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Rainfall concentration under olive trees
Agricultural Water Management 55 (2002) 53±70
Rainfall concentration under olive trees J.A. GoÂmeza, K. Vanderlindenb, J.V. GiraÂldezb, E. Fereresc,* a
National Soil Erosion Research Laboratory, USDA-ARS-MWA, 1196 Soil Building, West Lafayette, IN 47906, USA b Departamento de Agronomia, Universidad de CoÂrdoba, P.O. Box 3048, CoÂrdoba 14080, Spain c Instituto de Agricultura Sostenible, Consejo Superior de Investigaciones Cienti®cas, P.O. Box 4084, CoÂrdoba 14080, Spain Accepted 30 October 2001
Abstract To determine the existence of rainfall concentration beneath olive trees, throughfall and stem¯ow was measured in three olive trees during 12 rainfall events, using 36 rain gauges per tree and a stem¯ow collection system. Data from different rainfall events were aggregated to assess the spatial correlation in throughfall. Only one out of the three trees showed a clear spatial dependency structure. Rainfall concentration under the tree canopy, as a consequence of rainfall redistribution of throughfall, was relatively unimportant with few and sparse locations showing a percentage of throughfall with respect to rainfall in open area >100% and none above 125%. Throughfall showed a consistent storm to storm pattern in spatial distribution among high rainfall events, and nonconsistent patterns among low rainfall events. Stem¯ow was found to be the most important mechanism of canopy induced ¯ux concentration, in events where rainfall depth was large enough to saturate the olive canopy. Stem¯ow was estimated to in®ltrate in a radial area up to 0.5 m from the tree trunk, depending on tree characteristics and rainfall intensity. The area surrounding the tree trunk appears to be the most relevant area for potential research dealing with the in¯uence of concentrated canopy induced water ¯uxes on the transport of chemicals to deeper layers within the soil. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Rainfall redistribution; Stem¯ow; Olive trees; Spatial correlation
1. Introduction Interest in rainfall redistribution by plant canopies led Kiesselbach in 1916 (as quoted by Alva et al., 1999) to report how corn plants funnel rainwater down their stalk. Rainfall redistribution by canopies has been reported since then in potato (Saf®gna et al., 1976; * Corresponding author. Tel.: 34-957-499200; fax: 34-957-499252. E-mail address: [email protected] (E. Fereres).
0378-3774/02/$ ± see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 3 7 7 4 ( 0 1 ) 0 0 1 8 1 - 0
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Jefferries and MacKerron, 1985), corn (Parkin and Codling, 1990), citrus trees (Alva et al., 1999; Li et al., 1997; Kalma et al., 1968), sparse vegetation (NaÂvar and Bryan, 1990; Pressland, 1976); tropical rainforests (Schroth et al., 1999), and coniferous forests (Taniguchi et al., 1996), among others. Such interest is justi®ed by the importance of the impact of canopy interception on the hydrological balance (Gash and Morton, 1978; Domingo et al., 1994), the nutrient cycle of forests (Parker, 1983), the modi®cation of soil properties near tree trunks (Gersper and Holowaychuk, 1970), the recharge and transport of elements to subsurface water (Taniguchi et al., 1996), or the leaching of agrochemical products (Parkin and Codling, 1990). Olive, an evergreen tree crop, occupies over 2,000,000 ha throughout Spain, and it is broadly extended in the Mediterranean basin, where it has an important economical and environmental role in rural areas (de Graaf and Eppink, 1999). Previous works on forest canopies have found both, a systematic trend in throughfall spatial distribution (Whelan and Anderson, 1996; Robson et al., 1994) and a random distribution of throughfall (Loustau et al., 1992). This is attributed to differences among species, sites and rainfall characteristics. Previous research has quanti®ed rainfall partitioning in olive orchards in relation to tree leaf area index (GoÂmez et al., 2001). Works on another species (e.g. Taniguchi et al., 1996) suggest the importance of canopy induced concentrated water ¯uxes for the leaching of nutrients, or contaminants and agrochemical products to deep soil layers. The existence of pesticide (Troiano et al., 1997) or airbone contamination (Tsiros et al., 1998) has been shown in olive orchards, and in these situations concentrated ¯uxes could be determinant in the transport of contaminants. To assess the rainfall concentration potential of olive trees, an experiment was set up with two objectives: 1. Characterize the rainfall distribution beneath olive canopies. 2. Determine the zones of rainfall concentration and estimate the magnitude of the water fluxes in these zones.
2. Material and methods Rainfall interception was measured in three mature olive trees of the `Picual' cultivar. Tree spacing was 6 m 6 m, trees were 3 m high and their canopy size was approximately 4 m in diameter, giving about 40% ground cover. The three trees were chosen among those in close proximity that differed in leaf area density. The canopy of each experimental tree was characterized by measuring leaf area and canopy horizontal projection, following procedures of Villalobos et al. (1995). The leaf area by canopy projected surface, PLAI, of the experimental trees ranged from 3.4 to 5.46 (meter square leaf area per meter square ground surface area underneath the canopy). The experimental site was located at the Agricultural Research Center `Alameda del Obispo', CoÂrdoba, Spain, 358500 4000 N and 48510 0200 W. CoÂrdoba has a Mediterranean climate with an average annual precipitation of 606 mm concentrated from October to April. Interception was measured in the trees for 12 rainfall events from January to June, 1997. Thirty-six rain gauges, funnels 0.12 m in diameter connected to 1.5 l bottles, were placed beneath the canopy of each tree at 0.4 m
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Fig. 1. Location of the throughfall rain gauges for tree A.
height in a arrangement following four orthogonal axes (Fig. 1). This arrangement was maintained during the entire experiment. Each gauge was measured after any rainfall event, or after several individual events when independent collection was not possible. No attempt was made for correcting evaporation loss from the bottles. This was assumed to be small because (a) fast collection and reading after rainfall and (b) the small surface area of the bottleneck that reduced the diffusion of water vapor out of the bottle. Collected rainfall in a gauge, situated up to a normalized distance of 1.05 from the tree trunk was considered in the analysis of throughfall. Normalized distance is calculated as the ratio between the distance from the trunk center to the rain gauge and the distance from the trunk center to the canopy projection line in that radial direction. Throughfall, de®ned as the volume of rain caught in each rain gauge per unit area associated with each rain gauge, was reported as a percentage, by calculating the ratio between throughfall and rainfall, multiplied by 100. Stem¯ow was measured for each tree with a collector ring installed around each main trunk of the trees. Each collector was approximately 0.03 m in width, and was made of plastic material sealed to the tree trunk using silicone. Stem¯ow volume was transformed to depth [L] using the surface of the horizontal projection of the canopy. No attempt was made to link the throughfall measurements to measurable features of the trees (e.g. canopy gaps). A tipping bucket rain gauge (model ARG 100, Campbell Scienti®c#, Logan, UT) was used to record rainfall in an open area located about 20 m from one of the experimental trees, which were within 12 m of each other. Continuous records of wind speed and direction were obtained from a nearby meteorological station. 2.1. Spatial autocorrelation theory Regionalized variable theory (Isaaks and Srivastava, 1989; Goovaerts, 1997) was applied to assess the spatial correlation structure of throughfall. The existence of spatial
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dependence among the throughfall observations can be detected by means of the semivariogram, which is a measure for dissimilarity in function of the separation vector h between observational pairs. The experimental semivariogram ^g h can be computed using Eq. (1): 1 X z xi 2n h i1 n h
^g h
z xih 2
(1)
where z() represents throughfall and n(h) is the number of throughfall observation pairs at locations xi and xih, separated by a distance h. Eq. (1) requires a minimum number of observations, in order to obtain a representative sample of data pairs that contribute to the semivariogram value at each lag. Webster and Oliver (1992) recommend at least 100 sampling points. If not, resultant semivariograms may be noisy and difficult to interpret. In our study, throughfall was measured at 36 equally spaced rain gauges under each tree. An additional concern was the absence of observational pairs with separation distances <0.5 m, due to arrangement of the raingauges. This can lead to difficulties when inferring the magnitude of the nugget effect, that is the unexplained variability at small scales (Journel and Huijbregts, 1978, Section III.C.5). In order to overcome the small number of observations, we applied the methodology used by Sterk and Stein (1997) for mapping wind-blown mass transport. They joined the information on mass transport from several windstorms into one experimental semivariogram. Applying their methodology, we used the measurements from 12 rainfall events to assess the spatial correlation structure of throughfall under each tree, using Eq. (2) to compute the experimental semivariogram. n h
^g h
j 12 X 1 X zs xij 2n h j1 i1
zs xih;j 2
(2)
P where n h 12 j1 nj h and zs(xij) is the standardized throughfall of observation i from rainfall event j, given by: zs xij
z xij m mj
(3)
where mj and m are the mean throughfall during rainfall event j and the overall mean throughfall during the 12 rainfall events, respectively. Eq. (2) is applied to the observations of each tree and an appropriate theoretical model is fitted to each experimental semivariogram. Each one of the three theoretical models can be converted to 12 semivariogram models, one for each rainfall event. In order to accomplish this the sill parameter (Cj) is calculated using the overall mean throughfall, m, and the mean throughfall for event, mj: m 2 j (4) Cj C m We assumed the nugget effect equal to zero, and considered the range of spatial dependency of throughfall independent from rainfall depth, which means that the same range can be used for all rainfall events. Further assumptions made are first and second order stationarity and isotropy of the throughfall process.
