1

MONSOON DEPENDENT ECOSYSTEMS: IMPLICATIONS OF THE VERTICAL

DISTRIBUTION OF SOIL MOISTURE ON LAND SURFACE-ATMOSPHERE

INTERACTIONS

by

Zulia M. Sánchez-Mejía

_______________________

A Dissertation Submitted to the Faculty of the

SCHOOL OF NATURAL RESOURCES AND THE ENVIRONMENT

In Partial Fulfillment of the Requirements

For the Degree of

DOCTOR IN PHILOSOPHY

WITH A MAJOR IN WATERSHED MANAGEMENT AND ECOHYDROLOGY

In the Graduate College

THE UNIVERSITY OF ARIZONA

2013

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THE UNIVERSITY OF ARIZONA

GRADUATE COLLEGE

As members of the Dissertation Committee, we certify that we have read the dissertation

prepared by Zulia Mayari Sánchez-Mejía, titled Monsoon Dependent Ecosystems:

Implications of the vertical distribution of soil moisture on land surface-atmosphere

interactions and recommend that it be accepted as fulfilling the dissertation requirement

for the Degree of Doctor of Philosophy.

_______________________________________________________________________

Shirley A. Papuga Date: 5/31/2013

_______________________________________________________________________

Francina Dominguez Date: 5/31/2013

_______________________________________________________________________

Xubin Zeng Date: 5/31/2013

_______________________________________________________________________

Steve Archer Date: 5/31/2013

_______________________________________________________________________

Willem van Leeuwen Date: 5/31/2013

Final approval and acceptance of this dissertation is contingent upon the candidate’s

submission of the final copies of the dissertation to the Graduate College.

I hereby certify that I have read this dissertation prepared under my direction and

recommend that it be accepted as fulfilling the dissertation requirement.

________________________________________________ Date:___________________

Dissertation Director: Shirley A. Papuga

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STATEMENT BY AUTHOR

This dissertation has been submitted in partial fulfillment of the requirements for an

advanced degree at the University of Arizona and is deposited in the University Library

to be made available to borrowers under rules of the Library.

Brief quotations from this dissertation are allowable without special permission, provided

that an accurate acknowledgement of the source is made. Requests for permission for

extended quotation from or reproduction of this manuscript in whole or in part may be

granted by the head of the major department or the Dean of the Graduate College when in

his or her judgment the proposed use of the material is in the interests of scholarship. In

all other instances, however, permission must be obtained from the author.

SIGNED: Zulia M. Sánchez-Mejía

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ACKNOWLEDGEMENTS

This research was possible thanks to the doctoral studies fellowship by CONACYT-

Mexico (Consejo Nacional de Ciencia y Tecnología, National Council for Science and

Technology). Additional support was provided by the University of Arizona College of

Agriculture and Life Sciences (CALS), The Arizona University System Technology and

Research Initiative Fund (TRIF), The University of Arizona Office of the Vice President

for Research (VPR), SAHRA (Sustainability of Semi-Arid Hydrology and Riparian area)

under the STC Program of the National Science Foundation (NSF), NSF CAREER

Award # EAR-1255013, the Springfield Scholarship for Graduate Research in

Rangelands, the University of Arizona Global Change Interdisciplinary Program

Dissertation Improvement Grant, the William A. Calder III PhD Scholarship for Mexican

graduate students focused in conservation, the Kel M. Fox Watershed Scholarship, and

the Institute of the Environment travel support grant.

This research would not have been possible without the guidance and mentoring of my

major advisor Shirley Papuga and the rest of my doctoral committee: Francina

Dominguez, Xubin Zeng, Steve Archer, Willem van Leeuwen. I am especially thankful

for their encouragement, patience, and sharing of knowledge. Other professors that had a

large impact in my doctoral studies included Peter F. Ffolliott, Valerie Trouet, Katherine

Morrissey, Paul Brooks, and Jaime Garatuza.

I am very grateful to Russell Scott, Jim Shuttleworth, and Michael Crimmins for

providing field equipment that was necessary for the completion of this research.

I also am grateful to those volunteers who shared their time and made the hot sunny days

in the field fun and pleasant, including Retta Bruegger, Carrie Presnall, Russell Lyon,

Jose Arizpe, Alex Arizpe, and Xavier Zapata.

Significant progress in my language and critical thinking skills is thanks to the Papuga

Lab: Ami Kidder, Andy Neal, Bhaskar Mitra, Daniel Bunting, Evan Kipnis, Jessica

Swetish, Joseph Miller, Krystine Nelson, Lori Lovell, Maria Pilar Cendrero Mateo, and

Zack Guido.

I owe special thanks to all the support from Katie Hirschboeck, Lesa Langan-Du Berry,

Katie Hughes, Ashley Stewart, Amber, Khuyen Huynh, Chiyo Yamashita-Gill, Andy

Honaman, and Bryce.

I will like to thank all the members of the NRGSO (Natural Resources Graduate Student

Organization) for organizing talks, seminars, HH, and for sharing the good and not so

good moments of graduate school.

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Last but not least, I thank my sanity and emotional wellbeing to my parents Edgar and

Lulú, my siblings Ana and Camilo, my boyfriend Alexis Arizpe, Familia Arizpe, Familia

Nuñez, Familia Sanchez, Familia Mejia, and so many friends that have been there

through this process.

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DEDICATION

To creosotebush…

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TABLE OF CONTENTS

ABSTRACT ............................................................................................................... 11

CHAPTER 1: INTRODUCTION .............................................................................. 13

1.1 Background ..................................................................................................... 13

1.2 Research Objectives ........................................................................................ 18

1.3 Dissertation Format ......................................................................................... 21

CHAPTER 2: PRESENT STUDY............................................................................. 23

2.1 APPENDIX A: OBSERVATION OF A TWO-LAYER SOIL

MOISTURE INFLUENCE ON SURFACE ENERGY DYNAMICS AND

PLANETARY BOUNDARY LAYER CHARACTERISTICS IN A

SEMIARID SHRUBLAND .................................................................................. 23

2.2 APPENDIX B: QUANTIFYING THE INFLUENCE OF DEEP SOIL

MOISTURE ON ECOSYSTEM ALBEDO: THE ROLE OF

VEGETATION ..................................................................................................... 24

2.3 APPENDIX C: EMPIRICAL RELATIONSHIPS BETWEEN SOIL

MOISTURE, ALBEDO, AND THE PLANETARY BOUNDARY LAYER

HEIGHT: A TWO-LAYER BUCKET MODEL APPROACH ............................ 25

2.4 Future Research Opportunities ........................................................................ 26

2.4.1 Land surface- atmosphere interactions: dryland ecohydrology

modeling .............................................................................................................. 26

2.4.2 The shrubby “gray” –zone of woody encroachment ................................. 28

REFERENCES ......................................................................................................... 29

APPENDIX A: OBSERVATION OF A TWO-LAYER SOIL MOISTURE

INFLUENCE ON SURFACE ENERGY DYNAMICS AND PLANETARY

BOUNDARY LAYER CHARACTERISTICS IN A SEMIARID

SHRUBLAND ........................................................................................................... 42

1. Instruction .............................................................................................................. 45

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TABLE OF CONTENTS

2. Study Area and Methods ........................................................................................ 49

2.1 Study Area ....................................................................................................... 49

2.2 Data ................................................................................................................. 51

2.2.1 Radiation and Surface Energy ................................................................... 51

2.2.2 Soil Moisture ............................................................................................. 53

2.2.3 Atmospheric Sounding .............................................................................. 54

2.3 Soil Moisture Conceptual Framework ............................................................ 56

3. Results .................................................................................................................... 58

3.1 Soil Moisture Influence on Radiation and Surface Energy Components ....... 59

3.1.1 Wet and Dry Season .................................................................................. 59

3.1.2 Two-layer Conceptual Framework ............................................................ 60

3.2 Soil Moisture Influence on the Planetary Boundary Layer ............................. 62

4. Discussion .............................................................................................................. 63

References .................................................................................................................. 68

APPENDIX B: QUANTIFYING THE INFLUENCE OF DEEP SOIL

MOISTURE ON ECOSYSTEM ALBEDO: THE ROLE OF VEGETATION ........ 89

1. Instruction .............................................................................................................. 92

2. Study Area and Method ......................................................................................... 96

2.1 Study description ............................................................................................. 96

2.2 Field Campaign ............................................................................................... 97

2.2.1 Sampling design ......................................................................................... 99

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TABLE OF CONTENTS

2.2.2 Analysis ..................................................................................................... 100

2.3 Camera-Derived Greenness ............................................................................ 101

3. Results and discussions .......................................................................................... 102

3.1 Two-Layer Soil Moisture Influence on Greenness ......................................... 103

3.2 Two-Layer Soil Moisture Influence on Albedo .............................................. 104

3.3 Influence of Vegetation on Ecosystem Albedo via Access to Two-Layer

Soil Moisture ......................................................................................................... 106

3.4 Implications of Soil Moisture Drydown Differences between Bare and

Canopy Patches ..................................................................................................... 109

4. Conclusions ............................................................................................................ 111

References .................................................................................................................. 112

APPENDIX C: EMPIRICAL RELATIONSHIPS BETWEEN SOIL

MOISTURE AND THE PLANETARY BOUNDARY LAYER HEIGHT: A

TWO-LAYER BUCKET MODEL APPROACH ..................................................... 134

1. Instruction .............................................................................................................. 137

2. Methods.................................................................................................................. 140

2.1 Micrometeorological, Soil Moisture and Radiation Data ............................... 140

2.2 Atmospheric Sounding Data ......................................................................... 143

2.3 Development of Empirical Relationships ..................................................... 144

2.4. Two-Layer Bucket Model of Soil Moisture Dynamics ............................... 146

2.4.1 Model Parameters .................................................................................... 149

2.4.2 Model Parameters Input .......................................................................... 150

3. Results and discussion ........................................................................................... 153

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TABLE OF CONTENTS

3.1 Soil moisture and albedo influence on the planetary boundary layer height .. 153

3.2 Empirical relationships for estimating planetary boundary layer height ........ 154

3.3 Influence of change in precipitation regime on land surface –PBL

dynamics ............................................................................................................... 157

3.4 Implications of modeling two-layer soil moisture dynamics ........................ 160

References .................................................................................................................. 161

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ABSTRACT

Uncertainty of predicted change in precipitation frequency and intensity motivates the

scientific community to better understand, quantify, and model the possible outcome of

dryland ecosystems. In pulse dependent ecosystems (i.e. monsoon driven) soil moisture is

tightly linked to atmospheric processes. Here, I analyze three overarching questions; Q1)

How does soil moisture presence or absence in a shallow or deep layer influence the

surface energy budget and planetary boundary layer characteristics?, Q2) What is the

role of vegetation on ecosystem albedo in the presence or absence of deep soil moisture?,

Q3) Can we develop empirical relationships between soil moisture and the planetary

boundary layer height to help evaluate the role of future precipitation changes in land

surface atmosphere interactions?. To address these questions I use a conceptual

framework based on the presence or absence of soil moisture in a shallow or deep layer. I

define these layers by using root profiles and establish soil moisture thresholds for each

layer using four years of observations from the Santa Rita Creosote Ameriflux site. Soil

moisture drydown curves were used to establish the shallow layer threshold in the

shallow layer, while NEE (Net Ecosystem Exchange of carbon dioxide) was used to

define the deep soil moisture threshold. Four cases were generated using these thresholds:

Case 1, dry shallow layer and dry deep layer; Case 2, wet shallow layer and dry deep

layer; Case 3, wet shallow layer and wet deep layer, and Case 4 dry shallow and wet deep

layer. Using this framework, I related data from the Ameriflux site SRC (Santa Rita

Creosote) from 2008 to 2012 and from atmospheric soundings from the nearby Tucson

Airport; conducted field campaigns during 2011 and 2012 to measure albedo from

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individual bare and canopy patches that were then evaluated in a grid to estimate the

influence of deep moisture on albedo via vegetation cover change; and evaluated the

potential of using a two-layer bucket model and empirical relationships to evaluate the

link between deep soil moisture and the planetary boundary layer height under changing

precipitation regime. My results indicate that (1) the presence or absence of water in two

layers plays a role in surface energy dynamics, (2) soil moisture presence in the deep

layer is linked with decreased ecosystem albedo and planetary boundary layer height, (3)

deep moisture sustains vegetation greenness and decreases albedo, and (4) empirical

relationships are useful in modeling planetary boundary layer height from dryland

ecosystems. Based on these results we argue that deep soil moisture plays an important

role in land surface-atmosphere interactions.

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CHAPTER 1: INTRODUCTION

1.1 Background

Almost forty percent of the Earth’s land surface is classified as dryland. Uncertainty in

how these dryland regions will respond to changes in precipitation and temperature is of

great concern for the scientific community and society [D'Odorico et al., 2013;

Rodriguez-Iturbe, 2000; Weltzin et al., 2003; Wiegand and Jeltsch, 2000]. In particular

because the ecosystem services provided by this regions influence the well-being of about

35% of the human population [Dooley, 2005; van Jaarsveld et al., 2005; Vihervaara et

al., 2010].

Drylands are unique in that they are water-limited and potential evapotranspiration

generally exceeds precipitation [Newman et al., 2006]. In addition to these environmental

conditions, drylands face management challenge [Miller, 2005]related to land use change

which are often as a consequence of human and natural disturbance, e.g.

“desertification”, salinization, soil erosion [D'Odorico et al., 2013; Miller, 2005].

Some identified knowledge gaps in our understanding of dryland ecosystems include 1)

ecohydrological impacts of invasive species [Nagler et al., 2009], 2) evaporation

partitioning [Newman et al., 2010], 3) scale and non-linear implications [Asbjornsen et

al., 2011], 4) shrub-grass interactions[Miller, 2005; Newman et al., 2006], 5) soil

moisture dynamics in canopy-intercanopy transitions[Bhark and Small, 2003], 6) water

availability for deep-rooted species as precipitation events become more intense

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[Breshears et al., 2009; Raz-Yaseef et al., 2012], and 7) land surface - atmosphere models

with good representation of dryland ecohydrological triggers [Wang et al., 2012].

Arid and semiarid regions are characterized by “pulses” of precipitation [Huxman et al.,

2004; Lauenroth and Bradford, 2009] that have an effect on ecosystem functioning and

composition [Aguiar and Sala, 1999]. These “pulses” arrive as packets of moisture, as a

result of small or large precipitation events, that may differentially influence different

species within these arid and semiarid ecosystems. For instance in these regions, small

rainfall events (<5 mm) are especially important for certain functional types such as

grasses [Sala and Lauenroth, 1985] and soil crusts [Belnap et al., 2004].

In semiarid regions “pulses” of precipitation influence all of the components of the water

budget [Weltzin et al., 2003; Wilcox et al., 2006], and especially soil moisture [Entekhabi

et al., 1996]. Importantly, soil moisture links the ecological and climate system via

evapotranspiration and the surface energy budget [Chahine, 1992; Kustas et al., 1991;

Rodriguez-Iturbe, 2000]. Therefore, understanding mechanisms, interactions, thresholds,

and feedbacks between vegetation and the hydrologic cycle is of great importance

especially with anticipated changes in precipitation regimes [Rodriguez-Iturbe, 2000;

Seneviratne et al., 2010].

Soil moisture and vegetation are coupled in such a way that influences land surface

processes. This has been widely investigated especially in tropical and temperate forests

[Hutjes et al., 1998], e.g. by analyzing the effect of tropical deforestation [Berbert and

Costa, 2003; Gash and Shuttleworth, 1991] and temperate afforestation [Swann et al.,

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2012] on albedo and the consequence of this on the surface energy balance and

furthermore on the climate system.

Processes such as evaporation depend on if soil moisture is within reach of atmospheric

demand [Orlandini, 1999; Wythers et al., 1999], while soil moisture beyond this depth is

transferred through transpiration as a consequence of photosynthesis of vegetation

[Cavanaugh et al., 2011]. Evapotranspiration partitioning is a subject of ongoing research

due to the complexity of surface energy balance processes and measurements

[Cavanaugh et al., 2011; Oren et al., 1998; Scott, 2010; Williams et al., 2004], and of

bellow ground processes such as hydraulic redistribution [Caldwell et al., 1998;

Giambelluca et al., 2009; Unsworth et al., 2004]. The partitioning of this is of great

importance, for instance, where there is a need to quantify the effects of soil moisture on

bare and vegetated patches individually to better predict the influence of vegetation

change (i.e. woody encroachment) [Rothfuss et al., 2010; Sakaguchi and Zeng, 2009;

Yepez et al., 2005] on the climate system.

Woody vegetation in semiarid regions is expected to change the ratio of transpiration to

total evapotranspiration because of changes in leaf area, volume of roots, and phenology

[Huxman et al., 2005; Rodriguez-Iturbe et al., 1999]. Changes in the land surface

characteristics as a result of shifts in vegetation impact more than one land surface

process. For instance, canopy architecture and greenness can influence albedo [Asner,

1998]. Moreover, belowground changes as a result of shifts in vegetation, such as root

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distribution, also have the potential to influence land surface processes such as

transpiration [R Jackson et al., 2000].

The planetary boundary layer depends on mechanisms such as latent and sensible heat

partitioning, which can be influenced by transpiration if soil moisture (>20 cm) is

available [Basara and Crawford, 2002]. Latent and sensible heat from the land surface to

the atmosphere modify the lowest 100 to 3000 m, leading to the formation of this

planetary boundary layer [Stull, 1988]. Early studies on the planetary boundary layer

focused on the processes that affect air buoyancy and therefore the turbulence and height

of this layer, leading to linking the surface energy budget to the planetary boundary layer

[Bjerken, 1900; Ruzin, 1963]. More recently, research has focused on developing

diagnostic tools and relationships that couple land surface processes and the planetary

boundary layer [Chen et al., 2012; Kirtman et al., 2009; Santanello et al., 2013].

Albedo (the ratio of outgoing shortwave radiation to incoming shortwave radiation) is a

land surface property that is related to both vegetation and soil moisture [Charney, 1975;

Idso et al., 1975; Richardson et al., 2013]. For instance, as vegetation grows in the

presence of soil moisture, albedo decreases and as vegetation senesces, albedo increases,

making it a useful metric for analyzing phenological processes [Zhang et al., 2005]. As

another example, biophysical parameters such as albedo are used to study vegetation

processes using remote sensing in natural [Mendez-Barroso and Vivoni, 2010; Otterman,

1977] or agricultural [Mokhtari et al., 2013] systems.

