1. !
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Ecosystem Primary Productivity and
Resilience across Australian Drought and
Wet Cycles through Coupling Field Data,
Tower Fluxes and Satellite Imagery
AOGS Annual Symposium, Brisbane 25 June 2013
Alfredo Huete1
Contributions from:
Xuanlong Ma1
, Derek Eamus1
, Natalia
Restrepo-Coupe1
, Mark Broich1
,
Nicolas Bolain1
, James Cleverly1
,
Lindsey Hutley2
, Jason Berringer3
(1) University of Technology, Sydney
(2) Charles Darwin University
(3) Monash University BG17-A005
Alfredo HUETE, University of
Technology Sydney, Australia
BG17-A005

2. Introduction
• Recent large-scale, warm droughts have occurred in Australia,
China, North America, Amazonia, Africa, and Europe, resulting in
dramatic changes in vegetation productivity across ecosystems
with direct impact on societal needs, food security and basic
livelihood and water balance, and food security.
Tropical cyclones and the ecohydrology of Australia’s recent
continental-scale drought
Gavan S. McGrath,1
Rohan Sadler,2
Kevin Fleming,3,4
Paul Tregoning,5
Christoph Hinz,1,6
and Erik J. Veneklaas7
Received 8 November 2011; revised 20 December 2011; accepted 22 December 2011; published 9 February 2012.
[1] The Big Dry, a recent drought over southeast Australia,
began around 1997 and continued until 2011. We show that
between 2002–2010, instead of a localized drought, there
was a continent-wide reduction in water storage, vegetation
and rainfall, spanning the northwest to the southeast of
Australia. Trends in water storage and vegetation were
assessed using Gravity Recovery and Climate Experiment
(GRACE) and Normalized Difference Vegetation Index
(NDVI) data. Water storage and NDVI are shown to be
significantly correlated across the continent and the greatest
losses of water storage occurred over northwest Australia.
The frequency of tropical cyclones over northwest Australia
peaked just prior to the launch of the GRACE mission
in 2002. Indeed, since 1981, decade-scale fluctuations in
tropical cyclone numbers coincide with similar variation in
rainfall and vegetation over northwest Australia. Rainfall
and vegetation in southeast Australia trended oppositely to
the northwest prior to 2001. Despite differences between
[Ummenhofer et al., 2009; Smith and Timbal, 2012]. The
IOD is an irregular oscillation of sea surface temperature
and atmospheric circulation in and around the Indian Ocean
characterized by the Dipole Mode Index (DMI). In the
negative phase, with warmer waters off northwest Australia
the atmospheric circulation brings moisture across the
continent in a southeasterly direction [Ashok et al., 2003
Ummenhofer et al., 2009]. In the positive phase, southeas
Australia experiences lower rainfall. In early 2011, the
apparent end of the drought coincided with a strong La
Niña and the occurrence of a strongly negative DMI. We
hypothesized that a drought in southeast Australia may
therefore be associated with a continent-wide drought
oriented northwest to southeast across the continent.
[3] A warming trend in the equatorial Indian Ocean a
well as a tendency for stronger and more frequent positive
IOD events have been identified [Ashok et al., 2003; Ihara
et al., 2008]. Modeling efforts also support the hypothesi
GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L03404, doi:10.1029/2011GL050263, 2012
MODIS Satellite EVI

3. OUTLINE
Taking the pulse of the earth
IMPACTS
Remote Sensing with high
frequency observations in the
temporal domain open the door to
answering unique sets of questions
in metabolic processes of the Earth
System and in Global Ecology

