Post on 07-May-2023
REPORT
Mapping carbon flux uncertainty and selecting optimallocations for future flux towers in the Great Plains
Yingxin Gu • Daniel M. Howard •
Bruce K. Wylie • Li Zhang
Received: 22 August 2011 / Accepted: 14 December 2011 / Published online: 28 December 2011
� Springer Science+Business Media B.V. 2011
Abstract Flux tower networks (e.g., AmeriFlux,
Agriflux) provide continuous observations of ecosystem
exchanges of carbon (e.g., net ecosystem exchange),
water vapor (e.g., evapotranspiration), and energy
between terrestrial ecosystems and the atmosphere.
The long-term time series of flux tower data are essential
for studying and understanding terrestrial carbon cycles,
ecosystem services, and climate changes. Currently,
there are 13 flux towers located within the Great Plains
(GP). The towers are sparsely distributed and do not
adequately represent the varieties of vegetation cover
types, climate conditions, and geophysical and biophys-
ical conditions in the GP. This study assessed how well
the available flux towers represent the environmental
conditions or ‘‘ecological envelopes’’ across the GP and
identified optimal locations for future flux towers in the
GP. Regression-based remote sensing and weather-
driven net ecosystem production (NEP) models derived
from different extrapolation ranges (10 and 50%) were
used to identify areas where ecological conditions were
poorly represented by the flux tower sites and years
previously used for mapping grassland fluxes. The
optimal lands suitable for future flux towers within the
GP were mapped. Results from this study provide
information to optimize the usefulness of future flux
towers in the GP and serve as a proxy for the uncertainty
of the NEP map.
Keywords Net ecosystem production (NEP) �Regression tree models � Extrapolation �Mean absolute difference � Flux tower location �Great Plains
Introduction
Flux tower networks (e.g., AmeriFlux, Agriflux) have
provided continuous observations of ecosystem
exchanges of carbon (e.g., net ecosystem exchange),
water vapor (e.g., evapotranspiration), and energy
between terrestrial ecosystems and the atmosphere
since 1996 (Baldocchi et al. 2001; Law 2007; http://
public.ornl.gov/ameriflux/; http://daac.ornl.gov/FLUX
NET/fluxnet.shtml). The primary uses of flux tower data
are (1) to provide ground truth information to validate
Y. Gu (&)
ASRC Research & Technology Solutions, Contractor
to US Geological Survey (USGS) Earth Resources
Observation and Science (EROS) Center, 47914 252nd
Street, Sioux Falls, SD 57198, USA
e-mail: ygu@usgs.gov
D. M. Howard
Stinger Ghaffarian Technologies, Inc., Contractor
to USGS EROS, Sioux Falls, SD 57198, USA
B. K. Wylie
USGS EROS, Sioux Falls, SD 57198, USA
L. Zhang
Key Laboratory of Digital Earth, Center for Earth
Observation and Digital Earth, Chinese Academy
of Sciences, Beijing, China
123
Landscape Ecol (2012) 27:319–326
DOI 10.1007/s10980-011-9699-7
and verify remote-sensing-derived products (e.g., net
primary productivity, evaporation, and energy absorp-
tion) (Running et al. 1999; Sims et al. 2005; http://
daac.ornl.gov/FLUXNET/fluxnet.shtml), (2) to
develop, test, and apply terrestrial ecosystem models
and atmospheric models (Running et al. 1999; Baldoc-
chi et al. 2001; Law 2006; Jung et al. 2009; http://
public.ornl.gov/ameriflux/about-strategic_plan.shtml);
and (3) to integrate with remotely sensed data to quan-
tify gross primary production and net ecosystem pro-
duction (NEP) over large areas with high spatial and
temporal resolutions (Yang et al. 2007; Xiao et al. 2010;
Zhang et al. 2011). As a result, these long-term time
series of flux tower data are essential for studying and
understanding terrestrial carbon cycles, ecosystem ser-
vices, and climate changes (Running et al. 1999; Bal-
docchi et al. 2001; Gilmanov et al. 2003; Law 2006;
Reichstein et al. 2007; Wylie et al. 2007; Xiao et al.
2008; Jung et al. 2009).
The Great Plains (GP), which covers 14 states and
includes 17 ecoregions as defined by Omernik’s level
III Ecoregions (Omernik 1987), includes a variety of
vegetation cover types and a broad range of climate
conditions and plant productivities (Tieszen et al.
