THE QUANTITATIVE IMPORTANCE OF STEMFLOW:
AN EVALUATION OF PAST RESEARCH AND RESULTS FROM A STUDY IN
LODGEPOLE PINE (PINUS CONTORTA VAR. LATIFOLIA) STANDS IN SOUTHERN
BRITISH COLUMBIA

by
Adam Jon McKee
B.A. Thompson Rivers University, 2008

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE (ENVIRONMENTAL SCIENCE)

Thesis examining committee:
Darryl Carlyle-Moses (Ph.D.), Thesis Supervisor, Assistant Professor, Dept. of
Geography, Thompson Rivers University
Karl Larsen (Ph.D.), Committee Member, Associate Professor, Dept. of Natural Resource
Sciences, Thompson Rivers University
Rita Winkler (Ph.D., R.P.F.), Committee Member, Adjunct Professor, Dept. of Natural
Resource Sciences and Research Hydrologist, BC Ministry of Forests and Range
Delphis F. Levia (Ph.D.), External Examiner, Associate Professor, Depts. of Geography
& Plant and Soil Science, University of Delaware

Spring Convocation 2011
Thompson Rivers University

© Adam Jon McKee, 2010

Thesis Supervisory Committee

________________________
Dr. Darryl Carlyle-Moses, Supervisor

________________________
Dr. Karl Larsen, Committee Member

________________________
Dr. Rita Winkler, Committee Member

This thesis by Adam Jon McKee was defended successfully in an oral examination on
December 9, 2010 by a committee comprising:

________________________
Dr. Delphis F. Levia, External Examiner

________________________
Dr. Darryl Carlyle-Moses, Supervisor

________________________
Dr. Karl Larsen, Committee Member

________________________
Dr. Rita Winkler, Committee Member

ii

________________________
Dr. Lauchlan Fraser, Chair/Coordinator of Graduate Program Committee

________________________
Dr. Tom Dickinson, Dean of Science

________________________
Dr. Peter Tsigaris, Chair of the Examining Committee

This thesis is accepted in its present form by the Office of the Associate Vice President,
Research and Graduate Studies as satisfying the thesis requirements for the degree Master
of Science, Environmental Science.

………………………………
Dr. Nancy A. Van Wagoner
Associate Vice President, Research and Graduate Studies

iii

I, Adam Jon McKee, grant non-exclusive permission to the University Librarian of
Thompson Rivers University to reproduce, loan or distribute copies of my thesis in
microform, paper or electronic formats on a non-profit, royalty-free basis for the full term
of copyright protection. I, however, retain the copyright in my thesis.

________________________
Author

________________________
Supervisor

________________________
Date

iv

ACKNOWLEDGEMENTS

I would like to thank my supervisor, Dr. Darryl Carlyle-Moses, for inviting me to
join his research program, and for his input, support, and knowledge that made the
completion of this thesis possible. Thanks to my thesis committee for their time and
guidance: Dr. Rita Winkler, Dr. Karl Larsen, and Dr. Delphis Levia. Special thanks to the
entire research team for help with equipment setup, data collection, and for two great
summers: Chad Lishman, Pearce Sanders, Andrew Pillar, Jenn Golden, Warren
Giesbrecht, and Sarah Ostoforoff.
I would like to thank my family for their support over the past two years and
everyone that had to listen to me talk about “trees, water, and dirt”. Special thanks to
Katie Tallon for her support, editing skills, and field assistance when an extra person was
needed.
This project would not have been possible without the funding support provided
by the NSERC (Natural Sciences and Engineering Research Council of Canada)
Discovery Grant and the FIA-FSP (Forest Investment Account – Forest Science Program)
funds awarded to Dr. Darryl Carlyle-Moses.

v

ABSTRACT

Stemflow is a focused point source input of precipitation and nutrients at the base
of a tree or plant, and can have a significant impact on site hydrology. A review paper
examining the quantitative importance of stemflow, and a stemflow modelling paper
focused on juvenile lodgepole pine are presented in this thesis. Stemflow production
information from 145 different studies is presented in table format with the addition of
author-calculated funnelling ratios and plateau funnelling ratios when applicable. Plateau
funnelling ratios were calculated to provide an estimation of the rainfall depth required to
satisfy the storage capacity of a tree. Reference tables were used to identify interclimatic, inter-genera, and intra-genera variations in stemflow production. Plateau
funnelling ratios were used to identify shortcomings in current canopy interception
models. Finally, the reference tables were used to identify areas of the stemflow literature
where knowledge remains fairly weak. To date, no known studies have modelled
stemflow production for juvenile lodgepole pine (Pinus contorta var. latifolia).
Meteorological conditions, tree characteristics, and stemflow were sampled for two
juvenile lodgepole pine stands over the course of the 2009 growing season. Step-wise
multiple regression was used to assess which meteorological and tree architecture
variables influenced stemflow production for each research plot. Once predictor variables
were identified, models were produced for each stand and a generic model was produced
that applied to both plots. A model employing precipitation depth and crown projection
area successfully explained 71.3 % of the variation in stemflow production from sampled
trees.

Key words: Lodgepole pine (Pinus contorta var. latifolia), stemflow, stemflow funnelling
ratio, plateau funnelling ratio, forest hydrology

vi

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...................................................................................................... v
ABSTRACT ............................................................................................................................. vi
TABLE OF CONTENTS ........................................................................................................ vii
LIST OF TABLES ................................................................................................................... ix
LIST OF FIGURES ................................................................................................................. xi
LIST OF SYMBOLS .............................................................................................................. xii
CHAPTER 1. INTRODUCTION ............................................................................................. 1
LITERATURE CITED ......................................................................................................... 4
CHAPTER 2. A SYNTHESIS AND EVALUATION OF PAST RESEARCH ON THE
QUANTITATIVE IMPORTANCE OF STEMFLOW ............................................................. 7
INTRODUCTION ................................................................................................................ 7
METHODS ........................................................................................................................... 9
RESULTS ........................................................................................................................... 12
1. Temperate deciduous .................................................................................................. 12
2. Temperate coniferous and boreal ................................................................................ 14
3. Mixed deciduous and coniferous stands ..................................................................... 15
4. Tropical ....................................................................................................................... 16
5. Mediterranean ............................................................................................................. 16
6. Arid and semi-arid environments................................................................................ 18
7. Agroforestry ................................................................................................................ 19
DISCUSSION ..................................................................................................................... 19
CONCLUSION ................................................................................................................... 22
LITERATURE CITED ....................................................................................................... 23
APPENDIX – REFERENCE TABLES .............................................................................. 39
CHAPTER 3. MODELLING STEMFLOW PRODUCTION BY JUVENILE
LODGEPOLE PINE (PINUS CONTORTA VAR. LATIFOLIA) TREES IN SOUTHERN
BRITISH COLUMBIA, CANADA........................................................................................ 79
INTRODUCTION .............................................................................................................. 79
MATERIALS AND METHODS ........................................................................................ 81
Site description................................................................................................................ 81

vii

Meteorological data ........................................................................................................ 82
Stemflow collection ........................................................................................................ 84
Tree characteristics ......................................................................................................... 86
Statistical analysis ........................................................................................................... 87
Modelling procedure ....................................................................................................... 87
RESULTS ........................................................................................................................... 88
Funnelling ratios for lodgepole pine ............................................................................... 88
Abiotic and biotic influences on stemflow and the simulation of stemflow
production ....................................................................................................................... 89
Stemflow produced by a branchless tree ........................................................................ 99
DISCUSSION ................................................................................................................... 100
Lodgepole pine stemflow production ........................................................................... 101
Model assessment ......................................................................................................... 102
CONCLUSION ................................................................................................................. 104
LITERATURE CITED ..................................................................................................... 105
CHAPTER 4. CONCLUSION.............................................................................................. 109
LITERATURE CITED ..................................................................................................... 112

viii

LIST OF TABLES

Table 2.1. Species, location, stand information (diameter: Diam, tree density: Density,
stand basal area: BA), author(s), and alphanumeric code for temperate
deciduous studies
39
Table 2.2. Species, meteorological data (annual rainfall: PA, and study period rainfall:
PS), stemflow production information (funnelling ratio(s): F, and percentage
of gross rainfall diverted to stemflow: SF), and stemflow formulae for
temperate deciduous studies
42
Table 2.3. Species, location, stand information (diameter: Diam, tree density: Density,
stand basal area: BA), author(s), and alphanumeric code for coniferous and
boreal studies
46
Table 2.4 Species, meteorological data (annual rainfall: PA, and study period rainfall:
PS), stemflow production information (funnelling ratio(s): F, and percentage
of gross rainfall diverted to stemflow: SF), and stemflow formulae for
coniferous and boreal studies
49
Table 2.5. Species, location, stand information (diameter: Diam, tree density: Density,
stand basal area: BA), author(s), and alphanumeric code for mixed deciduous
and coniferous stands
52
Table 2.6. Species, meteorological data (annual rainfall: PA, and study period rainfall:
PS), stemflow production information (funnelling ratio(s): F, and percentage
of gross rainfall diverted to stemflow: SF), and stemflow formulae for mixed
deciduous and coniferous stands
52
Table 2.7. Species, location, stand information (diameter: Diam, tree density: Density,
stand basal area: BA), author(s), and alphanumeric code for tropical
studies
53
Table 2.8. Species, meteorological data (annual rainfall: PA, and study period rainfall:
PS), stemflow production information (funnelling ratio(s): F, and percentage
of gross rainfall diverted to stemflow: SF), and stemflow formulae for
tropical studies
57

ix

Table 2.9. Species, location, stand information (diameter: Diam, tree density: Density,
stand basal area: BA), author(s), and alphanumeric code for Mediterranean
studies
61
Table 2.10. Species, meteorological data (annual rainfall: PA, and study period rainfall:
PS), stemflow production information (funnelling ratio(s): F, and percentage
of gross rainfall diverted to stemflow: SF), and stemflow formulae for
Mediterranean studies
66
Table 2.11. Species, location, stand information (diameter: Diam, tree density: Density,
stand basal area: BA), author(s), and alphanumeric code for arid and semiarid studies
72
Table 2.12. Species, meteorological data (annual rainfall: PA, and study period rainfall:
PS), stemflow production information (funnelling ratio(s): F, and percentage
of gross rainfall diverted to stemflow: SF), and stemflow formulae for arid
and semi-arid studies
74
Table 2.13. Species, location, stand information (diameter: Diam, tree density: Density,
stand basal area: BA), author(s), and alphanumeric code for agroforestry
studies
76
Table 2.14. Species, meteorological data (annual rainfall: PA, and study period rainfall:
PS), stemflow production information (funnelling ratio(s): F, and percentage
of gross rainfall diverted to stemflow: SF), and stemflow formulae for
agroforestry studies
77
Table 3.1. Stand characteristics for Plots E and D

82

Table 3.2. Coefficient of determination (R2) and p-values associated with statistically
significant abiotic predictor variables of stemflow production for individual
study trees.
90

x

LIST OF FIGURES

Figure 3.1. View of Plot E from the northwest corner of the plot

83

Figure 3.2. View of Plot D from the centre looking south

83

Figure 3.3. Stemflow collar and collection container used in Plots E and D

85

Figure 3.4. Season-long stemflow funnelling ratios versus tree diameter for all healthy
lodgepole pine trees in Plots E, D, C, and B
89
Figure 3.5. Power relationship between slope values and tree diameter for healthy
lodgepole pine trees

91

Figure 3.6. Intercept values versus diameter showing a weak linear relationship and not
the power relationship shown by Park and Hattori (2002)
92
Figure 3.7. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for Plot E (
) and the 1:1 line (------)
94
Figure 3.8. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for Plot D employing the Plot E model (
) and the 1:1 line
(------)
94
Figure 3.9. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for Plot D (
) and the 1:1 line (------)
95
Figure 3.10. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for Plot E employing the simplified model (
) and the 1:1 line
(------)
96
Figure 3.11. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for Plot D employing the simplified model (
) and the 1:1 line
(------)
97
Figure 3.12. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for lodgepole pines in Plots E and D employing the generic model
(
) and the 1:1 line (------)
98
Figure 3.13. The percentage of rainfall that became stemflow versus rainfall depth at the
stand scale for Plot E (
) and Plot D (------), highlighting the rainfall
depth required for the commencement of stemflow production (1.6 mm) 99

xi

LIST OF SYMBOLS

#Brch – Number of branches

n – Number of samples

̅ – Average horizontal distance
𝐷

nstems – Number of stems

̅ – Average height
𝐻

P – Precipitation

A – Agroforestry

P’g – Rainfall depth required for

A – Canopy area

canopy saturation

a – Slope

PA – Annual rainfall

Angle2/3 – Branching angle at two

Pg – Rainfall

thirds the height of the tree

PS – Study period rainfall

Anglebottom – Branching angle at the

S – Semi-arid and arid

bottom of the tree

SF – Stemflow

b – Y-intercept

T – Tropical

BA – Basal area

TF - Throughfall

C – Temperate coniferous and boreal

V – Canopy volume

CBH – Circumference at breast height

X – Mixed deciduous and coniferous

CPA – Crown projection area

stands

D – Temperate deciduous

β1 – Regression coefficient

D – Tree diameter at the base

β2 – Regression coefficient

DBH – Diameter at breast height
Density – Tree density
Diam. – Tree or shrub diameter
F – Funnelling ratio
H – Tree height
I – Rainfall intensity
LAI – Leaf area index
M – Mediterranean
M – Proximity metric

xii

CHAPTER 1
INTRODUCTION

Rainfall intercepted by vegetation cover either passes through or drips from the
canopy as throughfall, moves down the bole or stem of the vegetation and reaches the
ground as stemflow, or remains on the vegetation canopy and is subsequently evaporated.
Of the aforementioned components, stemflow has received the least attention in the
hydrologic literature (Park and Hattori, 2002; Levia and Frost, 2003; Llorens and
Domingo, 2007). This is likely due to stemflow being volumetrically insignificant when
compared to throughfall and evaporation; however, its importance is far from irrelevant.
The first research examining the movement of intercepted rainfall down a tree’s bole was
conducted in the late 19th and early 20th centuries by Hoppe (1896) and Horton (1919).
This process was later termed “stemflow”, and is the focus of this thesis.
Despite a lower volume of water when compared to the other components of the
canopy water balance, stemflow is of hydrologic importance due to it being a focused
point source input of water at the base of a tree or plant (Herwitz, 1986). A principle
focus of this thesis deals with the stemflow funnelling ratio. The stemflow funnelling
ratio was first introduced in 1986 by Herwitz (1986) as a measure of how efficient a tree
or bush is at producing stemflow. The ratio is one that expresses the amount of water
directed to the base of a tree or plant during a rainfall event relative to the volume of
rainfall that would have been captured by an unobstructed rain gauge with a receiving
area equal to that of the tree / plant basal area. The stemflow funnelling ratio is calculated
as:
F= SF/(Pg ∙BA)

(1.1)

where F is the funnelling ratio (dimensionless), SF is stemflow volume (L), Pg is rainfall
(mm), and BA is the basal area of the tree’s truck or shrub’s stem (m2).
Stemflow research has been conducted worldwide focusing on a large variety of
species under varying climatic and hydrologic regimes. Findings have shown that
stemflow can be of hydrologic and biogeochemical significance, at least in certain
1

environments. Stemflow is an important source of moisture for plant growth and ground
water recharge as highlighted by a number of studies (Voigt, 1960; Tanaka et al., 1996;
Taniguchi et al., 1996; Whitford et al., 1997). For a Pinus densiflora (Japanese red pine)
forest in Japan, Taniguchi et al. (1996) found that stemflow was responsible for 20 % of
the groundwater recharge rate. Along with deriving the stemflow funnelling ratio,
Herwitz (1986) found that large volumes of stemflow could overwhelm the infiltration
capacity of soil and result in Hortonian overland flow and subsequently cause surface
erosion. Once thought to only occur under rainfall conditions, Herwitz and Levia (1997)
found that stemflow was also produced under winter conditions, with increased stemflow
volumes associated with mixed precipitation. Stemflow has been found to be a
concentrated source of nutrients and, in some cases, pollutants (Brinson et al., 1980;
Chang and Matzner, 2000; Schroth et al., 2001; Johnson and Lehmann 2006). Brinson et
al. (1980) found that stemflow contained high levels of organic carbon and phosphorus,
20.2 % and 16.8 %, respectively, of the total amount of organic carbon and phosphorus
reaching the forest floor. Stemflow can be important not only for the producer, but also
for surrounding vegetation. Stemflow and the nutrients contained within have been found
to create a “fertile island” effect, resulting in vegetation growth around a stemflow
producing tree or bush (Whitford et al., 1997). Stemflow models developed to date
include a variety of different predictor variables and have been produced for a number of
different tree and plant species. Depending on tree architecture and geographic location
of the tree(s) studied, a number of different predictor variables were employed by each
study. Branching angle (Herwitz, 1987; Návar, 1993; Martínez-Meza and Whitford,
1996), number of branches (Návar, 1993), tree height (Brown and Baker, 1970), storm
duration and intensity (Brown and Baker, 1970; Crockford and Richardson, 2000), crown
projection area (Brown and Baker, 1970; Aboal et al., 1999; Park and Hattori, 2001;
Pressland, 1973), and bark roughness (Horton, 1919; Aboal et al., 1999), are just some of
the variables that have been found to influence stemflow production across a number of
different species. Due to the variety of variables included in models produced to date, it is
difficult to transfer models between species. Also, when producing a model, it is

2

important to consider a large array of predictor variables. The stemflow production
ability of different species from around the globe, and the modelling of stemflow
production for juvenile lodgepole pine are the foci of this thesis.
Chapter 2 is a comprehensive review paper of stemflow production information
for research published prior to June 30, 2010. The goal of this paper was to compile
information relating to stemflow production for as many tree and plant species as
possible. Once compiled, this information was organized alphabetically by species within
seven different climate and vegetation classifications for ease of reference. This
information was then used to identify inter-climatic, inter-genera, and intra-genera
variations in stemflow production. Stemflow funnelling ratios were calculated for studies
that did not contain these metrics ratios, but contained the required information for their
calculation. Plateau funnelling ratios, the point at which funnelling ratios plateau, and the
associated rainfall depth, were calculated for entries that provided the necessary
information. This comprehensive review of stemflow production information will aid
future researchers and improve our understanding of inter- and intraspecific variations in
stemflow production. Past reviews have been conducted that provided stemflow
production information in table format, however, these tables simply summarized
stemflow production information related to the author(s)’ research or focused on a
particular region.
Chapter 3 is a stemflow modelling paper based on original field observations
conducted on the Bonaparte Plateau, north of Kamloops, British Columbia, Canada. The
goal of this chapter was to model stemflow production for juvenile lodgepole pine. Two
research plots were used to model stemflow production for trees with crown projected
areas ranging from 0.1 to 3.5 m3. The generic model produced explained 71.3 % of the
variation in stemflow production for individual lodgepole pines, or for entire stands
fitting the model criteria. In addition to the generic model, models for the individual
research plots are also presented, along with the findings that canopy structure in
combination with rainfall depth accurately explained variations in stemflow production
for juvenile lodgepole pine.

3

The rationale for Chapter 3 was the lack of knowledge concerning juvenile
lodgepole pine stemflow production, and the current mountain pine beetle (Dendroctonus
ponderosae) epidemic impacting British Columbia. The mountain pine beetle epidemic is
expected to kill 77 % of all merchantable pine in the Province by 2014 (BC Ministry of
Water, Land and Air Protection, 2004; Walton et al., 2007). The landscape of the Interior
of British Columbia will not only be changed visually for decades to come, site
hydrology will also change drastically as mature stands are replaced by juvenile stands at
various stages of regrowth. Past research has shown that mature lodgepole pine are
inefficient stemflow produces and do not produce large volumes, however little is known
about the stemflow production of juvenile lodgepole pines (Spittlehouse, 1998; McKee
and Carlyle-Moses, 2010). Due to the shift in stand composition that will occur over the
coming years, understanding how stands of juvenile lodgepole pine partition rainfall is
important as this may have impacts on streamflow production and thus potentially impact
water resource supplies and aquatic ecosystem health.
LITERATURE CITED
Aboal JR, Jimenez MS, Morales D, Hernandez JM. 1999. Rainfall interception in laurel
forest in the Canary Islands. Agricultural and Forest Meteorology 97: 73-86.
BC Ministry of Water, Land and Air Protection. 2004. Weather, Climate and the Future:
BC’s Plan. http://www.env.gov.bc.ca/air/climate/index.html#1
Brinson MM, Bradshaw HD, Holmes RN, Elkins JB Jr. 1980. Litterfall, stemflow, and
throughfall nutrient fluxes in an alluvial swamp forest. Ecology 61(4): 827-835.
Brown JH Jr., Barker AC Jr. 1970. An analysis of throughfall and stemflow in mixed oak
stands. Water Resources Research 6(1): 316-323.
Chang S, Matzner E. 2000. The effect of beech stemflow on spatial patterns of soil
solution chemistry and seepage fluxes in a mixed beech/oak stand. Hydrological
Processes 14: 135-144.
Crockford RH, Richardson DP. 2000. Partitioning of rainfall into throughfall, stemflow
and interception: effect of forest type, ground cover and climate. Hydrological
Processes 14: 2903-2920.

4

Herwitz SR. 1986. Infiltration-excess caused by stemflow in a cyclone-prone tropical
rainforest. Earth Surface Processes and Landforms 11: 401-412.
Herwitz SR. 1987. Raindrop impact and water flow on the vegetative surfaces of trees
and the effects on stemflow and throughfall generation. Earth Surface Processes
and Landforms 12: 425-432.
Herwitz SR, Levia DF Jr. 1997. Mid-winter stemflow drainage from bigtooth aspen
(Populus grandidentata michx.) in central Massachusetts. Hydrological Processes
11: 169-175.
Hoppe E. 1896. Regenmessung unter Baumkronen. Mitt. Aus des Forstlichen
Versuchswesen Oesterreichs 21: 1-75.
Horton RE. 1919. Rainfall Interception. Monthly Weather Review 47(9): 608-623.
Johnson MS, Lehmann J. 2006. Double-funnelling of trees: Stemflow and root-induced
preferential flow. Ecoscience 13(3): 324-333.
Levia DF Jr., Frost EE. 2003. A review and evaluation of stemflow literature in the
hydrologic and biogeochemical cycles of forest and agricultural ecosystems.
Journal of Hydrology 274: 1-29.
Llorens P, Domingo F. 2007. Rainfall partitioning by vegetation under Mediterranean
conditions. A review of studies in Europe. Journal of Hydrology 335: 37-54.
Martínez-Meza E, Whitford WG. 1996. Stemflow, throughfall and channelization of
stemflow by roots in three Chihuahuan desert shrubs. Journal of Arid Environments
32: 271-287.
McKee AJ, Carlyle-Moses DE. 2010. Stemflow: A potentially important point source of
water for growth. Linking Innovations and Networking Knowledge 11(2): 11-12.
Návar J. 1993. The causes of stemflow variation in three semi-arid growing species of
northeastern Mexico. Journal of Hydrology 145: 175-190.
Park H, Hattori S. 2002. Applicability of stand structural characteristics to stemflow
modelling. Journal of Forest Research 7: 91-98.
Pressland AJ. 1973. Rainfall portioning by an arid woodland in South-Western
Queensland. Australian Journal of Botany 21: 235-245.

5

Schroth G, Elias MEA, Uguen K, Seixas R, Zech W. 2001. Nutrient fluxes in rainfall,
throughfall and stemflow in tree-based land use systems and spontaneous tree
vegetation of central Amazonia. Agriculture, Ecosystems and Environment 87: 3749.
Spittlehouse D. 1998. Rainfall interception in young and mature conifer forests in British
Columbia. Proceedings 23rd Conference on Agricultural and Forest Meteorology.
Tanaka T, Taniguchi M, Tsujimura M. 1996. Significance of stemflow in groundwater
recharge. 2: A cylindrical infiltration model for evaluating the stemflow
contribution to groundwater recharge. Hydrological Processes 10: 81-88.
Taniguchi M, Tsujimura M, Tanaka T. 1996. Significance of stemflow in groundwater
recharge. 1: Evaluation of this stemflow contribution to recharge using a mass
balance approach. Hydrological Processes 10: 71-80.
Voigt GK. 1960. Distribution of rainfall under forest stands. Forest Science 6(1): 2-10.
Walton A, Hughes J, Eng M, Fall A, Shore T, Riel B, Hall P. 2007. Provincial-level
projection of the current Mountain Pine Beetle outbreak: Update of the infestation
projection based on the 2006 provincial aerial overview of forest health and
revisions to the “model”
(BCMPB.v4).http://www.for.gov.bc.ca/hre/bcmpb/BCMPB.v4.BeetleProjection.
Update.pdf
Whitford WH, Anderson J, Rice PM. 1997. Stemflow contribution to the ‘fertile island’
effect in creosotebush, Larrea tridentate. Journal of Arid Environments 35: 451457.

