Risk assessment of soil erosion in different rainfall scenarios by RUSLE model coupled with Information Diffusion Model: A case study of Bohai Rim, China

Catena 100 (2012) 74–82

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Risk assessment of soil erosion in different rainfall scenarios by RUSLE model coupled with Information Diffusion Model: A case study of Bohai Rim, China Lifen Xu, Xuegong Xu ⁎, Xiangwei Meng College of Urban and Environmental Sciences & Laboratory for Earth Surface Process of Ministry of Education, Peking University, Beijing 100871, PR China

a r t i c l e

i n f o

Article history: Received 14 June 2012 Received in revised form 20 August 2012 Accepted 24 August 2012 Keywords: Soil erosion risk RUSLE IDM Exceeding probability Geographically Weighted Regression Bohai Sea Rim

a b s t r a c t Risk assessment of soil erosion addresses the likelihood of the occurrence of erosion as well as its consequences. This in turn can provide precautionary and relevant suggestions to assist with disaster reduction. In light of the great threat of soil erosion to global soil resources, it is necessary to implement this type of risk assessment. This study aims to appraise the risk of soil erosion caused by water along the Bohai Sea region during the rainy season. A new method, namely the RUSLE–IDM coupled model, which embeds the IDM (Information Diffusion Model) into the RUSLE(Revised Universal Soil Loss Equation)model, is applied to reveal soil erosion risk in different scenarios, with rainfall exceeding the probability of 0.1 and 0.02 respectively. From this case study, three conclusions can be drawn as follows: (i) This coupled method can effectively examine soil erosion risk and show comparable results of different scenarios, which cannot only calculate the erosion amount, but also identify the likelihood; (ii) Soil erosion caused by water is serious from July to September, but comparatively speaking, the greatest amount of attention should be paid to the prevention of soil erosion in July, as the erosion amount at this time is times larger than during September; (iii) Vegetation coverage and soil erosion control practices are controllable and important factors for the future soil conservation in this area. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Soil erosion negatively impacts ecology and can lead to reduced crop productivity, worsened water quality, lower effective reservoir water levels, flooding, and habitat destruction (Park et al., 2011). In both the past and present day, soil erosion is one of the major and most widespread environmental threats. Risk assessment of soil erosion caused by water is indispensable to the creation of effective policies and measures on water and soil resource conservation. An increasing number of studies have examined soil erosion by water on different temporal and spatial scales. Approaches to the research in this field belong to one of two categories, namely: (i) on-site measurements, which are rather small scale point measurements on selected points, often constrained to irrigation experiments; (ii) off-site quantification modeling, which has the potential for revealing soil erosion on a large scale. The models can be further grouped into empirical and mechanism (Meusburger et al., 2010). Quantification modeling has become a widely accepted method that is considered the most scientific approach for future study, although experimental measurements are still fundamental and indispensable. Numerous soil erosion models have been developed, such as the Water Erosion Prediction Project (WEPP) model (Nearing et al., 1989), the Chemical, Runoff, and Erosion

⁎ Corresponding author. Tel.: +86 10 62767240. E-mail addresses: [email protected] (L. Xu), [email protected] (X. Xu). 0341-8162/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.catena.2012.08.012

