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A study of soil surface characteristics in a small watershed in the hilly, gullied area on the Chinese Loess Plateau
Catena 54 (2003) 31 – 44 www.elsevier.com/locate/catena
A study of soil surface characteristics in a small watershed in the hilly, gullied area on the Chinese Loess Plateau Guobin Liu a,b,*, Mingxiang Xu a, Coen Ritsema c a
Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, 26 Xinong Road, Yangling, Shaanxi Province, 712100, PR China b NW Sci-Tech University of Agriculture and Forestry, Yangling, Shaanxi Province, 712100, PR China c Alterra b.v. Green World Research, 6700 AA Wageningen, Netherlands
Abstract Soil surface characteristics are closely related to soil surface depressional storage, infiltration, runoff generation and soil erosion, especially in highly erodible loess soil. Soil surface random roughness, soil cohesion and aggregate stability are necessary parameters in the Limburg Soil Erosion Model (LISEM) and helpful in developing alternative land-use and conservation strategies. Over a period of 2 years, soil surface characteristics were measured continuously in Danangou, a small catchment in the hilly, gullied loess area in China, with the support of the Erochina project. The results show that soil surface random roughness, soil cohesion and aggregate stability differed significantly between land-use types. Ranking the types on soil roughness during the monitoring period yielded the sequence cropland>orchard and wasteland and fallow land>shrubland>woodland. The corresponding sequence for aggregate stability was approximately wasteland and woodland>cropland and orchard>shrubland>fallow land. Cohesion in the different land-use types in the catchment was smallest for cropland and largest for wasteland. There were no significant differences between croplands with different topographical characteristics. All of these soil surface characteristics showed significant temporal variation over the monitoring period. Temporal variations in roughness for shrubland and woodland over the 2-year period were significantly greater than those for other land-use types. There were also considerable variations in cohesion for shrubland, woodland and wasteland. In 1998, the weighted average values of roughness, aggregate stability and cohesion for the entire catchment were 1.113 cm, 13.6 drops and 0.131 kg/cm2, respectively, with C.V. values of 9.6%, 32.05% and 21.11%. The corresponding values for 1999 were 1.616 cm, 11.52 drops and 0.100 kg/cm2, with C.V. values of 16.0%, 15.41% and 33.33%. The temporal and spatial
* Corresponding author. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, 26 Xinong Road, Yangling, Shaanxi 712100, PR China. Tel.: +86-29-7012907; fax: +86-29-7012210. E-mail address: [email protected] (G. Liu). 0341-8162/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0341-8162(03)00055-9
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variations in soil surface characteristics in the catchment should be carefully taken into account in LISEM calibration and validation. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Soil surface characteristics; Temporal and spatial variation; LISEM model
1. Introduction Soil surface characteristics, especially random roughness, aggregate stability and soil cohesion, are closely related to soil surface depressional storage, infiltration, runoff generation and soil erosion (Yanlai and Guangyi, 1992; Ali, 1995; Cremers et al., 1996; Jianjun and Bergsma, 1995; Zimbone et al., 1996). Knowledge of soil surface characteristics is important for runoff and soil erosion modeling (Cremers et al., 1996). Bagnold (1941) first gave the definition and calculation method of soil surface roughness, however, the concept of soil surface roughness was mainly concerned with wind erosion and sand movement. Burnell and Larson (1969) studied infiltration as influenced by tillage-induced roughness. Soil surface roughness affects the amount of surface depressional storage, the proportion of the soil surface covered by water, the amount of rainfall excess needed to start runoff and the overland flow rate (Cremers et al., 1996; Huang and Bradford, 1990). Aggregate stability influences several aspects of a soil’s physical behavior, particularly water infiltration and soil erosion (Bissonnals, 1996). Casagrande (1936, 1938) appears to have been the first to study the characteristic of soil cohesion and its relation with soil stability. He also invested a Shear box, which is widely used in soil cohesion measurement in research (Head, 1994). These soil surface characteristics are basic parameters of several soil erosion prediction models (Lane et al., 1992; Wischmeir and Smith, 1978; Renard, 1997; De Roo et al., 1996). However, there have been few studies of temporal and spatial variations in soil surface characteristics (Liang and Yao, 1993; Cass et al., 1996; Cremers et al., 1996). In the Limburg Soil Erosion Model (LISEM model (De Roo et al., 1995, 1996)), soil surface random roughness is used to simulate rainfall storage in microdepressions, while aggregate stability is used to calculate splash detachment, and soil cohesion is used to estimate rill and inter-rill erosion (De Roo et al., 1995, 1996; Cremers et al., 1996; Li, 1998). Nowhere in the world is soil erosion such a serious problem as on the Chinese Loess Plateau. Aware of the extent and severity of the problem, the Chinese Government is promoting measures to control soil erosion. In these highly erodible loess soils, the irrational land-use styles, low vegetation coverage and cultivation on steep slopes have caused serious environmental problems. However, the complicated morphology of the loess plateau, with its hills and gullies, and the characteristics of the soil make it difficult for the available models to predict erosion. There is as yet no proper method to predict soil erosion and evaluate the effect of conservation measures (Ritsema, 1996). This paper reports on a study which was part of the Erochina research project, and in which several soil surface characteristics of different land-use types, slope aspects and slope degrees were continuously measured for a typical small watershed in the area of hills and gullies on the Chinese Loess Plateau (EROCHINA, 1997). Temporal and spatial variations were studied at both plot and catchment scale, and are expected to provide an
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important empirical basis for the calibration and validation of the LISEM model, as well as for the use of this model in developing alternative land-use methods and conservation strategies. The Danangou catchment (Latitude 36j53VN, Longitude 109j17VE) is situated in the center of the loess plateau in the northern Shaanxi Province of China. The catchment has an area of 3.5 km2 and its altitude ranges from 1000 to 1350 m. There are significant topographic variations within the study area, with typical loess hills and gully landforms. Prolonged human activity has destroyed much of the natural vegetation. Land-use types include sloping croplands, fallow land, grassland, shrubland, orchards and woodland. Crops mainly include potatoes (Solanum tuberosum), beans (Phaseolus valgaris), maize (Zea mays) and millet (Panicum miliaceum). The forest, all of which has been planted, is dominated by locust trees (Robinia pseudoacacia). The grassland is mainly covered by annuals such as sweet wormwood (Artemisia annua) and annual fleabane (Erigeron annuus). The region has a semiarid continental climate with an average annual temperature of 8.8 jC. Monthly mean temperatures range from 22.5 jC in July to 7 jC in January. The average annual precipitation is 562 mm, with great inter-annual variability; 60% of the rainfall falls between July and September. There are 159 frost-free days and an average of 2415 h of sunshine each year. The soils developing on the wind-accumulated loess parent material have an average thickness of 50– 80 m. The most common soil in the catchment is loessal, which is highly erodible, with an erosion modulus of 10,000 –12,000 tons km 2 year 1 (Jiang and Fan, 1990).