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Maps for throughfall were tailored using ordinary kriging with the corresponding semivariogram. Throughfall percentage (TP) was obtained through division by rainfall depth: 100 X lp z xpj Rj p1 q
TP xoj
(5)
where TP(xoj) is throughfall percentage at the interpolated location, xo, for rainfall event j. Rj the corresponding rainfall depth, lp are the best linear unbiased kriging weights for observations z(xpj) and q the number of neighbouring observations that are considered for the interpolation at location xo. The overall semivariogram model for each tree can be used to interpolate the individual TPs for each rainfall event, because the sill value, C, is only a proportionality factor for the kriging algorithm (Goovaerts, 1997, pp. 174±175), the nugget effect is assumed equal to zero, and the range is the same for all rainfall events. 2.2. Stemflow analysis The funneling ratio (FR) was used to analyze the concentration of incoming rainfall around the tree trunk as stem¯ow. It is de®ned after Herwitz (1986) as: FR
V BG
(6)
where V is the volume of stemflow [L3], B the trunk basal area [L2] and G the rainfall depth [L] in an open area. The in®ltrating area for the stem¯ow was calculated as a function of the stem¯ow rate and the in®ltration rate of the soil surrounding the tree trunk, according to Herwitz (1986). The stem¯ow rate was estimated from the rainfall intensity (at 10 min record interval) assuming that the transmission rate to the trunk was the same as the ratio of stem¯ow depth to rainfall depth for each event. This stem¯ow rate was used to compute the in®ltration area by means of Eq. (7), using a constant soil in®ltration rate of 81 mm h 1 measured near the tree trunks at the same time in another experiment using a portable rainfall simulator (MunÄoz, 1998). We assume this value to be representative of the steady in®ltration condition because the rates of in®ltration measured in the ®eld quickly achieved nearconstant values (MunÄoz, 1998). A
Sr Sic
(7)
where A is the infiltration area [L2], Sr the stemflow rate [LT 1] and Sic the final soil infiltration rate [L 1T 1]. The infiltration area was assumed to be radial as suggested previously by several authors (e.g. NaÂvar and Bryan, 1990; Tanaka et al., 1996), and supported by the fact that the trees were located on a flat area and a homogeneous soil, as determined by examination of soil cores in the orchard. The distance that the infiltrationexcess due to stemflow travels away from the tree trunk, Dr, was calculated using Eq. (8) (Herwitz, 1986): r A B d Dr (8) p 2
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where A is the infiltration area [L2], B the tree basal area [L2] and d the tree trunk diameter [L]. 3. Results and discussion Table 1 presents a summary of the observed data. The values are similar to those reported by De Luna (1994) for different olive trees in the same area, and in the range of the values found by Kalma et al. (1968) for orange trees in Mediterranean conditions. Compared to orange trees (Kalma et al., 1968), our data show slightly higher interception losses and smaller stem¯ow volumes for olive trees. Differences in canopy structure between species, PLAI, rainfall intensities and distribution and experimental procedure could explain these differences. 3.1. Throughfall distribution Fig. 2 analyzes the TP for the three olive trees for different rainfall depths by means of box and whisker plots. Fig. 2 shows 10 plots for each tree instead of 12 because two rainfall depths, 2.6 and 17.6 mm, occurred twice during the experimental period. Thus, box and whisker plots for these two rainfall depths are calculated using twice as much throughfall data as in the case for the other rainfall depths. TPs increase as rainfall increases and seems to stabilize at larger rainfall depths, beyond a certain threshold. This can also be seen from the scatterplot in Fig. 3, where rainfall is plotted against throughfall. The logarithmic ®t in Fig. 3, shows this tendency to stabilize at larger rainfall depths. Two rainfall depths in Fig. 2, 2.6 and 8.8 mm, differ slightly from the general tendency, probably because the measured throughfall for these events corresponds to several small rainfall episodes. There Table 1 Characteristics of the rainfall events observed in the experimenta Event Date
1 2 3 4 5 6 7 8 9 10 11 12
23 January 1997 6 June 1997 7±9 April 1997 18±20 April 1997 21 April 1997 22 April 1997 18 May 1997 23±25 May 1997 26 May 1997 30±31 June 1997 4 June 1997 6 June 1997
WD
E E SE SW W W E E SE SE E E
R
17.6 0.6 2.6 8.8 4.9 2.6 17.6 31.3 3.91 11.2 77.1 1.96
Tree A
Tree B
Tree C
S
T
FR
S
T
FR
0.79 0 0.03 0.08 0.05 0.01 0.25 1.49 0.05 0.43 3.81 0.01
11.21 0.04 0.77 2.43 2.69 0.64 9.23 18.52 1.55 6.90 55.33 0.51
48.4 0 14.0 9.3 11.0 1.9 15.1 51.4 12.9 41.5 53.7 3.6
1.59 0 0.08 0.20 0.06 0.02 0.37 3.05 0.07 0.90 7.90 0.01
8.48 89.2 0.10 0 0.63 27.7 3.08 22.6 2.68 11.5 0.64 6.3 8.48 20.6 18.7 96.1 2.68 18.1 6.52 79.8 49.5 101 0.60 4.21
S
T
FR
1.05 0 0.10 0.30 0.24 0.04 0.37 1.88 0.20 0.88 4.62 0.03
10.13 0.16 0.94 3.23 2.33 0.58 10.13 19.02 1.91 6.86 53.85 0.61
13 0 1.3 3.7 2.9 0.5 4.6 23.3 2.5 10.9 57.3 0.3
a WD: observed wind direction; R: rainfall (mm); T: throughfall (mm); S: stemflow (mm), FR: funnelling ratio (m3 m 3).
Fig. 2. Box and whisker plots of percentage throughfall against rainfall in the open, for each tree. The square inside the box represents the median value, while the box height includes the upper and lower quartile values. Bars represent the highest and the lowest values found up to 150% of the box height, measured from the upper and lower limits. The plots that correspond to rainfall depths of 2.6 and 17.6 mm are based on throughfall measurements from two events that each yielded the same rainfall depth.
Fig. 3. Scatterplot for TP against rainfall depth and logarithmic fit.
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were TP values above 100, that indicates concentration of rain at certain points due to the in¯uence of the canopy. Figs. 2 and 3 show that such concentration also occurs at very small rainfall depths, although it is more important under higher rainfall. This can also be observed from the positive skew of TP distributions at lower rainfall depths and its disappearance at higher rainfall depths. The overall throughfall performance in relation to rainfall depth varies slightly among trees, probably in¯uenced by the individual canopy characteristics. Occurrence of concentration and high throughfall rates also seem to be related to the speci®c canopy characteristics of each tree. In order to assess the in¯uence of prevailing wind direction on the overall throughfall performance we consider box and whisker plots for TP classed by prevailing wind direction during the rainfall event(s), as shown in Fig. 4. The highest throughfall rates are recorded for E and SE wind direction, but the interpretation of Fig. 4 is somewhat cumbersome since rainfall depths are different for each wind direction. Rainfall depths were 146.2 and 17.7 mm for E and SE, respectively, while they only averaged 16.3 mm for the other directions. E and SE wind directions correspond to the largest rainfall events. As a result, it was impossible to infer the effect of the prevailing wind direction on overall throughfall performance. Overall mean throughfall values during the 12 rainfall events amounted to 9.46, 9.16 and 10.62 mm, respectively, for trees A, B and C. Observations from only 36 rain gauges did not permit the calculation of meaningful directional semivariograms. As a result, it was not possible to check for anisotropy in the spatial variability structure of the throughfall
Fig. 4. Box and whisker plots for TP against prevailing wind direction. For further explanation, see Fig. 2 legend.
