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A rainfall simulator study of infiltration into arable soils
Agricultural Water Management, 21 (1992) 119-135
119
© 1992 Elsevier Science Publishers B.V. All rights reserved. 0378-3774/92/$05.00
A rainfall simulator study of infiltration into arable soils A. Wierda and A.W.L. Veen University of Groningen, Department of Physical Geography, Groningen, Netherlands (Accepted 29 January 1992 )
ABSTRACT Wierda, A. and Veen, A.W.L., 1992. A rainfall simulator study of infiltration into arable soils. Agric. Water Manage., 21:119-135. Since Hortonian surface runoff is one possible mechanism for the fast transport of agricultural chemicals from arable soils to surface water, more information is needed on its significance in agricultural areas. The present study concerns the sandy soils of the Dutch Cover Sands area, and is based on the approximate one-dimensional Mein-Larson infiltration model. This model was modified to incorporate the surface storage of water in the micro-relief, thus providing a tool to predict surface runoff on a scale of square metres. Infiltration measurements were carried out with a portable rainfall simulator. Factors which influence infiltration parameters appear to be the type of crop and tillage practices. Strong variability within fields was found due to the presence of compacted tracks caused by the passage of agricultural machinery. Comparison of infiltration characteristics to rainfall data indicates when and where small-scale surface runoffis expected. Modeling results indicate that this is mainly controlled by rainfall, and less by the variability in infiltration parameters. In spring and summer, fields growing potatoes and sugarbeets are the most sensitive to surface runoff, especially the compacted parts (tracks). Surface runoff also occurs occasionally on maize fields. Wheat appears to enhance infiltration capacities by creating cracks (preferential flowpaths) around the roots. This is especially significant after a dry period. Grasslands are also sensitive to ponding, but surface runoff is limited by the relatively large storage capacity of the sod layer. Though bare fields in the autumn and winter show the lowest infiltration rates and storage capacities, Hortonian surface runoff hardly occurs, due to the lower rainfall intensities in those seasons.
INTRODUCTION
For many years it has been assumed that Hortonian surface runoff, that is overland flow, rarely occurs on the sandy plains in the northern part of the Netherlands. Undoubtedly the streams draining this area are generally fed by groundwater flow (De Vries, 1974), and surface runoff contributes little to Correspondence to: A. Wierda, Kerklaan 30, 9751 N N Haren, The Netherlands.
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A. WIERDA AND A.W.L. VEEN
stream flow. However, concentration levels of pesticides in stream waters exhibit peaks of short duration (Eleveld et al., 1989), suggesting fast transport routes. Snyder and Woolhiser ( 1985 ) have shown, among others, that small quantities of surface runoff can contribute significantly to the pesticide pollution of surface water. Hortonian surface runoff occurs when the rainfall rate is higher than the infiltration capacity of the soil and after the storage capacity of the soil surface is exceeded. The physically most correct infiltration models are those based on the Richards equation. These can give an accurate solution of the flow of water in the soil (Skaggs and Kaheel, 1982). However, a number of real-world phenomena are difficult to include in these models. These include crusting, air entrapment and preferential flow. This makes it necessary for practical purposes to choose between a purely empirical approach or an approach using an approximate, time-dependent modeling method combined with field measurements (Skaggs and Kaheel, 1982 ). The latter approach, as it was adopted in the present study, allows for some extrapolation of field observations. Most of the approximate infiltration models describe infiltration under continuous ponding (Parlange and Haverkamp, 1989 ). However in the early seventies a one-dimensional flux infiltration model has been developed, based on the equation of Green and Ampt, to describe infiltration under steady rainfall (Mein and Larson, 1973). This model performs reasonably well (Skaggs and Kaheel, 1982; Plate et al., 1991 ), although it is now generally acknowledged that this type of model has serious shortcomings (Parlange and Haverkamp, 1989 ). In reality piston flow in the topsoil rarely occurs. Wierda et al. ( 1989 ) extended the model with a parameter for storage in the microreliefto enable the coupling to infiltration curves measured with a rainfall simulator. In this way a good estimate of the model parameters is obtained and the restrictions of the model are largely compensated. The overall result is the possibility to predict surface runoff on a small scale (of square metres ). The effects of tillage on infiltration parameters have been studied in detail by Rawls et al. (1983, 1989). However, on a landscape scale little is known of the effect of rainfall pattern and land use on the spatial and temporal distribution of surface runoff. This paper assesses and explains the variability in space and time of infiltration parameters over a catchment with various types of agricultural land use. The occurrence of small scale surface runoff as caused by this variability in relation to the rainfall pattern is predicted. However, the amount of surface runoff which reaches the stream is not predicted, as the heterogeneity across a field and the large-scale field storage have not been taken into account. With the assumption that some part of the predicted surface runoff will reach the stream, qualitative conclusions can be drawn on the occurrence of surface runoff on a catchment scale.
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Since the water of the Drenthsche Aa (a small stream) is used for the preparation of drinking water, this catchment was selected as the study site. The study is limited to arable land and intensively-used pastures because these land use types are the most likely sources of pesticides. METHOD A portable drop-plate rainfall simulator (Imeson, 1977 ) was used to determine infiltration characteristics. This rainfall simulator sprinkles an area of 1 × 0.5 m. The height of the dropplate is 2 m. Rainfall intensities can be varied between 5 and 100 m m / h . Details of the procedure are given in Wierda etal. (1989). As it is much more practical to determine the moment of surface runoff than the moment of ponding on a rough surface, the original model of Mein and Larson was modified in the following way. The original variable 'cumulative infiltration to ponding', Fp, was replaced by 'cumulative infiltration to runoff', Fr, by adding the surface storage B to Fp. Fr is coupled to the 'time to runoff', Tr, which is analogous to the original 'time to ponding', Tp. Since the observation site was small (0.5 m E) as compared with an entire field, only storage in the microrelief is considered and not the storage in macrodepressions. The consequence is that only small scale surface runoff can be simulated. The model is thus given by: F r =B-t- (Sav " M ) / ( ( R / K s ) - 1 )
(1)
where Fr is the cumulative infiltration to runoff (mm); B, the surface storage (mm); Say, the average (effective) suction at the wetting front (mm); M the moisture deficit (cm3/cm3), R -- rainfall intensity ( m m / h ) , and Ks the saturated conductivity ( m m / h ) . The measured time versus rainfall intensity curves have to be transformed to cumulative infiltration curves to fit the model. Firstly, they were fitted by non-linear regression (Marquardt, 1963 ) to Eqn. (2): R=x+y.T z
(2)
