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Agricultural and Forest Meteorology 100 (2000) 323–336

Measured and modelled rainfall interception loss from anagroforestry system in Kenya

N.A. Jackson∗Institute of Hydrology, Crowmarsh Gifford, Wallingford, Oxon OX10 8BB, UK

Received 17 May 1999; received in revised form 6 October 1999; accepted 11 October 1999

Abstract

Rainfall interception losses from an agroforestry system in semi-arid Kenya comprisingGrevillea robustaand maize weremeasured over a period of 33 months. These measurements showed that interception was slightly higher (10.2%) under treeswith no maize understorey, than in the intercropped treatment (9.8%), and was directly related to the degree of tree canopycover. Interception estimates using the reformulated version of the Gash analytical model were∼4% lower than measuredtotals, and were strongly dependent on both canopy cover and on monthly variations in the mean rainfall rate (R, mm h−1).©2000 Published by Elsevier Science B.V. All rights reserved.

Keywords:Rainfall interception; Agroforestry; Modelling;Grevillea robusta

1. Introduction

Agroforestry has been promoted as a possiblemeans of increasing the productive use of rainfallin water-limited environments, by using the waterwhich is usually inaccessible to conventional crop-ping systems; i.e. soil water reserves that are belowthe crop rooting zone and/or rainfall occurring outsidethe normal cropping seasons (Jackson et al., 1998).However, any potential increase in rainfall utilisationby agroforestry systems, when compared to eitherwoodlots or open crop fields, must be offset againstthe evaporation of rainfall intercepted by the likelygreater canopy size (tree and crop), and not thereforeavailable for crop growth (Wallace et al., 1995). In-terception losses must also be weighed against anyincreased abstraction of water from the crop rooting

∗ Corresponding author. Tel.:+44-1491-692336;fax: +44-1491-692424.E-mail address:[email protected] (N.A. Jackson).

zone by tree roots, a component of the water balancethat can be manipulated through practices such asroot and/or canopy pruning.

Agroforestry systems are often spatially complex innature, with both the tree and crop canopies affectingrainfall distribution and input to the soil surface. Sincequantitative investigations of water-use in agroforestrysystems are rare (Ong et al., 1991), we decided to in-vestigate the water-use of a hillslope agroforestry sys-tem in Kenya, combiningGrevillea robusta(A. Cunn.ex R. Br.) trees with a local variety of maize (Zea maysL. cv. Katumani). Water balance components otherthan rainfall interception are reported elsewhere; e.g.soil evaporation (Jackson and Wallace, 1999a; Wallaceet al., 1999), rainfall infiltration (Jackson and Wallace,1999b), soil water storage (Jackson et al., 1999), andtree and crop transpiration (Lott et al., 1997).

It was intended that the experiment would providedata on how rainfall distribution is modified by bothtree and crop canopies in an agroforestry system, aswell as demonstrate how these processes could be con-

0168-1923/00/$ – see front matter ©2000 Published by Elsevier Science B.V. All rights reserved.PII: S0168-1923(99)00145-8

324 N.A. Jackson / Agricultural and Forest Meteorology 100 (2000) 323–336

trolled by regular manipulation of the tree canopy,achieved through pruning. This paper compares themeasurement and modelling of rainfall interceptionby G. robusta(A. Cunn. ex R. Br.) trees in a tropicalagroforestry system in Kenya. The previously refor-mulated version of the Gash analytical model (Gash,1979; Gash et al., 1995) was used to model the inter-ception loss from the discontinuousGrevilleacanopy.

Interception estimates from tropical forests are of-ten more uncertain than those obtained in temperateclimates due to the complexity of the tree canopy struc-ture (Jackson, 1971; Bruijnzeel and Wiersum, 1987;Lloyd and Marques, 1988; Asdak et al., 1998a). Rain-fall interception loss in an agroforestry system willdepend on the extent of the tree canopy cover, and thisin turn depends on factors such as the tree plantingdensity at establishment, and subsequent thinning andpruning practices.

Most of the literature comprises studies from closedforest canopies, although some cases exist where in-terception by discontinuous tree canopies has beenstudied (Rao, 1987; Teklehaimanot and Jarvis, 1991;Valente et al., 1997; Asdak et al., 1998a). In terms oftropical forestry and agroforestry systems, Veracionand Lopez (1976) examined interception belowPinuskesiya stands, and interception under multi-storeysystems incorporating coffee and cocoa and shadetree species has been explored by Imbach et al.(1989). Tropical agroforestry systems often comprisefast-growing tree species grown in rotations as shortas 6 or 7 years. Both measurements and models ofinterception loss are needed to predict the effects ofthe tree component on the overall water balance of thesystem at all stages from establishment to the harvestof the trees.

