Precipitation–vegetation coupling and its influence on erosion on the Loess Plateau, China

Catena 64 (2005) 103 – 116 www.elsevier.com/locate/catena

Precipitation–vegetation coupling and its influence on erosion on the Loess Plateau, China Xu Jiongxin Institute of Geographical Sciences and Natural Resource Research, Chinese Academy of Sciences, Key Laboratory of Water Cycle and Related Land Surface Processes, Chinese Academy of Sciences, Beijing 100101, China Received 28 January 2004; received in revised form 7 July 2005; accepted 22 July 2005

Abstract The relationships between precipitation, vegetation and erosion are important and are unsolved issues in the field of earth surface processes. Based on data from the Loess Plateau of China, some non-linear relationships between forest cover (C f), mean annual rainfall erosivity (R e) and annual precipitation ( P m) have been found. A threshold has been identified at P m = 450 mm, that is, when P m is b 450 mm, C f is low and basically does not vary with P m; when P m exceeds 450 mm, C f increases rapidly. Furthermore, two thresholds are identified in the relationship between rainfall erosivity and annual precipitation. When P m is b300 mm, R e is low and basically does not vary with P m. When P m exceeds 300 mm, R e increases rapidly; when P m becomes N 530 mm, the rate at which R e increases with P m becomes higher. Based on these relationships, the non-linear relationship between erosion intensity and annual precipitation (i.e., the erosion intensity increases with annual precipitation to a peak and then declines) is explained. The implication of these thresholds for erosion control on the Loess Plateau is discussed. D 2005 Elsevier B.V. All rights reserved. Keywords: Erosion; Vegetation; Precipitation; Geomorphic threshold; Loess Plateau of China

E-mail address: [email protected]. 0341-8162/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.catena.2005.07.004

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1. Introduction The inter-relations among precipitation, vegetation and erosion are important research topics in Earth surface processes. After the pioneering work of Langbein and Schumm (1958), who found the specific sediment yield increases with effective precipitation to a peak followed by a decline based on data from river drainage basins in the United States, much research has been done. Since then, similar patterns of variation have been found by many workers (e.g., Douglas, 1967; Wilson, 1973; Walling and Webb, 1983) in various areas in the world, and thus, the specific sediment yield-precipitation relationship established by them has been called the Langbein–Schumm relation. In China, Xu (1994) tested this relation using data from ~700 river drainage basins and obtained similar result. Jansson (1988) and Xu (1994) found that erosion phenomena exhibit some zonal features at macroscopical spatial scales in the world in general and in China in particular, respectively (Jansson, 1988; Xu, 1994). Yair and Emzel (1987) studied relationships between annual rainfall and sediment yield in arid and semi-arid areas and Lavee et al. (1998) studied the impact of climate change on geomorphology and desertification along a Mediterranean-arid transect, both dealing with precipitation–vegetation–erosion relationships and thresholds. Due to the influence of vegetation, the relationship between rainfall and erosion is nonlinear and complex. The inter-relationship among the above three factors can be expressed by the flowchart in Fig. 1. Rainfall erosivity is a function of rainfall characteristics, and the erosivity of rainstorm runoff is closely related to rainstorm characteristics. In the monsooninfluenced Yellow River basin, there is a close, positive correlation between mean annual precipitation and mean annual daily maximum rainfall (Fig. 2). The greater the annual precipitation, the higher the rain intensity, then the higher the erosivities due to rainfall and rainstorm runoff. Thus, there is a positive correlation between precipitation and erosivity. The erosion-resistance of land surface can be divided into two parts, the erosion-resistance due to vegetation and that due to surface materials. The former depends on vegetation Annual precipitation

+

Vegetation types, biomass, erosion-resistance of vegetation

+, _

_

+

Rainstorm intensity, erosivity of rainfall and rainstorm runoff

+

Erosion intensity Fig. 1. A flowchart showing relationship between erosion intensity and annual precipitation. The symbols + and
at the arrows indicate positive and negative correlations, respectively.