for Agricultural Management system (CREAMS) (Knisel, 1980), the European Soil Erosion Model (EuroSEM) (Morgan et al., 1990), etc. Among these models, Revised Universal Soil Loss Equation (RUSLE) is one of the most widely-used (Renard et al., 1997; Wischmeier and Smith, 1978), and has been applied in areas of different sizes and environmental conditions (Angima et al., 2003; Cohen et al., 2005; Prasannakumar et al., 2012). The RUSLE model is very efficient, robust and simple, although it suffers from a number of drawbacks concerning extrapolation, spatial scale effects and the complexity of the entire soil erosion process (Li et al., 2011). In terms of soil erosion risk assessment, recent times have seen many studies emerge. Soil erosion risk assessments using the RUSLE model account for a large proportion of the undertakings in this field. Those which apply comprehensive methods are fewer in number yet gaining popularity. Such examples include soil erosion risk assessments on the basis of the DPSIR (Driving force–Pressure– State–Impact–Response) framework (Gobin et al., 2004); the ERA(Ecological Risk Assessment) framework, which uses concepts such as endpoints and exposure (Schowanek et al., 2007); those based on the reconstruction of the historical soil erosion facts to predict future potential erosion through the adoption of archeological methods (e.g. 137Cs) (Martinez et al., 2009); and others combining the Universal Soil Loss Equation (USLE) with the Pan-European Soil Erosion Risk Assessment (PESERA) model to assess the risk (Meusburger et al., 2010). Yet while the advancements regarding soil erosion risk have earned remarkable achievements, much more could still be improved. Risk is often defined as the probability of

L. Xu et al. / Catena 100 (2012) 74–82

occurrence of an event multiplied by the consequences of that event. It exists because not everything is known, as is the case with soil erosion risk assessment. However, earlier studies on soil erosion risk focus heavily on the current and static soil erosion, paying less attention to future and dynamic loss. The majority of studies directly calculate the amounts of soil erosion on average or from past measurement (Bou Kheir et al., 2006; Santini et al., 2010). A further point to be noted is that some of the named soil erosion risk assessments lack any mention of probability or uncertainty, which is one of the components vital to concept of risk. In view of the shortcomings analyzed above, this paper assesses the soil erosion risk caused by rainfall by fully considering the likelihood or possibility of potential loss. A relatively new and superior method in probability estimation, named the Information Diffusion Model (IDM) is employed in this study to estimate rainfall probability. IDM was first established by Huang (1997) and further developed by Huang et al. (1998), Liu et al. (1998) and Chen et al. (2006). The model employs fuzzy set theory to complement probability theory, with an additional dimension of uncertainty. This can effectively characterize the fuzziness of the probability by a possibility distribution in small sample optimization with inherent imprecision and scarcity of statistical data (Feng et al., 2010; Zou et al., 2012). After rainfall probability estimation determined by IDM, the results are embedded into the RUSLE model (elaborated in Section 3.2) to assess the soil erosion risk in an integrated manner. This coupled method is validated by the case study of the Bohai Sea Rim. Two rainfall scenarios in three months respectively are examined, facilitating a comparative analysis on soil erosion risk in this area. 2. The case-study area and materials 2.1. The case-study area The target area of this study was the region surrounding the Bohai Sea (Fig. 1), located at 34°22′N–43°26′N, 113°04′E–125°46′E, containing Beijing City, Tianjin City, Hebei Province, Liaoning Province and Shandong Province. Following the Pearl River Delta and the Yangtze River Delta, the study area has boomed economically since the 2000s. By its topographical configuration, this region can be divided into 4 sub-regions. They include: low mountains in the northeast at mainly 500–800 m in elevation, low mountains and hills in the east at about 500 m in elevation but with a few exceptions, mountains in the west with the elevation more than 1000 m, and the North China Plain in the middle at less than 50 m in elevation. In addition, the North China Plain is formed by fluvial sediments of the lower reaches of the Yellow River, the Huaihe River and the Haihe River. The Liao River is the main river in the northeast sub-region. These regions are hotbeds for floods from the latter half of July to the first half of August. One reason for this is the heavy rain that intensively falls in July, August and September, which often accounts for 50–70% of the entire year's precipitation. The average precipitation in summer along the Bohai Sea Rim is 407.9 mm (Duan and Yang, 2011) (based on data from the previous 50 years' rainfall). The Bohai Sea Rim is one of the regions with the most serious soil erosion, a problem which is exacerbated by unreasonable and sharp economic development which aggravates soil erosion through vegetation and grassland deterioration. The area of soil erosion has reached up to more than 1,000,000 km 2, which is approximately 15% of the total area of the region (Luo and Zhang, 2010). Soil erosion in some places has resulted in serious land degradation, as well as the silting of the river, lake and reservoir. Of particular concern is the downstream of the Yellow River, where the riverbed rises by about 10 cm every year (Luo and Zhang, 2010). Consequently, the