where R stands for rainfall intensity; T for time since the beginning of the experiment respectively; and x, y and z are fitting parameters. The fitted curves were numerically integrated to deduce cumulative infiltration versus infiltration rate curves. Saturated conductivity Ks is evaluated as the Y-asymptote of the infiltration rate vs. time curve. The moisture deficit M is calculated at each infiltration experiment as the difference between the volumetric moisture content of the soil before and after infiltration. Surface storage B is calculated from the modeled infiltration curves as the X-asymptote of the cumulative infiltration
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A. WIERDA AND A.W.L. VEEN
curve (Wierda et al., 1989). The average suction at the wetting front Sav presents a problem. The value of this parameter can be calculated using the soilwater content-hydraulic conductivity relation (Neuman, 1976). These data were not available and, since it remains impossible to derive an accurate value from standard grain size data, it was decided to use this parameter as a fitting factor. It appears from the literature that the value of Sav in loamy sand ranges between 20 and 100 m m water (Mein and Larson, 1973; Brakensiek, 1977, 1979; Rawls et al., 1983; Cosby et al., 1984; Rawls and Brakensiek, 1989). These values were taken as limits, within which the Say was optimized on the measured infiltration curves. As textural differences between experimental plots are small, constant effective suction was assumed. Optimization was done by least-squares fitting. The Root Mean Square (RMS) was calculated as an indication of the performance of the model. When using Eqn. ( 1 ) in combination with sprinkling infiltrometer measurements, the saturated conductivity has to be corrected for infiltration along an irregular wetting front due to preferential flowpaths and 'fingering' (Wierda et al., 1989 ). This means that the effective infiltrating surface is larger than the sprinkled area. This effect depends on the number of surface-vented macropores and the textural changes in the topsoil (Baker et al., 1990), while the Mein-Larson model assumes a homogeneous topsoil without macropores. The correction factor was found to be 0.75 in an earlier study (Wierda et al., 1989), but it was calibrated again in the area under consideration here. This factor was optimized to a minimal mean error in predicted Ks. After calibration of the infiltration model the occurrence of runoff was calculated. Based on field measurements the soil moisture deficit was estimated by assuming a dry topsoil possessed M = 0 . 3 when it had not rained for more than a week. A moist soil was considered to have M = 0.2 when no rainfall had occurred for the last 3 days, a wet topsoil with M = 0.1 when no rainfall had occurred for the previous day, and for a very wet soil, M = 0.05, when it had rained on the same day. This crude way of estimating M was considered reasonable because of the relatively low sensitivity of the infiltration model to this parameter. Data on hourly precipitation sums and the duration of of rainfall during each hour were available for 1989 from a nearby weather station (Eelde). From these data the mean rainfall intensity for each event was calculated. The highest hourly intensity per rainstorm was taken as the m a x i m u m intensity. In reality, higher peak intensities may have occurred. Consequently, the occurrence of Hortonian surface runoff may have been underestimated. In order to assess the likely; hood of occurrence of surface runoff from rainfall data and infiltration characteristics the following procedure was applied. Firstly, the infiltration characteristics were combined in low, mean and high values per type of land use. Infiltration characteristics for compacted and
A RAINFALLSIMULATORSTUDY OF INFILTRATION INTO ARABLESOILS
123
TABLE 1 Table of symbols describing probability and amount of surface runoff Value of infiltration parameters
Low Mean High
Rainstorm intensity Mean
Maximum
* ** ***
q+ + + + +
'normal' parts of fields were treated separately. Canopy storage and large scale field storage were neglected. Secondly, for every single rainstorm it has been calculated whether or not Hortonian overland flow would occur for each combination of either the mean and maximum rainfall intensity, with low, mean or high values of infiltration characteristics, over the five types of land use, and for compacted or noncompacted surface. Thirdly, if for any of these combinations surface runoff was found to have been likely, simple symbols have been given to indicate which combination of values of infiltration parameters and rainfall intensity was responsible for that event (shown in Table 1 ).
N
Fig. I. T h e c a t c h m e n t o f the D r e n t s c h e Aa. (asterisks denote experimental sites).
A.WIERDAANDA.W.L.VEEN
124
Frequency distribution Measured Ks (mm/h) 30-
25
~1o
0
5
10 15 20
25 30
35 40 45 50 60 70
80 90 100
Class
Fig. 2. Frequency distribution of the measured Ks (mm / h ).
Cumulative frequency distribution RMS {,%)
100-
90" 80'
70-
;i!ii::i
60"
~2 "~ 0
50"
D_
40-
i:~i?:! !: ~ii
!:?!
ii~ii ! ~i!
3020.
1o4 0
,~ /, <2 <5
i <10
<15
<25
<50
>50
Class
Fig. 3. Cumulative frequency distribution of the Root Mean Square of the relative error of predicted against measured infiltration curves.
A RAINFALL SIMULATOR STUDY OF INFILTRATION INTO ARABLE SOILS
125
Surface storage B Potatoes 20
16 12
8 4 I
0
IO0
I
1~
I
I
1~
2~
3OO
2~
day n'.
Saturated permeability Ks Potatoes 100
80
60
,|
40
0
i
20
o•
1;
oD
• 0 100
o
li
I
I
I
I
140
180
220
260
300
Daynr, []
KB01
,o
KBH3
A
SB31
V
Fig. 4. (Lower panel) Measured Ks on potato fields. (Upper panel) Calculated surface storage on potato fields. (For legend Figs. 5-9: each symbol denotes a different field; black = measured on a compacted part; white=measured on a non-compacted part; arrow-up=Ks higher than initial rainfall intensity. ) STUDY AREA
The catchment of the Drentsche Aa, 0.6 m to 21 m above M.S.L., has an area of approximately 26 000 ha (Fig. 1 ). Slopes are very slight, mostly less than 2%. The area is underlain by Pleistocene formations of fine, well-sorted eolian (loamy) sands, the so-called Cover sands. Podzols and plaggen soils (i.e., soils having a thick man-made surface layer produced by long-continued manuring) have developed, while in the subsoil glacial till, periglacial sands and clays occur (Stiboka, 1977; De Gans, 1981 ). The texture of the topsoil is mostly loamy sand. In valleys and depressions peaty soils occur. Mean annual precipitation is 812 mm, while the annual precipitation excess is about 335 m m (Streefkerk and van Hoorn, 1985 ). About three quarters of the region is
126
A. WIERDA AND A.W.L VEEN
Surface storage Sugarbeets
B
20
16
8
• " 0 100
140
180
oi 220
8
260
300
Dey'r~.
Saturated
permeability
Ks
Sugarbeets t o
100
80
60
40
D
° o
20
~
o IDA
i 100
i
L
I
i
140
180
220
260
300
Day'rr. o
~ 6
o
SB21
Fig. 5. (Lowerpanel) MeasuredKson sugarbeet fields. (Upper panel) Calculated surface storage on sugarbeetfields. used for agriculture. Half of this area consists o f grassland, while the other half is used for growing crops. Of the crops grown, potatoes (40%), sugarbeets ( 15% ), maize (15%) and wheat ( 10% ) are the most important (Mulder, 1986 ). From each o f these types o f land use two to four representative parcels o f land were selected. A total o f 120 infiltration experiments were carried out from April to October 1989. In 17 cases no surface ponding occurred, which means that the saturated conductivities exceeded the high initial rainfall intensities (60-100 m m / h ) , and that under the prevailing weather patterns no significant surface runoff is to be expected. Of the 103 experiments that produced surface ponding, 29 were carried out on potato fields, 25 on fields planted with sugarbeets, 16 on fields with wheat and 8 on grasslands. An additional 25 tests were performed on bare fields, both before or after the growing season.