2. Materials and methods

2.1. Site and climate

The rainfall interception measurements presented,were made at the ICRAF field station at Machakos,Kenya, 80 km south-east of Nairobi (1◦33′S, 37◦8′E)at an altitude of 1560 m. The site runs downhill tothe Maruba river at a slope of∼22%. The soil con-sists of a series of shallow (0.2–2 m) reddish-brownto brown sandy clay loams (well-drained luvisols —FAO soil classification). The surface 0.4 m was com-

paratively homogenous, while soil layers below thisvaried widely in gravel and clay content, with a num-ber of distinct horizons (Kibe et al., 1981; Huxleyet al., 1989). The soil is underlain by layers of firstweathered and then coherent rock (gneiss) at varyingdepths. A band of very shallow soils (0.2–0.6 m deep)ran across the site from the top north-west corner to-wards the bottom south-east corner. Soils were gen-erally deeper (0.7–1.5 m) above and below this band(Wallace et al., 1995).

Annual rainfall is bi-modal, with a short rainy sea-son usually lasting from late October to late December,and a longer rainy season running from late Februaryto the end of May. Monthly rainfall peaks in April andNovember, and little rainfall occurs between June andOctober (see Table 1 ). Rainfall during much of thetwo crop growth seasons is largely orographic in originas moisture-laden air travels from the coast westwardstowards the Kenyan highlands. Machakos district hasa very large inter-annual variation in monthly andseasonal rainfall (Huxley et al., 1989).

2.2. Experimental design

This rainfall interception study formed part of alarger agroforestry trial which was established inOctober 1991. This trial combined rows of maize withG. robusta, a rainforest tree species (Proteaceae) fromthe north of Australia, introduced to East Africa as anornamental in the last century. A detailed descriptionof the experimental design is given by Jackson et al.(1999).

The planting arrangements of the plots in whichrainfall interception was measured, consisted of asole tree (Td) treatment, whereG. robustatrees wereplanted in a 3 m× 4 m grid pattern, and an inter-cropped (CTd) treatment with trees planted in thesame grid pattern, but with maize planted in rows 1 mapart (0.3 m between plants along row), following thecontours of the slope. Maize was grown twice a year,and was planted at the start of each rainy season, afterat least 20 mm of rainfall had occurred.

2.3. Instrumentation

Gross rainfall was measured using a tipping-bucketraingauge (0.5 mm bucket, Rimco, Vic., Australia)positioned uphill from the plots, approximately 20 m

N.A. Jackson / Agricultural and Forest Meteorology 100 (2000) 323–336 325

Table 1Monthly and annual rainfall (mm) at Machakos: historical means and recorded values at the project site between tree planting (October1991) and the end of the project (June 1997)

Averagea 1991 1992 1993 1994 1995 1996 1997

January 50 23 28 283 1 38 20 3February 50 13 4 110 90 77 80 0March 105 48 5 41 87 152 91 58April 183 77 164 35 92 103 69 229May 56 89 68 14 15 32 57 66June 11 4 17 21 0 0 17 4July 4 11 7 1 5 6 2 n/aAugust 4 11 1 3 5 13 7 n/aSeptember 5 5 1 1 3 6 1 n/aOctober 43 47 40 10 61 48 0 n/aNovember 175 175 126 162 317 82 156 n/aDecember 96 150 214 118 144 95 10 n/aAnnual total 782 653 675 799 820 652 510 n/a

a Data for 9-year period (1963–1971) from Machakos (Maruba) Dam station.

from the nearest trees, and four manually recordedraingauges (125 mm diameter) at distances of between20 and 100 m from the nearest trees.

An automatic weather station (Didcot Instruments,Abingdon, UK) was sited on a tower above the trees inthe centre of the site, with the tree canopy extendingbetween 40 and 80 m in all directions. The mast wasraised at least once each season to ensure that the mea-surements were always made∼2 m above the meantree height. The variables measured at this positionwere: dry- and wet-bulb air temperatures (recorded us-ing an aspirated psychrometer); incident solar radia-tion (Model CM5, Kipp and Zonen, Netherlands); andwind speed and direction (Campbell Scientific, Leics.,UK), used to determine prevailing wind directions dur-ing rainfall events. Wind speed was also measured atvarious heights below and within the tree canopy. Allautomatic instruments were measured every 10 s, andhourly totals (for rainfall and wind speed) or averages(wind direction) were stored on a data logger (Model21, Campbell Scientific Instruments, UK).

Jackson (1971) demonstrated that estimates of trop-ical forest interception varied greatly as a result of highspatial heterogeneity in canopy architecture. Althoughthe agroforestry system was less spatially complexthan most tropical forests, the grid pattern of tree plant-ing meant that the canopy was markedly ‘clumped’.It was expected that such a canopy distribution mightlead to significant differences in throughfall over rela-tively small horizontal distances. Eschner (1967) notedthat considerable micro-scale variability in through-

fall existed between points below tree canopies, withstemflow sometimes accounting for significantly en-hanced and localised rainfall inputs to the soil surface.

Given this anticipated variation in throughfall,it would have been preferable to use large-scalesheet-type interception gauges of the sort describedby Calder and Rosier (1976) and Rao (1987). How-ever, the presence of the understorey maize crop madeit impossible to adopt this technique. In addition,as part of the larger experiment it was necessary toobtain measurements of net rainfall input to the soilat specific positions relative to the trees, to matchother measurements being made, such as soil watercontent (Jackson et al., 1999) and soil evaporation(Jackson and Wallace, 1999a). Therefore, throughfallwas measured in both the Td and CTd treatments us-ing manually recorded raingauges, measuring 125 mmdiameter.