4. • Understanding water and productivity relationships are key
issues in models that aim to predict how carbon and water
relationships will shift with projected changes in the frequency,
timing, amount and intensity of rainfall.
• The hydro-meteorological conditions that recently impacted N.
America and Australia are of the same order to those expected with
climate change, and thus offer an opportunity to investigate changes
and generalize vegetation responses to future climate change scenarios.
• “Natural experiments” have great power to study rainfall
variability and vegetation response.
LETTER doi:10.1038/nature11836
Ecosystem resilience despite large-scale altered
hydroclimatic conditions
Guillermo E. Ponce Campos1,2
, M. Susan Moran1
, Alfredo Huete3
, Yongguang Zhang1
, Cynthia Bresloff2
, Travis E. Huxman4
,
Derek Eamus3
, David D. Bosch5
, Anthony R. Buda6
, Stacey A. Gunter7
, Tamara Heartsill Scalley8
, Stanley G. Kitchen9
,
Mitchel P. McClaran10
, W. Henry McNab11
, Diane S. Montoya12
, Jack A. Morgan13
, Debra P. C. Peters14
, E. John Sadler15
,
Mark S. Seyfried16
& Patrick J. Starks17
Climate change is predicted to increase both drought frequency and
duration, and when coupled with substantial warming, will estab-
1
In field experiments, vegetation productivity is generally measured
as the above-ground net primary production (ANPP, or total new
8
Ponce-
Campos et
al. (2013)
Nature

5. 2010 MODIS
iEVI
• Tropical Rainfall
Measuring Mission
(TRMM) satellite
• Japan-USA joint project
• Launched 1997
3B43 Data
Product
2010 TRMM
Rainfall
- Methods
Remote sensing methods, by
observing broadscale vegetation
responses to climatic variability,
offer potentially powerful insights
into ecological questions on
observable timescales.

6. Many%remote%sensing%
seasonal%&%phenology%metrics%
can%be%derived%from%the%
annual%VI%cycle
1153
Fig. 2 phenological methodology schematic diagrams. (A) Original MODIS EVI time series1154
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Howard Springs (Eucalypt Woodlands)
0.2
0.3
0.4
0.5
0.6
2000 2002 2004 2006 2008 2010 2012
Date
EVI
●
●
Original
SSA reconstructed
(A)
● ●
●
●
1/2 1/2
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●SGS
1/2 1/2
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●EGS
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
PGS
LGS
0.2
0.3
0.4
Jul 2000 Oct 2000 Jan 2001 Apr 2001 Jul 2001 Oct 2001
Date
EVI
(B)
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
Fastest Greening Date
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
Fastest Browning Date
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
Minimum EVI before growing season
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Minimum EVI after growing season●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
Maximum EVI during growing season
−0.02
0.00
0.02
0.04
Jul 2000 Oct 2000 Jan 2001 Apr 2001 Jul 2001 Oct 2001
Date
d(EVI)/dt
(C)
Ma X. et al. (in review)

7. iEVI as proxy for ANPP
"377"
"378"
Figure 1.379"
380" Ponce-Campos et al 2013, Nature
Fig. 3 Graph shows the technical scheme to derive the phenological metrics based on double logistic fitted EVI time
series (solid line) and corresponding curvature change rate (dashed line). The light grey area is the integral of annual
EVI subtract the integral of Base EVI, which is used as surrogate for grass layer productivity (Pg). The dark grey area is
the integral of annual Base EVI, which is used as surrogate for the woody layer productivity (Pw). The annual total
productivity (Pt) is the sum of Pw and Pg.
y = 192.65x - 155.29
R = 0.8578
0
200
400
600
800
1000
1200
1400
1600
1800
0 2 4 6 8 10
MeanAnnualGPP(gCm-2)
Mean Annual iEVI
FOREST DESERT-GRASSLAND
SAVANNA SHRUB
OPEN FOREST SAVANNA WOODY SAVANNA