2. Materials and methods On the basis of the various land-use types, 18 plots were selected in the Danangou catchment in 1998 and 16 plots in 1999 (Table 1). To obtain a representative input for the LISEM model calibration, fields with different geographical situations were selected. Measurements were repeated every 2 weeks from April to October in each year. 2.1. Random roughness Soil surface random roughness (surface microtopography) is a measure of the irregularity of the soil surface. LISEM uses random roughness to calculate soil surface water storage and the point at which overland flow starts. Researchers used several measurement methods and indicators to calculate soil surface roughness, such as pin roughness meter (Cremers et al., 1996), chain method, roughness coefficient and indicator (Ali, 1995). Measurement in this paper was conducted using a special pin meter (Photo 1) and a digital camera. The measurement steps were as follows: (EROCHINA, 1997; Ritsema, 1996; Cremers et al., 1996): (a) The pin meter was placed in a position parallel to the slope, avoiding rills and plants.
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Land-use type
Field no.
Topographic types
Slope
Vegetation types
Field no.
Topographic types
Slope
Vegetation types
Crop
1, 4, 7, 8, 9, 13, 15, 16, 18
Steep slope 4, 7, 9 terrace 13; 16 gentle slope 1, 8, 15, 18
30 1; 5 15
Millet, soybean + oil crop, tomato, maize + soybean
1, 6, 7, 11, 12, 13, 14, 17
Steep slope 1, 6, 7 terrace 11 gentle slope 12, 13, 14, 17
25 0 12
Wood Shrub Orchard Fallow Waste
6; 14 12 5; 17
Slope Slope Slope Slope Slope
30; 8 15 8; 18
Acacia Caragana korshinskii Apple Grass Grass
5; 10 16 4; 9 8; 15 2; 3
Slope Slope Slope Slope Slope
24; 13 36 23; 19 9; 22 43; 28
Millet, soybean + oil crop, tomato, maize + soybean, buckwheat Acacia Caragana korshinskii Apple Grass Grass
2; 3; 10
15; 40; 18
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Table 1 Topographical factors and land-use types of the plots studied
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Photo 1. Pin meter.
(b) All pins were lowered to the ground, and a picture was taken with the digital camera. (c) This was repeated six times for the directions perpendicular and parallel to the slope. (d) The photo was edited in a computer, using a photo editor program (Profile meter program, Wagner and Yiming Yu, 1991). This made it possible to calculate the standard deviation of each pin’s height, based on the control standard height. This resulted in the roughness value and the average deviation in six repeats. 2.2. Aggregate stability 2.2.1. Method one—water-drop method The water-drop method (Low, 1954) was used to simulate the impact of raindrops on the aggregates. The moisture content of the aggregates was standardized to allow the results of different tests to be compared. About 200 g of surface sample was collected in each field. A filter paper was placed on sand in a bucket, and the sample was placed on the filter paper after having been passed through 5- and 4-mm sieves. A constant water level was provided in the bucket. After 24 h, a sample was placed on the 3-mm sieve below a drip-bottle and water drops were allowed to fall on the sample at a rate of about 50 drops/ min (Photo 2). The number of drops required to strike the aggregate passing through the sieve was counted. Aggregates (50) were tested for each sample and the average number of drops was calculated. 2.2.2. Method two—improved Yoder method One of the methods currently used to test aggregate stability is the wet sieve method (improved Yoder method) (Yoder, 1936; Zhu, 1989). This is the method most
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Photo 2. Equipment for the water-drop method.
commonly used to evaluate soil aggregate stability at the Chinese Loess Plateau. In order to make the best possible use of historical data on soil aggregate stability at the Loess Plateau, as well as to allow comparisons with the data obtained by the water-drop method, the relation between the water-drop method and the improved Yoder method was studied. A set of sieves (with aperture of 5, 2.5, 1, 0.5 and 0.25 mm) was prepared. The saturated soil sample (which had been wetted by capillary water to remove the air from the soil) was placed in the sieves. The sieves were then immersed in water and shaken up and
Photo 3. Shear tester with different vanes.
Table 2 Soil characteristics in different land-use types
1998
Steep slope Terrace Gentle slope Cropland (A.V.) Woodland Shrub Fallow Orchard Wasteland Weight average of catchment
Saturated cohesion (kg/cm2)
Aggregate (drops) 1999
1998
1999
1998
1999
Average
C.V. (%)
Average
C.V. (%)
Average
C.V. (%)
Average
C.V. (%)
Average
C.V. (%)
Average
C.V. (%)
1.52 1.35 1.37 1.41 0.82 0.97 – 1.33 1.12 1.11
8.31 11.85 10.27 6.47 19.86 24.04 – 9.71 10.83 9.61
1.93 1.73 1.73 1.80 0.85 0.89 1.08 1.42 1.80 1.62
11.64 45.21 18.35 20.41 21.75 65.85 9.99 10.37 10.37 13.79
12.87 9.95 11.10 11.31 15.09 9.11 – 11.34 16.69 13.60
50.61 29.95 33.53 37.75 26.50 32.78 – 51.29 45.94 32.05
8.23 11.39 6.29 8.64 17.07 8.93 7.28 11.81 13.39 11.52
19.09 63.89 9.84 32.01 63.34 40.98 17.08 12.60 49.84 15.41
0.084 0.086 0.075 0.082 0.133 0.144 – 0.105 0.183 0.131
20.10 25.49 19.39 18.78 35.44 61.88 – 29.55 30.80 21.11
0.066 0.058 0.061 0.062 0.151 0.078 0.100 0.086 0.108 0.100
26.07 30.24 25.76 23.09 35.13 18.76 8.05 11.99 82.63 33.33
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Random roughness (cm)
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down for 1 min. The aggregate left in each sieve was weighed and the percentage of each particle class was calculated. Mean weight diameter (MWD) (Van Bavel, 1949; Bissonnals, 1996) was also calculated as follows: MWD ¼
n X
Xi Wi
i1
MWD—mean weight diameter (mm); Xi — average diameter of each particle class (mm); Wi — percentage of aggregate relating to Xi (%). Samples of the topsoil (0 –10 cm) were taken at the Zhifanggou catchment and Yangling in July and August 1999, with the intention of measuring their aggregate stability using both methods. 2.3. Soil surface cohesion LISEM uses cohesion as a parameter to calculate erosion caused by overland flow. The cohesion at saturation level is therefore critical and care must be taken to measure fully wetted soils. A type of pocket shear tester (Photo 3) was used in the following steps (Zimbone et al., 1996): (a) The soil was thoroughly saturated, using a sprinkler. (b) The pocket vane tester was pushed into the ground, until the vanes were no longer visible. (c) The tester was carefully turned to the right, until the soil failed. If the value was above 8, the smaller vane was used. If the value was below 2, the larger vane was used. (d) Step 1 was repeated 5 –10 times. (e) The average cohesion value was calculated by multiplying the measured value by 0.02 for the large vane, 0.10 for the middle vane and 0.25 for the small vane (unit: kg cm 2). Average values of each parameter were calculated per measuring period for each of the different land-use types. The percentage coverage of each land-use type was then used to calculate the weighed average of each parameter for the entire catchment. All measurements of soil surface characteristics were evaluated using analysis of variance (Table 2).
