Satellite-Based Estimation of Evapotranspiration in Florida

David M. Sumner1,Jennifer M. Jacobs2, John R. Mecikalski3, and Michael Holmes4

1U. S. Geological Survey, Florida Integrated Science Center, Orlando, Florida

2University of New Hampshire, Department of Civil Engineering, Durham, New Hampshire3University of Alabama in Huntsville, Atmospheric Sciences Department, Huntsville, Alabama

4U. S. Geological Survey, Florida Integrated Science Center, Tampa, Florida

Varieties of ET

• Actual ET

• Reference ET- hypothetical surface ET

• Potential ET - ET when moisture is not limiting

- surface-dependent

Problem & Need

• Potential ET is common input for hydrologic models (surface dependent)

• Reference ET is needed for allocation of water (for real or hypothetical “reference” surface)

• PET and RET are inconsistently determined among the five Florida Water Management Districts

• Areally-continuous coverage of both PET and RET is lacking

• Potential ET = ET without water limitation

Actual ET = parameterized function of:PET, water level, soil moisture, and/or LAI

MODFLOW, MikeShe, HSPF, VS2D, etc.

MODFLOW ET conceptualization

Reference ET w/crop coefficients

• Reference ET computed using:- - weather station data- - selected RET equation

(varieties of Penman-Monteith, Blaney-Criddle, Hargreaves, etc.)

and crop coefficent specific to crop type and phase is applied as multiplier

AET = kcRET

Crop CoefficientsCrop Coefficients

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Original BC Shih Modified BC

Problem & Need

• Potential ET is common input for hydrologic models (surface dependent)

• Reference ET is needed for allocation of water (for real or hypothetical “reference” surface)

• PET and RET are inconsistently determined among five Florida Water Management Districts

• Areally-continuous coverage of both PET and RET is lacking

Objectives

Estimate reference and potential ET - - throughout State of Florida - - 1995 to 2004 … and beyond - - at 2 km spatial resolution - - at daily temporal resolution - - with spatial grid consistent with NEXRAD grid

RET computations

Meteorological data

Inverse distance weighting interpolation of numerous NOAA, UF, and WMD weather station data to 2-km grid.

UF and WMDNOAA

• Simple method

• Priestley-Taylor

• Penman-Monteith

PET models considered

slow RKET *

GRET now

as

apanow rr

rDcGRET

1

Comparison of PET models with AET measured during low Bowen ratio conditions

….. Supports choice of Priestley-Taylor PET

SFWMD / USGS ET station at WRWX in Polk County

Bowen ratio ET station in Everglades

Calculation of PET was performed with the Priestley-Taylor method

E = (Rn – G)

PET computations (daily)

Solar radiation measured via satellite …

… other variables estimated using spatial interpolation of land-based station data.

Required input = net radiation (Rn) = 1.26 = f(air temperature)G is assumed zero over a day

Solar and terrerestrial radiation

Net Radiation

Incoming solar (Rs)

Reflected solar = Rs

Longwave down (Ld)

Longwave up (Lu)

Net radiation = Rs – Rs +Ld - Lu

4-component radiation sensors (11) used to define means to estimate reflected solar and longwave terms

Longwave radiation simulation

Stefan-Boltzmann equationRadiation = T4

surface ~ 0.97 for soil/grass/snow

atmosphere =

f (vapor, temperature, cloudiness)

Clear sky clear (e) – Sellers (1965)

Cloudy sky (clear ,Rs/Ro) - Crawford and Duchon (1999)

Longwave radiation

Longwave radiation simulation

Stefan-Boltzmann equationRadiation = T4

surface ~ 0.97 for soil/grass/snow

atmosphere =

f (vapor, temperature, cloudiness)

Clear sky clear (e) – Sellers (1965)

Cloudy sky (clear ,Rs/Ro) - Crawford and Duchon (1999)

Satellite-based estimation of incoming solar radiation

Inco

min

g so

lar

radi

atio

n (

MJ/

m2/d

)R

efe

renc

e or

pot

entia

l ET

(m

m/d

)

Incoming solar radiation has strong explanatory value (> 80%) fortemporal variability of PET and RET in Florida

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Longwave terms are large … but solar terms exhibit most variability

