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Hydrological and Biogeochemical Fluxes of
Throughfall and Stemflow in Temperate Swamps
by
Matthew S. Malone
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Department of Geography and Planning
University of Toronto
© Copyright by Matthew S. Malone 2015
II
Hydrological and Biogeochemical Fluxes of Throughfall and Stemflow
in Temperate Swamps
Matthew S. Malone
Master of Science
Department of Geography and Planning
University of Toronto
2015
Abstract
This study quantifies the hydrological and biogeochemical inputs that precipitation (P)
provides to temperate swamp ecosystems. The meteorological influences on throughfall and
stemflow in two temperate swamps of southern Ontario over two growing seasons separated by a
major ice storm is presented. This study also presents the investigation of dissolved organic
carbon (DOC) and nitrogen (N) fluxes within throughfall and stemflow for two temperate
swamps of southern Ontario during the 2014 growing season. Field-based measurements in two
mixed-deciduous temperate swamps over the course of two growing seasons, combined with
chemical analyses from extensive laboratory work form the basis of the results presented here.
The quantification of P, DOC and N fluxes has rarely been studied in temperate swamp
ecosystems and is important to quantify as these inputs have large impacts on the physical and
chemical processes occurring at the wetland floor.
III
Acknowledgements
I would like to thank my supervisor, Prof. Tim Duval, for his constant guidance and
encouragement throughout my time at UTM. I am also grateful for the valuable experiences
provided by him, not only with this project but with the numerous other projects undertaken by
our research group. I would like to thank my committee members, Prof. Laura Brown and Prof.
Carl Mitchell, for their time and helpful feedback. This project would not have been successful
without the help and support of both former and current members of the Duval SWEHR Group.
The number of people that have helped me with this project is endless and I do not want to make
a list in case I forget someone…you know who you are and I am very grateful to each and every
one of you. A special thanks to the Carey Lab at McMaster for allowing me to invade their space
from time to time. Last, but certainly not least, I would like to thank my family, friends, and
those close to me who provided me with the guidance, encouragement, and distractions I needed
over the last 2 years of this project.
IV
Contents:
Chapter 1: Introduction ...............................................................................................................1
1.1 Background ................................................................................................................................1
1.2 Wetland Protection.....................................................................................................................3
1.3 Study Rationale ..........................................................................................................................4
1.4 Study Objectives ........................................................................................................................4
1.4.1 Hydrological Objectives .............................................................................................5
1.4.2 Biogeochemical Objectives ........................................................................................6
1.5 References ..................................................................................................................................9
Chapter 2: Rainfall partitioning in two swamps of southern Ontario: meteorological
influences before and after a canopy-damaging ice storm ...........................................13
2.1 Abstract ....................................................................................................................................13
2.2 Introduction ..............................................................................................................................13
2.2.1 Research Objectives ..................................................................................................18
2.3 Methods....................................................................................................................................18
2.3.1 Site Description .........................................................................................................18
2.3.2 Hydrological Instrumentation and Analysis .............................................................19
2.3.3 Temporal Stability Analysis .....................................................................................20
2.4 Results ......................................................................................................................................21
2.4.1 Stand Characteristics .................................................................................................21
2.4.2 Seasonal and Incident Precipitation ..........................................................................23
2.4.3 Throughfall ...............................................................................................................25
2.4.4 Spatiotemporal Variability in Throughfall................................................................27
2.4.5 Stemflow ...................................................................................................................29
2.4.6 Spatial Variability in Stemflow ................................................................................30
2.4.7 Interception ...............................................................................................................31
2.4.8 Influence of Rainfall Characteristics on Rainfall Partitioning ..................................32
2.5 Discussion ................................................................................................................................37
2.5.1 Rainfall Partitioning ..................................................................................................37
2.5.2 Influence of Rainfall Characteristics on Rainfall Partitioning ..................................41
2.5.3 Effects of an Ice Storm on TF and SF .......................................................................42
2.6 Conclusion ...............................................................................................................................45
2.7 References ................................................................................................................................46
V
Chapter 3: Dissolved carbon and nitrogen fluxes of throughfall and stemflow in two
mixed-deciduous swamps of southern Ontario .............................................................52
3.1 Abstract ....................................................................................................................................52
3.2 Introduction ..............................................................................................................................53
3.3 Methods....................................................................................................................................56
3.3.1 Site Description .........................................................................................................56
3.3.2 Hydrological Instrumentation and Analysis .............................................................57
3.3.3 Chemical Analysis ....................................................................................................59
3.4 Results and Discussion ............................................................................................................62
3.4.1 Aqueous Inputs – Precipitation, Throughfall and Stemflow ...................................62
3.4.2 Dissolved Organic Carbon ........................................................................................62
3.4.3 DOM Quality ............................................................................................................69
3.4.4 Nitrogen ....................................................................................................................73
3.4.5 Hot Phenomena of DOC and N Flux .......................................................................82
3.5 Conclusion ...............................................................................................................................84
3.6 References ................................................................................................................................85
Chapter 4: Conclusion .................................................................................................................90
VI
List of Tables
Chapter 2
2.1 Tree type, density, basal area, frequency and relative dominance for the maple site ..............22
2.2 Tree type, density, basal area, frequency and relative dominance for the ash site ..................22
2.3 Tree size distribution sampled within the maple site ...............................................................23
2.4 Tree size distribution sampled within the ash site ...................................................................23
2.5 Incident mean rainfall depths, durations and intensities for rainfall events during the 2013-
2014 study periods (May-September) ................................................................................24
2.6 Relationship between TF and SF to storm characteristics .......................................................33
2.7 Mean and standard deviation of rainfall amounts, durations and intensities for the 2013 and
2014 study periods ............................................................................................................36
2.8 Mean and standard deviation of incident TF, SF and interception for both sites during the
2013 and 2014 study periods ............................................................................................36
Chapter 3
3.1 Incident mean rainfall depths, durations and intensities for rainfall events during the 2014
study period (May-September). .........................................................................................58
3.2 DOM metrics used to analyze the quality of DOM within P, TF, and SF ...............................60
3.3 Rainfall, throughfall and stemflow chemical properties for the maple and ash swamps .......65
3.4 Hot phenomena for the maple and ash swamps .......................................................................83
VII
List of Figures
Chapter 1
1.1 Conceptual model of precipitation partitioning .........................................................................3
1.2 Map of maple and ash swamps near Terra Cotta, Ontario. Scale = 1:25000 .............................6
Chapter 2
2.1 Throughfall (mm) as a function of incident rainfall (mm) for the maple and ash sites in 2013
(A) and 2014 (B) ................................................................................................................26
2.2 TF Coefficient of Variation (%) as a function of incident rainfall (mm) for the maple and ash
sites in 2013 (A) and 2014 (B) ...........................................................................................27
2.3 Temporal stability plots normalized to 0 of median TF% for the maple site in 2013 (A), 2014
(B) and the ash site 2013 (C), and 2014 (D) ......................................................................28
2.4 Stemflow (mm) as a function of incident rainfall (mm) for the maple and ash sites in 2013
(A) and 2014 (B) ................................................................................................................30
2.5 SF Coefficient of Variation (%) as a function of incident rainfall (mm) for the maple and ash
sites in 2013 (A) and 2014 (B) ...........................................................................................31
2.6 Percent contribution of TF, SF, and IW to total P for the maple and ash sites both seasonally
and on an event-basis .....................................................................................................................32
2.7 Coefficient of variation (%) as a function of storm intensity for TF (A) and SF (C) and
coefficient of variation (%) as a function of storm duration for TF (B) and SF (D ..........34
2.8 Relationship between throughfall % and hours since last rainfall ...........................................35
2.9 TF (mm) as a function of rainfall (mm) for events < 5 mm for A- 2013 and B- 2014, in the
maple and the ash swamps .................................................................................................43
Chapter 3
3.1 Box plot comparison of DOM quantity and quality for TF and SF in the maple and ash
swamps ...............................................................................................................................64
3.2 Temporal progression of mean event DOM quantity and quality for TF and SF in the maple
and ash swamps, May-September, 2014 ............................................................................66
3.3 Box plot comparison of N-species between TF and SF in the maple and ash swamps ..........74
3.4 Temporal progression of mean event N concentrations for TF and SF in the maple and ash
swamps, May-September, 2014 .........................................................................................75
3.5 Temporal decrease in nitrate concentrations for TF and SF from May-September ................76
VIII
List of Figures cont’d
3.6 Proportion of NO3-, NH4
+ and DON contribution to TDN for each sampling event ...............79
1
Chapter 1:
Introduction
1.1 Background
Wetlands are defined as areas frequently inundated with water at or near the soil surface,
which have predominately hydric soils and the presence of vegetation adapted to waterlogged
conditions (Mitch and Gosselink, 2007). Wetland types are classified based upon several
characteristics including vegetation, hydrology, biogeochemical characteristics of the soil and
productivity (Mitch and Gosselink, 2007), with five main types of wetlands in Canada identified
as swamps, marshes, fens, bogs and shallow water systems (National Wetlands Working Group,
1997).
Wetlands are tremendously important ecosystems as they serve many functions and are
considered one of the most productive and economically valuable ecosystems in the world
(Moreno-Mateos et al., 2012). The functions of wetlands can be divided into four main
categories: physical and hydrological functions, biological and biogeochemical functions,
economic uses and societal functions. Physical and hydrological functions of wetlands lead to a
variety of societal benefits including the dispersal and/or storage of flood water, sediment
trapping, stabilization of river and stream banks, and the recharging of aquifers (Hughes and
Heathwaite, 1995). The main biological and biogeochemical functions of wetlands are that they
provide habitat for a large diversity of flora and fauna, they act as nutrient sources and/or sinks,
and they act as water purifiers by contaminant and excess nutrient uptake (Hughes and
Heathwaite, 1995; Mitch and Gosselink, 2007). Economic values include direct harvestable
2
goods such as hay, timber, fish and peat; as well as the indirect enhancement of water quality,
thereby requiring less resources allocated to treatment for human use (Hughes and Heathwaite,
1995). Finally, important societal uses of wetlands include acting as an educational tool; having
high aesthetic value; and serving as research, recreational and cultural areas (Hughes and
Heathwaite, 1995).
Swamps are wetlands characterized as peatland or mineral wetlands with the water table
at or below the surface, having minerogenous soils comprised of woody peat and organic matter
and 30% coniferous or deciduous plant cover taller than 1 metre (National Wetlands Working
Group, 1997). Swamps often have a water table below the major portion of the ground surface,
while still approximately 20 cm higher than the average groundwater level, with aerated
hummocks available for root growth of trees and/or tall shrubs (National Wetlands Working
Group, 1997). Temperate swamps are ecologically and economically important systems as they
provide habitat for many species of waterfowl, fish, amphibians and mammals; they improve
water quality of nearby aquatic areas by promoting sedimentation and plant uptake of nutrients
and contaminants; and help to mitigate flood intensity and frequency (Mitch and Gosselink,
2007).
Precipitation is a major component of the hydrologic cycle and, along with groundwater
inputs, dictate the degree of soil saturation during the growing season in headwater swamps
(McKillop et al., 1999). Understory precipitation, the proportion of rainfall that is not intercepted
by the canopy, can vary both spatially and temporally, thereby influencing water availability
within swamp systems. Understory precipitation can be broken down into two components:
throughfall and stemflow, while a proportion of rainfall is intercepted by the canopy (Figure 1.1).
Throughfall is the proportion of precipitation that makes it to the forest floor either directly
3
through gaps in the canopy or from branch or leaf drip (Hewlett, 1982; Cape et al., 1991; Price
and Carlyle-Moses, 2003), whereas stemflow is the proportion of understory precipitation
directly inputted at the tree bole via the flow of water along the leaves, twigs and branches of a
tree to the tree base (Levia and Frost, 2003). The precipitation that is retained within the canopy
is known as interception and is subsequently evaporated back into the atmosphere (Clements,
1971).
Figure 1.1: Conceptual model of precipitation partitioning.
1.2 Wetland Protection
Approximately 70% of wetland areas within southern Ontario have been lost due to
draining for agricultural and urban development, with upwards of 80% loss identified near major
urban centres (CVC Terrestrial Monitoring Program Report, 2010). Recent legislative acts within
Ontario, including the Ontario Planning Act, Oak Ridges Moraine Conservation Plan and
4
Greenbelt plan all include some level of protection for wetlands (Ducks Unlimited Canada,
2010). Legislative protection, combined with increased understanding of the importance of
wetlands and associated conservation efforts have decreased the rate of wetland destruction in
recent years around the world (Mitch and Gosselink, 2007). However, even with legislative
protection and plenty of resources allocated to conserve and restore wetlands across North
America, significant wetland losses continue to this day (Moreno-Mateos et al., 2012).
The losses of these important ecosystems presents the need to accurately monitor the
important physical, chemical and biological processes that occur within them in order to
effectively manage them. As precipitation is a major water contributor to most wetland systems,
quantifying the amount of precipitation entering temperate swamp systems is a major priority for
conservation authorities and land managers in southern Ontario.
1.3 Study Rationale
This study was designed to quantify the hydrological and biogeochemical inputs that
precipitation provides to temperate swamp ecosystems. Quantification of understory
precipitation has been studied in upland forests of southern Ontario (Price and Carlyle-Moses,
2003; Buttle and Farnsworth, 2012), boreal coniferous wetlands (Pelster et al., 2009), and
tropical swamps (Brinson et al, 1980), however, this type of research has not been studied in a
temperate swamp ecosystem. Quantifying the impacts of vegetation on the hydrologic cycle, in
particular the interception of precipitation, determines a significant portion of water available to
these important ecosystems (Guswa and Spence, 2012). Throughfall and stemflow water fluxes,
along with the solutes collected when passing through the canopy, will determine important
physical, chemical and biological processes occurring at the swamp floor.
5
1.4 Study Objectives
1.4.1 Hydrological Objectives
Chapter 2 was designed to quantify the amount of precipitation entering two temperate
swamps located near Terra Cotta, Ontario (Figure 1.2), thus, filling a research gap regarding
precipitation fluxes to temperate swamp ecosystems. With a desire to maintain wetland
ecosystems surrounded by urbanization and development in southern Ontario (CVC Terrestrial
Monitoring Program Report, 2010), this study provides a key component to water balance
calculations to ensure these systems are supplied with enough water after land-use changes occur
around them. This study also addresses anticipated precipitation partitioning dynamics when
changes in precipitation regimes and changing rainfall characteristics shift in the Greater Toronto
Area (GTA) due to climate change (IPCC, 2013). Rain events in the GTA are expected to
become more intense, with longer intervening dry periods between events (Cao and Ma, 2009).
As such, models that can accurately quantify precipitation partitioning based on storm
characteristics are vital to the accurate assessment of water availability. Finally, this chapter
assesses the impacts of a canopy-altering perturbation on precipitation partitioning in temperate
swamps. This aspect of the study came about after a severe ice storm hit the GTA in December,
2013, causing significant damage to the trees in both study swamps.
6
Figure 1.2: Map of maple and ash swamps near Terra Cotta, Ontario. Scale = 1:25000.
1.4.2 Biogeochemical Objectives
Chapter 3 was designed to assess the fluxes of dissolved organic matter (DOM) and
nitrogen (N) within throughfall and stemflow in two temperate swamps (Figure 1.2). The fluxes
of DOM and nitrogen have been shown to impact below-canopy biomass (Falkengren-Grerup,
1989), nutrient cycling (Chang and Matzner, 2000) and water quality both within the wetland
(Brinson et al., 1980) and in downstream ecosystems (Inamdar et al., 2012). However, there is a
research gap regarding the fluxes of DOM/N to temperate swamp ecosystems.
Background on DOM Quality Metrics
A Horiba Aqualog benchtop fluorometer was used to analyze TF and SF samples for
chemical characterization of DOM. This instrument works by directing energy into water
samples, causing electrons in fluorescing moieties to go into a higher energy state. When these
electrons go back to ground state (original state), they emit light in the form of a fluorescence
Maple Swamp
Ash Swamp
Ontario
7
signature that is detected by the instrument (Skoog et al. 1996). This method is more sensitive
than traditional absorbance measurements, but can be affected by factors such as pH, quenching,
temperature, redox and ionic strength (Skoog et al., 1996). Thus, fluorescence measurements
need to be corrected for inner filter effects and Raman scattering, as well as standardized in order
to be compared between different ecosystems and between different fluorometers (Nollet, 2007).
From the data generated by a fluorometer, a number of absorption and fluorescence metrics can
be analyzed:
Fluorescence Index:
The fluorescence index (FI) is a metric which is used to determine the source of DOM
within a water sample, whether that be from microbial or plant-derived material (Cory et al.,
2005). Values usually range between 1.2-1.8, with lower values indicating more terrestrially-
derived material and higher values microbial-sourced. In the context of this study, DOM in TF
and SF comes from the leachates of plant material that rainwater flushes away (Levia et al.,
2012). However, there is the potential for microbes living on the tree surfaces to contribute to the
DOM pool as they are able to break down tree leachates (Guggenberger and Zech, 1994),
thereby contributing metabolites to TF and SF. The FI is found by dividing the emission at 470
nm by the emission at 520 nm, at an excitation wavelength of 370 nm:
FI = em 470 nm @ ex 370 nm (2)
em 520 nm @ ex 370 nm
Freshness Index:
The freshness index (β:α) is used to determine how fresh the DOM is, with values closer
to 1 representing more recently produced DOM and values closer to 0 representing more
8
decomposed DOM (Fellman et al., 2010). The freshness index is found as the ratio of emission
intensity at 380 nm divided by the maximum intensity between 420-435 nm at an excitation of
310 nm (Parlanti et al., 2000; Wilson and Xenopoulos, 2009):
(β:α) = em 380 nm @ ex 310 nm (3)
max. em 420 – 435 nm @ ex 310 nm
Humification Index:
The humification index (HIX) indicates the humic substance content within a water
sample, with higher values indicating a larger degree of humification (Fellman et al., 2010).
Values closer to 1 represent more humified DOM and values closer to 0 represent less humified
DOM. To calculate HIX, an equation first postulated by Zsolsnay et al. (1999) and later modified
by Ohno (2002) is used:
HIX = Peak area (435 – 480 nm) @ ex 254 nm (4)
Peak area (300 - 345 nm + 435 – 480 nm) @ ex 254 nm
Thus, the HIX can be found by dividing the peak intensity area from 435 to 480 nm by the peak
intensity area from 300 to 345 nm plus the peak intensity area from 435 to 480 nm, at an
excitation of 254 nm (Fellman et al., 2010).
Spectral Slope Ratio:
The spectral slope ratio (SR) is a metric used to determine DOM source, quality and
diagenesis and is independent of DOM concentration (Cory et al., 2011). The SR can be found by
dividing the slope of the absorbance from 275-295 nm by the slope of the absorbance from 350-
400 nm. SR has a strong, inverse relationship with the relative molecular mass of organic
compounds within a water sample (Helms et al., 2008), with lower SR associated with
9
terrestrially-derived organic material and higher SR associated with autochthonous inputs (Cory
et al., 2011). The SR is calculated using the equation:
SR = Slope (abs. 275 - 295 nm) (5)
Slope (abs. 350 - 400 nm)
Specific UV Absorbance:
The amount of aromatic carbon, a measure of DOM bioavailability, can be identified in
water samples by dividing the specific UV absorbance (SUVA) at 254nm (per metre) by the
concentration of DOC (mg/l) (Weishaar et al., 2003), as a study performed by Korshin et al.
