1. Rick Allen, University of Idaho-Kimberly Nov., 2013, rev. Sept. 2015
John L. Monteith
How John Monteith's Formulation of the Penman-Monteith Equation Helped to Standardize the World of Reference Evapotranspiration
Rick Allen, University of Idaho-Kimberly
Nov., 2013, rev. Sept. 2015
Acknowlegements:
Luis Pereira, Univ. Lisbon
William Pruitt, UCD (deceased)
James L. Wright, USDA-ARS (retired)
Terry A. Howell, USDA-ARS (retired) Ayse Kilic, Univ. Nebraska

2. John Lennox Monteith
“In a career spanning over half a century, he is perhaps best known for the Penman–Monteith equation that has become the basis for guidelines for estimating irrigation water requirements used by FAO (Food and Agriculture Organization of the United Nations).”
“In 1954 John moved to Rothamsted Experimental Station as a Scientific Officer and began working under Penman, who was carrying out seminal research into how variation in weather conditions affected soil moisture. Penman had developed a method to predict the rate of evaporation from wet surfaces, but this did not take account of the complicating effects that vegetation imposed on water loss. By harnessing the analogy of electrical resistance, John showed how to account for surface conductance of water, and produced the Penman-Monteith equation that more correctly accounted for wind and surface effects.”
Chin Ong and Colin Black, 2012, Agricultural and Forest Meteorology 166–167

3. Penman-Monteith
Monteith not only saw the need for surface resistance in the Combination equation, but he appreciated that the empirical wind function of Penman should be replaced by the electrical analogue for aerodynamic resistance.
Penman:
Rn=net radiation, G = soil heat flux
= f (solar radiation)
f (temperature)
aerodynamic resistance = f (wind)
actual vapor pressure
= f (humidity)
f (crop)
ρa is density, cp is specific heat, λ is latent heat of vaporization, Δ is slope of sat. vapor pressure curve, γ is psychrometric constant

4. Challenge: NARR, NLDAS Gridded Weather data sets often need to be ‘conditioned’ before using to estimate ET from Irrigated Agriculture in Semiarid climates
Ambient
Conditions
Of Dry Areas
Overestimation when placed in a Penman-Monteith “Reference ET” Equation
zm measurement height
NASA/USDA Workshop on Evapotanspiration

5. ‘Reference’ Conditions Representing Irrigated Areas
Challenge: NARR, NLDAS Gridded Weather data sets may need to be ‘conditioned’ before using to estimate ET from Irrigated Agriculture in Semiarid climates
‘Reference’ Conditions
Representing
Irrigated Areas
Correct estimation when placed in a Penman-Monteith “Reference ET” Equation
zm measurement height
unadjusted
adjusted (conditioned)

6. Monteith vs Van Gogh
(both saw eddies clearly)

7. What is Reference ET? If there were no Reference ET:
Reference ET (ETref) is:
ET from a well-defined surface of dense vegetation that has reproducible ET that can be predicted using weather data
ETref represents the majority of weather-based effects on ET and can be based on physics , a
If there were no Reference ET:
We would have a specific ET equation for:
each crop
each stage of growth
much wasted effort in parameterizations

8. What is Reference ET? Two Current Vegetation Types for Reference ET:
Clipped Grass (ETo)
Cool season grass (fescue or perennial ryegrass)
Mowed to 8 to 15 cm height
Extensive cover (~ 50 m or more)
Full-cover Alfalfa (ETr)
Dense stand with no cutting effects
30 to 70 cm height

9. ASCE Penman-Monteith Manual 70 "Full Form"
ASCE Manual 70 – Jensen et al. (1990) – Found PM best performing for “Reference ET”
ASCE Penman-Monteith
for alfalfa, bulk surface resistance, rs = 45 sm-1 for full cover, 0.5 m height
for clipped grass, rs = 70 sm-1 for 0.12 m grass height
(Daily Timestep)
Manual 70
"Full Form"
Rn=net radiation, G = soil heat flux
= f (solar radiation)
f (temperature)
aerodynamic resistance = f (wind)
actual vapor pressure
= f (humidity)
f (crop)

10. ASCE Manual 70 Comparisons
Monthly Timesteps

11. ASCE Manual 70 Comparisons

12. ASCE Manual 70 Comparisons

13. 11/18/2018
CIGR, Bari, Italy, Sept. 10, 2013
The “Chaos” associated with ETref and Kc prior to FAO24  FAO56 (and ASCE 2005)
Wind Functions on the Penman Equation
WF = f(bias in anemometer, RH sensor, Rn est., ET meas.)
Tanner WF
Wright-Jensen WF
Hubbard WF
Kruse WF
Kohler WF
Pruitt WF
Kincaid WF
Sammis WF