1997). 13 grassland flux towers from Ameriflux,
Agriflux networks, and non-affiliated tower sites
located within the GP were used for developing NEP
mapping algorithms (Zhang et al. 2011; Fig. 1). These
flux towers are sparsely distributed throughout the
region and probably do not provide an adequate
representation of certain vegetation cover types,
climate conditions, or geophysical and biophysical
conditions (e.g., soil conditions, vegetation phenol-
ogy) of the GP. Strategic augmentation of the flux
tower networks will help capture a broader range of
climate and biome conditions in the GP and make
future carbon flux mapping more accurate and robust.
In a previous study, Hargrove et al. (2003) used an
‘‘unsupervised clustering’’ approach, which was pri-
marily based on climatic and physiographic drivers, to
identify southern Texas as underrepresented by the
AmeriFlux Network. In this study, we developed a
new approach that focuses on the model input data
gaps by using flux tower data, remote sensing,
geophysical and biophysical data, and weather vari-
ables. The main objective of this study is to assess the
Fig. 1 Names and locations
of the 13 flux towers within
the GP. The land cover types
as identified in the 2001
NLCD are shown
320 Landscape Ecol (2012) 27:319–326
123
uncertainty of the NEP maps developed by Zhang
et al. (2011) and to identify optimal locations for
future flux towers in the GP. Remote sensing and
weather-driven NEP models were developed using
two different extrapolation strategies (labeled 10 and
50% extrapolation) to assess areas and times when
current flux tower data well (or poorly) represented the
environmental and climate conditions across the GP
grasslands. Results from this study provide useful
information for ensuring future flux tower locations in
the GP effectively complement current and historical
grassland flux towers and provide a proxy for the
uncertainty of the NEP maps produced by Zhang et al.
(2011).
Study area
Our study area is the GP, where the main vegetation
cover types are grassland and cultivated crops. Other
land cover types include shrubs, evergreen and
deciduous forests, urban, wetlands, open water, and
barren lands. Figure 1 is the 2001 National Land
Cover Database (NLCD 2001) map for the GP (Homer
et al. 2004). The average annual temperature generally
increases from the Northern GP (less than 4�C) to the
Southern GP (exceeds 22�C) and the annual precip-
itation increases from the Western GP (less than
200 mm) to the Eastern GP (over 1100 mm) (Joyce
et al. 2001). The soil available water capacity (AWC),
which represents important soil and agronomic char-
acteristics and was derived from the State Soil
Geographic (STATSGO) database, is shown in
Fig. 2. The AWC values vary from 0 to 0.64 within
the GP. Examples of phenological metrics (i.e., long-
term median start of growing season time and long-
term average of growing season integrated normalized
difference vegetation index, NDVI), which represent
the biophysical characteristics of the vegetation, are
also shown in Fig. 2. These phenological metrics were
derived from 2000 to 2009 eMODIS (expedited
moderate resolution imaging spectroradiometer)
(Jenkerson et al. 2010) data and were based on Reed
et al. (1994) method.
Data and methods
Method for building GP NEP models
The data-driven piecewise regression NEP models
were developed from multiple flux tower observations
(the gap-filled NEP data), satellite-derived NDVI,
phenological metrics, precipitation and temperature,
photosynthetically active radiation, and AWC (Zhang
et al. 2011). The NEP gap-filling algorithms used the
30 min step data and light response curve analysis as
well as relationships with flux tower ‘‘slow data’’
Fig. 2 Soil and phenological conditions in grassland areas of the GP. a available water capacity; b long-term median SOST; and
c long-term averaged time integrated NDVI
Landscape Ecol (2012) 27:319–326 321
123
(atmospheric and soil variables) to fill short gaps in the
carbon flux estimates (Gilmanov et al. 2005). These
NEP models were used to map weekly NEP for the GP
grasslands but with varying degrees of extrapolations
allowed. Zhang et al. (2011) constrained the NEP
prediction to 10% beyond the training data range. In
this study, a second map was created with prediction
constrained to 50% beyond the training data range.