(1997) found that the aromatic ring of polar groups on hydroxyl, carbonyl, carboxyl, and esters
increases absorption at a wavelength of 254 nm. Higher aromaticity is associated with less
bioavailable DOM within water sources (Fellman et al., 2008) and can be found using the
following equation:
SUVA254 = abs. 254 nm (6)
[DOC] (mg/l)
Chapter 4 presents a summary of key findings and areas of future research.
1.5 References
Brinson, M.M., Bradshaw, H.D., Holmes, R.N. and Elkins, J.B. (1980). Litterfall, Stemflow,
and Throughfall Nutrient Fluxes in an Alluvial Swamp Forest. Ecology. 62 (4): 827-835
10
Buttle, J.M. and Farnsworth, A.G. (2012). Measurement and modeling of canopy water
partitioning in a reforested landscape: The Ganaraska Forest, southern Ontario, Canada.
Journal of Hydrology. 466: 103-114
Cao, Z., Ma, J. (2009). Summer severe-rainfall frequency trend and variability over Ontario,
Canada. Journal of Applied Meteorology and Climatology. 48: 1955-1960
Cape, J.N., Brown, A.H.F., Robertson, S.M.C, Howson, G., Paterson, I.S. (1991). Interspecies
comparisons of throughfall and stemflow at three sites in northern Britain. Forest
Ecology and Management. 46: 165-177
Carlyle-Moses, D.E. and Price, A.G. (2006). Growing-season stemflow production within a
deciduous forest of southern Ontario. Hydrological Processes. 20: 3651-3663
Chang, S.C. and 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
Clements, J.R. (1971). Evaluating summer rainfall through a multilayered Largetooth Apsen
community. Canadian Journal of Forest Research. 1: 20-31
Cory, R.M., McKnight, D.M. (2005). Fluorescence spectroscopy reveals ubiquitous presence of
oxidized and reduces quinones in dissolved organic matter. Environmental Science and
Technology. 39: 8142-8149
Cory, R.M., Boyer, E.W., McKnight, D.M. (2011). Spectral methods to advance understanding
of dissolved organic carbon dynamics in forested catchments. Forest Hydrology and
Biogeochemistry: Synthesis of Past Research and Future Directions. 216: 117-135
Credit Valley Conservation (CVC) (2010). Monitoring Wetland Integrity within the Credit
River Watershed. Chapter 1: Wetland Hydrology and Water Quality 2006- 2008. Credit
Valley Conservation. 79pp
Ducks Unlimited Canada. (2010). Southern Ontario Wetland Conversion Analysis. Final
Report. 51 pp
Falkengren-Grerup, U. (1989). Effect of stemflow on beech forest soils and vegetation in
southern Sweden. Journal of Applied Ecology. 26: 341-352
Fellman, J.B., D’Amore, D.V., Hood, E., Boone, R.D. (2008). Fluorescence characteristics and
biodegradability of dissolved organic matter in forest and wetland soils from coastal
temperate watersheds in southeast Alaska. Biogeochemistry. 88: 169-184
11
Fellman, J.B., Hood, E., Spencer, R.G.M. (2010). Fluorescence spectroscopy opens new
windows into dissolved organic matter dynamics in freshwater ecosystems: a review.
Limnology and Oceanography. 55: 2452-2462
Guswa, A.J., Spence, C.M. (2011) Effect of throughfall variability on recharge: application to
hemlock and deciduous forests in western Massachusetts. Ecohydrology
Helms, J.R., Stubbins, A., Ritchie, J.D., Minor, E.C., Kieber, D.J., Mopper, K. (2008).
Absorption spectral slopes and slope ratios as indicators of molecular weight, source and
photobleaching of chromophoric dissolved organic matter. Limnology and
Oceanography. 53: 955-969
Hewlett, J.D. (1982). Principles of Forest Hydrology. University of Georgia Press. Athens, GA.
215 pp
Hughes, J. and Heathwaite, L. (1995) Hydrology and Hydrochemistry of British Wetlands. John
Wiley and Sons, Ltd. New York, NY.
Inamdar, S., Shatrughan, S., Dutta, S., Levia, D., Mitchell, M., Scott, D., Bais, H., McHale P.
(2012). Fluorescence characteristics and sources of dissolved organic matter for stream
water during storm events in a forested mid-Atlantic watershed. Journal of Geophysical
Research. 116: G03043
IPCC. (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate
Change [Stocker, T.F., D. Qin, G. –K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A.
Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 1535 pp
Korshin, G.V., Li, C.W., Benjamin, M.M. (1997). Monitoring the properties of natural organic
matter through UV spectroscopy: a consistent theory. Water Resources Research. 31:
1787- 1795
Levia, D.F. and Frost, E.E. (2003). A review and evaluation of stemflow literature in the
hydrologic and biogeochemical cycles of forested and agricultural ecosystems. Journal
of Hydrology. 274: 1-29
McKillop, R., Kouwen, N., Soulis, E.D. (1999). Modeling the rainfall-runoff response of a
headwater wetland. Water Resources Research. 35: 1165-1177
Mitch, W.J., and Gosselink, J.G. (2007). Wetlands. John Wiley & Sons Inc., 4th Edition
Moreno-Mateos D., Power M.E., Comı´n F.A., Yockteng R. (2012). Structural and Functional
Loss in Restored Wetland Ecosystems. PLoS Bio. 10 (1)
National Wetlands Working Group. (1997). The Canadian Wetland Classification System.
University of Waterloo., 2nd Edition
12
Nollet, L. M. L. (2007). Handbook of Water Analysis, 2nd edition. CRC Press, Boca Raton,
Florida.
Ohno, T. (2002). Fluorescence inner-filtering correction for determining the humification index
of dissolved organic matter. Environmental Science and Technology. 36: 742–746
Parlanti, E., Worz, K., Geoffroy, L., Lamotte, M. (2000). Dissolved organic matter fluorescence
spectroscopy as a tool to estimate biological activity in a coastal zone submitted to
anthropogenic inputs. Organic Geochemistry. 31: 1765–1781
Pelster, D.E., Kolka, R.K., Prepas, E.E. (2009). Overstory vegetation influence nitrogen and
dissolved organic carbon flux from the atmosphere to the forest floor: Boreal Plain,
Canada. Forest Ecology and Management. 259: 210-219
Price, A.G. and Carlyle-Moses, D.E. (2003) Measurement and modelling of growing-season
canopy water fluxes in a mature mixed deciduous forest stand, southern Ontario,
Canada. Agricultural and Forest Meteorology. 119: 69-85
Skoog, D. A., F. J. Holler & T. A. Nieman, 1998. Principles of Instrumental Analysis, 5th ed.
Thomson Learning Inc., Florence, KY, USA.
Weishaar, J.L., Aiken, G.R., Bergamaschi, A., Farm, M.S., Fujii, E., Mopper, K. (2003).
Evaluation of specific ultraviolet absorbance as an indicator of the chemical
composition and reactivity of dissolved organic carbon. Environmental Science and
Technology. 37: 4702- 4708
Wilson, H.F., Xenopoulos, M.A. (2009). Effects of agricultural land use on the composition of
fluvial dissolved organic matter. Nature Geosciences. 2: 37- 41
Zsolnay, A., Baigar,E., Jimenez, M., Steinweg, B., Saccomandi, F. (1999). Differentiating with
fluorescence spectroscopy the sources of dissolved organic matter in soils subjected to
drying. Chemosphere. 38: 45–50
13
Chapter 2:
Rainfall partitioning in two swamps of southern Ontario: meteorological
influences before and after a canopy-damaging ice storm
2.1 Abstract
The meteorological influences on throughfall and stemflow in two temperate swamps of
southern Ontario was investigated over two growing seasons separated by a major ice storm.
Rainfall partitioning has rarely been studied in temperate swamp systems, while the influences of
an ice storm on throughfall and stemflow have never been investigated. Average event-based
throughfall was 70.5% of precipitation for the maple swamp in 2013 and 75.1% in 2014, while
average event-based throughfall for the ash swamp was 80.8% in 2013 and 85.7% of event
precipitation in 2014. Stemflow fluxes in both sites were much lower than expected, with SF
fluxes < 1% of incident precipitation for both years. This study shows that a maple-dominated
swamp produced less throughfall and similar stemflow amounts to an ash-dominated swamp;
while the amount of rainfall received at the swamp floor was greater after a canopy-altering ice
storm occurred. Additionally, these swamp systems produced slightly more throughfall, with
much less stemflow than comparable upland deciduous forests. With a much higher basal area
measured in the maple swamp than most upland studies, we can conclude that wetland trees
partition rainfall much differently than upland trees.
2.2 Introduction
Precipitation is a major component of the hydrologic cycle and, along with groundwater
inputs, dictate the degree of soil saturation during the growing season in headwater swamps
14
(McKillop et al., 1999). Understory precipitation, the proportion of rainfall that is not intercepted
by a forest canopy, can vary both spatially and temporally, thereby further influencing areas of
soil moisture. Understory precipitation can be influenced by many biotic (stand type, density,
LAI, etc.) and abiotic factors (storm characteristics) (Levia and Frost, 2006), and is divided into
two components: throughfall and stemflow. Quantifying the interactions between vegetation and
the hydrologic cycle will help to determine the effects of climate change, land-use change, and a
growing human population on water availability in and around wetlands (Guswa and Spence,
2012). The quantification of precipitation partitioning in forested wetlands will also help
determine the water available for important physical, chemical and biological processes, such as
soil recharge (Eschner, 1967; Bouten et al., 1992; Si, 2002; Zhou et al., 2002; Raat et al., 2002;
Schume et al., 2003), soil solution chemistry (Manderscheid and Matzner, 2000), nutrient
cycling (Chang and Matzner, 2000), species composition (Kramer and Holscher, 2009), and
streamwater chemistry (Beier, 1998). This quantification is also important to monitor and model,
as small changes in water fluxes have the ability to induce shifts and/or losses of species present
within wetland communities (Hughes and Heathwaite, 1995).
The characteristics of precipitation events (i.e. amount, duration and intensity of an
event), dictate the amount and spatiotemporal variability of understory precipitation observed in
a forested system. Throughfall is the proportion of precipitation that makes it to the forest floor
either by falling directly through gaps in the canopy or from canopy drip (Hewlett, 1982; Cape et
al., 1991; Price and Carlyle-Moses, 2003). Through an extensive literature review performed by
Price and Carlyle-Moses (2003), average throughfall amounts in coniferous, deciduous and
tropical rainforest canopies were observed to be 71, 80 and 85% of incident precipitation,
respectively, with higher throughfall observed during storms with higher incident rainfall
15
amounts (Guswa and Spence, 2011). Also, it has been observed that throughfall measurements
become more uniform and consistent with larger precipitation events (Pressland, 1977; Llorens
et al, 1997, Price et al., 1997; Price and Carlyle-Moses, 2003). The results show that smaller
rainfall events produce more variability in throughfall; whereas larger rainfall events decrease
throughfall variability. Variations in rainfall duration and intensity have also been shown to
influence throughfall, as it was found that long duration precipitation events produce more
throughfall than short duration events (Muzylo et al. 2012). In addition, long duration, low
intensity rain events produced similar throughfall amounts to long duration, high intensity rain
events, suggesting event duration is more important than intensity in dictating throughfall fluxes
(Muzylo et al. 2012).
Stemflow is the proportion of understory precipitation that reaches the forest floor by
way of the tree bole (precipitation that runs down the leaves, branches, and bark to the base of
the tree) and has preferential access to the soil matrix via the root system of the tree (Johnson and
Lehmann, 2006). This water source is important for soil water recharge and root growth, as
stemflow flow paths may dictate areas of higher soil moisture and plant available water, thereby
effecting species distribution (Andre et al., 2011). Average stemflow amounts for most forests
range between 3-6% of incident precipitation (Helvey and Patric, 1965; Mahendrappa, 1990;
Loustau et al., 1992; Levia and Herwitz, 1995, Price et al., 1997; Price and Carlyle-Moses,
2003). However, stemflow amounts can be much higher, such as in a smooth-barked beech stand
that generated means of 17% of incident precipitation (Delfs, 1967), or much lower, as found in
tropical forest stands where SF was < 1% of incident P (Kellman and Roulet, 1990; Veneklaas
and Van Ek, 1990). Similar to throughfall, stemflow amounts are highly correlated with incident
precipitation. Stemflow (as a % of incident rainfall) increases with increasing precipitation and
16
stemflow measurements become less variable with larger rainfall events (Loustau et al., 1992;
Price and Carlyle-Moses, 2003). Muzylo et al. (2012) found that intensity and duration both
influenced stemflow amounts in the leafed period as all events with high intensity (short and
long duration) and long duration (low and high intensity) produced higher amounts of stemflow
(1.5-1.8% of incident precipitation) than short duration, low intensity events (<0.5% of incident
rainfall). However, some studies have found a significant negative correlation between stemflow
production and rainfall intensity (Mauchamp and Janeau, 1993; Crockford and Richardson,
2000; Carlyle-Moses and Price, 2006).
Precipitation that does not make it through the canopy as throughfall or stemflow is
known as interception, resulting in a net loss of water from a stand as it is subsequently
evaporated back into the atmosphere. Mean rainfall interception values of 18.8% and 23% were
found for temperate and Mediterranean deciduous stands, respectively (Price and Carlyle-Moses,
2003; Muzylo et al., 2013); while coniferous stands have interception means much higher at 30-
33% (Barbier et al., 2009). These intercepted hydrological inputs are important as they can make
up a very large proportion of evapotranspiration fluxes in forest stands, from 13% in tropical
rainforests to 26% in some coniferous stands (Price and Carlyle-Moses, 2003).
This study was designed to quantify the hydrological and biogeochemical inputs that
precipitation provides to temperate swamp ecosystems. Quantification of understory
precipitation has been studied in upland forests of southern Ontario (Price and Carlyle-Moses,
2003; Buttle and Farnsworth, 2012), boreal coniferous wetlands (Pelster et al., 2009), and
tropical swamps (Brinson et al, 1980), however, this type of research has not been studied in a
temperate deciduous swamp ecosystem. This study also addresses anticipated precipitation
partitioning dynamics when changes in precipitation regimes and changing rainfall
17
characteristics shift in the Greater Toronto Area (GTA) due to climate change (IPCC, 2013).
Rain events in the GTA are expected to become more intense, with longer intervening dry
periods between events (Cao and Ma, 2009). As such, watershed models that can accurately
quantify precipitation partitioning based on storm characteristics are vital to the accurate
assessment of water availability.
This study was intended to be a two year-growing season (May-September, 2013 and
2014) project in order to provide a large dataset for a proper comparison between the two distinct
canopy types in addition to capturing inter-year rainfall variability. However, on December 22,
2013, a major ice storm hit the Greater Toronto Area (GTA) and caused significant damage to
forest canopies in the region. Many trees were damaged due to the weight of the ice and a large
amount of branch debris was found when an inspection of the study site occurred in April, 2014.
The damage caused by the ice storm provided a unique opportunity to assess how precipitation
partitioning dynamics were affected when a loss in canopy occurs. Similar to increases observed
for throughfall during leaf-less periods (Muzylo et al., 2012) and for stands with larger canopy
gaps (Crockford and Richardson, 2000), our hypothesis was that the loss in canopy would
produce increased throughfall quantities. Conversely, stemflow was hypothesized to decrease
after a reduction in canopy surface area, as stemflow from dead snags and stressed trees produce
less stemflow than healthy trees (Watters and Price, 1988; Frost and Levia, 2013). Ice storms
have an annual occurrence in eastern North America (Hellmer et al., 2015), a local return time of
20-100 years (Pasher and King, 2006), and can affect very large areas of the landscape (Hopkin
et al., 2003). Therefore, throughfall and stemflow quantification both before and after an ice
storm is important to study in order to identify if a shift in precipitation partitioning has occurred.
18
2.2.1 Research Objectives
This study was designed to address the following research objectives: [1] determine the
quantity of understory precipitation (as throughfall and stemflow) and interception in two mixed-
deciduous swamps of southern Ontario. [2] Determine the precipitation characteristics (i.e. event
amount, duration and intensity) that dictate throughfall and stemflow quantities [3] Quantify how
throughfall and stemflow fluxes differ between years separated by a canopy-altering ice-storm.
2.3 Methods
2.3.1 Site Description
The two study swamps, referred to as the maple and ash sites, due to the dominant tree
species present at each, are located 250 metres from one another and are 1.48 and 1.04 hectares,
respectively. The swamps are located just north of Terra Cotta, Ontario, Canada (43°44’26” N,
79°57’33’’ W, 365 m a.s.l.). The climate is characterized by an average annual temperature of
8°C and is classified as humid continental with 877 mm of total annual precipitation
(Environment Canada, 2015). Precipitation during the growing season in this region averages
374 mm (May 1-Sept. 30) that is dominated by low-intensity, high-duration events in the spring
and fall with high-intensity, short-duration convective storms mid-summer (Environment
Canada, 2015).
A forest inventory was performed by creating five, 10 metre by 10 metre plots in each
swamp (total area surveyed = 500m2). Within each plot, every tree with a diameter at breast
height (dbh) > 1cm was identified and measured for dbh. Unfortunately, remote sensing data was
unavailable for this area during the 2014 growing season. Therefore, we could not quantify the
exact amount of canopy loss caused by the ice storm.
19
2.3.2 Hydrological Instrumentation and Analysis
Incident precipitation amounts were obtained from the Credit Valley Conservation
Authority weather monitoring station located approximately 3.5 kilometres from the study sites
from May 1 to September 30, 2013 and 2014. This weather station was used because it was the
nearest possible location that did not have extensive forest cover. This station uses a tipping-
bucket rain gauge that measures total rainfall at half-hourly intervals, which were subsequently
used to calculate precipitation amounts, durations and intensities for each event.
Rainfall for each event was separated into classes based on event duration and intensity
as characterized by Muzylo et al., 2012. Short events are defined as having a duration <5 hours,
long events ≥5 hours, while low intensity events have rainfall intensities <2 mm/h and high
intensity events ≥2 mm/h. Thus, four types of rainfall classifications can be made: short/low
(S/L), short/high (S/H), long/low (L/L) and long/high (L/H).
Throughfall (TF) was collected using fifteen buckets elevated approximately 30 cm
above the surface to prevent splashing, with 18.7 cm diameter orifice at five sub-sites within
each swamp. These areas were marked and buckets were placed in the same spot in both 2013
and 2014. Altogether, 75 buckets were placed in each swamp with a total of 150 TF collectors
used in this study (total area covered at each swamp = 2.06 m2). Thus, this study used a
stationary gauge method, as the number of gauges and area covered was large enough that the
difference between using a stationary and roving gauge method should be negligible (Holwerda
et al., 2006; Ziegler et al., 2009). Immediately after each rainfall event, water in every collector
was measured using variable-sized graduated cylinders, depending on TF volume, to minimize
error between measurements.
20
Stemflow (SF) was collected from 40 designated trees in 2013 (20 in each site), with
another 10 trees added (5 at each site) during the 2014 growing season. SF was collected by
stapling half of a one-inch corrugated PVC tube in a spiral 1.5 times around the circumference of
the tree. This PVC tube was then caulked, using clear silicone, at the tree-tube interface so that
all of the water flowing down the tree would collect into the PVC tubing and flow into a funnel
that directed the water into high-density polyethylene bottles for measurement after each rain
event. To calculate equivalent SF depth (mm), the SF calculation method adapted from Price and
Carlyle-Moses (2003) was used:
SF = SF(l)avg. n (1)
FA
where SF is the estimated SF (mm) within a designated forest area (FA, in m2) with n number of
trees and SF (l)avg. the average SF volume from the collared tree. The designated forest area (FA)
used in Eq. (1) for each sub-plot was the 500 m2 forest inventory measured for each site..
