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PRECIPITATION PARTITIONING BY A NORTHERN HARDWOOD STAND, SOUTHERN ONTARIO, CANADA: PROCESSES AND VARIABUIïY
Darryl E. Carlyle-Moses
- - - - - - - - - - - - - - - - - - - - - - -
A thesis submitted in coafoxmity with the requirements for the degree of Master of Science Graduate Department of Geography
University of Toronto
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The author would U e to express his gratitude to the following people who have contributed the fruition of this thesis: Dr. A.& Price; Dr. L.E. Band; Dr. K. Deufto: Ms. R. AntuXies: ML K. Turner and Dr- G. Thaler. Speciai t h d s to m y mother and grandfather.
M e M e (Mike) Moses 1917 - 1996
whose love and courage wlll never be forgotten. this thesis is respectifidly dedlcated.
Precipitation Partitionfng By A Northern Hardwood Stand, Southern Ontario, Canada: Processes and Variabtiity
Darry1 E. Carlyle-Moses
Degree of Master of Science Graduate Department of Geography, University of Toronto
1996
Measurernents of gross precipitation, throughfâil, stemflow and Mer
interception loss in a northern hardwood stand in Southern Ontario were made during
the siimmer of 1995. Equations were derived for estimating the quantitative
importance of each of the foiiowing water fluxes under summer canopy conditions:
Throughfi* s tedow. canopy interception loss and total interception loss (canopy
interception loss + litter interception loss) and net precipitation entering the soïi.
Gross precipitation depth and intensity had a sïgniûcant effect (a = 0.05) on di water
fluxes while event duration, proportion of canopy openings and wind velocity above
the canopy dtd not.
The importance of ritter interception, whfch has been largely ignored in
previous interception stucires , was found to be. significant- Interception loss fkom the
litter layer was -6 % of gross precipitation and -25 % of total interception loss kom
the stand. Throughfiall, s t e d o w and canopy interception loss variabiiity was
determined and possible factors infiuencing the variability of these fluxes are
examiued.
TabIe of Contents
Acknowledgments .............................................................................. ....................................................................................... Dedication
Abstract .......................................................................................... ................................................................ List of Figures .......... .....
List of Tables ................................................................................... List of Plates .................................... .. .............................................
.................................................................................. 1: Introduction
1.1 Background ....................................................................... ............................................... 1.1.1 General Water Balance
....................... 1.1.2 Canopy Interception and Interception Loss
.................... ....................*....-.-- 1.1.3 Throughfall ....... 1.1.4 S t e d o w .............................................................
......................... 1.1.5 Litter Interception and Interception Loss
1.1.6 Total Interception Studies .......................................... . .
1.2 Objectives ....................................................................... . .
1.3 Definihon of Terms ............................................................. 1.4 Study Area .......................................................................
........................... 1.4.1 Geographic Location ......... .......... ............................................................ 1 -4.2 Vegetation
...................... 1.4.3 Climatology ...... .............. .................................................. 1.4.4 Hydrologie S etting
......................................... 1.4.5 Elevation and Topography
1.5 Methodology ............. .................. ................................ ....................................................................... 1.6 Limitations
.............................................................. ......... 2: Throughfall ..... 2.1 Throughfall and Gross Precipitation ................ .... ......................
vii
TabIe of Contents Continued
2.2 Additional Factors Iiifluencing Throughfd at the Plot Scale ..............
2.3 Throughfall as a Percent of Gross Precipitation .............................
.................................................. 2-4 Event Throughfall Variability
2.5 Gauge Requirements .......................................................... ....... ..... 2.6 Factors Influencing ThroughfaU Spatial Variability .......
.................................................................................... S ternflow
3.1 Stemflow and Gross Precipitation .............................................
3 -2 Additional Factors Innuencing Site S temflo w ............. ,.. ............. 3 -3 S temflow as a Percent of Gross Precipitation ...............................
. . 3 -4 S temflow Variability .............................................................
Interception Loss .......................... .... .......................................... .......................... 4.1 Canop y Interception Loss and Gross Precipitation
.................. 4.2 Additional Factors Influencing Canopy htercep tion Loss
4.3 Canopy Interception Loss as a Percent of Gross Recipitation ............. 4.4 Litter Interception Loss .. ................ ... ....................................
... 4.5 Litter Interception as a Percent of Gross Precipitation and Throug hfall
....... ........ ......... 4.6 Total Interception Loss and Net Precipitation .... ., ,.
..................... 4.7 Additional Factors Influencing Total Interception Loss
4.8 Total Interception Loss as a Percent of Gross Precipitaàon ................ 4.9 Net Precipitation ........................................ ,... ....................
Conclusion ...... ...,,. ......... .. ....................................................... Bibliography ........ ,...,.,. ................................................................. Appendices ............. .,. ....................................................................
List of Figures
Figure # Page #
The basin hydrologic cycle ....................................... Relationship between gross precipitation, interception and the terresuiai portion of the hydrologic cycle,, ................ Geographic location of study area ........-.......,,............. DBH frequency distribution of tree species within the study area.. . . . . . , . . . .. . . ,. . , . . . . . . . . . . . .. . . , . . . . . . . . . . . -. . . . -. .. . . . .. . . . . -O
Location of study site withiu the Credit River watershe d...., Distribution of throughfalI gauges. ....,,.. .,.... .. .-............ Gross precipitation and corresponding throughfall depths.. . Throughfall (mm) as a function of gross precipitation (mm) during the entire study period ........... ,...... .... ..- ........... ThroughfaU (mm) as a function of gross precipimion (mm) under summer foliage conditions ............................ .-.. A cornparison of the Helvey and Patric (1965) regression mode1 for throughfall with the one developed at Erindale .... Variation of predictive summer-long ùiroughfall inputs (mm) and number of events ......................,,.,.................-- Rt/R ratio as a function of summer-long gross precipitation and nurnber of events, .,... . ... .., .......-. .. ,. -.... -..... ..... .. Variation in slope of Rt linear regression equations as a function of gross precipitation .............-.-........ , ...... ThroughfaIi (55) as a function of gross precipitation during the entire study period ................................. . ...,...... Throughfall (%) as a fimction of gross precipitation under . . sumrner foliage cond~tions,.,.,.. ...... , ....................... .. 'ïhroughfall coefficient of variation as a function of throughfall (%) under summer canop y conditions ... . . ... .. . . Rt Max / Rt Min ratio as a function of gross precipitation (mm) under summer canop y conditions.. ...,... .. .,... . . - .- .- -. Summer throughfall (5%) as a function of distance from tree stem,., ... ...... .. ....... . ..... .. . .... ..... ... .... . ... .. ... Exponential rise to maximum semivariogram curves for 6 events during the summer portion of the study .-............,. Experimental semivariogram of t@oughfall(%) as a function of distance between gauges for a 1.5 mm event... . Experimental semivariogram of throughf' (%) as a function of distance between gauges for a 12.4 mm even t... Stemflow (mm) as a function of p s s precipitation (mm) under sumrner foiiage conditions .... - .,.. - ..... ,,.... ...... Variation of predictive summer-long s t e d o w inputs (mm) as a function of varying gross precipitation (mm) and ....... Rs / R ratio as a function of summer-long gross precipitation (mm) and number of events ....................... ~temflow (%) -as a function of gross precipitation depth (mm) under summer canopy conditions ............................ 96
List of Figures Continued
Page # Figure #
3.5
3.6
S t e d o w coefficient of variation as a function of gross precipitation (mm) under sumrner foliage conditions ........-... Variation of mean stemflow production (IYïree) as a function of tree species, tree condition and gross precipit&on depth (mm) under summer canop y conditions ........................... Summer - long sternflow production (L / Tree) as a fimction of DBH (cm) (all 20 sampled trees included) ..................... Summer - long stemflow production (L / Tree) as a function of DBH of Amencan beech stems ................................. Sumrner - Iong stemfiow production (L / Tree) as a function
of DBH of sugar maple stems ...................................... Siimmer - long stexnflow production (L / Tree) as a function of DBH of red oak stems ........................................... Representative proportion of to ta1 s t e d o w production generated by dominate eree species within the stand ............. Canop y interception loss (mm) as a function of gross precipitation (mm) under summer foliage conditions ... ......... Variation of predictive summer - long canopy interception loss (mm) as a fmction of varying gross precipitation (mm) and number of events ................................................ Ec / R ratio as a fimction of gross precipitation (mm) and number of events ............. .., ..................................... Canopy interception loss (%) as a function of gross precipitation (mm) under summer foliage conditions ............ Litter interception Ioss (mm) as a function of gross precipitation (mm) under summer foliage conditions ........,.,. Litter interception loss (%) as a function of gross precipitation (mm) under summer foliage conditions..,., ....... Total interception loss (mm) as a func tion of gross precipitation (mm) under summer foliage conditions ... ......... Total interception loss (%) as a function of gross precipitation (mm) under summer foliage conditions ..........,. Net precipitation (mm) as a function of gross precipitation (mm) under su-r foliage conditions ............ Net precipitation (%) as a function of gross precipitation (mm) under summer foliage conditions ............ Variation of predictive summer - long net precipitation (mm) as a function of varying gross precipitation (mm) and number of events.. .............................................. Rn / R ratio as a function of gross precipitation (mm) and number of events ............. ,., ....-...... .... ......................
List of Tables
Table # Page #
........... Quantitative characteristics of tree species witbin the plot Throughfall charactenstics ................................................ Examples of 1Uiea.r regression equations relating throughfall with gross precipitation in eastern North America .......................... Gross precipitation and throughfd values with corresponding rainfall intensity, raiddl duration, proportion of canopy openings and wind velocity values. ................................................ Step - wise regression for determining possible factors effecting throughfdl during the entire study period ............... .. .......... S tep - wise regression for determinhg possible factors effecting throughfall under summer canop y conditions. ........................ Number of gauges requires to keep standard error of mean throughfall under 5% of the mean using the Helvey and Pamc (1965) method ............................................................. Number of gauges requks to keep standard enor of mean throughfall under 5% of the mean using the Kimmings (1973) method ............................................................. Throughfall as function of species of nearest stem .................... Examples of extrerne spatial variability of throughfall values during the study penod .................................................. Exponential and linear semivariance models of throughfall catch as a function of distance fkom another gauge for 6 selected storms.
S .
S temflow charactensucs .................................................. Lmear regression equations for predictïng summer s t e d o w fiom gross precipitation inputs for eastem hardwood stands ............... Step - wise regression for determining possible factors effecting stemflow under surnmer canopy conditions.. ......................... Gross precipitation depth (mm) at which various stemfiow production values were achieved ........................................ Total site s t e d o w contribution as a function of species, frequency of stems and stemfiow production rates for sugar maple, red oak American beech and red maple ........................................... S tep - wise regression for detennining possible factors e f f e c ~ g canopy interception loss under siunmer canopy conditions .......... S tep - wise regression for determining possible factors effecting total interception loss under siirnmer canopy conditions .............
List of PIates
Plate #
Representirve portion of the Litter layer at the E ~ d a l e site ........... 20 Canadian Atmospheric Environment standard tipping rain gauge
...... employed in determînhg raùifd intensity and duration vaiues 26 Portion of subplot with fransect of 5 gauges in background ......... 28
............... Stemflow coliector at 1 of 20 trees in the study area ,,. 29 One of 120 litter pans situated within the study plot .................. 30
1: INTRODUCTION 1.1 Background
1.1.1 General Water Bnirince
The global hydrologie cycle is a closed system. However. at any srnalier scale
(e-g. basin scale) the water balance becomes an open system with inputs and outputs
(Figure 1.1). The water budget of an area is the baiance between the input of water
(from rainfall, snow melt, streamfiow,etc.) the oufflow of water (by means of
. evapotranspiration. overIand flow. subsurface flow and Stream fiow) and the
subsequent changes in the storage of that water (soiL rnoisture, groundwater and
surface storage). Thus, the water budget of a region may be written as a simple mass
bhnce equaffon:
(Eq. 1.1)
where dS equals changes in storage, dt equals change in time, 1 represents inputs
and Q represents outputs for the time period dt (Bras. 1990).
Often hydrologists and other pracfftloners require information concerning the
partïtioniag of incoming precipitation and the relative importance of each input,
transfer mechanism. storage and output. The water balance. encompassing these
components. may be expressed as:
(Eq. 13)
where R is gross precipitation, Ei represents total interception loss. AET represents
actual evapotranspiration (other than interception loss), OF is overland flow, w M e
EYAPO- P E a P r r A n o w 'IRARSPIRATTON 1
- -
-
-
L
Figure 1.1: The basin hydrologie cycie. Components in boxes represent storage.
) OYERLMD FLOW
S M , AGWS. and GTKR represent change in sou moisture. change in groundwater
storage. and groundwater recharge respective& @unne and Leopold. 1978).
Figure 1. L shows that not aZ1 precipitation actuaily W*ates into the mineral
soil layer- Some precipitation becomes owerlând flow wMe some is intercepted by the
vegetation canopy or litter iayer and is evaporated back to the atmosphere
(represenang an output in the water balance of a Land area). It is important to note
that interception is the process by which the canopy or Mer hyer intervenes with the
intercepted water that is retained by the vegetatîve surface and is subsequently
evaporated back to the atmosphere- Figure 1.2 illustrates the relationship between
precipitation. interception and the terrestxiaI portion of the hydrolIogic cycle
where R represents gross rainfall, Rn is net rainfall, Ei equals total interception ioss.
Ec and El are canopy interception and limer interception respectively, Rt represents
throughfafl and Rs equals stemflow (Dunne and Leopid. 1978: Dingrnan. 1993).
Vegetation and other obstructio~ that intercept all or a portion of the
precipitation are of partlcular importance ta hydrologists since net precfpitation plays
a significant role in detennfning soil moisture. -off and groundwater recharge.
Aïthough goss precipitation (espedally convectional precipitation) may be quite
spatialLy variable Wuff and Shipp. 1969). the cornpiex structure wittiiri forest stands
results in a greater degree of variability of net precipitation (Kittredge. 1948: Helvey
and Patric. 1965; Tekle-anot et a L 199 1: Keiliher et al-. 1992; Loustau et
aL. 1992:). Bouten et aL(1992) found that a how1edge of throughfall variabiüty can
provide insight lnto sofl moisture dynamics.
Figure 1.2: RelaUonship between gross precipitatton. interception and the terrestriai portion of the hydrologie regime, For expIanation of terms see tee.
ThroughfM and stemflow variabiItty may also be of importance to ecologists
and other researchers interested in the variability of mer decay, mîcrobiai activity,
piant distribution and nument input (Bellot and Escarre, 1991; Strojan et al,, 1987;
Taylor and Parkinson. 1988: Williams et al., 1990; Frego and Carleton. 1995).
1.1.2 Canopy Interception and Interception Loss
Canopy interception has been studied by a number of investigators and
although their findings are site specifk. interesting trends have developeà. With the
broad Ieaves of deciduous trees one could assume that these eees wouid intercept
more rainfali on average than their coder counterparts. However. by comparing 10
investigations conducted in deciduous forests wlth 1 1 conducted in coniferous
forests, Dunne and Leopold (1978) concluded that deciduous forests intercepted an
average of -13 percent of gross precipitation, wMe conifers averaged -22 percent.
Other studies not included in the work of Diinne and Leopold (1978) also support
these 5dings. StogsdLLl et ai- (1989) found th& interception averaged -26 percent
for a species of pine (Ptnus taeda) in South Carolina while Carlisle et ai. (1965) found
that interception by a species of oak (Quercus petraea) was 13.1 percent. It should be
noted that winter interception of rainfdl by deciduous trees is much lower than
coniferous trees since deciduous trees lose their Ieaves. Leaf loss may result in a
reductlon of interception from -13 percent to -8 percent in some deciduous species
and thus the total annual interception capacity of conlferous trees is much greater
than deciduous trees (CarLisle et al.. 1965). Although canopy interception loss is often
reported as a percent of gross precipitation, this number is meanfngless unless both
the number of events and the total gross precipitation amount are given (Helvey and
Patric. 1965).
Canopy interception has been investigated in forest stands consisting of
deciduous species such as oak (Hortoa. 19 19; Rogerson, 1960; Stuart, 1962; Carlisle
et aL.1966: Lawson, 1967; Dolman,1987). oak - maple (Storey, 1953). maple (Horton,
19 19), hickory (Horton, 19 191, hickory - poplar (Black, 1957), beech (Horton, 19 19;
Voigt, 1960: Robson et al-, 19941, beech - maple - elm [Gilbert, 1953) and beech - birch
- maple (Leonard, 1961); however, forests with a maple - oak - beech composition,
such as the stand at Erindale, have not been represented in the literature.
lntuitivelygnd from a review o f al l available l i terature, stemf low is the
dominant fonn of understory precipitation. Mahendrappa (1990) found that
throughfàli is greater in deciduous stands than for conlfers with maple (Acer spp.),
white birch (Betula papynrera) and aspen (Populus grandidentata) having mean
annual throuwall values of 77, 83, and 80 percent of gross precipitation.
respectively. while the values for white pine (pinus sfrobus) red pine (Pinus reshosa).
white spruce (Picea glauca), Baisam fir mies bafsarnea) red spruce (Picea rubens)
were reported as 65, 69, 73, 75, and 76 percent of gross precipitation, respectively.
Throughfhil under deciduous stands in f d leaf is typically - 80 percent of seasonal
gross precipitation (Helvey and Pamc, 1965) -
Various factors are thought to influence what proportion of precipitation will be
allocated to each component of the hydrologie cycle (including throughfall] withiu a
forest environment - These factors include: gross precipitation amount, rainfall
intensity, duration of the precipitation event, stand composition characteristics (e.g.
species, DBH distribution, height of vegetation cover, stand density), season,
antecedent conditions and variations in wind velocity and wlnd direction
(Horton, 19 19: Helvey, 1967; Czamowskl and Olszewski, 1 968; Delfs, 1967;
Strarosolszky and Stelczer, 198 7)-
1.1.4 Stemfiow
S t e d o w is often disregarded by investigators due to measwement difflculties
and cost (Helvey and Patrïc, 1965; Ward and Robinson, 1990). The exchsion of
stemfiow volume in interception studies involving coder stands may not
overestimate canopy interception loss vaiues signlficantly since research conducted
by investigators such as DeEs (1967) and Price et al. (1996) show that annual
stemflow volume is - 1 percent of gross precipitation in stands of both N o m y
spruce (P icea abies) and black spruce (P icea niuriana). However, other researchers
have found that a significant proportion of lncoming mfddI can be partitioned into
s t e d o w within conifer stands, Helvey (1967). for example, reported annual
stemflow values of 2 - 9 percent (depending on stand age) for white pine Pinus
strobus) while Ford and Deans ( 1 9 7 8 ) found that - 27 percent of annual gross
precipitation was partitioned into stemfïow witnfn a Sitka spruce (Picea sifchensts)
plantation.