Initially (in October 1994), sets of raingauges wereinstalled in the following arrangements. Six gaugeswere placed in one of the CTd plots, at various dis-tances (0.3–2.5 m) from the base of the tree (seeFig. 1), covering one quarter of the 12 m2 area occu-pied by each tree, and in such a way that each lay mid-way between two rows of maize. Six more gaugeswere installed in one of Td plots, in the same spatial ar-rangement. The raingauges were inspected after eachrainfall event and the volumes recorded. Lloyd andMarques (1988) demonstrated how the effect of spatialvariation in throughfall beneath a rainforest canopycould be reduced by randomly relocating interception


Fig. 1. Location of the raingauges used to measure throughfall over a 12 m2 area below theG. robustatree canopy (CTd intercroppedtreatment shown here). Six gauges () were deployed in November 1994, and the network was augmented with a further twelve gauges( ) in early 1995. An identical spatial arrangement was employed in the sole tree Td treatment.

gauges on a regular basis. To compensate for varia-tion in interception by tree canopies of different sizesand shapes in this study, the raingauges in both theTd and CTd were moved to identical positions arounddifferent, randomly chosen trees, after approximatelyfive rainfall events >5 mm had been recorded. Arealaverage interception over the 12 m2 per tree was de-termined by weighting the volumes recorded in indi-vidual gauges by the fractional area they represented.

An analysis of the wind direction during 885 hof rainfall showed that the bulk of the rain (73% ofvol., 81% of rainfall h) came from directions betweennorth-east and south-east. As there was measurableinterception at 2.5 m midway between the trees, somerainfall must have been inclined away from verticalfall paths and was therefore being intercepted bythe adjacent tree canopies, as reported in previousinterception studies (Eschner, 1967; Aldridge, 1975;

Herwitz and Slye, 1995). Therefore, the number ofgauges was increased early in 1995 to 18 per tree, sothat the entire 12 m2 area occupied by each tree wasmeasured (see Fig. 1).

To measure stemflow,Sf , 18 gauges were installedon trees in the Td plot and a further 18 gauges in theCTd plot, consisting of a flexible plastic collar whichwas sealed to the trunk of the tree with a non-toxic sil-icone compound about 0.75 m above the ground. Thecollars drained to plastic jerry-cans of 35 l capacity.Stemflow gauges were moved from tree to tree on aregular basis, in order to sample trees with varyingtrunk diameters and canopy sizes.

2.4. Tree canopy cover

There were no discernible seasonal fluctuations inleaf area index (L* ) during the experiment, nor did the


rate of leaf fall seem to be seasonally dependent. Thetrees were pruned at various stages during the exper-iment, usually prior to planting the crop, by remov-ing the lowermost 1 m of canopy. In November 1996the trees were severely pruned, removing all but theuppermost 1 m of canopy. This reduced the canopyvolume by approximately 85%, and was designed tominimise the competition for soil water between treesand crops as part of an associated experiment on rootcompetition (Smith et al., 1999).

Leaf area index (L∗) values for G. robusta wasestimated using a calibrated pipe model (Lott etal., 1997), for the entire period from establishment(October 1991) to the end of the study (June 1997).Estimates of projected crown area (Ap) were madeat various intervals throughout the experiment, usingcanopy radius measurements and assuming a circularcanopy distribution. An empirical relationship be-tween projected canopy area (m2) and leaf area indexwas determined, and is shown in Fig. 2:

Ap = −0.594(L∗)2 + 3.669(L∗);r2 = 0.932; n = 74 (1)

The curved nature of the relationship reflects the factthat the adopted pruning regime encouraged the trees

Fig. 2. Empirical relationship determined between the leaf areaindex (L∗) and the projected area (Ap, in m2) of G. robustacrowns at the Machakos site. The relationship has the form:Ap = −0.594(L∗)2 + 3.669(L∗).

to grow vertically more rapidly than laterally, andtherefore, towards the end of the experiment, treeleaf area index increased faster than did the projectedcanopy area. The relationship was used to calculatemonthly values ofAp, which were in turn convertedto fractional canopy cover values,c, (where a denseclosed canopy= 1) using a planting density of onetree per 12 m2.

2.5. Model description

Gash et al. (1995) revised the original (Gash, 1979)analytical rainfall interception model to improve esti-mation of interception from sparse tree canopies. Themodel, which is a simplification of the Rutter model(Rutter et al., 1971, 1975), assumes that daily rainfalloccurs as one single storm event each day, an assump-tion that Lloyd et al. (1988) considered valid given theprevailing rainfall conditions in much of the tropics,i.e. short but intense convective storms. Each of theserainfall events is considered to comprise three distinctphases: firstly, a period from the onset of rainfall un-til the canopy becomes saturated (wetting-up phase);secondly, a saturation phase; and lastly a period ofdrying out, from the point at which rainfall stops untilthe canopy and trunks are absolutely dry. It is assumedthat both the canopy and trunks dry out completelybetween rainfall events.

