SOIL MOISTURE CONTROLS ON SPATIAL AND TEMPORAL PATTERNS OF
CARBON DIOXIDE FLUXES IN DRYLANDS
by
Andrew Neal
_____________________
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF HYDROLOGY AND WATER RESOURCES
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
WITH A MAJOR IN HYDROLOGY
In the Graduate College
THE UNIVERSITY OF ARIZONA
2012
2
THE UNIVERSITY OF ARIZONA
GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation
prepared by Andrew Neal
entitled Soil Moisture Controls on Spatial and Temporal Patterns of Carbon Dioxide
Fluxes in Drylands
and recommend that it be accepted as fulfilling the dissertation requirement for the
Degree of Doctor of Philosophy
_______________________________________________________________________
Date: 11/23/2011
Paul Brooks
_______________________________________________________________________
Date: 11/23/2011
Shirley Papuga
_______________________________________________________________________
Date: 11/23/2011
Hoshin Gupta
_______________________________________________________________________
Date: 11/23/2011
Willem van Leeuwen
_______________________________________________________________________
Date: 11/23/2011
Steven Archer
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 Papuga
________________________________________________ Date:
Dissertation Director: Paul Brooks
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STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of 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 accurate acknowledgment of 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: Andrew L. Neal
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ACKNOWLEDGEMENTS
This dissertation would not have been possible without the assistance of my committee,
Steve Archer, Hoshin Gupta and Wim van Leeuwen, and particularly the guidance of my
co-advisors, Paul Brooks and Shirley Papuga.
Editorial, analytical, and moral support from Daniel Bunting, Jon Martin, Krystine
Nelson, and Zulia Sanchez Mejia has been invaluable. Kyle Hartfield and the students at
the Arizona Remote Sensing Center helped me through technical computing issues with
remote sensing data and spatial analysis. Rafael Rosolem and Jeff Gawad were very
accommodating to an out-of-town student coming back to finish his dissertation.
Funding for the research was provided by the Semi-Arid Hydrology and Riparian Areas
(SAHRA) NSF Science and Technology Center (NSF agreement EAR-9876800). Data
for this study were provided by the Ameriflux network and were used in accordance with
their fair-use policy. Data were also provided by the USDA-ARS Southwest Watershed
Research Center. Funding for these datasets was provided by the United States
Department of Agriculture, Agricultural Research Service.
Thank you to my parents, Donna and Rick, my brothers, Rich and Tom and my wife,
Hayley, who were my safety net throughout this process.
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DEDICATION
To Richard,
who planted the seed of inspiration and inquisitiveness
in a young boy,
and
To Hayley,
who has nurtured that seed in a grown man.
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TABLE OF CONTENTS
LIST OF FIGURES ............................................................................................................ 8
ABSTRACT........................................................................................................................ 9
ABSTRACT........................................................................................................................ 9
1.
INTRODUCTION .................................................................................................... 10
1.1.
Problem Statement ............................................................................................ 10
1.2.
Background Information............................................................................... 11
1.3.
Dissertation Format........................................................................................... 18
2.
METHODS ............................................................................................................... 20
3.
PRESENT STUDY................................................................................................... 22
3.1.
Appendix A: ENVIRONMENTAL CONTROLS ON ECOSYSTEM
RESPIRATION IN DRYLANDS: THE ROLE OF THE VERTICAL DISTRIBUTION
OF SOIL MOISTURE .................................................................................................. 22
3.2.
Appendix B: OVERCOMING LIMITATIONS TO REMOTE SENSING-
BASED UPSCALING OF CARBON FLUXES IN SEMIARID SYSTEMS ............. 23
3.3.
Appendix C: HYDROLOGIC REDISTRIBUTION SUBSIDIZES CARBON
FLUX IN SEMIARID LANDSCAPES........................................................................ 24
4.
FUTURE RESEARCH OPPORTUNITIES ............................................................. 26
5.
REFERENCES ......................................................................................................... 30
APPENDIX A: SOIL MOISTURE AND VEGETATION CONTROLS ON
ECOSYSTEM RESPIRATION IN DRYLANDS............................................................ 41
APPENDIX B: SOIL MOISTURE CONTROLS REMOTE SENSING-BASED
UPSCALING OF CARBON FLUXES IN A SEMIARID SHRUBLAND ..................... 89
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APPENDIX C: LATERAL HYDROLOGIC REDISTRIBUTION SUBSIDIZES
CARBON FLUX IN SEMIARID LANDSCAPES ........................................................ 131
APPENDIX D: USING LANDSAT ETM+ NDVI TO CLASSIFY VEGETATION
COVER ........................................................................................................................... 170
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LIST OF FIGURES
Figure 1. Drydown of shallow soil moisture (θ, 0-20 cm depth) following a storm. Points
indicate mean soil moisture, error bars are one standard deviation.................................. 15
Figure 2. Identification of transition points between negative (uptake) and positive
(release) NEE to define the deep soil moisture threshold (θ*deep). Gray bars indicate
individual instances of prolonged carbon uptake.............................................................. 16
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ABSTRACT
Dryland ecosystems provide a unique opportunity to study the effects of water limitation
on ecosystem activity. The sensitivity of these systems to small inputs of moisture is
well-documented, but the expression of water limitation in terms of carbon dioxide flux
between the ecosystem and atmosphere remains unclear. Applying a simple conceptual
approach to soil moisture dynamics, patterns in carbon flux become clear. Release of
carbon dioxide via respiration is primarily driven by moisture in the shallow soil, and
differences in respiration rates among plant functional types are only evident after
controlling for soil moisture. Alternatively, carbon uptake by a semiarid shrubs
ecosystem is largely driven by the availability of deep soil moisture. This link to deep soil
moisture improves spatial scaling of gross and net carbon uptake using remote sensing
data. Lateral redistribution of moisture on the landscape connects readily observed
physical features, namely topography, to ecosystem function, but redistribution is
generally not considered in carbon models. A simple runoff scheme coupled to a
conceptual model for carbon flux demonstrates the high degree of spatial heterogeneity in
carbon dioxide flux resulting from moisture redistribution. The importance of
redistribution in carbon modeling is highlighted by interannual variability in modeled
carbon fluxes under different rainfall characteristics (event size, event duration,
interstorm duration). The links between hydrology and ecology across spatial scales
become clearer when topographically-based moisture distribution is used as an
organizing variable. In all, this research identifies new avenues for research where
moisture dynamics are of central interest in dryland ecohydrology.
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1. INTRODUCTION
1.1. Problem Statement
This study examines the response of carbon dioxide fluxes in semiarid ecosystems
based on moisture availability. The prevalence of water-limitation in semiarid
environments emphasizes the importance of hydrologic processes (e.g. runoff,
infiltration, percolation) on ecological function. Semiarid systems exhibit different
carbon flux behavior in response to shallow and deep soil moisture (Kurc and Small
2007) Runoff redistributes moisture laterally which augments local moisture from
rainfall. Using specific patterns in the vertical profile of moisture and with simple runoff
models, we can better understand the dynamics of carbon flux. This framework allows us
to more clearly identify source/sink patterns of carbon dioxide exchange between the land
and atmosphere.
This study addresses three main sets of questions:
ƒ
How does soil moisture at different depths drive ecosystem respiration? Is that
difference more significant than the differences among vegetation functional types?
ƒ
Can soil moisture information be used to identify a relationship between net carbon
flux (and photosynthetic carbon uptake) and remote sensing data? How can this
relationship be applied spatially?
ƒ
Does the lateral redistribution of moisture as runoff subsidize local soil moisture to
extend periods of increased photosynthesis and respiration? What is the effect of
hydrologic redistribution on watershed-scale carbon flux?
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Addressing these questions from an empirical standpoint, the importance of vertical
(i.e. shallow versus deep) soil moisture is evident from previous studies as a control for
carbon dioxide uptake (Kurc and Small 2007). Further, moisture patterns may be
influenced by hydrologic redistribution, altering the spatial patterns of carbon flux.
Altogether, these results point toward new, spatially-distributed methods to quantify
carbon flux based on soil moisture as a direct driver of ecosystem processes.
1.2. Background Information
Semiarid environments are a major component of the terrestrial earth surface,
comprising approximately 40% of the land area (Reynolds et al. 2007). These areas are
important to global carbon balance, and may act as substantial sources or sinks for carbon
at a level that is globally significant (Wohlfahrt et al. 2008). Carbon cycling in semi-arid
systems may be altered under potential land use change, species invasion, climate change
and desertification (Conant and Paustian 2002; Conant et al. 2001; Lal 2004).
Quantifying the landscape-level carbon balance and understanding the drivers of carbon
flux will improve our ability to assess potential changes based on management and
climatic changes.
The net ecosystem exchange of carbon dioxide (NEE) is comprised of two main
components: the carbon dioxide released during all ecosystem respiration processes (Re)
and the gross primary production (GPP) associated with photosynthesis. In dryland
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environments, carbon fluxes have shown a close link to moisture availability, which has
led to the concept of precipitation pulses as drivers of ecosystem activity (Noy-Meir
1973). Precipitation pulses provide a simple tool for analyzing carbon fluxes (Huxman et
al. 2004), particularly in a modeling context (Ogle and Reynolds 2004; Reynolds et al.
2004). However, precipitation does not capture differences in moisture within the soil
column, which more closely influences ecosystem activity in vegetation and soil
microbes (Fernandez 2007).
Several studies have demonstrated the importance of soil moisture in controlling
carbon fluxes in semiarid environments (Bell et al. 2008; Kurc and Small 2007; Scott et
al. 2008; Thomas et al. 2009). The water-limitation inherent to these systems provides an
opportunity to explore the mechanisms of different soil moisture conditions on ecosystem
activity. For example, small storms may only wet the shallow soil (~20 cm deep).
Moisture in shallow soils is often short-lived due to evaporative demand (Wythers et al.
1999). This brief, shallow moisture favors Re (Barron-Gafford et al. 2011; Scott et al.
2006) over GPP, which requires more extensive moisture redistribution within plants.
Alternatively, large storms or a series of storms have been shown to yield more moisture
deeper in the soil profile (>20 cm deep) (Kurc and Small 2007). This moisture may be
preferentially used by plants, thus corresponding to increased GPP (Cavanaugh et al.
2011; Kurc and Benton 2010; Scott et al. 2006).
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Further, ecosystems respond dynamically to available moisture. Vegetation develops
complex root systems to access moisture from different locations in the soil (Cavanaugh
et al. 2011; Donovan and Ehleringer 1994; Scott et al. 2006). Plants may grow or
abandon roots to ensure moisture availability (Chaves et al. 2002) and often have
developed mechanisms to cope with water stress (Porporato et al. 2001). Plant
communities may also directly influence local soil moisture by trapping overland flow
(Moran et al. 2010; Pugnaire et al. 2004; Villegas et al. 2010). As a result, a simple
“bucket” conceptualization of root zone soil moisture may not adequately represent the
ability of a plant to access water.
As moisture moves down through the soil, we anticipate different responses in the
flux of carbon dioxide. Kurc and Small (2007) demonstrated a link between NEE and
evapotranspiration when deep soils were wet and shallow soils were dry. Extrapolating
this result, a conceptual model for soil moisture is used to analyze the patterns of carbon
dioxide flux. This conceptual model divides the soil into two zones: an upper, shallow
zone (0-20 cm) and lower, deep zone (20-60 cm). These depths were selected based on
the maximum depth of direct bare soil evaporation, at approximately 20 cm (Wythers et
al. 1999), and the maximum depth of available soil moisture data at the sites analyzed.
Data from soil moisture reflectometers at each site were averaged within the two zones.
Each individual instrument was assumed to measure an equal distance above and below
the depth of installation.
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The conceptual model identifies wet and dry periods for the two soil zones based on
different thresholds. The shallow soil zone threshold is determined at each site based on
exponential drydown curves associated with daily rainfall greater than 8 mm (Kurc and
Small 2007). Shallow soil moisture data for each rainfall event were averaged and an
exponential curve was fit to that average (Figure 1). The moisture threshold was defined
as the asymptote of the exponential drydown. Deep soil moisture thresholds were
determined from the transition between net carbon dioxide uptake and net release.
Transitions were identified when more than five days of carbon uptake were followed by
five or more days of net carbon release. The transition occurs on the first day of net
carbon release. The values of deep soil moisture for each transition were averaged to
produce a single threshold value for each site (Figure 2).
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Figure 1. Drydown of shallow soil moisture (θ, 0-20 cm depth) following a storm. Points
indicate mean soil moisture, error bars are one standard deviation.
Moisture conditions at any point in the landscape may also be influenced by lateral
redistribution of moisture (Guntner and Bronstert 2004; Ravi et al. 2010). Large storm
events in semiarid environments often produce infiltration-excess runoff (Goodrich et al.
1994), which may later re-infiltrate as runon when trapped by vegetation or having
(Ludwig et al. 2005; Wilcox et al. 2003). This runon moisture has been linked to
vegetation patterns on the landscape (Bhark and Small 2003; Ludwig et al. 2005) and
produces highly heterogeneous patterns in lateral soil moisture (Breshears et al. 2009;
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Caylor et al. 2006). These lateral moisture effects, in concert with the vertical moisture
effects described above, drive the patterns of activity in semiarid ecosystems (Belnap et
al. 2005).
Figure 2. Identification of transition points between negative (uptake) and positive
(release) NEE to define the deep soil moisture threshold (θ*deep). Gray bars indicate
individual instances of prolonged carbon uptake.
Understanding the function of coupled hydrologic (runoff/runon, infiltration) and
ecological (root hydraulic properties, plant physiological behavior, microbial activity)
systems is critical to predict carbon fluxes. This is particularly true in systems that
experience much of their rainfall during the summer growing season, as in the
southwestern United States (Gochis et al. 2006; Higgins and Shi 2000).
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From this study, we find that using soil moisture states can more clearly identify
expected patterns in carbon dioxide fluxes in semiarid landscapes. Simple models can be
used to link precipitation to soil moisture states (Huxman et al. 2004). Through these
models, the effect of seasonal precipitation on rates of GPP in the growing season
(Emmerich and Verdugo 2008; Potts et al. 2006) can be directly linked to landscape
hydrology. In the context of altered climate, the southwestern US may experience
reduced total annual rainfall (Diffenbaugh et al. 2005) although this may include either
increased or decreased seasonal rainfall (Fatichi et al. 2011; McAfee and Russell 2008;
Trenberth et al. 2003). Applying simulated precipitation data from a climate change
scenario with daily resolution, a simple soil moisture characterization could identify the
relevant soil moisture states to estimate both Re and GPP according to the results
presented here.
This study focuses on the ecohydrological response resulting from the distribution of
soil moisture on the landscape, focusing on the distribution of moisture both vertically
and laterally. The effect of the vertical moisture distribution is assessed with respect to
carbon dioxide flux in terms of Re (Appendix A) and NEE and GPP (Appendix B).
Lateral redistribution of moisture may enhance both Re and GPP fluxes by subsidizing
both shallow and deep soil moisture. The effect of hydrologic subsidy on carbon fluxes
and a discussion of relevant spatial scales are included (Appendix C). A method for
delineating vegetation cover types using satellite data is also included (Appendix D).
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1.3. Dissertation Format
A brief treatment of some of the analytical techniques used in this study is given in
the “Methods” section. This offers an overview of some of the technical aspects of eddy
covariance and remote sensing data collection and processing, which may not be included
in the subsequent chapters.
Following the methods is the “Present Study” which provides a summary of the
analyses and conclusions of three papers prepared for peer-reviewed publication. The
three articles are included as appendices, namely:
Appendix A: “Environmental Controls on Ecosystem Respiration in Drylands: the
Role of the Vertical Distribution of Soil Moisture”. In this paper I performed all analyses
interpreted results and drafted the manuscript, which is submitted at Journal of
Geophysical Research-Biogeosciences;
Appendix B: “Overcoming Limitations to Remote Sensing-Based Upscaling of
Carbon Fluxes in Semiarid Systems”. In this paper I performed all analyses, interpreted
results and drafted the manuscript, which will be submitted to Agricultural and Forest
Meteorology;
Appendix C: “Hydrologic Redistribution Subsidizes Carbon Flux in Semiarid
Landscapes”. In this paper I performed all analyses, interpreted results and drafted the
manuscript, which will be submitted to Advances in Water Resources.
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Altogether these three papers discuss findings that will enhance our understanding of
the mechanism of water limitation on ecosystem carbon dynamics. Also included is an
appendix describing work in development as part of the study in Appendix B. Appendix
D: “Using Landsat ETM+ NDVI to Classify Vegetation Cover” discusses an approach to
delineate parts of the landscape that contain specific vegetation types, specifically
creosote shrubs. This analysis was developed in conjunction with the analyses in
Appendix B, and a modified version of the method is in development to extend the results
found in Appendix B.
The dissertation concludes with a section on Future Research Opportunities, which
describe several potential studies that may extend the results presented here. These
studies delve further into the ecohydrological behavior of dryland ecosystems.
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2. METHODS
Direct, landscape-scale measurements of NEE are often made using the eddy
covariance method (Baldocchi et al. 1988). The eddy covariance method gives an
estimate of ecosystem carbon dioxide, water vapor and sensible heat flux based on highfrequency records of turbulent exchange in the atmosphere. Data are collected via a threedimensional wind speed and infrared gas analyzer for carbon dioxide and water
(Moncrieff et al. 1997). Associated micrometeorological measurements, e.g. relative
humidity, temperature, and net radiation are also collected to corroborate the flux data.
Data used in this study were accessed from the Oak Ridge National Laboratory
Ameriflux archive (http://public.ornl.gov/ameriflux/) at Level 2 quality control.
Various approaches have been developed to partition tower-measured NEE into GPP
and Re (Desai et al. 2008). They include correlation structures in the raw, high-frequency
data (Scanlon and Kustas 2010) or model approaches based on the 30-minute flux data.
These models may predict GPP, often via a light use efficiency (LUE) scheme (Lasslop
et al. 2010) or Re, through either biophysical models (e.g. BIOME-BGC, (Reichstein et
al. 2007). Often one model is used for either GPP or Re and the other is calculated as the
residual of observed NEE and the model output.
