Runoff Curve Numbers for Small Grain Under German Cropping Conditions

Journal of Environmental Management (1996) 47, 223–228

Runoff Curve Numbers for Small Grain Under German Cropping Conditions Karl Auerswald∗ and Josef Haider† ∗Lehrstuhl fu¨r Bodenkunke, TU Mu¨nchen, D-85350 Freising, Germany Received 12 April 1995; accepted 9 August 1995

The Curve Number (CN) method is used in many models to predict surface runoff depth and transport of dissolved agrochemicals. CNs were determined on 70 small plots at 8 sites and different crop stages with artificial rain. The measured CNs deviated greatly from the commonly used CNs in most cases. For growing crops, CN correlated closely with cover, regardless of whether the crop was spring or fall barley or rape. The CNs measured with artificial rain agreed well with CNs measured on larger plots with natural rain. A new table was developed that accounts for the resulting seasonal changes in CNs of different small grain crops. The use of this table will greatly improve runoff predictions under German cropping conditions. Predictions will be poor between harvest and subsequent plowing, because of the fast and unpredictable changes in CNs during this generally short period (average CN: 75; standard deviation: 15). On a very stony site, CNs were much lower than would be expected for the hydrological soil group A. If, however, stone cover (23–35%) was included in total cover, the CNs fell into the range of the regression developed for crop cover. In cases where stones are not embedded into a surface seal, but rather protect the soil as would a crop or mulch cover, they can similarly reduce runoff.  1996 Academic Press Limited Keywords: runoff, small grain, soil cover, rainfall simulation.

1. Introduction The SCS curve number (CN) method is an easy-to-use model for estimating runoff volume (SCS, 1972). It requires few input parameters and was incorporated in many erosion and matter flux models like CNS (Haith et al., 1984), CREAMS (Knisel, 1980), EPIC (Sharpley and Williams, 1990) and PRZM (Carsel et al., 1984). Because it is so easy to apply, it has also been used in the past under German cropping conditions (DVWK, 1984; Bronstert et al., 1993), although the necessary CNs have never been verified for this use. An accurate runoff prediction is especially important for estimating the transfer of dissolved substances like phosphorus or pesticides into surface water bodies. The existing CNs must therefore be verified or modified, if necessary. † Present address: Landesumweltamt NRW, Postfach 102363, D-45023 Essen, Germany. 223 0301–4797/96/070223+06 $18.00/0

 1996 Academic Press Limited

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Runoff curve numbers for small grain

T 1. Properties of the topsoil and the experimental sites (Plot sizes of 40, 7 and 187 m2 indicate the use of the Swanson-type simulator, the Kainz and Eicher simulator and natural rain, respectively) Site Component

V2

DU

SZ

RI

TH

FS

SY

SM

Bulk soil: Stones (>2 mm) (% wt)

<2

<2

<2

<2

<2

7–13

12–15

50–75

Fine earth: Sand (>63 lm) Silt (2–63 lm) Clay (<2 lm) Organic C (g/kg)