On average stemfiow is less s i w c a n t volumetrical& within conifer stands
than withfn deciduous stands (Lawson, 1967; Ward and Robinson, 1990)- Delfs (1 967)
found that stemfïow was - 17 percent of arinual precipitation for beech (Fagus
sylvafica) whiie Leonard (1961) reported annuai stemflow as -5 percent for an
American beech (Fagus grczndtJioiia) - sugar mapk Meer sacchanrm) stand in New
Hampshire and Mahendrappa (1990) found that annual s t edow was - 6, 6, and 4
percent of gross precipitation for maple (Acer spp-) aspen (Populus grandidentata) and
white birch (Betula papyniJera) respectively in an Acaàian forest. Thus, the exclusion
of stemflow measurernents during investigations involving decidugus trees will, on
average, result in greater error in estimating canopy interception loss values,
The amount of stemflow generated during a storm event is Iarreiy dependent
on the depth of gross precipitation (Kelvey and Pamc, 1965) bark roughness
(KIttredge, 1948: Helvey and Patric, 1965) and canopy architecture (Price et ai., 1996).
A certain depth of gross precipitation is required to initiate stemfiow (Le when the
storage capacity of the canopy and trunk is satisfkd). Based on the available
Literature the required deptfi to initiate stemflow may be as smaU as 1.3 mm for beech
stands (Voigt.1960: Leonard.1961) whiIe other species with rougher bark and
dlnering canopy architecture require greater depths (Horton, 1919; Black, 1957; Price
et al.. 1996). Kittredge (1948) and Helvey and Patric (1965) have summerized a
number of stemflow investigations. These s r n a r i e s coupled with the results of
Mahendrappa (1990) suggest that the s t e ~ o w - species relationship for deciduous
trees is as follows: beech > aspen > maple > ash > e h > basswood > hemlock > O&
> hickory- It is apparent £kom the proceeding k t that bark roughness does play a
role in s t e d o w production. 7Be smooth bark assocfated with beech and aspen allow
water to move quite easily d o m the bole of the tree whereas the rough bark of oak
and hickory irnpede fiow and allow for greater storage. Price et a' (1996) suggest that
throughfall variabiiity was high in a black spruce stand since water was flowing away
fkom the bole of individual trees to drip zones at the edge of the tree canopy.
Stemflow fluxes for conlfers may be sm-er than those for deciduous trees since the
canopy architecture of black spruce and many other contfers brings water away fi-orn
the tree bole while deciduous stands have a canopy architecture that tends to
concentrate water at individuaï boles-
1.1.5 Litter interception and Interception Loss
Helvey (1964) and Blow (1955) suggest that 2-5 percent of annual rainfidl
evaporates fiom litter in the southem Appalachians wbile litter interception loss
values of 6 - 9 percent were found by Dabral et al.(1963) for a n area of Iridia. LoshaIi
and Singh (1992) generated values of 8 to 11 percent for three litter types in the
Central Himaiayan region wfrile Pradham (1973) int-estigated the kterceptfon of
ralnfall using packed iitter sarnples and obtained values of -17 percent to - 27
percent depending on the species. The values given above are for annuai precipitation
totals. However, interception by iitter layers varies greatiy at the regional and plot
scale due to a number of spatial and temporal Innuences.
Ward and Robinson (1990) state that Iiffle fs known about the importance of
m e r Iayer interception, although it wLLl generally increase with a corresponding
ïnaease in the amount of precipitation- Law intensity, short duration rair&ll wiLi
have iittle or no impact on litter interception since the canopy wLZl intercept most. if
not aïï, incoming precipitation. High intensity and or long duration events wiU
surpass the holding capacfty of the canopy and will result in available water for the
Utter. However* once the holding capacity of the canopy has been reached. the litter
layer wiU intercept a greater portion of low volume and low intensiw raidhll. rather
than high duration. high lntensity rainfall (Dabral et ai.. 19631.
Ttvo main components of litter interception are the amount of precipitatron
reaching the litter and the water holding capacity of the litter- Since ail water
reaching the Utter hyer of a forest must pass through the canopy as either throughfidl
or stemflow, the total interception by the canopy is important. As mentioned above,
the amount of interception by deciduous forests fs Iess thsn conlferous forests. Thus,
the total amount of water available to the iitter. under simihr precipifation events,
would be greater for deciduous forests. especially during periods of leafout when the
canopy cover is at a minimum.
The water holding capacity of litter is dependent on the amount. type and
dryLng rate of the litter. The amount of Mer on the forest fbor varies grea* h m
region to region and at the plot scale. Kittredge (1948) and Helvey (1964) suggest
that the annual Utter productfon in the eastern Umted States ranges fiom -2.400 to
4,400 pounds per acre (-2700 - -4900 kg per ha). Not aII the Utter decomposes by
the next year and as a result the total accumulation weight can be ten times the
annuai deposition (Helvey and Pabric, 1965). Litter decomposition rates vary
according to species, cllniate and biobgicai acttvity- Generlly decomposition rates
are lowest in cooler regions with Iiffle r d ' Voigt et al.(1986) conducted a litter
biomass survey of the world and concluded that Mer varied fkom -2.25 kg/m2 for
boreal forests. -0.75 kglm2 for broad leafed forests to -0. lkglmz for tropical
forests, whiie Shanbs and Olson (196 1) found that for each l0 F (-0-560 C) rfse in
average annual temperature there was an -1 percent increase in "the rate of litter
decomposition- Although one may expect that with increasing decay there should be
less interception by the litter Layer. it has been suggested that the increased
fkagmentation of the decaying litter actu* increases absorption rates (Pitman. 1989)-
At the plot scaie the amount of iitter-faL1 can vary greatiy fkom year to year.
Sykes and Bunce (1970) investigated the fluctuations in mer-fa in a mixed
deciduous wodLand over a period of three years. Their Bndings show seasonal
variations wSth litter-fall (with most new Mer king added during the months of
September - November for northern latitude forests) and that an annual variation
erdsts as weU. The iinnual variation in leaf litter fhiï can be as high as 10 percent for
deciduous forests under natural conditions and thus can Lnfluence the interception
ability of the forest floor on an m u a l bask Spatial vmt ion also eïdsts at the plot
scale. The thichess of the Mer layer can be severaL centimetres in
depressions arrd other areas where the influence of wind fs sign.iflcantly reduced,
wàlle bare ground may be observed at higher regïons. such as halls. of the same
forest stand or where wind rnay be a s-cant factor such as near the forest edge.
The variation in the amount of litter on the ground within a forest stand is so great
that Blow (1955) concluded that forty 0.4 m2 plots were needed to maintain a
sampling error of 5 percent of the total Utter weight (Kelvey and Patric. f965).
Hehrey and Patric (1965) cornpied data on iitter interception in the eastern
United States and the& findings suggest that the deciduous Utter tu the eastem
United States had an average maximum water holding capacity of -220 percent by
dry weight while the average minimum holding capacity for these same forests was
-30 percent by weight. Therefore. the average deciduous iitter in the eastern United
States is able to intercept -190 percent by weight. m e r dryLng curves indicate that
over 50 percent of interception loss occurs withrn the 3 days after the Litter has
reached saturated conditions and that liffle water is lost after 1 O days of dxying.
Since Utter does not always dry completely between precipitation events, and many
events do not wet the iitter completely, knowiedge of the litter &@ng rate is crucial in
d e t e r m g the interception potentiai of a particular litter iayer. Thus, litter
interception is not onty Lnfluenced by the amount of precipitatlon but ako the
fkequency of precipitation events- In addition. Helvey ( 1964) suggests that the drying
of deciduous forest Iitter is more rapid during winter months than for summer
months. During the -ter months there is an increase in air movement wtthin the
forest stand and an increase in the total incorning soIar radiation reaching the forest
floor due to the absence of a leaf canopy.
1.1.6 Total Interception Studits
Hehrey and Patric(1965) indlcated that as of 1965 there had not been a
complete interception study (i.e.,simultaneous canopy and Utter interception Iosses on
a single area) reported. However. two years later Hebey (1967) pubiished a report on
the total interception loss fmm various stands of white pine (Plnus strobus L.). He
concluded that total interception ranged fiom - 15 - -26 percent of gross rainfall with
Utter interception loss accounang for -2 - -4 percent of gross rainfall depending on
the age of the stand. Therefore. the Utter interception component of the totai
interception loss ranged fkom - L3 - - 15 percent.
Although various components of the hydrologfc cycle have been extensive&
studied (e.g. ground water recharge). other components have largely been ignored.
Total interception bss inchdes both canopy interception loss and iitter Iayer
interception loss. Few studies have been devoted to deternilning the 'nydrologic
importance of Utter interception ( Blow. 1955; Curtis. 1960: Bernard. 1963; Dabral et
al.. 1963: Helvey, 1964: Pradhan, 1973:). while fewer have incorporated canopy
interception and Utter interception into the same investigation (Helvey. 1967; LoshaU.
and Sing, 1992).
Research which included both litter interception loss and canopy interception
loss values has done so either in conlferous stands mehrey, 1967) or in deciduous
stands not found in North America (Loshali and Sing, 1992 1. Thus, the need for an
interception study that inchdes both forms of interception loss in an eastem North
American deciduous forest is needed and has been suggested as an important area of
M e r research by HeIvey and Patric (1965).
1.2 Objectives:
The specltic objectives of the study are:
a) To measure the propor-tion of incoming precipitation that is
partitioned into canopy interception loss, litter interception loss.
throughfall. s tedow and net precipitation entering the minera1
soii within a maple-oak-beech s t d .
b) To characterize the size and spatial variabiiity of throughfall and
stedow.
c) To determine the influence that gross precipitation depth, rainfkiü
intensity, event duration, changes in canopy cover,and wuid speed has
on throughfall. stemfiow. canopy interception loss and total interception
loss quantities -
d) To develop a series of equations for predicting throughfall. stemflow,
canopy interception Ioss, litter interception foss, total interception Ioss
and net precipitation within a maple-oak-beech stand
Interception: The process of by which the canopy or litter layer intervenes with the direct en- of gross precipitation lnto the mineral sou-
Cross Precipitation: Precipitation that is measured in the open and is assumed to be equivalent to that above the vegetation canopy.
Understorg hecipitation: Precipitation that is either in the form of throughfhll or stemflow that passes through the vegetation canopy but has not been in contact with either the Litter layer or the mineral soil.
Net Precipttation: Precipitation that enters the mirierai sou directly or indirectly by drainage fkom the Utter Iayer,
Total fnterceptfon Loss: Precipitation that is retained by either the forest canopy or by the iitter layer and is subsequentiy evaporated back into the atmosphere.
Canopy Interception Loss: Precipitation that is intercepted by and evaporated fYom the canopy Layefis).
Litter Interception Loss: PrecipitaYon that is uitercepted by and evaporated from the Utter layer-
Througtzfall: The portion of understory precipitation that reaches the Utter iayer directly through spaces in the canopy or as canopy Ieaf drip.
StemjZow: The portion of understory precipitation that reaches the forest fioor by running down a e stems and bdes of trees.
Ratnfall Intensity: The rate of precipitation falling on the forest canopy (mm h-l).
Canopy Transitional Seasons: Periods in which the canopy cover is either rapidy inereasing Mue to growth in the spring) or decreasing (due to abscission in the autumn}.
Canopy Growing Season: Period tu which the canopy coverage is at a fairly stable maximum,
Canopy Dormant Season: Period in which the forest stand is devoid of a leaf canopy.
Cyclonic Preclpitation: Widespread precipitation associated with rising air in a low pressure system. Spatiai variability of precipitation is usually quite small
Convectional Precipitation: LocaLlzed precipitation associated with adiabatic cooling of rising air. Spatial variab11lty can be quite large.
Spatial Vmation: DiSeremes in an entity over a geographic area.
Tempord Variation: Differences in an en- at one point over a period of time-
1.4 Study AI-
1.4.1 Geogrcrphic tocatio=
Observations were made wlthin a hardwood forest plot f- 1 hectare in area)
within the Erindale Ecologlcal Research Area. ErfndaLe Campus. University of Toronto
în Mississauga. Ontario. Canada (790 39'8" W. 430 33'. 1" NI (Figure 1.3). The
Universal Transverse Mercator (UTM) coordinates of tbe forest plot are Zone 16 N=
4823043 and E= 607863 while the UTM coordinates of the open field [where gross
precfpitation measurements were made) are N= 4823072 and E= 607715-
Figure 1.3: Geographic Iocation of study area. Scale = 1 : I2000000
1.4.2 Vegetatiom
Dominant tree species witfiin the study plot were: sugar maple (Acer
sacchanun). red oak (guercus rubru) and American beech (Fagus gmndlfoi[a) while
secondary species were: red maple (Acer rubrum). hop-hombeam (Os-a uirgfniana),
white oak (guercus &a), white ash (Fraxuius amerlcm), basswood ( T i k mericana),
white bircih [Betula papyrifera) and biack cherry (Prunus serotha), Table 1- 1 provides
a s u m m q of the occurrence and size characteristics of these tree species whiïe
Figure 1.4 shows the tree DBH fiequency distribution for the experbnentai stand.
Note that Figure 2.4 does not include black cherry due the singie occurrence-
Table 1.1 : Quantitative characteristics of tree specf es witbin the study plot.
Spocies 1 R 1% of stand ! ' ~ l a n ~ ~ ~ ( c r n > f ~ . d . ~ ~ ~ ( c m ) ~
Sugar rnaple I 241 / 45.41 1 9.3 j 1 6.2 R d œ k 1 l30f 24.5) 44.43 43.5 Americm bmeh ! 98 [ 1 8.5 17-61 15
~d mapie I 22 i 4.1 / 34.3 i 36.1
~ ~ h o r n - b . s n j 141 2.6 1 17.6 1 1 5.9 White adc t 81 1 .si 45.8 ( 49 white& k 7f 1.3( 34.0 [ 34.1 Basswod 1 6f 1.1 28-41 24.4' White birch r 4f 0.8 35.2( 36.9 6 tack cherry
t 15 0.2 ( 30.9 f 30.9
T o t a l ! 531t 1 OO! 26.5f 24.5 Calculateci fkom data in Appendfx 1 - 1 - .
No major disturbances have occurred within the shdy plot for at l e s t 60
years. The upper canow (-12 -15 m above ground level) is dominated primarily by
red oak and larger sugar maples. The secondary canopy (-8 - 10 m in height) is
compoûed mostty of sugar maple and beech, while the majority of the white oak. red
maple. white ash, basswood and white birch constitute this iayer. Although a tertiary
= S'LZ
1
S Z Z
S'LL S'ZL S'L9
S'LZ
S Z Z
s-L L
S Z L
Ç'L
L
S ' t g
SOLS , szs 5 S'L* -
I S'ZC m S'LE -SZE S'LZ S'ZZ
SLS
S'ZS
S'Lw
sz* Ê u 2
S'LE - Q
SZE n =
S Z *
S'LE
S'ZC
S'LZ
SZ* , i SLE 5 0
szr - S.Z g E S Z Z 0 S L i g S'Z L S'L I s-z I
1
hyer (-3 - 7 m in height) exïsts, th& layer Is restricted to small isohted areas within
the forest plot. A variety of shrub and herb vegetatïon may be found in the study
area (Appendfx 1-2); however. sugar mapie seedhgs (averaging -50 cm in height)
p r e d o m t e the nez-ground vegetatïon cover. Tree density for the plot is estimated
at -512.6/ha with an average distance between trees of -4.6 m. The mean basal area
of the stand is -0.0552 mz per stem-
The Mer layer of the area is mostLy Ieaf debris fÏom the oak and maple species
plate 1.1)- Th& ïitter layer is -3 - 15 cm in depth where present- However. it has
been observed that certain isohted areas within the plot do not fiave a Mer cover of
any sgniilcance. These areas of sparse Iitter cover are most frequent around the base
Plate 1.1: Representaiive portion of the litter layer at the Erindaie site. Note the dominance of oak and maple leaf debris.
of trees. possibly a consequence of Horton flow resuiting fkom stexnfiow.
The study area has a mean annual precipitation input of -785 mm (standard
deviation of - 10 1 mm). whüe the mean precipitation for the perfod of Year Day 12 1 -
293 (the period of this study] is -41 1 mm (standard deviation of -75 mm) (Appendix
1-3)- Since 196 1 the maximum 24 hou rainfall in the area was -84 mm on August
22. 1968.
Precipitation type varies considerably within the region, Snow generally falls
durlng the *ter months of December - March, while m â l l generally occurs during
the months of April - November inclusive. It should be noted that a mixture of min.
fkeezing rain, sleet and snow generaIty oc- durlng seasona. transition perfods in
the region (generally during the months of March, ApriI.. November and Decemkr) .
This study concems itself on& with rainfall, Cyclonic rainf2d.i often occurs in the
spring, late summer and autumn. while convectional r a t n f ' occurs during the warm
summer months. These two forms of rainfall
the duration and intensity of rainfall events.
have clifferhg characteristics concerning
Since ratnfall. duration and intensity are
possible factors aecting throughfiidl. stemliow and subsequenfiy total interception
loss values, these hydrologic components may have a distincctlvve temporal variation
due to the variation of rainfall characteristics.