MEAN ANNUAL DAILY MAXIMUM RAINFALL (mm)

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105

1000 y = 56.755e0.0011x r2 = 0.164 n = 285, p <0.001

100

10 100

200

300

400

500

600

700

800

MEAN ANNUAL PRECIPITATION (mm) Fig. 2. The relationship between mean annual precipitation and mean annual daily maximum rainfall, based data from 285 rain gauges in the Yellow River basin.

conditions, and the latter on types and properties (e.g. lithology, grain size, and other soil physico-chemical properties) of surface material. To some degree, soil properties are also related to vegetation, in that pedogenic processes are strongly affected by vegetation. At larger spatial scales, vegetation is a zonal factor, and the pattern of its distribution is closely controlled by the distribution of annual precipitation. In undisturbed systems, precipitation determines vegetation types, net primary productivity and vegetation cover. In general, higher annual precipitation results in larger vegetation biomass, and therefore, stronger protection of vegetation against rainfall and water erosion, or higher erosionresistance of vegetation. Thus, a negative correlation between precipitation and erosionresistance due to vegetation appears. Therefore, due to the influence of vegetation, the relationship between precipitation and erosion becomes uncertain. The effect of precipitation on erosion is two-fold. When annual precipitation is high, rainfall erosivity is also high, and high intensity of erosion might appear. However, in the meantime, when mean annual precipitation is high, natural vegetation is dense, and the erosion-resistance due to vegetation is high. This factor may lead to an opposite result: a lower intensity of erosion. Thus, it can be said that, in a given area, if the erosion process is predominated by the effect of rainfall itself, and the effect of vegetation is secondary, then the correlation between erosion and mean annual precipitation is positive. If the erosion process is predominated by the effect of vegetation, and the effect of rainfall is secondary, then the correlation between erosion and mean annual precipitation is negative. Hence, it is demonstrated by the simple descriptive model in Fig. 1 that there may exist some non-linear, complex relationships between erosion and annual precipitation. Much evidence can be learnt from pre-existing studies, such as the pioneering work by Langbein and Schumm (1958) and many others (e.g. Douglas, 1967; Wilson, 1973; Walling and Webb, 1983; Xu, 1994). In this study, the descriptive model shown in Fig. 1 will be tested using data from the Loess Plateau, and thereby the Langbein–Schumm relation may be better understood by considering the precipitation–vegetation–erosion coupling.

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2. Outlines of study area and sources of data 2.1. Outline of study area The present study involves the whole Loess Plateau (Fig. 3) and the neighbouring desert area to the northwest. Located in an area covering arid, semi-arid and sub-humid climates, the mean annual precipitation varies from 200–700 mm in the northwest to southeast direction. In the same direction, natural vegetation types vary from arid desert, steppe to broad-leaf deciduous forest (Yang and Yuan, 1991). The study area is covered by a thick loess mantle and the grain size of loess becomes finer from northwest to southeast. Thus, surface material type can be categorized as eolian sand, sandy loess, typical loess and clayey loess in the northwest to southeast direction (Liu, 1964). In the study area, spatial gradient in physico-geographical conditions is high, and therefore, the spatial gradient in erosion and specific sediment yield is also high. Thus, it provides an ideal area to study the influence of precipitation–vegetation coupling on erosion and sediment yield. 2.2. Method, quantitative indices and data sources To quantitatively express the precipitation–vegetation coupling, some indices are introduced. Then, the relationships between these indices are studied, thereby to reveal the influence of precipitation–vegetation coupling on erosion processes. Apart from annual precipitation, the rainfall erosivity (R e) is introduced,Pwhich is defined by rainfall energy and the maximum 30 min rain intensity, i.e., R e = (E d I 30) (Wischmeier and Smith, 1978). Due to the difficulty in making direct field measurements,

Fig. 3. Location map of study area. The dashed lines are boundaries between climates; A is arid climate, SA is semiarid climate, SH is sub-humid climate and H is humid climate. The boundary lines between humid, sub-humid, semi-arid, and arid climates are shown as isolines of the dryness index 1.0, 1.5, and 4.0. The index of dryness K is P P defined as K = 0.16P / t, where P is mean annual precipitation, and t is the cumulative temperature during the period when the daily temperature is equal to or larger than 10 8C (Compilation Commission, 1982).

