A Remote Sensing Model Estimating Water Body Evaporation

1. A Remote Sensing Model Estimating Water Body Evaporation Junming Wang, Ted Sammis, Vince Gutschick Department of Plant and Environmental Sciences New Mexico State University 2008 International Workshop on Earth Observation and Remote Sensing Applications June 30- July 2, Beijing, China

2. Introduction Elephant Butte Lake

3. Introduction Figure 1. Map of Elephant Butte Reservoir and Las Cruces area, NM, USA. From maps.google.com

4. Introduction • Mexico had amassed a water deficit to the US since 1992 that reached 1.5 million acre-feet at its highest point, costing U.S. agricultural producers in the Rio Grande Valley $1 billion.

5. Evaporation loss • Part of the water delivery problems for both countries was the amount of water being used by reservoir evaporation in the upstream storage reservoirs.

6. Objective • The general objective of the research was to develop a remote sensing tool to estimate evaporation (E) loss (mm/day or m3) from reservoirs to aid international water delivery management.

7. Ground measurements of evaporation • inflow–outflow water balance method, pan measurement method, or eddy covariance method are time- and labor-intensive and one point measurement can not integrate the spatial variability of lake evaporation.

8. Remote sensing methods to estimate ET • SEBAL (surface energy balance algorithm for land) is a residual method of energy budget, developed by [Bastiaanssen et a., 1998] • It is more operational than other models for ET • Need to calibrate the parameters for water body

9. Method • Based on SEBAL, a Remote Sensing ET model was developed and validated for ASTER data for land ET • The model was modified for MODIS input data and was calibrate and validate using a water balance lake evaporation calculation .

10. Build the modelTheory Build the ASTERModel ETins = Rn - G - H R H n ETins G Graph from Allen, et. al., (2002)

11. Start Build the ASTER Model Satellite inputs: surface temperature and reflectance. Local weather inputs: solar radiation, humidity and wind speed Rn=f(Rs, reflectance) General flowchart NDVI=f(reflectance) G=f(NDVI, solar radiation, reflectance) H=f(NDVI, temperature, reflectance, solar radiation, wind speed) ETins=Rn-H-G End

12. Build the ASTER Model Validate the modelMeasurement sites Pecan orchard Alfalfa field

13. Validate the ASTER Model ET measurement Li Cor system

14. Validate the ASTER Model ET map mm/day

15. Validate the ASTER Model The pecan ET of simulation vs. observation.

16. Validate the ASTER Model

17. Calibration for MODIS model • Rn • C (G/Rn)

18. Rn from data in 2005 at Elephant Butte Lake(Almy, 2006)

19. G/Rn • Using Roosevelt lake E data (Water balance) ETins = Rn - G - H

21. mm

22. MODIS model validation Figure 5. Modelled ET from MODIS data taken on June 8, 2005. ET unit: mm/day.

23. ET values obtained from MODIS data compared with the ET values from ASTER data at Las Cruces, NM, USA for June 8, 2005, September 7, 2003, May 18, 2003, and September 4, 2002, .

25. Conclusions • For the summer time E estimate, the accuracy is within 1.5 mm/day. The evapotranspiration accuracy is about 85%. • The model is capable for aiding international water delivery management. • The average evaporation of Elephant Butte Reservoir in summer time was 5.6 mm/day.

26. Acknowledgements • This publication was made possible by a grant from the Southwest Consortium for Environmental Research and Policy (SCERP).

27. Thank You!