The role of TERN in studies of ecosystem  resilience across large-scale altered         hydroclimatic conditions       Gui...
Introduction      •             Recent large-scale, warm droughts have occurred in                    Australia, China, No...
•         As Earth’s climate continues to change, the frequency and                      intensity of warm droughts, extre...
•   Understanding water and productivity relationships are key    issues in models that aim to predict how carbon and wate...
Objectives• Investigate cross-biome productivity and  vegetation functional responses to recent  contrasting hydro-meteoro...
er nitrogen and light will influence ANPP more strongly. However,  n  7ng   ; both in locations with high MAP and in those ...
1800                    iEVI as proxy for ANPP                                                                            ...
The experimental sites encompass a range of precipitation regimes and capture similar biome                               ...
This indicates a cross-biome aerosol-contaminated pixels and observationsremove low-quality, cloud- and sensitivity to pro...
intrinsic sensitivity of plant communities to wat                                                                         ...
PDSI             collapse as biomes endure the significant drought-induced mortal                             0           ...
collapse as biomes endure the significant drought-induced mortality                    1,200                              ...
- future of Australia?                              • Tropical Rainfall                                Measuring Mission  ...
TRMMiEVI (mean)  iEVI (dry)   iEVI (wet)
20002003           Continental               WUE (2000-                 2010)2006                 •   Australia Tree      ...
Continental Scale RUE,  WUE of Average, Driest, and Wettest         years                         Average yearDriest year ...
4000"                                        3500"                                                                        ...
Productivity- rainfall per year along                NATT transect                                                        ...
CONTINENTAL   MVG class
Continental RUE for dry, mean, and wet                 years                                                              ...
CO,                      WUE%of%Major%Vegeta4on%Classes%                                        ANPP vs ET       100 is 43...
Pulse and decline resilience        measures (following Knapp & Smith, 2004, Science) iEVI pulse = (iEVIwet - iEVImean)/(i...
BrisbaneAdelaide                                        Sydney                                      iEVI pulse   iEVI decl...
Conclusions•   Extreme precipitation patterns have substantial effects on vegetation    production,•   Cross-ecosystem wat...
Thanks
continental Australia                      2004iEVI                                             2005         TRMM, rainfal...
iEVI Pulse - iEVI Decline  Positive values indicate where wet year pulses in  vegetation productivityexceeded dry year dec...
SE Australia                      WUE%of%Major%Vegeta4on%Classes%(SE%Australia)%               600$               500"    ...
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Alfredo Huete_The role of TERN in studies of ecosystem resilience across large-scale altered hydroclimatic conditions

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Alfredo Huete_The role of TERN in studies of ecosystem resilience across large-scale altered hydroclimatic conditions

  1. 1. The role of TERN in studies of ecosystem resilience across large-scale altered hydroclimatic conditions Guillermo E. Ponce-Campos1 Susan M. Moran1 Alfredo Huete2,3 Derek Eamus2,3 Acknowledgements Tim McVicar4 Randall Donohue4 Alex Held3,4 Kevin Davies, Natalia Restrepo-Coupe, Mark Broich2,3 ! Bureau of Meteorology ! (1) USDA-ARS ! (2) University of Technology, Sydney ! (3) TERN AusCover and OzFlux ! (4) CSIRO ! TERN 4th Annual Symposium, Canberra 20 February 2013
  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.Extreme Australian temperatures alter face of countrys heat mapTemperatures to soar past 50 Chttp://www.cbc.ca/news/technology/story/2013/01/08/sci-aussie-heat-map.html GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L03404, doi:10.1029/2011GL050263, 2012 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, [Ummenhofer et al., 2009; Smith and Timbal, 201