A RAINFALL SIMULATOR STUDY OF INFILTRATION
127
INTO ARABLE SOILS
RESULTS
The saturated conductivity varies strongly (Fig. 2)) despite the fact that a texture analysis of the experimental plots showed only small variations. The saturated conductivity can be as low as 9 mm/h or exceed 100 mm/h. However, most plots give values between 15-35 mm/h. The measured infiltration curves in the form of cumulative infiltration curves could be related to the modified Mein-Larson model because the results of the curve fitting were generally very good. Optimal calibration was achieved with a correction factor for the saturated conductivity K, of 0.65 and a value of 20 mm for the average suction S,,. The value of the correction factor appears to be independent of the type of land use. A group of infiltration curves could be distinguished for which this correction factor was too low. These measurements were mostly taken in the beginning of the project (with an inexperienced observer) or in conditions where it was difficult to estimate T,correctly (such as in grassland). We decided to keep the value of Surface 20
0
60
x 0’
KS
Maize
z x
j‘?oy”
permeability
+ 6
100 80 -
B
0
Saturated
E
storage
Maize
1’
.
4 8
20-
ii
‘8 .
. J
100
140
220
180
260
3w
hyrr. q
E!nM.s
Fig. 6. (Lower panel) on maize fields.
0
5822
Measured
asPh4s
KS on maize fields. (Upper
panel)
Calculated
surface star:
A. WIERDA
128 Surface
AND
A.W.L.
VEEN
B
storage Wheat
20
16
12 8 !i G
6
* 4
I 0
.5 0
0 100
140
160
Saturated
220
260
permeabi I i ty
300
KS
Wheat
60
.
P
140
100
4 * * 160
220
260
300
Dayrr. 0
-14
0
ml9
ia
5822
Fig. 7. (Lower panel) Measured KS on wheat fields. (Upper panel) Calculated surface storage on wheat fields.
the correction factor constant, as these deviations probably stem from inaccurate measurements. The frequency distribution of the RMS (Fig. 3) shows that 73% of the measured infiltration curves are described well (RMS < 10%)) while 5% of the measured infiltration curves do not tit the model (RMS> 25%). Variability of infiltration characteristics For each type of land use the observed saturated conductivities and the calculated storage factors are presented versus the day number of 1989 (Figs. 4 to 9). These figures include 17 cases where despite simulated rainfall intensities of 60 mm/h no runoff occurred, and thus only a lower limit of the infiltrability was obtained. These cases are marked by a small arrow pointing
A RAINFALL SIMULATOR STUDY OF INFILTRATION
Surface
storage
INTO ARABLE SOILS
129
B
Grassland
140
100
Saturated
220
180
permeabi
260
I i ty
300
KS
Grassland
v
100
140
220
100
260
300
cm-. q
ei=G3
Fig. 8. (Lower panel) Measured grasslands.
os=GR
KS on grasslands.
(Upper
panel) Calculated
surface storage c
upwards. Of the cases where the model did not lit, storage factors were n calculated. Measured saturated conductivities are included in the figures. For potato fields (Fig. 4) infiltration characteristics seem to vary great1 not only during the growing season, but also within a field. The temporal val ation of surface storage is large as a result of cultivation activities. Both t. saturated conductivity and the surface storage vary greatly across a field d to the occurrence of vehicle tracks. However, it has to be borne in mind tF soil cultivation activities in an area the size of the basin under considerat will not take place at the same time. Therefore, the temporal variation wlcan be seen in Fig. 4 (and in the Figs. 5 to 9) represents at the same t some spatial variation. Still, the potato fields show a clear temporal tren infiltration behaviour. Infiltration capacities tend to diminish towards end of the season.
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A. WIERDA AND A.W.L. VEEN
Surface
storage
Bare
B
land
20
140
100
le.0
Saturated
260
220
permeabi I ity
Bare
land
300
KS
‘OOB -
%
s 60 %40-
0
9
i*
20-i
*l: :f
0 260
220
180
140
100
300
byn. 0
KBOI
0
uB14
A
-22
v
?a21
0
s831
Fig. 9. (Lower panel) Measured KS on bare fields. (Upper panel) Calculated surface storage on bare fields.
Temporal variability on sugarbeet fields appears to be somewhat less (Fig. 5 ), with one exception. Infiltration characteristics on day 132 were high, because sugarbeets had just been sown on that particular field. This created so many macropores that it was not the infiltration through the soil matrix which was measured. On other days spatial variability within a field shows the same pattern as potato fields: infiltration rates drop to 50% or less of the ‘normal’ rates on the compacted parts, while differences between fields are relatively small. Maize lields (Fig. 6) show high values of saturated conductivity in the first half of the growing season. The variation is mainly caused by difference between tracks and the rest of the field. Surface storage factors remain fairly constant during the growing season. This also applies to saturated conductivities in the second half of the growing season. The late sowing of maize at the
131
A RAINFALL SIMULATOR STUDY OF INFILTRATION INTO ARABLE SOILS
TABLE 2 Calculated occurrence o f r u n o f f in t h e c a t c h m e n t over 1989 Date
04-06-1989 22-06-1989 08-07-1989 08-08-1989 07-10-1989
R ....
12.5 4.7 73.0 11.4 3.2
Rmax D a y t
17.3 10.0 73.0 30.0 9.0
20.0 12.8 21.9 18.3 8.7
Non-compacted PO
SU
MA
+
+
+
*** +++
*** ++
*** +++
Compacted WH
*
G R BA P O * + *** *
SU
MA WH
BA
** ** * * + + + *** *** *** * * * * * + +
Legend: Rm~,nm e a n
rainstorm intensity ( m m / h ) ; Rmax m a x i m u m r a i n s t o r m intensity ( m m / h ); Dayt, daily total rainfall ( m m ) . PO, Potato fields, not recently e a r t h e d up; SU, Sugarbeet fields, not recently weeded; MA, Maize fields; W H , W h e a t fields; G R , Grassland; a n d BA, Bare fields. For s y m b o l s see Table 1.
start of the long dry period which occurred in 1989 has resulted in a clear difference between the first and the second half of the growing season. In average years the effects of sowing on the infiltration behaviour of the soils will probably diminish faster, making the results obtained for the last part of the season representative for a larger part of the growing season. From visual observations it seems that wheat plants appear to influence the saturated conductivity during dry spells by creating cracks and large macropores around their roots. Rainfall tends to seal these cracks. Depending on the a m o u n t of rainfall previous to an infiltration experiment, quite different saturated conductivities are measured (Fig. 7). Infiltration rates on wheat fields were the highest encountered. The eight infiltration experiments on grassland have been carried out on only two fields (Fig. 8 ). The variation during the season in and between these two fields seems to be small, as the topsoil of a grassland remains undisturbed during the season. Variations within a field are also small and saturated conductivities on tracks do not differ largely. Bare land shows relatively little variation (Fig. 9 ). It is striking that soon after harvesting infiltration rates drop to low levels which remain about the same during the whole winter. Some variation occurs within a field but variation between fields is low. The large variation in surface storage stems from large variations in microrelief on the field after harvesting.
