.19 59.44 11.75 6.00 8.44

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Fig. 2 Maps of rainfall erosivity factor (R; A), soil erodibility factor (K; B), slope length and steepness factor (LS; C), cover management factor (C; D) and control practice factor (P; E).

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of R values were satisfactory, with R2 of 0.90 and RMSE of 693.15 MJ mm ha–1 h–1 yr–1. The map of K factor is illustrated in Fig. 2-B. The R2 and RMSE were 0.56 and 0.032 t ha h ha–1 MJ–1 mm–1 for the K factor in the DSM validation procedure. Fig. 2-B shows that the soils with K factor less than 0.01 t ha h ha–1 MJ–1 mm–1 are mostly found in the sandy soil with a caliche layer among desert regions (the Tarim Basin and Qaidam Basin). Soil which is hard to get detached and carried away by overland flow also shows small K value. The most erodible soils (K>0.022 t ha h ha–1 MJ–1 mm–1) are mostly observed in the eastern part of the Tianshan Mountains. Fig. 2-C describes the LS factor map of China. The lowest LS factor (<0.1) occurred in the areas of deserts, e.g., in the Tarim Basin and Qaidam Basin. Areas with low relief, such as the Yangtze Plain and North China Plain, also showed low LS values. Whereas the highest LS factors (>8) occurred in the western part of the Sichuan Basin, the Hengduan Mountains and the southern Himalayas. The C factor map is displayed in Fig. 2-D. The C values have a tendency to grow from the southeast to the northwest of China. The largest C values occurred in areas where almost no vegetation exist, for example, the Qaidam and Tarim Basins. The smallest C values were observed in the rain forests with evergreen trees, e.g., in the southern and northeastern China. The North China Plain and Yangtze Plain have relatively large C values. Fig. 2-E, namely the P factor map, shows the decrease in soil erosion resulting from 80°

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3.2. Soil loss potential in China A map of the potential annual soil erosion of China (1-km resolution) is shown in Fig. 3. The average potential erosion rate in China is 1.44 t ha–1 yr–1, which means around 1 180.28×106 tonnes of soil is potentially lost annually in China (Table 3). Southwestern China, especially the eastern Tibetan Plateau, the Hengduan Mountains and western Yunnan-Guizhou Plateau showed the greatest erosion potential (>10 t ha–1 yr–1). The smallest rates (<0.001 t ha–1 yr–1) were evident in northwestern China, particularly in the Tarim Basin, Qaidam Basin and southern Kunlun Mountains (Fig. 3). The Sanjiang Plain, Hetao Plain and eastern Yangtze Plain also showed low erosion rates (Fig. 3). The estimated average erosion rate in China was converted into seven erosion grades (Limited, Slight, Light, Moderate, Intense, Extremely Intense, and Severe) according to the rules listed in Table 2. Fig. 4 presents a map of China’s soil erosion condition at different grades. According to Table 2 and Fig. 4, limited erosion areas account for the largest proportion of erosional areas in China and mostly occur in the Yangtze Plain, North China Plain, northwestern and northeastern China, whereas Extremely Intense and Severe erosion areas were obligated to the 130°

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TENG Hong-fen et al. Journal of Integrative Agriculture 2019, 18(2): 251–264

lowest proportion of the erosional area in China and mostly appear in the eastern Tibetan Plateau and the Hengduan Mountains (Fig. 4).

3.3. Regional and land use assessment Table 3 describes the estimated average and total soil erosion potential in China. Hong Kong, Sichuan and Chongqing have the highest erosion rates (Table 3). Fujian, Yunnan and Hainan have the next highest erosion rates. Jiangsu, Shanghai and Jilin have relatively low erosion rates. Inner Mongolia, Tianjin and Heilongjiang have the lowest rates of erosion (Table 3). The total soil loss in southwestern China is more than 40% of the total annual soil loss of China. Northeast China, including Liaoning, Jilin and Heilongjiang, accounts for less than 1% of the total soil loss of China. Table 4 shows the estimated potential average and overall soil loss in China categorized by land use type. Other

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unused land and other forested woodlands have the largest erosion rates and total soil loss. Other forested woodland have experienced relatively minor total soil loss due to their limited areas over China (Table 4). Grasslands, bare land and dry land have the next highest erosion rates. The Gobi desert, sandy land and saline-alkali land have lower rates of erosion. Woodlands, wetlands and paddy fields have the lowest rates of erosion (Table 4). The total amount of these land use types accounts for approximately 1% of the entire annual soil loss from these areas.