  3. 3. • As Earth’s climate continues to change, the frequency and intensity of warm droughts, extreme precipitation patterns, and heat waves will alter in potentially different ways, ecosystem functioning and productivity with major impacts on 3. Comparison of iEVI relative difference of years with similar annual precipitation but different% (years in the Low and High groups, respectively; iEVI difference= (RUEHigh- RUELow)/ w*100)) across 11 sites. For each site, the years with similar annual precipitation were selected to carbon re the iEVI differences in the two groups. The inset shows the average iEVI difference combined ome types. Different letters indicate significant differences at P < 0.05." What is an extreme climate event? All extremes are relative to some expectation. An ex- ing will probably become more frequent in treme climate event is one that has appeared only rarely as warmer conditions mean some snowfall in the historical record, which goes back about 100 storms in the Sierra Nevada converts to rai years. For example, a 1-in-100 year flood is an extreme snow on the ground melts earlier in the yea event, as is a three-day heat wave that is hotter than 95% of all previous 3-day heat waves. Together, the frequency and intensity of we ANPP make up a distribution. The well-known be As Earth’s climate continues to change, the climate example of a distribution. Extreme events a extremes we experience will alter in potentially different fall on the ends of the distribution. One of ways. The intensity could change, or the frequency (or climate science is to understand how the d both). climate events is likely to change in the futu Some extremes could become more intense. Intensity Understanding how the frequency and inte refers to how different the climate extreme is from extremes changes in the future has implica normal conditions. For instance, as the climate warms, we could adapt to those changes. For instan MODIS Satellite EVI heat waves will likely become hotter than any seen since ing becomes more intense (a larger volume measurements began. ter), bigger flood control channels may be flooding becomes more frequent, perhaps On the other hand, some extremes could change their channels needed to drain roads that inconv frequency, which is to say, how often they occur. Flood- flood during heavy rains would be needed Old Probability of occurrence Climate 4. Relation of production across precipitation gradients for 11 sites for two groups (Low: R95p% < Zhang et al 2013, BiogeosciencesHigh: R95p% ≥ 20%). See Table 2 for R95p% definitions. The relations were significantly different two groups (F2, 106 = 18.51, P < 0.0001). New climate 34" More frequent heat waves1" Extreme precipitation patterns reduced terrestrial ecosystem production across2" biomes Cold Average Hot More intense heat waves As the climate changes, the distribution of events such as heat waves and 1 1 1 13" Yongguang Zhang *, M. Susan Moran , Mark A. Nearing , Guillermo E. Ponce Campos , Alfredo R. floods will change. Extreme events are those on the tails of the distribution, and could change in their intensity (for example, how hot a heat wave is) or4" Huete2, Anthony R. Buda3, David D. Bosch4, Stacey A. Gunter5, Stanley G. Kitchen6, W. Henry McNab7, their frequency (how often the event occurs). After IPCC (2001), Fig. 2.32.5" Jack A. Morgan8,Mitchel P. McClaran9, Diane S. Montoya10, Debra P.C. Peters11, Patrick J. Starks12
  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
  5. 5. Objectives• Investigate cross-biome productivity and vegetation functional responses to recent contrasting hydro-meteorological conditions in Australia and the Americas,• Investigate rain use efficiencies (RUE) and water use efficiencies (WUE) across biomes and over prolonged warm drought periods,• Conceptualize satellite- monitoring schemes for ecosystem threshold and resilience
  6. 6. er nitrogen and light will influence ANPP more strongly. However, n 7ng ; both in locations with high MAP and in those with low MAP, water Rainfall use efficiency (RUE) concept mit availability is tightly linked to biogeochemical constraints throughan i- mineralization processes and leaching20. Precipitation affects botho- nutrient availability through its effects on microbial activity andngn or al rs h er ty 18nt.ms a ANPP g/m2st,ngbe i- 17he;onre 7yP;ps it i- r- donm al th h ty i- ntofms Figure 1 Between-year variation in production across a precipitation gradient and aV, letters to natureng maximum rain-use efficiency. a, Plot of ANPP against PPT for 14 sites (see Methods foror i- .............................................................. I, abbreviations). Multi-year data give site-specific relationships by using linear regression s, for S. paradoxus versus S. lizards. ty 038/nature02597. Convergence across biomes tohe (see Supplementary Information). The overall relationship (bold line) derives from data *ANPP difficult to measure d a common rain-use efficiency re from all sites: ANPP ¼ 1011.7 £ (1 2 exp(20.0006 £ precipitation)); r 2 ¼ 0.77; spectives (eds Woods, C. A. & Sergile, e of the Antillean insectivoranPP P , 0.001. The inset shows theD.site-level slopes ,(ANPP plotted against precipitation) as aof 1 2,3 4 Travis E. Huxman *, Melinda Smith *, Philip A. Fay Alan K. Knapp , m. Mus. Novit. 