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process. Anisotropy would indicate that the process is more or less continuous or spatially correlated in one direction of the two-dimensional space, or that the spatial correlation range changes with direction. Anisotropy in the spatial correlation structure must be related to the particular con®guration of each tree canopy. Here we will assume an isotropic correlation structure. Fig. 5 displays the corresponding experimental semivariograms for the three trees together with a ®tted exponential model. The models were ®tted using the variowin 2.21 software (Pannatier, 1996). Experimental semivariograms are calculated for separation distances up to approximately half the maximum separation distance. The spatial correlation structure is most apparent for tree A. The small semivariogram values at the smallest lags indicate that only a small portion of the variability occurs at scales smaller than the minimum separation distance between observational points. The experimental semivariogram for tree B is very noisy, which makes it dif®cult to ®t a theoretical model, while the spatial correlation structure is less obvious for tree C. Since throughfall is determined by rainfall interception, splash and dripping from tree leaves and branches, these results could be a consequence of spatially correlated leaf surfaces and angles within the canopy (Whitehead et al., 1990). A random relocation of gauges between collection periods (Robson et al., 1994) instead of a ®xed location probably would have facilitated the interpretation of the experimental semivariograms. The existence of a structure in the spatial variability indicates that throughfall is spatially dependent up to a certain separation distance (the range) and that the observed throughfall is affected by the throughfall occurring at surrounding gauges. Similar results have also been found with observations in forest canopies (Whelan and Anderson, 1996; Robson et al., 1994). In order to detect possible throughfall concentration spots, for each rainfall event, TP was interpolated on a regular 20 cm 20 cm grid using ordinary kriging with Eq. (5) and a global search neighborhood. Subsequently, the results were represented on gray scale maps. Fig. 6 shows TP maps for 12 rainfall events in tree A. Values beyond the canopy projection line are mere extrapolations, however, it was dif®cult to eliminate them as those close to the drip line were also affected by the canopy. In order to compare rainfall events, a common gray scale from 0 to 100 was chosen, with zones with TPs larger than 100 marked in white. Fig. 6 shows that events with high rainfall depths induced higher TP values, with a patchy spatial distribution under the canopy. Events with the same prevailing wind direction and rainfall depth, i.e. events 1 and 7, do not necessarily show the same spatial throughfall pattern. This can be due to different rainfall intensities and/or to additional vegetative growth during the experimental period (January±June). Evidence for the effects of additional growth can be deduced from the comparison between event 1 (23 January) against event 12 (6 April; Fig. 6). The signi®cant leaf growth that takes place between the two dates, even though it did not change measurably the projected canopy area, can explain the somewhat anomalous pattern of event 1 in comparison to subsequent events of similar magnitude. The most intense rainfall events, all show a similar distribution pattern of TP (Fig. 6), a phenomenon previously observed by Whelan and Anderson (1996) in Norway spruce (Picea abies). For small storms and during the initial period of larger storms when the canopy is `wetting-up' a large fraction of the throughfall is due to rainfall falling through gaps in the canopy (Whelan and Anderson, 1996). The gap size and distribution are presumably modi®ed quickly during the growing season as discussed. This could explain the lack of a consistent tendency in the throughfall pattern within small storms.
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Fig. 5. Experimental semivariograms for throughfall (mm) and the fitted exponential models for each tree.
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Fig. 6. Kriged TP maps for tree A for the different rainfall events. Isolines represent the 100% throughfall contour. The vertical projection of the tree canopy is represented by the dashed line. The prevailing wind direction is indicated by the arrow in the lower left corner of each plot.
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Fig. 7 shows total TP maps for the three trees. Total TP grids for each tree are calculated as the sum of 12 throughfall grids, divided by the total rainfall depth observed during the 12 events (180.2 mm) and multiplied by 100. Due to the methodology used to obtain these maps, total TP patterns from Fig. 7 correspond roughly to the TP pattern of the most important rainfall events of Fig. 6. Total TP shows a similar patchy distribution for trees B and C, although a slight increase in concentration can be observed at some spots. Mariscal et al. (2000) found in trees of the same orchard, a variation in local leaf area density which was related to the radial distance to the tree trunk. In their Fig. 9, Mariscal et al. (2000)
Fig. 7. Maps of total TP for the three trees, calculated as the ratio between the sum of the 12 individual throughfall events and the total rainfall during the experimental period, multiplied by 100. The dashed line depicts the vertical projection of the tree canopy.
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Fig. 7. (Continued ).
showed that local leaf area density reaches a maximum value somewhere in-between the tree trunk and the canopy projection line. Our data suggest that low throughfall areas (black spots in Figs. 6 and 7) correspond approximately with areas of high local leaf area density within the canopy. An overall evaluation of the importance of concentration from these maps and the original data shows that no large signi®cant throughfall concentration spots were detected under any of the three canopies studied. Of particular interest is the area of in®ltration close to the tree trunk. Throughfall concentration in this zone would add up to stem¯ow and could cause additional ponding and runoff. However, none of the three maps of Fig. 7 show clear evidence of signi®cant throughfall concentration in the vicinity of the tree trunk. 3.2. Stemflow Stem¯ow represents a small fraction of the total rainfall that reaches the soil when normalized for the tree canopy surface (Table 1). However, this rainfall is concentrated in a very small area around the tree trunk and generates a zone of intense ¯ux. The FR for the 12 events were, 51, 85, and 60 for trees A, B and C, respectively. These values are in the upper range of the values reported by Herwitz (1986). The relatively high FR values of our trees result from having higher effective crown area/basal area ratios than those reported by Herwitz (1986). Fig. 8 shows that FR is related to rainfall depth, probably because the bark is saturated and the transmission through the trunk and branches increases with rainfall intensity. Such increase approaches an asymptotic limit for large rainfall events, due to the limited water transport capacity of the tree stem and branches (Fig. 8). It was found that the radius of the in®ltration area ¯uctuated during the storm and vary among storms. An analysis of the magnitude and spatial distribution of stem¯ow ¯uxes was
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Fig. 8. Relationship between funneling ratio and rainfall depth.
made for all the events and trees. Total in®ltration was calculated using the in®ltrating area according to the observed rainfall intensity and soil in®ltration rate and was related to the rainfall outside. Fig. 9 shows a signi®cant concentration of water (up to 1500% of the rainfall) in the immediate surroundings of the trunk of the tree A for representative rainfall events. This phenomenon causes stem¯ow to play an important role in the recharge of
Fig. 9. Flux concentration due to stemflow, as percentage of rainfall in open area, determined for tree A and four rainfall events.
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Table 2 Maximum radial distance from tree trunk, Dr (m) of the stemflow infiltration area, calculated for the observed events Event
Tree A (Dr)
Tree B (Dr)
Tree C (Dr)
1 2 3 4 5 6 7 8 9 10 11 12 Mean (m) Maximum (m) Tree diameter (m)
0.04 0 0.01 0.15 0.05 0 0.03 0.17 0.04 0.18 0.30 0 0.08 0.30 0.26
0.07 0 0.02 0.04 0.04 0.02 0.02 0.25 0.05 0.25 0.48 0 0.11 0.48 0.26
0.05 0 0.03 0.05 0.12 0.04 0.04 0.17 0.12 0.23 0.34 0.01 0.10 0.34 0.26
deeper layers of the soil (Tanaka et al., 1996; Pressland, 1976) and in the risk of leaching chemicals like nitrogen or pesticides (Parkin and Codling, 1990). Its relevance to the overall soil water ¯uxes will be determined by the total water depth, the area throughout which in®ltration occurs and the soil pro®le characteristics. Table 2 shows the area of in®ltration around the tree, which extends to approximately 0.3±0.5 m from the tree trunk. This value is similar to those reported by Voight (1960) in hemlock (Tsuga canadiensis [L.] Carr.) and red pine (Pinus resinosa Ait.), Tanaka et al. (1996) in Japanese red pine (Pinus densi¯ora Sieb. et Zucc.), and NaÂvar and Bryan (1990) in shrubs in a semi-arid environment, and it should be considered an estimate of the maximum area of in®ltration for the stem¯ow ¯uxes. The fact that this size is similar to that quoted for trees of greater crown canopy projection and total stem¯ow volumes can be explained by the low in®ltration rate of our soil and by the high FR values found in our experiment. When all the rainfall events were analyzed it became apparent that only events with large rainfall (probably also of high intensity) would increase signi®cantly soil water ¯uxes due to stem¯ow. The data in Fig. 9 shows that for small rainfall depths, e.g. event 5, the ¯ux concentration was in a narrow area and, due to the small amount of stem¯ow volume, its effect on the total ¯ux could be considered negligible. When total rainfall depth is greater, the area affected increases, e.g. events 4, 10 and 11 and the ¯ux concentration also increased. In this case, the area of signi®cant ¯ux concentration area was estimated approximately to be 0.17 m from the trunk of tree A, (Fig. 9). Similar results were obtained for trees B and C, with slight differences due to the differences in tree characteristics and total stem¯ow (data not shown). As pointed out by Herwitz (1986), the previous approach should be regarded as an average estimation of the extension of stem¯ow. Factors like spatial variation of in®ltration rate, microrelief or zones of preferential ¯ow could modify signi®cantly the in®ltration pattern of the stem¯ow for particular trees. However, the radial symmetry assumption has been successfully applied in previous stem¯ow analyses