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Generally albedo decreases with a darker surface [Twomey et al., 1986]. For instance, the

presence of soil moisture darkens bare soil and changes albedo [Idso et al., 1975; R D

Jackson et al., 1976; Lobell and Asner, 2002] and the green-up of vegetation does as well

[Asner, 1998; Berbert and Costa, 2003; Song, 1999; Zhang et al., 2005]. However,

whether the soil or the vegetation has a stronger influence on albedo is not clear

[Domingo et al., 2000], and at what depth the presence or absence of moisture will

influence the surface and as a consequence impact albedo is also not well understood

[Small and Kurc, 2003]. For instance, albedo plays a key role in sensible and latent heat

partitioning, especially when moisture is present in a shallow layer [Baldocchi et al.,

2004; Domingo et al., 2000; Small and Kurc, 2003], but less is understood of the effect

when there is moisture in a deep layer [Bracho et al., 2008].

Modeling efforts at different scales have improved our understanding of land surface-

atmospheric interactions [Pitman, 2003; Rosolem et al., 2010; Seneviratne et al., 2010;

Shuttleworth, 1994]. However there is still a need to link these interactions at multiple

scales and to engage in understanding on how ecosystems will trigger and feedback to

future climate perturbations [Asbjornsen et al., 2011].

To contribute to these knowledge gaps, my doctoral research is focused in the study of

the presence or absence of soil moisture at different depths and its overall influence on

radiation and surface energy budget dynamics and planetary boundary layer

characteristics. This research will result in three peer-reviewed research papers. My first

paper assesses the influence of two-layer soil moisture on radiation and surface energy

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dynamics and planetary boundary layer characteristics by coupling continuously

monitored ecosystem level measurements of soil moisture and surface energy fluxes

within a desert shrubland using the eddy covariance technique and atmospheric sounding

data. My second paper builds off of the findings from my first paper in the design of a

field campaign to address the differential influence of vegetation patches on ecosystem

albedo due to the influence of deep soil moisture on vegetation dynamics. Finally, in my

third paper, using my findings from my first and second papers I will incorporate deep

soil moisture dynamics into a simple land-surface modeling approach linking a bucket

model with empirical relationships to assess the value of including this information on

applications related to land-atmosphere interactions.

1.2 Research Objectives

The overall objective of this research is to contribute to watershed management and

ecohydrology in arid and semiarid ecosystems. Arid and semiarid ecosystems face many

challenges in both the near and distant future. The success of mitigation, conservation,

and managing strategies to address these challenges will depend on both scientific and

social effort .

The training that I have gained through this study include critical thinking, the use of

emerging tools such as eddy covariance, and experience in land surface – atmosphere

modeling, have prepared me for these challenges, enabling me to contribute in research

and mentoring as I return to Mexico.

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With this in mind, my research aims to answer three broad questions:

1) How does soil moisture presence or absence in a shallow or deep layer influence the

surface energy budget and planetary boundary layer characteristics?

Understanding soil moisture control on land-atmosphere interactions will become

increasingly important as global changes continue to alter the water availability of our

ecosystems. For my analysis, I used four years of data from the Santa Rite Creosote

Ameriflux site. I categorized each day of our record into (1) wet or dry seasons and (2)

one of four Cases within a two-layer framework based on the presence or absence of

moisture in a shallow and a deep soil layer. Using these categorizations I quantified the

soil moisture control on the radiation and surface energy budgets and planetary boundary

layer characteristics using both average responses and linear regression.

2) What is the role of vegetation on ecosystem albedo in the presence or absence of deep

soil moisture?

Because vegetation plays a critical role in ecosystem processes, understanding what

controls vegetation change and when is it triggered is especially important in arid and

semiarid regions. To address this, I made use of the two-layer conceptual framework. I

hypothesized that when the vegetation had access to soil moisture available only in the

shallow layer (Case 2), albedo would be higher than when the vegetation had soil

moisture available in the shallow and deep layer (Case 3) or deep layer (Case 4). With

this we know that deep soil moisture is linked to surface albedo. I further hypothesized

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that when the shallow layer is wet (Case 2 and 3), bare albedo will be lower than when

the shallow layer is dry (Case 1 and 4). To test this hypothesis I designed a field

campaign to measure albedo above canopy and bare patches for each Case. These Cases

were identified in real-time using data from a nearby meteorological station at a local

highschool ( http://sahuarita.cals.arizona.edu/). Field measurements were taken in two

locations within the footprint of an eddy covariance tower, at multiple bare and canopy

patches. Probability distribution functions (PDFs) were developed from these albedo data

collected from the field campaigns. I then populated a 100x100 grid of bare and canopy

cells with albedo values weighted by these PDFs, and increased the canopy cover from 0

to 100% to look at the influence of the deep moisture on albedo by its influence on the

vegetation. Long-term monitoring with pheno-cams proved an important tool for better

understanding the vegetation phenology that was linked to deep moisture.

3) Can we use empirical relationships between soil moisture and planetary boundary

layer height to evaluate future precipitation changes?

One major knowledge gap in dryland ecohydrology is a good representation of the major

triggers in land surface – atmosphere interactions and how they are expressed in

modeling contexts.

To address this need, I followed an empirical approach, identifying the relationships

between shallow and deep soil moisture in addition to albedo on the planetary boundary

layer height. I used polynomial relationships to estimate the planetary boundary layer

height as a function of combining shallow soil moisture, deep soil moisture and albedo.

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Then, I modeled soil moisture dynamics using a two-layer bucket model approach and

used these modeled results, and estimate albedo and the planetary boundary layer height.

Finally precipitation was modeled stochastically to generate treatments based on future

precipitation scenarios such as a 20% increase or decrease in average annual rainfall and

evaluate the potential for this empirical approach for looking at climate change

feedbacks.

Ultimately my doctoral research contributes to our ecohydrological knowledge on

thresholds, mechanisms, and interactions between the land surface and the atmosphere

through (1) analyzing long term data sets, (2) developing and analyzing field campaigns,

and (3) modeling exercises. Furthermore, my doctoral research is at the boundary

between ecohydrology and hydrometeorology, both interdisciplinary sciences applied to

better understand watershed processes and add to management and conservation

strategies.

1.3 Dissertation Format

Following the introduction is the “Present Study” which provides a summary of the

analyses and conclusions of three papers prepared for peer-reviewed publication, the first

submitted to Water Resources Research and the third to be submitted to the Advances in

Water Resources. The three articles included as appendices are as follow:

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APPENDIX A: OBSERVATION OF A TWO-LAYER SOIL MOISTURE

INFLUENCE ON SURFACE ENERGY DYNAMICS AND PLANETARY

BOUNDARY LAYER CHARACTERISTICS IN A SEMIARID SHRUBLAND

APPENDIX B: QUANTIFYING THE INFLUENCE OF DEEP SOIL

MOISTURE ON ECOSYSTEM ALBEDO: THE ROLE OF VEGETATION

APPENDIX C: EMPIRICAL RELATIONSHIPS BETWEEN SOIL MOISTURE,

ALBEDO, AND THE PLANETARY BOUNDARY LAYER HEIGHT: A TWO-

LAYER BUCKET MODEL APPROACH

Altogether these three papers discuss important findings to improve what we know of

ecohydrology in semiarid and arid environments, improve our understanding of how

“pulse” ecosystem respond to soil moisture dynamics, and how soil moisture at different

depths influences land surface – atmosphere interactions through influencing the surface

energy dynamics, vegetation characteristics, and the development of the planetary

boundary layer height.

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CHAPTER 2: PRESENT STUDY

The methodology, results, and conclusions of this study are presented in three papers

appended to this dissertation. These papers follow a logical progression from (1) the

analysis of continuous long term data, (2) the testing of hypotheses developed from that

analysis through a field campaign, and (3) a modeling exercise based on an empirical

approach that emphasizes the use of readily available data that support the concepts

developed through the analysis of the long term and field campaign data. The following

is a summary of important findings and take home points from each of these appendices.

2.1 APPENDIX A: OBSERVATION OF A TWO-LAYER SOIL MOISTURE

INFLUENCE ON SURFACE ENERGY DYNAMICS AND PLANETARY

BOUNDARY LAYER CHARACTERISTICS IN A SEMIARID SHRUBLAND

I present an observational analysis examining soil moisture control on surface energy

dynamics and planetary boundary layer characteristics. Understanding soil moisture

control on land-atmosphere interactions will become increasingly important as global

changes continue to alter the water availability of our ecosystems. For my analysis, I used

four years of data from the Santa Rite Creosote Ameriflux site. I categorized each day of

our record into (1) wet or dry seasons and (2) one of four Cases within a two-layer

framework based on the presence or absence of moisture in a shallow and a deep soil

layer. Using these categorizations I quantified the soil moisture control on the radiation

and surface energy budgets and planetary boundary layer characteristics using both

average responses and linear regression. My results highlight the importance of deep soil

24

moisture in land surface – atmosphere interactions. For instance, albedo for Cases with

any moisture were significantly lower than for a completely dry soil profile, even if this

moisture was only below the surface. Also, while the planetary boundary layer height

PBLh was largest when the whole soil profile was dry and smallest when the whole

profile was wet, even when shallow moisture was absent but deep moisture was present

the PBLh was significantly lower than when the entire profile was dry. This deep

moisture importance is likely site-specific and modulated through vegetation. Therefore

understanding these relationships also provides important insights into feedbacks

between vegetation and the hydrologic cycle and their consequent influence on the

climate system.

2.2 APPENDIX B: QUANTIFYING THE INFLUENCE OF DEEP SOIL MOISTURE

ON ECOSYSTEM ALBEDO: THE ROLE OF VEGETATION

I present an observational analysis from a field campaign designed to examine the

influence of two-layer soil moisture control on the albedo from bare and canopy patches.

Because changes in precipitation dynamics will continue to alter the water availability of

our ecosystems, understanding the feedbacks between the vegetation and the hydrologic

cycle and their consequent influence on the climate system will become increasingly

important. For my analysis, I conducted a series of field campaigns over two years (2011

– 2012) within the tower footprint of the Santa Rite Creosote Ameriflux site. The timing

of our campaigns always fell into one of four Cases within a two-layer framework based

25

on the presence or absence of moisture in a shallow and a deep soil layer. Using these

Cases I quantified the soil moisture control on the albedo of bare and canopy patches

using average responses. My results highlight the importance of deep soil moisture in

land surface – atmosphere interactions through its influence on aboveground vegetation

characteristics. Despite a dry shallow layer in both Cases, ecosystem albedo for the Case

when soil moisture was present in the deep layer was significantly lower than for the

Case when soil moisture was absent in the deep layer. Greenness of the vegetation

differed between these two Cases, higher in the Case with soil moisture in the deep layer,

suggesting that deep moisture is expressing itself at the surface through an increase in

greenness of the vegetation. Understanding these relationships between vegetation and

deep soil moisture will provide important insights into feedbacks between the hydrologic

cycle and the climate system.

2.3 APPENDIX C: EMPIRICAL RELATIONSHIPS BETWEEN SOIL MOISTURE,

ALBEDO, AND THE PLANETARY BOUNDARY LAYER HEIGHT: A TWO-LAYER

BUCKET MODEL APPROACH

My study presents a modeling approach to study the soil moisture and albedo control on

planetary boundary layer height (PBLh). Interactions between the land surface and the

atmosphere regulate the coupled soil moisture-precipitation system. This is especially

important in semiarid regions, where water resources are limited, as precipitation

dynamics are changing. I used data from the Santa Rita Creosote Ameriflux site and

Tucson Airport atmospheric sounding to generate empirical relationships between soil

26

moisture, albedo and PBLh. Empirical relationships show that ~50% of PBLh can be

explained by soil moisture and albedo. Then, a two-layer bucket model was used to

model soil moisture, and was coupled to these empirical relationships to model PBLh. As

part of this modeling exercise, I explored soil moisture dynamics under three different

precipitation regimes: current, decreased, and increased, to look at the influence on soil

moisture on land surface-atmospheric processes. While my precipitation regimes are

simple, they represent future precipitation regimes that can influence the two layers in our

conceptual framework: an increase in annual precipitation, could have an impact on deep

soil moisture and atmospheric processes if the precipitation event is intense, while a

decrease in annual precipitation, has more of an impact on the soil moisture at the

shallow layer. I observed that the response of soil moisture, albedo, and the PBLh will

depend not only on annual change, but on the frequency and intensity of this change. I

argue that because albedo and soil moisture data are readily available at multiple

temporal and spatial scales, developing empirical relationships that can be used in land

surface – atmosphere applications are of great value.

2.4 Future Research Opportunities

Here, I outline suggestions for future research opportunities that could follow research

development from my dissertation.

2.4.1 Land surface- atmosphere interactions: dryland ecohydrology modeling

27

A knowledge gap previously identified is that land surface - atmosphere models need a

better representation of dryland ecohydrological processes [Wang et al., 2012].

Ecohydrological processes in arid and semiarid regions can be better understood using a

two-layer conceptual framework [Kurc and Small, in review; Sanchez-Mejia and Papuga,

in prep; in review; Sanchez-Mejia et al., in review]. The reasoning for this is that the

presence or absence of soil moisture will trigger biological, hydrological, surface energy

and planetary boundary layer processes [Basara and Crawford, 2002; Cavanaugh et al.,

2011; Kurc and Small, 2004; Sanchez-Mejia and Papuga, in prep]. For instance, soil

moisture in the shallow layer of bare areas controls soil evaporation and bare soil albedo

[Idso et al., 1975; Sanchez-Mejia et al., in review], while soil moisture in the deep layer

of a shrub controls transpiration [Cavanaugh et al., 2011], greenness[Kurc and Benton,

2010] and canopy albedo [Sanchez-Mejia et al., in review]. However, these controls may

be limited to warm deserts and therefore may be site-specific. For instance, in the

Mojave desert the behavior of the controls could be different, so further research in site-

specific local triggers will enrich the good representation of drylands in land surface-

atmospheric interactions models.

While previous research has suggested diagnostic parameters such as stability or

evaporative fraction as controls on land surface- atmospheric interactions [Santanello et

al., 2013], I show that other influences such as albedo and evaporative fraction indirectly

incorporate the role of vegetation as part of the land surface influencing the planetary

boundary layer height. However, the hierarchy of these parameters and the control they

have on planetary boundary layer height in semiarid regions should be further analyzed.

28

I plan to explore the impact of vegetation change on land surface- atmospheric

interactions using a Daisy World approach in combination with the two-layer bucket

model to get insight on how future precipitation regime and percent cover will influence

soil moisture, albedo, and therefore the planetary boundary layer height.

The “Daisy World” is a model developed by [Watson and Lovelock, 1983] to examine

feedbacks between the climate and terrestrial systems, in which competition for bare

space is based on the albedo of dark and bright daisies, and the ideal temperature at which

they can survive. Modifies the model so that competition is based on “wet” or “dry”

daisies and incorporates a transpiration. Because it is a 2-D model, meaning that bare and

canopy patches can be represented by independent cells, I will use this approach and

integrate it with the two-layer bucket model.

2.4.2 The shrubby “gray” –zone of woody encroachment

Better understanding of the ecohydrological impact of woody encroachment is priority

for more than one science discipline [Newman et al., 2006]. Woody encroachment

research has focused on species such as creosotebush [Kurc and Small, 2004], mesquite

[Archer, 1990], pinon-juniper and on grasses and their possible responses to warmer and

drier climates . However less worked has focused in the “gray” zone that cacti represent

[Ansley and Castellano, 2007; Unland et al., 1996].

Plant physiology has an effect on soil moisture and transpiration processes , and therefore

cacti which have a specific Crassulacean Acid Metabolisme (CAM) mechanism for

29

avoiding water stress. However, little is known about what controls their phenological

changes and the associated changes in albedo that accompany those phenological

changes.

In future work it would be beneficial to use this field and modeling approach using more

than one functional type to better represent ecosystem dynamics in the face of less

precipitation and warmer temperatures.

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42

APPENDIX A: OBSERVATIONS OF A TWO-LAYER SOIL MOISTURE

INFLUENCE ON SURFACE ENERGY DYNAMICS AND PLANETARY

BOUNDARY LAYER CHARACTERISTICS IN A SEMIARID SHRUBLAND

Zulia Mayari Sanchez-Mejiaa and Shirley A. Papuga

a

Submitted to Water Resources Research

a School of Natural Resources and the Environment, University of Arizona,

Tucson,AZ 85719

43

Abstract

We present an observational analysis examining soil moisture control on surface energy

dynamics and planetary boundary layer characteristics. Understanding soil moisture

control on land-atmosphere interactions will become increasingly important as climate

change continues to alter water availability. In this study, we analyzed four years of data

from the Santa Rita Creosote Ameriflux site. We categorized our data independently in

two ways (1) wet or dry seasons, and (2) one of four Cases within a two-layer soil

moisture framework based on the presence or absence of moisture in shallow (0-20 cm)

and deep (20 - 60 cm) soil layers. Using these categorizations, we quantified the soil

moisture control on surface energy dynamics and planetary boundary layer characteristics

using both average responses and linear regression. Our results highlight the importance

of deep soil moisture in land surface – atmosphere interactions. The presence of deep soil

moisture decreased albedo by about 10 % and significant differences were observed in

evaporative fraction even in the absence of shallow moisture. The planetary boundary

layer height PBLh was largest when the whole soil profile was dry, decreasing by about 1

km when the whole profile was wet. Even when shallow moisture was absent but deep

moisture was present the PBLh was significantly lower than when the entire profile was

dry. This deep moisture importance is likely site-specific and modulated through

vegetation. Therefore understanding these relationships also provides important insights

into feedbacks between vegetation and the hydrologic cycle and their consequent

influence on the climate system.

44

Keywords:

albedo; available energy; evaporative fraction; eddy covariance; creosotebush; Larrea

tridentata, Santa Rita Experimental Range

45

1. Introduction

The land surface and the atmosphere are tightly coupled through the exchange of energy

and water [Nicholson, 2000; Shukla and Mintz, 1982; Shuttleworth, 1991]. Soil moisture

plays an important role in this exchange [e.g. Seneviratne et al., 2010; Vereecken et al.,

2008] through the partitioning of available energy into sensible and latent heating

[Brubaker and Entekhabi, 1996; Colby, 1984]. Soil moisture control on the exchange of

energy and water is especially strong in dryland ecosystems [Small and Kurc, 2003;

Vivoni et al., 2008; Williams and Albertson, 2004]. Because over 40% of the Earth’s land

surface can be classified as arid to semiarid [e.g. Okin et al., 2009; Reynolds et al., 2007],

understanding soil moisture control on the interactions between the land surface and the

atmosphere will be critical for anticipating feedbacks associated with global change

[Betts, 2000; D'Odorico et al., 2013; Taylor et al., 2002].