To calculate the various components of the inter-ception loss, the revised model requires several initialparameters (see Table 2). A value for canopy cover,c, is required, defined as the projected area of the treecanopy on the ground below. Canopy storage,Sc, is as-sumed to remain constant, however, the canopy cover,c, varies. Also required are daily rainfall,Pg, the trunkstorage capacity,St, the rainfall fraction redirected tostemflow,pt, and the ratio between the mean rainfallrate,R, and the mean evaporation rate (during rainfall)per unit area of canopy cover,Ec.

2.6. Model parameterisation

For purposes of data analysis, rainfall was sepa-rated into discrete storm events where these were de-fined as periods of rainfall with at least eight clearhours (with no rainfall) occurring both before andafter the event (Lloyd and Marques, 1988). The typi-cal storm duration was 4–5 h, 87% of which occurredovernight.


Table 2The parameters and analytical forms for components of rainfall interception loss from sparse tree canopies (after Gash et al., 1995)

Parameters

Daily rainfall, Pg (mm)Canopy storage capacity,Sc (mm)Rainfall necessary to fill canopy storage,P ′

g (mm)Trunk storage capacity,St (mm)Rainfall fraction redirected to stemflow,pt

Fractional canopy cover,cMean rainfall rate onto saturated canopy,R (mm h−1)Mean evaporation rate from saturated canopy,Ec (mm h−1)

Component of the interception loss Analytical formulation

From the tree canopy:For a number of storms,m, too small to saturate the canopy (Pg < P ′

g) c∑m

j=1Pg,j

Wetting up the canopy, forn storms which saturate the canopy (Pg ≥ P ′g) ncP ′

g − ncSc

Evaporation from the saturated canopy during rainfall (cEc/R)∑n

j=1(Pg,j − P ′g)

Evaporation from the canopy after rainfall stops ncSc

From the trunks:For a number of storms,q, that saturate the trunks (Pg ≥ St/pt ) qSt

For a number of storms,n − q, which do not (Pg < St/pt ) pt∑n−q

j=1Pg,j

From 885 h of rainfall recorded between 1991 and1993 (i.e. before interception measurements com-menced), the mean rainfall rate was determined to be2.28± 0.92 mm h−1. However, individual storms ofmore than 90 mm were recorded, as well as (very) oc-casional rainfall intensities of more than 15 mm h−1.For this reason, monthly rainfall rates (Rm) were alsocalculated, which varied between 0.5 and 3.2 mm h−1.Both the long term and monthly values were used inmodelling. Bruijnzeel and Wiersum (1987) found thatthe mean daily rainfall rate in a sparseAcaciaplanta-tion varied significantly at different times throughoutthe rainy season.

The mean evaporation rate during rainfall,Ec, was calculated as 0.23 mm h−1, using thePenman–Monteith formula, estimating values of netradiation (Rn) above the tree canopy from measuredsolar radiation (see Rao, 1987; Lloyd et al., 1988).The aerodynamic conductance,ga, was calculatedaccording to Valente et al. (1997), asga = fu, whereuis the windspeed andf is a constant. It was assumedthat the aerodynamic conductances for momentum,water vapour and sensible heat remained the same,and thereforef was calculated as:

f =(

k

ln[(z − d)/z0]

)2

(2)

wherek is the von Kármán constant (0.41),z is the ref-erence level,d is the zero-plane displacement height,andz0 is the roughness length, determined to be 0.75and 0.1, respectively, of the mean tree height (Rutteret al., 1975; Valente et al., 1997), which increasedfrom 0.5 to 9.5 m over the course of the experiment.

Although the effect onga of increasing tree heightwas taken into account by varying the value of the tworoughness parameters, it is possible that the nature ofthe relationship betweend andz0 and tree height mayhave varied over time, as pruning changed the shapeand distribution of the canopy. There is some uncer-tainty about determiningd and z0 in widely spacedcanopies (Jarvis et al., 1976). Pruning would widenthe gaps between individual tree canopies, often lead-ing to more effective turbulent exchange within thecanopy layer and greater conductances per tree.

Teklehaimanot et al. (1991) studied the effect oftree planting density on boundary layer conductanceand found thatga increased linearly as the spacingbetween trees grew larger. It is unlikely that removingthe lowermost branches would have had a large effecton the value ofga, as windspeeds below and within thetree canopy did not increase significantly. However,when radical pruning took place towards the end ofthe experiment, removing 85% of the canopy, windspeeds greatly increased and it is logical to assume


that the value ofga did also. However, as canopy coverwas less than 2% at this stage, any error in the valueof ga would account for a negligible percentage of theoverall interception.

As the canopy storage capacity (Sc) was known to beaffected by canopy architecture (Asdak et al., 1998b)it was expected to vary, depending on the height andshape of the trees as they grew, and on the degreeof canopy cover resulting from the pruning regimeadopted. Canopy storage capacity was determined us-ing the envelope method of Leyton et al. (1967), fromthe negative intercepts of linear regressions betweenthroughfall and gross rainfall (with a pre-defined slopeset to 1− pt).