6

CHAPTER 2
A SYNTHESIS AND EVALUATION OF PAST RESEARCH ON THE
QUANTITATIVE IMPORTANCE OF STEMFLOW

INTRODUCTION
The first notable research examining the manner in which tree canopies partition
rainfall was conducted in the late 19th and early 20th centuries by Hoppe (1896) and
Horton (1919). These papers identified that a portion of intercepted rainfall was diverted
down the trunk of the vegetation in question, a process later termed "stemflow". Despite
recent studies and reviews that have highlighted the hydrologic importance of stemflow,
it has received relatively little attention in the hydrologic literature when compared to the
two other canopy water balance components: throughfall and canopy interception loss
(Park and Hattori, 2002; Levia and Frost, 2003; Llorens and Domingo, 2007). Due to its
delivery being concentrated at the base of vegetation, stemflow has been found to be an
important point source input of water for soil moisture and groundwater recharge (Voigt,
1960; Tang, 1996; Taniguchi et al., 1996), a cause of Hortonian overland flow in certain
environments (Herwitz, 1986), and a significant source of nutrients and pollutants
(Brinson et al., 1980; Price and Watters, 1989; Chang and Matzner, 2000; Johnson and
Lehmann, 2006). The ability of vegetation to concentrate stemflow at their bases can be
expressed quantitatively using the stemflow funnelling ratio (Herwitz, 1986):
F = SF/(Pg ∙BA)

(2.1)

where F is the funnelling ratio (dimensionless), SF is stemflow volume (L), Pg is rainfall
(mm), and BA represents the tree basal area (m2).
Carlyle-Moses and Price (2006), in a northern hardwood stand in southern
Ontario under growing season conditions, found that stemflow funnelling ratios increased
with increasing rainfall depth until a peak was reached with funnelling ratios declining
with greater rainfalls. Similar results have been found for semi-arid shrubs in China (Li et
al., 2008) and in tropical tree plantations in Panama (Carlyle-Moses et al., 2010).

7

Carlyle-Moses and Price (2006) suggest that the peak funnelling ratio is reached once the
canopy becomes saturated and all areas capable of producing stemflow are doing so at
their maximum capacity. At greater rainfall depths the funnelling ratios should be
expected to decline since the numerator in Eq. 2.1 will be limited by the contributing area
of the canopy, while the denominator will increase in a linear fashion. Thus, the
derivation of stemflow funnelling ratios is not only of importance with regards to
determining the quantitative significance of stemflow as a point source of water for soil
moisture, groundwater and plant growth, but may also be used to determine the rainfall
depth required for the complete saturation of vegetation canopies and thus can aid in
canopy interception loss process and modelling studies (e.g. Carlyle-Moses et al., 2010).
A number of stemflow review papers have been published to date. Levia and
Frost (2003) provided a comprehensive overview of stemflow research by summarizing
and evaluating the different aspects of stemflow research. Levia and Frost (2003) also
provided recommendations for future research by drawing attention to areas where
further study is required and highlighting those areas that have already received
considerable attention. Other review papers and studies containing reviews have taken a
more focused approach, examining specific regions, climates, or species. Llorens and
Domingo (2007), for example, provided an in-depth review of stemflow research
conducted in the Mediterranean. Wei et al. (2005) reviewed a number of stemflow
studies conducted in China, while Johnson and Lehmann (2006) provided a review of
several different species under differing environmental conditions. Zinke (1967)
reviewed studies examining canopy interception in the United States, which included
stemflow production information for a number of different species. Barbier et al. (2009)
reviewed the canopy water balance differences between coniferous and broadleaved
species. All of these reviews provided valuable information regarding stemflow
production; however, none provided a comprehensive summary of stemflow production
data. Llorens and Domingo (2007) provided vast amounts of data for the Mediterranean;
however, they do not employ the stemflow funnelling ratio in their paper. A
comprehensive stemflow production review utilizing both stemflow as a percentage of

8

rainfall and stemflow funnelling ratios has not been undertaken to date and would
therefore be a valuable addition to the current knowledge base concerning this canopy
water balance component.
It was the goal of this review to provide a reference that summarizes the ability of
different tree species to produce stemflow. The purpose of this review was fourfold: (1)
to review the stemflow literature for papers containing information regarding stemflow
production; (2) to develop stemflow equations if the information was provided and the
author(s) had not already done so; (3) to calculate season-long funnelling ratios and
plateau funnelling ratios if the required information was provided by the author(s); (4) to
compile information relevant to a species’ ability to produce stemflow into table format.
It is my objective that a stemflow reference guide will be used by future researchers not
only to save time when conducting research, but also to aid in identifying inter- and
intraspecific variations in stemflow production by comparing studies of similar species.

METHODS
The Web of Science database by ISI Web of Knowledge and Google Scholar
were searched using the terms “stemflow”, “stem flow”, “funnelling ratio”, and
“funneling ratio”. Over 600 publications containing one or more of the above terms were
identified. Just over 100 of these publications published prior to June 30, 2010 were
included in this review. The reference sections of the publications found in the
aforementioned databases were then inspected for relevant studies not found in the
academic database search. Prior to their inclusion in this review, publications were
scrutinized to ensure that the data contained within was suitable for comparison with
other studies. In total, 145 studies containing stemflow data for a variety of species were
included in this review. Publications were examined for stemflow production
information, specifically: stemflow equations (relating stemflow to another variable),
stemflow funnelling ratios, the percentage of rainfall that became stemflow, and the
information required to produce a stemflow equation or stemflow funnelling ratio. If a
publication contained at least one of the aforementioned pieces of information it was
9

included in the reference table along with relevant stand, meteorological, and geographic
information including: tree species, geographic location, climate, tree diameter, tree
density, basal area, annual precipitation, study period precipitation, and, finally, the
citation. Information originally published in imperial units was converted to metric units
before being entered into the reference table; any data that underwent conversion was
followed by a superscript “con”.
If a stemflow equation was not provided by the author(s) of a specific study one
was calculated if individual event rainfall depth and accompanying stemflow
measurements were provided. Any calculated stemflow equations were followed by a
superscript “calc” in the reference tables provided in the Results section of this review.
For the purpose of inter- and intraspecific comparisons, stemflow funnelling ratios were
calculated when possible if the author(s) of a specific study did not provide them.
Calculated funnelling ratios were followed by a superscript “calc” in the reference tables.
Stemflow funnelling ratios were calculated in two ways depending on the data provided
by the author(s): if study period rainfall depth, percentage of rainfall that became
stemflow, and stand basal area were provided, then a stand level funnelling ratio for the
entire research period was calculated using Eq. 2.1 (e.g. 64.3calc); if a stemflow equation
relating stemflow volume or depth to rainfall depth was provided in conjunction with the
basal area for a stand or individual tree then a range of funnelling ratios were calculated
using rainfall depth values starting at 1 mm and increasing by 1 mm rainfall increments
until the funnelling ratios “plateaued”. For the purposes of this review the point at which
funnelling ratios plateaued occurred when the funnelling ratio value increased by < 1 %
compared to its previously calculated value at a rainfall depth 1 mm less. Once the
plateau was identified, the corresponding funnelling ratio and rainfall depth were
recorded in the reference table (e.g. 48.6 at 35 mmcalc). Based on the results of CarlyleMoses and Price (2006), these plateau values and associated rainfall depths are assumed
to be the maximum funnelling ratios produced when the canopy reaches full saturation
and the required rainfall to saturate the canopy, respectively.

10

Some entries in the reference tables contained more than one species; this is
because certain studies only provided multi-species stand scale stemflow production data.
For linear equations with a positive y-intercept, plateau values were not calculated
because a positive y-intercept implies that a tree has no storage capacity. If the
information required to calculate both the season-long and plateau funnelling ratios was
provided, both were included in the reference tables.
Special attention was paid to the methodology and results sections of selected
papers to determine if the stemflow information presented was at the individual or stand
scale level. Studies that provided stemflow information for an individual tree had “Lone”
entered under the tree density column of the reference tables and studies that provided
stemflow information for multiple individuals but with no reference to the entire stand
were identified as “Lone trees” or “Lone shrubs”. All other entries not marked as either
“Lone”, “Lone trees”, or “Lone shrubs” focused on the stand scale. In addition to the
percentage of rainfall that became stemflow for the study period or a range of values if
the author(s) did not provide a study period value, other information can be found in the
SF (%) column. The percentage of rainfall that became stemflow for specific periods or
stand conditions was provided for some studies, for example, leaved and leafless or
growing and dormant season periods, unlogged and logged, or summer and winter
conditions. In addition to season-long funnelling ratios and plateau funnelling ratios, the
funnelling ratio (F) column contains additional information for some studies. The event
high funnelling ratio, representing the maximum funnelling ratio observed for an
individual tree/shrub for a single event, was recorded for some entries. If multiple
stemflow percentages, funnelling ratios, or formulae are contained within one entry this is
because the entry contains information for multiple trees of the same species or data for
multiple years.
Once the comprehensive reference table was compiled, the information it
contained was organized by climate and vegetation type. Seven classifications were used
to organize the 326 entries: temperate deciduous (D), temperature coniferous and boreal
(C), mixed deciduous and coniferous stands (X), tropical (T), Mediterranean (M), semi-

11

arid and arid (S), and agroforestry (A). For each climate/vegetation classification two
tables were produced: one table containing stand information along with author(s); the
other containing stemflow production and meteorological data. Entries were sorted
alphabetically by species and given a code for ease of referencing and comparison
between tables. Within the seven categories, average, median, and a range of values were
calculated for stemflow and funnelling ratio values and compared at the genera and
category levels. If a single entry contained multiple years of data an average was
produced across those years for comparison with other entries. If a single entry contained
only a range of stemflow data it was not included in comparative analyses.
RESULTS
1. Temperate deciduous
From the available literature, stand-scale stemflow was found to average 5.1 %
(median = 3.9 %, n = 34) of growing-season or annual rainfall in temperate deciduous
forests, ranging from < 0.5 % in a Crataegus sativa – Acer campestre stand in southwest
England (Herbst et al., 2006, D10) to 17.1 % in an evergreen-broadleaf forest in Osaka,
Japan (Masukata et al., 1990, D11). Mean stemflow as a percentage of growing-season or
annual rainfall from nine studies conducted in Quercus genera dominated stands was 6.0
% (median = 4.0 %, range = 0.5 – 15.5 %), while it accounted for an average of 5.0 %
(median = 5.0 %, range = 2.0 – 9.6 %, n = 5) in Fagus forests. A notably high annual
stemflow value of 26 % was reported for a lone Stewartia monadelpha in Japan (Liang et
al., 2009, D63). Additional stemflow percentage values for other genera dominated and
mixed deciduous stands are presented in Table 2.1 and Table 2.2 found in this chapter’s
appendix.
The proportion of rainfall that contributes to stemflow typically increases under
leafless periods compared to leafed periods. For example, in a Q. alba – Q. velutina
forest in Rhode Island stemflow increased from 3.9 % of rainfall during the growing
season to 4.8 % under dormant conditions (Brown and Barker, 1970, D48). Similar
results were also found in a Nyssa aquatic - Taxodium distichum - Fraxinus caroliniana
12

stand in North Carolina, where stemflow averaged 2.5 % of the 639 mm of rainfall under
leafed-conditions and 4.5 % of the 466 mm of rainfall during the leafless period of the
study (Brinson et al., 1980, D 42). Calculated stand-scale funnelling ratios for the latter
stand increased from 3.6 during the leaved period to 6.5 during the leafless period.
Calculated and author-provided stand-scale growing season or annual funnelling
ratios in temperate deciduous forests averaged 26.6 (median = 15.6, n = 12), ranging
from 2.3 in a F. orientalis forest in Nowshahr, Iran (Ahmadi et al., 2009, D21) to 64.3 in
a Alnus glutinosa forest in Lancaster, England (Cape et al., 1991, D05). Growing season
or annual funnelling ratios for Quercus stands averaged 36.8 (median = 50, n = 5),
ranging from 7.6 to 61.3. A study examining individual Q. rubra reported season-long
funnelling ratios averaging 8.8 (median = 7.6, n = 7), with a range of 6.1 to 13.7 (CarlyleMoses and Price, 2006). Growing season funnelling ratios averaged 12.1 (median = 8.6,
range = 2.3 – 25.4, n = 3) for Fagus stands, and 32.7 (median = 32.4, range = 15.8 – 47.2,
n = 9) for individual trees. An entry for Acer saccharum (Carlyle-Moses and Price, 2006,
D03) had a notably high season-long funnelling ratio of 108.6 for an individual tree,
however the average season-long funnelling ratio for all A. saccharum trees included in
the study averaged 31.6 (median = 21.6, n = 7).
For temperature deciduous stands, calculated plateau funnelling ratios for the
growing season averaged 23.4 at 17 mm (median = 17.6 at 15 mm, n = 4) with a range of
9.0 at 12 mm for a mixed deciduous forest in Ontario, Canada (Price and Carlyle-Moses,
2003, D39), to 48.6 at 35 mm for a stand of A. glutinosa in Lancaster, England (Cape et
al., 1991, D05). Calculated plateau funnelling ratios for individual trees during the
growing season were much higher than those for stands. Plateau funnelling ratios
averaged 40.2 at 15 mm (median = 38.7 at 13 mm, n = 9), ranging from 1.6 at 27 mm for
a lone Liriodendron tulipifera in Maryland (Levia et al., 2010, D30) to 91.5 at 7 mm for a
lone Q. suber in California (Xiao et al., 2000, D62). Growing season plateau funnelling
ratios were calculated for three studies that examined individual Fagus which averaged
55.0 at 13 mm (median = 50.0 at 13 mm, n = 4), ranging from 42.1 at 16 mm (Staelens et
al., 2008, D27) to 82.3 at 13 mm (André et al., 2008, D23) for two F. sylvatica studies.

13

Quercus and Fagus are the two genera in temperate deciduous forests that have
received the greatest study in regards to stemflow with 16 entries each (Table 2.1; Table
2.2). Liriodendron, Acer, Nothofagus, Populus, and Betula also have multiple entries,
albeit less than Quercus and Fagus, while other genera, including Alnus and Stewartia
have only one entry. Some studies included a mix of genera with no discernable means of
separating the results in a genera specific fashion.

2. Temperate coniferous and boreal
For studies conducted in temperate coniferous and boreal stands, study period
stand scale stemflow averaged 5.0 % (median = 3.7 %, n = 50) of rainfall, with a range of
< 0.1 % for a stand of Larix cajanderi in Siberia, Russia (Toba and Ohta, 2005, C11) to
27 % for a stand of Picea sitchensis in Dumfriesshire, Scotland (Ford and Deans, 1978,
C21). Mean stemflow as a percentage of rainfall from 19 studies conducted in Pinus
dominated stands was 4.2 % (median = 2.7 %, range = < 0.1 – 15 %, n = 23). Studies
examining Picea and Larix reported season averages above and below Pinus,
respectively. Average stemflow as a percentage of rainfall from nine studies of Picea
dominated stands was 8.8 % (median = 6.4 %, range = 0.5 – 27 %, n = 9), while it
accounted for 2.0 % (median = 1.6 %, range = < 0.1 – 4 %, n = 5) for four studies of
Larix dominated stands. The two highest average annual stemflow values of 27.0 %
(Ford and Deans, 1978, C21) and 16.7 % (Teklehaimanot et al., 1991, C24) were
reported from P. sitchensis dominated stands in Scotland. Additional stemflow
percentage values for other genera dominated and mixed temperate coniferous stands are
presented in Tables 2.3 and 2.4.
Calculated and study provided stand-scale growing season or annual funnelling
ratios in temperature coniferous and boreal forests averaged 22.1 (median = 14.4, n = 12),
ranging from 0.9 for a stand of P. abies in Vosges, France (Viville et al., 1993, C16) to
69.8 for a stand of Ilex pedunculosa in Kyoto, Japan (Park and Hattori, 2002, C09). In
comparison to temperate deciduous stands, little stemflow funnelling ratio data has been
reported for temperate coniferous and boreal forests. Two studies (Cape et al., 1991;

14

McKee and Carlyle-Moses, 2010) reported season-long funnelling ratio averages for
Pinus of 19.7 (median = 17.2, range = 14.9 – 34.1, n = 4) and two studies (Cape et al.,
1991; Viville et al., 1993) for Picea averaging 16.1 (median = 10.4, range = 0.9 – 37.1, n
= 3). A nine year old stand of Chamaecyparis obtuse (Murakami, 2009, C05) had a
notably high season-long funnelling ratio of 81.3, however, over the next three years of
stand growth the season-long funnelling ratio dropped to 29.0.
Calculated plateau funnelling ratios for temperate coniferous and boreal stands
during the growing season averaged 12.4 at 51 mm (median = 8.8 at 47 mm, n = 7),
ranging from 0.8 at 59 mm for a stand of L. decidua (Cape et al., 1991, C13) to 26.1 at 39
mm for a stand of P. sylvestris (Cape et al., 1991, C47). Three stands of P. sylvestris had
average growing season plateau funnelling ratios of 15.8 at 51 mm (median = 13.7 at 39
mm, n = 3) and average winter plateau funnelling ratios of 22.6 at 35 mm (median = 19.2
at 37 mm, n = 3).
Pinus was found to be the dominant genus studied within the temperate
coniferous and boreal stands examined, followed by Picea (Table 2.3; Table 2.4). Larix,
Pseudotsuga, and Abies all had multiple entries; however they received far less attention
when compared to Pinus.

3. Mixed deciduous and coniferous stands
Studies that presented stemflow values for mixed coniferous and deciduous stands
were rare, with most studies providing data for individual species if the study stand
contained both coniferous and broadleaf species. Studies that did not separate data for
individual species within a mixed stand were assigned to this category. Stemflow as a
percentage of annual rainfall for four studies averaged 2.6 % (median = 2.5 %, range =
0.5 – 7 %, n = 5). A study in a coastal redwood forest in California (Reid and Lewis,
2009, X05) reported the only study period funnelling ratio in this category of 2.6.
Supplementary information for the presented stemflow data can be found in Tables 2.5
and 2.6.

15

4. Tropical
For interception studies conducted in tropical climates, annual stemflow values at
the stand level averaged 4.0 % (median = 1.6 %, n = 46), ranging from < 0.1 % for a
tropical montane rainforest in Columbia (Veneklass and Van Ek, 1990 as cited in Levia
and Frost, 2003, T57) to 30.5 % for a subtropical forest in Okinawa, Japan (Xu et al.,
2005, T05). Study period stemflow values from studies that examined individual trees
averaged 8.2 % (median = 2.7 %, n = 17), ranging from 0.01 % for a lone Cecropia
peltata in Puerto Rico (Holwerda et al., 2006, T06) to 39.7 for a lone Elaeocarpus
foveolatus in Queensland, Australia (Herwitz, 1986, T15). Additional stemflow
percentage values for other genera dominated and mixed tropical stands are presented in
Tables 2.7 and 2.8.
Calculated and published study period stemflow funnelling ratios at the stand
level averaged 18.7 (median = 12.4, n = 8) with a range of 0.8 for a natural montane
forest in Central Sulawesi, Indonesia (Dietz et al., 2006, T32) to 53.0 for a subtropical
forest in Okinawa, Japan (Xu et al., 2005, T05). Study period funnelling ratios for
individual trees averaged 41.2 (median = 11.0, n = 35), ranging from 0.5 for a Dacryodes
excelsa in Puerto Rico (Holwerda et al., 2006, T15) to 275.7 for a Prestoea montana in
Puerto Rico (Holwerda et al., 2006, T44).
At the stand scale only two plateau funnelling ratios could be calculated, 3.1 at 24
mm for a terra firme rainforest in Manus, Brazil (Cuartas et al., 2007, T58) and 8.7 at 22
mm for a lowland tropical forest in Sarawak, Malaysia (Manfroi et al., 2004; Manfroi et
al., 2006). At the individual level, plateau funnelling ratios averaged 46.8 at 23 mm
(median = 11.2 at 19 mm, n = 27), ranging from 0.7 at 34 mm for a D. excelsa in Puerto
Rico (Holwerda et al., 2006, T11) to 272.8 at 2 mm for a P. montana in Puerto Rico
(Holwerda et al., 2006, T40).

5. Mediterranean
Studies conducted in regions with Mediterranean climates reported annual stand
scale stemflow values that averaged 4.4 % (median = 3.0 %, n = 77), ranging from 0.2 %

16

for a stand of Eucalyptus melliodora in Canberra, Australia (Crockford et al., 1996, M09)
to 22.0 % for a stand of Juniperus oxycedrus in El Ardal, Spain (Belmonte, 1997;
Belmonte and Romero, 1998 as cited by Llorens and Domingo, 2007, M25). Study period
stemflow values for individual trees averaged 11.6 % (median = 4.8 %, n = 10), ranging
from 0.6 % for a Q. pyrenaica in Villasrubias, Spain (Moreno et al., 2001 as cited by
Llorens and Domingo, 2007, M84) to 42.5 % for a Rosmarinus officinalis in El Ardal,
Spain (Belmonte, 1997; Belmonte and Romero, 1998 as cited by Llorens and Domingo,
2007, M86). Annual stemflow values for stands of Pinus averaged 4.4 % (median = 3.0
%, range = 0.3 – 22.0 %, n = 29), while stands of Quercus averaged 3.5 % (median = 2.8
%, range = 0.3 – 12.5 %, n = 16). Stemflow values from four studies of Eucalyptus
averaged 2.2 % (median = 2.9 %, range = 0.2 – 4.0 %, n = 12), while five studies of
Fagus averaged 7.9 % (median = 6.5 %, range = 1.1 – 20.4 %, n = 8). Additional
stemflow percentage values for other genera dominated and mixed Mediterranean stands
are presented in Tables 2.9 and 2.10.
Calculated and previously published stand scale season-long funnelling ratios for
Mediterranean stands averaged 14.8 (median = 14.7, n = 51), ranging from 1.7 for P.
sylvestris stand in the Sierra de la Demanda (Santa Regina and Tarazona, 2001, M63) to
41.1 for Q. cerris in south-western Spain (Moreno et al., 2001 as cited by Llorens and
Domingo, 2007, M68). Individual trees averaged 47.8 (median = 34, n = 13), ranging
from 16.7 to 137 for two Q. ilex individuals (Bellot and Escarré, 1998, M70). Seasonlong funnelling ratios for Pinus dominated stands averaged 16.1 (median = 15.4, range =
1.7 – 32, n = 18), while stands of Quercus averaged 13.6 (median = 11.3, range = 3.1 –
41.1, n = 11). In contrast to the aforementioned Quercus stands, individual Quercus had
average study period funnelling ratios of 42.5 (median = 30.5, range = 16.7 – 137, n =
10). Study period stand scale funnelling ratios from Eucalyptus averaged 13.6 (median =
13.1, range = 4 – 21, n = 10), while Fagus stands averaged 16.4 (median = 11.9, range =
2.7 – 39.1, n = 4).
For Mediterranean vegetation at the stand level, only three plateau funnelling
ratios were calculated averaging 21.9 at 15 mm. Plateau funnelling ratios for individual

17

trees averaged 62.4 at 26 mm (median = 47.9 at 29, n = 17), ranging from 16.4 at 29 mm
for Q. ilex rotundifolia (Bellot and Escarré, 1998, M70) to 137.9 at 17 mm for E.
globulus (Bellot and Escarré, 1998, M01). Plateau funnelling ratios for individual
Phyllirea media averaged 76.1 at 27 mm (median = 77.6 at 30 mm, range = 19.9 at 22 –
118.1 at 9 mm, n = 5), while individual Quercus averaged 43.3 at 27 mm (median = 27.9
at 29 mm, range = 16.4 at 29 – 129.6 at 9 mm, n = 9). Forests comprised predominantly
of Pinus are the most studied in Mediterranean climates (30 entries in total, Table 2.9;
Table 2.10). Quercus, Eucalyptus, and Fagus are also well represented in this category
with 18, 11, and 8 entries, respectively.