Interest in the soil moisture influence on land surface-atmosphere interactions arises from

observations of feedbacks between soil moisture and precipitation, where high rainfall

may lead to increased soil moisture which, in turn, promotes increased rainfall, or vice

versa [Findell and Eltahir, 1997; Koster et al., 2003]. One possible mechanism for this

feedback is through direct contribution of surface moisture to precipitation through

increased evapotranspiration (ET) [Dekker et al., 2007; Dominguez et al., 2008; Eltahir

and Bras, 1996]. However, soil moisture may also influence on the surface energy budget

46

and its relationship to the development of the planetary boundary layer (PBL) [Betts et

al., 1996; Eltahir, 1998].

The surface energy budget can be summarized as Rn – G = LH + SH, where Rn is net

radiation, G is ground heat flux, LH is latent heat flux, and SH is sensible heat flux

[Pitman, 2003]. The available energy, Qa, is the amount of energy available for sensible

and latent heat exchange with the atmosphere, i.e. Rn – G. The evaporative fraction, EF,

is the fraction of Qa that is partitioned into latent heating, i.e. LH/Qa. The surface energy

budget is coupled with the radiation budget which can be summarized as Rn = SWn +

LWn, where SWn is net shortwave radiation and LWn is net longwave radiation. Net

shortwave radiation is influenced by the land surface through albedo, , i.e. the amount

of incoming shortwave radiation that is reflected. Net longwave radiation is influenced by

the temperature of the land surface.

The presence of soil moisture is expected to increase the EF [Betts and Ball, 1998;

Eltahir, 1998]. Higher EF is expected to lower the land surface temperature and therefore

outgoing longwave radiation [Brubaker and Entekhabi, 1996; Eltahir, 1998]. Further, an

increase in ET associated with increased soil moisture should increase water vapor in the

atmosphere, increasing the longwave radiation emitted back to the land surface [Eltahir,

1998; Miller et al., 2009]. Therefore, the combined effect of higher EF and higher ET

should lead to a higher LWn. Additionally, increased soil moisture should be associated

with increased SWn because wet soils tend to have a lower albedo than dry soils

47

[Cunnington and Rowntree, 1986; Small and Kurc, 2003; Twomey et al., 1986]. Overall

then, an increased Rn is expected with increased in surface soil moisture [Eltahir, 1998].

Finally, an increased Rn is expected to increase the energy transported into the PBL by

increasing Qa [Betts, 2000; Eltahir, 1998; Quinn et al., 1995].

These couplings between the land surface and the atmosphere have been shown to be

especially strong in semiarid ecosystems [e.g. Charney, 1975]. Previous research has

demonstrated the sensitivity of semiarid ecosystems to pulses of moisture [Austin et al.,

2004; Huxman et al., 2004; Loik et al., 2004], i.e. concentrations of moisture that come in

discrete “packets”. Pulses of moisture arrive either by frequent small storms that wet only

the shallow surface layer or large but infrequent storms that wet deeper soil layers [Kurc

and Small, 2007; Sala and Lauenroth, 1985; Yaseef et al., 2010]. The layering of this soil

moisture has important but poorly understood consequences for the partitioning the

vertical fluxes of energy and water in semiarid ecosystems.

In semiarid ecosystems, Qa increases as much as 80 W m-2

[Kurc and Small, 2004] with

increasing soil moisture at the surface , a magnitude larger than changes in Qa associated

with major land surface changes such as deforestation [Gash and Nobre, 1997] or shrub

encroachment [Kurc and Small, 2004]. Evaporation is also driven by moisture near the

surface [Cavanaugh et al., 2011; Kurc and Small, 2004]. However, transpiration tends to

be driven by large storms or by soil moisture at depths greater than 20[Cavanaugh et al.,

2011; Domingo et al., 1999; Scott et al., 2006; Zeppel et al., 2008] and beyond the reach

48

of atmospheric demand. Because transpiration and photosynthesis are inextricably linked,

regardless of vegetation type, net ecosystem uptake of carbon dioxide also tends to occur

only when moisture reaches depths greater than 20 cm [Kurc and Small, 2007; Kurc and

Benton, 2010]. Further, analysis of time-lapse digital photography has demonstrated that

green-up of creosotebush (Larrea tridentata), a widespread species in the Sonora,

Chihuahua, and Mojave deserts, is also driven by moisture that reaches depths greater

than 20 cm[Kurc and Benton, 2010]. These observations argue for the importance of the

consideration of deep soil moisture in the cycling of energy and water, and therefore land

surface -atmosphere interactions, in semiarid ecosystems.

Based on the contrasting influence of shallow and deep soil moisture on surface energy

dynamics in semiarid ecosystems, we have developed a paradigm for evaluating their

relative roles in land surface-atmosphere interactions. Our objective is to evaluate how

moisture in shallow (0-20 cm) and deep (20-60 cm) soil layers influences surface energy

dynamics and planetary boundary layer characteristics. Specifically we provide

quantitative estimates of (1) the magnitude of changes in the surface energy budget

associated with the presence or absence of soil moisture in each layer and (2) the

influence of soil moisture in each layer on planetary boundary layer characteristics. This

analysis is critical in assessing the importance of the inclusion of soil moisture in multiple

soil layers in models of land surface - atmosphere interactions. Additionally, our results

point to the consideration of size-of-storm in addition to timing and frequency when

49

evaluating climate change impacts in semiarid ecosystems and their feedbacks to the

atmosphere.

2. Study Area and Methods

2.1 Study Area

Our study site is located within the Santa Rita Experimental Range (SRER),

approximately 50 km south of Tucson, Arizona, USA. This site is co-located with the

Santa Rita Creosote (US-SRC) Ameriflux eddy covariance site (http://ameriflux.ornl.gov)

in the northern portion of the SRER (UTM: 12 R 515177, 3530284) at 950 m above sea

level (Figure 1). With adherence to Ameriflux protocol, 30 min averaged CO2, H2O, and

energy fluxes are calculated using 10 Hz measurements from an open path CO2/H2O

infrared gas analyzer (LI-7500, LI-COR Inc., Lincoln, NE, USA) and a 3-D sonic

anemometer (CSAT-3, Campbell Scientific Inc., Logan, UT, USA), both at 3.75 m. Data

are stored in a CR5000 data logger (Campbell Scientific Inc., Logan, UT, USA) and

downloaded every two weeks.

Site-specific mean annual precipitation is 294 mm (calculated from a 4-year record),

more than 50% of which occurs from July to September. Small rainfall events (< 5mm)

are most frequent; however, events > 20 mm make the largest contributions to the annual

rainfall. As typical for the region, this site is characterized by a bimodal precipitation

distribution. However, winter rains (December, January, February) account only for ~20

50

% of the annual precipitation while precipitation in the months of July, August, and

September account for about 60% of the annual precipitation. We define the monsoon

season as “the shortest continuous period of the year during which 50% of the annual

precipitation accumulates”. To determine the monsoon for our study site, we analyzed

long-term precipitation data from stations within 1 km, provided by the Santa Rita

Experimental Range Digital Database (http://ag.arizona.edu/SRER/data.html). According

to these data, the monsoon season occurs during the months of July, August, and

September, hereafter referred to as the “wet season.” The “dry season” for this site is

defined as the months of May and June (4% of total precipitation). Mean annual surface

temperature is about 20°C, with monthly mean temperatures ranging from about 10°C

during the winter to about 35°C during the summer.

The physical landscape of the flux site is gently sloping (slopes < 2%), and the soils are

sandy-loam with a 10% increase of clay and silt from 35 cm to 75 cm depth. The site has

23-24% canopy cover, of which 14-15% is creosote bush (Larrea tridentata) and 8% is

grasses, forbs, and cacti unpublished data. The root distribution in the soil profile differs

between bare and canopy patches; highest densities of roots are present at 10 and 35 cm

in bare patches, while canopy patches have their highest density at 25 cm (Figure 2).

These distributions were determined by excavating six 1-m soil pits, three bare and three

canopy, and extracting 10 cm x 10 cm soil samples every 5 cm. These soil samples were

brought back to the laboratory, weighed, and then placed in a drying oven for 24 hrs at 60

°C. After they were dried, they were reweighed. After this drying, roots were collected

51

through a tiered sieving process until no further roots could be identified with the naked

eye. The collected roots were then weighed and the root density was calculated as grams

of roots per kilograms of dry soil.

2.2 Data

In this study, we present data from 2008 to 2011. In this section, we introduce how the

data were collected and the instrumentation used. We also introduce how we calculate

site-averaged values by combining measurements from bare soil and plant canopies. All

surface and radiation variables were recorded every 30 minutes. Unless otherwise

indicated, half-hour data were aggregated to a 24-hr period starting at midnight local

time. Data gaps (DOY 81-133 and 166 – 190 in 2008, DOY 154-159 in 2009, DOY 261-

280 in 2010, DOY 30-34 in 2011) were caused by external disturbances to equipment or

power failures.

2.2.1 Radiation and Surface Energy

Incoming (SWin) and outgoing (SWout) shortwave radiation, as well as incoming (LWin)

and outgoing (LWout) long wave radiation are measured with a four-component net

radiometer (CNR1, Kipp & Zonen, Inc., Delft, Netherlands) installed at 2.75 m above the

surface and 10 m from the eddy covariance flux tower to avoid shading from other

sensors. Net radiation (Rn) is calculated as Rn = SWn-LWn where SWn is shortwave net

radiation and LWn is longwave net radiation. Shortwave net radiation is calculated as the

52

sum of SWin and SWout. Longwave net radiation is calculated as the sum of LWin and

LWout.

Half-hour sensible and latent heat fluxes (SH and LH, respectively) were obtained for

US-SRC from the Ameriflux website (http://ameriflux.ornl.gov). These fluxes have been

corrected for an apparent flux occurring from density fluctuations [Webb et al., 1980].

Additionally, we established and used a friction velocity (u*) threshold of 0.25 m s-1

[Blanken et al., 1998].

Soil heat flux is calculated using measurements from six heat plates (HFP01SC,

Hukseflux Thermal Sensors, Elektronicaweg, The Netherlands) of which three were

installed at a depth of 5 cm on bare ground between shrub canopies and three beneath

shrub canopy sites. Six soil temperature probes (TCAV-L50, Campbell Scientific Inc.,

Logan, UT, USA) are co-located with the heat flux plates. Ground heat flux (G) is

calculated from the soil heat flux and soil temperature using a combined calorimetric heat

flux approach [Kimball et al., 1976; Kurc and Small, 2004].

Surface albedo () is calculated as the ratio of outgoing to incoming shortwave radiation:

u

(1)

53

By removing ground heat flux from the net radiation, the available energy (Qa) that could

be transferred from the land surface to the atmosphere is calculated as:

a - (2)

The evaporative fraction (EF) is the ratio of LH to Qa, the fraction of available energy

that is used toward latent heating, and is calculated as [Shuttleworth, 2012]

(3)

Midday averages were calculated from 30-min data for , Qa, EF, and components of the

surface energy budget. Midday averages (10:00 am to 2:00 pm, UTC/GMT-7, Mountain

Standard Time, no daylight saving) are used because we assume this is the time when

available energy is at its maximum and incoming shortwave radiation is relatively stable.

2.2.2 Soil Moisture

Six soil moisture profiles were monitored since 2008 using factory-calibrated water

content reflectometers (CS616, Campbell Scientific Inc., Logan, UT, USA) at five

different depths (2.5, 12.5, 22.5, 37.5, and 52.5), in three sites for both bare and shrub

canopy locations. Average soil moisture at each depth was weighted based on bare

ground and shrub canopy percent cover (Eq.4) [Kurc and Small, 2004; Small and Kurc,

2003]:

54

- B (4)

where is volumetric soil moisture (m3m

-3) in the ecosystem, f is the fraction of canopy

cover for the site (14 % for the US-SRC), C is shrub canopy soil moisture, and B is bare

ground soil moisture.

Vertical moisture was aggregated into two different layers based on reach of atmospheric

demand, where the shallow layer (0-20 cm) is within reach and the deep (20-60 cm) is

beyond the reach of atmospheric demand. Weighted averages were based on the relative

contribution of depth to the shallow (Eq. 5) or deep (Eq.6) layers of the profile:

shallow 0. .5 0.5 .5 0. .5 (5)

deep 0. 5 .5 0. 5 .5 0. 5 5 .5 (6)

2.2.3 Atmospheric Sounding

Atmospheric sounding data were obtained from the Department of Atmospheric Science,

University of Wyoming (http://weather.uwyo.edu/upperair/sounding.html). The sounding

data correspond to the National Weather Service surface Tucson station (KTUS, WMO:

72274) located at the Tucson International Airport (UTM: 12 R 504112, 3555012). In

this study, we analyzed the PBL characteristics using sounding data at 00 UTC

(Coordinated Universal Time).

55

Planetary boundary layer height (PBLh) was determined by analyzing potential

temperature profiles (p) [Stull, 1988]. In a mixed layer, p remains constant with height,

therefore the height of the PBL can be determined by p/z (Figure 3), p is calculated

as follows:

(7)

where p (°K) is the potential temperature, T (°K) is temperature at each level , p0 (Pa) is

pressure at sea level, p (Pa) is pressure at each level, R is assumed to be Rd= (287 J K-

1kg

-1)

and cpd 1004 (J K

-1kg

-1 ).

An air parcel that travels from the surface adiabatically will reach a saturation point, i.e.

the lifting condensation level (LCL, m) in which the temperature of the air parcel and

dewpoint are equal. We can calculate the height of displacement from:

(8)

(9)

(10)

where T0 is the initial temperature, Tdew0 is the initial dewpoint temperature, d is the dry

adiabatic lapse rate, and dew is the dewpoint lapse rate. We assume a constant dry

adiabatic lapse rate with height d= 9.8 °C/km. Tdew0 is calculated as:

(11)

56

where Tdew0 is in °C, and e is the actual vapor pressure (see [Wallace and Hobbs, 2006]),

i.e.

(12)

where w is the mixing ratio (g/kg), = Rd/Rv= 0.622, and p is (kPa) is the pressure at the

T level. Finally, dew is calculated as:

(13)

where dew is in °C/km, g is 9.8 m s-2

and lv is the latent heat of vaporization (2.5 x106 J

kg-1

), derived from Clausius – lapeyron and Poisson’s equation [Tsonis, 2007; Wallace

and Hobbs, 2006].

In addition, data available from the soundings include convective available potential

energy (CAPE) and Precipitable Water for the whole sounding (PWAT). The CAPE is a

measure of a potentially unstable atmosphere. It represents the potential energy available

in an air parcel that can be transformed to kinetic energy in a buoyant updraft, i.e. CAPE

> 2500J/kg supplies enough energy for strong updrafts and therefore thunderstorms

[Renno and Ingersoll, 1996]. PWAT is an indicator of the moisture in the atmosphere.

PWAT values above > 30 mm generally suggest that thunderstorms are likely

[Kirkpatrick et al., 2011; Means, 2012].

2.3 Soil Moisture Conceptual Framework

Soil moisture drydown dynamics differ between the shallow and deep soil, therefore two

different approaches were used to define dry and wet periods in each layer (Figure 4a).

57

For the shallow layer, soil moisture drydown curves were used to determine a moisture

threshold. To do this, we first identified large storms, i.e. precipitation events >8 mm

[Sala and Lauenroth, 1982]. Soil moisture values for the 14 days following each of the

large precipitation events were used to develop an average drydown curve (Figure 4a).

Then, an exponential model (Eq. 9) was fit to this average drydown curve:

i- e -

(14)

where is volumetric soil moisture (m3 m

-3), t is the time in days from the rainfall event,

i is the soil moisture the first day after the rainfall event, f is the soil moisture the last

day of the drying curve, and is the exponential time constant [Kurc and Small, 2004;

Lohmann and Wood, 2003; Scott et al., 1997]. We identified the shallow soil moisture

layer threshold as the time when 1/3 of the moisture remains (i.e. at time ). For our site

this threshold occurred on day 4, when shallow was 0.1229 (more than two decimal places

are needed for this analysis). Therefore, the shallow layer was considered dry at moisture

values less than 0.1229.

The deep soil moisture threshold was determined by using carbon flux data from the eddy

covariance tower, specifically using the net ecosystem exchange (NEE) of CO2. In this

approach, we assume that uptake of CO2 (negative NEE) is an indicator of plant activity

and that plant activity only occurs when deep moisture is available for the plant to use

[Kurc and Small, 2007]. For this threshold analysis, we selected a 10-day window in

which NEE shifted from negative (uptake, 5 continuous days) to positive (release, 5

58

continuous days) (Figure 4a). This transition signal indicated that low soil moisture levels

in the deep layer were reducing photosynthetic activity. We assumed the fifth day of the

positive NEE (release of CO2) represented the time when soil moisture was unable to

continue to support plant activity (Figure 4a). For our four year period (2008-2011) we

extracted all 10 day windows that represented this transition and noted the soil moisture

value on the 5th

day of positive NEE for each. To be conservative, we used the maximum

value of the time series generated from soil moisture values on the fifth day. Therefore,

the deep layer was considered dry at deep < 0.1013.

These threshold values (shallow=0.1229 and deep=0.1013) were used to design the

conceptual framework (Figure 3b) composed of four Cases: 1) dry shallow soil (shallow <

0.1229) and dry deep soil (deep < 0.1013); 2) wet shallow soil (shallow > 0.1229) and dry

deep soil (deep < 0.1013); 3) wet shallow soil (shallow > 0.1229) and wet deep soil (deep >

0.1013); 4) dry shallow soil (shallow < 0.1229) and wet deep soil (deep > 0.1013) (Figure

4b).

3. Results

Here we demonstrate how soil moisture present or absent in shallow or deep soil layers

influences land surface processes. We do this by analyzing 1) the surface energy budget

components and 2) analyzing the planetary boundary layer both in the context of our both

in the context of wet and dry seasons and in the context of our two-layer conceptual

framework. Overall, from our 4 year record, the dry season consisted of 244 days, while

59

the wet season consisted of 368 days. Using our two-layer soil moisture framework

categorization, 62% (795 days) of our 4-year record relates to Case 1, 1% (18 days) to

Case 2, 14% (178 days) to Case 3, and 22% (286 days) to Case 4.

3.1 Soil Moisture Influence on Radiation and Surface Energy Components

3.1.1 Wet and Dry Seasons

When calculated using midday averages, differences in all radiation and surface energy

components were statistically significant between the wet and dry seasons, with the

exception of Rn and Qa (Table 1). The similar average Rn between seasons is a result of

higher average SWn during the dry season and higher LWn during the wet season (Table

1). The average ground heat flux G is positive during the dry season while negative

during the wet season (Table 1). However, G comprises less than 4% of Rn in either

season which leads to a negligible difference between wet and dry seasons (Table 1;

Figure 5b).