14 71 17 19

20 53 28 16

31 52 16 13

42 40 18 12

48 38 14 13

43 36 21 10

61 25 14 13

26 53 21 27

Soil depth (cm) Slope (%) Plot size (m2) Crop

160 5–6 40 rape

80 10 40 oats

150 9–14 40 barley

120 10–15 40 barley

55 10–13 7187 barley

80 16–19 7 barley

80 9–10 7 barley

70 11–14 40 barley

The CN method was developed to predict runoff depth in small rural catchments; however, the size of a small catchment is not defined. In the past it was applied to catchments of 0·25 ha to 1000 km2 in size (Boughton, 1989). Artificial rain on small plots has also been used to determine CNs (Rawls et al., 1980), even down to a size of 1 m2 (Glanville et al., 1984). Advantages are a higher accuracy in measurement, control of crop stage, soil and moisture, and, most important, rains can be used which are sufficiently large to determine the CN. According to Hawkins et al. (1985) the CN method should only be applied to storm depths of at least 0·46 of the maximum retention for average moisture conditions. Since few rainstorms in Germany meet this condition, CNs for different soil and crop conditions can hardly be determined using natural rain. Simulated rain will be used here, but the result will be compared to results from natural rainstorms on large plots. 2. Materials and methods Fall and spring barley, oats and rape were examined at different crop stages. Ground cover was measured with the meterstick method at low crop heights. For heights larger than 20 cm, cover was estimated visually. In total, 70 plots were analysed; 57 of them tested different crop stages after seedbed population while 13 examined different conditions between harvest and plowing. The plots were on conventionally farmed fields at eight sites. Site properties are given in Table 1. They encompass a wide range of conditions, but, with the exception of site SM, all soils belong to the hydrologic soil group C (“Moderately high runoff potential”). The results will therefore be analysed together except for SM. Rainfall was applied at intensities of 60–74 mm/h for one hour by two rainfall simulators with Veejet-80100 nozzles. According to Hawkins et al. (1985) the applied rain depth should be large enough to determine CNs down to 65 with sufficient precision. The rainfall simulator developed by Kainz and Eicher (Kainz et al., 1992) was used on

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225

100

90

CN (Group C)

80

70

60

50

40

0

10

20

30

40 50 60 Ground cover (%)

70

80

90

100

Figure 1. Curve numbers at different stages of crop growth (Site SY and FS were cropped with fall barley, site TH with spring barley, and site V2 with oilseed rape). The CNs of the stony soil SM were adjusted to hydrologic soil group C with Equation (3). The CNs of SM and natural rain were not included in the regression analysis but are shown for comparison. Φ=SY+FS; Ε=natural rain; Μ=V2; Β=TH; Χ= SM. Y=87−0·49X; N=51, R=0·91.

plots 4·6 m long and 1·5 m wide. A Swanson-type rainfall simulator (Auerswald, 1986) was used on plots 10 m long and 4 m wide. As no significant difference in CNs was observed between these two rainfall simulators, we do not further distinguish between them. Two plots, 34 m long and 5·5 m wide, were used on site FS with natural rain to examine whether the small plot size or the artificial rain biased the results. Rain depth was 120 mm, which fell in 9 days. One of the plots had only a small amount of cover (10% stones and weeds), the other was covered to 25–30% by spring barley. Runoff was collected continuously during each event with calibrated buckets. The CN was then computed from the measured runoff and rain depth according to: CN=5080/(50·8+P+2Q−(4Q2+5PQ)0·5)

(1)

where Q is total runoff and P is precipitation, both in mm. In two cases, the soil was drier than Antecedent Moisture Condition II (AMC II) and in two other cases it was wetter. The computed CN was accordingly adjusted to AMC II following Arnold et al. (1989) for these exceptions. 3. Results SCS Hydrology Manual (SCS, 1972) reports a CN of 83 and 84 for small grain at AMC II and the hydrologic soil group C, where the larger value reflects conditions of poor and the smaller those of good crop growth. In contrast to this small range of predicted CNs, the measured CNs ranged from 45–99. Between seedbed preparation and harvest, CNs decreased with increasing percentage of ground cover (Figure 1). This was described by the following equation (N=51; P=0·91):

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Runoff curve numbers for small grain

CN=87−0·49 Cover

(2)