0

100

200

300

400

500

600

0 50 100 150 200 250

LW up

LW down

solar down

solar up

Day of year 2008

Rad

iatio

n, in

W/m

2

Rad

iatio

n, in

W/m

2Solar radiation terms explain most (~ 84% at central Florida station) of temporal variability in net radiation

Day of year 2008

-200

-100

0

100

200

300

400

0 50 100 150 200 250

net solar

net longwave

net radiation

Frequency (%) of clear sky conditionsFrom: Climatic Atlas of Clouds over Land and Ocean by Warren and Hahn (2007)

Solar radiation is strongly affected by cloud cover … and Florida is relatively cloudy.

Large spatial variability in cloud cover --->

Large spatial variability in incoming solar radiation and ET

GOES East 8 and 12 geostationary satellites provide spatially (1-km in Florida) and temporally (30 minute) data,

capturing spatial and diurnal changes in cloud cover and solar radiation

Polar-orbiting satellites (MODIS, AVHRR, Landsat) provide less frequent monitoring.

Gautier-Diak-Masse model – simple radiative transfer model incorporating:

1. Clouds

2. water vapor absorption

3. Raleigh/Mie scattering

4. ozone absorption

Gautier et al. (1980)

Diak and Gautier (1983)

Diak et al. (1996)

1. 2-week minimim noon albedo

2. Is pixel cloudy?

3. If so, solve for cloud albedo.

4. Solve for incident solar radiation (full SW bandwidth)

Approach

GOES albedo =

solar albedo

GOES “visible” bandpass

Albe

do

AMS Agriculture and Forest Meteorology Conf.

Orlando, Florida 30 April 2008

GDM model has shown regional utilityOtkin et al. (2005) :

Method has error on theorder of ~7-8% during clear-skyconditions, and ~17% duringcloudy-sky conditions.

Calibration of GDM incoming solar radiation product for Florida

1) Clear-sky conditions

2) Cloudiness bias correction

3) Temporal bias correction

Uncorrected

Clear-Day Comparison Initial Model Calibration

CorrectedCorrected +4%

Clear-Day Comparison Initial Model Calibration

Calibration of GOES daily solar product under cloudy conditions

19 pyranometer stations~ 36,000 station-days over 1995-2004

GOES daily solar bias related to cloudiness

Mean solar radiation ~ 190 w/m^2

… winter solstice ~ 100 w/m^2

… summer solstice ~ 270 w/m^2

Temporal bias in initial solar product

Measured vs. GOES insolation

Orange = southBlue = north

Error statistics of 9 “validation” stations during calibration path

Initial Clear-sky Cloudiness Temporal trend

Examples of incoming solar radiation daily product

Summer Winter

Mean annual solar cross-sections across Florida

Data availability

10 year dataset (1995-2004) and summary report available on webFISC Hydrologic Data Portal ….. http://hdwp.er.usgs.gov/

Daily 2-km values throughout Florida:

Potential ETReference ETIncoming solar radiationAir temperature (min and max)Relative humidity (min and max)Wind speed (mean)

Work to extend dataset thru 2007 will be completed in September 2008

Applications:Applications:

1.1. PET and RET for hydrological modeling (surface & ground water) PET and RET for hydrological modeling (surface & ground water)

2.2. RET for water allocation RET for water allocation

3.3. Solar radiation distributions for studies of:Solar radiation distributions for studies of:ecosystem (e.g., implications for Primary Production)ecosystem (e.g., implications for Primary Production)oceans (e.g., light availability in subsurface)oceans (e.g., light availability in subsurface)

4.4. Monitoring for climate change (cloud cover and solar radiation)Monitoring for climate change (cloud cover and solar radiation)

Note: Florida is flat …. expansion to more topographically rugged areas Note: Florida is flat …. expansion to more topographically rugged areas will require consideration of slope and aspect in computation of will require consideration of slope and aspect in computation of incident solar radiation ....incident solar radiation ....……. including differentiation of diffuse and direct solar radiation. including differentiation of diffuse and direct solar radiation

Extend approach Nationally?

1. Puts hydrologic modelers on common PET framework.

2. Aid to Nationwide climate monitoring