The mean annual temperature in the Mississauga area ïs +8.0 OC with mean
monthly temperatures v a m g fiom -4.8 OC (January) to i20.6 O C (July): representing
a range of 25.4 OC. The mean temperature for the period of Year Day 12 1 - 293 is
-i 17-4 O C , while the mean d e maxjmum and mean daily minimum temperature for
the same time peniod is -+2 1.0 O C and -+11.5 OC respectively. The area is
characterized as king in the Dbf category of the ~O~pen-~eiger climate classification
system (1953 version). This category ïs described as a cold boreal forest chnate with
warm sumrners (mean temperature below 22 OC, but with more than four months
with a mean temperature greater than 10 OC) and having adequate precipitation
throughout the year for vegetatlve growth (Briggs et al.. 1989) The area has been
designated as part of the Erie ecoregion by Envkronment Canada. The vegetation in
this ecoregion is described as northern hardwood with some Caro- species
(United States Environmentai Protection Agency and Environment Canada. 1987).
The e-rimental site is Iocated dong the banks of the Credit River -6.25 km
west-north-west of the river-s mouth (located at Lake Ontario) (Figure 1.5). The area
of the drainage basin is -850 km2 (Crombie, 1992)- The total lengt. of the river is -90
km (MInistry of Envlronment and Energy. 1994). Much of the drainage basin has
undergone substantiai urban and farmkmd development since the latter portion of the
nineteenth centuxy- As a result. the forested regions of the dralnage basln have
decreased. Thts is especblly true in the southern portions of the basin where large
wban areas such as Mississauga now exist. Thus, the inauence of forested plots on
the hydrolow of the area has and will continue to be a h e d .
Chapman and Putnam (1966) M u d e d the Erindale Campus in a physiograpblc
regîon known as the South Slope- The region is a till plain underMn by shale ofthe
Dundas formation- Turner (1975) conducted a groundt~ater m y within the study
area and found that the study pIot is located withln an area that has moist sand and
grave1 deposlts -3.0 m thick wlth the depth to the shale bedrock befng -8-0 m. The
water table was estimated to be an average -1.2 m below the surface during the
summer-
The experimental site is -137 m above mean sea level and -62 m above Lake
Ontario- The topography of the study site can be described as gently sloplng towards
the east with a large knon (- 1500 m2, -2 m above the rest of the site) located In the-
south-west portion of the site.
Gross precipitation was measurt-d ( h m May I - October 20. 1995 ( Year Day
12 1 - 293 ) inclusive) in an open field with two white polyethylene containers (29 cm
î n diameter and 36 cm in height with an orifice area of -0.066 m2 1 - Each gauge
was situated so that there were no o~tructions extending into the conical space
defined by a 450 angle centred on the gauge opemg- By employing the PalWhder
Basic+ Global Positioning System (GPS) it was determined that the gross precipitation
gauges were situated -150 m west-north-west of the geographic centre of the forest
study plot-
The volume of precipitation that was caught by each gauge was measured with
1000. 250 and or 10 millilitre graduated cyiinders- The arithmetic mean of the two
gauges was assumed to be the actual gross precipitation. MillLmeWe equivalent of
gross preclpitation from the volume of water in each gauge foIlowing a ralnfall event
was d e t e m e d using the foUowing equation:
where R equals gross precipitation (mm), Vg represents the volume of water contained
wtthiri the gauge (LI and r ïs the radius of the gauge ortfïce (ml-
The intensity and duration of raLnfall events during the study period were
determined using a Canadian Atmospheric Environment standard tipping bucket rain
gauge equipped wïth an Alter shield (Piate 1.2). The tippi~g bucket raïn gauge was
25
located in the open field -10 m west of the two goss precipitation gauges.
employed in obtaining rainfall intensity and duration values during the study pe&d.
Throughfall volume was measured with the same type of containers (n = 85)
used to measure gross precipitation. Seventeen gauges were phced at each
randomly located subplot (n = 5 ) (Figure L -6; Plate 1 -3). At each subplot, 3 gauges
were placed (at a spacing of - L m) dong a randomly selected transect radiating fYom
each of 4 trees. The 5 rernaining throughfhll gauges were placed along a randomly
assigned transect radia- from a randomly selected point with3n the subplot.
Plate 1.3: Pomon of subplot with transect of 5 gauges in back ground.
ThrougNall volume was measured for each of 34 rainfaU events and the
rnillimetre equivalent at each gauge site was determined using Quation 1.7 with
througMd substituted for gross precipitation-
Stemffow was measured for 20 representative trees (4 in each subplot) during
the shidy period. Plastic concave corrugated tubing (approximately 2.5 cm wide) was
stapled and caulk sealed around the circumference of each tree with the terminus of
the tubing located within a collection container (Plate 1.4). The volume of stemflow
was found by measuring the contents of each 'of the coliecting containers with
Plate 1.4: Stemflow coïïector at 1 of 20 irees in the study area.
caJibrated containers and graduated cyïinders. Milllmeefxe equlvatent of stemfbw was
derlved by employing the followtng tree per area equation:
RS = Rs~- n / A (Eq. 1.8)
where Rs îs the estimated stemflow for a given area of forest (A) (ma) with n number
of trees, Rsm- is the mean stemfïow of the 20 representative trees (L).
Interceptron by the crown and understory vegetation was assumed to be the
ciifference between the gross precipitation values measured at the open field and the
sum of througMail and stemflow values obtained at the forest site. Litter interception
was measured with 120 perforated aluminium pans (each with a diameter of 2 1 cm
and a height equal to the surrounding depth of litter) (Plate 1.5).
Plate 1.5: One of 120 Utter pans situated within the study plot
Two Utter pans were situated within lm of each of 12 throughfall gauges at
each subplot. Litter interception loss was determined usïng gravimetric methods. The
weight of each Mer pan was measured (with a portable -ta1 scale) before each
event and then soon after the event had ceased . Millimetre equlvalent values for
each pan were calcdated using the foilowing:
where El equals interception by the Iitter layer (mm). PanPt and Panpre represent the
weight of the Mer pan (kg) afkr a ralnfdl event and weight of litter pan (kg) before an
event respectiveSr, while r is the radius of the Iitter pan (m).
Canopy cover values were obtatned using visual estimation methods whlle wind
velocity values measured for the region by Environment Canada were used.
1.6 Limitations:
An attempt was made to minimixe any measurement error. Observations
regardlng the performance of stemflow. throu&hfd. Iitter interception. and gross
rainfàil measurement contalners were conducted during each rainfaU event and ail
data that the author felt were not reiiable was omitted fkom any @sis performed in
this study. Data that was not omitted may be erroneous due to incorrect
measurement readings (e.g, interprecatisn of water volume us= a graduated
cyiinder), incorrect recording, evaporation fkom collecting containers before
measurements were made or shght differences in gauge orifice diameters; however,
these errors are assumed to be negliigible.
The data does represent the majority of precipitatlon that feu during the study
period, Some of the srnaIl events (usually under 1 mm) were not included in the
shidy. However, of the 35 events that "te author was aware of 34 were measured
and included fn the study. One event was not included due to tampering with
measurement containers by vandals.
The positional error assodated with the iocation of trees. throughfâll containers
and other iristriimentatiori (measured with the use of a total statfon survey
instrument) is assumed to have a mean value of zero with a standard deviation of an
estbnated 10 mm- Error incurred during the estimation of canopy cover and with the
reporting and recording of casual observations is assumed to be neg,ligible.
Table 2- 1 Lis- all the raidaIl events measured (n = 34) during the study
period and the corresponding throughfall descriptive statistics for the entire
experimentd plot while Figure 2.1 illustrates the range of gross preclpltation and
corresponding throughfall depths (mm) during the same time period. Of the -392 mm
of gross precipitation measured during the study period -307 & 14 mm (-78 + 4 %)
reached the forest floor as throughfall. Although raiddl intensity. event duration.
canopy cover and wind speed were highly variable during the study period, the
relatiomhip between throughf5il (mm) and g r a s precipitation (mm) during the entire
study period was found to be a positive Iinear relationship with a high coeoicient of
correlation (r = 0.99) [Figure 2.2) were:
Rt = 0.82 R - 0-40 (r2 = 0.99. SE of E = 0.58 n = 34) CEQ. 2.1)
where Rt is throughfâii Cmm) and R ïs gross precipitation (mm)-
Ail interception studies that provide linear equations describwg the rehtiomhip
between througnfall fmm) and gros precipitation (mm) list either growing season
equations. dormant season equations or both (e.g. Leonard. 196 1; Helvey and
Fatric, 1965: Lawson, 1967). However, studies that provide equations developed
under growing season conditions do not provide de- as to whether the througtifd
Table 2.1: Throughfal l character 1st lcs for each measured event dur ing the study per lod.
S6)
Evrnt 8
1 . 2 ---- 3
4 - -- - 5
6 ----.. .
7 - . 8 --- 9 ---
IO --..
11
12
- 13
14
1s 7
16 --'-
17
18
19, -
Yrrr Day -- 125 ----- 129 -------- 130
---y--
137
144
148 - - -..-.- --,-
153 . . . .
158 ~--.-.
1 713 --.--
- 1 00
181 . 185
195
197
201 - 204
209
213
21 5
R (mm) -.---- 1.9
7,O -.-A.-
20 ...-. 2 1
22
216 ..-..-..---... 21 7
223
27.41 ........... -..< ... 8,O
17.4 -- 2 3 --.
24
2 5 - 26
27
28
2 9 - 30
3 1 32
3 3
34
R i Mern (mm) - R i Mtsn (Xi R I S.Divlmm) R14.0tv.(X) Ai . Corl,Var, R i Mln, (X) R~M3J%l, M . R m g t I X l .- - 1 S 77 -.-- --. o. 1 7 -- --- 59 90 30 --- - - --- 6,O 85 O. 6 8 9.8 66 I l 8 5 2 --.---- -.------ ..--. -- -- ----.--- --.-
R i HedAmm) -..--- 1 .S ---- 5.9
23.7 . - ...... ... .. - 6.3
0.2 79 -- -- O. 8 8 10.2 43 93 5 1 - --- ---- ---- --- 9.5 7 7 1,l 9 -- -- -
11.8 5 5 1 03 ----- 48 -- 2,6 67 - .----- O S 12 17,4 42 9 8 56 ---
32.8 82 3,8 1 O 11.8 54 1 04 50 . ...---.. .-.. ..... ..- . .-, .-.- ---- .. --- .----.-. -,-. .... ...-.. ........ .-. . . . . . . . . . . . . . . .-- - , . . . . . . . . . . . . . . . . . . . . 0.0 5 5 0-4 2 1 38,7 O 100 9 0
4.8 6 4 0.7 9 13,8 4 3 1 O0 5 7 -- ---.---.-- .-- ---- ----- --- .a--- - ----.. .-...--.- ,-. 5.1 77 1 ,O 16 20.3 4 5 122 7 7 .---..-. .. .-----.--- ----..-. --*-- ---+---- -.. -.-.-..-..-- .--.--.--..-. -. --. --.. -" 2.0 69 0.4 12 18.1 36 112 -- -- ---- --- 7 6
6.4 75 1.1 --- --- ---. 13 17.2 ---, 46 -- - 116 - 7 O
6.4 79 0,7 8 11.2 48 96 -- --y --- 4! 3.1 7 3 0.5 12 16.1 49 108 5 8 -- --- --- ---- ---- 0,O 1 1 O. 1 16 144.2 - O 8 O ----- -- 80
0.4 2 8 0.2 15 55.1 O ----- -- ----- 80 80
6.9 7 9 1,1 12 1 S.S -.--- . -- ----. 54 120 66 -
5.4 80 O. 7 1 O 12.6 49 126 7 7 --a.--- ---
74 0,8 11 14.7 42 123 . 5.6 ----- ---- 8 1 -- 9,3 84 1.4 -- --- - 12 14,8 5 7
I - ----- '16 7 . --
Rt Mtd.(X) - 77 -- 8 4 ----- 8 O ---- 77
10.3 --- 12,4 ---,
86 .- . .-..-." .
7 9
--- . 224
226a
226b
243 ,-,,
248 -- 260
262
264
269
276 - 278 ---- 293
8.3 .-- 9.5
23,Q 8 7 2,7 l O 11.3 69 115 46 '
. . . . . . . . . --* .,..-. ....._................. --a. . _-. ...... --.. ... - ..- ..- . . . _ . . . . . . . -. .- 6.4 8 1 1.4 18
.----"- 2,s
.~--
0.8
45.5 -. 1.8 ,,---
3,9
6.8 --,--
8 3 .----.- 3,7 - . 1.3
10.5
91.3 ---- 11.5
3.9 -- 40.1 .-.- ........ .. .
1,7 . . 7.5
---.p.-
6.6 -- -.-..- .* 2,9 - --. --,
8.6 ~ -- -- 8,1
------ d -a---.----- .--- .---- ---- -...---,..-- 112 52 .--*-*.... *-
1.1 5 O 0.4 17 33.1 9 92 84 ,----- .--- -- --- ------.-- .------
16 O, 1 12 O.' - - - - . - - --- 74.2 O 7 3 73 - -.------ - - 37.5 8 3 3.9 9 10.4 67 1 1 1 --- --- -.-.---- 44 - -
0.9 5 4 0.2 I l 20.3 82 5 5 , " " .,-,, . . - - - . - - - - - .21 - --.---.. -.,-..-,..--
2.5 6 5 0.4 1 1 16.9 3 8 93 5 5 -- ---- ----. ---.....- 4.9 7 2 O. 7 1 O 14.3 44 9 8 5 4 A--*......*. -- .----. ----. ----- -*-.,- ..--. - -.--- ..-a -----..------ 5,4 66 1.1 13 18.7 37 101 6 4
--, -.-.--- - +----- ....- - ..--.----... 2.2 59 0.6 16 ---- .--
27 267 --.-. 130 157 - - "
0 3 24 O. 1 8 33.5 6 ---- ---- 4 2 37 --- 7.2 69 1.3 13 18.5 34 1 07 7 2 -- ~-- -----
73.3 80 12.1 . 13 16.5 39 -- -- ------ - 158 119 -.-- ----- 9.6 8 4 1.4 12 14.3 43 118 7 5
---- ---- -- 4-
22.5 40 160 120 ------ .--- --..-- 14.5 8 3 1.5 8 10.1 60 ---. -
1.1 ---
37.7
14.5
-- 4.3
0.4
1.5
8.7
6.7 --
49 --- 14 --. 8 3 -
83
7,7 -- 72 -- 83
3.1 1 74
0.01 4
0,9 --,....,,, 2,s
0.4
53
64
26
4.8 ---.- 5,4
-.---*-
_ 2 . L 0.3
6.9
72.4 - 9.3
6.9
7 1 .. - 66 ---- 5 8
2 5
6 6
7 9 - 8 1
80
5.4 1 8 O
Gross Prec lp l ta t lon and Correspondlng Throughtall Depths (mm) for Events Grcater than 10 mm Durlng the Study Period
22 4
Event #
Gross ~ r e c i p l tatlon and Correspondlng Throughfal l Depths (mm) for Events Less than 10 mm During the Study Perlod
Gross Preclpltatlon 7--.-.-.--,.- 7-- Throughfall
. . . . . . . . . .
Figure 2.1: Gross preclpi tatlon and correspondlng throughfal l depths (mm) measured dur Ing the study per lod. Note the var i abi I i t y of throughfall catch for events less than 10 mm.
O O O O O O O O O O
a ab r- (O VI -t m N .-
( W U ) l I W V ~ ~ ~ J U
measurements were taken solely under stable canopy cover conditions (late spring to
eariy autumn) or if they included data generated during canopsf cover transition
periods (early spring and Xate autumn). Of the 34 events included in the study, 33
occurred with a leaf canopy present (Events 2-34). Two of these events (Events 2
and 3) occurred while the canopy was undergoing spring growth. while 3 rainfall
events (Events 32 to 34) occurred wMe the cânopy was undergoing abscission-
Canopy cover was at a fairiy stable maximm durirrg the period in which Events 4
througti 3 1 occurred. It shouId be noted; however. that an Oak skeletonizer
(Bucculatrt;rc ainslieUa) infestation did reduce the canopy cover try -4 to 5 percent
durlng the Mer 16 events [Events 16 to 3 1). The reLationship between throughfkùi
(mm) and gross precipitation (mm) under varying c a n o ~ cover conditions can be
summarized by the foUowlng equations:
where Eq- 2.2 represents the relatlonship between throughfii (mm) and gross
precipitation (mm) while there was at least some leaf cover present. whlle Eq. 2.3. 2.4
and 2.5 apply to the all-siimmer canopy conditions, summer canopy conditions before
defoliation by the Oak skeletonizer, and during the presence of the Oak skeletonizer.
respectfvely.
The ciifferences in the intercepts of ai l of the above regressfon equations (Eq-
2.2 - 2.5) are non-signiAcant (Cr = 0.05)- Ln tems of slope. it was found that the
dope of Eq.2.5 was signtûcantly different fkom Eq. 2.2 whiïe ali other equations were
not srmiificantty dlnerent (Or = 0-05). Thus, the dope of the regression Une relating
throughfi volume to gross precipitation durlng summer foliage conditions whiïe an
Oak skeletonizer infiestation was tw place was found to be signiûcantly steeper
than the dope associated with the throu@all- gross predpitation relationship during
the entire study period-
The -4 - 5 percent reduction in cânopy cover due to the defoliating behavfour
of the Oak skeletoaizer dtd not result in a corresponding increase in throughfall of -4-
5 percent. PredicUve amounts of throughf2alI were dertved by employing the
regression equatfon developed during summer canopy cover conditions prior to the
pest infestation to gross precipitation values generated under the pest infestation
period and vice versa The gross precipitation amount during the summer canopy
period before the Oak skeletonizer infestation was found to be - 98 mm and the
c o r r e s p o n ~ amount of throughfdl associated with this gros raid'& was -74 mm
1-76 %1. The predictive amount of throughfaU that wouId have occurred if Ibis pest
had been present durrng U s time period Wear Day 137 - 201) was caïculated to be
-76 mm (-77 %), representing an increase in throuehfail of - 1 %- The gmss
precipitation amount during the Oak skeletonizer infesta-tlon period Wear Day 203 -
269) was determined to be - 141 mm with - 1 14 mm (-8 1 %) f à l b g to the forest flwr
as throughfall. However, the predictive throughfall amount if no pest infestaUon had
occurred is -1 10 mm (- 78 %), representing a decrease of -3 %. These fhdings
suggest that the overd infïuence of Oak skeletonizer defoiiation on thxoughfall
generation is an increase in throughfâiï of -2 %- However, this fs an absolute value
and does not take into account that the two regression equations (Eq- 2-4 and 2.5)
were not similftcantiy Werent (a = 0.05). Thus, it can not be stated with any
confîdence tiiat there was a difference between plot - scde throughfMl depth before
and during the infestation.