In order to scale tower-measured carbon fluxes, researchers rely on satellite
remote sensing data to provide information about the land surface over a broad spatial
area (Wylie et al. 2003). Because satellites only detect surface conditions, scaling models
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based on remote sensing data predict GPP as a function of vegetation conditions
(Mahadevan et al. 2008; Sims et al. 2005; Sims et al. 2008; Xiao et al. 2010). These
models use reflectance data that are sensitive to vegetation as inputs, often in the form of
a vegetation index such as the normalized difference vegetation index (NDVI, (Tucker
1979) or the enhanced vegetation index (EVI, (Huete et al. 2002). Vegetation indices
respond to the presence of photosynthetic leaf surfaces (Verhoef 1984), thus GPP models
that use vegetation indices are explicitly or implicitly LUE models (Turner et al. 2002;
Turner et al. 2005). In fact, more recent satellite missions have included data products for
absorbed photosynthetic radiation (Knyazikhin et al. 1999; Myneni et al. 2002) and other
indices related to photosynthetic absorption, e.g. the photosynthetic radiation index (PRI,
(Garbulsky et al. 2010) in an attempt to better characterize plant response to radiation.
A major drawback of LUE models is the coupling of water limitation with
photosynthetic efficiency (Garbulsky et al. 2010). From a remote sensing standpoint,
detection of moisture characteristics is difficult, because satellites only detect surface
reflectance across limited spectra (Fensholt and Sandholt 2003). In order to overcome
this, new indices, such as the normalized difference water index (NDWI, (Fensholt and
Sandholt 2003)), based on the absorption of infrared radiation or land surface temperature
(LST, (Sims et al. 2008)) have been used to represent the moisture characteristics of the
landscape. Other studies have used ground-based estimates of water vapor flux to
represent moisture conditions on the ground (Sjöström et al. 2011).
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3. PRESENT STUDY
The methods, results, and conclusions of this study are presented in the papers
appended to this dissertation. The following is a summary of the most important findings
in these documents.
3.1. Appendix A: ENVIRONMENTAL CONTROLS ON ECOSYSTEM
RESPIRATION IN DRYLANDS: THE ROLE OF THE VERTICAL
DISTRIBUTION OF SOIL MOISTURE
This study examined the influence of vertical (shallow versus deep) soil moisture on
Re. The findings of this analysis include the poor predictive relationships for Re based on
soil temperature (Tsoil) and volumetric soil moisture, individually. When soil moisture
was divided into wet and dry states in shallow and deep soil zones, distinctions between
Re fluxes emerged. Predictive relationships based on Tsoil in the four soil moisture cases
indicated higher rates of Re when shallow soils were wet. These relationships held across
vegetation type, and differences in respiration rates associated with vegetation type
(grass, shrub and savannah) became clear only in the context of the wet/dry soil moisture
cases.
The results of this study indicate the importance of soil moisture as a driver of Re in
semiarid ecosystems. However, Re does not respond as a continuous function of soil
moisture, but rather based on a binary wet-dry classification. Other variables—Tsoil,
vegetation type—only yield meaningful relationships with Re after the wet-dry
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classification is performed. We point toward the potential of the soil moisture cases to
identify particular sources of carbon dioxide (e.g. autotrophic maintenance and growth,
heterotrophic metabolism) at the landscape scale.
3.2. Appendix B: OVERCOMING LIMITATIONS TO REMOTE SENSINGBASED UPSCALING OF CARBON FLUXES IN SEMIARID SYSTEMS
This study explored empirical scaling relationships between remote sensing data and
tower-based NEE and GPP measurements. The goal was to develop a simple function
linking the temporally-detailed tower data to spatially-extensive satellite remote sensing.
The results indicated that the relationship between tower-measured carbon fluxes and
vegetation index data for the tower source area was sensitive to soil moisture. The wetdry soil moisture cases were used to explore the relationships between EVI and NEE and
EVI and GPP based on moisture availability. Correlations between EVI and carbon
fluxes improve near the tower when deep soils are wet.
The connection between carbon flux, especially carbon uptake, and EVI suggests that
deep soil moisture is critical to relax the water stress on semiarid vegetation. Reducing
water stress leads to a stronger relationship between vegetation indices, representing the
solar radiation reflected by plants, and carbon flux. This method offers an approach to
scaling carbon fluxes over wider areas in water-limited systems, given knowledge of soil
moisture conditions.
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3.3. Appendix C: HYDROLOGIC REDISTRIBUTION SUBSIDIZES CARBON
FLUX IN SEMIARID LANDSCAPES
This study considers the potential for soil moisture at any point in the landscape to be
influenced by lateral flow within the watershed. Redistribution of moisture due to
topographic effects can influence the ecosystem carbon flux in semiarid landscapes
depending on runoff/runon processes. Using a simple model for runoff and moisture
routing, the relationship between topography, hydrology and ecosystem dynamics
emerges. Runoff that collects downslope supports longer periods of carbon uptake lower
in the watershed.
Comparing carbon flux with moisture redistribution to carbon flux with no runoff
indicates that the ability of semiarid ecosystems to withstand dry conditions is due in part
to hydrologic features. The results suggest that topography can be used as a predictor of
carbon flux resulting from the redistribution of moisture toward areas that collect runoff
from upslope areas. Spatial heterogeneity in carbon flux resulting from this redistribution
is critical to improve knowledge of ecohydrological processes at larger spatial scales in
drylands.
3.4. APPENDIX D: USING LANDSAT ETM+ NDVI TO CLASSIFY
VEGETATION COVER
This study tested an approach to land cover classification using vegetation index data
and knowledge of vegetation phenology. The goal was to identify parts of the landscape
that are dominated by creosote bush, an evergreen desert shrub. Anticipating minimal
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change in NDVI between growing and non-growing seasons, the method classified the
landscape based on seasonal change in NDVI values. The method gives an approximation
for creosote distribution, but ignores potential variability in herbaceous plant cover,
which may be more or less prevalent depending on a variety of factors, including
seasonal moisture conditions. A new method, corroborating Landsat data with high
resolution (1 meter) remote sensing data, is in development as a result of this study.
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4. FUTURE RESEARCH OPPORTUNITIES
The results presented in this dissertation identify the importance of moisture
distribution, both vertically within the soil and laterally via runoff, on carbon dioxide
exchange in dryland ecosystems. They also suggest further study that will enhance the
understanding of semiarid ecosystems both under current conditions and as dynamic
systems. Ongoing research in dryland ecohydrology continues to develop new
understanding of coupled vegetation and hydrologic behavior. The research proposed
here can extend that frontier across spatial and temporal scales.
Relationships between shallow and deep soil moisture and carbon dioxide fluxes
presented in this dissertation are based on sites where the majority of rainfall occurs
during the summer months—coincident with the typical growing season. Increased
respiration and carbon uptake associated with soil moisture is thus inherently tied to
seasonal climate and ecosystem phenology. Left unresolved is the question of how
ecosystems that receive most of their annual precipitation in winter (e.g. Mediterranean
or Great Basin systems) respond to shallow moisture versus deep moisture. Time lags
between winter moisture inputs and spring and summer carbon flux response become the
basis to explore the mechanism for plant and ecosystem responses. Synthesizing such
results with those presented here can help unify understanding of dryland response to
moisture, whether they receive precipitation in summer or winter.
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The response of ecosystem respiration to shallow soil moisture is demonstrated here
based on respiration over large areas and with limited soil moisture measurement. But
spatial heterogeneity in soil moisture due to variable infiltration rates (Bhark and Small
2003), canopy shading (Breshears et al. 1997), and litter (Villegas et al. 2010) may
introduce variability between canopy and bare soil carbon fluxes. A reasonable question,
then, is: does the vertical moisture profile influence carbon fluxes over small spatial (and
possibly temporal) scales? Detailed analysis of carbon fluxes in bare and canopy areas,
potentially including isotopic analysis to partition flux sources, would describe the effect
of spatially varying soil moisture on respiration.
Isotopic analysis of carbon dioxide fluxes could also be used to determine if the four
moisture cases favor autotrophic or heterotrophic respiration. A working hypothesis that
emerged from this study was that respiration under fully dry soils represented background
levels of respiration for both autotrophs and heterotrophs; that shallow soil moisture was
the driver for heterotrophic respiration; and that deep soil moisture was associated with
growth respiration in vegetation. Relationships between soil moisture and respiration
components will explain the processes and pathways to better understand carbon
dynamics in the future.
This study indicates the importance of incorporating runoff in carbon dioxide flux
estimates at watershed scales. However, the experiment performed here used a limited
precipitation data set over a relatively small area. Rainfall is known to be highly variable
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in space and time, including variability within a single storm event, between seasons and
between years. Rainfall variability can be characterized by intensity, duration, and
frequency. From the findings here, reduced storm frequency and intensity would be
expected to reduce runoff and thus carbon dioxide uptake.
Results presented in Appendices A and B suggest different sensitivities of ecosystem
properties to shallow and deep soil moisture, i.e. ecosystem respiration responds to
shallow moisture while carbon uptake is sensitive to deep soil moisture. The connection
between shallow and deep soil moisture and ecosystem behavior is an emergent property
of dryland ecosystems. Exploring the correlation structure and frequency patterns
between shallow and deep moisture and partitioned carbon and water fluxes would
describe the sensitivity of ecosystems to potential change and identify positive feedbacks
between vegetation and soil moisture. Further, detailed frequency analysis would identify
time scales associated with stress thresholds on vegetation dynamics. As more research
sites continue to maintain long-term data records, this research will be easier to facilitate
and more fruitful in potential outcomes.
The remote sensing data applied in this study was at a relatively coarse spatial scale
relative to the processes of interest. Identifying subgrid vegetation patterns and temporal
landscape change are complicated by the lack of resolution. New data products derived
from multiple satellite sensors may enable improvements in both spatial and temporal
resolution via a data fusion approach. Applying these new tools will enable consistent
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analysis of long-term trends in dryland ecosystem dynamics at the landscape scale by
linking data from current sensors to legacy systems.
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5.
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Bell, C., McIntyre, N., Cox, S., Tissue, D., & Zak, J. (2008). Soil microbial responses to
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between Microbial and Hydrologic Processes in Arid and Semiarid Watersheds.
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APPENDIX A: SOIL MOISTURE AND VEGETATION CONTROLS ON
ECOSYSTEM RESPIRATION IN DRYLANDS
Andrew L. Neal1, Shirley A. Kurc1, 2, Paul D. Brooks1, Russell L. Scott3
A.L. Neal. Department of Hydrology and Water Resources, University of Arizona, 1133
James E. Rogers Way, Tucson, AZ 85721. (aneal@email.arizona.edu)
S.A. Kurc. School of Natural Resources and the Environment, University of Arizona, P.O. Box 210043, Tucson, AZ 85721 (kurc@email.arizona.edu)
P.D. Brooks. Department of Hydrology and Water Resources, University of Arizona,
1133 James E. Rogers Way, Tucson, AZ 85721. (brooks@email.arizona.edu)
R.L. Scott. Southwest Watershed Research Center, United States Department of
Agriculture, Agricultural Research Service, 2000 E. Allen Road, Tucson, AZ 85719
(russ.scott@ars.usda.gov)
1
Department of Hydrology and Water Resources, University of Arizona.
2
School of Natural Resources and the Environment, University of Arizona.
3
Southwest Watershed Research Center, United States Department of Agriculture,
Agricultural Research Service.
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Abstract
Terrestrial ecosystem respiration (Reco) is a major component of the global carbon system
and is sensitive to a variety of physical and biological factors, including temperature,
moisture and vegetation. Describing the sensitivity of Reco to these factors is particularly
important to quantify how expected changes to temperature, rainfall, and vegetation will
affect Reco and net carbon balance. Here, we examine the relationships between several
ecosystem characteristics (soil temperature, vegetation type, and soil moisture) and
nighttime Reco from eddy flux towers throughout the southwestern USA. Soil moisture
data provided the best prediction of Reco and was most useful in the context of a wet/dry
classification scheme based on shallow and deep soil zones. Soil moisture thresholds for
the classification scheme were determined using drydown curves (shallow soil, < 20 cm)
and net ecosystem carbon flux (deep soil, 20-60cm). In contrast, we found that soil
temperature alone was a poor predictor for Reco and that including information about
vegetation type did little to improve that prediction. The two zone wet/dry soil moisture
classification also indicates different sensitivity to moisture among vegetation types. The
ability of a threshold-based soil moisture-based approach to capture widespread patterns
in Reco underscores the importance of water-limitation for both soil microbial and plant
respiration in drylands, and provides a useful tool for future analyses of carbon cycling
under climate or vegetation change in these landscapes.
Keywords: ecohydrology; semiarid environments; thresholds; water limitation; carbon
flux; shrubland; grassland
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1. Introduction
Drylands are critical to the global carbon cycle because they store approximately 15% of
soil organic carbon [Jenerette and Lal, 2005; Lal, 2004; Scott et al., 2009] and constitute
about 40% of the terrestrial earth surface [Reynolds et al., 2007]. The net carbon flux in
drylands can vary between source and sink behavior on seasonal to interannual scales [cf.
Scott et al., 2009; Wohlfahrt et al., 2008], because it are largely controlled by moisture
pulses characteristic of drylands [Noy-Meir, 1973]. Carbon fluxes may also vary under
conditions of vegetation change, e.g. woody plant encroachment [Huxman et al., 2005;
Scott et al., 2006a]. Due to their expansive land area, any change in the carbon dynamics
of drylands is likely to have a large impact on the global carbon system [Lal, 2004]. The
sensitivity of these ecosystems to moisture inputs (both frequency and intensity)
underscores the need for improved understanding of the mechanisms driving carbon
fluxes under potentially altered climate [Vargas et al., 2012]. This is particularly true for
understanding the carbon release via ecosystem respiration (Reco) [Raich and Schlesinger,
1992; Schlesinger and Andrews, 2000].
Ecosystem respiration is the release of carbon dioxide both aboveground and below.
Aboveground respiration is predominantly autotrophic plant growth and maintenance
respiration, while belowground soil respiration is composed of both heterotrophic
(microbial) and autotrophic (plant and mycorrhizal) sources [Hanson et al., 2000]. Over
long periods of time, soil respiration is dominant in Reco except during intense vegetation
growth [Franzluebbers et al., 2002, Law et al. 1999]. Although soil temperature is
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generally considered the primary driver of Reco [Bahn et al., 2010; Cable et al., 2011;
Fang and Moncrieff, 2001], in dryland ecosystems, Reco is also driven by soil moisture
[Almagro et al., 2009; Conant et al., 2004; Grünzweig et al., 2009; Liu et al., 2009;
Sponseller, 2007; Xu et al., 2004]. The temperature and moisture effect in respiration is
linked directly to biochemical processes [Davidson et al., 2012] at multiple time scales
[Vargas et al., 2012].
To characterize the influence of soil moisture on Reco in dryland ecosystems, conceptual
methods have used rainfall as a proxy [Huxman et al., 2004; Ogle and Reynolds, 2004;
Reynolds et al., 2004, Vargas et al., 2012]. However, rainfall must infiltrate the soil
before it becomes useful to plants and soil microbial communities [Bhark and Small,
2003; Kurc and Small, 2004; Moran et al., 2010; Ng and Zhan, 2007; Wainwright et al.,
2000] resulting in a temporal lag between rainfall and Reco response [Vargas et al.,
2010b]. Therefore, soil moisture (θ) is more appropriate for describing the influence of
changes in water availability controlling the biological processes involved in Reco [Bell et
al., 2008; Kurc and Small, 2007; Potts et al., 2006; Scott et al., 2008; Thomas et al.,
2009]. Soil moisture is particularly important between rainfall events as persistent
moisture sustains continued ecosystem activity [Kurc and Small, 2007]. Following a
storm, θ can vary vertically in the soil as a function of the magnitude and time since that
storm [Laio et al., 2001], soil hydraulic properties [Porporato et al., 2002] and
topographic and plant hydraulic redistribution [Scott et al., 2008]. This vertical change in
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θ directly influences ecosystem function [S. A. Kurc and Small, 2007; Yaseef et al., 2010]
such as net primary productivity and soil respiration [Thomey et al., 2011].
At the onset of the wet season, when the shallow soil is wet but the deeper soil remains
dry, microbial activity can be high [Barron-Gafford et al., 2011], while plant
transpiration [Cavanaugh et al., 2010; Scott et al., 2006b] and carbon flux [Kurc and
Benton, 2010] are typically low. However, when the surface is dry and deep soil is wet,
such as occurs several days following a storm, plants transpire and photosynthesize
[Cavanaugh et al., 2010; Kurc and Benton, 2010], which has been linked to autotrophic
respiration [Vargas et al., 2011] but may inhibit heterotrophic respiration [BarronGafford et al., 2011; Tang and Baldocchi, 2005]. We note here the lack of a widely
agreed upon description of autotrophic and heterotrophic respiration response to eventbased moisture inputs [Carbone et al., 2008]. The influence of shallow and deep soil
moisture on the components of Reco derives from differences in the temporal scales of
water availability [Loik et al., 2004]. Shallow soil moisture (< 20 cm soil depth) is
relatively short-lived due to the high evaporative demand of arid ecosystems [Wythers et
al., 1999]. Alternatively, deep soil moisture (> 20 cm below the surface) is more
persistent with minimal impact from direct atmospheric effects; thus it is available for
uptake via plant root systems [Caldwell and Richards, 1989; Williams and Albertson,
2004]. Because of its influence on physical and biological processes, the vertical θ
distribution and the timing of moisture availability at different depths is critical for Reco in
dryland ecosystems.
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The objective of this study was to examine the importance of shallow versus deep θ on
Reco across dryland ecosystems in the southwestern USA. We seek a unifying framework
to describe Reco in dryland environments. To accomplish this, we synthesize data from
five sites, which span vegetation types typical of the region. We focus on the influence of
θ in the context of other expected environmental controls: soil temperature (Tsoil) [Fang
and Moncrieff, 2001; Luo and Zhou, 2006], vegetation type [Barron-Gafford et al., 2011;
Cable et al., 2011]. To simplify θ variability in the vertical dimension, we used a
conceptual framework based on two soil zones shallow and deep soil moisture described
above. We applied a classification scheme that divides soil moisture into two states (i.e.,
wet and dry) using threshold soil moisture values for the two zones, resulting in four
specific wet/dry cases. We evaluate the distribution of Reco rates based on the four soil
moisture cases and vegetation type.