There is an inevitable degree of subjectivism in-volved in the envelope approach (Rowe, 1983), whonoted that seasonal variations, rainfall intensities andwind speeds had a marked effect on determiningScusing the Leyton et al. (1967) method. Data from172 rainfall events recorded at various stages dur-ing the experiment were used to deriveSc values ofbetween 0.71 and 0.93 mm. In general, as the treecanopies expanded,Sc increased. Pruning reducedSc, but to varying degrees, as might be expectedfrom removing either the lowermost branches orfoliage higher up the tree. Variation inSc also re-flected monthly and seasonal variations in the rainfallintensity.

Fig. 3. Changes in the fractional cover,c, from theG. robustacanopies in the sole tree (thick line) and intercropped (thin line) treatments,estimated from the formula from Fig. 2. Arrows denote times at which the canopies were pruned. Bars show weekly rainfall during thelong (February–July) and short (October–January) rainy seasons.

Trunk storage capacities for smooth barked Aus-tralian rainforest tree species likeGrevilleawere foundto be much lower than rough barked equivalents (Her-witz, 1985), suggesting that stemflow from theGrevil-lea in our experiment might be significant. Due to theobserved variability in stemflow from trees of differentsizes,St andpt were derived following the procedureof Lloyd et al. (1988). Separate linear regressions ofstemflow against gross rainfall were determined for 42trees. The averages of the intercepts (0.185±0.03 mm)and of the slopes (0.026± 0.007) were taken as esti-mates ofSt andpt, respectively. Monthly values of thecanopy cover,c, were calculated using values ofAp,as shown in Fig. 3.

3. Results and discussion

Fig. 3 shows the monthly changes in fractional treecanopy cover,c, in the Td and CTd plots during the ex-periment. After 21

4 years of growth after planting, treecanopy cover,c, in the Td and CTd plots in January1994 was 0.27 and 0.18 respectively. The values ofcin the CTd plots reflect the smaller tree size resultingfrom earlier resource competition between trees andcrops after establishment (Lott et al., 1997). Remov-ing the lowermost 1 m of canopy at this point reducedc to 0.16 and 0.11 in the Td and CTd treatments, re-spectively.


Table 3Comparison of gross rainfall,Pg, canopy throughfallTf , and stemflow,Sf , and rainfall interception,I, in the agroforestry (CTd) and soletree (Td) plots, for 126 rainfall events between November 1994 and June 1997

Parameters Units Sole tree plots Agroforestry (tree+ crop) plots

Area averagea By base of treesb Between treesc Area average By base of trees Between trees

Pg mm 1583 1583 1583 1583 1583 1583Tf mm 1400 1338 1444 1409 1379 1452

(%) (88.4) (84.5) (91.2) (89.0) (87.1) (91.7)Sf mm 11 22 – 10 19 –

(%) (0.7) (1.4) – (0.6) (1.2) –Id mm 172 223 139 164 185 131

(%) (10.9) (14.1) (8.9) (10.4) (11.7) (8.3)

a Tf , Sf and I expressed per 12 m2 ground area occupied per tree. Values of throughfall are means of interception gauges weightedaccording to ground areas they represent.

b Means of raingauges closest to the base of the trees (0.3 m) in each treatment.c Means of raingauges furthest from the trees (2.5 m) in each treatment.d Interception,I, defined asPg − Tf − Sf .

Canopy cover increased steadily following prun-ing, throughout the dry season, before increasingrapidly in both treatments following the onset of the1994–95 short rains. Both treatments were prunedagain, between the rainy seasons, reducingc from0.45 to 0.29 and from 0.40 to 0.24 in the Td and CTdtreatments, respectively. The final pruning, in Novem-ber 1996, reduced the canopy size by approximately85%, and hence reduced the canopy cover,c, from thelargest values observed during the study (Td = 0.54;CTd = 0.46) to values similar to those observed dur-ing establishment (0.02 for both treatments). Trees inboth treatments recovered quickly, exhibiting valuesof c of ∼0.21 by the end of the experiment.

Table 3 summarises measurements of rainfall in-terception, I, made between November 1994 andthe end of the experiment in June 1997. During thisperiod, variation in both storm intensity and dura-tion, as well as in the degree of canopy cover ledto a wide range of interception values for individualrainfall events. At just over 10%, rainfall interceptionwas similar to values of 11% reported for a sparseMediterraneanEucalyptusplantation (Valente et al.,1997), montane stands ofP. kesiyaof varying densi-ties (from 8 to 13%, Veracion and Lopez, 1976), andmontane stands of bamboo and cypress in Kenya (be-tween 10 and 15%, Pereira, 1973). However, rainfallinterception in theGrevillea/maize system was muchlower than values reported for both Ugandan mon-tane forest (∼35%, Hopkins, 1960), or multi-storeyagroforestry systems in Costa Rica (>30%, Imbach et

al., 1989), where the vegetation in these other studieswas considerably more dense.