75

surrounding environment has been negatively influenced and worsened by soil erosion. 2.2. Materials Quantitative and semi-quantitative data in this paper has been collected from academic literature and websites. The main data is categorized into the following databases, which is listed in Table 1. All data has been converted into raster at 1 km grid cell, so that spatial analysis can be done in the same cell size and map projection. 3. Methods 3.1. Information Diffusion Model (IDM) Information diffusion is to turn a single point sample into a set-value sample as a way of solving the problem of incomplete information. The simplest model of information diffusion is the normal diffusion model. The main steps of normal information diffusion are as follows: 3.1.1. Definition Let X = {xi|i = 1,2, …, n} be a given sample, and U = {uj|j = 1, 2,…, m}, u1 b u2 b u3… b um be the discrete universe of X, with a given step lengthΔ, Δ ≡ uj − uj −1, j = 1,2, 3,…,m. xiand ujare called the sample point and the monitoring point respectively. 3.1.2. Selecting a step length of the universe The choice of U and Δ is quite arbitrary in traditional IDM, which renders different results from different researchers. In the advanced IDM by Chen et al. (2006), U and Δ are restricted in a consistent frame. Furthermore, to make full use of the information, let 

 maxðX Þ− minðX Þ= m−1 . U ¼ minðX Þ−Δ 2 ; maxðX Þ þ Δ 2 , and Δ ¼ 3.1.3. Diffuse the information Then diffuse the information carried by xi to uj to gain fi(uj) using the normal information diffusion illustrated by the following equation:     xi −uj 1 f i uj ¼ pffiffiffiffiffiffi exp½− ; uj ∈U 2h2 h 2π

ð1Þ

where h is the normal diffusion coefficient calculated by Eq. (2) (Huang et al., 1998). 8 1:6978ðb−aÞ=ðm−1Þ 1bm≤5 > > < 1:4456ðb−aÞ=ðm−1Þ 6bm≤7 h¼ 1:4230ðb−aÞ=ðm−1Þ 8bm≤9 > > : 1:4208ðb−aÞ=ðm−1Þ 10≤m

ð2Þ

where b = max(X); a = min(X). Let

Di ¼

m   X f i uj :

ð3Þ

j¼1

Then generalize the distribution of sample information by Eq. (4).     f i uj uxi uj ¼ : Di

ð4Þ

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L. Xu et al. / Catena 100 (2012) 74–82

Fig. 1. The study area.

Thus diffuse sample points {x1, x2, …, xn} to each uj m   X   q uj ¼ uxi uj :

ð5Þ

j¼1

Therefore the frequency of a sample falling at uj can easily be estimated according to Eq. (7).     q uj : ð7Þ p uj ¼ Q Finally, the probability value of exceeding uj should be

Then sum q(uj)

m   X   p uj p u≥uj ¼ m   X q uj : Q¼ j¼1

k¼j

ð6Þ where p(u ≥ uj) is the required risk estimate value.

ð8Þ

L. Xu et al. / Catena 100 (2012) 74–82

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Table 1 Dataset list. Dataset

Content

Format

Data source

Precipitation

Monthly precipitation of 296 rainfall stations from 1973 to 2002 Subsoil sand fraction, silt fraction and clay fraction, topsoil organic carbon SRTM dataset with 1 km spatial resolution SPOT VGT dataset with 1 km spatial resolution from 2001 to 2008 GLC2000 dataset with 1 km spatial resolution, including 15 kinds of land use types in the target area Province boundary, river at 1:10,000 scale

Excel

http://www.cdc.cma.gov.cn

Grid Grid Grid

Made by Shi et al. (2002), downloaded from http://westdc.westgis.ac.cn http://westdc.westgis.ac.cn http://westdc.westgis.ac.cn

Grid

http://westdc.westgis.ac.cn

Vector

National Administration of Surveying, Mapping and Geo-information

Soil DEM NDVI Land use Basic geographical information

by Wischmeier and Smith (1978). S is estimated by taking step coupling methods (Liu et al., 1994; McCool et al., 1989).

