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

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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.

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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

LIST OF REFERENCES

Appel J. 1995. Hydrogeologic Framework of the High Creek Calcareous Fen, South Park, Park County, CO. MS thesis, University of New Mexico.

Beckett SH, Blaney HF, Taylor CA. 1930. Irrigation Water Requirement Studies of Citrus and Avocado Trees in San Diego County, California, 1926 and 1927. University of California College of Agriculture, Agricultural Experiment Station, Berkeley, California, Bulletin 489, April. Beringer J, Lynch AH, Chapin FS, Mack M, Bonan GB. 2001. The representation of arctic soils in the land surface model: The importance of mosses. Journal of Climate 14: 3324 – 3335. Blaney HF, Criddle WD. 1949. Consumptive Use and Irrigation Water Requirements of Crops in Colorado:Washington, U.S. Soil Conservation Service, 55p, August. Blanken PD. 2001 – 2002. Personal communication. Blanken PD, Rouse WR. 1994. The role of willow-birch forest in the surface energy balance at arctic treeline: Arctic and Alpine Research 26: 403 - 411. ______ 1995. Modelling evaporation from a high subarctic willow-birch forest: International Journal of Climatology 15: 97 - 106. _____ 1996. Evidence of water conservation mechanisms in several subarctic wetland species. Journal of Applied Ecology 33: 842 – 850. Bowen IS. 1926. The ratio of heat losses by conduction and by evaporation from any water surface: Phys. Rev. 27: 779 - 787. Brand C, Carpenter AT. 1999. Hydrogeologic and Vegetation Monitoring Plan for the High Creek Fen Preserve in Park County, CO: Prepared for The Nature Conservancy.

143

Burba GG, Verma SB, Kim J. 1999. A comparative study of surface energy fluxes of three communities (Phragmites australis, Scirpus acutus, and open water) in a prairie wetland ecosystem. Wetlands 19: 451 – 457. Chambers JL, Hinckley TM, Cox GS, Metcalf CL, Aslin RG. 1985. Boundary- line analysis and models of leaf conductance for four oak-hickory forest species. Forest Science 31: 437 – 450. Chen JM, Blanken PD, Black TA, Guilbeault M, Chen S. 1997. Radiation Regime and Canopy Architecture in a Boreal Aspen Forest. Agricultural and Forest Meteorology 86: 107 – 125. Cooper DJ. 1996. Water and Soil Chemistry, Floristics, and Phytosociology of the Extreme Rich High Creek fen in South Park, Colorado, USA: Canadian Journal of Botany 74: 1801 - 1811. Cusack ST. 2002. Hard rock miner in South Park. Personal communication. Dawson TE, Bliss LC. 1989. Intraspecific variation in the water relations of Salix arctica, an arctic-alpine dwarf willow. Oecologia 79: 322 – 331. Denver Post. December 8th, 2000. De Pury DGG, Farquhar GD. 1997. Simple scaling of photosynthesis from leaves to canopies without the error of big-leaf models. Plant, Cell and Environment 20: 537 – 557. Dougherty PM, Hinckley TM. 1981. The influence of severe drought on net photosynthesis of white oak (Quercus alba). Canadian Journal of Botany 59: 335 – 341. Dugan PJ, Jones TA. 1992. Ecological change in wetlands: a global overview. In Moser M, Prentice RC, van Vessem J. (eds.). 1992. Water fowl and wetland conservation in the 1990s: A global perspective. IWRB Special Publication 26. Ehrlich PR. 1994. Energy use and biodiversity loss. Phil. Trans. R. Soc. Lond. B 344: 99 – 104. Jarvis PG. 1976. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Phil. Trans. R. Soc. Lond. B 273: 593 – 610.