The insigniflcant increase in throuehfall during the infestation period may have
been due to the interception capacities of trees at a lower height than most of the red
oak trees and the fact that oak trees on& represent 24.5 % of the trees on the plot-
Iiz addition. the surfaces of oak leaves af5ected by the Oak skeletonizer that remained
in the canopy must be taken into account. Approximately 50 - 70 % of oak leaves
remained wifhin the canopy and the surface of these leaves became quite rigid with
crevasses and other indentations caused by the insect larvae- Thus, intuitively, a
portion of the decreased interception capacity of red oak trees due to leaves that have
f a e n to the forest floor wouid be compensated by the coarser surface and increased
surface area of the rem- leaves. For the purpose of this research. since no
signiflcant Ciifference in throughfM was found between the pre-infestation and
infestation periods during the siimmer portion of the study. the grovvïng season
canopy equation includes aU rainfaU events that developed during the penlod of May
17 to September 26, 1995 Wear Day 137 - 269) (EQ.2.3). Figure 2.3 illustrates the
relationship between throughf" (mm) and gross precipitatlon (mm) for this tMe
period.
Previous research bas genersrrted various h e a r equaaons for the purpuse of
modeiljng througbfidl (mm) fkom groiss precipitation amounts (mm)- Heivey and Patric
(1965) have summarised aIl interception studles conducted withiri eastern North
American deciduous forests prior ta 1965, Hehrey and Pamc (1965) performed an
approximate test ofuniformity on aLl a m b l e equations and found that the following
equation provided the best estimate of growing season throughfalI in mature. mixed
hardwood forests in the eastern Zlnited States:
where Equation 2-6 has been converted from EngIish [in.) to S I units (mm)-
Although Helvey and Patxïc ( 1965) stated that Equation 2-6 was appficable to
most hardwd stands in the eastern United States, no research findings were
incorporated that rneasured t h r o u ~ h i l in a maple - oak - beech forest such as the
one located at the Erindale site,
Linear models developed in eastem North American deciduous forests can
produce variable results* Employïmg Eq* 2.6 to total growing season gross
precipitation (-259 mm for 28 events) produced an estimated throughfâll of -2 12 mm
(- 82%) while the modelled througalfhll value for the Erindale site ïs -201 ?13 mm (-
78 +5 %) and the rneasured throudhfi was - 20 1 2 7 mm- Thus, the equation
developed by Hehrey and Patric (1965) over estlmated mean throughfall by - 1 1 mm (4
%), but was within the range of the standard error of - 5 %. ALthough the predfcted
season - long throughfhll amount generated by the Hebey and Patric (1965)
regression model was not sirrriificantly dinerent (a = 0.05) f?om the value generated
by the Erindale madel. individual throughfàll events - > 20 mm the modeis do Mer
Other modeis (equations presented in Table 2.2) also wer esthnated tbxoughfall
(BIack, 1957 (- 80 %): LeonardJ961 (- 82 %): and Stuart.1962 (- 83 %)) whiie others
(Storey, 1953 (- 68 %): Rogerson, 1960 1- 76 %): and Voigt. 1960 1- 63 %)) under
estimated tfifs portion of understorey precipitation. However, onïy the work of Storey
(1953). Voigt (1960) and Stuart (19621 predicted throurrhfdi beyond the standard
error range of the model derived at Erlndale with the esmates of Storey (1953) and
Voigt (1960) exceeding the 95 % conûdence limih The low estimates produced by
Voigt (1 960) and Storey (1953) are a possible consequeme of the modeIs king
generated h m data obtained under very dense vegetatlon cover (Storey (1953) =
dense rhododendron understorey; Voight (1960) = -LOO % beech canopy cover)-
Table 2.2. Exampies of other Wear equations relating throughhil to gross precipitation in deciduous forest stands of eastem North America,
i Author Date Equat i o n Computation
1957 Biadr : 1 R t = 0-900R - 0.91 1 Convem (rom in to mm*
Gilbert f 1953 ! R t = 0.830R - 0.43 f m
Leonard f 1961 1 R t = 0.898R - 0.76 ( _ Rogerson ! 1960 1 R t = 0.872R - 1.04 S torey 1953 1 R t = 0.697R - 0.13 1 Stuart i 1962 ! R t = 0.888R - 0.58 f ,
-
~ o i g h t ! 1960 f R t = 0.698R - 0.66 ! D
wote: Equations computed by Hehrey and Patric [ I965) but given In Engiish units-
A Compar ison of the Helvey and Patr ic ( 1 965) Throughf a l l (mm) - Gross Precipi t a t ion (mm) Regression Model and the Regression Model
Developed from Data Generated at the Erindale S i t e
Erlndale Model
Notc Dashd llnss = 95 % confldince bamk around the Er lndalr rnodel.
20 25 30
Gross Preclpitation (mm)
Figure 2.4: A comparlson o f the Helvey and Patrlc ( 1 965) regresslon model and the model developed at the Erlndale SI te. I
AU of the models that over esthnated throughfall for the ErindaIe site were
developed in regions (south eastern United States or the north eastern maritime
states) with larger mean annual rainfdl rates and higher mean event values than the
southem Ontario region experiences. Thus. the Helvey and Patric (1965) model
(based rnostly on data obt-ed in these higher rainfdl areas) may hâve k e n
developed with a mean rainfall value ïarger than that used in Eq.2.3. This will resuIt
in a tenden- to over esthnate seasonal throughfdl in regions characterized as having
srnalier events. In contrast. the result produced by the Gilbert (1953) Iinear
model (- 78 %) is quite comparable to the result produced by Eq.2.3. The Gilbert
(1953) study was conducted in a forest with a composition simik to the Erindde site
(3 dominate spcies, with 2 species king the same (beech and maplel). In addition.
the study was conducted in Ohfo and thus was developed in a region that experiences
simlkir râinfkli patterns to that of southem Ontario.
The large range of estimates (63 - 83 %) supports the objection against the
regression approach due to the Linilted applicability of results to forest stands outside
the area for which they were developed (Dolman, 1987). This has lead to the
development of conceptual models such as those proposed by Rutter (1971). Gash
(1979) and Mulder (1985). However. the accuracy of these models in predicling
throughfall has not been @en much support in the Ilterature. Dolman (1987). for
example, found that the Gash (1979) and Mulder (1985) models produced estimates
of throughfhll that were erroneous by an average of 9.0 and 9.7 %, respectfvely, for
the 1983.1984 and 1985 groartng seasons for an oak forest in The Netherlanàs. The
reiatively large errors associated with using these models to estixnate throughfhll
44
suggests that the employment of Iinear models developed in areas with simihr stand
and climatic characteristics may produce throughfâU estimates that are more
accurate- h addition, the field information required to establish the canopy -
structure parameters and the need for hourly meteorological data would Bmit the use
of conceptual modeIs were and or when such requirements couId not be met (e-g.
remote areas). In these instances, empIoying Iinear models developed under similax
conditions may be more appropriate.
When estimating summer throu.ghfMl for budget purposes the number of
events that occur and the corresponding depth of gross precipitation wiil infhence
what proportion of gross precipitation be aiiocated into throughfall (Figures 2 -5
and 2-6). Gross precipitation depths resuiting fiom a few events wiIl produce greater
throughfall depths than the same amount of gross precipitation generated by a larger
number of events, For exampIe, based on Eq.2.3. if summer gmss precipitation
amounted to 150 mm and was derived Born 10 events the estimated throughfall
depth would be - 120 mm (- 80 %). while the throughfall deptb for the same depth of
gross precipitation associated with 50 events wodd on& be - 94 mm (- 63 %), It
should also be noted that as summer - long gross precipitation depth increases the
influence of varying n (number of events) on fhrougtifhX depth decreases. This is a
resuit of iarger events generating slmiiar thrOu@all values (as a % of Soss
precipitation). This relationship is exanined m e r in Section 2.3-
When Umar equations are employed for throughfdl prediction purposes. the
amount of error (as a percent of measured througMall) associated wïth larger events
is usually significantfsr Iess than the error associated wïth smaller throughfhil events.
Varletlon of Predlctlve Summer - Long Throughtell lnpute (mm) as a Functlon of Varylng Gross Preclpltetlon (mm) Inputa and Number o f Events - (Based on Eq.2.5) -
175 200 225 250 275 300 325 350 375 400
Gross Preclpltatlon (mm)
1 gurr 2.5: Verlatlon o f predlctlve summer - long throughfall (mm) as a function of total gros5 preclpltatlon depth (mm) and number of events.
R t I R Ratlo es e Functlon o f Summer Long Gross Preclpltatlon end Nurnber 01 Storm Events es Predlcted trom Eq.2.5
200 250 300 350 400 Groas P r e c l p l t a t l o n (mm)
Figure 2.6: Var ia t ion of pred lc t lve sumrner - long throughfal l (percent of gross prec lp l ta t lon) as a tunct lon o f t o t a l gross p rec lp l ta t lon depth ;(mm) and number of events.
Thus. seasonal and annual estimates of throughfâli fkom equations such as Eq.2.3
and Eq-2.6 provide adequate predications of througMan for these time periods since
iarge precipitation events, in many instances, comprise the buIk of the totai
thoughfhU8 and thus the error is small. Although seasonal and annuai estimates of
throughfall are important for hydrologïc budgets of forested watersheds, throughfMl
estimates of single events may also be important (e-g. forest 8re prediction and
management),
Linear models. such as Eq.2.3. often produce eslimates of throurrhfall that are
of a negative value for small event mputs (in the case of Eq. 2 -3, estimates remain
negathre untü gross precipitation exceeds -0.8 mm wen though tfiroughfhll was
recorded during an - 0.4 mm event during the study period). In addition. the
predicttve event throughfii for smaller events can be quite erroneous, Durfng an - 1.5 mm gross precipitation event. for example, the amount of throughfbii generated
was measured at - 0-4 mm whiïe the predictive throughfidl amount (dertved by
employing Eq. 2.3) was -0.6 mm. Thus, the h e a r modei predicted that tfirou&hfall
was - 0.2 mm (- 50 %) more than what it was measured to be-
A student's t test was performed to determine if there was a sigdlcant
Werence 0 = 0,051 between the slopes of regression Unes under varying gross
precipitation depths (mm)- It was found that the slope of the linear regression
relating throughfhlï (mm) to gmss precipitation (mm) for events less than or equal to - 1.5 mm was significantly dinerent thau the dope for events greater than - 1.5 mm
(Figure 2.7). Perhaps the majority of throughfàll generated by events less than or
For example, - 58 % of a l measured thnmghfafl (mm) at the Eihdaie stte iesuifed from just 4 of the 34 measured events. 48
y . . S a n
equai to - 1.5 mm is 'f?ee7 tbroughfall (i.e..throughfh.U that passes through the c m
without c o q in contact with that canopy) and throughfall associated with events
greater than - 1.5 mm is comprised of both 'fi-ee' throughfall and canopy drfp ( i,e
water retained on vegetation surfaces begins to overcome surface tension forces at
gross precipitation depths of - 1.5 mm). As a result of the increased throughfhil fkom
canopy drip a steeper dope of regression wouid be applied to events greater than - 1-5 mm than for those less ttian tbis depth.
Due to the error that can be associated with erilp1oyi.q Eq.2.3 when estimatfng
mean throughfall for small single events. the author suggests that the following
equation be used for predictive purposes for single events less than or equai to 1.5
mm occurring during summer foliage conditions:
AIthough the slope and intercept of Eq.2.7 is signlftcantLy different from Eq.2.3 (a =
0.05). caution should be used in employing Eq.2.7 since O- 4 obsenmtions were
used for the generation of Eq.2.7. However. the Mering slope and intercept of
-2.7 suggests that in regions were srnail ra inf i events (Le. c 1.5 mm) make up a
sizable pmon of total precipitation. equations such as Eq.2.3 may signiflcantly over
estimate season - long throuéhfdl- It should also be noted that From Eq.2.7
throughf" occus once gross precipltation equals - 0-3 mm. thus for gros
precipitation depths < 0.3 mm. throughf2dl should be recorded as O.
2 2 Additional Fact~rs Muencing ThmuQhPall at the Plot S c a k
As noted in Section 2.1, gross precipitation is the prfmaq factor in estlmating
throughfdl depth. However. other factors can and appear to have a n iufluence in
determining what portion of gross precipitation will be partitioned into t h r o u a a
The factors analysed in this section. in adàition to gross precipitation depth are: mean
rainfall intensity (Eo). rainfall duration (Dl, canopy cover openirig (Co) and mean wind
speed (Wv) (values of each possible factor for each event k t e d in Table 2.3). Step
wise regression analysis was pecformed to determine which possible factors have a
sïgntûcant influence on throughfall depth (Table 2 -4 for total study period and Table
2.5 for the summer foiiage portion of the study period). It is important to note that
there are various means of performing step wise regression (e.g., Zar,L974;
Mantel. 2970). The s tep wise regression analysis performed in this and subsequent
sections of this study empIoy the method described by Zar (1974).
From Table 2-4 and 2-5 it is evident that both rainfhll depth and mean rafnfaii
ïntensity have a signtficant (a = 0.05) effect on throughfall depth while w i ~ d velocity,
stonn duration and portion of canopy openlng did not The resulting multiple
regression equations (including râinfall depth and intensity) for both the total study
period t h r o u g m (Eq.2.8) and for the summer portion of the study (Eq.2-91 are:
Table 2.3: Gross prrclpl tstlon and throughfal t valuei wl th correspondlng ralnfall Intensl ty, ralnt al1 durstlon, proportlon of canopy openlngs and wlnd veloclty values,
Wlnd Velocl ty -. -----. - .-- (krn/h) . . . .
lote; Ralnfell lnttnelty and duratlon values calculatad t rom tlpplng ralngauge output, whlle proportlon of canopy openlng values were determlned by vlsual eatlmat ton, Wlnd velocl ty est lmated f rom Envlronment Canade (1 995) date,
Table 2-4: Step wise regression for determining possible factors influencing throughfail depth (mm) draing the entire study pefiod.
VARIABLE b SE ot E t - STATlSTlC Cn - rn -1 )* CRITICAL -tfj a
R (mm)
150 (mm/h)
D (hl
Co ( r a t i o )
W v ( k m / h )
R (mm)
150 (mrn/h)
D (hl -
Co (ratio)
R (mm)
l50 (mrn/h)
Co (ratio)
R (mm)
repesents number observations and nurnber of factors k i n g examined.
**The critical - t vaiue used : t (a 2) =0.05.
Table 2.5: Step wise regression for determining possible factors influencing throughf ail depth (mm) dunng summer fdiage conditions.
VARIABLE b SE of E t - STATISTIC (n - m -1 1" CRITICAL -te* a
R (mm)
150 (mm/h)
D (hl
Co (rat io)
Wv (krn/h)
R (mm)
ISO (mrnlh)
Co ( rat io)
W ( k m / h )
R (mm1
150 (rnrnih)
Co (ratio)
R (mm)
ISO (mm/h) 0.027 0.00 5 5.1 00 * (n - m - 1 ) reprexnts degees of freedom where n = number of obsmtions and rn =
number of factors being examined, ** The aibicai - t vafue used : t (a 2) 4 .OS.
The posiUve coefBcient value for rainfidl intensity within both Eq.2.8 and
Eq.2.9 indicates that wïth iacreasing intensity, througMM (reiame to gross
precipitation depth), wili also increase. For examp1e. the W e s t throughfâli value (as
a percent of gross precipitation] was determined to be -87 %. This event (Event #20)
generated 27.4 mm of gross precipitation in a time span of only -25 minutes (ko = -
65.6 mm / h). Two iarger events occuxred during the summer (40. 1 and 45 -5 mm)
but both of these storms produced smailer values of throughfZdl(%) (82 and 83 %,
respectively). These two events both had much lower rainfall intensities associated
with them (&O = -4.6 and - 1 1.6 mm / h. respectively). The relatlonship between
throughfhll and rainfall intensity was found to hold tme for srnail events as well. For
example, a 2.9 mm event generated -69 % throu&hfall while a 3.7 mm event
produced -59 % througbfii even though the latter took place when the canopy had
k e n reduced by -5% and wind vebcities during both events were simlEar (-13 and
-1 1 km / h, respectively). The orifv sigrilficânt measured Merence was minîii
intensity which was calculated to be -7 mm / h for the 2.9 mm event and -3.2 mm /
h for the 3-7 mm event-
H@er intensity storms may result In increased throughfall due to a
combination of factors. Laws and Parsons (1943) concluded that rafndrop sizes
increased, on average, with lncreasing rainfall intensifies. These researchers found
that the mean drop diameter assocîated with 1-2 mm / h events was - 1.3 mm, while
the mean diameter of rain drops associated with 12.7 mm / h storm events was - 1.9
mm, In addition, the kinetic eners (J / m2 per mm of rain) associated with events
increases exponentlally with increasing intensity (Selby, 1982)- The larger drop sizes
and correspondlng kinetic energy values associated with higher intensity storms
ailows for a larger portion of raindrops to bypass the surface tension forces exerted
by the vegetatian canopy- In addition. =ter that ts held in storage on the ieaves and
other vegetation swfaces may be expeiïed Born those surfaces by the incoming frirrh
fntensity raindrops. thus reducing the storage capacity of the canopy during that
p a r t i e event and consequently increasing throughfii
It should be noted that the staudard error of estimate values associated with
Eq.2. l and Eq.2 -3 are reduced when râinfall intensity is incorporated into a h e a r
equation. For the entire study period the standard error of estimate for Eq.2.l
(throughfall depth (mm] as a fiuiction of gross precipitation depth (mm)] improves ITom
0-58 to 0.45 when intensity is taken into account. Under summer foWge conditions
the standard error associated with the throughfall - gross precipitation Iinear equation
(Eq.2.3) decreases fkom 0.45 to 0.32.