While overall, average SH was lower during the wet season than the dry season (Table 1),

SH dominated the partitioning between SH and LH in both seasons. However, average

LH was significantly higher during the wet season than the dry season by about 81 Wm-2

(Table 1). Because the difference in average midday available energy Qa was negligible

between the seasons, this lower SH and higher LH led to a significantly higher average

midday EF during the wet season than the dry season (Table 1; Figure 5c).

60

Average midday surface albedo was significantly higher during the dry season (0.192)

than during the wet season (0.173) (Table 1; Figures 5a). This difference of 0.02

corresponded to about a 10% decrease in albedo under wet conditions.

3.1.2 Two-Layer Conceptual Framework

When calculated using midday averages, differences in radiation and surface energy

components were always statistically significant between conceptual framework Cases

when presence of soil moisture in the shallow layer differed, i.e. Cases 1 and 4 were

always significantly different than Cases 2 and 3 (Table 1).

Average Rn was lowest for Case 2 when moisture was present in the shallow layer but

absent in the deep layer (Table 1). This was a result of significantly lower average SWn

for Case 2 than for the other Cases (Table 1). The average ground heat flux G was

positive for Cases 1 and 4 when moisture was absent in the shallow layer but negative for

Cases 2 and 3 when moisture was present in the shallow layer (Table 1). Similarly,

average SH was significantly higher for Cases 1 and 4 when moisture was absent in the

shallow layer than for Cases 2 and 3 when moisture was present in the shallow layer

(Table 1 As for wet and dry seasons, regardless of Case, SH dominated the partitioning

between SH and LH, with the exception of Case 2 where LH is slightly higher than SH

(Table 1). Average LH was significantly higher for Cases 2 and 3 when moisture was

present in the shallow layer than for Cases 1 and 4 (Table 1).

61

Average midday available energy Qa was highest for Case 4 (499 W m-2

) when moisture

was present in the deep layer but absent in the shallow layer (Table 1; Figure 5e). This is

a consequence of Rn being largest for Case 4 (Table 1). Because Case 4 is likely under

“drying” conditions, sparse cloud cover probably leads to the high SWn (Table 1).

Average midday available energy Qa was lowest for Case 2 (290 W m-2

) when moisture

was present in the shallow layer but absent in the deep layer (Table 1; Figure 5e). Again,

in Case 2 Rn was lowest (Table 1). Because Case 2 is likely under short duration

“wetting” conditions from small storm before the surface has time to dry, clouds likely

lead to the low SWn (Table 1). As a consequence, differences in Qa were significant even

when similar moisture conditions were present in the shallow layer (Table 1). Despite the

differences in Qa between the Cases, Qa did not appear to be strongly influenced by

increasing soil moisture in either the shallow layer (R2

= 0.03) or the deep layer (R2 =

0.01) (Figure 6e,f).

EF was highest for Cases 2 (0.475) and 3 (0.419) when moisture was present in the

shallow layer (Table 1; Figure 5f). While EF was lowest when moisture was absent in the

shallow layer, EF was significantly higher for Case 4 than for Case 1 (Table 1, Figure

5f), presumably because the moisture in the deep layer can be used for transpiration. EF

tended to increase with increasing soil moisture in either layer, but this influence was

stronger for the shallow (R2 = 0.66) than for the deep soil layer (R

2 = 0.32) (Figure 6c,d).

62

Difference in albedo from a complete dry Case (4) to wet Case (3) is about 0.01 (Table 1;

Figure 5d ); again this corresponded to about a 9% decrease in albedo under wet

conditions. Albedo for Cases with any moisture at all (Cases 2, 3, 4) were significantly

lower than for a completely dry soil profile (Case 1), even if this moisture was only

below the surface (Case 4) (Table 1; Figure 5d ). Further, albedo tended to decrease with

increasing soil moisture in either layer, but this influence was stronger for the shallow (R2

= 0.38) than for the deep soil layer (R2 = 0.15) (Figure 6a,b).

3.2 Soil Moisture Influence on the Planetary Boundary Layer

The PBLh was significantly lower when soil moisture was present in shallow layer (Cases

2 and 3) (Table 2). The PBLh extended the most when the whole soil profile was dry

(Case 1) and extended the least when the whole profile was wet (Case 3) (Table 2).

However, even when moisture was absent in the shallow layer but was present in the

deep layer (Case 4), the height of the PBL was significantly lower than when the entire

profile was dry (Case 1) (Table 2). Further, the PBLh tended to decrease with decreasing

albedo (R2=0.21) under the presence of moisture, but this influence was stronger for the

shallow (R2 = 0.32) than for the deep soil layer (R

2 = 0.10) (Figure 7).

The LCL was highest when whole soil profile was dry (Case 1) and was lowest when the

shallow layer was wet (Cases 2 and 3) (Table 2). Moisture in the deep layer tended to

decrease the LCL whether or not moisture was present in the shallow layer (Cases4)

63

(Table 2). In general, the CAPE was lowest when the shallow layer was dry (Cases 1 and

4) (Table 2). The CAPE was highest when the whole profile was wet (Case 3), and is

statistically different from other cases which were not statistically different from one

another. PWAT was lowest when the entire soil profile was dry (Case 1) and highest

when the entire soil profile was wet (Case 3) (Table 2). Only in Case 3 was PWAT

greater than 30 mm (Table 2), suggesting that thunderstorms were likely only under Case

3 conditions.

4. Discussion

Our data demonstrate the importance of the presence of soil moisture on the surface

energy dynamics in a semiarid shrubland and therefore its importance in modeling local

land surface – atmosphere interactions. Consistent with previous theory [Betts and Ball,

1998; Eltahir, 1998], the presence of soil moisture resulted in an increase of EF (Table 1,

Figure 5c,f). However, our data also suggest that regardless of the presence of moisture in

the shallow layer, the presence of moisture in the deep layer also increases the EF (Figure

5f). This is most likely a consequence of the shrubs being able to access moisture in the

deep layer for transpiration [Cavanaugh et al., 2011; Zeppel et al., 2008], even when

moisture from the shallow layer is unavailable for latent heating. Also consistent with

previous theory [Eltahir, 1998; Miller et al., 2009], higher LWn was associated with the

presence of soil moisture, as much as 60 W m-2

between wet and dry seasons (Table 1).

In fact, LWn was higher when moisture was present in the deep layer but absent from the

64

shallow layer (Case 4) than when the entire profile was dry (Case 1), though this

difference was not significant.

Consistent with previous studies [Cunnington and Rowntree, 1986; Small and Kurc,

2003; Twomey et al., 1986], albedo tended to be lower under wet conditions than dry

conditions (Table 1; Figure 5d). In fact, albedo was significantly lower when moisture

was present in the deep layer but absent from the shallow layer (Case 4) than when the

entire profile was dry (Case 1). This suggests that moisture deep in the profile has an

influence on the characteristics of the land surface. We suspect that because the shrubs

have access to deep moisture through their roots, that the moisture in the deep layer is

altering the vegetation at the surface, which is altering the albedo. Supporting this notion,

a recent study showed that in a semiarid shrubland, deep moisture, beyond the reach of

atmospheric demand, is responsible for the greening of vegetation in these shrubland

ecosystems[Kurc and Benton, 2010]. Because greening of vegetation influences albedo

[Asner, 1998; Berbert and Costa, 2003; Song, 1999], the idea that deep soil moisture

influences ecosystem albedo is reasonable.

Generally speaking, despite lower albedos under wet conditions than dry conditions SWn

was higher under dry than wet conditions (Table 1), which was not expected [Small and

Kurc, 2003]. This is likely a result of the use of a two-layer soil moisture conceptual

framework to classify processes through time rather than looking for cloudy versus clear

sky days. The use of cloudy days also has implications for the values of Rn. Contrary to

65

expectations [Eltahir, 1998] , our data shows that Rn is not significantly different between

wet and dry conditions, even between an entirely dry (Case 1) and an entirely wet (Case

3) soil profile (Table 1). These negligible differences in Rn associated with soil moisture,

are reflected in similar negligible differences in Qa (Table 1). Therefore at our site, we

did not observe the expected increase in Qa associated with moisture driven increase in Rn

to increase the energy transported into the PBL [Betts, 2000; Eltahir, 1998; Quinn et al.,

1995]. Because changes in PBL characteristics are observed under different moisture

conditions at our site (Table 2), other soil moisture driven processes must be influencing

the PBL. For instance, increasing albedo [presumably by drying the soil] is associated

with increasing PBLh (Figure 7c).

Because the two-layer conceptual framework is based thresholds influenced by wetting

from precipitation pulses [Sala and Lauenroth, 1985], our data also demonstrate the

importance of storm size and the consequent temporal dynamics of the “layering” of soil

moisture associated with storm size on the surface energy components and planetary

boundary layer characteristics. To summarize, for each day within our study period, the

shrubland falls into a Case (1 to 4) (Figure 8e,j) based on the presence or absence of

moisture in a shallow and a deep layer (Figure 3b). Generally before precipitation events

both soil layers are dry (Case 1). After small precipitation events, the shallow layer is

wetted, but the deep layer remains dry (Case 2; Figure 8a,f), after a larger precipitation

event the deep layer is wetted in addition to the shallow layer (Case 3; Figure 8 e,j).

However, Case 3 is more persistent following larger storms (Figure 8a,f). Following both

66

large and small storms, the surface energy budget is modified by a decrease in albedo

(Figure 8d,i), an increase in LH (Figure 8c,h), and a decrease in SH (Figure 8c,h). These

changes correspond to a shrinking of the PBL after both small and large precipitation

events (Figure 8b,g). Importantly, the persistence of soil moisture in the deep layer

through Case 3 and Case 4 (Figure 8e,j) is closely linked to the height of the PBL (Figure

8b,g) and the components of the surface energy budget (Figure 8c,h). This suggests that

larger storms may have a greater influence on land-surface atmosphere interactions than

small storms, and that this influence is linked to the presence of deep soil moisture.

The value of continuous measurements of atmospheric and hydrological properties to

investigate land-atmosphere interactions has been previously highlighted [e.g. Baldocchi

et al., 2001; Basara and Crawford, 2002] link the importance of deep soil moisture to

surface energy dynamics by showing linear relationships between variability in soil

moisture at 20 – 60 cm depths and variability of multiple atmospheric properties. In their

study, the strength of the relationships between soil moisture and SH, LH, and EF were

always largest at depths between 20 and 60 cm depth rather than at the surface. The

results from our study support highlighting the importance of deep soil moisture in the

consideration of land surface-atmosphere interactions.

Insights from our research reflect the importance of considering site-specific nature of the

role of soil moisture in land surface atmosphere interactions, both in a shallow and deep

layer. Because the mechanism by which deep soil moisture influences surface energy

67

dynamics and planetary boundary characteristics is likely through transpiration [Yaseef et

al., 2010], understanding site-specific controls on feedbacks between vegetation and the

hydrologic cycle is critically important for land surface – atmosphere research [Chahine,

1992; Dekker et al., 2007; Scheffer et al., 2005]. For instance, root density with depth is

likely important in the determination of the depth at which soil moisture most strongly

influences surface energy dynamics. At our shrubland site, roots are concentrated at

depths > 20 cm (Figure 2), and therefore the strength of the relationship between

moisture in the deep soil layer with radiation and surface energy components is

reasonable. Furthermore, site-specific soil characteristics will also play a role in the

layering of soil moisture and in the development of root density with depth [Gregory et

al., 1987]. For instance, in desert ecosystems of the southwestern United States, a caliche

layer , “bedrock”, or a argilic horizon may inhibit root growth and soil moisture

movement beyond depths of 40 to 60 cm [Hennessy et al., 1983].

Additional sources of uncertainty must be considered in the interpretation of the surface

energy dynamics. Firstly, the temporal distribution of the Cases is subject to variability in

solar zenith angle throughout the year. Differences in solar zenith angle is likely to have a

confounding effect on the albedo values for each Case [Wang et al., 2005] as Cases

tended to be associated with particular times of year, e.g. Case 3 always occurred during

the wet season. Additionally, our two-layer framework does not discern between cloudy

or clear sky days, which may add uncertainty to our findings. Clouds influence the

incoming shortwave radiation and other surface energy components. While variations in

68

albedo are not necessarily driven by cloud cover at local scales [Small and Kurc, 2003],

at regional scales this may be an important source of uncertainty because cloud cover is

not necessarily homogeneous [van Leeuwen and Roujean, 2002].

Acknowledgments.

This research was supported in part by The University of Arizona College of Agriculture

and Life Sciences (CALS), The Arizona University System Technology and Research

Initiative Fund (TRIF), The University of Arizona Office of the Vice President for

Research (VPR), SAHRA (Sustainability of semi-Arid Hydrology and Riparian areas)

under the STC Program of the National Science Foundation (NSF), NSF CAREER

Award # EAR-1255013 and a doctoral studies fellowship provided through Mexican

National Council for Science and Technology (CONACYT).

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77

Table 1. Midday average values of season and case analysis. Here we present net

radiation (Rn; Wm-2

), net shortwave radiation (SW; Wm-2

), net longwave radiation (SW;

Wm-2

), ground heat flux (G; Wm-2

), sensible heat flux (SH; Wm-2

), latent heat flux (LH;

Wm-2

), available energy (Qa, Wm-2

), albedo, and evaporative fraction (EF).

Dry Wet Case 1 Case 2 Case 3 Case 4

n 244 368 795 18 178 286

Rn 510 A 515

A 444

b 296

c 410

b 490

a

SWn 722 A 662

B 613

a 369 c 521

b 648

a

LWn -212 B -147

A -169

b -73a -111

a -158

b

G 18 A -3

B 23

a -6 c -9

c 9

b

SH 286 A 213

B 232

a 113 b 139

b 220

a

LH 23 B 105

A 32

c 116 a 105

a 72

b

Qa 526 A 508

A 467

b 290 d 400

c 499

a

Albedo 0.193 A 0.173

B 0.182

a 0.167 c 0.163

c 0.174

b

EF 0.090 B 0.325

A 0.132

c 0.475 a 0.419

a 0.249

b

p-Value <0.01, A,B

Season t-test, a,b,c,d

Case t-test, n=under each

category

78

Table 2. The planetary boundary layer characteristics analyzed using a two-layer soil

moisture framework. We present planetary boundary layer height (PBLh; m), lifting

condensation level (LCL; m), convective available potential energy (CAPE; J kg-1

),

Precipitable Water (PWAT; mm).

Case 1 Case 2 Case 3 Case 4

n 32 11 32 32

mean SE mean SE mean SE mean SE

PBLh 2983a 79 1969

c 992 1855

c 787 2592

b 764

LCL 4859a 112 2797

c 399 2599

c 136 3503

b 154

CAPE 0b 0 104

b 46 239

a 55 61b

b 25

PWAT 6c 0.48 30

a 4 33

a 2 20

b 2

p-Value <0.01,t-test a,b,c,d

, n= under each case, SE standard error mean

79

List of Figures.

Figure 1. Location of study site. The Santa Rita Experimental Range (SRER) is shown in

solid gray and the white circle indicates the location of Santa Rita Creosote Ameriflux

tower. The distribution range of L. tridentata is shown for Arizona in light gray semi-

circles, and the dominated areas in the SRER are shown in solid black.

Figure 2. Root density (g/kg, root/soil) under bare (fill) and canopy (open). Each line

represents the average from three independent profiles, error bar shows standard

deviation.

Figure 3. The planetary boundary height is establish following the P/h [Stull, 1988],

here we present a typical profile from Case 1 to demonstrate the cutoff.

Figure 4.Our two-layer soil moisture conceptual framework. Soil moisture is influenced

by its vertical distribution whether is at reach of atmospheric demand (0-20 cm) or not

(20-60 cm). We use a drydown curve to determine the soil moisture threshold in the

shallow layer (a), and a relationship between carbon uptake and soil moisture to

determine the soil moisture threshold in the deep layer (a). Using these thresholds, all

days within the study period are categorized into Cases (b), where Case 1 represents the

dry state, Case 2 represents small precipitation events, Case 3 represents large

80

precipitation events, and Case 4 represents drying of the surface after a large precipitation

event.

Figure 5. Average diurnal cycles of albedo, available energy (Qa), and evaporative

fraction (EF), by season (a,b,c): wet (black line) and dry season (gray line) and by Case

(d,e,f): Case 1 (thick gray line), Case 2 (thin gray line), Case 3 (thick black line) and Case

4 (thin black line).

Figure 6. Linear regressions between: albedo and (a) shallow soil moisture (shallow) and

(b) deep soil moisture (deep); evaporative fraction (EF) and (c) shallow soil moisture and

(d) deep soil moisture; and available energy (Qa) and (e) shallow soil moisture and (f)

deep soil moisture.

Figure 7. Linear regressions between the planetary boundary layer height and (a) shallow

soil moisture (shallow), (b) deep soil moisture (deep), and (c) albedo.

Figure 8. Summary showing two examples of (a,f) precipitation events and how they

influence (b,g) planetary boundary layer height (PBLh), (c,h) surface energy components

sensible heat (SH) and latent heat (LH) heat, (d, i) albedo, and Case (e, j) over time as the

soil dries.

81

Figure 1. Location of study site. The Santa Rita Experimental Range (SRER) is shown in

solid gray and the white circle indicates the location of Santa Rita Creosote Ameriflux

tower. The distribution range of L. tridentata is shown for Arizona in light gray semi-

circles, and the dominated areas in the SRER are shown in solid black.

82

Figure 2. Root density (g/kg, root/soil) under bare (fill) and canopy (open). Each line

represents the average from three independent profiles, error bar shows standard

deviation.

83

Figure 3. The planetary boundary height is establish following the P/h [Stull, 1988],

here we present a typical profile from Case 1 to demonstrate the cutoff.

84

Figure 4.Our two-layer soil moisture conceptual framework. Soil moisture is influenced

by its vertical distribution whether is at reach of atmospheric demand (0-20 cm) or not

(20-60 cm). We use a drydown curve to determine the soil moisture threshold in the

shallow layer (a), and a relationship between carbon uptake and soil moisture to

determine the soil moisture threshold in the deep layer (a). Using these thresholds, all

days within the study period are categorized into Cases (b), where Case 1 represents the

dry state, Case 2 represents small precipitation events, Case 3 represents large

precipitation events, and Case 4 represents drying of the surface after a large precipitation

event.

