Orginal Article

The influence of rainfall and land use patterns on soil erosion in multi-scale watersheds: A case study in the hilly and gully area on the Loess Plateau, China

  • WANG Jun , 1 ,
  • ZHONG Lina 1, 2 ,
  • ZHAO Wenwu 3 ,
  • YING Lingxiao 1
  • 1. Land Consolidation and Rehabilitation Center, Key Laboratory of Land Consolidation and Rehabilitation, Ministry of Land and Resources, Beijing 100035, China
  • 2. School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
  • 3. College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China

Author: Wang Jun (1970-), Professor, specialized in landscape ecology and land sustainable use. E-mail:

Received date: 2017-04-07

  Accepted date: 2018-01-20

  Online published: 2018-10-25

Supported by

National Natural Science Foundation of China, No.41771207, No.41171069


Journal of Geographical Sciences, All Rights Reserved


Soil erosion has become a major global environmental problem and is particularly acute on the Loess Plateau (LP), China. It is therefore highly important to control this process in order to improve ecosystems, protect ecological security, and maintain the harmonious relationship between humans and nature. We compared the effects of rainfall and land use (LU) patterns on soil erosion in different LP watersheds in this study in order to augment and improve soil erosion models. As most research on this theme has so far been focused on individual study areas, limited analyses of rainfall and LU patterns on soil erosion within different-scale watersheds has so far been performed, a discrepancy which might influence the simulation accuracies of soil erosion models. We therefore developed rainfall and LU pattern indices in this study using the soil erosion evaluation index as a reference and applied them to predict the extent of this process in different-scale watersheds, an approach which is likely to play a crucial role in enabling the comprehensive management of this phenomenon as well as the optimized design of LU patterns. The areas considered in this study included the Qingjian, Fenchuan, Yanhe, and Dali river watersheds. Results showed that the rainfall erosivity factor (R) tended to increase in these areas from 2006 to 2012, while the vegetation cover and management factor (C) tended to decrease. Results showed that as watershed area increased, the effect of rainfall pattern on soil erosion gradually decreased while patterns in LU trended in the opposite direction, as the relative proportion of woodland decreased and the different forms of steep slope vegetation cover became more homogenous. As watershed area increased, loose soil and craggy terrain properties led to additional gravitational erosion and enhanced the effects of both soil and topography.

Cite this article

WANG Jun , ZHONG Lina , ZHAO Wenwu , YING Lingxiao . The influence of rainfall and land use patterns on soil erosion in multi-scale watersheds: A case study in the hilly and gully area on the Loess Plateau, China[J]. Journal of Geographical Sciences, 2018 , 28(10) : 1415 -1426 . DOI: 10.1007/s11442-018-1553-2

1 Introduction

Soil is a key basic ecosystem component and therefore an important raw material for human life and production (Cerdà et al., 2015; Mabit et al., 2015). Soil erosion has, however, become more and more widespread globally in recent years and is especially prevalent on the Loess Plateau (LP), China; this phenomenon is now one of the major ecological environmental issues worldwide that is likely to influence the survival and development of humans (Cerdà et al., 2014). It is therefore vitally important to prevent and control soil erosion in order to improve the environment, protect ecological security, and to enable harmonious and sustainable development between humans and nature (Govers, 2014; Prosdocimi, 2015).
Research on the impacts of rainfall and land use (LU) on soil erosion has recently become hot topics. For example, Ochoa et al. (2016) analyzed the relationship between a number of causative factors including climate, topography, and land cover (LC) at small-scale watersheds and demonstrated the impacts of these factors on soil erosion. In earlier work, Paroissien et al. (2015) established a model to simulate the impacts of LU and climate change on soil erosion at the basin scale, and applied this approach to an analysis of annual mean rate of soil erosion in Mediterranean region. The impacts of LU/LC on soil erosion on the LP of China were discussed by Wei et al. (2006) who considered the influence of different rainfall patterns; the results of this study showed that the main rainfall factors influencing soil erosion included concentration as well as high intensity and short duration events. Thus, a series of LU types with the ability to resist runoff erosion were ordered in this study from most to least in the sequence of sea-buckthorn, weed, Chinese pine, alfalfa, and wheat. In addition, Zhuang et al. (2012) applied the universal soil loss equation (USLE) model to analyze the impacts of LU and rainfall changes on soil erosion in the Xiaojiang River Watershed, part of the Jinsha River in southwestern China. The results of this study highlighted the fact that high intensity soil erosion was mainly distributed at altitudes between 1,600 m and 2,800 m in the downstream part of the Xiaojiang River Watershed. Noteworthily, the bulk of previous research on this topic has been focused mainly on individual watersheds (Iserloh et al., 2013; Shi et al., 2013; Arhem and Fredén, 2014; Jomaa et al., 2014; Gessesse et al., 2015); few comparative analyses that address the impacts of rainfall and LU on soil erosion at multi-scale watersheds have been performed. Thus, exploring soil erosion patterns and their variations with rainfall and LU impacts in different watershed areas is important, not only to the research on soil erosion but also to the comprehensive management of regions susceptible to these processes.
Thus, adopting “scale-pattern-process” theory as the basis for research in this area, and considering LU, topography, soil, rainfall, and other factors, the soil loss (SL) evaluation index was initially proposed via the application of methods for calculating related factors in the context of the Revised Universal Soil Loss Equation (RUSLE) model (Zhao et al., 2008). A number of previous studies have suggested, however, that the SL index can only provide an approximation of the main processes of soil loss as a component of erosion evaluation; Fu et al. (2006) utilized this index in earlier work at small-scale watersheds, calculating it step-by-step based on relevant landscape ecology theory and the main processes of soil erosion. This led to the proposal of a multi-scale SL index (Fu et al., 2006), while Zhao et al. (2012) provided an additional method for assessment of the vegetation cover and management (C) factor between the SL index and the RUSLE model in their study in the Yanhe River Watershed within the hilly and gully area of the LP. The results showed that compared to the C factor, the SL index could more accurately describe the impacts of LU patterns on soil erosion while at the same time providing clear scientific reference data enabling reductions in land losses via pattern adjustments within watersheds.
The SL index was improved in this study and applied to the unique hilly and gully terrain area on the LP in northern Shaanxi Province, China. The aim of this study was to explore the effects of rainfall and LU patterns on soil erosion at different watershed scales.

2 Study area

The hilly and gully area on the LP was selected as the study area for this research because it suffers serious soil erosion. This region has a total area of 17,488 km2 (108°45′-110°25′E and 36°10′-37°55′N, and includes the Qingjian, Fenchuan, Yanhe, and Dali river watersheds (Figure 1). Numerous grooves have formed on the ground surface because of long-term runoff erosion as this area is widely covered by loess soils, having gentle slopes and ridges dissected into tugged topography. With obvious climatic seasonality, average annual rainfall is 513.8 mm and more than 90% of precipitation occurs in May and September. The annual distribution of surface runoff is also concentrated within the flooding season (July-September; sometimes more than 70% of annual runoff can be resulted from just a few rainstorms.
Figure 1 The location of the study area, including watersheds, hydrological and rainfall stations

3 Data collection and methods

3.1 Data

Horizontal and vertical distance as well as slope data were extracted from a digital elevation model (DEM) generated from a 1:5,000,000 scale topographic map using the software ArcGIS 9.3. This enabled the calculation of annual average values for vegetation cover from 2006 to 2012 using Normal Differential Vegetation Index (NDVI) data at a spatial resolution of 500 m extracted from moderate-resolution imaging spectroradiometer (MODIS) (http://www.gscloud. cn/) data. Rainfall, sediment discharge, and other hydrological data collected at 57 rainfall observation stations within the study area were extracted from the China Hydrological Yearbook: Hydrological Information of the Yellow River Watershed. The locations of hydrological and rainfall stations are shown in Figure 1.