3261, 1–20 (1999). e Caribbean region: implications for 6 7 8 5 9 M. Rebecca Shaw , Michael E. Loik , Stanley D. Smith , David T. Tissue ,m- function of MAP:C.ANPP ¼ Weltzin , William T. Pockman , Osvaldo E. Sala ,precipitation)); r2 ¼ 0.51; *First ‘fun’ test MODIS EVI data ps 999). John Zak , Jake F. 0.388 £ (1 2 exp(20.0022 £ s. Annu. Rev. Ecol. Syst. 27, 163–196 9 7 10 13 14 11 12 15 Brent M. Haddad , John Harte , George W. Koch , Susan Schwinning , r-n- P , 0.001. b, AnSmall & David G.max derived from the slope of the minimum precipitation and Eric E. overall RUE Williams l history of Solenodon cubanus. Acta 16 17 ldhe the correspondingand Evolutionaryall sites (solidArizona, Tucson, Arizona 85721, þ 0.42 £ PTTmin. Closed the Dominican Republic. 1–128, Ecology ANPP for Biology, University of line): ANPP ¼ 86.1 1re circles, minima; open circles, remaining2004 California95% confidence intervals. Huxman et alSynthesis, Santa Barbara, lines, USA mhy: molecular evidence for dispersal 89, 1909–1913 (1992). 2 National Center for Ecological Analysis and data; dotted 93101, USA Mus. Nat. Hist. 115, 113–214 (1958).ar Arrows showConnecticut 06511, USAand for sites Biology, Yale University, New Haven, high precipitation. average slopes Evolutionary with low, medium and 3 Department of Ecology Species Level (Columbia Univ. Press, th (ed. Benton, M. J.) 117–141 (Oxford 4 Natural Resources Research Institute, Duluth, Minnesota 55811, USA Department of Biology, Colorado State University, Fort Collins, | 10 JUNE 2004 | www.nature.com/nature 5 NATURE | VOL 429 Colorado 80523, Ponce et al ISRSE, Sydney 2011ure Publishing Group i- ntals (eds Szalay, F. S., Novacek, M. J. USA 6
  7. 7. 1800 iEVI as proxy for ANPP 1600 y = 192.65x - 155.29 R = 0.8578 Mean Annual GPP (g C m-2) 1400 1200 1000 800 600 400 200 0 0 2 4 6 8 10 Mean Annual iEVI FOREST DESERT-GRASSLAND 377" " SAVANNA SHRUB OPEN FOREST SAVANNA WOODY SAVANNA 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 annualEVI 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. 378" " 379" Figure 1. 380" Ponce-Campos et al 2013, Nature
  8. 8. The experimental sites encompass a range of precipitation regimes and capture similar biome Location of study sites (with known in-situ fieldtypes on both continents (Table A1). Results for USDA and Australia sites were compared withpreviously-published results based on a dataset composed primarily of Long-term EcologicalResearch (LTER) sites covering 14 sites with measurements made during the period from about information)1980-1999, hereafter referred to as the LTER dataset (Table A1; Figures A1-A3). • In N America, LTER, LTAR sites and on-site meteorological and in- situ data available co-located. 67° W 66° W Luquillo (LU) ! ( 18° N 18° N Source: NOAA (http://www7.ncdc.noaa.gov) 67° W 66° W NRCS (http://www.wwc.nrcs.usda.gov/climate)Figure A1. Location of the USDA experimental sites with mean annual precipitation. • Use NVIS and TERN for key Biome types • Use of Google-Earth to assess similar types but close to BOM HQ stations. Check MODIS 1 time series for any LCC/ disturbances Ponce et al (2013) Nature
  9. 9. This indicates a cross-biome aerosol-contaminated pixels and observationsremove low-quality, cloud- and sensitivity to prolonged warm drought for the Australia sites.made at large sensor zenith angles (.30u). The retained high-quality pixels were The PDSI is a measure of drought and wet spells, in which PDSI 5 0 is normal, a 1,200 LTER75–98 USDA00–09 Australia01–09averaged to represent the EVI 2 = 0.95 that2site0.77 16-day period, resulting in a value for R = and 23 is moderate drought, 24 is extreme drought, and excess precipitation is R2 = 0.87 R10-year EVI time series forUSDAsite. Australia01–09 a 1,200 LTER0.001 each 0.001 P < 0.05 represented by a positiveof plant production for the time period from The response PDSI. We obtained the PDSI to precipitation P < 75–98 P < 00–09 1980 to 2009 to identify the average drought conditions across the USDA, LTER the 0.95 R2 = TIMESAT23 to smooth the quality- The next step2was to useR2 = software tool0.77 R = 0.87 andduring the prolonged warm drought showed strong 1,000assurance-filtered time series<data and P < 0.05 