85

Figure 5. Average diurnal cycles of albedo, available energy (Qa), and evaporative

fraction (EF), by season (a,b,c): wet (black line) and dry season (gray line) and by Case

(d,e,f): Case 1 (thick gray line), Case 2 (thin gray line), Case 3 (thick black line) and Case

4 (thin black line).

86

Figure 6. Linear regressions between: albedo and (a) shallow soil moisture (shallow) and

(b) deep soil moisture (deep); evaporative fraction (EF) and (c) shallow soil moisture and

(d) deep soil moisture; and available energy (Qa) and (e) shallow soil moisture and (f)

deep soil moisture.

87

Figure 7. Linear regressions between the planetary boundary layer height and (a) shallow

soil moisture (shallow), (b) deep soil moisture (deep), and (c) albedo.

88

Figure 8. Summary showing two examples of (a,f) precipitation events and how they

influence (b,g) planetary boundary layer height (PBLh), (c,h) surface energy components

sensible heat (SH) and latent heat (LH) heat, (d, i) albedo, and Case (e, j) over time as the

soil dries.

89

APPENDIX B: QUANTIFYING THE INFLUENCE OF DEEP SOIL MOISTURE ON

ECOSYSTEM ALBEDO: THE ROLE OF VEGETATION

Zulia Mayari Sanchez-Mejiaa, Shirley Anne Papuga

a, and Jessica Blaine Swetish

a

Submitted to Water Resources Research

a School of Natural Resources and the Environment, University of Arizona,

Tucson, AZ 85719

90

Abstract.

As changes in precipitation dynamics continue to alter the water availability in the

ecosystem, understanding the feedbacks between the vegetation and the hydrologic cycle

and their influence on the climate system is critically important. We designed a field

campaign to examine the influence of two-layer soil moisture control on bare and canopy

albedo dynamics in a semiarid shrubland ecosystem. We conducted this campaign during

2011 and 2012 within the tower footprint of the Santa Rite Creosote Ameriflux site.

Albedo field measurements fell into one of four Cases within a two-layer soil moisture

framework based on the presence or absence of moisture in a shallow and a deep soil

layer. The average response of albedo from canopy and bare patches to soil was

quantified using these Cases. Using these averages, we explore the influence of

vegetation and soil moisture on ecosystem albedo in a gridded framework. Our results

highlight the importance of deep soil moisture in land surface – atmosphere interactions

through its influence on aboveground vegetation characteristics. For instance we show

how green-up of the vegetation is triggered by deep soil moisture, and link deep soil

moisture to a decrease in canopy albedo. Understanding relationships between vegetation

and deep soil moisture will provide important insights into feedbacks between the

hydrologic cycle and the climate system.

91

Keywords:

semiarid shrubland; greenness; eddy covariance; creosotebush; Larrea tridentata, Santa

Rita Experimental Range

92

1. Introduction

Arid and semiarid environments are susceptible to major land surface alterations such as

desertification [e.g. Carrion et al., 2010; Otterman, 1974] and shrub encroachment [e.g.

Asner et al., 2003; Grover and Musick, 1990; Van Auken, 2000] that result from climate

change and anthropogenic pressure. These changes are important as the land surface

strongly influences the dynamics of energy and water fluxes within these environments

[Gu et al., 2007]. Previous studies have shown that feedbacks between the land surface

and the atmosphere may, for instance, increase drought persistence [Charney, 1975] and

affect the planetary boundary layer development through wet or dry periods [Nicholson,

2000].

These feedbacks can be quite dynamic in space and time in arid and semiarid

environments due to the strong linkages between soil moisture and vegetation [Philippon

et al., 2005; Rodriguez-Iturbe, 2000; Rodriguez-Iturbe et al., 1999]. For instance,

moisture changes due to changes in precipitation intensity and frequency in arid and

semiarid regions are likely to affect vegetation cover [Nicholson, 2000; Thomey et al.,

2011]. Furthermore, vegetation cover, type, and phenology can exert a strong influence

on the radiation budget [e.g. Villegas et al., 2010a]. Therefore variations on land-surface

atmospheric interactions are to be expected with changes in precipitation dynamics

[Overpeck and Udall, 2010].

93

In arid and semiarid regions, soil conditions have a large effect on surface energy fluxes

and atmospheric conditions [e.g. Ek and Cuenca, 1994; Nicholson, 2000], particularly by

changing the albedo of the surface. Albedo generally decreases with increasing soil

moisture content [Idso et al., 1975; Small and Kurc, 2003; K Wang et al., 2005].

Therefore, soil moisture is a key component in controlling the variation of surface albedo

[Entekhabi et al., 1996; Fuller and Ottke, 2002; G Wang et al., 2007].

Land cover also plays a significant role in surface energy dynamics of arid and semiarid

regions through modifying albedo. Generally speaking, surfaces with scarce vegetation

cover tend to have higher albedo than more vegetated landscapes [e.g. Charney, 1975;

Nicholson et al., 1998]. The influence of land cover on albedo can vary temporally in

semiarid and arid regions which are so strongly linked to moisture inputs [Huxman et al.,

2004]. For instance, surface albedo has been shown to be lower during the wet season

due to an increase in leaf area index and greenness of the vegetation in semiarid

landscapes [Colwell, 1974; G Wang et al., 2007]. Remote sensing observations in

semiarid ecosystems have also shown a decrease in albedo during the monsoon season

that is associated with an increase in vegetation greenness [Mendez-Barroso et al., 2009;

Wu et al., 1995].

The dynamics of precipitation in semiarid regions exert a strong control over soil

moisture [Huxman et al., 2004] and vegetation dynamics [Hastings et al., 2005], and

therefore should also influence albedo. For instance, in these areas, small frequent

94

precipitation events moisten the surface layer of the soil[Sala and Lauenroth, 1982] .

Because this moisture darkens the color of the soil surface [Y Wang et al., 2011], land

surface albedo has been shown to decrease after these small storms [Duchon and Hamm,

2006; Small and Kurc, 2003]. On the other hand, large infrequent precipitation events in

these semiarid regions are capable of wetting the soil deeper below the surface [Huxman

et al., 2004; Kurc and Small, 2007; Schwinning and Sala, 2004]. This deep moisture

stimulates the vegetation [Grover and Musick, 1990; Noy-Meir, 1973], and leads to a

green up of the shrub canopy[Kurc and Benton, 2010]. Greening and thickening of

canopy foliage have been shown to decrease albedo [Xuezhen et al., 2013; Zhang and

Walsh, 2006]. Therefore, large precipitation events and therefore deep soil moisture is

likely to influence ecosystem albedo in arid and semiarid regions.

Furthermore, different plant functional types will allocate roots at different depths in the

soil to access water [Asbjornsen et al., 2011; R B Jackson et al., 2000; Loik et al., 2004].

This conceptual thinking has been established to study ecosystems in what is known as

the two-layer hypothesis [Mahrt and Pan, 1984; Walter, 1972; Wiegand et al., 2006].

Because certain plants in arid and semiarid ecosystems can access longer lasting water

from deeper soil layers [Raz-Yaseef et al., 2012], there is growing interest in knowing the

effects of this deep moisture in surface and atmospheric processes [Basara and

Crawford, 2002; Siqueira et al., 2009], especially on albedo [Sanchez-Mejia and Papuga,

in review].

95

The aim of this research is to evaluate how the presence or absence of moisture in two

different soil layers influences ecosystem albedo through modifying aboveground

characteristics of the vegetation. We demonstrate that changes in vegetation due to deep

soil moisture availability will result in changes in albedo, thereby linking deep soil

moisture and albedo. We do this by modifying a simple two-layer soil moisture

conceptual framework of four Cases: (1) a dry shallow layer (0 – 20 cm) with a dry deep

layer (20 – 60 cm), (2) a wet shallow layer with a dry deep layer, (3) a wet shallow layer

with a wet deep layer and (4) a dry shallow layer with a wet deep layer [Sanchez-Mejia

and Papuga, in review]. For the purposes of this study we evaluate each Case for a bare

patch and for a shrub (creosotebush, Larrea tridentada) patch (Figure 1a). We

hypothesize that the absence of deep soil moisture ases and will result in a “dry”

and light-colored canopy, while the presence of soil moisture in the deep layer (Cases 3

and 4 will result in a “wet” and dark-colored (greener) canopy (Figure 1a). We further

hypothesize that a “dry” land surface, either through dry surface soil ase bare, ase 4

bare) or through dry vegetation (Case 1 canopy, Case 2 canopy) should yield high albedo

values Figure b . Alternatively, a “wet” land surface, either through wet surface soil

(Case 2 bare, Case 3, bare) or through darker (greener) vegetation (Case 3 canopy, Case 4

canopy) should yield low albedo values. Using this framework, we demonstrate the role

of deep soil moisture on land surface-atmosphere interactions through its influence on

vegetation.

96

2. Study Area and Methods

2.1 Site description

The study site is located within a 250 m footprint of an eddy covariance (EC) tower

located in a creosotebush dominated ecosystem on the Santa Rita Experimental Range

(SRER), approximately 50 km south of Tucson, Arizona [Kurc and Benton, 2010;

Sanchez-Mejia and Papuga, in review]. In addition to typical water and carbon fluxes,

incident and reflected solar radiation are measured at 2.75 m above the surface and at 10

m from the EC tower with a four-component net radiometer (CNR1, Kipp & Zonen, Inc.,

Delft, Netherlands). Six soil moisture profiles are also monitored at the EC tower with

water content reflectometers (CS616, Campbell Scientific Inc., Logan, UT, USA) at

seven different depths (2.5, 12.5, 22.5, 37.5, 52.5, 67.5 and 82.5 cm) in 3 bare and 3

shrub canopy sites. Additionally, within the footprint of this EC tower are three time-

lapse digital cameras (pheno-cams) used to monitor plant phenology .

Long-term annual precipitation average for the closest rain gage is 260 mm (Santa Rita

Experimental Range Digital Database (http://ag.arizona.edu/SRER/data.html). According

to these data, most of the rain occurs from July, August, and September (~ 60%), while

winter rains (December, January, February) account for ~ 20%. Mean annual surface

temperature is ~20°C, with monthly mean temperatures ranging from ~10°C during the

winter to ~35°C during the summer.

97

Vegetation cover at the site is about 24%, from which 14% is creosotebush (Larrea

tridentata); while herbaceous cover and cacti account for nearly 10% [Kurc and Benton,

2010]. The topography is relatively flat (slopes < 2%), and soils are sandy loams, with a

10% increase of clay and silt, from 35 cm to 75 cm depth. Root profile shows vertical and

horizontal heterogeneity similar to what has been described in a conceptual model for

shallow-extracting woody plants [Breshears and Barnes, 1999]; root density under bare

patches is highest at 10 and at 35 cm depth, while under the canopy it is highest at 25 cm

depth [Sanchez-Mejia and Papuga, in review].

2.2 Field Campaign

Field campaigns were conducted in 2011 and 2012 to assess the influence of deep soil

moisture on albedo of bare and vegetated patches. The soil profile was viewed in two

layers where the surface (0 – 20 cm) and deep (20 – 60 cm) layers can differ in moisture

content as per Figure 1 and Sanchez-Mejia and Papuga [in review]. Over a two year

period, albedo measurements were made at eight bare and eight vegetated patches on

three different occasions in each of the Cases.

To sample in Case 1 (dry/dry), measurements were obtained during periods with no

precipitation events for at least 2 months. To sample in Case 2 (wet/dry), measurements

were taken just after a small precipitation events (<8 mm) in July, 2011 and July, 2012,

respectively so that the shallow layer will be wet but the deep layer would be dry. To

sample Case 3 (wet/wet), measurements were taken just after a large precipitation events

98

(>8 mm) in August and September, 2011 and August and September 2012 so that both

the shallow and deep layers would be wet. And finally, to sample Case 4 (dry/wet),

measurements were taken several days (~ 4) after the large precipitation events used in

Case 3. Real-time data from the nearby Sahuarita High School meteorological station

(sahuarita.cals.arizona.edu) located 13 km from the EC tower site was used to track

precipitations events. Soil moisture conditions used for the four Cases are summarized in

Figure 2.

We verified that our sampling days matched the soil moisture condition for each Case by

using the soil moisture profile data. Vertical moisture distribution was aggregated at the

surface (0-20 cm) and deep (20-60 cm) using weighted averages [Sanchez-Mejia and

Papuga, in review]. The fractions are based on the contribution of each layer to shallow

or deep moisture as below:

shallow 0. .5 0.5 .5 0. .5 (1)

deep 0. 5 .5 0. 5 .5 0. 5 5 .5 (2)

We then used the thresholds determined in Sanchez-Mejia and Papuga [in review] to

determine in hindsight the actual Case status for each sampling period. Generally

speaking, Case 1 was sampled during dry season of the spring before the onset of the

monsoon, Case 2 was sampled in the late spring when small rainstorms dominated the

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precipitation, Case 3 was sampled in the summer when large rainstorms were present as a

result of the established monsoon, and Case 4 was sampled just after Case 3 but before

additional rainfall occurred (Figure 2).

2.2.1 Sampling Design

Measurements were made at two sites within the EC tower footprint, approximately 120

m apart. The first sampling site was approximately 100 m northwest of the tower and the

second 20 m south of the tower. We made measurements at four shrub canopy and four

bare soil patches at each sampling site, for a total of 8 shrub canopy patches and 8 bare

ground patches. At each patch, we measured reflected shortwave radiation using an

Eppley PSP Precision Spectral Pyranometer (The Eppley Laboratory, Inc., Newport,

Rhode Island) attached to a tripod and connected to a CR10x datalogger (Campbell

Scientific, Inc., Logan, Utah) for a two minute time period between 12:00 to 2:00 pm,

when incoming shortwave radiation is maximized. The tripod was oriented so that the

sensor was 0.3 m above the canopy of each shrub and 0.3 m above the ground level of

each bare patch. The height of the sensor ensured that the majority of the source area for

the measurement was directly from the feature of interest.

In addition to shortwave radiation measurements, we also made qualitative measurements

at each patch. Downward looking photos above canopy (Figure 3) and bare patches were

taken to follow leaf color and abundance and bare ground color. We also recorded leaf,

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branch, and soil color, and presence or absence of blooms, flowers, seed pods, and litter

(Table 1).

2.2.2 Analysis

Albedo () was calculated as = SWout/SWin using the incoming shortwave radiation

(SWin [W m-2

]) from the EC tower and the reflected shortwave radiation (SWout [W m-2

])

from the field campaign measurements. Because the EC data is averaged every 30

minutes, we used the 2-minute measurement to determine a half-hour SWout and then

calculated a half hourly albedo. The eight bare measurements were pooled together and

then averaged as were the eight canopy measurements.

Field campaign albedo measurements from bare and canopy were used to generate

probability distribution functions (PDFs) describing the frequency of albedo values

around the mean for each Case (Figure 4), using a normal distribution:

(3)

where is the mean or expected distribution, is the standard deviation, and 2 is the

variance.

We then used these PDFs in a simple model in which each cell of a 100 x 100 grid was

populated with either a bare (0) or canopy (1) patch [e.g. Baldocchi et al., 2005]. Albedos

101

for each patch were assigned based on the PDFs. In this virtual landscape, vegetation

cover can be modified and bare and canopy albedo values are coupled to the landscape.

For our study, we increased cover from 0 to 100% and then calculated ecosystem albedo

as a function of vegetation cover for each soil moisture Case, i.e.:

(4)

where, e is ecosystem albedo, f is the percent canopy, b is bare albedo and c is canopy

albedo.

With this model we can investigate the influence of vegetation cover on albedo in the

presence or absence of moisture in a shallow or deep layer. In this exercise, we assumed

that a grid cell was either completely covered by a shrub (canopy) or completely

uncovered (bare). While this assumption is a simplification of canopy architecture and

vegetation composition, it is a starting point that can eventually be strengthened with a

dynamic vegetation model [e.g. Bonan et al., 2003; Krinner et al., 2005].

2.3 Camera-Derived Greenness

The three pheno-cams were originally placed to account for different sizes and aggregate

structure of the creosotebush community within the footprint of the EC tower. These

digital cameras (Moultrie Game Spy I-60) are mounted on a pole at 1m (lens-to-ground

distance), oriented with a field-of-view parallel to the ground surface, facing northward to

maximize sunlight and to minimize shading[Kurc and Benton, 2010].

102

Regardless of cloud cover, the daily solar noon image from each camera was analyzed for

greenness by utilizing a constant rectangular region of interest (ROI) [Richardson et al.,

2007], approximately 400 by 2800 pixels, selected to maximize creosotebush density by

integrating several individuals within each image [Kurc and Benton, 2010]. Each ROI is

then analyzed for greenness Ig using the following relationship Richardson et al. [2007]:

(5)

where Green is the average green intensity of the ROI, Red is the average red intensity,

and Blue is the average blue intensity. We then calculate a daily Ig study site by averaging

the Ig from each of the three pheno-cams . Daily Ig data is limited to 2011 due to theft of

these cameras from the field site[Kurc and Benton, 2010].

3. Results and Discussion

Here we demonstrate how soil moisture present or absent in shallow or deep soil layers

influences albedo and suggest how this is modulated by the vegetation. We do this by

analyzing 1) vegetation greenness and 2) bare and canopy patch scale albedo in the

context of our two-layer conceptual framework (Figure 1).

103

3.1 Two-Layer Soil Moisture Influence on Greenness

As hypothesized, we found the canopies of the shrubs to be the greenest (i.e. highest Ig)

when soil moisture was present in the deep layer (Cases 3 and 4) (Figure 5). Despite the

fact that creosotebush is an evergreen shrub, change in greenness is visible via the

greenness index because of changes in LAI (leaf area index) [Migliavacca et al., 2011]

associated with moisture in the soil beyond the reach of atmospheric demand that triggers

vegetation processes[Cavanaugh et al., 2011; Kurc and Small, 2007; Kurc and Benton,

2010] . While Ig in Case 4 (mean= 0.71) was not significantly different than Case 3 (mean

= 0.67), there was higher variability in Case 3 than in Case 4 that could be associated

with meteorological conditions such as cloudiness [Migliavacca et al., 2011] (Figure 5).

Because Case 4 tended to follow Case 3 (e.g. Figure 2), how moisture in the deep layer

sustains canopy characteristics at the surface [e.g. Eastham et al., 1984; Ezcurra et al.,

1992; Monson and Smith, 1982; Neufeld et al., 1988; Ong et al., 1985] must be

considered.