14. History of Standardized Reference ET
Adoption of ASCE PM for ETo by FAO in 1990 with reduced form format
Request by Irrigation Association in 1999 to the ASCE Technical Committee on ET in Irrigation and Hydrology to “standardize” a single reference ET method to:
promote transfer of crop and landscape coefficients
add clarity to calculation of reference ET
Selection of ASCE PM by ASCE committee as basis for 2005 standardization

15. FAO Expert Meeting on ET -- 1990
Essentially Unanimous agreement to adopt the Penman-Monteith as their sole Reference
John Monteith
(yellow)
Bill Pruitt
(pink)
Jan Doorenbos
(green)
Marvin Jensen
(orange)
Alain Perrier
(blue)

16. Reasons for Standardization of Reference ET
Common, national basis for expression of Evaporative Demand
Consistency in Calculation of Evapotranspiration
Facilitate transfer of Crop and Landscape Coefficients

17. Considerations in Reference ET Type
Two different reference crops, alfalfa and clipped grass are used, with usage generally divided among western States
Grass reference ETo has a long history of application in urban areas and for agriculture in much of the U.S.
Alfalfa ETr has a long history for agricultural application in the midwest and northwestern U.S.
Alfalfa ETr is taller and ‘rougher’ and leafier than clipped grass and better represents an ‘upper’ bound on ET that is set by energy availability for ET
Families of crop coefficients have been developed (and are required) for each reference type.
Theoretical arguments for both short and tall reference crops have been made by Perrier (1980) and Pereira et al. (1999)

18. An argument for alfalfa ETr is that Maximum Kcb tends toward 1.0
and aerodynamic
characteristics of ETr are similar to field crops. Therefore, better transferability across climates.
11/18/2018

19. ASCE Penman-Monteith Manual 70 "Full Form"
for alfalfa, bulk surface resistance, rs = 45 sm-1 for full cover, 0.5 m height
for clipped grass, rs = 70 sm-1 for 0.12 m grass height
"Full Form"
Rn=net radiation, G = soil heat flux
= f (solar radiation)
f (temperature)
aerodynamic resistance = f (wind)
actual vapor pressure
= f (humidity)
f (crop)
ρa is density, cp is specific heat, λ is latent heat of vaporization, Δ is slope of sat. vapor pressure curve, γ is psychrometric constant

20. ASCE / FAO Penman-Monteith
Standardized
f (time step, reference type)
Fixed Height
Fixed Conductance (1/(bulk surface resistance))
f (time step, reference type, day/night)
Same Reduced Form as the FAO-56 Penman-Monteith

21. Standardized Reference ET
American Society of Civil Engineers Setup
The single standardized Penman-Monteith equation can be applied to a) grass and alfalfa and b) for daily or hourly timesteps
(FAO-56 PM ETo )
(ASCE PM ETr hourly)

22. The ASCE Standardized Reference Evapotranspiration Equation
ASCE Task Committee on Standardization of Reference ET in 2002 during setting of parameters – geographic coverage in US
Ivan A.Walter, Colorado, chair
Richard Allen, Idaho, vice-chair
Ron Elliott, Oklahoma
Brent Mecham, Colorado
Marvin Jensen, Colorado
Daniel Itenfisu, Oklahoma
Terry A. Howell, Texas
Rick Snyder, California
Paul Brown, Arizona
Simon Eching, California
Tom Spofford, Oregon
Mary Hattendorf, Washington
Richard Cuenca, Oregon
James L. Wright, Idaho
Derrell Martin, Nebraska

23. Reference ET comparisons (hourly vs. daily, etc.)
2005 ASCE Standardization
ASCE study: 49 sites and 81 site years

24. Comparisons with Measurements
Weighing Lysimeter System at Kimberly, Idaho
Dr. James L. Wright, USDA-ARS

25. ASCE Stdzd. PM
(tall reference)
at Kimberly, Idaho
hourly timestep
shows same response as measurements

26. Kimberly, Idaho, Daily Alfalfa ET 1969-1971
Periods of Full Cover
SEE = 1.0 mm d-1

27. Daily measurements (lysimeter) of ET from full cover alfalfa
Daily ET

28. ASCE PM -- Daily vs. Hourly Timestep
Davis, California
CIMIS Station
2008 – 2012
Grass Reference

29. ASCE PM -- Daily vs. Hourly Timestep
Davis, California
CIMIS Station
2008 – 2012
Alfalfa Reference

30. Robustness of the Penman-Monteith
The alfalfa reference version has ‘the right’ weighting on wind speed to mimic maximum ET demand. Having wind speed (aero. resistance) on numerator and denomenators provides some dampening on the impact of increasing wind speed.
aerodynamic resistance = f (wind)