Occurrence of surface runoff Table 2 shows the calculated occurrence of small scale surface runoff per type of land use during 1989, using the symbols from Table 1. On at least five days during 1989 small scale surface runoff is likely to have occurred in the drainage basin of the Drenthsche Aa. On 4 June, surface runoff on the non-
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A. WIERDA AND A.W.L. VEEN
compacted soils with potatoes, sugar beets and maize was limited to slowly infiltrating soils during the period of maximum rainfall intensity. The noncompacted soils with wheat did not produce surface runoff on that date, whereas surface runoff might arise even at the mean rainfall intensity of the non-compacted grass soils. On the compacted parts of potato and sugar beet fields, runoff occurred at mean rainfall intensity and even on soils with average infiltrability. However, on maize and wheat, overland flow was limited to soils with low infiltrability. In 8 July, on the other hand, runoff occurred on almost all soils regardless of compaction, infiltrability and type of crop, with the exception of wheat where overland flow only occurred on the soils with low infiltrability. DISCUSSION
A strong seasonal variation in infiltration parameters is found that generally depends on land use and related management practices. The spatial variation within fields is often large and overshadows variations between fields. This is in good agreement with conclusions reached by Starr (1990) from infiltration experiments in corn fields. Bare land has the lowest saturated hydraulic conductivities, combined with relatively low surface storage factors. Spatial and temporal variations within and between fields are small during the winter season. The saturated conductivities of grassland (although based on few data) are found to be low as well. This is in disagreement with Hino et al. ( 1987 ), who concluded from laboratory experiments that grass tends to increase infiltration rates. In reality the management practices (harvesting etc.) may have compressed the top soil layer without disturbing it, thus diminishing infiltration capacities. Surface storage was found to be high because of the thick sod layer, which is in accordance with previous results from large scale rainfall simulation experiments (Oosterom, 1979 ). Of the investigated crops, soils with wheat show high to very high saturated conductivities. This is explained by the rupture of the soil by the growing plants during dry periods. Potato and maize fields have intermediate infiltration characteristics. Spatial variability within fields is high, due to tracks, but variation between fields is relatively low. Temporal variability during the growing season is high in potato fields, caused by mounding of the potato rows. Sugarbeet fields generally show low infiltration and storage capacities, with high spatial variability within fields and less between fields. Only in the very beginning of the season the effects of sowing can be distinguished. Small-scale Hortonian surface runoff may occur during the growing season when rainfall reaches the highest intensities under the prevailing climate. It is determined primarily by the rainfall intensity, and may occur even on the
A RAINFALL SIMULATOR STUDY OF INFILTRATION INTO ARABLE SOILS
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soils which are considered to be highly permeable, and which have been subjected to mechanical treatment resulting in an open surface layer. The variation in infiltration characteristics is of secondary importance, as can be deduced from simultaneous occurrence under most types of landuse. Roughly speaking, in an area the size of the drainage basin of the Drenthse Aa, parcels of land with either moderate or low infiltrability will always be available so that the high rainfall intensities during early summer tend to be the controlling factor. In the autumn and winter of 1989, Hortonian surface runoffwas rare, even though bare fields had the lowest of all measured infiltration and storage capacities. Because of the wet soils and high groundwater tables in the winter, saturated overland flow could occur however. CONCLUSIONS At the catchment scale the described method proved to be useful in research on infiltration and overland flow because of the combination between realistic field data and physically based modelling. The variability in infiltration parameters as related to land use and time of the year could be assessed. In combination with the rainfall pattern an estimate of the distribution of small scale surface runoff over the year and over the various types of landuse was obtained. Hortonian surface runoff is likely to occur regularly during the summer season in the study area and therefore can be a source of pollution of surface waters with pesticides or other agrochemicals. In the future, this phenomenon should be taken into account in the planning of the land use in agricultural areas, even those with relatively permeable soils. ACKNOWLEDGEMENTS The financial support of the Provinces of Groningen and Drenthe, of the Gemeentelijk Waterbedrijf Groningen and of the Zuiveringschap Drenthe is gratefully acknowledged. We thank Henk de Groot for his assistance with the field experiments and Wim Klaassen for his useful comment on the original manuscript.
REFERENCES Brakensiek, D.L., 1977. Estimatingthe effectivecapillarypressure in the Green and Ampt infiltration equation. WaterResour. Res., 13: 680-682. Brakensiek, D.L., 1979. Commentson 'Empiricalequations for some soil hydraulicproperties' by RogerB. Clapp and GeorgeM. Hornberger.WaterResour. Res., 15" 989-990.