The piecewise regression approach divides the
multi-dimensional domain of environmental variables
into many segments and derives a multiple regression
equation for each segment. The training data for the
model (sets of environmental data for the flux tower
site-year combinations) are then modeled, and the
resulting extreme values (NEPmax and NEPmin) are
recorded for each segment. Two extrapolated ranges
are then defined for each segment: a 10% extrapolation
(by adding 10% of the range to the NEPmax and
subtracting 10% from the NEPmin) and a 50% range
(by adding 50% to the NEPmax and subtracting 50%
from the NEPmin). Two maps (10 and 50% extrapo-
lations) were generated by applying the piecewise
regressions to the full set of data inputs. If the NEP for
a pixel was beyond the limited extrapolation range, it
was reset to the limit of the extrapolation range.
Identify optimal locations for future flux towers
in the GP
Model interpolation occurs when predictions are made
within the range of the training data that are used to
create the model (Fig. 3, blue box). Model
extrapolation, which is inherently less reliable than
interpolation, occurs when model predictions extend
beyond the training data range (Fig. 3, red and yellow
areas). Theoretically, a pixel that incorporates envi-
ronmental input variables (e.g., NDVI, weather, soils,
and phenology) that conform to the characteristics
observed in the flux tower training data would be
assigned a similar NEP value in both the 10 and 50%
extrapolation models (blue and yellow areas in Fig. 3).
On the other hand, the environmental variables of a
pixel that are poorly represented by any flux tower in
the network would likely have different NEP values
computed using the 10 and 50% extrapolation models
(red areas in Fig. 3). In a sense, the degree of
difference between the two NEP (10 and 50%
extrapolation) for a given pixel (the Y-axis in
Fig. 3) are used to identify the degree to which the
environmental conditions of the pixel (the X-axis in
Fig. 3) are different than the training data. If a lot of
pixels in an area are different than the training data,
that is an indication that a new flux tower in that area
would help explain and model that set of environ-
mental conditions.
The absolute differences between the two NEP data
sets (10 and 50% extrapolation limits) were calculated
for weekly NEP estimates from 2000 to 2008. The
weekly absolute differences were averaged to yearly
time steps, referred to as mean absolute difference
(MAD), to quantify interannual variations in model
extrapolation. Yearly MAD values were then averaged
to a multi-year (2000–2008) MAD (MMAD). Areas
with high MMAD imply that these regions are poorly
Fig. 3 A hypothetical
example of quantifying the
degree of extrapolation in
the NEP model. Modeled
NEP will be on the heavy
blue line if environmental
conditions are interpolated
within the domain of the
training data, on the heavy
yellow line if the 10%
constraint is used, or the
heavy red line if the 50%
constraint is used
322 Landscape Ecol (2012) 27:319–326
123
represented by conditions at flux tower sites in the
years used by Zhang et al. (2011). The high MMAD
areas have a lower prediction reliability of NEP and
would be considered as candidates for new flux towers.
Unique environmental grassland conditions may
occur infrequently or sporadically in areas with highly
variable annual MAD. Therefore, we excluded areas
where the interannual coefficient of variation (CV) for
MMAD was high (i.e., CV of the 9-year MAD is
greater than 20%). This will ensure the proposed flux
tower locations have consistently different environ-
mental conditions than existing flux towers, not just
from one or two outlier years.
Results and discussion
Figure 4 is the 2000–2008 MMAD map and shows the
13 flux towers in the GP. Areas with high MMAD
imply that the remote sensing, soil, climate, and
weather conditions are poorly represented by the flux
towers sites and years used by Zhang et al. (2011).
These high MMAD areas would be suitable for
potential new flux towers. High MMAD areas (i.e.,
MMAD [ 0.75 g C m-2 day-1) are mainly located
in the following three regions (within the red ovals in
Fig. 4): (1) western Kansas (within the Central GP);
(2) central Texas (mainly located within the South-
western Tablelands, Central GP, Central Oklahoma/
Texas plains); and (3) southern Texas (mainly in the
Southern Texas Plains and Western Gulf Coastal
Plains). Areas with high interannual CV of MMAD
(black in Fig. 4), mainly located in the middle and
western parts of the GP, are not recommended for
future new flux towers because of the variable
environmental conditions.