3.4. Comparison of erosion rates with runoff plot assessments To assess the skill of our modelling, we compared the predicted potential soil erosion rates with erosion rates measured on runoff plots collected from the literature (Table 5). All plots were selected according to the

Table 3 Estimates of annual soil loss potential across China Region Southwest China

Southern China

Southeast China

Central China Northern China

Northeast China

Northwest China

Tibetan Plateau Inner Mongolia China

Province/Region Sichuan Chongqing Yunnan Guizhou Hong Kong Hainan Guangdong Guangxi Fujian Taiwan Zhejiang Jiangxi Anhui Jiangsu Shanghai Hubei Hunan Shaanxi Shanxi Shandong Hebei Henan Beijing Tianjin Liaoning Jilin Heilongjiang Gansu Xinjiang Ningxia Tibet Qinghai Inner Mongolia

Erosion rate (t ha–1 yr–1) 5.73 5.07 4.54 2.72 6.83 4.4 1.88 1.71 5.07 3.84 1.24 0.92 0.96 0.19 0.16 1.41 1.14 2.78 0.88 0.72 0.49 0.39 0.34 0.06 0.45 0.13 0.06 1.72 0.58 0.35 1 1.45 0.12 1.44

SD (t ha–1 yr–1) 15.58 12.74 11.7 7.74 16.98 107.5 40.38 26.42 20.37 25.74 9.55 5.52 5.34 7.5 5.66 6.19 5.62 7.49 1.97 9.4 4.13 1.47 1.12 0.42 5.36 0.97 0.26 9.6 5.49 0.74 3.77 6.32 0.39 12.28

Total soil loss (×106 t yr–1) 261.03 39.96 168.32 47.07 0.51 13.33 28.37 38.15 57.06 12.04 10.86 14.02 10.99 1.23 0.05 22.76 22.49 54.89 13.01 8.5 7.99 5.26 0.43 0.04 5.72 2.35 2.6 59.87 73.43 1.7 97.35 86.48 12.45 1 180.28

Proportion of soil loss of each province/region (%) 22.12 3.39 14.26 3.99 0.04 1.13 2.4 3.23 4.83 1.02 0.92 1.19 0.93 0.1 0 1.93 1.91 4.65 1.1 0.72 0.68 0.45 0.04 0 0.48 0.2 0.22 5.07 6.22 0.14 8.25 7.33 1.06 100

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Fig. 4 Map of soil erosion grade of China at 1-km resolution.

Table 4 Estimates of annual soil loss potential across China by land use types Land use type Other unused land Other forested woodland Medium-coverage grassland High-coverage grassland Low-coverage grassland Bare land Dry land Shrubbery land Sparsely forested woodland Gobi Sandy land Saline-alkali land Woodland Wetland Paddy field

Mean soil loss (t ha–1 yr–1) 13.20 6.79 3.34 2.78 1.72 1.72 1.26 0.61 0.57 0.25 0.25 0.22 0.07 0.07 0.02

SD (t ha–1 yr–1) 27.88 30.95 11.53 9.43 10.09 36.66 6.63 1.10 1.17 1.20 32.05 27.91 0.15 1.02 0.78

following criteria: (i) under natural rainfall conditions and (ii) obtained from direct measurements with site and plot-data descriptions for interpretation were selected. A total of 237 plot data from 22 studies were used in the validation. For each plot, the erosion rate was recorded. The comparison was made by taking erosion rates for both measured and predicted values for the plot locations. The relationship between our predictions and the plot measurements is shown in Fig. 5; it has a R2 of 0.72 and a RMSE of 22.14 t ha–1 yr–1. Our prediction underestimated the relatively high erosion rate (>100 t ha–1 yr–1). In general, the potential erosion rates that modelled using RUSLE compares well with erosion rates calculated using runoff plots.

Total soil loss (×106 t yr–1) 115.92 33.96 354.20 269.32 152.79 4.90 160.74 29.14 19.64 11.62 13.89 2.85 9.76 0.50 1.06

Proportion of soil loss of each land use type (%) 10.09 2.95 30.82 23.43 13.29 0.43 13.99 2.54 1.71 1.01 1.21 0.25 0.85 0.04 0.09

4. Discussion Previous studies of soil erosion in China were mainly focused on regional (Zhao et al. 2003; Hu et al. 2010; Fu et al. 2011; Zhang et al. 2013; Wei et al. 2014; Ma et al. 2016; Sun et al. 2016; Zhang and McBean 2016; Wu et al. 2017) or watershed scale (Li et al. 2010; Miao et al. 2012; Wang et al. 2013, 2017; Zhang et al. 2014; Du et al. 2016; Ma et al. 2016), and only a few of them were conducted at the national scale (Guo et al. 2015; Wang X et al. 2016; Yue et al. 2016; Chen et al. 2017). Our research is the first attempt to rapidly quantify soil erosion potential in China with

