!
!
!
!
!
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Ecosystem Primary Productivity and
Resilience across Australian Drought and
Wet Cycles through Coupling Field ...
Introduction
• Recent large-scale, warm droughts have occurred in Australia,
China, North America, Amazonia, Africa, and E...
OUTLINE
Taking the pulse of the earth
IMPACTS
Remote Sensing with high
frequency observations in the
temporal domain open ...
• Understanding water and productivity relationships are key
issues in models that aim to predict how carbon and water
rel...
2010 MODIS
iEVI
• Tropical Rainfall
Measuring Mission
(TRMM) satellite
• Japan-USA joint project
• Launched 1997
3B43 Data...
Many%remote%sensing%
seasonal%&%phenology%metrics%
can%be%derived%from%the%
annual%VI%cycle
1153
Fig. 2 phenological metho...
iEVI as proxy for ANPP
"377"
"378"
Figure 1.379"
380" Ponce-Campos et al 2013, Nature
Fig. 3 Graph shows the technical sch...
Rainfall use efficiency (RUE) concept
s, for S. paradoxus versus S.
lizards.
038/nature02597.
spectives (eds Woods, C. A. ...
iEVI (mean) iEVI (dry) iEVI (wet)
AnnuallyintegratedEVI
Annually integrated EVI (sum across all of Australia)
2001 2002 20...
Continental Scale RUE
of Average, Driest,
and Wettest years
Driest year
Wettest year
Average year
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...
Productivity- rainfall per year along
NATT transect
Regression lines become more linear with
drier years
Driest year
y"="$...
●
●
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●
●
●
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●
●
●
●
●
●
●
●
●
●
●
●
...
A1
●
●
●
●
●
●
Howard Springs
Adelaide Rivers
Daly River
Dry River
Sturt Plains
Ti Tree
A2
●
●
●
●
●
●
A3
●
●
●
●
●
●
A4
●...
050100150
Precipitation(mm)
Jun
2001
Dec
2001
Jun
2002
Dec
2002
Jun
2003
Dec
2003
Jun
2004
Dec
2004
Jun
2005
Dec
2005
May
...
Pulse and decline resilience
measures
(following Knapp & Smith, 2004, Science)
iEVI pulse = (iEVIwet - iEVImean)/(iEVImean...
iEVI declineiEVI pulse
Difference (Pulse - Decline)
Southeast Australia
WUE$=$0.78,$$R²$=$0.99$
WUE$=$0.66,$$R²$=$0.98$
WU...
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...
Continental
WUE (2000-
2010)
2000
2003
2006
2010
• Australia Tree
Cover fraction
(Donohue et al.
2006)
Conclusions
• It is possible to monitor ecosystem resilience with a satellite-
metric, but vital to have long term experim...
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Ecosystem primary productivity and resilience across Australian drought and wet cycles through coupling field data, tower fluxes and satellite imagery_Huete

  1. 1. ! ! ! ! ! ! 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. 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. 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. 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. 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. 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 ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ●● ● ● ● ● ● ●●● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ● ●● ● ●●● ● ● ●●●●●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●● ● ● 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. 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. 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 ng m an o- ng or rs er 18 . a st, be 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 i- he re P ps r- d m h i- of V, or I, ty d of m- n- 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. 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. 10. Continental Scale RUE of Average, Driest, and Wettest years Driest year Wettest year Average year
  11. 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. 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. 13. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● (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 ● ● ● ● ● ● ● ● ● ● ● ● 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. 14. A1 ● ● ● ● ● ● Howard Springs Adelaide Rivers Daly River Dry River Sturt Plains Ti Tree A2 ● ● ● ● ● ● 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 ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ●● ● ●● ● ● ● ●●● ● ● ●●● ● ● ● ● ●●● ●●● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ●●● ● ● ● 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 ● ●●●●● ●●● ● ●● ● ●● ●●●●● ● ● ●●● ● ●●●● ● ●● ● ●● ●●●●● ● 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. 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 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 01234 GEP(gC*m−2*d−1) ● ● RMSE: SGS = 70.7d, PGS = 19.8d, EGS = 99.6d, LGS = 70d (D) 1215 59 ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● 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 ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● 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. 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. 17. iEVI declineiEVI pulse Difference (Pulse - Decline) Southeast Australia WUE$=$0.78,$$R²$=$0.99$ WUE$=$0.66,$$R²$=$0.98$ WUE$=$0.62,$$R²$=$0.94$ 0$ 100$ 200$ 300$ 400$ 500$ 600$ 0$ 200$ 400$ 600$ 800$ 1000$ ANPP,%g%m(2% Evapotranspira4on,%mm%yr(1% WUE%of%Major%Vegeta4on%Classes%(SE%Australia)% Dry$Year$ Mean$Year$ Wet$Year$ Melbourne Brisbane Sydney Canberra Adelaide
  18. 18. Continental RUE for dry, mean, and wet years RUE$=$0.33,$$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. 19. Continental WUE (2000- 2010) 2000 2003 2006 2010 • Australia Tree Cover fraction (Donohue et al. 2006)
  20. 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.

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