2018 SWCS International Annual Conference July 29-August 1, 2018 Albuquerque Convention Center

1. All About Discovery!TM
New Mexico State University
aces.nmsu.edu
College of Agricultural, Consumer and Environmental Sciences
The College of Agricultural, Consumer and Environmental Sciences is an engine for economic and community development
in New Mexico, improving the lives of New Mexicans through academic, research, and Extension programs.
Comparison of some reference
evapotranspiration equations under semiarid
conditions with limited climatic data
Koffi Djaman1, Michael O'Neill (ret.)1, Lamine Diop2, Ansoumana Bodian2, Samuel Allen1,
Komlan Koudahe3 and Kevin Lombard1
1 Department of Plant and Environmental Sciences, New Mexico State University, Farmington, NM, USA
2 Université Gaston Berger, Saint Louis, Sénégal
3 ADA Consulting Africa, Lomé, Togo

2. Introduction
Adapted from FAO (1986): http://www.fao.org/docrep/S2022E/s2022e07.htm#3.1%20influence%20of%20climate%20on%20crop%20water%20needs%20(eto)
Theoretical Eto
Formulas using
Climate Data
Reference evapotranspiration (ETo) is an important parameter in
hydrological, agricultural and environmental studies.
• Accurate estimation of ETo helps to improve water management
and increase water use productivity and efficiency.
There are several methods to determine Eto:
• experimental, using direct measurements such as an
evaporation pan, lysimeters, or Eddy covariance, or
• theoretical, using measured climatic data
plugged into various formulas, e.g.,
the Penman-Monteith method,
Hargreaves and Samani, Snyder,
Blaney-Criddle, Valiantzas, others.

3. Adapted from FAO (1986): http://www.fao.org/docrep/S2022E/s2022e07.htm#3.1%20influence%20of%20climate%20on%20crop%20water%20needs%20(eto)
Effect of Major Climatic Factors on Crop Water Needs
Definition of the reference crop evapotranspiration (ETo)
ETo is the rate of evapotranspiration from a large area, covered by
green grass, 8 to 15 cm tall, which grows actively, completely shades
the ground and which is not short of water .

4. Need for the Study
The Penman-Monteith ETo equation enjoys worldwide adoption as one of
the most accurate ETo equations, however, the number of requested climatic
variables makes its application questionable under limited data conditions.
Penman-Monteith Method
(standardized ASCE form)
Adapted from: https://www.kimberly.uidaho.edu/water/asceewri/ascestzdetmain2005.pdf

5. Methods
• The objective of this study was to evaluate the Penman-Monteith
ETo equation under limited climatic data vs. 34 other ETo
equations that request few climatic variables, selected for their
simplicity and broad adaptability.
• Long-term datasets (from 2009-2017) from five weather stations
in New Mexico were included, based on their completeness and
geographic/climatic variation.
Weather Station – NMSU Farmington Ag Science Center.
Consists of air temperature, relative humidity, solar radiation, wind
speed, wind direction, soil temperature, and rain depth sensors
recorded by Campbell Scientific data logger every hour, with wireless
transmission to an on-site network computer system. Data are
reported to New Mexico Climate Center: http://weather.nmsu.edu.
PAN and other data are recorded at another on-site station affiliated
with National Weather Service, accessible at Western Regional
Climate Center http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?nm3142
and ASCF: http://farmingtonsc.nmsu.edu/weather-daily-data.html.

6. NMSU Ag Experiment Stations included in climate data analysis
Leyendecker Plant Science
Research Center, Las Cruces
Sustainable Agriculture Science
Center at Alcalde
Fabian Garcia
Science Center,
Las Cruces
Agricultural Science Center at
Tucumcari
Agricultural Science
Center at Farmington

7. Data requirements of the models used in this study

9. Reference evapotranspiration estimation methods
The evaluated models were classified into three groups according to their data
requirements (McMahon et al., 2013).
• Combination Models:
Penman-Monteith, Valiantzas 4, Valiantzas 5.
• Temperature- or Radiation-Based Models:
Berti et al. (2014), Djorji et al. (2016), Hargreaves and Samani (1985),
Romanenko (1961), Ahooghalandari (2016), Caprio (1974), Hargreaves (1975),
Irmak (2003), Trajkovic (2007), Droogers and Allen (2003), Jensen-Haise (1963),
Tabari (2011), Abtew (1996), Makkink (1967), Valiantzas (2012).
• Mass Transfer Models:
Albrecht (1950), Brockamp and Wenner (1963), Dalton (1802), Mahringer (1970),
Meyer (1926), Penman (1963), Trabert (1896), WMO (1966), Rohwer (1931).