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(Herwitz, 1986; NaÂvar and Bryan, 1990; Tanaka et al., 1996), and in our work allows the estimation of the signi®cance of stem¯ow in soil water ¯uxes. According to the analysis based on Figs. 6, 7 and 9, we conclude that stem¯ow is signi®cantly more important than throughfall as a source of rainfall concentration and increased water ¯uxes into the soil. For medium and large rainfall events its extension and magnitude is signi®cant in comparison to values previously reported for another trees. 4. Conclusions TP increased asymptotically with rainfall depth, but no conclusions could be drawn on its relation to prevailing wind direction. Experimental semivariograms were calculated combining observations from several rainfall events into one variogram for each tree. One tree showed a clear spatial dependence of throughfall measurements, while this was less obvious for the other two. This spatial dependence could be a consequence of spatial correlation in the organization of canopy leaves and branches. There is some uncertainty about the spatial dependence due to the limitations of the experimental design, lack of observational points at small distances, and no random relocation between collection periods. Our results suggest that throughfall under olive canopies can be expected to be spatially correlated, but further research will be necessary to determine whether the poor spatial correlation structure that we observed in some cases is due to inef®ciency of the sampling design, or to variability among individual trees. TP gray scale maps were tailored, using ordinary kriging, for individual rainfall events and total TP maps for each tree. Total throughfall maps were dominated by the throughfall pattern of the largest rainfall events, although no signi®cant concentration spots were observed due to throughfall. Only relatively small concentration occurred, with a patchy distribution that loosely corresponded to open or less dense areas in the tree canopy, as determined by visual inspection. The total rainfall fraction that is concentrated at the base of the tree trunk in the form of stem¯ow is relatively high in comparison to other species. This results in an increase in soil water far greater than that due to throughfall, after a certain threshold rainfall causes the canopy to saturate. The area in which stem¯ow in®ltrates was estimated to be within 0.3±0.5 m from the trunk, with signi®cant ¯ux concentration up to 0.2 m, depending on individual tree characteristics and rainfall intensity. This suggests avoiding the application of agrochemical products that may be subject to leaching around the tree base, given that soil water ¯uxes may become high in this area. It also suggests that this area could act as a preferential ¯ow path for airborne contaminants leached by rainfall into the soil. Subsequent studies should be devoted to evaluate the volume and chemical composition of stem¯ow, as well as to characterize the spatial patterns of wetting fronts beneath olive canopies. Acknowledgements This research was sponsored by the Spanish Ministry of Education and Science (CICYT OLI96-222) and by INIA grant CAO98-015. The paper was written while the senior author
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was a visiting scientist at the National Soil Erosion Laboratory, on a grant from the Spanish Ministry of Education and Science. This support is gratefully acknowledged. References Alva, A.K., Prakash, O., Hornsby, A.G., 1999. Distribution of rainfall and soil moisture content in the soil profile under citrus tree canopy and at the dripline. Irrig. Sci. 18, 109±115. de Graaf, J., Eppink, L.A.A.J., 1999. Olive oil production and soil conservation in southern Spain, in relation to EU subsidy policies. Land Use Policy 16, 259±267. De Luna, E., 1994. ProteccioÂn del suelo contra la erosioÂn en el cultivo del olivo, PhD Thesis, Department of Agronomy, University of CoÂrdoba, Spain. Domingo, F., Puigdefabregas, J., Moro, M.J., Bellot, J., 1994. Role of vegetation cover in the biogeochemical balances of a small afforested catchment in southeastern Spain. J. Hydrol. 159, 275±289. Gash, J.H.C., Morton, A.J., 1978. An application of the Rutter model to the estimation of the interception loss from Thetford forest. J. Hydrol. 38, 49±58. Gersper, P.L., Holowaychuk, N., 1970. Effects of stemflow water on a Miami soil under a beech tree. I. Morphological and physical properties. Soil Sci. Soc. Am. Proc. 34, 779±786. GoÂmez, J.A., Giraldez, J.V., Fereres, E., 2001. Rainfall interception by olive trees in relation to leaf area, Agric. Water Manag. 49, 65±76. Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation, Oxford University Press, New York. Herwitz, S.R., 1986. Infiltration-excess caused by stemflow in a cyclone-prone tropical rainforest. Earth Surf. Process. Landforms 11, 401±412. Isaaks, E.H., Srivastava, R.M., 1989. An Introduction to Applied Geostatistics, Oxford University Press, New York. Jefferries, R.A., MacKerron, D.K.L., 1985. Stemflow in potato crops. J. Agric. Sci. 105, 205±207. Journel, A.G., Huijbregts C.J., 1978. Mining Geostatistics, Academic Press, London. Kalma, J.D., Stanhill, G., Urieli, E., 1968. Rainfall interception and stemflow in an orange plantation. Israel J. Agric. Res. 18, 3±15. Li, Y.C., Alva, A.K., Calvert, D.V., Zhang, M., 1997. Stemflow, throughfall, and canopy interception of rainfall by citrus tree canopies. Hortscience 32, 1059±1060. Loustau, D., Berbigier, P., Granier, A., El Hadj Moussa, F., 1992. Interception loss, throughfall and stemflow in a maritime pine stand. I. Variability of throughfall and stemflow beneath the pine canopy. J. Hydrol. 138, 449± 467. Mariscal, M.J., Orgaz, F., Villalobos, F.J., 2000. Modelling and measurement of radiation interception by olive canopies. Agr. For. Meteorol. 100, 183±197. MunÄoz, J., 1998. EvolucioÂn de propiedades del suelo y del estado del aÂrbol seguÂn sistemas de manejo en un olivar de secano de AndalucõÂa, Master thesis, University of Cordoba, Spain. NaÂvar, J., Bryan, R., 1990. Interception loss and rainfall redistribution by three semi-arid growing shrubs in northeastern Mexico. J. Hydrol. 115, 51±63. Pannatier, Y., 1996. VARIOWIN: Software for Spatial Data Analysis in 2D, Springer, New York. Parker, G.G., 1983. Throughfall and stemflow in the forest nutrient cycle. Adv. Ecol. Res. 13, 57±133. Parkin, T.B., Codling, E.E., 1990. Rainfall distribution under a corn canopy: implications for managing agrochemicals. Agron. J. 82, 1166±1169. Pressland, A.J., 1976. Soil moisture redistribution as affected by throughfall and stemflow in an arid zone shrub community. Aust. J. Bot. 24, 641±649. Robson, A.J., Neal, C., Ryland, G.P., Harrow, M., 1994. Spatial variations in throughfall chemistry at the small plot scale. J. Hydrol. 158, 107±122. Saffigna, P.G., Tanner, C.B., Keeney, D.R., 1976. Non-uniform infiltration under potato canopies caused by interception, stemflow and hilling. Agron. J. 68, 337±342. Schroth, G., da Silva, L.F., Wolf, M., Tixeira, W.G., Zech, W., 1999. Distribution of throughfall and stemflow in multi-strata agroforestry, perennial monoculture, fallow and primary forest in centra Amazonia, fallow and primary forest in centra Amazonia, Brazil. Hydrol. Process. 13, 1423±1436.
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Sterk, G., Stein, A., 1997. Mapping wind-blown mass transport by modeling variability in space and time. Soil Sci. Soc. Am. J. 61, 232±239. Tanaka, T., Taniguchi, M., Tsujimura, M., 1996. Significance of stemflow in groundwater recharge. 2. A cylindrical infiltration model for evaluating the stemflow contribution to groundwater recharge. Hydrol. Process. 10, 81±88. Taniguchi, M., Tsujimura, M., Tanaka, T., 1996. Significance of stemflow in groundwater recharge. 1. Evaluation of the stemflow contribution to recharge using a mass balance approach. Hydrol. Process. 10, 71± 80. Troiano, J., Nordmark, C., Barry, T., Johnson, B., 1997. Profiling areas of groundwater contamination by pesticides in California. Phase II. Evaluation and modification of a statistical model. Environ. Monit. Assess. 45, 301±318. Tsiros, J.X., Haidouti, C., Chronopoulou, A., 1998. Airborne fluoride contamination of soils and olive trees near an aluminum plant. Measurements and simulations. J. Environ. Sci. Health Part A 33, 1309±1324. Villalobos, F.J., Orgaz, F., Mateos, L., 1995. Non-destructive measurement of leaf area in olive (Olea europaea L.) trees using a gap inversion method. Agric. For. Meteorol. 73, 29±42. Voight, G.K., 1960. Distribution of rainfall under forest stands. Forest Sci. 6, 2±11. Webster, R., Oliver, M.A., 1992. Sample adequately to estimate variograms of soil properties. J. Soil Sci. 43, 177±192. Whelan, M.J., Anderson, J.M., 1996. Modelling spatial patterns of throughfall and interception loss in a Norway spruce (Picea abies) plantation at the plot scale. J. Hydrol. 186, 335±354. Whitehead, D., Grace, J.C., Godfrey, M.J.S., 1990. Architectural distribution of foliage in individual Pinus radiata D. Don crowns and the effects of clumping on radiation interception. Tree Physiol. 7, 135±155.