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some empirical equations are used to calculate the R e index (Wischmeier and Smith, 1978). Wang et al. (1998) adopted the following empirical equation to calculate the mean annual rainfall erosivity (R e) on the Loess Plateau: 0:989 1:312 I1440 Re ¼ 0:015P0:107 I60

ð1Þ

where P is mean annual precipitation (mm), I 60 and I 1440 are mean annual maximum 60 min and 24 h rain intensities, respectively. The unit of R e is mt cm/(ha h). The application of this formula proves that the influence of rain erosivity on specific sediment yield on the Loess Plateau can be well explained by the rain erosivity (Wang et al., 1998). Based on data from hydrometric stations, they calculated the R e values in the Loess Plateau region, and their results (Wang et al., 1998). The data of R e from 152 hydrometric stations are used in the present study. The precipitation data (mean annual precipitation and annual daily maximum rainfall) used in this study are from 261 county meteorological stations. This study is made on the basis of counties, i.e., various counties are taken as basic units for study. Forest cover (C f), defined as the areal percentage of forest of each county, is used to express vegetation characteristics and erosion-resistance due to vegetation. For convenience, the areal percentage of non-forest (C b), calculated as 100% minus C f, is also used sometimes, to express the erodibility of land surface. The data of forest cover from 261 counties are from Wang et al. (1991), who measured the C f from TM satellite images in 1985. The Loess Plateau is an area where human impacts on natural vegetation can be traced back 2000 years. To express the condition of bnaturalQ vegetation, we use the index of potential vegetation biomass, or the index of Net Primary Productivity (NPP, in t/(ha yr)). Several studies on estimating NPP have been made in the Loess Plateau region. For example, Zhu (1993) proposed a modified method to estimate NPP, and calculated the NPP values for each county in this area. The calculated NPP by his formula can well explain the zonal distribution of natural vegetation in the Yellow River basin (Zhu, 1993). Thus, his results are used in the present study. Totally, data of NPP from 282 counties are available. The erosion intensity (I e) used in this study is defined as erosion amount per km2 per year, and the data of I e for each county in the study area come from some previous study made by other researchers (Team for Integrated Scientific Investigation on the Loess Plateau, Chinese Academy of Sciences, 1992; Tang, 1990). They estimated erosion intensity through interpretation of satellite images that was supported by a statistical model established between I e and some influencing factors such as vegetation, landform and surface material. The data of I e for establishing the model were mainly from experimental slope-plot and small experimental catchment measurements. Using the model and based on the data of influencing factors interpreted from TM satellite images, the I e values for various units were calculated, and the I e value for each county was obtained. Thus, the I e is not specific sediment yield measured at hydrometric stations in drainage basins, but erosion amount per km2 over a given county, including the amount of sheet, rill and gully erosions. The data of I e were from the soil erosion investigation widely undertaken on the Loess Plateau in the mid-1980s, as part of a national key project on integrated investigation of natural resources. From this investigation, data

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from 64 counties are available, which were published (Team for Integrated Scientific Investigation on the Loess Plateau, Chinese Academy of Sciences, 1992; Tang, 1990) and used in the present study. The accuracy of the data is not high, but the spatial difference was well shown. Because the data involved in this study are from different sources, the number of samples are different. As pointed out above, the NPP data are from 282 counties, annual rain erosivity from 152 hydrometric stations, precipitation data from 285 counties, forest cover from 261 counties, and erosion intensity from 64 counties.