While moisture is available in the shallow layer in Case 2, this moisture leaves the

shallow layer within about two days[R Jackson et al., 1976; Kurc and Small, 2004],

presumably before the shrubs are able to use it[Kurc and Small, 2007] . Therefore it is not

surprising that Ig was lowest for Cases when moisture was absent from the deep layer

(Cases 1 and 2) (Figure 5). Further, Ig was lower by 0.3 when moisture was present in the

shallow layer (Case 2) than when it was not (Case 1) (Figure 5). This finding confirms

our hypothesis that the canopy is perceived as “drier” with absence of deep soil moisture,

104

and is consistent with a response mechanism to drought were evergreen desert shrubs

shed leaves [Hamerlynck and Huxman, 2009]. An alternative mechanism for the lower Ig

in Case 1 than in 2 could be a result of canopy-litter interactions [van Leeuwen and

Huete, 1996] that result from the physical removal of leaves during precipitation events

that led to the moisture in Case 2. Overall, these results support the differences between

Cases when moisture was present in the deep layer (Cases 3 and 4) and Cases when

moisture was absent in the deep layer (Cases 1 and 2) with respect to the canopy portion

of our two-layer conceptual framework (Figure 1).

3.2 Two-Layer Soil Moisture Influence on Albedo

Ecosystem albedo was calculated as an average from measurements of incoming and

outgoing shortwave radiation at the EC tower for days aligning with the field campaign.

Ecosystem albedo (Eq. 2) was highest ( = 0.205) when the entire soil profile was dry

(Case 1), and in general albedo was higher for Cases when soil moisture was absent from

the shallow layer (Cases 1 and Case 4) than when the shallow layer was wet (Cases 2 and

3) (Figure 6). Lower land surface albedo is expected when the surface soil is wet

[Fritschen, 1967; Small and Kurc, 2003; Twomey et al., 1986], so this is not surprising.

Land surface albedo is influenced by the presence of vegetation [Idso, 1972], and

therefore, we suspect that ecosystem albedo for Case 4 ( = 0.192) was lower than for

Case 1 due because the shrubs were accessing deep moisture available in Case 4 and

changing their reflectivity through greening [Kurc and Benton, 2010] .

105

We hypothesized that bare albedo values for Cases when the shallow layer was dry

(Cases 1 and 4) would be similar, and that values for Cases when the shallow layer was

wet (Cases 2 and 3) would be similar (Figure 1). However, while albedo values were

highest for Cases 1 ( = 0.249) and 4 ( = 0.220) (Figure 6b), the albedo values were

statistically different. This suggests that surface characteristics in Case 4 differ from

those in Case 1. We suspect this is through the presence of litter in Case 4 that

accumulates after rainfall events which is not as prevalent in Case 1 (Table 1).

When evaluating albedo for canopy patches, we expected that the albedo values for Cases

when the deep layer was wet (Cases 3 and 4) would be comparably lower than the albedo

values for Cases when the deep layer was dry (Cases 1 and 2) (Figure 1), indicative of the

influence of deep soil moisture on shrub canopy greenness. In fact, when evaluated for

canopy patches, albedo values were lowest and statistically similar for Case 3 ( = 0.166)

and 4 ( = 0.165) despite their differences in shallow moisture (Figure 6c). Canopy

albedo for Cases 3 and 4 was significantly different from Case 1 (= 0.193) and 2 (=

0. 8 Figure 6c . In addition, ases and have a “sparser” canopy than ase and 4

(Figure 3), resulting in a complex canopy-bare soil interaction. Because of this complex

interaction, we suspect that albedo from Case 2 appears significantly lower than in Case

, mostly because of the influence of the wet soil “under” the canopy.

As expected, albedo is always highest in the absence of moisture in the shallow and deep

layer (Case 1) (Figure 6a, b, and c). Under these conditions, both the soils and the

106

vegetation tend to be “lighter” in color Figure , Figure 5, Table and therefore more

reflective. In Case 1, albedo is higher in the bare patches (=0.249) than in the canopy

patches (=0.193) (Figure 6b, c). Ecosystem albedo calculated from tower data (=.205)

falls between these values (Figure 6a), reflecting the influence of both bare and canopy

albedo on the ecosystem.

Differences between albedo values from bare and canopy patches are largest for Cases

when the shallow layer is dry (Cases 1 and 4) (Figure 6b, c), suggesting the presence of

vegetation influences the ecosystem albedo most under conditions when the surface of

the soil is dry, despite the characteristics of the canopy (Figure 3). Albedo values from

bare and canopy patches are most similar under Case 2 (Figure 6b, c), when the shallow

layer is wet but the deep layer is dry. Under this condition, the vegetation is “dry”, light

color, with scarce leaves (Figure 3)[Kurc and Benton, 2010]. This suggests that under

these conditions the leaf area index (LAI) of the vegetation is low enough so that the wet

soil conditions pass through the canopy [van Leeuwen and Huete, 1996] and influence the

shrub albedo signal [Asner, 1998].

3.3 Influence of Vegetation on Ecosystem Albedo via Access to Two-Layer Soil

Moisture

In semiarid ecosystems, scarce vegetation results in higher albedo due to 1) the amount of

bare ground exposed [Charney, 1975; Otterman, 1974] and 2) how quickly this bare

ground loses moisture [R Jackson et al., 1976; Wythers et al., 1999]. To investigate this,

107

we used PDFs of patch scale albedo Figure 4 within the context of a simple “grid

model”, in which each cell of a 00 x 00 grid was populated with either a bare (0) or

canopy (1) patch [e.g. Baldocchi et al., 2005]. We assigned an albedo value for each

patch based on the PDFs. For our study, we increased cover from 0 to 100% for each

Case (Figure 7a), and calculated an ecosystem albedo (Equation 4) as an average of the

patches within the grid. We remind the reader that in this exercise, we assumed that a

grid cell was either completely covered by a shrub (canopy) or completely uncovered

(bare), i.e. a sparse canopy patch would not influence the grid differently than a full

canopy patch.

We expected that at 0% cover, ecosystem albedo values for Case 1 and 4 would be

similar as would ecosystem albedo values for Case 2 and 3, representing the conditions of

the bare soil patches (Figure 7a). When moisture was absent in the shallow layer but

present in the deep layer (Case 4), we expected that as cover increased ecosystem albedo

would converge toward the ecosystem albedo for Case 3 because the deep layer moisture

is accessible by the vegetation under these conditions creating “wet” and therefore “dark”

canopies (Figures 3 and 5). Therefore, at 00% cover the surface should appear “dark” in

color, regardless of the color of the soil below the canopy (Figure 7a). Further, when

moisture was present in the shallow layer but absent in the deep layer (Case 2), we

expected that as cover increased ecosystem albedo would converge toward the ecosystem

albedo for Case 1 because moisture is not accessible by the vegetation under these

conditions, creating “dry” and therefore “light” canopies Figures and 5 . Therefore, at

108

100% cover the surface should appear “light” in color regardless of the color of the soil

beneath it (Figure 7a). Finally, we expect that the convergence of Case 4 to Case 3 should

be stronger than the convergence of Case 2 to Case 1 because canopies present under the

conditions of Cases 3 and 4 should have higher LAI because they are under less water

stressed conditions. Therefore, the canopies in Cases 3 and 4 should have less influence

from the soil beneath the canopy than Cases 1 and 2.

The biggest change in ecosystem albedo with increasing cover (~ 8% decrease) was for

Case 4 (a dry shallow layer and a wet deep layer) (Figure 7b). This result suggests that at

this site, the albedo of “wet” vegetation with high Ig (Figure 5) is very different than the

albedo of “dry” surface soil Figure b . ase also underwent a large decrease in

ecosystem albedo with increasing cover (~5%), becoming more similar to Case 2 (Figure

7b), as expected. The smallest change in ecosystem albedo with increasing cover (~ 2%)

occurred when the shallow layer was wet but the deep layer was dry (Case 2). This

suggests that, at least at this site, the albedo of “dry” vegetation Figure with low Ig

(Figure 5) is more similar to the albedo of wet surface soil than to the albedo of dry

surface soil.

As expected, the albedo values for Cases 3 and 4 converged with increasing cover

(Figure 7b). In fact, the ecosystem albedo for Case 4 was nearly identical to the

ecosystem albedo for Case 3 after cover was increased to 100%, supporting the

hypothesis that the canopies sufficiently cover the soil surface under these conditions

109

such that the ecosystem albedo is not influenced by it. While the albedo values of Cases 1

and 2 also began to converge when cover was increased from zero to 100%, ecosystem

albedo for Cases 1 and 2 were still quite different (Figure 7b). This difference was nearly

as large as the difference between Cases 2 and 3 at 100% cover. This supports the

hypothesis that the canopies do not sufficiently cover the soil surface under these

conditions and therefore the ecosystem albedo is still influenced by the soil surface.

Finally, according to our toy grid model, albedo values for Cases 2 and 3 should look

identical at around 30% cover (Figure 7b). Additionally, albedo values for Cases 2 and 4

should look identical at around 75% cover (Figure 7b).

3.4 Implications of Soil Moisture Drydown Differences between Bare and Canopy

Patches

Based on previous studies, we expected that the length of time that a bare and canopy

patch remained in each Case would be different and that these differences would have

implications for land surface – atmosphere modeling applications that identify bare and

canopy patches within a two-layer soil moisture framework. In particular, we expected

evaporation to be higher in bare patches [Villegas et al., 2010b] so that bare patches

would remain in Cases 2 and 3 for less time than canopy patches.

Similar to methods using in studies aimed at evaluating the characteristic timescales of

the evaporation response in land surface models [Lohmann and Wood, 2003; Scott et al.,

110

1997], we modeled the decrease in soil moisture in the surface layer and in the deep layer

as an exponential relationship through time [Hunt et al., 2002; Kurc and Small, 2004]:

(6)

where, is volumetric soil moisture [m3m

-3], t is the number of days since the rainfall

event, 1 is the soil moisture observed on the first day following the rainfall event, f is

the soil moisture on the last day of the drydown, and is a best fit exponential time

constant. We only used “large storms” > 8 mm in our analysis .

Interestingly, we found that the shallow layers of canopy patches actually dried more

quickly (Figure 8b; = 3.15 days) than the shallow layers of bare patches (Figure 8a; =

4.6 days), by more than one full day. This suggests that contrary to our initial

expectation, bare patches would remain in Cases 2 and 3 longer than canopy patches

would. Infiltration is expected to be higher under canopy patches than under bare patches

[Bhark and Small, 2003] due in part to increased interception and stemflow [e.g.

Reynolds et al., 1999], and the movement of moisture belowground through root

macropores [e.g. Mitchell et al., 1995]. Evapotranspiration from these canopy shallow

111

layers may be large relative to bare shallow layers because of larger pore spaces in the

soil beneath the canopy or compaction of soil in the bare patches.

4. Conclusions

Our findings highlight that only considering shifts in vegetation [e.g. Beltran-Przekurat

et al., 2008] or changes in surface soil moisture [e.g. Small, 2001] may limit our

understanding of land surface-atmosphere interactions. In particular, we suggest that

understanding how deep soil moisture is expressed at the surface through the vegetation

may have implications for surface energy dynamics and planetary boundary layer

characteristics [Sanchez-Mejia and Papuga, in review]. We expect that this is especially

important in semiarid ecosystems, which are largely confined to the water-limited, rather

than energy-limited conditions that influence evapotranspiration dynamics [R Jackson et

al., 1976]in addition to other land surface-atmosphere exchanges. In these semiarid

ecosystems, moisture only moves beyond a shallow surface layer after large rainfall

events or series of events [Kurc and Small, 2007]. While precipitation is likely to

continue to decrease in semiarid areas [e.g. Woodhouse et al., 2010], whether this

reduced precipitation arrives as smaller rainfall events or more infrequent large rainfall

events will have important consequences for the vegetation [Heisler-White et al., 2008;

Huxman et al., 2004] and therefore the partitioning of energy in these ecosystems. These

changes will result in further feedbacks with the climate system [e.g. Notaro et al., 2006;

Penuelas et al., 2009].

112

Acknowledgments.

This research was supported in part by The University of Arizona College of Agriculture

and Life Sciences (CALS), The Arizona University System Technology and Research

Initiative Fund (TRIF), The University of Arizona Office of the Vice President for

Research (VPR), SAHRA (Sustainability of semi-Arid Hydrology and Riparian areas)

under the STC Program of the National Science Foundation (NSF), NSF CAREER

Award # EAR-1255013 and a doctoral studies fellowship provided through Mexican

National Council for Science and Technology (CONACYT). Patch scale shortwave

radiation measurements were made possible through field equipment provided by Dr.

Russell Scott. Additional field assistance for the campaign measurements was provided

by Lori Lovell, Evan Kipnis, Krystine Nelson, and Daniel Bunting.

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123

Table 1. Qualitative data generated from the field campaign. Bloom, flower, seed pod,

and litter characteristics are presented as a fraction of the number of patches with the

given characteristic, where n= 8 for both canopy and bare patches.

Case 1 Case 2 Case 3 Case 4

Canopy Patches

Leaf color yellow-brown light green dark green dark green

Branch color light gray dark gray dark gray medium gray

Blooms 0/8 2/8 8/8 3/8

Flowers 0/8 0/8 8/8 6/8

Seed pods 2/8 3/8 4/8 7/8

Bare Patches

Soil color light brown brown brown light brown

Litter 2/8 5/8 5/8 7/8

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Figure Captions.

Figure 1. a) The four Cases of our two-layer soil moisture conceptual framework broken

into bare and canopy patches. Gray coloring represents “wet” soil and “dark” green

canopies; white represents “dry” soil and “light” green canopies. b The expected relative

magnitude of albedo associated with bare and canopy patches for each Case.

Figure 2. Time series (2011-2012) of a) daily precipitation, b) shallow (black line) and

deep (gray line) soil moisture, and c) day on which the field campaign was conducted for

each Case (Case 1 open circles, Case 2 filled circles, Case 3 filled squares, Case 4 open

squares). Soil moisture was aggregated at the shallow (0-20 cm) and deep (20-60 cm)

depths using weighted averages as described in [Sanchez-Mejia and Papuga, in review]

Figure 3. Examples of images taken during the field campaigns at each shrub where

albedo was measured for a) Case 1, b) Case 2, c) Case 3. Images for Case 4 are missing

and therefore not included here.

Figure 4. Probability distribution functions (PDFs) for albedo derived from field

campaign measurements over bare and canopy and canopy patches for each Case: a) Case

1, bare; b) Case 2, bare; c) Case 3, bare; d) Case 4, bare; e) Case 1, canopy; f) Case 2,

canopy; g) Case 3, canopy; and h) Case 4, canopy. Albedo class intervals are defined as,

125

1 (<0.12), 2(0.12-0.14), 3(0.14-0.16), 4(0.16-0.18),5(0.18-0.20), 6(0.20-0.22), 7(0.22-

0.24), 8(0.24-0.26), 9(>0.26).

Figure 5. Mean and standard deviation of greenness (Ig) computed from daily 2011

images that were categorized using two-layer soil moisture conceptual framework. The

different letters represent statistical significance p-Value <0.01.

Figure 6. Mean and standard deviation of a) ecosystem albedo, b) bare albedo, and c)

canopy albedo for each Case. The different letters represent statistical significance p-

Value <0.01.

Figure 7. a) Changes in the two-layer soil moisture framework with increasing percent

canopy cover from 0 to 100% for each Case and b) Changes in albedo resulting from

increasing percent canopy cover from 0 to 100% for each Case.

Figure 8. Drydown curves for shallow soil moisture in a) bare and b) canopy patches.

The markers indicate measurements and the line is an exponential fit, in the time

constant which represents the day at which soil moisture returns to within one third of its

pre-storm value.

126

Figure 1.

Figure 1. a) The four Cases of our two-layer soil moisture conceptual framework broken

into bare and canopy patches. Gray coloring represents “wet” soil and “dark” green

canopies; white represents “dry” soil and “light” green canopies. b The expected relative

magnitude of albedo associated with bare and canopy patches for each Case.

127

Figure 2.

Figure 2. Time series (2011-2012) of a) daily precipitation, b) shallow (black line) and

deep (gray line) soil moisture, and c) day on which the field campaign was conducted for

each Case (Case 1 open circles, Case 2 filled circles, Case 3 filled squares, Case 4 open

squares). Soil moisture was aggregated at the shallow (0-20 cm) and deep (20-60 cm)

depths using weighted averages as described in [Sanchez-Mejia and Papuga, in review]

128

Figure 3.

Figure 3. Examples of images taken during the field campaigns at each shrub where

albedo was measured for a) Case 1, b) Case 2, c) Case 3. Images for Case 4 are missing

and therefore not included here.

129

Figure 4.

Figure 4. Probability distribution functions (PDFs) for albedo derived from field

campaign measurements over bare and canopy and canopy patches for each Case: a) Case

1, bare; b) Case 2, bare; c) Case 3, bare; d) Case 4, bare; e) Case 1, canopy; f) Case 2,

canopy; g) Case 3, canopy; and h) Case 4, canopy. Albedo class intervals are defined as,

1 (<0.12), 2(0.12-0.14), 3(0.14-0.16), 4(0.16-0.18),5(0.18-0.20), 6(0.20-0.22), 7(0.22-

0.24), 8(0.24-0.26), 9(>0.26).

130

Figure 5.

Figure 5. Mean and standard deviation of greenness (Ig) computed from daily 2011

images that were categorized using two-layer soil moisture conceptual framework. The

different letters represent statistical significance p-Value <0.01.

131

Figure 6.

Figure 6. Mean en standard deviation of a) ecosystem albedo, b) bare albedo, and c)

canopy albedo for each Case. The different letters represent statistical significance p-

Value <0.01.

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Figure 7.

Figure 7. a) Changes in the two-layer soil moisture framework with increasing percent

canopy cover from 0 to 100% for each Case and b) Changes in albedo resulting from

increasing percent canopy cover from 0 to 100% for each Case.

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Figure 8.

Figure 8. Drydown curves for shallow soil moisture in a) bare and b) canopy patches.

The markers indicate measurements and the line is an exponential fit, in the time

constant which represents the day at which soil moisture returns to within one third of its

pre-storm value.

134

APPENDIX C: EMPIRICAL RELATIONSHIPS BETWEEN SOIL MOISTURE,

ALBEDO, AND THE PLANETARY BOUNDARY LAYER HEIGHT: A TWO-

LAYER BUCKET MODEL APPROACH

Zulia Mayari Sanchez-Mejiaa and Shirley A. Papuga

a

For Submission Advances in Water Resources, Summer 2013

a School of Natural Resources and the Environment, University of Arizona,

Tucson, AZ, 85719

135

Abstract.

