Diurnal Patterns and Microclimatological Controls on Stomatal Conductance and Transpiration at
High Creek Fen, Park County, Colorado.
Heide Maria Baden,
Department of Geography, University of Colorado,
Boulder.
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This Master Thesis has been defended before the following committee:
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Acknowledgements This research was funded in part by The Nature Conservancy. Additional
support was granted by the Germanistic Society of America and the Graduate School of this University. I thank Terri Schulz of The Nature
Conservancy for her support in the field and on the defense committee. I especially thank Peter Blanken for outstanding and persistent advice. I
further thank Karen Weingarten, our graduate secretary for immeasurable patience and support. Last but not least I thank my parents for their
everlasting love.
Fuer die Regenbogenkinder
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TABLE OF CONTENTS
SIGNATURE PAGE........................................................................... ii
ACKNOWLEDGEMENTS AND DEDICATION.............................. iii
TABLE OF CONTENTS.................................................................... iv LIST OF TABLES.............................................................................. vii LIST OF FIGURES............................................................................ viii
LIST OF PHOTOGRAPHS................................................................ xii
LIST OF SYMBOLS........................................................................... xiii
CHAPTER 1. INTRODUCTION
1.1. OBJECTIVES OF THIS RESEARCH……………......... 1 1.2. THE SCALE OF THE DISCIPLINE………………......... 4 1.3. EVAPORATION AND EVAPOTRANSPIRATION......... 5
CHAPTER 2. LITERATURE REVIEW
2.1. LITERATURE REVIEW OF EARLY WORKS..……...... 9
2.1.1. I.S. BOWEN AND THE BOWEN RATIO…………........ 9 2.1.2. H.L. PENMAN AND POTENTIAL EVAPORATION…. 10 2.1.3. C. WARREN THORNTHWAITE ……………………… 13 2.2. THE FIELDS OF AGRO-AND BIOMETEOROLOGY.. 18 2.3. BIOCLIMATOLOGY AND HUMAN HEALTH……….... 19
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2.4. AGROMETEOROLOGY AND CROPS……………...... 21 2.5. RECENT PUBLICATIONS…………………………....... 26 2.5.1. JOHN L. MONTEITH…………………………………..... 26 2.5.2. BIOMETEOROLOGICAL MODELING……………….... 29 2.6. CONCLUSION………………………………..………….. 33
CHAPTER 3. BACKGROUND
3.1. INTRODUCTION…………..…………………………….. 36 3.2. PHOTOSYNTHESIS AND ENERGY BALANCE ..…... 38 3.3. STUDY SITE DESCRIPTION ………………………….. 45 3.3.1. TOPOGRAPHY, HYDROGEOLOGY, AND
HISTORY………………………………………………..... 45 3.3.2. CLIMATE AND ENERGY BALANCE AT
HIGH CREEK FEN……..………………………………... 51 3.3.3. VEGETATION AT HIGH CREEK FEN……………….... 53 3.4. THE FOUR SITES AND THEIR INHABITANTS…….... 55 3.5. STUDY HYPOTHESES……......................................... 60 3.5.1. PROBLEM STATEMENT 1: DOES HEIGHT ABOVE
GROUND INFLUENCE PHYSIOLOGICAL RESPONSES WITHIN AN INDIVIDUAL SPECIES?.... 61
3.5.2. PROBLEM STATEMENT 2: DOES SOIL MOISTURE CONTROL RATES OF STOMATAL CONDUCTANCE AND TRANSPIRATION FROM SAME SPECIES IN DIFFERING LOCATIONS?........................................... 62 3.5.3. PROBLEM STATEMENT 3: WHEN EXPOSED TO THE SAME MICROCLIMATE, DO DIFFERENT SPECIES
VARY IN STOMATAL CONDUCTANCE AND TRANSPIRATION?....................................................... 63
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CHAPTER 4. METHODS
4.1. INTRODUCTION……..........……………………………. 65 4.2. ON-SITE CLIMATE STATION………………………...... 65 4. 3. METHODS OF DATA COLLECTION AT THE FOUR
SITES.......................…………………………………….. 67 4.4. THE DATA SET………………………………………….. 71 4.4.1. DATA SET PREPARATION…………………………….. 74
CHAPTER 5. RESULTS 5.1. INTRODUCTION……………………………………....... 77 5.2. METEOROLOGICAL DATA OBSERVED
BY THE TOWER……………………………………….... 77 5.3. RESULTS FOR PROBLEM STATEMENT 1………….. 78 5.4.1. RESULTS FOR PROBLEM STATEMENT 2.a……….. 90 5.4.2. RESULTS FOR PROBLEM STATEMENT 2.b……….101 5.5. RESULTS FOR PROBLEM STATEMENT 3………....105
CHAPTER 6. DISCUSSION.....................................131
CHAPTER 7. CONCLUSION.....................................137 REFERENCES.................................................................................142 APPENDIX A....................................................................................147 APPENDIX B....................................................................................148
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LIST OF TABLES
Table 5.1. Minima, maxima, and means of transpiration [E] in mmol m-2 s–1 and stomatal conductance [g] in mol m-2 s–1 for S. monticola at z = 40, 70, 100 cm.
Table 5.2. Transpiration [E] measured from three distinct heights of S. monticola measured on DOY 188 (July 7th), 2001 expressed in mmol m-2 h–1 and g H2O m-2 h-1.
Table 5.3. Minima, maxima, means, and standard deviations of in
the wet [ (w)] and dry [ (d)] location. Ranges were 8 and 6% for the wet and dry location, respectively.
Table 5.4. Comparing the means of transpiration [E] and stomatal conductance [g] for the two populations (d) and (w) via a paired samples t-test, results show paired samples correlations for E and g of S.candida in dry and wet location as highly significant.
Table 5.5. Comparing paired samples differences of transpiration [E] and stomatal conductance [g] show a higher predictability of the differences in g (80.2 % confidence) than differences in E (35 % confidence).
Table 5.6.a. Transpiration [E], expressed in mmol m-2 h-1 and g m-2 h-1, on DOY 174 (June 23rd), 2001, from S. candida (d) in soil moisture
[ ] ~45 % and S. candida (w) in ~50 %. Table 5.6.b. Transpiration in the wet location [E (w)] exceeds transpiration in the dry location [E (d)] by 30.0 %. Hence, S.candida
(w) in ~50% transpired one third more than S.candida (d) in ~45%. Table 5.7 Transpiration [E] from all six species on DOY 191 (July 10th), 2001 expressed in mmol and grams H2O m-2 s-1 as well as h-1. Fluxes are listed in decreasing order from top to bottom. Table 5.8. Mean daily stomatal conductance [g] from all six species on DOY 191 (July 10th), 2001 expressed in mol m-2 s-1 as well as h-1.
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LIST OF FIGURES
Figure 3.1. Map shows the northwestern part of the Garo quadrangle topographic map; the study site located near High Creek is circled; the Colorado index map shows the location of Park County. Figure 3.2. Soil moisture transect from southeast (0) to northwest (1000 m) taken across the fen on July 1st, 2001. With distance
increments of 33 m, 31 data points were recorded. Low values represent areas outside the fen. Figure 4.1. Wetting and Drying Curve of 1500 cm3 High Creek Fen Soil determined in the laboratory. Wetting: 20x75 ml of H2O were added to the oven-dried soil in increments of 5 minutes; through this process, actual soil moisture was continuously increased by 5 %, and HydroSense delay times were recorded. Drying: soil was repeatedly placed in oven, weighed, and delay times were recorded, until no further weight was lost. The following fit was created for all data
points: = - 55.36 + 62.74 ms +13.97 ms2.
Figure 4.2. HydroSense Calibration Curve from both wetting and drying curve data; to view the fit from this new calibration, this figure shows how the originally reported delay time increasingly
overestimates increasing actual volumetric water content [ ] by a factor of up to 2 at saturation. Figure 5.1. Vapor pressure deficit [VPD] and air temperature [TA] as
observed by the tower for DOY 188 as decimal time, where 188 = 00:00:00 hours on July 7th, and 188.5 = noon. Graph shows that VPD is a function of TA.
Figure 5.2.a. Stomatal conductance [g] for S. monticola from leaves at heights of z = 40 cm, z = 70 cm, and z = 100 cm. Figure 5.2.b. Transpiration [E] and from leaves of S. monticola at heights of z = 40, z = 70, and z = 100 cm. Figure 5.3.a. Leaf temperature [TL] of S.monticola and quantum flux [Q] measured at a leaf at 40 cm height show that the plant’s TL does not react to Q. Also, compared to the incident radiation at z = 100, this height of z = 40 catches a larger amount more quickly in the morning (e.g., from 06:30 until 07:00, the leaf receives 100 to 850
mol m-2 s-1).
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Figure 5.3.b. Leaf temperature [TL] of S. monticola and quantum flux [Q] measured at a leaf of 70 cm height.
Figure 5.3.c. Leaf temperature [TL] of S. monticola and quantum flux [Q] measured at a leaf located at 100 cm tree height. Compared to the other heights, this part of the plant reacts with TL most aggressively to a change in Q. Figure 5.4.a. Regression of transpiration rates (E) of S. candida in the dry location against E from S. candida in the wet location as mmol H2O transpired m-2 s-1.
Figure 5.4. b. Regression of stomatal conductances (g) of S. candida in the dry location against g of S. candida in the wet location expressed as molar flux through stomatal magnitude m-2 s-1.
Figure 5.5.a. Transpiration [E] for S. candida on DOY 174 in a dry (d) and wet (w) location show a visible, although not statistically significant difference in mmol of E released m-2 s-1 throughout the day; the mid-day data gap is due to temporary system failure.
Figure 5.5.b. Stomatal conductance [g] for S.candida in the dry (d) and wet (w) location again show a visible, however, not statistically significant difference in the flux of mol m –2 s-1 of g on DOY 174 (summer solstice).
Figure 5.6. The scatter plot shows mean daily transpiration [E] in
dependence upon soil moisture []. Plant locations 1 – 3 were grouped as the drier locations, 4 – 6 as the mesic, and 7 – 9 as the
wet, close to saturated locations. E from case 3 with av = 20.8 % did not differ from the average E values produced by cases 7 and 9. Figure 5.7. The scatter plot shows mean daily stomatal
conductance [g] in dependence upon soil moisture []. Again, cases 1 – 3 were grouped as the drier locations, 4 – 6 as the mesic, and 7 – 9 as the wet, close to saturated locations.
Figure 5.8. Stomatal conductance [g] plotted against quantum flux [Q] for all six species investigated at High Creek Fen. Data may be compared with general statements made about C3 plants in Nobel (1999).
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Figure 5.9. Stomatal conductance [g] in dependence upon leaf temperature [TL] of all six species investigated at High Creek Fen. Data may be compared with general statements made about C3 plants in Nobel
(1999), where photosynthetic rate doubles between 20 and 30 C, and
maximizes between 30 and 40 C. Figure 5.10. Stomatal conductance [g] as controlled by vapor pressure deficit [D] surrounding all six plant species investigated at High Creek Fen. Usually, g can be expected to decrease exponentially with increasing D. Since D is highly correlated with TL, most data points are expected to fall into the same quadrant from both this, and the previous figure (5.11.).
Figure 5.11. Stomatal conductance[g] regressed with soil moisture
[] measured in the separate locations of the six plants researched in the fen; this graph should not be interpreted as revealing soil moisture tolerance ranges – respective plants may grow in areas not
represented here. However, all spectra of B. glandulosa as well as
most spectra of S. candida should be found in this graph; the researcher searched the fen for locations of these species that
encompassed the complete range in this fen. Generally, all plant underlying soils were saturated between 50 and 55 %.
Figure 5.12. Transpiration [E] and stomatal conductance [g] from Betula glandulosa on DOY 191 (July 10th), 2001. This species reaches gmax around 10:00 a.m., and then gradually decreases g over the afternoon, when TL and D become limiting. As seen from Table 5.7., B. glandulosa ranks highest in E compared to the other five species. Figure 5.13. Transpiration [E] and stomatal conductance [g] from Carex aquatilis on DOY 191; here, mid-day stomatal depression effecting necessary reduction of the quantity of water vapor demand by the atmosphere is evident. Compared to gmax from B. glandulosa and S. brachycarpa, gmax from C. aquatilis is a third, and half as large as that of S. monticola. S. candida exceeds it by a factor of 2.5. Figure 5.14. Transpiration [E] and stomatal conductance [g] from
Salix brachycarpa on DOY 191. Again, mid-day stomatal depression to reduce water stress is evident. Morning conductance allows this species to still rank third in E compared to the other five species.
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Figure 5.15. Transpiration [E] and stomatal conductance [g] from Salix candida on DOY 191; compared to the previously seen (5.12 – 5.14) flux developments over time, the silver willow shows a high morning, toward evening gradually decreasing g. Nevertheless, mid-day stomatal depression is visible, as well as a second depression starting after 14 hours solar time (15:10 MDT), when the tower showed a solar flux of 1008 W m-2. Stomatal conductance increased after 15 hours (16:10 MDT), when intensity of radiation dropped again.
Figure 5.16. Transpiration [E] and Stomatal conductance [g] from Salix monticola on DOY 191. As also seen from Table 5.7., this species seems best adapted to its environment, since it has the strongest E of all compared plants. Clouds were over the area when the steep drop in stomatal conductance occurred around 13:30 hours solar time. Possible explanation for the drop in g may be a TL of 32.8
C at this time, which may have caused the partial stomatal closure. Figure 5.17. Transpiration [E] and Stomatal conductance [g] from Salix planifolia on DOY 191 show the typical behavior of an unstressed plant with no mid-day stomatal depression. Ranking 5th in E and g (Tab. 5.7.) might allow a stress-free life in this environment.
Figure 5.18. Stomatal conductance [g] from B. glandulosa, S. candida, C. aquatilis, S. monticola, S. brachycarpa, and S. planifolia on DOY 191. On this daily basis, C. aquatilis conducted least, S. monticola most. See Tables 5.7. and 5.8. for numeric details.
Figure 5.19. Transpiration [E] from B. glandulosa, S. candida, C. aquatilis, S. monticola, S. brachycarpa, and S. planifolia on DOY 191. On this daily basis, S. planifolia conducted least, B. glandulosa most amounts of H2O. S. planifolia was also the least stressed (no mid-day stomatal depression). See Table 5.7. and 5.8. for numeric details.
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LIST OF PHOTOGRAPHS
Title page Sunrise over High Creek Fen in Summer 2001.
Photograph 3.1. Cumulus Cloud over High Creek Fen (view to NE) in Summer 2001.
Photograph 3.2. View across the fen from NW (transect survey pole) to SE shows approximate transect location; the location of the meteorological tower is included on transect.
Photograph 3.3. Dense ground-cover of willow, birch, and sedge at High Creek Fen, Summer 2001. Blue Spruce in the background greatly influence turbulence at the site.
Photograph 3.4. Betula glandulosa (Swamp Birch) in a drier location at High Creek Fen. Summer 2001. This species occurs in a range of
locations where 15 % < < 60 %.
Photograph 3.5. Close view of the thick, dark-green leaves of Salix candida (Silver Willow). Although not measured, S.candida’s physiology suggests multi-storied, dense chlorophyll pigmentation.
Photograph 3.6. Salix monticola
Photograph 3.7. Salix brachycarpa
Photograph 4.1. On-site climate station in Summer 2001
Photograph 4.2. Porometer measurements by Researcher; machine strapped on via belt, storage module attached to belt on the back, cuvette in right hand.
Photograph 7.1. High Creek Fen looking west toward the Mosquito Range.
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LIST OF SYMBOLS
Symbol Definition Units
D Atmospheric Water Vapor Deficit kPa
E Transpiration mmol m-2s-1
g Stomatal conductance mol m-2s-1
gmax Maximum stomatal conductance mol m-2s-1
E Latent heat flux W m-2
K Incoming shortwave radiation W m-2
K Reflected shortwave radiation W m-2
L Incoming longwave radiation W m-2
L Reflected longwave radiation W m-2
Volumetric soil moisture %
Q Quantum flux mol m-2s-1
RH Relative humidity %
Rn Net radiation W m-2
TA Air temperature C
Tdew Dew point temperature C
TL Leaf temperature C
TS Soil temperature C
CHAPTER 1. INTRODUCTION
1.1. OBJECTIVES OF THIS RESEARCH
While broad-scale climates of the Earth‘s major vegetative
regions have been well studied, a fine-scale investigation of local
environments is required to understand the influence of both
atmosphere and soil on local vegetation dynamics. An area‘s
microclimate often distinguishes itself from the regional climate by
peculiarities such as soil texture, topography, or biomass (Rouse 2000).
As functions of microclimate, water and solar energy are among the
main lifelines for plants, and their abundance and availability are
therefore a question of precise locality. Assessing the sensitivity of
plants from different regions to soil moisture and microclimate allows
researchers to establish a gauge for these plants‘ susceptibility to
disturbances such as drainage and climate change.
Net all-wave radiation and its partitioned sensible and
evaporative heat flux are extremely important components of both the
energy and water balances of an area, especially those of high- latitude
and alpine wetlands, which partition up to 80% of their net radiation into
the evaporative, or latent heat flux [E] (Rouse 2000). Plants that
inhabit these areas therefore constitute a considerable local source of
2
water vapor to the atmosphere. Results from measuring and modeling
the E over such surfaces can aid researchers in improving current
climate models (Beringer et al. 2001).
This research focuses on fine-scale exchange of both water and
energy between the soil, the plant, and the atmosphere in a 750-acre
fen in central Colorado. In particular, the combined effects of the
atmospheric vapor pressure deficit, solar energy flux, leaf temperature,
and soil moisture availability on plant stomatal conductance and
transpiration of water vapor during the photosynthetically active part of
the day were examined. While a complete list of resources controlling
plant physiological responses includes N and CO2 (Kazda 1995), this
research investigates water and energy resources. Understanding their
role, their spatial and temporal distribution at certain locations, and their
availability and use in relationship to particular plant species was the
goal of this research.
