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

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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

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(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

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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

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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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