The optimal locations (within the red ovals in
Fig. 4) derived from this study represent a variety of
climate, geophysical, and biophysical conditions. For
example, there is only one flux tower (Freeman Ranch,
TX) located in the southern part of the GP. The soil
condition (AWC) in the southern part of the GP varies
from 0.18 to *0.48 cm/cm. The growing season
integrated NDVI (GSN), which represents a proxy of
ecosystem productivity (Tieszen et al. 1997), varies
from 7 to*30. The start of season time (SOST), which
is an important variable for vegetation phenology, also
varies from Julian day 40 (early February) to *Julian
day 95 (early April). Therefore, it is necessary to build
new flux towers to better represent the vegetation, soil,
and climate conditions of this southern part of the GP.
This optimal area is also in good agreement with the
previous research results from Hargrove et al. (2003),
who identified the Southern GP as an area that is poorly
represented by flux towers.
In contrast, although there is only one flux tower
located within the Nebraska Sandhills ecoregion
(Gudmundsen Ranch site, a grassland site that is not
in an upland part of the Sandhills), the MMAD is very
low (\0.5 g C m-2 day-1) in this ecoregion. Our
study suggests that it would not be a priority to build
new flux towers in the Sandhills ecoregion even though
it contains a very large area and there is only one flux
tower. The unique dry climate and biophysical condi-
tions (e.g., sandy soil with low AWC, similar SOST
from early March to early April) suggest that the
environmental conditions are similar in the Sandhills
ecoregion, and an additional flux tower would not be
necessary. This demonstrates that our approach can
distinguish areas that are poorly represented by the
current flux tower network from those that are well
represented in terms of climate conditions (e.g.,
precipitation and temperature), soil conditions, and
vegetation phenology. In this study, we used a coarse
resolution AWC map derived from the STATSGO data
set in order to be consistent with data sets used by
Zhang et al. (2011). This coarse AWC map does not
capture the fine scale differences; this could be
improved by using the Soil Survey Geographic
(SSURGO) AWC data.
We also found that there was a dramatic MMAD
difference, occurring over short distances, in the
southeastern edge of the Sandhills and the northern
portion of the Central GP ecoregion (Figs. 2a, 4). The
environmental conditions of this region include an
earlier SOST and a higher productivity (as proxied by
GSN) than the Sandhills ecoregion. This region would
be a candidate for a new flux tower.
This study provides a general indication of the
optimal locations for the future flux towers at a 250-m
spatial resolution based on the climate, geophysical,
and biophysical conditions. Subsequent assessments
on the local (high resolution) environmental condi-
tions (e.g., micrometeorological conditions necessary
for the eddy covariance, vegetation homogeneity and
low relief within the fetch area, reasonably accessible
for maintenance) of the optimal locations are needed
before building the new flux towers.
Landscape Ecol (2012) 27:319–326 323
123
Fig. 4 2000–2008 MMAD map (unit: 0.01 g C m-2 day-1) excluding areas where the CV (interannual CV of the 9-year MAD) is
greater than 20% (black area). Regions with high MMAD are optimal locations for the future flux towers, as highlighted with red ovals
324 Landscape Ecol (2012) 27:319–326
123
Conclusions
This study assessed a large set of flux tower data to
determine how well their environmental conditions or
‘‘ecological envelopes’’ extended across the GP and
identified optimal locations for future flux towers in
the GP. Remote sensing and weather-driven NEP
models derived from different extrapolation limita-
tions (10 and 50%) were used to identify areas that
poorly represent ecological conditions within the GP.
The optimal lands suitable for future flux towers
within the GP were mapped. Results from this study
provide information to optimize the usefulness of
future flux towers in the GP and serve as a proxy for
the uncertainty of the NEP maps.
Acknowledgments This work was performed under USGS
contracts G08PC91508 and G10PC00044, and funded by the
USGS Geographic Analysis and Monitoring Program in support
of Renewable Energy-Biofuels initiative. The authors thank
Norman B. Bliss, Kurtis Nelson, Lei Ji, and Thomas Adamson
for their valuable suggestions and comments. The authors thank
Tagir G. Gilmanov for gap filling of flux tower NEP data. The
authors thank D. Billesbach, C. Owensby, and J. Heilman (the
PIs of the Gudmundsen, Rannels Flint Hills, Fort Reno, and
Freeman Ranch sites) for providing their unpublished flux tower
data. Any use of trade, product, or firm names is for descriptive
purposes only and does not imply endorsement by the US
Government.
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