Salicaceae (willow), Betulaceae (birch), and Cyperaceae
(sedges) are typical examples of wetland species of the arctic, alpine,
and boreal tundra regions. As meteorological and soil moisture
conditions exert limitations and affect the magnitude of plant
transpiration [E], this research focused on analyzing the effect of
variation in the spatial and temporal magnitudes of these environmental
3
variables on stomatal conductance [g]. First, g and E rates from one
individual of Salix monticola at three different heights (40 cm, 70 cm,
and 100 cm above ground) were compared. This was to assess
whether there was a significant difference in the magnitudes in g and
the plant‘s stomatal responses at different heights. Second, g and E of
two specimens of Salix candida situated in a dry (40-45 % volumetric
soil moisture, ) and a wet (50-55 %) location were compared. Third, g
and E of nine Betula glandulosa situated in dry (with an average of
18 %), mesic (35 %), and wet (51 %) locations were compared. Part
two and three of the study analyzed this soil moisture variability to
which plants in different microclimatological locations were exposed,
and evaluated intraspecific variation in g and E based upon soil
moisture abundance. Lasty, differences in g and E between six
different species exposed to the same environmental conditions were
examined. Species included were Salix monticola, S. brachycarpa, S.
planifolia, S. candida, Carex aquatilis and Betula glandulosa. This
fourth part of the study determined whether different species have
differing adaptations to the same microclimatological conditions.
Results of all four studies enhanced the understanding of local
vegetation dynamics in this high altitude wetland.
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1.2. THE SCALE OF THE DISCIPLINE
This research defines the microenvironment as the area that
surrounds an animate object, e.g. a plant, an animal, or a human being.
The scale beyond which neither the object, nor its environment have a
direct or indirect influence on each other shall be the limit to the micro
scale. Micrometeorology concerns itself with the processes that occur
within or closely above the atmospheric boundary layer, beyond which
the Earth‘s surface has little influence on the atmospheric processes.
The height of the boundary layer varies constantly with wind and
temperature. On a calm day with a large sensible heat flux, the height
of the boundary layer reaches its maximum. Correspondingly, ―areas
experiencing greater wind speeds tend to have shorter vegetation, such
as cushion plants in alpine tundra or the procumbent forms on coastal
dunes‖ (Nobel 1999). Inside the atmospheric boundary layer, turbulent
(wind-driven) transport is the predominant motion of the gas molecules
that make up the air. This research investigates the lower boundary of
the atmospheric boundary layer ending where the plant roots do not
reach any further. This soil-plant-atmosphere is the region of direct
hydrogeologic influence on the plant and its atmospheric environment.
However, potential upwelling of water from even deeper regions in the
ground must be considered.
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1.3. EVAPORATION AND EVAPOTRANSPIRATION
Evaporation and, in the presence of transpiring plants,
evapotranspiration are of the few basic climatic factors that scientists
are neither able to estimate easily, nor extrapolate from remotely
sensed data. They are important variables, because their values are
needed to assess the water, and the energy budget of all organisms.
Measurements taken on the ground are highly dependent on
numerous physical factors that include temperature, radiation, humidity,
soil moisture, and ground heat flux. As a mandatory agent to the
photosynthetic process, water is needed to dissolve carbon and keep
leaf surfaces cool. If a plant‘s water supply is at its end, i.e. the roots
cannot draw up any more water from the ground, the plant will dry up
completely. Plants have evolved physiological features to acclimate
themselves to their microclimate, and the physiology and phenology of
a plant tell a lot about the climate of the area.
As Lieth (1997) mentions in his abstract on phenological
monitoring, the "data on vegetation development provided by the
phenologists during the last two centuries are about the most reliable
information available for the evaluation of global trends of
environmental parameters." As an example of this, Blanken
(pers.comm. 2000) stated that the decrease in stomatal density on the
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leaves of plants over the past 1000 years is evidence that plants are
getting more efficient at photosynthesis as atmospheric CO2
concentrations have increased.
Evaporation has been and still is especially important in arid
climates such as the Southwestern United States, where this study has
been conducted. Here, the water supply for E depends on the relatively
small amount of precipitation that is received (often in the form of snow)
as well as underground aquifers that occasionally allow their water to
surface in streams. In these arid regions, the usually dry air constantly
demands water vapor from the surface of the earth, and its inhabitants.
Stream and ground water flow may be an important contributor
to the water supply of vegetated surfaces. As in the case of High Creek
Fen in Park County, Colorado, evapotranspiration exceeds precipitation
by a factor of 3 (Blanken, pers. comm. 2002). This fact ponders the
question where the additional water may be added to the system. The
hydrogeological processes seem to provide moisture to the
microenvironment through lateral in- and outputs of water from surface
and subsurface flow systems, such as those Rouse (1998) observed in
similar ecosystems. This goes to show that evaporation is not at all
strictly a function of infiltrated water just through precipitation. To
explain the amount of water evaporated by a surface, it is therefore
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necessary to acquire information about the hydrogeological features of
the ground beneath it. The potential amount of water available to the
plants at High Creek Fen is yet to be estimated through local research.
The prime factor that drives evapotranspiration, radiation, must
be investigated. Incident solar radiation is measured at the site by a
permanently installed pyranometer. If a cloud passes over the area, the
incident radiation is diminished, leading to several feedback processes,
which will be discussed in the later sections. The second factor that
accounts for the amount of E, the saturation vapor pressure deficit of
the air, gives an estimate of the evaporative demand at the surface.
The presence of plants on the surface greatly modifies the
energy balance and the partitioning between evaporation and
transpiration. Evaporation from the non-vegetated part of the surface,
as well as the amount of water transpired by the plant [E] yield
evapotranspiration [E]. The plants‘ transpiration rates are influenced
by the same physical factors as the rates of evaporation, however, their
need to conserve water will induce stomatal resistances that lower the
rate of transpiration. Stomata are the physiological means of plants to
regulate water loss and CO2 uptake throughout the photosynthetic
process. Biochemical triggers like hormones regulate stomatal
resistance, i.e. the partial closure of stomata, which, depending on the
8
saturation vapor pressure deficit [D], may lower the rate of transpiration.
9
CHAPTER 2. LITERATURE
2.1. LITERATURE REVIEW OF EARLY WORKS
Questions that explore the role of evapotranspiration in the water
budget and bioclimate have quite a long history, as well as an extended
field of origin. Scientific articles can be found since before the turn of
the 20th century, many of the early ones published in the U.S.
Department of Agriculture Bulletin; many articles on evaporation and
evapotranspiration came from several different scientific fields,
including physics and meteorology, agro-ecology, as well as hydrology,
soil science, botany and plant physiology. Having mentioned the
interconnectedness of micrometeorology to almost all physical
sciences, the first part of this chapter is focused on several earlier
publications that brought new thoughts and findings into the field.
2.1.1. I. S. BOWEN AND THE BOWEN RATIO
Bowen (1926) experimented with evaporation as a measurement
of latent heat loss in comparison to sensible heat loss. With his paper
on the Bowen Ratio, he introduced the ratio of the sensible heat flux [H]
to E, which typically ranges from 0.1 for an irrigated crop to 5 for
desert environments. The Bowen Ratio when combined with the
energy balance, is used in a great number of papers that concern
10
themselves with energy fluxes (e.g., Blanken and Rouse 1994, Burba et
al. 1999, Takagi 1998). Such values tell a knowledgeable climatologist
a lot about the place where it was measured, even if she has not been
there personally – much like the morphology of a plant gives away the
nature of its surrounding microclimate.
2.1.2. H.L. PENMAN AND POTENTIAL EVAPORATION
In 1947, the British meteorologist H.L. Penman modeled
evaporation in his well-known paper titled ―Natural evaporation from
open water, bare soil, and grass‖ published in the Proceedings of the
Royal Society of London, describing pan evaporation experiments, as
well as evaporation from soil and vegetation. His experiments only
looked at potential evapotranspiration, i.e. from water-saturated
surfaces. Although this did not account for stomatal conductance as a
resistance to the magnitude of plant transpiration, he laid the
groundwork for the still widely used Penman-Monteith combination
equation which models E as controlled by plant physiological
parameters.
In his introduction, Penman states, that ―a complete survey of
evaporation from bare soil and transpiration from crops should take into
account all relevant factors [but that his current] account will be largely
11
restricted to [considering processes] after thorough wetting of the soil
by rain or irrigation, when soil type, crop type and root range are of little
importance.‖ Penman goes into the physical requirements for the
occurrence of evaporation, which are ―a supply of energy to provide the
latent heat of vaporization [i.e. solar radiation] and some mechanism for
removing the vapor, i.e. there must be a sink for vapor.‖ His arguments
consider the laminar boundary layer in which non-turbulent, but
diffusive movement of air takes place. This is an important concept in
the aerodynamic considerations made when calculating fluxes at the
leaf level.
Penman‘s discussion on the energy balance introduces the
important concept of assumptions. In bioclimatological modeling,
assumptions must be made in order to translate the reality into
mathematical formulae. While the assumption of horizontal
homogeneity, for example, works well for oceans and lakes, it is an
assumption also made in most canopy flux models, so that x and y
coordinates are negligible, and all statistical moments (mean, variance,
skewness, kurtosis) are forced into the vertical z coordinates. Often
unrealistic to natural environments, assumptions allow scientists to rule
out possibilities by making reliable estimates, much like a predator
12
circling its prey (i.e. the research question). It is a slow, yet useful way
of approaching the solution to a hypothesis.
The assumption Penman makes is that the factor of heat storage
is negligible, a factor that indeed can be assumed zero for
measurements at the leaf level, however not at the canopy level
(Monson 2000). Penman admits that ―obtaining a reliable daily mean
value of the dew point temperature remains one of the main
experimental problems to be solved‖—data that with nowadays‘
technology is easily obtained (for example a chilled-mirror hygrometer).
Penman gives a detailed description of the instruments used. However,
to be meticulous about the description of the exact type or make of an
instrument, gives experienced micrometeorologists and other scientists
appropriate insight into potential errors of a measurement. It is also
mentioned that the accuracy of the cloudiness factor is a hard one to
obtain. The reason may be that although pyranometers had been
invented, measurements for 24 hours a day were taxing, whereas
nowadays, data loggers take the place of a measurement-reading
scientist (let‘s invent an automatic porometer).
The article is a cornerstone work in micrometeorology. Its
terminology is still used in today‘s lectures. The use of units is
confusing to metric scale users, since they are miles per day for wind
13
velocity, and switch between inches per month and mm/day for
evaporation, but fortunately the scientific community today is in the
process of collectively changing to the (more sensible) metric system.
2.1.3. C. WARREN THORNTHWAITE
Thornthwaite incorporated evaporation into his global climate
classification model (1951). His quantitative method distinguished
aridity from humidity in climates of the Low-Latitudes, Mid-Latitudes,
and High-Latitudes as a function of potential evaporation and soil-water
storage capacity reflected in the plants‘ need for water, which generally
increases from the poles toward the equator. His climate classification
is still used in geographic education. However, for the
microclimatologist, this kind of classification is of lesser interest. More
important here were Thornthwaite‘s contributions to bioclimatology on
the micro scale. The following paragraphs will explore some thoughts
of Thornthwaite and his group of scientists at Johns Hopkins University
in New Jersey.
A monograph on bioclimatology, compiled in 1954 by
Thornthwaite, May, and Mather, consists of several articles on the
effects of the physical environment on life, including human issues like
health and housing. In the book‘s preface, May points out that in light
14
of its omnipresence on Earth, bioclimatology‘s ―scope is tremendous‖.
The authors see the field in its early stage, where ―various niches of
ignorance will be filled as more […] data becomes available‖
(Thornthwaite et al. 1954). According to May, and not surprisingly, the
first man to concern himself with the field was the Greek Hippocrates.
His work that May refers to is Airs, Waters, and Places, a treatise that
deals with ―the action of climate on living things‖. Another interesting
part in the preface explains May‘s view on the variation of climate.
―Climates vary not only between the poles and the equator, between the level sea and the tops of the mountains, but between a hollow as big as the palm of one‘s hand in a field and a similar depression several feet away. All these variations occur according to natural laws, some of which man has discovered and learned to understand, some of which remain mysterious and represent the field of research for tomorrow.‖
May describes the processes between climate and physical
environment, which are constantly modifying each other, as in ―a race
towards a state of equilibrium that will never be reached‖.
From this same compilation, an article by Thornthwaite and
Mather (1953) titled ―Climate in Relation to Crops‖ gives interesting
historical facts about the first developments of bioclimatology, including
information on the 17th century French scientist Réaumur, who
developed an index in 1735 that attempts to quantify the heat required
15
for a plant to reach maturity. The index was acquired by summing the
degrees of mean daily air temperatures during certain stages of
development of a plant. Réaumur called this sum the ―thermal
constant‖ for the particular plant (Thornthwaite et al.1954), based on his
observations. Thornthwaite later explains how Réaumur was wrong
since ―his thermal constants were not constant,‖ but showed that one
plant in higher latitudes yielded a smaller constant than the same plant
in lower latitudes. Thus, ―less heat was required in cold climates than in
warm to bring about a given amount of development‖ and a cold year
had a smaller thermal constant than a warm year. Thornthwaite et al.
conclude their paragraph about Réaumur‘s heat index that ―the many
changes and refinements that have been introduced in recent years
have not removed the basic deficiencies of the heat unit theory.‖
Although this method did not render successful for crop
scheduling, its theory seems quite interesting. Keeping in mind that it
was developed 265 years ago, the ideas show scientific ingenuity and
expertise. Also, its findings harmonize with the zonal idea of climatic
regions, and with some climatic imagination, show May‘s idea that life is
modified by the environment, while at the same time the environment is
modified by life in a ―race for equilibrium‖.
16
Thornthwaite and Mather (1953) develop a list of concerns about
the current needs of the field of bioclimatology, and later describe their
method that stems from research with their group of bioclimatologists in
New Jersey. This approach will be outlined later. According to them,
the needs of the discipline in 1953 were a collection of observational
data, since the Federal Weather Service was obviously not able to
deliver anything but regional data, thus giving information on
―observations […] inadequate to the solution of most problems.‖ They
argue ―the climate of a region as determined by means of the
standardized observations is more or less of an abstraction‖ and ―the
region is a composite of innumerable local climates‖ including ravines,
south-facing slopes, hill tops, meadows, corn fields and woods. They
go on to say that ―the climates of areas of very limited extent are called
microclimates. They are clearly the ones that concern the farmer, the
agronomist and the biologist‖ (Thornthwaite et al. 1954).
The authors point out the importance of approaching the
problems, of, e.g., the effects of frost, drought or extremely high
temperatures on plants, from both the climatological as well as the
biological side through the cooperation of scientists from the respective
groups. This call for synthesis has, as far as I am concerned, been
increasingly heard, maybe because most attempts at integration have
17
proven very successful. This success could be attributed to the first
ecological principle, that all things are interrelated.
As with synthesis, another suggestion from the authors is the
development of a climatic calendar that organizes the observational
data according to the relationship between climate and plants. The
development of such a device could help ―schedule successive
plantings of vegetable crop to yield uniform harvest.‖ The Laboratory of
Climatology at Seabrook devised a method to control soil moisture,
targeting the ―twin problems of crop and irrigation scheduling.‖ Their
goal was not to just observe peas and corn, but to devise a more
comprehensive method that links the ―water used by plants in
transpiration and growth [to] the rate of plant development.‖
A well-developed discussion on the water budget of plants is
given, that introduces the term evapotranspiration. The ―return flow of
water from the ground to the atmosphere‖ is a ―climatic factor as
important as precipitation‖ that is not only dependent on climate, but
also ―related to certain vegetation and soil factors [such as] type and
stage of development of the vegetation, the method of cultivation, the
soil type, and above all the moisture content of the soil.‖ The
discussion goes on to distinguish the actual from the potential
evapotranspiration; the latter is reached only in a well-hydrated soil. Its
18
value is ―independent of soil type, kind of crop, or mode of cultivation
and is, thus, a function of climate alone.‖
The abstract explains further facts about plant processes. The
wording ―green plants manufacture food within their leaves by a
process called photosynthesis, using water from the soil and carbon
dioxide from the air as raw materials‖ may bring a smile to today‘s
reader‘s faces; it seems amazing that this article is not even 50 years
old, yet goes to show that Thornthwaite can truly be counted as one of
the forefathers of bioclimatology.
It should seem viable that young, beginning scientists owe much
gratitude to people like Thornthwaite‘s group, who explain these early
developments of bioclimatology with such patiently detailed vocabulary
and well-chosen examples that make understanding of the subject
easily possible. The words used are free of scientific vanity and their
sole purpose is straightforward communication.
2.2. THE FIELDS OF AGRO--AND BIOMETEOROLOGY
Agro- and biometeorology have made it their goal to elucidate
the relationships between organisms and their physical environment.
Both fields take the science of pure micrometeorology a step further, as
their questions concern themselves with the interactions of life forms
19
with their surrounding climatic situations. Incentives to tackle the
complexity of these relationships have been given by the potential
advantages of understanding these interactions, from maximizing the
yield of a crop to healing human diseases.
The first issue of the International Journal of Bioclimatology and
Biometeorology (this name later changed to International Journal of
Bioclimatology) was published in 1957. It featured four parts. One
concerned general bioclimatology, the second dealt with plant –
microclimate interactions. The third and fourth parts explored effects of
climate on animals and humans. The plant-related topics include a
paper on the influence of soil preparation on the microclimate of weedy
clear-cut fields before reforestation. Also, topics discussed guidelines
for bioclimatological measurements and whether microclimate can be
predicted (Pascale 1957).