3. Results and discussion 3.1. Dynamics of random roughness in the catchment Random surface roughness is an important variable in runoff and soil erosion modeling since it correlates closely with surface runoff, soil infiltration, surface heat balance, evaporation, etc. (Ali, 1995; Yanlai and Guangyi, 1992; Cremers et al., 1996). In terms of soil and water conservation, greater roughness increases infiltration and
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decreases surface runoff. Soil surface roughness is mainly influenced by cultivation, vegetation, soil type and rainfall (Cremers et al., 1996). The results of 2 years of measurements showed that the random roughness was significantly different for the various land-use types (Table 2). The 1998 measurements showed that the various landuse types could be ranked for this parameter as follows: cropland>orchard>wasteland>shrubland>woodland. The ranking for 1999 was cropland and wasteland>orchards>fallow land>shrubland>woodland. Thus, the rankings for the 2 years were identical except for the higher value for wasteland in 1999. The temporal variations for shrubland and woodland in these 2 years were much greater than those for the other land-use types, while the temporal variations in roughness for shrubland and cropland were much greater in 1999 than in 1998. There was no significant temporal variation in roughness for the other land-use types (Fig. 1). However, there were two peaks of roughness in cropland and orchards in both years. Farming activities had obvious effects on roughness, increasing it in June and September. The soil surface was severely disturbed by farming activities in this period, such as fertilizing, weeding and harvesting. The roughness of cropland on steep slopes was markedly higher than that on the terraces and on gently sloping land. The effects of tillage on soil surface roughness have been reported in several previous studies. The roughness measured in our experiment was comparable with that reported from other studies (Cremers et al., 1996). However, the different soil erosion rates for different land-use types and croplands with different topographical characteristics cannot be explained by the random roughness. The weighed mean roughness in the catchment as a whole had a value of 1.113 cm in 1998, with a C.V. of 9.6%, and one of 1.616 cm in 1999, with a C.V. of 16.0%. For the purpose of LISEM calibration, it is important to pay attention to the temporal variations in roughness for the different land-use types, in that parameters showing great temporal variation should be measured just before calibration. 3.2. Characteristics of soil aggregate stability 3.2.1. Dynamics of aggregate stability in the catchment Soil aggregate stability is closely related to anti-scourability and erodability (Bissonnals, 1996; Liu, 1997). The variance analyses showed that, while there were significant
Fig. 1. Soil random roughness in different land-use types.
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Fig. 2. Soil aggregate stability in different land-use types.
differences in aggregate stability between land-use types in 1998, no significant differences were found in 1999. Ranking the different land-use types for aggregate stability yields the following sequence for 1998: wasteland>woodland>cropland and orchards> shrubland. The sequence for 1999 was slightly different: woodland>wasteland>cropland and orchards>shrubland (Fig. 2). On the whole, soil aggregate stability showed a close positive relation with organic matter content (Bissonnals, 1996) (Table 3). Decomposing stick, leaves, weeds and roots resulted in greater accumulation of organic matter in woodland and wasteland, while the orchard soils had a higher organic matter content because of intensive management (more organic fertilizer input). The weighed mean Table 3 Soil aggregate stability and organic matter content Sample No.
Mean weight diameter (mm)
Percentage of 3.5-mm aggregate (%)
Average (drops)
Organic (%)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
2.25 3.31 2.61 2.77 0.22 2.02 2.89 1.42 2.28 0.59 1.74 2.35 0.40 0.19 0.25 0.79 1.08 0.44 1.05 1.06 1.19 0.38
6.8 7.6 12.3 18.9 3.4 13.9 7.4 5.8 6.9 4.3 12.3 9.5 3.1 1.1 2.5 8.2 10 3.9 5 3.8 2.5 1.8
10.7 20.3 16.1 111.8 5.0 13.6 9.7 10.1 14.9 8.0 19.0 15.2 6.5 4.3 4.2 22.5 32.0 9.6 8.0 8.3 26.4 55.0
2.75 2.28 2.33 3.58 0.68 1.85 1.45 1.12 0.88 0.54 1.43 0.99 0.55 0.39 0.57 0.84 0.66 0.56 0.74 0.67 1.27 1.61
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aggregate stability for the catchment as a whole was 13.6 in 1998, with a C.V. of 32.05%, against 11.52 in 1999, with a C.V. of 15.41% (Table 2). The results do not indicate any obvious differences for croplands with different topographical characteristics. The main soil properties influencing aggregate stability are soil texture, clay mineralogy, organic matter content, type and concentration of cations, sesquioxide content and CaCO3 content (Bissonnals, 1996). Of these soil properties, organic matter content plays the greatest role in aggregate stability. The significant differences in aggregate stability for the various land-use types in the catchment were due to the differences in organic matter content. The small temporal variation in aggregate stability also demonstrated that aggregate stability was a relatively stable property. 3.2.2. Comparison of aggregate stability values measured by the water-drop method and the improved Yoder method The results of a linear regression analysis comparing aggregate stability values measured by the two methods showed that the MWD values found with the wet sieve method were not significantly correlated with the values found using the waterdrop method. However, there was significant correlation between the percentage of 3.5-mm aggregates and aggregate stability measured with water-drop method. The percentage of 3.5-mm aggregates can be used instead of the number of drops to express aggregate stability. The results also showed that soil organic matter content was significantly correlated with MWD value percentage of 3.5-mm aggregates and with the aggregate stability measured with the water-drop method, demonstrating that organic matter has a positive and beneficial effect on the formation of soil aggregates (Fig. 3). 3.3. Characteristics of soil cohesion and its dynamics in the catchment Soil cohesion is one of the indices of soil resistance, which represents the soil’s ability to resist destruction by sheet flow in water erosion. Generally speaking, soil erosion shows a negative relation with soil cohesion. Soil cohesion is related to factors such as soil type, organic matter content and soil water content (Jianjun and Bergsma, 1995). Variance analysis of the measured data showed that there were significant differences in soil cohesion between cropland and the other land-use types in both years. The cohesion in wasteland differed significantly from that in the other land-use types, while the values for orchards differed significantly from those for shrubland in 1998. In 1999, cohesion in woodland was significantly different from that of the other land-use types. Ranking the various land-use types for cohesion results in the following sequence for 1998: wasteland>shrubland>woodland>orchards>cropland; the corresponding sequence for 1999 was: woodland>wasteland>fallow land>orchard>shrubland>cropland. Cropland had the lowest cohesion values, while wasteland and woodland had the highest values of the various land-use types in the catchment. This means that cropland was the most erodible type, while wasteland and woodland were less erodible. The spatial variation in cohesion for the various land-use types in the catchment was considerable and should be taken into consideration in LISEM
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Fig. 3. Relation between aggregate stability values measured with two methods.
calibration. The results also showed that there was significant temporal variation over the year. In 1998, the temporal variation in cohesion was smallest for cropland and greatest for shrubland (Fig. 4), while in 1999, the smallest cohesion value was found for fallow land, and wasteland showed much higher temporal variation. On the whole, cohesion values in shrubland, woodland and wasteland varied greatly. The results did not show obvious differences for croplands with different topographical characteristics. The weighed average for soil cohesion in whole catchment was 0.131 kg/cm2 in 1998, with a C.V. of 21.11%, and 0.100 kg/cm2 in 1999, with a C.V. of 33.33% (Table 2). The great temporal variation in soil cohesion over the 2 years causes uncertainty in soil erosion predictions.
