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

Introduction Elephant Butte Lake

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

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. 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.

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

Objective The general objective of the research was to develop a remote sensing tool to estimate evaporation (E) loss (mm/day or m) from reservoirs to aid international water delivery management. The general objective of the research was to develop a remote sensing tool to estimate evaporation (E) loss (mm/day or m 3 ) from reservoirs to aid international water delivery management.

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

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] 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 It is more operational than other models for ET Need to calibrate the parameters for water body Need to calibrate the parameters for water body

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

Build the model Theory ETins = Rn - G - H R n G H ETins Graph from Allen, et. al., (2002) Build the ASTER Model

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

Validate the model Measurement sites Pecan orchard Alfalfa field Build the ASTER Model

ET measurement Li Cor system Validate the ASTER Model

ET map mm/day Validate the ASTER Model

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

Calibration for MODIS model Rn Rn C (G/Rn) C (G/Rn)

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

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

mm

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

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,.

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

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