Significant spatial variation in throughfall was ob-served below the canopy, e.g. in the 1994–95 shortrains shown in Fig. 4, as was reported by previous au-thors (Lloyd et al., 1988; Asdak et al., 1998a). Greaterinterception was observed in both treatments, at po-sitions closest to the trees (0.3 m), as compared withboth the area average and the value at 2.5 m fromthe trees (i.e. midway between four trees, see Fig. 1),confirming the strong dependence of throughfall onthe degree of canopy cover as well as the positionof the gauge with respect to the tree trunk (Eschner,1967).

Stemflow volumes were influenced by the individ-ual tree size, i.e. canopy cover and trunk diameter, andled to large variation inSf between trees. The arealaverage contribution to interception by stemflow wassmall, at∼0.7%, as is generally the case (Jackson,1971; Dykes, 1997). However, it is worth noting thatpartitioning of rainfall between throughfall, canopystorage and stemflow can lead to significant spatialheterogeneity in water distribution below the canopy.Although for the purposes of this paper, stemflow wascalculated on the basis of the 12 m2 area occupied byeach tree, field observations confirmed that stemflowconcentrated water into a small area around the baseof a tree, a process described as rainwater funnelling(Herwitz, 1986, 1987) and reported in other studies(Jordan, 1978; Prebble and Stirk, 1980; Bellot andEscarre, 1998).


Fig. 4. An example of the spatial variation in throughfall below the tree canopy, from the 1994–95 short rains. Gross rainfall (Pg) iscompared with: the areal average throughfall over the 12 m2 occupied by each tree (see Fig. 1), and throughfall recorded by the base ofthe tree (0.3 m) and between the trees (2.5 m). Also shown are the monthly increases in fractional cover,c, and daily rainfall).

3.1. Comparisons between measured and modelledinterception

In order to compare measured rainfall interceptionloss under continually varying canopy cover with thatestimated using the revised Gash model, 4 periods dur-ing the experiment were chosen, and examined sepa-rately. The first period started soon after the start of the1994–95 short rains (see Fig. 3), on 1 November 1994and ended just before the tree canopies were prunedon 10 March 1995. The second period started just af-ter pruning, on 15 March 1995 and finished at the endof November, 1995. The fourth interval covered theend of the 1996 long rains, (May and June 1996) aperiod when tree canopy cover was at a maximum, andwas used to compare interception with that recordedduring the final period, which started just after the

tree canopies had been severely pruned (4 November1996) and lasted until the end of the experiment (2June 1997).

The comparisons between monthly observed andmodelled interception losses are shown in Table 4.Initial estimates from the model where the long term(1991–1993) value ofR was used, underestimated theoverall measured interception by 33 mm (20%), withmore than 50% of this discrepancy occurring duringthe first period studied. It is possible that as the net-work of interception gauges was not augmented untilearly 1995, part of this discrepancy may be accountedfor, by the errors in the measured interception occur-ring between December 1994 and February 1995.

Moderate pruning of the tree canopy cover halfwaythrough March 1995, by removing the lowermost 1 mof foliage, caused a slight reduction in both measured


Table 4Comparison of gross rainfall,Pg, with measured and modelled interception by theGrevillea canopies in the sole tree (Td) plots over fourperiods throughout the experiment

Pg Measured Interception estimated by Gash modelinterceptiona

Using long-term mean value ofRb Using monthly meanRm values

mm mm (%) mm (%) mm (%)

Period 1 — before moderate pruningNovember 94 317 22 (6.9) 21 (6.6) 27 (8.5)December 94 144 20 (13.9) 14 (9.7) 17 (11.8)January 95 38 7 (18.4) 4 (10.5) 4 (10.5)February 95 77 14 (18.2) 10 (13.0) 9 (11.7)March 95c 81 11 (13.6) 8 (9.9) 8 (9.9)

657 74 (11.3) 57 (8.7) 65 (9.9)

Period 2 — following moderate pruningMarch 95 56 5 (8.9) 6 (10.7) 6 (10.7)April 95 103 10 (9.7) 9 (8.7) 9 (8.7)May 95 32 4 (12.5) 4 (12.5) 5 (15.6)August 95 13 0 (0.0) 2 (15.4) 3 (23.1)September 95 6 0 (0.0) 1 (16.7) 1 (16.7)October 95 48 9 (18.8) 4 (8.3) 4 (8.3)November 95 82 17 (20.7) 9 (11.0) 13 (15.9)

340 45 (13.2) 35 (10.3) 41 (12.1)

Period 3 — before severe pruningMay 96 57 9 (15.8) 10 (17.5) 12 (21.1)June 96 017 7 (41.2) 4 (23.5) 6 (35.3)

74 16 (21.6) 14 (18.9) 18 (24.3)

Period 4 — following severe pruningNovember 96 156 0 (0.0) 2 (1.3) 3 (1.9)January 97 3 0 (0.0) 0 (0.0) 0 (0.0)March 97 58 3 (6.9) 1 (1.7) 2 (3.4)April 97 229 14 (6.1) 13 (5.7) 16 (7.0)May 97 66 9 (13.6) 6 (9.1) 9 (13.6)