P < 0.001 P 0.001 standardize the MODIS EVI time series Australia sites. On the basis of this site-specific PDSI (see Supplementary ANPPG orG or ANPPm(g) m–2)analysis for consistent cross-site comparisons. The TIMESAT filtering option 1,000 agreement15–18 the ANPP/precipitation relations with Information and Supplementary Table 3) and reports of continental-scale droughtknown 800the adaptive Savitzky–Golay filter24 was applied over the time series as extent and severity (summarized in the main text), the period of altered ANPP ANPPS (g S –2 800 ‘Mean’ yearsfor smoothing the data and suppressing noise by replacing each data value yi,i 5 1,...,n by a linear combination of nearby values in a window, where reported during the late twentieth century (mean hydroclimatic conditions was determined to be 2000–2009 for USDA sites and 2001–2009 forfor each site over the naming convention USDA00–09 and values Australia sites, reflected by multi-year study periods). 600 n Australia01–09, respectively. X 600 cj yizj LTER75–98 ð1Þ Evapotranspiration model. Estimates of evapotranspiration at different biomes j~{n The lowest mean RUE reported for biomes with were obtained using a model of mean annual evapotranspiration formulated with 400 USDA LTER75–9800–09and the weights were cj 5 1/(2n 1 1), and the data value yi was replaced by the data from moremean precip can be explained largely by non- highest than 250 catchment-scale measurements from around the world12. 400 Australia USDA00–09 01–09 The two-parameter model offers an approach for estimation of mean annual eva-average of the values in the window. The moving-average methodLTER preserved the Australia01–09 biological components of the hydrological cycle. 200 00–09area and mean position of a seasonal peak, but altered both the width and height. potranspiration (ET) on the basis of changes in annual precipitation (P) (mm yr21) 00–09 LTER and the percentage of forest cover ( f ), where 200The latter properties were preserved by approximating the underlying data valuewith the value obtained from a least-square fit to a polynomial, rather than theaverage in00 window. For each data value2,000 1, 2,…, n a quadratic polynomial the 1,000 2 yi, i 5 3,000 4,000 Fractional forest cover Fractional herbaceous plants cover 0 zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{ zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{ 0 1was fitted as f(t) 5 c1 1 c2t 1 c3t Precipitation points in3,000 0 1,000 to all 2n 1 1 (mm yr–1) moving window and 2,000 the 4,000 1,410 1,100the value yi was replaced with the value of the polynomial at position ti (ref. 24). Precipitation (mm yr–1) B 1z2 1z0:5 CThe advantage of this method was that it preserved features of the distribution ET~ @ Bf P z (1{f ) P CP ð2Þ b LTER75–98 USDA00–09 Australia01–09 Average of all years 1,410 P 1,100 P Asuch as relative maxima, minima and width, which01–09 Average‘flattened’ by b LTER75–98 USDA00–09 Australia were usually of all years 1z2 z 1z z 1,200 WUEm = 0.65 WUEm = 0.73 WUEm = 0.73 P 1,410 P 1,100other1,200 WUE = 0.65 WUE = 0.73 WUE = 0.73 adjacent-based averaging techniques. R2 m0.88 of integrating EVI2values from TIMESAT and avoid To simplifyRthe=0.88 2 = process R2 = 0.98 m R2 = 0.98 R2 m 0.77 = R = 0.77 Zhang et al. (2001) ET model P < 0.0001parameterization, 0.0001 P < 0.0001 P < 0.0001 The model has two portions as depicted in equation (2), with the left side P < we integrated over the entire year for every site. Therefore, the P < 0.0001 P < 0.0001 1,000integrating EVI to obtain iEVI was based on using the default para-process of 1,000 accounting for the fractional forest cover and the right side accounting for the - Ecosystem water-use efficiency (WUEm) was ANPPG or ANAPSS(g m–2) )meters found when TIMESAT was initiated. After smoothing the series, we pro- fractional herbaceous plant cover (non-forested). The main advantage of this –2ceeded to extract an offset of 0.05 of each 16-day EVI value to reduce effects of soil model over more traditional models is theprecipitation gradient. constant across the entire derivation from data readily available G or ANAP (g m 800 800exposure. Our process was standardized by applying the same procedures to each at theThere were no significant differences among - catchment scale. For the USDA00–09 data set, the information about thedata set used. percentage of non-forested areas was obtained from contacts at each location. ForMeteorological data. Daily precipitation and temperature were measured at in the WUEm for the three dataof the percentage of non-forested areas 600 Australia01–09 data set, estimations sets - indicating all biomes 600situ stations associated with the experimental sites. 