3.2 Methods

Rainfall and LU patterns refer to the distribution of these parameters in terms of slope and horizontal and vertical distance from river systems. Thus, the closer, more topographically varied, and higher the rainfall and LU pattern unit, the greater the contribution of LU and rainfall pattern to the sediment output of the watershed, and vice versa. We therefore utilized the LU and rainfall pattern indices based on the SL index (Zhao et al., 2008), as outlined below.
The SL index was calculated as follows:
\[SL=\frac{f(R,K,T,C)}{f(R,K,T)}\ (1) \]
where R denotes the rainfall erosivity factor, C denotes the vegetation cover and management factor, K denotes the soil erodibility factor, T denotes the terrain feature factor, and f denotes the function of the SL index at different scales. In this context, SL is a dimensionless factor with values that range between 0 and 1; thus, a larger SL value implies a greater contribution of LU pattern to soil loss, and vice versa.
The rainfall pattern (SLR) index was calculated based on R and its potential capacity to contribute to soil erosion based on the SL index, as follows:
\[S{{L}_{R}}=\frac{\sum{S\times H\times D\times R}}{\sum{S\times H\times D}}\ \ (2) \]
where S denotes the slope factor index, while H and D are the horizontal and vertical distance indices of soil loss, respectively.
The LU pattern (SLC) index was calculated based on the C factor and drawing on the SL index, as follows:
\[S{{L}_{C}}=\frac{\sum{S\times H\times D\times C}}{\sum{S\times H\times D}}\ (3) \]
Calculation of R in this study was based on a simple algorithm for rainfall erosivity within the hilly and gully area on the LP (Zhang, 2003; Zhong, 2015), as follows:
\[R=0.849\times \alpha \sum\nolimits_{j=1}^{k}{{{({{X}_{j}})}^{\beta }}}-29.651\ (4) \]
where R denotes the monthly rainfall erosivity [MJ·mm/(ha·h)], while Xj denotes erosive rainfall on day j. Erosive rainfall request rainfall is greater than, or equal to, 12 mm, if it is erosive rainfall equal to rainfall, or is otherwise calculated as zero; thus:
\[\beta =0.8363+18.44P_{{{d}_{12}}}^{-1}+24.455P_{{{y}_{12}}}^{-1}\ (5) \]
$\alpha =21.586{{\beta }^{-7.1891}} (6)$
where Pd12 denotes daily average rainfall greater than, or equal to, 12 mm, while Py12 refers to the annual average daily rainfall greater than, or equal to, 12 mm.
The relationship between the C factor and crop coverage is complicated. Thus, the first of these two variables was calculated as follows (Cai et al., 2000):
\[{{F}_{c}}=(NDVI-NDV{{I}_{\text{min}}})/(NDV{{I}_{\text{max}}}-NDV{{I}_{\text{min}}})\ (7) \]
where Fc refers to the extent of vegetation coverage, while NDVImin and NDVImax denote the minimum and maximum values of the NDVI within the study area, respectively.
The soil loss distance index reflects differences in the degree of contribution of various LU types to river sediment as a function of distance from the water system (Zhao et al., 2008). These LU type distances can be further divided both horizontally and vertically (Figure 2); thus, di denotes the horizontal distance of soil erosion at point i in a small watershed, while hi denotes the vertical distance at the same point. Applying the Straight Line function of the Spatial Analyst tool in the software ArcGIS v9.3 enabled us to extract the horizontal distance of point i from the river system based on the spatial distribution map. In contrast, extracting a measure for vertical soil erosion was relatively more complicated, and was achieved in a series of distinct steps. Water vector data was first converted to a raster format and a grid value of 1 was defined prior to application of the raster calculator module in ArcGIS v9.3. The water grid was then multiplied with DEM data to obtain a map comprising elevation values which were then extended outwards so that the water surface covered the entirety of a small watershed (i.e., the surface of a river system). Finally, a vertical distance map of soil loss was obtained by subtracting DEM data from values for water level elevation; horizontal (D) and vertical (H) distance indices were then calculated based on corresponding soil losses.
Figure 2 Schematic diagram of horizontal distance (hi) and vertical distance (di) of soil loss (Zhao et al., 2008)
Values of D were calculated as follows:
${{D}_{i}}=\frac{{{D}_{\max }}-{{d}_{i}}}{{{D}_{\max }}} (11)$
where${{D}_{i}}$ denotes the soil erosion distance index at one point within a watershed, while Dmax
denotes the maximum value of this index within a watershed, and di denotes the soil erosion distance at a given point within a watershed. Soil erosion distance in this context refers to the minimum straight line distance from one point along the sediment transport path to the water system within a watershed.
Values of H were calculated as follows:
${{H}_{i}}=\frac{{{H}_{\max }}-{{h}_{i}}}{{{H}_{\max }}} (12)$
where ${{H}_{i}}$ denotes the soil erosion vertical distance index at one point within a watershed, while
Hmax denotes the maximum value of soil erosion vertical distance, and hi denotes the vertical distance of soil erosion at a given point within a watershed.
Values of S were calculated as follows (Liu et al., 1994).
\[{{{S}'}_{i}}=21.91\sin \theta -0.96\ (13) \]
where ${{S}_{i}}$ denotes the slope factor index, θ refers to the slope gradient, ${{{S}'}_{i}}$ refers to the slope factor, and \({{{S}'}_{\min }}\) and \({{{S}'}_{\max }}\)denote the minimum and maximum slope factors, respectively, within the watershed.