512 26 (5.1) 22 (4.3) 30 (5.9)

Overall totals1583 161 (10.2) 128 (8.1) 154 (9.7)

a Mean interception per 12 m2 ground area occupied per tree, including stemflow values.b R is the mean rainfall rate (2.28 mm h−1) recorded between 1991 and 1993, before measurements commenced. Monthly rainfall rates,

Rm were calculated for each month during the experiment in which measurements were made (see text for details).c As pruning occurred mid-way through March, two rainfall totals are provided for this month: the first from 1 March to10 March inc.,

and the second from 15 March to 31 March inc.

and modelled interception, but by the end of Period2 (Nov 95) the tree canopies had re-grown and in-terception losses were larger. More severe pruning inNovember 1996, where 85% of the canopy was re-moved, showed a more marked effect on interception.High interception losses observed before pruning inPeriod 3, were reduced to zero (measured) or minimal(modelled) interception following pruning.

On a month-to-month basis, the model tended tounder- or over-estimate interception between 1 and

8 mm, and as several of these months showed meanrainfall rates (Rm) that varied from the long termaverage used (2.28 mm h−1 ), the model was re-runentering (Rm) in each case. The results of this estima-tion are also shown in Table 4. This second modellingrun underestimated the total observed interception byonly 7 mm (∼4%). Although initially (Nov 94), mod-elled interception was greater than that recorded, themodel adequately estimated interception, on a monthlytimescale, to within 3–5 mm.


The fact that the model still underestimated inter-ception losses might be due in part to consideringrainfall as one storm event per day, when in fact themonthly mean number of ‘discrete’ rainfall events perday varied between 1.0 and 2.2 over the period studied.One might expect a higher interception loss from sev-eral small storms in a day than from one larger event(Jackson, 1971; Bruijnzeel and Wiersum, 1987), andthat isolated short periods of rain (1–3 h) occurringwithin a ‘single’ prolonged event might be expectedto result in significant errors in the interception esti-mated by the Gash analytical model (Mulder, 1985;Dykes, 1997).

The calculations showed that relatively smallamounts of water were lost through evaporation eitherthe canopy wetted up (4%) or during storms whenit is too small for canopy saturation to occur (3%).By far the largest evaporative losses occurred eitherwhile the canopy remained saturated (54%) or as itdried out following rainfall (30%). Evaporation from

Fig. 5. (a) Modelled interception losses byG. robustacanopies in the intercropped (CTd, ) and sole-tree (Td, ) treatments. Also shownare monthly estimates of the gap fraction (space between tree canopies) in the CTd ( ) and Td ( ) treatments. Arrows denote times atwhich the canopies were pruned.(b) Monthly gross rainfall amounts (bars) shown for comparison.

the trunks accounted for a little under 9% of the totalevaporative loss.

Given that this revised overall model estimatewas close to the measured cumulative interception,and that it covered a period where the fractionalcanopy cover,c, ranged between 0.02 and 0.54 (themaximum observed), it was considered acceptableto use the model to estimate cumulative intercep-tion over the period before interception measure-ments commenced, i.e. from October 1991 to Oc-tober 1994. During this period, it was impossibleto determine canopy storage capacity (Sc) values,and therefore the earliest recorded measurement(November–December 1994) was substituted, al-though this may have lead to a slight over-estimateof interception in the early stages of the exper-iment. As before, monthly values of the canopycover, c, were determined from projected area val-ues (see Fig. 3). Monthly mean rainfall rates (Rm)were calculated from hourly rainfall data, and ranged


between 0.5 and 5.7 mm h−1. The results are shown inFig. 5.

Overall, between October 1991 and June 1997, thecumulative gross rainfall was 4106 mm and the cu-mulative modelled interception by the trees grownwith (CTd) and without (Td) an understorey of maizewas 236 mm (5.8%) and 286 mm (7.0%), respectively.However, monthly interception losses were generallylow during the first 3 years after the trees were planted,and the cumulative interception up to 1 November1994, was 59 mm (2.6%) and 80 mm (3.6%) for theCTd and Td treatments, respectively, during whichtime viable yields of maize were obtained from theintercropped plots (Lott et al., 1997).

Highest monthly interception losses were recordedduring the 1994–95 short rains, which were signifi-cantly greater than the seasonal average rainfall (seeTable 1). Interception losses decreased after this point,then increased again, following the trends in monthlyrainfall, as well as variation in the free throughfallcoefficients that resulted from pruning managementpractices. Maize yields were substantially reducedfrom this point onwards (see Lott et al., 1997), asa result of competition for water between trees andcrops. Drastic pruning in late 1996 reduced intercep-tion losses as a percentage of gross rainfall to valuessimilar to those observed during the first 3 years afterplanting, with increased maize growth beneath thetrees (data not shown).