2.3. BIOCLIMATOLOGY AND HUMAN HEALTH
The fourth section in the first edition of the above journal shows
that early concerns of bioclimatology stemmed not only from agricultural
incentives, but also from questions regarding climate's direct effects on
human beings. Those questions were, for example, acclimation to high
20
altitudes, predictability of asthma attacks, and the influence of
meteorological fronts on the general wellness of people.
Just one year later, in 1958, the medicinal journal Fundamenta
balneo-bioklimatologica was established, which deals with the
atmospheric influences on living organisms. According to Jordan
(1981), balneo-bioclimatology is both a subsection of bioclimatology
and balneology, i.e. therapy through baths, and it stands for applied
therapy through climate. I cite Jordan here not to go into detail about
balneo-bioclimatology, but because his thoughts are a valuable
contribution to understanding the development of bioclimatology. He
begins by citing Alexander von Humboldt's definition of climate as "all
changes in the atmosphere that noticeably affect our organs," thereby
speaking of the dialectic system of humans and their physical
surroundings. Jordan goes on to explain the difference between
looking at stimulus and response versus stimulus and responsibility.
'Stimulibility', or the readiness to be stimulated by outside processes,
modifies the reaction, and therefore the responsibility of an organism.
Changes occur along rhythmic or periodic processes. Jordan shares a
further thought by proposing that reactions can initiate either positive or
negative feedback mechanisms, since the stimulus may modify one
21
rhythm and that rhythm may then modify the response in either
direction.
This little excursion proves quite interesting, especially when
relating it to the mass and energy balances of vegetated surfaces. On
a sunny day, the balance of energy loss and gain at the surface can be
disturbed by the passage of a thick cloud. This occurs because the
cloud intercepts the path of the radiation, which again results in a net
heat loss at the surface of the earth. The now cooling surface will
diminish the water vapor concentration gradient between the surface
and the air (warmer air can hold more moisture), as well as cause a
lower temperature gradient, the results being less evapotranspiration
and a lower rate of sensible heat transfer. When the new gradients
have caused their respective responses to be adjusted, a new energy
balance has been established (Monson 2000).
2.4. AGROMETEOROLOGY AND CROPS
After this intermezzo of how bioclimatology affects humans
directly, this part of the chapter offers to look at literature that deals with
the climate's effects on human food, i.e. crops as an indirect relation to
humans. As mentioned above, evaporation and evapotranspiration are
very important processes especially in arid regions. Irrigation to
22
maximize crop yield has primarily been researched in those areas,
where dry conditions called for water resource management. During
the 1930s (in the late 1940s together with Criddle), Blaney researched
evaporation as well as evapotranspiration especially in the
Southwestern U.S. Their work, published primarily through the U.S.
Soil Conservation Service, developed ways of estimating ―consumptive
use and irrigation water requirements (Blaney and Criddle 1949).‖ A
number of other scientists also explored optimized timing of irrigation
(Van Bavel and Wilson 1952) in the pursuit of water resource
conservation (Veihmeyer 1951).
A study from the College of Agriculture at Berkeley, California
shows approaches taken toward irrigation methods in the late 1920s.
The authors Beckett, Blaney, and Taylor (1930) research the amount of
water required for irrigation to produce a successful crop of Avocado
and Citrus trees in San Diego County. The goal of the study was not
just crop maximization, but finding optimal irrigation efficiency, since
water resources were scarce and expensive even in the 1920s.
"Efficiency of irrigation is defined as the percentage of the water applied
that is shown in soil-moisture increase in the soil mass occupied by the
principal rooting system of the crop." The authors describe the
watersheds, classify soils and climate, and map the rainfall and soil
23
moisture patterns down to four feet depth. Detailed observations,
including height and age of trees, root development and the interval
between irrigation lead the authors to an "estimated seasonal
requirement [of water] at maturity." The study finds an average water
resource efficiency of 60% "under good irrigation practice." Finally, the
authors make several predictions about certain crops and their
particular irrigation needs during, e.g. a period of drought of "more than
6 weeks". An important result of the study was that, "as long as the soil
moisture is above the wilting point, the moisture content has no
measurable effect on the rate of moisture extraction," a warning to not
waste water through excessive irrigation.1
From the Commission for Agrometeorology (CAgM) of the World
Meteorological Organization (WMO), four agrometeorologists
(Seemann et al. 1979) chose to compile a book titled
"Agrometeorology," since students of this young discipline had no
complete reference book to study by. In this book, J. Seemann, who is
obviously an advocate of the meso-scale, or topoclimatology, defends
the topic of his choice with this abruptly ending sentence
"macroclimatology is based on a wide network of measurements and
does not register the special features resulting from topographical
1 I just recently visited Riverside County in CA, and was amazed by the amount of avocado and citrus trees. I am sure that Blaney and his fellow scholars laid the groundwork for this intensive use of irrigation in agriculture.
24
differentiation of the terrain, whereas the microclimate comprises areas
which are far too small.‖ 2 One can only guess, for what purposes his
statement would make sense, but maybe he was talking about a mid- to
large-size farm. And indeed, the microclimate can vary between two
areas just a few meters apart, yielding a problem with the accuracy of
larger scale prediction of e.g., highly accurate crop cycles.
However, Chirkov, the second author of the book
"Agrometeorology" is more precise when giving his ideas about
microclimate. He explains, "microclimate of meadows, fields, forest
fringes, glades, and lakes is produced by the disparity in the radiative
heating of the subjacent surface." Chirkov facilitates the agricultural
point of view toward microclimate by asking where to expect frost, when
to expect frost-free periods, and what the differences are between
south-facing versus north-facing slopes in respect to optimal time of
sowing. He coins the term "phytoclimate" as the "meteorological
conditions produced amongst plants" and therefore as a modified
microclimate that is "controlled by the structure of the plant cover [i.e.
height, density] and the width of inter-row spaces." Chirkov relates
species, habitus, age of plant community, density of stand (plantation),
as well as the sowing or planting method, illumination intensity, air and
2 I did not explore Seemann's article any further, but found his statement rather
funny and therefore worthy of being shared here.
25
soil temperature and humidity, and wind intensity values, to come to the
conclusion that the phytoclimate must be considered closely in order to
make predictions of any sort. He gives the example that a vegetated
soil can have a temperature difference of up to 25 C compared to a
soil in an open location.
For accurate information on planting, sowing, or irrigating, he
suggests that vertical measurements must be taken (an approach
fundamental to current-day research) and the fields‘ distances to a
reservoir or a forest strip are to be assessed. The data shall then be
compared to that of the nearest weather station. Maps shall be made
that mirror the practical importance of data for the plant development
and crop formation, an idea that resembles Thornthwaite's crop
calendar.
Finally, Chirkov suggests that for agricultural purposes, the
microclimate can be improved, e.g. in cold or humid climates by ridging
the surface to reduce overhumidification, or in arid regions by thinning
out timber to preserve moisture. Another strategy to reduce wind and
turbulence, and therefore soil erosion, according to Chirkov, is to plant
forest strips in between fields that are 25 times their height apart. If the
trees of the forest strips were 20 meters tall, Chirkov suggests one
26
forest strip every 500 meters. However, he does not go into potential
soil water competition between trees and crops.
2.5. RECENT PUBLICATIONS
After the groundwork of biometeorology has been highlighted, it
is worthy to now explore several paragraphs on contemporary work,
especially focusing on John L. Monteith, since he still plays a large role
in today‘s cutting edge of synthesizing science. Several other
researchers and their attempts to model mass and energy balances will
also be outlined. In the conclusion, the researcher‘s own view and
future goals about her place in the discipline will be mentioned.
2.5.1. JOHN L. MONTEITH
In ―Vegetation and the Atmosphere‖ (1975), one of Monteith‘s
many books, he states that ―micrometeorology is the measurement and
analysis of the state of the atmosphere near the surface of the earth
whether life is present or not. His main objective was to ―provide a
quantitative framework‖ for describing processes such as heat and
mass transfer in terms of the prevalent mechanisms that operate
through radiative heat exchange, turbulent diffusion, or conduction of
heat in the soil. Like his fellow Penman, Monteith stresses the
27
importance of considering the distribution of sources and sinks of heat,
mass, and momentum in the canopy, mechanisms that are currently still
being explored by biometeorologists, and that are hard to quantify
directly.
Interestingly, Monteith mentions the dialectic that
―micrometeorologists have tended to regard vegetation as a steady
state system [which it is not, whereas] plant physiologists have tended
to overlook the significance of the state of the system [i.e. the
atmosphere].‖ With this comment, he stresses the importance of
sharing insights amongst scientists from seemingly separate fields. He
praises the recent contributions biochemists have made to ―our (i.e. the
meteorologists‘) understanding of physiological mechanisms elucidating
biochemical pathways, interactions, and feedback.‖
Monteith‘s thought on biometeorologic models ― [which] link
adjacent levels of organization from cell to leaf, leaf to plant, plant to
community‖ is that ―the input to such models is a set of equations
(received by assumptions) relating the rates of processes to the states
which govern these rates.‖ An example has been outlined in the last
paragraph of the section on human health. The processes Monteith is
talking about are physical and chemical, and his following elaborations
stress the intricate and complex interrelationships between the ―state of
28
the environment, the state of the plant, and the nature of the relevant
physical and physiological mechanisms.‖ Monteith expanded
Penman‘s energy balance equation to the Penman-Monteith
combination equation, in which he considers the effects of physiology
on aerodynamic and stomatal resistances. His modification allows
scientists to predict processes much more accurately.
In a later section, he mentions micrometeorology‘s contributions
to ecology, which include such application of physical principles to the
―relationship of states to processes.‖ Such principles are Newton‘s Law
of Motion explaining the transfer of momentum; the First Law of
Thermodynamics elucidating the radiation balance; the Conservation of
Mass for water balance; Ohm‘s Law for understanding resistance, and
Fick‘s Law to explain diffusion.
Conclusively, Monteith suggests the importance of applying
micrometeorologic knowledge to ameliorate crop successes, to
understand the relationship between weather and disease, or even the
parasite susceptibility of a host, that is often related to ―certain physical
states like temperature and humidity.‖ To achieve this, Monteith calls
for ecological records to be ―interpreted by interdisciplinary teams of
physicists and biologists‖ while keeping in mind that progress in this
field can only be maintained with a ―sensible balance between all these
29
essentials: development of instruments and recording systems,
interpretation of measurements, construction of mathematical models,
and most of all, the collaboration of micrometeorologists and ecologists
prepared to learn from each other.‖
Monteith has followed this vision. In 1995's "Accomodation
between Transpiring Vegetation and the Convective Boundary Layer",
outlines the interactions of meteorology and vegetation, giving special
regard to feedback mechanisms in the relationships of soil-plant, plant-
surface layer, and surface layer-planetary boundary layer. These
include the crucial balancing role of stomata in the physical
dependencies of fluxes and resistances to fluxes. Monteith's paper is
an extraordinary example of recent synthesis, as it combines the latest
findings of biochemistry, physiology, and environmental physics.
2.5.2. BIOMETEOROLOGICAL MODELING
Current research on the microclimatological boundary-layer
scale is extremely active. The field has been influenced by many of the
physical sciences, as each field‘s advances of knowledge contribute to
the understanding of the whole complex web of complicated processes.
With technological innovations, intricate measurements of biosphere—
atmosphere interactions have been made possible, e.g. the eddy-
30
covariance technique that simultaneously measures large-scale fluxes
of certain entities, e.g. CO2 concentration and vertical wind speed
(Monteith and Unsworth 1990) using highly accurate (and expensive)
sonic anemometers. The Penman-Monteith combination equation is
used in several papers that have been referenced (Blanken and Rouse
1994, Chen et al. 1997, Takagi 1998, Burba et al. 1999) to model
evapotranspiration at the leaf- and the canopy level, taking into account
the boundary layer conductance as meteorological conditions change,
i.e. stormy versus calm weather, or dry versus moist air. Generally,
measurements can be recorded with minimal time constraints, and
computer software allows for statistical modeling and plotting of the
data. Biometeorologic modeling is important in the attempt to make
predictions of future events. In an era where the conservation of
species richness has become a general concern, the modeling of
nutrient and surface water cycles becomes a helpful tool in
understanding multidimensional interactions between the many agents
of a biome.
Rey Benayas et al. (1999) approach the quantification of species
richness by modeling the relationship of "- and -diversity" of species
to "moisture status and environmental variation". In their study,
"environmental status is measured as actual evapotranspiration." This
31
approach deems especially interesting, since the loss of wetlands due
to development has been rapid. While many states have a
development prohibition of wetlands intended for their general
protection as densely populated, species rich areas, money still seems
to have the last word too often, and development of wetland areas is
still a possible threat to their inhabitants (refer to MaryPIRGS, 1999,
when The University of Maryland wanted to build a new stadium on a
wetland and succeeded).
A large amount of current research focuses on exploring
biometeorological processes in forests, wetlands, and grassland
vegetation. Some papers are part of a joint effort of exploring major
regions of the earth, and those regions‘ importance on a global level.
An example of such a project is the Boreal Ecosystem-Atmosphere
Study (BOREAS), which according to Chen et al. (1997) "has the goal
of understanding the contribution of boreal ecosystems to the global
carbon budget and their response to global change". He goes on to
explain that "solar energy is the driving force for biological activities
resulting in the observed energy and gas fluxes". He further elaborates
that the canopy structure, i.e. over- and understory features "requires
special attention in the radiation modeling". Overall goals of Chen et
al.‘s study were to compare the radiation balance inside the canopy" at
32
different times throughout the growing season and to assess general
patterns of leaf area index (LAI) over a "nearly complete seasonal
cycle." LAI is an important variable that needs to be measured to
model canopy stomatal conductance. Measured in square meters of
leaf area over square meters of ground, this index quantifies the
magnitude of photosynthetic potential, i.e. the leaf area above ground
through which gas exchange can occur, best pictured in the comparison
between a tropical forest (LAI~12) and a desert with sparse vegetation
(LAI~0.2). In his concluding discussion, Chen et al. state that LAI is
important not only because it "defines the photosynthetically active leaf
surface area responsible for plant growth and CO2 uptake", but also
since it delivers an estimate of rainfall that is intercepted by the leaves.
Lastly, he includes how the latest efforts to estimate LAI have improved
the applicability of remotely sensed data on canopy structure.
Rouse (1998) uses a water balance model to generate data for
General Circulation Models (GCM's) that attempt to predict future
climatic scenarios. As Rouse determined in his study on a subarctic
sedge fen, the increase in air temperature over the next decades will
lead to a drier environment of the present day fen, unless precipitation
increases by more than 20%. He goes on to predict several scenarios,
including extremely wet and extremely dry years, and their effects on
33
the fen habitat. With such a significant change in the water balance of
fens like this, the decrease in species richness is almost certain. A
critique of GCM's however, was made by Blanken (pers. comm. 2001).
According to him, "GCM's still fall apart today", because the missing
data about soil make-up and moisture is not measurable through
satellite observations.
The application of models contains multiple sources for potential
error, because their derivations rely on assumptions that are only barely
true in certain scenarios. If the research area in question deviates from
the scenario described in the model, e.g. a crop field could qualify for
the assumption of horizontal homogeneity, not though a forest, the
scientist will have to correct for these deviations, or chose a different
model altogether. It is the responsibility of the scientist to use
statistical models in a sensible way, and to refrain from tasks that are
too complex for the human mind to explain.
2.6. CONCLUSION
The field of biometeorology has made invaluable progress over
the last decades, and much of this success stems from the continuing
effort of scientists to synthesize their specialized research. The reader
may ask where the discipline is headed, and where the goals for future
34
research should be placed. In 1969's "Geography and Public Policy",
Gilbert White emphasized the importance of "translating findings into
changed public policy". The pursuit of a profession should undoubtedly
be linked with the incentive to make a change for the better. For why
should geography "fabricate a nifty discipline about the world while that
world and the human spirit are degraded?" In tune with Gilbert White's
spirit, one has to ask, what are the "truly urgent questions" of today,
and whether researchers are able to tackle research questions "in the
light of possible social implications?" as there are bountiful problems to
be solved, both on the local and the global scale.
A change for the better to which everyone can contribute through
personal input and research reaches out toward reestablishing
inalienable rights not only for human beings, but also for every species
that inhabits this planet. Also, other geographical fields like urban
geography are developing proposals that increase sustainability in
cities, ideas that may decrease people's needs to migrate further and
further into other species' habitats. Interdisciplinary, physical research
in biometeorology will be a necessary and powerful tool in changing
public policy. Understanding ecosystems and all agents that steer
them, as well as potential changes in biomes through anthropogenic
impact may enable inspired researchers to succeed in reaching their
35
goals, engaging all sources of creativity. Here's to Gilbert White: "We
must work with all our heart and mind".
36
CHAPTER 3. BACKGROUND
3.1. INTRODUCTION
The role of E in the water and energy balance of high latitude
wetlands is well documented (e.g., Blanken and Rouse 1994, Rouse
2000). Further, studies quantifying this flux have been conducted on
fairly homogenous areas like forest canopies or sedge meadows (e.g.,
Blanken and Rouse 1995), and stomatal conductance has been scaled-
up to the canopy level using a leaf area index (e.g., Chen et al. 1997,
De Pury and Farquhar 1997). Additionally, habitat loss and decreasing
biodiversity have recently found increasing attention in both public and
academic spheres. Whereas Ehrlich (1994), Pimm et al. (1995), and
Myers et al. (2000) focused on biodiversity hotspots and conservation
priorities, Blanken and Rouse (1996) investigated fine-scale processes
in specific habitats and assessed the ecological and meteorological
characteristics that explain the existence of particular plant
communities. Lastly, Rey Benayas et al. (1999) developed an index
that correlates E of an area to its biodiversity.
Wetlands in particular are known for both their exceptional
properties to filter water and to provide habitat for species that depend
on a unique combination of environmental factors, forming an oasis for
example, for waterfowl that often travel several thousands of kilometers
37
to satisfy their physiological demands at such sites. Plant diversity of
such areas is often remarkable; therefore, varying spatial and temporal
distributions of limiting or controlling factors deserve special attention.