4 Results

4.1 Characteristics of factors in SLR and SLC

4.1.1 Characteristics of rainfall erosivity factor (R)
Values of R for 57 rainfall observation stations within the hilly and gully study area on the LP for the period from 2006 to 2012 were calculated by using the simple algorithm presented by Zhang et al. (2003), and a distribution map was generated (Figure 3). Results showed that R values for the Yanhe and Dali river watersheds remained generally low, while the opposite was the case for the watersheds of the Qingjian and Fenchuan rivers. Similarly, R values for upstream regions of the Fenchuan and Qingjian river watersheds remained relatively high while those for downstream regions were relatively low over the time period of this study; the opposite pattern was detected in the Yanhe and Dali river watersheds. Two upstream zones with high R values were identified within the Qingjian and Fenchuan river watersheds, while low value zones were identified in the upstream area of the Dali River Watershed and in the mid-upstream of the Yanhe River Watershed. The inter-annual variation in R within the study area was illustrated in Figure 4; these data showed that R tended to increase from 2006 to 2012 (P=0.05), although values were relatively low in both 2008 and 2010 because the volume of rainfall was also relatively small in these years.
Figure 3 The spatial distribution of R factor from 2006 to 2012 within the hilly and gully area on the LP
Figure 4 Inter-annual variation in R within the hilly and gully area on the LP from 2006 to 2012
4.1.2 Characteristics of C
The distribution of C values was shown in Figure 5. The results suggested that C values for the Dali River Watershed in the north of the study area remained relatively high throughout the study period while those of the Fenchuan River Watershed in the south were relatively low. At the same time, C values tended to decline along a north-south transect across the study area over the course of this research. The results presented in Figure 6 were generated by calculating annual average C values and reveal an overall downward trend from 2006 to 2012 (P=0.05). This LP study area is noteworthy because it has experienced the implementation of a policy to return farmland to forests and grasslands since 1998; the results of this process have been remarkable as vegetation coverage has significantly increased, and ecological environmental quality has significantly improved since 2000. Vegetation coverage in this region has also consistently increased year by year due to the continuous return of farmland to forests and grasslands (Zhong and Zhao, 2013); a close relationship between the C and vegetation coverage was revealed, while Equations (7) to (9) showed that these values decreased from 2006 to 2012.
Figure 5 The spatial distribution of C within the hilly and gully area on the LP from 2006 to 2012
Figure 6 Inter-annual variation in values of the C within the hilly and gully area on the LP from 2006 to 2012
4.1.3 Other factors
Minimum line distances and the vertical distance between points and the water system were extracted from DEM data alongside slope gradient values to enable the calculation of D, H, and S (Figure 7).
Figure 7 The patterns of D, H, and S within the hilly and gully area on the LP

4.2 The impact of rainfall patterns on soil erosion within multi-watersheds

Values of SLR from 2006 to 2012 for 13 small watersheds were calculated as part of this study. These results enabled evaluation of the impact of rainfall pattern on soil erosion within multi-watersheds via the calculation and analysis of correlation coefficients between SLR and sediment discharge data for the 13 small watersheds (P=0.05). Figure 8 revealed that the impacts of rainfall pattern on soil erosion gradually decreased as watershed area increased. In other words, the smaller the watershed area, the greater the contribution of rainfall patterns to soil erosion, and vice versa.
Figure 8 The effect of rainfall pattern on soil erosion in different watersheds in the hilly and gully area on the LP

4.3 The impact of LU patterns on soil erosion in multi-watersheds

Values of SLC from 2006 to 2012 for 13 small watersheds were calculated as part of this study. These data enabled evaluation of the impact of LU patterns on soil erosion within multi-watersheds via calculation and analysis of correlation coefficients between SLC and sediment discharge data for the 13 small watersheds (P=0.05). Figure 9 showed that the impact of LU patterns on soil erosion gradually increased in concert with watershed area. In other words, the smaller a watershed area, the smaller the contribution of LU patterns to soil erosion, and vice versa.
Figure 9 The effect of LU patterns on soil erosion in different watersheds within the hilly and gully area on the LP

4.4 A comparative analysis of the effect of rainfall and LU patterns on soil erosion within different watershed areas

The data presented in Figure 10 was generated by comparing the correlation coefficients between either SLR or SLC and sediment discharge (Figure 10). These data show that correlation coefficients between SLR and sediment discharge were greater than those seen for SLC in the case of the seven watersheds in Caoping, Xinghe, Qingyangcha, Zaoyuan, Lijiahe, Zichang, and Linzhen, while the opposite was true for the six watersheds within Ansai, Suide, Xinshihe, Yan’an, Yanchun, and Ganguyi. Indeed, watershed area increased from left to right along the horizontal axis (Figure 10); this meant that the effect of rainfall pattern on soil erosion was larger than that of LU pattern when a watershed was relatively smaller, and vice versa at relatively larger sizes.
Figure 10 Comparison of the effects of rainfall and LU patterns on soil erosion in 13 watersheds within the hilly and gully area on the LP

4.5 Causal analysis

It is clear that soil erosion is the combined result of rainfall, LU, topography, soil type, and other factors. A great many variables influence this complex process; the results presented in this study showed that the influence of rainfall on soil erosion was greater than that of LU pattern when the watershed area was smaller. While, the influence of LU pattern on soil erosion was more dominant as watershed area increased; it is clear, for example, that different patterns of LU influence the emergence of runoff by changing underlying surface features. In order to ensure ongoing survival and development, humans have constantly modified their land surface environments, and such changes in LU patterns are one of the important ways that anthropogenic activities adapt to the environment (Wu et al., 2014). Thus, the influence of LU patterns on soil erosion actually reflects human activities; the return of farmland to forests and grasslands has been the dominant human activity within this study area since 1998 and has led to significant increases in vegetation coverage, largely changing local LU patterns.
Ding et al. (2015) have noted that vegetation cover types tend to be monospecific as watershed area increases within the hilly and gully area on the LP in northern Shaanxi Province when the proportion of grassland increases in concert with a reduction in forested land and farmland. And compared to grassland and cultivated land, forests tend to have much better soil-water storage and conservation capacities. This change of vegetation is the main reason for the increase of the effects of LU pattern on soil erosion as watershed area increases. This also indicates that the effect of human activities on soil erosion increases when watershed areas are relatively larger.
It has also been noted in previous work that soil erosion within a watershed is mainly the result of splash, sheet, and rill erosive effects (Liu, et al., 2004). Specifically, rills cut down into the soil and subsoil and gradually develop into shallow gullies; runoff therefore collects in these gullies, cuts down into their base, and extends channels and heads of gullies stretch upwards to form broken dissected surface owing to the gully erosion. The amount of sediment yield caused by this process nevertheless remains small and stable when splash erosion dominates, while the direct impacts of rainfall are relatively more significant. It is the case, therefore, that the sediment yield caused by erosion increases when these processes are dominated by shallow gully mechanisms; under such situations, rainfall, soil, and LU are all significant factors influencing erosion, while sediment is mainly derived from the collapse of cut channel walls and the development of trenches. The sediment yield that results from erosion increases significantly when linear processes, including gravity erosion, become dominant (Jia et al., 2005); consequently, soft soil and the crisscrossed ravine networks that form increase the probability of gravitational erosion and enhance the impacts of both soil and topography.