1.0 1.0 annual precipitation were made using Google Earth. Total WUEm (g m–2 mm–1) WUEm (g m–2 mm–1) 21(sum of daily precipitation, mm yr ), mean annual precipitation (MAP)a(mean 0.8 0.8 a a a retained their intrinsic sensitivity to water availability a aof annual precipitation over the study period, mm yr21) and mean maximum 23. during prolonged,TIMESAT–adrought conditions. 400 400 0.6 0.6 Jonsson, P. & Eklundh, L. warm program for analyzing time-series of ¨ 0.4 0.4temperature (mean of average monthly maximum temperature, uC) were com- satellite s ensor data. Comput. Geosci. 30, 833–845 (2004). 0.2 0.2puted for the hydrological year, defined as the 12-month period from October– 24. - This suggestsM. J. E. Smoothing andgoverning how species Savitzky, A. & Golay, that the rules differentiation of data by simplified 200 200 0 0 least s quares procedures. Anal. Chem. 36, 1627–1639 (1964).September in the Northern Hemisphere and May–April in the Australia LTER USDA Southern LTER USDA Australia 25. are organized in terms of Weather Bureau Res. Paper no.45 (1965). Palmer, W. C. Meteorological Drought their tolerance ofHemisphere. The warm season was defined as April–September–0.81USDA–1.34 26. Thornthwaite, C. W. An approach toward a rational classification of climate. ~0 ~0 for–0.81 –1.34sites PDSIand November–April for Australia sites. 0 PDSI hydrological stress are robust despite extended Geogr. Rev. 38, 55 (1948).PDSI. The0 0 PDSI200 computed with800 Thornthwaite equation26 using1,800 25 was 400 600 the 1,000 1,200 1,400 1,600 a self- 27. Wells, N., Goddard, S. & Hayes, M. J. A self-calibrating Palmer Drought Severity 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800calibrating PDSI implementation that automatically calibrated the behaviour of perturbation by low precipitation. Index. J. Clim. 17, 2335–2351 (2004). Evapotranspiration (mm yr–1) –1 Evapotranspiration (mm yr ) Ponce-Campos et al 2013, Nature
  10. 10. intrinsic sensitivity of plant communities to wat shared capacity to tolerate low annual precipitatioa LTER75–98 WUEx = 0.66 USDA00–09 WUEx = 0.79 Australia01–09 Driest years WUEx = 1.01 Driest years to high annual precipitation. These findings p model of ecosystem resilience at the decadal scal 1,200 R2 = 0.81 R2 = 0.94 R2 = 0.82 hydroclimatic conditions that are predicted for P < 0.001 P < 0.001 P < 0.001 1,000 (Fig. 4). During the driest years, the high-produ water limited to a greater extent resulting in highANPPG or ANPPS (g m–2) LTER75–98 that encountered in less productive, more arid ec 800 USDA00–09 Australia01–09 that when all ecosystems are primarily water lim LTER00–09 maximum WUEe will be reached (WUEx) that 600 1.2 with further reductions in water availability. Furt WUEx (g m–2 mm–1) b 1.0 a that as cross-biome WUEe reaches that maximum 0.8 a 400 0.6 will approach WUEx because production will b 0.4 water supply and less so by nutrients and light (F 0.2 200 0 With continuing warm drought, the single linea LTER USDA Australia ~0 –0.81 –1.34 spiration relation that forms the common cross- PDSI collapse as biomes endure the significant drough 0 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 Evapotranspiration (mm yr–1) 1,200b LTER75–98 USDA00–09 Australia01–09 Wettest years Predicted WUEx 1,200 1,000 WUEn Australia01–09 - Plotting the driest 2 n = 0.67each multi-year record, yield WUEn = 0.57 WUE years in WUEn = 0.65 a maximum ecosystem WUE (WUEx) R2 = 0.77 R = 0.94 R2 = 0.70 Predicted across all biomes for each of the three data sets. 1,000 P < 0.001 P < 0.001 P < 0.001 800 WUEx ANPP (g m–2) - Most biomes were primarily water limited during the driest years of the early twenty-first century ANPPG or ANPPS (g m–2) 600 drought, overshadowing limitations imposed by other resources even at high- productivity sites. 800 1.2 WUEx (g m–2 mm–1) 1.0 Grassland 0.8 - This indicates a cross-biome sensitivity to prolonged warm drought where ecosystems 400 0.6 600 sustain productivity in the driest years by increasing their WUEe. 0.4 En (g m–2 mm–1) 1.0 0.8 0.2 a a 0.6 a 200 Primarily Limited by 0 400 0.4 water several 0.2 Ponce-Campos et al 2013, Nature 0 limited resources