Rainfall concentration under olive trees J.A. GoÂmeza, K. Vanderlindenb, J.V. GiraÂldezb, E. Fereresc,* a
National Soil Erosion Research Laboratory, USDA-ARS-MWA, 1196 Soil Building, West Lafayette, IN 47906, USA b Departamento de Agronomia, Universidad de CoÂrdoba, P.O. Box 3048, CoÂrdoba 14080, Spain c Instituto de Agricultura Sostenible, Consejo Superior de Investigaciones Cienti®cas, P.O. Box 4084, CoÂrdoba 14080, Spain Accepted 30 October 2001
Abstract To determine the existence of rainfall concentration beneath olive trees, throughfall and stem¯ow was measured in three olive trees during 12 rainfall events, using 36 rain gauges per tree and a stem¯ow collection system. Data from different rainfall events were aggregated to assess the spatial correlation in throughfall. Only one out of the three trees showed a clear spatial dependency structure. Rainfall concentration under the tree canopy, as a consequence of rainfall redistribution of throughfall, was relatively unimportant with few and sparse locations showing a percentage of throughfall with respect to rainfall in open area >100% and none above 125%. Throughfall showed a consistent storm to storm pattern in spatial distribution among high rainfall events, and nonconsistent patterns among low rainfall events. Stem¯ow was found to be the most important mechanism of canopy induced ¯ux concentration, in events where rainfall depth was large enough to saturate the olive canopy. Stem¯ow was estimated to in®ltrate in a radial area up to 0.5 m from the tree trunk, depending on tree characteristics and rainfall intensity. The area surrounding the tree trunk appears to be the most relevant area for potential research dealing with the in¯uence of concentrated canopy induced water ¯uxes on the transport of chemicals to deeper layers within the soil. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Rainfall redistribution; Stem¯ow; Olive trees; Spatial correlation
1. Introduction Interest in rainfall redistribution by plant canopies led Kiesselbach in 1916 (as quoted by Alva et al., 1999) to report how corn plants funnel rainwater down their stalk. Rainfall redistribution by canopies has been reported since then in potato (Saf®gna et al., 1976; * Corresponding author. Tel.: 34-957-499200; fax: 34-957-499252. E-mail address: [email protected] (E. Fereres).
0378-3774/02/$ ± see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 3 7 7 4 ( 0 1 ) 0 0 1 8 1 - 0
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Jefferries and MacKerron, 1985), corn (Parkin and Codling, 1990), citrus trees (Alva et al., 1999; Li et al., 1997; Kalma et al., 1968), sparse vegetation (NaÂvar and Bryan, 1990; Pressland, 1976); tropical rainforests (Schroth et al., 1999), and coniferous forests (Taniguchi et al., 1996), among others. Such interest is justi®ed by the importance of the impact of canopy interception on the hydrological balance (Gash and Morton, 1978; Domingo et al., 1994), the nutrient cycle of forests (Parker, 1983), the modi®cation of soil properties near tree trunks (Gersper and Holowaychuk, 1970), the recharge and transport of elements to subsurface water (Taniguchi et al., 1996), or the leaching of agrochemical products (Parkin and Codling, 1990). Olive, an evergreen tree crop, occupies over 2,000,000 ha throughout Spain, and it is broadly extended in the Mediterranean basin, where it has an important economical and environmental role in rural areas (de Graaf and Eppink, 1999). Previous works on forest canopies have found both, a systematic trend in throughfall spatial distribution (Whelan and Anderson, 1996; Robson et al., 1994) and a random distribution of throughfall (Loustau et al., 1992). This is attributed to differences among species, sites and rainfall characteristics. Previous research has quanti®ed rainfall partitioning in olive orchards in relation to tree leaf area index (GoÂmez et al., 2001). Works on another species (e.g. Taniguchi et al., 1996) suggest the importance of canopy induced concentrated water ¯uxes for the leaching of nutrients, or contaminants and agrochemical products to deep soil layers. The existence of pesticide (Troiano et al., 1997) or airbone contamination (Tsiros et al., 1998) has been shown in olive orchards, and in these situations concentrated ¯uxes could be determinant in the transport of contaminants. To assess the rainfall concentration potential of olive trees, an experiment was set up with two objectives: 1. Characterize the rainfall distribution beneath olive canopies. 2. Determine the zones of rainfall concentration and estimate the magnitude of the water fluxes in these zones.
2. Material and methods Rainfall interception was measured in three mature olive trees of the `Picual' cultivar. Tree spacing was 6 m 6 m, trees were 3 m high and their canopy size was approximately 4 m in diameter, giving about 40% ground cover. The three trees were chosen among those in close proximity that differed in leaf area density. The canopy of each experimental tree was characterized by measuring leaf area and canopy horizontal projection, following procedures of Villalobos et al. (1995). The leaf area by canopy projected surface, PLAI, of the experimental trees ranged from 3.4 to 5.46 (meter square leaf area per meter square ground surface area underneath the canopy). The experimental site was located at the Agricultural Research Center `Alameda del Obispo', CoÂrdoba, Spain, 358500 4000 N and 48510 0200 W. CoÂrdoba has a Mediterranean climate with an average annual precipitation of 606 mm concentrated from October to April. Interception was measured in the trees for 12 rainfall events from January to June, 1997. Thirty-six rain gauges, funnels 0.12 m in diameter connected to 1.5 l bottles, were placed beneath the canopy of each tree at 0.4 m
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Fig. 1. Location of the throughfall rain gauges for tree A.
height in a arrangement following four orthogonal axes (Fig. 1). This arrangement was maintained during the entire experiment. Each gauge was measured after any rainfall event, or after several individual events when independent collection was not possible. No attempt was made for correcting evaporation loss from the bottles. This was assumed to be small because (a) fast collection and reading after rainfall and (b) the small surface area of the bottleneck that reduced the diffusion of water vapor out of the bottle. Collected rainfall in a gauge, situated up to a normalized distance of 1.05 from the tree trunk was considered in the analysis of throughfall. Normalized distance is calculated as the ratio between the distance from the trunk center to the rain gauge and the distance from the trunk center to the canopy projection line in that radial direction. Throughfall, de®ned as the volume of rain caught in each rain gauge per unit area associated with each rain gauge, was reported as a percentage, by calculating the ratio between throughfall and rainfall, multiplied by 100. Stem¯ow was measured for each tree with a collector ring installed around each main trunk of the trees. Each collector was approximately 0.03 m in width, and was made of plastic material sealed to the tree trunk using silicone. Stem¯ow volume was transformed to depth [L] using the surface of the horizontal projection of the canopy. No attempt was made to link the throughfall measurements to measurable features of the trees (e.g. canopy gaps). A tipping bucket rain gauge (model ARG 100, Campbell Scienti®c#, Logan, UT) was used to record rainfall in an open area located about 20 m from one of the experimental trees, which were within 12 m of each other. Continuous records of wind speed and direction were obtained from a nearby meteorological station. 2.1. Spatial autocorrelation theory Regionalized variable theory (Isaaks and Srivastava, 1989; Goovaerts, 1997) was applied to assess the spatial correlation structure of throughfall. The existence of spatial
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dependence among the throughfall observations can be detected by means of the semivariogram, which is a measure for dissimilarity in function of the separation vector h between observational pairs. The experimental semivariogram ^g h can be computed using Eq. (1): 1 X z xi 2n h i1 n h
^g h
z xih 2
(1)
where z() represents throughfall and n(h) is the number of throughfall observation pairs at locations xi and xih, separated by a distance h. Eq. (1) requires a minimum number of observations, in order to obtain a representative sample of data pairs that contribute to the semivariogram value at each lag. Webster and Oliver (1992) recommend at least 100 sampling points. If not, resultant semivariograms may be noisy and difficult to interpret. In our study, throughfall was measured at 36 equally spaced rain gauges under each tree. An additional concern was the absence of observational pairs with separation distances <0.5 m, due to arrangement of the raingauges. This can lead to difficulties when inferring the magnitude of the nugget effect, that is the unexplained variability at small scales (Journel and Huijbregts, 1978, Section III.C.5). In order to overcome the small number of observations, we applied the methodology used by Sterk and Stein (1997) for mapping wind-blown mass transport. They joined the information on mass transport from several windstorms into one experimental semivariogram. Applying their methodology, we used the measurements from 12 rainfall events to assess the spatial correlation structure of throughfall under each tree, using Eq. (2) to compute the experimental semivariogram. n h
^g h
j 12 X 1 X zs xij 2n h j1 i1
zs xih;j 2
(2)
P where n h 12 j1 nj h and zs(xij) is the standardized throughfall of observation i from rainfall event j, given by: zs xij
z xij m mj
(3)
where mj and m are the mean throughfall during rainfall event j and the overall mean throughfall during the 12 rainfall events, respectively. Eq. (2) is applied to the observations of each tree and an appropriate theoretical model is fitted to each experimental semivariogram. Each one of the three theoretical models can be converted to 12 semivariogram models, one for each rainfall event. In order to accomplish this the sill parameter (Cj) is calculated using the overall mean throughfall, m, and the mean throughfall for event, mj: m 2 j (4) Cj C m We assumed the nugget effect equal to zero, and considered the range of spatial dependency of throughfall independent from rainfall depth, which means that the same range can be used for all rainfall events. Further assumptions made are first and second order stationarity and isotropy of the throughfall process.