5 Discussion

In their earlier work, Hu et al. (2014) proposed that the value of R factor had decreased from the south to the northwest between 1971 and 2012 in Shaanxi Province. The results of this study lend themselves to a similar conclusion; calculated R values for the Yanhe and Dali river watersheds in the northern part of the study area were relatively smaller while values for the Qingjian and Fenchuan river watersheds in the south were relatively higher. Numerous scholars have examined the spatiotemporal characteristics of vegetation coverage on the LP and have suggested that an increase occurred from 2006 to 2012 (Liu et al., 2011; Wei et al., 2017). And it was also shown that vegetation coverage is high in the southeast and low in the northwest of the plateau. Because the negative correlation in vegetation coverage and C factor, the C factor has tended to decline from 2006 to 2012 and was low in the south and high in the north. The conclusion of Wei et al. (2006) that there is great difference between runoff and soil erosion given different rainfall and LU patterns is supported by this study.
The SL index includes soil erosion spatial distribution information and also reflects loss processes at different scales. However, the relevant factors that comprise this index remain too numerous and complicated to calculate. Horizontal and vertical distance from the river system as well as slope factor following deletion of the superfluous factor in LU pattern were used in this study to generate an alternative LU pattern index that could reflect the effects of this variable on soil erosion. A further rainfall pattern index was also proposed in this study that was based on the LU pattern index. Data for these revised indices are easy to collect, and they are relatively simple to calculate; use of these approaches can therefore more accurately reflect the impacts of rainfall and LU patterns on soil erosion.
Soil erosion is the combined result of rainfall, LU, soil type, topography, tillage, management, and other factors. The emphasis in this paper has been on analyzing the impacts of rainfall and LU patterns on soil erosion without taking their spatial correlations into account. Thus, examining the spatial distribution patterns of rainfall, LU, soil type, topography, tillage, management methods, and other factors, as well as considering the relationships and correlations between them and their influence on soil erosion as watershed areas change should be explored in future work.

6 Conclusions

Building based on the SL index, and combining horizontal and vertical distance from the river system in tandem with slope factor, pattern indices for rainfall and LU are developed in this paper and their impacts on soil erosion in multi-watersheds on the LP in northern Shaanxi Province are discussed. A number of clear conclusions are presented as a result of this research.
(1) Values for R from 2006 to 2012 within the study area tended to generally increase, while the opposite trend was seen in C values.
(2) As watershed area increased, the impact of rainfall pattern on soil erosion within the study area tended to gradually decrease, while the impact of LU pattern gradually increased. When the watershed area was small, the impact of rainfall pattern on soil erosion tended to be larger than that of LU pattern. The opposite was suggested when watershed area was large.
(3) As watershed area increased, the proportion of forested land tended to decrease and vegetation cover types tended to be monospecific. This phenomenon was the main explanation for an overall increase of the effect of LU patterns on soil erosion within watersheds. At the same time, the effect of rainfall on soil erosion tended to be relatively large when a watershed was relatively smaller, while the impacts of soil and topography on erosion increased in concert with watershed area.

The authors have declared that no competing interests exist.

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Cerdà A, Jordán A, Zavala Let al., 2014. The contribution of mulches to control high soil erosion rates in vineyards in eastern Spain.EGU General Assembly Conference Abstracts, 16: 16127.Soil erosion take place in degraded ecosystem where the lack of vegetation, drought, erodible parent material and deforestation take place (Borelli et al., 2013; Haregeweyn et al., 2013; Zhao et al., 2013). Agriculture management developed new landscapes (Ore and Bruins, 2012) and use to trigger non-sustainable soil erosion rates (Zema et al., 2012). High erosion rates were measured in agriculture land (Cerdà et al., 2009), but it is also possible to develop managements that will control the soil and water losses, such as organic amendments (Marqués et al., 2005), plant cover (Marqués et al., 2007) and geotextiles (Giménez Morera et al., 2010). The most successful management to restore the structural stability and the biological activity of the agriculture soil has been the organic mulches (García Orenes et al; 2009; 2010; 2012). The straw mulch is also very successful on bare fire affected soil (Robichaud et al., 2013a; 2013b), which also contributes to a more stable soil moisture content (García-Moreno et al., 2013). The objective of this research is to determine the impact of two mulches: wheat straw and chipped branches, on the soil erosion rates in a rainfed vineyard in Eastern Spain. The research site is located in the Les Alcusses Valley within the Moixent municipality. The Mean annual temperature is 13 02C, and the mean annual rainfall 455 mm. Soil are sandy loam, and are developed at the foot-slope of a Cretaceous limestone range, the Serra Grossa range. The soils use to be ploughed and the features of soil erosion are found after each thunderstorm. Rills are removed by ploughing. 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Robichaud, P.R., Wagenbrenner, J.W., Lewis, S.A., Ashmun, L.E., Brown, R.E., Wohlgemuth, P.M. 2013b. Post-fire mulching for runoff and erosion mitigation. Part II: Effectiveness in reducing runoff and sediment yields from small catchments. Catena 105, 93-111. Wang, L., Tang, L., Wang, X., Chen, F. 2010. Effects of alley crop planting on soil and nutrient losses in the citrus orchards of the Three Gorges Region. Soil and Tillage Research 110, 243-250. Wu J., Li Q., Yan L. 1997. Effect of intercropping on soil erosion in young citrus plantation - a simulation study. Chinese Journal of Applied Ecology 8, 143-146. Zema, D. A., Bingner, R. L., Denisi, P., Govers, G., Licciardello, F., Zimbone, S. M. 2012. Evaluation of runoff, peak flow and sediment yield for events simulated by the AnnAGNPS model in a belgian agricultural watershed. Land Degradation & Development, 23: 205- 215. DOI 10.1002/ldr.1068 Zhao, G., Mu, X., Wen, Z., Wang, F., Gao, P. 2013. 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Cerdà A, Pelayo Ó G, Pereira Pet al., 2015. The wild geographer avatar shows how to measure soil erosion rates by means of a rainfall simulator.EGU General Assembly Conference Abstracts, 17: 15878.This contribution to the immersed worlds wish to develop the avatar that will teach the students and other scientists how to develop measurements of soil erosion, surface runoff and wetting fronts by means of simulated rainfall experiments. Rainfall simulation is a well established and knows methodology to measure the soil erosion rates and soil hydrology under controlled conditions (Cerdà 1998a; Cerdà, 1998b; Cerdà and Jurgensen, 2011; Dunkerley, 2012; Iserloh et al., 2012; Iserloh et al., 2013; Ziadat and Taimeh, 2013; Butzen et al., 2014). However, is a method that requires a long training and expertise to avoid mismanagement and mistaken. To use and avatar can help in the teaching of the technique and the dissemination of the findings. This contribution will show to other avatars how to develop an experiment with simulated rainfall and will help to take the right decision in the design of the experiments. Following the main parts of the experiments and measurements the Wildgeographer avatar must develop: 1. Determine the objectives and decide which rainfall intensity and distribution, and which plot size to be used. Choose between a laboratory or a field rainfall simulation. 2. Design of the rainfall simulator to achieve the objectives: type of rainfall simulator (sprayer or drop former) and calibrate. 3. The experiments are carried out. 4. The results are show. Acknowledgements To the "Ministerio de Economía and Competitividad" of Spanish Government for finance the POSTFIRE project (CGL2013- 47862-C2-1-R). The research projects GL2008-02879/BTE, LEDDRA 243857 and PREVENTING AND REMEDIATING DEGRADATION OF SOILS IN EUROPE THROUGH LAND CARE (RECARE)FP7-ENV-2013- supported this research. References Butzen, V., Seeger, M., Wirtz, S., Huemann, M., Mueller, C., Casper, M., Ries, J. B. 2014. Quantification of Hortonian overland flow generation and soil erosion in a Central European low mountain range using rainfall experiments. Catena, 113, 202-212. Cerdà, A. 1998a. Effect of climate on surface flow along a climatological gradient in Israel. A field rainfall simulation approach. Journal of Arid Environments, 38, 145-159. Cerdà, A. 1998b. The influence of aspect and vegetation on seasonal changes in erosion under rainfall simulation on a clay soil in Spain. Canadian Journal of Soil Science, 78, 321-330. Cerdà, A., Jurgensen, M. F. 2011. Ant mounds as a source of sediment on citrus orchard plantations in eastern Spain. A three-scale rainfall simulation approach. Catena, 85(3), 231-236. Dunkerley, D. 2012. Effects of rainfall intensity fluctuations on infiltration and runoff: rainfall simulation on dryland soils, Fowlers Gap, Australia. Hydrological Processes, 26(15), 2211-2224. Iserloh, T., Ries, J.B., Arnaez, J., Boix Fayos, C., Butzen, V., Cerdà, A., Echeverría, M.T., Fernández-Gálvez, J., Fister, W., Gei08ler, C., Gómez, J.A., Gómez-Macpherson, H., Kuhn, N.J., Lázaro, R., León, F.J., Martínez-Mena, M., Martínez-Murillo, J.F., Marzen, M., Mingorance, M.D., Ortigosa, L., Peters, P., Regüés, D., Ruiz-Sinoga, J.D., Scholten, T., Seeger, M., Solé-Benet, A., Wengel, R., Wirtz, S. 2013. European small portable rainfall simulators: a comparison of rainfall characteristics. Catena, 110, 100-112. Doi: 10.1016/j.catena.2013.05.013 Iserloh, T., Ries, J.B., Cerdà, A., Echeverría, M.T., Fister, W., Gei08ler, C., Kuhn, N.J., León, F.J., Peters, P., Schindewolf, M., Schmidt, J., Scholten, T., Seeger, M. (2012): Comparative measurements with seven rainfall simulators on uniform bare fallow land. Zeitschrift für Geomorphologie, 57, 193-201. DOI: 10.1127/0372- 8854/2012/S-00118. Ziadat, F. M., Taimeh, A. Y. 2013. Effect of rainfall intensity, slope and land use and antecedent soil moisture on soil erosion in an arid environment. Land Degradation & Development, 24: 582- 590. DOI 10.1002/ldr.2239