Fig. 4. Soil saturated cohesion in different land-use types.
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4. Conclusion The various land-use types showed significant differences in soil surface random roughness, soil cohesion and aggregate stability. Ranking the types for roughness in the 2 years yields the sequence cropland>orchards and wasteland and fallow land>shrubland> woodland. The corresponding sequence for aggregate stability for the 2 years was approximately wasteland, woodland>cropland and orchards>shrubland>fallow land. Of all the land-use types in the catchment, cohesion was smallest in cropland and largest in wasteland. There were no significant differences for croplands with different topographical characteristics. All of these soil surface characteristics showed significant temporal variation during the monitoring periods. The temporal variation in soil surface roughness in these 2 years was much greater for shrubland and woodland than for the other land-use types. Cohesion values varied greatly for shrubland, woodland and wasteland. In 1998, the weighed averages of roughness, aggregate stability and cohesion in the catchment as a whole were 1.113 cm, 13.6 drops and 0.131 kg/cm2, respectively, with C.V. values of 9.6%, 32.05% and 21.11%. The corresponding values for 1999 were 1.616 cm, 11.52 drops and 0.100 kg/cm2, with C.V. values of 16.0%, 15.41% and 33.33%. LISEM calibration and validation should pay close attention to temporal variation in roughness for the different land-use types, in that parameters that show great temporal variation should be measured just before calibration and all kinds of land-use types should be considered.
Acknowledgements The research was supported by Erochina project, and also supported by China national key foundational development and planning project (G2000018606), knowledge innovation project of Chinese academy of science (KZCX1-06) and special foundation for preceding research field of ISWC.
References Ali, S., 1995. Measuring soil surface roughness using the chain method. Sci-Tech Information of Soil and Water Conservation (Chinese) 1, 14 – 16. Bagnold, R.A. (Ed.), 1941. The Physics of Blown Sand and Desert Dunes, vol. 256. Methuen, London, pp. 78 – 90. Bissonnals, Y.L.E., 1996. Aggregate stability and assessment of soil crustability and erodibility: theory and methodology. European Journal of Soil Science 47, 425 – 437. Burnell, R.E., Larson, W.E., 1969. Infiltration as influenced by tillage-induced roughness and pore-space. Soil Science Society of America Journal Proceedings 333, 449 – 452. Casagrande, A., 1936. Characteristic of cohesionless soils affecting the stability of slope and earth fill. Journal of the Boston Society of Civil Engineering, January, 13 – 32. Casagrande, A., 1938. The shear resistance of soils and its relation to the stability of earth dams. Proceedings Soil and Foundation Conference of the U.S. Engineer Department, Boston.
44
G. Liu et al. / Catena 54 (2003) 31–44
Cass, A., McKenzie, N., Cresswell, H., 1996. Physical indicators of soil health. In: Walker, J.W., Reuter, D.J. (Eds.), Indicators of Catchment Health—a Technical Perspective. CSIRO Publishing, Melbourne, pp. 89 – 107. Cremers, N.H.D.T., van Dijk, P.M., De Roo, A.P.J., Verzandvoort, M.A., 1996. Spatial and temporal variability of soil surface roughness and the application in hydrological and soil erosion modeling. Hydrological Process 10, 1035 – 1047. De Roo, A.P.J., Wesseling, C.G., Jetten, B.G., Ritsema, C.J., 1995. LISEM, a User Guide. DLO-Staring Centre, Wageningen, pp. 7 – 15. De Roo, A.P.J., Wesseling, C.G., Ritsema, C.J., 1996. LISEM: a single-event physically based hydrological and soil erosion model for drainage basins. I: Theory, input and output. Hydrological Processes 10 (8), 1107 – 1118. EROCHINA, 1997. A Participatory Approach for Soil and Water Conservation Planning, Integrating Soil Erosion Modeling and Land Evaluation, to Improve the Sustainability of Land Use on the Loess Plateau in Northern China. DLO-Staring Centre, Wageningen. Head, K.H., 1994. Manual of soil laboratory testing, 2nd ed. Compressibility, Shear Strength and Permeability, vol. 2. Pentech Press, London. Huang, C.H., Bradford, J.M., 1990. Depressional storage for Markov – Gaussian surfaces. Water Resource Research 26 (9), 2235 – 2242. Jiang, D., Fan, X., 1990. Study on the benefit and optimal disposition for measures of soil and water conservation in hilly and gully region in Loess Plateau. Studies on Ecological Agriculture with Soil and Water Conservation in Loess Hilly Gully Region. Tianze Press, China, pp. 87 – 100. Jianjun, P., Bergsma, E., 1995. Determination of soil erosion class using soil infiltration rate and soil shear resistance. Journal of Soil and Water Conservation (Chinese) 9 (2), 93 – 96. Lane, L.J., Renard, K.G., Foster, G.R., Laflen, M., 1992. Development and application of modern soil erosion prediction technology—the USDA experience. Australian Journal of Soil Research (30), 893 – 912. Li, T., 1998. Evaluation of land use systems for water and soil conservation in Groesbeek, the Netherlands, using LISEM. Wageningen, DLO-Staring Centre. Msc. Thesis. P5 – 7. Liang, C.X., Yao, X.Z., 1993. Study of the spatial variation of red soil physics in hilly region in China. Acta Pedologica Sinica 30 (1), 69 – 78. Liu, G., 1997. The study on soil anti-scourability and related issues in the Loess Plateau. Research of Soil and Water Conservation (Chinese) 4 (5), 122 – 128. Low, A.J., 1954. The study of soil structure in the field and the laboratory. Journal of Soil Science 5 (1), 57 – 74. Renard, K.G., 1997. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Equation (RUSLE). USDA Agriculture Handbook No. 703. Ritsema, C.J., 1996. A Participatory Approach for Soil and Water Conservation Planning, Integrating Soil Erosion Modeling and Land Evaluation, to Improve the Sustainability of Land Use on the Loess Plateau in Northern China, Research Proposal DLO-Staring Centre, Wageningen. Van Bavel, C.H.M., 1949. Mean weight diameter of soil aggregates as a statistical index aggregation. Soil Science Society of America Journal Proceedings 14 (1), 20 – 23. Wagner, L.E., Yiming Yu, 1991. Digitization of profile meter photographs. Transactions of the ASAE 34, 412 – 416. Wischmeir, W.H., Smith, D.D., 1978. Predicting rainfall erosion losses. A Guide to Conservation Planning. USDA Handbook No. 537. Yanlai, L., Guangyi, L., 1992. Soil surface roughness and wind erosion. Soil Science Progress (Chinese) 20 (6), 38 – 42. Yoder, R.E., 1936. A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. Journal of American Society of Agronomy 28, 337 – 351. Zhu, X., 1989. Soil and Agriculture in the Loess Plateau. Chinese Agricultural Press, Beijing, pp. 216 – 218. Zimbone, S.M., Vickers, A., Morgan, R.P.C., Vella, P., 1996. Field investigations of different techniques for measuring surface soil shear strength. Soil Technology 9, 101 – 111.
