Develop a remote sensing tool to estimate evaporation loss from reservoirs
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Develop a Remote Sensing Tool to Estimate Evaporation Loss from Reservoirs. Junming Wang, Ted Sammis, Vince Gutschick Department of Plant and Environmental Sciences New Mexico State University Ramiro Lujan

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Develop a Remote Sensing Tool to Estimate Evaporation Loss from Reservoirs

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Develop a remote sensing tool to estimate evaporation loss from reservoirs

Develop a Remote Sensing Tool to Estimate Evaporation Loss from Reservoirs

Junming Wang, Ted Sammis, Vince Gutschick

Department of Plant and Environmental Sciences

New Mexico State University

Ramiro Lujan

Comisión Internacional de Límites y Aguas (CILA, the Boundary and Water Commission in Mexico)

2008 SCERP Annual Technical Conference, Arizona State University Memorial Union Tempe, Arizona, December 5-6, 2008


Introduction

Introduction

A treaty requires that US (Mexico) delivers certain amount water to Mexico (US) each year.

However, in some drought years, it may not be followed. Farmers complained.

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

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.


Introduction1

Introduction

Elephant Butte Lake


Introduction2

Introduction

Figure 1. Map of Elephant Butte Reservoir and Las Cruces area, NM, USA.

From maps.google.com


Objective

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.


Ground measurements of evaporation

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.


Remote sensing methods to estimate et

Remote sensing methods to estimate ET

  • Surface energy balance algorithm for land (SEBAL) is a residual method of energy budget, developed by [Bastiaanssen et al., 1998]

  • It is more operational than other models for ET

  • Need to calibrate the parameters for water body


Method

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 calibrated and validated using a water balance lake evaporation calculation .


Build the model theory

Build the modelTheory

Build the ASTERModel

ETins = Rn - G - H

R

H

n

ETins

G

Graph from Allen, et. al., (2002)


Develop a remote sensing tool to estimate evaporation loss from reservoirs

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


Validate the model measurement sites

Build the ASTER Model

Validate the modelMeasurement sites

Pecan orchard

Alfalfa field


Et measurement

Validate the ASTER Model

ET measurement

Li Cor

system


Et map

Validate the ASTER Model

ET map

mm/day


The pecan et of simulation vs observation

Validate the ASTER Model

The pecan ET of simulation vs. observation.


Develop a remote sensing tool to estimate evaporation loss from reservoirs

Validate the ASTER Model


Calibration for modis model

Calibration for MODIS model

  • Rn

  • C (G/Rn)


Rn from data in 2005 at elephant butte lake almy 2006

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


Develop a remote sensing tool to estimate evaporation loss from reservoirs

G/Rn

  • Using Roosevelt lake E data (Water balance)

ETins = Rn - G - H


Develop a remote sensing tool to estimate evaporation loss from reservoirs

mm


Modis model validation

MODIS model validation

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


Develop a remote sensing tool to estimate evaporation loss from reservoirs

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

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.


Publications

Publications

Referred Journal Paper

Wang, J. and T. W. Sammis. 2008. Sensitivity Analysis on Remote Sensing Evapotranspiration Algorithm-Surface Energy Balance Algorithm for Land. ASABE Transaction. Submitted.

 Wang, J., T.W. Sammis, and V.P. Gutschick. 2008. Review of Satellite Remote Sensing Use in Forest Health Studies. Applied Remote Sensing. Submitted.

Wang, J., T.W. Sammis, and V.P. Gutschick. 2008. Remote Sensing of Water Body Evaporation. Applied Remote Sensing. Submitted.

Wang, J. and T. W. Sammis. 2008. New Automatic Band and Point Dendrometers for Measuring Stem Diameter Growth. ASABE Ag. Engineering. In press.

Conference Proceedings

Wang, J., T.W. Sammis, and V.P. Gutschick. 2008. A Model Estimating Lake Evaporation Using MODIS Data. International Geoscience and Remote Sensing Symposium. 2008 IEEE International Geoscience & Remote Sensing Symposium. July 6-11. 2008. Boston, Massachusetts, U.S.A.

Wang, J., T.W. Sammis, and V.P. Gutschick. 2008. A Remote Sensing Model Estimating Water Body Evaporation. 2008 International Workshop on Earth Observation and Remote Sensing Applications. June30-July2. Beijing, China.


Evaporation loss at amistad reservoir in mexico

Evaporation loss at Amistad Reservoir in Mexico


Internet site http hydrology1 nmsu edu

Internet Site http://hydrology1.nmsu.edu/


Acknowledgements

Acknowledgements

  • Dr. Thomas Schmugge at NMSU provided ASTER data

  • Graduate research assistants.

  • USGS provided the water balance data.

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


Develop a remote sensing tool to estimate evaporation loss from reservoirs

Thank You!


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