Ding Jingyi, Zhao Wenwu, Wang Junet al., 2015. Scale effect of the impact on runoff of variations in precipitation/vegetation: Taking northern Shaanxi loess hilly-gully region as an example.Progress in Geography, 34(8): 1039-1051. (in Chinese)Water conservation capability of the loess plateau increased significantly since the Grain for Green project started. At the same time, the amount of runoff significantly reduced, resulting in the question of how to balance the water conservation needs and the need for water supply in the region. In order to explore causes of the runoff reduction and provide support for balancing different needs for water at different scales, the variation of precipitation and vegetation as well as the scale effect of its impact on runoff in northern Shaanxi loess hilly-gully region during 2006 to 2011 were examined. The results show that: Precipitation of flood season gradually reduced from southeast to northwest in the study area. During the research period, vegetation restored significantly, nearly 80% of vegetated area was improved, especially in areas with poor vegetation conditions. Precipitation and runoff had significant positive correlation. With the increase of sub-watershed area,the correlation of precipitation and runoff increased, which shows a clear scale effect. The main reason was that land use structure above 15 of slope changed along with the spatial scale egetation types at steep slope tended to be homogenous and forest that has significant water conservation function decreased along with the increase in sub-watershed area. The correlation between NDVI and runoff was neither significant nor forming a clear relationship with the area of sub-watersheds, therefore the scale effect of vegetation influence on runoff was not obvious.


Fu Bojie, Zhao Wenwu, Chen Lidinget al., 2006. Multi-scale soil loss evaluation index.Chinese Science Bulletin, 51(16): 1936-1943. (in Chinese)Exploring the relationships between land use and soil erosion at different scales is a fron-tier research field and a hot spot topic in contempo-rary physical geography. Based on the scale-pattern-process theory in landscape ecology and with consideration of such influential factors as land use, topography, soil and rainfall, this paper ap-plies the scale transition method to establishing a soil loss evaluation index at different scales and puts forward a research path and methodology for mul-tiscale soil loss evaluation indices. The multiscale soil loss evaluation index is applied to the evaluation of relationships between land use and soil erosion and the research of soil erosion evaluation at multiple scales. It provides a new method for optimizing the design of regional land use patterns and integrated multiscale research.


Gessesse B, Bewket W, Bräuning A, 2015. Model-based characterization and monitoring of runoff and soil erosion in response to land use/land cover changes in the Modjo watershed, Ethiopia.Land Degradation & Development, 26(7): 711-724.Abstract The Modjo watershed has experienced significant land use/land cover (LULC) change and soil erosion. This study examines changes in surface runoff generation and soil erosion in response to the LULC dynamics. To simulate runoff and sediment yield, the geographic information system-interfaced Soil and Water Assessment Tool (SWAT) model was used. Model sensitivity, calibration and validation analyses were carried out, and the efficiency of the model was evaluated using simulated and measured discharge data. The two scenario model simulation goodness-of-fit measures verified that the SWAT model performed very well during calibration and validation periods for daily and monthly time steps (Nash–Sutcliffe efficiency65>650·79 and root mean square error–observation standard deviation ratio65611</sup>65y611. Nearly 95·2% of the watershed is experiencing moderate to severe soil loss rates ranging from 14·7 to 37·565Mg65ha61165y611. In the remaining parts of the watershed, soil loss rates range from 4·4 to 14·765Mg65ha61165y611. Surface runoff generation and soil erosion varied widely by soil, LULC types and slope positions. The observed environmental change would lead to further land degradation, with negative implications on the livelihoods of local people unless appropriate conservation measures are implemented. Copyright 08 2014 John Wiley & Sons, Ltd.


Govers G, Van Oost K, Wang Z, 2014. Scratching the critical zone: The global footprint of agricultural soil erosion.Procedia Earth and Planetary Science, 10: 313-318.Agricultural activities have drastically increased soil erosion rates and it is therefore likely to have important effects on the Earth's Critical Zone. In this paper we first investigate to what extent agricultural soil erosion can be quantified. We then combine this information with our current understanding of Critical Zone processes to assess the impact of agricultural soil erosion on soil functioning (biogeochemical cycling, hydrology, crop productivity) and to identify areas where additional research is needed to complete our understanding of how agricultural soil erosion affects the Earth's Critical Zone at different spatial and temporal scales.