Interception (IW) was calculated by subtracting total understory precipitation (TF + SF)
from total above canopy precipitation (P) for each rain event:
IW = P - (TF + SF) (2)
2.3.3 Temporal Stability Analysis
A temporal stability analysis was performed to determine whether TF measurements
were stable between the two study seasons, with two types of persistence possible (Kiem et al.,
2005). The first type is general persistence and refers to the TF collectors that significantly
deviate from the median in the interquartile range of values when collectors are arranged from
lowest to highest normalized TF medians. The second type is extreme persistence, which refers
21
to the collectors that significantly deviate from the median in the lower (dry) and upper (wet)
quartiles of the ranked collectors. Normalized TF% values (Ť) were calculated using the
equation:
Ťi,E = (δi,E – MδE)(MADδE)-1 (3)
where δi,E are the normalized variable δ at sample point i of event E, MδE the median of δ in
event E and MADδE the mean absolute deviation of δ in event E. The plot for each event and TF
collector, ranked from minimum to maximum mean normalized TF% (Ťi,E) shows the deviation
of a collector from the median for all sampling points (Zimmerman et al., 2008).
2.4 Results
2.4.1 Stand Characteristics
The dominant tree species in the maple site were large silver (Acer saccharinum)
(average dbh = 62 cm) and sugar maples (Acer saccharum) (average dbh = 36 cm), making up
approximately 94% of the basal area (m2 ha-1) at this site (total site basal area = 144 m2 ha-1).
There was also a high density of small (<10 cm dbh) eastern hophornbeam trees present (1080
trees ha-1) as well (Tables 2.1 and 2.3). The ash site had a much smaller total basal area (32.3 m2
ha-1) and was much more heterogeneous. Dead snags had the highest relative dominance
(27.7%), based on basal area (m2 ha-1), with black ash (18.6%) and eastern hophornbeam
(11.1%) the next highest (Tables 2.2 and 2.4). Dead trees had an average dbh of 12 cm, while
black ash and eastern hophornbeam had an average dbh of 8 cm. Less prevalent trees at these
sites include American basswood (Tilia americana), trembling aspen (Populus tremuloides),
green ash (Fraxinus pennsylvanica), paper birch (Betula papyrifera), alternate leaved dogwood
22
(Cornus alternifolia), balsam fir (Abies balsamea), mountain maple (Acer spicatum), red osier
dogwood (Cornus sericea), white cedar (Thuja occidentalis) and yellow birch (Betula
alleghaniensis) (Tables 2.1-2.4).
Table 2.1: Tree type, density, basal area, frequency and relative dominance for the maple site.
Tree Type Density (trees/ha) Basal Area (m2/ha) Frequency (%) Relative Dominance (%)
Silver Maple 200 101.2 8 69.70
Sugar Maple 280 35.3 11.2 24.35
Eastern Hophornbeam 1080 5.0 43.2 3.46
Green Ash 380 1.2 15.2 0.85
Black Ash 320 1.1 12.8 0.78
American Basswood 100 0.6 4 0.44
Dead 20 0.4 0.8 0.28
Paper Birch 100 0.2 4 0.14
Alternate Leafed Dogwood 20 0.01 0.8 0.00
Total 2500 144.4 100 100
Table 2.2: Tree type, density, basal area, frequency and relative dominance for the ash site.
Tree Type Density (trees/ha) Basal Area (m2/ha) Frequency (%) Relative Dominance (%)
Dead 560 9.0 17.6 27.65
Black Ash 900 6.0 28.3 18.62
Eastern Hophornbeam 460 3.6 14.5 11.09
Silver Maple 80 2.8 2.5 8.75
White Cedar 240 2.7 7.5 8.45
Yellow Birch 320 2.6 10.1 7.94
Trembling Aspen 60 2.2 1.9 6.68
American Basswood 180 1.4 5.7 4.43
Balsam Fir 140 1.1 4.4 3.46
Paper Birch 100 0.5 3.1 1.52
Sugar Maple 80 0.4 2.5 1.35
Red Osier Dogwood 20 0.011 0.6 0.03
Mountain Maple 40 0.0 1.3 0.02
Total 3180 32.3 100 100
23
Table 2.3: Tree size distribution sampled within the maple site.
Species
DBH (cm)
0-9.9 10-19.9 20-29.9 30-39.9 40-49.9 >50
Silver Maple 1 - 1 2 3 3
Sugar Maple 1 2 2 4 1 4
Eastern Hophornbeam 49 4 - 1 - -
Green Ash 15 4 - - - -
Black Ash 14 2 - - - -
American Basswood 3 2 - - - -
Dead - 1 - - - -
Paper Birch 5 - - - - -
Alternate Leafed Dogwood 1 - - - - -
Table 2.4: Tree size distribution sampled within the ash site.
Species
DBH (cm)
0-9.9 10-19.9 20-29.9 30-39.9 >40
Dead 12 13 2 1 -
Black Ash 28 16 1 - -
Eastern Hophornbeam 15 7 1 - -
Silver Maple - 2 2 - -
White Cedar 7 4 1 - -
Yellow Birch 11 3 2 - -
Trembling Aspen - 2 - 1 -
American Basswood 8 - 1 - -
Balsam Fir 5 2 - - -
Paper Birch 5 - - - -
Sugar Maple 3 1 - - -
Red Osier Dogwood 1 - - - -
Mountain Maple 2 - - - -
2.4.2 Seasonal and Incident Precipitation
Incident precipitation totalled 518 mm over 29 rain events from May 1 to September 30,
2013, with 395 mm observed over the same time period in 2014 (n=29) (Table 2.5). The 30-year
24
normal rainfall from May-September for this area is 374 mm (Environment Canada, 2015). 2013
was a much wetter season than normal (4th wettest in last 30 years), while 2014 was only slightly
above normal (12th wettest).
Table 2.5: Incident mean rainfall depths, durations and intensities for rainfall events during the 2013-
2014 study periods (May-September).
Event
# Date
Rainfall
Depth
(mm)
Rainfall
Duration
(h)
Rainfall
Intensity
(mm/h)
Event
# Date
Rainfall
Depth
(mm)
Rainfall
Duration
(h)
Rainfall
Intensity
(mm/h)
1 11-05-13 15.5 11.5 1.35
30 10-05-14 1.6 2.5 0.64
2 13-05-13 1.6 5.5 0.29
31 13-05-14 4.3 3 1.43
3 15-05-13 0.8 1.5 0.53
32 14-05-14 9.5 3.5 2.71
4 21-05-13 5.9 2 2.95
33 16-05-14 17.8 13 1.37
5 22-05-13 25.2 3 8.40
34 03-06-14 26 7.5 3.47
6 23-05-13 0.9 2 0.45
35 09-06-14 6.8 5.5 1.24
7 24-05-13 2.6 5.5 0.47
36 12-06-14 14.7 6.5 2.26
8 30-05-13 54.1 14.5 3.73
37 13-06-14 1.6 2 0.80
9 03-06-13 36.2 12 3.02
38 18-06-14 18 3 6.00
10 07-06-13 7.4 9.5 0.78
39 25-06-14 11.8 7.5 1.57
11 11-06-13 34.7 16.5 2.10
40 03-07-14 7.6 5 1.52
12 14-06-13 4.2 4 1.05
41 04-07-14 0.7 2 0.35
13 18-06-13 7.3 7.5 0.97
42 08-07-14 14 5 2.80
14 26-06-13 2.6 2 1.30
43 09-07-14 18.9 5.5 3.44
15 29-06-13 23.7 8 2.96
44 14-07-14 8.3 4 2.08
16 07-07-13 14.5 15 0.97
45 15-07-14 7.8 5.5 1.42
17 10-07-13 58.4 16 3.65
46 21-07-14 4.7 9 0.52
18 20-07-13 13.9 4 3.48
47 28-07-14 28.7 13.5 2.13
19 28-07-13 6.6 3.5 1.89
48 02-08-14 7.6 1 7.60
20 30-07-13 1.5 3.5 0.43
49 13-08-14 29.3 9 3.26
21 01-08-13 63.1 13.5 4.67
50 17-08-14 2.6 2.5 1.04
22 08-08-13 18.5 7 2.64
51 21-08-14 4.4 2.5 1.76
23 27-08-13 19.5 6.5 3.00
52 03-09-14 21.7 11 1.97
24 28-08-13 11.3 4.5 2.51
53 06-09-14 42.1 11 3.83
25 31-08-13 3.5 4 0.88
54 11-09-14 46.2 10.5 4.40
26 08-09-13 21.2 7.5 2.83
55 13-09-14 3.2 4 0.80
27 12-09-13 11.5 3.5 3.29
56 16-09-14 1.8 5 0.36
28 22-09-13 48 18 2.67
57 22-09-14 26.7 3.5 7.63
29 01-10-13 3.7 8 0.46
58 30-09-14 6.4 3.5 1.83
Average event-based precipitation for May-September, 2013, was 17.9 mm with events
ranging from 0.8 mm (event 3) to 63.1 mm (event 21). Total rainfall duration was 220 hours
25
(average event duration of 7.6 hours), with events ranging from 2 hours (events 4, 6 and 14) to
16.5 hours (event 11). Average rainfall intensity was 2.20 mm/h with intensities ranging from
0.29 mm/h (event 2) to 8.40 mm/h (event 5).
Average event precipitation for May-September, 2014, was 13.6 mm with events ranging
from 0.7 mm (event 41) to 46.2 mm (event 54). Total rainfall duration was 175 hours (average
event duration of 5.8 hours), with events ranging from 1 hour (events 48) to 13.5 hours (event
47). Average rainfall intensity was 2.42 mm/h with intensities ranging from 0.35 mm/h (event
41) to 7.63 mm/h (event 57).
2.4.3 Throughfall
Seasonal TF for the maple swamp in 2013 was 429 mm, equalling 83% of total rainfall
during the study period, while average event-based TF was 70.5 ± 26.7% of P (events ranged
from 20.6 ± 6.5% to 130.2 ± 30.4%). During 2014, total seasonal TF was 329 mm (83% of total
P), with average event-based TF of 75.1 ± 21.5% and events ranging from 27.9 ± 30.4% to 128.2
± 36.3%. TF for the ash swamp in 2013 was 451 mm, which was 87% of total rainfall, with
average event-based TF 80.8 ± 28.4% of precipitation (events ranged from 30.4 ± 8.9% to 153.4
± 28.4%). During 2014 in the ash site, total TF was 335 mm (85% of total P), with average
event-based TF of 85.7 ± 24.8% and events ranging from 55.4 ± 13.8% to 179.5 ± 45.4%. Figure
2.1A presents the linear regression of TF (mm) in relation to P (mm) for each rainfall event in
2013. The regression that best fits the scatter for the maple (TFM1) and ash (TFA1) sites in 2013
were:
TFM1 = 0.918P – 1.60; r2= 0.93; P < 0.001; n=29 (4)
TFA1 = 0.916P – 0.823; r2= 0.87; P < 0.001; n=29 (5)
26
From Eq. (4), we find that the canopy storage in the maple canopy is 1.75 mm, as this amount of
P is necessary for TF production in 2013, with 0.90 mm of P necessary for TF production in the
ash site (Eq. 5) during the same time period.
Figure 2.1: Throughfall (mm) as a function of incident rainfall (mm) for the maple and ash sites in 2013
(A) and 2014 (B). Linear regressions are plotted as a solid line (maple) and dashed line (ash).
Figure 2.1B presents the linear regression of TF (mm) in relation to P (mm) for each rainfall
event in 2014. The regressions that best fit the scatter for the maple (TFM2) and ash (TFA2) sites
in 2014 are:
TFM2 = 0.876P – 0.586; r2= 0.95; P < 0.001; n=29 (6)
TFA2 = 0.841P – 0.103; r2= 0.94; P < 0.001; n=29 (7)
From Eq. (6), there was a canopy storage capacity of 0.67 mm in the maple site in 2014, with a
storage capacity of 0.12 mm in the ash site (Eq. 7). Thus, the maple site required 62% less
rainfall for TF production in 2014, while the ash site required 87% less P.
A B
27
2.4.4 Spatiotemporal Variability in Throughfall
Within a site, TF (as a % of incident P) was found to be highly variable during smaller
rainfall events, with TF measurements becoming less spatially variable with larger P depths. A
high coefficient of variation (CV % = 100 x relative standard deviation/mean) was seen with
small P events (<5 mm) in 2013 (range 30-80%) and 2014 (30-109%) for both sites. As rainfall
events increase (>5 mm) we found that the CV% became more consistent, reaching a minimum
asymptote at 22% for the maple and 24% for the ash site in 2013 (Figure 2.2A). This pattern was
similar in 2014, with the CV% becoming more consistent with larger events (minimum
asymptote of CV% = 19% for maple, 26% for ash) (Figure 2.2B).
Figure 2.2: TF Coefficient of Variation (%) as a function of incident rainfall (mm) for the maple and ash
sites in 2013 (A) and 2014 (B). First-order inverse regressions are plotted as a solid line (maple) and
dashed line (ash).
The number of collectors that significantly deviated from the median was consistent in
the lower quartile for the maple site in 2013 and 2014 (Figure 2.3). This means that the number
of collectors that stayed extremely persistently drier than the median for this site was the same
over the course of both years. However, the ash site had ~33% increase in collectors that had
A B
28
extremely persistent dry values in 2014. The interquartile range values showed no collectors that
deviated significantly from 0 in either direction for both sites over the course of the two study
seasons, meaning that there were no collectors that had generally persistent wetness or dryness
over the course of the study. The amount of collectors with extremely persistent wet
measurements decreased by 34% from 2013 to 2014 in the maple site, whereas this amount
increased by 50% in the ash site from 2013 to 2014 (Figure 2.3).
Figure 2.3: Temporal stability plots normalized to 0 of median TF% for the maple site in 2013 (A), 2014
(B) and the ash site 2013 (C), and 2014 (D). The collectors are ranked by their medians from smallest to
largest and plotted along the x-axis. Error bars show ± 1 SD.
A B
C D
29
2.4.5 Stemflow
SF for the maple site was 3.1 mm, equalling 0.6% of total rainfall during the study period
in 2013 and average SF 0.4 ± 0.3% of event-based P. In 2014, SF for the maple site totalled 2.4
mm (0.6% of total P), with average incident SF equalling 0.5 ± 0.3% of P. SF for the ash site
totalled 4.2 mm in 2013, equalling 0.8% of total rainfall, with average SF 0.6 ± 0.5% of incident
P. In 2014, SF for the ash site totalled 2.8 mm (0.7% of total P), with average incident SF
equalling 0.5 ± 0.4% of P.
Figure 2.4A shows the linear regression of SF (mm) in relation to P (mm) for each rainfall event
in 2013. The regression that best fits the scatter for the maple (SFM1) and ash (SFA1) sites in 2013
are:
SFM1 = 0.006P – 0.003; r2= 0.86; P < 0.01; n=29 (8)
SFA1 = 0.008P + 0.005; r2= 0.84; P < 0.01; n=29 (9)
From Eq. (8), a 0.43 mm rain depth is necessary for SF production in the maple site in 2013,
with any rainfall amount producing SF in the ash site (Eq. 9).
Figure 2.4B shows the linear regression of SF (mm) in relation to P (mm) for each rainfall event
in 2014. The regression that best fits the scatter for the maple (SFM2) and ash (SFA2) sites in 2014
are:
SFM2 = 0.007P – 0.012; r2= 0.79; P < 0.01; n=29 (10)
SFA2 = 0.008P – 0.014; r2= 0.83; P < 0.01; n=29 (11)
From Eq. (10), we find that 1.76 mm of P is necessary for SF production in the maple site in
2014, with 1.71 mm of rainfall necessary to produce SF in the ash site (Eq. 11). Thus, both sites
30
require much more rainfall in 2014 in order for SF production to begin when compared to 2013,
likely due to the loss in canopy cover.
Figure 2.4: Stemflow (mm) as a function of incident rainfall (mm) for the maple and ash sites in 2013 (A)
and 2014 (B). Linear regressions are plotted as a solid line (maple) and dashed line (ash).
2.4.6 Spatial Variability in Stemflow
Similar to trends seen for TF, SF (as a % of incident P) was highly variable with smaller
rainfall events. A high coefficient of variation (%) is seen with small P events (<20 mm) at both
sites in 2013 (range 16-150%) and 2014 (21-107%). As rainfall events get larger (>20 mm) we
find that the CV% followed an exponential decay model for both the maple and ash sites in 2013
and 2014 (Figure 2.5). This is different from the TF model, where a first order-inverse curve fit
best (a minimum asymptote was reached).
A B
31
Figure 2.5: SF Coefficient of Variation (%) as a function of incident rainfall (mm) for the maple and ash
sites in 2013 (A) and 2014 (B). Exponential decay regressions are plotted as a solid line (maple) and
dashed line (ash).
2.4.7 Interception
Interception (IW) at the maple site totalled 102.6 mm in 2013, equalling 19.8% of total
rainfall during the study period, while average incident IW was 31.2 ± 23.3% of P. During 2014,
total IW was 54.4 mm (13.8 % of total P), with average incident IW of 20.1 ± 19.5% in the maple
site. IW for the ash site in 2013 totalled 100.0 mm, equalling 19.3% of total rainfall, with average
IW 24.0 ± 18.2% of incident precipitation. During 2014, total IW was 46.3 mm (11.7 % of total
P), with average incident IW of 13.1 ± 13.8% in the ash site. Figure 2.6 presents the percent
contribution of TF, SF, and IW to total P.
A B
32
%
0
10
20
30
40
50
60
70
80
90
100
110
TF
SF
IW
0
10
20
30
40
50
60
70
80
90
100
110
Figure 2.6: Percent contribution of TF, SF, and IW to total P for the maple and ash sites both seasonally
and on an event-basis.
2.4.8 Influence of Rainfall Characteristics on Rainfall Partitioning
Rainfall duration and intensity influenced TF amounts at both the maple and ash site
during both study seasons (Table 2.6). The linear regression equations revealed that 2 hours of
rainfall was needed for TF production in the maple site in 2013, with 1.7 hours of rainfall needed
to produce TF in the ash site. In 2014, that duration decreased to 0.69 hours for TF production in
the maple site, with 0.37 hours of rainfall needed to produce TF in the ash site. According to the
linear regression equations, a rainfall intensity of 0.53 mm/h was needed for TF production in the
maple site in 2013, with an intensity of 0.49 mm/h required to produce TF in the ash site,
however, both sites required any rainfall intensity above 0 mm/h in order to produce TF in 2014.
While the relationship between rainfall intensity and TF amount in 2014 was statistically
significant (p<0.01 for both sites), the strength of this relationship was much weaker in 2014 (r2
= 0.27 and r2 = 0.28 for maple and ash, respectively) than in 2013 (r2 = 0.51 and r2 = 0.50 for
maple and ash, respectively).
Seasonal Event-Based
Maple 2013
Maple 2014
Maple 2013
Maple 2014
Ash 2013
Ash 2014
Ash 2013
Ash 2014
33
Table 2.6: Relationship between TF and SF to storm characteristics. Slope of linear regression presented
with r2 values below.
TF- Maple TF- Ash SF-Maple SF-Ash
2013 2014 2013 2014 2013 2014 2013 2014
Duration
(h)
2.67
r2=0.57
2.24
r2=0.51
2.65
r2=0.54
2.14
r2=0.50
0.09
r2=0.53
0.08
r2=0.34
0.10
r2=0.45
0.08
r2=0.30
Intensity
(mm/h)
9.96
r2=0.51
2.94
r2=0.27
10.12
r2=0.50
2.88
r2=0.28
0.07
r2=0.55
0.03
r2=0.38
0.09
r2=0.62
0.04
r2=0.41
Note: All regressions are significant at P <0.01, n=29 for all fluxes.