3. Results 3.1. Precipitation–vegetation coupling Based on the above indices and data, the variations of erosion-resistance due to vegetation and rainfall erosivity with mean annual precipitation have been analysed. In Fig. 4a, forest cover (C f) is plotted against mean annual precipitation ( P m), based on data from 261 counties in the Loess Plateau region. An overall correlation can be seen between C f and P m (r = 0.584, p b 0.001). Thus, the existence of positive correlation between erosion-resistance due to vegetation and annual precipitation shown in Fig. 1 has been demonstrated. To show some threshold points in the variation of C f with P m, the relationship between them is plotted in a semi-log coordinate (Fig. 4b), which indicates a non-linear variation. Although the points are scattered, they may be fitted by two straight lines with different slopes, with the break at P m c 450 mm. To identify the location of the break, the points were divided to two groups, and then the regression equations were calculated separately and the regression lines fitted. Then, the location of the break, i.e., the intersection of the two regression lines, was calculated by jointly solving the related two regression equations. When P m is b450 mm, the slope of the fitted line is very gentle, meaning a very low rate at which C f increases with P m. However, when P m becomes N 450 mm, the slope of the fitted line is steep, indicating that the rate at which C f increases markedly with P m. The relationship between mean annual rainfall erosivity (R e) and mean annual precipitation ( P m) has been plotted in Fig. 5a, based on data from 152 counties. An overall trend that R e increases with P m can be seen, and the correlation coefficient between them r = 0.77 ( p b 0.001). Again, the positive correlation between rainfall erosivity and annual precipitation shown in Fig. 1 has been demonstrated. It can be seen from Fig. 5a that the variation of R e with P m is also non-linear. There are two breaks that divide the fitted line to three straight lines with different slopes. To identify the location of the two breaks, the points were divided to three groups, and then the regression equations were calculated separately and the regression lines fitted. Then, the location of the break, i.e., the intersection of the two regression lines, was calculated by jointly solving the related two regression equations. From the left side to the right, the slope of the fitted line increases gradually. When P m is b 300 mm, the slope is very gentle, indicating that R e does not vary much with P m. When P m becomes N300 mm, the slope becomes much steeper; when P m becomes N530 mm, the slope increases further. This indicates that the rate at which R e

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100

FOREST COVER (%)

(a)

10

y = 1.1851e0.0044x r2 = 0.3414 p <0.01 n = 261

1

0.1 100

200

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MEAN ANNUAL PRECIPITATION (mm) 80

FOREST COVER (%)

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(b)

y = 46.969Ln(x) - 278.46 r2 = 0.169

60 50 Pm = 450 mm

40 30 20

y = 6.9416Ln(x) - 33.26 r2 = 0.286

10 0 100

1000

MEAN ANNUAL PRECIPITATION (mm) Fig. 4. Relationship between forest cover and annual precipitation in ordinary (a) and semi-log coordinates (b).

increases with P m has increased twice, for P m N 300 mm and P m N 530 mm, respectively. Thus, P m = 300 mm and P m = 530 mm can be regarded as two thresholds in variation of R e with P m. To reveal the combination of the C f–P m and R e–P m relationships, comparison has been made by plotting the two relationships on the same coordinate (Fig. 5b). To see the relationship clearer, the points were omitted and only the fitted lines remain (Fig. 5c). It can be seen that, when P m is given, the rates at which C f and R e increase with P m are different. When P m is b300 mm, C f, R e and the rates at which C f and R e vary with P m are all at low levels. When P m varies from 300–450 mm, both C f and the rate at which C f increases with P m are still low, but R e increases rapidly. The rapid increase of C f with P m occurs when P m becomes N 450 mm. Notably, P m at this point is smaller than its value at the point where R e starts to increase rapidly with P m, 530 mm. When P m exceeds 530 mm, the rate at which R e increases with P m increases again, a feature that matches the high rate at which C f increases with P m.

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RAINFALL EROSIVITY (m.t.cm/(ha.h))

110

400

(a)

y = 363.65Ln(x) - 2148.4 r2 = 0.252

350 300

Pm = 530 mm

250 y = 160.61Ln(x) - 871.17 r2 = 0.384

200 150

100 y = 14.604Ln(x) - 43.145 r2 = 0.0618 50

Pm = 300 mm

0 100

1000

MEAN ANNUAL PRECIPITATION (mm)

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80 FITTED LINE FOR RAINFALL EROSIVITY FITTED LINE FOR FOREST COVER RAINFALL EROSIVITY FOREST COVER

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EROSION INTENSITY (t/(km2.yr))

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FOREST COVER ( %) Fig. 6. Plots of erosion intensity against annual precipitation (a) and forest cover (b). The fitted curves are drawn by eye.