Hu Lin, Su Jing, Sang Yongzhiet al., 2014. Spatial and temporal characteristics of rainfall erosivity in Shaanxi Province.Arid Land Geography, 37(6): 1101-1107. (in Chinese)Based on the daily rainfall data of 96 meteorological sites in Shaanxi Province whose sequence is completed. The rainfall erosivity mode was used to calculate the rainfall erosivity of Shaanxi.(This mode was set up on the basis of daily rainfall and proposed by Zhang Wenbo.)The spatial distribution map was drawn of erosive rainfall(ER)and rainfall erosivity(RE)with the aid of GIS tools and the inverse distance weighted interpolation method.This paper analyzed both the spatial distribution characteristics of yearly and four-seasonal ER and RE,and annual variation characteristics. The results show as follows:(1)There are significant differences of average annual erosion rainfall,the erosive rainfall of northern Shaanxi is least about 279.3 mm,the second is Guanzhong region about366.4 mm,the largest erosion is in southern Shaanxi region about 589.1 mm. The change of average annual rainfall erosivity is between 781.6-7 690,the average annual rainfall erosivity of southern Shaanxi region is most about3 499,the second is Guanzhong region about 1 881,the least rainfall erosivity is in northern shaanxi about 1 471.4.The spatial distribution of erosive rainfall is consistent with rainfall erosivity in Shaanxi Province,they all show decreasing trend from the south to the north,rainfall erosivity is extremely significant with rainfall and erosive rainfall.(2)Rainfall erosivities have high concentration in a year;they are large in summer and autumn,small in winter and spring. In northern Shaanxi and Guanzhong region,the largest rainfall erosivitis are in August,respectively account for 35.7% and 27.6%,the rainfall erosivities from July to September respectively account for 78.6% and 72%. In southern Shaanxi,the largest rainfall erosivity is in July,accounts for 28.6%,the rainfall erosivities from July to September and from May to October respectively account for 68.9% and 94.7%. Thus it can be found that the soil and water loss is mainly concentrated from July to September in northern Shaanxi and Guanzhong region,but it is possible to occur large soil erosion from May to October in southern Shaanxi,the risk of erosion is increasing progressively from the north to the south.(3)The interannual change of rainfall erosivity in Shaanxi Province is very obvious,the Cv value is in 33.1%-77.3%,the differences and fluctuation degree of annual rainfall erosivity in different regions are all relatively large;Cv of rainfall erosivity in north region is larger than that in south region. The Cv value of southern Shaanxi is relatively small,the second is in Guanzhong region,and the larger is in northern Shaanxi. The maximum rainfall erosion in Fugu is 5 303.7,about up to 30.8 times the minimum rainfall erosion.

Iserloh T, Fister W, Marzen Met al., 2013. The role of wind-driven rain for soil erosion-an experimental approach.Zeitschrift für Geomorphologie, Supplementary Issues, 57(1): 193-201.Recent research has shown that wind can have a significant influence on velocity, impact angle and kinetic energy of raindrops, and subsequently increases soil erosion. The aims of this study were to 1) quantify the influence of wind on water erosion, 2) specifically observe the difference in processes between windless rain (WLR) and wind-driven rain (WDR) simulations and 3) test the device's and test sequence's practicability.The Portable Wind and Rainfall Simulator (PWRS), recently developed at Trier University for plot-scale in situ assessment of differences in soil erosion with and without the influence of wind on raindrops, was used. To facilitate extraction of the influences of WDR on soil erosion, to avoid systematic errors, and to reduce variability between test plots, a defined order of four consecutive test runs was established: 0) wind simulation, 1) WLR simulation on dry soil, 2) WLR simulation on moist soil, 3) WDR simulation. The tests were conducted on homogenous sandy substrate deposited on an area of 15.2 m x 60 m with uniform and smooth surface and low inclination (1 degrees) in the Willem Genet Tunnel of Wageningen University. The results show an increase of eroded sediment ranging from 113% up to 1108% for WDR simulations in comparison to WLR simulations. The increase in runoff was considerably lower (15% to 71%), resulting in an increase of sediment concentration between 56% and 894%. The results indicate an immense impact of WDR on soil erosion of sandy cohesionless substrate. The experimental setting and measurement proved reliable and reproducible and enables a clear process observation and quantification in the field.


Jia Yuanyuan, Zheng Fenli, Yang Qinke, 2005. Distributed water erosion prediction model for small watershed in loess plateau.Journal of Hydraulic Engineering, 36(3): 328-332. (in Chinese)Based on the grid digital elevation model (DEM), a distributed water erosion prediction model for small watershed in loess plateau is established. The model is composed of hydrological component and erosion component. In the hydrological component, the processes of rainfall, interception, surface storage formed by micro-depression, infiltration, overland flow and channel flow are taken into account, and the implementation of runoff confluence calculation is carried out on the basis of kinetic wave theory. The erosion component includes the splash detachment, detachment rate formed by interrill flow, rill flow, ephemeral gully flow and channel flow, and the calculation of sediment yield is realized according to the principle of dynamic balance of mass. The simulation result of single rainfall event with rainfall intensity higher than medium grade shows that the accuracy of the prediction is acceptable.


Jomaa S, Barry D A, Brovelli Aet al., 2012. Rain splash soil erosion estimation in the presence of rock fragments.Catena, 92: 38-48.78 Rain splash soil erosion was estimated in the presence of rock fragments. 78 Proportionality between rain splash erosion and area exposed was tested. 78 For controlled conditions, soil erosion is proportional to surface area exposed. 78 For field data, erosion is not proportional to the area exposed to raindrops.


Leys A, Govers G, Gillijns Ket al., 2010. Scale effects on runoff and erosion losses from arable land under conservation and conventional tillage: The role of residue cover.Journal of Hydrology, 390(3): 143-154.A literature survey as well as our own observations on runoff and soil losses measured under conventional and conservation tillage ( CT) show that differences in runoff and erosion between both tillage techniques are scale-dependent: the difference in runoff and erosion response between conservation and conventional tillage increases with the length of the plot/field considered. The relative scale effect is more important for erosion than for runoff. The scale effect implies that plot measurements may lead to an underestimation of the effectiveness of conservation tillage in reducing runoff and erosion at the field or catchment scale. We tested experimentally the hypothesis that this scale-dependency can (partly) be explained by the occurrence of runoff transmission losses along the hillslope. In a 2.3 m long soil tray, filled with a silty loam soil, a seedbed was simulated and covered with different amounts of straw and maize residues. Our data showed that transmission losses can indeed be important and that they depend on cover percentage, discharge, time of discharge application and residue type. A simple model exercise shows that, under realistic assumptions, the effect of transmission losses on runoff and erosion on arable land may be highly significant. At present, most erosion models explicitly or implicitly assume a linear increase of runoff with slope length: taking into account transmission losses may contribute to a better estimation of runoff and soil losses. Since no interaction effect between cover percentage and inflow rate was found, the relative difference in runoff and soil loss between bare and residue-covered surfaces did not change with scale. Thus, other factors than those observed are responsible for the observed increasing differences between conservation and conventional tillage with increasing scale.


Liu B Y, Nearing M A, Risse L M, 1994. Slope gradient effects on soil loss for steep slopes.Transactions of the ASAE, 37(6): 1835-1840.The effect of hillslope length on soil loss, often termed the slope length factor, is one of the main and most variable components of any empirical model. In the most widely used model, the Universal Soil Loss Equation (USLE), normalized soil loss, L, is expressed as a power function of slope length, λ, as L=(λ/22.1)m, in which the slope exponent, m, is 0.2, 0.3, 0.4, and 0.5 for different, inc...


Liu Qingquan, Li Jiachun, Chen Liet al., 2011. Dynamics of overlan flow and soil erosion (II): Soil erosion.Advances in Mechanics, 34(4): 493-506. (in Chinese)This paper briefly introduced the basic characteristics and main types of soil erosion on hillslopes, and summarized the research advances in the dynamic process and prediction model of soil erosion on the hillslopes, including the surface-sealed soils, splash erosion, sheet flow (interrill) erosion, rill erosion, sediment transport of overland flow, critical slope gradient of soil erosion, and prediction modeling of soil erosion. Also, the future trends of soil erosion dynamics are discussed.