6. Arid and semi-arid environments
Stemflow values for arid and semi-arid communities averaged 5.9 % (median =
5.9 %, n = 18), ranging from 0.7 % for Grevillea robusta in Machakos, Kenya (Jackson,
2000, S16) to 18.0 % for Acacia aneura in Queensland, Australia (Pressland, 1973, S01).
Individual plants had higher values averaging 7.7 % (median = 6.3 %, n = 10), ranging
from 0.6 % for a Prosopis laevigata in Nuevo Leon, Mexico (Návar, 1993; Návar and
Bryan, 1990, S28) to 20 % for a Anthyllis cytisoides in Almería, Spain (Domingo et al.,
1994; Llorens and Domingo, 2007, S05). Additional stemflow percentage values for
other genera dominated and mixed arid or semi-arid communities are presented in Tables
2.11 and 2.12.
Calculated and previously published season-long funnelling ratios at the
community level averaged 61.3 (median = 51.0, n = 8) with a range of 21.1 for a matorral
community of the Sierra Madre Oriental , Mexico (Carlyle-Moses, 2004, S22) to 153.5
for Caragana korshinskii in Gaolan, China (Li et al., 2008, S08). Only three entries
provided funnelling ratio data for individual plants, averaging 28.7 (median = 16.8, n = 3)
and ranging from 11.7 for a A. farnesiana in Nuevo Leon, Mexico (Návar, 1993; Návar
and Bryan, 1990, S02) to 57.7 for a D. texana in Nuevo Leon, Mexico (Návar, 1993;
Návar and Bryan, 1990, S13). One plateau funnelling ratio was calculated for a tree in an
arid or semi-arid climate. A lone Ficus benjamina in an urban setting (Queretaro City,

18

Mexico) had a plateau funnelling ratio of 16.8 at 5 mm (Guevara-Escobar et al., 2007,
S14).

7. Agroforestry
Eight studies that examined a variety of crop species reported an average study
period stemflow value of 7.3 % (median = 1.5 %, n = 14) with a range of 0.6 % for a plot
of Zea mays and Grevillea robusta in Kenya, Africa (Jackson, 2000, A12) to 24.7 % for a
plantation of Bactris gasipaes in Manaus, Brazil (Schroth et al., 1999; Schroth et al.,
2001, A02). Calculated or previously published study period funnelling ratios for three
studies averaged 10.8 (median = 8.1, n = 5), ranging from 3.8 for an agroforest in Central
Sulawesi, Indonesia (Dietz et al., 2006, A01) to 25.3 for a Musa sp. plantation in
Guadeloupe (Cattan et al., 2007, A08). Supplementary information for the presented
stemflow data can be found in Tables 2.13 and 2.14.

DISCUSSION
Carlyle-Moses and Price (2006) were the first to note that stemflow funnelling
ratios could be used to determine the depth of rainfall required to satisfy the storage
capacity of a tree. Once the canopy of a tree has reached complete saturation, the
stemflow funnelling ratio will plateau and decrease if rainfall continues. The rainfall
depth that corresponds to the funnelling ratio plateau indicates the point at which the
canopy has reached complete saturation. Calculated plateau funnelling ratios are only as
accurate as the linear equations on which they are based; therefore, the rainfall depth
provided with each plateau funnelling ratio is an estimation of the point at which
complete canopy saturation occurred. Holwerda et al. (2006, T40) provided a linear
equation that produced a plateau funnelling ratio of 272.8 at 2 mm. Such a small storage
capacity is either due to large amounts of scatter not reflected in the linear equation, or
the plant in question had a much lower storage capacity compared to similar plants
included in the study. The limitations of using a linear equation to determine funnelling
ratio plateaus can be seen in some table entries where the author provided season-long
19

funnelling ratios that were higher than calculated plateau values. Bellot and Escarré
(1998) provided season-long funnelling ratios for Q. ilex (M70) which in some instances
were up to 7 times higher than the plateau funnelling ratio. Calculated plateau values may
in reality be higher or lower due to scatter that is not reflected in a linear equation but is
observed when values are graphed.
The use of plateau funnelling ratios to identify the rainfall depth required to
satisfy the storage capacity of a tree has implications for canopy water balance modelling.
Current canopy water balance models underestimate the amount of rainfall required to
reach complete canopy saturation (Carlyle-Moses and Price, 2007). Carlyle-Moses et al.
(2010) suggested that stemflow funnelling ratios could be used to provide a more
accurate estimation of the rainfall depth required to saturate the canopy (P’g). Using the
improved Gash model, Carlyle-Moses et al. (2010) produced P’g values for five species
in a tropical forest in Panama. Calculated P’g values for A. mangium, G. sepium, G.
ulmifolia, O. pyramidale, and P. quinata were 1.33 mm, 1.10 mm, 1.18 mm, 0.93 mm,
and 1.00 mm, respectively, however, author calculated funnelling ratios plateaued at
rainfall depths of 14.5 mm, 18.3 mm, 18.8 mm, 14.8 mm, and 26.8 mm, respectively. The
calculated plateau funnelling ratios and the accompanying rainfall depths found in this
paper further support the initial findings by Carlyle-Moses and Price (2006) that
stemflow funnelling ratios increase until a threshold rainfall depth is reached,
subsequently identifying the rainfall depth required for canopy saturation.
Based on the available data, genera comparisons between climate classes were
only possible for Quercus, Fagus, and Pinus. Intra-genera analyses showed that there was
no statistical difference for Quercus (p = 0.23), Fagus (p = 0.28), and Pinus (p = 0.77)
between the different climate/vegetation classifications. Quercus in the temperate
deciduous class had stemflow values that averaged 5.7 % (median = 4.0 %) while
Quercus in the Mediterranean class averaged 3.5 % (median = 2.8 %). Ranges reported in
both classes were similar at 0.5 – 15.5 % and 0.3 – 12.5 %, respectively. Fagus in the
temperate deciduous class averaged 5.0 % (median = 5.0 %) while Mediterranean Fagus
values averaged 7.9 % (median = 6.5 %). Values for Fagus were reported at 2.0 – 9.6 %

20

for temperate deciduous and 1.1 – 20.4 % for Mediterranean. In keeping with the findings
for broadleaved genera, Pinus varied only slightly between classes. Temperate coniferous
Pinus had stemflow values that averaged 4.1 % (median = 2.7 %) while Mediterranean
values averaged 4.4 % (median = 3.0 %). Reported temperate coniferous and
Mediterranean Pinus values had ranges of < 0.1 – 14.0 % and 0.3 – 22.0 %, respectively.
Inter-climatic variation between stemflow values did not vary as greatly as
expected and no statistical difference was observed between climate/vegetation
classifications. Average stemflow values for climate classes ranged from 2.6 % for mixed
stands to 7.3 % for agroforestry, while median values ranged from 1.5 % for agroforestry
to 5.9 % for semi-arid and arid environments. Excluding the classes with limited entries
(agroforestry and mixed stands) average stemflow values ranged only 1.9 %, from 4.0 %
for Tropical to 5.9 % for arid and semi-arid communities; however, median values had a
range of 4.3 %. Values for temperate deciduous stands were expected to differ from
temperate coniferous and boreal stands; however, as previously stated, no statistical
difference was observed (p = 0.90). Both categories had similar reported stemflow
values, averaging 5.1 % (median = 3.9 %) and 5.0 % (median = 3.7 %), respectively.
Reported stemflow funnelling ratios for these two classes were also very similar with an
average of 26.6 (median = 15.6) for temperate deciduous stands and 22.1 (median = 14.4)
for temperate coniferous and boreal stands. These findings are not in keeping with those
of Barbier et al. (2009) that found broadleaved species to have higher stemflow values
when compared to coniferous species. Similar average values for temperate deciduous
and temperate coniferous and boreal stands presented in this review may be due in part to
an inherent bias. Only publications containing measured stemflow data were included,
therefore those that stated stemflow was insignificant or used findings from a previous
study were given no weight. From the available literature it appears that the majority of
water balance studies that do not measure stemflow do so for coniferous stands (Baker et
al., 1985; Fenn et al., 2000; Gholz et al., 1985; Johannes et al., 1986; Lankreijer et al.,
1999; Pypker et al., 2005). This trend is due to the generalization that all mature conifers

21

have low stemflow production, and because some studies employ data from previous
studies due to similarities in location or vegetation.

CONCLUSION
Stemflow production data for a multitude of tree and shrub species was organized
into table format totalling 326 entries. Information was sorted alphabetically by species
and given a reference code within seven different climate and vegetation classifications.
Reference tables were designed in such a way that future researchers will be able to
quickly access information of interest to aid in comparisons between differing studies and
species. Stemflow production was found to be highly variable for categories with a large
number of entries; these findings are in keeping with the findings of Llorens and
Domingo (2007) for studies conducted in the Mediterranean.
As noted by Llorens and Domingo (2007) a lack of standardization makes
combining and comparing information in a comprehensive review difficult. Specifically,
the way in which stemflow production is reported yields problems because stemflow as a
percentage of rainfall cannot be compared directly to a funnelling ratio. The funnelling
ratio is the superior method for reporting stemflow production when compared to
reporting stemflow as a percentage of gross rainfall, however, stemflow as a percentage
of gross rainfall is a more widely used method. This is partly due to the fact that the
funnelling ratio was not introduced until 1986 (Herwitz, 1986). It is paramount that
authors report detailed stand characteristics and stemflow funnelling ratios along with
percentages of rainfall that became stemflow. Detailed stand characteristics allow for
more accurate comparisons between studies and take up little space in one’s publication.
Stemflow funnelling ratios should be reported because they aid in comparisons between
individual trees or stands. The stemflow funnelling ratio allows for the assessment of
stemflow production efficiency across species due to the inclusion of basal area in the
funnelling ratio calculation.
A review of the information contained within the reference tables highlighted
several areas of stemflow research that remain understudied. As noted by Levia and Frost
22

(2003), knowledge regarding winter stemflow generation for both deciduous and
coniferous species remains weak to date. In temperate coniferous climates, our
knowledge of stemflow production for genera other than Picea, Pinus, Pseudotsuga,
Larix, and Abies is limited. Studies of deciduous species focused heavily on Quercus and
Fagus, therefore future research involving different deciduous genera would add new
information to the existing stemflow literature. Due to the species diversity found in
tropical forests these ecosystems require more attention to further our understanding of
interspecific variation in stemflow production. However, it is understandable that tropical
forests with high species diversity have received less attention when compared to other
forest types due to the logistical challenges of accurately sampling stemflow in these
diverse forests.
Stemflow can be beneficial or detrimental to agriculture depending on differing
circumstances, therefore, the further examination of rainfall portioning for agroforests
and crop species is recommended. For many tree and bush species found in the Interior of
British Columbia the stemflow literature is lacking. Particularly abundant, the sagebrush
(Artemisia tridentata) has received no attention in the stemflow literature; however, other
members of the genera have been examined in China (Yang et al., 2008). Pine species
found in the Interior of British Columbia have also received little attention when
compared to other species in the genera. Due to the hydrologic importance of stemflow it
is paramount that we continue to enhance the stemflow literature by examining species
and aspects of stemflow production that have received little or no attention.

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38

APPENDIX – REFERENCE TABLES
Table 2.1. Species, location, stand information (diameter: Diam, tree density: Density, stand basal area: BA), author(s), and
alphanumeric code for temperate deciduous studies.

23.0
22.2 ± 0.6

Density
(Trees/ha)
2470
403

BA
(m2/ha)
1.1

-

Lone trees

8.9

Code

Species

Location

Diam. (cm)

D01
D02

Acer rubrum
Acer rubrum

D03

Acer saccharum

New Brunswick, Canada
Eastern Kentucky, USA
Mississauga, Ontario,
Canada

Author
Mahendrappa (1974)
Alexander and Arthur (2010)
Carlyle-Moses and Price
(2006)

California, USA

-

-

-

Rowe (1948) (Zinke, 1967)

D05
D06

Aesculus californica
Ceanothus cuneatus
Alnus glutinosa
Betula papyrifera

Lancaster, England
New Brunswick, Canada

15.0

2510
4303

14
-

D07

Betula platyphylla

Mao'er Shan, China

-

-

-

Cape et al. (1991)
Mahendrappa (1974)
Wei & Zhou (1991) (Wei et
al., 2005)

Massachusetts, USA

-

Lone trees

-

Levia (2004)

Bristol, UK

-

632

-

Davie and Durocher (1997)

Swindon, U.K.

-

-

-

Herbst et al. (2006)

Osaka, Japan

10 - 20

767

-

Masukata et al. (1990)

-

Lone trees

3.3

74.9
10.3
15.2
14.4
29.6
48.6
-

Lone
Lone
Lone
Lone
Lone
Lone

-

D04

D08
D09
D10
D11

Broad-leaved deciduous
forest
Castanea sativa
Quercus rubra
Crataegus monogyna
Acer campestre
Evergreen-broadleaf
forest

D12

Fagus grandifolia

D13
D14
D15
D16
D17
D18
D19

Fagus grandifolia
Fagus grandifolia
Fagus grandifolia
Fagus grandifolia
Fagus grandifolia
Fagus grandifolia
Fagus grandifolia

Mississauga, Ontario,
Canada
Maryland, USA
Maryland, USA
New Haven, Connecticut
Maryland, USA
Maryland, USA
Maryland, USA
Maryland, USA

Carlyle-Moses and Price
(2006)
Levia et al. (2010)
Levia et al. (2010)
Voigt (1960)
Van Stan and Levia (2010)
Van Stan and Levia (2010)
Van Stan and Levia (2010)
Van Stan and Levia (2010)

39

1651con
1458con
1789con
112
-

32.8con
30.3con
29.6con
86.2
-

Ahmadi et al. (2009)
André et al. (2008)

D21
D22

Fagus grandifolia
Acer saccharum
Betula alleghaniensis
Fagus orientalis
Fagus sylvatica

D23

Fagus sylvatica

Chimay, Belgium

D24
D25

Fagus sylvatica
Fagus sylvatica

Steigerwald, Germany
Thuringia, Germany

49.5
17.8
29.3
37

D26

Fagus sylvatica

Hampshire, UK

-

-

-

D27

Fagus sylvatica

Ghent, Belgium

68

-

D28

Hardwood forest

Georgia, USA

5 - 23

-

Bryant et al. (2005)

D29
D30
D31
D32
D33
D34
D35

Liriodendron tulipifera
Liriodendron tulipifera
Liriodendron tulipifera
Liriodendron tulipifera
Liriodendron tulipifera
Liriodendron tulipifera
Liriodendron tulipifera

Maryland, USA
Maryland, USA
Maryland, USA
Maryland, USA
Maryland, USA
Maryland, USA
Maryland, USA

73.1
71.1
33.7
16.5
27.3
67.5
-

Lone
1200
1150
975
Lone
Lone
Lone
Lone
Lone
Lone
Lone

Chang and Matzner (2000)
Krämer and Hölsher (2009)
Neal et al. (1991); Neal et al.
(1993)
Staelens et al. (2008)

-

Levia et al. (2010)
Levia et al. (2010)
Levia et al. (2010)
Van Stan and Levia (2010)
Van Stan and Levia (2010)
Van Stan and Levia (2010)
Van Stan and Levia (2010)

D36

Lithocarpus-Castanopsis
association with
bryophytes

China

-

-

-

Liu et al. (2002) (Johnson and
Lehmann, 2006)

D37

Lithocarpus-Castanopsis
association with
bryophytes

China

-

-

-

Liu et al. (2003) (Johnson and
Lehmann, 2006)

Reefton, New Zealand

-

-

-

Rowe (1979)

Mississauga, Ontario,
Canada

-

442

38.5

Price and Carlyle-Moses
(2003)

D20

D38

D39

Mixed beech-podocarphardwood stand
Mixed deciduous forest
Quercus rubra
Acer saccharum
Fagus grandifolia
Acer rubrum

New Hampshire, USA

-

Leonard (1961)

Nowshahr, Iran
Chimay, Belgium

Lone trees

-

André et al. (2008)

286
228

20.5
36

40

Oyarzún et al. (2004) (Johnson
and Lehmann, 2006)
Godoy et al. (1999) (Johnson
and Lehmann, 2006)

D40

Nothofagus betuloides

Chile

-

-

-

D41

Nothofagus pumilio

Chile

-

-

-

D42

Nyssa aquatica
Taxodium distichum
Fraxinus caroliniana

Pitt County, North
Carolina, USA

> 2.5
< 2.5

2730
2681

69

Brinson et al. (1980)

Lone trees

-

Herwitz and Levia (1997)

5649
Lone
350

-

Mahendrappa (1974)
Dunford and Niederhof (1944)
Xiao et al. (2000)
Toba and Ohta (2005)

2595
2916
2520
1087
1236
840
1507
1804
2150

11.8
14.7
13.4
16.0
19.6
22.3
22.4
24.0
19.9

Brown and Barker (1970)

D43

Populus grandidentata

New Braintree,
Massachusetts, USA

D44
D45
D46
D47

Populus grandidentata
Populus sp.
Pyrus calleryana
Quercus acutissima

New Brunswick, Canada
Colorado, USA
California, USA
Nagoya, Japan

37.0
37.5
35.0
34.8
32.0
16.0
22

D48

Quercus alba
Quercus velutina

Rhode Island, USA

Stand 1a: 7.1
1b: 7.4
1c: 7.4
2a: 11.2
2b: 10.9
2c: 16.8
3a: 9.7
3b: 9.1
3c: 7.4

D49

Quercus coccinea

Eastern Kentucky, USA

27.7 ± 0.5

-

-

D50

Quercus mongolica

Mao'er Shan, China

-

-

-

D51
D52
D53

Quercus montana
Quercus petraea
Quercus petraea

26.1 ± 0.6
-

5000

20

D54

Quercus rubra

Eastern Kentucky, USA
Chimay, Belgium
Lancaster, England
Mississauga, Ontario,
Canada

-

Lone trees

20.7

D55

Quercus rubra

-

-

-

-

D56

Quercus rubra

Massachusetts, USA

63.8

Lone

-

Alexander and Arthur (2010)
Wei and Zhou (1991) (Wei et
al., 2005)
Alexander and Arthur (2010)
André et al. (2008)
Cape et al. (1991)
Carlyle-Moses and Price
(2006)
Durocher (1990) (Levia and
Frost, 2003)
Levia (2004)

41

D57
D58
D59

Quercus serrata
Quercus serrata
Quercus serrata

Shirasaka, Japan
Yamashiro, Japan
Nagoya, Japan

7.2
6.9

5070
3502
2852

-

D60

Quercus sp.

Miyaluo, China

-

-

-

D61
D62

Quercus spp.
Quercus suber

Nuevo Leon, Mexico
California, USA

312
Lone

-

D63

Stewartia monadelpha

Kyoto, Japan

16.1
12.5
S1 - 22.3
S2 - 23.7
S3 - 29.1
S4 - 21.8
S5 - 20.3
S6 - 27.9

Park and Hattori (2002)
Park and Hattori (2002)
Toba and Ohta (2005)
Lei et al. (1994a,b) (Wei et al.,
2005)
Silva and Rodrigues (2001)
Xiao et al. (2000)

Lone trees

-

Liang et al. (2009)

Table 2.2. Species, meteorological data (annual rainfall: PA, and study period rainfall: PS), stemflow production information
(funnelling ratio(s): F, and percentage of gross rainfall diverted to stemflow: SF), and stemflow formulae for temperate
deciduous studies.
Code

Species

PA (mm)

PS (mm)

SF (%)

F

Formula(s)

D01
D02

Acer rubrum
Acer rubrum

1130

-

5.6
-

-

D03

Acer saccharum

785

213.80

-

21.5
P > 4.3 mm
21.6
7.2
30.5
108.6
16.1
22.7
14.6

D04

Aesculus californica
Ceanothus cuneatus

-

-

14.6

-

SF = 6.02 lnP - 0.071I - 8.9a b c
Units: SF (L) P (mm) I (mm/h)

-

42

D05

Alnus glutinosa

-

1983/84: 1583
1984/85: 1690

D06
D07

Betula papyrifera
Betula platyphylla
Broad-leaved deciduous
forest
Castanea sativa
Quercus rubra
Crataegus monogyna
Acer campestre

676

-

3.9
4.6

48.6 at 35 mmcalc
64.3calc
38.6 at 37 mmcalc
64.3calc
-

1210

-

-

Winter: 6 - 21

-

-

31

2.4

-

-

650

1350

< 0.5

-

SF (mm) = 0.0015P (mm) - 0.0118

1467

1976: 1726.5
1977/78: 974.1

20.3
13.8

-

SF (mm) = 0.18(P (mm) - 3.6)
SF (mm) = 0.145(P (mm) - 5.8)

D08
D09
D10

D11

Evergreen-broadleaf forest

9±2
9±2

D12

Fagus grandifolia

785

213.80

-

D13
D14
D15
D16
D17
D18
D19

1221
1221
1143
1200
1200
1200
1200

-

9.6
-

1270

-

5.0

D21

Fagus grandifolia
Fagus grandifolia
Fagus grandifolia
Fagus grandifolia
Fagus grandifolia
Fagus grandifolia
Fagus grandifolia
Fagus grandifolia
Acer saccharum
Betula alleghaniensis
Fagus orientalis

-

309.9

2.0

P > 4.3 mm
15.8
24.0
32.4
39.3
57.0 at 10 mmcalc
38.2
47.2
26.9
37.4
15.5 at 11 mmcalc
16.8 at 11 mmcalc
17.1 at 11 mmcalc
2.3calc

D22

Fagus sylvatica

1044

-

-

-

D20

Summer: SF (mm) = 0.092P (mm) 0.837
Winter: SF (mm) = 0.074P (mm) 0.74
-

SF = 14.50 ln P - 0.15I - 20.8
Units: SF (L) P (mm) I (mm/h)
SF (L) = 5.82 P (mm) + 5.75calc
SF (L) = 0.52 P (mm) - 0.45calc
SF (mm) = 0.0563P (mm) - 0.061con
SF (mm) = 0.0029P1.7315 (mm)
Leaved: SF (L/mm) = 0.09CBH
(cm) - 4.31d
Leafless: SF (L/mm) = 0.17CBH
(cm) - 9.16

43

D23

Fagus sylvatica

1044

-

-

D24
D25
D26

Fagus sylvatica
Fagus sylvatica
Fagus sylvatica

750
544 - 662
800

691
1223
640

5.2calc
3.1calc
5.0

D27

Fagus sylvatica

755

Leafed: 769.9
Leafless: 677.9

6.4
9.5

D28
D29
D30
D31
D32
D33
D34
D35

Hardwood forest
Liriodendron tulipifera
Liriodendron tulipifera
Liriodendron tulipifera
Liriodendron tulipifera
Liriodendron tulipifera
Liriodendron tulipifera
Liriodendron tulipifera

830
1221
1221
1221
1200
1200
1200
1200

752.8
-

0.7
-

38.7 at 13 mmcalc
82.3 at 13 mmcalc
25.4calc
8.6calc
42.1 at 16 mmcalc
31.7calc
61.4 at 13 mmcalc
47.1calc
3.3 at 28 mmcalc
1.6 at 27 mmcalc
8.5 at 21 mmcalc
19.2
14.4
3.1
12.2

D36

Lithocarpus-Castanopsis
association with bryophytes

2165

-

2.8

-

-

D37

Lithocarpus-Castanopsis
association with bryophytes

2165

-

2.0

-

-

1950

6220

1.5

-

-

785

259.3

3.7 ± 0.9

9.0 at 12 mmcalc
9.6calc

SF (mm) = 0.039P (mm) - 0.005

7111
5332

-

1.4
9.0

-

-

D38

D39

D40
D41

Mixed beech-podocarphardwood stand
Mixed deciduous forest
Quercus rubra
Acer saccharum
Fagus grandifolia
Acer rubrum
Nothofagus betuloides
Nothofagus pumilio

SF (L) = 1.09P (mm) - 1.65
SF (L) = 6.29P (mm) - 9.65
SF (L) = 0.41 DBH2.04 (cm)e
SF (mm) = 0.098P (mm) - 0.209
SF (mm) = 0.140P (mm) - 0.209
SF (L) = 1.78P (mm) - 11.19calc
SF (L) = 0.81P (mm) - 4.70calc
SF (L) = 0.92P (mm) - 3.47calc
-

44

D42

Nyssa aquatica
Taxodium distichum
Fraxinus caroliniana

-

466
639

D43

Populus grandidentata

1190

-

D44
D45
D46
D47

Populus grandidentata
Populus sp.
Pyrus calleryana
Quercus acutissima

599.4
446
-

487.7
428

Leafless: 4.5
Leaved: 2.5
3.3

6.5calc
3.6calc
4.8calc

5.4
9.0
9.9
7.8
8.4
6.1
1.1
8
2.5

5.2
12.0
8.5
9.9
14.7
37.2 at 1 mmcalc
18.8 at 19 mmcalc
36.7 at 16 mmcalc
21.0 at 20 mmcalc
30.2 at 15 mmcalc

Leafless: SF (L) = 6.287DBH (cm) 2.421
Leaves: SF (L) = 0.864DBH (cm) 50.512

-

SF (mm) = 0.0794P (mm) - 0.0012
Growing all: SF = 0.041P - 0.127
Dormant S1: SF = 0.057P - 0.127
Dormant S2: SF = 0.048P - 0.152
Dormant S3: SF = 0.077P - 0.152
All units in mm
Leaved: SF (L/mm) = 0.08CBH
(cm) - 4.62
Leafless: SF = 0.16CBH (cm) 10.20

D48

Quercus alba
Quercus velutina

1119.38

-

Growing:
3.9
Dormant:
4.8

D49
D50
D51

Quercus coccinea
Quercus mongolica
Quercus montana

1130
450 - 550
1130

-

15.5
-

9.5
7.6

D52

Quercus petraea

1044

-

-

-

-

1983/84: 1583
1984/85: 1690

10 ± 2
10 ± 2

50calc
43.2 at 28 mmcalc
50calc

Summer: nd
Winter: SF (mm) = 0.11P (mm) 0.66

-

P > 4.3 mm
10.4
7.4
7.6
7.0
9.3
6.1
13.7

SF = 25.55 lnP - 0.50I - 38.6
Units: SF (L) P (mm) I (mm/h)

D53

D54

Quercus petraea

Quercus rubra

785

213.80

45

D55
D56

Quercus rubra
Quercus rubra

1210

-

4.0
-

Event high: 70.0

D57

Quercus serrata

-

4187.9

9.9

61.3

D58

Quercus serrata

-

2955.5

5.0

55.6

D59

Quercus serrata

735.4

3.0

-

D60

Quercus sp.