Wind velocity does not seem to have a n influence on the amount of gross
precipitation that is partitioned into throughfdl. Czamowski and Olszewsld (1968)
also found that there was no clear relatiomhip between throu&hfall and thrs
meteorologicai variable. Intuitively, higher wind velocities should result in hcreased
motion of leaves and other vegetation surfaces and as a result water king held ixl
storage would be "shalcen" h m these surfaces and thus generate throu&bfall,
However, observations made in the field by the author should be noted. It was
obsenred that although the steady fdI of drops of water could be heard after rain
events had ceased. the author noted that Uttle, if any. canopy drip was reach;lng the
forest fioor. These same observations were made even during periods with strong
wLnà veiocities above the canopy.
Czamowski and Olszewski (1968) found that wind velocity was 10 - 20 times
less inside a deciduous forest than in the open. However, the upper canopy layer
experiences higher wind velocities than do the lower canopy layers due to the
increasing drag imposed on the horizontal flow of wind at heights closer to the forest
floor (Oke, 1987). Thus, drip caused by wind motion probably occurs at a higher
proportion from the upper canopy Iayer and less f5om lower canopies. Intultively, a
large proportion of these drips would then be intercepted and held in storage by the
lower canopy iayerts). In addition, any tncrease in canopy drfp produced by wind, as
a result of the increased movement of vegetathre surfaces, may be compensated by
the increase in evaporation ( and thus interception loss) generated by this wind.
Evaporation of intercepted water may take place during a rainfall event (especially
during events that have periods of rain stoppage). Singh and Szeicz (L9791, for
example. found interception losses exceeding 10 mm for a stand with a canopy
capaclty of 2.4 mm.
2.3 ThroughfPll as a Percent of Gross Precipitation
Aithough a strong Linear relatfonship belween throughfhll (mm) and gross
precipitation (mm) errists. the relatfonship between throughfall (as a % of gross
precipitation) ( Rt %) and gross precipitation (mm) was determined to be best
represented by the exponentiai rise to rnazcfmum mode1 rather than a Iinear one for
both the total study period throughfiall (Figure 2.8) and surnmer canopy throughfkU
[Figure 2 -9) which take the form of Eq.2 - 10 and Eq.2.L 1. respectively:
w%) = 83.0 (1-Exp(-0.40 R)) (r2 = 0.80. SE of E = 9.1. n = 34) (Eq.2.10)
Rt(%) = 83,5(1- EX^(-0.35 R)) (r2 = 0.88. SE of E = 7.5. n = 28) (Eq.2.11)
Exponential rise to maximum rnodels clearly indicate the approximate depth of
gross precipitation requlred to produce signiRcant changes in the way the canopy
interacts wi th gross precipitation. Figure 2.9 in confunction wi th Eq-2-11 suggests
that the way the srimmer canopy partitions incoming rauiiauiiall changes Ii-om a linear to
a curviiinear relattonship when rafnfd depth reaches -4 - 5 mm. When gross
precipitation reaches a Ievel of -9 - t l mm the curvilinear relationship gives way to a
stable straight-Une relationship in which the portion of gross precipitation that is
partitioned into throughfdl by the canopy rem- quasi-constant. These changes
coincide with the development of stemfbw and are discussed in greater detaiï in
Chapter 3 (Stemflow and Stemflow VariabilifSI)-
2.4 Event 't'hr0ughfh.U Variability:
The coefficient of variation for throughfd was found to be a curviIlnear
function of the percent of gross precipitation being partiffoned into throughfall (Figure
2- 10)- Events with a k g e portion of the incoming water being partitioned into
throughfdi have much srnalier spatial variabillties associated with them than do
smalIer throughf-ail (as a % of gross precipitation) generating events. For example. of
the 45.5 mm of rain that feu on the site during the late afternoon / earw evening of
August 14. 83 % reached the forest floor as throughfdi and had a coefncient of
variation value of 10 %, whereas the coefficient of variation was calculated to be 14%
% for the 1 1 % of throughfhli generated by the 0-4 mm event of July 16. The
relationship between throuehfâIl(% of gross precipitation) and the correspnding
coefficient of variation of that throughfall for the summer portion of the study period
was found to best represented by the following:
where Rt(c.v.) represents the coefncient of variation of throughfa and Rt[%) is the
percent of gross precipitation parUtioaed into throughf=all.
To Illustrate the relationship between throu&hfall variation and gross
precipitation m e r , the ratto between the maximum throughfhll measurement and
the minimum throughfall measurement for each summer event was determined. This
ratio provided an indication of how variable an event was: with a small ratio value
Throughf al l Coeff ic ient of Var ia t ion as a Function of Throughfal t ( X o f Gross Precipi tation) Under Summer Canopy Conditions -May 17 -
September 26. 1995
-.-----. .-.---- y-. --------- ---- ---. - ----.- ...--. .
--1 ~ - ~ ~ ~ z f e ~ = ~ ~ :
Throughfall (% of Gross Precipitation)
Figure 2.10: Throughta l l coefficient of v a r l a t l o n as a functlon o f the percent o f gross p r e c l p l t a t l o n belng partloned l n t o throughf a l 1 under summer f o l iage conditions.
suggesting a Xow degree of variabiuty and a larger ratio value indicating a higher
degree of vâriabillty. When the ratio of madmln throughfàiI was plotted against
summer g r o s precipitation depths the relationship was found to be curviiinear with a
negative slope (Figure 2- 11). The negative slope of the Une reveals that the variabiïify
of throughfall catch [expresseci as the ratio between the maximum and minimum
throughfall catch for each event) decreases with lncreasïug gross precipitation. The
relationship between the Rt Max / Rt Min ratio and gross precipitation (mm) values
under summer growing season conditions for the maple - oak - beech forests at
Erindale may be estimated by the foUowing equation:
Rt M a / Rt Min Ratio = 10 0-823 + R 0.436 (r2 = 0.54. SE of E = 2- 1. n = 25) (EQ,Z.l3)
where Rt Max / Rt Min Ratio is the ratio of the maximum point througWaIX and
minimum point throughfMl values obtalned at the Erlndale site during an event.
The average Rt M a x / Rt Min ratio for summer events was found to be -2.8 X.
However, smaller events produced much ïarger ratio values. A 1.3 mm event, for
example, produced a ratio value of -7.4 X, and the Rt Max / Rt Mln ratio for a 1.7
mm event was -12.2 X. The ratio values for three events could not be determined
because the mtnrmiini vahe was 0- Although these events were srnaii (0.4. 0-8 and
1.5 mm). the maximum throughfdl values were relatlvefy high ( 80, 73 and 80 %,
respective@). Variability of throughfiall catch was not as hig,h for larger storm events,
but SUU signiflcant ( dl events greater than or equal to 12.4 mm had ratio values
rangïng fkom 1.7 X to 1.9 X). These fhdings fiirther demonstrate the high degree of
point varïabiiity for throughfall. especially during smsùl events- Price et al. (1996)
found that the average Rt Max / Rt Min ratio for a biack spruce (Plcea mariana) stand -
was -9.1 X. Even gross precipitation depths as large as 37.2 mm produced
significant ratio values (-3-4 X). Thus. these fhdings, when compared to the results
obtained at the Erindale site, suggest the spatiai variability of thoughfiall reaching the
forest floor rnay be of a higher degree for conlferous stands thiin for stands composed
of deciduous aee species. This increased variability may be a consequence of
conrfers. such as black spruce. channeIIing water away ftom the tree bole (as
opposed to most deciduous trees) to drip zones of hi#$ througbfall (Price et al.. 1996).
Variation of throughfhU catch by the 85 gauges reached a minimum (C-V = - 10 - 13 %) once gross precipitation equalled - 11 to 12 mm. Other studies
(Leonard, 1961: Helvey and PaMc, 1965) also suggest that no sigaiBcant reduction in
the spatial variation of througMaU is achleved once rainfail depth exceeds - 12 mm.
Thus, it can be deduced that for most deciduous forests in eastern North Amerlca the
way the canopy stores and expels water fYom the surface of leaves, stems and
trunks of trees is highly dependent on the variation of canopy cover. Ieaf smface
structure and size. wind dlrection and velocity. rainfaU mtensity and rainfall duration
for events - d l mm. However, once rainfdl depth becomes -> 11 - 1 2 mm.
presumably when the storage capacity of the entire canopy has been satlsfied. the
spatial variation of thr0ugMM.l is highJy dependent on the spatial variation of the
canopy structure with the variability of wind speed. wind dlrection, rainfall intensie
and rainfdl duration having iiffle influence on the overall variability of throughfall
catch-
When rain drops corne in contact with a vegetation surface a portion of these
drops are intercepted and coiiect together- When enough water is coliected on the
surface, srnace tension is overcome and the water begins to be expeiïed fiom storage
(usually fiom one point on the vegetation surface)- Thus. although mean througMall
will always be less than 100 % under forest canopy conditions, point throughfMl may
exceed this amount Of the 34 storms sampled. 2 1 bad point throughfâli values
greater than or equal to 100 % of gross rainfall. It shouId be noted that on& 3 of the
12 events less than or equd to 3.89 mm had point throughfd greater thsn or equal to
100 % wMe 17 of the 22 events greater than or equaï to 426 mm had these
point throughfall values associated with them (note: these hi@ throughfbii depths
were observed with aU 8 storms greater than or equal to 11.1 mm).
Although point throughfdl depth was found to exceed gross precipitation depth
at a point. the relative frequency of these points was found to be small (Appendix 2.1
a . 2.2)- The highest fiequency of these hi@ throughf2dl points was found to be
12-9 % for Event 20 (27-4 mm), Thus, - 1290 m2 / ha of the forest floor at the site
experienced these relatively high throughfhll depths during this event- However. the
mean fkequency of these hi@ t h r o u ~ d l values for the entire study period was
calculated to be - 2-7 % / event ( - 270 mz / ha), The greatest pint throu&hfall
depth relative to gross precipitation depth was recorded on October 5. 1995 Wear
Day 278) when -144 mm was caught by Gauge 523 during the 91.3 mm event.
Thus. the throu&hfall depth at this point was - 158 % of the gross precipitation.
Durrng three events (Events 14.15 and 24) no throu@d was caught by some
of the gauges on the site. During Event 14 (- 0.4 mm). for example. no throu&hfal2
was present h~ - 37 % of the gauges. The three events that were associated with
gauges catching no throughfMl were ail sm&( 0.4 - 1.5 mm). The frequency of no
throu&hf& catch at a point decreased rapidly with increasing gcoss prectpitation
depth, Approximateiy 6 and 5 % of the gauges empIoyed on the site received no
throu&hf& input during 0.8 and 1.5 mm events, respectWei.. It shouïd be noted that
one event less than 1.5 mm (Event 3 1 (1 -3 mm)) did have throughfall in aü of the
gauges as did other small events (Event 7 (1.7 mm) and Event 26 (1-8 mm)). Thus, it
can be deduced that during events less than -1 -5 mm. throughfi rnay not reach the
ground at various points at the Erindale stand. This f h c 3 . q agrees weïi with earlier
ELndings reported in Section 2- 1. in Section 2- L it was specuiated that much of the
throughfalt generated by events less than 1.5 mm was -freea throughfhll. Since the
remmder of the water is held in storage by the canopy this wouid result in iarge
portions of the forest flmr experfencing no throughfâü Input. The above findings
provide further support to the notion that 'fkee* throu&hf& is the dominant water flm
duririg small (Le. < 1.5 mm) r e events.
Due to the variabiüty of throughfalt at the forest plot scale, a certain number
of gauges is required to hold the standard error under 5% of mean throughfhll.
Hehrey and Pamc (1965) found that by employing the following standard error
equation the number of throughfdl observations needed to maintain a ievel of
reliability can be estimated:
where N represents the numkr of observations. SD is the standard deviation of the
t h r o u ~ ~ rneasurements and SE is the percent error that can be tderated multipiied
by the mean tbrougMal1 for each event-
Table 2-6 shows that the number of gauges required to maintain the standard
error at under 5 % of mean throughf! signiûcantly decreases with increasing gross
precipitation amount. Estimatlng the mean throu&hfi wittiin the error tolerance levei
of 5 % for rainf" events tess than 1 mm would req-e -526 gauges- The large
number of gauges required. coupled with the assumed hydroIogicai insirrriiftcance of
these events. suggests that emp1-g this many gauges is not only impractical. but is
also not warranted. The amount of gauges required to sample throu&bfall adequately
Is also high (n = 61) for events rangïng fiom I to 2 mm. However. the througbfall
associated with events > 2 mm can be estimated wi- 5 % of the mean by
employtng -22 gauges. The mean of the total summer throughfhll for the Erindale
study site was found to be estimated within 5 % with the use of only 8 gauges. Since
hydrologically important events are in the > 2 mm range and the total summer
throughfâil mean can be estlrnated with even fewer gauges, the author suggests that
-22 gauges should be employed for most throuwall investigation purposes.
J3itImati.g the mean throu@all -thin 5 % at either the 68 or 95 % confidence Ieveh
for events less than 2 mm would result in the need for a . unreasonable number of
gauges.
Table 2.6: Number gauges needed to keep standard error ofthe throughfaI3 mean estimate under 5 % uslng the H e m and EWric ( 1965) method (Eq.2- 14).
R (IlUn) 1 nom Rt (mm) Mean a o f Gauges Range o f of Gauges
c l 1 O, 1 526 1 221 - 830 1-21 0.8 61 16 - 122 2-41 2.1 1 22 1 1 1 -44
4-id 5.6 11 S - 20 10-1 4 9.5 1 8 ! 6 - 9
It shouid be noted that Eq.2.14 produces esilmates wifh on& a 68 %
confidence Ievel. Kfmmings (1973) suggested that estimates with a 95 % coddence
level can be achieved using either -2- 15 or Eq.2.16:
where t ïs the student's t value for a desired confidence interval at a given probabiiity
level and CI, is the desired confidence level as a percentage of the mean througiifall.
Kimmings (1973) points out th& the number of coUectors indicated by the
Helvey and Patric (1965) equation (Eq.2 - 14) ( 68 % confidence) is much lower than
that given by either Eq.2.15 or Eq.2.16 (95 % confXdence): in fact 1 1 (t2) as large.
Equation 2.14 wiU generate. for the same number of coliectors, either a 68 %
confidence intervai equal to 5 % of the throu@rùl mean or a 95 % confidence interval
equal to LO % of the mean (Kïmmings. 1973). Table 2.7 lists the number of throughfâll
coUectors required in order to keep the standard error of the mean under 5 % at the
95 % confidence level.
Table 2.7: Nurnber gauges needed to keep standard error of the throughtàü mean estfmate under 5 % using the Kimmings ( 1973) method fEq2.15 & 2- 16)-
R (nnd 1 Mern R t Cmm) Mem # of Gauges Range of S of auges
cl 1 O. 1 1 2024 1 859 - 3188 1-2 1 0.8 244 74 - 479 2-4 ! 2.1 1 95 1 55 - 178
4-10 1 5.6 55 38 - 88 10-1s ! 9.5 1 42 1 37 - 46
>15 1 r - -- - - - - - -
27.2 34 32 - 36
Puckett (199 1) concluded that - 11 collectors were needed to sample
throu&hfall volume within 10 % of the mean [a! = 0.05) within a mixed deciduous
stand in Virginia. However. it should be noted that Pukett (1991) O- samp1ed 5
storms and alï were greater than 10 mm. From the data presented in ' ï abe 2.6 it ïs
evident that storms less than 10 mm required a greater number of gauges due to the
increased variabiiiiy of throughfall associated with these srnalier events- Helvey and
PaMc (1965) suggested that - 15 gauges wodd be s a c e n t to sample -storms of ali
&esw (Mean k 10 % (cl = 0.05). However. this is not evident from Table 2-5 which
suggests that mean througMall generated by events less than 2 mm req-ed a much
hrger number of gauges to be sampled-
Based on the results presented in Tables 2.6 and 2-7 the numkr of gauges to
be exnployed during a study is dependent on the coddence and error level desired
and the minimum event depth at whfch the error and confidence limits apply. Costs
versus benefits must aiso be considered since an - 4 - 5 foId increase in costs
associated with gauge purchases and - 4 - 5 fold iucrease in sampling Ume wili onfy
result in a 5 % decrease of the Standard Error about the mean (nom Mean 2 10 % to
Mean _+ 5 %)- In addition, the costs assocBted with gauge purchases and sampiiug
time to estimate the mean at a gtven confidence and wïthrn a certain error Ilmit
increases dramatically with decreasing storm size.
Aithough onfy one estimate of throughfdl under no foliage conditions was
obtafned during the study period, it is evident that the number of gauges needed to
estimate the mean at a @en Ievd of accuracy is much less thau for follage growth
werstorey conditions. The 1.9 mm rainfhil event (Event 1) generated -1.5 mm of
throughfdi (77 %) with a coefficient of variation of 8.5 %- This degree of variability is
lower than for any other throughfhii measurements obtained during the study (even
for events with depths of 40.1.45-5 and 91-3 mm). As a result of the Iow degree of
VarjabIIity associated with th& event, onïy 3 gauges would have been needed to
estanate the mean throughfàll within 10 -% at the 95 % confidence interval, Simiïar
fFndings have ken found by other researchers. Hehrey and Pamc (1965). for
example. found that throughfd in the range of - 5 - 10 mm required 18 gauges to
estimate mean throughfhïl within 10 % (a = 0.05) during the growing season and O*
6 gauges were needed during dormant conditions in a deciduous forest stand. Thus.
it is clear that not oniy is the amount of throughfi reaching the forest ffoor increased
under dormant leafïess conditions but the spatial variability (and as a resuit the
number of collectors required) is s w c a n t ï y decreased-
I€ research is geared towards examinmg througMâU variabiiity and not just
throu@all volume than the use of bucket gauges is recommended. However. if onïy
throughfdl volume fs of tnterest the use of troughs may be more appropriate
(ICostelnik et aL,1989). Heivey and Patric (1965) reported that. based on the work of
Stuart (1962). 20 % less trough gauges (- 122 cm x 13 cm) than 20.3 cm bucket
gauges were needed to sample throughfdï volume within 5 % of the mean at the 68 %
confidence level at an oak-hickory stand in centrai Pennsybania However, the fact
that a 350 % increase in gauge catch area ody resulted in a 20 % reduction in the
numumber of gauges needed to be employed (Hebey and ï?airïc. 1965). and that t ro~gh
networks are ususlhf more expensive and difficult to constn~ct and maintain than
bucket networks. the author suggests that bucket gauges are more appropriate for
throughfall studies regardless if it is throughfall varlabillty or volume that is of
interest,
2.6 Factors Influencing ThroughfWi Spatial Variabflity:
Variation in the distance fkom tree trunk undoubtably influences the variation
of throughfaii catch under single trees (Horton, 1919: Stout and McMahon, 196 1).