Rainfall duration and intensity also influence SF amounts at both the maple and ash site
during both study seasons (Table 2.6). From the appropriate linear regression equations, we find
that 1.3 hours of rainfall is needed for SF production in the maple site in 2013, with 0.45 hours of
rainfall needed to produce SF in the ash site. Similar to trends noticed for TF, less time is needed
to produce SF in 2014, with 0.60 hours is needed for SF production in the maple site and 0.12
hours of rainfall needed to produce SF in the ash site. From the linear regression equations in
Table 2.6, a rainfall intensity of 0.51 mm/h is needed for SF production in both the maple and
ash sites in 2013. This decreases substantially for 2014, where both sites require any rainfall
intensity in order to produce SF. However, the strength of the relationship between SF and both
event duration and intensity in 2014 was less than that in 2013 (Table 2.6).
TF and SF measurements became less variable when storms were either long in duration
or had a high intensity (Figure 2.6) The results show that short rain events and events with low
intensity produced much more variability in both TF and SF measurements than long duration or
high intensity events. However, these trends were much more pronounced for TF (Figure 2.6A
and 2.6B) than for SF (Figure 2.6C and 2.6D). As such, these results should be treated with
caution when analyzing the effects of event duration and intensity on SF variability.
34
Figure 2.7: Coefficient of variation (%) as a function of storm intensity for TF (A) and SF (C) and
coefficient of variation (%) as a function of storm duration for TF (B) and SF (D).
All rainfall events were classified based on event duration (short or long) and intensity
(low or high) and are presented in Tables 2.7 and 2.8. The number of events, along with rainfall
amount, duration and intensity averages are shown for each classification in Table 2.7, with
corresponding TF and SF quantities presented in Table 2.8. The lowest TF% (TF as a % of
event-based P) at both sites in 2013 were seen for the short duration/low intensity (S/L)
classification (47.5% and 66.9% for the maple and ash sites, respectively), while both sites had a
50% increase in TF% for the S/L class in 2014 (63.1% and 85.8% for the maple and ash sites,
respectively). While the S/L class produced the smallest TF% at the maple site in both years, this
class was the second highest producer of TF in the ash site in 2014. Both sites had less variability
A B
C
V
C
D
V
C
35
between classes in 2014 than 2013, with TF% values ranging from 47.5% to 84.8% in the maple
in 2013, while maple TF% ranged from 63.1% to 85.2% in 2014. Similarly, the range of TF% for
the ash site in 2013 was 66.9% to 100.2%, while in 2014 the range was much smaller (80.3% to
94.1%). The largest TF% in 2013 came from the S/H class, with 84.8% in the maple and 100.2%
in the ash. However, the L/L class was the highest TF% class in 2014, with 85.2% and 94.1%
seen in the maple and ash sites, respectively. This change may represent a shift (driven by the ice
storm) from rainfall intensity to rainfall duration acting as the main driver of TF%.
Some studies have attempted to find a relationship between throughfall (as a percent of
total precipitation) and time since last rainfall (Zimmermann et al., 2008). As was seen with
Zimmerman et al. (2008), there was no apparent relationship between TF% and hours since last
rainfall in this study (Figure #).
Figure 2.8: Relationship between throughfall % and hours since last rainfall.
36
Table 2.7: Mean and standard deviation of rainfall amounts, durations and intensities for the 2013 and
2014 study periods. S/L (short/low), S/H (short/high), L/L (long/low), L/H (long/high). 2013 2014
S/L S/H L/L L/H S/L S/H L/L L/H
Mean
(Standard Deviation)
Mean
(Standard Deviation)
# of Events 7 5 8 9 10 6 6 7
Rainfall
Amount (mm)
2.9
(2.1)
13.6
(7.1)
10.9
(10.9)
38.1
(18.1)
3.4
(2.2)
14
(7.4)
11.8
(6.7)
29.4
(11.4)
Rainfall Duration (h)
2.9
(1.1)
3.4
(0.96)
9.9
(4.1)
11.4
(4.3)
3.2
(1.1)
3.3
(1.3)
8.6
(3.0)
9.1
(2.8)
Rainfall Intensity
(mm/h)
0.9
(0.5)
4.1
(2.4)
0.9
(0.6)
3.2
(0.7)
1.1
(0.6)
4.8
(2.6)
1.3
(0.5)
3.3
(0.8)
Table 2.8: Mean and standard deviation of incident TF, SF and interception for both sites during the
2013 and 2014 study periods. S/L (short/low), S/H (short/high), L/L (long/low), L/H (long/high).
2013 2014
S/L S/H L/L L/H S/L S/H L/L L/H
Mean
(Standard Deviation)
Mean
(Standard Deviation)
# of Events 7 5 8 9 10 6 6 7
MAPLE
Throughfall (%
of Incident P)
47.5
(12.5)
84.8
(36.8)
72.8
(27.6)
78.5
(19.2)
63.1
(28)
81.1
(9.3)
85.2
(20.1)
78.5
(13.7)
Stemflow (% of
Incident P)
0.1
(0.1)
0.8
(0.5)
0.4
(0.3)
0.6
(0.1)
0.2
(0.2)
0.7
(0.4)
0.5
(0.3)
0.6
(0.2)
Interception (% of
Incident P)
52.5
(13.0)
17.5
(34.9)
30.5
(19.5)
22.9
(15.4)
33.9
(23.7)
18.1
(9.5)
7.8
(12.6)
12.6
(13.4)
ASH
Throughfall (%
of Incident P)
66.9
(10.2)
100.2
(45.7)
81.8
(30.1)
79.9
(22.2)
85.8
(36.4)
83.5
(8.9)
94.1
(23)
80.3
(16.3)
Stemflow (% of
Incident P)
0.2
(0.1)
1.2
(0.7)
0.6
(0.3)
0.7
(0.2)
0.2
(0.3)
0.8
(0.5)
0.6
(0.3)
0.7
(0.2)
Interception (% of
Incident P)
31.9
(10.5)
16.0
(30.4)
23.4
(18.0)
22.7
(15.1)
17.9
(17.8)
12.7
(10.8)
4.3
(6.7)
14.0
(12.6)
SF% (SF as a % of incident P) patterns across both years and study sites were very
similar: S/H events produced highest amounts of SF, L/H produced the second most, L/L third
37
most and S/L events produced the smallest SF%. This indicates that rainfall intensity was the
main driver of SF production, as both high intensity classes produced more SF than the low
intensity classes and the S/H produced more SF than the L/H class.
Interception patterns between both sites in 2013 were similar in that S/L events had the
highest I% (I as a % of incident P) at 52% and 31.2% for the maple and ash sites, respectively.
This was followed by L/L (29.3% and 21.8% for maple and ash, respectively), L/H (20.9% for
maple and 20% for ash) and finally S/H (17% for maple and 15.3% for ash). Similar to I% in
2013, the S/L class had the highest I% for both sites in 2014 (33.5% and 17.5% for maple and
ash, respectively), though these values were much lower than in 2013. Also, the L/L class in
2014 showed the lowest I% rates (7.2% and 3.5% for maple and ash, respectively), which was
different than in 2013 where the S/H class had the lowest I%. These results show that low
intensity events had the highest I% for both sites in 2013, while in 2014, short duration events
had the highest I% (even though S/H was 2% lower than L/H for the ash site).
2.5 Discussion
2.5.1 Rainfall Partitioning
On a season-long basis, TF fluxes in this study were similar, yet slightly higher than
those observed in temperate deciduous forested stands. Seasonal TF (% of total P) observations
of 77-83% within a maple, birch, pine and aspen stand in eastern Canada (Mahendrappa, 1990)
and 77.5% in a red oak, sugar maple and American beech stand of southern Ontario (Price and
Carlyle-Moses, 2003) had slightly lower TF totals than this study. On an event-basis, TF (as a %
of incident P) was higher in this study by 6.2-21.4% than those reported in upland studies. Price
and Carlyle-Moses (2003) reported an event TF (as a % of incident P) of 64.3% while Kiem et
38
al. (2005) reported a similar value of 63.8% for a deciduous stand during the growing season in
the Pacific Northwest, USA. The swamps of our study are unique in the amount of TF that they
produce, with both dense (maple) and sparse (ash) producing higher amounts of TF than upland
systems. The difference in TF between sites may be a result of the total basal area
(approximation of percent canopy cover) of each site. The maple swamp had a basal area of 144
m2 ha-1 and produced 2-4% less TF than the ash swamp, which had a basal area of only 32 m2 ha-
1. This suggests that a very dense swamp would be necessary to produce TF totals similar to that
of upland systems, as Price and Carlyle-Moses (2003) had a stand with a basal area of 38 m2 ha-1,
yet produced less TF than the maple swamp in this study. While there was a high density of
small trees (<10 cm dbh) in the ash swamp, these small trees likely resulted in less canopy cover
and will continue to produce higher TF totals until it develops into a more mature stand. As such,
an investigation regarding the influence of stand characteristics on TF in temperate swamps is
warranted.
Variation in TF measurements for this study also showed similar trends to previous
studies, where smaller rain events (< 5 mm) have coefficients of variation > 30% (Pressland,
1977; Llorens et al, 1997; Price and Carlyle-Moses, 2003). Additionally, trends of larger events
(> 5 mm) having fairly constant coefficients of variation (Llorens et al., 1997; Price et al., 1997;
Price and Carlyle-Moses, 2003) were found in this study as well. However, the coefficient of
variation remains slightly higher once it reaches a constant plateau than previous studies values
of 5-15% (Cantu Silva and Okumura, 1996; Llorens et al., 1997; Price and Carlyle-Moses,
2003). Both sites over the course of both study years show higher variability in TF measurements
with larger events than those in previous studies, with coefficient of variation values ranging
from 19.2-25.5%. This may be due to the complex nature of the swamp canopies, particularly the
39
high density and large number of understory American basswoods in the maple swamp and the
high heterogeneity of tree species within the ash swamp. While Price and Carlyle-Moses (2003)
had a good mix of tree species, Cantu Silva and Okumura (1996) and Llorens et al. (1997) were
investigating TF in monospecific white oak and scots pine stands, respectively, which may
account for low TF variability with higher rainfall events.
The proportion of TF collectors that deviated significantly from the median in this study
(10.7-16%) was much lower than 31-46% found in two conifer stands and one deciduous stand
in the Pacific Northwest, USA (Kiem et al., 2005), 31% in a lodgepole pine stand in British
Columbia, Canada (Carlyle-Moses and Lishman, 2015) and 65% in an open tropical rainforest
(Zimmermann et al., 2008). Fathizadeh et al. (2014), had 8.5% of collectors deviate significantly
from the mean in a Persian oak stand (using ±1 SD) which was similar to the results found in this
study. Differences in persistence between studies have be attributed to stand type, density, spatial
heterogeneity and climate (Kiem et al., 2005). This study may have low temporal persistence
(neither many consistently wet or dry collectors), similar to that of Fathizadeh et al. (2014),
because deciduous stands have varying TF patterns over the course of a season attributed to
seasonal changes in canopy structure (Loescher et al., 2002; Zimmermann et al., 2008). These
results agree well with the spatial variability in TF for these swamps, as there are no areas
consistently receiving more or less TF than others, leading to high spatial variability and low
temporal persistence. The results show that there are areas receiving higher/lower TF amounts
during individual events (leading to high spatial variability), yet these areas do not remain
consistently high/low throughout the growing season (hence, low temporal persistence).
Seasonally, SF values in this study were much lower than the 3-6% of incident P reported
in upland deciduous forests (Mahendrappa, 1990; Price and Carlyle-Moses, 2003), and were
40
more similar to tropical forest sites where SF was observed to be <1% of incident P (Kellman
and Roulet, 1990; Veneklaas and Van Ek, 1990). These results show that temperate deciduous
swamp stands, whether dense like the maple site or sparse like the ash site, produced slightly
higher TF amounts while producing much less SF than upland forests. This may be due to active
shedding of water from tree surfaces, as the water retained on leaf surfaces after precipitation
decreases evapotranspiration (ET) and can be beneficial to the plant in times of drought (Koch
and Barthlott, 2009). Thus, wetland plants may develop hydrophobic surfaces, due to a constant
water supply at their roots over the course of the growing season, which would shed water from
their leaves and maximize photosynthesis. This active shedding of water away from the tree bole
causes both higher TF (increased canopy drip) and lower SF (removal of water from tree
surfaces to increase ET). Because there is sufficient water supply available in swamp soils
throughout the growing season, these trees are not required to actively collect and funnel water to
their roots to act as a water supply during times of water shortage.
Variability of SF measurements also followed a similar trend to previous studies, where
large rain events produce less variability in SF measurements (Pressland, 1973; Loustau et al.,
1992; Price and Carlyle-Moses, 2003). These studies found that as rainfall amounts increased, SF
coefficient of variation (%) decreased until reaching a constant CV%, generally higher than TF
CV% (Pressland, 1973; Loustau et al., 1992, Price and Carlyle-Moses, 2003). However, the
results of this study indicate that as rainfall amounts increase, variation in SF measurements
decrease exponentially. These results indicate that increases in rain magnitude will continue to
cause variations in SF measurements to decrease, rather than plateau at a constant CV%.
Season-long calculated interception amounts were similar in 2013 at both sites to
averages of 18.8-23.6% for comparable upland stands (Mahendrappa, 1990; Price and Carlyle-
41
Moses, 2003; Barbier et al., 2009; Muzylo et al. 2012). In 2014, interception values in this study
were much lower than those reported in previous studies (Mahendrappa, 1990; Price and Carlyle-
Moses, 2003; Barbier et al., 2009; Muzylo et al. 2012), and were closer to seasonal interception
values of 13.1-14.9% for white birch and aspen species in eastern Canada (Mahendrappa, 1990)
and 11.7% reported in an evergreen broadleaved forest of Japan (Masukata at al., 1990) due to
the increase in TF in 2014. On an event-basis, interception in the maple site in 2013 (31.2%) was
similar to a nearby study site value of 34.3% (Price and Carlyle-Moses, 2003), while in the same
year the ash site was only 24%. In 2014, both study sites had much smaller mean interception
rates than those previously reported in upland deciduous stands, also as a result of increased TF,
while SF was a very minor input and did not influence interception values in these two swamps.
2.5.2 Influence of Rainfall Characteristics on Rainfall Partitioning
There are few studies relating precipitation characteristics, particularly event duration and
intensity, to rainfall partitioning (as noted in the review by Levia and Frost, 2006). Our study
showed that TF and SF both have a positive, linear relationship to event duration and intensity
(Table 2.6) and that variability in TF and SF measurements decreases with longer rain events and
higher intensity storms (Figure 2.6). With changing rainfall regimes in southern Ontario, most
notably an increase in summertime severe-rainfall events (Cao and Ma, 2009), these results help
to predict TF and SF amounts in swamps based not only on the amount of rain, but on storm
duration and intensity as well. The results of this study show that with more severe-rainfall
events, classified as either (1) duration < 3 h and intensity ≥ 50 mm h-1 or (2) duration > 3 h and
intensity ≥ 4.2 mm h-1 (Cao and Ma, 2009), TF and SF amounts in these swamps will likely
increase as well. Because short duration/high intensity events produced more TF, increases in
these types of events will likely lead to a disproportionate increase in TF. Meteorological
42
influences on TF and SF after canopy-altering perturbations can also be assessed with these
results, for example, TF amounts being dictated by intensity in 2013, while in 2014, TF amounts
were equally influenced by both duration and intensity (Table 2.6 and Table 2.8).
This study shows that SF increased with increasing storm intensity, which is opposite to
relationships stated in upland study sites (Mauchamp and Janeau, 1993; Crockford and
Richardson, 2000; Carlyle-Moses and Price, 2006). Additionally, both high intensity classes
(S/H and L/H) showed the highest SF% in this study, whereas Muzylo et al. (2012), found that
long duration/low intensity events produced the most SF. Herwitz (1985) suggested that high
intensity events created higher flow velocities on tree branches that are more likely to exceed the
flow capacity of those branches. As a result, water slowed by bark obstructions is pushed from
behind by faster moving water, causing it to drip off of the branch and not contribute to stemflow
fluxes. Our contrasting results may be due to multiple factors, for example, Carlyle-Moses and
Price (2006) and Muzylo et al. (2012) had higher mean intensity per event (means of 11.2 and
5.2 mm/h for events classified as high intensity, respectively). Alternatively, this may be a result
of bark and leaf water storage capacities, as differences in water storage capacity within the
leaves and bark of different tree species can influence SF volumes (Herwitz, 1985; Levia and
Herwitz, 1995). As such, there is a need for more work on plant-SF interactions in temperate
swamps.
2.5.3 Effects of an Ice Storm on TF and SF
It is evident that the loss in canopy caused by the ice storm resulted in an increase in TF
from 2013 to 2014. Though 2013 was a much wetter year with a higher number of larger storms,
event-based TF% was lower by ~5% for both swamps in 2013. There were also smaller rainfall
43
amounts, durations and intensities required at both sites in order for TF production to begin after
the ice-storm occurred. TF% has been observed to plateau after enough rain has surpassed the
storage capacity of the canopy (approx. 5-10 mm of rain) (Pressland, 1973; Price and Carlyle-
Moses, 2003). TF amounts for rain events < 5 mm in 2013 and 2014 demonstrated that the ice
storm had reduced the storage capacity of the canopy, as more TF was observed for rain events <
5 mm after the canopy loss occurred (Figure 2.7). According to the linear regressions in Figure
2.7, a 2.5 mm event after the ice storm would produce 39% and 51% more TF in the maple and
ash swamps, respectively, than before the ice storm.
Figure 2.9: TF (mm) as a function of rainfall (mm) for events < 5 mm for A- 2013 and B- 2014, in the
maple swamp (black) and the ash swamp (red).
The results from the temporal stability analysis show that the amount of extremely
persistent dry collectors remained the same at the maple site between years but increased at the
ash site post-ice storm by about 33%. This increase in dry collectors after the ice storm may be
the result of a shift and/or reduction in the number of drip points due to fewer horizontally-
inclined branches that are more prone to drip (Levia and Herwitz, 2002), as there is evidence that
A B
y = 0.54x + 0.22 r2 = 0.96
y = 0.43x + 0.05 r2 = 0.87
y = 0.92x - 0.11 r2 = 0.72
y = 0.86x - 0.44 r2 = 0.82
44
horizontally configured branches are more prone to break during an ice storm (Lemon, 1961).
The number of extremely persistent wet collectors doubled at the ash site, with this increase
attributed to increased direct TF as a result of larger canopy gaps. Conversely, the number of
extremely persistent wet collectors decreased by about 50% at the maple site from 2013 to 2014.
This would indicate that the more dense maple site was either less damaged than the ash site or
that drip points changed due to a change in canopy structure and were not captured by the
collectors in the maple site during the 2014 study period.
Canopy loss caused by the ice storm resulted in a decrease in SF at both sites in 2014.
The decrease in SF% for short duration/high intensity events in 2014 (events which are observed
to produce higher SF% in both years), combined with an increase in total S/H classified events in
2014 provide evidence of this trend. Additionally, the amount of rainfall required for SF
production to begin after the ice storm was much higher than before the damage to the canopy
occurred and there was less time required and less intense rain events required for SF production
post-ice storm. Rainfall intercepted by more horizontally configured leaves, twigs and branches
increases the average residence time of water flowing over these surfaces, thus slowing (yet also
contributing to) SF production (Herwitz, 1985), while vertical branches deliver SF to the tree
bole quicker (Levia and Herwitz, 2002). Post-ice storm trees with decreased surface areas,
combined with a reduction in more horizontally-inclined surfaces (Lemon, 1961), lead to quicker
delivery of water to the tree bole, while still producing a decreased total water flux to the tree
bole.