-

2.3

-

-

D61
D62

Quercus spp.
Quercus suber

700 1000
639
446

SF (mm) = (0.0124(DBH (cm))1.455)
Pg - (0.018(DBH (cm))1.825)
SF (mm) = (0.0077(DBH (cm))1.500)
P (mm) - (0.0195(DBH (cm))2.031)
-

974
-

91.5 at 7 mmcalc

SF (mm) = 0.148P (mm) - 0.0589

D63

Stewartia monadelpha

1523

-

0.5
15
S1 - nd
S2 - 26.0
S3 - 10.3
S4 - 14.7
S5 - 3.3
S6 - 6.6

-

-

a

SF = Stemflow
P = Precipitation
c
I = Rainfall intensity
d
CBH = Circumference at breast height
e
DBH = Diameter at breast height
b

Table 2.3. Species, location, stand information (diameter: Diam, tree density: Density, stand basal area: BA), author(s), and
alphanumeric code for coniferous and boreal studies.

18.0

Density
(Trees/ha)
2959

BA
(m2/ha)
-

New Hampshire, USA

-

-

-

-

-

-

-

Code

Species

Location

Diam. (cm)

C01

Abies balsamea

New Brunswick, Canada

C02

Abies balsamea

C03

Abies lasiocarpa

Author
Mahendrappa (1974)
Olson et al. (1981)
Niederhof and Wilm (1943)
(Zinke, 1967)

46

C04

Abies lasiocarpa
Picea glauca x engelmannii

Penticton, BC, Canada

-

1470

-

Spittlehouse (1998)

C05

Chamaecyparis obtusa

Honshu, Japan

1997: 5.8
1999: 7.0
2000: 8.1

2944

-

Murakami (2009)

C06

Chamaecyparis obtusa

Tokyo, Japan

21.5

932

-

C07

Chamaecyparis obtusa

Tokyo, Japan

21.5

932

-

C08

Fitzroya cupressoides

C09

Koichiro et al. (2001); Kuraji et
al. (2001)
Koichiro et al. (2001); Kuraji et
al. (2001)

-

-

-

Oyarzún et al. (1998)

Ilex pedunculosa

Cordillera de la Costa,
Chile
Kyoto, Japan

3.5

15

-

Park and Hattori (2002)

C10

Juniperus sp.

Texas, USA

-

-

-

Owens et al. (2006)

C11

Larix cajanderi

Siberia, Russia

-

840

-

Toba and Ohta (2005)

C12

Larix decidua

Edinburgh, Scotland

-

3900

30

Cape et al. (1991)

C13

Larix decidua

Aberdeen, Scotland

-

1600

50

Cape et al. (1991)

C14

Larix gmelinii

Genhe, China

-

-

-

C15

Larix laricina

Canada

-

-

-

C16

Picea abies

Vosges, France

-

575

53.3

Zhou (2003) (Wei et al., 2005)
Lilienfein and Wilcke (2004)
(Johnson and Lehmann, 2006)
Viville et al. (1993)

C17

Picea abies

Lancaster, England

-

3200

35

C18

Picea engelmannii

-

-

-

-

C19

Picea glauce

New Brunswick, Canada

17.0

3767

-

Cape et al. (1991)
Niederhof and Wilm (1943)
(Zinke, 1967)
Mahendrappa (1974)

C20

Picea rubens

New Brunswick, Canada

16.0

4841

-

Mahendrappa (1974)

C21

Picea sitchensis

Dumfriesshire, Scotland

25 - 36

-

-

Ford and Deans (1978)

C22

Picea sitchensis

-

-

-

Johnson (1990)

C23

Picea sitchensis

Balquhidder, Scotland
Carnation Creek, BC,
Canada

-

1500

-

Spittlehouse (1998)

15

156
277
625
3000

-

Teklehaimanot et al. (1991)

C24

Picea sitchensis

Edinburgh, Scotland

47

C25

Picea sitchensis

Aberdeen, Scotland

-

3600

125

C26

Pinus arandi

Miyaluo, China

-

-

-

C27

Pinus contorta

2.0 - 14.6

-

-

C28

Pinus contorta

Mayson Lake, British
Columbia, Canada
Penticton, BC, Canada

-

720

-

C29

Pinus contorta

-

-

-

-

C30

Pinus contorta

Colorado, USA

Pinus densiflora

Tsukuba, Japan

-

Taniguchi et al. (1996)

C32

Pinus densiflora

Northern Japan

2300
1700
1444

-

C31

20.4
19.8
-

Cape et al. (1991)
Lei et al. (1994a,b) (Wei et al.,
2005)
McKee and Carlyle-Moses
(2010)
Spittlehouse (1998)
Wilm and Dunford (1948)
(Zinke, 1967)
Dunford and Niederhof (1944)

-

Toba and Ohta (2005)

C33

Pinus densiflora

Northern Japan

-

1678

-

Toba and Ohta (2005)

C34

Pinus densiflora

Northern Japan

-

355

-

Toba and Ohta (2005)

C35

Pinus elliottii

Guangzhou, China

30

400

-

C36

Pinus koraiensis

Mao'er Shan, China

-

-

-

C37

Pinus palustri

Georgia, USA

10

2050

-

Tang (1996)
Zhou et al. (1994) (Wei et al.,
2005)
Bryant et al. (2005)

C38

Pinus pseudostrobus

Nuevo Leon, Mexico

32.4

246

-

C39

Pinus radiata plantation

-

-

-

-

C40

Pinus radiata

-

-

-

-

C41

Pinus resinosa

New Brunswick, Canada

22.0

1882

-

Silva and Rodrigues (2001)
Crockford and Khanna (1997)
(Levia and Frost, 2003)
Crockford and Richardson
(1990) (Levia and Frost, 2003)
Mahendrappa (1974)

C42

Pinus resinosa

New Haven, Connecticut

20.3

500

-

Voigt (1960)

C43

Pinus strobus

New Brunswick, Canada

21.0

2151

-

Mahendrappa (1974)

C44

Pinus strobus

North Carolina, USA

-

-

-

Helvey (1967)

C45

Pinus sylvestris

Siberia, Russia

-

1492

-

Toba and Ohta (2005)

C46

Pinus sylvestris

Lancaster, England

-

2270

36

Cape et al. (1991)

C47

Pinus sylvestris

Edinburgh, Scotland

-

3900

44

Cape et al. (1991)

C48

Pinus sylvestris

Aberdeen, Scotland

-

2700

95

Cape et al. (1991)

48

C49

Pinus tabulaeformis

Miyaluo, China

-

-

-

C50

Pinus taeda

-

-

-

C51

Pinus taeda
Pinus palustris

Georgia, USA

14 - 21

-

Bryant et al. (2005)

C52

Pinus wallichiana

Himachal Pradesh, India

-

556
367
189
1200

Lei et al. (1994a,b) (Wei et al.,
2005)
Hoover (1953) (Zinke, 1967)

29

Singh (1987)

C53

Pseudotsuga menziesii

Malalcahuello, Chile

25.9

1143

60.3

Iroumé and Huber (2002)

C54

Pseudotsuga menziesii

Oregon, USA

-

-

-

Rothacher (1963) (Zinke, 1967)

C55

Pseudotsuga menziesii

-

1050

-

Spittlehouse (1998)

C56

Pseudotsuga menziesii

-

1090

-

Spittlehouse (1998)

C57

Tsuga canadensis

24.1

-

-

Voigt (1960)

C58

Tsuga heterophylla

-

480

-

Spittlehouse (1998)

Cowichan Lake, BC,
Canada
Cowichan Lake, BC,
Canada
New Haven, Connecticut
Carnation Creek, BC,
Canada

Table 2.4. Species, meteorological data (annual rainfall: PA, and study period rainfall: PS), stemflow production information
(funnelling ratio(s): F, and percentage of gross rainfall diverted to stemflow: SF), and stemflow formulae for coniferous and
boreal studies.
Code

Species

PA (mm)

PS (mm)

SF (%)

F

Formula(s)

C01
C02
C03

Abies balsamea
Abies balsamea
Abies lasiocarpa
Abies lasiocarpa
Picea glauca x
engelmannii

-

389
-

3.5
3-8
-

-

SF (L) = 2.312P (mm) - 6.342con

3316

454

< 0.5

-

-

C04

49

C05

Chamaecyparis obtusa

1467.7

C06
C07
C08

Chamaecyparis obtusa
Chamaecyparis obtusa
Fitzroya cupressoides

2279
2279
4000

1997: 1259.7
1998: 1509.4
1999: 1673.2
2000: 1431.2
2156.4
1862.9
4098

C09

Ilex pedunculosa

-

-

-

69.8

C10
C11

Juniperus sp.
Larix cajanderi

600 - 900
-

1176 - 3209
59.13

5.0
0.0

-

SF (mm) = (0.0047(DBH (cm))2.174) Pg (0.0428(DBH(cm))1.150)
SF (mm) = 3.5 x (1 - e-0.103 x P) (mm)
Slope = 0.62 x 10-4

C12

Larix decidua

-

1984/85: 783
1985/86: 1053

4±1
3±1

8.5 at 63 mmcalc 13.3calc
15.3 at 52 mmcalc 10.0calc

Summer: SF (mm) = 0.041P (mm) - 0.984
Winter: SF (mm) = 0.07P (mm) - 1.26

C13

Larix decidua

-

C14
C15
C16

Larix gmelinii
Larix laricina
Picea abies

1400

1984/85: 1023
1985/86: 986
1710.6

1±0
0.4 ± 0.1
3.3
1.6
0.5

0.8 at 59 mmcalc 2.0calc
3.2 at 72 mmcalc 0.8calc
0.9calc

Summer: SF (mm) = 0.006P (mm) - 0.132
Winter: SF (mm) = 0.027P (mm) - 0.81
-

C17

Picea abies

-

1983/84: 1583
1984/85: 1690

13 ± 3
14 ± 3

21.1 at 47 mmcalc 37.1calc
34.6 at 33 mmcalc 40calc

Summer: SF (mm) = 0.16P (mm) - 2.4
Winter: SF (mm) = 0.16P (mm) - 1.28

C18
C19
C20
C21
C22
C23

Picea engelmannii
Picea glauce
Picea rubens
Picea sitchensis
Picea sitchensis
Picea sitchensis

-

-

2130
3316

454

-

SF (L) = 0.668P (mm) - 4.933con
-

C24

Picea sitchensis

1000

441.78

6.4
2.3
27.0
3.0
9.0
0.5
1.0
2.9
16.7

-

-

1639

5.9
2.8
3.8
4.3
12.0
12.0
2.0

81.3
30
31.4
29
-

-

50

C25

Picea sitchensis

C26

Pinus arandi

C27
C28
C29
C30

-

1984/85: 1023
1985/86: 986

13 ± 3
14 ± 3

8.8 at 37 mmcalc 10.4calc
10.4 at 15 mmcalc 11.2calc

Summer: SF (mm) = 0.15P (mm) - 1.5
Winter: SF (mm) = 0.15P (mm) - 0.3

-

5.0

-

-

Pinus contorta
Pinus contorta
Pinus contorta
Pinus contorta

700 1000
600
3316
599.4

52.3
454
396

14.9
-

C31

Pinus densiflora

1222

1291

C32
C33
C34
C35
C36
C37
C38

1500
676
830
639

152.2
269
174.6
724.8
974

-

SF (mm) = 0.0136P (mm) – 0.0896
SF (mm) = 0.0061P (mm) – 0.0729
slope = 0.16
SF (mm) = 0.088P (mm) – 0.432
-

-

-

3.1 - 3.9

-

-

-

-

11.2

-

-

C41
C42
C43

Pinus densiflora
Pinus densiflora
Pinus densiflora
Pinus elliottii
Pinus koraiensis
Pinus palustri
Pinus pseudostrobus
Pinus radiata
plantation
Pinus radiata
plantation
Pinus resinosa
Pinus resinosa
Pinus strobus

< 0.5
1.5
0.5
1.2
5.2
2.7
3.3
9.4
3.8
2.0
0.6

1143
-

-

0.7
1.2
5.3

-

C44

Pinus strobus

-

-

8.8
4.3
2.3

-

C45

Pinus sylvestris

-

C46

Pinus sylvestris

-

49.75
1983/84: 1583
1984/85: 1690

0.0
7±1
6±1

13.7 at 76 mmcalc 19.4calc
19.2 at 28 mmcalc 16.7calc

10 yrs old - SF = 0.00 + 0.09Pcon
35 yrs old - SF = -0.254 + 0.06Pcon
60 yrs old - SF = -0.254+ 0.03Pcon
All units (mm)
Slope = 0.31 x 10-3
Summer: SF (mm) = 0.087P (mm) - 2.871
Winter: SF (mm) = 0.088P (mm) - 0.528

C47

Pinus sylvestris

-

1984/85: 783
1985/86: 1053

15 ± 3
13 ± 3

26.1 at 39 mmcalc 34.1calc
38.6 at 41 mmcalc 29.5calc

Summer: SF (mm) = 0.16P (mm) - 1.76
Winter: SF (mm) = 0.24P (mm) - 2.88

C39
C40

-

51

C48

Pinus sylvestris

C49

Pinus tabulaeformis

C50

Pinus taeda
Pinus taeda
Pinus palustris
Pinus wallichiana

C51
C52

1984/85: 1023
1985/86: 986

10 ± 2
8±2

7.5 at 37 mmcalc 10.5calc
10.0 at 37 mmcalc 8.4calc

Summer: SF (mm) = 0.098P (mm) - 0.98
Winter: SF (mm) = 0.13P (mm) - 1.3

-

2.6

-

-

-

-

-

SF (mm) = 0.222 P (mm) - 0.457con

830

752.8

0.5

-

-

-

-

2.7

-

700 1000
-

C53

Pseudotsuga menziesii

2341

3805

6.0

C54
C55
C56
C57
C58

Pseudotsuga menziesii
Pseudotsuga menziesii
Pseudotsuga menziesii
Tsuga canadensis
Tsuga heterophylla

3316
3316
1143
3316

454
454
454

0.3
9.0
4.0
5.9
1.0

9.3 at 15 mm

calc

10

calc

SF (mm) = 0.065P (mm) - 0.131

-

-

Table 2.5. Species, location, stand information (diameter: Diam, tree density: Density, stand basal area: BA), author(s), and
alphanumeric code for mixed deciduous and coniferous stands.
Code

Species

Location

Diam.
(cm)

Density
(Trees/ha)

BA (m2/ha)

Author

X01

Dry sclerophyll forest

-

-

-

-

Crockford and Richardson (1990)
(Levia and Frost, 2003)

Ibaraki, Japan

-

-

-

Iida et al. (2005)

Georgia, USA

16 - 18

711

-

Bryant et al. (2005)

Georgia, USA

14
60

1411

-

Bryant et al. (2005)

Fort Bragg, California,
USA

-

341
108
89

61
31
5.5

Reid and Lewis (2009)

X02
X03
X04
X05

Pinus densiflora
Quercus myrsinaefolia
Eurya japonica
Quercus alba
Pinus taeda
Quercus berberidifolia
Pinus palustris
Sequoia sempervirens
Pseudotsuga menziesii
Lithocarpus densiflorus

52

Table 2.6. Species, meteorological data (annual rainfall: PA, and study period rainfall: PS), stemflow production information
(funnelling ratio(s): F, and percentage of gross rainfall diverted to stemflow: SF), and stemflow formulae for mixed deciduous
and coniferous stands.
Code

Species

PA (mm)

PS (mm)

SF (%)

F

Formula(s)

X01

Dry sclerophyll forest
Pinus densiflora
Quercus myrsinaefolia
Eurya japonica
Quercus alba
Pinus taeda
Quercus berberidifolia
Pinus palustris
Sequoia sempervirens
Pseudotsuga menziesii
Lithocarpus densiflorus

-

-

4.8

-

-

1207

1984/1985: 1213
2001/2002: 1246

1.2
8.5

-

SF (mm) = 0.0186P (mm) – 0.119
SF (mm) = 0.101P (mm) – 0.297

830

684.9

0.5

-

-

830

724.8

0.5

-

-

1285

1316

2.5

2.6calc

-

X02
X03
X04
X05

Table 2.7. Species, location, stand information (diameter: Diam, tree density: Density, stand basal area: BA), author(s), and
alphanumeric code for tropical studies.
Code

Species

Location

Diam.
(cm)

Density
(Trees/ha)

BA
(m2/ha)

Author

T01

Acacia mangium

Soberania, Panama

-

Lone

-

Park and Cameron (2008); Carlyle-Moses
et al. (2010)

T02

Amazonian terra firme
rainforest

Manaus, Amazonas, Brazil

-

3000

-

Lloyd et al. (1988)

T03

Balanops australiana

27.9
39.1

Lone trees

-

Herwitz (1986)

T04

Cardwellia sublimis

Northeast Queensland,
Australia
Northeast Queensland,
Australia

40.2

Lone

-

Herwitz (1986)

53

Castanopsis sieboldii,
Schima wallichii, and
Rapanea neriifolia
dominated stand

Ryukyus, Japan

> 3.0

6625

57.5calc

Xu et al. (2005)

T06

Cecropia peltata

Luquillo Mountains,
Puerto Rico

21
24
19
18

Lone trees

-

Holwerda et al. (2006)

T07

Cecropia peltata

Rio Piedras, Puerto Rico

-

-

-

Scatena (1990)

T08

Ceratopetalum virchowii

Northeast Queensland,
Australia

25.0
46.1
43.3

Lone trees

-

Herwitz (1986)

T09

Cerrado (native savanna)

Brazil

-

-

-

Lilienfein and Wilcke (2004) (Johnson and
Lehmann, 2006)

T10

Cunningshamia
lanceolata plantation

Huitong, China

-

-

-

Tian et al. (1994) (Wei et al., 2005)

Lone trees

-

Holwerda et al. (2006)

-

-

Scatena (1990)

T05

32
54
49
32
41
59
-

T11

Dacryodes excelsa

Luquillo Mountains,
Puerto Rico

T12

Dacryodes excelsa

Rio Piedras, Puerto Rico

T13

Dimorphandra
macrostachya and
Euceraea nitida

Canaima, Venezuela

> 10
< 10

950
4530

29.7
9.2

Dezzeo and Chacón (2006)

T14

Dimorphandra
macrostachya and
Euterpe sp.

Canaima, Venezuela

> 10
< 10

1060
3400

40
7

Dezzeo and Chacón (2006)

T15

Elaeocarpus foveolatus

48.1

Lone

-

Herwitz (1986)

T16

Elaeocarpus sp.

45.0

Lone

-

Herwitz (1986)

Northeast Queensland,
Australia
Northeast Queensland,
Australia

54

T17

Eschweilera spp.

Manaus, Brazil

21.0

-

-

Schroth et al. (1999); Schroth et al. (2001)

T18

Eucalyptus melanophloia

Australia

-

-

-

T19

Eucalyptus mixed cross

Congo

-

-

-

T20

Eucommia ulmoides

4.5 ± 1.1

6478

-

T21

Evergreen montane forest

Hunan Province, China
Zamora-Chinchipe,
Ecuador

Prebble and Stirk (1980) (Johnson and
Lehmann, 2006)
Laclau et al. (2003) (Johnson and
Lehmann, 2006)
Cao et al. (2008)

-

-

-

Fleischbein et al. (2005, 2006)

T22

Gliricidia sepium

Soberania, Panama

-

Lone

-

T23

Guazuma ulmifolia

Soberania, Panama

-

Lone

-

T24

Large timber extraction
forest

Central Sulawesi,
Indonesia

-

5495
3740
4052

41.1
53.6
34.6

Dietz et al. (2006)

Park and Cameron (2008); Carlyle-Moses
et al. (2010)
Park and Cameron (2008); Carlyle-Moses
et al. (2010)

Lowland dipterocarp
forest
Lowland evergreen rain
forest

Malaysia

-

-

-

Manokaran (1979)

Central Kalimantan,
Indonesia

> 10

-

-

Vernimmen et al. (2007)

T27

Lowland tropical forest

Kalimantan, Indonesia

-

Unlogged: 581
Logged: 278

38.6
13.8

Asdak et al. (1998)

T28

Lowland tropical forest

Sarawak, Malaysia

-

6856

43.3

Manfroi et al. (2004); Manfroi et al. (2006)

T29

Mixed pine broadleaf

Dinghushan, China

-

-

-

Yan et al. (2003) (Wei et al., 2005)

T30

Monsoon evergreen
broadleaf

Dinghushan, China

-

-

-

Yan et al. (2003) (Wei et al. 2005)

T31

Monsoon pine forest

Dinghushan, China

-

-

-

Yan et al. (2003) (Wei et al. 2005)

T32

Natural montane forest

Central Sulawesi,
Indonesia

-

2272
1806
3455

68.6
50
51.1

Dietz et al. (2006)

T33

Nectandra sp.

La Mancha, Veracruz,
Mexico

-

-

-

Kellman and Roulet (1990)

T25
T26

55

T34

Nectandra sp.

La Mancha, Veracruz,
Mexico

-

-

-

Kellman and Roulet (1990)

T35

Ochroma pyramidale

Soberania, Panama

-

Lone

-

Park and Cameron (2008); Carlyle-Moses
et al. (2010)

T36

Oenocarpus bacaba

Manaus, Brazil

15.5

-

-

Schroth et al. (1999); Schroth et al. (2001)

T37

Pachira quinata

Soberania, Panama

-

Lone

-

Park and Cameron (2008); Carlyle-Moses
et al. (2010)

T38

Pinus canariensis

-

-

-

Kittredge et al. (1941) (Zinke, 1967)

T39

Pinus massoniana

Hunan Province, China

2628

T40

Prestoea montana

Luquillo Mountains,
Puerto Rico

9.2 ± 3.4
15
16
16
15
18
17
15
17

T41

Quercus copeyensis

Costa Rica

T42

Quercus copeyensis

T43
T44
T45

T46
T47

Cao et al. (2008)

Lone trees

-

Holwerda et al. (2006)

-

-

-

Costa Rica

-

-

-

Quercus copeyensis

Costa Rica

-

-

-

Rain forest
Rain forest with high
abundance of
ectomycorrhizal trees
Rain forest with low
abundance of
ectomycorrhizal trees
Semi-deciduous
monsoon forests

Sabah, Malaysia

-

-

-

Hölscher et al. (2003) (Johnson &
Lehmann, 2006)
Hölscher et al. (2003) (Johnson &
Lehmann
Hölscher et al. (2003) (Johnson &
Lehmann
Sinun et al. (1992)

Korup, Cameroon

>5

301

-

Chuyong et al. (2004)

Korup, Cameroon

>5

303

-

Chuyong et al. (2004)

Jianfengling, China

-

-

-

Zeng (1994) (Wei et al., 2005)

56

Sloanea berteriana

Luquillo Mountains,
Puerto Rico
Rio Piedras, Puerto Rico

Lone
trees
-

T50

Small timber extraction
forest

Central Sulawesi,
Indonesia

-

T51

Stunted heath forest

T52

Stunted heath forest

T53

Tall heath forest

T54

Terra firme rainforest

T55

Tristania sp.