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Maps for throughfall were tailored using ordinary kriging with the corresponding semivariogram. Throughfall percentage (TP) was obtained through division by rainfall depth: 100 X lp z xpj Rj p1 q
TP xoj
(5)
where TP(xoj) is throughfall percentage at the interpolated location, xo, for rainfall event j. Rj the corresponding rainfall depth, lp are the best linear unbiased kriging weights for observations z(xpj) and q the number of neighbouring observations that are considered for the interpolation at location xo. The overall semivariogram model for each tree can be used to interpolate the individual TPs for each rainfall event, because the sill value, C, is only a proportionality factor for the kriging algorithm (Goovaerts, 1997, pp. 174±175), the nugget effect is assumed equal to zero, and the range is the same for all rainfall events. 2.2. Stemflow analysis The funneling ratio (FR) was used to analyze the concentration of incoming rainfall around the tree trunk as stem¯ow. It is de®ned after Herwitz (1986) as: FR
V BG
(6)
where V is the volume of stemflow [L3], B the trunk basal area [L2] and G the rainfall depth [L] in an open area. The in®ltrating area for the stem¯ow was calculated as a function of the stem¯ow rate and the in®ltration rate of the soil surrounding the tree trunk, according to Herwitz (1986). The stem¯ow rate was estimated from the rainfall intensity (at 10 min record interval) assuming that the transmission rate to the trunk was the same as the ratio of stem¯ow depth to rainfall depth for each event. This stem¯ow rate was used to compute the in®ltration area by means of Eq. (7), using a constant soil in®ltration rate of 81 mm h 1 measured near the tree trunks at the same time in another experiment using a portable rainfall simulator (MunÄoz, 1998). We assume this value to be representative of the steady in®ltration condition because the rates of in®ltration measured in the ®eld quickly achieved nearconstant values (MunÄoz, 1998). A
Sr Sic
(7)
where A is the infiltration area [L2], Sr the stemflow rate [LT 1] and Sic the final soil infiltration rate [L 1T 1]. The infiltration area was assumed to be radial as suggested previously by several authors (e.g. NaÂvar and Bryan, 1990; Tanaka et al., 1996), and supported by the fact that the trees were located on a flat area and a homogeneous soil, as determined by examination of soil cores in the orchard. The distance that the infiltrationexcess due to stemflow travels away from the tree trunk, Dr, was calculated using Eq. (8) (Herwitz, 1986): r A B d Dr (8) p 2
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where A is the infiltration area [L2], B the tree basal area [L2] and d the tree trunk diameter [L]. 3. Results and discussion Table 1 presents a summary of the observed data. The values are similar to those reported by De Luna (1994) for different olive trees in the same area, and in the range of the values found by Kalma et al. (1968) for orange trees in Mediterranean conditions. Compared to orange trees (Kalma et al., 1968), our data show slightly higher interception losses and smaller stem¯ow volumes for olive trees. Differences in canopy structure between species, PLAI, rainfall intensities and distribution and experimental procedure could explain these differences. 3.1. Throughfall distribution Fig. 2 analyzes the TP for the three olive trees for different rainfall depths by means of box and whisker plots. Fig. 2 shows 10 plots for each tree instead of 12 because two rainfall depths, 2.6 and 17.6 mm, occurred twice during the experimental period. Thus, box and whisker plots for these two rainfall depths are calculated using twice as much throughfall data as in the case for the other rainfall depths. TPs increase as rainfall increases and seems to stabilize at larger rainfall depths, beyond a certain threshold. This can also be seen from the scatterplot in Fig. 3, where rainfall is plotted against throughfall. The logarithmic ®t in Fig. 3, shows this tendency to stabilize at larger rainfall depths. Two rainfall depths in Fig. 2, 2.6 and 8.8 mm, differ slightly from the general tendency, probably because the measured throughfall for these events corresponds to several small rainfall episodes. There Table 1 Characteristics of the rainfall events observed in the experimenta Event Date
1 2 3 4 5 6 7 8 9 10 11 12
23 January 1997 6 June 1997 7±9 April 1997 18±20 April 1997 21 April 1997 22 April 1997 18 May 1997 23±25 May 1997 26 May 1997 30±31 June 1997 4 June 1997 6 June 1997
WD
E E SE SW W W E E SE SE E E
R
17.6 0.6 2.6 8.8 4.9 2.6 17.6 31.3 3.91 11.2 77.1 1.96
Tree A
Tree B
Tree C
S
T
FR
S
T
FR
0.79 0 0.03 0.08 0.05 0.01 0.25 1.49 0.05 0.43 3.81 0.01
11.21 0.04 0.77 2.43 2.69 0.64 9.23 18.52 1.55 6.90 55.33 0.51
48.4 0 14.0 9.3 11.0 1.9 15.1 51.4 12.9 41.5 53.7 3.6
1.59 0 0.08 0.20 0.06 0.02 0.37 3.05 0.07 0.90 7.90 0.01
8.48 89.2 0.10 0 0.63 27.7 3.08 22.6 2.68 11.5 0.64 6.3 8.48 20.6 18.7 96.1 2.68 18.1 6.52 79.8 49.5 101 0.60 4.21
S
T
FR
1.05 0 0.10 0.30 0.24 0.04 0.37 1.88 0.20 0.88 4.62 0.03
10.13 0.16 0.94 3.23 2.33 0.58 10.13 19.02 1.91 6.86 53.85 0.61
13 0 1.3 3.7 2.9 0.5 4.6 23.3 2.5 10.9 57.3 0.3
a WD: observed wind direction; R: rainfall (mm); T: throughfall (mm); S: stemflow (mm), FR: funnelling ratio (m3 m 3).
Fig. 2. Box and whisker plots of percentage throughfall against rainfall in the open, for each tree. The square inside the box represents the median value, while the box height includes the upper and lower quartile values. Bars represent the highest and the lowest values found up to 150% of the box height, measured from the upper and lower limits. The plots that correspond to rainfall depths of 2.6 and 17.6 mm are based on throughfall measurements from two events that each yielded the same rainfall depth.
Fig. 3. Scatterplot for TP against rainfall depth and logarithmic fit.
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were TP values above 100, that indicates concentration of rain at certain points due to the in¯uence of the canopy. Figs. 2 and 3 show that such concentration also occurs at very small rainfall depths, although it is more important under higher rainfall. This can also be observed from the positive skew of TP distributions at lower rainfall depths and its disappearance at higher rainfall depths. The overall throughfall performance in relation to rainfall depth varies slightly among trees, probably in¯uenced by the individual canopy characteristics. Occurrence of concentration and high throughfall rates also seem to be related to the speci®c canopy characteristics of each tree. In order to assess the in¯uence of prevailing wind direction on the overall throughfall performance we consider box and whisker plots for TP classed by prevailing wind direction during the rainfall event(s), as shown in Fig. 4. The highest throughfall rates are recorded for E and SE wind direction, but the interpretation of Fig. 4 is somewhat cumbersome since rainfall depths are different for each wind direction. Rainfall depths were 146.2 and 17.7 mm for E and SE, respectively, while they only averaged 16.3 mm for the other directions. E and SE wind directions correspond to the largest rainfall events. As a result, it was impossible to infer the effect of the prevailing wind direction on overall throughfall performance. Overall mean throughfall values during the 12 rainfall events amounted to 9.46, 9.16 and 10.62 mm, respectively, for trees A, B and C. Observations from only 36 rain gauges did not permit the calculation of meaningful directional semivariograms. As a result, it was not possible to check for anisotropy in the spatial variability structure of the throughfall
Fig. 4. Box and whisker plots for TP against prevailing wind direction. For further explanation, see Fig. 2 legend.