Post-ice storm interception was much lower than pre-ice storm due to the increase in
event-based TF observed after the canopy loss occurred, while SF was a minor input both years.
A reduction of intercepted precipitation, due to the canopy loss as a result of the ice storm, would
45
allow 38.5 mm more rain into the maple swamp and 40.7 mm more rainfall into the ash swamp
during a normal growing season (374 mm from May-September), based on decreases in incident
IW. This is equivalent to TF produced by 2-3 moderate to large rainfall events, and represents a
significant water flux (~10% of growing season rainfall) to the wetland floor. An ice storm
represents one form of natural perturbation that can decrease canopy cover and therefore increase
the total amount of water received at the forest/swamp floor. As such, shifts in precipitation
partitioning dynamics post-canopy loss in forested ecosystems will likely enhance the influence
of TF fluxes on important physical, chemical and biological processes, while the role of SF will
diminish.
This study provides land managers and conservation authorities with the ability to predict
how much precipitation is entering temperate swamps. With a desire to maintain wetland
ecosystems surrounded by urbanization and development in southern Ontario (CVC Terrestrial
Monitoring Program Report, 2010), this study provides key components to water balance
calculations, with the goal that these systems are supplied with enough water after land-use
changes occur around them. Additionally, this study will help to predict precipitation partitioning
dynamics when changes in precipitation regimes and changing rainfall characteristics shift due to
climate change, and when recurring perturbations such as ice storms cause damage to stand
canopies.
2.6 Conclusion
Throughfall and stemflow are important sources of water to a swamp floor. The amount
of water that makes it through a swamp canopy as TF and SF has now been characterized in a
temperate swamp ecosystem, for which there was a lack of previous research. Key information
46
regarding the various meteorological influences on precipitation partitioning in temperate
swamps have also been identified. This is also the first study to characterize the influence of a
canopy-altering perturbation on TF and SF in a forested ecosystem.
Average event-based throughfall was 70.5% of precipitation for the maple swamp in
2013 and 75.1% in 2014, while average event-based throughfall for the ash swamp was 80.8% in
2013 and 85.7% of event precipitation in 2014. Stemflow fluxes in both sites were much lower
than previously studied mixed-deciduous stands, with SF fluxes < 1% of incident precipitation
for both years. The trees in the maple swamp intercepted more rainfall than the ash swamp due to
a higher total site basal area; while the amount of rainfall received at the swamp floor was
greater after the canopy-altering ice storm occurred. This is evidenced by the increase in TF for
smaller rain events and decreases in intercepted P for short duration/low intensity events.
Additionally, these swamp systems produced slightly more throughfall, with much less stemflow
than comparable upland deciduous forests. With a much higher basal area measured in the maple
swamp than most upland studies, we can conclude that wetland trees partition rainfall much
differently than upland trees.
2.7 References
Andre, F., Jonard, M., Jonard, F., Ponette, Q. (2011). Spatial and temporal patterns of throughfall
volume in a deciduous mixed-species stand. Journal of Hydrology. 400: 244-254
Barbier, S., Balandier, P., and Gosselin, F. (2009). Influence of several tree traits on rainfall
partitioning in temperate and boreal forests: a review. Ann. For Sci. 66: 602-613
47
Beier, C. (1998) Water and elemental fluxes calculated in a sandy forest soil taking spatial
variability into account. Forest Ecology and Management. 101: 269-280
Bouten, W., Heimovaara, T., Tiktak, A. (1992). Spatial pattern of throughfall and soil water
dynamics in a Douglas fir stand. Water Resources Research. 28: 3227-3233
Brinson, M.M., Bradshaw, H.D., Holmes, R.N., Elkins, J.B. (1980). Litterfall, stemflow, and
throughfall nutrient fluxes in an alluvial swamp forest. Ecology. 61: 827-835
Buttle, J.M. and Farnsworth, A.G. (2012). Measurement and modeling of canopy water
partitioning in a reforested landscape: The Ganaraska Forest, southern Ontario, Canada.
Journal of Hydrology. 466: 103-114
Cantu Silva, I., Okumura, T. (1996). Throughfall, stemflow and interception in a mixed white
oak forest (Quercus serrata Thunb.). Journal of Forest Resources. 1: 123-129
Cao, Z., Ma, J. (2009). Summer severe-rainfall frequency trend and variability over Ontario,
Canada. Journal of Applied Meteorology and Climatology. 48: 1955-1960
Cape, J.N., Brown, A.H.F., Robertson, S.M.C, Howson, G., Paterson, I.S. (1991). Interspecies
comparisons of throughfall and stemflow at three sites in northern Britain. Forest
Ecology and Management. 46: 165-177
Carlyle-Moses, D.E. and Price, A.G. (2006). Growing-season stemflow production within a
deciduous forest of southern Ontario. Hydrological Processes. 20: 3651-3663
Carlyle-Moses, D.E., Lishman, C.E. (2015). Temporal persistence of throughfall heterogeneity
below and between the canopies of juvenile lodgepole pint (Pinus contorta).
Hydrological Processes. In press.
Chang, S.C. and 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
Credit Valley Conservation (CVC) (2010). Monitoring Wetland Integrity within the Credit River
Watershed. Chapter 1: Wetland Hydrology and Water Quality 2006- 2008. Credit Valley
Conservation. 79p
Crockford, R.H. and Richardson, D.P. (2000). Partitioning of rainfall into throughfall, stemflow
and interception: effect of forest type, ground cover and climate. Hydrological Processes.
14: 2903-2920
Delfs, J. (1967) Interception and stemflow in stands of Norway spruce and beech in West
Germany. Forest Hydrology. 179-185
48
Eschner, A.R. (1967). Interception and soil moisture distribution. In: Sopper, W.E., Lell, H.W.
(Eds.), Forest Hydrology. Pergamon, Oxford, 191-200
Fathizadeh, O., Attarod, P., Keim, R.F., Stein, A., Amiri, G.Z., Darvishsefat, A.A. (2014).
Spatial heterogeneity and temporal stability of throughfall under individual Quercus
brantii trees. Hydrological Processes. 28: 1124-1136
Frost, E.E., and Levia, D.F. (2013). Hydrologic variation of stemflow yield across co-occuring
dominant canopy trees of varying mortality. Ecohydrology. 7: 760-770
Guswa, A.J., Spence, C.M. (2011). Effect of throughfall variability on recharge: application to
hemlock and deciduous forests in western Massachusetts. Ecohydrology.
Hellmer, M.C., Rios, B.A., Ouimet, W.B., Sibley, T.R. (2015). Ice storms, tree throw, and
hillslope sediment transport in northern hardwood forests. Earth Surface Processes and
Landforms. 40: 901-912
Helvey, J.D. and Patric, J.H. (1965). Canopy and litter interception of rainfall by hardwoods of
eastern United States. Water Resources Research. 1: 193-206
Herwitz, S.R. (1985). Interception storage capacities of tropical rainforest canopy trees. Journal
of Hydrology. 77: 237-252
Hewlett, J.D. (1982). Principles of Forest Hydrology. University of Georgia Press. Athens, GA.
Holwerda, F., Scatena, F.N., Bruijnzeel, L.A. (2006). Throughfall in a Puerto Rican lower
montane rain forest: A comparison of sampling strategies. Journal of Hydrology. 327:
592-602
Hopkin, A., Williams, T., Sajan, R., Pedlar, J., Nielsen, C. (2003). Ice storm damage to eastern
Ontario forests: 1998-2001. The Forestry Chronicle. 79: 47-53
Hughes, J. and Heathwaite, L. (1995). Hydrology and Hydrochemistry of British Wetlands. John
Wiley and Sons, Ltd. New York, NY.
IPCC, 2007: Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and
III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change
[Core Writing Team, Pachauri, R.K and Reisinger, A. (eds.)]. IPCC, Geneva,
Switzerland, 104 pp
Johnson, M.S. and Lehmann, J. (2006). Double-funneling of trees: Stemflow and root-induced
preferential flow. Ecoscience. 13 (3): 324-333
Kiem, R.F., Skaugset, A.E., Weiler, M. (2005). Temporal persistence of spatial patterns in
throughfall. Journal of Hydrology. 314: 263-274
49
Kellman, M., Roulet, N. (1990). Stemflow and throughfall in a tropical dry forest. Earth Surface
Processes and Landforms. 15: 55-61
Koch, K. and Barthlott, W. (2009). Superhydrophobic and superhydrophilic plant surfaces: an
inspiration for biomimetic materials. Philosophical Transactions of the Royal Society.
367: 1487-1509
Kramer, I and Holscher, D. (2009). Rainfall partitioning along a tree diversity gradient in a
deciduous old-growth forest in Central Germany. Ecohydology. 2: 102-114
Lemon, P.C. (1961). Ecology of ice storms. Bulletin of the Torrey Botanical Club. 88: 21-29
Levia, D.F. and Herwitz, S.R. (1995). Interspecific variation of bark water storage capacity of
three deciduous tree species in relation to stemflow yield and solute flux to forest soils.
Catena. 64: 117-137
Levia, D.F. and Herwitz, S.R. (2002). Winter chemical leaching from deciduous tree branches as
a function of branch inclination angle in central Massachusetts. Hydrological Processes.
16: 2867-2879
Levia, D.F. and Frost, E.E. (2006). Variability of throughfall volume and solute inputs in
wooded ecosystems. Progress in Physical Geography. 30: 605-632
Llorens, P., Poch, R., Latron, J., Gallart, F. (1997). Rainfall interception by a Pinus sylvestris
forest patch overgrown in a Mediterranean mountainous abandoned area. I. Monitoring
design and results down to the event scale. Journal of Hydrology. 199: 331-345
Loustau, D., Berbigier, P., Granier, A., El Hadj Moussa, F. (1992). Interception loss, throughfall
and stemflow in a maritime pine stand. I. Variability of throughfall and stemflow beneath
the pine canopy. Journal of Hydrology. 138: 449-467
Mahendrappa, M.K. (1990). Partitioning of rainwater and chemicals into throughfall and
stemflow in different forest stands. Forest Ecology and Management. 30: 65-72
Manderscheid, B. and Matzner, E. (2000). Spatial and temporal variation of soil solution
chemistry and ion fluxes through the soil in a mature Norway spruce (Picea abies (L.)
Karst.) stand. Biogeochemistry. 30: 99-114
Masukata, H., Ando, M., Ogawa, H. (1990). Throughfall, stemflow and interception of rainwater
in an evergreen broadleaved forest. Ecological Research. 5: 303-316
Mauchamp, A. and Janeau, J.L. (1993). Water funneling by the crown of Flourensia cernua, a
Chihuahuan desert shrub. Journal of Arid Environments. 25: 299-306
McKillop, R., Kouwen, N., Soulis, E.D. (1999). Modeling the rainfall-runoff response of a
headwater wetland. Water Resources Research. 35: 1165-1177
50
Mottonen, M., Jarvinen, E., Hokkanen, T.J., Kuuluvainen, T., Ohtonen, R. (1999). Spatial
distribution of soil ergosterol in the organic layer of a mature Scots pine (Pinus sylvestris
L.) forest. Soil Biology and Biochemistry. 3: 503-516
Muzylo, A., Llorens, P., Domingo, F. (2012). Rainfall partitioning in a deciduous forest plot in
leafed and leafless periods. Ecohydrology. 5: 759-767
Pasher, J., King, D.J. (2006). Landscape fragmentation and ice storm damage in eastern Ontario
forests. Landscape Ecology. 21: 477-483
Pelster, D.E., Kolka, R.K., Prepas, E.E. (2009). Overstory vegetation influence nitrogen and
dissolved organic carbon flux from the atmosphere to the forest floor: Boreal Plain,
Canada. Forest Ecology and Management. 259: 210-219
Poland, T.M. and McCullough, D.G. (2006). Emerald ash borer: invasion of the urban forest and
the threat to North America’s ash resources. Journal of Forestry. 103: 118-124
Pressland, A.J. (1973). Rainfall partitioning by an arid woodland (Acacia Aneura F. Muell) in
south-western Queensland. Australian Journal of Botany. 21: 235-245
Price, A.G., Dunham, K., Carleton, T., Band, L. (1997). Variability of water fluxes through the
black spruce (Picea mariana) canopy and feather moss (Pleurozium schreberi) carpet in
the boreal forest of northern Manitoba. Journal of Hydrology. 196: 310-323
Price, A.G. and Carlyle-Moses, D.E. (2003). Measurement and modelling of growing-season
canopy water fluxes in a mature mixed deciduous forest stand, southern Ontario, Canada.
Agricultural and Forest Meteorology. 119: 69-85
Raat, K.J., Draaijers, G.P.J., Schaap, M.G., Tietema, A., Verstraten, J.M. (2002) Spatial
variability of throughfall water and chemistry and forest floor water content in a Douglas
fir forest stand. Hydrology and Earth System Sciences. 6: 363-374
Schume, H., Jost, G., Katzensteiner, K. (2003). Spatio-temporal analysis of the soil water content
in a mixed Norway spruce (Picea abies (L.) Karst.)- European beech (Fagus sylvatica L.)
stand. Geoderma. 112: 273-287
Si, B.C. (2002). Spatial and statistical similarities of local soil water fluxes. Soil Science of
America Journal. 66: 753-759
Vaneklaas, E.J., Van Ek, R. (1990). Rainfall interception in two tropical montane rain forests,
Colombia. Hydrological Processes. 4: 311-326
Watters, R.J. and Price, A.G. (1988). The influence of stemflow from standing dead trees on the
fluxes of some ions in a mixed deciduous forest. Canadian Journal of Forest Research.
18: 1490-1493
51
Zhou, Q.Y., Shimada, J., Sato, A. (2002). Temporal variations of the three-dimensional rainfall
infiltration process in heterogeneous soil. Water Resources Research. 38: 1-15
Ziegler, A.D., Giambelluca, T.W., Nullet, M.A., Sutherland, R.A., Tantasarin, C., Volger, J.B.,
Negishi, J.N. (2009). Throughfall in an evergreen-dominated forest stand in northern
Thailand: Comparison of mobile and stationary methods. Agricultural and Forest
Meteorology. 149: 373-384
Zimmermann, A., Germer, S., Neill, C., Krusche, A.V., Elsenbeer, H. (2008) Spatio-temporal
patterns of throughfall and solute deposition in an open tropical rain forest. Journal of
Hydrology. 360: 87-102
52
Chapter 3:
Dissolved carbon and nitrogen fluxes of throughfall and stemflow in two
mixed-deciduous swamps of southern Ontario
3.1 Abstract
This chapter presents the investigation of dissolved organic carbon (DOC) and nitrogen
(N) fluxes within throughfall and stemflow for two temperate swamps of southern Ontario
during the 2014 growing season. This type of research has rarely been studied in temperate
swamp systems and is important to quantify due to the large influences these fluxes have on the
physical and chemical processes occurring at the wetland floor. DOC fluxes were found to be
higher in the ash-dominated swamp than the maple-dominated swamp due to the higher
abundance of dead snags; while throughfall DOC fluxes were much higher than stemflow DOC
fluxes in both sites. The bioavailability of dissolved organic matter (DOM) within these swamps
was higher than reported upland forests. This was due to a combination of the tree species
instrumented; as well as the importance of highly bioavailable TF in these systems. The ash site
produced more bioavailable DOM than the maple site and can be attributed to the higher
abundance of actively decomposing dead snags. N fluxes were less than bulk precipitation fluxes
in both swamps, indicating that an uptake of N occurred within the swamp canopy. This research
suggests that the forest species composition has important controls on N-cycling through the
canopy and that swamp and upland species differ in their interactions.
53
3.2 Introduction
Climatic influences can have a profound effect on precipitation interception and resulting
wetland hydrology (Crockford and Richardson, 2000). Temperature, wind speed and direction
and precipitation characteristics (i.e. amount, duration and intensity) are the major climatic
factors that influence incoming precipitation and interception in forested ecosystems (Crockford
and Richardson, 2000). The amount of incoming precipitation not intercepted by the canopy,
which is redistributed as throughfall and stemflow, dictates a significant amount of water
available to wetland ecosystems. Moreover, throughfall and stemflow have been found to
accumulate numerous solutes as these aqueous fluxes pass through the forest canopy, including
organic matter and nutrients, which are then transported to the soil at the forest floor. Spatial and
temporal variability of throughfall and stemflow have been found to influence processes such as
soil water recharge (Eschner, 1967; Bouten et al., 1992; Si, 2002; Zhou et al., 2002; Raat et al.,
2002; Schume et al., 2003), soil solution chemistry (Manderscheid and Matzner, 2000), nutrient
cycling (Chang and Matzner, 2000), species composition (Kramer and Holscher, 2009), and
streamwater chemistry (Beier, 1998). Additionally, coloured dissolved organic matter (CDOM)
within these fluxes can absorb solar radiation, thereby positively impacting primary production
in aquatic environments by acting as an attenuator of harmful UV-B radiation, but also limiting
the amount of light available for photosynthesis (Bracchini et al., 2004). While proven to be
important inputs to forested ecosystems, there is a lack of knowledge regarding the flux of
dissolved organic matter (DOM) and nitrogen (N) contained within throughfall and stemflow to
temperate swamp systems.
The quantity and quality of dissolved organic matter (DOM) within throughfall and
stemflow and the spatial and temporal variations of these fluxes influence soil and surface water
54
pH, carbon and nutrient availability, and the transport and magnification of heavy metals (Dalva
and Moore, 1991). Furthermore, the lability of DOM within these fluxes determines which pools
of DOM are accessible for microbial uptake and will dictate the types of carbon cycling
pathways that are likely to occur once the DOM has reached the soil.
Atmospheric deposition can be an important source of nitrogen (N) to many wetland
ecosystems, where ammonium (NH4+), nitrate (NO3
-) and organic-N wet deposition in
throughfall and stemflow often represent a significant portion of new N to these systems
(Bowden, 1987). Since N is often the most limiting nutrient in flooded soils (Mitch and
Gosselink, 2007), the quantification of N-species deposited in wetlands via wet deposition is
crucial to understand the amount of N available for biogeochemical cycling. Studies have found
that enrichment of all N-species can occur when rain passes through a forest canopy and is
converted to throughfall and stemflow (Brinson et al., 1980; Hamdan and Schmidt, 2012), while
uptake of inorganic N (NH4+ and NO3
-) in the canopy has been found as well (Bellot and Escarre,
1991; Levia, 2002; Pelster et al., 2009). Thus, the spatial and temporal variations in N deposition
and uptake via throughfall and stemflow have large implications on the biogeochemical cycling
that may occur at the swamp floor.
The degree of throughfall and stemflow solute enrichment or uptake as precipitation is
transported through the canopy is based upon a number of factors; such as contact time with the
vegetation, stand species composition, rainfall characteristics, seasonality and stand
characteristics (density, LAI and branch architecture) (Mina, 1965; Levia and Frost, 2003). The
amount of enrichment that occurs and the total flux of solutes in throughfall and stemflow
dictates where hot spots and hot moments of biogeochemical processing and transport are likely
to occur. A biogeochemical processing hot spot is an area (often within a soil matrix) that
55
experiences higher rates of biogeochemical reaction rates than the surrounding areas, whereas a
processing hot moment is defined as a short period of time when an area experiences higher
biogeochemical reaction rates between long intervening periods (McClain et al., 2003). A
biogeochemical transport hot spot is an area where the flux of solutes is higher than surrounding
areas, while a transport hot moment is a short period during which solute fluxes are greater than
intervening time periods (Vidon et al., 2010). These phenomena include many different
biogeochemical compounds, with the main focus on carbon, nitrogen and phosphorus cycling
(McClain et al., 2003; Zhu et al., 2012; Gu et al., 2012). Hot phenomena are usually initiated by
an accumulation of chemicals that are primed to undergo biogeochemical reactions/transport and
need water to either mobilize and/or need the proper medium for the reactions to occur. Since the
production of TF and SF is water-driven and has been shown to accumulate various solutes
(Brinson et al., 1980; Mahendrappa, 1990; Bellot and Escarre, 1991; Liu and Sheu, 2003;
Hamdan and Schmidt, 2012; and many others), the flux of TF and SF to swamp soils have the
potential to act as transport hot phenomena.
