Presentation is loading. Please wait.

Presentation is loading. Please wait.

Land Surface Evaporation 1. Key research issues 2. What we learnt from OASIS 3. Land surface evaporation using remote sensing 4. Data requirements Helen.

Similar presentations


Presentation on theme: "Land Surface Evaporation 1. Key research issues 2. What we learnt from OASIS 3. Land surface evaporation using remote sensing 4. Data requirements Helen."— Presentation transcript:

1 Land Surface Evaporation 1. Key research issues 2. What we learnt from OASIS 3. Land surface evaporation using remote sensing 4. Data requirements Helen Cleugh and Ray Leuning CSIRO Atmospheric Research

2 1.Key Research Issues (a) Why ET? Quantify evaporation (ET) at landscape (ecosystems, catchments, regions) scales: Water limits productivity in Australian ecosystems –Managing landscapes for food & fibre –Managing landscapes for carbon ET is the largest output in the water balance and is the only part that can be managed (except for irrigation): –Managing landscapes for water - soil moisture and runoff are the small difference Surface energy balance important for weather & climate

3 The challenge …. We cant cover everything all of the time … in-situ observations: (lysimeters, flux towers) aircraft observations: (fluxes, concentrations) modelling: (leaf …. region) cover almost nothing but most of the time cover almost everything but hardly ever only pretends to cover everything all of the time satellite observations: (AVHRR, MODIS … ) cover everything all of the time but not what we want! Modified from Dr HP Schmid, Indiana University

4 1.Key Research Issues (b) Methods to quantify ET Multiple space and time scales: –Local, regional, continental; –Spatially distributed or lumped –Sub-diurnal, daily or seasonal Monitoring - combining in situ + remotely sensed observations + land surface model –Remote sensing algorithms –Data assimilation approaches Modelling - prognostic and diagnostic: –Initialisation, parameterisation and testing – especially for Australian ecosystems

5 2. What we learnt from OASIS

6 Flux variation and coherence along OASIS transect From Leuning et al (2004)

7 2. Spatially-averaged evaporation – combining aircraft and flux towers (Isaac et al, 2004) Aircraft data rich in space, sparse in time Tower data sparse in space, rich in time Combine aircraft and tower measurements –Aircraft: measure spatially varying properties (diurnally invariant): Surface properties ( L ai ) Evaporative fraction ( e ) –Flux tower: measure diurnal variation at fixed points in space: Near surface meteorology ( S, A, D, U, T ) –Spatial and temporal evaporation fields using Penman Monteith equation with appropriate forcing

8 Evaporation – Penman Monteith with a simple conductance model g sx and evaporative fraction ( e ) constant during daylight hours

9 Spatial variability at local scale - contours of evaporation ratio, max. surface conductance

10 Evaporation – performance of a simple model combining aircraft and tower data From Isaac (2004)

11 Regional evaporation at OASIS October, 1995

12 with a linear expression for the surface conductance: and MODIS estimates of LAI A new approach using Penman-Monteith model 3. Land surface evaporation using remote sensing

13 Maximum canopy conductance vs antecedent rainfall and NDVI

14 Measured MODIS Tumbarumba Virginia Park

15 4. Data requirements to address research questions Aircraft provides spatially resolved: –Surface conditions (diurnally invariant) - soil moisture, LST, NDVI, LAI, albedo, g smax (derived) –Surface fluxes ( x, z and t, but not continuous) –Concentration fields ( x, z and t, but not continuous) Ground-based, sparse sites to capture time variation: –Surface meteorology and fluxes (CO2 and water) –Calibration data (soil moisture, LAI, NDVI, LST, albedo) Land surface model requirements: –ABL profiles, met. forcing, physiological parameters –Vegetation description –Antecedent data (rainfall, soil moisture, fluxes….)

16 4. Extrapolation – mesoscale models Use flux station data to validate mesoscale model locally Use model to examine spatial correlation length scales Use model to estimate fluxes at regional and continental scales

17 Model validation – need data and initialisation

18 3. Land surface evaporation using remote sensing (a) N DVI – T sR models (triangle method) N DVI From Owens et al, 1998 T sR

19 But relationship between N DVI and T sR not well defined for long time series From Carlson and Tracy, 2000

20 (b) Aerodynamic model where: T sA and T a : aerodynamic surface and air temperatures T sA assumed to be equivalent to T sR Measured differences between T sA & T sR are small … 3. Land surface evaporation using remote sensing

21

22 …..sensible heat flux predictions are poor

23 2. Regional and continental scales: The multiple constraint approach

24 –Surrogate Bowen ratio does not capture seasonal variation in E at mesic sites: 3. Results a) Model Intercomparison

25 Estimating regional evaporation: comparing atmospheric budget, aircraft and flux towers

26 G s = c L. L AI using an average c L G s = c L. L AI using site specific c L


Download ppt "Land Surface Evaporation 1. Key research issues 2. What we learnt from OASIS 3. Land surface evaporation using remote sensing 4. Data requirements Helen."

Similar presentations


Ads by Google