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Table 5 Data sources used for validation Region Fujian

Plot number 18 21 3 3 1 5 1 3 6 4 10 2 18 18 5 9 12 22 6 15 12 12 3 18 10

Gansu Guangxi

Guizhou Heilongjiang Henan Hubei Inner Mongolia Jiangsu Jiangxi Jilin Liaoning

Erosion rate pledicted by model (t ha–1 yr–1)

Shandong Sichuan Yunnan Zhejiang

Soil erosion rate (t ha–1 yr–1) 32.57 32.48 1.89 1.79 0.26 3.62 0.02 1.27 11.19 3.59 1.32 4.9 2.67 8.78 10.38 20.66 60.39 20.28 12.82 25.45 7.28 11.34 4.58 405.11 3.87

700 R =0.72 2

600

RMSE=22.14

500 400 300 200 100 0 0

100 200 300 400 500 600 700 Erosion rate derived from plot (t ha–1 yr–1)

Fig. 5 Comparison between erosion rates predicted by the model used in our study and those derived from runoff plots for 237 sites. RMSE, root mean square error.

the most current and public sharing data sets. The effect of high steep topography (steep and long slopes) is most visible in the southwestern China, which has the highest potential erosion rates, even though relatively high vegetation cover exists in this area. Due to the combination effect of high R and K values, the potential erosion risk in

Reference Huang et al. (2007) Ding et al. (2006) Zhu et al. (2003) He et al. (1992) Huang et al. (2010) Huang and Liang (1999) Huang et al. (2012) Zhang et al. (2001) Chen et al. (2006) Zhao and Wei (2009) Huang et al. (2000) Xiang et al. (2001) Zheng et al. (2006) Chen et al. (2006) Zhang et al. (2009) Fan et al. (2005) Zuo et al. (2003) Chen et al. (2006) Han et al. (2007) Chen et al. (2006) Sun et al. (1997) Liu et al. (2007) Chen et al. (2002) Yang (1999) Yang et al. (2004)

southern China is also remarkable. The Tibetan Plateau, which includes Tibet and Qinghai, has a relatively high potential mean and total soil loss because of the large areas it occupies (Table 3). The potential erosion risk on flat land of the Sanjiang Plain, the North China Plain and the Middle-Lower Yangtze River, which are the main grainproducing areas in China, is low because of the flat terrain and advanced farming management, therefore, the amounts of soil erosion resulting from water runoff could be negligible. The Loess Plateau, which is one of the most severely eroded area in the world, showed a significant declining trend of soil erosion thanks to ecological construction and the increment in vegetation cover. Over the last ten years, the Grain-to-Green Program has been witnessed to be effective in improving land cover and decreasing erosion risk in the Loess Plateau (Sun et al. 2014). Soil loss potential on the high canopy cover land, particularly on the woodland and shrubbery land, is lower than that on the low canopy cover land, such as other forested woodland, low-coverage grassland and bare land. Grasslands are mainly distributed in western China at high altitude, high-coverage grasslands that accounted for more than half ground cover showed lower potential erosion rates than medium-coverage grasslands. Dense vegetation cover acts a pivotal part in reducing the loss of soil by decreasing the velocity of raindrops and protecting them from directly scouring soil surface particles (Wei et al. 2010). The dense