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process. Anisotropy would indicate that the process is more or less continuous or spatially correlated in one direction of the two-dimensional space, or that the spatial correlation range changes with direction. Anisotropy in the spatial correlation structure must be related to the particular con®guration of each tree canopy. Here we will assume an isotropic correlation structure. Fig. 5 displays the corresponding experimental semivariograms for the three trees together with a ®tted exponential model. The models were ®tted using the variowin 2.21 software (Pannatier, 1996). Experimental semivariograms are calculated for separation distances up to approximately half the maximum separation distance. The spatial correlation structure is most apparent for tree A. The small semivariogram values at the smallest lags indicate that only a small portion of the variability occurs at scales smaller than the minimum separation distance between observational points. The experimental semivariogram for tree B is very noisy, which makes it dif®cult to ®t a theoretical model, while the spatial correlation structure is less obvious for tree C. Since throughfall is determined by rainfall interception, splash and dripping from tree leaves and branches, these results could be a consequence of spatially correlated leaf surfaces and angles within the canopy (Whitehead et al., 1990). A random relocation of gauges between collection periods (Robson et al., 1994) instead of a ®xed location probably would have facilitated the interpretation of the experimental semivariograms. The existence of a structure in the spatial variability indicates that throughfall is spatially dependent up to a certain separation distance (the range) and that the observed throughfall is affected by the throughfall occurring at surrounding gauges. Similar results have also been found with observations in forest canopies (Whelan and Anderson, 1996; Robson et al., 1994). In order to detect possible throughfall concentration spots, for each rainfall event, TP was interpolated on a regular 20 cm 20 cm grid using ordinary kriging with Eq. (5) and a global search neighborhood. Subsequently, the results were represented on gray scale maps. Fig. 6 shows TP maps for 12 rainfall events in tree A. Values beyond the canopy projection line are mere extrapolations, however, it was dif®cult to eliminate them as those close to the drip line were also affected by the canopy. In order to compare rainfall events, a common gray scale from 0 to 100 was chosen, with zones with TPs larger than 100 marked in white. Fig. 6 shows that events with high rainfall depths induced higher TP values, with a patchy spatial distribution under the canopy. Events with the same prevailing wind direction and rainfall depth, i.e. events 1 and 7, do not necessarily show the same spatial throughfall pattern. This can be due to different rainfall intensities and/or to additional vegetative growth during the experimental period (January±June). Evidence for the effects of additional growth can be deduced from the comparison between event 1 (23 January) against event 12 (6 April; Fig. 6). The signi®cant leaf growth that takes place between the two dates, even though it did not change measurably the projected canopy area, can explain the somewhat anomalous pattern of event 1 in comparison to subsequent events of similar magnitude. The most intense rainfall events, all show a similar distribution pattern of TP (Fig. 6), a phenomenon previously observed by Whelan and Anderson (1996) in Norway spruce (Picea abies). For small storms and during the initial period of larger storms when the canopy is `wetting-up' a large fraction of the throughfall is due to rainfall falling through gaps in the canopy (Whelan and Anderson, 1996). The gap size and distribution are presumably modi®ed quickly during the growing season as discussed. This could explain the lack of a consistent tendency in the throughfall pattern within small storms.
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Fig. 5. Experimental semivariograms for throughfall (mm) and the fitted exponential models for each tree.
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Fig. 6. Kriged TP maps for tree A for the different rainfall events. Isolines represent the 100% throughfall contour. The vertical projection of the tree canopy is represented by the dashed line. The prevailing wind direction is indicated by the arrow in the lower left corner of each plot.
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Fig. 7 shows total TP maps for the three trees. Total TP grids for each tree are calculated as the sum of 12 throughfall grids, divided by the total rainfall depth observed during the 12 events (180.2 mm) and multiplied by 100. Due to the methodology used to obtain these maps, total TP patterns from Fig. 7 correspond roughly to the TP pattern of the most important rainfall events of Fig. 6. Total TP shows a similar patchy distribution for trees B and C, although a slight increase in concentration can be observed at some spots. Mariscal et al. (2000) found in trees of the same orchard, a variation in local leaf area density which was related to the radial distance to the tree trunk. In their Fig. 9, Mariscal et al. (2000)
Fig. 7. Maps of total TP for the three trees, calculated as the ratio between the sum of the 12 individual throughfall events and the total rainfall during the experimental period, multiplied by 100. The dashed line depicts the vertical projection of the tree canopy.
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Fig. 7. (Continued ).
showed that local leaf area density reaches a maximum value somewhere in-between the tree trunk and the canopy projection line. Our data suggest that low throughfall areas (black spots in Figs. 6 and 7) correspond approximately with areas of high local leaf area density within the canopy. An overall evaluation of the importance of concentration from these maps and the original data shows that no large signi®cant throughfall concentration spots were detected under any of the three canopies studied. Of particular interest is the area of in®ltration close to the tree trunk. Throughfall concentration in this zone would add up to stem¯ow and could cause additional ponding and runoff. However, none of the three maps of Fig. 7 show clear evidence of signi®cant throughfall concentration in the vicinity of the tree trunk. 3.2. Stemflow Stem¯ow represents a small fraction of the total rainfall that reaches the soil when normalized for the tree canopy surface (Table 1). However, this rainfall is concentrated in a very small area around the tree trunk and generates a zone of intense ¯ux. The FR for the 12 events were, 51, 85, and 60 for trees A, B and C, respectively. These values are in the upper range of the values reported by Herwitz (1986). The relatively high FR values of our trees result from having higher effective crown area/basal area ratios than those reported by Herwitz (1986). Fig. 8 shows that FR is related to rainfall depth, probably because the bark is saturated and the transmission through the trunk and branches increases with rainfall intensity. Such increase approaches an asymptotic limit for large rainfall events, due to the limited water transport capacity of the tree stem and branches (Fig. 8). It was found that the radius of the in®ltration area ¯uctuated during the storm and vary among storms. An analysis of the magnitude and spatial distribution of stem¯ow ¯uxes was
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Fig. 8. Relationship between funneling ratio and rainfall depth.
made for all the events and trees. Total in®ltration was calculated using the in®ltrating area according to the observed rainfall intensity and soil in®ltration rate and was related to the rainfall outside. Fig. 9 shows a signi®cant concentration of water (up to 1500% of the rainfall) in the immediate surroundings of the trunk of the tree A for representative rainfall events. This phenomenon causes stem¯ow to play an important role in the recharge of
Fig. 9. Flux concentration due to stemflow, as percentage of rainfall in open area, determined for tree A and four rainfall events.
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Table 2 Maximum radial distance from tree trunk, Dr (m) of the stemflow infiltration area, calculated for the observed events Event
Tree A (Dr)
Tree B (Dr)
Tree C (Dr)
1 2 3 4 5 6 7 8 9 10 11 12 Mean (m) Maximum (m) Tree diameter (m)
0.04 0 0.01 0.15 0.05 0 0.03 0.17 0.04 0.18 0.30 0 0.08 0.30 0.26
0.07 0 0.02 0.04 0.04 0.02 0.02 0.25 0.05 0.25 0.48 0 0.11 0.48 0.26
0.05 0 0.03 0.05 0.12 0.04 0.04 0.17 0.12 0.23 0.34 0.01 0.10 0.34 0.26
deeper layers of the soil (Tanaka et al., 1996; Pressland, 1976) and in the risk of leaching chemicals like nitrogen or pesticides (Parkin and Codling, 1990). Its relevance to the overall soil water ¯uxes will be determined by the total water depth, the area throughout which in®ltration occurs and the soil pro®le characteristics. Table 2 shows the area of in®ltration around the tree, which extends to approximately 0.3±0.5 m from the tree trunk. This value is similar to those reported by Voight (1960) in hemlock (Tsuga canadiensis [L.] Carr.) and red pine (Pinus resinosa Ait.), Tanaka et al. (1996) in Japanese red pine (Pinus densi¯ora Sieb. et Zucc.), and NaÂvar and Bryan (1990) in shrubs in a semi-arid environment, and it should be considered an estimate of the maximum area of in®ltration for the stem¯ow ¯uxes. The fact that this size is similar to that quoted for trees of greater crown canopy projection and total stem¯ow volumes can be explained by the low in®ltration rate of our soil and by the high FR values found in our experiment. When all the rainfall events were analyzed it became apparent that only events with large rainfall (probably also of high intensity) would increase signi®cantly soil water ¯uxes due to stem¯ow. The data in Fig. 9 shows that for small rainfall depths, e.g. event 5, the ¯ux concentration was in a narrow area and, due to the small amount of stem¯ow volume, its effect on the total ¯ux could be considered negligible. When total rainfall depth is greater, the area affected increases, e.g. events 4, 10 and 11 and the ¯ux concentration also increased. In this case, the area of signi®cant ¯ux concentration area was estimated approximately to be 0.17 m from the trunk of tree A, (Fig. 9). Similar results were obtained for trees B and C, with slight differences due to the differences in tree characteristics and total stem¯ow (data not shown). As pointed out by Herwitz (1986), the previous approach should be regarded as an average estimation of the extension of stem¯ow. Factors like spatial variation of in®ltration rate, microrelief or zones of preferential ¯ow could modify signi®cantly the in®ltration pattern of the stem¯ow for particular trees. However, the radial symmetry assumption has been successfully applied in previous stem¯ow analyses