3.2. RUSLE model RUSLE, one of the most widely-used models, provides a clear perspective from which to understand the interaction between rainfall and soil erosion. A ¼ R  K  LS  C  P

β

L ¼ ðλ=22:13Þ

.h i ð sinθ=0:0896Þ 3  ð sinθÞ0:8 þ 0:56 . β¼

1 þ ð sinθ=0:0896Þ 3  ð sinθÞ0:8 þ 0:56

ð9Þ

where A is the computed spatial average soil loss in the month selected for R (t·km −2·month−1); R is the rainfall–runoff erosivity factor (MJ·mm·km−2·h−1·month−1); K is the soil erodibility factor (t·km2·h·km −2 MJ−1·mm −1); LS is the slope length–steepness factor (dimensionless); C is the cover management factor (dimensionless); and P is the erosion control practice factor (dimensionless, ranging between 0 and 1). (1) Rainfall erosivity factor (R): R is an indicator of the potential erosivity, which is determined as a function of the volume, intensity and duration of a rainfall. For the monthly R, it is often calculated according to daily data or monthly data. Xu et al. (2007) used 2894 rainfall events over 25 years in 10 hydrology gauge stations to build and compare monthly rainfall erosivity by daily data and monthly data of Beijing, respectively. The result shows that these two models have similar precision with square of regression coefficient equaling 0.72. Therefore, the value of R is calculated as Eq. (10) according to Xu et al. (2007). Rm ¼ 0:689P

1:474

ð13Þ

λ ¼ Flowa ccumulation  cellsize

ð14Þ

8 ∘ < 10:8 sinθ þ 0:03 θb5 S ¼ 16:8 sinθ−0:5 5∘ ≤θb10∘ : 21:9 sinθ−0:96 θ≥10∘

ð15Þ

where θ is the slope of DEM (%); λ is the horizontal slope length; and b is the index related to slope. (4) Vegetation coverage and management factor (C): C is defined as the ratio of soil loss from land cropped under specific conditions to the corresponding loss from clean-tilled, continuous fallow. Taking full advantage of the Normalized Difference Vegetation Index (NDVI) data, C is calculated according to the equation of Gutman and Ignatov (1998).

ð10Þ

where Rm is the rainfall erosivity in mth month (MJ·mm· km − 2·h − 1·month − 1); and P is the average precipitation in the month. (2) Soil erodibility factor (K): K is resistant to both detachment and transport but closely related to its grain size, drainage potential, structural integrity, organic content and cohesiveness. The 2nd Soil Survey Data of China at 1:1000,000 scale are applied to estimate K by using the method of William et al. (Sharpley and Williams, 1990). K ¼ f0:2 þ 0:3 exp½0:0256SAN ð1−SIL=100Þg  0:3

 SIL 0:25C   1:0− CLA þ SIL C þ expð3:72−2:95C Þ

 0:7SN 1  1:0− SN 1 þ expð−5:51 þ 2:95SN 1 Þ

ð12Þ

ð11Þ

where SAN, SIL, CLA are respectively the subsoil sand fraction, the silt fraction and the clay fraction (%). C is the topsoil carbon content (%). SN1 =1−SAN/100. (3) Slope length and steepness factor (LS): LS is an accelerating factor for rainfall erosion. L is computed by using the method developed

C ¼ 1−

NDVI−NDVI min NDVI max −NDVI min

ð16Þ

(5) Erosion control practices factor (P): P is determined as the ratio between the soil losses expected for a certain soil conservation practice and that of up- and ‐down slope plowing (Liu et al., 2001). P is usually estimated based on land use type. Here P refers to the research result of Li et al. (2011) (Table 2). All the calculation of these factors is performed on the software platform of ArcGIS9.3. It's noteworthy of the transforming slope degree to radian when calculating L factor and S factor, because the default setting of the raster calculator tool in ArcGIS for trigonometric function calculation is radian. Table 2 P value of different land use types. Land Farmland Wood- Shrub- Water Seaside Swampy City Waste use type land land wetland wetland land P