  11. 11. PDSI collapse as biomes endure the significant drought-induced mortal 0 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 Evapotranspiration (mm yr–1) 1,200 WUEx USDA00–09 Wettest years b LTER75–98 USDA00–09 Australia01–09 Wettest years Predicted WUEx 1,200 1,000 WUEn Australia01–09 WUEx WUEn = 0.57 WUEn = 0.67 WUEn = 0.65 LTER75–98 R2 = 0.77 R2 = 0.94 R2 = 0.70 Predicted WUEx WUEn 1,000 P < 0.001 P < 0.001 P < 0.001 800 75–09 ANPP (g m–2) ANPPG or ANPPS (g m–2) 800 600 1.2 a WUEx (g m–2 mm–1) 1.0 b Grassland 0.8 600 400 0.6 0.4 WUEn (g m–2 mm–1) 1.0 0.8 0.2 c a a 0.6 a 200 Primarily Limited by 0 400 LTER USDA Australi 0.4 water several ~0 –0.81 –1.34 limited resources PDSI 0.2 0 200 0 LTER USDA Australia 0 200 400 600 800 1,000 1,200 1,400 1,600 1,80 ~0 –0.81 –1.34 Evapotranspiration (mm yr–1) PDSI 0 Figure 4 | A conceptual model of ecosystem resilience during altered 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 Evapotranspiration (mm yr–1) hydroclimatic condition. A summary of WUEe results in this study (solid lines), overlain with the predicted behaviour of WUEx (brown dashed line) aFigure 3 | Ecosystem resilience across biomes and hydroclimatic WUEn (blue dashed line) along a continuum of sites limited primarily by waconditions. a, b, Maximum (WUEx) (a) and minimum (WUEn) (b) water-use and by other resources with an arbitrary distinction made here atefficiency (slope of the ANPP/evapotranspiration) in the driest and wettest evapotranspiration 5 700 mm yr21 for illustration only (black dashed line)years, respectively, based on all sites for each data set, plus the three LTER00–09 Predictions are based on forecasts of continuing warm drought1. The inset Comparison for the wettest years during the drought (2003–2009) compared to thevalidation sites. The insets illustrate the differences in WUEx (a) and WUEn illustrates the decrease in WUEx with PDSI for subsets of the LTER75–98 (n 5(b) with mean PDSI for the study periods and locations. Columns labelled with USDA00–09 (n 5 5) and Australia01–09 (n 5 2) data sets limited to grassland wettest years during the earlier hydro-climatic conditions from 1975–1998.the same letter are not significantly different (P . 0.05) across hydroclimatic sites, where columns labelled with the same letter are not significantly differconditions. (P . 0.05). For the wettest years in both periods, there was a minimum value O N T H 2 0 1n3),| commonN A T U R E 00 M (WUE V O L 0 0 0 | to all biomes and similar acrossMacmillanhydroclimatic rights reservedindicating a cross-biome ©2013 both Publishers Limited. All periods, capacity to respond to high annual precipitation, even during periods of warm drought.
  12. 12. collapse as biomes endure the significant drought-induced mortality 1,200 WUEx USDA00–09 Predicted WUEx 1,000 WUEn Australia01–09 WUEx LTER75–98 800 Predicted WUEx WUEn Conceptual model of 75–09 ANPP (g m–2) ecosystem resilience 600 1.2 a WUEx (g m–2 mm–1) 1.0 b Grassland 0.8 400 0.6 0.4 0.2 c 200 Primarily Limited by 0 LTER USDA Australia water several ~0 –0.81 –1.34 limited resources PDSI 0 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 Evapotranspiration (mm yr–1) Figure 4 | A conceptual model of ecosystem resilience during altered hydroclimatic condition. A summary of WUEe results in this study (solid water limited to a greater extent resulting in During the driest years, the high-productivity sites become lines), overlain with the predicted behaviour of WUEx (brown dashed line) and WUEn (blueWUEe line) along a continuum of sites limited primarily by water more arid ecosystems. higher dashed similar to that encountered in less productive,e and by other resources with an arbitrary distinction made here at evapotranspiration 5 700 mm yr21 primarily water (black dashed line). When all ecosystems are for illustration only limited, a cross-biome maximum WUEe is reached (WUEx)9 Predictions are based on forecasts of continuing warm drought1. Thewater availability. that cannot be sustained with further reductions in inset illustrates the decrease in WUEx with PDSI for subsets of the LTER75–98 (n 5 4),h USDA00–09 (n 5 5) and Australia01–09 (n 5 e would collapse as biomes endure the significant drought-induced The common cross-biome WUE2) data sets limited to grassland sites, where columns has been extensively documented over different decade. mortality that labelled with the same letter are not significantly the past (P . 0.05). We hypothesize that this loss of resilience associated with dieback would probably occur first for 0 0 M O N T H 2 0 1 3 | VO L 0 0 0 | N AT U R E | 3Limited. All rights reservedrespond most rapidly to precipitation variability (that is, grasslands). ecosystems that Ponce-Campos et al 2013, Nature
  13. 13. - future of Australia? • Tropical Rainfall Measuring Mission (TRMM) satellite 3B43 Data Product2010 MODIS iEVI • Japan-USA joint project • Launched 1997 Remote sensing methods, by observing broadscale vegetation responses to climatic variability, offer potentially powerful insights2010 TRMM into ecological questions on Rainfall observable timescales. 