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Baker, R.S. and Hillel, D., 1990. Laboratory tests of a theory of fingering during infiltration into layered soils. Soil Sci. Soc. Am., J., 54: 20-30. Cosby, B.J., Hornberger, G.M., Clapp, R.B. and Ginn, T.R., 1984. A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res., 20: 682-690. Eleveld, R., Bosch, A.D. and van de Wetering, H.T.J., 1989. Transportmechanismen van bestrijdingsmiddelen naar her oppervlaktewater. HLS-report No. 89-39, Groningen. De Gans, W., 1981. The Drentsche Aa valley system. Thesis, Amsterdam: Rodopi, 132 pp. Hino, M., Fujitaa, K. and Shutto, H., 1987. A laboratory experiment on the role of grass for infiltration and runoffprocesses. J. Hydrol., 90: 303-325. Imeson, A.C., 1977. A simple field portable rainfall simulator for difficult terrain. Earth Surface Process., 2:431-436. Marquardt, D.W., 1963. An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Indust. Appl. Math., 11: 431-441. Mein, R.G. and Larson, C.L., 1973. Modeling infiltration during a steady rain. Water Resour. Res., 9: 384-394. Mulder, J.K., 1986. Bestrijdingsmiddelen in relatie tot de drinkwatervoorziening van het GWG. HLS report Groningen. Neuman, S.P., 1976. Wetting front pressure head in the infiltration model of Green and Ampt. Water Resour. Res., 12: 564-566. Oosterom, H.P., 1979. Opzet en uitvoering van een vooronderzoek naar oppervlakkige afstroming op lage zandgrond. ICW Nora nr. 1149. Wageningen: ICW. Parlange, J.Y. and Haverkamp, R., 1989. Infiltration and ponding time. ln: Unsaturated flow in hydrologic modelling: Theory and Practice. Proceedings of the NATO Advanced Research Workshop on unsaturated flow in Hydrologic modeling, Arles, France, June 1988. NATO ASI series, Series C: Vol. 275. Dordrecht. Plate, E., Buck, W., Bronstert, A. and Schiffler, G.R., 199 I. A multidisciplinary project for modelling transport processes in a small rural catchment. In: Hydrological basis of ecologically sound management of soil and groundwater. Proceedings of the Vienna Symposium, August 1991. IAHS Publ. No. 202. Rawls, W.J., Brakensiek, D.L. and Soni, B., 1983. Agricultural management effects on soil water processess. Part l : Soil water retention and Green and Ampt infiltration parameters. Trans. ASAE, 26(6): 1747-1752. Rawls, W.J. and Brakensiek, D.L., 1989. Estimation of soil water retention and hydraulic properties. In: Unsaturated flow in hydrologic modeling: Theory and Practice. Proceedings of the NATO Advanced Research Workshop on unsaturated flow in Hydrologic modeling, Arles, France, June 1988. NATO ASI series, Series C: Vol. 275. Dordrecht. Skaggs, R.W. and Kaheel, R., 1982. 'Infiltration' in the Haan, C.T., H.P. Johnson and D.L. Brakensiek (Eds), 'Hydrological modeling of small watersheds', Am. Soc. Agric. Engineering, Monograph, 5: 121-161. Snyder, J.K. and Woolhiser, D.A., 1985. Effects of infiltration on chemical transport into overland flow. Trans. ASAE, 28: 1450-1457. Starr, J.L., 1990. Spatial and temporal variation of ponded infiltration. Soil Sci. Soc. Am. J., 54: 629-636. Stihoka (1977). Bodemkaart van Nederland: Kaartbladen 12 Oost en 12 West plus toelichting (schaal 1 : 50.000). Stiboka, Wageningen. Streefkerk, J. and Van Hoorn, J.W. (1985). Hydrologisch onderzoek in het stroomdal van de Drentsche Aa. Report SBB Utrecht.
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Vries, J.J., de, 1974. Groundwater flow systems and streamnets in the Netherlands. Thesis, Free University, Amsterdam. Wierda, A., Veen, A.W.L. and Hutjes, R.W.A., (1989). Infiltration at the Tai rain forest (Ivory Coast): Measurements and modeling. Hydr. Proc., 3:371-382.
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© 1992 Elsevier Science Publishers B.V. All rights reserved. 0378-3774/92/$05.00
A rainfall simulator study of infiltration into arable soils A. Wierda and A.W.L. Veen University of Groningen, Department of Physical Geography, Groningen, Netherlands (Accepted 29 January 1992 )
ABSTRACT Wierda, A. and Veen, A.W.L., 1992. A rainfall simulator study of infiltration into arable soils. Agric. Water Manage., 21:119-135. Since Hortonian surface runoff is one possible mechanism for the fast transport of agricultural chemicals from arable soils to surface water, more information is needed on its significance in agricultural areas. The present study concerns the sandy soils of the Dutch Cover Sands area, and is based on the approximate one-dimensional Mein-Larson infiltration model. This model was modified to incorporate the surface storage of water in the micro-relief, thus providing a tool to predict surface runoff on a scale of square metres. Infiltration measurements were carried out with a portable rainfall simulator. Factors which influence infiltration parameters appear to be the type of crop and tillage practices. Strong variability within fields was found due to the presence of compacted tracks caused by the passage of agricultural machinery. Comparison of infiltration characteristics to rainfall data indicates when and where small-scale surface runoffis expected. Modeling results indicate that this is mainly controlled by rainfall, and less by the variability in infiltration parameters. In spring and summer, fields growing potatoes and sugarbeets are the most sensitive to surface runoff, especially the compacted parts (tracks). Surface runoff also occurs occasionally on maize fields. Wheat appears to enhance infiltration capacities by creating cracks (preferential flowpaths) around the roots. This is especially significant after a dry period. Grasslands are also sensitive to ponding, but surface runoff is limited by the relatively large storage capacity of the sod layer. Though bare fields in the autumn and winter show the lowest infiltration rates and storage capacities, Hortonian surface runoff hardly occurs, due to the lower rainfall intensities in those seasons.
INTRODUCTION
For many years it has been assumed that Hortonian surface runoff, that is overland flow, rarely occurs on the sandy plains in the northern part of the Netherlands. Undoubtedly the streams draining this area are generally fed by groundwater flow (De Vries, 1974), and surface runoff contributes little to Correspondence to: A. Wierda, Kerklaan 30, 9751 N N Haren, The Netherlands.
120
A. WIERDA AND A.W.L. VEEN
stream flow. However, concentration levels of pesticides in stream waters exhibit peaks of short duration (Eleveld et al., 1989), suggesting fast transport routes. Snyder and Woolhiser ( 1985 ) have shown, among others, that small quantities of surface runoff can contribute significantly to the pesticide pollution of surface water. Hortonian surface runoff occurs when the rainfall rate is higher than the infiltration capacity of the soil and after the storage capacity of the soil surface is exceeded. The physically most correct infiltration models are those based on the Richards equation. These can give an accurate solution of the flow of water in the soil (Skaggs and Kaheel, 1982). However, a number of real-world phenomena are difficult to include in these models. These include crusting, air entrapment and preferential flow. This makes it necessary for practical purposes to choose between a purely empirical approach or an approach using an approximate, time-dependent modeling method combined with field measurements (Skaggs and Kaheel, 1982 ). The latter approach, as it was adopted in the present study, allows for some extrapolation of field observations. Most of the approximate infiltration models describe infiltration under continuous ponding (Parlange and Haverkamp, 1989 ). However in the early seventies a one-dimensional flux infiltration model has been developed, based on the equation of Green and Ampt, to describe infiltration under steady rainfall (Mein and Larson, 1973). This model performs reasonably well (Skaggs and Kaheel, 1982; Plate et al., 1991 ), although it is now generally acknowledged that this type of model has serious shortcomings (Parlange and Haverkamp, 1989 ). In reality piston flow in the topsoil rarely occurs. Wierda et al. ( 1989 ) extended the model with a parameter for storage in the microreliefto enable the coupling to infiltration curves measured with a rainfall simulator. In this way a good estimate of the model parameters is obtained and the restrictions of the model are largely compensated. The overall result is the possibility to predict surface runoff on a small scale (of square metres ). The effects of tillage on infiltration parameters have been studied in detail by Rawls et al. (1983, 1989). However, on a landscape scale little is known of the effect of rainfall pattern and land use on the spatial and temporal distribution of surface runoff. This paper assesses and explains the variability in space and time of infiltration parameters over a catchment with various types of agricultural land use. The occurrence of small scale surface runoff as caused by this variability in relation to the rainfall pattern is predicted. However, the amount of surface runoff which reaches the stream is not predicted, as the heterogeneity across a field and the large-scale field storage have not been taken into account. With the assumption that some part of the predicted surface runoff will reach the stream, qualitative conclusions can be drawn on the occurrence of surface runoff on a catchment scale.