However, intuittvely, the relationshtp between throughfhli catch and distance fkom a
tree stem shodd not be as strong in forest environments where overlapping canopies
of various trees largel. negates the Wuence of drip points at the outer fringes of
individual trees- in orC:-r to investigate the relationship between througMbil catch
and distance fiom tree stem, the amount of throughfdi coiiected by 85 gauges during
19 summer foliage events (Appendix 2.3) was plotted ag-st the distance fiom their
nearest tree neighbour (Figure 2- 12). The relationship is a hyperbolic one that takes
the form of the foUowing:
where ZRt represents the sum of a.Il throughfall(% of totai gross precipitation)
collected by a gauge at some distance (m) from a tree stem (Dt) a s a result of the - 172 mm of gross precipitation that feil during the 19 sampled events.
a o u g h the r2 value (0.231 obtained d-g the regression analysis of the data
suggests that the throughfall - distance h-om tree relationshtp is weak to weakly
moderate. the mean throughfall value obtalned by gauges c 1.25 m fkom a tree stem
(127.9 r 11-1 mm (74.5 r 6.5 %)) was signrtlcantly ciiffereut (a = 0.05) fkom the mean
throughf'measured in gauges > 1.25 (136.3 2 10.2 mm (79.4 2 5.9 %)). The trend
in the data suggests that throughf3dI does, in general decrease with decresing
distance fiom the tree stem- Thus, at the Emme site during summer follage
conditions, distance kom tree stem expiainî some of the observed variation,
However. as Figure 2- 12 illustrates, there is a great deal of throughMI variability that
is not expïained by gauge placement in relation to tree stems-
Other investigators have also found that the rehtionship between
throughhli amount and distance h m tree stems under forest conditions is weak
Loustau et aL(1992), for exampie. found that the Iïnear reiationship between
throughfalI (mm) and distance fkom maritime pine (PInus phaster) stems had an rz
value of 0.04 (n = 52) for seven sampled storm events- Hoover (1953). and Trfmble
and Weitzman (1954) have &O conchded that distance h m tree stem is not a
sigd3cant Muence on throughfhil catch under forest conditfons-
Stout and McMahon (196 11 investigated the spatial and temporal relationship
betvween throughMl and distance fiom tree stem uoder various tree species canopies-
For red oak (woods). during summer folïage conditions, they found that the Merence
between throu@a2t catch was signlficant when near stem throu&hf&ll values were
compared to values generated near the edge of the tree canopy. However. the value
at the base of the tree was significantly m e r (statistically) than the value obtaïned at
a further distance Born the trunk of the tree, Stmilar patterns have ais0 ken
observed by Robson et aL(L993) within a beech forest in England and by Ford and
Deans (1978) within a young spruce stand. AIthough this pattern of hfgher
throughfhli near tree stems is not what one would expect, it can occur. For exampIe.
Gauge 12 1 at the Erindale site (Iocated -1 .O m away Erom the base of a red oak stem
(DBH = -49 cm)) caught -239 mm of throughfhii during the summer foitage season
whlle Gauges 122 and 123 (located -2.0 and 3.0 m nom the same stem respective&
and dong the same transect as Gauge 121) caught -220 and -196 mm, respectlvely.
Given that the gross precipitation for this tlme period was -259 mm. the throurrhfi
measured at Gauge 121 was -92 % of gross precipitation m e Gauge 122 recetved
-85 % and Gauge 123 recewed -76 %. Thus. the season long throughfill catch at
the gauge nearest to the ixee stem was - 16 % greater tban the gauge - 3.0 m fkom
the tree and - 7 % greater than the gauge -2-0 m fkom the me.
Season-long throughfidl as a function of species type was also examineci- It
was found that througb.fMi depth under the canopies of dtnerent species w i t b the
ErfnWe plot was not significantly Merent (a = 0-05) [Table 2.7)- From the data
presented in the Table 2.6 it is evident that throughfhïl under one species is
surprtsingly s i . to throughfall under other species. The slightfv smaller throu-
depths under beech canopies may be a consequence of the increase in stemflow
generated by these species (see Chapter 3: Stemflow ând Stedow Varaibility). It ïs
important to note that even through mean distance fiom tree stem and mean DBH of
tree stem varied in relation to tree species. mean throughfall under the crowns of
dinerent species remainecl relatively constant. The gmatest Merence in mean
throughfhll wâs between throughfi under American beech and througbfall under red
oak crowns. However. this dinerence in mean throughfkü was on.& 0-9 mm and
@en the standard deviauons around the means this Merence is insgniflcant-
Table 2.8: Throughfd as a fundion of species of nearest tree stem. R = 17 1 -7 mm ( L 9 eventsl, Error estimates reported as standard devfation of the mean.
1 i h k a l bM (am MsanbotanaenrmGaugetm ManThrPuqntatmm) f WfmthraiQI8Dm1
Sugar Map l e 18 2 7 1 1.6 t 0.9 1 1323k6.1 f 77.1 2 3.6 Red Oak 1 46 + 6 1.6 I 1 .O 133.6 2 6.0 1 77.8 + 3.5
American Beech 1 K
21 1 7 1 2.1 2 1 .O 1 1320 I 5.5 I 76.9 I 3.2
Cape et ai. (199 1) made interspecies cornparisons of throughfhU in northern
Britain and &O concluded that there was virtually no difference between season-long
throughfd depth under different deciduous tree crowns within the same stand For
example. throughfall input under sessile oak crowns was calcuhted to be 73 + 9 %
and 75 I 9 % of gross precipitation while throughfd under common aider in the same
forest was 72 I 9 % and 75 & 9 % respectively d-g two study periods- Helvey and
Patric (1965) reported no sigdicant Merence in throughfhil under the crowns of an
oak-hickory stand (Kovner. 1955), a yeilow poplar-hickory stand (30 years of age)
(Coweeta Hydrologie Laboratory. 1939- 1944). and a yeUow poplar-hickory stand (50
years of age) (Black. 1957) at Coweeta. North Carolina. Lawson f 1967) and Cape et
aL(l99 1) found sgniflcant dinerences in throughfall catch under crowns of deciduous
species when compared with coniters withtn the same stand. However. Lawson
(1967) did not report if there was any difference between throughfhll under dinerent
deciduous species. Based on previous research imdertakings and the resdts
obtained during the Erindale study, the author suggests that although throughfdl may
vary under different stands of dinerent deciduous species (Kittredge. 1948: Helvey
and Patric, 1965). season-long throu&hfall under varying deciduous species crowns is
not significantly dinerent when these species are located within the same stand. The
high degree heterogeneity of canopy characteristics throughout mixed stands resuits
in. ironically. increased spatial homogeneity of throughfall variability (Le. if one part of
77
a stand was comprised of one species ând another portion cornprised of another
species. througtifhII variation between the two areas wodd be greater than if both
portions of the stand were comprised of a mix of these two species).
ThrougMMI at a point is probably more dependent on the degree of canopy
cover above that point than the distance fkom the tree stem. Casual observations
made at the Erïndale site by the author sugges-t tbat these canopy openings appear
to be randomly located within the canopy and usuaUy quite srnail (often less than 0.02
d). Thus. infmitkvely~ throughfàll at a point wLIl be random in pattern since the
amount a d structure of canopy openings appears to be random as weiL Since
significant changes in canogy physiolog)r can occur over relattvely short distances,
thrortghfidl volume should sùso vary over short distances- During the course of the
field work component of this study the author noticed that the amount of throughfidl
catch at one gauge can be quite Werent from the catch by a gauge in close proximity
(e.g. -1.0 m). For example, during a 27.4 mm fainfall event Gauge 533 caught 34.8
mm while Gauge 532 (-1.0 m fkom Gauge 533) caught 23.4 mm (representing a
Merence of -1 1.4 mm). Table 2.8 provides examples of some other large dinerences
in throughfall over relative@ short ci3.stances measured during the study period-
From Table 2.8 it Is evident that considerable variation of throughfaIl can occur
over srnail distances on the forest floor. Puckett (1991) reported similar findings for a
33.0 mm rainfaI1 event, Some of the larger differences reported ày Puckett (199 1) for
this event included tliroughfii variations of 122 % between gauges 2 m aprt and 56
% for gauges situated 0.5 m apart. Due to the high degree of throughfall variabiZIty
between two gauges Iocated in close prordniity to each other and the fact that
Table 2.9: Examples of extreme spatial variability of throughfail during the 1995 study period at ErindaIe. M#& (rnrnj Gauges Distanœ(rn) Difference(rnrn) 1 Dierenœ(%)
the throughfd distance f3om stem. and ciifference in species crown relationships are
weak. the author suggests that, at least for the Erindale plot, point throu&hfhl.i is
spaWy independent of throu&hfall at another point.
To determine if throughfall at one point fs spatially dependent on the
throughfall at another point an analysfs of semivariance was conducted on the
throughfidi catch of 6 summer storm events of varying depths. The distances
between each gauge were determined and the ciifference in throughfall between the
gauges at vaxying distances was calcdated (Appendix 2.4). The semivariance of
throughfhii catch (as a % of gross precipitation) as a function of distance between
gauges was detennined by the foliowing:
where y(h) represents the semivariance. Z(xi) is the throughfdl at one gauge and Z(xi +
h) is the throughfhll caught at a second gauge and h is the distance 0 between the
two gauges- The values of each of these parameters can found in Appendix 2.5 for
the 6 sâmpled storms.
A plot of semivariance against the lag produces a semivariogram. By fitting an
exponential rise to maximum modeï to the scatter of a semivariogram one can usmïly
determfne the distance at which an entity is no longer spathlly dependent on the
value of another entity- This is done by fin* the iag value at which the range (Le.
the point at which the exponentiaï rise to maximum modei has reached its maximum)
is met (Burrough, 1986)-
Figure 2- 13 illustrates the exponentfal rise to maximum curves for the 6
events. The distance at which a sill is reached is hïghïy variable i3om one event to
another (firom no- sill to a slu at -7 m)- Evidence that throu&hfall ïs not spatiaïly
dependent cornes ftom the fact that the expnential mode1 demed fkom the data
generated during Event 5 suggests that there is no increase in semivariance mth
increasing distance for this 3.9 mm event. In addition, both Event 5 and 11 are
associated with negative Iinear modeïs, suggesting that spatiai variabiüty decreases
with ïncreasing distance- In general. the data generated for the 6 seIected events
were so varied that neither an exponentlal rise to maximum or a Ilnear mode1 seemed
appropriate (Table 2.10; Figures 2.14 and 2- 15)- The hïgh degree of noise associated
wlth the models (note the r2 d u e s kted in Table 2- 10) suggests that throughfhll at
one point under the forest canopy is not spathily reïated to thr0qhfh.U at another
point (note: the minïmiim distance examine was 1 m). These fbdings suggest that
there is no aàvantage in estimatlng point thmughfàii by knowing the t f r r o u ~ depth
80
at a point in close proximity to the point of interest and that point tnr0ughh.ü
estimates are probabiy best served by assigning the mean throug,hfhU value generated
at the plot. Thus,contour maps of throughfidi. at least at the Erindale site. wouid be
rn-ess and inappropriate.
The results obtained concerning point throughf'i'i and its relationship between
distance h m stem and distance fkom another point within the forest suggests that
througMall is spa- independependent within mixed forest communitïes and its
unpredictabIe variabIUty is due to the complex overstorey of these environments.
Table 2-10: Exponential and mear semivariance modeb of throughfall catch as a fiinction of distance mm another gauge 6 seIected storm events
i Event # ~Depth ( Ihear ~ d d Exp Model i
4 124 1 y = 2 6 x + 6 1 1 y = 84.4( 1 -exp(-0,55*x)
5 1 3.9- 1 y = -6.2 x + 141 1 0.07 y = 71.4(1 -cxp(36.6*x)) 1 0.00 1 N I A
. 11 [ 8.6 y = -6 .2x+ 216 y = 1 8 4 5 1 -exp( t.3*x)) 2.5 i
15 1 1.5 1 y = 15.1 x + 140 1 0.06 fy = 2726(1 -exp(-0.39*x)II 0.07 1 7 i
20 f 27.4 y = 2 5 x + 89 1 0.0 1 f y = 1 1 3.M 1 -exp(-0-57*~)11 0.04 1 4.5 r t 1
( 25 i 45.5 y = 4 . 8 ~ + 56 1 0.03 [y = 1 1 1 S 1 -exp(-0.27*x~)) 0.03 6.5 Note: The distance to siil values given in this table is not the actual distance in which the SU meurs but rather is the distance% which very Wtie increase in semivariance is generated by in<rreashg distance (Le the la@.
3 Stemfïow and StemfIow VariabiUty
3.1 Stemfïow and Gross Frtcipitation:
During the 27 events for which stemfIow was measured (Table 3.1). gross
precipitation was measured at - 2 14 mm, throughfàii at -164 mm and stemflow at -9
mm. Thus, stemflow was -4 percent of gross precipitation and -5 percent of
understory precipitation during these events. As a fonn of understory precipitation,
ste-w was found to be volumetricaUy Iess significant than throu&hfall: however.
biochemicaiiy, stemfïow may be more signifxant (Carlisle et aL, 1967: Gersper and
Hollowaychuck, 1970: Eaton et al., 1973; Price and Watters, 1989; Robson et
aL. 1993)-
Stemfïow (Rs) (mm) showed a strong posittve mear relationship with gros
precipitation depth (mm) was found to be strongly correlated with @oss precipitation
[Figure 3.1). The equation of the line takes the form of:
where Rs represents stemflow (mm).
A thorough search of the literature produced no prewus stemfiow research
conducted in a maple - oak - beech forest stand. However, Hehrey and i?atric (1965)
suggested that the foiiowing equation prwfdes an adequate estlmate of stemflow in
eastern hardwood stands:
Table 3.1: Stemf low characterlstlcs for lndivtdual events measured during the study per lod.
an- al = P O
RS = 0.04 R - 0-13
Where Eq.3-2 has been converted fiom imperid to SI units.
Gfven the depth of s o s s precipitation (-2 14 mm) and the number of events in
*ch stemflow was measured at the Erindale site, Eq.3.2 produces an estirnate of - 5 mm of stemflow representing -2 - 3 percent of summer-long gross precipitation.
Thus, the Hekey and Patric (1965) equation underestimates stemfiow for the ErindaIe
site by - 4 mm or -1 - 2 percent of gross precipitation. However, Hehrey and Patrie
(1965) suggest that t-g previous research equations (Table 3.3) into account one
can derive an appïîcable equatton for estimating stemflow for a particuliir stand.
Since the Erfndale stand is a mixed forest commumity the average of each regression
equation for each Erindale species [mage, oak and beech) iisted in Table 3.2 was
calculated and then muitipïîed by their representative fraction of the stand- The
average linear regression equation for maple was derWed firom onfy one equation. oak
fiom five and beech fkom two. The resuling equations take the form of:
Taking the average of -3.3 - 3.5 to derive an equation for Erindale wouid not
be appropriate since the fkequency of occurrence for each species differs. Taking the
fkequencies of each species into account (te. a weighted average) the foiiowing
Table 3.2: Linear regression equations for predicting summer stemflow fkom gross precipitation for eastern hardwood stands.
%
Author t
Date: Species Equation .Est f o r Erindale (mm) Diff, from Eq-3-1 (mm
Horton ( 1919 i Beech f R s = 0.09R - 0.12 { 1 6
i 1 6
Voigt 1 1960 j Beech R s = O.09R - 0-20 1 4 1 4
Gi Ibert ) 1953 1 Beech-Maple-Elm 1 Ro = O.06R - 0.05 1 12 2
Leonard 1 1961 ! ~ e e c b - ~ i r ~ n - ~ a p l e f Rs = 0.06R - 0.05 ) 12 1 2
Hortan ) 7919 i Heple Rs-0-OSR-0 .12 1 8 -2
Stuart 1 1962 Oaks i Rs- 0-BSR -023 5 5 -5
Rogerson ( l96Oi Oaks 1 R s - 0 - W R - 0 . 1 0 6 1 -4
Horton 1 1919 ! Oaks ( R s - 0 . m - 0 - 1 0 1 2 -8
K ~ p p s l s c h ~r watershedt 1939 1 Oaks 1 R s - 0-O2R - 0-08 i 2 t -8 1
Coweeta Hyd. Lab. [ 1938 i Oaks f R s = 0.01 R - 0.05 f -9
Storey 1 1 9 5 3 ! Oaks-Maple i R s - 0 . 0 2 R - 0 - 0 5 ! 3 ! -7
Horton 1 1919 1 Hickory R f - 0.01 R - 0-03 ! 1 -9 -
Btack 19- \ ~ i c k o r y - ~ o p ~ a r j R r = 0.03R - 036 1 -3 I -1 3
Source: Hehrey and Patrie ( 19651 Table 3- Note: equations converted to SI uriits.
equatlon was derived for esUmating stemflow at the Erindale site:
RS = 0-05R - 0.13
Equation 3.6 is the same as Eq.3.3 due to the high fkequency of mapIe species
at the site and the fact that the equations for oak and beech cancel each other.
Equation 3.6 yields a value of -7 mm ( 3% of gross precipitation), Thus, the value
derlved from Eq-3-6 is closer to the measured stemflow depth at Erindale (-9 mm
(-4%)) than the value derivecl fkom employing Eq.3.2 (-5 mm (-2 - 3 %))-
3.2: Mditiod Factors iilflucncing Site Stemflow:
Aithough a strong iinear relatlonship exists between stemfhw and gmss
precipitation, it fs evident from the r2 value for -3.1 and the scatter tllustrated in
Flgure 3. L tbat other factors apart fi-om gross precipitation determine stemfïow depth.
