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Estimating Surface-Atmosphere Exchange at Regional Scales Peter Isaac 1, Ray Leuning 2 and Jörg Hacker 3 1 School of Geography and Environmental Science,

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Presentation on theme: "Estimating Surface-Atmosphere Exchange at Regional Scales Peter Isaac 1, Ray Leuning 2 and Jörg Hacker 3 1 School of Geography and Environmental Science,"— Presentation transcript:

1 Estimating Surface-Atmosphere Exchange at Regional Scales Peter Isaac 1, Ray Leuning 2 and Jörg Hacker 3 1 School of Geography and Environmental Science, Monash University 2 CSIRO Marine and Atmospheric Research 3 Airborne Research Australia, Flinders University of South Australia

2 What Does The Title Mean? Surface-Atmosphere Exchange –fluxes of momentum, heat, H 2 O and CO 2 Regional Scales –of the order of 100 km –bigger than patch scale accessible by micro-meteorological techniques –smaller than continental scale accessible by inversion techniques

3 Why Do We Want Regional Scale Fluxes of H 2 O and CO 2 ? Validation of land-surface models (LSMs) Estimation of parameters for LSMs at regional scales –avoids some of the issues in scaling-up Investigation of catchment scale hydrology Validation of inventory-based carbon budgets

4 What Was OASIS? Observations At Several Interacting Scales –multi-organisation experiment –held near Wagga Wagga, New South Wales –intensive field campaigns in October 1994 and 1995 1994 was a severe drought year, not used –8 flux towers, G109 research aircraft, free- flying and tethered balloon systems, FTIR spectrometer, TDL, flask samples, LandSat imagery

5 OASIS 1995 Wagga Wagga to Urana 100 km transect ~1 mm/km rainfall gradient paired flux towers at 3 locations aircraft to provide link from patch to regional scale

6 Aircraft Flight Patterns Paddock Grid Transect

7 Tower-Aircraft Comparison Low level allows comparison of means, variances and covariances Correction for temperature sensor response time Correction for surface heterogeneity reduces mean bias to: –7 Wm -2 for F h –-7 Wm -2 for F e Paddock Flights

8 Observing Regional Scale Fluxes No single observation technique available that covers all temporal and spatial scales –flux towers direct measurement with good temporal but poor spatial coverage –aircraft direct measurement with good spatial but poor temporal coverage –remote sensing indirect measurement with good spatial and good temporal coverage

9 Modelling Regional Scale Fluxes Limitations of modelling only approach –need regional-scale values for model parameters often known at leaf or patch scale but not regional –need regional-scale values for fluxes to validate model often only available at patch scale –models incomplete or approximations

10 Best Of Both Worlds Combine observational and modelling techniques to use strengths of each –direct measurement (towers or aircraft) of fluxes used to infer surface properties –interpolation of surface properties over region using remotely sensed data –use surface properties in a model to estimate regional scale fluxes

11 Surface Properties Evaporative fraction,  E Maximum stomatal conductance, g sx Bowen ratio,  Water use efficiency, W UE

12 Assumptions Combined approach uses 2 assumptions applicable in well-watered situations at time scales of several days –temporal evolution of fluxes is primarily driven by diurnal and synoptic trends in meteorology solar radiation, temperature, humidity, wind speed –spatial variation in fluxes is primarily driven by heterogeneity in surface properties stomatal conductance, soil moisture, roughness

13 Constraints Combined approach is subject to 2 constraints –bulk meteorological quantities show good spatial (point to point) correlation meteorology at a single location can be used for a region (tile approach in GCMs) –surface properties show little (ideally no) diurnal variation measurement of surface property at any time during day is representative of whole day

14 Spatial Correlation

15 Diurnal Trend  E and g sx show small diurnal variation at most sites  shows large diurnal variation W UE mixed

16 Spatial Variability Good agreement between aircraft and tower measurements Spatial variability consistent with rainfall gradient  E, g sx,  and W UE all show some variation with synoptic conditions

17 Variability Along Transect

18 Remote Sensing : NDVI LandSat 7 ETM Lack of F C measurements at Urana will bias regional F C based on tower data

19 Source Area Weighted NDVI L = -30 m u * = 0.5 ms -1 z 0 = 0.03 m  WD = 20 deg 80% ~ 18,000 m 2 80% ~ 216,000 m 2 Horst & Weil, 1992 etc

20 Source Areas of Tower and Aircraft Data

21 Surface Properties and NDVI  E = 1.7*NDVI - 0.7 r 2 =0.73  = -6*NDVI + 6 r 2 =0.75 g sx = 57*NDVI + 34 r 2 =0.66 W UE = -36*NDVI + 22 r 2 =0.85

22 Push Forward To Fluxes Interpolate g sx and W UE across region using linear relationship to NDVI Use bulk meteorology (F A, S , D, T a, u) from central location (Browning) plus interpolated surface properties to estimate regional F E F H calculated as F H = F A - F E F C calculated as F C = W UE x F E

23 Comparison With Observations Daily averages of F E, F H and F C Modelled values from g sx and Penman-Monteith equation F E under-predicted at Wagga Wagga and over-predicted at Urana F C under-predicted at Wagga Wagga

24 Comparison Of Techniques Obs is average of sites g sx -PM is combined approach ICBL is integral convective boundary layer approach (Cleugh et al 2004) DARLAM/SCAM is coupled mesoscale/LSM (Finkele et al, 2003)

25 Limitations Daytime only Relationship between surface properties and NDVI is empirical –Site and time specific –W UE relationship not strong Soil moisture not included –Effect of soil moisture on surface conductance is passed on to estimate of g sx (or  E )

26 Consequences Variation of g sx (or other surface properties) along transect is an artifact of not including soil moisture NDVI is a strong function of L ai and L ai is a strong function of soil moisture Relationship between g sx (or other surface properties) and NDVI is likely to be a consequence of neglecting soil moisture

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