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(Herwitz, 1986; NaÂvar and Bryan, 1990; Tanaka et al., 1996), and in our work allows the estimation of the signi®cance of stem¯ow in soil water ¯uxes. According to the analysis based on Figs. 6, 7 and 9, we conclude that stem¯ow is signi®cantly more important than throughfall as a source of rainfall concentration and increased water ¯uxes into the soil. For medium and large rainfall events its extension and magnitude is signi®cant in comparison to values previously reported for another trees. 4. Conclusions TP increased asymptotically with rainfall depth, but no conclusions could be drawn on its relation to prevailing wind direction. Experimental semivariograms were calculated combining observations from several rainfall events into one variogram for each tree. One tree showed a clear spatial dependence of throughfall measurements, while this was less obvious for the other two. This spatial dependence could be a consequence of spatial correlation in the organization of canopy leaves and branches. There is some uncertainty about the spatial dependence due to the limitations of the experimental design, lack of observational points at small distances, and no random relocation between collection periods. Our results suggest that throughfall under olive canopies can be expected to be spatially correlated, but further research will be necessary to determine whether the poor spatial correlation structure that we observed in some cases is due to inef®ciency of the sampling design, or to variability among individual trees. TP gray scale maps were tailored, using ordinary kriging, for individual rainfall events and total TP maps for each tree. Total throughfall maps were dominated by the throughfall pattern of the largest rainfall events, although no signi®cant concentration spots were observed due to throughfall. Only relatively small concentration occurred, with a patchy distribution that loosely corresponded to open or less dense areas in the tree canopy, as determined by visual inspection. The total rainfall fraction that is concentrated at the base of the tree trunk in the form of stem¯ow is relatively high in comparison to other species. This results in an increase in soil water far greater than that due to throughfall, after a certain threshold rainfall causes the canopy to saturate. The area in which stem¯ow in®ltrates was estimated to be within 0.3±0.5 m from the trunk, with signi®cant ¯ux concentration up to 0.2 m, depending on individual tree characteristics and rainfall intensity. This suggests avoiding the application of agrochemical products that may be subject to leaching around the tree base, given that soil water ¯uxes may become high in this area. It also suggests that this area could act as a preferential ¯ow path for airborne contaminants leached by rainfall into the soil. Subsequent studies should be devoted to evaluate the volume and chemical composition of stem¯ow, as well as to characterize the spatial patterns of wetting fronts beneath olive canopies. Acknowledgements This research was sponsored by the Spanish Ministry of Education and Science (CICYT OLI96-222) and by INIA grant CAO98-015. The paper was written while the senior author
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was a visiting scientist at the National Soil Erosion Laboratory, on a grant from the Spanish Ministry of Education and Science. This support is gratefully acknowledged. References Alva, A.K., Prakash, O., Hornsby, A.G., 1999. Distribution of rainfall and soil moisture content in the soil profile under citrus tree canopy and at the dripline. Irrig. Sci. 18, 109±115. de Graaf, J., Eppink, L.A.A.J., 1999. Olive oil production and soil conservation in southern Spain, in relation to EU subsidy policies. Land Use Policy 16, 259±267. De Luna, E., 1994. ProteccioÂn del suelo contra la erosioÂn en el cultivo del olivo, PhD Thesis, Department of Agronomy, University of CoÂrdoba, Spain. Domingo, F., Puigdefabregas, J., Moro, M.J., Bellot, J., 1994. Role of vegetation cover in the biogeochemical balances of a small afforested catchment in southeastern Spain. J. Hydrol. 159, 275±289. Gash, J.H.C., Morton, A.J., 1978. An application of the Rutter model to the estimation of the interception loss from Thetford forest. J. Hydrol. 38, 49±58. Gersper, P.L., Holowaychuk, N., 1970. Effects of stemflow water on a Miami soil under a beech tree. I. Morphological and physical properties. Soil Sci. Soc. Am. Proc. 34, 779±786. GoÂmez, J.A., Giraldez, J.V., Fereres, E., 2001. Rainfall interception by olive trees in relation to leaf area, Agric. Water Manag. 49, 65±76. Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation, Oxford University Press, New York. Herwitz, S.R., 1986. Infiltration-excess caused by stemflow in a cyclone-prone tropical rainforest. Earth Surf. Process. Landforms 11, 401±412. Isaaks, E.H., Srivastava, R.M., 1989. An Introduction to Applied Geostatistics, Oxford University Press, New York. Jefferries, R.A., MacKerron, D.K.L., 1985. Stemflow in potato crops. J. Agric. Sci. 105, 205±207. Journel, A.G., Huijbregts C.J., 1978. Mining Geostatistics, Academic Press, London. Kalma, J.D., Stanhill, G., Urieli, E., 1968. Rainfall interception and stemflow in an orange plantation. Israel J. Agric. Res. 18, 3±15. Li, Y.C., Alva, A.K., Calvert, D.V., Zhang, M., 1997. Stemflow, throughfall, and canopy interception of rainfall by citrus tree canopies. Hortscience 32, 1059±1060. Loustau, D., Berbigier, P., Granier, A., El Hadj Moussa, F., 1992. Interception loss, throughfall and stemflow in a maritime pine stand. I. Variability of throughfall and stemflow beneath the pine canopy. J. Hydrol. 138, 449± 467. Mariscal, M.J., Orgaz, F., Villalobos, F.J., 2000. Modelling and measurement of radiation interception by olive canopies. Agr. For. Meteorol. 100, 183±197. MunÄoz, J., 1998. EvolucioÂn de propiedades del suelo y del estado del aÂrbol seguÂn sistemas de manejo en un olivar de secano de AndalucõÂa, Master thesis, University of Cordoba, Spain. NaÂvar, J., Bryan, R., 1990. Interception loss and rainfall redistribution by three semi-arid growing shrubs in northeastern Mexico. J. Hydrol. 115, 51±63. Pannatier, Y., 1996. VARIOWIN: Software for Spatial Data Analysis in 2D, Springer, New York. Parker, G.G., 1983. Throughfall and stemflow in the forest nutrient cycle. Adv. Ecol. Res. 13, 57±133. Parkin, T.B., Codling, E.E., 1990. Rainfall distribution under a corn canopy: implications for managing agrochemicals. Agron. J. 82, 1166±1169. Pressland, A.J., 1976. Soil moisture redistribution as affected by throughfall and stemflow in an arid zone shrub community. Aust. J. Bot. 24, 641±649. Robson, A.J., Neal, C., Ryland, G.P., Harrow, M., 1994. Spatial variations in throughfall chemistry at the small plot scale. J. Hydrol. 158, 107±122. Saffigna, P.G., Tanner, C.B., Keeney, D.R., 1976. Non-uniform infiltration under potato canopies caused by interception, stemflow and hilling. Agron. J. 68, 337±342. Schroth, G., da Silva, L.F., Wolf, M., Tixeira, W.G., Zech, W., 1999. Distribution of throughfall and stemflow in multi-strata agroforestry, perennial monoculture, fallow and primary forest in centra Amazonia, fallow and primary forest in centra Amazonia, Brazil. Hydrol. Process. 13, 1423±1436.
70
J.A. GoÂmez et al. / Agricultural Water Management 55 (2002) 53±70
Sterk, G., Stein, A., 1997. Mapping wind-blown mass transport by modeling variability in space and time. Soil Sci. Soc. Am. J. 61, 232±239. Tanaka, T., Taniguchi, M., Tsujimura, M., 1996. Significance of stemflow in groundwater recharge. 2. A cylindrical infiltration model for evaluating the stemflow contribution to groundwater recharge. Hydrol. Process. 10, 81±88. Taniguchi, M., Tsujimura, M., Tanaka, T., 1996. Significance of stemflow in groundwater recharge. 1. Evaluation of the stemflow contribution to recharge using a mass balance approach. Hydrol. Process. 10, 71± 80. Troiano, J., Nordmark, C., Barry, T., Johnson, B., 1997. Profiling areas of groundwater contamination by pesticides in California. Phase II. Evaluation and modification of a statistical model. Environ. Monit. Assess. 45, 301±318. Tsiros, J.X., Haidouti, C., Chronopoulou, A., 1998. Airborne fluoride contamination of soils and olive trees near an aluminum plant. Measurements and simulations. J. Environ. Sci. Health Part A 33, 1309±1324. Villalobos, F.J., Orgaz, F., Mateos, L., 1995. Non-destructive measurement of leaf area in olive (Olea europaea L.) trees using a gap inversion method. Agric. For. Meteorol. 73, 29±42. Voight, G.K., 1960. Distribution of rainfall under forest stands. Forest Sci. 6, 2±11. Webster, R., Oliver, M.A., 1992. Sample adequately to estimate variograms of soil properties. J. Soil Sci. 43, 177±192. Whelan, M.J., Anderson, J.M., 1996. Modelling spatial patterns of throughfall and interception loss in a Norway spruce (Picea abies) plantation at the plot scale. J. Hydrol. 186, 335±354. Whitehead, D., Grace, J.C., Godfrey, M.J.S., 1990. Architectural distribution of foliage in individual Pinus radiata D. Don crowns and the effects of clumping on radiation interception. Tree Physiol. 7, 135±155.