8. Rainfall use efficiency (RUE) concept
s, for S. paradoxus versus S.
lizards.
038/nature02597.
spectives (eds Woods, C. A. & Sergile,
e of the Antillean insectivoran
m. Mus. Novit. 3261, 1–20 (1999).
e Caribbean region: implications for
999).
s. Annu. Rev. Ecol. Syst. 27, 163–196
l history of Solenodon cubanus. Acta
the Dominican Republic. 1–128,
hy: molecular evidence for dispersal
89, 1909–1913 (1992).
Mus. Nat. Hist. 115, 113–214 (1958).
Species Level (Columbia Univ. Press,
(ed. Benton, M. J.) 117–141 (Oxford
ntals (eds Szalay, F. S., Novacek, M. J.
..............................................................
Convergence across biomes to
a common rain-use efficiency
Travis E. Huxman1
*, Melinda D. Smith2,3
*, Philip A. Fay4
, Alan K. Knapp5
,
M. Rebecca Shaw6
, Michael E. Loik7
, Stanley D. Smith8
, David T. Tissue9
,
John C. Zak9
, Jake F. Weltzin10
, William T. Pockman11
, Osvaldo E. Sala12
,
Brent M. Haddad7
, John Harte13
, George W. Koch14
, Susan Schwinning15
,
Eric E. Small16
& David G. Williams17
1
Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721,
USA
2
National Center for Ecological Analysis and Synthesis, Santa Barbara, California
93101, USA
3
Department of Ecology and Evolutionary Biology, Yale University, New Haven,
Connecticut 06511, USA
4
Natural Resources Research Institute, Duluth, Minnesota 55811, USA
5
Department of Biology, Colorado State University, Fort Collins, Colorado 80523,
USA
6
letters to nature
Huxman et al 2004
*ANPP difficult to measure
Methods are usually inconsistent
er
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rs
er
18
.
a
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17
;
on
y7
;
it
i-
on
al
th
ty
nt
ms
ng
i-
he
re
PP
ps
r-
ld
m
th
i-
nitrogen and light will influence ANPP more strongly. However,
both in locations with high MAP and in those with low MAP, water
availability is tightly linked to biogeochemical constraints through
mineralization processes and leaching20
. Precipitation affects both
nutrient availability through its effects on microbial activity and
ANPPg/m2
n
7
;
it
i-
n
al
h
ty
nt
ms
ng
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re
P
ps
r-
d
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h
i-
of
V,
or
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ty
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of
m-
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he
re
ar
Figure 1 Between-year variation in production across a precipitation gradient and a
maximum rain-use efficiency. a, Plot of ANPP against PPT for 14 sites (see Methods for
abbreviations). Multi-year data give site-specific relationships by using linear regression
(see Supplementary Information). The overall relationship (bold line) derives from data
from all sites: ANPP ¼ 1011.7 £ (1 2 exp(20.0006 £ precipitation)); r 2
¼ 0.77;
P , 0.001. The inset shows the site-level slopes (ANPP plotted against precipitation) as a
function of MAP: ANPP ¼ 0.388 £ (1 2 exp(20.0022 £ precipitation)); r2
¼ 0.51;
P , 0.001. b, An overall RUEmax derived from the slope of the minimum precipitation and
the corresponding ANPP for all sites (solid line): ANPP ¼ 86.1 þ 0.42 £ PTTmin. Closed
circles, minima; open circles, remaining data; dotted lines, 95% confidence intervals.
Arrows show average slopes for sites with low, medium and high precipitation.
NATURE | VOL 429 | 10 JUNE 2004 | www.nature.com/nature
urePublishing Group
Ponce et al ISRSE, Sydney 2011

9. iEVI (mean) iEVI (dry) iEVI (wet)
AnnuallyintegratedEVI
Annually integrated EVI (sum across all of Australia)
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
We may be seeing an accumulated stress effect here..TRMM standard anomalies
Broich, M (in
preparation)