A study of soil surface characteristics in a small watershed in the hilly, gullied area on the Chinese Loess Plateau Guobin Liu a,b,*, Mingxiang Xu a, Coen Ritsema c a
Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, 26 Xinong Road, Yangling, Shaanxi Province, 712100, PR China b NW Sci-Tech University of Agriculture and Forestry, Yangling, Shaanxi Province, 712100, PR China c Alterra b.v. Green World Research, 6700 AA Wageningen, Netherlands
Abstract Soil surface characteristics are closely related to soil surface depressional storage, infiltration, runoff generation and soil erosion, especially in highly erodible loess soil. Soil surface random roughness, soil cohesion and aggregate stability are necessary parameters in the Limburg Soil Erosion Model (LISEM) and helpful in developing alternative land-use and conservation strategies. Over a period of 2 years, soil surface characteristics were measured continuously in Danangou, a small catchment in the hilly, gullied loess area in China, with the support of the Erochina project. The results show that soil surface random roughness, soil cohesion and aggregate stability differed significantly between land-use types. Ranking the types on soil roughness during the monitoring period yielded the sequence cropland>orchard and wasteland and fallow land>shrubland>woodland. The corresponding sequence for aggregate stability was approximately wasteland and woodland>cropland and orchard>shrubland>fallow land. Cohesion in the different land-use types in the catchment was smallest for cropland and largest for wasteland. There were no significant differences between croplands with different topographical characteristics. All of these soil surface characteristics showed significant temporal variation over the monitoring period. Temporal variations in roughness for shrubland and woodland over the 2-year period were significantly greater than those for other land-use types. There were also considerable variations in cohesion for shrubland, woodland and wasteland. In 1998, the weighted average values of roughness, aggregate stability and cohesion for the entire catchment were 1.113 cm, 13.6 drops and 0.131 kg/cm2, respectively, with C.V. values of 9.6%, 32.05% and 21.11%. The corresponding values for 1999 were 1.616 cm, 11.52 drops and 0.100 kg/cm2, with C.V. values of 16.0%, 15.41% and 33.33%. The temporal and spatial
* Corresponding author. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, 26 Xinong Road, Yangling, Shaanxi 712100, PR China. Tel.: +86-29-7012907; fax: +86-29-7012210. E-mail address: [email protected] (G. Liu). 0341-8162/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0341-8162(03)00055-9
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variations in soil surface characteristics in the catchment should be carefully taken into account in LISEM calibration and validation. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Soil surface characteristics; Temporal and spatial variation; LISEM model
1. Introduction Soil surface characteristics, especially random roughness, aggregate stability and soil cohesion, are closely related to soil surface depressional storage, infiltration, runoff generation and soil erosion (Yanlai and Guangyi, 1992; Ali, 1995; Cremers et al., 1996; Jianjun and Bergsma, 1995; Zimbone et al., 1996). Knowledge of soil surface characteristics is important for runoff and soil erosion modeling (Cremers et al., 1996). Bagnold (1941) first gave the definition and calculation method of soil surface roughness, however, the concept of soil surface roughness was mainly concerned with wind erosion and sand movement. Burnell and Larson (1969) studied infiltration as influenced by tillage-induced roughness. Soil surface roughness affects the amount of surface depressional storage, the proportion of the soil surface covered by water, the amount of rainfall excess needed to start runoff and the overland flow rate (Cremers et al., 1996; Huang and Bradford, 1990). Aggregate stability influences several aspects of a soil’s physical behavior, particularly water infiltration and soil erosion (Bissonnals, 1996). Casagrande (1936, 1938) appears to have been the first to study the characteristic of soil cohesion and its relation with soil stability. He also invested a Shear box, which is widely used in soil cohesion measurement in research (Head, 1994). These soil surface characteristics are basic parameters of several soil erosion prediction models (Lane et al., 1992; Wischmeir and Smith, 1978; Renard, 1997; De Roo et al., 1996). However, there have been few studies of temporal and spatial variations in soil surface characteristics (Liang and Yao, 1993; Cass et al., 1996; Cremers et al., 1996). In the Limburg Soil Erosion Model (LISEM model (De Roo et al., 1995, 1996)), soil surface random roughness is used to simulate rainfall storage in microdepressions, while aggregate stability is used to calculate splash detachment, and soil cohesion is used to estimate rill and inter-rill erosion (De Roo et al., 1995, 1996; Cremers et al., 1996; Li, 1998). Nowhere in the world is soil erosion such a serious problem as on the Chinese Loess Plateau. Aware of the extent and severity of the problem, the Chinese Government is promoting measures to control soil erosion. In these highly erodible loess soils, the irrational land-use styles, low vegetation coverage and cultivation on steep slopes have caused serious environmental problems. However, the complicated morphology of the loess plateau, with its hills and gullies, and the characteristics of the soil make it difficult for the available models to predict erosion. There is as yet no proper method to predict soil erosion and evaluate the effect of conservation measures (Ritsema, 1996). This paper reports on a study which was part of the Erochina research project, and in which several soil surface characteristics of different land-use types, slope aspects and slope degrees were continuously measured for a typical small watershed in the area of hills and gullies on the Chinese Loess Plateau (EROCHINA, 1997). Temporal and spatial variations were studied at both plot and catchment scale, and are expected to provide an
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important empirical basis for the calibration and validation of the LISEM model, as well as for the use of this model in developing alternative land-use methods and conservation strategies. The Danangou catchment (Latitude 36j53VN, Longitude 109j17VE) is situated in the center of the loess plateau in the northern Shaanxi Province of China. The catchment has an area of 3.5 km2 and its altitude ranges from 1000 to 1350 m. There are significant topographic variations within the study area, with typical loess hills and gully landforms. Prolonged human activity has destroyed much of the natural vegetation. Land-use types include sloping croplands, fallow land, grassland, shrubland, orchards and woodland. Crops mainly include potatoes (Solanum tuberosum), beans (Phaseolus valgaris), maize (Zea mays) and millet (Panicum miliaceum). The forest, all of which has been planted, is dominated by locust trees (Robinia pseudoacacia). The grassland is mainly covered by annuals such as sweet wormwood (Artemisia annua) and annual fleabane (Erigeron annuus). The region has a semiarid continental climate with an average annual temperature of 8.8 jC. Monthly mean temperatures range from 22.5 jC in July to 7 jC in January. The average annual precipitation is 562 mm, with great inter-annual variability; 60% of the rainfall falls between July and September. There are 159 frost-free days and an average of 2415 h of sunshine each year. The soils developing on the wind-accumulated loess parent material have an average thickness of 50– 80 m. The most common soil in the catchment is loessal, which is highly erodible, with an erosion modulus of 10,000 –12,000 tons km 2 year 1 (Jiang and Fan, 1990).