3.2. Pattern of variation in relation with precipitation–vegetation coupling The combination of the rates at which C f and R e increase with P m between different thresholds of P m, as shown in Fig. 5c, directly controls the way in which erosion intensity varies with mean annual precipitation. Based on data from 64 counties in the Loess Plateau region, annual erosion intensity in t/(km2 yr) has been plotted against mean annual precipitation in Fig. 6. The relationship is similar to that of Langbein and Schumm (1958). Erosion intensity increases with annual precipitation to a peak, followed by a decline when annual precipitation increases further. The mean annual precipitation of P m = 450 mm at the peak values of erosion intensity can be regarded as an important threshold. Another Fig. 5. Relationship between rainfall erosivity (R e), forest cover (C f) and annual precipitation (P m). (a) R e versus P m; (b) Comparison between R e–P m and C f–P m relationships; (c) Comparison between R e–P m and C f–P m relationships, only showing the fitted lines.

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threshold appears at P m = 300 mm. When P m is b300 mm, the slope of the fitted curve is very gentle, meaning that erosion intensity increases very slowly with P m. However, when P m exceeds 300 mm, the slope of the fitted curve becomes steep suddenly, indicating a sharp increase of erosion intensity with annual precipitation. Comparing Fig. 6a with Fig. 5c, the mechanisms by which the precipitation–vegetation coupling influences the way in which erosion intensity varies with annual precipitation can be explained. In Fig. 5a and c showing the relationship between rainfall erosivity and annual precipitation, a break also appears at P m = 300 mm, the same location as the break on the right side of the curve in Fig. 6a, as pointed out earlier. In Figs. 4b and 5c showing the relationship between forest cover and annual precipitation, a break appears at P m = 450 mm. When P m is b 450 mm, C f is very low and shows little change with P m; When P m exceeds 450 mm, C f increases rapidly with P m. Thus, the following points can be recognized: (1) When P m is b300 mm, erosion-resistance of vegetation expressed as C f is low, but rainfall erosivity is also low and insufficient to cause noteworthy erosion on bare land. Thus, erosion intensity is very low. (2) When annual precipitation is N 300 mm but is b 450 mm, erosion-resistance of vegetation is still low, but rainfall erosivity increases rapidly. This means that, within this range of annual precipitation, the effect of rainfall erosivity prevails over the effect of erosion-resistance of vegetation, and erosion process is controlled by rainfall erosivity. Hence, erosion intensity increases rapidly with annual precipitation, a feature that can be clearly seen from Fig. 6a. (3) When annual precipitation crosses the 450 mm threshold, forest cover starts to increase rapidly, and the role the erosion-resistance plays in erosion process is strengthened markedly. This would make the effect of vegetation on erosion gradually exceed the effect of rainfall erosivity and become the predominating control. From this point, the previous trend that erosion intensity increases with annual precipitation would turn to the opposite one. This turning point corresponds to the peak in erosion intensity in Fig. 6a. (4) Fig. 5c shows another break on the rainfall erosivity–annual precipitation curve at P m = 530 mm. When P m exceeds this value, the rate at which rainfall erosivity increases with P m increases further. It can be seen from this figure that, at this time, forest cover is sufficiently high, and resistance of vegetation still prevails over rainfall erosivity. Therefore, erosion intensity still decreases with annual precipitation. The relationship between erosion intensity and forest cover has been plotted in Fig. 6b, which shows that with the increase in forest cover, erosion intensity increases to a peak and then decreases. For the counties involved on the left side of the peak, the low forest cover can be related to low annual precipitation. The erosion process there is predominated by rainfall erosivity, rather than by erosion-resistance of vegetation. Thus, the increase of erosion intensity with annual precipitation reflects the relationship between rainfall erosivity and erosion intensity, rather than the relationship between erosion-resistance of vegetation and erosion intensity. For the counties involved on the right side of the peak, the relatively high forest cover results from relatively high annual precipitation. The erosion process there is predominated by erosion-resistance of vegetation, rather than by