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121
Since the water of the Drenthsche Aa (a small stream) is used for the preparation of drinking water, this catchment was selected as the study site. The study is limited to arable land and intensively-used pastures because these land use types are the most likely sources of pesticides. METHOD A portable drop-plate rainfall simulator (Imeson, 1977 ) was used to determine infiltration characteristics. This rainfall simulator sprinkles an area of 1 × 0.5 m. The height of the dropplate is 2 m. Rainfall intensities can be varied between 5 and 100 m m / h . Details of the procedure are given in Wierda etal. (1989). As it is much more practical to determine the moment of surface runoff than the moment of ponding on a rough surface, the original model of Mein and Larson was modified in the following way. The original variable 'cumulative infiltration to ponding', Fp, was replaced by 'cumulative infiltration to runoff', Fr, by adding the surface storage B to Fp. Fr is coupled to the 'time to runoff', Tr, which is analogous to the original 'time to ponding', Tp. Since the observation site was small (0.5 m E) as compared with an entire field, only storage in the microrelief is considered and not the storage in macrodepressions. The consequence is that only small scale surface runoff can be simulated. The model is thus given by: F r =B-t- (Sav " M ) / ( ( R / K s ) - 1 )
(1)
where Fr is the cumulative infiltration to runoff (mm); B, the surface storage (mm); Say, the average (effective) suction at the wetting front (mm); M the moisture deficit (cm3/cm3), R -- rainfall intensity ( m m / h ) , and Ks the saturated conductivity ( m m / h ) . The measured time versus rainfall intensity curves have to be transformed to cumulative infiltration curves to fit the model. Firstly, they were fitted by non-linear regression (Marquardt, 1963 ) to Eqn. (2): R=x+y.T z
(2)
where R stands for rainfall intensity; T for time since the beginning of the experiment respectively; and x, y and z are fitting parameters. The fitted curves were numerically integrated to deduce cumulative infiltration versus infiltration rate curves. Saturated conductivity Ks is evaluated as the Y-asymptote of the infiltration rate vs. time curve. The moisture deficit M is calculated at each infiltration experiment as the difference between the volumetric moisture content of the soil before and after infiltration. Surface storage B is calculated from the modeled infiltration curves as the X-asymptote of the cumulative infiltration
122
A. WIERDA AND A.W.L. VEEN
curve (Wierda et al., 1989). The average suction at the wetting front Sav presents a problem. The value of this parameter can be calculated using the soilwater content-hydraulic conductivity relation (Neuman, 1976). These data were not available and, since it remains impossible to derive an accurate value from standard grain size data, it was decided to use this parameter as a fitting factor. It appears from the literature that the value of Sav in loamy sand ranges between 20 and 100 m m water (Mein and Larson, 1973; Brakensiek, 1977, 1979; Rawls et al., 1983; Cosby et al., 1984; Rawls and Brakensiek, 1989). These values were taken as limits, within which the Say was optimized on the measured infiltration curves. As textural differences between experimental plots are small, constant effective suction was assumed. Optimization was done by least-squares fitting. The Root Mean Square (RMS) was calculated as an indication of the performance of the model. When using Eqn. ( 1 ) in combination with sprinkling infiltrometer measurements, the saturated conductivity has to be corrected for infiltration along an irregular wetting front due to preferential flowpaths and 'fingering' (Wierda et al., 1989 ). This means that the effective infiltrating surface is larger than the sprinkled area. This effect depends on the number of surface-vented macropores and the textural changes in the topsoil (Baker et al., 1990), while the Mein-Larson model assumes a homogeneous topsoil without macropores. The correction factor was found to be 0.75 in an earlier study (Wierda et al., 1989), but it was calibrated again in the area under consideration here. This factor was optimized to a minimal mean error in predicted Ks. After calibration of the infiltration model the occurrence of runoff was calculated. Based on field measurements the soil moisture deficit was estimated by assuming a dry topsoil possessed M = 0 . 3 when it had not rained for more than a week. A moist soil was considered to have M = 0.2 when no rainfall had occurred for the last 3 days, a wet topsoil with M = 0.1 when no rainfall had occurred for the previous day, and for a very wet soil, M = 0.05, when it had rained on the same day. This crude way of estimating M was considered reasonable because of the relatively low sensitivity of the infiltration model to this parameter. Data on hourly precipitation sums and the duration of of rainfall during each hour were available for 1989 from a nearby weather station (Eelde). From these data the mean rainfall intensity for each event was calculated. The highest hourly intensity per rainstorm was taken as the m a x i m u m intensity. In reality, higher peak intensities may have occurred. Consequently, the occurrence of Hortonian surface runoff may have been underestimated. In order to assess the likely; hood of occurrence of surface runoff from rainfall data and infiltration characteristics the following procedure was applied. Firstly, the infiltration characteristics were combined in low, mean and high values per type of land use. Infiltration characteristics for compacted and
A RAINFALLSIMULATORSTUDY OF INFILTRATION INTO ARABLESOILS
123
TABLE 1 Table of symbols describing probability and amount of surface runoff Value of infiltration parameters
Low Mean High
Rainstorm intensity Mean
Maximum
* ** ***
q+ + + + +
'normal' parts of fields were treated separately. Canopy storage and large scale field storage were neglected. Secondly, for every single rainstorm it has been calculated whether or not Hortonian overland flow would occur for each combination of either the mean and maximum rainfall intensity, with low, mean or high values of infiltration characteristics, over the five types of land use, and for compacted or noncompacted surface. Thirdly, if for any of these combinations surface runoff was found to have been likely, simple symbols have been given to indicate which combination of values of infiltration parameters and rainfall intensity was responsible for that event (shown in Table 1 ).
N
Fig. I. T h e c a t c h m e n t o f the D r e n t s c h e Aa. (asterisks denote experimental sites).
A.WIERDAANDA.W.L.VEEN
124
Frequency distribution Measured Ks (mm/h) 30-
25
~1o
0
5
10 15 20
25 30
35 40 45 50 60 70
80 90 100
Class
Fig. 2. Frequency distribution of the measured Ks (mm / h ).
Cumulative frequency distribution RMS {,%)
100-
90" 80'
70-
;i!ii::i
60"
~2 "~ 0
50"
D_
40-
i:~i?:! !: ~ii
!:?!
ii~ii ! ~i!
3020.
1o4 0
,~ /, <2 <5
i <10
<15
<25
<50
>50
Class
Fig. 3. Cumulative frequency distribution of the Root Mean Square of the relative error of predicted against measured infiltration curves.
A RAINFALL SIMULATOR STUDY OF INFILTRATION INTO ARABLE SOILS
125
Surface storage B Potatoes 20
16 12
8 4 I
0
IO0
I
1~
I
I
1~
2~
3OO
2~
day n'.