In semiarid regions, where water resources are limited and precipitation dynamics are

changing, understanding land surface-atmosphere interactions that regulate the coupled

soil moisture-precipitation system is key for resource management and planning. We

present a modeling approach to study soil moisture and albedo controls on planetary

boundary layer height (PBLh). We used data from the Santa Rita Creosote Ameriflux site

and Tucson Airport atmospheric sounding to generate empirical relationships between

soil moisture, albedo and PBLh. Empirical relationships show that ~50% of the variation

in PBLh can be explained by soil moisture and albedo. Then, a two-layer bucket model

was used to model soil moisture, and was coupled to these empirical relationships to

model PBLh. We explored soil moisture dynamics under three different mean annual

precipitation regimes: CURRENT, INCREASE, and DECREASE, to look at the

influence on soil moisture on land surface-atmospheric processes. While our precipitation

regimes are simple, they represent future precipitation regimes that can influence the two

soil layers in our conceptual framework: an increase in annual precipitation, could have

an impact on deep soil moisture and atmospheric processes if the precipitation event is

intense, while a decrease in annual precipitation, has more of an impact on the soil

moisture at the shallow layer. We observed that the response of soil moisture, albedo, and

the PBLh will depend not only on changes in annual precipitation, but also on the

frequency and intensity of this change. We argue that because albedo and soil moisture

data are readily available at multiple temporal and spatial scales, developing empirical

136

relationships that can be used in land surface – atmosphere applications are of great

value.

Keywords

Precipitation regime, creosotebush, Larrea tridentata, eddy covariance, atmospheric

sounding, Santa Rita Experimental Range

137

1. Introduction

In his well cited scenario, [Charney, 1975] proposed a positive feedback between the

sparse desert and the dry atmosphere. Since then, for semiarid to arid regions worldwide,

coupled atmosphere-vegetation models have demonstrated how small scale land surface-

atmosphere interactions can influence climate variability at the larger scales [Claussen,

1997; Henderson-Sellers, 1993; Zeng et al., 1999]. Vegetation has also been shown to

have a strong influence on large scale hydrological processes [e.g. Ghan et al., 1997].

Ultimately, the climate system is largely dependent on the hydrologic cycle that this

vegetation influences [Chahine, 1992]. In fact, studies have suggested that small scale

positive feedbacks between vegetation and the hydrologic cycle may have the ability to

elicit non-linear responses with important large scale consequences [Dekker et al., 2007;

Rietkerk et al., 2002; Scheffer et al., 2005]. Capturing the synergies between hydrologic

processes at different space and time scales is necessary for appropriate modeling the

influence of vegetation and the hydrologic cycle on the climate system [Chahine, 1992;

Lyon et al., 2008]. These synergies are often expressed through soil moisture dynamics

[Chen and Dudhia, 2001]. Soil moisture is an important link in the hydrological cycle as

it couples precipitation and ecosystems [D'Odorico et al., 2010; Rodriguez-Iturbe, 2000;

Santanello et al., 2011; Seneviratne et al., 2010].

[Manabe, 1969] pioneered research on better understanding the effects of soil moisture

dynamics on the climate system by using a “bucket model” approach – where single root-

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zone layer is essentially a bucket of moisture. The simplicity of this bucket model, and

others like it, make it an attractive method for looking at soil moisture control on

ecosystem processes. For instance, recent studies have focused on using the bucket model

approach for understanding ecosystem scale evapotranspiration dynamics [Guswa et al.,

2002; Porporato et al., 2004], and link this to transpiration and carbon assimilation[Daly

et al., 2004; Kurc and Small, in review] . Additionally, it has been used in combination

with “slab” models to look at the sensitivity of the planetary boundary layer development

in relation to soil moisture heterogeneity [Quinn et al., 1995]. At the larger scale, this

approach continues to be used in General Circulation Models (GCMs) [Pitman, 2003].

This simple bucket model has been shown to adequately represent soil moisture dynamics

and their influence on water cycling in ecosystems where precipitation events are large

and sufficiently frequent enough to wet the entire root zone [Salvucci, 2001]. However, in

arid to semiarid ecosystems, water and carbon cycles have been shown to be controlled

by soil moisture availability two discrete layers [Breshears et al., 1997; Kurc and Small,

in review]. Small precipitation events, which only wet the top surface layer [Sala and

Lauenroth, 1982], trigger evaporation-dominated evapotranspiration [Kurc and Small,

2004; Scott et al., 2006]. Large precipitation events provide soil moisture to the deep

layer [Kurc and Small, 2007; Raz-Yaseef et al., 2012] that is used toward transpiration

[Cavanaugh et al., 2011] and carbon uptake [Kurc and Benton, 2010; Scott et al., 2006].

Further, both shallow and deep soil moisture have been shown to have an effect on the

surface energy budget components, albedo, and planetary boundary characteristics

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[Basara and Crawford, 2002; Sanchez-Mejia and Papuga, in review; Sanchez-Mejia et

al., in review; Small and Kurc, 2003]. Therefore, modifying the bucket model to include

two-layers will be important for representing soil moisture dynamics of arid to semiarid

areas in land surface – atmosphere applications.

The characteristics of the planetary boundary layer (PBL) reflect the effects of the surface

energy dynamics of the land surface, which play a large role in the development of the

climate system. Therefore, knowledge of the PBL height, and how it changes, is

important for understanding the influence of the land surface on the climate system.

Previous efforts to obtain PBL characteristics have tended to rely on in situ measurements

[e.g. Betts and Ball, 1994] or complex models [e.g. Diak and Whipple, 1993], which are

not ideal for applications that need daily estimates of PBL characteristics [Santanello et

al., 2005] . However, because of the strong relationships between the PBL height and

land surface characteristics such as soil moisture [Basara and Crawford, 2002] and

albedo [Sanchez-Mejia and Papuga, in review] that are generally available daily through

micrometeorological datasets or through remote sensing products, empirical relationships

that relate PBL height to land surface characteristics show promise [Santanello et al.,

2005] for improving our understanding of PBL dynamics and its feedback to the climate

system.

Given the sensitivity of the PBL to both shallow and deep soil moisture [Sanchez-Mejia

and Papuga, in review], the objectives of this study are to 1) to develop empirical

methods of estimating daily PBL height based on readily attainable data that has been

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shown to influence the PBL, such as albedo and surface soil moisture; and 2) link the

two-layer bucket model to the empirical relationships to predict changes in PBL

dynamics under different rainfall regimes that represent different climate change

scenarios.

2. Methods

2.1 Micrometeorological, Soil Moisture and Radiation Data

Field observations used in this study were from the Santa Rita Creosote Ameriflux site

(US-SRC; http://ameriflux.ornl.gov) in a creosotebush (Larrea tridentata)-dominated

shrubland located near the northern-most boundary of the Santa Rita Experimental Range

(SRER) in southern Arizona. Annual precipitation at the site is bimodal and averages ~

350 mm yr-1

(http://ag.arizona.edu/SRER/data.html), ~ 55% occurring in summer (July,

August, September) and 15% occurring in winter (December, January, February). The

mean annual surface temperature is ~20°C, with monthly mean temperatures ranging

from ~10 °C during the winter to ~35°C during the summer. Soil is sandy-loam with a

10% increase of clay and silt from 35 cm to 75 cm depth. Highest densities of roots are

present at 10 and 35 cm in bare ground patches, while creosote bush canopy patches have

their highest density at 25 cm [Sanchez-Mejia and Papuga, in review].

Observations included four years of measurements (2008 – 2012) from an eddy

covariance tower with typical micrometeorological instrumentation [Moncrieff et al.,

141

2000; Shuttleworth, 1993]. Precipitation was recorded using a tipping-bucket rain gauge.

Volumetric water content was measured using Campbell Scientific factory-calibrated

water content reflectometers at depths of 2.5, 12.5, 22.5, 37.5 and 52.5 cm replicated in

six profiles, three under shrub canopies and three in bare soil patches. Incoming (SWin)

and outgoing (SWout) shortwave radiation, as well as incoming (LWin) and outgoing

(LWout) long wave radiation were measured with a four-component net radiometer

(CNR1, Kipp & Zonen, Inc., Delft, Netherlands).

Average soil moisture at each depth was calculated using a weighting based on bare

ground and shrub canopy percent cover [Kurc and Small, 2004; Small and Kurc, 2003],

i.e.:

- B (1)

where is volumetric soil moisture (m3m

-3), f is the fraction of shrub canopy cover (14 %

for the US-SRC), C is shrub canopy soil moisture, and B is bare soil moisture. Average

soil moisture values were then aggregated for two layers: shallow (0-20 cm) and deep

(20-60 cm). Aggregations were calculated as weighted averages based on the contribution

of each depth [Sanchez-Mejia and Papuga, in review]:

shallow 0. .5 0.5 .5 0. .5 (2)

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deep 0. 5 .5 0. 5 .5 0. 5 5 .5 (3)

Soil moisture was then categorized into four cases based on the presence or absence of

moisture in the upper “shallow” layer and the lower “deep” layer [Sanchez-Mejia and

Papuga, in review]: Case 1 corresponds to a dry shallow and dry deep layer, Case 2

corresponds to a wet shallow and dry deep layer, Case 3 corresponds to a wet shallow

and wet deep layer, and Case 4 corresponds to a dry shallow and wet deep layer. The

presence or absence of moisture in each layer was determined based on soil moisture

thresholds derived based on soil moisture observations at the site [Sanchez-Mejia and

Papuga, in review]. Soil moisture thresholds for our site were identified for the

ecosystem based on percent shrub cover using 2009 – 2011 data (Equation 1), and also

for bare and shrub canopy patches using 2009 – 2012 data and are presented in Table 1.

For each day, these thresholds are then used to categorize soil moisture into one of the

four Cases.

Half-hourly surface albedo () was calculated as the ratio of outgoing to incoming

shortwave radiation, i.e.:

out

in (4)

For albedo, midday averages, as opposed to daily averages, were calculated from 30-min

data. We used midday averages (10:00 am to 2:00 pm, UTC/GMT-7, Mountain Standard

143

Time, no daylight savings) because we assume that this is the time when available energy

is at its maximum and incoming shortwave radiation is relatively stable.

2.2 Atmospheric Sounding Data

Atmospheric sounding data were obtained from the Department of Atmospheric Science,

University of Wyoming (http://weather.uwyo.edu/upperair/sounding.html). The sounding

data correspond to the National Weather Service surface Tucson station (KTUS, WMO:

72274) located at the Tucson International Airport (UTM: 12 R 504112, 3555012). In

this study, we analyzed the PBL characteristics using sounding data at 00 UTC

(Coordinated Universal Time).

Planetary boundary layer height (PBLh) was determined by analyzing potential

temperature (P) profiles[Stull, 1988]. In a mixed layer, P remains constant with height,

therefore the height of the PBL can be determined by P/z, where P is calculated as

follows:

(5)

where P (°K) is the potential temperature, T (°K) is temperature at each level , p0 (Pa) is

pressure at sea level, p (Pa) is pressure at each level, R is assumed to be Rd= (287 J K-

1kg

-1)

and cpd 1004 (J K

-1kg

-1 ).

144

Atmospheric stability (ML; oK m

-1) in the mixed layer relates to the change of P with

height; the thermal PBL development is influenced by this parameter[Santanello et al.,

2005]. We calculated stability as:

(6)

where P is potential temperature (°K), h is height (m), t refers to the top of PBL and s to

the surface just above the inversion zone[Santanello et al., 2005].

2.3 Development of Empirical Relationships

Data for the development of empirical relationships were selected based on soil moisture

dynamics defined by the Cases (described in Section 2.1). From 2008 to 2011, we

identified the days corresponding to each Case and selected representative days for each

Case based on surface and sounding data available (Case1=30, Case2=7, Case3=32, Case

4=32) for a total of 101 days. Case 2 occurred infrequently, and therefore comparisons

must be made cautiously in this analysis. For these 101 days we obtained atmospheric

soundings (Section 2.2), shallow and deep soil moisture (Section 2.1), and albedo

(Section 2.1). We used these data to develop empirical relationships between the land

surface and the PBLh using a polynomial approach.

The PBLh has been shown to be controlled by land surface parameters such as soil

moisture, atmospheric stability, evaporative fraction, and 2-m potential temperature

145

[Basara and Crawford, 2002; Santanello et al., 2005; Santanello et al., 2011; Santanello

et al., 2013]. Additionally, albedo has shown to have an important role as well in land

surface – atmosphere interactions [Jackson et al., 1976; Sanchez-Mejia and Papuga, in

review]. [Santanello et al., 2005]developed an empirical relationship between the PBLh

and soil moisture and stability for their site in the southern Great Plains using a 2nd

order

polynomial:

(7)

where p0…p5 are the polynomial coefficients. Using atmospheric stability (Eq. 6) and

shallow soil moisture (Eq. 2), we adapted this empirical relationship for our site using all

of days of data from 2008 – 2011 (Figure 1) and also by categorizing the data by Case as

described above (Figure 2). We report our site-specific coefficients in Table 2.

Because previous studies have highlighted the sensitivity of PBLh to albedo and soil

moisture, including deep soil moisture, [Basara and Crawford, 2002; Sanchez-Mejia and

Papuga, in review] and albedo is a more readily available parameter than PBLh stability,

we also develop an empirical relationship between the PBLh and shallow soil moisture

(Eq. 2) and albedo (Eq. 4) using the 2nd

order polynomial approach (Figure 3), i.e.:

(8)

146

We also develop an empirical relationship between the PBLh and shallow soil moisture

(Eq. 2) and deep soil moisture (Eq. 3) using the 2nd

order polynomial approach (Figure 3),

i.e.:

(9)

These relationships are developed using all of 101 days of Case data from 2008 – 2011;

we report the polynomial coefficients in Table 2. Lastly, we develop a 2nd

order

polynomial empirical relationship using both shallow (Eq. 2) and deep soil moisture (Eq.

3) with albedo (Eq. 4):

(10)

and report the polynomial coefficients in Table 2.

2.4 Two-Layer Bucket Model of Soil Moisture Dynamics

To model shallow and deep soil moisture dynamics, we modified the standard root zone

bucket model [Daly et al., 2004; Guswa et al., 2002; Laio et al., 2001; Rodriguez-Iturbe

et al., 1999] into a two-layer bucket model (Figure 4) [Kurc and Small, in review]. In this

two-layer bucket model, the root zone is divided into an upper shallow layer where

evaporation drives soil moisture, and a lower deep layer where transpiration controls soil

moisture [Kurc and Small, in review]. Leakage can occur from the upper to the lower

147

layer, but no redistribution is allowed from the lower to the upper layer [Kurc and Small,

in review]. The following equations describe this two-layer bucket model:

(11)

(12)

where, n is porosity, ZE is the depth of the upper layer, ZT is the depth of the lower layer,

sE is the relative soil moisture content in the upper layer, sT is the relative soil moisture

content in the lower layer I(sE,t) is the rate of infiltration, E(sE) is the rate of evaporation,

T(sT) is the rate of transpiration, L(sE) is the rate of leakage from the upper to the lower

layer, and L(sT) is the rate of leakage from the lower layer out of the system. Parameters

used for our specific study site are listed in Table 3.

As for evapotranspiration in the standard bucket model, losses from the system due to

evaporation E(sE) and transpiration T(sT) are described as piecewise functions of sE and

sT, where sE and sT are constant above a certain s* and decrease linearly to the

hygroscopic point sh in the case of E [Guswa et al., 2002]and to the wilting point sw in the

case of T [Guswa et al., 2002; Laio et al., 2001], i.e.:

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(13)

(14)

where Emax and Tmax are the maximum daily rates under wet conditions [Guswa et al.,

2002]. Parameters used for our specific study site are listed in Table 3.

Previous studies in similar ecosystems have shown that leakage below the root zone (60 –

80 cm) is rare [Liu et al., 1995], or suggest that the lower layer never gets enough wet to

leak [Kurc and Small, in review]. However, because we consider increased precipitation

in our analysis, we include a leakage term from the lower layer in our model. Movement

out of the layers is only considered to be downward in our model. Two components

comprise this movement: (1) behavior of hydraulic conductivity and (2) a simple

empirically derived linear relationship regulating water movement out of the layer [Kurc

and Small, in review], i.e. 1% of the soil moisture in the upper layer is moved into the

lower layer and 0.1% of the soil moisture in the lower later is moved out of the bottom of

the system at any time step. We ignore hydraulic redistribution [e.g. Ryel et al., 2004;

Scott et al., 2008] between the layers mostly due to lack of information of this

mechanism for our study site. As such, leakage is modeled as a simple linear relationship

[Daly et al., 2004; Kurc and Small, in review]:

149

(15)

(16)

where, Ksat is saturated hydraulic conductivity in cm d-1

and b is the exponent of the

retention curve where

is the soil water potential at saturation. Parameters

used for our specific study site are listed in Table 3.

2.4.1 Model Parameters

The depth of the upper layer ZE is set as 0 – 20 cm to minimize the influence of

atmospheric demand on the lower layer (Table 3). A root density profile has been

developed for this shrubland site and is reported in [Sanchez-Mejia and Papuga, in

review]. As was found for a similar shrubland[Kurc and Small, 2007] , while roots were

found to depths of 1 m in both the bare and canopy areas of our shrubland, the highest

root densities were found above 60 cm. Therefore, a maximum depth of 60 cm, i.e. ZT =

20 – 60 cm, was used in our modeled scenarios.

Soil texture at our study site classified as a sandy loam [Kurc and Benton, 2010], and

therefore we assigned a porosity (n) of 0.43 (Table 3). [Laio et al., 2001] suggest values

for the parameters sfc,sh, sw, and s* are based on the sandy loam soil texture using soil

water potential at different pressures using soil moisture retention curves . However, the

150

model is very sensitive to these parameters[Kurc and Small, in review] , and therefore

while we adopt the suggested value of 0.46 [Table 3; Laio et al., 2001], we recognize that

the wilting and hygroscopic point at our site are lower than what they report, i.e. at our

shrubland sh of 0.075 and sw of 0.085 are more appropriate (Table 3). These parameters

depend on both vegetation and soil properties, and wilting point values have been shown

to be defined as low as 0.03 [Guswa et al., 2002].

Partitioning evapotranspiration into its components of transpiration and evaporation is a

challenging enterprise, and has not been fully conducted at our shrubland site. Because

we do not have site-specific partitioning measurements, we defined the model parameters

of maximum evaporation from the upper layer and maximum transpiration from the deep

layer based on research conducted similar semiarid ecosystems [Kurc and Small, 2004;

Moran et al., 2009; Reynolds et al., 2000; Villegas et al., 2010]. Using these studies, we

define an individual maximum value for evaporation and transpiration at an ecosystem,

bare, and canopy level (Table 3).