Duration of gross precipitation event, the intensity of the event, wind velocity and
canopy cover were examined ( by means of step wise regression d y s i s ) to
determine their influence. if any. on steaiflow production within the plot (Table 3.2)
The step wise regression @sis suggests that only ralnfall depth [mm) and
intensity (mm/h) have a sirrnlficant (a = 0-05) effect on stemflow depth. The
multiple regression equation encompassing these two factors is:
From E+3-7 it is evident that when gros precipitation is held constant
increasing ralnfidl intensity results in a decreke in stemflow. intultiveIy, stemfhw
would be less durlng higher intensity events since more p s precipitation passes
through the canow to generate throughikdl, Converse@, during iight rain showers,
stemllow may be greater, Presumably, dduring these low intensity events. once the
storage capacity of Ieaves and branches is BUed, water will have a tendency to flow
towards the bole of the tree rather than being explied as throughfall due to the lower
Table 3-31 Step wise regession for determining possible factors inffuencing stemfiow depth (mm) dwing summer foliage conditions.
VARIABLE b SE of E t - STATISTIC (n - rn -1 I f CRITICAL -tfj O
R (mm)
150 (mm/h)
D tn)
Co (ratio)
Wv (km/h)
R (mm)
156 (mrn/b)
Co (rat io)
Wv (km/h)
R (mm)
150 (mm/h>
Co (ratio)
R (mm)
150 (mrn/h) -0.009 0.003 -2.878
' (n - m - 1) represents deqees of freedam where n = number of observations and m = number of factors being examined.
*The critical- t value useci : t {a 2) 4 .05.
kinetic energy of incoming rain drops-
Previous research (Coweeta HydroIogic Laboratory. 1938; North AppaLachian
Esrperimentai Watershed, 1939; Black, 1957) has shown that stemflow generaily
ïncreases (&L proportion to gross precipftafion) during Ieafless dormant season
condftions. Hehrey and Patric (1965) suggest that stemflow increases durfng leafless
conditï011~ since wind-dfiven rain can strike stems more reaw than under summer
canopy conditions when the presence ofleaves and the relative& stable air (causing
raindrops to fall parael to the stems) decreases the fifiequency in which rain strikes
the stems. Although stemflow was not measured under Ieafless conditions. the
positive coefficient values for portion of canopy cover openings suggests that stemfiow
increased with the decrease in canopy cover caused by the Oak Skeletonlzer.
However, the dinerence in stemflow production is not signiflcant (a = 0.05).
Like throughf~, season-long stemflow is dependent on the depth and number
of gross precipitation events that occur Figures 3-2 and 3-31. However, the
dichotomy between varying stemflow depths is small relattve to the Werence in
tbroughfau for varyïng sirmmer-Iong g r o s precipitation depths and number of
events. The general trend suggests that s k e r s with large gross precipitation
inputs deltvered üy a relatively small number of events will produce the greatest
stemflow values (bath volumetricaltTy and as a percent of gross precipitation) at the
Erindaie site-
O E n r t ..... C C E C C
3.3 Stemflow as a Percent of Gross Prccipitation:
Although summer-long stemflow equaUed -4 percent of gross precipitation, the
percentage per storm varied considerâbly (fkom O - -8 %). Smaller storm events
generated- Uttle or no s t e d o w whiîe larger events produced the higher Rs/R ratios.
However, a 17.4 mm event generated - 8% stedow wMe only -2% of a 27.4 mm
ment was partitioned into stedow. Thus. gross precipltation depth alone is not
aiways a good indicator of stemflow production. However, the general trend in the
data suggests that the reIationship between stemflow and gross precipitatlon is a
positive hyperbolic one (Figure 3-41 that takes the form:
where Rs (%) ïs the percentage of gross precipitation that generates stemflow.
Sttmf low ( X of Gross Preclpltitlon) a!, r Function of Gross Preclpltatlon Depth (mm) Under Summer Canopy Condltlons, Erlndalt
1995
O 15 20 25 30 35 40
Groris Preclpltatlon (mm)
Figure 3.4: Stemflow product ion (X of gross prec lp i ta t lon) as a functlon of gross p r e c i p i t a t i o n depth (mm).
The amount of stemfbw produced during an event was found to be -y
variabIe among the 20 sampled trees- For events that do generate stemflow it was
found that, in generaL the smaïler the event the greater the varfabiIIty (Figure 3.5).
The high coemcient of variation values for steniflow (-79 - 28L%) is due, in part, to
the dtversity of species found at the Erindale site and the& m g DBH. branch
architecture. and condition me or dead indtviduals]- Figure 3-6 illustrates the
variation of stemflow (expressed in liters) produced by the various tree species
sampled (sugar maple. red oak. American beech, and red maple), while Figure 3.7.
3-8, 3.9 and 3-10 show the effect that DBH bas on stemflow quantities for aU species
combined, beech. sugar maple and red oak. respective& as a huiction of storm size,
When species type and condition is not taken into account the correlation
between DBH and stemflow production is weak (r = 0-35, n = 7): however, when
these factors are considered the correlation between stemflow production and DBH
increases (beech r = 0-99, a = 4; sugar mapIe r = 0.55, n = 7; red oak (Live) r = 0.62.
n = 7). However, althou# the linear reiationship was positive for beech and red oak,
it was found that for the sugar mapies samplea the slope of the iinear regression was
negattve. Thus. wfth the possible exception of beech, the DBH relationship is weak
for both red oak and for sugar maple- Given the large standard error associated with
the slope of the regression Une for both red oak and sugar maple, it is clear that the
number of observations needed to estabiish the relationship between DBH and
stemflow must be increased ( e-g. smce the standard error associated with the sugar
mape - DBH regression dope is so large it includes, at the 95 % confidence Ilmits,
Summer-Long Stemflow Production (L/Tree) es e Functlon o f DBH for Amcrlcan Becch Stems
_C_C1____ -- t--t------t-----t --- ---+ 16 18 20 22 24 26 28 30
DBH (cm)
Flgure 3.8: Var la t lon In season - long s temf low productlon (L / tree) as a functlon of DBH forbeech trees.
Summer-Long Stemf low Production (L/Tree) as a Funct Ion o f DBH for Red Oak Stems
Rs (L/Tree) 12.6 DBH - 292.3 rA2 0.39 SE of E 105.5 n n 7
DBH (cm)
; Figure 3.1 0: Varlat lon In season - long stemf t ow production (L / tree) as a funct Ion of DBH for red oak trees. I l
both positive and negative slopes-
During relatively large rainfd events s t e d o w volumes at individual trees may
be quite large. For example, -105 litres of water was coilected fYom the stem of a red
oak (DBH = 50 cm) during a 40- 1 mm event whLIe -80 litres was collected fiom a
beech stem (DBH = 29 cm) during a 17-4 mm event. Sm& events produce little or
no s t e d o w since a certain depth of gross precipitation is required to f3.U the storage
capacity of the stem. StemfIow was not recorded for any of the 20 sampled trees
during events less than L -7 mm and it was not until gross precipitation reached a
depth of 3-9 mm that stemflow was was fourid in all coUection containers (Table 3.4).
Stemflow generation fiom sugar maple (n = 7) and American beech (n = 4) stems
began at gross precipitation depths rangïng kom 1.7 - 2.2 mm, while stemflow began
on individual red oak stems (n = 7) during events ranging fi-om 1.7 - 2.9 mm,
S t e d o w from. a dead red oak and fkom a red maple did not commence until g r o s
precipitation was 3.9 mm. It should be noted that during svents in the range of - 2 -
10 mm the author observeci that ste-w was concentrated in varying sized bands of
flow coming down the boles of trees with the stems of most trees completely wetted
durlng events > 11 mm.
Although beech and sugar maple stems form the secondary canopy, stemfiow
was initiated, on average, on these stems during smaller gross precipitation depths
than for red oak stems. This is probably a consequence of the rehtively smooth bark
(thus, decreasing surface tension forces acting on the water] of these sugar maple
and beech stems (Horton, 1919; Kittredge.1961). The s m d e r DBH sizes of sugar
maple and beech stems c o d d &O be a factor in that these smaiier stems will,
Table 3.4: Dcpth of gross prec i p l tation In which var b u s stemf low production values (1 / tree) were measured for each tree.
intuitively, have smaller storage capacities- However, it should be noted that for both
sugar maple and beech it was fouad that, on average, stems wlth farger DBH sizes
produced s t e ~ o w at smaller gross precipitation depths than did stems with smaller
DBH sizes- The reason for this is not clear; however, the author speculates that the
smooth bark associated with these trees does not permit much resistance against the
flow coiïected by the increased collection area of these larger stems- For red oak
stems no pattern was found in relation to DBH and depth of gross precipitation
required to initiate s tedow; however, the condition of the tree may be a factor- Flow
did not begin on a dead red oak stem until gross precipitation reached a depth of 3.9
mm and even then it was only a trace amount. AU stems of five red oaks produced at
least 0.5 iitres of stemfiow durfng events ranging fkom 3.7 - 3.9 mm; however it was
not untiï a 27.4 mm event that s t e d o w of this quanffty occurred on the dead red oak
stem.
Sugar maples were found to produce more stemflow than the other species for
the smaUest event ( 1.7 mm). However, beech produced the ïargest mean volumes per
stem for events ranging in depth fkom 2.2 - 3.9 mm and red oak stems generated the
greatest mean volumes per tree for events greater than 3.9 mm. Although oak stems
have rougher bark than their counterparts, average stemfiow per tree was probably
greater for this species d m g events larger than 3.9 mm sirice they are taiier (thus,
receiving more incoming precipitation than understory trees (sugar maple and beech))
(Leonard, 196 1) and they have larger coUection areas than the other species.
Due to the Merences in stemflow production (L/Tree) by different tree
species and the frequency in which these species occur at the Erindaie stand, certain
species wiU contribute a larger proportion of total site stemfiow than others. Alttiough
red oak only constifxte 26.5 % of the sugar maple-red oak- beech-red maple
composition of the Erindaïe stand. the relatively large stemfiow input makes this
species an important contributor of total site stemfIow (Figure 3.1 1 and Table 3.5).
From Table 3.5 it is evident that for events < 4 mm (n = 6) beech and sugar
maple dominate stemflow confrïbution to the forest floor (average = 44 and 41 % of
totaI stemfbw, respectively), while red oak and red maple contribute - 16 and O % of
totai site stemfiow, respectively. However, the reïationship between s t e d o w
production and species type changed when gross precipitation exceeded 4 mm.
During these ïarger events red oak, on average, generated - 39 % of total stemflow
input per stom. whiïe the contribution by both sugar maple and beech decreased
(-29 and 3 1 %. respectively). Red maple input remained insignificant for kge r
events with average contribution rates of - 1 %. 'Thus, although a certain species may
dominate in terms of quantity of stems (in this case sugar maple) t h does not mean
that this species WU dominate stemflow input. Table 3.5 aïs0 b t s the mean stemflow
contribution (%) - stem frequency (%) ratio for each measured species. This ratio
provides an indication of stemfbw efficiency for each species. For gross precipitation
depths c 4 mm the species that generated the greatest amount of stemflow relative to
their frequency of occurrence were, in decreasing magnitude: beech, sugar maple,
red oak and red maple, and for stonn events > 4 mm: beech, red oak, sugzu maple
and red maple. Both red oak and red maple emciencies fncreased with increased
gross precipitation, whLle sugar maple and beech efBciencies decreased.
4
Table 3.5 Total eite etemflow contribution as a function of epeciee, frequency of sterne and stemflow podudlon ratw for euger maple, red oak, beech and red maple at the Erindiilr study site, eumrner 19%.
- - - - - - - - - - - - - - - - - - - -
Specles W o f S,Msple,Beech, Ra Contrlbullon As Contrlbut Ion RI Contrlbutlon Rs Contrlbutlon An Contrlbutlon / AI Contrlbutlon /
R.Oait end Rhieple Range ( X I for Mcan (X I for Renge ( W l for Moan ( X I for Frequtncy Rnt Io Frequency R i t Io
Compa~lt lon R 44 mm R ~4 mm R)4 mm R b4 mm tor R (4 mm tor ~ ( 4 mm
Representstlve Proportlon of Total Stemt low Productlon Generited by Sugar Maple, Red Oak, Amcrican Beech end Red Maple Specles Wlthln the
Erlndsle Stand
Gross Preclpltatlon Depth of Event (mm)
III Rad Mmple
Amirlcrn &@ch
li! hgrr u i p h
O RldOlh
Figure 3.1 1: Representlve propor t lon o f t o t a l s temf low generated by each tree specles as a functlon of gross p r e c l p l t a t i o n depth. i
4 Interception Loss .
4.1 Canopy Interception Loss and Gross Redpitation:
Canopy interception loss can not be measured directly, and thus estimates of
this flux must be made fkom subtracting the sum of ehroughfaü and stemflow form
gross precipitation depth. For the 27 summer events (-2 14 mm) in which stemfiow
and throughfaIi measurernents were taken canopy interceplion loss was - 41 mm r
(-19 % of gross precipitation) (Appendix 4.1)- By estimamg stemflow depth (fiom
Eq-3.11) for the 45.5 mm event in which stemfiow waç not measured, the total
canopy interception loss for a 2 8 events was -46 mm (-18 % of gross precipitation),
Figure 4.1 iilustrates the hear relationship between gross precipitation depth
(mm) and canopy interception loss (Ec) (mm) for the 27 events. The equation of the
Une takes the fonn of:
Although Helvey and Patric (1965) did not-provide an equation for estimating
canopy interception loss in eastem hardwood stands, this equation can be easily
computed fiom their equations for growing season throughfall and growlng season
stemflow equations:
Cenopy Interception Loss (mm) as a Function of Gross Precipitatlon (mm) Under Summer Canopy Condl t Ions. Er lndele 1 995
a
I - r - - - 4 1 1 -t-./------ 1 O 5 1 O 15 20 25 30 35 40 45
Oross Prtclpltstlon (mm)
l Figure 4.1: Canopy Interception loss (pm) as a functlon of gross preclpltatlon depth (mm).
Appl-ying Eq.4.2 to the data generated at the Erindale site yields a canopy
interception loss estimate of -38 mm 1-18 % of gross precipitation) for the 27 events.
This vzlue is consistent with the value derived fkom measurernents at the Eruidale
site ( -4 L mm (- 19 % of gross precipitation)).
Estirnates of summer-long canopy interception Zoss (mm) wiil Vary in response
to variations ui botfi total gross precipitation depth and the number of events that
occur d u ~ g a particuIar season (Figures 4.2 and 4.3). Cape et al. (199 1) caiculated
interception loss fkom larch (La* decidua) to be 15 % of gross precipitation during
the period of April 1984 - March 1985; however. for the same species on the same
plot interception loss was determined to be 24 % during the period of April 1985 -
March 1986. Thus. Large variations in canopy interception losses can occur fi-orn
year to year.
Since canopy interception ( as a percent of gross precipitation) is a function of
gross precipitation and number of events, reports of canopy interception loss values
(%) are meâningless unless a Linear regression equation (preferably with the standard
error of estimate given) or both precipitation depth and fkequency characteristics are
provided. Although this point was raised by Helvey and Patric (1965) over 30 years
ago, much of the interception fiterature sînce then has failed to provide these
Variation of Sesson-Long Canopy lnterceptlon Loss (mm) as a Funct Ion of Gross Precipitatlon (mm) and Number of Events
100 1 50 200 250 300 350 400
Gross Prcclpltatlon (mm)
Flgure 4.2: Canopy ln tercept lon loss (mm) as a functlon of al1 summer gross p rec lp i t a t l on depth (mm) and numbbpy of events.
Ec / R Ratlo as a Function o f Gross Precipitation (mm) and Number of Evtnts
1 O0 150 200 250 300 350 400
Gross Preclpltetlon (mm)
Figure 4.3: Canopy Intercept ion loss (percent o f gross preclpl tat lon) as a functlon of al1 summer gross prec ip i ta t lon depth (mm) and number of events.
characteristics (e.g.. Sïngh. 1987: Williams et al.. 1987; Ahmad-Shah and Rieley, 1989:
Cape et al-, 199 1 ) In additton many texts publish interception loss vaiues for
comparaBve purposes but with no mention of raidail characteristics (e-g,, Dunne and
Leopold, 1978; Briggs et al. L989: Dolman. 1993;). Zinke (1967) provided a compIete
summary of canopy interception loss values detennined by various researchers in the
United States- However, many of the reported interception values were glven as a
percent with no Iinear regression equation or storm characteristics given and thus
meanin@ cornparisons of canopy interception loss can not be determined. It was
determined by Price et al- (1996) that canopy interception loss fjrom a black spruce
(Picea mariana) stand was - 23 % of gross precipitation- This value was derived fkom
1 I storm events totaUïng - 106 mm. Given the same gross precipitation depth and
number of storms, Eq.4.1 yields a value of - 18 mm (- 17 %), Thus. given the same
depth of gross precipitation over 11 storm events. the Erindale stand would uitercept
and evaporate -6 % less incoming precipitation than the bhck spruce stand. It is of
the authors opinion that this is a much more meaningful way of c o m p m g canopy
interception loss from differing vegetative surfaces.
4.2 Addition Factors riiflutllcing Canopy interception Loss:
As mention above, canopy ioterceptron loss is dependent on both throughfall.
and stedow. Thus, dthough gross precipitation and canopy interception Ioss were
found to be highly correlated during this study (r = 0-89, n = 27)- other factors
should play a role since throughfall and stemflow were found to be influenced by an
additional factor (rainfd intensity], Step wise regression anaLysis was performed
(Table 4- 1) and the results indicate that, of the possible factors tested, only rarnfall a-
depth and intensity have a sgdicant (a! = 0-05) effect on canopy interception. The
equation of the line is:
From the above equation it is evident that rainfhll intensity, when combined
with gross precipitation values, gives the best estimate of canopy interception loss at
the Erindale site. The standard error associated with this equation (Eq.4.3) is 0.4 1
thus, representing a 0.04 mm improvement over the standard error of estimate
associated with Eq.4-1. Thus, for predictive purposes the author suggests that
incorporating r a t n f i intensity with gross precipitation depth yields the best estfmate
of canopy interception loss at the Erindale site (Eq.4.3).
Although an exhaustive search of the literature did not produce other equations
relatlng rainfa11 intensity to canopy interception loss, research fhdlngs by CarLlsle et
116
Table 4.1 T Step wise regession for determinhg possible factors infiuencing canopy interception loss (mm) during summer foliage cond-fiions.