  14. 14. TRMMiEVI (mean) iEVI (dry) iEVI (wet)
  15. 15. 20002003 Continental WUE (2000- 2010)2006 • Australia Tree Cover fraction (Donohue et al. 2010 2006)
  16. 16. Continental Scale RUE, WUE of Average, Driest, and Wettest years Average yearDriest year Wettest year
  17. 17. 4000" 3500" TRMM 2010 Annual&Rainfall&Northern Australia 3000" 2500" rainfall Mean" 2000" 2001" 2002"Tropical Transect 2000" 2003" 2004" 1500" 2005" 2006" 1000" 2007" 500" driest 2008" 2009" 2010" TRMM(min)" 0" 6" 62" 230" 286" 342" 510" 566" 622" 790" 846" 902" 1070" 1126" 1182" 1014" 118" 174" 398" 454" 678" 734" 958" NATT&transect,&km& 0.30# 4.00# iEVI#(mean)# 3.50# 6.00# MODIS 0.25# 5.00# 3.00# iEVI Mean# annual%iEVI% 0.20# 2000# 4.00# TRMM# 2001# 2.50# (mean)# 2002# 3.00# 2003# iEVI% 2004# 0.15# 2.00# 2.00# 2005# 2006# 2007# 2008# 1.50# 1.00# 0.10# 2009# 2010# iEVI(min)# 0.00# 1.00# 6# 62# 230# 286# 342# 510# 566# 622# 790# 846# 902# 1070# 1126# 1182# 1014# 118# 174# 398# 454# 678# 734# 958# 0.50# TRMM- NATT%transect,%km% 0.05# rainfall 0.00# 0.00# 1# 9# 17# 25# 41# 49# 57# 65# 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# 33# 73# Transect%distance%
  18. 18. Productivity- rainfall per year along NATT transect Darwin NhulunbuyRegression lines become more linear with drier years Halls Creek Tennant Creek Mount Isa Driest year Alice Springs Birdsville Warburton Tuesday, 14 August 12 Site- based productivity - rainfall 7" 6" Wet tropical 5" savanna Annual&iEVI& 4" y"="$8E$05 R²"="0 3" N10" y"="$0.0005 N50" R²"=" 2" N100" y"="$0.000 R²"= 1" Semi-arid Mulga N150" y"="0.00 is there an inherent 0" (Acacias) N200" R² y"="0.00 R² maximum RUE? 0" 1000" Annual&Rainfall,&mm& 2000" 3000" 4000"
  19. 19. CONTINENTAL MVG class
  20. 20. Continental RUE for dry, mean, and wet years 1000 800 RUE%of%Major%Vegeta1on%Classes% dry mean 500$ 450$ 600 wet 400$ 400 350$ ANPP,%g%m(2% 300$ 200 250$ RUE%of%Major%Vegeta1on%Classes% 200$ 500$ 450$ 0 150$ 400$ 350$ Dry$Year$ RUE$=$0.33,$$R²$=$0.83$ ANPP,%g%m(2% 100$ 300$ 250$ Mean$Year$ RUE$=$0.25,$$R²$=$0.78$ 200$ 50$ 150$ Wet$Year$ RUE$=$0.17,$$R²$=$0.69$ Dry$Year$ RUE$=$0.33,$$R²$=$0.83$ 100$ 0$ 50$ Mean$Year$ RUE$=$0.25,$$R²$=$0.78$ Wet$Year$ 0$ 500$ 1000$ 1500$ 2000$ 2500$ RUE$=$0.17,$$R²$=$0.69$ 0$ 0$ 500$ 1000$ 1500$ 2000$ 2500$ Precipita1on,%mm%yr(1% Precipita1on,%mm%yr(1%Continental RUE for major vegetation classes