2.4.2 Model Precipitation Input

The water from precipitation enters the upper layer of the model as:

(17)

where i corresponds to the time step and PPT is precipitation.

151

We calibrated the two-layer model using three years (2009 – 2011) of daily precipitation

from the micrometeorological tower as input and compared observed soil moisture in the

shallow and deep layers to modeled soil moisture (Figure 5). Linear regression between

observations and model output suggest that shallow moisture dynamics (R2= 0.60) are

better captured than deep moisture dynamics (R2= 0.12). In general, the modeled shallow

soil moisture follows the peaks and drydowns of the observed data. While deep soil

moisture follows the trends of the observed data, the magnitude does not reflect the

observations. We note that the model initialization results in a lag at the beginning of the

modeling time series which is resolved as however as moisture accumulates. In addition

we note that modeling the higher and lower values of deep soil moisture is complex,

despite the fact that the model follows the trend of the observed deep soil moisture. This

is a consequence for the development of Cases and the use of our Case thresholds (Table

1). When categorizing our model data by Case, it is clear that deep soil moisture is

misrepresented. For instance, Case 1 days decrease from 608 (observed) to 587 (model),

while Case 3 days increase from144 (observed) to 216 (model), similarly an increase is

observed in Case 4 (Table 4). When partitioning the ecosystem into bare and canopy to

analyze model soil moisture versus observed, the model overestimates the deep soil

moisture under bare (Table 4, Case 4) and underestimates deep soil moisture under

canopy (Table 4, Case 2 and 4).

To develop soil moisture scenarios for our Cases based on shift in the precipitation

regime, we model precipitation input as a stochastic process [Daly et al., 2004;

152

Rodriguez-Iturbe et al., 1999]. At a scale where soil-moisture recycling can be neglected,

the rainfall input can be generated as an external force independent from the soil moisture

state [Laio et al., 2001]. The response of vegetation and therefore land surface- PBL

interactions in a water-limited ecosystem is tightly coupled to the stochastic nature of soil

moisture given by precipitation [Katul et al., 2007]. Because of this, rainfall follows a

series of “events” in time with frequency ) and depth () following a typical Poisson

process [Bonser et al., 1985], and therefore the frequency in time is found using a typical

Poisson distribution:

(18)

where, Px(x) is the probability of observing x events in a time period and is equal to the

average rate of occurrence of the event.

The depth () of the precipitation event can be calculated by using an exponential

function based on the average rainfall amount [Laio et al., 2001; Porporato et al., 2002].

This methodology to simulate daily precipitation regime is recognized to be a common

practice [Laio et al., 2001]. Frequency at the site was calculated from daily precipitation

data from 2008 to 2012, where the amount of days between storms was averaged,

resulting in 10 days average between storms. However, during a dry period a maximum

of 80 days without storms can be observed, while during the monsoon period a single day

between storms can be observed. Based on possible future changes in precipitation

153

[NAST, 2000; Trenberth et al., 2003; Weltzin et al., 2003], we assume the average 10 day

precipitation frequency and alter the average annual intensity from 8.3 mm with a 20%

increase and a 20% decrease.

3. Results and Discussion

3.1 Soil moisture and albedo influence on the planetary boundary layer height

Our analysis takes advantage of a two-layer soil moisture conceptual framework. Using

this framework, we categorize our time series data into four soil moisture cases based on

the presence or absence of moisture in the upper “shallow” layer and the lower “deep”

layer [Sanchez-Mejia and Papuga, in review]. Using representative days from each of

these categories (Figure 6a), we can see that the PBLh is sensitive to the soil moisture

derived Cases. Overall, the PBLh is largest when the shallow layer is dry (Cases 1 and 4)

and smallest when the shallow layer is wet (Cases 2 and 3). As has been demonstrated in

recent studies [Sanchez-Mejia and Papuga, in review], moisture presence in the deep

layer does tend to shrink the PBLh, for Case 3 the PBLh is generally lower than for Case 2

and for Case 4 the PBLh is generally lower than for Case 1 (Figure 6a). Obviously,

shallow soil moisture is highest for Case 2 and Case 3– but it is noticeably higher under

Case 3 (Figure 6b), which likely is influencing the increased shrinkage of the PBLh under

that moisture condition. Albedo is lowest in Case 2 and Case 3 (Figure 6c), which

supports the concept that surface soil moisture is influences the albedo [Small and Kurc,

154

2003] which is influencing the PBLh [Jackson et al., 1976; Sanchez-Mejia and Papuga, in

review].

We note that the scale at which soil moisture and albedo is measured in comparison to an

atmospheric sounding is a source of uncertainty in our analysis. For instance, spatial

variability in soil moisture associated with terrain can introduce complexity during moist

periods at multiple scales [Western et al., 1999], having an influence on surface fluxes

and ultimately the PBL [Giorgi and Avissar, 1997].

3.2 Empirical relationships for estimating planetary boundary layer height

[Santanello et al., 2005] argued for the need to develop empirical relationships that

focused on observable properties of the PBL and the land surface rather than model of

complex processes. Further, they emphasize using daily variability in these properties,

using the PBL as an integrator of surface conditions at the regional scale. A statistical

analysis found that the PBL was most sensitive to atmospheric stability ML, and that soil

moisture also played a significant role . Therefore, they developed an empirical

relationship to estimate PBLh from daily ML and (Equation 7). Because the empirical

relationship only captured about 60% of the variance between the variables, the predicted

range of PBLh was smaller than the actual range, which did have consequences when

using those PBLh estimates in other applications . We do note that the atmospheric

sounding from which PBLh is estimated has a larger source area than the , therefore a

difference between the actual and predicted PBLh is not necessarily unexpected.

155

Based on their findings, we calculated the ML for our site (Equation 6) and defined our

coefficients (Table 2) for their empirical relationship for PBLh using ML and shallow

(Equation 7, Figure 1). Using this approach, the variables explained about 50% of the

variance in PBLh; the estimated PBLh values fall between 298 m to about 4600 m (Figure

1), whereas the actual PBLh values have a range from about 300 m to about 4600 m with

a majority of those between 1500 – 2500m (Figure 6a). When we look at these by Case

(Figure 2), we see that the estimations for PBLh improve for the Cases when shallow soil

moisture is present, i.e. Case 3 (R2 = 0.51) and Case 2 (R

2 = 0.97). Additionally, the

differences in ranges between the actual PBLh values (Figure 6a) and the estimated PBLh

values (Figure 1) almost disappear for these Cases.

Despite reasonable predictions of PBLh using ML and , we calculated ML using

atmospheric sounding data and therefore we presume that this parameter is not any easier

to access than the PBLh itself. Strong relationships between PBLh and albedo and PBLh

and soil moisture in both the shallow and deep layer have been previously identified

[Figure 6; Basara and Crawford, 2002; Sanchez-Mejia and Papuga, in review]. There is

value in having identified these relationships in part because observations of albedo and

soil moisture are readily available at multiple temporal and spatial scales [Asbjornsen et

al., 2011; Wang et al., 2012] – ranging from micrometeorological datasets [Baldocchi et

al., 2001; Mueller et al., 2011] to remotely sensed data [Mills, 1990; Nemani et al.,

156

1993]. Furthermore, soil moisture has been predicted from albedo [Jackson et al., 1976]

and vice versa [Sanchez-Mejia and Papuga, in review].

To explore the ability to predict PBLh using these readily available parameters, we

adopted the polynomial approach of [Santanello et al., 2005]. We developed an empirical

relationship to estimate PBLh from daily and shallow (Equation 8), from daily shallow and

deep (Equation 9), and from daily , shallow and deep (Equation 10). Our analysis shows

that and shallow explained about 40% of the variance in PBLh (Figure 7a). From the

surface contours, relationships between the variables and the PBLh are highlighted, e.g. at

high and shallow, the PBLh develops more (Figure 7a). This is expected because under

these conditions, energy is partitioned towards turbulence (sensible heat) [Otterman et

al., 1993]. Estimating PBLh with shallow and deep did not show improvement (R2 = 0.40;

Figure 7b), while using , shallow, and deep provided the best estimates (R2 = 0.42; Figure

7c). The addition of deep moisture does not necessarily improve the predictability of the

PBLh, in our analysis. We suspect this is in part due to the lack of the ability of the model

to adequately capture deep moisture dynamics. That being said, deep moisture data are

not as readily available as shallow soil moisture, e.g. remotely sensed soil moisture

generally only captures the near surface signal [e.g. Calvet and Noilhan, 2000].

Therefore, based on the availability of the variables and their influence on the PBLh, we

suggest that the empirical relationship predicting PBLh from and shallow (Equation 8) is

a reasonable approach.

157

It should be noted again that the consideration of scale will be important in the derivation

and use of these empirical relationships. This is because atmospheric soundings represent

a scale on the order of 10 km2, while flux towers generally represent a scales on the order

of 100 m2 [Asbjornsen et al., 2011]. How much these empirical models represent the land

surface that is linked to the PBL, will depend on surface heterogeneity, and the response

of the PBL to integrated fluxes over a larger spatial scale.

3.3 Influence of change in precipitation regime on land surface - PBL dynamics

With a combination approach using the modified two-layer bucket model of soil moisture

dynamics and the empirical relationships developed in this study, we estimate the PBLh

for three different modeled precipitation regimes (Equation 18), two based on possible

future changes in precipitation [NAST, 2000; Trenberth et al., 2003; Weltzin et al., 2003]:

(1) the current 10 day precipitation frequency with an average annual intensity of 8.3

mm, hereafter referred to as CURRENT; (2) the current 10 day precipitation frequency

with a 20% increase in average annual intensity, hereafter referred to as INCREASE; and

3) the current 10 day precipitation frequency with a 20% decrease in average annual

intensity, hereafter referred to as DECREASE. Using these precipitation regimes as

inputs into the two-layer bucket model, we obtain a daily soil moisture value for both the

shallow upper (Equation 11) and the deep lower layer (Equation 12) and calculate this for

both bare and canopy patches. Using the percent cover, we calculate ecosystem soil

moisture as a weighted average (Equation 1). Using modeled ecosystem shallow soil

moisture, we estimate albedo using a linear relationship [Sanchez-Mejia and Papuga, in

158

review]. Then, we use our empirical relationships (Equations 8, 9 and 10) to estimate the

PBLh.

Model results from the CURRENT precipitation regime (Figure 8) at the study site yield

total precipitation of 293 mm, which is consistent with our site characteristics. Shallow

ecosystem soil moisture ranges from 6.7% to 17.5% and generally following the

dynamics of the precipitation, increasing after every storm. Deep ecosystem soil moisture

ranges from 8.5% to 10.8%, only increasing after large storms. Albedo ranges from 15 %

to 19%, with a tendency toward an overall decrease following large storms. The PBLh is

variable depending on the empirical relationship: for f(,sh) PBLh ranges from 1567 m to

2919 m, for f(sh,d) PBLh ranges from 1353 m to 3130 m, and for (sh,d,) PBLh ranges

from 1725 m to 3202 m.

Model results from the INCREASE precipitation regime (Figure 9) at the study site yield

total precipitation of 433 mm. Shallow ecosystem soil moisture ranges from 6.9 % to

27.9 %. Deep ecosystem soil moisture ranges from 8.5 % to 11.6 %, only increasing after

large storms. Albedo ranges from 11.5% to 18.4%, which is just slightly lower than the

CURRENT scenario. The PBLh is variable depending on the empirical relationship: for

f(sh,) PBLh ranges from 1001 m to 2892 m, for f(sh,d) PBLh ranges from 1237 m to

3193 m, and for (sh,d,) PBLh ranges from 1622 m to 4792 m.

159

The DECREASE precipitation regime (Figure 10) results in annual average rainfall of

244 mm. Shallow soil moisture ranges from 6.6% to 19.2%. Deep soil moisture ranges

from 8.5% to 11%. Albedo ranges from 14.4 to 18.5 %. The PBLh is variable depending

on the empirical relationship: for f(sh,) PBLh ranges from 1419 m to 2939 m, for

f(sh,d) PBLh ranges from 1301 m to 2080 m, and for (s,d,) PBLh ranges from 1697 m

to 3237 m.

When categorizing by cases using the two-layer conceptual framework, the CURRENT

and INCREASE scenario present more days of Case 4 (CURRENT= 47%, INCREASE =

57%), while the scenario DECREASE has more days of Case 1 (45%).

We explored two simple possibilities for an increase and decrease by 20% of

precipitation inputting the system. We are conservative in this analysis especially, since

regional modeling is not completely consistent [Weltzin et al., 2003]. The decrease in

average precipitation could affect soil moisture-vegetation-atmosphere interactions

depending on the frequency and intensity of the events [Knapp et al., 2008]. In any case,

this framework allows for the exploration of changes in precipitation dynamics and the

influence these changes might have on the PBLh, an indicator of larger scale land surface

– atmosphere interactions.

160

3.4 Implications of modeling two-layer soil moisture dynamics

We explored an ecohydrological application using a two-layer bucket model (Figure 4).

Our results show that a two-layer approach is feasible and that using this approach, we

can simulate both shallow and deep layer soil moisture dynamics for use in multiple

applications. Notably however, shallow soil moisture is better represented than deep soil

moisture (Figure 5), leaving room for further improvement, perhaps through the inclusion

of hydraulic redistribution [Guswa, 2012; Nadezhdina et al., 2010; Ryel et al., 2004].

We also look at the two-layer soil moisture dynamics of bare and canopy patches

individually (Figures 8, 9, and 10). To do this, we take into account that roots do not

distribute homogeneously beneath the surface [Breshears et al., 2009; Loik et al., 2004],

and allocate more roots under the canopy than under the bare patches [Sanchez-Mejia and

Papuga, in review]. Because of the smaller fraction of roots under the bare patches, we

assume a smaller maximum transpiration (Tmax) from the bare patches than the canopy

patches for our site. Because these root distributions do generalize to all vegetation and

soil types, parameterizations should be based off of specific site characterizations. With

this in mind, the partitioning of the soil into a shallow and deep layer and the surface into

canopy and bare patches, we can improve our understanding the dynamics of evaporation

and transpiration partitioning by applying this two-layer model, especially if we know

where is water coming from [Cavanaugh et al., 2011; Kurc and Small, 2007; Yepez et al.,

2007].

161

Acknowledgments.

This research was supported in part by University of Arizona College of Agriculture and

Life Sciences (CALS), The Arizona University System Technology and Research

Initiative Fund (TRIF), The University of Arizona Office of the Vice President for

Research (VPR), SAHRA (Sustainability of Semi-Arid Hydrology and Riparian area)

under the STC Program of the National Science Foundation (NSF), NSF CAREER

Award # EAR-1255013, the UA Springfield Scholarship for Graduate Research in

Rangelands, the UA William A. Calder III PhD Scholarship for Mexican graduate

students focused in conservation, and the UA Kel M. Fox Watershed Scholarship.

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Table 1. Shallow layer soil moisture threshold and (exponential time constant), and

deep layer soil moisture threshold.

Ecosystem Bare Canopy

threshold threshold threshold

shallow 0.1229 3.5 0.1251 3.6 0.1142 2.7

deep 0.1013 0.1015 0.1003

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Table 2. Coefficients of 2nd

order polynomial empirically-derived relationships

173

Table 3. Parameters used in two-layer bucket model based on vegetation and soil similar

to our study site. The selection process describes the source from which the parameter

was derived.

Symbol Value Selection Process

Layer depth

ZE 0-20 cm Estimated from root profiles

ZT 20-60 cm Estimated from root profiles

Soil parameters

n 0.43 [Cosby et al., 1984]

Ksat 80 cm d-1

[Laio et al., 2001]

B 4.9 [Laio et al., 2001]

s* 0.46 [Laio et al., 2001]

sfc 0.56 [Laio et al., 2001]

sh 0.075 [Laio et al., 2001]

sw 0.085 [Laio et al., 2001]

Atmospheric loss parameters

Ecosystem Emax 0.45 cm d-1

[Moran et al., 2009;

Reynolds et al., 2000;

Villegas et al., 2010]

Ecosystem Tmax 0.5 cm d-1

Bare Patch Emax 0.5 cm d-1

Bare Patch Tmax 0.4 cm d-1

Canopy Patch Emax 0.4 cm d-1

Canopy Patch Tmax 0.55 cm d-1

174

Table 4. Number of days for each Case applying two-layer soil moisture conceptual

framework to classify observed and model data. Ecosystem refers to ecosystem soil

moisture idem for bare and canopy.

Case 1 Case2 Case3 Case4

Ecosystem

Observed 608 13 144 247

Model 587 18 216 274

Bare

Observed 604 15 145 248

Model 557 15 151 372

Canopy

Observed 670 6 106 246

Model 669 74 242 110

175

List of Figures

Figure 1. Planetary boundary layer height PBLh (m) estimated as a function of a) shallow

soil moisture (sh) and atmospheric stability.

Figure 2. Planetary boundary layer height PBLh (m) estimated as a function of a) shallow

soil moisture (sh) and atmospheric stability (ML) for a) Case 1, b) Case 2, c) Case 3, and

d) Case 4.

Figure 3. Planetary boundary layer height PBLh (m) estimated as a function of a) shallow

soil moisture (sh) and albedo (), and b) deep soil moisture (d) and albedo.

Figure 4. Modified two-layer bucket model

Figure 5. Two-layer bucket soil moisture model performance compared to observed data.

Figure 6. The variability in (a) planetary boundary layer height (PBLh), (b) shallow soil

moisture, (c) deep soil moisture, and (d) albedo with respect to Case (1, 2, 3, 4).

Figure 7. Observed versus model PBLh as a function of a) shallow soil moisture and

albedo, b) shallow and deep soil moisture, c) shallow and deep soil moisture and albedo.

176

Figure 8. Land surface and PBLh dynamics based on modeling approach using

CURRENT average annual rainfall (8.3 mm) and storm frequency (10 days) as input. E

refers to ecosystem, B to bare, and C to canopy.

Figure 9. Land surface and PBLh dynamics based on modeling approach using a 20%

INCREASE in average annual rainfall (9.96 mm) and storm frequency (10 days) as input.

E refers to ecosystem, B to bare, and C to canopy.

Figure 10. Land surface and PBLh dynamics based on modeling approach using a 20%

DECREASE in average annual rainfall (6.64 mm) and storm frequency (10 days) as

input. E refers to ecosystem, B to bare, and C to canopy.

177

Figure 1.

178

Figure 2.

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Figure 3.

180

Figure 4.

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Figure 5.

182

Figure 6.

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Figure 7.

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Figure 8.

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Figure 9.

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Figure 10.