Saturated permeability Ks Potatoes 100
80
60
,|
40
0
i
20
o•
1;
oD
• 0 100
o
li
I
I
I
I
140
180
220
260
300
Daynr, []
KB01
,o
KBH3
A
SB31
V
Fig. 4. (Lower panel) Measured Ks on potato fields. (Upper panel) Calculated surface storage on potato fields. (For legend Figs. 5-9: each symbol denotes a different field; black = measured on a compacted part; white=measured on a non-compacted part; arrow-up=Ks higher than initial rainfall intensity. ) STUDY AREA
The catchment of the Drentsche Aa, 0.6 m to 21 m above M.S.L., has an area of approximately 26 000 ha (Fig. 1 ). Slopes are very slight, mostly less than 2%. The area is underlain by Pleistocene formations of fine, well-sorted eolian (loamy) sands, the so-called Cover sands. Podzols and plaggen soils (i.e., soils having a thick man-made surface layer produced by long-continued manuring) have developed, while in the subsoil glacial till, periglacial sands and clays occur (Stiboka, 1977; De Gans, 1981 ). The texture of the topsoil is mostly loamy sand. In valleys and depressions peaty soils occur. Mean annual precipitation is 812 mm, while the annual precipitation excess is about 335 m m (Streefkerk and van Hoorn, 1985 ). About three quarters of the region is
126
A. WIERDA AND A.W.L VEEN
Surface storage Sugarbeets
B
20
16
8
• " 0 100
140
180
oi 220
8
260
300
Dey'r~.
Saturated
permeability
Ks
Sugarbeets t o
100
80
60
40
D
° o
20
~
o IDA
i 100
i
L
I
i
140
180
220
260
300
Day'rr. o
~ 6
o
SB21
Fig. 5. (Lowerpanel) MeasuredKson sugarbeet fields. (Upper panel) Calculated surface storage on sugarbeetfields. used for agriculture. Half of this area consists o f grassland, while the other half is used for growing crops. Of the crops grown, potatoes (40%), sugarbeets ( 15% ), maize (15%) and wheat ( 10% ) are the most important (Mulder, 1986 ). From each o f these types o f land use two to four representative parcels o f land were selected. A total o f 120 infiltration experiments were carried out from April to October 1989. In 17 cases no surface ponding occurred, which means that the saturated conductivities exceeded the high initial rainfall intensities (60-100 m m / h ) , and that under the prevailing weather patterns no significant surface runoff is to be expected. Of the 103 experiments that produced surface ponding, 29 were carried out on potato fields, 25 on fields planted with sugarbeets, 16 on fields with wheat and 8 on grasslands. An additional 25 tests were performed on bare fields, both before or after the growing season.
A RAINFALL SIMULATOR STUDY OF INFILTRATION
127
INTO ARABLE SOILS
RESULTS
The saturated conductivity varies strongly (Fig. 2)) despite the fact that a texture analysis of the experimental plots showed only small variations. The saturated conductivity can be as low as 9 mm/h or exceed 100 mm/h. However, most plots give values between 15-35 mm/h. The measured infiltration curves in the form of cumulative infiltration curves could be related to the modified Mein-Larson model because the results of the curve fitting were generally very good. Optimal calibration was achieved with a correction factor for the saturated conductivity K, of 0.65 and a value of 20 mm for the average suction S,,. The value of the correction factor appears to be independent of the type of land use. A group of infiltration curves could be distinguished for which this correction factor was too low. These measurements were mostly taken in the beginning of the project (with an inexperienced observer) or in conditions where it was difficult to estimate T,correctly (such as in grassland). We decided to keep the value of Surface 20
0
60
x 0’
KS
Maize
z x
j‘?oy”
permeability
+ 6
100 80 -
B
0
Saturated
E
storage
Maize
1’
.
4 8
20-
ii
‘8 .
. J
100
140
220
180
260
3w
hyrr. q
E!nM.s
Fig. 6. (Lower panel) on maize fields.
0
5822
Measured
asPh4s
KS on maize fields. (Upper
panel)
Calculated
surface star:
A. WIERDA
128 Surface
AND
A.W.L.
VEEN
B
storage Wheat
20
16
12 8 !i G
6
* 4
I 0
.5 0
0 100
140
160
Saturated
220
260
permeabi I i ty
300
KS
Wheat
60
.
P
140
100
4 * * 160
220
260
300
Dayrr. 0
-14
0
ml9
ia
5822
Fig. 7. (Lower panel) Measured KS on wheat fields. (Upper panel) Calculated surface storage on wheat fields.
the correction factor constant, as these deviations probably stem from inaccurate measurements. The frequency distribution of the RMS (Fig. 3) shows that 73% of the measured infiltration curves are described well (RMS < 10%)) while 5% of the measured infiltration curves do not tit the model (RMS> 25%). Variability of infiltration characteristics For each type of land use the observed saturated conductivities and the calculated storage factors are presented versus the day number of 1989 (Figs. 4 to 9). These figures include 17 cases where despite simulated rainfall intensities of 60 mm/h no runoff occurred, and thus only a lower limit of the infiltrability was obtained. These cases are marked by a small arrow pointing
A RAINFALL SIMULATOR STUDY OF INFILTRATION
Surface
storage
INTO ARABLE SOILS
129
B
Grassland
140
100
Saturated
220
180
permeabi
260
I i ty
300
KS
Grassland
v
100
140
220
100
260
300
cm-. q
ei=G3
Fig. 8. (Lower panel) Measured grasslands.
os=GR
KS on grasslands.
(Upper
panel) Calculated
surface storage c
upwards. Of the cases where the model did not lit, storage factors were n calculated. Measured saturated conductivities are included in the figures. For potato fields (Fig. 4) infiltration characteristics seem to vary great1 not only during the growing season, but also within a field. The temporal val ation of surface storage is large as a result of cultivation activities. Both t. saturated conductivity and the surface storage vary greatly across a field d to the occurrence of vehicle tracks. However, it has to be borne in mind tF soil cultivation activities in an area the size of the basin under considerat will not take place at the same time. Therefore, the temporal variation wlcan be seen in Fig. 4 (and in the Figs. 5 to 9) represents at the same t some spatial variation. Still, the potato fields show a clear temporal tren infiltration behaviour. Infiltration capacities tend to diminish towards end of the season.
130
A. WIERDA AND A.W.L. VEEN
Surface
storage
Bare
B
land
20
140
100
le.0
Saturated
260
220
permeabi I ity
Bare
land
300
KS
‘OOB -
%
s 60 %40-
0
9
i*
20-i
*l: :f
0 260
220
180
140
100
300
byn. 0
KBOI
0
uB14
A
-22
v
?a21
0
s831
Fig. 9. (Lower panel) Measured KS on bare fields. (Upper panel) Calculated surface storage on bare fields.