VARIABLE b SE of E t - STATISTIC Cn - m -1 )* CRITICAL -t- a
R (mm)
150 (mm/h)
D (hl
Co (ratio)
Wv (km/h)
R (mm)
150 (mm/h)
Co (ratio)
Wv (km/h)
R (mm)
150 (mmlh)
Wv (km/h)
R (mm)
t (n - m - 1 ) represents degees of freedom where n = number of observations and m = nurnber of factors being examined.
** The critical - t value used : t (a 2) =0.05.
al. (1965). Delfs (19671 and Williams et ai. (1987) gïve further support to the notion
that with increasing rainfall intensity canopy interception losses decrease. For
example. DeKs (1966) found that canopy interception loss was on& 2 % during a 74-6
mm rainfall that feu within 3.5 hours (150 -2 1 mm/h), while 25 % was intercepted
during a continuous 70.5 mm event that occurred over a period of 50 hours (150 -1 -
2 rnrn/h)-
4.3 Canopy Interception Loss as a Percent of Gross Precipitation:
Canopy interception loss was found to be greatest (as a percent of gross
precipitation] during srna events and Iess for larger events. For exampte -89 % of
gross precipitation was intercepted and evaporated fkom the canopy during a 0.4 mm
event whïie only 8 % of the gross precipitation was lost to evaporation fiom the
canopy duriag a 17.4 mm event, Figure 4.4 mustrates the c m e a r relationship
between gross precipitation depth (mm) and canopy interception loss (%). From
Figure 4.4 it appears that caaopy interception (%) decreases rapidy with increasing
gross precipitation depth untii a depth of - 5 mm is reached. Once gross
precipitation depths exceed 10 mm, canopy interception loss (%) becomes quasi-
constant at - 10 %. The equation of the line is:
4.4 Litter Interception Loss:
Litter Ïnterception loss values have onJy been reported for North American
hardwood forests by a few authors le-g. Blow. 1955; CZrrtfS, 1960: Helvey. 1964)-
Typicd values for the southern Appalachians (where most of the available data were
generated) range f?om 2 - 5 % of gross precipitation (Helvey and Pamc, 1965). From
Table 4.2 it is evident that. at least at the Erindale site, Mer interception loss is a
sigdicant porüon of total interception bss. Litter interception loss vaiues ranged
Çom -0.0 - 1-3 mm (-1 - 17 % of gross precipitation) per storm event. Knterception
loss from Litter was - 6 % for the summer portion of the study and - 5% for summer
plus fall storms. The slightly higher values reported in this study may be a
consequence of variations in litter type, decomposition rates and or time of
rneasurement in cornparison to other studies. Litter interception loss ranged from 7 -
39 % of total interception loss (canopy + litter) per storm.
Unlike throughfall. stemflow or canopy interception loss. the relationship
between litter interception loss and gross precipitation was not found to be Ilnear. but
rather hyperbolic (Figure 4.5). The equation of the iïne is:
where El represents iitter interception loss (mm). Equation 4.5 is for ail
measurements taken during the study period. while Eq.4.6 is for measurements
taken under summer foliage conditions.
4.5 Litter Interception as a Percent of Gross Precipitation and
Throuahfnll:
Litter interception loss values (as a percent of gross precipitation) did not show
a continuai decrease with gross precipitation. Values for events less than or equal to
1.5 mm were Iess than for events in the range of 1.7 - 8.1 mm, while values for
events > 8.1 mm were the srnaest (as a % of gross precipitaaon) (Appendix 4.2)-
This is possibly a consequence of the large canopy interception rates during these
smaU events (Ward and Robinson, 1990). However, iitter interception loss (as a
percent of throughfall) showed a curvillriear relationship with throughfdl (mm). Figure
4.6 illustrates this relationship for the srimmer portion of the study- The equation is:
where El (%Rt) represents the percent of throughfail that is intercepted and
evaporated fr-om the Litter layer.
From Figure 4.6 it is evident that iitter interception as a percent of throughfall
decreases rapidly (from - 76 %) with increasing throu&hfd depth unUi throughfd
reach -1 - 3 mm. At these depths, litter interception is typicaUy -5 % of Rt.
O 0 0 0 0 0 O 0 0 O O O Q ) a b b r C ) V I P m - - F
4.6 Total Interception Loss and Net Precipitation:
Total hterception loss was determined for 27 events in which measurements
of throughfii. stemfïow and Utter interception were taken (n = 27). Total interception
loss for the summer events was - 23 % of gross precipitation (2 14 mm). However,
total interception loss varied with storm depth (Appendlx 4.3). Values ranged fkom 12
- 96 % of gross precipiîation with lower interceptfon loss values associated with
greater gross precipitation amounts- Figure 4.7 shows the Unear reiationship
between total interception loss (Ei) (mm) and gross precipitation (mm) which takes the
form of the foUowing:
Total interception Loss (mm) as a Functlon a f Gross Precipitation (mm) Under Summer Canopy Condl tlons, Erlndale 1995
l
4.7 Additional Factors Innuencing Total Interception Loss:
Rainfall intensity. duraffon of storm event, changes in canopy cover and wind
velocity were examined to determuie their influence on total interception loss. By
means of multiple regession and regressional step andysis (Table 4.2) it was fourid
that the foïïowfng equation incorporates factors that significântly (a = 0-05) effect total
interception loss:
Thus, when rainfail intensity ïs incorporated with rafnfhiï depth the best
estimates of total interception loss (fL-om the parameters studied during this research)
are achieved.
i ame 4.2: xep Wise regession ror aerermining posstoie tactors rnnuenang totai interception Ioss (mm) during su rnmer foliage conditions.
(n - m - 1) represerrts degees of freedom where n = n u m k of observations and rn = number of factors b&g examined.
**The aiticai - t value used : t (a 2) 4.05.
4.8 Total Interception Loss as a Percent of Cross Precipitation:
Total interception loss [as a percent of gmss precipitatfon) decreased from - 96
% during events c Lmm to - 12 - 17 % for events > 10mm. The relationship between
total interception loss (%) and gross precipitation was found to be a curvtlInear
rektionship (Figure 4-8) that takes the forrn of the foIiowing:
where Ei (%) represents the percent of gross precipitation (RI that is Iost due to
canopy and litter interception.
4.9 Net Pncipitation
The portion of gross precipitation that enters the mineral sou 5 termed net
precipitation. -Thus. al l throu&hfall and stemflow that is not lost to Litter interception
becomes net precipitation- Figures 4.9 and 4.10 illustrate the reIationship between
net precipitation (mm) and gross precipitation (mm] and net precipitation (% of gross
precipitation) and gross precipitation (mm) respectively- The equations are:
where R n is net precipitation (nim) and Rn (%) is net precipitation as a percent of
gross precipitation-
Net precipitation on a seasonaï basis with vary in that it depends on both the
gross precipitation amount and the nequency of events (Figures 4.1 1 and 4.12).
Thus, temporal and spatiai variation of the season - long gross precipitation inputs
will result in Mering amounts of water passing into the mineral sou.
Since net precipitation is the inverse of total interception loss, gross
precipitation depth and intensiiy wLLl &O significantiy (a = 0.05) effect net
precipitation. The foliowing equation appliies:
Rn= 0.86 R + 0.02 150 - 1.05 (r2 = 0.99. SE of E = 0.50, n = 27) (Q.4.13)
Net Preclpltatlon (mm) as a Functlon of Gross Precipitation Depth (mm) Under Sumrner Canopy Condl t ions
O 5 1 O 15 20 25 30 35 40 45
Gross Prtclpi tet lon (mm)
Figure 4.9: Net prec ip l ta t lon as a functlon of g r o s preclpl tat ion depth (mm) under summer f o l lage condl t ions.
. . , , ..,-----.--........
Variation of Season-Long Net Preclpltatlon (mm) as a Functlon of Gross Preclpltation (mm) and Number of Events
Gross Preclpltatlon (mm)
Figure 4.1 1: Net p rec lp l ta t lon as a funct lon of summer - long gross preclpltat lon depth (mm) and nurnber of events.
5 Conclusion
Rainfall partïtioning by the sugar maple - red oak - American beech stand at
Erindale varied witb both gross precipitation depth and intensity. Throuehfi,
stedlow, canopy interception loss, litter interception loss and total interception loss
d W g the summer were found to be - 77 -c_ 3, 4 -c_ 1, 19 -c_ 3. 6 2 3 and 25 I 5
percent of summer - long gross precipitation. respecWy. while net precipitation
entering the mineral soi1 was -75 2 5 percent. Significant factors influencing water
fluxes were rainfall depth and intensity. The variables of proportion of canopy
openings. wind velocity and event duration did not have a significant effect on any
water flux. Throughfall increased with increasing gross precipitation depth and
intensity, whiie stemflow increased with increasmg gross precipitation and decreasing
intensity. Interception losses generally decreased with increasing gross precipitation
depth and intensity.
Throughfall and stemtlow (as a percent of g ros precipitation) increased rapidly
with gross precipitation depth unOU gross precipitation reached - 10 - 12 mm. For
events > LO - 12 mm both throughfdl and stemflow [as a percent of gross
precipitation remained fafrly constant. Throughfali and s tedow variab111ty also
remained quasi constant once this threshold was reached-
With regards to summer - long throughfaiI depth. sgniflcant dtaerences (a =
0.05) between throughfall depth at varying distances were found. The relationship
between throughfall depth and distance from Wee stem was found to be hyperbolic.
suggesting that throughfdl depths rapidly increase untü a certain distance has been
136
reached from the stem, af-ter wfuch the increase is on& graduai- No signiticant
merence (a = 0.05) was found between throughfi depths under varyfng tree
species and throughfdi depth at one point was not highly correlated with throughfall
depth at another point, regardless of prorami@
Stemflow variation was substantiai. However, given the Merences bark,
height and branch architecture among the trees sampled this variation is not
surprising. Although much of the literature suggests that stemflow generally
decreases with increasing bark roughness, red oak, whicsh has rougher bark then
both sugar maple and American beech, produced Larger quantiffes of stemflow, on
average, than did any other species. This is possibly a consequence of the height of
these trees (red oak dominated the upper canopy level) which, intuitively, permitted
them to receive more gross precipitation. However, it should be noted that both
sugar maple and beech trees initiated sternflow, on tiverage, during smaiïer gross
precipitation depths than did red oak trees.
Litter interception loss was found to - 6 percent of gross precipitation- This
represents - 25 percent of total interception loss and Nustrates the importance of
taking the iitter Iayer into consideration when estimating interception loss f?om a
forest stand.
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Appendix 1-2 Shrub and Herb Veeetation of the Erindale ExPerimental Site
Gemniummacurtltrrnr Gemnïummbertiarwm GeumcaRQdeRSe n-lisvirginiana W y s ~ p a t u r c r Lonicemdlofca Lonicenztwrtarfca M-mrlmcanadense M e d e o I a ~ t n i a m MonoulfZom OnJwpsiS asperyolia Paa compressa Podop~Uumpeltafum Pdyg-p-- n r n a q u i l i n u m Ranunculusabortiuus Rhusmdfcans Ribescy~osbatl Rubus (bhddenyl Srnilacinaracernosa Sambucuspubens Smiïaxherbacea S o ~ o m n a d e n s i s S o ~ o c a e s i a ThaLictmm dtolcum TriLUum gmRdiiomtn
Source: J. Geraphty (1972). -BI0 330" laimratory rrport. Department of Biology. ErLndale Cokge. University of Toronto.
0-
64,2 60.7
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7 -
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12.3 .. S..- . .-- I3/ . -..,-- 132
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74.8 75.7 44.8 84.6 78,B 62.3 83.8
111.7
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"" -.-..- 76.6 -. .-.--.. 70.3 ---- 750 ---- 82.0' - 89.8 -.,---- 75.0 .-. # .... -.*- 74,2 - .-,, --,-.* 70.3 --- 683 ---- 79,7 - -- -- 85.9 ---- 72.7 5 ~ 2 -----
7&1 . -- 76,6
-- 213 2 3 - .---- z- ---
26' --- 2c ------ 2d - .---.-- 2e
----,
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87.7 - 83.2 -
110,6
---. 82.1
78.6'--82.1 --- 7 8 , ~ ' - . ---- - 84.2
a..- - -7 fi --a
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89.9'-83.0 - 86.3'-68.7
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71.4 80.9 48.4
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--A
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--- 82,8 -
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-..--A
77,6 -.,. ..-- 82,l --
-75.8 --.- 89.9
" -.-- '- 91.8 -.,.- 03.4 - 82.2 - 92.9 .+-- 7- -., . --- 77.0 -. ,..- 68.2 - 82,9 ---- 78,8 .*-.-.-- 89.6 -- 85.0
-m- --- --.--A--
77.1 ---- 82.8
80.6 76.8
76.9 69.1 76,2
10S.O 104.0 8 m -
-- 60,2 - 6 4
"
75.3 70.5 78.4 68,Q 82.0 58,2 71.4
100.4 99,3
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58.4
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'2'js . .,- - 4
-- -.. 2 222 . ----- 223
*-"S. - 2Jt - .--- 332 ---
-- 233 211 -----
1 1 - 78.1 -- 80,8-
46,8 -- 1 1 5 , ~ '
--- 70,O
78.2
S . . "0 .
65.1 --. , , --- 75.2 -- 78.8 - 81.2 87.4- --- 77.3 85,8 ----
101J - 08.4 81.2 -- 88,0' 88,8
(15.7
S9.QI 62.4
79.7 80.4
54,2-- 74J 69,8- 68.8
50.7
- ".ï$,o -- --. - .. 83.6 -- 80.3 ---- 08.1-
-9-
71.9 -- 75.8 75.0
' 81.2
70.7 77.0
76,s 77,2
. - -.-- 653 .--- 8 8 3 -- 73.8 -- 8S9' "'-.. 79.2 .- ,... --..- 71.7 --- 80,4
8 5 . 0 x - -- 85.0' ---- 730" .-.- .- .*-- 7 1 , ..-* -- f a - .- 87.9 - 89,ê
a----
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--.-- 77.7 --.- 74,8
Sf.B 7 4 . 1 8 8 , ~ '
65.1 82.5 88.7 -
- 883
84.2 7 5 . 5
72.4 06,2
68.0
8 0.0
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14.5 - SO,Q'.
12
70.3 -- 75.0
' 48,4 95,2 84.3 76.5
04J
" "d'". "
17.7 . -,,-. 8 1 82.1 90.8 87.4 68,s 78.6 - 8B07
61.5 -- a s , ~ ' - - 45.7. 8S,Q 8S,2 --
68.4 75.7 .- 92.4 -- 71,6 73.7 84.7 7Q,1 81,O.
68.7' 82,2 ---- 78.0
63,5 6 0 , 0 5 5 . 2 -
86.5 77.8 76.8
O . 88.0
8 3 . 0 ' 7 6 , ~ 66.7 64.8
..--. 63,7
8 ) , 8 - 100.0
' ---- 81.6 70.5 82.7 02.2 79.9 .-...-. 78.3 --- .-.. 80.2 --- 86.6 - 73.8 - 00,O 85.4 83.5 93.9
"
--- 82.1
78.2 82.7 87.8
27.8 20.4 42.4 253 18.7 31.5 17.3
24,1 22,8 35.8 25,ë
45.6 -
67.1
102.8 83A 70.1 78.0
86.1 75,8 -,.- 71.2 ---- 64.8 82,4 61.7 75.1 80,8
- 80.8 57,7 70.5 - 62.3
88,8 78.3
.-- --- 62,O -
734 ' 3 5 7 8 --
- 08,8 - - 54.2 . - 62,s 56.8 533 68.8 53,6 54.2 62,O 73,4
24,l 36.4 31.5 40.7
66.3 725 66.3 83,Q
78.8 81.6 67.3-
7 7 -87,4
80.7
- 73.7 - 86.8 -- 733-
1,8 14.5 10.8 0.0,
-- 833 --
' 92.0' --,
85.1
13
58.4 - 70.5 48.1 8S,1
100.7 81.5
63,0-85,211.8--- 107.5
- 64.7
743'-7P.l -
Q 2 , 8 ' - 1 7 6 50.3
53.8
. "-- 77.2'- 86.3 - 81.0 74.1 79.8 60A 15.8 -
8 & 3 ' - 8 3 7 2 .--y 733
72.3 84.8 95,s 81.9 65,2 76,O 793
68,s - 54.0
704 87,3
87.4 S4,6 523
73.3 55,8 71.7 -- 84,3 - -. - 74,1
67.0 78.7
81.2
85.8 87.Q 6Q.B 77.7
77.7 78.8 01.8
63.0 --- 78.0
748 --...-.- 77,4 - - - - -. 89.8 - - .----- 67.0- -- 81.1 - 78.0 71.8'
14
3,8 1,1) 0,O
14,s 21.8
7 3 7 3
72.7
15
34.5 19,1
- 73,s 70.0 ---- 57,8 -- 09,4 06.0 82.2 96.3
5 7 . 7 Ë 8 , 7 76.8
7 0 . 2 ' 8 0 , 1 86.3 87.6 81.4 77.0 .-.---- 82,s
88,9- 54.3
- 78.2 --.- 81.3
""""" 72,1 2
43.6 14.5 0.0
i8,2 7 3
59.8 --.
---.--a-
83.3 - - 62.2 .----- 72,8 74.0 68,8 68,Q 57,8 71.6 01.3
115.0. 90.0 82,s 73.4 81,7 75.2 74,1 02.6
74,3 7 4 --
75,4
- -2 i i ;491 ,8 ' - 66,O
81,B- 76.1
62.7 83.4 91.4
- 80.0 85.7 ---
' 84.4 ---- 75,O
85.8 78.3 79,4
72.0 73.9 73.2'
-* .. - - - - 69.5
50,1-6~-~ 47.6 38.8 523
80,2 74.6 73.4
70,4 663 693 .------- 73,9 -. - - .- 72.4 -. 62.2
"'-- 86,7 .-- 82.7
- 1 6 , S - - ë 6 3 --
tS,4 35,0-'
82,l
28.4
17.9-
31.5 8 3 , 2 t 5 , i 7 3 2 , 7 = - 7 4 . 3
80.0 8 . 2 85,6
94.1
75.7 740 - - I L 2 88,4 78.7 74.2 79.5 74.8 -
72,2
- '
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