Liu Zhihong, Guo Weiling, Yang Qinkeet al., 2011. Vegetation cove rchanges and their relationship with rainfall in different physiognomy type areas of Loess Plateau.Science of Soil and Water Conservation, 9(1): 16-23. (in Chinese)Vegetation play a very important role in controlling the soil and water loss in Loess Plateau.Based on the NOAA/AVHRR month maximum value composite NDVI(Normalized Difference Vegetation Index)data of in July from 1988 2005,the spatial and temporal distribution of NDVI in different physiognomy areas of Loess Plateau was analyzed and the vegetation changes between the pre-and-post phases of the Conversion from Farmland to Forest was compared.The correlation between NDVI and rainfall in the same term was established to analyze the rainfall impact on NDVI.And also the impact on NDVI from the policy of Conversion from Farmland to Forest was discussed roughly.The results show that the average NDVI is 0.29 in the whole Loess Plateau,between 0.30-0.40 in plain,stony mountain,low loess mountain and loess tableland,between 0.18-0.22 in Liang shape loess hill,Mao shape loess hill,and sand loess hill,and lower than 0.15 in other regions.The vegetation changes between the pre-and-post phases of the Conversion from Farmland to Forest are different and show a zonary distribution from north-east to south-west in different physiognomy areas with a gentle increase in whole Loess Plateau about 4%,a 10% increase in the principle part of Loess Plateau,a little decrease in wind erosion sandy hill and Mao shape loess hill region.Moreover,the NDVI in Wuqi County has a great increase with 40%,higher far than the average increase value 14% in same Liang shape loess hill region.They are positive correlations(R20.60) between the maximum NDVI in July and the cumulate rainfall from May to July in those physiognomy type areas except in the rocky mountainous region,low loess mountain region and plain region.Some conclusions have been obtained that rainfall plays a decisive role in the spatial distribution of NDVI,and also determine the NDVI increase and decrease in the principle part of Loess Plateau in different time phases.The policy of the Conversion from Farmland to Forest has exerted an active action on the increase of vegetation cover through the model demonstration of Wuqi County for its available fund,a suitable revegetation selection between grass and forest based on its terrain and rainfall condition,and a perfect protection measure.


Mabit L, Zapata F, Dercon Get al., 2014. Assessment of soil erosion and sedimentation: The role of fallout radionuclides.Iaea Tecdoc Series, 3: 181-201.

Ochoa P A, Fries A, Mejía Det al., 2016. Effects of climate, land cover and topography on soil erosion risk in a semiarid basin of the Andes.Catena, 140: 31-42.61The relation climate–altitude–topography and their influence over erosivity were analyzed.61The conservation of ground cover is key to preventing soil erosion in tropical dry forest.61Seasonality effects on soil erosion process in semiarid watersheds of the Andes.61The relation between climate, soil and land cover was verified at mountain basin scale.


Paroissien J B, Darboux F, Couturier Aet al., 2015. A method for modeling the effects of climate and land use changes on erosion and sustainability of soil in a Mediterranean watershed (Languedoc, France).Journal of Environmental Management, 150(1): 57-68.61Soil erosion rate and soil life expectancy are combined into a sustainability index.61Current sustainability of soils in the watershed is low.61Only the Environmental law scenario strongly improves soil sustainability.61The method is designed to be flexible and to help decision maker.


Peter K D, Doleire-Oltmanns S, Ries J Bet al., 2014. Soil erosion in gully catchments affected by land-levelling measures in the Souss Basin, Morocco, analysed by rainfall simulation and UAV remote sensing data.Catena, 113: 24-40.61Impacts of land-levelling measures in the Souss valley are analysed.61Combining experimental rainfall simulation and aerial surveying using an UAV.61Runoff is 1.4 and sediment erosion even 3.5 times higher on levelled areas.61Gully eroded about 720m3 of soil in only one rain period, area 0.054m lowered.61Total soil loss is only 5% of the area, but 95% of the gully (including side-branches).


Prosdocimi M, Cerdà A, Tarolli P, 2015. Soil water erosion on Mediterranean vineyards. A review based on published data.EGU General Assembly Conference Abstracts, 17: 4034.61Soil water erosion on vineyards is variable due to anthropic and natural factors.61Average erosion rates measured by means of rainfall simulation was 77.6gm612h611.61Average soil loss measured by means of runoff plots was 2.4Mgha611.61Average erosion rate measured by means of erosion method was 9.3Mgha611yr611.61General trends between erosion rates and triggering factors can be found.


Shi Z, Ai L, Li Xet al., 2013. Partial least-squares regression for linking land-cover patterns to soil erosion and sediment yield in watersheds. Journal of Hydrology, 498: 165-176.There are strong ties between land cover patterns and soil erosion and sediment yield in watersheds. The spatial configuration of land cover has recently become an important aspect of the study of geomorphological processes related to erosion within watersheds. Many studies have used multivariate regression techniques to explore the response of soil erosion and sediment yield to land cover patterns in watersheds. However, many landscape metrics are highly correlated and may result in redundancy, which violates the assumptions of a traditional least-squares approach, thus leading to singular solutions or otherwise biased parameter estimates and confidence intervals. Here, we investigated the landscape patterns within watersheds in the Upper Du River watershed (8973 km(2)) in China and examined how the spatial patterns of land cover are related to the soil erosion and sediment yield of watersheds using hydrological modeling and partial least-squares regression (PLSR). The results indicate that the watershed soil erosion and sediment yield are closely associated with the land cover patterns. At the landscape level, landscape characteristics, such as Shannon's diversity index (SHDI), aggregation index (AI), largest patch index (LPI), contagion (CONTAG), and patch cohesion index (COHESION), were identified as the primary metrics controlling the watershed soil erosion and sediment yield. The landscape characteristics in watersheds could account for as much as 65% and 74% of the variation in soil erosion and sediment yield, respectively. Greater interspersion and an increased number of patch land cover types may significantly accelerate soil erosion and increase sediment export. PLSR can be used to simply determine the relationships between land-cover patterns and watershed soil erosion and sediment yield, providing quantitative information to allow decision makers to make better choices regarding landscape planning. With readily available remote sensing data and rapid developments in geographic information system (GIS) technology, this practical and simple PLSR approach could be applied to a variety of other watersheds. (C) 2013 Elsevier B.V. All rights reserved.


Wei H, Fan W, Ding Zet al., 2017. Ecosystem services and ecological restoration in the northern Shaanxi Loess Plateau, China, in relation to climate fluctuation and investments in natural capital.Sustainability, 9(2): 199.