Temporal variability on sugarbeet fields appears to be somewhat less (Fig. 5 ), with one exception. Infiltration characteristics on day 132 were high, because sugarbeets had just been sown on that particular field. This created so many macropores that it was not the infiltration through the soil matrix which was measured. On other days spatial variability within a field shows the same pattern as potato fields: infiltration rates drop to 50% or less of the ‘normal’ rates on the compacted parts, while differences between fields are relatively small. Maize lields (Fig. 6) show high values of saturated conductivity in the first half of the growing season. The variation is mainly caused by difference between tracks and the rest of the field. Surface storage factors remain fairly constant during the growing season. This also applies to saturated conductivities in the second half of the growing season. The late sowing of maize at the
131
A RAINFALL SIMULATOR STUDY OF INFILTRATION INTO ARABLE SOILS
TABLE 2 Calculated occurrence o f r u n o f f in t h e c a t c h m e n t over 1989 Date
04-06-1989 22-06-1989 08-07-1989 08-08-1989 07-10-1989
R ....
12.5 4.7 73.0 11.4 3.2
Rmax D a y t
17.3 10.0 73.0 30.0 9.0
20.0 12.8 21.9 18.3 8.7
Non-compacted PO
SU
MA
+
+
+
*** +++
*** ++
*** +++
Compacted WH
*
G R BA P O * + *** *
SU
MA WH
BA
** ** * * + + + *** *** *** * * * * * + +
Legend: Rm~,nm e a n
rainstorm intensity ( m m / h ) ; Rmax m a x i m u m r a i n s t o r m intensity ( m m / h ); Dayt, daily total rainfall ( m m ) . PO, Potato fields, not recently e a r t h e d up; SU, Sugarbeet fields, not recently weeded; MA, Maize fields; W H , W h e a t fields; G R , Grassland; a n d BA, Bare fields. For s y m b o l s see Table 1.
start of the long dry period which occurred in 1989 has resulted in a clear difference between the first and the second half of the growing season. In average years the effects of sowing on the infiltration behaviour of the soils will probably diminish faster, making the results obtained for the last part of the season representative for a larger part of the growing season. From visual observations it seems that wheat plants appear to influence the saturated conductivity during dry spells by creating cracks and large macropores around their roots. Rainfall tends to seal these cracks. Depending on the a m o u n t of rainfall previous to an infiltration experiment, quite different saturated conductivities are measured (Fig. 7). Infiltration rates on wheat fields were the highest encountered. The eight infiltration experiments on grassland have been carried out on only two fields (Fig. 8 ). The variation during the season in and between these two fields seems to be small, as the topsoil of a grassland remains undisturbed during the season. Variations within a field are also small and saturated conductivities on tracks do not differ largely. Bare land shows relatively little variation (Fig. 9 ). It is striking that soon after harvesting infiltration rates drop to low levels which remain about the same during the whole winter. Some variation occurs within a field but variation between fields is low. The large variation in surface storage stems from large variations in microrelief on the field after harvesting.
Occurrence of surface runoff Table 2 shows the calculated occurrence of small scale surface runoff per type of land use during 1989, using the symbols from Table 1. On at least five days during 1989 small scale surface runoff is likely to have occurred in the drainage basin of the Drenthsche Aa. On 4 June, surface runoff on the non-
132
A. WIERDA AND A.W.L. VEEN
compacted soils with potatoes, sugar beets and maize was limited to slowly infiltrating soils during the period of maximum rainfall intensity. The noncompacted soils with wheat did not produce surface runoff on that date, whereas surface runoff might arise even at the mean rainfall intensity of the non-compacted grass soils. On the compacted parts of potato and sugar beet fields, runoff occurred at mean rainfall intensity and even on soils with average infiltrability. However, on maize and wheat, overland flow was limited to soils with low infiltrability. In 8 July, on the other hand, runoff occurred on almost all soils regardless of compaction, infiltrability and type of crop, with the exception of wheat where overland flow only occurred on the soils with low infiltrability. DISCUSSION
A strong seasonal variation in infiltration parameters is found that generally depends on land use and related management practices. The spatial variation within fields is often large and overshadows variations between fields. This is in good agreement with conclusions reached by Starr (1990) from infiltration experiments in corn fields. Bare land has the lowest saturated hydraulic conductivities, combined with relatively low surface storage factors. Spatial and temporal variations within and between fields are small during the winter season. The saturated conductivities of grassland (although based on few data) are found to be low as well. This is in disagreement with Hino et al. ( 1987 ), who concluded from laboratory experiments that grass tends to increase infiltration rates. In reality the management practices (harvesting etc.) may have compressed the top soil layer without disturbing it, thus diminishing infiltration capacities. Surface storage was found to be high because of the thick sod layer, which is in accordance with previous results from large scale rainfall simulation experiments (Oosterom, 1979 ). Of the investigated crops, soils with wheat show high to very high saturated conductivities. This is explained by the rupture of the soil by the growing plants during dry periods. Potato and maize fields have intermediate infiltration characteristics. Spatial variability within fields is high, due to tracks, but variation between fields is relatively low. Temporal variability during the growing season is high in potato fields, caused by mounding of the potato rows. Sugarbeet fields generally show low infiltration and storage capacities, with high spatial variability within fields and less between fields. Only in the very beginning of the season the effects of sowing can be distinguished. Small-scale Hortonian surface runoff may occur during the growing season when rainfall reaches the highest intensities under the prevailing climate. It is determined primarily by the rainfall intensity, and may occur even on the
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soils which are considered to be highly permeable, and which have been subjected to mechanical treatment resulting in an open surface layer. The variation in infiltration characteristics is of secondary importance, as can be deduced from simultaneous occurrence under most types of landuse. Roughly speaking, in an area the size of the drainage basin of the Drenthse Aa, parcels of land with either moderate or low infiltrability will always be available so that the high rainfall intensities during early summer tend to be the controlling factor. In the autumn and winter of 1989, Hortonian surface runoffwas rare, even though bare fields had the lowest of all measured infiltration and storage capacities. Because of the wet soils and high groundwater tables in the winter, saturated overland flow could occur however. CONCLUSIONS At the catchment scale the described method proved to be useful in research on infiltration and overland flow because of the combination between realistic field data and physically based modelling. The variability in infiltration parameters as related to land use and time of the year could be assessed. In combination with the rainfall pattern an estimate of the distribution of small scale surface runoff over the year and over the various types of landuse was obtained. Hortonian surface runoff is likely to occur regularly during the summer season in the study area and therefore can be a source of pollution of surface waters with pesticides or other agrochemicals. In the future, this phenomenon should be taken into account in the planning of the land use in agricultural areas, even those with relatively permeable soils. ACKNOWLEDGEMENTS The financial support of the Provinces of Groningen and Drenthe, of the Gemeentelijk Waterbedrijf Groningen and of the Zuiveringschap Drenthe is gratefully acknowledged. We thank Henk de Groot for his assistance with the field experiments and Wim Klaassen for his useful comment on the original manuscript.
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