T56

Tropical dry forest

T48

Sloanea berteriana

T49

T57
T58
T59
T60
T61
T62
T63
T64

382

-

Holwerda et al. (2006)

2020
3855
2420

55.5
67
41.4

Scatena (1990)

> 10

-

-

Vernimmen et al. (2007)

Small
trees

-

-

Vernimmen et al. (2007)

> 10

-

-

Vernimmen et al. (2007)

> 10

670

33.7

Cuartas et al. (2007)

-

-

-

Vernimmen et al. (2007)

-

-

-

Kellman and Roulet (1990)

Columbia

-

-

-

Manaus, Brazil
San Carlos de Rio Negro,
Venezuela
San Carlos de Rio Negro,
Venezuela
Araracuara, Colombia

3.8 - 52.2

3000

-

Veneklaas and Van Ek (1990) (Levia and
Frost, 2003)
Lloyd and de Marques (1988)

-

11217

-

Jordan (1978)

-

2736

-

Jordan (1978)

-

-

-

Marin et al. (2000)

Vernicia fordii
Vismia guianensis,
Myrcia sp.
Clusia sp.

Hunan Province, China

7.3 ± 2.1

2000

-

Cao et al. (2008)

Canaima, Venezuela

> 10
< 10

130
1030

2
2

Dezzeo and Chacón (2006)

Vismia spp.

Manaus, Brazil

3.5

19500

Tropical montane
rainforest
Tropical rain forest
Tropical rain forest (228
species)
Tropical rain forest (100
species)
Tropical rainforest

Central Kalimantan,
Indonesia
Central Kalimantan,
Indonesia
Central Kalimantan,
Indonesia
Manaus, Brazil
Central Kalimantan,
Indonesia
La Mancha, Veracruz,
Mexico

Dietz et al. (2006)

Schroth et al. (1999); Schroth et al. (2001)

57

Table 2.8. Species, meteorological data (annual rainfall: PA, and study period rainfall: PS), stemflow production information
(funnelling ratio(s): F, and percentage of gross rainfall diverted to stemflow: SF), and stemflow formulae for tropical studies.
Code

Species

PA (mm)

PS (mm)

SF (%)

F

Formula(s)

T01

Acacia mangium
Amazonian terra firme
rainforest

2127

158.1

2.7 ± 2.0

38.7 at 14.5 mm 20.3calc

-

2391

4804

-

-

SF (mm) = 0.036P (mm) - 0.15

T02

calc

7800

25.2
3.3calc
3.8calc

112
7
11

1998: 4320
1999: 2231
2000: 3424

32.1
27.6
31.7

55.8calc
48.0calc
55.1calc

SF (L) = 0.07P (mm) - 0.58
SF (L) = 0.14P (mm) - 1.49
SF (L) = 0.13P (mm) - 0.92
SF (L) = 0.14P (mm) - 0.03

T03

Balanops australiana

6500

7800

T04

Cardwellia sublimis
Castanopsis sieboldii
Schima wallichii
Rapanea neriifolia
dominated stand

6500
2680

T05

-

T06

Cecropia peltata

3000 - 4000

2246

0.01

1.5 at 33 mmcalc 1.2calc
2.2 at 38 mmcalc 1.6calc
3.5 at 30 mmcalc 3.0calc
5.3 at 5 mmcalc 5.5calc

T07

Cecropia peltata

-

-

9.8

-

-

calc

T08

Ceratopetalum virchowii

6500

7800

18.6
26.2calc
7.7calc

100
33
20

-

T09

Cerrado (native savanna)

1656

-

0.8

-

-

T10

Cunningshamia
lanceolata plantation

1550

-

0.2

-

-

3.9 at 36 mmcalc 2.9calc
2.3 at 19 mmcalc 2.2calc
1.8 at 30 mmcalc 1.5calc
0.7 at 34 mmcalc 0.5calc
1.9 at 38 mmcalc 1.4calc
1.7 at 35 mmcalc 1.2calc
-

SF (L) = 0.43P (mm) - 4.14
SF (L) = 0.63P (mm) - 1.95
SF (L) = 0.44P (mm) - 3.12
SF (L) = 0.08P (mm) - 0.69
SF (L) = 0.35P (mm) - 3.65
SF (L) = 0.62P (mm) - 5.72
-

T11

Dacryodes excelsa

3000 - 4000

2246

0.3

T12

Dacryodes excelsa

-

-

1.5

58

T13

T14
T15

Dimorphandra
macrostachya and
Euceraea nitida
Dimorphandra
macrostachya and
Euterpe sp.
Elaeocarpus foveolatus

2548

2215

6.9

17.7calc

-

2548

2215

8.4

17.9calc

-

6500

7800

39.7calc

50

-

calc

T16
T17

Elaeocarpus sp.
Eschweilera spp.

6500
2622

7800
2672

3.2
0.1

9
-

-

T18

Eucalyptus melanophloia

718

-

0.8

-

-

T19

Eucalyptus mixed cross

1502

-

1.6

-

-

T20

Eucommia ulmoides

1347.2

2086.1

7.6

-

-

T21

Evergreen montane forest

2048

2504

1.0

-

T22
T23

Gliricidia sepium
Guazuma ulmifolia

2127
2127

T24

Large timber extraction
forest

2437 - 3424

T25
T26
T27

Lowland dipterocarp
forest
Lowland evergreen rain
forest
Lowland tropical forest

255.1
264.2
220
185
259

1.5 ± 0.21
2.3 ± 0.28
0.7
0.7
0.6

74.8 at 18.3 mm 29.7
105.1 at 18.8 mm 37.7calc
1.7calc
1.3calc
1.7calc

2030 - 3050

2381

0.6

-

SF (L/100 sq.m) = 0.008 P (x102
L/100 sq.m) - 2.6797

3625 ± 560

2995

0.2

-

SF (ml/mm) = 4.2 BDH (cm) - 32.2

2862

2199
3563

Logged: 1.4
Unlogged: 0.3

-

SF (m3) = 0.008 + 0.019BA (m2)a
SF (m3) = 0.002 + 0.019BA (m2)

3.5
2.8
3.0

T28

Lowland tropical forest

2740.5

Yr 1: 2292
Yr 2: 2439
Yr 3: 2668

T29

Mixed pine broadleaf
Monsoon evergreen
broadleaf

1900

-

6.5

8.7 at 22 mmcalc
Year 1: 8.1calc
Year 2: 6.5calc
Year 3: 6.9calc
-

1900

-

8.3

-

T30

calc

-

SF (mm) = 0.046 P (mm) - 0.18
SF (ml/mm) = -11.6 + 122.4
log10(DBH (cm))
-

59

T31

Monsoon pine forest

1900

T32

Natural montane forest

2437 - 3424

T33

Nectandra sp.

1300

T34

Nectandra sp.

130

215
165
148
32
16
16

1.9
0.6
0.3
0.5

0.9calc
0.6calc
1.0calc

-

-

Event high: 111.9

-

-

Event high: 135.3

-

calc

T35

Ochroma pyramidale

2127

269.6

0.9 ± 0.6

29.9 at 14.8 mm 10.3

T36
T37
T38
T39

Oenocarpus bacaba
Pachira quinata
Pinus canariensis
Pinus massoniana

2622
2127

2672
232.6

29.8 at 26.8 mm 12.2calc

1347.2

2086.1

0.7
1.3 ± 0.3
0.03 - 13
2.4

T40

Prestoea montana

3000 - 4000

2246

2.7

T41
T42
T43
T44

Quercus copeyensis
Quercus copeyensis
Quercus copeyensis
Rain forest
Rain forest with high
abundance of
ectomycorrhizal trees

2830
2900
2900
-

3627

5011

T45

-

2.2
16.1
16.6
1.9

206.9 at 10 mmcalc 214.0calc
132.5 at 14 mmcalc 133.9calc
63.3 at 15 mmcalc 63.2calc
115.3 at 19 mmcalc 110.7calc
11.2 at 23 mmcalc 10.3calc
73.5 at 6 mmcalc 76.0calc
272.8 at 2 mmcalc 275.7calc
53.1 at 7 mmcalc 55.1calc
-

SF (mm) = 0.03P (mm) - 0.508con
SF (L) = 4.05P (mm) - 3.94
SF (L) = 3.03P (mm) - 5.11
SF (L) = 1.47P (mm) - 2.95
SF (L) = 2.41P (mm) - 7.09
SF (L) = 0.35P (mm) - 1.49
SF (L) = 1.76P (mm) - 0.55
SF (L) = 4.87P (mm) - 0.10
SF (L) = 1.30P (mm) - 0.67
-

5370

2.2

-

-

T46

Rain forest with low
abundance of
ectomycorrhizal trees

5011

5370

1.5

-

-

T47

Semi-deciduous
monsoon forests

1650 - 2650

-

3.0

-

-

60

T48

Sloanea berteriana

3000 - 4000

2246

0.6

T49

Sloanea berteriana

-

T50

Small timber extraction
forest

2437 - 3424

480
315
300

1.0
0.7
0.9
0.6

9.7 at 33 mmcalc 7.7calc
6.3 at 35 mmcalc 4.7calc
14.4 at 27 mmcalc 12.6calc
2.1 at 36 mmcalc 1.6calc
1.3calc
1.3calc
1.4calc

T51

Stunted heath forest

3625 ± 560

2995

0.4

-

SF (ml/mm) = 3.2DBH (cm) - 10.0

T52

Stunted heath forest

3625 ± 560

2995

1.0

-

SF (ml/mm) = 49.0DBH (cm) + 2.6

T53

Tall heath forest

3625 ± 560

2995

0.8

-

SF (ml/mm) = 1.1DBH (cm) + 6.53
SF (ml/mm) = 3.3DBH (cm) +
13.74

T54

Terra firme rainforest

2442

3064.2

0.7

3.1 at 24 mmcalc

SF (mm) = 0.013P (mm)- 0.06

T55

Tristania sp.

3625 ± 560

2995

0.6

-

SF (ml/mm) = 35.4DBH (cm) - 27.6

T56

Tropical dry forest
Tropical montane
rainforest
Tropical rain forest
Tropical rain forest (228
species)
Tropical rain forest (100
species)

1300

304

0.7

-

-

-

-

< 0.1

-

-

2442

2721

1.8 ± 1

-

-

-

2861

7.1

-

-

-

3087

1.8

-

-

0.9
0.9
1.5
1.1

-

T57
T58
T59
T60

SF (L) = 0.29P (mm) - 2.31
SF (L) = 0.27P (mm) - 2.48
SF (L) = 0.28P (mm) - 1.57
SF (L) = 0.13P (mm) - 1.25
-

T61

Tropical rainforest

3100

3273.8
3293.0
3158.4
3120.9

T62

Vernicia fordii

1347.2

2086.1

3.6

-

Plot 1 - SF = 0.0015P1.53
Plot 2 - SF = 0.0020P1.467
Plot 3 - SF = 0.0029P1.423
Plot 4 - SF = 0.0031P1.325
Units: SF(mm) P(mm)
-

T63

Vismia guianensis,
Myrcia sp. and Clusia sp.

2548

2215

2.0

50.0calc

-

T64

Vismia spp.

2622

2672

20.3

-

SF (L/mm) = 0.026DBH (cm) - 0.03

61

a

BA = Basal area

Table 2.9. Species, location, stand information (diameter: Diam, tree density: Density, stand basal area: BA), author(s), and
alphanumeric code for Mediterranean studies.
Code

Species

Location

Tarragona, Spain

Density
(Trees/ha)

BA
(m2/ha)

Author

Lone trees

-

Bellot and Escarré (1998)

67

-

13.5

1792

25

12.7

1760

24.6

Ferreira (1992)

7.3

1664

17.3

Ferreira (1992)

14.2

1010

-

Valente et al. (1997)

Diam. (cm)
2.8
3.2
5.4
7.0
10.5
41.2
39.2

M01

Arbutus unedo

M02

Castanea sativa

M03

Eucalyptus globulus

M04

Eucalyptus globulus

M05

Eucalyptus globulus

M06

Eucalyptus globulus

M07

Eucalyptus macrorhyncha

Canberra, Austalia

23

292

7.3

Crockford et al. (1996)

M08

Eucalyptus mannifera

Canberra, Austalia

23

433

10.8

Crockford et al. (1996)

M09

Eucalyptus melliodora

Canberra, Austalia

15

100

1.4

Crockford et al. (1996)

M10

Eucalyptus nitens

Collipulli, Chile

-

1560

29.6

Huber and Iroumé (2001)

M11

Eucalyptus nitens

Collipulli, Chile

-

850

19.5

Huber and Iroumé (2001)

M12

Eucalyptus nitens

Collipulli, Chile

-

633

15.9

Huber and Iroumé (2001)

M13

Eucalyptus rossii

Canberra, Austalia

21

700

14.6

M14

Fagus moesiaca

Pindous MTS, Greece

-

-

-

Crockford et al. (1996)
Michopoulos et al. (2001)
(Llorens and Domingo, 2007)

Argemil, Tras-osMontes, Portugal
Pousadas, Agueda
basin, Portugal
Cabeço Cão, Agueda
basin, Portugal
Serra de Cima, Agueda
basin, Portugal
Herdade da Espira,
Portugal

Portela and Pires (1995) (Llorens
and Domingo, 2007)
Ferreira (1992, 1996) (Llorens
and Domingo, 2007)

62

M15

Fagus sylvatica

Mont Lozère, Lozère,
France

10.2

4270

52.2

Didon-Lescot (1996, 1998)
(Llorens and Domingo, 2007)

M16

Fagus sylvatica

Toscana, Italy

28.5
28.5

-

-

Giacomin and Trucchi (1992)

M17

Fagus sylvatica

24.3

889

41.2

M18

Fagus sylvatica

39.7

327

40.3

M19

Fagus sylvatica

10.3

4356

35.15

M20

Fagus sylvatica

36.2

345

35.46

M21

Fagus sylvatica

Selva Piana, Abruzo,
Italy
Piano Nuda,
Campanioa, Italy
Brasimone, EmiliaRomagna, Italy
Pian Cansiglio, Veneto,
Italy
Burgos-Logroño, Spain

4 - 20

526

-

Moreno et al. (2001) (Llorens and
Domingo, 2007)
Moreno et al. (2001) (Llorens and
Domingo
Moreno et al. (2001) (Llorens and
Domingo
Moreno et al. (2001) (Llorens and
Domingo
Tarazona et al. (1996)

M22

Fitzroya cupressoides
Fraxinus ornus
Quercus pubescentis

Hueicolla, Chile
Istrian Peninsula,
Slovenia

-

1100

58

Huber and Iroumé (2001)

-

3100

-

Šraj et al. (2008)

M24

Holm-oak forest

Tarragona, Spain

-

9178

37.9

M25

Juniperus oxycedrus

El Ardal, Murcia,
Spain

-

Lone

-

M26

Laurel forest

>6

1693

33.7

M27

Mixed broadleaved

Agua Garcia
Mountains, Tenerife
Hueicolla, Chile

-

530

99.6

Bellot and Escarré (1998); Bellot
et al. (1999)
Belmonte (1997); Belmonte and
Romero (1998) (Llorens and
Domingo, 2007)
Aboal et al. (1999); Aboal et al.
(2002)
Huber and Iroumé (2001)

M28

Mixed broadleaved

Mariquina, Chile

-

335

-

Huber and Iroumé (2001)

M29

Mixed broadleaved

Malalcahuello, Chile

-

367

47

M30

Nothofagus dombeyi

Chile

-

-

-

M31

Nothofagus obliqua
Nothofagus alpina
Nothofagus dombeyi

Nacimiento, Chile

37.6
43.4
26
26
26

3500
133
200

14.8
29.6

Huber and Iroumé (2001)
Uyttendaele and Iroumé (2002)
(Johnson and Lehmann, 2006)
Huber and Iroumé (2001)

Lone trees

-

M23

M32
M33

Olea europaea

Malalcahuello, Chile
Coraba, Spain

Iroumé and Huber (2002)
Gomez et al. (2002)

63

M34

Phyllirea media

Tarragona, Spain

3.2
3.8
6.5
7.0
13.7

M35

Picea abies

Lozère, France

27

395

22

M36

Pinus hapepensis

El Ardal, Murcia,
Spain

-

Lone

-

M37

Pinus nigra L.

23.1
25.7

1533
867

64.5
44.9

M38

Pinus pinaster

M39

Pinus pinaster

M40

Pinus pinaster

M41

Pinus pinea

M42

Lone trees

-

Bellot and Escarré (1998)

Don Bruno, Sila Greca,
Italy
Barrosa, Agudea Basin,
Portugal
Bordeaux, France
Herdade da Espira,
Portugal
Petit-Saint-Jean, Delta
Rhone, France

32.1

400

32.8

9 - 15

800

-

Didon-Lescot (1996); DidonLescot (1998) (Llorens and
Domingo, 2007)
Belmonte (1997); Belmonte and
Romero (1998) (Llorens and
Domingo, 2007)
Iovino et al. (1998) (Llorens and
Domingo, 2007)
Ferreira (1992, 1996) (Llorens
and Domingo, 2007)
Loustau et al. (1992)

33.7

312

-

Valente et al. (1997)

-

800

-

Pinus pinea

Languedoc, France

20.2

800

33.9

M43

Pinus radiata

Valdivia, Chile

-

733

60

Ibrahim et al. (1982) (Llorens and
Domingo, 2007)
Rapp and Ibrahim (1978) (Llorens
and Domingo, 2007)
Huber and Iroumé (2001)

M44

Pinus radiata

Valdivia, Chile

-

973

65.9

Huber and Iroumé (2001)

M45

Pinus radiata

Valdivia, Chile

-

467

51.6

Huber and Iroumé (2001)

M46

Pinus radiata

Valdivia, Chile

-

194

34.9

Huber and Iroumé (2001)

M47

Pinus radiata

Nacimiento, Chile

-

2000

-

Huber and Iroumé (2001)

M48

Pinus radiata

Nacimiento, Chile

-

443

-

Huber and Iroumé (2001)

M49

Pinus radiata

Collipulli, Chile

-

460

19.5

Huber and Iroumé (2001)

M50

Pinus radiata

Collipulli, Chile

-

220

12

Huber and Iroumé (2001)

M51

Pinus radiata

Collipulli, Chile

-

833

13.4

Huber and Iroumé (2001)

M52

Pinus radiata

Collipulli, Chile

-

395

6.8

Huber and Iroumé (2001)

M53

Pinus radiata

San Ignacio, Chile

-

1206

27.1

Huber and Iroumé (2001)

64

M54

Pinus radiata

San Ignacio, Chile

-

549

13.7

Huber and Iroumé (2001)

M55

Pinus radiata

San Ignacio, Chile

-

1143

22.1

Huber and Iroumé (2001)

M56

Pinus radiata

San Ignacio, Chile

-

417

8.8

Huber and Iroumé (2001)

M57

Pinus radiata

Laja, Chile

-

926

11

Huber and Iroumé (2001)

M58

Pinus radiata

Laja, Chile

-

1087

16.5

Huber and Iroumé (2001)

M59

Pinus radiata

Canberra, Australia

18

1708

35.1

M60

Pinus radiata

Chile

-

-

-

M61

Pinus sylvestris

S.J. Pena, Aragón,
Spain

18.6

1080

52.3

M62

Pinus sylvestris

Mediterranean

-

2400

39

Crockford et al. (1996)
Uyttendaele and Iroumé (2002)
(Johnson andLehmann, 2006)
Alvera (1976) (Llorens and
Domingo, 2007)
Llorens (1997) (Llorens and
Domingo, 2007)

M63

Pinus sylvestris

Sierra de la Demanda,
Spain

-

581

29.6

M64

Pinus sylvestris

Salamanca, Spain

19.8

1700

-

M65

Pinus sylvestris

Burgos-Logroño, Spain

30 - 40

581

-

M66

Pseudotsuga menziesii

-

1143

97

M67

Quercus cerris

12.5

2131

25.9

M68

Quercus cerris

14.1

1623

25.3

M69

Quercus cerris

Malalcahuello, Chile
Carrega, EmigiaRomagna, Italy
Monte Rufeno, Lazio,
Italy
Monteromano, Lazio,
Italy

-

2375

-

1.9
4.1
4.6
6.0
6.2
11.7
12.6
15.1
19.1
23.4

Lone trees

-

M70

Quercus ilex

Tarragona, Spain

Santa Regina and Tarazona (2001)
Santa Regina (1995) (Llorens and
Domingo, 2007)
Tarazona et al. (1996)
Huber and Iroumé (2001)
Moreno et al. (2001) (Llorens and
Domingo, 2007)
Moreno et al. (2001) (Llorens and
Domingo, 2007)
Moreno et al. (2001) (Llorens and
Domingo, 2007)

Bellot and Escarré (1998)

65

M71

Quercus ilex

Montpellier, France
Colognole, Toscana,
Italy
La Castanya, Montseny
Range, Spain
St Pere Vilamajor,
Montseny Range,
Spain

4 - 12

6885

M72

Quercus ilex

M73

Quercus ilex

M74

Quercus ilex

M75

12.7

2366

30.2

11.3

2127

26.5

Rodrigo and Avila (2001)

12

1753

22.3

Rodrigo and Avila (2001)

Quercus ilex rotundifolia

Évora, Portugal

0.5 ± 0.11

35 - 45

-

David et al. (2006)

M76

Quercus ilex rotundifolia

Munovela, Salamanca,
Spain

24.9

Lone

-

M77

Quercus ilex rotundifolia

Guadalperón, Cáceres,
Spain

25.5

Lone

-

M78

Quercus petraea

Carrega, EmigiaRomagna, Italy

12.5

2131

25.9

M79

Quercus pubescens

Settimo, Crati, Italy

2.2

3250

1.8

M80

Quercus pubescentis
Carpinus orientalis
croaticus

Istrian Peninsula,
Slovenia

-

900

-

M81

Quercus pyrenaica

15.2

820

14.9

M82

Quercus pyrenaica

25.4

406

20.6

M83

Quercus pyrenaica

16.5

738

15.8

M84

Quercus pyrenaica

11

1043

9.9

M85

Quercus suber

Odemira, Portugal

15.8

-

-

M86

Rosmarinus officinalis

El Ardal, Murcia,
Spain

-

Lone

-

Navasfrias, Salamanca,
Spain
El Payo, Salamanca,
Spain
Fuenteginaldo,
Salamanca, Spain
Villasrubias,
Salamanca, Spain

Limousin et al. (2008)
Moreno et al. (2001) (Llorens and
Domingo, 2007)

Calabuig et al. (1978) (Llorens
and Domingo, 2007)
Mateos (2001); Mateos and
Schnabel (1998) (Llorens and
Domingo, 2007)
Moreno et al. (2001) (Llorens and
Domingo, 2007)
Iovino et al. (1998) (Llorens and
Domingo, 2007)
Šraj et al. (2008)
Moreno et al. (2001) (Llorens and
Domingo, 2007)
Moreno et al. (2001) (Llorens and
Domingo, 2007)
Moreno et al. (2001) (Llorens and
Domingo, 2007)
Moreno et al. (2001) (Llorens and
Domingo, 2007)
Pereira de Almeida and Riekerk
(1990)
Belmonte (1997); Belmonte and
Romero (1998) (Llorens and
Domingo, 2007)

66

M87

El Ardal, Murcia,
Spain

Thymus vulgaris

-

Lone

-

Belmonte (1997); Belmonte and
Romero (1998) (Llorens and
Domingo, 2007)

Table 2.10. Species, meteorological data (annual rainfall: PA, and study period rainfall: PS), stemflow production information
(funnelling ratio(s): F, and percentage of gross rainfall diverted to stemflow: SF), and stemflow formulae for Mediterranean
studies.
Code

Species

PA
(mm)

PS (mm)

SF (%)

F

Formula(s)

M01

Arbutus unedo

570

1296.26

3.8

72.3 at 23 mmcalc
79.9 at 29 mmcalc
137.9 at 17 mmcalc

M02

Castanea sativa

1133

2490

0.2

-

SF (L) = 0.096P (mm) + 0.060
SF (L) = 0.072P (mm) - 0.318
SF (L) = 0.234P (mm) - 1.480
SF (L) = 0.625P (mm) - 1.603
SF (L) = 0.425P (mm) + 2.276
-

