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 - PowerPoint PPT PresentationTRANSCRIPT
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).