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Globally distributed evapotranspiration using remote sensing and CEOP data Eric Wood, Matthew McCabe and Hongbo Su Princeton University.

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Presentation on theme: "Globally distributed evapotranspiration using remote sensing and CEOP data Eric Wood, Matthew McCabe and Hongbo Su Princeton University."— Presentation transcript:

1 Globally distributed evapotranspiration using remote sensing and CEOP data Eric Wood, Matthew McCabe and Hongbo Su Princeton University

2 Evapotranspiration (ET) provides the link between the energy and water budgets at the land surface. Developing a globally robust algorithm for the prediction of surface energy fluxes is a significant challenge The purpose of this analysis is to evaluate the adaptability of varied techniques to different climatic conditions and land cover classifications Also, what is the potential for using operational products in achieving routine prediction of evapotranspiration Introduction

3 MOD-16 Evapotranspiration Product  Princeton University funded to research a MODIS based ET product (July, 2004)  Based primarily on the SEBS model, although other approaches are being explored – (can one model work in all environments/all conditions)  Global product – but locally validated – hence need for thorough evaluation – CEOP sites!!!  Princeton is keen to partner with other groups to investigate the best means of forwarding the planned MODIS product – model intercomparison, field experiments etc… 1 Su, B. (2002). The surface energy balance system (SEBS) for the estimation of turbulent heat fluxes. Hydrol. Earth Sys. Sci. 6(1): 85-99

4 Modeling Evapotranspiration Use the Surface Energy Balance Model (SEBS) 1 to determine daily/pentad/monthly ET predictions. Combining available land surface information, meteorological, surface temperature, vegetation, radiation data Make use of available MODIS products – supplement with other sources of data (LIS) Undertake comparisons with other approaches over varied environments…

5 Components of the radiation balance are used to determine the net radiation (R n ) – SW , α, ε, T s, LW  SEBS Model Description R n – G = H + LE Rn = (1- α) SW  + ε LW  - εσ The ground heat flux (G) is parameterized as a function of fractional cover 1 – LAI/NDVI relationships 1 Kustas, W. P. and C. S. T. Daughtry (1990). Estimation of the soil heat flux/net radiation ratio from spectral data. Agric. For. Meteorol. 49: 205-223

6 SEBS Model Description Wind, air temperature, humidity (aerodynamic roughness, thermal dynamic roughness) SEBS calculates H using similarity theory: Various sub-modules for calculating needed components…

7 SEBS Model Description CEOP observations used to assess estimates of evapotranspiration determined using different approaches. Forcing data from validation tower sites supplemented with MODIS data to produce estimates of surface fluxes.

8 Evaluating SEBS Model Results Issues of measurement accuracy, frequency, type… Intensive field experiments offer excellent detail, but are temporally limited Continuous measurements are usually spatially sparse… What is the best/most efficient combination of these. Global product – but locally validated Predictions are only as good as the evaluation data!!!

9 Global Evaluation - CEOP Data Coordinated Enhanced Observation Period provides globally distributed data sets from which estimates of ET can be produced. Located over a variety of landscapes and hydro- climatologies they offer: Data to assess global scale application Allow comparison of different model output SEBS, modified Priestley-Taylor, modified Penman-Monteith # and GLDAS model output # Boegh, E. et al. (2002). Evaluating evapotranspiration rates and surface conditions using Landsat TM to estimate atmospheric and surface resistance. Remote Sensing of Environment, 79(2-3): 329-343.

10 ET Predictions with CEOP Data Time series of GLDAS ~ 0.25º 3-hrly time step 4 sites at global locations: Netherlands, Germany, USA, Brazil

11 ET Predictions with CEOP Data

12 SEBS with Operational Data Run SEBS with: 1.CEOP data + MODIS 2.GLDAS + MODIS 3.GLDAS model output for the EOP-1 Results are limited by lack of coincident sensor/observation data

13 SEBS with Operational Data Mean values approximate reasonably well (within 20%), but instantaneous data are somewhat variable Bondville (corn) indicates particular difficulty – issues of representative flux measurement, site characteristics, capturing vegetation dynamics with RS data Berms – considering it is forested – illustrates pleasing results

14 Scale Issues in ET Modeling How does our ability to predict ET relate to the scale of the observations?? What is the role of surface heterogeneity? How do different model forcings scale – surface temp, vegetation, meteorology? Is there a relationship with other hydrological variables – or do the time scales of the process reduce their influence? How do you get information between satellite overpasses

15 Summary and Conclusions Correct estimation of radiation components is critical Evaluation data Scale of measurements ….

16 Hydrological Measurement from Space 1.Rainfall 2.Runoff 3.Soil Moisture 4.Evapotranspiration 5.Surface Temperature 6.Vegetation Information Rainfall Water level - altimetry Vegetation - LAI Surface temperature

17 Presentation Overview 1. Background 2. Overview and Evaluation of SEBS 3. CEOP Analyis 4. Summary

18 SEBS Description

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