We hypothesize that the dynamics of Reco will depend on the presence of moisture in the
soil. Specifically, we expect that Reco is controlled by temperature only when moisture is
present, particularly in the shallow soil. We expect the highest Reco rates to be associated
with wet conditions in shallow soil because of the concentration of labile carbon
available for respiration in the upper soil [Jobbágy and Jackson, 2000]. Reco will also be
elevated when moisture is present in the deep zone, whether or not there is moisture in
the shallow zone, because of root water uptake stimulating autotrophic activity.
Vegetation type is also expected to have an influence Reco [Barron-Gafford et al., 2011;
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Jin et al., 2010] based on the physiological differences in plant functional types. Any
trend in Reco based on vegetation is expected to follow from the pattern in soil moisture,
based on the strength of soil moisture controls across plant functional types.
2. Methods
2.1. Sites
A total of five dryland sites are used in this study (Figure 1). These semiarid sites were
selected because they include a vertical profile of continuous soil moisture measurements
to at least 50 cm depth and all have similar soil textures and climates. The sites also span
a range of grass and shrub vegetation types that characterize semiarid environments [S. A.
Kurc and Small, 2007; S.A. Kurc and Benton, 2010; Scott et al., 2006]. See Table 1 for
details on site characteristics. First, we included two creosotebush-dominated shrublands,
one in the Chihuahuan desert of New Mexico at the Sevilleta Long Term Ecological
Research (LTER) site (Sev Shrub) and one in the Sonoran desert at the Santa Rita
Experimental Range (SRER, SRER-C). These sites are similar climatically, with mean
annual temperatures of 17.5 °C and 21 °C and mean annual rainfall of 230 and 260 mm,
respectively. Second, we also included two semidesert grasslands, one at the Sevilleta
LTER (Sev Grass) and one at the transition zone between the Chihuahuan and Sonoran
deserts in the Walnut Gulch Experimental Watershed (Kendall). The Sev Grass site
experiences the same climate as the Sev Shrub site. Kendall has a mean annual
temperature of 17 °C and mean annual rainfall of 351 mm. Third, we included a fifth site
representing a mesquite savannah, also located at the SRER (SRER-M). Climate at
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SRER-M is slightly wetter and cooler than at SRER-C, with mean annual temperature of
19 °C and mean rainfall of 310 mm. All sites receive the majority of annual rainfall
during the summer months (June-September).
Instrumentation used at the individual sites includes standard open-path eddy covariance
equipment, i.e. three-dimensional anemometers, infra-red gas analyzers and other
micrometeorological equipment [see Baldocchi et al., 2001, and Table 1]. Additional
instrumentation includes soil temperature thermistors within the upper 10cm of soil depth
at all sites and soil moisture sensors at several depths, at least as far as 50cm below the
surface (Table 1). Multi-year datasets in this region reveal that infiltration is usually
limited to the upper ~50 cm of soil in the hot, rainy season and ~100 cm during the cool
and occasionally wet winters [Scott et al., 2000].
2.2. Ecosystem Respiration Data
Data at the five sites were collected from 2002-2009 (see Table 1 for exact range at each
site) and include years representing relatively wet, dry and normal precipitation patterns
at each site, as reported by site investigators (see Table 1 for references). The data used
here were processed at Level 3 according to Ameriflux processing standards and were
used according to the Ameriflux fair use policy (http://public.ornl.gov/ameriflux). This
level of processing applies a standard set of protocols to quality control net ecosystem
exchange of CO2 (NEE) and calculation of Reco [Reichstein et al., 2005].
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49
Nighttime NEE was used to represent Reco [Lavigne et al., 1997]. To further constrain
NEE to measure only respiration, this study used data collected between 00h00 and
05h00 local time at all sites. We note that these late-night periods are often characterized
by a well-formed stable boundary layer and thus decreased mixing in the atmosphere;
during these time periods, assumptions of eddy covariance methods are likely violated
[Stull, 1988]. To remedy this, a friction velocity (u*) filter was identified for each site
[Goulden et al., 1996]. Filter values were either based on those reported by site
investigators or, in the case of SRER-C, derived for that site individually. A u* filter of
0.2 ms-1 was applied for SRER-C; the filter was identified by the minimum filter value
where annual net carbon flux converges to a stable value [Goulden et al., 1996]. In total,
the filters reduced the available Reco data by 70% of the total recorded data between
00h00 and 05h00. Similar rates of nighttime data loss are reported elsewhere in studies
using eddy covariance systems [Goulden et al. 1996, Lavigne et al., 1997], and nighttime
NEE has widely been use to study Reco [Mahecha et al., 2010].
After filtering, daily average flux rates were calculated. For the remaining data, daily
average values were only determined if at least three non-filtered data points remained.
These daily-level fluxes (units: mg m-2 s-1) were analyzed in conjunction with other dailylevel site data of the forcing variables (i.e., soil moisture and soil temperature).
Anticipating an exponential relationship between Tsoil and Reco [Lloyd and Taylor, 1994],
we use a linearized form of an exponential model [Luo and Zhou, 2006], of the form:
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Reco = ln a + bTsoil
(1).
The parameters of equation 1 are also used to derive an apparent Q10 value, to describe
the rate of change in Reco based on a 10 °C change in Tsoil [Xu and Qi, 2001]. The
apparent Q10 is calculated as:
Q10 = e10b
(2).
2.3. Ancillary Data and Soil Moisture Cases
Soil moisture, soil temperature and rainfall data were collected with micrometeorological
measurements as noted in section 2.1. Information about vegetation type at each site was
drawn from the Ameriflux website. Soil moisture data were averaged, and rainfall
summed, to daily values, which ensured that any moisture inputs prior to nighttime Reco
periods were included. Soil temperature data from the same nighttime period as the Reco
data were averaged temporally to produce a daily Tsoil value which corresponded to the
Reco data. Tsoil varies by approximately 3°C across all sites during the night.
Soil moisture data from different depths were combined to yield two soil zones, one
shallow and one deep, for each of the five sites. The shallow soil zone includes the upper
20cm of the soil and the deep soil zone ranges from 20-60cm. Assuming that each sensor
measured an equal distance above and below the sensor, weighted average values of daily
(24 hour) soil moisture were determined at each site for the two soil zones for the entire
day. Soil temperatures were analyzed in connection with soil moisture conditions on the
immediate preceding day, i.e. soil moisture conditions for the day correspond with Tsoil
and Reco for the following night. A wet/dry classification was developed from the two50
51
zone soil moisture record [Kurc and Small, 2007]. The wet/dry classifications yielded
four soil moisture cases: dry shallow and dry deep zones (Case 1); wet shallow and dry
deep zones (Case 2); dry shallow and wet deep zones (Case 3); and wet shallow and wet
deep zones (Case 4).
Vegetation type and soil moisture case are used as categorical variables in this study. To
determine the relative importance of each categorical variable tested, the exponential
relationship (Equation 1) of each variable was assessed by the goodness-of-fit, measured
as the coefficient of determination (r2). An individual variable (e.g. grasslands) that exerts
stronger control over Reco should produce a better exponential fit compared to a weaker
category variable (e.g. shrublands). Categories (e.g. vegetation type), which produced
good individual model fits exert a stronger control on respiration than those with weaker
fits.
We used an analysis of covariance model (ANCOVA) to compare Reco models (Equation
1) among moisture cases and vegetation types. This ANCOVA test yields individual
models for each categorical variable, and allows those parameters to be compared
directly. Because the Reco distributions are not Gaussian, differences in Reco among
categories were assessed by the nonparametric Kruskal-Wallis test [Milton and Arnold,
2003]. Median Reco values were compared using the Bonferroni test to identify
differences in distributions of Reco rates among on moisture cases and vegetation types.
This test was chosen because it is conservative under conditions of unequal sample size
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[Benjamini and Hochberg, 1995]. Model parameters from the ANCOVA were also
compared using the Bonferroni test.
3. Results
The four soil moisture cases follow a similar pattern in time for all five sites, driven by
the seasonality of rainfall (e.g. Figure 2). Fully dry soils characterized by Case 1 occur
for much of the year, from the end of the summer wet season in September through to the
next wet season in June. Occasional low intensity winter rains wet the soil enough to
produce Case 3 and 4 events that may persist for several days before the soils return to
Case 1. In general, the first 3-5 days at the start of the wet season fall into Case 2,
establishing wet conditions in the shallow soil. Cases 3 and 4 occur intermittently
throughout the wet season among the five sites, totaling 50 (± 29) and 94 (± 48) days,
respectively. Cases 1 and 4 each account for approximately 35% of soil moisture states in
all sites, while Case 3 represents 15% and Case 2 approximately 5% of the soil moisture
time series (these do not total 100% due to instances of soil moisture probe malfunction;
not shown).
The weakness of Tsoil alone as a predictor of Reco is demonstrated by a poor exponential
model fit (r2 = 0.0877, p <0.001; Figure 3a). Many of the Reco values are clustered around
zero and peak Reco is reached around 25 °C, with declining respiration at higher
temperatures. The exponential fit does not capture the variability in the data, indicating
that variables other than Tsoil influence respiration rates in dryland environments (Figure
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3a). Similarly, shallow soil moisture (θs) is a poor predictor of Reco (Figure3b). Low Reco
rates occur even when soils are relatively wet, and high Reco rates are possible even with
relatively little water in surface soil. Ecosystem respiration peaks at intermediate levels of
θs (~ 0.10 m3 m-3) when temperatures are high (Tsoil > 20 °C). These conditions generally
correspond to moisture cases 2 and 4.
When Reco data are separated based on the wet/dry soil moisture cases, distinct patterns of
Reco variability become evident (Figure 2). Respiration is higher in Cases 2 and 4 when
soil moisture is present at the surface (0.06 and 0.07 mg C m-2 s-1 respectively) than in
Cases 1 and 3 when soil moisture is absent at the surface (0.03 and 0.03 mg C m-2 s-1, see
Table 2). The trends in (nighttime) Reco contrast with the pattern in NEE (Figure 2),
which is closely linked to the availability of deep soil moisture. When deep soil moisture
is available (Cases 3 and 4), NEE is generally less than zero (mean NEE is -0.03 and 0.10 mg C m-2 s-1, respectively) indicating net carbon uptake (Figure 2, day of year 200275). Alternatively, dry deep soils (Cases 1 and 2) are more likely to correspond to net
carbon release (mean NEE values of 0.02 and 0.03, respectively) (Figure 2, day of year 1200).
When the data are divided based on their soil moisture case and then Reco is predicted
based on the typical Tsoil exponential model, the soil moisture control on respiration is
further reinforced (Figure 4). For instance, the best relationship between Reco and Tsoil (r2
= 0.44, p < 0.001) occurs in Case 2 when soil moisture is high in the surface, but absent
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from the deep layer (Figure 4b). A strong exponential relationship with Tsoil was also
found in Case 4 (r2 = 0.41, p < 0.001; Figure 4d), when both shallow and deep soil zones
are wet. When moisture is only present in the deep soil (Case 3) a relationship exists
although not as strong as in Cases 2 and 4 (r2 = 0.21, p < 0.001; Figure 4c). In Case 1
when the soil is dry, Reco is generally near-zero, even at high temperatures (Figure 4a).
Reco is more responsive to increasing temperature when moisture is present in the soil,
especially near the surface (Figures 4b, c and d). The largest Reco fluxes occur in Case 4,
(Figure 4d), but Reco is also high when only the shallow soil is wet (Figure 4b). By
contrast, when only the deep soil is wet, the response of Reco to Tsoil is diminished (Figure
4c). Expressed as an apparent Q10 value (Equation 2), the dry soil conditions of Case 1
have a Q10 of 1.5 (± 0.09), while Cases 2 and 3 have a Q10 of 2.5 (± 0.28 and ±0.22,
respectively) and Case 4 has a value of 4.0 (±0.23).
In contrast to expectations [Barron-Gafford et al., 2011; Cable et al., 2012], little
difference was found in Reco rates among the three vegetation types (Table 2). The
grassland sites have average Reco rates of 0.04 ± 0.02 mg m-2 s-1 (error range is the
standard error of the mean), while rates in the shrublands average 0.045 ± 0.03 mg m-2 s-1
and the savannah average 0.06 ± 0.02 mg m-2 s-1 (Table 2). The difference between the
grass and shrub Reco rates is not distinguishable at 95% confidence in the Bonferroni test
and the distribution of Reco over the three vegetation types is similar (not shown).
Marginal means for all three vegetation types are within the 95% confidence in the
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Bonferroni test under an ANCOVA model which includes Tsoil with vegetation type, i.e.
with the effect of Tsoil removed.
Differences in Reco trends among the three vegetation types become clear when we
separate the data based on soil moisture cases and then vegetation type, (Figure 5). As
expected, in Case 1, when moisture is absent from the soil profile, all three vegetation
types have similarly distributed and low Reco values. However, in Cases 2 and 4 when
soil moisture is present at the surface, savannah Reco is significantly greater than Reco in
both shrub and grass. The distribution of Reco rates in Case 4 includes larger fluxes at all
sites compared to Case 2. In Case 3, when moisture is only available deep in the soil, the
savannah and shrub sites have mean Reco values greater than the grassland (Table 2).
4. Discussion
At the daily level, temperature alone is insufficient to explain Reco as indicated by the
weak model as a function of Tsoil (Figure 3a). When Reco is analyzed as a function of θ, a
threshold response becomes apparent (Figure 3b). The Reco-θs pattern was the impetus for
exploring a threshold-based classification system for soil moisture in a two-zone soil
column. The threshold response indicates that a small amount of moisture can activate
Reco, but that Reco is not a direct function of soil moisture (Figure 3b). Further, Reco
responds differently to wet conditions in the shallow and deep soil zones (Figure 4, Table
3). Only after soils become wet (Cases 2-4) are other factors limiting for Reco [Austin et
al., 2004].
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56
The soil moisture dynamic is conceptually consistent with the prevailing “bucket” model
of soil moisture [Knapp et al., 2008] but delineates between the response to shallow and
deep moisture. As this study demonstrates, the location of moisture in the soil may
influence the rate of Reco (Figure 4). Shallow and deep moisture stores may also facilitate
different components of Reco, i.e. autotrophic and heterotrophic respiration, depending on
the level of photosynthetic activity in the vegetation (Figures 3, 4).
Shallow and deep soil moisture vary at different rates. Shallow moisture responds rapidly
based on the frequency and intensity of storm events and evaporative demand [Kurc and
Small, 2004; Sala and Lauenroth, 1982] while deep soil moisture varies over longer
times (seasonal to annual scales; [Amenu et al., 2005]) and is dependent on soil hydraulic
properties [Porporato et al., 2002] and plant hydraulic redistribution [Scott et al., 2008]
among other factors. As a result, the responses in Reco identified here will vary under two
thresholds with two frequency patterns (Figure 6). Understanding future behavior of these
systems will depend on the ability to forecast shallow and deep moisture variability under
changing climate [Cayan et al., 2010].
4.1. The Effect of Soil Moisture on Reco
Our results demonstrate that soil moisture is a strong control on carbon flux in drylands
and drives different responses based on the depth of moisture. The influence of soil
moisture on plant water uptake indicates that deep soil moisture triggers vegetation
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57
activity, including autotrophic respiration [Borgogno et al., 2010; Porporato et al., 2001].
Other studies have suggested threshold-type responses for soil respiration based on
available soil moisture or storm size [Potts et al., 2006; Sponseller, 2007]. Thresholds in
prior studies were defined for precipitation (either at the event or antecedent rainfall),
rather than soil moisture or matric potential. This study points toward soil moisture
thresholds as a more appropriate paradigm for ecosystem analyses.
The clearest pattern in Reco emerges when analyzed based on the vertical distribution of
soil moisture. The cases identify distinct ranges of Reco flux values. When soil moisture is
present at the surface (Cases 2 and 4) Reco rates are significantly higher (Figure 5)
compared to when moisture is absent at the surface (Case 1 and 3). Moisture deeper in
the profile (Case 3) slightly elevates Reco (Figure 5) compared to the fully dry conditions,
although not significantly so. By comparison, the 24-hour trend in NEE indicates more
net respiration under Cases 1 and 2, while Cases 3 and 4 are more frequently associated
with net carbon uptake. Taken altogether, this suggests the importance of shallow
moisture to Reco (both day and night) which is likely linked to soil microbial respiration.
Similarly, the correspondence with net carbon uptake in Cases 3 and 4 suggests that Reco
in these periods may be dominated by plant respiration.
Strong exponential Reco-Tsoil relationships emerge under the soil moisture case analysis
compared. The best exponential fits across all categorical variables occur in Cases 2 and
4 (Table 4). When moisture is present in the soil column as in Cases 2-4, Reco is nearly an
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order of magnitude more sensitive to Tsoil compared to when the soil is dry. However,
when soils are dry (Case 1), ecosystems are moisture-limited, and respiration is minimal
across a range of temperatures (Figure 5a). In this case, the exponential model is weak.
Soil moisture cases provide a tool to identify times when moisture limitations are relieved
and other variables, such as Tsoil, may influence Reco. The characteristic response of Reco
to temperature varies among these cases, based on the location of moisture in the soil.
The different responses suggest different sensitivities at the ecosystem scale that emerge
from autotrophic and heterotrophic respiration responses, in contrast to a single,
ecosystem-level response [Cable et al., 2011; Chen et al., 2009].
The differences among Reco rates in the three vegetation types are closely linked to a Tsoil
effect. Our analysis shows that those differences are small and may be sensitive to the test
applied (no difference was found in the marginal means in the ANCOVA analysis, not
shown). This clarifies a movement in the literature pointing toward vegetation controls on
respiration processes via canopy-induced microclimates, microbial species assemblages
and root-driven soil moisture manipulation [Barron-Gafford et al., 2011; Breshears et al.,
1997; Metcalfe et al., 2011; Scott et al., 2008]. Differences among the three vegetation
types are only found after analyzing for the soil moisture distribution (Figure 5). For
example, the Reco rates at the savannah site and shrub sites are generally greater than at
the grassland sites, in Cases 2 and 3, although they are similar when considered outside
the moisture cases. The differences in Reco associated with community and plant function
are secondary to the effect of soil moisture.