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vegetation slows land surface water flow and its root system decreases runoff by improving soil characteristics (Sun et al. 2014). Terrace fields, which usually occur on hillsides with a specific slope, have the lowest erosion risks because of the support practice of ridges. The preservation of ridges is crucial for water and soil conservation on steep slopes (Panagos et al. 2015b). In this study, soil erosion in China is estimated based on the RUSLE. RUSLE refers to the net loss of soil by soil erosion, which calculates the quantity of sediment that lands the end of a hillslope. It does not include areas of the slope that experience net deposition. Estimates of potential gross soil loss is inherently inconsistent with net estimates of erosion, as soil can be redeposited elsewhere in the study area. RUSLE has been noted to overestimate soil erosion (Teng et al. 2016), and efforts have been made to improve the erosion factors in the RUSLE model to better estimate soil erosion (Xie et al. 2016; Zhang et al. 2017; Feng et al. 2018). This study will apply improved methodologies in evaluating soil erosion potential in China. Our gross estimates of soil loss potential in China are significantly lower than those derived from runoff plot assessments (Table 5 and Fig. 5), especially in southwestern China. The inconsistency between this study’s erosion estimates and former soil erosion research might be due to the new methodologies and more up to date data sources that were collected to calculate erosion factors in this study, especially the improvement of R and K factors. Rainfall data from rain gauges were used to calculate R values. Estimation and mapping of R factor over large areas is mainly based on spatial interpolation of limited ground gauges. The accurate estimation of R is highly dependent on the spatial distribution of the rain gauges and model that used in the interpolation procedure. Our R factor results were likely to be better because the merged rainfall data were used to estimate daily rainfall and the downscaling methodology was applied to produce a high spatial resolution map of R across China. A local study, which was carried out by Fan et al. (2013) and specific to Tibet, confirmed large R values in the southeastern tropical rainforest areas of approximately 12 189 MJ mm ha–1 h–1 yr–1. Our estimates of the R factor over this area are consistent with those of Fan et al. (2013). National studies conducted by Liu et al. (2013) and Panagos et al. (2017) showed spatial patterns very similar to our R factor distribution in China. Liu et al. (2013) applied daily rainfall data from 590 meteorological stations to calculate the R factor in China, and our estimations of the average R value for the different climate regions in China were well within their R value ranges. Panagos et al. (2017) used high-temporal resolution rainfall records to assess the global R values and found that China has an average value of 1 600 MJ mm ha–1 h–1 yr–1

with an extreme erosivity value over 15 000 MJ mm ha–1 h–1 yr–1 in the southeastern coastal zones. The most commonly utilized K estimators include RUSLE (Renard et al. 1997), the Geometric Mean Diameter based model (Römkens et al. 1988), and EPIC (Sharply and Williams 1990). Zhang et al. (2008) assessed the suitability of these methods which were used to estimate the K factor of Chinese soils from natural runoff plots at 13 sites in eastern China, and draw a conclusion that all estimated K values exhibited considerably higher than the measured K values for these sites in eastern China. In this study, an improved method proposed by Zhang et al. (2008) based on the EPIC model was used to calculate the K factor in China. Moreover, comprehensive soil property data, which is currently available, and environmental factors that might influence the process of soil loss were incorporated into the mapping of the K factor. Zhang et al. (2007) estimated K values in China and showed that the K values are typically in the range of 0.007–0.02. Wang et al. (2013) measured K values in China based on long-term observations of natural runoff plots and reported that the most observed values were concentrated in a range of 0.015–0.035. Our K values bear a resemblance to the results of Zhang et al. (2007) and correspond to the range of Wang et al. (2013). Conventionally, the C factor refers to the combined effect of canopy cover, canopy height, residual cover, below-ground biomass and time; the P factor could be measured according to the support practice in China. However, measuring these factors across China would be difficult and costly. In this study, the C and P factors are involved with land use type. The methods that we used in this study might not fully expound the content of the C and P factors. Besides, some uncertainties that produced in the R downscaling and K mapping procedures will remain in the subsequent soil erosion estimates and thus influence the accuracy of the results. Since soil can be redeposited elsewhere in the landscape, our estimates are intrinsically larger when compared with net estimates of erosion. Future efforts should be focused on algorithm improvements and the uncertainty analysis of erosion factors.

5. Conclusion This study quantitatively estimated and mapped soil erosion potential in China using RUSLE. All of the data that we used for calculating erosion factors are the most detailed and publicly available data set for China. Our estimates are comparable to those made by other studies at the national and local scales. The greatest erosion rates mainly occur in southwestern China, whereas desert areas showed the lowest erosion rates. More than 60% of the total areas of China are influenced by soil erosion in a limited way. Other

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unused land and other forested woodlands have the highest erosion rates, whereas woodland, wetlands and paddy fields have the lowest rates of erosion. The RUSLE is useful for understanding and evaluating soil erosion potential over large areas with sparse data. As the most quantitative estimation of soil erosion potential with publicly available data in China, our estimates could support national and regional policy regarding land degradation in China.

Acknowledgements This work was supported by the National Natural Science Foundation of China (41461063 and 41571339), the China Postdoctoral Science Foundation (2018M630682), the Research Fund of State Key Laboratory of Soil and Sustainable Agriculture, Nanjing Institute of Soil Science, the Chinese Academy of Sciences (Y412201430).

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Exe cu tive Editor-in-Chief ZHANG Wei-li Guest Editor SHI Zhou Managing editor SUN Lu-juan