M03

Eucalyptus globulus

1600

156.8

2.9

11.6calc

-

M04

Eucalyptus globulus

1600

223.4

2.9

11.8calc

-

calc

-

M05

Eucalyptus globulus

1600

335.7

2.9

16.8

M06

Eucalyptus globulus
Eucalyptus
macrorhyncha
Eucalyptus mannifera

600

1545.80

1.7

-

-

679

805

0.3calc

4

-

805

1.1

calc

10.6

-

calc

18.6

-

M07
M08

679

M09

Eucalyptus melliodora

679

805

0.2

M10

Eucalyptus nitens

1540

1996/97: 1039
1997/98: 1858
1998/99: 734

4
4
4

13.5calc

SF (mm) = 0.014P (mm) + 20.65

M11

Eucalyptus nitens

1540

1996/97: 1039
1997/98: 1858
1998/99: 735

3
3
3

15.4calc

SF (mm) = 0.014P (mm) + 20.65

M12

Eucalyptus nitens

1540

1996/97: 1039

2.0

12.6calc

SF (mm) = 0.014P (mm) + 20.65

67

M13

Eucalyptus rossii

679

805

3.1calc

21

-

M14

Fagus moesiaca

-

-

8.0

-

-

M15

Fagus sylvatica

1900

1537.5

M16

Fagus sylvatica

2027

-

M17

Fagus sylvatica

1300

-

M18

Fagus sylvatica

1500

1552.5

20.4
13.8
15.0
4.1
1.1

39.1

calc

-

-

-

-

-

2.7

calc

-

calc

-

M19

Fagus sylvatica

1800

1139

6.4

18.2

M20

Fagus sylvatica

1900

-

Fagus sylvatica

895

2.0
6.0
5.6
8.3

5.6calc

M21

1366.5
1986: 812.8
1987: 1669.8
1988: 1911

-

-

M22

Fitzroya cupressoides

3500

1982/83: 4603

9.0

15.5calc

-

M23

Fraxinus ornus
Quercus pubescentis

1000
1300

1318

4.5 ± 0.8

-

-

M24

Holm-oak forest

570

1296.26

12.1

M25

Juniperus oxycedrus

228

89.7

22.0

M26

Laurel forest

733

626

6.9

M27

Mixed broadleaved

2500

1982/83: 3563

4.0

4.0calc

SF (mm) = 0.014P (mm) + 20.65

2
1
1
7
1
1
1
1

-

SF (mm) = 0.014P (mm) + 20.65

8.0

17.0calc

SF (mm) = 0.014P (mm) + 20.65

M28

Mixed broadleaved

2400

1986: 2973
1987: 2268
1988: 1538
1989: 1643
1990: 2287
1991: 2355
1993/94: 2690
1994/95: 2066

M29

Mixed broadleaved

2350

1998/99: 1347

30.4 at 16 mmcalc
31.9calc
19.2 at 11 mmcalc
20.5calc

SF (mm) = 0.133P (mm) - 0.285
SF (mm) = 0.0719P (mm) – 0.0805

68

M30

Nothofagus dombeyi

1982

-

2.0

-

M31

Nothofagus obliqua

1200

1991/92: 1973

3.0

-

M32

Nothofagus alpina
Nothofagus dombeyi

SF (mm) = 0.014P (mm) + 20.65
calc

2341

3805

7.0

16.0 at 19 mm
15.8calc
51
85
60
77.6 at 39 mmcalc
117.1 at 33 mmcalc
118.1 at 9 mmcalc
47.9 at 30 mmcalc
19.9 at 22 mmcalc
3.2calc

SF (mm) = 0.0509P (mm) - 0.1814calc
SF (mm) = 0.1055P (mm) - 0.3962calc
SF (mm) = 0.0606P (mm) - 0.1012calc
SF (L) = 0.087P (mm) - 0.958
SF (L) = 0.175P (mm) - 1.393
SF (L) = 0.428P (mm) - 0.324
SF (L) = 0.239P (mm) - 1.643
SF (L) = 0.361P (mm) - 1.477
-

-

-

-

-

3.4calc

-

-

-

-

-

SF (mm) = 0.085P (mm) - 0.263

M33

Olea europaea

606

180.17

3.9
7.9
5.4

M34

Phyllirea media

570

1296.26

1.6

M35

Picea abies

1900

1537.5

0.7

M36

Pinus hapepensis

228

217.8

M37

Pinus nigra L.

1179

M38

Pinus pinaster

1600

M39

Pinus pinaster

920

M40

Pinus pinaster

600

990.1
1987: 139.4
1988: 97.5
1988: 190.3
1989: 82.5
1366.20

1.7
0.7
0.8
1.1
3.4
4.9
2.7
4.2
0.3

M41

Pinus pinea

494

-

2.3

-

-

769
1982: 2389
1983: 1628
1984: 2059
1985: 2295
1986: 2341
1987: 1841

2.3
13
12
12
11
10
9

calc

6.8
21.7calc
20.0calc
20.0calc
18.3calc
16.8calc
15.0calc

-

1992/93: 2925
1993/94: 2075

10
9

15.2calc
13.7calc

M42

Pinus pinea

648

M43

Pinus radiata

2150

M44

Pinus radiata

2150

SF (mm) = 0.106P (mm) - 72.29

SF (mm) = 0.106P (mm) - 72.29

69

M45

Pinus radiata

2150

1992/93: 2925
1993/94: 2075
1994/95: 2394
1996/97: 2574
1997/98: 1676
1992/93: 2925
1993/94: 2075
1994/95: 2394
1996/97: 2574
1997/98: 1676

8
8
8
8
8
6
5
5
5
6

15.5calc

SF (mm) = 0.106P (mm) - 72.29

17.2calc
14.3calc
14.3calc
14.3calc
17.2calc

SF (mm) = 0.106P (mm) - 72.29

M46

Pinus radiata

2150

M47

Pinus radiata

1200

1991/92: 1971

5.0

-

SF (mm) = 0.106P (mm) - 72.29

M48

Pinus radiata

1200

1991/92: 1972

3.0

-

SF (mm) = 0.106P (mm) - 72.29

M49

Pinus radiata

1540

SF (mm) = 0.106P (mm) - 72.29

Pinus radiata

1540

8.3calc
16.7calc

SF (mm) = 0.106P (mm) - 72.29

M51

Pinus radiata

1540

14.9calc

SF (mm) = 0.106P (mm) - 72.29

M52
M53
M54
M55
M56
M57
M58
M59

Pinus radiata
Pinus radiata
Pinus radiata
Pinus radiata
Pinus radiata
Pinus radiata
Pinus radiata
Pinus radiata

1540
1000
1000
1000
1000
1000
1000
679

3
3
1
2
2
2
2
1.0
5.0
4.0
6.0
2.0
1.0
3.0
11.2calc

15.4calc

M50

1996/97: 1039
1997/98: 1858
1996/97: 1039
1997/98: 1858
1996/97: 1039
1997/98: 1858
1998/99: 734
1996/97: 1039
1998/99: 1005
1998/99: 1005
1998/99: 1005
1998/99: 1005
1998/99: 1038
1998/99: 1038
824

14.7calc
18.5calc
29.2calc
27.1calc
22.7calc
9.1calc
18.2calc
32

SF (mm) = 0.106P (mm) - 72.29
SF (mm) = 0.106P (mm) - 72.29
SF (mm) = 0.106P (mm) - 72.29
SF (mm) = 0.106P (mm) - 72.29
SF (mm) = 0.106P (mm) - 72.29
SF (mm) = 0.106P (mm) - 72.29
SF (mm) = 0.106P (mm) - 72.29
-

M60

Pinus radiate

1982

-

22.0

-

-

M61

Pinus sylvestris

931

858

0.8

-

-

M62

Pinus sylvestris

850

-

1.3

-

-

calc

-

M63

Pinus sylvestris

886

1254

0.5

1.7

M64

Pinus sylvestris

985

1021

10.8

-

70

M65

Pinus sylvestris

895

1986: 600.7
1987: 1281.4
1988: 1678.7

0.35
0.5
0.4

-

M66

Pseudotsuga menziesii

2350

1998/99: 1346

6.0

6.2calc

3.1

12.0

calc

-

calc

-

M67

Quercus cerris

1200

748

SF (mm) = 0.106P (mm) - 72.29

M68

Quercus cerris

1000

991.5

10.4

41.1

M69

Quercus cerris

-

-

6.3

M70

Quercus ilex

570

1296.3

6.6

M71

Quercus ilex

908

1605

12.5

62.8 at 35 mmcalc 60.9
37.9 at 34 mmcalc 32.0
46.8 at 36 mmcalc 44.8
34.0
129.6 at 9 mmcalc 137.0
20.7 at 24 mmcalc 21.6
21.5 at 29 mmcalc 21.8
27.9 at 25 mmcalc 29.0
16.4 at 29 mmcalc 16.7
26.2 at 25 mmcalc 27.2
-

SF (L) = 0.024P (mm) - 0.217
SF (L) = 0.072P (mm) - 0.944
SF (L) = 0.106P (mm) - 1.015
SF (L) = 0.069P (mm) + 0.899
SF (L) = 0.430P (mm) - 0.349
SF (L) = 0.273P (mm) - 1.218
SF (L) = 0.347P (mm) - 2.305
SF (L) = 0.619P (mm) - 2.977
SF (L) = 0.603P (mm) - 3.825
SF (L) = 1.393P (mm) - 6.703
SF (mm) = 0.16P (mm) - 0.98

M72

Quercus ilex

900

861.5

3.4

11.3calc

-

calc

-

M73

Quercus ilex

876

1275.2

2.7

10.2

M74

Quercus ilex

876

1048.2

5.3

23.8calc

-

M75

Quercus ilex rotundifolia

665

1736.4

0.3

-

-

M76

Quercus ilex rotundifolia

432

-

0.6

-

-

M77

Quercus ilex rotundifolia

516

755

0.7

-

-

M78

Quercus petraea

1200

748

4.7

18.1calc

-

M79

Quercus pubescens
Quercus pubescentis
Carpinus orientalis
croaticus
Quercus pyrenaica

1021
1000
1300
1580

-

0.3

-

-

1318

2.9 ± 0.6

-

-

1056.7

0.9

6.0calc

-

M80
M81

71

M82
M83

Quercus pyrenaica
Quercus pyrenaica

1245
720

933.3

0.64

3.1calc

-

0.8

5.1

calc

-

calc

-

624.7

M84

Quercus pyrenaica

872

825

0.6

6.1

M85

Quercus suber

-

-

1.3

-

-

M86

Rosmarinus officinalis

228

181.3

42.5

-

-

M87

Thymus vulgaris

228

181.3

31.2

-

-

Table 2.11. Species, location, stand information (diameter: Diam, tree density: Density, stand basal area: BA), author(s), and
alphanumeric code for arid and semi-arid studies.
Code

Species

Location

Diam.
(cm)

Density
(Trees/ha)

BA
(m2/ha)

Author

S01

Acacia aneura

South-Western Queensland,
Australia

-

-

-

Pressland (1973)

S02

Acacia farnesiana

Nuevo Leon, Mexico

12.75

Lone shrubs

-

Návar (1993); Návar and Bryan (1990)

S03

Acacia rigidula
Adenocarpus
decorticans

Nuevo Leon, Mexico

-

-

-

Filabres, Almeria, Spain

12.4

Lone

-

Almería, Spain

-

Lone

-

Návar et al. (1999)
Domingo et al. (1994); Llorens and
Domingo (2007)
Domingo et al. (1998); Llorens and
Domingo (2007)

S04
S05

Anthyllis cytisoides

Mu Us, China

-

-

-

Yang et al. (2008)

S07

Artemisia
sphaerocephala
Bumelia celastrina

Nuevo Leon, Mexico

-

-

-

Návar et al. (1999)

S08

Caragana korshinskii

Gaolan, China

-

-

-

S09

Cistus laurifolius

Filabres, Almeria, Spain

8.3

Lone

-

S10

Condalia hookeri

Nuevo Leon, Mexico

-

-

-

Li et al. (2008)
Domingo et al. (1994); Llorens and
Domingo (2007)
Návar et al. (1999)

S11

Cordia boissieri

Nuevo Leon, Mexico

-

-

-

Návar et al. (1999)

S12

Diospyros palmeri

Nuevo Leon, Mexico

-

-

-

Návar et al. (1999)

S06

72

S13

Diospyros texana

Nuevo Leon, Mexico

9.22

Lone shrubs

-

Návar (1993); Návar and Bryan (1990)

S14

Ficus benjamina

Queretaro City, Mexico

22.4

Lone

-

Guevara-Escobar et al. (2007)

S15

Flourensia cernua

New Mexico, USA

-

-

-

Martínez-Meza and Whitford (1996)

S16

Grevillea robusta

Machakos, Kenya

-

-

-

Jackson (2000)

S17

Hedysarum scoparium

Shaanxi, China

-

-

-

Li et al. (2009)

S18

Larrea divaricata

Viedma, Argentina

-

-

-

Cecchi et al. (2006)

S19

Larrea tridentata

Las Cruces, New Mexico

-

-

-

Abrahams et al. (2003)

S20

Larrea tridentata

New Mexico, USA

-

-

-

Martínez-Meza and Whitford (1996)

S21

Larrea tridentata

-

Lone shrubs

-

Whitford et al. (1997)

S22

Matorral community

-

-

16.2

Carlyle-Moses (2004)

S23

Pinus halepensis

Las Cruces, New Mexico
Santa Rosa de Iturbide,
Mexico
Yatir forest, Israel

-

360

-

S24

Pinus nigra

Filabres, Almeria, Spain

5.8

Lone

-

S25

Pinus pinaster

Filabres, Almeria, Spain

12.8

-

-

S26

Pithecellobium pallens

Nuevo Leon, Mexico

-

-

-

Shachnovich et al. (2008)
Domingo et al. (1994); Llorens and
Domingo (2007)
Domingo et al. (1994); Llorens and
Domingo (2007)
Návar et al. (1999)

S27

Prosopis glandulosa

New Mexico, USA

-

-

-

Martínez-Meza and Whitford (1996)

S28

Prosopis laevigata

Nuevo Leon, Mexico

10.6

Lone shrubs

-

Návar (1993); Návar and Bryan (1990)

S29

Prosopis laevigata

Nuevo Leon, Mexico

-

-

-

Návar et al. (1999)

S30

Quercus emoryi

Arizona, USA

11.7 - 45.9

-

-

Haworth and McPherson (1995)

S31

Reaumuria soongorica

Gaolan, China

-

-

-

S32

Retama sphaerocarpa

Almería, Spain

1.7

Lone

-

S33

Salix psammophila

Shaanxi, China

-

-

-

Li et al. (2008)
Domingo et al. (1994); Llorens and
Domingo (2007)
Li et al. (2009)

S34

Salix psammophila

Mu Us, China

-

-

-

Yang et al. (2008)

S35

Tamarix ramosissima

Gaolan, China

-

-

-

Li et al. (2008)

73

S36
S37

Tamaulipan
thornscrub
Zanthoxylum fragara

Nuevo Leon, Mexico

2.3 - 3.9

-

-

Návar et al. (1999)

Nuevo Leon, Mexico

-

-

-

Návar et al. (1999)

Table 2.12. Species, meteorological data (annual rainfall: PA, and study period rainfall: PS), stemflow production information
(funnelling ratio(s): F, and percentage of gross rainfall diverted to stemflow: SF), and stemflow formulae for arid and semi-arid
studies.
Code

Species

PA
(mm)

PS (mm)

SF (%)

F

Formula(s)

S01

Acacia aneura

-

618.55

18.0

-

S02

Acacia farnesiana

-

230

0.6

11.7

BA(0-0.01): SF = 1.446P - 0.026P2 - 2.235
BA(0.01-0.03): SF = 2.198P + 0.191
BA(0.03-0.065): SF = 6.047P - 6.842
BA(>0.065): SF = 8.085P - 5.128
P(0-6.25): SF = 0.026BA + 1.631
P(6.25-12.5): SF = 0.076BA + 7.751
P(12.5-25): SF = 0.162BA + 0.369
P(>25): SF = 0.280BA + 13.810
Units: P (mm) SF (L) BA (cm2)
-

S03

Acacia rigidula
Adenocarpus
decorticans
Anthyllis
cytisoides
Artemisia
sphaerocephala
Bumelia
celastrina

805

489.1

-

-

SF (mm) = 0.038P (mm) - 0.085

395

650

4.4

-

-

300

-

20.0

-

-

395

173

2.7

41.5

SF (mm) = 0.024P (mm) + 0.015

805

489.1

-

-

SF (mm) = 0.014P(mm) - 0.019

S04
S05
S06
S07
S08

Caragana
korshinskii

263

-

7.2

153.5 ± 66.2
Event high: 292

SF (mm) = 0.079P (mm) - 0.028
SF (mm) = 0.107P (mm) - 0.036I (mm/h) - 0.056

S09

Cistus laurifolius

395

650

7.2

-

-

74

S10

Condalia hookeri

805

489.1

-

-

SF (mm) = 0.013P (mm) - 0.040

S11

Cordia boissieri

805

489.1

-

-

SF (mm) = 0.027P (mm) - 0.066

S12

Diospyros palmeri

805

489.1

-

-

SF (mm) = 0.034P (mm) - 0.080

S13

Diospyros texana

-

230

5.6

-

S14

Ficus benjamina

548

152

2.4

57.7
16.8 at 5 mmcalc
17.2calc

S15

Flourensia cernua

230

-

Summer: 10.6
Winter: 10.5

S16

Grevillea robusta
Hedysarum
scoparium

782

1583.00

0.7

395

-

3.4

S18

Larrea divaricata

350

-

Disturbed: 3.6
Intact: 7.2

-

S19

Larrea tridentata

245

-

6.7

-

SF = 0.0361P - 0.1512
SF = 0.0722P - 0.3483
All units in mm
SF (cm/h) = 0.16A (cm2) P (cm/h)

S20

Larrea tridentata

230

-

10

-

SF (L) = 17·0 + 20·5V (m3)b

S21

Larrea tridentata
Matorral
community

235
635 ±
145

-

16.8 ± 1.9

-

-

8.5 ± 1.9

21.1

SF = [21.13(P x BA)]nstemc
Units: SF (L) P (mm) BA (m2)

S23

Pinus halepensis

280

2000/01: 306
2001/02: 307
2002/03: 341.5

2.1
1.4
1.5

-

SF (mm) = 0.02P (mm) - 0.06

S24

Pinus nigra Ar.

395

650

12.3

-

-

S25

395

650

1.5

-

-

805

489.1

-

-

SF (mm) = 0.037P (mm) - 0.068
SF (L) = 10·3A (m2) + 7.6

230

Summer: 5.4
Winter: 5.3
0.6

-

S28

Pinus pinaster
Pithecellobium
pallens
Prosopis
glandulosa
Prosopis laevigata

11.1

-

S29

Prosopis laevigata

805

489.1

-

-

SF (mm) = 0.005P (mm) - 0.012

S17

S22

S26
S27

230

SF (mm) = 0.0248P (mm) – 0.007calc

-

SF (L) = 3·9A (m2) + 4.8a

77.8
Event high: 203

SF (mm) = 0.034P (mm) - 0.046

75

S30

Quercus emoryi

600

-

-

-

ln(SF) = 8.65 + 0.036(A) - 11e-1.36(P)
Units: SF(ml) CA(m2) P(mm)

S31

Reaumuria
soongorica

263

-

3.7

53.2 ± 25.7
Event high: 97

SF (mm) = 0.065P (mm) - 0.13
SF (mm) = -0.103 + 0.066P (mm) - 0.019I (mm/h)

300

-

7.0

-

-

395

-

6.3

48.7
Event high: 117

SF (mm) = 0.063P (mm) - 0.139

395

173

7.6

69.4

SF (mm) = 0.057P (mm) + 0.136

263

-

2.2

24.8 ± 15.3
Event high: 54

SF (mm) = 0.039P (mm) - 0.083
SF (mm) = 0.041P (mm) - 0.001I (mm/h) - 0.070

805

489.1

3.0 ± 1.9

-

-

805

489.1

-

-

SF (mm) = 0.007P (mm) - 0.012

S32
S33
S34
S35

Retama
sphaerocarpa
Salix
psammophila
Salix
psammophila
Tamarix
ramosissima

Tamaulipan
thornscrub
Zanthoxylum
S37
fragara
a
A = Canopy area
S36

b

V = Canopy volume

c

nstems = Number of stems

Table 2.13. Species, location, stand information (diameter: Diam, tree density: Density, stand basal area: BA), author(s), and
alphanumeric code for agroforestry studies.
Density (Trees/ha)

BA (m2/ha)

Author

8.6
23.7
26.5

Dietz et al. (2006)

16.5
<8

1706
2705
2612
625
1875

-

2500

-

Code

Species

Location

Diam. (cm)

A01

Agroforest

Central Sulawesi,
Indonesia

-

A02

Bactris gasipaes

Manaus, Brazil

A03

Bactris gasipaes

Manaus, Brazil

-

Schroth et al. (1999);
Schroth et al. (2001)
Schroth et al. (1999);
Schroth et al. (2001)

76

Schroth et al. (1999);
Schroth et al. (2001)
Schroth et al. (1999);
Schroth et al. (2001)
Opakunle (1989)
(Levia and Frost, 2003)

A04

Bertholletia excelsa

Manaus, Brazil

8.4

93

-

A05

Bixa orellana

Manaus, Brazil

-

156

-

A06

Cacao plantation

-

-

-

-

A07

Manihot esculenta
Zea mays
Oryza sativa

West Java,
Indonesia

-

-

-

van Dijk et al. (2001)

A08

Musa sp.

-

-

-

Cattan et al. (2007)

A09

Musa sp.

Capesterre-BelleEau, Guadeloupe
Roseau, St Lucia

-

-

Harris (1997)

A10

Phyllostachys pubescens

Munakata, Japan

12.4
13.4
13.7

6800

-

Onozawa et al. (2009)

A11

Theobroma grandiflorum

Manaus, Brazil

5.5

93

-

Schroth et al. (1999);
Schroth et al. (2001)

A12

Zea mays
Grevillea robusta

Machakos, Kenya

-

-

-

Jackson (2000)

Table 2.14. Species, meteorological data (annual rainfall: PA, and study period rainfall: PS), stemflow production information
(funnelling ratio(s): F, and percentage of gross rainfall diverted to stemflow: SF), and stemflow formulae for agroforestry
studies.
Code

Species

PA (mm)

PS (mm)

SF (%)

F

Formula(s)

calc

2622

293
172
214
2672

0.7
0.9
1.0
24.7

8.1
3.8calc
3.8calc
-

SF (L/mm) = 5.32 - 0.224 DBH (cm)

Bactris gasipaes

2622

2672

20.6

-

SF (L/mm) = 0.114DBH (cm) - 0.09

Bertholletia excelsa

2622

2672

0.8

-

SF (L/mm) = 0.303DBH (cm) - 2.59

A01

Agroforest

2437 3424

A02

Bactris gasipaes

A03
A04

-

77

A05

Bixa orellana

2622

2672

0.1

-

-

A06

Cacao plantation
Manihot esculenta
Zea mays
Oryza sativa

-

-

2.0

-

-

2600

1995: 1577
1999: 1642

2.4
3.9

-

SF (L/m2)= 0.054TF (mm)a

A07

A08

Musa sp.

3850

Vegetative: 164
Flowering: 158
Bunch: 151

25.6
24.1
17.9

Ve: 20
Fl: 28
Bu: 28

F = 11.2LAIb c

A09

Musa sp.