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Other respiration studies have focused on smaller spatial and temporal scales, and have
identified sub-daily hysteresis resulting from temperature [Barron-Gafford et al., 2011],
and plant and microbial community differences [Bell et al., 2008; White et al., 2009].
Here, we focused on large-scale landscape Reco at daily time steps rather than focusing on
short-term, plot or point measurements of soil respiration. Our approach integrates the
variability resulting from small scale processes, e.g. spatial heterogeneity of vegetation
and microbial communities. At these scales, the differences in respiration rates between
vegetation communities are minimal (Table 2) and only become apparent in the context
of soil moisture distribution (Figure 5).
4.2. Implications of Moisture Cases
The four moisture cases described here provide a useful approach to analyze ecosystem
dynamics in drylands. Changes in carbon fluxes in response to rainfall event size [Knapp
et al., 2008; Munson et al., 2010] suggest the importance of soil hydrologic
characteristics and this study identifies the different roles shallow and deep water play in
driving ecosystem activity. Water limitation and moisture pulse response have been
identified in a variety of other semi-arid landscapes in different environmental conditions
based both on carbon fluxes and metabolic rates [Jenerette et al., 2008; Sponseller,
2007]. Moisture patterns at different scales, e.g. seasonal and interannual, have been
identified in the response of Reco to moisture [Bowling et al., 2010]. The two-zone
method described here provides direct, explicit approach to identifying water limitation.
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The two-zone moisture approach was applied here at a whole ecosystem scale. Research
at microsites near SRER-M pointed out the importance of satisfying moisture limitations
to stimulate soil respiration and the subsequent role of vegetation in respiration processes
[Barron-Gafford et al., 2011]. Differences among the three vegetation types in the
present study become clear when analyzed on the basis of the four soil moisture cases.
The greatest difference in mean Reco rates occurs in Case 2, when moisture is only present
at the surface. During those periods, the mean respiration rate in the savannah is over 0.1
mg m-2 s-1, compared to 0.025 mg m-2 s-1 at the shrub sites and 0.01 mg m-2 s-1 at the
grasslands. When moisture limitations are met in the deep soil as well, the savannah
ecosystem generally respires at a rate greater than that of the grassland and shrubland
sites. This may be due to greater diversity in the savannah relative to the grass and shrub
environments [Zak et al., 2003]. A higher respiration rate in savannahs is consistent with
expected results under woody plant encroachment along a riparian corridor [Scott et al.,
2006a].
The importance of soil moisture cases on Reco may depend on seasonal characteristics of
rainfall patterns and vegetation phenology. Different rainfall regimes may produce
different moisture thresholds and alter the pattern of moisture cases. A similar
dependence on the seasonality of moisture has been found in other semi-arid systems, e.g.
on grasslands adjacent to the Sev Grass site [Thomey et al., 2011, Carbone et al., 2011].
Alternatively, some communities may be less sensitive to the timing of moisture inputs
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based on their ability to manipulate soil moisture, such as sagebrush [Bates et al., 2006].
Overall, the results shown here identify soil moisture as the primary factor determining
subsequent effects on respiration [Cable et al., 2011]. For future studies, the soil moisture
cases described here enable simple analysis in the context of water immediately available
to plants and soil microbes [Fernandez, 2007].
The differences in Q10 values in the present study compared to that of Cable et al. [2011]
suggest the dynamic response of Reco to moisture is important for understanding Reco in
drylands. The large Q10 values in Cases 2, 3 and 4 indicate the potential to dramatically
increase Reco when the ecosystem is wet, particularly in Case 4 when the entire soil
column is wet and the sensitivity to Tsoil is greatest. Plant physiological characteristics
and climate factors vary across dryland environments and may alter the influence of the
soil moisture distribution on Reco. Comparing the Reco responses of the three vegetation
types, we find more frequent high Reco rates at the savannah site compared to the
grassland and shrubland sites (Table 2). In landscapes undergoing a shift from grass- to
shrub-dominance, we may expect a change in the sensitivity of Reco to moisture inputs,
with particularly large Reco fluxes during the transition, in a mixed landscape. This pattern
of increased fluxes during vegetation transition has been described for riparian areas
[Scott et al., 2006a] and at smaller spatial scales in terms of soil respiration [Cable et al.,
2012]; we show that it is consistent in uplands as well.
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In Cases 1 and 2, when deep soils are dry, Reco and daytime NEE are both positive, while
Reco and nee have opposite signs (i.e. NEE is negative) when deep soils are wet. As a
result, we may expect a large part of that Reco is associated with heterotrophic respiration,
while the vegetation is less active due to moisture stress. Similarly, when the deep soil
zone is wet, water is more readily accessed by vegetation, which may promote
autotrophic respiration. The potential relationship between the components of Reco can be
explored in the context of moisture availability at wider spatial scales than in previous
studies, which have relied on upscaling results from soil collar respiration data
[Richardson et al., 2006]. Such an approach could help to bridge gaps in understanding
respiration across wider spatial and temporal scales [Vargas et al., 2010a]. This research
points toward scaling relationships that consider the vertical distribution of moisture as a
potential driver of autotrophic and heterotrophic processes when combined with NEE
data or experimental approaches.
5. Conclusions
Among the variables tested, soil moisture was a strong predictor of Reco in the context of
a two-zone wet/dry classification scheme. Meeting the small moisture requirement in the
two soil zones enables a wide range of Reco flux values. This classification scheme
underscores the importance of the water-limitation mechanism in dryland environments.
Our results further highlight the location of that moisture within the soil column as an
important factor in ecosystem behavior. Moisture in the surface layer leads to higher Reco
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rates than moisture deeper in the soil and Reco responds to Tsoil at different rates based on
moisture in the two soil zones.
The occurrence of different soil moisture cases is largely controlled by the magnitude of
and time between storms and soil hydraulic properties. As a result of potential changes in
precipitation patterns, the timing and frequency of soil moisture cases may also change.
Applying forecasts of precipitation under altered climate is among the potential
applications of the soil moisture cases identified here. The response of soil moisture to
variable rainfall inputs, and the ability to satisfy threshold values of soil moisture,
provides a clear approach to assessing the complexities associated with carbon flux in
drylands.
Appendix A: Defining Soil Moisture Thresholds
Wet/dry thresholds for the upper soil zone were determined based on exponential
drydown curves following rainfall events large enough to yield substantial changes in soil
moisture, i.e. > 8 mm d-1 [ Kurc and Small, 2004]. The terminal soil moisture value at the
end of the mean drydown curve for each site was used as the shallow soil moisture
threshold. The lower soil zone threshold was determined from the soil moisture value on
the first of at least five consecutive days of positive net carbon flux (release) after a
minimum five-day period of negative (uptake) net carbon flux. This threshold was chosen
to identify the value of θ in the deep soil when θ becomes a limiting factor on
photosynthetic uptake. Several transition periods occur in each site record and an average
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soil moisture value was used based on all uptake/release transitions. The wet/dry
classification was performed independently for each site (see Table 2 for threshold
values).
The shallow soil zone threshold is determined based on exponential drydown curves for
each site. The curves are generated from the mean shallow soil moisture values following
daily rainfall events greater than 8 mm. This rainfall event size was selected to identify
storms that yield enough precipitation to increase soil moisture over several days [Kurc
and Small 2007] Shallow soil moisture data for each 8 mm rainfall event in the site
record were averaged and an exponential curve was fit to that average, using the Matlab
curve fitting toolbox (Figure A1). The moisture threshold was defined as the asymptote
of the exponential drydown.
Deep soil moisture thresholds were determined from the transition between net carbon
dioxide uptake and net release, i.e. from NEE < 0 to NEE > 0. Transitions were identified
when more than five consecutive days of carbon uptake were followed by five or more
days of net carbon release. The deep soil moisture transition occurs on the first day of net
carbon release in such a sequence. The values of deep soil moisture for each transition
were averaged for each site record to produce a single threshold value for each site
(Figure A2). Threshold values for the shallow and deep soil zones are given in Table A1.
Acknowledgements
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Funding for this research was provided by the SAHRA (Sustainability of semi-Arid
Hydrology and Riparian Areas) Science and Technology Center of the National Science
Foundation (NSF agreement EAR-9876800) and the University of Arizona Water
Sustainability Program. The authors would like to thank the Ameriflux network for
providing public access to the data used in this study. All data were used according to the
Ameriflux fair-use policy. G. Barron-Gafford, D. Bunting, Z. Guido, H. Neal, K. Nelson,
Z. Sanchez-Mejia, and R. Vargas made helpful contributions to earlier versions of this
manuscript.
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Table Captions
Table 1. Characteristics for the five Ameriflux sites used in this study. Length of data and
years of record describe publicly available data from the Ameriflux website.
Table 2. Mean (± standard error) and standard deviation of R eco rates based on secondary
controls. Results of the Bonferroni comparison tests are also given. Comparison tests
were performed uniquely for each grouping, i.e. moisture cases, vegetation type, and
combined moisture cases and vegetation type.
Table 3. Regression coefficients and coefficient of determination (r2) for exponential
models based on different categorizations. Letters indicate parameter groups based on a
Bonferroni test. Parameters for vegetation type models all belong to the same group.
Table A1. Threshold values for the shallow and deep soil zones for each of the five sites.
Method for determining thresholds are described in section 2.3
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Figure Captions
Figure 1. Map showing study sites. Contours indicate annual rainfall (National
Oceanographic and Atmospheric Administration (NOAA) Climate Maps of the US,
http://hurricane.ncdc.noaa.gov/cgi-bin/climaps/climaps.pl).
Figure 2. Time series of precipitation (P), NEE and Reco at the SRER-M site. Colors
indicate the soil moisture conditions, which transition from predominantly Case 1 in the
dry (winter) season through Cases 2, 3 and 4 in the wet (summer) season.
Figure 3. (a) Respiration as a function of soil temperature at all sites. Fit is an exponential
curve, derived from the linearized form in equation 1. (b) Respiration as a function of
shallow soil moisture (θs). Note the increase in Reco variability when soil moisture is
above approximately 0.03 m3 m-3. Fit is piecewise linear with a breakpoint at 0.28 m3 m3
.
Figure 4. Reco-Tsoil regressions categorized by soil moisture cases: (a) Case 1, (b) Case 2,
(c) Case 3, and (d) Case 4.
Figure 5. Boxplot of Reco distributions categorized by soil moisture case and vegetation
type.
Figure 6. Revised soil moisture model, after the “bucket” model of Knapp et al [2008].
The solid black line represents average soil moisture while the undulating line indicates
variation around the average. Red dashed lines indicate the upper and lower thresholds
for moisture-controlled ecosystem activity.
Figure A1. Drydown of shallow soil moisture (θ) at the Sev Shrub site. Points indicate
mean soil moisture, error bars are one standard deviation.
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Figure A2. Identification of transition points between negative (uptake) and positive
(release) NEE to define the deep soil moisture threshold (θ*deep) at SRER-M. Gray bars
indicate individual instances of prolonged carbon uptake.
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77
Site
Sevilleta Grass
Sevilleta Shrub
Kendall Grass
SRER Mesquite
SRER Creosote
Reference
[Kurc and Small, 2007]
[Kurc and Small, 2007]
[Moran et al., 2009]
[ Scott et al., 2008]
[ Kurc and Benton, 2010]
Vegetation Type
% Cover
Root depth
(cm)
Veg. height
(m)
Grassland (Bouteloua eripoda)
Open Shrubland (Larrea tridentata)
Grassland (B. eripoda, Eragrostis lehmanniana)
Woody Savanna (Prosopis velutina)
Open Shrubland (L. tridentata)
50
30
40
35
24
100
100
n/r
n/r
n/r
0.3
0.75
n/r
4
1.7
n/r--not reported
1
Soil Type
MAP
(mm)
MAT
(°C)
Soil Moisture Depths
(cm)
loamy sand
sandy loam
sandy loam
sandy loam
sandy loam
230
230
357
310
260
17.5
17.5
17
19
21
2.5, 12.5, 22.5, 37.5, 52.5
2.5, 12.5, 22.5, 37.5, 52.5
5,15,30,50
5,10,20,30,50
2.5, 12.5, 22.5, 37.5, 52.5
Soil Moisture
Sensor
CS500
CS500
TDR100
CS616
CS616
Length of Record
(years)
3
3
4
4
2
(2002-2004)
(2002-2004)
(2004-2007)
(2004-2007)
(2008-2009)
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78
N
Mean (mg m2 s-1)
2422 0.0498 (±0.001)
All
Vegetation Type
924
Grass
423
Shrub
1075
Savannah
Soil Moisture Case
701
Case 1
108
Case 2
499
Case 3
1046
Case 4
Moisture Case-Vegetation
184
Case 1 Grass
165
Shrub
352
Savannah
39
Case 2 Grass
25
Shrub
44
Savannah
255
Case 3 Grass
53
Shrub
191
Savannah
413
Case 4 Grass
145
Shrub
488
Savannah
St. Dev. (mg m2 s-1)
0.0673
0.0370 (±0.002)
0.0457 (±0.003)
0.0625 (±0.002)
0.0461
0.0631
0.0806
a
b
b
0.0255 (±0.003)
0.0635 (±0.002)
0.0292 (±0.006)
0.0737 (±0.003)
0.0347
0.0742
0.0328
0.0829
a
b
a
b
0.0263 (± 0.004)
0.0251 (± 0.005)
0.0252 (± 0.003)
0.0214 (± 0.010)
0.0344 (± 0.012)
0.1174 (± 0.009)
0.0205 (± 0.004)
0.0329 (± 0.008)
0.0398 (± 0.004)
0.0547 (± 0.003)
0.0622 (± 0.005)
0.0933 (±0.003)
0.0337
0.0382
0.0336
0.0277
0.0277
0.0867
0.0244
0.0291
0.0397
0.0566
0.0711
0.0990
a
a
a
a,b
a,b,c,d
c,d
a
a,b
b
b,c
b,c
c,d
78
79
All
Vegetation Type
Grass
Shrub
Savannah
Soil Moisture Case
Case 1
Case 2
Case 3
Case 4
A
0.0189
b
0.0465
r2
0.0877
0.0189
0.0241
0.0209
0.0383
0.0325
0.0436
0.0591
0.0453
0.0678
0.0046 (a)
0.0055 (a)
0.0024 (b)
0.0026 (b)
0.0431 (a)
0.0942 (b)
0.0933 (b)
0.1375 (c)
0.0508
0.4423
0.2115
0.4087
All fits are significant at
p < 0.001
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80
Kendall SRER-M
0.07
0.04
Shallow
0.13
0.06
Deep
SRER-C
0.09
0.1
Sev Grass
0.11
0.11
Sev Shrub
0.09
0.10
80
81
Figure 1.
81
82
Figure 2.
82
83
Figure 3.
83
84
Figure 4.
84
85
Figure 5.
85
86
“Bucket” soil zone
Shallow soil zone
Deep soil zone
Figure 6.
86
87
Figure A1.
87
88
Figure A2.
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89
APPENDIX B: SOIL MOISTURE CONTROLS REMOTE SENSING-BASED
UPSCALING OF CARBON FLUXES IN A SEMIARID SHRUBLAND
Andrew L. Neal1, Shirley A. Kurc1,2, Willem J.D. van Leeuwen2,3, Paul D. Brooks1
for submission to Agricultural and Forest Meteorology
A.L. Neal. Department of Hydrology and Water Resources, University of Arizona, 1133
James E. Rogers Way, Tucson, AZ 85721. (aneal@email.arizona.edu)
S.A. Kurc. School of Natural Resources and the Environment, University of Arizona, P.O. Box 210043, Tucson, AZ 85721 (kurc@email.arizona.edu)
W.J.D. van Leeuwen. School of Natural Resources and Environment, University of
Arizona, 1955 E. 6th Street Tucson, AZ 85721 US
P.D. Brooks. Department of Hydrology and Water Resources, University of Arizona,
1133 James E. Rogers Way, Tucson, AZ 85721. (brooks@email.arizona.edu)
1
Department of Hydrology and Water Resources, University of Arizona.
2
School of Natural Resources and the Environment, University of Arizona.
3
School of Geography and Development, University of Arizona
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90
Abstract
The application of spatially-distributed models of carbon dioxide flux using remote
sensing data is a rapidly developing line of research. Validation of such models requires
linking remote sensing data to ground measurements of carbon dioxide exchange,
generally collected by eddy covariance towers. In semiarid shrublands, this link is
complicated by sparse vegetation cover and temporal variability in vegetation activity in
response to soil moisture. We find that remotely sensed vegetation greenness near the
tower is not strongly correlated to tower CO2 fluxes during dry soil moisture conditions.
We determine that increased deep soil moisture identifies a relationship between
greenness and carbon uptake via photosynthesis (r2 = 0.37 for net carbon flux, r2 = 0.75
for gross primary productivity). The spatial vegetation distribution around the tower is
shown to have little effect on the link between greenness and carbon fluxes. Together the
two analyses enable improved spatially-distributed modeling of carbon flux from remote
sensing data.
Keywords: net ecosystem exchange (NEE), gross primary productivity (GPP), Moderate
Resolution Imaging Spectroradiometer (MODIS), enhanced vegetation index (EVI),
Sevilleta Long Term Ecological Research (LTER) site, ecohydrology
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1. Introduction
Carbon dioxide fluxes, particularly terrestrial fluxes, may substantially influence
atmospheric and biogeochemical cycles (Friedlingstein et al. 2006; IPCC 2007). Spatially
explicit carbon flux estimates can help quantify the net ecosystem exchange of carbon
(NEE) between the land and the atmosphere (Gilmanov et al. 2005; Wylie et al. 2003),
often generated using models of NEE based on satellite remote sensing data, such as
vegetation indices, temperature and leaf area index (Goerner et al. 2011; King et al. 2011;
Wu et al. 2010; Xiao et al. 2010; Zhang et al. 2011). Several approaches to link ground
measurements of NEE to satellite data have produced reasonable carbon flux estimates at
regional to continental scales (Mahadevan et al. 2008; Wylie et al. 2003; Xiao et al. 2011;
Xiao et al. 2010). These models depend on the link between vegetation greenup as
observed by satellites and the subsequent uptake of carbon by photosynthesis (or gross
primary productivity, GPP) (Sims et al. 2006a; Sims et al. 2006b; Wu et al. 2010).
