develop a remote sensing tool to estimate evaporation loss from reservoirs

Develop a Remote Sensing Develop a Remote Sensing Tool to Estimate Evaporation Tool to Estimate Evaporation

Loss from ReservoirsLoss from ReservoirsJunming Wang, Ted Sammis, Vince Junming Wang, Ted Sammis, Vince

GutschickGutschickDepartment Department ofof Plant and Environmental Plant and Environmental

Sciences Sciences New Mexico State UniversityNew Mexico State University

Ramiro LujanRamiro Lujan Comisión Internacional de Límites y Comisión Internacional de Límites y

Aguas (CILA, the Boundary and Water Aguas (CILA, the Boundary and Water Commission in Mexico)Commission in Mexico)2008 SCERP Annual Technical Conference, Arizona State

University Memorial Union Tempe, Arizona, December 5-6, 2008

IntroductionIntroduction A treaty requires that US (Mexico) delivers A treaty requires that US (Mexico) delivers

certain amount water to Mexico (US) each certain amount water to Mexico (US) each year.year.

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

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

Evaporation lossEvaporation lossPart of the water delivery Part of the water delivery

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

IntroductionIntroduction

Elephant Butte Lake

IntroductionIntroduction

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

ObjectiveObjective The general objective of the The general objective of the

research was to develop a research was to develop a remote sensing tool to remote sensing tool to estimate evaporation (E) loss estimate evaporation (E) loss (mm/day or m(mm/day or m3) from ) from reservoirs to aid international reservoirs to aid international water delivery management. water delivery management.

Ground measurements of Ground measurements of evaporationevaporation

Inflow–outflow water balance Inflow–outflow water balance method, 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 and one point measurement can

not integrate the spatial not integrate the spatial variability of lake evaporation.variability of lake evaporation.

Remote sensing methods to Remote sensing methods to estimate ETestimate ET

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

It is more operational than other models It is more operational than other models for ETfor ET

Need to calibrate the parameters for Need to calibrate the parameters for water bodywater body

MethodMethod Based on SEBAL, a Remote Sensing Based on SEBAL, a Remote Sensing

ET model was developed and ET model was developed and validated for ASTER data for land validated for ASTER data for land ETET

The model was modified for MODIS The model was modified for MODIS input data and was calibrated and input data and was calibrated and validated using a water balance validated using a water balance lake evaporation calculation .lake evaporation calculation .

Build the modelBuild the modelTheoryTheory

ETins = Rn - G - H

Rn

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 ModelSatellite inputs: surface

temperature and reflectance. Local weather inputs: solar

radiation, humidity and wind speed

ETrdailyETrinsETinsETdaily

Validate the modelValidate the modelMeasurement sitesMeasurement sites

Pecan orchard

Alfalfa field

Build the ASTER Model

ET measurementET measurement

Li Cor system

Validate the ASTER Model

ET mapET map

mm/day

Validate the ASTER Model

The pecan ET of simulation The pecan ET of simulation vs. observation.vs. observation.

0123456789

02/1

3/02

05/2

4/02

09/0

1/02

12/1

0/02

03/2

0/03

06/2

8/03

10/0

6/03

01/1

4/04

04/2

3/04

Time (day)

ET

(mm

/day

)ObservationModel

Validate the ASTER Model

Validate the ASTER Model

Calibration for MODIS Calibration for MODIS modelmodel

Rn Rn C (G/Rn)C (G/Rn)

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

2006) 2006) y = 0.887x - 2.6497

R2 = 0.8173

-5

0

5

10

15

20

25

0 5 10 15 20 25 30

Daily Rs (MJ/day)

Daily

Rn

(MJ/

day)

G/RnG/Rn Using Roosevelt lake E data (Water Using Roosevelt lake E data (Water

balance)balance)ETins = Rn - G - H

mm

MODIS model validationMODIS model validation

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

ET values obtained from MODIS data ET values obtained from MODIS data compared with the ET values from ASTER data compared with the ET values from ASTER data

at Las Cruces, NM, USA for June 8, 2005, at Las Cruces, NM, USA for June 8, 2005, September 7, 2003, May 18, 2003, and September 7, 2003, May 18, 2003, and

September 4, 2002, .September 4, 2002, .y = 0.9576xR2 = 0.9578

0

1

2

3

4

5

6

0 1 2 3 4 5 6 7

ASTER ET (mm/day)

MO

DIS

ET

(mm

/day

)

012345678

E (m

m/da

y)

06 0

2 20

0606

04

2006

06 0

5 20

0606

09

2006

06 2

0 20

0607

10

2006

07 2

0 20

0607

22

2006

07 2

4 20

0607

25

2006

08 0

7 20

0608

09

2006

08 0

2 20

0508

11

2005

08 2

2 20

0508

25

2005

08 2

7 20

05

Date

MeasuredModel ed

ConclusionsConclusions For the summer time E estimate, the For the summer time E estimate, the

accuracy is within 1.5 mm/day. The accuracy is within 1.5 mm/day. The evapotranspiration accuracy is about evapotranspiration accuracy is about 85%. 85%.

The model is capable for aiding The model is capable for aiding international water delivery management.international water delivery management.

The average evaporation of Elephant The average evaporation of Elephant Butte Reservoir in summer time was 5.6 Butte Reservoir in summer time was 5.6 mm/day. mm/day.

PublicationsPublicationsReferred Journal PaperReferred Journal PaperWang, J. and T. W. Sammis. 2008. Sensitivity Analysis on Remote Sensing Wang, J. and T. W. Sammis. 2008. Sensitivity Analysis on Remote Sensing

Evapotranspiration Algorithm-Surface Energy Balance Algorithm for Land. Evapotranspiration Algorithm-Surface Energy Balance Algorithm for Land. ASABE Transaction. Submitted.ASABE Transaction. Submitted.

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

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

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

Conference ProceedingsConference ProceedingsWang, J., T.W. Sammis, and V.P. Gutschick. 2008. A Model Estimating Lake Wang, J., T.W. Sammis, and V.P. Gutschick. 2008. A Model Estimating Lake

Evaporation Using MODIS Data. International Geoscience and Remote Evaporation Using MODIS Data. International Geoscience and Remote Sensing Symposium. 2008 IEEE International Geoscience & Remote Sensing Sensing Symposium. 2008 IEEE International Geoscience & Remote Sensing Symposium. July 6-11. 2008. Boston, Massachusetts, U.S.A.Symposium. July 6-11. 2008. Boston, Massachusetts, U.S.A.

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

Evaporation loss at Evaporation loss at Amistad Reservoir in Amistad Reservoir in

MexicoMexico

Internet Site Internet Site http://hydrology1.nmsu.ehttp://hydrology1.nmsu.e

du/du/

AcknowledgementsAcknowledgements Dr. Thomas Schmugge at NMSU Dr. Thomas Schmugge at NMSU

provided ASTER dataprovided ASTER data Graduate research assistants.Graduate research assistants. USGS provided the water balance data. USGS provided the water balance data. This publication was made possible by This publication was made possible by

a grant from the Southwest a grant from the Southwest Consortium for Environmental Consortium for Environmental Research and Policy (SCERP).Research and Policy (SCERP).