144

Johnson B. 1998. The Calcareous Fens of Park County, Colorado: Their Vegetation, Environmental Functioning, and the Effects of Disturbance. Prepared for US EPA Region VIII, CO Dept. of Natural Resources, and Park County. Jordan H. 1981. Balneoklimatologie--Eine Zielstellung im Mensch-Umwelt Konzept. Sitzungsberichte der saechsischen Akademie der Wissenschaften zu Leipzig; Band 114, Heft 6. Akademie Verlag Berlin. Kazda M. 1995. Changes in alder fens following a decrease in the ground water table: results of a geographical information system application. Journal of Applied Ecology 32: 100 – 110. Leuning R, Kelliher FM, De Pury DGG, Schulze E-D. 1995. Leaf nitrogen, photosynthesis, conductance and transpiration: Scaling from leaves to canopies: Plant, Cell and Environment 18: 1183 – 1200. Lieth H. 1997. Aims and methods in phenological monitoring. Progress in Biometeorology 12: 1 - 21. Backhuys Publishers, Leiden, The Netherlands. MaryPIRGS .1999., Personal communication. Misantoni D. 2002. Senior geologist in South Park. Personal communication. Monson R. 2000. Biosphere—Atmosphere Interactions. In Print. Monteith JL. 1995. Accomodation between transpiring vegetation and the convective boundary layer: Journal of Hydrology 166: 251 - 263. ______1975. Vegetation and the Atmosphere. Academic Press Inc. London. Monteith JL, Unsworth MH. 1990. Principles of Environmental Physics. Edward Arnold, London. Moser M, Prentice C, Frazier S. 1996. A global overview of wetland loss and degradation. Ramsar Convention on Wetlands Conference Proceedings 10. Myers H, Mittermeier RA, Mittermeier CG, da Fenseca GAB, Kent J. 2000. Biodiversity hotspots for conservation priorities. Nature 403: 853 – 858.

145

Nobel PS. 1999. Physicochemical & Environmental Plant Physiology. Academic Press, San Diego. OECD/IUCN. 1996. Guidelines for aid agencies for improved conservation and sustainable use of tropical and sub-tropical wetlands. OECD, Paris. Oke TR. 1996. Boundary Layer Climates. Routledge, New York. Pascale AJ. 1957. Richtlinien fuer bioklimatologische Untersuchungen von Wintergetreiden. International Journal of Bioclimatology and Biometeorology 1: First Bioclimatological Congress, Vienna. Penman HL. 1947. Natural evaporation from open water, bare soil,

and grass. Proceedings of the Royal Society of London 193: 120 - 145. Petrone RM, Rouse WR, Marsh P. 2000. Comparative surface energy budgets in western and central subarctic regions of Canada. International Journal of Climatology 20: 1131 – 1148. Pimm SL, Russell GI, Gittleman IL, Brooks TM. 1995. The future of biodiversity. Science 269: 347 – 350. Rey Benayas JM, Colomer MGS, Levassor C. 1999. Effects of area, environmental status and environmental variation on species richness per unit area in Mediterranean wetlands. Journal of Vegetation Science 10: 275 - 280. Rouse WR. 2000. The energy balance and water balance of high-latitude

wetlands: controls and extrapolation. Global Change Biology 6: 59 - 68.

______ 1998. A water balance model for a subarctic sedge fen and its application to climate change: Climatic Change 38: 207-234. Sanderson J. 2001. Bio-ecologist at Colorado State University. Personal communication. Sanderson J, March M.1996. Extreme Rich Fens of South Park, Colorado: Their Distribution, Identification, and Natural Heritage Significance. Colorado Natural Heritage Program.

146

Schulz T. 1998. Final Report on the Restoration of Wetlands Degraded by Peat Mining at High Creek Fen Preserve, Park County, Colorado. The Nature Conservancy. Schulz T. 2002. Steward ecologist at The Nature Conservancy. Personal communication. Seemann J, Chirkov YI, Lomas J, Primault B. 1979. Agrometeorology. Springer-Verlag, Berlin. Shawe. 1995. Sinkhole map ……Map MF-2295. US Geological Survey. Takagi K. 1998. Diurnal hystereses of stomatal and bulk surface conductances in relation to vapor pressure deficit in a cool-temperate wetland. Agricultural and Forest Meteorology 91:177-191.

Thornthwaite CW. 1940. Atmospheric Moisture in Relation to Ecological Problems: Ecology 21: 17-28.

______ Mather JR. 1953. Climate in Relation to Crops. In: Recent Studies in

Bioclimatology: A Group. Meteorological Monograph, American Meteorological Society.

______ May A, Mather JR. 1954. Monograph on Bioclimatology.

U.S. Census Bureau. 2000. Van Bavel CHM, Wilson TV. 1952. Evapotranspiration estimates as criteria for determining time of irrigation: Agriculture and Engineering 33: 417 - 420. Veihmeyer FJ. 1951. Elimination of wasteful uses of water and salvage of uneconomic water consumption: Paper presented at Regional Conference for the Conservation of Natural Resources, 2d, Water Sec., Berkeley, California. White G. 1969. Geography and Public Policy. The Professional Geographer 24, May 1972: 101-104. Winter TC. 2000. The vulnerability of wetlands to climate change: A hydrologic landscape perspective. Journal of the American water resources association 36: 305 – 311.

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APPENDIX A

Carex aquatilis (Watersedge)

148

APPENDIX B

CD-ROM: Photographs of High Creek Fen