-

-

10.0

13

-

A10

Phyllostachys pubescens

1697

2105

15.3

-

-

A11

Theobroma grandiflorum

2622

2672

0.1

-

-

782

1583.00

0.6

-

-

Zea mays
Grevillea robusta
a
TF = Throughfall
A12

b

F = Funnelling ratio

c

LAI = Leaf area index

78

CHAPTER 3
MODELLING STEMFLOW PRODUCTION BY JUVENILE LODGEPOLE PINE
(PINUS CONTORTA VAR. LATIFOLIA) TREES IN SOUTHERN BRITISH
COLUMBIA, CANADA

INTRODUCTION
Stemflow is rainfall that has been intercepted by vegetation cover and
subsequently directed down the stem or trunk of the plant or tree to its base. The ability
of vegetation to produce stemflow can be described quantitatively using the stemflow
funnelling ratio (Herwitz, 1986), which represents the ratio between the stemflow volume
collected at the base of the plant’s stem or tree’s bole to the volume of rainfall that would
have been collected by a rain gauge having an area equal to that of the base of the plant
stem / tree bole in the absence of vegetation cover. The stemflow funnelling ratio is
calculated as (Herwitz, 1986):
F= SF/(Pg ∙BA)

(3.1)

where F is the funnelling ratio (dimensionless), SF is stemflow volume (L), Pg is rainfall
depth (mm), and BA is the basal area of the plant’s stem or tree’s bole (m2).
Stemflow has received relatively little attention in the hydrologic literature due to
its volumetric insignificance at the plot-scale and beyond when compared to throughfall
and canopy interception loss (Levia and Frost, 2003). However, stemflow may still be of
hydrological and biogeochemical importance since it is a focused point source of water at
the base of a plant or tree (Herwitz, 1986; Levia and Frost, 2003). The importance of
stemflow as a source of soil moisture has been highlighted by a number of studies (Voigt,
1960; Tanaka et al., 1996; Taniguchi et al., 1996; Whitford et al., 1997). Taniguchi et al.
(1996) found that 20 % of groundwater recharge within a red pine forest in Japan
originated as stemflow, while in a rainforest in Queensland, Australia, Herwitz (1986)
showed that large concentrations of stemflow can exceed the infiltration capacity of soil
and result in Hortonian overland flow subsequently causing erosion. Stemflow has also
been found to be a concentrated source of nutrients and, in some cases, pollutants
79

(Brinson et al., 1980; Chang and Matzner, 2000; Schroth et al., 2001; Johnson and
Lehmann, 2006).
Only two studies have examined stemflow production by tree species in the
Interior of British Columbia, with both of these studies being conducted within mature
coniferous stands. Spittlehouse (1998) reported a stemflow fraction of < 0.5 % of a 454
mm May – October study period rainfall record for both a mature Pinus contorta var.
latifolia (lodgepole pine) stand, and a mature Picea glauca x engelmannii (hybrid white
spruce) - Abies lasiocarpa (subalpine fir) forest, while Moore et al. (2008) reported that
stemflow comprised 0.2 % of the rainfall over two growing seasons within a mature
lodgepole pine – hybrid white spruce – subalpine fir stand. The results of these studies
suggest that stemflow is a minor component of the canopy water balances of mature
coniferous forests in the Interior of British Columbia.
British Columbia is currently undergoing a Dendroctonus ponderosae (mountain
pine beetle – MPB) epidemic which has been forecast to kill ~ 77 % of all merchantable
pine in the province by 2014 (Walton et al., 2007). In addition, the frequency of wildfires
in British Columbia is projected to increase as a consequence of global climatic change
(BC Ministry of Water, Land and Air Protection, 2004). Due to these disturbances, many
of the province’s interior watersheds will see a shift in land-cover dominated by mature
conifers to stands at various stages of juvenile re-growth. This shift in stand composition
brings with it many uncertainties, including the impacts on site hydrology. One aspect of
the forest water balance that may be altered is the quantitative importance of stemflow.
McKee and Carlyle-Moses (2010) found that juvenile lodgepole pine trees produced
more stemflow compared to mature trees; however, no studies to date have examined
factors influencing stemflow production from juvenile lodgepole pine.
Despite studies that have highlighted the influence of a multitude of variables on
stemflow production (Levia and Frost, 2003), the majority of stemflow simulation
models produced to date only utilize one independent variable, normally rainfall depth or
plant / tree diameter. However, other variables have also been shown to exert a control on
the quantity of stemflow produced, including, branching angle (Herwitz, 1987; Návar,

80

1993; Martínez-Meza and Whitford, 1996), number of branches (Návar, 1993), tree
height (Brown and Baker, 1970), storm duration and intensity (Brown and Baker, 1970;
Crockford and Richardson, 2000), crown projection area (Brown and Baker, 1970;
Pressland 1973; Aboal et al., 1999; Park and Hattori, 2001), and bark roughness (Horton,
1919; Aboal et al., 1999). Logistically, it would be difficult to collect sufficient data to
include all of the potential factors influencing stemflow production; however, the
inclusion of more than one predictive variable would lead to more accurate modelling
(Levia and Frost, 2003) and improve our understanding of how tree architecture and
meteorological conditions influence stemflow production.
The objective of this research was threefold: (1) to identify the abiotic and biotic
factors that influence stemflow production by lodgepole pine, (2) to incorporate the most
influential of these factors into a predictive model of stemflow yield from this forest type,
and (3) to evaluate the spatial transferability of the developed model.

MATERIALS AND METHODS
Site description
Measurements of incident rainfall and stemflow were made from 1 June, 2009 to
31 October, 2009 at the Mayson Lake Hydrological Processes Study Area (MLk) located
approximately 60 km NNW of Kamloops, British Columbia on the Thompson-Bonaparte
Plateau at 51o 13’ N, 120o 24’ W. The MLk, located at an elevation of ~1260 m a.m.s.l.,
is situated within the Montane Spruce Biogeoclimatic Zone (MSdm2), a zone typified by
cold winters and moderately short, warm summers (Lloyd et al., 1990). The nearest longterm meteorological station with a comparable elevation to the study site, 1155 m a.m.s.l,
is Bridge Lake 2 (Meteorological Service of Canada Climate Station ID = 1160986). This
station, located approximately 41 km NNW of the study area, has a mean annual rainfall
depth of approximately 600 mm (1980 – 2000) with approximately half falling during the
growing-season (mid-May to September, inclusive) in the form of rain. Snow is the
dominant form of precipitation during the dormant season. Mean annual temperature at

81

the Bridge Lake 2 station is 3.7 oC with mean monthly values ranging from -7.8 oC in
December to 14.2 oC in July and August.
Data were largely collected from two plots situated within juvenile lodgepole
pine dominated stands. These two juvenile stands, designated Plots E and D (Figures 3.1
and 3.2, respectively), were replanted after commercial harvesting. Detailed tree and
stand characteristics for Plots E and D can be found in Table 3.1. Stemflow was also
measured in three additional plots: Plot A – a mature pine-spruce-fir stand of ~ 125 years
of age with most pine at the MPB grey attack stage, Plot B – a pine dominated stand of ~
27 years of age at the red / grey MPB attack stage, and Plot C – a stand of ~ 16 years of
age largely comprised of healthy pine, although a few individuals were at the green or red
MPB attack stage. Plots A, B, C, and E measured 72 m by 40 m in size, while Plot D
measured 160 m by 24 m.

Table 3.1. Stand characteristics for Plots E and D.
Stand Age (yrs)
Avg. Tree Diameter (cm)
Avg. Tree Height (m)
Avg. Tree CPA (m2)
Tree density (trees ha-1)
BA (m2 ha-1)
Avg. Number of branches per tree
Pine
Composition (%)
Subalpine Fir
Relative Dominance of Pine (%)

Plot E
~7
2.7
1.42
0.43
8476

Plot D
~9
3.5
2.01
0.63
7974

7.4
23
86
14
94

10.9
32
79
21
89

Meteorological data
The meteorological station used for this study was situated in the centre of Plot E
and was equipped with an Onset® Wind Speed and Direction Smart Sensor (product # SWCA-M003) and an Onset® Temperature / Relative Humidity Smart Sensor (product #
S-THA-M002). Measurements of wind speed, temperature and relative humidity were
taken 2 m above the principal tree canopy on a 30 second time-step and averaged and
82

Figure 3.1. View of Plot E from the northwest corner of the plot.

Figure 3.2. View of Plot D from the centre looking south.
83

logged on 10 minute time-step using a Hobo® Micro Station data logger (product # H21002). Rainfall depth and intensity measurements were taken in locations closest to each
plot that allowed for unobstructed measurement. At each location rainfall was measured
using an Onset ® Data Logging Rain Gauge (product # RG-3-M) with an orifice diameter
of 15.4 cm and a resolution of 0.2 mm tip-1 as well as a cylindrical polyethylene gauge
having a diameter of 29.0 cm in which the volume of collected rainfall was measured
using a graduated cylinder. Rainfall measurements for Plots A, B, and C were taken in a
fire break located ~ 630 m from the geographic centre of Plot A and ~ 560 and 310 m
from the centres of Plots B and C, respectively. Rainfall measurements for Plots D and E
were taken with both an Onset® rain gauge and a cylindrical polyethylene gauge situated
in clearings no further than ~ 90 m from the centres of each of the two plots. A rain event
was defined for this study as a period of rainfall bounded by periods of eight hours with
no measurable rainfall, as this was the observed maximum time required for the juvenile
pine canopies and boles to dry completely.

Stemflow collection
Stemflow was sampled from lodgepole pine trees only. Plots A, B, and C
contained seven, seven, and five stemflow collection systems, respectively. Stemflow in
these three plots was collected using stemflow collars constructed from 2.5 cm diameter
corrugated flexible tubing that was cut in half lengthwise, then wrapped 360o around the
tree on a downward angle and secured with nails and silicone sealant (Levia, 2004). An
uncut piece of corrugated tubing running from the stemflow collar diverted the
intercepted stemflow to a collection container at the base of the tree. Stemflow collection
containers in these three plots ranged in capacity from 4 to 20 L depending on the
expected stemflow production of each tree.
Stemflow in Plots D and E was sampled more intensely than the other plots
because previous research showed that juvenile lodgepole pine trees were more efficient
stemflow producers when compared to mature pine trees (McKee and Carlyle-Moses,
2010). Thirty-six and thirty-seven trees were sampled for stemflow in plots D and E,

84

respectively. Twelve relatively small, 12 medium, and 12 large trees were sampled in
Plot D in order to achieve a representative sample. The same sampling method was used
in Plot E with the addition of one medium tree. Adjacent to Plot E, one small, one
medium, and one large tree had their branches and trunk needles removed. Stemflow
collars were attached to these trees in an attempt to further understand the influence of
abiotic factors by eliminating tree architecture completely. Sample trees were located
outside of Plot E on the northeast edge to insure that experiments inside the plot were not
influenced by anthropogenic damage to these trees. Each stemflow collar in these two
plots was constructed using fabricated plastic funnels that were cut vertically, then
wrapped and sealed to the tree near the base of the bole using silicone sealant (Figure
3.3). A plastic tube with a diameter of ~ 1.0 cm connected the inner portion of the
stemflow collar to a 4 L collection container for subsequent measurement. All stemflow
collars in the five plots were tested weekly to determine if any leakage may have
occurred due to tree growth and/or animal disturbance. If a stemflow collar had a leak it
was noted and promptly repaired and any data collected since the prior test was
discarded. Collected stemflow was measured after each rainfall event using a graduated
cylinder.

Figure 3.3. Stemflow collar and collection container used in Plots E and D.
85

Tree characteristics
Stand level characteristics were recorded along with individual tree characteristics
for trees associated with stemflow collection. The point-quarter method (Mueller et al.,
1974) was used to determine tree density, species frequency, and basal area information
required to determine stand scale stemflow production for Plots D and E. In order to
relate stemflow production to tree architecture, tree characteristics were recorded for each
plot. In Plots A, B, and C, tree diameter and height were recorded for all trees being
sampled for stemflow. As the focus of this research was on juvenile trees, more detailed
tree characteristics were recorded in Plots D and E. In these two plots, tree height,
number of branches, canopy width, branching angle, and tree diameter (at the base just
under the first branch) were recorded for each tree sampled for stemflow. North, south,
east, and west facing branches were selected at the base and top of the tree, as well as at
one-third and two-thirds the tree height. Branching angle where the branch met the tree
bole was recorded for each of these branches, measured from the horizontal yielding a
positive or negative angle. Canopy width was also derived for the four sampled levels for
each tree by taking the average horizontal distance from the outermost extent of the
branch projecting in a northerly direction to that projecting in a southerly direction and
the outermost extent of the easterly branch to that of the branch extending westward.
A proximity metric was developed for Plots D and E to determine if sheltering by
neighbouring trees had an influence on stemflow production. All trees whose canopies
extended to within a 45o cone of the base of a stemflow tree were recorded. The distance
of each of the neighbouring trees from the tree sampled for stemflow, as well as the
height of those trees, were recorded. The proximity statistic was then calculated for each
stemflow tree as:
̅ /𝐷
̅
𝑀 = 𝑛 ∙𝐻

(3.2)

where M is the proximity metric (dimensionless), n is the number of trees whose heights’
extended to within a 45o cone centred on the base of the tree sampled for stemflow, while
̅ and 𝐷
̅ are the average height (m) and average horizontal distance (m) from the tree
𝐻
sampled for stemflow of the n trees.

86

Statistical analysis
Statistical analysis and modelling was performed using Microsoft® Office Excel
2010 (Microsoft Corporation, Redmond, WA, USA) spreadsheet application and
Minitab® 15 (Minitab Inc., State College, PA, USA) statistical software. Excel 2010 was
used for data organization and graphing, while Minitab 15 was used to perform stepwise
multiple regression analysis. Levels of statistical significance reported in this study were
at the p < 0.05 level.

Modelling procedure
Park and Hattori (2002) suggested that the slope, a, and the intercept, b,
associated with the linear relationship between stemflow depth (mm) and rainfall depth
(mm) [i.e., Stemflow = a · Rainfall + b] for a single tree or an entire stand may be related
to the tree / stand diameter at breast height (DBH) in the form of power relationships:
a = A(DBH)β1

(3.3)

b = B(DBH)β2

(3.4)

where A, B, β1, and β2 are regression coefficients, while DBH is diameter at breast
height.
In contrast to Park and Hattori (2002), the slope (a) and intercept (b) values in this
study were compared against a number of different abiotic and biotic factors to determine
which factor(s) had a statistically significant influence on stemflow production. Biotic
factors were analysed on the event basis and abiotic factors were analysed on a per tree
basis. The analysis was conducted in this manner because a multiple regression could not
be conducted with all independent variables versus stemflow volume due to some
variables changing from tree to tree, while others only changed from event to event. This
resulted in linear equations replacing Eqs. 3.3 and 3.4 containing one or more variables.
Regression between event rainfall depth (mm) and associated stemflow volume (L) was
conducted for each tree in order to produce a and b values. Stepwise multiple regression
was then conducted to determine which variables explained variations in a and b for Plot
E and for Plot D. The candidate biotic predictor variables were: total number of branches,

87

tree height (m), tree diameter (cm), the proximity metric (dimensionless), canopy width
(m) and branching angle (o), at the top, two-thirds of the height, one-third of the height,
and at the bottom of the tree. The candidate abiotic predictor variables were rainfall depth
(mm), intensity (mm h-1), storm duration (h), maximum wind gust speed (m s-1), as well
as storm duration (h), wind speed (m s-1), and vapour pressure deficit (kPa) when rainfall
intensity ≥ 0.4 mm h-1.
Once Plot E and Plot D models have been produced, they will be examined to
determine if common variables exist between the two, and if they do, simplified models
will be produced using those variable(s). Data sets used to produce simplified models will
then be combined if their slopes and intercepts are not significantly different. The
regression process employing common variable(s) will then be repeated using the
combined dataset to produce a generic model of stemflow prediction for these stands.

RESULTS
Funnelling ratios for lodgepole pine
For the research period, cumulative rainfall for 22 events ranged from 126.0 mm
in Plot E to 135.6 mm in Plot D, with individual events ranging in size from 0.5 to 41.3
mm. Plots E and D produced a total of 102.5 and 77.1 L of stemflow, respectively, from
20 of the sampled trees in each plot whose stemflow collection systems were operational
throughout the study period (~ 5.1 L tree-1 in Plot E and 3.9 L tree-1 in Plot D). Plot-scale
stemflow for Plots E and D were estimated at 1.8 % of rainfall for both stands, assuming
that the juvenile sub-alpine fir trees had similar stemflow production abilities to that of
the juvenile pine. Given that crown projection area (CPA) represented ~ 3750 m2 ha-1 in
Plot E and ~ 5200 m2 ha-1 in Plot D, a total of 5.1 and 3.7 % of rain falling within the
crown areas was portioned into stemflow, respectively. The season-long funnelling ratio
for pines within Plot E averaged 24.3, while individual trees had season-long funnelling
ratios up to 69.3, with a single event maximum of 95.9 (tree diameter = 1.6 cm, rainfall =
7.0 mm). The season-long funnelling-ratio for Plot D averaged 22.2, while individual

88

trees in this plot had season-long funnelling ratios as great as 60.4, with a single event
maximum of 111.7 (tree diameter = 3.3 cm, rainfall = 17.4 mm).
The exponential decay relationship between season-long funnelling ratios and tree
diameter (cm) is shown in Figure 3.4. Figure 3.4 contains only data for healthy lodgepole
pine trees from which stemflow was collected over the entire study period. The largest
healthy lodgepole pine sampled was 18.1 cm in diameter; however, pine at various stages
of MPB attack, nine in total, ranged in size from 8.6 to 39.5 cm in diameter. These dead
pine trees had an average season-long funnelling ratio of 2.3, ranging from 0.01 to 17.6,
with the latter value being derived from the smallest dead tree.

Season-Long
Stemflow Funnelling Ratio

75
60
45
F = 93.0 D-1.14
R² = 0.46

30
15
0
0

5

10
Tree Diameter (cm)

15

20

Figure 3.4. Season-long stemflow funnelling ratios versus tree diameter for all healthy
lodgepole pine trees in Plots E, D, C, and B.
Abiotic and biotic influences on stemflow and the simulation of stemflow production
Multiple regression analysis, which included the linear transformations of some of
the data, revealed that each of the biotic predictor variables used in this study, with the
exception of proximity, had a statistically significant influence (p < 0.05) on stemflow
volume at the individual rainfall event scale for at least one event. However, canopy
89

width at various levels, branching angle at various levels, and diameter at the base were
the most prominent. Since a multitude of variables were shown to have a statistically
significant influence on stemflow production for different rainfall events, it was decided
that all biotic variables, with the exception of proximity, would be included in the
multiple regression for predicting the values of a and b in Eqs. 3.3 and 3.4, respectively.
When examining abiotic variables, it was found that only one variable, rainfall depth (p <
0.001), was consistently statistically significant throughout. Rainfall depth explained over
80 % of the variation in stemflow production for 31 of the 34 trees tested in Plot E. Trees
that were influenced by a variable aside from rainfall are listed in Table 3.1. Storm
duration was statistically significant for nine trees, maximum gust during the storm was
significant for two trees, and duration of the storm when rainfall intensity was greater
than 0.4 mm hr-1 was significant for one tree. Only duration explained between 5 % and
11 % of the variation in stemflow for three trees, while for the remaining trees, duration,
maximum gust speed, and duration when rainfall intensity ≥ 0.4 mm hr-1 explained less
than 3 % of the stemflow variation. Due to these findings, rainfall depth was the only
abiotic variable selected for inclusion in the final model.
Table 3.2. Coefficient of determination (R2) and p-values associated with statistically
significant abiotic predictor variables of stemflow production for individual study trees.
Tree
1
4
12
16
18
20
24
26
28
34

Rainfall Depth
(mm)
0.96 (p < 0.001)
0.91 (p < 0.001)
0.80 (p < 0.001)
0.97 (p < 0.001)
0.92 (p < 0.001)
0.77 (p < 0.001)
0.94 (p < 0.001)
0.93 (p < 0.001)
0.97 (p < 0.001)
0.97 (p < 0.001)

Duration
(h)
0.03 (p = 0.001)
0.03 (p = 0.004)
0.11 (p = 0.001)
0.03 (p = 0.001)
0.09 (p = 0.007)
0.02 (p < 0.001)
0.05 (p < 0.001)
0.01 (p = 0.008)

Max. Gust
(m s-1)
0.01 (p = 0.048)
0.01 (p = 0.003)
-

Duration while intensity
≥ 0.4 mm h-1 (h)
0.01 (p = 0.016)
-

90

Linear regression equations were developed between stemflow volume (L) and
rainfall depth (mm) for individual trees in Plot E. The derived slope (a) and intercept (b)
values were then plotted against the diameter of the individual trees sampled for
stemflow. According to Park and Hattori (2002) a versus DBH and b versus DBH should
produce power relationships. Although a versus tree diameter (cm) was found to follow a
power relationship, b versus tree diameter (cm) did not (Figures 3.5 and 3.6,
respectively).
In this study, since all of the biotic variables with the exception of proximity, and
not just diameter, had a statistically significant influence on stemflow production for at
least one event, and because the relationship between plotted b values and diameter was
found to be weak, stepwise multiple regression using a and b as dependent variables was
conducted to determine which biotic factors best explained variation in slope and
intercept values.
0.14
0.12

Slope (a)

0.1
a = 0.004 DBH1.671
R² = 0.79

0.08
0.06
0.04
0.02
0
0

1

2

3

4

5

6

7

8

Diameter (cm)
Figure 3.5. Power relationship between slope values and tree diameter for healthy
lodgepole pine trees.

91

0.1

Y-Intercept (b)

0.05
0
-0.05
-0.1
b = -0.018 DBH + 0.020
R² = 0.35

-0.15
-0.2
0

1

2

3

4

5

6

7

8

Diameter (cm)
Figure 3.6. Intercept values versus diameter showing a weak linear relationship and not
the power relationship shown by Park and Hattori (2002).
Upon performing the regression analysis multicollinearity was observed. Tree
diameter at the base, height, number of branches, and canopy width at differing levels
were highly correlated, resulting in the removal of diameter when performing analyses on
slope and intercept values. The result passed the multicollinearity test, however
correlation between independent variables remained fairly high. This was resolved by
replacing the four separate canopy width measurements with one variable, CPA. Crown
projection area solved all multicollinearity problems and also increased the accuracy of
the model. Crown projection area (p < 0.001) and branching angle at two-thirds the
height of the tree (Angle2/3) (p = 0.001) explained 76.8 % of the variation in a. Branching
angle at the bottom of the tree (Anglebottom) (p = 0.004) and CPA (p < 0.001) explained
55.2 % of the variation in b. Following are the two equations that were used in
conjunction with Eq. 3.7 to produce a predictor model of stemflow volume as a function
of biotic and abiotic factors in Plot E:
a = 0.04 CPA + 0.001 Angle2/3 - 0.008

(3.5)

92

b = - 0.06 CPA + 0.003 Anglebottom - 0.0007

(3.6)

SF = a Pg + b

(3.7)

where SF is stemflow volume (L) and Pg is rainfall depth (mm).
The next stage of the analysis was to determine the performance of the stemflow
model in simulating observed versus predicted stemflow volumes within Plot E, the plot
in which the model was developed (Piñeiro et al., 2008). The model was successful in
predicting 83.0 % of the variation in stemflow production for Plot E (Figure 3.7). Total
predicted stemflow volume was 147.3 L and observed stemflow volume totalled 144.0 L,
an overprediction of only 2.3 %. Analysis of the slope and intercept associated with the
linear equation of observed versus predicted values found that they did not differ
significantly from one and zero, respectively.
In order to assess the spatial transferability of the Plot E model, it was applied to
Plot D data. The above procedure was repeated to determine the performance of the
model when applied to a different plot. The model was found to explain 74.1 % of the
variations in observed data; however, for large rainfall events the model greatly
overestimated the amount of stemflow produced (Figure 3.8). Although the intercept of
predicted versus observed stemflow (L) was not significantly different from zero, the
slope was found to be significantly different from one.
In an attempt to understand why the Plot E model greatly overestimated stemflow
production when applied to Plot D, Plot D stemflow was modeled in the same manner as
Plot E using stepwise multiple regression. This was to determine if other variables aside
from the ones highlighted during the Plot E analysis were important for predicting
stemflow production in Plot D. Crown projection area (p = 0.006), number of branches
(#Brch) (p = 0.038), and branching angle at the bottom of the tree (p = 0.036) explained
46.6 % of the variation in a. Number of branches (p = 0.013) explained 17.4 % of the
variation in b. Following are the two equations that were used in conjunction with Eq. 3.7
for Plot D:
a = 0.02 CPA + 0.001 Anglebottom + 0.001 #Brch + 0.006

(3.8)

b = - 0.002 #Brch - 0.008

(3.9)

93

5

Observed Stemflow (L)

4.5
4
3.5
3
2.5
2
1.5

Observed SF = 1.03 Predicted SF - 0.01
R² = 0.83

1
0.5
0
0

1

2
3
4
5
Predicted Stemflow (L)
Figure 3.7. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for Plot E (
) and the 1:1 line (------).
8

Observed Stemflow (L)

7
Observed SF = 0.65 Predicted SF - 0.012
R² = 0.74

6
5
4
3
2
1
0
0

1

2

3
4
5
Predicted stemflow (L)

6

7

8

Figure 3.8. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for Plot D employing the Plot E model (
) and the 1:1 line (------).

94

The process used in Plot E to test the performance of the model was repeated for Plot D.
The resulting Plot D model predicted 78.1 % of the variation in stemflow production
(Figure 3.9).