Recent data indicate a 53% loss of U.S. wetlands between 1780
and 1980 (Moser et al. 1996), and data for Colorado estimate an annual
loss of 60 acres in the state alone (Denver Post, Dec 8, 2000). This
loss is mainly due to Colorado‘s population increase and concurrent
growth of development and water demand. Colorado ranks eighth in
the list of states with the largest net population gains recorded from
1995 to 2000 (U.S. Census Bureau 2000). Working to keep biodiversity
loss minimal, The Nature Conservancy (TNC), a global organization
dedicated to the preservation of endemic species and natural
communities, has purchased over 50,000 acres of land in Colorado with
the objective to preserve and restore native species and biological
communities. Brand and Carpenter (1999) have stated that TNC
strives for ecologically intelligent decisions through collaboration with
scientists to characterize future site management strategies.
High Creek Fen, a 750-acre extreme rich fen 2850 meters above
sea level (a.s.l.) near Fairplay, CO, is part of TNC‘s preserve system.
TNC, as well as the scientific community in general, is lacking accurate
data for this type of ecosystem in the Rockies. This research fills part
38
of this knowledge gap, and lays the groundwork for the formation of
successful management strategies to be implemented by TNC over the
next several years.
3.2. PHOTOSYNTHESIS AND ENERGY BALANCE
Through photosynthesis, plants use the sun‘s photosynthetically
active radiation (PAR), referred to in this work by quantum flux [Q], to
produce the energy required for the synthesis of carbohydrates. Q,
which represents the flux of PAR in the visible spectrum, is included in
the sun‘s electromagnetic field between 0.4 and 0.7 m. Cell water
necessary for photosynthesis evaporates through the stomata at rates
that are determined by the magnitude of stomatal conductance in
addition to other factors. Inevitable while stomata are opened, the loss
of water due to a water vapor deficit of the ambient air surrounding the
leaf additionally offers evaporative cooling to the leaf‘s surfaces. Up to
the point where physiological constraints or N availability limit the
turnover rate of the Calvin cycle, Q is a strong driving force in the
photosynthetic process (Monson 2000).
The maximization of photosynthetic potential is accounted for by
physiological differences in plants, differences such as density of
chlorophyll pigments, leaf thickness, LAI, and density of stomata per
39
leaf area (Monson 2000). Increased density of chlorophyll pigments,
roughly translatable into the ―greenness‖ of the leaf, allows the plant to
absorb energy faster than lighter-colored leaves that have a lesser
amount of chlorophyll per leaf area. Thicker leaves allow the plant to
capture more Q. These details strongly influence the plants‘ ability to
make maximum use of the photon energy. Furthermore, the overall
budget of potential CO2 assimilation of a plant depends on its LAI.
Additionally, distribution of stomata takes different densities according
to the urgency to minimize water loss. For example, tropical leaves
compared to xerophytic leaves have dense versus sparse
concentrations of stomata, respectively. Because leaf surfaces are the
interfaces of plant correspondence and mass and energy exchanges
with the overlying boundary layer, investigating all leaf processes is
important.
For a plant, the visible wavelengths are not the only solar energy
spectrum of interest. All wavelengths outside the visible range are
important to the plant, because they culminate in the total amount of
energy available at the surface of the plant‘s habitat. Thermal energy,
which partially translates into air temperature, is another factor that
determines the rate of photosynthesis. Optimal leaf temperatures [TL]
40
for C3 plants usually range between 30 and 40 C, but plants can also
alter their optimum to match their typical environment (Nobel 1999).
The overall intensity of solar radiation that reaches the plant
depends on the solar angle, which is a function of the time of day and
year, latitudinal position, and leaf orientation. Additionally, depth and
density of the atmosphere above the plant determine the amount of
energy (and actual CO2 concentration, which depends on atmospheric
pressure, and may therefore be considered lower at High Creek Fen
than at sea level) that arrives at the surface of the earth. Intuitively, the
sun‘s intensity will lessen with cloud cover. A thin atmosphere, present
over high elevation sites, allows for less absorption of solar radiation
during its way through the atmosphere, and thus has a more intense
impact on the surface compared to thicker cloud cover, or an
environment at sea level.
The net radiation (Rn) consists of the incident short-wave
radiation that strikes an area (K) minus the amount that is reflected off
that surface (K), plus the incoming long-wave radiation (L) minus the
amount that is radiated from that same area (L), the latter is a function
of the surface temperature and emissivity at a particular location.
Hence, we have the equation
Rn = (K- K) + (L - L) (1).
41
Energy at the surface can be expressed in Watts per square meter
(W m-2), or in micromol per square meter per second (mol m-2 s–1).
The energy available for absorption (transmittance, and reflectance) by
the leaf is a strong determining factor in the photosynthetic process and
the energy balance over an area.
Micrometeorologists like to follow the fate of the net radiation in
its distribution at the impacted surface, because it is a distinct way of
looking at the environmental dynamics of an area. The net radiation is
partitioned into three main terms, i.e. the energy is distributed into the
heating of air (H), the transformation from water into water vapor,
(evaporation or E), and into the heating of the ground (soil heat flux
[G]). It follows that
Rn = H + E + G (2).
Usually, due to the dense ground cover at High Creek Fen, the
lesser part of the net radiation goes into the heating of the ground.
(Over areas with bare soil, however, the partitioning changes.) The
distribution of Rn between H and E is often expressed as the Bowen
ratio (), where = H/ E. Generally, the Bowen ratio takes on
numbers between 0 and 5, where the latter would typify an extremely
xeric, and the former an intensely humid environment. Another effect of
Rn at the surface is upon Tair and the temperature dependent
42
atmospheric water vapor deficit [D]. D exerts another strong control
over plant transpiration. As stated above, water vapor diffuses from
intercellular air spaces and the stomata into the atmosphere. The flux
rate is subject to the differences in water vapor concentration between
the inside of the leaf (assumed to be 100 %) and the surrounding air;
the steepness of the gradient determines the flow rate. Diffusion of
water vapor from the plant into the atmosphere, based on the second
law of thermodynamics, or the law of entropy, can therefore
mathematically be expressed as follows:
E = -K cH2O / z, (3)
where K is the molecular diffusion coefficient for water vapor (from
higher to lower concentration), and cH2O / z is the difference in water
vapor concentration over the height of the leaf boundary layer, which
again is a function of wind speed. Strong winds will thin the boundary
layer over the leaf, increasing the gradient. A low relative humidity,
usually present at the daily peak of Q, forces water out of the plant
faster than a high relative humidity, which is generally common for the
morning hours. Hypothetically, the relatively constant wind at High
Creek Fen delivered warm, dry air from the arid Mosquito Range and
Park area in the west, and therefore increased the evaporative demand
43
at the surface. Hence, the large E above the fen is combined with dry
air (D max = 5 kPa).
Due to physiological constraints, a strong demand for water
vapor out of the leaf will likely lead to stomatal depression or full
stomatal closure. This adaptation allows a plant to control the amount
of water vapor leaving its stomata, since too great of a demand for
water vapor out of the leaf would result in cautation of water inside the
xylem and death of the plant. Soil moisture [ ] at the fen was plentiful
during the whole growing season, assuring the plants in their respective
locations a generally lesser stressed summer than may be expected
from plants located in semi-arid environments.
The daily pattern of varied considerably between sites; soil
moisture recharge occurred either through atmospheric deposition, e.g.,
rain or dewfall (surface recharge) or through groundwater movement
(subsurface recharge). Intuitively, soil moisture can be expected to
gradually decrease during a day where photosynthesis occurs, reaching
a minimum at the photosynthetic peak, both due to root water extraction
and evaporation from the bare soil surface. At the densely vegetated
fen, however, stayed high throughout the day, and was only slightly
influenced to a downward direction throughout a period of little rain at
the end of July 2001, when measured at the tower showed a
44
minimum of 93 %, which is to be considered saturated soil. In
contrast, investigating soil moisture control in non-saturated locations
allowed for testing of differences in intra-specific stomatal responses to
living in drier versus wetter areas of the fen. Summer 2001‘s studies on
B. glandulosa and S. candida both showed soil moisture control on g
and E. Attention to such physical and physiological factors as detailed
above is paramount in assessing the processes that govern plant
processes. These observations will now be communicated in light of
the above.
Photograph 3.1. Cumulus cloud (Cu) over High Creek Fen (view to NE) in Summer 2001. Although never again in this exact shape, Cu commonly form in areas adjacent to the fen during the summer season in early or late afternoon.
45
3.3. STUDY SITE DESCRIPTION
In the following paragraphs, the research site is described from
personal observation and as communicated through the literature.
First, a general description of the site‘s topography, hydrogeology, and
history, and last a focus on the environmental factors given by its
geographical location and local dynamics, including the energy balance,
microclimate, and soil moisture will be given.
High Creek Fen (Photograph 3.1.) is the largest remaining
natural fen in the South Park region of Colorado (Brand and Carpenter
1999). It is currently a nature preserve that has been managed by TNC
since 1990. The 750- acre wetland is located at 3906‘00‖N,
10557‘30‖W at an elevation of 2850 m, between the towns of Fairplay
and Buena Vista Figure 3.1.).
3.3.1. TOPOGRAPHY, HYDROGEOLOGY, AND HISTORY
Topographically, South Park lies in a flat valley surrounded by
the Mosquito Range to the west, the Kenosha and Taryall Ranges to
the north, and the Rampart Range to the east. The wetland, located
just east of Black Mountain (igneous remnant), shows a gentle change
in elevation from its highest (2850 m a.s.l.) northwest corner to its
lowest (2810 m a.s.l.) southeast corner.
46
Geologically, (visible from a geologic map of the area) High
Creek Fen is underlain by easterly dipping Cambrian through
Pennsylvanian sedimentary rocks (quartzite, shale, and dolomite)
deposited on a Precambrian basement complex of gneiss and schist
(the Idaho Springs Formation). These easterly dipping sedimentary
rocks represent the eastern limb of the Sawatch Anticline to the west.
The bedrock geology is obscured at High Creek Fen by surficial
deposits of Quarternary gravels and alluvium, and the underlying
geology has been inferred by projecting the geology of the adjacent
Mosquito Range to the east (Misantoni 2002).
Hydrogeologically, the fen is subject to complex variables. The
ground water pattern is influenced by both the Creek as well as the
make up of the material described above. Following the gentle slope,
High Creek supplies the fen grounds with fresh (and relatively warm)
spring water from the northwest, and leaves the area to the southeast.
Additionally, the underlying formations contain several aquifers, e.g.,
the Leadville and Quarternary aquifers. Several scenarios concerning
the delivery of ground water into the alluvial substrate and fen soil are
viable: (1) ground water is recharged from aquifers through several
Paleozoic strata by ways of faults and fractures (Shawe 1995, Appel
1995) that reach into the alluvium through its semi-permeable bottom
47
layer, or (2) ground water is recharged from one formation only, (e.g., a
layer of shale forms an aquifer) topped again by a semi-permeable
layer reaching into the alluvium, or (3) the alluvium is itself an aquifer
with an impermeable bottom layer, and recharge is either not yet
necessary (last glacial period only ended 10,000 years ago), or is
partially achieved from surface water. While the shallow ground water
level at High Creek Fen may be due to any, all of, or additions to the
above scenarios, the ground water level was relatively constant
throughout the years 1995 – 1998 (Johnson 1998) and 2000/ 2001
(tower data). The water supply to the fen, however, may be threatened
by water-use projects such as the ―South Park Conjunctive Use Project‖
(now fallen through), in which the city of Arvada would have been
supplied with water from this region. While it is unknown whether a
drop in the water table at the fen would likely occur after one or 100
years, such projects present a definite threat to sufficient supply of for
the already dry environments surrounding the fen, including several
ranches, i.e. livelihoods of the locals.
The high E during the summer months as well as relatively
constant even after atmospherically dry days both mandate a
perpetually active groundwater recharge. A transect of taken
diagonally across the fen with a water content reflectometer revealed
48
values between 8% outside the fen and 60% within the fen with soil
texture ranging from clay to silt with varying organic matter contents.
This transect of taken throughout the fen in summer 2001 (Figure
3.1.) and an accompanying photograph to gain perspective on the
transect (Photograph 3.2.) can be viewed below.
Photograph 3.2. View across the fen from NW (transect survey pole) to SE shows approximate transect location; the location of the meteorological tower is included on transect. Note: this picture was taken in Winter 2001/ 2002, while the transect data graphed below (Figure 3.1.) was collected July 1st 2001.
49
0
10
20
30
40
50
60
0 200 400 600 800 1000 1200
Volu
metr
ic S
oil
Mois
ture
[%
]
Distance [m]
Tow er
Figure 3.1. Soil moisture transect from southeast (0) to northwest (1000 m) taken across the fen on July 1st, 2001. With distance
increments of 33 m, 31 data points were recorded. Low values represent areas outside the fen.
50
Photograph 3.2. View across the fen from NW (transect survey pole) to SE shows approximate transect location; the location of the meteorological tower is included on transect. Note: this picture was taken in Winter 2001/ 2002, while the transect data graphed above (Figure 3.1.) was collected July 1st 2001.
Historically, small portions of High Creek Fen were disturbed
during a short period of peat mining from the 1970s until the mid- 1980s
(Schulz 1998), when 22 of the 750 acres were mined. Since 1992,
attempts have been made to restore plant communities (Sanderson,
pers.comm. 2001). Disturbance also occurred while High Creek Fen
was open to grazing by cattle and sheep since 1860 and prior to that by
51
bison, elk and antelope (Brand and Carpenter 1999). Apart from the
above, High Creek Fen has remained undeveloped and largely
undisturbed.
3.3.2. CLIMATE AND ENERGY BALANCE AT HIGH CREEK FEN
The harsh climate of High Creek Fen is characterized by intense
solar radiation, strong winds, and little precipitation. Due to its high
elevation, on cloudless days, High Creek Fen is exposed to a solar
peak of 2500 mol m-2 s -1 during 10:00 and 15:00 hours mountain
daylight time (MDT) throughout the height of the growing season; this
amount is 1.25 times higher than the average sea-level peak of 2000
mol m-2 s –1. Winds typically originate from the northwest; peak
observations of up to 150 km per hour have been made on the ridges to
the N and W, e.g., Boreas Pass and Windy Ridge (Cusack, personal
communication 2001). While the Mosquito Range to the west of the fen
functions as a rain shadow most of the time, convective clouds
(Photograph 3.1.) are common in the summer time; they supply most of
the precipitation recorded throughout the year. As stated above, is
generally recharged by the ground water of High Creek Fen and barely
influenced by local precipitation. The mean total annual precipitation
between 1961 and 1997 at the nearby weather stations Antero
52
Reservoir and Fairplay was measured to be 234 mm and 352 mm
respectively (Brand and Carpenter 1999). Those long-term recordings
also show that 40% of this precipitation falls in July and August. On-
site measurements, while on a different scale, indicate that 121 mm
precipitated onto the fen in the summer of 2001. Thus, High Creek
Fen‘s location exhibits extreme conditions of little precipitation and high
solar radiation; high soil moisture (Figure 3.1.) and special soil
chemistry and nutrients are conditional for the relatively dense and lush
vegetation present throughout the site (Blanken, pers. comm. 2001).
While High Creek Fen is exposed to the above-mentioned
regional meteorology, its microclimate differs from those of the
surrounding areas. During the photosynthetically active hours of the
days of this study, TS ranges were small, e.g., 2.5 or 3.5 C; such small
difference between minimum and maximum TS during daylight hours is
mainly due to the high volumetric moisture content of the soil,
perpetuated by an insulating, dense ground cover. Further, the diurnal
trend of D over the fen has a distinct shape and large amplitude. In the
morning, D has been measured as low as 0.2 kPa (in this case, 80 %
relative humidity). At the warmest part of the day, D can be as high 2.3
kPa (in this case, 25% relative humidity), both due to the solar heating
of the air, and the increasing, dry winds typically from the northwest.
53
Maximum D was measured by the porometer over S. monticola at 5
kPa with a TL = 36 C and Q = 1800 mol m-2 s-1 and = 40 %.
Due to its high elevation, the vegetation of High Creek Fen is
comparable to that of high-latitude wetlands of the boreal and tundra
regions (with exception of the perma-frost layer), where, as mentioned
above, E can comprise close to 80% of the net radiation. At High
Creek Fen, preliminary measurements of E using the Bowen Ratio
suggest that E is an important component of the wetland‘s water cycle,
and also, that the source of the water that is available for plant
transpiration cannot solely be local precipitation, but must primarily be
supplied by deeper rock units, or adjacent uplands.
3.3.3. VEGETATION AT HIGH CREEK FEN
The growing season lasts from early June until mid- September;
the ground is thawed from May throughout October. The vegetation
pattern can broadly be divided into upland and wetland types (Brand
and Carpenter 1999). The vegetation of the wetland exhibits great
variety in comparison with the adjacent upland areas (Cooper 1996,
Sanderson and March 1996). A description of both upland and wetland
species can be found in Cooper (1996) and Brand and Carpenter
(1999).
54
Wetland habitats include hummock communities, meadow
communities, spring fen communities, and a sodic flat community
(Cooper 1996). Dominant shrubs of the wetland are several willow
species, including silver willow (Salix candida), myrtleleaf willow (Salix
myrtillifolia), planeleaf willow (Salix planifolia), mountain willow (Salix
monticola) and barren-ground willow (Salix brachycarpa). Also
abundant are dwarf birch (Betula glandulosa), which inhabit mostly the
hummock and meadow communities, but also border the drier sodic flat
communities, as well as the moist spring fen areas. While kobresia is
the dominant grass throughout the fen, abundant especially at the
wetland‘s platform are sedges, mainly water sedge (Carex aquatilis)
(Photograph 3.3.). Furthermore, the existence of several state-rare and
globally-rare plants at High Creek Fen, including porter feathergrass
(Ptilagrostis porterii) and pale blue-eyed grass (Sisyrinchium pallidum)
supports TNC‘s recent suggestion that the fen is a globally significant
site. The species diversity at High Creek Fen is exceptional, deserves
scientific attention, and may be dependent upon protection from
anthropogenic disturbance such as a lowering of the water table.