2. Materials and methods On the basis of the various land-use types, 18 plots were selected in the Danangou catchment in 1998 and 16 plots in 1999 (Table 1). To obtain a representative input for the LISEM model calibration, fields with different geographical situations were selected. Measurements were repeated every 2 weeks from April to October in each year. 2.1. Random roughness Soil surface random roughness (surface microtopography) is a measure of the irregularity of the soil surface. LISEM uses random roughness to calculate soil surface water storage and the point at which overland flow starts. Researchers used several measurement methods and indicators to calculate soil surface roughness, such as pin roughness meter (Cremers et al., 1996), chain method, roughness coefficient and indicator (Ali, 1995). Measurement in this paper was conducted using a special pin meter (Photo 1) and a digital camera. The measurement steps were as follows: (EROCHINA, 1997; Ritsema, 1996; Cremers et al., 1996): (a) The pin meter was placed in a position parallel to the slope, avoiding rills and plants.
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Land-use type
Field no.
Topographic types
Slope
Vegetation types
Field no.
Topographic types
Slope
Vegetation types
Crop
1, 4, 7, 8, 9, 13, 15, 16, 18
Steep slope 4, 7, 9 terrace 13; 16 gentle slope 1, 8, 15, 18
30 1; 5 15
Millet, soybean + oil crop, tomato, maize + soybean
1, 6, 7, 11, 12, 13, 14, 17
Steep slope 1, 6, 7 terrace 11 gentle slope 12, 13, 14, 17
25 0 12
Wood Shrub Orchard Fallow Waste
6; 14 12 5; 17
Slope Slope Slope Slope Slope
30; 8 15 8; 18
Acacia Caragana korshinskii Apple Grass Grass
5; 10 16 4; 9 8; 15 2; 3
Slope Slope Slope Slope Slope
24; 13 36 23; 19 9; 22 43; 28
Millet, soybean + oil crop, tomato, maize + soybean, buckwheat Acacia Caragana korshinskii Apple Grass Grass
2; 3; 10
15; 40; 18
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Table 1 Topographical factors and land-use types of the plots studied
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Photo 1. Pin meter.
(b) All pins were lowered to the ground, and a picture was taken with the digital camera. (c) This was repeated six times for the directions perpendicular and parallel to the slope. (d) The photo was edited in a computer, using a photo editor program (Profile meter program, Wagner and Yiming Yu, 1991). This made it possible to calculate the standard deviation of each pin’s height, based on the control standard height. This resulted in the roughness value and the average deviation in six repeats. 2.2. Aggregate stability 2.2.1. Method one—water-drop method The water-drop method (Low, 1954) was used to simulate the impact of raindrops on the aggregates. The moisture content of the aggregates was standardized to allow the results of different tests to be compared. About 200 g of surface sample was collected in each field. A filter paper was placed on sand in a bucket, and the sample was placed on the filter paper after having been passed through 5- and 4-mm sieves. A constant water level was provided in the bucket. After 24 h, a sample was placed on the 3-mm sieve below a drip-bottle and water drops were allowed to fall on the sample at a rate of about 50 drops/ min (Photo 2). The number of drops required to strike the aggregate passing through the sieve was counted. Aggregates (50) were tested for each sample and the average number of drops was calculated. 2.2.2. Method two—improved Yoder method One of the methods currently used to test aggregate stability is the wet sieve method (improved Yoder method) (Yoder, 1936; Zhu, 1989). This is the method most
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Photo 2. Equipment for the water-drop method.
commonly used to evaluate soil aggregate stability at the Chinese Loess Plateau. In order to make the best possible use of historical data on soil aggregate stability at the Loess Plateau, as well as to allow comparisons with the data obtained by the water-drop method, the relation between the water-drop method and the improved Yoder method was studied. A set of sieves (with aperture of 5, 2.5, 1, 0.5 and 0.25 mm) was prepared. The saturated soil sample (which had been wetted by capillary water to remove the air from the soil) was placed in the sieves. The sieves were then immersed in water and shaken up and
Photo 3. Shear tester with different vanes.
Table 2 Soil characteristics in different land-use types
1998
Steep slope Terrace Gentle slope Cropland (A.V.) Woodland Shrub Fallow Orchard Wasteland Weight average of catchment
Saturated cohesion (kg/cm2)
Aggregate (drops) 1999
1998
1999
1998
1999
Average
C.V. (%)
Average
C.V. (%)
Average
C.V. (%)
Average
C.V. (%)
Average
C.V. (%)
Average
C.V. (%)
1.52 1.35 1.37 1.41 0.82 0.97 – 1.33 1.12 1.11
8.31 11.85 10.27 6.47 19.86 24.04 – 9.71 10.83 9.61
1.93 1.73 1.73 1.80 0.85 0.89 1.08 1.42 1.80 1.62
11.64 45.21 18.35 20.41 21.75 65.85 9.99 10.37 10.37 13.79
12.87 9.95 11.10 11.31 15.09 9.11 – 11.34 16.69 13.60
50.61 29.95 33.53 37.75 26.50 32.78 – 51.29 45.94 32.05
8.23 11.39 6.29 8.64 17.07 8.93 7.28 11.81 13.39 11.52
19.09 63.89 9.84 32.01 63.34 40.98 17.08 12.60 49.84 15.41
0.084 0.086 0.075 0.082 0.133 0.144 – 0.105 0.183 0.131
20.10 25.49 19.39 18.78 35.44 61.88 – 29.55 30.80 21.11
0.066 0.058 0.061 0.062 0.151 0.078 0.100 0.086 0.108 0.100
26.07 30.24 25.76 23.09 35.13 18.76 8.05 11.99 82.63 33.33
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Random roughness (cm)
37
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down for 1 min. The aggregate left in each sieve was weighed and the percentage of each particle class was calculated. Mean weight diameter (MWD) (Van Bavel, 1949; Bissonnals, 1996) was also calculated as follows: MWD ¼
n X
Xi Wi
i1
MWD—mean weight diameter (mm); Xi — average diameter of each particle class (mm); Wi — percentage of aggregate relating to Xi (%). Samples of the topsoil (0 –10 cm) were taken at the Zhifanggou catchment and Yangling in July and August 1999, with the intention of measuring their aggregate stability using both methods. 2.3. Soil surface cohesion LISEM uses cohesion as a parameter to calculate erosion caused by overland flow. The cohesion at saturation level is therefore critical and care must be taken to measure fully wetted soils. A type of pocket shear tester (Photo 3) was used in the following steps (Zimbone et al., 1996): (a) The soil was thoroughly saturated, using a sprinkler. (b) The pocket vane tester was pushed into the ground, until the vanes were no longer visible. (c) The tester was carefully turned to the right, until the soil failed. If the value was above 8, the smaller vane was used. If the value was below 2, the larger vane was used. (d) Step 1 was repeated 5 –10 times. (e) The average cohesion value was calculated by multiplying the measured value by 0.02 for the large vane, 0.10 for the middle vane and 0.25 for the small vane (unit: kg cm 2). Average values of each parameter were calculated per measuring period for each of the different land-use types. The percentage coverage of each land-use type was then used to calculate the weighed average of each parameter for the entire catchment. All measurements of soil surface characteristics were evaluated using analysis of variance (Table 2).