The focus of this study was to address the following research objectives: [1] determine
the concentration and fluxes of dissolved organic matter and the quality of DOM for TF and SF
in two temperate mixed-deciduous swamps. [2] Determine the concentration and fluxes of N-
species (NO3-, NH4
+, DON and TDN) produced by TF and SF in this ecosystem. [3] Describe the
factors influencing DOM and N transport hot phenomena in temperate swamps.
56
3.3 Methods
3.3.1 Site Description
The two study swamps were located approximately 250 metres from one another and are
referred to as the maple and ash sites, due to the dominating tree species present in each. The
maple swamp is 1.48 hectares and has a canopy height of approximately 30 metres, while the ash
site is 1.04 hectares (canopy height approx. 15 metres), and both are located just north of Terra
Cotta, Ontario, Canada (43°44’26” N, 79°57’33’’ W, 365 m a.s.l.). The dominant tree species in
the maple site were large silver (Acer saccharinum) and sugar maples (Acer saccharum) (94% of
the total basal area at this site). The total site basal area was 144 m2 ha-1, with a high density of
small (<10 cm dbh) eastern hophornbeam trees present (1080 trees ha-1) as well. The ash site had
a much smaller total basal area (32.3 m2 ha-1) and was much more heterogeneous. Dead snags
had the highest relative dominance (27.7%), based on basal area (m2 ha-1), with black ash
(18.6%) and eastern hophornbeam (11.1%) the next highest.
The climate of this area is classified as humid continental with average annual
temperature of 8°C and 877 mm of total annual precipitation (Environment Canada, 2015).
Rainfall amounts in this region average 374 mm from May 1 to September 30 (Environment
Canada, 2015), and is dominated by high-intensity, short-duration convective storms mid-
summer and low-intensity, high-duration events in the spring and fall. The two sites are located
on top of the Niagara Escarpment, an environmentally protected area under the Greenbelt
Program (OMMAH, 2005), that has rocky outcrops, steep slopes and a generally thin soil layer
(CVC Terrestrial Monitoring Program Report, 2010).
57
3.3.2 Hydrological Instrumentation and Analysis
Incident precipitation amounts were obtained from May 1 to September 30, 2014, from
the Credit Valley Conservation Authority weather monitoring station located approximately 3.5
kilometres from the two study sites. The weather station uses a rain gauge that measures total
rainfall at half-hourly intervals, which were then used to calculate rainfall amounts, durations
and intensities for each event. A total of 16 events were analyzed for chemical characterization
of rain water, throughfall and stemflow (Table 3.1). Only these dates were selected as these
events produced enough volume of TF and/or SF to allow for proper chemical analyses, while
some dates were discarded due to possible sample contamination (too long before
refrigeration/filtering).
58
Table 3.1: Incident mean rainfall depths, durations and intensities for rainfall events during the 2014
study period (May-September).
Event
# Date
Rainfall
Depth
(mm)
Rainfall
Duration
(h)
Rainfall
Intensity
(mm/h)
1 10-05-14 1.6 2.5 0.64
2 13-05-14 4.3 3 1.43
3 14-05-14 9.5 3.5 2.71
*4 16-05-14 17.8 13 1.37
*5 03-06-14 26 7.5 3.47
*6 09-06-14 6.8 5.5 1.24
*7 12-06-14 14.7 6.5 2.26
8 13-06-14 1.6 2 0.80
*9 18-06-14 18 3 6.00
*10 25-06-14 11.8 7.5 1.57
11 03-07-14 7.6 5 1.52
12 04-07-14 0.7 2 0.35
*13 08-07-14 14 5 2.80
*14 09-07-14 18.9 5.5 3.44
*15 14-07-14 8.3 4 2.08
*16 15-07-14 7.8 5.5 1.42
17 21-07-14 4.7 9 0.52
*18 28-07-14 28.7 13.5 2.13
19 02-08-14 7.6 1 7.60
*20 13-08-14 29.3 9 3.26
21 17-08-14 2.6 2.5 1.04
*22 21-08-14 4.4 2.5 1.76
*23 03-09-14 21.7 11 1.97
*24 06-09-14 42.1 11 3.83
25 11-09-14 46.2 10.5 4.40
26 13-09-14 3.2 4 0.80
27 16-09-14 1.8 5 0.36
*28 22-09-14 26.7 3.5 7.63
29 30-09-14 6.4 3.5 1.83
*Events sampled for chemical analysis.
One TF collector for chemical sampling was placed randomly within each of the 5
subplots at each site, with a total of 10 TF collectors used in this study. The collectors were made
using 200 cm2 HDPE funnels fitted into 1 L HDPE bottles with rubber stoppers and attached to
wooden dowels that raised the collectors 0.3 m off of the ground to prevent splashing water from
entering the bottles. There were an additional 75 TF collector buckets (orifice diameter = 18.7
59
cm) placed in each swamp to measure total TF amount after each rainfall. These buckets were
used to calculate depth of TF and the proportion of TF relative to P.
SF was collected by stapling a one-inch corrugated PVC tube split in half, spiralled 1.5
times around the circumference of the tree. The PVC tube was caulked, using clear silicone, at
the tree-tube interface so that water flowing down the tree collected into the PVC tubing and
flowed into a funnel (Crockford and Richardson, 1990). The water was then directed via clear
vinyl tubing into HDPE bottles for measurement after each rain event. SF depth was calculated
using Eq. 1 (Chapter 2).
SF collectors were attached to a variety of tree types and size classes selected for
analysis. In total, 50 trees were selected for SF chemical analysis with 25 trees instrumented in
each swamp. In the maple swamp there were 7 eastern hophornbeam (Ostrya virginiana), 9 sugar
maples (Acer saccharum), 6 silver maples (Acer saccharinum), 2 American basswoods (Tilia
americana) and 1 dead snag instrumented for SF analysis. In the ash swamp, SF collectors were
attached to 10 black ash (Fraxinus nigra), 7 dead snags, 5 eastern hophornbeam (Ostrya
virginiana) and 3 trembling aspen (Populus tremuloides).
3.3.3 Chemical Analysis
After each rainfall, HDPE bottles containing TF and SF were emptied into two-50 mL
sterile polypropylene sample containers and taken to the lab for chemical analysis. All samples
were vacuum-filtered using 0.45 µm glass fibre filter paper, with duplicates of each sample
preserved with concentrated sulphuric acid (H2SO4) to pH < 2. Approximately 20 mL was
transferred into amber vials to prevent photo-degradation for samples being analyzed for
absorbance and fluorescence properties. Strict QA/QC protocols were followed for all chemical
60
analyses; including the running of duplicates, calibration standards and matrix spikes, when
necessary.
Dissolved Organic Carbon
A Picarro iTOC CRDS analyzer was used to determine DOC concentrations, with
approximately 10 mL of sample required to determine DOC with another 20 mL needed
occasionally to perform matrix spike tests. This instrument uses a sodium persulfate digestion
method that oxidizes carbon present within water samples, in the presence of a platinum catalyst,
to produce CO2. The CO2 produced was subsequently measured by a non-dispersive infrared
analyzer and converted to a DOC concentration in mg/l.
DOM Quality
A Horiba Aqualog benchtop fluorometer was used to analyze TF and SF samples for
chemical characterization of DOM. A number of different absorbance and fluorescence metrics
were calculated and used to analyze DOM quality within TF and SF (Table 3.2).
Table 3.2: DOM metrics used to analyze the quality of DOM within P, TF, and SF.
Index Description
SUVA254 Higher Values Indicate Higher Aromaticity of DOM (Less
Bioavailable)
Spectral Slope Ratio
(SR)
Inversely Related to Relative Molecular Mass of DOM
Fluorescence Index
(F.I.)
1.3: Plant-Derived DOM
1.55: Mix of Plant-Derived and Microbial-Processed DOM
1.8: Microbial-Processed DOM
Freshness Index (β:α) Higher Values Indicate Fresher DOM
(More Bioavailable)
Humification Index
(HIX)
Higher Values Indicate Higher Humification of DOM (Less
Bioavailable)
61
Nitrogen
TF and SF samples were also analyzed for total dissolved nitrogen (TDN), dissolved
organic nitrogen (DON), ammonium (NH4+) and nitrate (NO3
-) concentrations. After sample
preparation, approximately 7 mL were analyzed colourmetrically on a Lachat Quikchem 8500
Series 2 FIA Analyzer for NH4+-N and NO3
--N concentrations. A potassium persulphate
digestion was performed using 10 mL of sample and 5 mL of digestion reagent (USGS, 2003), to
determine total dissolved nitrogen (TDN) within samples. The samples were mixed thoroughly
and placed in a hot-water bath at 100°C for 60 minutes, converting all N-species to NO3-, and
then re-analyzed on the Lachat FIA analyzer to determine TDN. The dissolved organic
proportion (DON) was determined by subtracting the inorganic forms (NH4+ and NO3
-) from
TDN concentrations. A validation experiment was performed to establish that the persulfate
digestion was recovering all of the N-species within the water sample, with the results showing
>95% recovery of N from a urea solution prepared in deionized water.
All concentrations and indices were compared using one-way repeated measures
ANOVA (SigmaPlot 11.0) to test for statistical significance between concentrations, fluxes and
DOM metrics in TF and SF at both sites and for seasonality. Seasonal fluxes of DOC and N were
calculated as average P, TF or SF depth per m2 multiplied by the average chemical concentration
for each event, summed and presented as a total deposition (kg or g ha-1). To arrive at estimates
of seasonal chemical fluxes, site-averaged concentrations of the banding events were used for
events with missing data, with the exception of the first 3 events for N-fluxes (May 16
concentration was used for all), the first two events of July for DOC (July 8 concentration was
used), and the last event for DOC and N (September 22 concentration was used). Since all flux
62
data were positively skewed, hot phenomena were defined as events with fluxes of DOC or N >
90th percentile of all observations (Mitchell et al., 2008).
3.4 Results and Discussion
3.4.1 Aqueous Inputs – Precipitation, Throughfall and Stemflow
Incident precipitation totalled 395 mm over 29 events from May 1 to September 30,
2014. This compares with a 30-year normal of 374 mm from the beginning of May until the end
of September (Environment Canada, 2015). Average event precipitation for May-September,
2014, was 13.6 mm with events ranging from 0.7 mm to 46.2 mm. Chemical analyses were
performed on samples for 16 of the 29 events (Table 3.1), with the exception of DOC and
SUVA254 (only last 10 of 16 events analyzed). Total TF in the maple site (TFM) was 329 mm,
which was 83% of total P, while the ash site (TFA) had a total TF of 335 mm (85% of total P).
Total SF in the maple site (SFM) was 2.4 mm or 0.6% of total P, while total SF in the ash site
(SFA) was 2.8 mm or 0.7% of total P. For a full description of rainfall characteristics and
resulting precipitation partitioning, refer to Chapter 2.
3.4.2 Dissolved Organic Carbon
DOC Concentrations
DOC concentrations were significantly higher for SF than for TF and P in both swamps
(Figure 3.1) for July-September (JAS). Average DOC concentrations were highest for SFA (86.6
mg/l), followed by SFM (61.3 mg/l) with much less DOC in TF (9.4 and 5.6 mg/l in the ash and
maple sites, respectively) (Table 3.3). P DOC concentrations were the lowest of all with an
average of just 3.0 mg/l. SFA DOC concentrations were consistently higher than all other fluxes
63
throughout the sampling period (Figure 3.2). TFA DOC concentrations, while always
significantly below SF DOC concentrations at both sites, were consistently higher (although not
significantly) than TFM throughout the period of measurement (Figure 3.2).
64
Figure 3.1: Box plot comparison of DOM quantity ([DOC] for SF in large graph, TF and P in small
graph) and quality between TF and SF in the maple and ash swamps. The length of each box represents
the interquartile range, the horizontal bar the median, the whiskers the 10th and 90th percentiles, and the
black dots outliers. Plots with different letter notations vary significantly at a P < 0.05.
a a a
b
b ab a b a
a b a
b
b a bc ab c
a b a c a a c
(β:α
)
65
Table 3.3: Rainfall, throughfall and stemflow chemical properties for the maple and ash swamps. Mean
concentrations/index values and total deposition are presented with standard deviations in brackets. Rainfall Throughfall Stemflow
P Maple
Swamp
Ash
Swamp
Maple
Swamp
Ash
Swamp
Concentration
DOC (mg l-1) 3.0 (2.7) 5.6 (3.0) 9.4 (6.5) 61.3 (52.9) 86.6 (112. 8)
SUVA254 (l mg m-1) 2.05 (2.9) 3.72 (1.3) 3.23 (1.0) 3.50 (0.8) 3.25 (0.6)
SR 3.13 (1.1) 3.72 (0.9) 2.99 (1.0) 3.58 (0.4) 3.24 (0.7)
F.I. 1.52 (0.1) 1.41 (0.1) 1.47 (0.1) 1.42 (0.1) 1.46 (0.1)
Freshness 2.90 (0.1) 0.79 (0.1) 0.79 (0.1) 0.61 (0.1) 0.64 (0.1)
HIX 0.35 (0.1) 0.47 (0.1) 0.47 (0.1) 0.72 (0.1) 0.68 (0.1)
NO3- (mg l-1) 0.6 (0.6) 0.5 (0.7) 0.3 (0.3) 0.5 (0.8) 0.5 (2.2)
NH4+ (mg l-1) 0.5 (0.6) 0.1 (0.2) 0.1 (0.3) 0.2 (0.2) 0.2 (0.8)
DON (mg l-1) 0.7 (0.3) 1.5 (0.6) 1.5 (0.6) 3.4 (1.5) 3.1 (1.3)
TDN (mg l-1) 1.4 (0.9) 1.9 (0.5) 1.8 (0.5) 4.1 (1.6) 3.6 (1.3)
Deposition
*DOC (kg ha-1) 8.9 (0.48) 11.1 (0.56) 20.4 (1.20) 1.03 (0.06) 1.61 (0.10)
NO3- (g ha-1) 1680 (70.4) 1210 (47.7) 700 (28.1) 9.4 (0.5) 13.1 (0.62)
NH4+ (g ha-1) 1493 (63.3) 212.7 (6.8) 197 (9.0) 3.5 (0.2) 5.7 (0.29)
DON (g ha-1) 7460 (230) 4800 (175) 4400 (144) 85.0 (3.5) 86.3 (3.36)
TDN (g ha-1) 13300 (409) 5660 (200) 4820 (156) 97.2 (4.0) 104.5 (3.93)
* DOC fluxes are from July-September only.
66
Figure 3.2: Temporal progression of mean event DOM quantity and quality for TF and SF in the maple
and ash swamps, May-September, 2014.
The values in this study for [DOC] in P (mean = 3.0 mg/l) were very similar to the range
of values found in the literature (2.2 – 4.7 mg/l), while [DOC] in TF were generally lower (5.6
67
and 9.4 mg/l for TFM and TFA, respectively) than those found in previous studies with [DOC]
ranging from 7.0 – 25.0 mg/l (McDowell and Likens, 1988; Liechty et al., 1995; Liu and Sheu,
2000; Chang and Matzner, 2000; Pelster et al., 2009; Hamdan and Schmidt, 2012). Mean SF
[DOC] for both sites were higher than most [DOC] in the literature (ranging from 7.2 – 59.6
mg/l) (Liu and Sheu, 2000; Chang and Matzner, 2000; Levia et al., 2012), however, SF [DOC] in
a Douglas-fir stand was found to be higher than SFM, with an average of 82.7 mg/l (Hamdan and
Schmidt, 2012), while SF [DOC] in a black spruce stand in Minnesota (Kolka et al., 1999), was
much higher (174.4 mg/l) than both SFM and SFA.
DOC concentrations in TFA were higher than TFM, while SFA also had higher
concentrations than SFM (Table 3.3). It was originally hypothesized that TFM would have higher
DOC concentrations than TFA, as more interaction between P and the canopy would occur since
the maple swamp had a much larger tree density than the ash swamp. One possible explanation
for higher TFA DOC concentrations would be the interaction and dripping of P off of dead snags.
Dead snags had a high relative dominance in the ash swamp and produced high DOC
concentrations in SF (mean DOC concentration = 188 mg/l). When dead snag SF DOC
concentrations are removed from seasonally-averaged SFA [DOC], the mean drops from 86.6
mg/l to 53 mg/l. Furthermore, some dead snags produced extremely high [DOC], with
concentrations >200 mg/l surpassed 13 times and concentrations >500 mg/l surpassed 5 times.
DOC concentrations >500 mg/l have not been recorded for TF or SF in previous research. Thus,
it appears that dead snags leach more DOC than live trees, resulting in an enrichment of TFA.
DOC concentrations through the canopy generally declined from July to the end of
September; however, this decrease was significant (P <0.05) for SFM only. A decrease in DOC
concentration from summer to early-fall has been noted in other studies (McDowell and Likens,
68
1988; Dalva and Moore, 1991) and can be linked to the amount of canopy cover present in
deciduous forests. Higher leaf leaching (Tukey, 1970) and leaf washing (Levia et al., 2012) of
DOC occurs in July (peak of the growing season) when compared to later in the growing season
(when deciduous trees are shedding their leaves). The more modest decrease in DOC
concentrations in the ash site likely occurred due to the dominance of the dead trees leaching
DOC regardless of leaf stage of living trees.
Precipitation amount, duration and intensity did not influence DOC concentrations
throughout the study period, other than for TFM, where a significant negative relationship
(y = -0.125x + 8.0, r2 = 0.45, P <0.05) was found between storm size (mm) and TFM DOC
concentration.
DOC Deposition
Total deposition of DOC for JAS was highest for TF in the ash swamp at 20.4 kg ha-1
(Table 3.3), followed by TFM (11.1 kg ha-1). SF DOC fluxes were significantly lower than both
TF, with only 1.61 and 1.03 kg ha-1 deposited as SFA and SFM, respectively. Total DOC
deposition in the ash site (TF + SF) was 22 kg ha-1, which is an approximately 2.5x enrichment
when compared to P DOC flux (8.9 kg ha-1), while total DOC deposition in the maple site was
12.1 kg ha-1 (enrichment of 1.4x P DOC flux).