55
Photograph 3.3. Dense ground-cover of willow, birch, and sedge at High Creek Fen, Summer 2001. Blue spruce in the background greatly influence turbulence at the site.
3.4. THE FOUR SITES AND THEIR INHABITANTS
All sites served as environments to investigate the importance of
soil moisture, water vapor deficit of the atmosphere, leaf temperature,
and solar radiation on stomatal conductance and plant transpiration.
Spatially, is highly variable, and while some plants, e.g., B.
glandulosa seem to be tolerant of a wide spectrum, others, such as S.
candida are restricted to a narrower range.
56
The research sites were chosen to control for , plant composition
and accessibility. Measurements of leaf conductance, transpiration,
vapor pressure deficit, leaf temperature, and solar radiation were taken
on several randomly chosen days dispersed throughout the growing
season from early June until late August 2001. Additionally, soil
moisture measurements were taken at each plant. Data were collected
from sunrise until sunset, weather permitting. This study focused on six
plant species abundant in the fen: Betula glandulosa, Salix candida,
Carex aquatilis, Salix monticola, Salix brachycarpa, and Salix planifolia.
B. glandulosa (Photograph 3.4.) grows on sites varying in from
15% to 60%, constituting a good indicator for potential soil moisture
control on its stomatal conductance and transpiration.
57
Photograph 3.4. Betula glandulosa (Swamp Birch) in a drier location at High Creek Fen, Summer 2001. This species occurs in a range of
locations where 15 % < < 60 %.
In contrast, S. candida (Photograph 3.5.) was not found in areas
with less than 35% average volumetric soil moisture. However, it was
chosen as a study organism since these plants are state-rare glacial
relicts, which are not found anywhere else in the Southern Rocky
Mountain region but at the South Park fens. Assessing their
environmental constraints is of great interest to the botanical
community, and existing work on this plant species in Manitoba,
Canada (Blanken and Rouse 1996) allowed a general comparison
between the plant‘s behavior on a latitudinal gradient.
58
Photograph 3.5. Close view of the thick, dark-green leaves of Salix candida (silver willow). Although not measured, leaf appearance suggests a multi-storied photosynthetic apparatus and dense chlorophyll pigmentation.
C. aquatilis is the most abundant sedge in portions of High Creek
Fen, offering necessary data for future mapping of transpiration
throughout the fen. A sample of one specimen can be seen in
Appendix A. S. monticola (Photograph 3.6.) is the most abundant
59
willow of the South Park region (Sanderson, pers. comm. 2001), and
comparing its environmental constraints with those of the rare S.
candida was an integral part of this project, as this allowed a look for
potential constraints to S. candida’s occurrence in these latitudes. S.
brachycarpa (Photograph 3.7.) and S. planifolia were chosen to further
the investigation of on-site willows for comparison of stomatal response
of different willow species to varying environmental factors.
Photograph 3.6. S. monticola Photograph 3.7. S. brachycarpa
60
3.5. STUDY HYPOTHESES
The research presented here investigates interactions of the
environmental factors explained above. It explains the nature of the
correlations between stomatal conductance [g] and transpiration [E]
from the leaf with the meteorological and soil moisture conditions that
exert limitations and affect the magnitude of transpiration. This
research is expected to explain several processes and therefore to
enhance the understanding of the interrelationships between
meteorological and plant physiological processes. In particular, it
shows a spatial variability of E corresponding to the heterogeneity of
the vegetative surfaces. It strives to explain the nature of the
correlation of g and E from the leaf with the meteorological conditions
that exert limitations on the plant physiological processes. This
research expands former analyses to include the effects of on the
magnitude of E; is expected to be also highly variable throughout the
fen. This research focused on testing three specific hypotheses, which
are outlined below.
61
3.5.1. PROBLEM STATEMENT 1: DOES HEIGHT ABOVE GROUND
INFLUENCE PHYSIOLOGICAL RESPONSES WITHIN AN
INDIVIDUAL SPECIES?
Stomatal conductance and E from distinct heights in an
individual plant above ground may vary because light absorption in the
leaf depends on the magnitude and partition between direct and diffuse
radiation that reaches to the vertical leaf layers of a plant, and because
the plant itself creates its own microclimate that may, for example, alter
the vapor pressure deficit of the air surrounding the leaf [D] so that a
leaf at the top of the plant may experience a higher D than a leaf in the
middle of the plant. Such differences would lead to diverging values of
g and E from different heights above ground, and if sufficiently large,
would have to be considered when extrapolating from the leaf to the
canopy level. Hence, the magnitudes of g and E from three leaves of
the same plant (S. monticola) at heights of z = 40, 70, and 100 cm
above ground were compared.
It was hypothesized that no significant differences in both g and
E from the three leaf levels of the same plant would be found.
62
3.5.2 PROBLEM STATEMENT 2: DOES SOIL MOISTURE
CONTROL RATES OF STOMATAL CONDUCTANCE AND
TRANSPIRATION FROM THE SAME SPECIES IN DIFFERING
LOCATIONS?
Soil moisture in High Creek Fen is incomparably higher than that
of its immediate surroundings, i.e. most of the Southern Rocky
Mountains. One goal of this study was to assess a species‘ sensitivity
to water stress, and to suggest scenarios that may occur with an abrupt
lowering of the water table due to increasing anthropogenic water
demand. Hence, the control of on the magnitudes of g and E was
quantified for both B. glandulosa and S. candida. B. glandulosa was
chosen because of its occurrence in locations with a wide range of as
well as its abundance within the Southern Rocky Mountain region, and
S. candida was chosen both because of its narrow range of and its
extraordinary occurrence in the latitudes where this fen is located.
To investigate S. candida‘s response to (Problem Statement
2.a), g and E from two individuals were compared. Their respective
mean equaled ~45 % at the drier, and 50 % at the wetter site. The
plants were 20 m apart, were approximately the same height, and
appeared to be of similar age. A significant difference in the magnitude
63
of the average g and E from the plants in the different soil moisture
categories was hypothesized.
To test discrepancies in g and E from B. glandulosa (Problem
Statement 2.b), nine plants located in differing soil moisture conditions,
three with mean= 18 %, three with mean= 35 %, and three within fully
saturated soil (mean= 60 %) were compared. The plants were within a
radius of 50 m of each other.
3.5.3. PROBLEM STATEMENT 3: WHEN EXPOSED TO THE SAME
MICROCLIMATE, DO DIFFERENT SPECIES VARY IN STOMATAL
CONDUCTANCE AND TRANSPIRATION?
Variability in g and E from different species must be understood
when quantifying or modeling E above a site like High Creek Fen,
where a great variety of species is represented. A comparative
investigation was designed to assess the physiological differences
between species, to determine plant sensitivity to water stress, and to
identify certain plants as early-warning indicators to changes in the
amount of plant available soil moisture at the fen. A site representative
of the fen was chosen to record g and E from different species, i.e. B.
glandulosa, S. candida, C. aquatilis, S. monticola, S. brachycarpa, and
S. planifolia that were within a radius of five meters of each other in
64
order to minimize microclimatic and site differences. Especially, the
effects of Q, TL, D, and on the magnitudes of the g and E from the six
plants were investigated. A significant difference between the six rates
of g at any point in the day was hypothesized, and E was expected to
differ among species.
The testing of all three hypotheses was to enhance the
understanding of arctic and high elevation wetland species, allow for a
comparison of physiological distinctions between common and rare
plants of the area, and help assess the sensitivity of high elevation
plants to microclimatic variability, soil moisture availability, and
disturbance.
65
CHAPTER 4. METHODS
4.1. INTRODUCTION
Data were collected by three different methods: a continuously
running on-site meteorological tower, a LICOR LI1600M steady state
porometer, and a Campbell Scientific CS620 HydroSense Quickdraw
water content reflectometer. Solar time was calculated after Oke
(1996). The following five sections outline the details of (1) the tower,
(2) the four sites investigated by the porometer and the water content
reflectometer, (3) the calibration procedure of the water content
reflectometer (CS620), (4) the characteristics of the data set, and (5)
the statistical data analysis.
4.2. ON-SITE CLIMATE STATION
An on-site climate station measured the following meteorological
variables within a radial footprint of ~300 meters (Photograph 4.1.):
precipitation, wind direction and speed, solar incoming radiation [W m-
2], net radiation [Rn], air and soil temperature [TA] and [TS], soil moisture
[ ], dew point temperature [Tdew], E and H, measured through the
micrometeorological Bowen Ratio Energy Balance (BREB) technique
(Blanken and Rouse 1994). All data were recorded in 20-minute
66
intervals with a Campbell Scientific 23X data logger from July 2000 until
present.
Photograph 4.1. On-site climate station in summer 2001.
The climate station was equipped with solar panels. The data served
as an additional, independent source of meteorological information to
the manually collected summer 2001 data from the investigated four
sites, which are described below.
67
4.3. METHODS OF DATA COLLECTION AT THE FOUR SITES
Soil moisture data from the top 12 cm of the soil were collected
with a HydroSense QuickDraw CS620 Water Content Reflectometer
(Campbell Scientific, Inc.). The volumetric soil moisture was calculated
and reported by the probe from the millisecond delay time created when
high frequency electromagnetic energy traveled along the length of the
probe rods and functioned as a measure of the dielectric permittivity of
the soil, which is directly related to the average amount of soil moisture
included in the equivalent soil depth. The probe reported this delay
time as a wave period (rather than a wave frequency); hence, the delay
time was directly proportional to the volumetric water content of the soil.
Calibrated for a sandy loam typical agricultural soil, the
HydroSense increasingly overestimated volumetric soil moisture at High
Creek Fen by a factor of up to 2 at saturation; less overestimation
occurred in lower (Figure 4.2.). To determine the true volumetric
water content, a calibration for the soil moisture probe was developed in
the laboratory. Here, the probe was placed in a completely dry (oven-
dried) sample of High Creek Fen soil. A known volume (75 ml) of water
was added in five-minute increments, while the millisecond delay time
and volumetric water content percentages reported by the probe were
recorded. After saturation ( = 65%), the process was reversed; the
68
soil was repeatedly placed in the oven and weighed to determine the
amount of water that had been vaporized, while milliseconds and
percentages reported by the probe were recorded. The resulting
wetting and drying curve can be seen in Figure 4.1. below.
A second order quadratic regression line with the equation
= - 55.36 + 62.74 ms +13.97 ms2 (4)
was fit through the combined data points of both wetting and drying
curves, where denotes actual volumetric soil moisture, and ms the
delay time reported by the probe in milliseconds. The resulting High
Creek Fen calibration equation was used to determine the actual, not
factory-calibrated, water content of the soil measured in the field. The
disparity between ordinary and newly achieved calibration can be
internalized with the inspection of the second order quadratic
polynomials in Figure 4.2., where both reported, and calibrated data are
integrated in a multiple scatter.
69
Figure 4.1. Wetting and Drying Curve of 1500 cm3 High Creek Fen Soil determined in the laboratory. Wetting: 20x75 ml of H2O were added to the oven-dried soil in increments of 5 minutes; through this process, actual soil moisture was continuously increased by 5 %, and HydroSense delay times were recorded. Drying: soil was repeatedly placed in oven, weighed, and delay times were recorded, until no further weight was lost. The following fit was created for all data points:
= - 55.36 + 62.74 ms +13.97 ms2.
70
Figure 4.2. HydroSense Calibration Curve from both wetting and drying curve data; to view the fit from this new calibration, this figure shows how the originally reported delay time increasingly overestimates
increasing actual volumetric water content [ ] by a factor of up to 2 at saturation.
71
Throughout the field season, was measured at the root area of
every plant. The calibration was applied to all measurements. To
achieve a mean value, at least three measurements were recorded per
plant per cycle. The duration of one cycle depended on the number of
plants investigated per site; this temporal resolution is outlined in the
data set section below.
4.4. THE DATA SET
In addition to data described above, the data set for this
summer‘s project consisted of roughly 120 hours of measurements with
the LI-1600M, and the corresponding hours of data collected at the
climate station. The data collected at all four sites had differing
temporal resolutions for each specific plant site. Since only one LI-
1600M was available, and only one researcher (me, Photograph 4.2.) at
the site to operate this instrument, g was recorded as three abaxial and
three adaxial measurements of the same leaf of one plant (all leaves
were exterior); then, the researcher moved to the next plant at the
specific site, continuing this process until arriving back at the first plant
to complete one cycle. Hence, data for every plant were recorded
every 0.4 to 2.5 hours, depending on the number of plants in the cycle.
72
To address ―Problem Statement 1,‖ three bi-axial g
measurements were taken from, respectively, one leaf located on the
lower, medium, and upper part of S. monticola to correlate the
magnitude of solar quantum flux received at these three levels with the
respective rates for g and E at each level of this one individual plant. g
for each leaf level was recorded every 0.5 hours.
To address ―Problem Statement 2.a,‖ three measurements for
both abaxial and adaxial sides of the leaf were taken for each of the
nine plants of B. glandulosa in 15-minute increments, which yielded a
temporal resolution of 2.5 hours per cycle. At the loss of high temporal
resolution, this site offered great spatial resolution at a high statistical
significance.
To address ―Problem Statement 2.b,‖ three measurements of g
from S. candida were taken for both leaf surfaces in 20-minute
increments, which yielded a temporal resolution of 1 hour per cycle.
To address ―Problem Statement 3,‖ g was also measured three
times for both sides of the leaf, treating each of the six species in 15-
minute increments, yielding a cycle of 1.5 hours before returning to the
same plant.
73
Photograph 4.2. Porometer measurements by Researcher; battery-powered machine strapped on via belt, storage module attached to belt on the back, cuvette in right hand.
The measurements were recorded into a storage module and,
connected to a personal computer via a communication box (Campbell
Scientific CS532), were downloaded into statistical graphing software
(EXCEL, SPSS, KaleidaGraph). The LI-1600M simultaneously
measured conductance [g], cuvette and leaf temperature [TC and TL],
the relative humidity [RH], and quantum flux from the sun [Q], recorded
the flow rate of dry air necessary to keep the cuvette at its constant
relative humidity of 2%, and computed transpiration. The last value,
74
however, was ignored in favor of computing transpiration [E] from
conductance and vapor pressure deficit of the air surrounding the leaf
[D], which again was calculated from TL (copper-constantan
thermocouple) and RH, therefore taking the actual, not relative water
vapor content of the air into account.
All data were derived from 3x2 matrices, which are explained as
follows: Because all leaves were amphistomatous with stomata on both
sides of the leaf, three rows of data give stomatal conductance [gs] and
concurrent independent variables [Q, TL, D, and ] for the bottom
(abaxial surface [e.g., gs ab]; column one), and three for the top (adaxial
surface [e.g., gs ad]; column two) of the leaf. g2s refers to stomatal
conductance from both sides of the leaf. Thus, a mean and a standard
deviation for all variables regarding each observation were derived.
See the paragraph 3.4.1. on ―data set preparation‖ below for a detailed
description of how the researcher arrived at the final variables.
3.4.1. DATA SET PREPARATION
The following paragraph explains the mathematical and
statistical relationships between separate measurements of bottom and
top sides of one leaf. For example, to arrive at the 84 observations of
g, Q, TL, D, and for S. candida (located in = 45 % and = 50 %) as
75
mean values, the 3x2 matrices were merged into one observation, i.e.
one case with a mean and a standard deviation. Thus, for the analysis
of ―Problem Statement 2.b.,‖ 504 rows of variables were merged.
Visible in the SPSS statistical analysis output tables and all other
figures included in this work are the means of, e.g., 3 measurements of
g2s and 6 measurements of TL. Hence, the dataset consisted of 84 (42
in the drier and 42 in the wetter location) cases of Yi = g, where g
stands for the average conductance g2s (from both sides of the leaf)
with average independent variables X1= Q, X2 = TL, X3 = D, and X4 = .
g2s from both sides of the leaf had to be calculated according to the
following equation,
sabb
sabb
sadb
sadbs
gg
gg
gg
ggg
2 (5)
where gb is the boundary layer conductance; the instrument measured
conductance of both the stomata and a boundary layer conductance
developed in the instrument‘s cuvette. Hence, the recorded values had
to be corrected, so that g2s was the intrinsic stomatal conductance for
an amphistomatous leaf corrected for boundary layer conductance
developed within the cuvette, and gsad and gsab were the adaxial and
abaxial stomatal conductances, respectively corrected for the cuvette
76
boundary layer conductance. The instrument‘s boundary layer
conductance [gb in mol m-2 s-1] was determined by placing a wet filter
paper over the cuvette, and taking a mean of 20 reported
conductances. For the cuvette opening of 2 cm2 (leaf area measured)
mean boundary layer conductance was 848 mmol m-2 s-1, and for 1 cm2
the instrument rendered a mean of 1549 mmol m-2 s-1.
The dependent variables g and E were correlated with the
independent variables Q, TL , D, and . The multiple regressions,
therefore, included the variability in both the spatial and the temporal
distribution of the environmental factors listed above. The multiple
regression coefficients were compared with separate bivariate
regression coefficients that were achieved by regressing individual soil
and atmospheric factors with stomatal conductance and transpiration.
Blanken and Rouse (1995) suggest that all variables mentioned above
relate to each other multiplicatively.