10. Continental Scale RUE
of Average, Driest,
and Wettest years
Driest year
Wettest year
Average year

11. Northern Australia
Tropical Transect
0.00#
0.05#
0.10#
0.15#
0.20#
0.25#
0.30#
0.00#
0.50#
1.00#
1.50#
2.00#
2.50#
3.00#
3.50#
4.00#
1#
9#
17#
25#
33#
41#
49#
57#
65#
73#
81#
89#
97#
105#
113#
121#
129#
137#
145#
153#
161#
169#
177#
185#
193#
201#
209#
217#
225#
233#
241#
249#
257#
265#
273#
281#
289#
297#
iEVI%
Transect%distance%
iEVI#(mean)#
TRMM#
(mean)#
MODIS
iEVI
TRMM-
rainfall
0"
500"
1000"
1500"
2000"
2500"
3000"
3500"
4000"
6"
62"
118"
174"
230"
286"
342"
398"
454"
510"
566"
622"
678"
734"
790"
846"
902"
958"
1014"
1070"
1126"
1182"
Annual&Rainfall&
NATT&transect,&km&
Mean"
2000"
2001"
2002"
2003"
2004"
2005"
2006"
2007"
2008"
2009"
2010"
TRMM(min)"
2010
driest
TRMM
rainfall

12. Productivity- rainfall per year along
NATT transect
Regression lines become more linear with
drier years
Driest year
y"="$8E$05x"+"4.6665"
R²"="0.027"
y"="$0.0005x"+"4.9852"
R²"="0.452"
y"="$0.0001x"+"3.405"
R²"="0.011"
y"="0.0006x"+"1.7864"
R²"="0.223"
y"="0.0015x"+"1.4189"
R²"="0.517"
0"
1"
2"
3"
4"
5"
6"
7"
0" 1000" 2000" 3000" 4000"
Annual&iEVI&
Annual&Rainfall,&mm&
N10"
N50"
N100"
N150"
N200"
Wet tropical
savanna
Semi-arid Mulga
(Acacias)
Site- based productivity - rainfall
is there an inherent
maximum RUE?
Figures
Figure 1 Study area. Left panel: Major Vegetation Groups map; righ
sites along the transect.

13. ●
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●
●
●
(A) Howard Springs (Eucalypt Woodlands) (B) Adelaide Rivers (Tropical Eucalypt Woodlands)
(C) Daly River (Eucalypt Woodlands) (D) Dry River (Eucalypt Open Forests)
(E) Sturt Plains (Tussock Grasslands) (F) Ti Tree (Acacia Woodlands)
2000−2001
2001−2002
2002−2003
2003−2004
2004−2005
2005−2006
2006−2007
2007−2008
2008−2009
2009−2010
2010−2011
2011−2012
2000−2001
2001−2002
2002−2003
2003−2004
2004−2005
2005−2006
2006−2007
2007−2008
2008−2009
2009−2010
2010−2011
2011−2012
2000−2001
2001−2002
2002−2003
2003−2004
2004−2005
2005−2006
2006−2007
2007−2008
2008−2009
2009−2010
2010−2011
2011−2012
Sep Nov Jan Mar May Jul Oct Dec Feb Apr Jun Aug
Sep Nov Jan Mar May Jul Nov Jan Mar May Jul Sep
Oct Dec Feb Apr Jun Aug Dec Feb Apr Jun Aug Oct
Date
Year
● SGS
PGS
EGS
0.4
0.3
0.2
EVI
R = 0.83, p < 0.001
250 mm
Mean uncertainty of MODIS EVI product
●
●
●
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●
●
●
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0 100 200 300 400 500 600 700 800 900 1000
Annual precipitation (mm yr
1
)
EVIAmplitude
Ti Tree
●
●
●
●
●
●
●
●
●
●
●
●
2000−
2001−
2002−
2003−
2004−
2005−
2006−
2007−
2008−
2009−
2010−
2011−
1187
Fig. 5 Relationship between EVI amplitude and annual precipitation for Ti Tree site1188
Woodland, 133.249°E 22.283°S) over Jul 2000- Jun 2012 time period. Horizontal re1189
line indicates the mean uncertainty of MODIS EVI product (0.02 EVI unit). Vertical blu1190
line indicates the minimal requirements of annual rainfall for reliable phenology detec1191
Tree site. Red shaded area indicates the low annual rainfall region with EVI seasonal a1192
was lower than MODIS data error that reliable phenology could not be retrieved.1193
seasonal EVI
amplitude vs MAP
Ma et al (submitted)