The link between satellite data and NEE is strongest in humid regions (Sims et al. 2008;
Tang et al. 2011), where the predominant limitation to vegetation activity is energy from
solar radiation (Badeck et al. 2004; Jolly et al. 2005). In these humid regions, vegetation
phenology, e.g. leaf-out, is largely determined by seasonal energy availability, and occurs
in synchrony with plant carbon uptake via photosynthesis (Sims et al. 2006b; Tang et al.
2011); Figure 1a). The synchrony of greenup and carbon uptake in humid ecosystems
reduces the complexity of modeling carbon flux based on remote sensing observations.
Further, the relative uniformity of vegetation cover, as opposed to bare soil (Asrar et al.
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1992; Gao et al. 2000), has further simplified the application of simple regression models
linking satellite data to NEE in humid regions (Sims et al. 2006b; Tang et al. 2011).
However, not all ecosystems are energy-limited. For instance, in water-limited semiarid
ecosystems, plant carbon uptake is strongly linked to intermittent moisture pulses
(Huxman et al. 2004; Noy-Meir 1973). Moisture pulses can have very different effects on
soil moisture, and thus on carbon flux, depending on the magnitude of a rainfall event.
For example, small storms, which occur relatively frequently (Sala and Lauenroth 1982)
generally wet the shallow soil and are associated with a net release of carbon dioxide
(Kurc and Benton 2010; Kurc and Small 2007; Scott et al. 2006) Larger, infrequent
storms are required to wet the deeper soil layers (Kurc and Small 2007), which leads to
net carbon uptake (Cavanaugh et al. 2011; Kurc and Benton 2010; Notaro et al. 2010).
While carbon fluxes are closely linked to moisture pulses, vegetation greenness may not
correlate strongly with carbon flux. Desert shrubs under water stress may retain their
green pigment (Hamerlynck and Huxman 2009), but will restrict their photosynthetic
activity in order to minimize transpiration losses (Ogle and Reynolds 2002). Many desert
shrubs, including creosotebush (Larrea tridentata), retain leaves despite lengthy dry
conditions (Lajtha and Whitford 1989) and can respond quickly when moisture is again
available (Franco et al. 1994). Because of these physiological adaptations, NEE and GPP
are often poorly linked to remotely sensed vegetation greenness (Sims et al. 2006a).
Satellite data may not capture vegetation response to moisture over short time scales
(Sjöström et al. 2009), due to the sensitivity of semiarid ecosystems to brief, localized
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storms that yield small amounts of soil moisture (Huxman et al. 2004; Sala and
Lauenroth 1982).
Further complicating the relationship between satellite-observed vegetation greenness
and NEE is that semiarid ecosystems tend to be sparsely vegetated when compared to
more mesic ecosystems (Smith et al. 1990; Xiao and Moody 2005). While the seasonal
pattern of vegetation change in sparse ecosystems has been described (Fensholt and
Sandholt 2003; Xiao and Moody 2005), this sparseness may limit the ability to precisely
resolve ecosystem carbon dynamics following rainfall events. The limitation is likely due
to the complex mixtures and contributions of vegetation and soil spectral reflectance
values, as well as the effects of moisture limitations on vegetation (Sims et al. 2006a;
Turner et al. 2005). A high percent vegetative cover simplifies the interpretation of
satellite vegetation data (Asrar et al. 1992; Gao et al. 2000), because changes in
vegetation index value can be ascribed to vegetation alone. However, the link between
satellite-observed greenness and carbon flux still depends on the density of vegetation,
based on the connection between leaf area and vegetation indices (Fensholt et al. 2004).
The main objective of this study is to improve the utility of remotely sensed data to
upscale carbon data in semiarid ecosystems. We contend that the weak link between EVI
and NEE in semiarid shrublands is due to a disconnect between observable features
(greenness) and plant physiology (GPP, NEE) based on water stress (Figure 1). We
identify when satellite-observed vegetation greenness is more closely related to carbon
flux, as a function of soil moisture. The relationship with deep soil moisture will yield
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reasonable estimates of carbon uptake for the region on the landscape that is consistent
with the tower area.
Specifically, we address the following hypotheses for this Creosote dominated area. First,
we hypothesize that the relationship between greenness and carbon flux (and specifically
between greenness and carbon uptake) will be strongest when moisture is available deep
in the soil, because it is available to vegetation over long periods of time and is not lost to
direct evaporation. Second, we hypothesize that the relationship between EVI and GPP
will identify when satellite-observed changes in vegetation directly relate to carbon
uptake, which we anticipate to occur with deep soil moisture.
To address our hypotheses, we examine the link between satellite data and carbon flux in
the context of a soil moisture classification scheme that defines wet and dry conditions
suitable for carbon flux activity. Wet and dry periods are defined on the basis of physical
processes in the shallow soil (the so called “drydown” period) and ecosystem water stress
in the deep soil (Neal 2012). Using this classification scheme, we isolate periods when
water limitation is relaxed so that observed reflectance predicts carbon uptake, e.g.
following large storms during the wet season. We test these characteristics at a semiarid
upland shrub-dominated site using NEE and GPP data collected via eddy covariance
(Kurc and Small 2007) and EVI data from the Moderate Resolution Imaging
Spectroradiometer (MODIS).
2. Data
2.1. Site Description
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Our flux tower study site is located within the semiarid southwestern United States (US).
The Sevilleta Shrubland (34.3349 °N, 106.7442 °W) is part of the Ameriflux network and
is located in the Sevilleta National Wildlife Refuge in central New Mexico. Mean annual
precipitation at the site is 230 mm with > 50 % of the annual rainfall occurring JulySeptember; mean annual temperature is 17.5 °C (Kurc and Small 2007). The site is
predominantly creosotebush (L. tridentata) and percent vegetative cover is approximately
30%. Vegetation is generally uniform and the terrain is flat for at least 500 m upwind of
the tower, which includes the footprint of the tower (Hsieh et al. 2000).
The site is instrumented for continuous measurements of carbon, water and energy flux
using the eddy covariance method (Baldocchi 2003). Latent heat and carbon fluxes are
directly measured at the tower using an infrared gas analyzer (Licor 7500, Licor
Biosciences, Lincoln, NE) and a three-dimensional sonic anemometer (CSAT, Campbell
Scientific, Logan, UT). Soil moisture data are collected half-hourly using time-domain
reflectometers (CS616, Campbell Scientific) for both canopy and bare soil at five depths
(2.5, 12.5 22.5, 37.5, and 52.5 cm). These values are averaged across all depths to
produce one soil moisture profile for the site. Soil moisture data are later recombined into
shallow (0-20 cm) and deep (20- 60 cm) zones (see section 3.1). Tower flux data were
collected for three years (2002-2004) and include relatively wet (2004, 324 mm) and dry
(2003, 140 mm) years in terms of rainfall, as well as a year of typical rainfall amounts
(2002; see Kurc and Small 2007).
2.2. Remote Sensing Data and Processing
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2.2.1. Vegetation indices
MODIS vegetation index data (MOD13Q1; (Huete et al. 2002)) were retrieved from the
Oak Ridge National Laboratory Data Archive and Acquisition Center (ORNL DAAC,
http://daac.ornl.gov/MODIS/modis.shtml) as 7 km x 7 km scenes. The vegetation index
scenes have a 250-m pixel grid with the central pixel containing the flux tower (see
section 3.1). Data files were downloaded in ASCII text format and processed in Matlab
(Mathworks, Inc.; Natick, MA).
We chose the vegetation index products over other MODIS products (e.g. leaf area index,
land surface temperature) because of its finer spatial resolution (250-m vs. 500-m for leaf
area index and 1-km for land surface temperature). EVI is less sensitive to soil and
atmospheric effects compared to other indices, namely the normalized difference
vegetation index (NDVI; (Huete et al. 2002). MODIS EVI products are available at 250
m resolution in 16-day composite records.
2.2.2. Temporal decomposition
MODIS data composites are designed to ensure that the final data products are of the
highest possible quality with respect to sensor view geometry (Huete et al. 2002).
Because the day of observation is retained in the MODIS Collection 5 composite data
products (Didan and Huete 2006), we “decompose” the images based on that information
to build a time series based on the day of observation and the availability of individual
pixel values.
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By applying EVI on the date the data are recorded, we hoped to better represent the shortterm dynamics of semiarid systems. Decomposition by time stamping the observations
alters the overall variability in the EVI record. We better capture the finer variability in
carbon fluxes associated with short duration events using decomposition, by associating
the remote sensing data with fluxes measured on the same day.
2.2.3. Percent Cover Maps
High resolution (1-m) data from the Airborne Data Acquisition and Registration (ADAR)
5500 (Positive Systems, Inc.; see (Benkelman and Behrendt 1992) was used to generate
fine-scale vegetation cover information. Data were collected on August 23, 2001 in four
channels in the visible and near-infrared spectra (blue, green, red, near infrared).
Supervised classification of the area surrounding the tower was used to identify creosote
shrub cover from bare ground. After classification, the 1-m pixels were averaged up to
the 250-m resolution of MODIS pixels. A more in-depth discussion of the classification
process and results is given in Appendix A. Overall classification accuracy was 92.6%
and the kappa coefficient is 85.1%.
Because of the scale of this data and the generally low growth rates of large, mature
creosote shrubs (Allen et al. 2008; Medeiros and Pockman 2010) we assume the percent
cover mapping based on the ADAR image is representative of the percent cover for the
entire study period. These percent cover data are used in comparison with the MODIS
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EVI data near the tower to determine the importance of increased shrub density on the
EVI-NEE relationship.
3. Approach
3.1. Flux Data Analysis
Tower flux data were collected at 10 Hz and aggregated into 30-minute records. Halfhourly fluxes were then averaged to daily values for comparison to satellite-derived
vegetation indices. At the daily level, molar NEE was converted to mass flux [g m-2 d-1].
Soil moisture data were combined to produce values over two depths: 0-20cm and 20-60
cm. These two soil zones were then classified into “wet” and “dry” conditions. The
shallow soil zone was classified based on drydown curves. Drydown following all storm
events >8 mm were averaged and fit to an exponential model. The terminal soil moisture
value of the mean curve was used as the transition point between wet and dry conditions.
We defined the deep soil moisture threshold on transitions between positive NEE (> 0)
and negative NEE (< 0). Values of deep soil moisture on the first of five consecutive days
of positive NEE following at least five consecutive days of negative NEE were sampled.
Each instance of this transition was recorded, and a mean value of deep soil moisture
under the positive/negative NEE transition was used as the threshold. Five consecutive
days of positive NEE were used to ensure consistent net carbon flux behavior. The
transition indicates that photosynthetic activity is limited based on soil water stress. From
the wet/dry threshold values we can define four distinct soil moisture cases such that
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Case 1 is a fully dry soil; Case 2 is wet in the shallow soil only; Case 3 is wet in the deep
soil only; and Case 4 is wet throughout the soil column.
3.2. MODIS-EVI and Tower-NEE Relationships
The link between vegetation greenness—identified by EVI—and NEE was evaluated
using the Pearson correlation coefficient. Correlations were calculated for each pixel
relative to the tower NEE flux over the three year record at the tower. We expect EVI and
NEE to be negatively correlated, i.e. increasing EVI is associated with decreasing
(negative) NEE, representing carbon uptake by vegetation. EVI-NEE relationships were
also considered for pixels within the tower footprint and from a larger 1.25-km (5 x 5
pixels) area around the tower.
Data also were analyzed in a temporal framework based on the soil moisture cases
described above. In addition, we examine the spatial patterns of correlation that emerge
based on the soil moisture cases. Linear regressions were performed on NEE as a
function of EVI to identify the best model to scale NEE based on the vegetation
characteristics in the tower footprint. Data from individual pixels were used in the
regression analysis, as well as the mean EVI value in the footprint and in the 1.25-km
tower area. Models were selected based on the coefficient of determination and
significance of the fit (only p < 0.05 were retained).
4. Results
4.1. EVI-NEE Relationships
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EVI is not strongly correlated to NEE over much of the study area (Figure 1). Among the
significant correlations, only 15 of the 25 pixels within a 1.25-km grid around the tower
yield significant correlations (p < 0.05, r values range between -0.3 and -0.5). Strong
correlation to tower NEE mostly occurs far from the tower, including a region
approximately 1.5 km to the west, and a region to the southeast (Figure 1). Correlations
are low close to the tower and in the tower footprint, indicating potential effects of
landscape heterogeneity that is represented by the remotely sensed vegetation data.
The time series of NEE and EVI clarifies the temporal patterns associated with EVI-NEE
correlations (Figure 2). EVI roughly follows the trend for NEE during periods of net
carbon uptake (NEE < 0; e.g. October 2002, Figure 2). However, net respiration periods
exhibit poor correlation between NEE and EVI (e.g. 2003, Figure 2). Correlation between
increasing EVI and carbon uptake is clearest in the wet and normal years (2002, 2004)
but weak in the dry year (2003). Increasing EVI and net carbon uptake both follow the
timing of rainfall inputs. Moisture inputs from rainfall drive vegetation activity and net
uptake in the normal and wet years. That correlation is clearest in the wet/dry contrast
following precipitation events in the wet year (2004). In the three years of the study, all
three regions of interest—the tower footprint, 1.25-km tower area and highly correlated
pixels—express similar timing in EVI for the start and end of growing periods.
4.2. Deep Moisture Effects on EVI-NEE Relationships
The vertical distribution of soil moisture improves correlation between EVI and NEE
near the tower under certain conditions (Figure 3). In Cases 1 and 2, when the deep soil is
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dry (Figures 3a, b), the correlation is weak across the study area (mean r = 0.02 and 0.16,
respectively; note that negative r values indicate stronger correlation, as noted above).
When there is moisture deep in the soil, in Cases 3 and 4 (Figures 3c, d), correlation
between EVI and NEE is improved in much of the study area (mean r = -0.40 and -0.38,
respectively). This is especially true in Case 4, when the entire soil column is wet, with
average correlation values near the tower of -0.28, compared to 0.10 in Case 1 (Figure 3,
inset). When the entire soil column is wet, the first MODIS pixel west of the tower,
which contains the largest contributing area to the tower, has a correlation coefficient of 0.64. Higher correlations when deep soils are wet highlight the importance of soil
moisture in identifying the relationship between EVI and NEE.
The improved correlation (-0.64 in the tower footprint compared to -0.38 over the entire
data set), particularly in Case 4, is clear when it is used as the basis for generating direct
relationships between EVI and NEE (Figure 4). Cases 1 and 2, when deep soils are dry,
do not produce clear, significant relationships between EVI and NEE in the footprint or
in the 1.25-km tower region (Figure 4a, b). A slight trend toward increasing carbon
uptake (2 g m-2 d-1 at EVI = 0.15) at higher EVI exists but is not significant. Significant
correlations (p < 0.05) emerge in the footprint region during Case 4, when the entire soil
column is wet, (Figure 4d, Table 1), as well as in the 1.25-km tower region in Case 3
(Table 1).
Using a gross primary productivity (GPP) dataset for this tower (Kurc and Small 2007),
determined as the residual between NEE and modeled respiration, we can examine the
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water limitation effect on vegetation (Figure 5). The tight link between EVI and
productivity when deep soil moisture is available is clear (Figure 5c, d, Table 2). The soil
moisture conditions that led to greater EVI-NEE correlation yield very strong predictive
relationships for GPP as well (r2 = 0.88 based on footprint EVI and 0.95 based on 1.25km tower region EVI). As in the EVI-NEE relationships, we also find a strong trend for
GPP when only the deep soils are wet (Figure 5c, r2 = 0.96 in the tower region).
Combining the two cases that represent deep soil moisture (Cases 3 and 4) we can build
models for NEE and GPP as a function of EVI that reflects the overall carbon response
(Figure 6). These trends provide predictive relationships for NEE and GPP (Table 3).
Deep soils are wet for approximately 26% of the data record in this study, thus this
relationship can be applied to scale tower fluxes for one-quarter of the three year span.
4.3. Spatial Analysis
The performance of EVI-NEE relationships is independent of the percent vegetation
cover in each pixel identified from the ADAR data (Figure 7). Percent cover in the tower
area ranged from 12% to 43%, with an average cover of 25%—similar to the 30%
reported by Kurc and Small (2007) for the study site. Over the range of percent cover
based on the spatially-aggregated ADAR data, EVI-NEE correlation coefficients do not
change significantly in the four soil moisture cases (Figure 7) or overall (not shown).
This insensitivity to vegetation cover is confirmed in the range of EVI values based on
percent cover, which are consistent over the entire data record as a function of percent
cover (not shown). Maximum and minimum EVI values are nearly unchanged as a
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function of percent cover, with a mean minimum EVI of 0.095 and mean maximum EVI
of 0.170 across all percent woody cover values.
5. Discussion
This study assessed the spatial and temporal links between remotely sensed vegetation
activity and carbon flux measured at an eddy covariance tower. The analysis
demonstrates the limitations that exist when using vegetation indices to scale flux
measurements in semiarid shrublands. These limitations include sub-pixel vegetation
heterogeneity and the vertical soil moisture distribution of the region. Analyzing data
based on soil moisture improved correlation between vegetation indices and carbon flux
in the tower area, enabling spatial scaling of carbon flux based on MODIS EVI when the
deep soils are wet.
5.1. Soil Moisture Control on EVI-NEE Relationships
The soil moisture cases are a strong organizing factor in EVI-NEE and EVI-GPP
relationships in terms of their overall predictive power (Figures 4, 5; Tables 1, 2). When
deep soils are dry, the link between vegetation greenness and carbon flux is relatively
weak (Figures 4a, b). Only after moisture percolates to the deep soil is there a link
between EVI and carbon flux (Figures 4c, d, 5c, d). The need to represent moisture
dynamics in remote sensing-based models of carbon flux for semiarid systems has been
previously documented (Sjöström et al. 2011). However, other studies have linked carbon
flux to energy partitioning using the evaporative fraction (the ratio of latent heat flux to
available energy). In applying data regarding the vertical soil moisture profile, we can
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quantify the response of vegetation to moisture directly available to roots (Fernandez
2007).