31. USDA-ARS Bushland Lysimeters
Data from Dr. T.A. Howell, USDA-ARS
The Kc ratio of ETlys to ETref remains constant with increasing Etref.
The ASCE Penman-Monteith alfalfa reference ET “keeps up” with measurements.

32. USDA-ARS Bushland Lysimeters
Data from Dr. T.A. Howell, USDA-ARS
The Kc ratio of ETlys to ETref remains constant with increasing wind speed, illustrating that the PM equation has appropriate sensitivity to wind speed.
The ASCE Penman-Monteith alfalfa reference ET “keeps up” .

33. USDA-ARS Bushland Lysimeters
Data from Dr. T.A. Howell, USDA-ARS
The Kc ratio of ETlys to ETref remains constant with increasing wind speed, illustrating that the PM equation has appropriate sensitivity to wind speed

34. Limitations of the Combination Equation
In derivation of the Combination Equation from Energy Balance:
Rn – G = H + λE
The following definition for Δ is implicit for application with air temperature:
11/18/2018

35. Why the Penman and Penman-Monteith Combination Equations are Largely only Useful for Reference ET
At Reference ET conditions, LE ~ Rn – G and H ~ 0.
Therefore, Ts ~ Ta.
When Ts is much warmer than Ta, four important things occur:
creates error
Ignoring stability correction, ψm, ψh , creates error
Use of standard, noncorrected estimation of outgoing long wave, RLout that is based on Ta, can create substantial error (up to 100 W m-2)
Estimation of G should consider influence of Ts.
Therefore, Ts should be estimated using recursive iteration.
However, Any recursive iteration of H and combination equations for Ts, in essence, causes the combination equation to revert back to the component equations for H and LE
11/18/2018

36. Challenge: NARR, NLDAS Gridded Weather data sets often need to be ‘conditioned’ before using to estimate ET from Irrigated Agriculture in Semiarid climates
Ambient
Conditions
Of Dry Areas
Overestimation when placed in a Penman-Monteith “Reference ET” Equation
zm measurement height
NASA/USDA Workshop on Evapotanspiration

37. ‘Reference’ Conditions Representing Irrigated Areas
Challenge: NARR, NLDAS Gridded Weather data sets may need to be ‘conditioned’ before using to estimate ET from Irrigated Agriculture in Semiarid climates
‘Reference’ Conditions
Representing
Irrigated Areas
Correct estimation when placed in a Penman-Monteith “Reference ET” Equation
zm measurement height
unadjusted
adjusted (conditioned)

38. “Evaporation” of the Combination Equation
When Ts is much warmer than Ta, and iteration estimation of Ts is performed, the Combination equation reverts to the original Rn – G = H + λE components and it ‘evaporates’:
becomes:
Ts is a part of each of
these four equations.
Therefore, we should just
use the separate equations,
not the combination.
11/18/2018

39. Computing Reference ET

40. The Ref-ET Data File

41. The Ref-ET Definition File

42. Selecting the Data Parameters and Units

43. Ref-ET Weather Station Data

44. Ref-ET Output and Equations

45. QAQC of weather data

46. Clear Sky Curve 24-hour Measured Solar Radiation
Corrected by multiplying by 1.14 for day 90 to day 250 for year 1992
x 1.16 for day 90 to day 240 for year 1993
Corrected

47. Towards Automated QAQC of Weather Data
Accurate Ag. Weather Data is a Must!
Example of QAQC process of Max. Daily RH% at UC Davis CIMIS station
Base adjustments on ratios between theoretical clear sky solar radiation and top percentiles of measured data
Before Correction – Sensor Drift
Max Daily RH%
After Correction – No Sensor Drift
Max Daily RH%

48. NARR Tair vs. Irrigated vapor pressure - Idaho
Three-hourly vapor pressure data from NARR over Kimberly, ID, during May 2008, compared with measurement at USBR Agrimet weather station (TWFI) near Kimberly. The Agrimet station is over irrigated grass and has double the humidity than the NARR data that is impacted by ambient data from nonirrigated weather sources.