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rainfall erosivity. Thus, with the increase in forest cover, erosion intensity decreases. It should be pointed out that the forest cover values at which the peak values of erosion intensity appear are only 15–20%, not sufficiently high to prevent erosion. This is because that, as to the absolute values, the present-day forest cover in most of the counties on the Loess Plateau does not reflect natural conditions, and most of the forests are secondary woods after the destruction by man in history. However, the pattern of variation shown in Fig. 6b still makes qualitative sense. 3.3. Explanation of the Langbein–Schumm relationship Langbein and Schumm (1958) explained the non-linear relationship between specific sediment yield and annual effective precipitation by considering the effect of vegetation as a function of precipitation. The occurrence of low values of specific sediment yield at low 1000

(a)

1/Cf

1000

y = 18.308e0.0036x r2 = 0.591, n = 152, p<0.001 100

1/Cf

RAINFALL EROSIVITY (m.t.cm/(ha.h))

RAINFALL EROSIVITY

100 10

r2 10 100

y = 84.378e-0.0044x = 0.341, n = 261, p<0.001 1

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RAINFALL EROSIVITY (m.t.cm/(ha.h))

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1/NPP y = 18.308e0.0036x r2 = 0.591, n = 152, p<0.001

350 300

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y = 1E+09x-3.6341 r2 = 0.801, n = 283 p<0.001

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MEAN ANNUAL PRECIPITATION (mm) Fig. 7. Rainfall erosivity and vegetation-determined erosional resistance varying with annual precipitation. (a) Rainfall erosivity and 1 / C f varying with precipitation; (b) Rainfall erosivity and 1 / NPP varying with precipitation.

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effective precipitation is due to lower runoff totals, and that at high effective precipitation is due to an increasingly abundant vegetation cover that affords better protection against erosion. Based on data from the Loess Plateau, an explanation can be given to the nonlinear relationship between erosion intensity and annual precipitation. As pointed out earlier, erosion intensity depends on both rainfall erosivity and erosionresistance due to vegetation. Land surface erodibility can be related to several factors, including soil physico-chemical and land cover properties. The latter is closely related to vegetation. Vegetation can directly protect land surfaces against erosion. Furthermore, vegetation increases soil organic matter content, improving its physico-chemical properties and thereby lowering soil erodibility. Hence, generally, the denser the vegetation cover, the lower the land surface erodibility. In this consideration, land surface erodibility can be indexed as the reciprocal of forest cover, 1 / C f. The meaning of C f is the total land area corresponding to unit area of forest. Thus, the higher the 1 / C f value, the larger the ratio of bare land. For bnaturalQ conditions, vegetation condition can be expressed by the net primary productivity (NPP), and then erodibilty may be expressed as 1 / NPP. Using these two indices, the rainfall erosivity (R e) and land surface erodibility (1 / C f and 1 / NPP) have been plotted against annual precipitation ( P m) in Fig. 7a and b, respectively. Although the points in Fig. 7a are scattered, some trends can be clearly seen, i.e., R e is positively correlated with P m, and 1 / C f is negatively correlated with P m. On the left side of the graph where P m is low, erodibility (1 / C f) of land surface is high but rainfall erosivity (R e) is low. On the right side of the graph where P m is high, land surface erodibility is low but rainfall erosivity is high. In both cases, erosion intensity is unlikely to attain high values. Only in areas with medium values of annual precipitation, rainfall erosivity and land surface erodibility are all relatively high, and thus, peak values of erosion intensity appear, as shown in Fig. 6a. The relationships shown in Fig. 7b, where 1 / NPP is used as the index of land surface erodibility, are similar to those in Fig. 7a, and the same result can be seen. Thus, Fig. 7 explains the formative cause of the Langbein–Schumm relation.