The link between deep soil moisture and carbon uptake is likely associated with
improved plant access to moisture below the shallow soil zone, where evaporative
demand quickly dries soil (Wythers et al. 1999). With moisture in the deeper soil, the
vegetation responds more strongly both in terms of leaf characteristics and photosynthetic
uptake. This pattern of greater carbon uptake when deep soils are wet is evident in
tower-derived GPP data (Figure 5). This strong correlation underscores the link between
moisture availability and vegetation activity in semiarid shrublands. When the moisture
limitation is relaxed, productivity is more directly related to vegetation greenness, and
productivity also dominates the net carbon flux (Figure 5).
In the cases where the entire soil is dry or only the shallow soil is wet, ecosystem carbon
flux is difficult to predict from EVI. This may be due to various physiological responses
to plant water stress (Cavanaugh et al. 2011; Ignace and Human 2009; Muldavin et al.
2008; Potts et al. 2008) as well as non-plant activity, e.g. soil microbial respiration
(Barron-Gafford et al. 2011). In these periods, carbon flux can either be positive (release)
or negative (uptake) without a clear link between EVI and NEE (Figures 3a, b). As a
result, the correlation between EVI and NEE remains low across much of the study area
during this period (Figure 3a, b). Further study of these periods in the context of
respiration models (Barron-Gafford et al. 2011) and vegetation water use efficiency
strategies (Franco 1994, Ogle 2002), may clarify the drivers of NEE.
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5.2. Spatial Patterns in NEE-EVI Relationships
The spatial pattern in correlation between EVI and NEE indicates that correlation is
generally weak in regions that are close to the tower (Figure 1). This precludes the direct
application of MODIS EVI to spatially scale carbon flux for this semi-arid creosote
dominated system. Nevertheless, the study area can be sub-divided into a region that is
similar in vegetation cover to the tower area. Further, because the EVI-NEE relationship
is not sensitive to the percent vegetation cover, the single model for NEE and GPP based
on deep soil moisture can be used for the entire creosote region irrespective of vegetation
density (Figure 7).
While the seasonal and interannual dynamics of EVI (Figure 2) can be linked to increases
in both leaf area and vegetation cover (Glenn et al. 2008), the relatively low growth rates
for creosote (Medeiros and Pockman 2010) suggest that any changes in percent cover are
more likely associated with small, seasonal herbaceous plants (Muldavin et al. 2008; Xia
et al. 2010). This trend is particularly relevant for the creosote-dominated areas near the
tower, where the high-resolution ADAR data were used to describe percent cover. The
low growth rate of creosote suggests that any potential changes in percent cover from
year to year are likely from the expansion of herbaceous plants. At the same time, the
creosote growth rate and its canopy dominance indicates that greenness detected at the
250-m scale in MODIS EVI on a seasonal basis is predominantly associated with leaf
area.
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Developing a more robust assessment for vegetation cover by plant functional type or
species represents an avenue for further research based on this study that would improve
the ability to generalize tower fluxes. The consistency of EVI under different cover
percentages near the tower suggests that, for creosote-dominated parts of the landscape,
the EVI-NEE relationships may be more closely related to leaf area, which has been
indicated by other studies using EVI (Xiao et al. 2004; Zhang et al. 2005). Identifying
more clearly the change in percent cover and type over time would allow the long-term
application of the EVI-NEE relationship developed here.
5.3. Temporal Patterns in EVI-NEE Relationships
We find the decomposed time series, where data were assigned to the day of observation,
allows for better resolution of the temporal relationships between EVI values at a given
pixel and the daily flux tower record. Because these semiarid ecosystems are highly
sensitive to short-term moisture inputs, the decomposed time series allows us to resolve
the finer temporal variability for individual pixels that produce viable, high quality data
on certain days. Relationships built on these individual pixels enable carbon flux models
that fully capture brief events, such as the wet shallow/dry deep conditions described in
soil moisture Case 2.
The timing of EVI increase and decrease is consistent across all pixels, though the
magnitude of EVI may differ. The best correlated pixels reach maximum EVI values
similar to the footprint, but also have lower EVI during dry periods (Figure 2). This
increased dynamic range improves correlation, although these pixels may not be
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representative of the vegetation that is measured at the tower. Spatial and temporal
differences in vegetation composition and density are not explicitly analyzed here, but
provide a promising avenue to extend this work.
The carbon flux models developed here are dependent on deep soil moisture to clarify the
relationship between MODIS EVI and NEE (and GPP). From a temporal standpoint, the
application of those models is dependent on the frequency and intensity of rainfall that
yields sufficient deep soil moisture. Interannual variation in rainfall patterns, such as
between the relatively dry year 2003 and the wet year 2004, may reduce or increase the
frequency of deep soil moisture, subsequently reducing or increasing the applicability of
the model.
5.4. Application for Carbon Flux Modeling
The soil moisture cases indicate that the vertical distribution of available moisture is
critical to link EVI to NEE and GPP in shrublands (Figures 4-6). This is consistent with
other findings on plant water use in semiarid ecosystems (Muldavin et al. 2008;
Porporato et al. 2002; Weltzin and McPherson 1997) and underscores the complex
connection between vegetation greenness and carbon flux among shrublands.
The improved performance of EVI in predicting NEE under wet conditions in deep soil
suggests the limitations of simple light use efficiency-based models of primary
productivity on these landscapes. While these models may incorporate a water-limitation
component, it is often based on root zone soil moisture (Knyazikhin et al. 1999;
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Mahadevan et al. 2008; Xiao et al. 2011). However, the link between plant greenup and
carbon uptake is not as straightforward in semiarid shrublands. Only when moisture
limitation has been satisfied in the deep soil do we see a clear trend between greenness
and NEE in much of the landscape (Figure 4d, 5d). At other times, water use restrictions
may lead respiration to dominate the NEE signal, and other models will be needed to
appropriately estimate carbon flux.
6. Conclusions
In this study we demonstrate the importance of soil moisture as a limiting factor when
using satellite remote sensing to spatially scale carbon flux data. Using a simple
conceptualization of the vertical soil moisture profile, we isolate deep soil moisture as the
key variable linking vegetation indices to net carbon flux. This finding reinforces the
importance of water availability as the key limitation to carbon flux in semiarid systems.
Because carbon flux is more sensitive to EVI when deep soils are wet, scaling NEE and
GPP is straightforward under those conditions.
By combining the temporal characteristics of deep soil moisture with a spatial analysis of
the vegetation distribution, we demonstrate the ability to resolve some of the
complexities associated with spatially-distributed carbon flux modeling in heterogeneous
semiarid environments. Among the complicating effects associated with modeling carbon
flux from EVI, we demonstrate methods to isolate moisture conditions from greenness
and resolve sub-grid variability in vegetation conditions for a semiarid shrubland. While
the availability of deep soil moisture is limited in time—approximately 25% of the time
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series includes wet conditions in the deep soil—it provides a direct, mechanistic approach
to resolve the effect of water limitation on the light-use efficiency construct that underlies
EVI-NEE and EVI-GPP relationships.
Having identified the period when water is not limiting, further studies applying speciesspecific water use efficiency information could improve carbon flux estimation when
deep soils are dry. For example, applying stomatal conductance or plant water potential
data for creosote in conjunction with flux tower measurements would connect the
landscape processes described here to the plant-level response.
Acknowledgements
K. Hartfield in the Arizona Remote Sensing Center assisted with the processing of
ADAR data. S. Archer, D. Bunting, Z. Guido, K. Nelson, and Z. Sanchez-Mejia
improved previous drafts of the manuscript. Ameriflux data were used under the fair-use
policy. Funding for collection of data at the Sevilleta Long Term Ecological Research site
was provided by the National Science Foundation Long Term Ecological Research
program.
Appendix A. Vegetation Cover Accuracy Assessment
We classify shrub and bare soil areas on the landscape with a supervised classification
scheme based on 1-m ADAR data. While line-intercept vegetation transect data are
available near the tower, they do not include the creosote-dominated region immediately
upwind of the tower, which is the focus of this study. As a result, the accuracy
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assessment for the classification was performed using a subset of the training data, based
on user-identified pixels. While this approach may lead to “optimistic” accuracy values,
i.e. an overly favorable estimate of classification, the classification performed was
intended to provide a simple estimate of vegetation cover. Differentiation between types
of cover is relatively restricted, since the classification was focused on a region that is
known to be predominantly shrub cover. Performance statistics for the classification are
given in Table A1.
This classification was then be resampled to the MODIS EVI 250-m scale as percent
cover. While complications may arise associated with identification of members for
classification groups and edge effects, this analysis gives us a first-cut at relating carbon
fluxes to percent cover on the landscape. The percent cover in the tower area ranged from
12 to 43%, with an average cover of 25%--similar to the 30% reported by Kurc and Small
(2007) for the study site. More research is necessary to provide detailed vegetation
information along with spectral data to build spatially extensive, fine scale land cover
maps for the entire site.
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120
Case 1
Case 2
Case 3
Case 4
best pixel
mean
best pixel
mean
best pixel
mean
best pixel
mean
β0
0.39
2.70
-3.29
-4.29
-36.93
-2.21
5.85
1.62
Footprint
β1
14.02
-8.10
29.27
36.87
273.37
20.22
-53.97
-22.49
r2
0.00
0.00
0.06
0.05
0.23
0.01
0.41
0.11
β0
-11.24
-0.23
-9.32
-6.16
67.73
5.57
3.23
0.47
Tower Area
β1
128.75
17.61
97.86
62.28
-503.33
-42.12
-33.17
-11.46
r2
0.37
0.01
0.54
0.13
0.78
0.13
0.49
0.01
Table 1. Linear regression coefficients and coefficient of variation for regressions of
NEE on EVI. Regressions were based on pixels from the two pixels in the tower
footprint and the 5 x 5 pixel area around the tower. Best pixel coefficients represent
the pixel that has the best fit for each soil moisture case. Mean values are based on the
average EVI for the footprint or tower area. Bold indicates regressions are significant
(p < 0.05).
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121
Case1
Case2
Case3
Case4
best pixel
Mean
best pixel
Mean
best pixel
Mean
best pixel
Mean
β0
-1.15
-1.30
-1.73
-0.18
-4.63
-2.97
-4.30
-4.18
Footprint
β1
12.10
13.58
17.68
5.97
39.92
26.76
43.00
42.82
r2
0.15
0.14
0.18
0.01
0.64
0.25
0.88
0.85
β0
-5.74
-0.53
-3.76
0.23
-5.31
-3.44
-3.16
-3.03
Tower Area
β1
56.73
7.37
32.93
1.67
45.35
30.64
34.86
32.72
r2
0.35
0.02
0.50
0.00
0.96
0.39
0.95
0.54
Table 2. Linear regression coefficients and coefficient of variation for regressions of
GPP on EVI. Best pixel coefficients represent the pixel that has the best fit for each
soil moisture case. Mean values are based on the average EVI for the footprint or
tower area. Bold indicates regressions that are significant (p < 0.05).
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122
NEE
best pixel
Mean
β0
6.30
2.02
GPP
best pixel
Mean
-6.21
-6.25
Footprint
β1
-53.73
-21.00
55.91
54.94
r2
0.25
0.04
β0
6.73
1.77
Tower Area
β1
-56.64
-19.63
r2
0.37
0.07
0.60
0.63
-5.95
-4.42
53.05
40.47
0.78
0.54
Table 3. Regression coefficients and coefficients of variation for NEE and GPP
models based on the presence of deep soil moisture. The “best pixel” model for NEE
and GPP is identified as the best model based on the highest value of the coefficient
of variation.
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123
Number in Class
Training
Reference Accuracy
Number of Samples
Vegetation
Bare
Vegetation
95.8
2038
1952
86
Bare
93.8
2142
133
2009
4180
2085
2095
93.6
95.9
Total
Reliability
Number in Class
Test
Reference Accuracy
Number of Samples
Vegetation
Bare
Vegetation
93.3
705
658
47
Bare
91.8
705
58
647
1410
716
694
91.9
93.2
Total
Reliability
Overall training performance = 94.8%. Training κ = 89.5%. Overall test performance =
92.6%. Test κ = 85.1%.
Table A1. Accuracy assessment statistics for the training and test periods of the
supervised classification used to define vegetation cover.
123
124
Figure 1. Spatial plot of correlation coefficient between tower NEE and MODIS EVI.
Negative values indicate increasing correlation. Pixels that had correlations that were
not significant at p < 0.05 are set to zero.
124
125
Figure 2. Time series of NEE (gray bars, left axis), precipitation (black bars, right inset axis) and EVI (right axis). Average values
of EVI are shown for the tower footprint (open circles) and the 1.25-km region around the tower (points). Both regions show a
general trend of increasing EVI slightly before the start of carbon uptake (NEE < 0). Missing EVI data in 2003 are due to quality
control issues on the MODIS sensor.
125
126
(a)
(b)
(c)
(d)
Figure 3. Correlation coefficients between tower NEE and MODIS EVI based on soil
moisture cases 1 (a), 2 (b), 3 (c) and 4 (d). Inset in Figure 4d shows the values for the
1.25-km region surrounding the tower, which are greater in Case 4 than in other cases.
127
Figure 4. NEE as a function of EVI for soil moisture cases 1 (a), 2 (b), 3 (c), and 4 (d).
Points represent the EVI from the pixel with the best fit (see Table 1) plotted with NEE
measured at the tower. Gray bars indicate the standard deviation of EVI for all pixels in
the 5 x 5 pixel area around the tower. Dashed lines show the linear regression of NEE as
a function of EVI, with reported coefficient of determination and p-value. The positive
trend in NEE in the upper figures and negative trend in the lower figures is clear on the
individual pixel level.
128
Figure 5. Gross primary productivity as a function of EVI for soil moisture cases 1 (a), 2
(b), 3 (c), and 4 (d). Trends in GPP as a function of EVI are clearer for an individual pixel
rather than based on the mean trend (see Table 2), with a slight decrease in GPP with EVI
when deep soils are dry and increasing GPP with EVI when deep soils are wet.
129
Figure 6. NEE (top) and GPP (bottom) as a function of tower area EVI. Circles are when
deep soils are dry, triangles when deep soils are wet. Linear fits are based on the wet deep
soil conditions. Data points are for the individual pixel that has the highest coefficient of
determination (see Table 3).
130
Figure 7. Correlation between EVI and NEE as a function of percent cover in each 250-m
pixel, under each of the four moisture conditions (arranged as in Figure 3). The link
between EVI and NEE shows little variation based on percent cover irrespective of soil
moisture conditions.
131
APPENDIX C: LATERAL HYDROLOGIC REDISTRIBUTION SUBSIDIZES
CARBON FLUX IN SEMIARID LANDSCAPES
Andrew L. Neala, Paul D. Brooksa, Shirley A. Kurca,b
a
Department of Hydrology and Water Resources, University of Arizona
1133 James E. Rogers Way, Tucson, AZ, USA.
2
School of Natural Resources and Environment, University of Arizona,
Biological Sciences East, Tucson, AZ, USA
for submission to Advances in Water Resources
Abstract
Lateral hydrologic redistribution controls the spatial availability of resources for
ecosystem productivity, but generally is not considered in models of carbon flux. By
linking simple models of lateral hydrologic redistribution and ecosystem carbon
dynamics, we explicitly quantified the effect of spatially variable moisture on carbon
flux. Simulations were performed on nested subwatersheds at Walnut Gulch
Experimental Watershed in southern Arizona, USA. Lateral hydrologic redistribution
approximately doubled plant available water near the watershed outlet relative to
precipitation alone. Net carbon uptake was nearly eight times higher at high topographic
index (TI), i.e. near the outlet, compared to low TI. Integrated across catchments,
hydrologic redistribution resulted in 25-120% higher carbon uptake relative to a model
that did not consider redistribution. Precipitation characteristics also influenced carbon
uptake via runoff and rainfall magnitude was more important than interstorm interval on
132
carbon. These results indicate that spatial heterogeneity in moisture availability controls
carbon dynamics based on TI.
1. Introduction
Terrestrial carbon dioxide fluxes are an important component of global biogeochemical
cycles [1, 2] but responses to climate, particularly changes in the amount, timing, and
duration of moisture available for vegetation, are unclear [3-5]. The spatial and temporal
availability of plant available water is controlled by the timing, duration, and intensity of
rainfall and infiltration [6, 7] which have received significant attention over the last
decade [7, 8]. Topographically driven redistribution of runoff also affects plant available
water, but largely has been studied from the standpoint of understanding flowpaths and
residence times of streamflow [9, 10], with less attention paid to how this affects
moisture availability to vegetation [11, 12]. Observations of available moisture at the
watershed level indicate that vegetation optimizes water use across climate regimes [1316], suggesting that catchments offer a natural study domain for quantifying
ecohydrological interactions. Refining these relationships between rainfall, available
moisture, and carbon dioxide flux requires coupling hydrologic and ecological processes
within a watershed, where moisture availability is dynamic in space and time [17].
Semiarid ecosystems are particularly sensitive to climate [18], with carbon flux generally
limited to brief periods following rainfall, often described as “pulses” [19-21]. This
sensitivity has led to insights into how semiarid vegetation is distributed on the landscape
133
[22, 23] and how the ecosystem responds to moisture dynamically across scales [24, 25].
The explicit representation of lateral moisture redistribution following intense rainfall
may indicate a different carbon flux response at the catchment scale than predicted
without incorporating redistribution [26].
Lateral redistribution of moisture is largely controlled by the topography and connectivity
of a catchment and supplements precipitation-derived moisture at points lower along a
flowpath [27, 28], particularly in semiarid systems [29, 30]. As a result of this moisture
redistribution, the topography of a catchment is linked to hydrologic partitioning [31],
respiration [32] and reduced plant water stress [33]. Therefore, moisture and net carbon
flux are associated with geomorphic characteristics, such as topography. Moisture
redistribution also promotes feedback loops for vegetation [28, 34-37], where vegetation
structure enhances nutrient and moisture availability below the canopy [35, 38-40].