14. A1
●
●
●
●
●
●
Howard Springs
Adelaide Rivers
Daly River
Dry River
Sturt Plains
Ti Tree
A2
●
●
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●
●
●
A3
●
●
●
●
●
●
A4
●
●
●
●
●
●
2001−2002
2005−2006
2007−2008
2010−2011
22
20
18
16
14
12
22
20
18
16
14
12
22
20
18
16
14
12
22
20
18
16
14
12
128 130 132 134 136
Latitude°S
210−1−2
(A) PPT
B1
B2
B3
B4
2001−2002
2005−2006
2007−2008
2010−2011
128 130 132 134 136
Aug Oct Dec Feb
(B) SGS
C1
C2
C3
C4
2001−2002
2005−2006
2007−2008
2010−2011
128 130 132 134 136
Jan Mar May
(C) PGS
D1
D2
D3
D4
2001−2002
2005−2006
2007−2008
2010−2011
128 130 132 134 136
Jun Aug Oct Dec
(D) EGS
E1
E2
E3
E4
2001−2002
2005−2006
2007−2008
2010−2011
128 130 132 134 136 138
0 100 200 300
(E) LGS
Fig. 6 Spatial patterns of vegetation phenology over the NATT study area along with rainfall
56
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● ●●●
●●●
●
●
●
Wet average
Dry average
SGS
PGS
EGS
(A)
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5
Latitude °S
Date
●
Eucalypt Open Forests
Eucalypt Woodland
Acacia Forests and Woodlands
Other Forests and Woodlands
Eucalypt Open Woodlands
Tropical Eucalypt Woodlands
Acacia Open Woodlands
Acacia Shrublands
Hummock Grasslands
●
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●●●
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●
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●
●●●
●
●●●● ● ●● ●
●●
●●●●●
●
LGS
(B)
0
60
120
180
240
300
12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5
Latitude °S
LGS(Days)
1206
Spatial patterns in
vegetation phenology
Ma et al
(submitted)