Wei Wei, Chen Liding, Fu Bojieet al., 2006. Soil and water loss affected by landuse under different rainfall patterns in the semi-arid loess hilly area.Bulletin of Soil and Water Conservation, 26(6): 19-23. (in Chinese)Study on soil and water loss affected by landuse/cover under different rainfall types plays a significant role in soil erosion control and vegetation restoration,which can give scientific guidance to the practice.Based on 14 years of measurements in the experimental hydrologic plots,different rainfall patterns were classified.Precipitation amount,duration and maximum 30 min intensity were selected as the comprehensive index to divide the local rainfall events into three different patterns.Generally,pattern 1 is the aggregation of those with medium intensities,durations and amounts.Pattern 2 is the aggregation of rainfall events with such features as high intensities and short durations.Pattern 3 is the aggregation of those with low intensities and long durations.Accordingly,runoff and erosion features of five landuse types governed by these three rainfall patterns,as well as their features in different years,are all stressed.The main results are shown as follows.Firstly,from the static point of view,the lands characterized by the mean runoff coefficients and mean erosion moduli are in the order of seabuckthornnatural grassChinese Pinealfalfawheat.The reason why alfalfa land has severe runoff and erosion may be related to its growing characteristics and human disturbance.Secondly,runoff and erosion under rainfall pattern 2 hold the most serious position,followed by pattern 1 and pattern 3.This means that rainfall events with high intensities and shorter durations play dominant roles in causing soil and water loss in the semiarid area.Lastly,perennial plants such as seabuckthorn and Chinese pine show a very clear trend that runoff coefficients and erosion modulus decrease with time.Soil and water loss is serious in the first several years after plantation,then decreases obviously and gets stable at a lower level.Therefore,more attention should be paid to the different stages of vegetation succession.


Wu Linna, Yang Sheng Tian, Liu Xiaoyanet al., 2014. Response analysis of land use change to the degree of human activities in the Beiluo River basin since 1976.Acta Geographica Sinica, 69(1): 54-63. (in Chinese)Land use change as an important factor reflects the degree of human activities. Analysis of spatial-temporal change of land use is an effective way to reveal the degree of land use change. Based on the remote sensing and GIS, the authors used man computer interactive image processing methods to acquire the land use data in 1976, 1998 and 2010, and analyzed the spatial-temporal variation of land use in Beiluo River basin from the rate of land use change, the direction of land use transformation, and the degree of land use. The results can be obtained as follows. (1) The integrated dynamic degree of land use increased from 0.61 during 1976-1998 to 6.66 during 1998-2010, the area of arable land and grassland decreased gradually, the rate was increased from 2.00% and 2.69% to 26.20% and 23.33% respectively, while the area of forest land and residential land increased gradually, the rate of the former increased from 5.93% during 1976-1998 to 59.68% during 1998-2010, and that of the latter decreased from 6.59% during 1976-1998 to 3.52% during 1998-2010. (2) The direction of land use change showed similar characteristics during the two periods. Forest land was converted from arable land and grassland, and a small part of residential land was converted from arable land. (3) The integrated degree of land use change ranged from -2-1 during 1976-1998 to -27-4 during 1998-2010. The authors proved that the impact of human activities on the natural environmental showed increasing trends, that arable land, grassland, forestland and residential land were mainly converted, that affected areas were mainly distributed in the upper basin, i.e., Wuqi county, Fuxian county, Ganquan county, Huangling county and Luochuan county, while the increasing area of forest land was much larger than the decreasing area of both arable land and grassland area, and the increasing area of residential land.


Zhang Wenbo, Fu Jinsheng, 2003. Rainfall erosivity estimation under different rainfall amount.Resources Science, 25(1): 35-41. (in Chinese)

Zhao W W, Fu B J, Chen L D, 2012. A comparison between soil loss evaluation index and the C-factor of RUSLE: A case study in the Loess Plateau of China.Hydrology and Earth System Sciences, 16(8): 2739-2748.

Zhao Wenwu, Fu Bojie, Guo Xudong, 2008. The methods and GIS techniques for calculating multi-scale soil loss evaluation index.Progress in Geography, 27(2): 47-52. (in Chinese)Exploring the relationships between land use and soil erosion at different scales is a frontier research field and a hot spot topic in contemporary physical geography.As a new soil erosion model,the multiscale soil loss evaluation index(SL) has good prospects for analyzing the relationships between land use and soil erosion at multiple scales.Based on the SL equations and SL-factor meanings at different scales,this paper explains the detailed methods and GIS techniques for SL and SL-factor calculation at slope scale,small watershed scale and watershed scale.The SL-factor include rainfall erosivity factor,soil erodibility factor,topographic factor and cover/management factor.And the GIS techniques include kriging interception,field calculation,raster calculation,expanding zones,and so on.It is expected that the methods and GIS techniques of SL calculation will be helpful for SL development and the profound research of land use and soil erosion at multiple scales.


Zhong Lina, 2015. Analysis of multi-scale effect of rainfall pattern and land use pattern on soil erosion: A case study in the hilly and gully area of the Loess Plateau. Beijing: Beijing Normal University. (in Chinese)

Zhong Lina, Zhao Wenwu, 2013. Detecting the dynamic changes of vegetation coverage in the Loess Plateau of China using NDVI data.Science of Soil and Water Conservation, 11(5): 57-62. (in Chinese)The Loess Plateau is one of the most environmentally vulnerable regions in China.Detecting the dynamic changes of vegetation coverage in the Loess Plateau will benefit the regional vegetation restoration and ecosystem rehabilitation.Based on the spatial data analysis software GeoDa and statistical analysis module of ArcGIS,the SPOT-VGT NDVI( normalized difference vegetation index) data on August 21 of 1998,2003,2008,and 2012 were used to represent the annual maximum vegetation coverage,and the dynamic changes of vegetation coverage over time were detected.The results showed that,vegetation coverage in the Loess Plateau decreased from 1998 to 2003 but increased afterwards from2003 to 2012; NDVI showed a significant spatial autocorrelation; the location and extent of the cold spots of NDVI were relatively stable,while the location and scope of the hotspots changed obviously,and were mainly distributed in Shaanxi and Shanxi provinces.

Zhuang Jianqi, Ge Yonggang, 2012. Assessment of the soil loss associated with land use and precipitation change in the Xiaojiang River basin, Southwest China.Resources and Environment in the Yangtze Basin, 21(3): 288-295. (in Chinese)Global change lead to a series of environmental effects.Soil erosion is one of the most sensitive environmental effects to global change.This paper select Xiaojiang basin which one of the most fragile ecological environment as study object.Land use and precipitation changes of three periods was obtained using remote senses and rainfall stations,meanwhile the soil erosion response to land use and precipitation change were analyzed according to USLE model and basic geographical information data.The results show that the precipitation has there periods in which the precipitation is low from 1981 to 1990 is high from 1991 to 2000,and the precipitation decreased sharply from 2001.The average soil loss values is 70.58,80.08 and 79.81 t/(hm2a) in 1987,1995 and 2005 respectively,and the percent of area of above moderate degree is 29.92%,33.83% and 33.18% in 1987,1995 and 2005 respectively.The extreme high degree of soil loss is 9.15%,12.81% and 12.63% in 1987,1995 and 2005 respectively and increased continued.At meanwhile,we analysis the distribution and change in different altitude and slop.The USLE model can be used to assess the soil loss response to global change.It also indicates that USLE associated to GIS model is a useful and efficient tool for evaluating and mapping soil erosion risk and providing scientific basis for land resource management and economic activities.