Estimated growing-season (May-September) TF DOC fluxes from this study (TFM = 18.3
kg ha-1, TFA = 33.8 kg ha-1) were in the low range of values found in the literature, with most
fluxes found between 29-95 kg ha-1 (Chang and Matzner, 2000; Liu and Sheu, 2003; Clarke et
al., 2007; Pelster et al., 2009). DOC fluxes in this study followed a similar trend to the results
found in other studies, where TF DOC fluxes were the largest (Brinson et al., 1980; McDowell
69
and Likens, 1988; Liu and Sheu, 2003; Pelster et al, 2009; Hamdan and Schmidt, 2012),
followed by P DOC fluxes, while SF DOC were the lowest in all cases, except for SF from
Douglas-fir trees in British Columbia, Canada (Hamdan and Schmidt, 2012). Estimated growing
season (May-September) SF DOC fluxes (SFM = 1.7 kg ha-1, SFA = 2.7 kg ha-1) were similar to
some of the results found in the literature, with a range of 0.6-6.3 kg ha-1 (Chang and Matzner,
2000; Liu and Sheu, 2003; Pelster et al., 2009). However, SF DOC fluxes were much lower in
this study than American beech (49 kg ha-1) and yellow poplar (20 kg ha-1) trees in Maryland,
USA (Levia et al., 2012). Even though DOC concentrations in this study were higher than both
American beech (14.9 mg/l) and yellow poplar (59.6 mg/l) from Levia et al. (2012), the lack of
SF production greatly reduced the flux of SFM and SFA DOC. Therefore, temperate swamp TF
and SF DOC fluxes were lower than most upland forested systems due to the lower [DOC] in
TF, combined with lower total P than most other studies, while the lack of SF production caused
lower SF DOC deposition.
3.4.3 DOM Quality
SUVA254 was higher for the maple site (Table 3.3), with SFM having significantly higher
SUVA254 than both TFA and SFA (Figure 3.1). While SUVA254 for TFM had the highest average
value (3.72), it was not significantly higher than TFA (average SUVA254 = 3.23) or SFA (average
SUVA254 = 3.25) (Table 3.3). SUVA254 values for TFM were consistently high (highest for 5 of
the 8 sampling dates), with SFM generally second highest throughout the sampling period (Figure
3.2). Spectral slope ratios (SR) were significantly higher in the maple swamp (both TF and SF)
than those in the ash (Figure 3.1). Average SR for TFM and SFM were 3.72 and 3.58, respectively,
while SR for TFA and SFA were 2.99 and 3.24, respectively (Table 3.3). TFM was the only flux
that was affected by storm characteristics, where a significant positive relationship (y = 0.04x +
70
3.2, r2 = 0.25, P <0.05) was found between TFM SR and rainfall amount (mm), while there were
no significant relationships found between SUVA254 and any rainfall characteristics.
TF and SF fluorescence index (F.I.) values ranged from 1.1-2.0, with SFA having
significantly higher mean F.I. (1.46) than both maples fluxes, while TFA (1.47) had significantly
higher mean F.I. than TFM (Figure 3.1). All mean F.I. values indicate that the contribution of
plant-derived DOM was larger than microbial-processed DOM. Throughout the growing season
(May-September), SFA was consistently higher than SFM, while TFM had consistently low F.I.
when compared to the other inputs (Figure 3.2). TF DOM was significantly more fresh and less
humified (β:α = 0.79, HIX = 0.49 for TF at both sites) than SFA (β:α = 0.64, HIX = 0.68) and
SFM (β:α = 0.61, HIX = 0.72) (Figure 3.1 and Table 3.3). Furthermore, SFA was significantly
fresher and less humified than SFM, while TF DOM was consistently fresher and less humified
than SF throughout the growing season (Figure 3.2). TFM and TFA experienced a large increase
in HIX during the mid-growing season period, increasing by 74% (0.34 to 0.59) from May to
mid-July, followed by decreases towards the end of the study period. This peak was also present
for SF inputs but was much more muted compared to TF (Figure 3.2). There were no
relationships found between storm characteristics and β:α, while TFM was the only flux that had
HIX values affected by storm characteristics. A significant (P <0.05) negative relationship was
found between TFM HIX and rainfall amount.
When comparing the bioavailability of DOM between TF and SF within a site, the
SUVA254, SR, and F.I. metrics do not show any significant differences. However, DOM β:α and
HIX values from this study show that TF DOM was more recently produced and is less humified
than SF DOM (P <0.05). Less P was necessary for TF production (0.67 and 0.12 mm for TFM
and TFA, respectively), causing the solutes on the canopy surfaces to be flushed away and
71
regenerated more often than SF. With larger amounts of P necessary for SF production (1.76 and
1.71 mm for SFM and SFA, respectively), the flushing of solutes took more time and required
larger P events, resulting in the build-up of older, more complex DOM compounds. Furthermore,
SF must travel further along the surface of the tree, which has been shown to increase the
enrichment of soluble lignin degradation by-products with high aromaticity into hydrologic
fluxes (Guggenberger et al., 1994; Levia et al., 2012). This causes a decrease in β:α and an
increase in HIX in SF and results in higher bioavailable DOM within TF at the stand scale.
Both TF and SF in the ash swamp had higher F.I. values than corresponding fluxes in the
maple swamp, indicating more contribution from microbial metabolites. This may be due to the
higher presence of dead snags, which are actively decomposed by microbial and fungal
communities (Janisch and Harmon, 2002), and therefore release more labile DOM compounds.
The results indicating that TFM and SFM acquire more plant-derived material than TFA and SFA
are further supported by the SUVA254 results. These values indicate that TFM and SFM have
increased aromaticity (lower bioavailability) when compared to TFA and SFA (Table 3.3), as
aqueous inputs with increased aromaticity are expected with a more plant-derived DOM pool
(Fellman et al., 2008). Moreover, SF DOM β:α was higher and HIX lower in the ash site, likely
due to less contact time of SF with trees in this site due to a shorter canopy than the maple site.
Therefore, most DOM quality metrics indicate that ash swamp TF and SF DOM is more
bioavailable than the corresponding fluxes in the maple swamp.
Over the course of the growing season, many of the DOM metrics did not show any
obvious seasonal trends (Figure 3.2). TF β:α values were high for the first measurement of the
growing season (May 16), but became fairly uniform after. HIX was the only metric that had a
seasonal trend, with a noticeable HIX peak for TF (and a small peak for SF) present in the
72
middle of the growing period (Figure 3.2). This peak may be a result of the canopy coming to
full leaf cover, providing the largest leaf surface area up to that point in the growing season. Rain
events during this period would cause the most P and leaf contact, thereby increasing the HIX of
TF and SF. The decline towards the end of the season may be due to the decline in total canopy
cover, or that larger P events occurred during the end of the season, causing a dilution effect.
This is supported by the negative relationship between TFM HIX and P magnitude, although this
relationship was not found for TFA.
SR values were lower for TF and SF in American beech, yellow poplar, and red maple
stands (1.2-2.1) during the growing season in Maryland, USA (Inamdar et al., 2011; Levia et al.,
2012), indicating our study had trees that produced TF and SF DOM with relatively lower
molecular mass (SR ranging from 2.99-3.72). Using the linear regression by Weisharr et al.
(2003), Inamdar et al. (2011) found similar, yet slightly lower aromatic compound content in TF
(median SUVA254 of ~2.8, aromatic content = 21.9%) compared to this study (TF median
SUVA254 = 3.06 for TFA, 3.58 for TFM, aromatic content = 23.6% and 27.0%, respectively). On
the other hand, Levia et al. (2012) found much more aromatic compounds in SF than these
swamps. Mean growing season SUVA254 were 6.3 (44.7% aromatic content) and 8.8 (61.0%
aromatic content) for American beech and Yellow poplar stands, respectively (Levia et al.,
2012), compared to mean SUVA254 values of 3.50 (26.5% aromatic content) for SFM and 3.25
(24.8% aromatic content) for SFA.
TF HIX was much lower in this study, with median HIX values of 0.47 for TFM and 0.48
for TFA, compared to median TF HIX of ~0.8 for an upland forest in Maryland, USA (Inamdar et
al., 2011). While SF had a larger degree of humification than TF in this study, likely due to the
enrichment of older, more complex humic structures as the water moved down the leaves and
73
branches to the base of the tree, there were no SF HIX values found from previous studies with
which to compare. Higher SR, combined with lower SUVA254 and HIX values, are generally
associated with increased bioavailability (Cory et al., 2011). Therefore, TF and SF in temperate
swamps can be considered more bioavailable than upland forest systems.
3.4.4 Nitrogen
N Concentrations
Nitrate concentrations were very similar between bulk P (0.6 mg/l), TF (0.3-0.5 mg/l at
both sites) and SF (0.5 mg/l) (Figure 3.3 and Table 3.3). However, average nitrate fluxes were
significantly higher, by 1-3 orders of magnitude, in both P and TF when compared to SF (Table
3.3). Very low nitrate fluxes in SF (9.4 and 13.1 g ha-1 for SFM and SFA, respectively) can be
attributed to the very low SF volumes produced in both swamps (0.6-0.7% of total P). NO3-
concentrations decreased exponentially from mid-May to the end of September (Figure 3.4) and
this decrease was found to be significant for all TF and SF inputs (Figure 3.5).
74
Figure 3.3: Box plot comparison of N-species between TF and SF in the maple and ash swamps. The
length of each box represents the interquartile range, the horizontal bar the median, the whiskers the 10th
and 90th percentiles, and the black dots outliers. Plots with different letter notations vary significantly at a
P < 0.05.
a
a a b b b
a a b b b c a
75
Figure 3.4: Temporal progression of mean event N concentrations for TF and SF in the maple and ash
swamps, May-September, 2014.
76
Figure 3.5: Temporal decrease in nitrate concentrations for TF and SF from May-September. TFM- red
solid line, TFA- blue solid line, SFM- red dashed line, SFA- blue dashed line. All linear regressions are
significant (P < 0.05). Note: concentrations have been ln transformed.
Nitrate concentrations in P, TF and SF were in the lower range of those found in the
literature, where NO3- concentrations range from 0.05-2.70 mg/l (Brinson et al., 1980; Bellot and
Escarre, 1991; Chang and Matzner, 2000; Levia, 2002; Pelster et al., 2009; Hamdan and
Schmidt, 2012), while the same was true for ammonium, with ranges found in the literature
between 0.1-6.6 mg/l (Brinson et al., 1980; Chang and Matzner, 2000; Pelster et al., 2009;
Hamdan and Schmidt, 2012). Concentrations of NO3--N and NH4
+-N decreased through the
swamps’ canopies, indicating uptake of inorganic nitrogen by the canopy. Inorganic-N uptake in
the canopy has occurred before, as lichen sequestration of nitrate leached from trees and from SF
absorption resulted in a net uptake of NO3- from pignut hickory trees in Massachusetts (Levia,
2002), while inorganic-N retention occurred in the canopy of upland deciduous, coniferous and
wetland coniferous stands in Alberta, Canada (Pelster et al., 2009). The mechanisms behind N
retention in the canopy are either foliar uptake, absorption onto the leaf surface or assimilation
by microorganisms in the canopy (Wilson, 1992; Lovett, 1994; Krupa, 2003). However, there are
77
findings that support the enrichment of inorganic-N as P passes through the canopy, such as in a
beech and oak stand in northern Bavaria, Germany (Chang and Matzner, 2000) and for big leaf
maple and Douglas fir stands in British Columbia (Hamdan and Schmidt, 2012).
Nitrate concentrations in TF and SF clearly declined over the course of the growing
season, with highest concentrations measured in May and June and an exponential decline until
the end of the study period (Figure 3.5). Increased microbial activity (with resulting
denitrification), as well as plant uptake of NO3- in the soil at the start of the growing season has
the potential to reduce the amount of NO3- in the soil later in the growing period (Dittman et al.,
2007). As such, the retention of NO3- by foliar uptake or absorption into the leaves, beginning in
the middle of the growing season when NO3- in the soil may become less available, may act as a
supplement for this limiting nutrient to the trees in these swamps.
Ammonium concentrations were found to be significantly higher in P and SF than for TF
at both sites (Figure 3.3), with P having the highest average NH4+ concentrations of 0.5 mg/l
(Table 3.3). Average SF NH4+ concentrations (0.2 mg/l at both sites) were slightly higher than
TF (0.1 mg/l at both sites) and were found to be significantly higher at a P < 0.05 (Figure 3.3).
However, average NH4+ fluxes were significantly higher (by 2-3 orders of magnitude) in both P
and TF when compared to SF (Table 3.3). Once again, very low SF production was the reason
for such small NH4+ fluxes in both swamps. Throughout the growing season, NH4
+
concentrations were highest for SFA on most sampling dates, with all fluxes showing a peak in
NH4+ concentrations mid-July (Figure 3.4).
Dissolved organic nitrogen (DON) concentrations were significantly higher for SF than
for TF at both sites (Figure 3.3). Average DON concentrations ranged from 3.1-3.4 mg/l for SF,
78
while TF [DON] was 1.5 mg/l for both sites (Table 3.3). Again, the very low SF volumes
resulted in much less DON flux (2 orders of magnitude less) when compared to P and TF DON
fluxes. Seasonally, SFM concentrations were generally higher than SFA, with little variation in
[DON] throughout the season (Figure 3.4). TFM and TFA have similar [DON] throughout the
growing season, however, TFA tended to be higher until mid-July, where TFM [DON] became
slightly higher (Figure 3.4).
The concentrations of DON in P, TF and SF in this study were comparable, although SF
was higher, than previously reported values of 0.3-2.1 mg/l (Brinson et al., 1980; Chang and
Matzner, 2000; Pelster et al., 2009; Hamdan and Schmidt, 2012). P had the lowest concentration,
with TF and SF enrichment occurring as P passed through the canopy (Table 3.3). As DON
concentrations were an order of magnitude larger than NO3- and NH4
+ in TF and SF, TDN
concentrations show similar trends to that of DON (Table 3.3). However, SFM TDN (4.1 mg/l)
was significantly higher than SFA (3.6 mg/l) and both TF inputs (1.9 and 1.8 mg/l for TFM and
TFA, respectively) (Figure 3.3), likely as a result of higher DON concentrations (Table 3.3).
These results suggest that the washing off of dry-deposited DON or the leaching of weak organic
acids likely caused an increase in DON concentrations for TF and SF (Draaijers et al., 1997).
Forest canopy N uptake may be actively controlled, leading to a depletion of inorganic-N and an
enrichment of [DON] in TF and SF as P passes through the canopy.
Total dissolved nitrogen (TDN) concentrations were significantly higher for SFM than
SFA and TF at both sites (Figure 3.3). Additionally, SFA TDN concentrations were significantly
higher than TF at both sites (Figure 3.3). Average TDN for SFM was 4.1 mg/l, while for SFA
[TDN] was 3.6 mg/l and for TFM and TFA [TDN] were 1.9 and 1.8 mg/l, respectively (Table
3.3). TDN fluxes in P were an order of magnitude larger than TF and 2-3 orders of magnitude
79
larger than SF (Table 3.3). The contribution of DON to TF and SF TDN dominated throughout
the growing season (Figure 3.6), as the concentration of DON was higher than NO3- and NH4
+
for TF and SF for all sampling dates except Event 7 for TFM and TFA (Figure 3.6).
Figure 3.6: Proportion of NO3
-, NH4+ and DON contribution to TDN for each sampling event for A: TF-
Maple, B: TF- Ash, C: SF- Maple, D: SF- Ash.
Throughout the study period, SFM TDN concentrations were nearly always the highest;
however, SFA had similarly high values as well (Figure 3.4). The concentration of TDN in SF
was higher at the start and end of the growing period with a dip in concentrations seen in mid-
July (Figure 3.4). When comparing TDN in TF, TFM TDN concentrations were usually higher
than TFA throughout the growing season (Figure 3.4). Both TF fluxes followed a similar trend to
SF ([TDN] decreasing in mid-July); however, this decrease was not as large as was seen for SF.
A B
C D
80
The only rainfall characteristic that affected N concentrations was event intensity, showing a
significant (P <0.05) negative relationship with TFM TDN concentration.
N Deposition
Estimated growing-season nitrate fluxes for TF in this study were higher than those found
in upland deciduous (100 g ha-1), upland coniferous (205g ha-1) and wetland coniferous (165 g
ha-1) stands in Alberta, Canada (Pelster et al., 2009), yet they were lower than a beech stand
(2800 g ha-1) in Germany (Chang and Matzner, 2000), and a holm oak, strawberry tree, and
green olive stand (1930 g ha-1) in Spain (Bellot and Escarre, 1991). Estimated growing-season
NO3- SF fluxes in this study were similar to Pelster et al. (2009), with approximately 1 g ha-1 of
NO3- deposition, while SFM and SFA NO3
- deposition were much lower than the approximately
200 g ha-1 found for SF in other studies (Bellot and Escarre, 1991; Chang and Matzner, 2000).
NO3- fluxes in this study were higher for TF than those of Pelster et al. (2009) due to a
combination of higher NO3- concentrations and higher growing-season P totals, whereas lower
NO3- concentrations in TF and SF were the main cause of lower NO3
- fluxes when compared to
Bellot and Escarre (1991) and Chang and Matzner (2000). The latter studies had rainfall totals
similar to or less than this study, yet the fluxes of nitrate were higher due to higher TF and SF
NO3- concentrations. Therefore, both local climate, particularly the amount of growing-season
rainfall, and stand species composition have major influences on the flux of NO3- in forested
systems.
Estimated growing-season ammonium fluxes for TF in this study were approximately
half of Pelster et al. (2009), with all three stands in their study producing NH4+ TF fluxes of ~400
g ha-1. NH4+ fluxes in this study were also much lower than the 3164 g ha-1 growing-season flux
81
found in a Mediterranean stand (Chang and Matzner, 2000). Estimated growing-season NH4+ SF
fluxes in this study were similar to the two coniferous sites (one upland and one wetland), with
fluxes of 8.2 and 2.5 g ha-1, respectively, while lower than the upland deciduous site (NH4+ flux
= 24.7 g ha-1) (Pelster et al., 2009). Estimated growing-season NH4+ fluxes in a Mediterranean
stand was much higher, however, with a total deposition of 205 g ha-1 (Chang and Matzner,
2000). Therefore, the uptake of ammonium in the canopy produced lower NH4+ concentrations
for TF, causing lower NH4+ fluxes than upland forests, while for SF, very low water delivery
combined with low [NH4+] produced lower NH4
+ fluxes.
Estimated growing-season DON fluxes for TF in this study were higher than those found
in Chang and Matzner (2000), Pelster et al. (2009), and Sleutel et al. (2009), where DON
depositions between 400-2900 g ha-1 were found. Estimated growing-season TDN fluxes for TF
were similar (approx. 4800 g ha-1) to those found in an alluvial swamp in Florida (Brinson et al.,
1980), while they were 4-5x greater than TDN fluxes in Alberta, Canada (Pelster et. al, 2009),
and about half of the growing-season TDN fluxes of a beech/oak stand in Germany (Chang and
Matzner, 2000). Estimated growing-season SF DON fluxes in this study were higher than those
found in Alberta, Canada, where a range of fluxes between 2.0-60 g ha-1 were found (Pelster et
al., 2009), while DON deposition was slightly lower than in a beech/oak stand, with ~120 g DON
ha-1 (Chang and Matzner, 2000). Estimated growing-season SF TDN fluxes in this study were
higher than those found in Alberta, Canada, where a range of fluxes between 4.0-80 g ha-1 were
found (Pelster et al., 2009), while TDN deposition was lower than in a beech/oak stand in
Germany (~530 g TDN ha-1) (Chang and Matzner, 2000), and an alluvial swamp in Florida (~330
g ha-1) (Brinson et al., 1980). Growing-season TF DON and TDN fluxes were generally greater
in temperate swamps than those in upland sites, with the exception of one study for TDN (Chang
82
and Matzner, 2000), and can be attributed to relatively high [DON] contained within TF. SF
DON and TDN fluxes were similar to upland studies, even with low SF aqueous inputs,
suggesting that the leaching of DON from trees in temperate swamps is higher than most other
systems. Furthermore, the deposition of DON contributed to > 80% of TDN deposition in this
study for both TF and SF. This is greater than the 50-70% DON found in previous studies for TF
(Qualls et al., 1991; Pelster et al., 2009; Hamdan and Schmidt, 2012) and 20-60% in SF (Chang
and Matzner, 2000; Hamdan and Schmidt, 2012).