77
CHAPTER 5. RESULTS
5.1. INTRODUCTION
The results from the above data analysis are listed below,
starting with a short background on the season‘s meteorological
recordings by the tower, and then going on to the outcomes from
―Problem Statement 1‖, ―Problem Statements 2.a. and 2.b.‖, and lastly
to ‖Problem Statement 3.‖
5.2. METEOROLOGICAL DATA OBSERVED BY THE TOWER
The data reported in the succeeding sections (which address
problem statements 1, 2.a, 2.b, and 3.) refer to the diurnal minima,
maxima, and means of variables measured by the tower on the
respective days of porometer measurements. In contrast, for a general
overview of the whole growing season, the data referred to in this first
paragraph covers June 16th through September 15th, or the 167th
through 258th days of year (DOY) 2001. Average rainfall of the season
was 0.057 mm h-1, with a cumulative total of 121 mm during the three
months. Rainfall distribution varied tremendously, with little
precipitation from June 16th until July 31st (~31 mm) and more intense
and longer showers (accumulating to ~90 mm) between August 1st and
September 14th. Of the whole 91 days, 38 days were completely dry
78
(longest dry period lasted for five straight days), 10 days received a
trace (0.1 mm) each, and the remaining 43 days received between 0.3
and 10.3 mm, save the record on DOY 218 (August 6th) when a total of
16 mm accumulated during 6 hours from late afternoon until midnight.
On this seasonal scale of 91 days, average wind speed was 2.1
m s-1, and the maximum was 11 m s-1 (recorded at 17:30 hours on DOY
171 or June 20th). Soil temperature reached a maximum of 15.5C at
19:30 hours on DOY 188 (July 7th), and a minimum of 5.3 C at 9:00
hours on DOY 168 (June 17th).
5.3. RESULTS FOR PROBLEM STATEMENT 1
The problem statement 1 addresses the question of whether
height above ground influences physiological responses within an
individual species, i.e. within S. monticola. First, general meteorological
data for the day of these measurements (DOY 188) will be given.
Then, results from porometer measurements will be analyzed.
Air temperature measured at the varied from a minimum of a
nightly TA min of 4.3C at 4:00 hours MDT and a daily TA max of 26.8 C at
16:00 hours MDT. Atmospheric water vapor deficit near the tower was
altogether low with a nightly Dmin of 0 kPa and a daily Dmax of 2.6 kPa.
79
-0.5
0
0.5
1
1.5
2
2.5
3
0
5
10
15
20
25
30
187.8 188 188.2 188.4 188.6 188.8 189 189.2
VPD [kPa]
Celsius T Air
Vapor
Pre
ssure
Defic
it [k
Pa]
Air T
em
pera
ture
[degre
es C
els
ius]
Decimal Time of Day (MDT)
Figure 5.1. Vapor pressure deficit [VPD] and air temperature [TA] as
observed by the tower for DOY 188 as decimal time, where 188 = 00:00:00 hours on July 7th, and 188.5 = noon. Graph shows that VPD is a function of TA.
80
Soil temperature climbed from 12 C in the morning hours to
15.5 C at 19:00 hours. Wind speed was light to moderate with an
average of 2.5 m s-1 between 7:00 hours and 23:00 hours MDT.
Maximum wind speed on this day was 4.5 m s-1.
The data revealed that z = 40 had the overall minimum and z =
70 the overall maximum for E (Table 5.1.). Both z = 40 and z = 70 held
the minimum of g, and z = 100 had the overall maximum of g. z = 70
had the highest mean of the three mean transpiration rates with 4.93
mmol m-2 s-1, but the mean stomatal conductances of the three levels
were very similar. Accordingly, no statistically significant differences
from the three levels for either E or g were found.
The following Table (5.2.) shows E per hour integrated over time
and as a mean value from the 15.5 hours of continuous measurements
on DOY 188. Hence, the numbers below give, when multiplied by 15.5,
the area under the curves of E from Figure 5.2.b.
These values give a different perspective on the plant heights‘
cumulative production of water vapor [E] than what has been seen from
Table 5.1. There, the mean E in mmol m-2 s–1 was highest at z = 70, but
here, mean cumulative E in mmol m-2 h–1 was highest from z = 100. This
points to the fact that S. monticola‗s cumulative transpiration was highest
81
from the top leaf (and potentially, leaves), which may be explained by Q
and TL, and D while obviously, was the same for all three heights.
The following results were obtained for rates of g and E from
leaves at three different heights [z] of S. monticola (Table 5.1) and from
these values‘ integration over time (Table 5.2.) on DOY 188 (July 7th),
2001.
82
Table 5.1. Minima, maxima, and means of transpiration [E] with n=20 observed over 15.5 hours from ~4:30 a.m. until ~8:00 p.m. solar time in mmol m-2 s–1 and stomatal conductance [g] in mol m-2 s–1 for S. monticola at z = 40, 70, 100 cm.
Emin Emax Emean gmin gmax gmean
z = 40cm 0.13 8.83 4.51 0.01 0.23 0.115
z = 70cm 0.27 10.06 4.93 0.01 0.2 0.121
z =100cm 0.4 7.37 4.47 0.02 0.34 0.124
Table 5.2. Transpiration [E] measured from three distinct heights of S. monticola measured on DOY 188 (July 7th), 2001 expressed in mmol m-2 h–1 and g H2O m-2 h-1.
E (mmol m-2 h–1) E (g m-2 h-1)
z = 40 cm 17792.32 320.26
z = 70 cm 17501.85 315.03
z = 100 cm 18668.89 336.04
83
When comparing g from all three heights (Figure 5.2.a.), and
taking into consideration the rates of Q as well as TL as visible from the
respective accompanying graphs (Figures 5.3.a., 5.3.b., and 5.3.c.), z =
100 cm displays the lowest g; in the morning, conductance
(accompanied by low D) was still high, but gradually decreased during
the course of the day. In contrast, rates of g for both z = 70 cm and z =
40 cm more directly follow the path of their received Q. Figure 5.2.b.
also shows a dip in E some time after noon, when TL (and D) was
highest, but z = 100 shows a large dip (partial stomatal closure) around
solar noon, pointing to potential water stress.
While the ―5.3.‖ figures show lowered Q (cloud cover) at that
time, the light level for z = 100 cm still exceeds 1000 mol m-2 s-1,
hence cannot be considered limiting to g. At about 13:20 hours solar
time, the passage of a cloud is visible in all three graphs (Figures 5.3.a
– c). This Q affected the top and middle height of the plant less than
the lowest leaf height. With a difference of ~1100 mol m-2 s-1, Q
dropped from 1800mol m-2 s-1 to 700 mol m-2 s-1 at z = 100 cm; at z =
70 cm, the cloud caused a slightly larger drop from 1750 mol m-2 s-1 to
500 mol m-2 s-1 (Q = 1250 mol m-2 s-1). The bottom of the plant was
affected with the largest decrease of 1500 mol m-2 s-1 from 1800 mol
m-2 s-1 to 350 mol m-2 s-1. As a result, TL decreased by 3 C at z = 100
84
cm, while for both z = 70 and z = 40 cm, TL was not affected by a
change in Q; at that time, measurements show a slight increase in TL ,
which 1 hour later blend into the characteristic afternoon decrease of
leaf temperature.
At the height of z = 40 cm it is shown how both E and g follow
the path of the quantum flux very closely. At the height of 70 cm this
pattern is still visible, but at 100 cm, i.e. the top of the plant, g shows
only a slight influence by the quantum flux received here, and further, it
shows a partial stomatal closure around solar noon, again pointing
toward potential water stress.
The Hypothesis (stated in 3.5.1.) of no significant difference
between g and E from different heights of S. monticola could not be
rejected. However, the plant behaved differently at all investigated
heights. For comparison between individuals, therefore, data should be
collected from comparable locations of the plant; data in the following
analyses all stemmed from the top of the respective research subject,
and furthermore, originated from the same leaf of each individual.
85
E [100 cm]
g [100 cm]
0
0.07
0.14
0.21
0.28
0.35
E
g [70 cm]
g [m
ol m
-2 s-1] a
t 40
cm
6:40:00 10:00:00 13:20:00 16:40:00 20:00:00
E
g [40 cm]
Solar Time
Figure 5.2.a. Stomatal conductance [g] for S. monticola from leaves at heights of z = 40 cm, z = 70 cm, and z = 100 cm.
86
0
2
4
6
8
10
12
E [100 cm]
g [100 cm]E
[m
mo
l m
-2 s
-1]
E [70 cm]
g
6:40:00 10:00:00 13:20:00 16:40:00 20:00:00
E [40 cm]
g
Solar Time
Figure 5.2.b. Transpiration [E] and from leaves of S. monticola at heights of z = 40, z = 70, and z = 100 cm.
87
10
15
20
25
30
35
40
0
500
1000
1500
2000
2500
6:40:00 10:00:00 13:20:00 16:40:00 20:00:00
TL
Q
Mean L
eaf T
em
pera
ture
[ T
Lin
degre
es C
els
ius] at 40 c
m
Quantu
m F
lux [Q
in
mol m
-2 s-1] a
t 40 c
m
Solar Time
Figure 5.3.a. Leaf temperature [TL] of S. monticola and quantum flux [Q] measured at a leaf at 40 cm height show that the plant‘s TL does not react to Q. Also, compared to the incident radiation at z = 100, this height of z = 40 catches a larger amount more quickly in the morning
(e.g., from 06:30 until 07:00, the leaf receives 100 to 850 mol m-2 s-1).
88
10
15
20
25
30
35
40
0
500
1000
1500
2000
2500
5:33:20 8:20:00 11:06:40 13:53:20 16:40:00 19:26:40
TL
QM
ean L
eaf T
em
pera
ture
[ T
L in
degre
es C
els
ius] at 70 c
m
Quantu
m F
lux [ Q
in
mol m
-2 s-1] a
t 70 c
m
Solar Time
Figure 5.3.b. Leaf temperature [TL] of S. monticola and quantum flux [Q] measured at a leaf of 70 cm height.
89
10
15
20
25
30
35
40
0
500
1000
1500
2000
2500
5:33:20 8:20:00 11:06:40 13:53:20 16:40:00 19:26:40
TL
QLeaf T
em
pera
ture
[ T
L in
degre
es C
els
ius] at 100 c
m
Quantu
m F
lux [ Q
in
mol m
-2 s-1] a
t 100 c
m
Solar Time
Figure 5.3.c. Leaf temperature [TL] of S. monticola and quantum flux [Q] measured at a leaf located at 100 cm tree height. Compared to the other heights, this part of the plant reacts with TL most aggressively to a change in Q.
90
5.4.1. RESULTS FOR PROBLEM STATEMENT 2.a.
The problem statement solved here asked whether controls
rates of g and E from the same species, i.e. S. candida in differing
locations. Again, general meteorological data reported for DOY 174
from the tower will be given first, and HydroSense as well as porometer
measurements will be analyzed thereafter.
Data reported by the meteorological tower for day showed that
the air temperature [TA] reached a minimum of -1C at 4:00 hours
Mountain Daylight Time (MDT). A TA of 5 C was recorded at 7:00
hours, reaching 17C at 10:00 hours, the maximum of 23C around
15:00 hours, and slow cooling until 20C at 19:00 hours preceded faster
cooling, through which 5C were again reached at 22:00 hours. That
day, wind speed averaged of 3 m s-1 with a maximum of 7 m s-1 at 14:00
hours. The soil temperature warmed up from a 7 C minimum at 8:30
hours to a daily maximum of 10 C at 21:00 hours. No rain recharged
the area on this day; however, 1.7 mm fell the two preceding days.
The following results were obtained for the differences in for S.
candida situated in locations that differed in their mean by five
percent (Table 5.3.).
91
Table 5.3. Minima, maxima, means, and standard deviations of in
the wet [ (w)] and dry [ (d)] location with (N) as number of measurements. Ranges were 8 and 6% for the wet and dry location, respectively.
N Minimum Maximum Mean Std. Dev.
(w) 41 47.27 53.11 50.64 1.65
(d) 41 40.44 48.50 44.96 2.23
92
The drier location had a mean of 45 %, the wetter location had
a mean of 50 %. This distinction is labeled in the diurnal graphs
(Figures 5.5. and 5.6.) as E (d) for the plant with mean ~45 % and as
E (w) with mean ~50 %. A statistical regression analysis of all three
measured days did not show a significant difference between the
plants‘ responses at the two locations. E(d) & E(w) and g(d) & g(w)
were highly correlated with r2 = 0.68 (r = 0.882) for E and 0.59 (r =
0.771) for g (Table 5.4. and Figure 5.4.a. and b.). While the means of E
and g are both higher for the plant in the wetter location, so are the
range, standard deviation and standard error (Table 5.3.). Comparing
paired samples differences of E and g yielded a higher predictability of
the differences in g (80.2 % confidence) than differences in E (35 %
confidence); paired differences in g can only be predicted with 80.2 %
confidence (100 % - 18.8%, i.e. the two-tailed significance) and paired
differences is E with 35 % confidence (100 % - 65 %, Table 5.5.).
93
Table 5.4. Comparing the means of transpiration [E] and stomatal conductance [g] for the two populations (d) and (w) via a paired samples t-test, results show paired samples correlations for E and g of S. candida in dry and wet location as highly significant.
N Correlation Significance
Pair 1 {E (w) & E (d)} 42 .822 .000
Pair 2 {g (w) & g (d)} 42 .771 .000
Table 5.5. Comparing paired samples differences of transpiration [E] and stomatal conductance [g] show a higher predictability of the differences in g (80.2 % confidence) than differences in E (35 % confidence).
94
Paired Samples Test
.1879 2.6615 .4107 -.6415 1.0173 .458 41 .650
1.568E-02 7.598E-02 1.172E-02 -8.00E-03 3.935E-02 1.337 41 .188
E_W - E_DPair 1
G_W - G_DPair 2
Mean
Std.
Deviation
Std. Error
Mean Low er Upper
95% Conf idence
Interval of the
Dif ference
Paired Dif ferences
t df
Sig.
(2-tailed)
0
5
10
15
20
0 5 10 15 20
E
E (
w)
in m
mol m
-2 s
-1
E (d) in mmol m-2 s
-1
95
Figure 5.4.a. Regression of the transpiration rates (E) of S. candida in the dry location against E from S. candida in the wet location as mmol H2O transpired m-2 s-1.
0
0.1
0.2
0.3
0.4
0.5
0 0.1 0.2 0.3 0.4 0.5
g
g (
w)
in m
ol m
-2 s
-1
g (d) in mol m-2
s-1
96
Figure 5.4. b. Regression of stomatal conductances (g) of S. candida in the dry location against g of S. candida in the wet location expressed as molar flux through stomatal magnitude m-2 s-1.
However, despite these statistically insignificant results, the
graphs (Fig 5.5.a. and 5.5.b.) below from Day Of Year 174 (June 23rd,
summer solstice) show that the plant in the drier location allows less g
and E, and therefore imply less water stress to be experienced at
location (w). The figures 5.5.a. and 5.5.b. below graphically show the
difference in E and g between locations (d) and (w). Additionally, an
alternative approach to quantifying and comparing the two was chosen.
To contrast E from the plants at the two sites, rates of E were
expressed as the cumulative amounts over a period of eight hours for
the two plants. Results were expressed in mmol m-2 h-1 as well as
grams H2O transpired m-2 h-1. A significant difference of 30% more E
from the plant located in higher soil moisture was computed (Table
5.6.a and b).
97
0
2
4
6
8
10
12
14
0
2
4
6
8
10
12
14
9:46:40 12:13:20 14:40:00 17:06:40 19:33:20
E(d)
E(w)
E [m
mol m
-2 s
-1] in
soil
mois
ture
of ~45%
(d
) E [m
mol m
-2 s-1] in
soil m
ois
ture
of ~
50%
(w
)
Solar Time
98
Figure 5.5.a. Transpiration [E] for S. candida on DOY 174 in a dry (d) and wet (w) location show a visible, although not statistically significant difference in mmol of E released m-2 s-1 throughout the day; the mid-day data gap is due to temporary system failure.
0
0.1
0.2
0.3
0.4
0.5
0
0.1
0.2
0.3
0.4
0.5
9:57:20 12:26:40 14:56:00 17:25:20 19:54:40
g(d)
g(w)
g [m
ol m
-2 s
-1] in
soil
mois
ture
of ~45%
(d
) g [m
ol m
-2 s-1] in
soil m
ois
ture
of ~
50%
(w
)
Solar Time
99
Figure 5.5.b Stomatal conductance [g] for S. candida in the dry (d) and wet (w) location again show a visible, however, not statistically significant difference in the flux of mol m –2 s-1 of g on DOY 174 (summer solstice).
Table 5.6.a. Transpiration [E], expressed in mmol m-2 h-1 and g m-2 h-1, on DOY 174 (June 23rd), 2001, from S. candida (d) in soil
moisture [ ] ~45 % and S. candida (w) in ~50 %.
Table 5.6.b. Transpiration in the wet location [E (w)] exceeds transpiration in the dry location [E (d)] by 30.0 %. Hence, S. candida (w)
in ~50% transpired one third more than S. candida (d) in ~45%.
E [mmol m-2 h-1] E [g m-2 h-1]
S. candida (d) 23690.8 426.4
S. candida (w) 30739.2 533.3
100
While extrapolation of this water vapor flux over time does not
quantify CO2 assimilation (photosynthetic rate), the comparison allows
stating that, if all else were equal, the plant in location (w) had a higher
metabolic rate than the plant in location (d), and therefore, higher soil
moisture gave S. candida (w) a resource advantage over S. candida
(d).
It may further be concluded that an overall drop in average
volumetric soil moisture of only 5% at High Creek Fen would generally
increase water stress of S. candida, and lead to an ultimate loss of the
species at those locations that are now supplied with soil moisture
ranking at the lower threshold of the optimum percentage, i.e. at those
locations with < 35%. On the other hand, the plant in the drier
location may have a higher water-use-efficiency [WUE], which is
defined as the fraction of grams of CO2 assimilated to the grams of
water lost (transpired) in the process (Nobel 1999). It may be argued
E (w)/ E (d) 1.30
E(d)/ [E (w)– E(d)] 3.36
101
that the plant in the drier location may be more adapted to a future,
sudden drop in the water table. However, it would seem quite an
evolutionary step for the plant in the drier soil to assimilate 30% more
CO2 per water lost than a closely located fellow individuals, and in that,
equal its neighbor in WUE. Mechanisms to maximize WUE have been
studied; examples of such are ―osmotic adjustment and changes in the
bulk tissue elastic modulus‖ to allow higher turgor pressure in the
tissues, and therefore delay desiccation (Dawson and Bliss 1989).