0.4

1

1

0

0

0.7

0

1

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4. Results 4.1. Exceeding probability by IDM By utilizing the IDM method, the exceeding probability of monthly rainfall at each station was calculated. The station named Raoyang with station code 54606 provides an example of the exceeding probability curves (Fig. 2). The exceeding probability on the vertical axis refers to the probability of the site experiencing heavier rainfall than the given rainfall. Comparatively, the three curves have a similar turning point at p = 0.02 but decline differently after the turning point. The curve of July declines slowly, clearly displaying a tailing phenomena, while the curves of September and August reach the zero point more quickly. The more quickly the curve reaches the zero point, the higher the corresponding maximum precipitation of the month is. According to these curves, the correspondent precipitation at a given exceeding probability can be obtained. If the given probability is 0.02, then the theoretical precipitation will be 497.9 mm in July, 356.2 mm in August, and 105.8 mm in September. It is evident that precipitation in July is the highest, followed by August and then September. Furthermore, July is quite prone to heavy rain, especially when the exceeding probability is lower than 0.02.

4.2. Factors for RUSLE modeling Each factor for RUSLE modeling can be evaluated using Eqs. (10)–(16). Fig. 3 shows the factors for soil erosion risk during July in the rainfall scenario with p = 0.1. Other scenarios of assessment factors can also be calculated in a similar fashion. Fig. 3 clearly shows the distribution of each factor. The figure of R (p = 0.1) factor shows several high value centers nearby to the river, while the Northwestern mountain area rains lightly. The figure of K factor examines the soil erodibility in detail. There is an approximate trend of the distribution: the area of low mountains and hills in Shandong Province and the mountain area in western Hebei Province have low soil erodibility; the North China Plain is in high value of soil erodibility. The figure of C factor shows the vegetation coverage and management efficiency of soil erosion inhibition. The value ranges from 0.243902 to 1, where 1 represents that the site is quite prone to soil erosion because of ineffective management, while 0.243902 indicates a low probability of soil erosion based on this factor. The figures of S factor, L factor and P factor have a similar distribution trend, which is high value in mountains and hills area and low value in the plain. Some differences exist in specific areas, such as

the northwestern corner of Hebei province with low value of S factor but high value of P factor, and the coastal zone with sporadic high value of P factor but consistent low value of S factor.

4.3. Soil erosion risk According to the theoretical precipitation calculated by IDM, rainfall at p = 0.1 and p = 0.02 are extracted respectively. Then, it is assumed that the other factors are similar to the current situation in future, in view of clearly examining soil erosion risk caused by rainfall and the great uncertainty of prediction of other factors. Thus, the final amounts of soil erosion in July, August and September are estimated by embedding IDM into RUSLE model. To clearly display differences between soil erosion risk at different precipitation exceeding probability, an optional way is classifying the assessment results. According to “Standards for classification and gradation of soil erosion SL 190-2007”, the grading standard for annual soil erosion is as follows: tolerable erosion (b 200 t·km −2), slightly erosion (200–2500 t·km −2), medium erosion (2500–5000 t·km −2), strong erosion (5000–8000 t·km −2), very strong erosion (8000– 15,000 t·km −2), and destructive erosion (>15,000 t·km −2). In view of this article focusing on monthly soil erosion, the grading standard is set approximately to 1/12 of the annual soil erosion grading standard. Thus the assessments are accordingly graded into four classes. They are very low soil erosion risk (b20 t·km −2·month −1), low soil erosion risk (20–200 t·km −2·month −1), medium soil erosion risk (200–700 t·km −2·month −1), and high soil erosion risk (>700 t·km −2·month −1). As can be seen from Fig. 4, soil erosion in July, August and September has the same distribution pattern. Mountains in northwestern Hebei Province, low mountains in eastern Liaoning province, and part of hills in Shandong province have the severest soil erosion because of steep slopes, improper land use and relatively heavy rain; while most of lowland in river basin has light soil erosion because here the land either has gentle slopes or has benefited from effective environmental management and protection. However, although the distribution of soil erosion is similar in different months at different rainfall exceeding probability, differences among them still exist. The absolute mean amount of soil erosion under different precipitation scenarios is 145.9 t·km − 2·month − 1(in July at p = 0.1), 208.6 t· km − 2·month − 1(in July at p = 0.02), 124.3 t·km − 2·month − 1(in August at p = 0.1), 168.5 t·km−2·month−1(in August at p = 0.02), 34.2 t·km−2·month−1(in September at p = 0.1) and 58.0 t·km−2· month−1(in September at p =0.02) respectively. The soil erosion amount increases greatly in July from p = 0.1 to p = 0.02. This indicates