CNs were in the predicted range only during early stages of crop growth. Simulations on four uncovered seedbed plots had been carried out on site SY with an average CN of 87 and a 95% confidence interval of ±3. On site FS, eight plots were tested in two different years. The average CN was 84 with a 95% interval of confidence of ±0·5. Both confidence intervals show the high reproducibility for CN determination. For later stages of crop growth, predictions with the CNs of SCS (1972) will overestimate runoff. No difference in the behavior of fall and spring barley or rape could be detected. Neither was there any difference between sites nor plots of different sizes. The CNs of the 34 m long plots with natural rain were 85 for the bare and 70 for the covered plots. Both CNs are close to the predictions according to Equation (2) (Figure 1), but were not included in the regression analysis. This indicates that the CNs measured with simulated rain on small plots can also be used for natural rain on larger plots. After harvest, average CN was 75, with a standard deviation of 15 and a range of 50–99 (N=13). This wide range was the result of different harvest and tillage methods; residues were left or removed and the soil was untilled or tilled with different machines and to varying depths. On some plots green manure was sown in and its growing stage changed quickly. No system was found to describe the variation in CNs. Removal of residues increased CNs in most cases. The largest CN (99), however, occurred on a field where the residues were left in situ and covered the soil in a thick mat. Almost all the rain ran off this mat without wetting the soil. Additionally, in practice, the different operations and the growth of weeds or green manure changes the CN several times after harvest, although this period between harvest and plowing lasts only one month in most rotations. Therefore, only the average CN (75) is applicable after harvest. On the very stony soil at site SM, CNs were much smaller than the CNs on the other sites. If, however, the CNs were adjusted to hydrologic soil group C with Equation (3) and plotted against total cover (stones and crop), they lay around the regression line obtained for crop cover from the other sites (Figure 1). Stones covered 23–35% of the soil and contributed two-thirds or more to total cover. On this site stones protected the soil surface from sealing like mulch or plant cover and thus reduced runoff. Poesen et al. (1994), however, showed that stones do not reduce runoff in all cases. They can also enhance runoff when embedded into a surface seal. Therefore, in contrast to plant and mulch cover, Equation (2) is not universally applicable to stone cover. It applies only where surface runoff can infiltrate beneath the stones which protect the soil from the beating action of the raindrops.

4. Discussion With the original Curve Number method, the runoff volume for a certain hydrologic soil group is supposed to depend mainly on the type of crop (“Land Use or Cover”, e.g. Row Crops or Small Grain) and the applied “Treatment or Practice” (e.g. Straight Row, Contoured or Terraced). Only two classes (good, poor) of ground cover are distinguished. This results in large prediction errors in most cases. Where measured crop cover is available, the predictions can be improved greatly using Equation (2). If the percentage crop cover is not known, predictions can be improved using the

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T 2. Seasonal changes of Curve Numbers (CNs) for small grain under German cropping conditions (all values for AMC II and hydrologic soil group C) Crop Wheat Month January February March April May June July August September October November December

Barley

Fall

Spring

Fall

Spring

Rye Fall

Oats Spring

Rape Fall

73 68 64 51 44 44 44 44 — 86 76 73

— — 87 73 46 44 44 44 — — — —

54 54 49 44 44 44 44 — 86 73 51 51

— — 88 74 51 46 46 46 — — — —

64 64 54 46 44 41 41 41 — 86 81 61

— — 88 73 49 44 44 44 — — — —

54 54 44 39 39 39 39 39 81 64 49 49

CN values given in Table 2. These values were derived from the average cover development of different crops given by Schwertmann et al. (1987) and Equation (2). It is valid for AMC II and the hydrologic soil group C. For deviating AMC, the CNs of Table 2 can be adjusted according to Arnold et al. (1989). For different hydrologic soil groups, the following regression equations developed from the CN values given by Arnold et al. (1989) for different crops can be applied to compute the CN of the groups A, B and D from the CN of group C. The differences between the hydrologic soil groups have not been verified under German cropping conditions, but it is assumed that the relation between soil groups is not affected by location or cropping condition. CNA=−52+1·42CNC

(3)

CNB=−21+1·16CNC

(4)

CND=4+0·99CNC

(5)

Furthermore, all measurements were made on fields planted in the slope direction, which is by far the most common condition. Assuming that the beneficial effect of contouring or terracing is not affected by location or cropping condition, regressions between straight row CNs and contour CNc or terracing CNt can be used: CNc=−0·2+0·97CNs

(6)

CNt=0·6+0·92CNs

(7)

With these regressions, CNs for any combination of crop cover, soil group and cropping pattern (up-and-down slope, contoured, terraced) can be predicted. A comparison of the different factors affecting runoff volumes shows that cover has by far the greatest influence on runoff. For a CNC of 75, the CN of different soil groups ranges between

228

Runoff curve numbers for small grain

50 and 78, CNc being 73, and CNt 70. The influence of cover, however, ranges from 43–87, according to Equation (2). Therefore, runoff volume predictions for German cropping conditions can be improved considerably using the CNs of the newly developed Table 2. Cover probably also has an influence on runoff generation under other cropping conditions. The verification of the CN values given by SCS (1972) should also be carried out for these cases. The authors acknowledge the help of S. Glasauer, Berkeley, in improving the English manuscript.

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