5.4.2. RESULTS FOR PROBLEM STATEMENT 2.b.
Similarly to 5.4.1., the problem statement solved here asked
whether controls rates of g and E from the same species, here B.
glandulosa in differing locations. Again, HydroSense as well as
porometer measurements follow the general meteorological data
reported by the tower.
Data reported by the tower for this DOY 170 (June 19th, 2001)
were the following: TA was above 5 C during daylight (5:30 – 22:00
hours MDT), and below 5 C at night. At 3:40 hours, a TA min of -0.2 C
and at 15:40 (exactly 12 hours later), a TA max of 22.9 C were recorded.
During the time of porometer measurements (5:20 – 13:00 hours MDT),
TA climbed steadily to 18 C at a rate of ~1.6 C h-1. Soil temperature
102
had a minimum of 6.5 C at 8:40 hours and a maximum of 10.2 C at
18:40 hours MDT, and averaged 7 C. Atmospheric water vapor deficit
was lowest at 6:00 hours with 0.06 kPa and peaked with 2.2 kPa at
16:20 hours that afternoon, and averaged 0.6 kPa. Solar incoming
radiation peaked at 13:30 hours (shortly after solar noon) at 1100 W m-
2. No rain had been falling for at least three days prior, and while at
the tower for that day (av = 95 %) equaled the average of the week
(DOY 167 – 173), it was below the seasonal average of 99%. Average
wind speed during porometer measurements was 1.7 m s-1.
Porometer measurements showed the basic trend of decreasing
rates of g and E with increasing soil moisture (Figures 5.6. and 5.7.)
103
0
2
4
6
8
10
10 20 30 40 50 60
1
2
34
5
6
7
89
E [m
mol m
-2 s
-1]
Soil Moisture [%]
Figure 5.6. The scatter plot shows mean daily transpiration [E] in
dependence upon soil moisture []. Plant locations 1 – 3 were grouped as the drier locations, 4 – 6 as the mesic, and 7 – 9 as the wet, close to
saturated locations. E from case 3 with av = 20.8 % did not differ from the average E values produced by cases 7 and 9.
104
0
0.2
0.4
0.6
0.8
1
10 20 30 40 50 60
1
2
34
5
6
7
89
g [m
ol m
-2 s
-1]
Soil Moisture [%]
Figure 5.7. The scatter plot shows mean daily stomatal conductance
[g] in dependence upon soil moisture [ ]. Again, cases 1 – 3 were grouped as the drier locations, 4 – 6 as the mesic, and 7 – 9 as the wet, close to saturated locations.
105
A correlation between the nine plants‘ respective and average E
(averaged over 9 hours of measurements) showed an r2 of 0.24, at a
significance of 0.177. While not statistically significant, the tendency of
B. glandulosa to decrease E with increasing was visible from the
Figure 5.6., where a scatter plot shows E in relation to . Plant
locations were grouped into three categories: dry, mesic, and wet,
where cases 1 – 3 were three plants representative of dry locations, 4 –
6 of the mesic, and 7 – 9 of the wet, close to saturated locations.
However, E from case 3 with av = 20.8 % did not differ from the
average values produced by cases 7 and 9 with av = 60 %.
Stomatal conductance regressed with av yielded a higher r2 of
0.32 at a significance of 0.112 (Figure 5.7.). For lack of a larger
sample, one can only suggest increased water stress with increased .
However, it is interesting to note the general difference in behavior of S.
candida compared to B. glandulosa: while S. candida tends to thrive in
high , B. glandulosa generally thrives in more moderate to dry
conditions.
5.5. RESULTS FOR PROBLEM STATEMENT 3.
The problem statement solved in this section asked whether
different species, i.e. B. glandulosa, C. aquatilis, S. brachycarpa, S.
106
candida, S. monticola,and S. planifolia vary in g and E when exposed to
the same microclimate.
This section outlines and compares the controls on g from all six
species as exerted by Q, TL, D, and , but also includes general
information about typical controls on g from C3 plants to elucidate the
parameters at hand.
Stomatal conductance in dependence upon Q for all six species
investigated at High Creek Fen shows different intra-specific responses
in g to Q at the leaf surface. Data may be compared with general
statements made about typical C3 plants in Nobel (1999), where
photosynthetic rate was observed as directly proportional to Q until
about 50 mol m-2 s-1, and light saturation was reached when Q
exceeded 600 mol m-2 s-1. Then, assuming that all environmental
parameters were at optimum, physiological constraints like the
concentration of CO2 in the chlorophyll and the chlorophyll density may
take the turn to limit the turnover rate of the photosynthetic cycle. For
example, S. candida’s response in Figure 5.8. shows a boundary line of
maximum stomatal conductance, where above 200 mol m-2s–1, an
increase in Q will not result in much of an increase in g; above that
point, other, physiological or meteorological processes limit the rate of
photosynthesis.
107
Regarding leaf temperature control on g, six measurements
were taken from TL . According to Nobel (1999), photosynthesis usually
doubles going from 20 to 30 C. Also, the optimal temperature for
photosynthesis can acclimate (usually by 2-15 C) to match the
average ambient air temperature of the environment. Further, typical
C3 plants maximize their photosynthetic rate between 30 and 40C, and
after the optimum has been reached, decrease the rate with further
increasing temperature. Below or above optimum temperatures lead to
lower g. The optimum temperature for S. candida, for example, seems
to be around 22 C, as seen on the graph below (Figure 5.9.). This
graph implies that g will have a positive relationship with TL when
approaching the optimum temperature, and a negative relationship
when exceeding the optimum temperature of 22 C.
The following two graphs (Figure 5.8. and 5.9.) show all six
plants‘ responses of g when regressed against Q and TL. Figure 5.8.
shows that S. monticola reached light saturation at 500 mol m-2 s-1. S.
candida saturated with light close to 200 mol m-2 s-1. S. planifolia did
not reach light saturation until 550 mol m-2 s-1. S. brachycarpa
saturated as high as 650 mol m-2 s-1. B. glandulosa seemed to reach
light saturation at 400 mol m-2 s-1, however, increased g can be
108
detected up to 2000 mol m-2 s-1. C. aquatilis did not follow the typical
pattern, as g decreased with increasing Q.
As shown in Figure 5.9., S. monticola and B. glandulosa reached
gmax at an optimum TL of 27 C. For S. monticola, many measurements
of g were probably limited by too high a leaf temperature (30 – 35 C).
(Its diurnal behavior can be followed in the example of DOY 191 from
Figure 5.16.) The large decrease in g seen in the afternoon stemmed
from a TL as high as 32.8 C. B. glandulosa showed a steep decline in
g when TL exceeded 27 C. Its tolerance for temperatures below 27 C
is much higher than for those above the optimum TL. S. candida and S.
planifolia reached their optimum TL at 22 C. The behavior of S.
planifolia gives a good example for plants capable of high rates of g
during lower leaf temperatures. S. candida raises the suspicion of
miraculously keeping its TL constant after reaching 31 C, while
109
S. brachycarpa
S. monticolaB. glandulosa
0
0.2
0.4
0.6
S. candida
0
0.2
0.4
0.6
Sto
mata
l cond
ucta
nce [
g] in
mol m
-2
s -1
S. planifolia
0 500 1000 1500 2000 2500
mol m-2
s-1
C. aquatilis
0 500 1000 1500 2000 2500
0
0.2
0.4
0.6
Quantum Flux [ Q] in
Figure 5.8. Stomatal conductance [g] plotted against quantum flux [Q] for all six species investigated at High Creek Fen. Data may be compared with general statements made about C3 plants in Nobel (1999).
110
5 10 15 20 25 30 35 40
S. planifolia
in degrees Celsius
S. monticolaB. glandulosa
0
0.2
0.4
0.6
S. candida
0
0.2
0.4
0.6
Sto
ma
tal co
nd
uct
an
ce
[g
] in
mo
l m-2
s-1
S. brachycarpa
C. aquatilis
5 10 15 20 25 30 35 40
0
0.2
0.4
0.6
Leaf Temperature [ TL]
Figure 5.9. Stomatal conductance [g] in dependence upon leaf temperature [TL] of all six species investigated at High Creek Fen. Data may be compared with general statements made about C3 plants in Nobel
(1999), where photosynthetic rate doubles between 20 and 30 C, and
maximizes between 30 and 40 C.
111
decreasing g as a trade-off for increased D above the leaf. The
optimum TL for S. brachycarpa lies between 21 and 24 C. While the
true optimum temperature is not determinable from these data, the
temperature range that allowed reaching > 50% of gmax (0.44 mol m-2 s-
1) appears to be narrow. C. aquatilis preferred a lower TL with its
optimum at 15 C, yet it did not decrease its g much at higher
temperatures (e.g., 32 C). If all else was equal (which it is not, since
the measurements shown in these plots originated from different days
throughout the growing season), one could conclude that S. monticola
has the largest range of possible leaf temperatures, or in other words,
that TA had the strongest influence on TL of S. monticola, while S.
candida with a range of only 23 C seemed least influenced by TA. A
possible reason for this fact may be that S. candida possesses leaves
that allow it to create a unique temperature environment where the
fuzzy hairs act as temperature buffers, i.e. increase the boundary layer
resistance.
The next two graphs (Figure 5.10. and 5.11.) show the same
data, here in terms of the respective controls of D and on g. As stated
above, H2O vapor diffuses from the insides of the stomata into the
atmosphere at a rate determined by the H2O vapor concentration
112
0 1 2 3 4 5
S. planifolia
[ D ] in kPa
S. monticola
B. glandulosa
0
0.2
0.4
0.6
S. candida
0
0.2
0.4
0.6
Sto
ma
tal co
nd
uct
an
ce
[g
] in
mo
l m-2
s-1
S. brachycarpa
C. aquatilis
0
0.2
0.4
0.6
0 1 2 3 4 5
Vapor Pressure Deficit
Figure 5.10. Stomatal conductance [g] as controlled by vapor pressure deficit [D] surrounding all six plant species investigated at High Creek Fen. Usually, g can be expected to decrease exponentially with increasing D. Since D is highly correlated with TL, most data points are expected to fall into the same quadrant from both this, and the previous figure (5.9.).
113
S. monticola
0
0.2
0.4
0.6
B. glandulosa
S. candida
0
0.2
0.4
0.6
Sto
ma
tal co
nd
uct
an
ce
[g
] in
mo
l m-2
s-1
S. planifolia
10 20 30 40 50 60
in %
S. brachycarpa
C. aquatilis
10 20 30 40 50 600
0.2
0.4
0.6
Volumetric Soil Moisture
Figure 5.11. Stomatal conductance[g] regressed with soil moisture [] measured in the separate locations of the six plants researched in the fen; generally, all plant underlying soils were saturated between 50 and 55 %.
114
gradient between the inside of the leaf and the surrounding air and the
stomatal resistance to this flux. Soil moisture [ ], necessary for several
metabolic functions, exerts a strong control over photosynthetic activity.
Immediately visible for S. monticola in Figure 5.10. is its greater
tolerance for a larger D; however its optimum lies in moister air with D
around 2.5 kPa. S. planifolia and S. brachycarpa again show a sharp
decrease in g after their shared optima at a D of 1.6 kPa were reached.
C. aquatilis is tolerant of dry air, and S. candida still reached >50% of
gmax (0.57 mol m-2 s-1) at D >3.5 kPa.
When comparing the range of B. glandulosa with that of S.
candida, and remembering the previously made statement from section
3.4., it showed that B. glandulosa is tolerant of a wider range in soil
moisture (15 – 55 %), namely drier areas than S. candida, which cannot
be found in areas containing an average less than 25%. Further,
did not limit g from B. glandulosa, while S. candida showed a tendency
to increase g with increasing . The remaining four species‘ behaviors
may be interpreted as follows: S. brachycarpa was situated in an area
with ranging between 25 and 50 %. In this range, g was highest
between times when was between 30 and 45 %. C. aquatilis
experienced gmax in soil with = 50 %. An imaginary boundary line
115
drawn across the gmax values at their respective soil moistures shows a
steep increase in g with increasing soil moisture, a fact to be expected
from water sedge. A similar positive relationship between g and can
be seen with S. planifolia (40 % < opt< 55 %), while S. monticola and S.
brachycarpa seem to find optimum conditions in their locations with
respective ranges of 33 – 48 % and 35 – 40 %. For these two
species, no immediate tendency can be made out (Figure 5.11.).
While this analysis should have led to the ability to predict g for
each particular plant from the environmental variables outlined above,
the data set is not large enough to conduct such modeling. As stated
by Jarvis (1976) and Chambers et al. (1985), it is difficult to relate a
single environmental variable to a change in g when dealing with actual
field data, and a sufficiently large sample is required for accurate
prediction. This data set also showed that (1) the set of environmental
conditions reported included too narrow a range (one growing season
only) to provide a successful basis for boundary-line analysis
(Dougherty and Hinckley 1981) and (2) rather a combination of several
conditions than solely one single environmental factor may have
determined g (for example, it is conceivable that gmax for each particular
plant during a single day was partially determined by the minimum soil
116
temperature reached the night before, a relationship that was not
analyzed).
After this analysis of the controls on g, a closer look at a
comparison between these plants behaviors during a single day, i.e.,
exposure to the same environmental conditions shall follow. The data
stems from DOY 191 (July 10th, 2001), a day which was reported by the
tower to have fully saturated soil, TS av = 13.5 C (TS min =11.55 C, TS
max = 14.9 C), TA av = 16 C (TA min = 4 C at 5:20 hours MDT, TA max =
23 C at 16:30 hours MDT), and Dav = 0.86 kPa (Dmax = 1.66 kPa, also
at 16:30 hours MDT). Solar incoming radiation increased gradually
from 0 to 1000 W m-2 between 6:00 and 11:30 hours MDT; this flux
stayed similar (between 900 and 1000 W m-2) until clouds rolled in at
14:30 hours. From 15:20 an (again gradual) decrease in incoming solar
radiation from ~1000 W m-2 until 0 at sundown (20:40 hours MDT) took
place. One tenth of a mm of rain fell between 14:20 and 14:40, and
wind speed averaged 3 m s-1 (strongest in late afternoon, with a
maximum of 10.3 m s-1 at 18:20 hours MDT).
The respective behavior of the six different species is illustrated
in the following figures (5.12. – 5.17.), first, in six separate graphs of
respective g and E, then in two graphs (5.18. and 5.19.) that
respectively include information of all species, suitable for easier
117
comparison among differing rates of g and E. Preceding are two tables
(Table 5.7. and 5.8.) that give the average fluxes of E and g for all six
species investigated at the fen; averages were achieved by
extrapolating g over the time measurements were conducted on DOY
191, which accumulated to 15.6 hours, or 56,160 seconds.
118
Table 5.7 Transpiration [E] from all six species on DOY 191 (July 10th), 2001 expressed in mmol and grams H2O m-2 s-1 as well as h-1. Fluxes are listed in decreasing order from top to bottom.
E[mmol m-2
s-1
] E [g(H2O) m-2
s-1
] E [mmol m-2
h-1
] E [g(H2O)m-2
h-1
]
B. glandulosa 7.0 0.13 25237 454.26
S. monticola 6.6 0.12 23636 425.44
S. brachycarpa 5.9 0.11 21399 385.17
S. candida 5.3 0.10 19107 343.93
S. planifolia 4.9 0.09 17695 318.51
C. aquatilis 3.0 0.05 10624 191.23
119
Table 5.8. Mean daily stomatal conductance [g] from all six species on DOY 191 (July 10th), 2001 expressed in mol m-2 s-1 as well as h-1.
g [mol m-2 s-1] g [mol m-2 h-1]
B. glandulosa 0.362273 1304.182
S. monticola 0.224626 808.653
S. brachycarpa 0.382705 1377.739
S. candida 0.285534 1027.924
S. planifolia 0.205949 741.4163
C. aquatilis 0.105449 379.6166
120
0
4
8
12
16
0
0.1
0.2
0.3
0.4
0.5
0:00:00 6:00:00 12:00:00 18:00:00 24:00:00
Eg
E [
mm
ol m
-2 s
-1] g
[ mol m
-2 s-1]
Solar Time
Figure 5.12. Transpiration [E] and stomatal conductance [g] from Betula glandulosa on DOY 191 (July 10th), 2001. This species reaches gmax around 10:00 a.m., and then gradually decreases g over the afternoon, when TL and D become limiting. As seen from Table 5.7., B. glandulosa ranks highest in E compared to the other five species.
121
Eg
0
4
8
12
16
0
0.1
0.2
0.3
0.4
0.5
0:00:00 6:00:00 12:00:00 18:00:00 24:00:00
g [ m
ol m
-2 s-1]
E [
mm
ol m
-2 s
-1]
Solar Time
Figure 5.13. Transpiration [E] and stomatal conductance [g] from Carex aquatilis on DOY 191; here, mid-day stomatal depression effecting necessary reduction of the quantity of water vapor demand by the atmosphere is evident. Compared to gmax from B. glandulosa and S. brachycarpa, gmax from C. aquatilis is a third, and half as large as that of S. monticola. S. candida exceeds it by a factor of 2.5.
122
0
4
8
12
16
0
0.1
0.2
0.3
0.4
0.5
0:00:00 6:00:00 12:00:00 18:00:00 24:00:00
Eg
E [
mm
ol m
-2 s
-1] g
[ mol m
-2 s-1]
Solar Time
Figure 5.14. Transpiration [E] and stomatal conductance [g] from
Salix brachycarpa on DOY 191. Again, mid-day stomatal depression to reduce water stress is evident. Morning conductance allows this species to still rank third in E compared to the other five species.