  21. 21. CO, WUE%of%Major%Vegeta4on%Classes% ANPP vs ET 100 is 430 500$ WUE%of%Major%Vegeta4on%Classes% 500$ 450$ 450$ 600 400$ 400$ 350$ 350$ ANPP,%g%m(2%ANPP,%g%m(2% 300$ 300$ 400 250$ 250$ 200$ 150$ 200$ Dry$Year$ WUE$=$0.54,$$R²$=$0.91$ 200 100$ 150$ Mean$Year$ WUE$=$0.50,$$R²$=$0.89$ 50$ Dry$Year$ WUE$=$0.54,$$R²$=$0.91$ 100$ Wet$Year$ WUE$=$0.46,$$R²$=$0.84$ 0$ Mean$Year$ WUE$=$0.50,$$R²$=$0.89$ 50$ 0$ 200$ 400$ 600$ 800$ 1000$ 0 Wet$Year$ WUE$=$0.46,$$R²$=$0.84$ Evapotranspira4on,%mm%yr(1% 0$ 0$ 200$ 400$ 600$ 800$ 1000$ Continental WUE for Evapotranspira4on,%mm%yr(1% dry, mean, and wet years Continental WUE for major vegetation classes
  22. 22. Pulse and decline resilience measures (following Knapp & Smith, 2004, Science) iEVI pulse = (iEVIwet - iEVImean)/(iEVImean)iEVI decline = (iEVImean - iEVIdry)/(iEVImean)Delta (Pulse/decline) = iEVI pulse - iEVI decline-same applies to TRMM pulse and decline
  23. 23. BrisbaneAdelaide Sydney iEVI pulse iEVI decline Canberra Melbourne Southeast Australia WUE%of%Major%Vegeta4on%Classes%(SE%Australia)% Difference (Pulse - Decline) 600$ 500$ 400$ ANPP,%g%m(2% 300$ 200$ Dry$Year$ WUE$=$0.78,$$R²$=$0.99$ 100$ Mean$Year$ WUE$=$0.66,$$R²$=$0.98$ Wet$Year$ WUE$=$0.62,$$R²$=$0.94$ 0$ 0$ 200$ 400$ 600$ 800$ 1000$ Evapotranspira4on,%mm%yr(1%
  24. 24. Conclusions• Extreme precipitation patterns have substantial effects on vegetation production,• Cross-ecosystem water use efficiency (WUEe) and RUE will increase with prolonged warm drought until reaching a threshold that will break down ecosystem resilience,• It is possible to monitor ecosystem resilience with a satellite-metric, but vital to have long term experimental monitoring sites• It is unclear and ambiguous what ecosystem collapse would look like from space.• An important goal would be to assess environmental and economic costs associated with variations in ANPP.• Better information for strategic resource management and adaptation practices during altered hydro-meteorological conditions.• Societal needs to detect, predict, and manage changes in complex managed systems that threaten to undermine resource sustainability and security.
  25. 25. Thanks
  26. 26. continental Australia 2004iEVI 2005 TRMM, rainfall iEVI TRMM, rainfall
  27. 27. iEVI Pulse - iEVI Decline Positive values indicate where wet year pulses in vegetation productivityexceeded dry year declines
  28. 28. SE Australia WUE%of%Major%Vegeta4on%Classes%(SE%Australia)% 600$ 500" y"="0.6857x" R²"="0.98168" 450" y"="0.5287x" R²"="0.92009" 500$ y"="0.9061x" 400" R²"="0.94744" 350" 400$ANPP,%g%m(2% 300" 300$ 250" 200" 200$ 150" Dry$Year$ WUE$=$0.78,$$R²$=$0.99$ Dry"Year" 100" 100$ Mean$Year$ WUE$=$0.66,$$R²$=$0.98$ Mean"Year" 50" Wet$Year$ WUE$=$0.62,$$R²$=$0.94$ Wet"Year" 0$ 0" 0$ 0" 200$ 200" 400$ 400" 600$ 600" 800$ 800" 1000$ 1000" Evapotranspira4on,%mm%yr(1%

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