15. 050100150
Precipitation(mm)
Jun
2001
Dec
2001
Jun
2002
Dec
2002
Jun
2003
Dec
2003
Jun
2004
Dec
2004
Jun
2005
Dec
2005
May
2006
0.20.30.40.50.6
Howard Springs (Eucalypt Woodlands) 1
EVI
● ● ● ● ●
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●
2468
GEP(gC*m−2*d−1)
● ● ●
● ●
RMSE: SGS = 10.3d, PGS = 7.2d, EGS = 13.1d, LGS = 16.8d(A)
●
EVI
GEP
SSA EVI
SSA GEP
●
●
EVI−SGS
EVI−PGS
EVI−EGS
GEP−SGS
GEP−PGS
GEP−EGS
050100150
Precipitation(mm)
Feb
2008
Aug
2008
Feb
2009
Aug
2009
Feb
2010
Aug
2010
Feb
2011
Jul
2011
0.200.300.400.50
Howard Springs (Eucalypt Woodlands) 2
EVI
●
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●
2468
GEP(gC*m−2*d−1)
●
● ●
RMSE: SGS = 14d, PGS = 12d, EGS = 9d, LGS = 10.9d
(B)
020406080100
Precipitation(mm)Mar
2007
Sep
2007
Mar
2008
Sep
2008
Mar
2009
Sep
2009
Mar
2010
Sep
2010
Mar
2011
Aug
2011
0.200.300.400.50
Daly River (Eucalypt Woodlands)
EVI
● ● ● ●
●
●
●
●
●
●
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●
●
●
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●
●
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●
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●
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●
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●
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● ● ● ● ●
●
●
●
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● ● ●
●
●
●
●
●
●
●
●
●
23456
GEP(gC*m−2*d−1)●
● ● ●
RMSE: SGS = 19.1d, PGS = 10d, EGS = 20.8d, LGS = 19.8d
(C)
050100150
Precipitation(mm)
Feb
2009
Aug
2009
Feb
2010
Aug
2010
Feb
2011
Aug
2011
Feb
2012
Jul
2012
0.100.140.18
Ti Tree (Acacia Woodlands)
EVI
●
●
●
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● ●
●
●
01234
GEP(gC*m−2*d−1)
●
●
RMSE: SGS = 70.7d, PGS = 19.8d, EGS = 99.6d, LGS = 70d
(D)
1215
59
●
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●
●
●
● ●
●●
EVI = 0.181 + 0.0333 GEP
R2
= 0.51 p < 1e−04
EVI = 0.2064 + 0.0316 GEP
R2
= 0.59 p < 1e−04
0.2
0.3
0.4
0.5
0.6
2.5 5.0 7.5
GEP
EVI
● Greenup phase
Browndown phase
(A) Howard Springs (Eucalypt Woodlands)
● ●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
EVI = 0.1643 + 0.0335 GEP
R
2
= 0.75 p < 1e−04
EVI = 0.1075 + 0.0503 GEP
R
2
= 0.78 p < 1e−04
0.2
0.3
0.4
0.5
2 3 4
GEP
EVI
● Greenup phase
Browndown phase
(B) Daly River (Eucalyp
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
EVI = 0.1052 + 0.0234 GEP
R2
= 0.83 p < 1e−04
EVI = 0.1105 + 0.0607 GEP 0.016 GEP
2
+ 0.0015 GEP
3
0.100
0.125
0.150
0.175
0.200
0.225
0 1 2 3 4 5
GEP
EVI
● Greenup phase
Browndown phase
(C) Ti Tree (Acacia Woodlands)
1222
Fig. 10 Relationships between 16-day aggregated flux tower GEP and MODI1223
three savanna sites. (A) Howard Springs (Eucalypt woodlands); (B) Daly1224
woodlands) and (C) Ti Tree (Acacia woodlands). Seasonal hysteresis effect1225
between EVI and GEP was maximal at the Ti Tree Mulga site, whilst the gree1226
near-linear relationship, and the browndown phase showed enhanced non-1227
phase was defined as the period from season start (SGS) to season pe1228
browndown phase was defined as the period from PGS to season end (EGS).1229
each site and each dataset. Shaded areas indicate time periods that had continuous missing gaps1220
present in flux GEP data.1221
●
●
●
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●
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●
●
●
●
●
●
●
● ●
●●
EVI = 0.181 + 0.0333 GEP
R2
= 0.51 p < 1e−04
EVI = 0.2064 + 0.0316 GEP
R2
= 0.59 p < 1e−04
0.2
0.3
0.4
0.5
0.6
2.5 5.0 7.5
GEP
EVI
● Greenup phase
Browndown phase
(A) Howard Springs (Eucalypt Woodlands)
● ●
●
●
● ●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
EVI = 0.1643 + 0.0335 GEP
R
2
= 0.75 p < 1e−04
EVI = 0.1075 + 0.0503 GEP
R
2
= 0.78 p < 1e−04
0.2
0.3
0.4
0.5
2 3 4 5 6
GEP
EVI
● Greenup phase
Browndown phase
(B) Daly River (Eucalypt Woodlands)
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
EVI = 0.1052 + 0.0234 GEP
R2
= 0.83 p < 1e−04
EVI = 0.1105 + 0.0607 GEP 0.016 GEP
2
+ 0.0015 GEP
3
0.100
0.125
0.150
0.175
0.200
0.225
EVI
● Greenup phase
Browndown phase
(C) Ti Tree (Acacia Woodlands)
each site and each dataset. Shaded areas indicate time periods that had contin1220
present in flux GEP data.1221
●
●
●
● ●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
● ●
●●
EVI = 0.181 + 0.0333 GEP
R2
= 0.51 p < 1e−04
EVI = 0.2064 + 0.0316 GEP
R2
= 0.59 p < 1e−04
0.2
0.3
0.4
0.5
0.6
2.5 5.0 7.5
GEP
EVI
● Greenup phase
Browndown phase
(A) Howard Springs (Eucalypt Woodlands)
● ●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
EVI = 0.1643 + 0.0335 GEP
R
2
= 0.75 p < 1e−04
EVI = 0.1075 + 0.0503 GEP
R
2
= 0.78 p < 1e−04
0.2
0.3
0.4
0.5
2 3 4
GEP
EVI
● Greenup phase
Browndown phase
(B) Daly River (Eucalyp
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
EVI = 0.1052 + 0.0234 GEP
R2
= 0.83 p < 1e−04
EVI = 0.1105 + 0.0607 GEP 0.016 GEP
2
+ 0.0015 GEP
3
0.100
0.125
0.150
0.175
0.200
0.225
0 1 2 3 4 5
GEP
EVI
● Greenup phase
Browndown phase
(C) Ti Tree (Acacia Woodlands)
Comparisons with Flux Tower
sites along NATT
Ma et al
(submitted)