123
0
4
8
12
16
0
0.1
0.2
0.3
0.4
0.5
0:00:00 6:00:00 12:00:00 18:00:00 24:00:00
Eg
E [
mm
ol m
-2 s
-1] g
[ mol m
-2 s-1]
Solar Time
Figure 5.15. Transpiration [E] and stomatal conductance [g] from Salix candida on DOY 191; compared to the previously seen (5.12 – 5.14) flux developments over time, the silver willow shows a high morning, toward evening gradually decreasing g. Nevertheless, mid-day stomatal depression is visible, as well as a second depression starting after 14 hours solar time (15:10 MDT), when the tower showed a solar flux of 1008 W m-2. Stomatal conductance increased after 15 hours (16:10 MDT), when intensity of radiation dropped again.
124
0
4
8
12
16
0
0.1
0.2
0.3
0.4
0.5
0:00:00 6:00:00 12:00:00 18:00:00 24:00:00
Eg
E [
mm
ol m
-2 s
1] g
[ mol m
-2 s-1]
Solar Time
Figure 5.16. Transpiration [E] and Stomatal conductance [g] from Salix monticola on DOY 191. As also seen from Table 5.7., this species seems best adapted to its environment, since it has the strongest E of all compared plants. Clouds were over the area when the steep drop in stomatal conductance occurred around 13:30 hours solar time. Possible explanation for the drop in g may be a TL of 32.8
C at this time, which may have caused the partial stomatal closure.
125
0
4
8
12
16
0
0.1
0.2
0.3
0.4
0.5
0:00:00 6:00:00 12:00:00 18:00:00 24:00:00
Eg
E [
mm
ol m
-2 s
-1] g
[ mol m
-2 s-1]
Solar Time
Figure 5.17. Transpiration [E] and Stomatal conductance [g] from Salix planifolia on DOY 191 show the typical behavior of an unstressed plant with no mid-day stomatal depression. Ranking 5th in E and g (Table 5.7.) might allow a stress-free life in this environment.
126
Betula glandulosa ranked highest in E compared to the other five
species (Table 5.7.). Figure 5.12. shows E and g from B. glandulosa,
which reaches gmax around 10:00 hours, then gradually decreased g
over the afternoon, when TL and D became limiting. No partial mid-day
stomatal closure was recorded for this species. Increased water loss
due to high TA and large D just before solar noon triggered the drop in g
visible in the figure. The graph shows a less than average decrease in
g between 13:00 and 14:30 hours solar time, a period in the day during
which clouds were reported by the tower (from 14:30 to 15:30 hours
MDT, which is ~13:20 to 14:20 hours solar time), hence TA and D
dropped, reducing the water stress in the plant, allowing higher
conductance.
Carex aquatilis ranked last in the amount of E lost over time; its
gmax of 380 mol m-2 h-1 was only a third of those of B. glandulosa and S.
brachycarpa, and half of that of S. monticola and S. planifolia. S.
candida’s gmax was 2.5 times that of C. aquatilis, which performed a
partial mid-day stomatal closure, here (Figure 5.13.) exactly at solar
noon.
Salix brachycarpa ranked third among the six compared rates of
E (Table 5.7.). Partial stomatal closure was apparent at solar noon,
preceded by gmax in the morning (9:00 to 10:00 hours solar time) and
127
succeeded by a second mid-afternoon (13:00 hours solar time)
increase in g until 16:00 hours solar time, resulting in a bimodal
distribution of both E and g (Figure 5.14).
Compared to the previously seen (5.12 – 5.14) flux
developments over time, Salix candida (Figure 5.15.) showed a high
morning g, which gradually decreased toward evening. Nevertheless,
partial mid-day stomatal closure was visible, as well as another closure
starting after 14 hours solar time (15:10 MDT), when the tower showed
TA max, Dmax and a solar flux of 1008 W m-2. Stomatal conductance
increased after 15 hours (16:10 MDT), when intensity of radiation
dropped again.
Salix monticola ranked second in E after B. glandulosa (Table
5.7.), and had the strongest E of all compared willows. This species is
also known as the most abundant willow species in the South Park
Region. Clouds were over the area when the steep drop in g occurred
around 13:30 solar time. This drop may, however, have been due to a
TL as high as 32.8 C, i.e., past the optimum of 27 C; this high
temperature may have caused the partial stomatal closure.
Salix planifolia ranked fifth in both E and g when compared to all
six species, and showed the typical behavior of an unstressed organism
with no mid-day stomatal depression (Figure 5.17.).
128
0
0.1
0.2
0.3
0.4
0.5
ES. mont icola
ES. planifolia
0
0.1
0.2
0.3
0.4
0.5
0:00:00 6:00:00 12:00:00 18:00:00 24:00:00
Time
E
S. candida ES. brachycarpa
0
0.1
0.2
0.3
0.4
0.5
g [ m
ol m
-2 s-1]
E
B. glandulosa
E
C. aquatilis
0:00:00 6:00:00 12:00:00 18:00:00 24:00:00
Solar
Figure 5.18. Stomatal conductance [g] from B. glandulosa, S. candida, C. aquatilis, S. monticola, S. brachycarpa, and S. planifolia on DOY 191. On this daily basis, C. aquatilis conducted least, S. monticola most. See Tables 5.7. and 5.8. for numeric details.
129
S. planifolia
g-pl
0:00:00 6:00:00 12:00:00 18:00:00 24:00:00
Time
S. mont icola
g-mo
S. candida
g-ca
0
4
8
12
16
E [
mm
ol m
-2 s
-1]
S. brachycarpa
g-br
B. glandulosa
g -gl
0
4
8
12
16
0
4
8
12
16
C. aquatilisg
0:00:00 6:00:00 12:00:00 18:00:00 24:00:00
Solar
Figure 5.19. Transpiration [E] from B. glandulosa, S. candida, C. aquatilis, S. monticola, S. brachycarpa, and S. planifolia on DOY 191. On this daily basis, S. planifolia conducted least, B. glandulosa most amounts of H2O. S. planifolia was also the least stressed (no mid-day stomatal depression). See Table 5.7. and 5.8. for numeric details.
130
Figure 5.18 shows E and g from C. aquatilis, S. brachycarpa,
and S. monticola. C. aquatilis and S. brachycarpa both performed a
partial stomatal closure at solar noon, but S. monticola showed its
lowest mid-day g at 13:30 hours solar time, possibly limited by TA and
D, and recovered higher rates of g after the clouds had appeared.
Similarly, Figure 5.19 shows the remaining rates of E and g from B.
glandulosa, S. candida, and S. planifolia. It also shows S. candida’s
partial stomatal closure at solar noon. S. planifolia experienced the
least stress, especially visible at solar noon, S. candida the most. B.
glandulosa had the highest g in the morning hours, and also
experienced less stress during the warm afternoon hours.
131
CHAPTER 6. DISCUSSION
First, these results strongly support those flux models that
differentiate between sunlit and shaded leaves, e.g., the sun/ shade
model, where ―separate extinction coefficients [are] used for diffuse and
direct beam radiation‖ (Leuning et al. 1995). Responses in TL to
changes in Q varied with height. Such a distinction becomes especially
important when calculating the CO2 assimilation rates from plants
subject to N limitation. Identifying differences in the availability of Q to
individual leaves over the course of one day will positively increase the
accuracy of flux prediction. For example, in the case of S. monticola
and the results above, the use of a single value of Q measured at the
top of the plant when predicting g from a canopy of plants would
probably have led to an overestimation of the flux. Further, on a more
practical note, one might use this knowledge about light-use efficiency
in plants to estimate the amounts of fertilizers needed when growing
plants for the purpose of agriculture, and might expand such a sun/
shade model to incorporate the factor of and hence, to further
optimize irrigation practices. While no study was conducted at High
Creek Fen to determine whether using the same leaf on the top of the
plant (which she did), may have resulted in possible leaf ―tiring‖ or
irritation and may have affected the plants responses, it is known that
132
using the same leaf rather than switching between several distinct
leaves is the lesser evil in terms of accuracy (Blanken. pers.comm.
2001).
As Colorado‘s population continues to increase dramatically, the
demand for water as a finite resource grows steadily. Coupled with an
arid climate, a future potential overall drop of the water table may be
considered. This loss could affect High Creek Fen in a way that S.
candida may vanish from there in the future. Instead, B. glandulosa
may take over a larger area. This has also been hypothesized by
Blanken and Rouse (1995) for a high subarctic willow – birch forest.
Naturally, as well as due to the lack of knowledge about the durability of
the water resource that High Creek Fen feeds upon today, it is hard to
predict whether a 5% decrease in average soil moisture is likely to
happen in the near future. Depending on the time span over which
such a reduction may occur one might even consider a gradual
acclimation of the species to the change in over time. Further, it has
not been tested whether the differences in E from S. candida and B.
glandulosa were as pronounced as their differences in actual water-use
efficiency, a statement that could only be made after measuring CO2
assimilation rate.
133
Still, if ever changed past the general respective tolerance
ranges of S. candida ( ) or B. glandulosa ( ), ―relocation‖ or a type
of successive change over the time during or following that change can
be imagined.
Some thoughts on the meaning of the last analysis on the
controls on g and E from all six plants may be worth pondering. All
species examined at High Creek Fen except for S. candida (its
existence here as a boreal relict is still a mystery) have been found in
other places throughout the region; C. aquatilis was mostly found in or
close to rivers, but B. glandulosa and the other willows were abundant
in other valleys blessed with sufficient resources for their flourishment.
Hence, the general findings from this study may partially be applied to
other areas in the South Park region and beyond.
From the appearance in the diurnal graphs of S. planifolia and B.
glandulosa at High Creek Fen (Figures 5.12. and 5.17.) it can be seen
that these organisms were less stressed than those found in subarctic
ecosystems such as the Hudson Bay area, where Blanken and Rouse
(1996) found mid-day stomatal depression in S. planifolia and B.
glandulosa of the subarctic. S. candida’s diurnal curve (Figure 5.15.)
shows a similar shape to the diurnal pattern recorded by Blanken and
Rouse(1996) in Manitoba on June 29th 1995, but mid-day stomatal
134
depression was not recorded on any days in Manitoba, whereas at High
Creek Fen, S. candida partially closed its stomata due to temperature
stress. C. aquatilis in Manitoba shows the same behavior of stomatal
depression at solar noon (Figure 5.13.); however, maximum stomatal
conductance measured at High Creek Fen was lower when compared
to data from June 29th and July 16th in Manitoba. In all four cases of (1)
B. glandulosa, where gmax = 0.55 mol m-2 s-1 (0.9 mol m-2 s-1 in
Manitoba), (2) S. candida gmax =0.55 mol m-2 s-1 (0.9 mol m-2 s-1 in
Manitoba), (3) S. planifolia gmax =0.52 mol m-2 s-1 (1.0 mol m-2 s-1 in
Manitoba), and (4) C. aquatilis gmax =0.5 mol m-2 s-1 (1.0 mol m-2 s-1 in
Manitoba), g was lower at High Creek Fen. Maximum transpiration
compared as follows: B. glandulosa and S. candida had a larger Emax at
High Creek Fen (14 mmol m-2 s-1 and 10 mmol m-2 s-1 as opposed to
their respective 7 mmol m-2 s-1 and 8 mmol m-2 s-1 in Manitoba). At High
Creek Fen, S. planifolia‘s Emax = 10 mmol m-2 s-1 and C. aquatilis‘s Emax
= 8 mmol m-2 s-1; in Manitoba, these numbers were similar on the days
measured (June 29th and July 16th 1995). Generally, these differences
can be explained through differences in ambient conditions such as TA
and D, but a comparison helps in creating a bigger picture that
describes the dynamics of such hygrophilous ecosystems.
135
The general atmospheric warming trend of the last few decades
and four consecutive years of relatively dry conditions has affected Park
County especially this year of 2002. The jet stream has moved further
south, leaving the atmosphere dry, allowing for little precipitation. Few
convective afternoon showers have been observed for this growing
season as of August 12, 2002, and rivers are running low this year. In
light of this drought and a possible continuation of this trend (global
warming) in the future, the vulnerability of the species explored
throughout this study may be inquired, of which an attempt has been
made in the following paragraph.
For S. monticola, which has also been quoted as the most
abundant willow of the South Park region, the current drought situation
may be least threatening. Its tolerance for dry air (large D) as well as
its high TL optimum prepares this species well for dry summers. The
same can be said about B. glandulosa, which seems well adapted to
low soil moisture conditions, and also displays a high TL optimum. The
large range of acceptable D and TL for S. brachycarpa and C. aquatilis
has these two also prepared for a warmer climate (C. aquatilis,
however, would have trouble with actual limitations in .) S. planifolia
and S. candida would experience an increased ambient air temperature
as a disadvantage, as their TL optima are in the lower 20s. This
136
scenario may especially cause S. candida to be out-competed by those
plants with a higher TL optimum, as this species is already a sparse
populace of High Creek Fen. However, Schulz (2002) stated that once
established, S. candida proves to be a very resistant species. As of the
controls of Q on g, all plants have their respective light saturation
points, i.e. B. glandulosa and S. candida at 400 mol m-2 s-1, S.
monticola at 500 mol m-2 s-1, S. planifolia at 550 mol m-2 s-1 and S.
brachycarpa at 650 mol m-2 s-1 but in light of the reliably strong
Colorado sun no actual, long-term limitation can be expected. Species
composition plays a determining role in modeling the E from an
ecosystem canopy, as, for example, E from C. aquatilis is a third of the
rate of E from B. glandulosa. The distinct results from all six species
should hopefully encourage all modelers to integrate species diversity
into their mathematics.
137
CHAPTER 7. CONCLUSION
Due to the high E from densely-vegetated areas in comparison
to non-vegetated or sparsely-vegetated surfaces, wetlands especially
contribute to the water vapor recharge of the atmosphere; their loss,
accompanied by a larger sensible heat flux, would set off a positive
feedback of warming surface temperatures (Blanken and Rouse 1996).
The importance of wetlands to the magnitude of E has also been
shown by Petrone et al. (2000), who found that drylands compared to
wetlands evaporate substantially less, even if located in similar areas
with comparable atmospheric conditions. Hence, global wetland loss
over the last century due to anthropogenic disturbance may be more
tightly connected to global warming than thought until now. Estimates
show that the world may have lost as much as 50 % of the wetlands
since 1900; during the first half of the century, loss was largest in
northern countries, but pressure has increased in tropical and
subtropical areas since the second half of the past century (OECD
1996). Today, few wetlands are free from anthropogenic threat (Dugan
and Jones 1992).
According to Winter (2000), the vulnerability of wetlands to
climate change fall between two extremes: those primarily dependent
upon precipitation are highly vulnerable, and those dependent primarily
138
on discharge from regional ground water flow are less at stake because
ground water flow systems have buffering capacity to climate change.
High Creek Fen, while not dependent on local precipitation but rather
recharged from regional ground water flow, which is ultimately derived
from snowmelt in higher-elevation areas. As long as atmospheric
warming and hence an increase in potential evaporation from the creek
does not significantly alter its water table, one may conclude that High
Creek Fen is less susceptible to climate change than those wetlands
primarily dependent on atmospheric deposition. Knowing that ~40 % of
the annual precipitation occurs in the summer, the 121 mm recorded at
the fen for summer 2001 fell well within the expected amount as
reported in the 35-year mean total annual precipitation for Antero
Reservoir (234 mm) and Fairplay (352 mm). However, a continued,
long term lack of sufficient precipitation in the creek‘s watershed may
change the ground water regime of the area. Moreover, since High
Creek Fen is the most southern representative of this ecosystem type in
North America, with floristically most similar fens found in Ontario,
Montana and Wyoming (Cooper 1996), it is conceivable that the global
warming trend will disturb this most southerly occurring fen in a long-
term perspective.
139
This research has given rise to the hypothesis that a five percent
decrease in av would compromise optimal conditions for S. candida,
and may lead to its outcompetition by, for example, B. glandulosa.
Further impairment of the current status of High Creek Fen could arise
with an alteration of timing and duration of spring time inundation. A
considerable difference in spring flood at High Creek Fen has been
observed for May 2001 compared to May 2002, the latter after a winter
of little to no snow (communication with South Park locals). However,
Schulz (2002) stated that flooding is an unusual event for High Creek
Fen. S. candida as opposed to other willows that have been observed
in dryland locations, but also all remaining hydrophytes of the fen may
be dependent on certain threshold durations of flooding; these may
have already been lacking in the past, and High Creek Fen may have
already entered the course of changing into an upland system. No
such evidence has been found by the researcher, except that S.
candida compared to its immediate neighbors often appeared frail with
low LAI. But, artificially altering spring flood duration through ditches
without understanding the exact water regime of the fen with all its
variables, for example, sediment transport, would only lead to negative
results.
140
On an ecosystem average of all six investigated species, 350
grams (H2O) m-2 (leaf surface) h-1 were transpired on July 10th, 2001
(mean value from Table 5.7.). Future measurements should include
both species distribution and their respective LAI, as well as data
collection extension over several growing seasons to gauge the actual
amount of water vapor leaving this area over time. These data should
be incorporated in current climate models to yield more realistic
estimates of future climate scenarios. In terms of management
strategies for High Creek Fen, long-term data from transpiration should
be correlated with concurrent, extensive water table measurements to
better understand the relationship between water table height, soil
moisture, and their effects on the health of the ecosystem, so that the
potential dependency of the plants on a threshold height of the water
table can be assessed.
141
Photograph 7.1. High Creek Fen looking west toward the Mosquito Range.
142
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APPENDIX A
Carex aquatilis (Watersedge)
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APPENDIX B
CD-ROM: Photographs of High Creek Fen