Concentration of resources can, in turn, support enhanced uptake and release of carbon
[41-43]. Landscape connectivity joins patches to hillslope and catchment scales [44, 45],
and provides the mechanism to concentrate resources along flowpaths [28, 46, 47]. Thus,
spatial redistribution of moisture alters resource availability within a watershed.
While previous research has shown greater rates of carbon dioxide uptake in areas that
concentrate soil moisture from runoff [23, 26] and reduced rates of carbon dioxide uptake
in areas that generate runoff [27], most models of carbon flux in semiarid systems do not
consider moisture redistribution explicitly at the catchment scale [48, 49]. This is based
134
on the assumption that runoff infiltrates along relatively short distances [50, 51].
However, intense storms characteristic of monsoon precipitation dynamics in semiarid
systems often produce runoff due to induced soil anisotropy, increasing overland flow
over long distances [28, 52]. As a result, runoff-based moisture redistribution is likely to
be more significant during the wet season. In order to better understand carbon dynamics
in semiarid ecosystems, lateral moisture redistribution and carbon flux should be linked
in a simple model framework that captures runoff behavior at appropriate spatial scales.
The objective of this paper is to quantify spatially explicit carbon fluxes resulting from
moisture redistribution. Here, we combine a topographically-driven model of moisture
routing with a simple model of vegetation activity to quantify the spatial distribution of
carbon flux. We use a distributed runoff approach that routes moisture based on a 30-m
grid [53] with a simple runoff process. We hypothesize that explicit accounting of
moisture redistribution will indicate spatial heterogeneity in vegetation-driven carbon
dynamics currently underrepresented in most carbon flux models. We determine the
influence of moisture redistribution on ecosystem carbon fluxes using a simple model of
carbon flux response to moisture inputs [54]. We discuss these results in the context of
topographic structure and interannual precipitation variability.
2. Methods
2.1. Site Description and Data
135
In this study, we used digital elevation data and daily rainfall and runoff data from the
Walnut Gulch Experimental Watershed (WGEW) in southern Arizona. Data span six
years (1999-2004) representing a range of wet and dry conditions (data archived at
www.tucson.ars.ag.gov/dap). WGEW is located at the ecotone between the Sonoran and
Chihuahuan deserts and is characterized by a mix of desert shrub and grass species [55].
Mean annual temperature is 17°C [56] while annual precipitation is approximately of 350
mm [57] and falls primarily during the North American Monsoon from July through
September [58]. Annual discharge from the outlet of WGEW typically is <1% of
precipitation, indicating that most of the precipitation is lost to evapotranspiration [59].
Runoff at WGEW is generally infiltration-excess as opposed to saturation-excess [60].
We focused this study on subwatersheds 102, 104 and 106 in the Lucky Hills shrubland
at WGEW (Figure 1, inset), because the subwatersheds are instrumented with runoff
flumes, are nested, and their spatial scale ensures minimal effect of spatial heterogeneity
of rainfall and runoff generation [29]. Lateral redistribution of moisture was determined
using runoff data measured at flumes in the WGEW subwatersheds [59].
2.2. Hydrologic Redistribution
We quantified catchment-scale hydrologic redistribution and available water using a
distributed modeling approach. Data from the flumes at the outlet of each subwatershed
were used to quantify the runoff ratio (runoff over precipitation) from individual rainfall
events. Because of the relatively small spatial extent of the three subwatersheds, we were
able to use precipitation data from a rain gauge in subwatershed 106 to represent the
136
entire watershed. Additionally, because runoff is primarily infiltration-excess in our study
area, runoff was calculated as a fraction of current rainfall above a minimum threshold
and was routed through the watershed rapidly as a function of topography [25],
Topographic characteristics of the watershed were developed based on the topographic
index (TI). TI characterizes the relative balance between runoff lost from any point on the
landscape relative to the flow into that point from upslope [61]. TI was derived for our
subwatersheds from a 30-m digital elevation model (DEM) as:
⎛ A
TI = ln⎜⎜
⎝ tan β
⎞
⎟⎟
⎠
(1)
where A is the area upslope of a pixel and β is the slope at that pixel using the Spatial
Analysis tools in ArcGIS (v. 9.3, ESRI, Inc., Redlands, CA).
A general runoff scheme [e.g. 62] was applied to simulate runoff for each 30-m grid cell.
The water balance for each cell is based on the following equation:
Q = k (P + QU )
(2)
where Q is the runoff from a given grid cell (mm), P is daily precipitation (mm), QU is
the runoff from all contributing cells (mm) and k is the runoff ratio as a function of TI.
Values of k were identified empirically based on runoff ratios (Q/P, determined from
daily Q data for each subwatershed and P from the raingauge in the watershed) for all
subwatersheds at Lucky Hills. The scale-dependence of runoff volumes was satisfied by
linking k to TI [63-65], similar to a scaled runoff coefficient approach used in other
137
studies [e.g. 66]. Runoff was routed along flowpaths determined from the DEM using the
flow direction tool in ArcGIS (v. 9.3, ESRI, Inc., Redlands, CA). Runoff model
performance was measured against flume data at the outlet of the three subwatersheds
using the root mean squared error (RMSE). Low values of RMSE indicate good estimates
of runoff over the whole watershed.
In our model, the effective precipitation (Peff) is defined as the depth of moisture
remaining on a given 30-m cell after runoff is removed, i.e.:
Peff = P + QU − Q
(3).
Peff is used as the input for carbon flux modeling with runoff, i.e. Peff replaces P in
equations 4 and 5. To compare results of the runoff model to measured water balance, the
available moisture (P-Q) and fractional available moisture, (P-Q)/P, were determined for
the three subwatersheds and compared to Peff at the outlet of each subwatershed.
Variability in Peff over the entire watershed was confirmed to be conservative in each year
of simulation, with area-weighted ΣPeff/P ≈ 1.
2.3. Carbon Flux
To quantify the importance of moisture redistribution in the dynamics of carbon dioxide
flux, we developed a simple model of ecosystem response to rainfall inputs. This model
is based on a conceptual relationship between the magnitude of a rainfall event and the
duration of ecosystem (vegetation and microbial) activity that follows [67]. The duration
of microbial and other heterotrophic respiration (Rh) is calculated as:
138
Dh = min ( 0.2P , 2 )
(4)
where Dh is the duration of Rh in days. Similarly, the duration of carbon uptake by
photosynthesis (Da, in days), termed gross primary productivity (GPP), and autotrophic
respiration (Ra) are determined as:
⎧⎪ min(0.17P + 0.14, 7)
Da = ⎨
⎪⎩
0
P≥5 .
P<5
(5)
Equations 4 and 5 were used to simulate ecosystem activity on the 30-m grid under no
runoff conditions (as above) as well as with runoff included (i.e. Peff instead of P).
Carbon flux values for shrub functional types [54] were used to calculate GPP, Ra and Rh
under “relaxed” and “active” states. Active states occur when Da and Dh are greater than
zero; otherwise GPP, Ra and Rh are at their relaxed values. Our model focuses on the
photosynthesis and respiration responses to growing-season rainfall inputs only when the
canopy is assumed to be fully developed (July-September) and does not incorporate the
growth of new leaf area at the start of the season [54]. Using this assumption, we expect
to capture the majority of carbon flux for these semiarid systems which receive most of
their rainfall during the growing season (July-September) [67]. Estimates of GPP, Ra and
Rh were combined into the net ecosystem exchange of carbon dioxide (NEE = -GPP + Ra
+ Rh) based on the sum of each of the three fluxes for the growing season.
The model assumes that vegetation is uniformly distributed and static. Therefore all
points in the watershed have the same carbon flux magnitudes (i.e. the same values of
139
GPP, Ra and Rh) under relaxed and active states and the flux values do not vary between
years. Any two points can differ in relaxed or active states based on the variability in Peff
(equation 3). Each point under a given relaxed or active state will have the same flux rate,
but the number of days that any point is active (Da > 0 and/or Dh > 0) can vary. The
model treats each year independently rather than serially based on vegetation dynamics.
It simulates the expected gains and losses in carbon on the basis of moisture
redistribution but does not apply those changes as vegetation growth or soil carbon loss.
Whether carbon flux results in changes to leaf area, patch density and/or soil carbon is
outside the scope of this model.
3. Results
3.1. Moisture Redistribution
Growing season precipitation ranged between 159 mm and 274 mm from 1999-2003
(Table 1), the period during which runoff data were consistently available for all three
subwatersheds. Catchment water balance for the three subwatersheds indicates that much
of the water that enters the subwatersheds during the growing season via precipitation is
retained, i.e. not lost to Q. Runoff ranged from 1 mm to 48 mm, generally with higher
runoff depths in the upper subwatersheds 102 and 106, compared to the lower
subwatershed 104. Fractional available moisture, (P-Q)/P, ranged between 0.81 and 0.99
in subwatershed 104. The distributed runoff model operating on a per-pixel basis
generated flow estimates at the outlet of the three subwatersheds comparable to measured
Q (Table 2). Root mean squared error (RMSE) for runoff in subwatershed 104 was 0.40
140
mm, but smaller subwatersheds had greater error (RMSE = 0.46 mm in subwatershed 102
and RMSE = 0.71 mm in subwatershed 106, Table 2). Mean errors relative to measured
flow were 14%, 7% and 6% for subwatersheds 104, 102 and 106, respectively, indicating
this simple model captures topographically driven moisture redistribution.
Areas of the watershed with TI values less than 7.5 experienced a deficit of available
water relative to P (Peff < 1.0), while areas with TI values larger than 7.5 exhibited next
subsidy of water with upslope (Figure 2). Areas with the highest TI values had Peff
approximately double the seasonal P demonstrating the role of moisture redistribution
from low TI to high TI along flowpaths concentrating moisture in downslope locations.
Overall, the majority of the catchments (~43%) were net sources of runoff with Peff <1.0,
approximately 41% exhibited Peff near 1.0 and relatively small parts of the watershed
(15%) at high TI received a greater amount of moisture than they would directly from
rainfall.
3.2. Carbon Flux Modeling
The duration of autotrophic activity increased with increasing TI (Figure 3) from a low of
approximately 25 days to a maximum of 75 days. A logistic fit best represents this trend
(Figure 3), because Da has a lower limit based on the minimum Peff in the catchment and
a maximum based on the accumulation of flow near the watershed outlet and the duration
of the summer growing season (100 days).
141
Similarly, NEE was more negative, i.e. carbon uptake was greater, with increasing TI.
The range of NEE values span positive and negative values of NEE up to TI = 8.5, above
which NEE was always negative within the range of annual precipitation values (83274mm). NEE appears to follow a logistic shape, increasing toward a limit with
increasing TI, which is expected given its relationship to Da Individual logistic curves
for each year (Figure 5) vary in their upper and lower bounds and the inflection points
shift from TI = 9.3 to TI = 11.2 between wet (year 1999) and dry (2004) conditions.
Interestingly, interannual variability in NEE is lowest at TI = 6.3 (72 g m-2 season-1) but
was highest at TI = 10.3 (242 g m-2 season-1, Figure 5).
NEE in the driest year varied between 20 and 29 g m-2 season-1 among the subwatersheds.
However, NEE under dry ecosystem conditions was 10 g m-2 season-1, suggesting that
small rainfall amounts stimulate net carbon loss. Interannual range of NEE varied
considerably as a function of TI, increasing from 72 g m-2 season-1 at TI = 6.3 to 222 g m2
season-1 at TI = 10.3 (Figure 7). Although TI is linked to the runoff/runon
characteristics at each pixel, flow connectivity can also be used to describe the effect of
moisture redistribution on carbon flux. As more pixels contribute runoff to areas of flow
convergence, Peff increases. This is reflected in the increase in carbon uptake as a function
of relative connectivity (Figure 7). Runoff connectivity also increases the variability in
NEE on an interannual basis (Figure 7) reaching a peak of 230 g m-2 season -1 at 45% of
maximum connectivity.
142
Comparing results of the carbon flux model with and without runoff, lateral redistribution
results in higher rates of carbon uptake in the three subwatersheds compared to the model
without runoff (Figure 8). As growing season precipitation decreased, the model with
hydrologic redistribution exhibits greater change in NEE relative to the model without
runoff, i.e. the slope of the linear trend is steeper when runoff is included than when it is
not included. The component fluxes of NEE, GPP, Ra and Rh, increase with increasing
TI. GPP and Ra grow at a faster rate compared to Rh, and thus dominate the net carbon
flux (Figure 9). For example, Rh varied between 220 and 225 g m-2 season-1, while GPP
varied between 456 and 827 g m-2 season-1 based on TI. As a function of seasonal P, the
insensitivity of Rh leads to greater carbon loss (Ra + Rh) in the driest year. Over the six
study years, mean watershed Rh varied between 187 and 263 g m-2 season-1 while GPP
varies from 288 to 541 g m-2 season-1.
4. Discussion
4.1. Runoff and Carbon Flux Modeling
Available moisture, P-Q, reflects the water retained in a catchment that is available to
infiltrate and thus promote ecosystem function [14]. Our model indicates areas within a
watershed where runoff contributions yield substantially more available moisture than
precipitation (Figure 2). By simulating the water balance on 30-m pixels, we identify the
influence of spatially explicit flowpaths on Peff within each of the three nested
subwatersheds. This relaxes the assumptions of uniform catchment-scale moisture [14,
143
16, 68, 69] and simple bifurcating flowpath models [31] to simulate carbon dioxide
exchange along physically-based flowpaths.
Our analysis shows that available moisture from the runoff model is comparable to
measured available moisture in the three subwatersheds (Tables 1, 2). Substantially
greater volumes of moisture (Peff > 2) become available via upslope runoff because of
flow convergence [63, 70] particularly close to the watershed outlet (Figure 2). This
runoff infiltrates downslope because of enhanced infiltration rates [11, 71]. Runoffvegetation feedbacks have been observed at patch scales [22, 72] and linked to increased
infiltration from runoff [39, 73-75]. The role of vegetation in controlling infiltration is not
directly considered in this study, but areas where moisture is retained (i.e. at high TI
where Pfrac> 1, Figure 2) support longer periods of vegetation activity (Figure 3).
Values of Da indicate the effective length of the growing season based on the availability
of moisture (as Peff) such that the lowest regions in the catchment are active for
approximately 50 days longer than those at the top of the watershed. Longer growing
periods result in an increase in the rate of carbon uptake relative to upslope areas (Figure
3). The interannual variability in Da (and NEE) establishes the upper and lower bounds of
vegetation carbon flux activity that result from different rainfall amounts. Values of mean
Da (ranging from 25 to 75 days) indicate vegetation activity is dependent on topography.
The model shows that areas lower in the watershed support longer periods of vegetation
activity because the accumulated runoff from upslope increases Peff and therefore
144
increases soil moisture via infiltration. The shape of the logistic curve of Da describes
how the landscape structure drives ecosystem response through runoff. For example, the
steep increase in Da between TI = 8 and TI = 10 reflects the consistent accumulation of
moisture in these areas. The relative rate of moisture accumulation diminishes above TI =
10 as less runoff is generated and more moisture is stored. Thus, the rate of increase in Da
slows per unit value of TI.
The runoff model used here yields reasonable estimates of runoff (i.e. low RMSE and
relative error) based on subwatershed outflow estimates and indicates that redistribution
of moisture to downslope areas increases the amount of water available for ecosystem
use. However, we note that accumulation of moisture in this study is accounted for as
precipitation, not as infiltrated moisture, which is treated implicitly. Spatial differences in
soil moisture have been identified across a range of soil-vegetation combinations [50, 76,
77], due to soil characteristics (e.g. hydraulic conductivity) and vegetation effects (e.g.
macropores and preferential flowpaths) influencing infiltration. These moisture patterns
directly affect plant water and nutrient availability [78-81]. Site-level differences in
vegetation and soil properties may yield different sensitivities in the response of carbon
flux to moisture, e.g. reduced sensitivity of vegetation to moisture inputs on fine-textured
soils [80]. To validate the results from our study in other environments, detailed
examination of the physical and vegetation effects on spatial moisture heterogeneity is
needed.
145
4.2. Topographic Distribution of Carbon Fluxes
Prior efforts to model carbon dioxide flux response to “pulse”-type moisture inputs have
not dealt with moisture redistribution directly [48, 49, 82]. Using our model, net carbon
uptake is lower at low TI (Figure 4), and greater at high TI (maximum mean flux 200 mg
m-2 over the growing season). Low TI regions may be net sources of carbon dioxide to
the atmosphere during dry conditions, primarily due to Rh consuming carbon previously
stored in soil [83]. Based on the structure of our runoff model, TI predicts spatially
distributed carbon fluxes (Figure 4) resulting from moisture redistribution. Topographic
controls on net carbon flux shown here are similar to observed and simulated vegetation
patterns from other studies, which indicate increased vegetation activity where moisture
(and other resources) are concentrated along hillslopes [22, 27, 31, 32, 35].
We demonstrate interactions between topography, flow routing, and NEE, on the basis of
moisture redistribution that are conceptually similar to continental scale work on
hydrologic partitioning that relates topographic structure to a dimensionless metric of
catchment hydrologic partitioning, the Horton index [16, 31]. The Horton Index, which is
the ratio of catchment-scale ET to plant available water, varies spatially due to moisture
redistribution along a flowpath [31] and by vegetation type [13]. The increase in carbon
uptake lower in the watershed is conceptually consistent with increased partitioning in
favor of evapotranspiration [31], linking the increase in vertical flux of moisture to
carbon uptake along flowpaths.
146
The spatial distribution of NEE rates also varies based on the degree of hydrologic
connectivity, the relative length of all flowpaths contributing to a point (Figures 6, 7).
These results suggest that quantifying hydrologic connectivity is necessary to simulate
ecosystem function because total watershed net carbon flux rates are between 25% and
120% lower (i.e. indicating more carbon uptake) than when runoff is not considered.. As
contributing area increases, mean carbon uptake increases, and the range of NEE
increases (Figures 6, 7). Hydrologic connectivity is particularly important to represent
hydrologic features in semiarid landscapes [63, 84] by quantifying the potential runoff
contribution to each point following a storm.