16. Pulse and decline resilience
measures
(following Knapp & Smith, 2004, Science)
iEVI pulse = (iEVIwet - iEVImean)/(iEVImean)
iEVI decline = (iEVImean - iEVIdry)/(iEVImean)
-same applies to TRMM pulse and decline
Delta (Pulse/decline) = iEVI pulse - iEVI decline

17. iEVI declineiEVI pulse
Difference (Pulse - Decline)
Southeast Australia
WUE$=$0.78,$$R²$=$0.99$
WUE$=$0.66,$$R²$=$0.98$
WUE$=$0.62,$$R²$=$0.94$
0$
100$
200$
300$
400$
500$
600$
0$ 200$ 400$ 600$ 800$ 1000$
ANPP,%g%m(2%
Evapotranspira4on,%mm%yr(1%
WUE%of%Major%Vegeta4on%Classes%(SE%Australia)%
Dry$Year$
Mean$Year$
Wet$Year$
Melbourne
Brisbane
Sydney
Canberra
Adelaide

18. Continental RUE for dry, mean, and wet
years
RUE$=$0.33,$$R²$=$0.83$
RUE$=$0.25,$$R²$=$0.78$
RUE$=$0.17,$$R²$=$0.69$
0$
50$
100$
150$
200$
250$
300$
350$
400$
450$
500$
0$ 500$ 1000$ 1500$ 2000$ 2500$
ANPP,%g%m(2%
Precipita1on,%mm%yr(1%
RUE%of%Major%Vegeta1on%Classes%
Dry$Year$
Mean$Year$
Wet$Year$
RUE$=$0.33,$$R²$=$0.83$
RUE$=$0.25,$$R²$=$0.78$
RUE$=$0.17,$$R²$=$0.69$
0$
50$
100$
150$
200$
250$
300$
350$
400$
450$
500$
0$ 500$ 1000$ 1500$ 2000$ 2500$
ANPP,%g%m(2%
Precipita1on,%mm%yr(1%
RUE%of%Major%Vegeta1on%Classes%
Dry$Year$
Mean$Year$
Wet$Year$
1000
800
600
400
200
0
dry mean
wet
Continental RUE for major vegetation classes

19. Continental
WUE (2000-
2010)
2000
2003
2006
2010
• Australia Tree
Cover fraction
(Donohue et al.
2006)

20. Conclusions
• It is possible to monitor ecosystem resilience with a satellite-
metric, but vital to have long term experimental monitoring sites
• Cross-ecosystem water use efficiency (WUEe) and RUE will
increase with prolonged warm drought until reaching a threshold
that will break down ecosystem resilience,
• Better information for strategic resource management and
adaptation practices during altered hydro-meteorological
conditions.
• An important goal would be to assess environmental and economic
costs associated with variations in ANPP.
• Societal needs to detect, predict, and manage changes in
complex managed systems that threaten to undermine resource
sustainability and security.