4. Conclusions Based on data from the Loess Plateau of China, some non-linear relationships between forest cover (C f), rainfall erosivity (R e) and annual precipitation ( P m) have been found. A threshold has been identified at P m = 450 mm, that is, when P m is b 450 mm, C f is low and varies little with P m; when P m exceeds 450 mm, C f increases rapidly. Furthermore, two thresholds are identified in the relationship between rainfall erosivity and annual precipitation. When P m is b300 mm, R e is low and basically does not vary with P m. When P m exceeds 300 mm, R e increases rapidly; when P m becomes N530 mm, the rate at which R e increases with P m becomes higher. Based on these relationships, the non-linear variation between erosion intensity and annual precipitation (i.e. the erosion intensity increases with annual precipitation to a peak and then declines) is explained. The discovery of the above thresholds has implications for erosion control on the Loess Plateau. The fact that only when the annual precipitation exceeds 450 mm the forest cover increases rapidly with annual precipitation reflects the zonal variations of natural vegetation in the study area. This implies that forest vegetation cannot form if annual

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precipitation is b450 mm, unless there are some local favourable conditions for moisture availability. Thus, annual precipitation of 450 mm should be taken as the limit for largescale reforestation, i.e., in areas with annual precipitation b450 mm, woodland is not appropriate, and large-scale reforestation should not be recommended. The species of shrubs and grasses (e.g., xerophytic grasses) are suitable for vegetation restoration. Furthermore, because in the areas where annual precipitation is b450 mm, the erosion process is predominated by rainfall erosivity and the role of erosion-resistance of vegetation is only secondary, vegetation measures such as tree-planting should not be taken as major erosion control measures there. Instead, engineering measures including land terracing and sediment trapping works such as reservoirs and checkdams should be emphasized. Shrub- and grass-planting should be the dominant types of vegetation restoration, rather than tree planting. In the areas where annual precipitation is N 450 mm, vegetation is the major factor that determines erosion intensity. Thus, vegetation restoration should be taken as the major measures for erosion control, in combination with engineering works. In the hilly gullied areas on the Loess Plateau, sediment derived from gully erosion makes major contributions to the sediment load of the Yellow River (Tang, 1990). By vegetation measures only, gully erosion, especially mass-wasting on gully walls, cannot be controlled effectively. Thus, engineering works, such as sediment trapping through reservoir and checkdams, are required. Acknowledgments The financial support from the National Natural Science Foundation of China and the Yellow River Water Conservancy Commission (50239080, 40271019) is gratefully acknowledged. All hydrometrical data used in this study came from the Yellow River Basin Water Resources Commission, and I wish to express my deep gratitude to all workers at the gauging stations, whose hard work made this study possible. Thanks are also expressed to Dr. M.A. Fullen and an anonymous reviewer, for their invaluable comments. References Compilation Commission, 1982. Physical Geography in China: Historical Perspectives. Science Press, Beijing. (in Chinese). Douglas, I., 1967. Man, vegetation and sediment yield of river. Nature 215, 925 – 928. Jansson, M., 1988. A global survey of sediment yield. Geografisca Annaler 70A, 81 – 98. Langbein, L.B., Schumm, S.A., 1958. Yield of sediment in relation to mean annual precipitation. Transactions American Geophysical Union 39, 1076 – 1084. Lavee, H., Imeson, A.C., Sarah, P., 1998. The impact of climate change on geomorphology and desertification along a Mediterranean-arid transect. Land Degradation and Development 9, 407 – 422. Liu, D.S., 1964. Loess in the Middle Yellow River Drainage Basin. Science Press, Beijing (in Chinese). Tang, K.L. (Ed.), 1990. Soil Erosion in the Loess Plateau Region and Its Control. Publishing House for Science and Technology of China, Beijing. 246 pp. (in Chinese). Team for Integrated Scientific Investigation on the Loess Plateau, Chinese Academy of Sciences, 1992. Data on Environment, Natural Resources and Social-Economy in the Loess Plateau Region. Publishing House for Chinese Economy, Beijing. 575 pp. (in Chinese).

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