Fig. 2. The exceeding probability curves of precipitation in July, August and September respectively.

L. Xu et al. / Catena 100 (2012) 74–82 79

Fig. 3. Factors of RUSLE model. R factor refers to Rainfall erosivity (MJ·mm·km−2·h−1); K factor denotes to soil erodibility (t·km2·h·km−2 MJ−1·mm−1); C factor represents vegetation coverage and management (dimensionless), L factor and S factor show slope and length-steepness respectively; P factor means erosion control practices (dimensionless).

80 L. Xu et al. / Catena 100 (2012) 74–82

Fig. 4. Soil erosion risk at different precipitation exceeding probability.

L. Xu et al. / Catena 100 (2012) 74–82

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Table 3 Area statistics of different risk degree. Area (km2)

July (p = 0.1)

July (p= 0.02)

Aug. (p= 0.1)

Aug. (p = 0.02)

Sep. (p = 0.1)

Sep. (p = 0.02)

Very low risk (0–20) Low risk (20–200) Medium risk (200–700) High risk (>700)

263,441 137,367 70,933 25,375

257,850 122,927 74,725 41,614

271,186 141,874 64,326 19,866

258,793 134,166 72,780 31,401

342,512 136,143 17,493 968

310,017 147,794 34,647 4658

that as the rainfall is heavier, soil erosion amount will increase greatly. Soil erosion risk in July is the highest, followed by August and then September (see Table 3 in detail). 5. Discussion It's widely known that rainfall plays a key role in soil erosion by water. But how the other factors contribute to the soil erosion? Comparing the distribution of factors shown in Fig. 3 with that of soil erosion risk given in Fig. 4, the spatial pattern of L factor, S factor and P factor, to the sight, is similar to that of soil erosion risk. To detect the contribution of each factor to soil erosion, 500 random points are generated by Hawths Analysis Tools for ArcGIS9. Then each point extracts value from layers of L factor, S factor, P factor, C factor, K factor, R factor and final soil erosion modulus, respectively. Normalize the extracted value into 0–1, and then analyze the relative contribution by using the tool of Geographically Weighted Regression (GWR) embedded in ArcGIS9.3. GWR constructs a separate equation for every feature in the dataset incorporating the dependent and explanatory variables of features falling within the bandwidth of each target feature, which is the superiority of GWR compared with global regression models. The GWR result shows that the determinant of soil erosion varies among different sites, but most of them still have a spatial cluster pattern. S factor contributes most in the mountain area of southwestern Hebei province, where the steeper slope aggravates the soil erosion making here the highest soil erosion risk. P factor plays an important role in the North China Plain and Liao River Plain to prevent these areas being eroded, meanwhile gentle slope is also a contributor to constrain the soil erosion. C factor becomes the determinant in low mountains and hills in Liaodong Peninsula and west of Liaoning province, where the higher vegetation coverage protects these areas well. Some researchers find that the USLE algorithms to predict field erosion are highly sensitive to slope gradients, which may result in overestimation on slopes steeper than 30% (Liu et al., 1994). However, S factor isn't the main determinant of soil erosion in the study area based on the GWR result. Slopes in this region ranged from 0 to 24.1°, among which slopes steeper than 30% only take 1.3% of the total area. Comparatively speaking, scientific land use and effective vegetation management play a more important role in prevention of soil erosion. For the future work on soil conservation, much attention should pay to these two factors, which is not only because of the GWR detecting result above, but also owing to their more controllability in practice. From the view of risk definition, directly using a certain past state of soil erosion as the risk predictor of soil erosion in future is not a scientific approach. Addressing the future uncertainty in risk assessment not only has significant theoretical meaning, but also can guide the effective management of soil erosion. This paper takes full consideration of rainfall uncertainty based on the method of IDM. The previous methods such as linear regression and directly setting a rainfall level according to experience fail to explain the probability of rainfall. IDM compensates this shortage by providing the rainfall value and its probability. However, further effort is required to conduct a consistent and comprehensive risk assessment referring to uncertainty of all related factors (Park et al., 2011). There exists, of course, many technical difficulties, such as the combination and compounding of uncertainty, the modeling of