3.4.5 Hot Phenomena of DOC and N Flux
At the stand scale, hot moments of DOC flux occurred for Events 20 and 25 in the maple
swamp and for Events 20 and 23 in the ash swamp (Table 3.4). These events are characterized by
relatively high rainfall amounts (all events over 20 mm), with Event 25 the highest recorded rain
event of the 2014 growing season (46.2 mm). These results seem logical, as larger rain events
produce more TF and SF that can entrain more DOC than small rain events. This implies that
DOC fluxes are not limited by the supply of DOC in the canopy; rather, DOC fluxes are limited
by rainfall magnitude. With an increase in summertime severe-rainfall events predicted for
southern Ontario (Cao and Ma, 2009), the magnitude of individual DOC hot moments is likely to
increase in these temperate swamp systems as well. Another factor to consider for DOC hot
moments is antecedent P conditions. Event 23 produced a hot moment in the ash site and was
preceded by a very long dry period (~2 weeks without P) and only 7 mm of total P in the 3 weeks
leading up to the event. This may have caused the build-up of DOC in the canopy, especially for
actively decomposing dead snags, that was subsequently flushed in a larger than normal DOC
pulse for an event of that size.
83
Table 3.4: Hot phenomena for the maple and ash swamps presented as the event number with fluxes (TF
+ SF) > 90th percentile of observations for each chemical flux. Maple Ash
DOC Event 20, 25 Event 20, 23
NO3- Event 4, 5, 7 Event 4, 5, 7
NH4+ Event 16, 18, 23 Event 10, 11, 13
DON Event 20, 24, 25 Event 23, 24, 25
TDN Event 20, 24, 25 Event 23, 24, 25
*Number of events: n=17 for DOC, n=29 for all N-fluxes.
Nitrate hot moments occurred for Events 4, 5 and 7 in both swamps (Table 3.4). These
hot moments were influenced by the magnitude of rainfall, as they occurred only during events
above the median rainfall amount for both sites. However, seasonality also had a large effect on
NO3--N hot moments, with all of the hot NO3
--N flux moments occurring during the early
growing season (May-early June). This represents an important moment of nutrient delivery to
microbes and plants at the forest floor and may cause higher biogeochemical cycling and plant
uptake at a crucial point in the growing season.
Hot moments of ammonium fluxes were influenced by storm size for the maple site (2 of
3 occurred during storms > 20 mm). On the other hand, seasonality affected hot moments of
ammonium fluxes in the ash site, with all occurring during a two-week time period in late June-
early July (Table 3.4). As the rain characteristics for each event were the same for both sites,
differing canopies uptake ammonium during different times of the growing season. However, the
mechanisms behind this phenomenon are uncertain at this time and require further investigation.
Rainfall magnitude had a large influence on DON and TDN hot moments. Two of the
three largest rain events produced disproportionately higher DON and TDN fluxes for the ash
swamp (Table 3.4). Again, the build-up of DON in actively decomposing dead snags in the ash
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site for Event 23 caused a larger than normal DON flux than would normally occur for an event
of that size. The 3 largest events were hot moments of DON and TDN flux in the maple swamp
(Table 3.4). Similar to trends in DOC fluxes, an increase in summertime severe-rainfall events
will likely increase the magnitude of DON/TDN hot moments in these temperate swamps as
well.
3.5 Conclusion
Throughfall and stemflow act as important inputs of DOM and N to a swamp floor. The
enrichment or uptake of DOC/N through a swamp canopy has now been characterized in a
temperate swamp ecosystem, for which there was a lack of previous research. Key information
regarding the spatial and temporal controls on DOC/N hot transport in temperate swamps have
also been identified. Furthermore, this is the first study to characterize the quality of DOM
delivered under a swamp canopy, which can have important implications on many physical and
chemical processes at the swamp floor, particularly carbon cycling.
DOC fluxes were higher in the ash swamp than the maple swamp, due to the higher
abundance of dead snags, with TF acting as the dominant contributor of DOC in these sites.
However, both swamps had less DOC deposition than upland studies. The bioavailability of
DOM within these swamps was higher than beech/poplar studies, with the ash site producing
more bioavailable DOM than the maple site. Higher DOM quality in this study is likely due to a
combination of the tree species instrumented; as well as the importance of highly bioavailable TF
in these systems (SF had less hydrological input than other studies). Nitrogen fluxes were less
than bulk precipitation fluxes in both swamps, indicating that an uptake of N occurred within the
swamp canopy. Nitrogen fluxes were higher in the ash swamp than the maple swamp for all N-
85
species, and may be a result of less interaction (less tree density) between P and the canopy in
the ash swamp. Both swamps had intermediate nitrate and total dissolved nitrogen fluxes when
compared to upland studies. Furthermore, these sites had lower ammonium fluxes than upland
forests, while the flux of dissolved organic nitrogen was higher than most upland forests. This
suggests that forest species composition has important controls on N-cycling through the canopy
and that swamp and upland species differ in their interactions.
3.6 References
Beier, C. (1998) Water and elemental fluxes calculated in a sandy forest soil taking spatial
variability into account. Forest Ecology and Management. 101: 269-280
Bellot, J., Escarre, A. (1991). Chemical characteristics and temporal variations of nutrients in
throughfall and stemflow of three species in Mediterranean holm oak forest. Forest
Ecology and Management. 41: 125-135
Bouten, W., Heimovaara, T., Tiktak, A. (1992). Spatial pattern of throughfall and soil water
dynamics in a Douglas fir stand. Water Resources Research. 28: 3227-3233
Bracchini, L., Loiselle, S., Dattilo, A.M., Mazzuoli, S., Cozar, A., Rossi, C. (2004). The spatial
distribution of optical properties in the ultraviolet and visible in an aquatic ecosystem.
Photochemistry and Photobiology. 80: 139-149
Brinson, M.M., Bradshaw, H.D., Holmes, R.N., Elkins, J.B. (1980). Litterfall, stemflow, and
throughfall nutrient fluxes in an alluvial swamp forest. Ecology. 61: 827-835
Cao, Z., Ma, J. (2009). Summer severe-rainfall frequency trend and variability over Ontario,
Canada. Journal of Applied Meteorology and Climatology. 48: 1955-1960
Chang, S.C. and 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
Clarke, N., Wu, Y., Strand, L.T. (2007). Dissolved organic carbon concentrations in four
Norway spruce stands of different ages. Plant Soil. 299: 275-285
86
Cory, R.M., Boyer, E.W., McKnight, D.M. (2011). Spectral methods to advance understanding
of dissolved organic carbon dynamics in forested catchments. Forest Hydrology and
Biogeochemistry: Synthesis of Past Research and Future Directions. 216: 117-135
Credit Valley Conservation (CVC) (2010). Monitoring Wetland Integrity within the Credit
River Watershed. Chapter 1: Wetland Hydrology and Water Quality 2006- 2008. Credit
Valley Conservation. 79p
Crockford, R.H., Richardson, D.P. (1990). Partitioning of rainfall in a eucalypt forest and pine
plantation in southeastern Australia: II. Stemflow and factors affecting stemflow in a dry
sclerophyll eucalypt forest and a Pinus radiata plantation. Hydrological Processes. 4:
145-155
Crockford, R.H. and Richardson, D.P. (2000). Partitioning of rainfall into throughfall, stemflow
and interception: effect of forest type, ground cover and climate. Hydrological
Processes. 14: 2903-2920
Dalva, M., Moore, T.R. (1991). Sources and sinks of dissolved organic carbon in a forested
swamp catchment. Biogeochemistry. 15: 1-19
Dittman, J.A., Driscoll, C.T., Groffman, P.M., Fahey, T.J. (2007). Dynamics of nitrogen and
dissolved organic carbon at the Hubbard Brook Experimental Forest. Ecology. 88:
1153-1166
Draaijers, G.P.J., Erisman, J.W., Van Leeuwen, N.F.M., Romer, F.G., Te Winkel, B.H.,
Veltkamp, A.C., Vermeulen, A.T., Wyers, G.P. (1997). The impact of canopy exchange
on differences observed between atmospheric deposition and throughfall fluxes.
Atmospheric Environment. 31: 387-397
Eschner, A.R. (1967). Interception and soil moisture distribution. In: Sopper, W.E., Lell, H.W.
(Eds.), Forest Hydrology. Pergamon, Oxford, 191-200
Fellman, J.B., D’Amore, D.V., Hood, E., Boone, R.D. (2008). Fluorescence characteristics and
biodegradability of dissolved organic matter in forest and wetland soils from coastal
temperate watersheds in southeast Alaska. Biogeochemistry. 88: 169-184
Gu, C., Anderson, W., Maggi, F. (2012). Riparian biogeochemical hot moments induced by
stream fluctuations. Water Resources Research. 48: W09546
Guggenberger, G., Zech, W., Schulten, H.R. (1994). Formation and mobilization pathways of
dissolved organic matter: evidence from chemical structural studies of organic matter
fractions in acid forest floor solutions. Organic Geochemistry. 21: 51–66
Hamdan, K., Schmidt, M. (2012). The influence of bigleaf maple on chemical properties of
throughfall, stemflow, and forest floor in coniferous forest in the Pacific Northwest.
Canadian Journal of Forest Research. 42: 868-878
87
Helms, J.R., Stubbins, A., Ritchie, J.D., Minor, E.C., Kieber, D.J., Mopper, K. (2008).
Absorption spectral slopes and slope ratios as indicators of molecular weight, source
and photobleaching of chromophoric dissolved organic matter. Limnology and
Oceanography. 53: 955-969
Inamdar, S., Shatrughan, S., Dutta, S., Levia, D., Mitchell, M., Scott, D., Bais, H., McHale P.
(2011). Fluorescence characteristics and sources of dissolved organic matter for stream
water during storm events in a forested mid-Atlantic watershed. Journal of Geophysical
Research. 116: G03043
Janisch, J.E., Harmon, M.E. (2002). Successional changes in live and dead wood carbon stores:
implications for net ecosystem productivity. Tree Physiology. 22: 77-89
Kolka, R.K., Nater, E.A., Grigal, D.F., Verry, E.S. (1999). Atmospheric inputs of mercury and
organic carbon into a forested upland/bog watershed. Water, Air and Soil Pollution. 113:
273–294
Kramer, I and Holscher, D. (2009). Rainfall partitioning along a tree diversity gradient in a
deciduous old-growth forest in Central Germany. Ecohydology. 2: 102-114
Krupa, S.V. (2003). Effects of atmospheric ammonia (NH3) on terrestrial vegetation: a review.
Environmental Pollution. 124: 179-221
Levia, D.F. and Herwitz, S.R. (2002). Winter chemical leaching from deciduous tree branches as
a function of branch inclination angle in central Massachusetts. Hydrological Processes.
16: 2867-2879
Levia, D.F. and Frost, E.E. (2003). A review and evaluation of stemflow literature in the
hydrologic and biogeochemical cycles of forested and agricultural ecosystems. Journal
of Hydrology. 274: 1-29
Levia, D.F., Van Stan, J.T., Inamdar, S.P., Jarvis, M.T., Mitchell, M.J., Mage, S.M., Scheick,
C.E., Mchale, P.J. (2012). Stemflow and dissolved organic carbon cycling: temporal
variability in concentration, flux, UV-Vis spectral metrics in a temperate broadleaved
deciduous forest in the eastern United States. Canadian Journal of Forest Research. 42:
207-216
Liechty, H.O., Kuuseoks, E., Mroz, G.D. (1995). Dissolved organic carbon in northern
hardwood stands with differing acidic inputs and temperature regimes. Journal of
Environmental Quality. 24: 927–933
Liu, C.P., Sheu, B.H. (2003). Dissolved organic carbon in precipitation, throughfall, stemflow,
soil solution, and stream water at the Guandaushi subtropical forest in Taiwan. Forest
Ecology and Management. 172: 315-325
88
Lovett, G.M. (1994). Atmospheric deposition of nutrients and pollutants in North America: an
ecological perspective. Ecological Applications. 4: 629-650
Mahendrappa, M.K. (1990). Partitioning of rainwater and chemicals into throughfall and
stemflow in different forest stands. Forest Ecology and Management. 30: 65-72
Manderscheid, B. and Matzner, E. (2000). Spatial and temporal variation of soil solution
chemistry and ion fluxes through the soil in a mature Norway spruce (Picea abies (L.)
Karst.) stand. Biogeochemistry. 30: 99-114
McClain, M.E., Boyer, E.W., Dent, C.L., Gergel, S.E., Grimm, N.B., Groffman, P.M., Hart,
S.C., Harvey, J.W., Johnston, Mayorga, C.A.E., McDowell, W.H. and Pinay, G. (2003).
Biogeochemical Hot Spots and Hot Moments at the Interface of Terrestrial and Aquatic
Ecosystems. Ecosystems. 6: 301-312
Mina, V. N. (1965). Leaching of certain substances from woody plants by rain, and its
importance in the biological cycle. Pochvovedenie. 6: 7-17
Mitch, W.J., and Gosselink, J.G. (2007). Wetlands. John Wiley & Sons Inc., 4th Edition
Mitchell, C.P.J, Branfireun, B.A., Kolka, R.K. (2008). Spatial characteristics of net
methylmercury production hot spots in peatlands. Environmental Science and
Technology. 42: 1010-1016
Moore, T.R., Jackson, R.J. (1989). Dynamics of dissolved organic carbon in forested and
disturbed catchments, Westland, New Zealand. Water Resources Research. 25: 1331-
1339
Ontario Ministry of Municipal Affairs and Housing (OMMAH). 2005. Greenbelt Plan. 57p.
http://www.mah.gov.on.ca/Page189.aspx#greenbelt. (accessed February 2015)
Patton, C.J., Kryskalla, J.R. (2003). Methods of analysis by the U.S. Geological Survey
National Water Quality Laboratory- Evaluation of alkaline persulfate digestion as an
alternative to Kjeldahl digestion for determination of total and dissolved nitrogen and
phosphorus in water. USGS Water Resources Investigations Report. 03-4174
Pelster, D.E., Kolka, R.K., Prepas, E.E. (2009). Overstory vegetation influence nitrogen and
dissolved organic carbon flux from the atmosphere to the forest floor: Boreal Plain,
Canada. Forest Ecology and Management. 259: 210-219
Qualls, R.G., Haines, B.L., Swank, W.T. (1991). Fluxes of dissolved organic nutrients and
humic substances in a deciduous forest. Ecology. 72: 254-266
Raat, K.J., Draaijers, G.P.J., Schaap, M.G., Tietema, A., Verstraten, J.M. (2002) Spatial
variability of throughfall water and chemistry and forest floor water content in a
Douglas fir forest stand. Hydrology and Earth System Sciences. 6: 363-374
89
Schume, H., Jost, G., Katzensteiner, K. (2003). Spatio-temporal analysis of the soil water
content in a mixed Norway spruce (Picea abies (L.) Karst.)- European beech (Fagus
sylvatica L.) stand. Geoderma. 112: 273-287
Sleutel, S., Vandenbruwane, J., De Schrijver, A., Wuyts, K., Moeskops, B., Verheyen, K., De
Neve, S. (2009). Patterns of dissolved organic carbon and nitrogen fluxes in deciduous
and coniferous forests under historic high nitrogen deposition. Biogeosciences. 6: 2743-
2758
Tukey, H.B. (1970). The leaching of substances from plants. Annual Review of Plant
Physiology. 21: 305-324
Vidon, P., Allan, C., Burns, D., Duval, T.P., Gurwick, N., Inamdar, S., Lowrance, R., Okay, J.,
Scott, D., Sebestyen, S. (2010). Hot Spots and Hot Moments in Riparian Zones: Potential
for Improved Water Quality Management. Journal of the American Water Resources
Association (JAWRA). 1-21
Weishaar, J.L., Aiken, G.R., Bergamaschi, A., Farm, M.S., Fujii, E., Mopper, K. (2003).
Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition
and reactivity of dissolved organic carbon. Environmental Science and Technology. 37:
4702- 4708
Wilson, E.J. (1992). Foliar uptake and release of inorganic nitrogen compounds in Pinus
sylvestris L. and Picea abies (L.) Karst. New Phytologist. 120: 407-416
Zhou, Q.Y., Shimada, J., Sato, A. (2002). Temporal variations of the three-dimensional rainfall
infiltration process in heterogeneous soil. Water Resources Research. 38: 1-15
Zhu, Q., Schmidt, J.P., Bryant, R.B. (2012). Hot moments and hot spots of nutrient losses from
a mixed use watershed. Journal of Hydrology. 414-415: 393-404
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Chapter 4:
Conclusion
The cumulative results of this study quantify the hydrological and biogeochemical inputs
that precipitation provides to temperate swamp ecosystems of southern Ontario. More
specifically, the quantification of water, dissolved organic matter and nitrogen delivered by
throughfall and stemflow has now been characterized for these important ecosystems.
The meteorological influences on throughfall and stemflow in two temperate swamps of
southern Ontario have now been investigated and provide key water balance information to
conservation authorities and land managers wishing to maintain these important ecosystems.
Furthermore, precipitation partitioning after a canopy-damaging ice storm has now been
investigated. Results from Chapter 2 show average event-based throughfall of 70.5% of
precipitation for a maple swamp in 2013 and 75.1% in 2014. Average event-based throughfall
for an ash swamp was 80.8% in 2013 and 85.7% of event precipitation in 2014. Stemflow fluxes
in both swamps were < 1% of incident precipitation for both years. As such, land managers may
only require throughfall estimates in order to quantify the amount of precipitation reaching the
floor in swamp ecosystems, as the amount of stemflow input is almost negligible. Seasonal TF%
in this study were 83% (both years) for the maple swamp, while in the ash swamp seasonal TF%
were 85% and 87% in 2013 and 2014, respectively. These seasonal TF% were found to be higher
than comparable upland deciduous systems, while seasonal SF% of <1% in both sites was much
less than upland forests. Furthermore, this study showed that the amount of rainfall received at
the swamp floor was greater after the ice storm occurred. Increases in event-based throughfall by
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~5% at both sites, combined with slight decreases in stemflow provide evidence of this
conclusion.
Chapter 3 presented the investigation of dissolved organic carbon (DOC) and nitrogen
(N) fluxes within throughfall and stemflow for two temperate swamps during the 2014 growing
season. DOC fluxes were found to be higher in the ash-dominated swamp than the maple-
dominated swamp. Throughfall fluxes dominated the input of DOC to these sites. Stemflow
DOC fluxes were much lower due to the very small hydrologic inputs that stemflow provided in
these systems. The bioavailability of dissolved organic matter (DOM) in the ash site was higher
than the DOM of the maple site, resulting from differing interactions between throughfall and
stemflow and the different canopy types. Nitrogen fluxes in throughfall and stemflow were less
than precipitation fluxes in both swamps, indicating that an uptake of nitrogen occurred within
the swamp canopy. Nitrogen fluxes were also higher in the ash swamp, resulting from a more
open canopy that sequestered less nitrogen than the maple swamp for all nitrogen species.
Therefore, forest species composition/density have important controls on nitrogen cycling
through the canopy.
Recommendations for future research related to this project include; linking remote
sensing data that quantifies the amount of canopy loss after a perturbation to rainfall partitioning
in forested systems; investigating the effects of the Emerald Ash Borer on rainfall partitioning
(particularly in ash-dominated forests); determining the fate of dissolved carbon and nitrogen
once it reaches the swamp floor (hot spots of C- and N-cycling within the soil); and investigating
chemical fluxes contained within throughfall and stemflow of swamps that were not addressed in
this study (i.e. K, P, Mg, Na, Cl and S delivery).