3. Results and discussion 3.1. Dynamics of random roughness in the catchment Random surface roughness is an important variable in runoff and soil erosion modeling since it correlates closely with surface runoff, soil infiltration, surface heat balance, evaporation, etc. (Ali, 1995; Yanlai and Guangyi, 1992; Cremers et al., 1996). In terms of soil and water conservation, greater roughness increases infiltration and
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decreases surface runoff. Soil surface roughness is mainly influenced by cultivation, vegetation, soil type and rainfall (Cremers et al., 1996). The results of 2 years of measurements showed that the random roughness was significantly different for the various land-use types (Table 2). The 1998 measurements showed that the various landuse types could be ranked for this parameter as follows: cropland>orchard>wasteland>shrubland>woodland. The ranking for 1999 was cropland and wasteland>orchards>fallow land>shrubland>woodland. Thus, the rankings for the 2 years were identical except for the higher value for wasteland in 1999. The temporal variations for shrubland and woodland in these 2 years were much greater than those for the other land-use types, while the temporal variations in roughness for shrubland and cropland were much greater in 1999 than in 1998. There was no significant temporal variation in roughness for the other land-use types (Fig. 1). However, there were two peaks of roughness in cropland and orchards in both years. Farming activities had obvious effects on roughness, increasing it in June and September. The soil surface was severely disturbed by farming activities in this period, such as fertilizing, weeding and harvesting. The roughness of cropland on steep slopes was markedly higher than that on the terraces and on gently sloping land. The effects of tillage on soil surface roughness have been reported in several previous studies. The roughness measured in our experiment was comparable with that reported from other studies (Cremers et al., 1996). However, the different soil erosion rates for different land-use types and croplands with different topographical characteristics cannot be explained by the random roughness. The weighed mean roughness in the catchment as a whole had a value of 1.113 cm in 1998, with a C.V. of 9.6%, and one of 1.616 cm in 1999, with a C.V. of 16.0%. For the purpose of LISEM calibration, it is important to pay attention to the temporal variations in roughness for the different land-use types, in that parameters showing great temporal variation should be measured just before calibration. 3.2. Characteristics of soil aggregate stability 3.2.1. Dynamics of aggregate stability in the catchment Soil aggregate stability is closely related to anti-scourability and erodability (Bissonnals, 1996; Liu, 1997). The variance analyses showed that, while there were significant
Fig. 1. Soil random roughness in different land-use types.
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Fig. 2. Soil aggregate stability in different land-use types.
differences in aggregate stability between land-use types in 1998, no significant differences were found in 1999. Ranking the different land-use types for aggregate stability yields the following sequence for 1998: wasteland>woodland>cropland and orchards> shrubland. The sequence for 1999 was slightly different: woodland>wasteland>cropland and orchards>shrubland (Fig. 2). On the whole, soil aggregate stability showed a close positive relation with organic matter content (Bissonnals, 1996) (Table 3). Decomposing stick, leaves, weeds and roots resulted in greater accumulation of organic matter in woodland and wasteland, while the orchard soils had a higher organic matter content because of intensive management (more organic fertilizer input). The weighed mean Table 3 Soil aggregate stability and organic matter content Sample No.
Mean weight diameter (mm)
Percentage of 3.5-mm aggregate (%)
Average (drops)
Organic (%)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
2.25 3.31 2.61 2.77 0.22 2.02 2.89 1.42 2.28 0.59 1.74 2.35 0.40 0.19 0.25 0.79 1.08 0.44 1.05 1.06 1.19 0.38
6.8 7.6 12.3 18.9 3.4 13.9 7.4 5.8 6.9 4.3 12.3 9.5 3.1 1.1 2.5 8.2 10 3.9 5 3.8 2.5 1.8
10.7 20.3 16.1 111.8 5.0 13.6 9.7 10.1 14.9 8.0 19.0 15.2 6.5 4.3 4.2 22.5 32.0 9.6 8.0 8.3 26.4 55.0
2.75 2.28 2.33 3.58 0.68 1.85 1.45 1.12 0.88 0.54 1.43 0.99 0.55 0.39 0.57 0.84 0.66 0.56 0.74 0.67 1.27 1.61
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aggregate stability for the catchment as a whole was 13.6 in 1998, with a C.V. of 32.05%, against 11.52 in 1999, with a C.V. of 15.41% (Table 2). The results do not indicate any obvious differences for croplands with different topographical characteristics. The main soil properties influencing aggregate stability are soil texture, clay mineralogy, organic matter content, type and concentration of cations, sesquioxide content and CaCO3 content (Bissonnals, 1996). Of these soil properties, organic matter content plays the greatest role in aggregate stability. The significant differences in aggregate stability for the various land-use types in the catchment were due to the differences in organic matter content. The small temporal variation in aggregate stability also demonstrated that aggregate stability was a relatively stable property. 3.2.2. Comparison of aggregate stability values measured by the water-drop method and the improved Yoder method The results of a linear regression analysis comparing aggregate stability values measured by the two methods showed that the MWD values found with the wet sieve method were not significantly correlated with the values found using the waterdrop method. However, there was significant correlation between the percentage of 3.5-mm aggregates and aggregate stability measured with water-drop method. The percentage of 3.5-mm aggregates can be used instead of the number of drops to express aggregate stability. The results also showed that soil organic matter content was significantly correlated with MWD value percentage of 3.5-mm aggregates and with the aggregate stability measured with the water-drop method, demonstrating that organic matter has a positive and beneficial effect on the formation of soil aggregates (Fig. 3). 3.3. Characteristics of soil cohesion and its dynamics in the catchment Soil cohesion is one of the indices of soil resistance, which represents the soil’s ability to resist destruction by sheet flow in water erosion. Generally speaking, soil erosion shows a negative relation with soil cohesion. Soil cohesion is related to factors such as soil type, organic matter content and soil water content (Jianjun and Bergsma, 1995). Variance analysis of the measured data showed that there were significant differences in soil cohesion between cropland and the other land-use types in both years. The cohesion in wasteland differed significantly from that in the other land-use types, while the values for orchards differed significantly from those for shrubland in 1998. In 1999, cohesion in woodland was significantly different from that of the other land-use types. Ranking the various land-use types for cohesion results in the following sequence for 1998: wasteland>shrubland>woodland>orchards>cropland; the corresponding sequence for 1999 was: woodland>wasteland>fallow land>orchard>shrubland>cropland. Cropland had the lowest cohesion values, while wasteland and woodland had the highest values of the various land-use types in the catchment. This means that cropland was the most erodible type, while wasteland and woodland were less erodible. The spatial variation in cohesion for the various land-use types in the catchment was considerable and should be taken into consideration in LISEM
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Fig. 3. Relation between aggregate stability values measured with two methods.
calibration. The results also showed that there was significant temporal variation over the year. In 1998, the temporal variation in cohesion was smallest for cropland and greatest for shrubland (Fig. 4), while in 1999, the smallest cohesion value was found for fallow land, and wasteland showed much higher temporal variation. On the whole, cohesion values in shrubland, woodland and wasteland varied greatly. The results did not show obvious differences for croplands with different topographical characteristics. The weighed average for soil cohesion in whole catchment was 0.131 kg/cm2 in 1998, with a C.V. of 21.11%, and 0.100 kg/cm2 in 1999, with a C.V. of 33.33% (Table 2). The great temporal variation in soil cohesion over the 2 years causes uncertainty in soil erosion predictions.