4.3. Carbon Flux Response to Interannual Precipitation Variability
The frequency and timing of large storms in 2000 (P = 237mm) led to more runoff and
thus more carbon uptake near the watershed outlet, where the effect of runoff generation
is strongest, (-274 gCO2 m-2 season-1) than during the wettest year (1999; NEE = -260
gCO2 m-2 season-1, P = 274mm). Greater C uptake in the year 2000 primarily occurred
due to moisture subsidy in areas of high TI values following larger storms. For example,
the year 2000 had a larger mean storm compared to 1999 (6.6 mm and 6.2 mm,
respectively) but the average interstorm period was 3.9 days (compared to 2.7 in 1999).
These observations emphasize the importance of the stochastic nature of rainfall on
ecohydrologic function in drylands [7, 85], and further how rainfall interacts with
landscape and lateral hydrologic redistribution
147
Prior studies of ecosystem sensitivity to rainfall indicate larger carbon fluxes (GPP, Ra
and Rh) when total P increases [4, 86], but that increase is also dependent on the
frequency and magnitude of storms [8, 87]. The change in carbon fluxes is further evident
when runoff is incorporated into the model framework (Figures 4-8). By including runoff
when simulating carbon dynamics, our results suggest that NEE becomes more sensitive
to growing season P (Figure 8), evident from steeper slopes of P-NEE relationships
compared to NEE estimated without runoff. Future climate states are expected to have
altered P regimes, including reduced seasonal P or reduced storm frequency [88, 89].
Our results suggest changes in precipitation could reduce carbon uptake more than
expected without considering runoff (Figure 8). For example, total rainfall in 2002 was
40 mm less than in 2000, but the average storm was approximately 0.5 mm larger and
average interstorm periods were similar (3.7 and 3.8 days respectively). However, the
distribution of interstorm periods is more strongly skewed toward shorter interstorm
periods (0-2 days) in 2000 compared to 2002 (not shown). Carbon uptake was
approximately 10% greater in 2000 at the watershed outlet (Figure 4). Alternatively,
comparing years with similar interstorm distributions (2002 and 2003) but different mean
event size (7.1 mm and 5.7 mm, respectively), carbon uptake rates at the watershed outlet
are 85% greater in 2002. While details of P characteristics and their effect on NEE are
not the focus of this study, results from this limited dataset indicate the reduction in mean
storm size may have a greater effect on carbon dynamics than the duration between
storms. Further study of the complexities of P dynamics on runoff generation and
148
ecosystem response is needed to fully describe the connections between storm size,
interstorm duration and carbon flux.
Enhancement of Rh occurs in the driest year of the study (2004) leading to net carbon loss
from the ecosystem in both the runoff and no runoff models (Figure 7). But contrary to
the minimal effect of runoff anticipated from patch studies [54], our model shows that
lateral moisture redistribution supports carbon uptake lower in the watershed (Figure 4)
due to small amounts of runoff accumulating downslope. Because GPP is more sensitive
to moisture than Rh (Figure 9), the accumulation of small runoff volumes may be
sufficient to reverse an area from a source to a sink of carbon based on high connectivity
near the bottom of the watershed (Figure 7; [90]).
The link between Peff (Figure 2) and longer growing periods, based on Da (Figure 3) may
also indicate regions where vegetation may better resist climate stress because more
moisture is available. Only locations with TI > 10 were net carbon sinks in the driest year
(2004, Figure 4); the remainder of the watershed was a net source of carbon to the
atmosphere. If climate shifts toward less frequent storms and lower seasonal P, i.e.
toward rainfall regimes similar to year 2004, then locations at TI > 10 are more likely to
sustain carbon uptake (Figures 4, 8) compared to locations that are consistently water
stressed.
4.4. Understanding Semiarid Landscapes Across Scales
149
This study estimates the spatial heterogeneity in carbon dioxide that may not be captured
at larger scales, e.g. in studies of regional models or catchment water balance, or that is
lost using integrated measurements, e.g. eddy covariance [13, 91, 93-95]. These carbon
flux estimates bridge the spatial resolution gap from plot and patch scale studies, such as
investigations of landscape evolution and vegetation pattern [22, 96], to larger scales. By
applying this approach at an intermediate spatial scale, several new avenues for study
become available. These include comparisons among patch vegetation dynamics [22, 23],
plant hydraulic behavior [97-99], landscape models of water and nutrient distribution [91,
100] and large-scale coupling of hydrologic partitioning and vegetation [13, 16].
Our results indicate a scale-dependence in carbon dioxide flux that is similar to the scaledependence of catchment hydrologic partitioning and vegetation [31], and both trends
depend on the accumulation of moisture along flowpaths. Trends of NEE as a function of
TI (Figure 5) suggest that NEE stabilizes between -250 and -300 g m-2 season-1 at TI > 12
in the wettest years. In the driest year, NEE stabilizes at -100 g m-2 season-1 around TI =
14. Both of these TI thresholds suggest that the scale-dependence of NEE is minimal at
scales slightly larger than the Lucky Hills subwatersheds (4.5ha). Larger scales are
required in dry years in order to concentrate enough moisture to support persistent carbon
uptake.
The coupled hydrologic and carbon model used here is consistent with the concept of
islands of fertility—that areas of the landscape concentrate resources and subsequently
150
experience greater biological activity [35]. The hydrologic routing component of the
model predicts moisture availability which in turn supports biological function. While our
results assume static, uniform vegetation distribution, vegetation dynamics have minimal
effect on annual carbon flux compared to precipitation [101]. Vegetation dynamics may
alter the spatial distribution of productivity and respiration [102, 103] but the overall
effect on net carbon flux is not immediately clear and warrants further study. Changes in
precipitation, and resulting changes in hydrologic redistribution, have a greater effect on
areas where biological function is enhanced due to runoff (Figures 4, 6, 7). This study
identifies locations on the landscape where carbon flux is sensitive to change—
specifically areas of high TI—and estimates the magnitude of that variability (~250 mg
m-2 season-1).
5. Summary
This study quantifies the high degree of spatial heterogeneity of carbon dioxide flux
(~200 g m-2 season-1 difference within the watershed) that results from lateral moisture
redistribution in semiarid landscapes. By applying topographic information in a simple
runoff model, we determine that substantial amounts of water are redistributed to points
lower in the watershed. This redistribution of moisture supports longer periods of
ecosystem carbon flux, resulting in higher rates of carbon dioxide uptake during the
growing season. The response of carbon flux to interannual variations in seasonal
precipitation is modulated by topography, such that larger than expected carbon fluxes
occur when runoff is considered. As a result, topography and interannual precipitation
151
control the pattern of carbon flux in a watershed. These results reinforce connections
among topography, hydrologic and ecological functions in semiarid ecosystems. Explicit
simulation of redistribution as performed here is important to understand the long-term
response of these ecosystems to changing climate, as including runoff increased the
sensitivity to total seasonal rainfall.
Acknowledgements
Funding for this research was provided by SAHRA STC 9876800, NSF EAR 0910831,
EAR-0724958, and DE-SC0006968. Datasets were provided by the USDA-ARS
Southwest Watershed Research Center. Funding for these datasets was provided by the
United States Department of Agriculture, Agricultural Research Service. The authors
would like to thank S. Archer for helpful comments on an early draft of this manuscript.
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159
Year
1999
2000
2001
2002
2003
Total
P
274
237
188
200
159
1059
Q
48
45
1
34
25
154
106
P-Q P-Q/P
226
0.82
192
0.81
187
0.99
166
0.83
134
0.84
905
Q
29
40
4
17
15
105
102
P-Q P-Q/P
245
0.90
197
0.83
184
0.98
182
0.91
144
0.91
953
Q
24
32
2
12
11
81
104
P-Q
250
205
186
188
148
978
P-Q/P
0.91
0.86
0.99
0.94
0.93
Table 1. Precipitation, runoff, available moisture (P-Q), and the ratio of available
moisture to P for the three subwatersheds for each growing season. Moisture that does
not leave the subwatersheds via runoff remains available on the landscape to support
ecosystem activity. Values of (P-Q)/P are comparable to [59, 104]
160
Year
1999
2000
2001
2002
2003
Total
RMSE
Qobs
48
45
1
34
25
154
106
Qmod
P-Qmod/P
18
0.93
15
0.94
12
0.94
13
0.94
9
0.94
67
0.96
0.71
102
Qobs Qmod P-Qmod/P
29
29
0.89
40
24
0.90
4
20
0.90
17
21
0.89
15
15
0.90
105
109
0.94
0.46
104
Qobs Qmod P-Qmod/P
24
35
0.87
32
29
0.88
2
24
0.87
12
25
0.87
11
18
0.89
81
131
0.92
0.40
Table 2. Table of measured growing season runoff (Qobs, mm) and simulated runoff (Qmod, mm) for the study period and
simulated wetness No runoff data were available for comparison in 2004. The underestimation of runoff in subwatershed 106
is likely due to inaccurate routing resulting from its small size (~5400 m2 or 6 30-m pixels). Despite differences between Qmod
and Qobs, the fraction of available moisture is similar to measured values (see Table 1).
161
101
105
103
106
102
104
Figure 1. Site map of WGEW showing the subwatersheds in the Lucky Hills region.
162
Figure 2. Mean fraction of rainfall available (Pfrac) on the landscape as a function of
topographic index. Pfrac values are determined as the ratio of Peff to P on an event basis
and averaged over the study period. Error bars indicate one standard deviation of Pfrac.
The line indicates a Pfrac of 1, which represents the distribution of P if runoff is not used
to model carbon flux.
163
Figure 3. Mean duration of vegetation activity (Da) for subwatershed 104 based on the
distributed runoff method. Error bars indicate one standard deviation of Da. The linear fit
(dashed line) has a coefficient of determination of 0.36. The logistic fit shown has a
coefficient of determination of 0.96, and indicates an inflection point in the Da trend at TI
= 9.8.
164
Figure 4. NEE as a function of TI for each year of the study period. NEE becomes more
negative (i.e. carbon uptake increases) with increasing TI. The range of variation is
lowest at TI = 6.3 (72 g m-2 season-1) but is high when TI is high (TI = 10.3, range of
NEE = 242 g m-2 season-1).
165
Figure 5. NEE as a function of TI (as in Figure 4) with logistic curves fitted for each year.
All curves fit at r2 > 0.95, except for 2003 at r2 = 0.88.
166
Figure 6. Interannual range of NEE (ΔNEE) as a function of TI. The lowest ranges, i.e.
least variable NEE between years, are found around TI = 6 and the most variable at TI >
10.
167
Figure 7. Mean NEE (left panel) and interannual variability in NEE (ΔNEE) as a function
of the relative connectivity of each pixel (λ). Connectivity is the relative number of pixels
that contribute flow to a point in the watershed, e.g. λ = 0.5 means half as many pixels
contribute to that point as contribute to the outlet.
168
Figure 8. NEE response to precipitation in the three subwatersheds, as indicated in each panel. Black points represent NEE
estimated with runoff and gray points indicate NEE with no runoff. The slope for the no runoff model is shown in the left
panel. All fits are significant at p<0.01.
169
Figure 9. Mean component fluxes of NEE as a function of TI (left panel) and seasonal P
(right panel). Note that GPP and (Ra + Rh) have similar magnitudes around TI = 6. Also,
(Ra+Rh) exceeds GPP in the driest year.
170
APPENDIX D: USING LANDSAT ETM+ NDVI TO CLASSIFY VEGETATION
COVER
1. INTRODUCTION
The analysis in this appendix was developed for the study in Appendix B as a method to
develop more precise landscape classification. This classification was intended to identify
regions that included vegetation characteristics similar to the source area immediately
surrounding the eddy covariance flux tower. The analysis examined the change in
vegetation greenness detected via the Landsat Enhanced Thematic Mapper Plus (ETM+).
Vegetation greenness near the study site is described as bimodal, with peaks in the spring
and summer (Pennington and Collins 2007). Because of the evergreen nature of creosote
shrubs, the change in greenness associated with creosote-dominated parts of the
landscape should be minimal compared to those dominated by seasonal vegetation, e.g.
grasses and forbs (Peters et al. 1997)
A revised version of this approach, using data from the Landsat Thematic Mapper sensor
and using data throughout the three-year study period, is in development as a complement
to the current analysis in Appendix B. The new analysis, in conjunction with highresolution spatial data, should better depict changes in fractional vegetation cover over
the study period. With a method to identify changes in fractional cover, the effect of leaf
area on vegetation indices over the same time period can be more accurately described.
171
2. METHODS
Data from the Landsat 7 ETM+ were analyzed to resolve the sub-grid variability in
MODIS scenes from Appendix B. Landsat scenes were downloaded for January and
August 2002. Landsat data were corrected for atmospheric effects using the LEDAPS
tool (Masek et al. 2006). Individual Landsat ETM+ bands were used to compute the
normalized difference vegetation index (NDVI), calculated as:
NDVI =
ρ NIR − ρ red
ρ NIR + ρ red
(2)
Landsat ETM+ NDVI was used to determine relative land cover dominance between
evergreen creosotebush and seasonal vegetation (grasses and forbs). Seasonal change in
NDVI (ΔNDVI) was used to characterize the distribution of shrubs based on the
difference between scenes taken during the growing (August) and non-growing (January)
season. The areas dominated by creosote (an evergreen shrub) are expected to have
relatively stable NDVI values compared to other vegetation types (i.e. grasses, forbs and
deciduous shrubs). Landsat ΔNDVI data were resampled to the 250-m scale to
correspond to MODIS pixels in Appendix B.
3. RESULTS
The seasonal vegetation dynamics, described by ΔNDVI in the Landsat image, are
minimal in the region close to the tower, i.e. the value of NDVI does not change (Figure
1). High ΔNDVI (~0.3) at the 30-m Landsat scale is evident to the northeast of the tower,
172
along the edge of the shrub/grass ecotone (Kurc and Small 2007), as well as along the
drainages . The diminished EVI (annual footprint ΔEVI = 0.06) and NDVI (ΔNDVI in
the footprint ~ 0.10) variability near the tower is associated with the evergreen character
of creosotebush, which reduced correlation to tower NEE. From the ΔNDVI values, we
delineate regions that have vegetation characteristics similar to the tower footprint, and
apply scaling rules in those locations, where similar flux patterns can be expected.
To produce an estimate of carbon flux when deep soils are wet, the ΔNDVI values from
the Landsat ETM+ image were resampled to MODIS EVI resolution, i.e. from 30-m to
250-m. Resampled pixels with ΔNDVI values less than or equal to the tower area were
considered similar to the tower. Applying the mean tower area equations for NEE and
GPP identified in Table 3, we can simulate carbon fluxes for the creosote-dominated
region when deep soils are wet. The average annual NEE for these pixels for the entire
three year record is -6.19 g m-2 d-1 and mean GPP is 8.95 g m-2 d-1. The spatial pattern of
NEE in 2002 (Figure 9), indicates consistent carbon uptake when deep soils are wet, and
similar net flux rates across the study area. Carbon uptake is slightly greater at the head
of drainages in the south and north of the study area.
4. DISCUSSION
MODIS EVI pixels that were highly correlated to tower flux exhibit a greater dynamic
range of EVI, suggesting differences in vegetation between correlated pixels and the
tower area, potentially due to increased bare space or annual vegetation. To the northeast
of the tower the landscape transitions from shrub-dominated to grass-dominated. A
173
stream crosses the scene toward the western edge, and wide swales drain the
northwestern edge of the creosote region. The ΔNDVI values in the Landsat image
corroborate the differences in vegetation characteristics along the drainages and in the
northeast region of the study area (Figure 8). The source area of the tower is
characterized by relatively uniform cover by creosote shrubs and flat terrain (Kurc and
Small 2007). Linking spatial patterns in the Landsat and MODIS images, we identify a
region that is similar to the tower area, based on the maximum ΔNDVI in the tower
source area.
The seasonality of vegetation, described by ΔNDVI, indicates two main patterns (Figure
8). The central portion of the study area, which is dominated by creosote, has a low range
of NDVI values, indicating minimal change in vegetation characteristics (leaf area or
fractional cover) between the growing and non-growing seasons. This result is expected
in light of the perennial nature of creosote and the low density of vegetation cover (Qi et
al. 1994). Alternatively, locations toward the margins of the study area and in the
drainages have a greater range of NDVI from growing to non-growing periods,
suggesting these areas are more densely vegetated and include seasonal vegetation rather
than the perennial creosote near the tower.
The goal of the ΔNDVI analysis was to provide a simple approach to determine regions
in the MODIS scene (7 km x 7 km) that have vegetation similar to the tower source area,
so the NEE and GPP models could be applied over similar vegetation conditions.
174
While the example shown (Figure 9) is applied only for one year (2002, the same year as
the Landsat NDVI figures), concerns were raised about the long-term applicability of this
method under interannual vegetation change. For example, assuming that the distribution
of creosote shrubs based on an analysis in 2002 was static over the three year study
period. Also, the ability to discern cover type reliably using NDVI on the basis of
phenology was a concern if this approach were applied over the three year period.
5. CONCLUSIONS AND FUTURE WORK
The ΔNDVI analysis to identify different vegetation communities in a scene may be
suitable for simple demonstration purposes to highlight the NEE and GPP models
developed in Appendix B. However, there remain potential complications in the
interpretation of the results, namely, the application of the method over longer time spans
in which vegetation characteristics may change. Further, because the model developed in
Appendix B used MODIS EVI (and not NDVI), methodological consistency would be
best served by reanalyzing using Landsat EVI products. Improvements to this approach
would also include repeat analysis to monitor changes in vegetation distribution over
time. This could be done by linking Landsat data to high-resolution remote sensing data,
such as the U.S. Department of Agriculture’s National Agricultural Imagery Program
(NAIP) data sets, which provide more detailed information about vegetation cover.
6. REFERENCES
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Figure 1. ΔNDVI between the annual maximum (August) and minimum (January) NDVI
in 2002. Upper image shows ΔNDVI at the 30-m resolution of Landsat and the lower
image is at the resampled 250-m resolution. ΔNDVI values near the tower (marked with a
cross) are all below 0.1, which was used as the threshold to identify regions that are
creosote-dominated.
177
Figure 2. Map of total NEE (g m-2 d-1) in 2002 during periods when deep soils are wet.
Regions that have NEE of 0 g m-2 d-1 are outside the creosote-dominated area determined
from the resampled Landsat image (Figure 8).