5

Observed Stemflow (L)

4.5
4
3.5
3
2.5
2
1.5
1

Observed SF = 0.98 Predicted SF - 0.009
R² = 0.78

0.5
0
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Predicted Stemflow (L)
Figure 3.9. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for Plot D (
) and the 1:1 line (------).
Total predicted stemflow volume was 127.9 L and observed stemflow volume totalled
119.6 L, an overprediction of 6.9 %. Analysis of the slope and intercept found that they
did not differ significantly from one and zero, respectively.
Both the Plot E and Plot D models successfully modelled stemflow production,
however for the most part they employed different variables to do so. Due to the variety
of variables used in each model, a simplified model was also developed. In an attempt to
produce a generic model, stemflow was re-modelled for both plots using only CPA,
which was the most influential variable common to both of the more complex models.
Crown projection area explained 65.8 % of the variation in a (p < 0.001) for Plot E and
31.8 % for Plot D (p < 0.001), along with 40.5 % of the variation in b (p < 0.001) for

95

Plot E and 16.7 % for Plot D (p = 0.015). Equations 3.10 and 3.11 were used in
conjunction with Eq. 3.7 to produce the simplified Plot E model (Figure 3.10):
a = 0.05 CPA + 0.003

(3.10)

b = -0.07 CPA – 0.005

(3.11)

5

Observed Stemflow (L)

4.5
4
3.5
3
2.5
2
1.5
Observed SF = 1.03 Predicted SF - 0.005
R² = 0.77

1
0.5
0
0

1

2

3

4

5

Predicted Stemflow (L)
Figure 3.10. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for Plot E employing the simplified model (
) and the 1:1 line (------).
Equations 3.12 and 3.13 were used in conjunction with Eq. 3.7 to produce the simplified
Plot D model (Figure 3.11):
a = 0.02 CPA + 0.019

(3.12)

b = -0.04 CPA – 0.038

(3.13)

The process previously used to access model performance was repeated for both
the simplified Plot E and Plot D models. The Plot E model explained 77.3 % of the
variation in cumulative stemflow production by individual trees, while predicting an all
sample tree production of 143.5 L compared to the observed 144.0 L, an underprediction
of 0.3 %. The Plot D model explained 74.3 % of the variation in cumulative stemflow

96

production by individual trees, while predicting an all-sample tree production of 130.0 L
compared to 119.6 L, an overprediction of 8.7 %.

5

Observed Stemflow (L)

4.5
4
3.5
3
2.5
2
1.5
1
Observed SF = 0.96 Predicted SF - 0.007
R² = 0.74

0.5
0
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Predicted Stemflow (L)
Figure 3.11. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for Plot D employing the simplified model (
) and the 1:1 line (------).
The culmination of the modelling process was the production of a generic model
that could be used to determine stemflow production for both Plot E and Plot D. Slope (a)
and intercept (b) values were produced in the same manner as the previous models. Slope
and intercept values for Plot E and Plot D did not differ significantly and were combined
for the regression analysis culminating in one model which was applicable to both stands,
resulting in Eqs. 3.14 (p < 0.001) and 3.15 (p < 0.001). This model applies to individual
trees or stands with trees having CPAs in the range of 0.01 to 3.5 m3. Trees with this CPA
range had associated diameters ranging from 1.6 cm to 8.8 cm (CPA = 0.078 D1.55, R2 =
0.81) and tree heights (H) ranging from 0.85 m to 4.89 m (CPA = 0.201 H1.58, R2 = 0.77).
The model can be seen in Figure 3.12 and was produced using Eqs. 3.14 and 3.15, in
conjunction with Eq. 3.7:

97

a = 0.03 CPA + 0.015

(3.14)

b = - 0.05 CPA – 0.023

(3.15)

6

Observed Stemflow (L)

5
4
3
2
1

Observed SF = 0.99 Predicted SF - 0.006
R² = 0.71

0
0

1

2

3

4

5

6

Predicted Stemflow (L)
Figure 3.12. Observed stemflow volume versus predicted stemflow volume derived from
Eq. 3.7 for lodgepole pines in Plots E and D employing the generic model (
) and the
1:1 line (------).
The generic model explained 71.3 % of the variation in stemflow production for juvenile
lodgepole pine stands, while predicting an all sample tree production of 274 L compared
to the observed 264 L, an over prediction of 3.8 %. The slope did not differ significantly
from one nor did the intercept differ significantly from zero. Equations 3.14 and 3.15
were used in conjunction with the relationship between CPA and D and applied to tree
frequency data for each stand to generate stand scale estimates of stemflow volume and
percentage of rainfall portioned into stemflow (Figure 3.13). The relationship between
the percentage of rainfall that became stemflow and rainfall depth also highlights the
point at which stemflow production commences: 1.6 mm of rainfall for both Plots E and
D.

98

3

Stemflow (% of rainfall)

2.5
2
1.5
1
0.5
0
0

5

10

15

20

25

30

35

40

Rainfall Depth (mm)
Figure 3.13. The percentage of rainfall that became stemflow versus rainfall depth at the
stand scale for Plot E (
) and Plot D (------), highlighting the rainfall depth required for
the commencement of stemflow production (1.6 mm).
Stemflow produced by a branchless tree
Three branchless trees were sampled for stemflow in an attempt to further
understand the influence of abiotic factors on stemflow production. However, these trees
suffered very high data loss due to leaking stemflow collars. As a result, only the large
tree had a complete dataset and the incomplete data sets of the small and medium trees
were discarded. Stepwise multiple regression was conducted using stemflow volume as
the dependent variable and only meteorological conditions as independent variables. The
result of the analysis was Eq. 3.16 which explained 94.8 % of the variation in stemflow
production. Stemflow production for a tree with no canopy was influenced by the amount
of rainfall and the duration of the storm. Stemflow increased as the amount of rainfall
increased but decreased as the duration of a storm increased. The decrease due to
increased storm length is most likely due to evaporation from the trunk, including
evaporation during breaks in the storm.
SF = 37.9 Pg – 5.2 Dur – 63.8

(3.16)
99

A tree lacking a canopy was expected to have minimal stemflow production and be an
inefficient stemflow producer. However, this was not the case for this lone branchless
tree, as it had an average season-long funnelling ratio of 10.9 and an event high
funnelling ratio of 19.8.

DISCUSSION
The results show that juvenile lodgepole pine trees are far more efficient stemflow
producers than their mature counterparts due to differences in tree morphology. It is
therefore not surprising that in comparison to other canopy water balance studies, with a
sampling emphasis on mature lodgepole pine, we have observed much higher stemflow
production. Juvenile lodgepole pine dominated stands partitioned up to ~ 2 % of
incoming rainfall into stemflow and individual trees are highly efficient producers, with
event funnelling ratios as high as 111.7. In contrast to our findings, Moore et al. (2008) in
a mature pine – hybrid white spruce – subalpine fir stand at the Mayson Lake study site,
found that stemflow represented ~ 0.2 % of season-long rainfall, while Spittlehouse
(1998) calculated that < 0.5 % of rainfall became stemflow for a mature lodgepole pine
stand in Penticton, BC. A comparison between the results of this present study and those
presented by Moore et al. (2008) suggest that within the study area, juvenile lodgepole
pine stands divert ~ 10 times more rainfall to stemflow than do mature coniferous stands.
Dunford and Niederhof (1944), however, reported higher values of 1.5 % of rainfall
becoming stemflow for a lodgepole pine stand in Colorado. Unfortunately, very few
stand characteristics necessary for accurate comparisons were provided by these authors.
Dunford and Niederhof (1944) provided the average canopy area (3.25 m2), which is just
over four times larger than the average canopy area observed in Plot E. Thus, although it
is evident that the Dunford and Niederhof (1994) study took place in an older stand, it is
not clear if it was a mature stand or one at the pole-stage. Spittlehouse (1998) listed tree
heights ranging from 22 – 26 m, much taller than the 0.85 – 3.48 m observed in Plot E.
As one can see, these studies both examine trees that are considerably larger than those

100

examined in this study, highlighting the lack of attention juvenile lodgepole pine canopy
water balances have received in the hydrologic literature.

Lodgepole pine stemflow production
The stand-scale funnelling ratios for Plots E and D were 24.3 and 22.2,
respectively, while the highest observed season-long funnelling ratio for an individual
tree was 69.3, and the single event lone tree maximum was 111.7. These findings suggest
that during the study period the base of these trees received an average stemflow input
having an equivalent depth of 3060 and 3010 mm, respectively – some 5 times the
average annual precipitation depth. Unfortunately, no other studies have provided
funnelling ratios for pine, let alone lodgepole pine, making comparisons with other pine
stands impossible. Only one other study has derived funnelling ratios for a coniferous
species. Murakami (2009) derived funnelling ratios for Chamaecyparis obtuse (Japanese
cypress) over a four year period, and found that funnelling ratios decreased from 81.3 to
29 with increasing stand age (9 – 12 yrs of age). The funnelling ratios derived in this
study compare well with findings in other forest environments. Herwitz (1987), for
example, observed a maximum season-long funnelling ratio from a lone Balanops
australiana of 112 in a tropical rainforest, while Van Stan and Levia (2010) found that
season-long funnelling ratios varied from 3.1 to 19.2 and 26.9 to 47.2 for lone
Liriodendron tulipifera (yellow poplar) and Fagus grandifolia (American beech) trees,
respectively. Návar (1993) recorded a season-long high funnelling ratio of 128 for a
single Diospyros texana; however, he also recorded a large variation in season-long
funnelling ratios ranging from 13 to 128 over 15 sampled shrubs. Season-long funnelling
ratios in this present study also varied by an order of magnitude (6.9 to 69.3). Juvenile
lodgepole pines are efficient stemflow producers, however, the large variation observed
at the individual tree scale highlights the impact tree characteristics can have on the
amount of rainfall partitioned into stemflow.

101

Model assessment
In addition to rainfall depth, a number of tree characteristics allowed for the
accurate modelling of stemflow production from juvenile lodgepole pine. The
identification of rainfall depth as the only prominent abiotic factor that influenced
stemflow production is in keeping with the findings of Cape et al. (1991). Tree
characteristics were used to explain variation in the slope and intercept values of the
regression between stemflow volume and rainfall depth. The slope of the regression
represents stemflow production and the intercept value represents the storage capacity of
the tree. The use of multiple predictor variables resulted in increased model accuracy
when compared to using only one variable like tree diameter (Park and Hattori, 2002). It
is important to note that the usefulness of adding additional variables to increase model
accuracy will vary depending on the species of study. Comparing our findings with those
of Park and Hattori (2002) is a perfect example of the differences that occur when
modelling species or specimens with differing morphology.
Stemflow production for the Plot E model increased as crown projection area and
the branching angle at two-thirds the tree height increased. A tree with a wider canopy
will produce more stemflow as it is able to capture more rainfall, while increasing the
upward branch inclination will result in more efficient flow along those branches
(Herwitz, 1987). However, a tree with a wider canopy will have a larger storage capacity,
and as the angle of the lower branches of the tree becomes more negative, more water
will drip from that canopy, contributing to throughfall rather than stemflow. The model
incorrectly assumed that canopy drip was becoming storage and thus the intercept of the
model should be viewed as representing both canopy + trunk storage and throughfall in
the form of canopy drip. The application of the Plot E model (Eqs. 3.5 and 3.6) to Plot D
resulted in a gross overestimate of the amount of stemflow produced. The poor
performance of Eqs. 3.5 and 3.6 when applied to Plot D was believed to be the result of a
variable that was highly influential in Plot E but not in Plot D. In an attempt to identify
this variable, a new stemflow model was produced for Plot D using the same
methodology as Plot E. The Plot D model (Eqs. 3.8 and 3.9) identified that stemflow

102

production increased as crown projection area and number of branches increased, but
decreased as the inclination of the bottom branches of the tree became more negative.
The storage capacity of trees in Plot D was dependent upon the number of branches: as
the number of branches increased, the storage capacity of the tree increased. Both Plot E
and Plot D models identified CPA as the prominent variable; however, both models also
contained other variables not common to both. The Plot D model included number of
branches, highlighting that more rainfall will be intercepted by a denser canopy. Upon
analysis of the variables contained in the Plot E and Plot D models, it was found that
Angle2/3 and #Brch were statistically different between the two plots, p = 0.035 and p =
0.056, respectively. Therefore, this was the likely cause of the overestimation of
stemflow production observed when applying the Plot E model to Plot D resulted in an
incorrect estimation of stemflow production. Our findings that branching angle (Herwitz,
1987; Návar, 1993; Martínez-Meza and Whitford, 1996; Aboal et al., 1999), canopy area
(Ford and Deans, 1978; Martínez-Meza and Whitford, 1996), and number of branches or
canopy density (Martínez-Meza and Whitford, 1996) have a significant influence on
stemflow production for trees sampled in this study are consistent with past studies.
As CPA was the only common and most influential variable between the models
produced for each plot, new simplified models were produced using only CPA as a
predictor of stemflow production and storage capacity. Our finding that CPA was the
most influential biotic predictor of stemflow production for both plots is in keeping with
the findings of Davie and Durocher (1997) and Aboal et al. (1999). The simplified
models for Plot E (Eqs.3.10 and 3.11) and Plot D (Eqs. 3.12 and 3.13) explained only 5.5
% and 3.8 % less variation in stemflow production, respectively. This is most likely due
to an overall decrease in the importance of canopy architecture for a lodgepole pine stand
as it matures, and more of a reliance on total rainfall. The final modelling stage involved
combining Plot E and D data to produce a generic equation for juvenile pine stands using
CPA. This model explained 71.3 % of the variation in stemflow production for individual
lodgepole pines or entire stands using CPA and gross rainfall. The model is applicable to
individual trees or stands that contain trees with CPAs ranging from ~ 0.1 to 3.5 m3.

103

The finding that a lone branchless tree had a season-long funnelling ratio of 10.9
suggests that meteorological conditions influence stemflow production for a tree lacking
a canopy. If rain was to fall vertically then the funnelling ratio of a branchless tree would
be less than one due to trunk storage. Therefore, rain must be falling on an angle and
stemflow production is therefore dependent on rainfall intensity and wind speed (Herwitz
and Slye, 1995; Xiao et al., 2000).

CONCLUSION
Field research conducted during the 2008 growing season showed that healthy
juvenile lodgepole pine trees are far more efficient stemflow producers than mature
individuals. Due to these findings, two stands of juvenile lodgepole pine were heavily
sampled for stemflow during the 2009 growing season. Stemflow production for both
juvenile stands was successfully modelled using two predictor variables: rainfall depth
and crown projected area. Additional variables could be added to the individual models
for each plot; however, the increase in accuracy for the sampled stands was insignificant
when compared to the variation in stemflow production explained by the aforementioned
variables. However, it is important to note that the identification of different biotic
variables at the plot scale highlights the fact that as trees age, the biotic factors that
influence stemflow production change. Therefore, modelling stemflow production is
more accurate when more than one variable is employed, in contrast to using only one as
the majority of studies have done to-date. However, gathering the data required for the
inclusion of additional variables in one’s model is no small task. If a researcher is
restrained by resources or time, crown projected area can be used in conjunction with
rainfall depth to produce a generic model for juvenile lodgepole pine that, at least in this
study, accurately predicted stemflow volumes.
The inclusion of detailed stand characteristics in one’s methodology is paramount
for comparison with other studies. It is therefore important that future publications
include detailed stand characteristics for ease of intra- and interspecific stemflow
production comparisons. Due to the findings that juvenile lodgepole pine are efficient
104

stemflow producers, further research into the hydrologic importance of the stemflow
produced by these trees, including the potential impact on soil moisture and groundwater
recharge, is merited.

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Brinson MM, Bradshaw HD, Holmes RN, Elkins JB Jr. 1980. Litterfall, stemflow, and
throughfall nutrient fluxes in an alluvial swamp forest. Ecology 61(4): 827-835.
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Cape JN, Brown AHF, Robertson SMC, Howson G, Paterson IS. 1991. Interspecies
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Processes 14: 135-144.
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partitioning within deciduous canopy. II. Model parameterization and testing.
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and the effects on stemflow and throughfall generation. Earth Surface Processes
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wind-driven rainfall. Journal of Hydrology 168: 205-226.
Horton RE. 1919. Rainfall Interception. Monthly Weather Review 47(9): 608-623.
Johnson MS, Lehmann J. 2006. Double-funnelling of trees: Stemflow and root-induced
preferential flow. Ecoscience 13(3): 324-333.
Levia DF Jr., Frost EE. 2003. A review and evaluation of stemflow literature in the
hydrologic and biogeochemical cycles of forest and agricultural ecosystems.
Journal of Hydrology 274: 1-29.
Levia DF Jr. 2004. Differential winter stemflow generation under contrasting storm
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stemflow by roots in three Chihuahuan desert shrubs. Journal of Arid Environments
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water for growth. Linking Innovations and Networking Knowledge 11(2): 11-12.
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throughfall and stemflow in tree-based land use systems and spontaneous tree
vegetation of central Amazonia. Agriculture, Ecosystems and Environment 87: 3749.
Spittlehouse D. 1998. Rainfall interception in young and mature conifer forests in British
Columbia. Proceedings 23rd Conference on Agricultural and Forest Meteorology.
Spittlehouse D. 2008. Annual water balance of forest and burnt stands. Streamline
Watershed Management Bulletin 12(1): 3-4.
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recharge. 2: A cylindrical infiltration model for evaluating the stemflow
contribution to groundwater recharge. Hydrological Processes 10: 81-88.
Taniguchi M, Tsujimura M, Tanaka T. 1996. Significance of stemflow in groundwater
recharge. 1: Evaluation of this stemflow contribution to recharge using a mass
balance approach. Hydrological Processes 10: 71-80.
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CHAPTER 4
CONCLUSION

In comparison to other components of the vegetation canopy water balance,
stemflow has received the least attention in the hydrologic literature (Levia and Frost,
2003). Despite being volumetrically insignificant at the plot scale and beyond when
compared to throughfall and interception loss, stemflow is hydrologically important
because it is a focused input of precipitation at the base of a tree or plant (Herwitz, 1986).
Over the course of a century of study, the important impact stemflow can have on site
hydrology has been highlighted time and time again. Stemflow can have implications for
groundwater recharge, erosion, and vegetation growth (Voigt 1960; Brinson et al., 1980;
Herwitz, 1986; Tanaka et al., 1996; Taniguchi et al., 1996; Whitford et al., 1997; Chang
and Matzner, 2000; Schroth et al., 2001; Johnson and Lehmann, 2006). Due to the
importance of stemflow as highlighted by prior studies, it is imperative that we strive to
increase our knowledge by studying different vegetation species under differing
geographic and climatic conditions, as well as under different age and condition (e.g.,
disturbance, planting arrangement, etc.) scenarios.
Stemflow production data was compiled for studies published prior to June 30,
2010, which contained one or more of the following: a stemflow equation, percentage of
rainfall that became stemflow, or stemflow funnelling ratios. The information was
organized by species and partitioned into seven climate and vegetation classifications.
Once organized, stemflow funnelling ratios and plateau funnelling ratios were calculated
for studies that provided the necessary information. Upon table completion, the data was
examined to identify inter-climatic, intra-climatic, and inter-genera relationships. Plateau
funnelling ratios were used to estimate the amount of rainfall required to satisfy the
storage capacity of a tree or bush. When compared to the values used in current canopy
water balance models, rainfall amounts found using the plateau funnelling ratio method
were much greater, highlighting a large underestimation in current models. Finally, the
data contained within the reference tables was used to highlight areas where knowledge
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remains fairly weak, and to identify particular genera which have received the most
attention to date.
After examination of the stemflow literature it was noted that stemflow
knowledge for species found in the Interior of British Columbia was lacking. Due to the
changes in the landscape that will occur as a result of the Mountain Pine Beetle epidemic,
an examination of stemflow production for lodgepole pine was undertaken. Spittlehouse
(1998) showed that stemflow production was fairly low for mature lodgepole pine,
however, no studies to-date had examined juvenile lodgepole pine. Field research
conducted for the 2008 growing season showed that juvenile lodgepole pine were much
more efficient stemflow producers when compared to mature trees (McKee and CarlyleMoses, 2010). Due to these findings, two stands of juvenile lodgepole pine were heavily
sampled over the 2009 growing season with the goal of identifying the meteorological
conditions and tree characteristics that influence stemflow production. The dataset
gathered for this thesis further supports the findings of McKee and Carlyle-Moses (2010)
that juvenile lodgepole pine produce significant volumes of stemflow. Analysis of the
dataset resulted in the successful production of three stemflow models, one for each
individual research plot, and a comprehensive model encompassing the entire dataset.
These models employed multiple variables, highlighting the importance of considering a
wide array of variables when modelling stemflow production.
Reviewing the quantitative importance of stemflow not only produced a reference
guide for future researchers, it also highlighted the shortcomings of current canopy water
balance models. Calculated plateau funnelling ratios were used to estimate the amount of
rainfall required to satisfy the storage capacity of a tree or plant. The rainfall depths
associated with the calculated plateau funnelling ratios suggest that current methods of
estimating the required rainfall depth to saturate a vegetation canopy (e.g. Valente et al.,
1997) may be erroneous. Examination of the review tables also highlighted the
importance of including detailed stand characteristics which aid in inter-study
comparisons.

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With regards to the field study, logistics limited the number of plots used to
produce the dataset required for developing the stemflow models to two. Plots E and D
contained 37 and 36 samples, respectively, that were representative of their respective
stands. The inclusion of more samples from other locations with differing tree
architecture would have resulted in a more comprehensive model, or multiple models
categorized by tree size. Despite the limitations due to sampling logistics, the final model
explained 71.3 % of the variation in stemflow production for both juvenile stands. This
model provides the basis for the development of a broadly applicable model that would
allow hydrologists to calculate stemflow production for individual juvenile lodgepole
pine or for lodgepole pine stands outside the geographic area of this study.
Based on the findings that plateau funnelling ratios can be used to estimate the
amount of rainfall required to satisfy the storage capacity of a tree, and that current
models greatly underestimate the storage capacity, new canopy water balance models
must be produced that accurately estimate canopy storage. The use of the stemflow
funnelling ratio in the stemflow literature should be expanded because it is an effective
way of explaining a tree’s or stand’s ability to produce stemflow. The inclusion of
detailed stand characteristics is paramount for comparison with other studies. It is
therefore important that future studies include detailed stand characteristics for ease of
inter- and intra-specific stemflow production comparisons.
Juvenile lodgepole pine trees are efficient stemflow producers and are capable of
producing large volumes of stemflow, up to 10 times more than their mature
counterparts. Based on that finding alone, more research is required to determine the
hydrological and ecological implications of stemflow production from juvenile lodgepole
pine. What are the implications for site hydrology? Specifically, is stemflow from
juvenile lodgepole pine important for soil moisture and groundwater recharge? Is this
water flux also an important source of nutrients for growth for this tree species? Further
investigation into the hydrological and biogeochemical importance of stemflow from
juvenile lodgepole pine is paramount due to the uncertainties surrounding the potential

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impacts of mountain pine beetle, wildfire, and climate change on the hydrology and
ecology of British Columbia’s Interior.

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rainforest. Earth Surface Processes and Landforms 11: 401-412.
Johnson MS, Lehmann J. 2006. Double-funnelling of trees: Stemflow and root-induced
preferential flow. Ecoscience 13(3): 324-333.
Levia DF Jr., Frost EE. 2003. A review and evaluation of stemflow literature in the
hydrologic and biogeochemical cycles of forest and agricultural ecosystems.
Journal of Hydrology 274: 1-29.
McKee AJ, Carlyle-Moses DE. 2010. Stemflow: A potentially important point source of
water for growth. Linking Innovations and Networking Knowledge 11(2): 11-12.
Schroth G, Elias MEA, Uguen K, Seixas R, Zech W. 2001. Nutrient fluxes in rainfall,
throughfall and stemflow in tree-based land use systems and spontaneous tree
vegetation of central Amazonia. Agriculture, Ecosystems and Environment 87: 3749.
Spittlehouse D. 1998. Rainfall interception in young and mature conifer forests in British
Columbia. Proceedings 23rd Conference on Agricultural and Forest Meteorology.
Tanaka T, Taniguchi M, Tsujimura M. 1996. Significance of stemflow in groundwater
recharge. 2: A cylindrical infiltration model for evaluating the stemflow
contribution to groundwater recharge. Hydrological Processes 10: 81-88.
Taniguchi M, Tsujimura M, Tanaka T. 1996. Significance of stemflow in groundwater
recharge. 1: Evaluation of this stemflow contribution to recharge using a mass
balance approach. Hydrological Processes 10: 71-80.

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113