The Penman–Monteith equation used to estimatethe evaporation rate is strongly dependent on theboundary layer conductance,ga. When looking atthe effects that thinning had on rainfall interception,Whitehead et al. (1989) assumed thatga was unaf-fected by tree spacing, although it is generally consid-ered to be the most important characteristic of forestcanopies that determines the extent of interceptionlosses (Jarvis and Stewart, 1978). As mentioned ear-lier there are problems associated with determiningdand z0 at wide tree spacings (Jarvis et al., 1976), asoccurred in some agroforestry systems. Therefore it isworth considering how greater temporal variation inga might have affected the model simulations carriedout in this study.

By inverting the Penman–Monteith equation, andignoring the net radiation terma as being negligibleduring rainfall, Teklehaimanot et al. (1991) used anaveraging method to determinega at different spacings

of 10 m tall Sitka spruce trees. As the gap between thetree canopies increased from 0 to 2 m,ga increasedfrom ∼0.6 to 2.1 m3 s−1.

In the experiment reported here, a comparableincrease in gap size occurred when the trees wereseverely pruned, moving from a situation where ad-jacent tree canopies overlapped to a gap of between2 and 3 m between canopy edges. However, unlike astudy dealing with similar sized trees at different spac-ings, in this experiment as gap size increased it wasat the expense of canopy size, i.e. through pruning.

It is likely that a large increase inga would haveoccurred immediately after severe pruning took place,and the average value ofE used would no longer beappropriate, as subsequent evaporation rates wouldbe significantly higher. This would have resultedin under-estimated interception losses in the periodimmediately after severe pruning. However, giventhat the aim of the modelling exercise was to sim-ulate cumulative interception from planting throughto a harvestable timber stage, and that even a largeunder-estimation during periods where interceptionlosses were small (between 1.3 and 3.4%, see Table4) would be negligible in terms of the overall fractionof incoming rainfall, it is convenient to accept themodel as it stands.

As remarked on earlier, agroforestry systems arespatially complex. When looking at how conventionalmethods of determiningga may be inappropriatein some cases, other factors affecting the Penman–Monteith equation may also be worth examining.An actively transpiring understorey crop may leadto a lower vapour pressure deficit below and aroundthe tree canopy, affecting evaporation rates of rain-fall intercepted by the tree canopy, when comparedwith a woodlot with nothing but crusted bare soilbelow. Clearly there is a need for more research intointerception processes within agroforestry systemsand their interaction both with the other componentsof the water balance, and with canopy manipulationthrough pruning.

4. Concluding remarks

The results from this study indicate that intercep-tion losses from relatively closely spaced agroforestrytrees depend on both the degree of canopy cover and


the variation in rainfall rate throughout each year. Ataround 10% of gross rainfall lost through interceptionbetween 1994 and 1997 (when the tree canopies werelarge), this compares with estimated savings of around15% of rainfall at the same site due to reductions insoil evaporation (Es) due to shading by the tree canopy(Wallace et al., 1999).

However, an overall assessment of whether the agro-forestry system really does use rainfall more effec-tively than a combination of woodlots and open fieldscan only be made when all components of the waterbalance are compared. This includes surface runoff,tree and crop transpiration and changes in soil waterstorage. Some of these processes have already beendescribed (Jackson and Wallace, 1999a, b; Jackson etal., 1999, Smith et al., 1999) and an overall assessmentwill appear in forthcoming papers.

The application of long term (historical) valuesof R derived from rainfall data from earlier years,caused the model to substantially under-estimaterainfall interception losses, whereas the incorporationof monthly (current) variation in rainfall rate intothe model, substantially improved the interceptionestimate. In this study, hourly rainfall data were avail-able with which to calculate monthly values forR.However, similar data necessary to determine howR varies from month-to-month in other locations, orfrom earlier periods may be scarce.

Further examination of rainfall records might re-veal whether monthly variations in mean rainfall ratemight be predicted, e.g. if higher values ofR mightbe expected during months of heavy rainfall, or atthe onset of the seasonal rains, whereas low valuesmight be expected during drier months with smallerstorms occurring with much lower frequencies. Giventhat pruning tends to be implemented at the onsetof the rains (reduced ground cover) and that the treecanopy recovers late in the rainy season (increasedcover), the interaction betweenc andR could then beinvestigated more fully.

It is concluded that the revised version of the Gashmodel (Gash et al., 1995) is suitable to model in-terception in sparse agroforestry systems over peri-ods where both rainfall and canopy characteristics donot alter significantly. Over longer periods, cautionshould be taken in applying long term average values,and modelling these periods in discrete intervals ispreferred.

Acknowledgements

This publication is an output from a research project(R4853 and R6364) funded by the Forestry ResearchProgramme of the Department for International De-velopment of the UK. However the Department forInternational Development can accept no responsibil-ity for any information provided or views expressed.The water balance research project at Machakos,Kenya was carried out with the support and collab-oration of ICRAF. The assistance of Professor C.K.Ong, Mr Boniface Muli, and Mr Peter Muia Mbathafrom ICRAF is greatly appreciated. The author is alsograteful to Dr James Lott from the University of Not-tingham, who provided the leaf area index data, and toProf. Jim Wallace and Mr Colin Lloyd of the Instituteof Hydrology who provided advice on the analysisand modelling of the rainfall interception data.

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