Fig. 4. Soil saturated cohesion in different land-use types.
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4. Conclusion The various land-use types showed significant differences in soil surface random roughness, soil cohesion and aggregate stability. Ranking the types for roughness in the 2 years yields the sequence cropland>orchards and wasteland and fallow land>shrubland> woodland. The corresponding sequence for aggregate stability for the 2 years was approximately wasteland, woodland>cropland and orchards>shrubland>fallow land. Of all the land-use types in the catchment, cohesion was smallest in cropland and largest in wasteland. There were no significant differences for croplands with different topographical characteristics. All of these soil surface characteristics showed significant temporal variation during the monitoring periods. The temporal variation in soil surface roughness in these 2 years was much greater for shrubland and woodland than for the other land-use types. Cohesion values varied greatly for shrubland, woodland and wasteland. In 1998, the weighed averages of roughness, aggregate stability and cohesion in the catchment as a whole were 1.113 cm, 13.6 drops and 0.131 kg/cm2, respectively, with C.V. values of 9.6%, 32.05% and 21.11%. The corresponding values for 1999 were 1.616 cm, 11.52 drops and 0.100 kg/cm2, with C.V. values of 16.0%, 15.41% and 33.33%. LISEM calibration and validation should pay close attention to temporal variation in roughness for the different land-use types, in that parameters that show great temporal variation should be measured just before calibration and all kinds of land-use types should be considered.
Acknowledgements The research was supported by Erochina project, and also supported by China national key foundational development and planning project (G2000018606), knowledge innovation project of Chinese academy of science (KZCX1-06) and special foundation for preceding research field of ISWC.
References Ali, S., 1995. Measuring soil surface roughness using the chain method. Sci-Tech Information of Soil and Water Conservation (Chinese) 1, 14 – 16. Bagnold, R.A. (Ed.), 1941. The Physics of Blown Sand and Desert Dunes, vol. 256. Methuen, London, pp. 78 – 90. Bissonnals, Y.L.E., 1996. Aggregate stability and assessment of soil crustability and erodibility: theory and methodology. European Journal of Soil Science 47, 425 – 437. Burnell, R.E., Larson, W.E., 1969. Infiltration as influenced by tillage-induced roughness and pore-space. Soil Science Society of America Journal Proceedings 333, 449 – 452. Casagrande, A., 1936. Characteristic of cohesionless soils affecting the stability of slope and earth fill. Journal of the Boston Society of Civil Engineering, January, 13 – 32. Casagrande, A., 1938. The shear resistance of soils and its relation to the stability of earth dams. Proceedings Soil and Foundation Conference of the U.S. Engineer Department, Boston.
44
G. Liu et al. / Catena 54 (2003) 31–44
Cass, A., McKenzie, N., Cresswell, H., 1996. Physical indicators of soil health. In: Walker, J.W., Reuter, D.J. (Eds.), Indicators of Catchment Health—a Technical Perspective. CSIRO Publishing, Melbourne, pp. 89 – 107. Cremers, N.H.D.T., van Dijk, P.M., De Roo, A.P.J., Verzandvoort, M.A., 1996. Spatial and temporal variability of soil surface roughness and the application in hydrological and soil erosion modeling. Hydrological Process 10, 1035 – 1047. De Roo, A.P.J., Wesseling, C.G., Jetten, B.G., Ritsema, C.J., 1995. LISEM, a User Guide. DLO-Staring Centre, Wageningen, pp. 7 – 15. De Roo, A.P.J., Wesseling, C.G., Ritsema, C.J., 1996. LISEM: a single-event physically based hydrological and soil erosion model for drainage basins. I: Theory, input and output. Hydrological Processes 10 (8), 1107 – 1118. EROCHINA, 1997. A Participatory Approach for Soil and Water Conservation Planning, Integrating Soil Erosion Modeling and Land Evaluation, to Improve the Sustainability of Land Use on the Loess Plateau in Northern China. DLO-Staring Centre, Wageningen. Head, K.H., 1994. Manual of soil laboratory testing, 2nd ed. Compressibility, Shear Strength and Permeability, vol. 2. Pentech Press, London. Huang, C.H., Bradford, J.M., 1990. Depressional storage for Markov – Gaussian surfaces. Water Resource Research 26 (9), 2235 – 2242. Jiang, D., Fan, X., 1990. Study on the benefit and optimal disposition for measures of soil and water conservation in hilly and gully region in Loess Plateau. Studies on Ecological Agriculture with Soil and Water Conservation in Loess Hilly Gully Region. Tianze Press, China, pp. 87 – 100. Jianjun, P., Bergsma, E., 1995. Determination of soil erosion class using soil infiltration rate and soil shear resistance. Journal of Soil and Water Conservation (Chinese) 9 (2), 93 – 96. Lane, L.J., Renard, K.G., Foster, G.R., Laflen, M., 1992. Development and application of modern soil erosion prediction technology—the USDA experience. Australian Journal of Soil Research (30), 893 – 912. Li, T., 1998. Evaluation of land use systems for water and soil conservation in Groesbeek, the Netherlands, using LISEM. Wageningen, DLO-Staring Centre. Msc. Thesis. P5 – 7. Liang, C.X., Yao, X.Z., 1993. Study of the spatial variation of red soil physics in hilly region in China. Acta Pedologica Sinica 30 (1), 69 – 78. Liu, G., 1997. The study on soil anti-scourability and related issues in the Loess Plateau. Research of Soil and Water Conservation (Chinese) 4 (5), 122 – 128. Low, A.J., 1954. The study of soil structure in the field and the laboratory. Journal of Soil Science 5 (1), 57 – 74. Renard, K.G., 1997. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Equation (RUSLE). USDA Agriculture Handbook No. 703. Ritsema, C.J., 1996. A Participatory Approach for Soil and Water Conservation Planning, Integrating Soil Erosion Modeling and Land Evaluation, to Improve the Sustainability of Land Use on the Loess Plateau in Northern China, Research Proposal DLO-Staring Centre, Wageningen. Van Bavel, C.H.M., 1949. Mean weight diameter of soil aggregates as a statistical index aggregation. Soil Science Society of America Journal Proceedings 14 (1), 20 – 23. Wagner, L.E., Yiming Yu, 1991. Digitization of profile meter photographs. Transactions of the ASAE 34, 412 – 416. Wischmeir, W.H., Smith, D.D., 1978. Predicting rainfall erosion losses. A Guide to Conservation Planning. USDA Handbook No. 537. Yanlai, L., Guangyi, L., 1992. Soil surface roughness and wind erosion. Soil Science Progress (Chinese) 20 (6), 38 – 42. Yoder, R.E., 1936. A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. Journal of American Society of Agronomy 28, 337 – 351. Zhu, X., 1989. Soil and Agriculture in the Loess Plateau. Chinese Agricultural Press, Beijing, pp. 216 – 218. Zimbone, S.M., Vickers, A., Morgan, R.P.C., Vella, P., 1996. Field investigations of different techniques for measuring surface soil shear strength. Soil Technology 9, 101 – 111.