secondary and higher order consequences, the modeling of processes which is poorly understood from the scientific perspective, and the dangers of “number” manipulation and pseudo-science in assessments of large, complex systems (Kates, 1978). In short, many aspects deserve further study, however modeling is a sound substitute for experiments. Where possible, mechanism models should be introduced to reveal risks. The ultimate response to environmental threats, however, lies neither with improved assessment nor with communication but with the reduction of the threat itself. We need, therefore, some global abominations–some avoidances, some risks to be averted–not because it is impossible to cope successfully with any of these potential hazards, but because it may be impossible to cope successfully with all of them (Kates, 1978). 6. Conclusion This paper provides a case study concerning soil erosion risk at different rainfall exceeding probabilities. The following conclusions can be drawn from this research: (1) This coupled method can effectively examine the soil erosion risk distribution and show comparative results of different scenarios. Most mountainous areas are in high risk of soil erosion caused by water while lowland in river basin areas suffers lower risk. Soil erosion at p = 0.1 is less than that at p = 0.02. Furthermore, the difference of soil erosion modulus between p = 0.1 and p = 0.02 will increase as average rainfall increases. (2) July, August and September are the main months for soil erosion caused by water, but comparatively speaking, greatest attention should be paid to the prevention of soil erosion in July, the erosion amount of which is times larger than during September. (3) High vegetation coverage and effective soil erosion control practices are the important factors for prevention of soil erosion. Much attention should be paid to these two factors in future soil conservation work. Acknowledgments This work has been financially supported by the National Natural Science Foundation (NSFC) Project (No. 40830746, No. 41271102). Some of the data set has been provided by the Environmental and Ecological Science Data Center for West China, the National Natural Science Foundation of China (http://westdc.westgis.ac.cn); the Harmonized World Soil Database and the China Meteorological Data Sharing Service System. All assistance received has been greatly appreciated. References Angima, S.D., Stott, D.E., O Neill, M.K., Ong, C.K., Weesies, G.A., 2003. Soil erosion prediction using RUSLE for central Kenyan highland conditions. Agriculture, Ecosystems & Environment 97 (1–3), 295–308. Bou Kheir, R., Cerdan, O., Abdallah, C., 2006. Regional soil erosion risk mapping in Lebanon. Geomorphology 82 (3–4), 347–359. Chen, Z.F., Huang, C.F., Zhang, J.X., 2006. The interior-outer-set models based on diffusion functions. Fuzzy Systems and Mathematics (1), 42–48 (in Chinese). Cohen, M.J., Shepherd, K.D., Walsh, M.G., 2005. Empirical reformulation of the universal soil loss equation for erosion risk assessment in a tropical watershed. Geoderma 124 (3–4), 235–252.

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