10. Parameters Alcalde Fabian Garcia Farmington Leyendecker Tucumcari
Latitude (Deg. North) 36.09 32.28 36.69 32.20 35.20
Longitude (Deg. West) -106.06 -106.77 -108.31 -106.74 -103.69
Elevation (m) 1735 1186 1720 1176 1246
Wind speed (m/s) 1.19 1.82 2.45 1.81 3.30
Tmax (oC) 21.09 26.50 20.94 26.12 24.07
Tmin (oC) 2.22 9.82 4.33 7.67 7.52
Tmean (oC) 11.65 18.16 12.63 16.89 15.79
Rhmax (%) 83.86 65.21 68.63 77.34 71.78
Rhmin (%) 22.88 18.60 19.70 19.09 22.29
Rhmean (%) 53.37 41.90 44.17 48.21 47.03
Rs (MJ m-2) 18.62 21.23 19.84 19.63 19.80
Study sites: Weather stations with long term average climatic conditions

11. Evaluation Criteria

12. Relationship between
the daily Penman-
Monteith reference
evapotranspiration
estimates (P-M ETo)
and the climatic
variables (pooled data
of all five locations).
Here, temperature and
solar radiation showed
the best correlation,
along with vapor
pressure deficit.

13. Relationship between
Penman-Monteith ETo
model (P-M ETo) with
full data and Penman-
Monteith ETo with
missing climatic data
(pooled data).
P-M performed
reasonably well with a
single missing climatic
variable, and
satisfactorily in some
cases with two missing
variables.

14. (b)
Regression slope (a) and coefficient of determination (b) between Penman-Monteith and
the selected ETo equation at Alcade, Fabian Garcia, Farmington, Leyendecker and
Tucumcari. Here, values for (a) and (b) that are close to “1” indicate a good fit.

15. Root mean square error (a) and mean bias error (b) between Penman-Monteith and the
selected ETo equation at Alcade, Fabian Garcia, Farmington, Leyendecker and Tucumcari.
Here, values for (b) that are close to “0” indicate a good fit in terms of minimum difference.;
negative numbers indicate that the equation is under-estimating ETo, and positive numbers
indicate that the equation is over-estimating ETo.
(b)

16. Comparison of daily
ETo estimated by
methods versus
Penman-Monteith
model (P-M ETo) and
the best simple ETo
equations with
regression slope
greater than 0.90 and
R2 greater than 0.80
(pooled data).
Note excellent
agreement for some
equations.

17. Results
• The Penman-Monteith ETo equation performed well under missing
solar radiation, relative humidity and wind speed. The Penman-
Monteith ETo was estimated with PE lower than 10% and RMSE lower
than 0.40 mm/day, MAE lower than 0.25 mm/day with linear
regression slope varying from 0.973 to 1.030 and R2 from 0.97 to 0.99.
• Under missing relative humidity data, the Penman-Monteith generally
performed well with regression slope that varied from 0.847 to 0.961
and R2 from 0.96 to 0.99, RMSE from 0.25 to 1.05 mm/day.
• The best performance was shown at Alcade and the poorest at
Tucumcari. Using the long-term average wind speed at each weather
station, P-M performed very well with regression slope between 0.973
and 0.999 and coefficient of determination R2 as higher than 0.86, PE
lower than 16% and very low MBE varying from -0.01 to 0.09 mm/day.
• However, P-M tended to underestimate daily ETo at values greater
than 8 mm/day under missing wind speed and when more than one
climate variable was missing.

18. Results
• Thus, the Penman-Monteith ETo equation showed good
performance under missing solar radiation, relative
humidity and wind speed and could still be adapted under
limited data conditions across New Mexico.
• However, it tended to underestimate daily ETo when more
than one climatic variable data is missing.

19. Results
• Among the simple ETo equations, four of the Valiantzas
equations, along with the Makkink, Calibrated Hargreaves,
Abtew, Jensen-Haise, and Caprio equations, were the best
performing ones compared to the Penman-Monteith
equation and could be the best alternative ETo estimation
methods.
• There is not a “one size fits all” approach to determining
ETo. Calculation of ETo depends on various factors and even
the same formula applied to two different sites in a region
can have differing quality of results.

20. Conclusions
• These alternative equations could be used by irrigation
managers, producers, engineers and university researchers
to improve water management across the dry semiarid and
arid zones across New Mexico.
• They could also be considered for use in other semiarid
areas where both water resources and historical climatic
data are limited.

21. References
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop Evapotranspiration-Guidelines
for computing crop water requirements. FAO Irrigation and Drainage paper 56.
FAO, Rome.
ASCE-EWRI (2005) The ASCE standardized reference evapotranspiration equation.
In: Allen RG, Walter IA, Elliot RL, et al. (eds.) Environmental and Water Resources
Institute (EWRI) of the American Society of Civil Engineers, ASCE, Standardization
of Reference Evapotranspiration Task Committee Final Report, 213pp. Reston, VA:
American Society of Civil Engineers (ASCE).
Djaman K, Koudahe K, Allen S, O’Neill M, Irmak S (2017) Validation of Valiantzas’
reference evapotranspiration equation under different climatic conditions.
Journal of Irrigation and Drainage Engineering 6:3 DOI: 10.4172/2168-
9768.1000196.
Djaman et al. Manuscript under review by Theoretical and Applied Climatology.

22. All About Discovery!TM
New Mexico State University
aces.nmsu.edu
College of Agricultural, Consumer and Environmental Sciences
The College of Agricultural, Consumer and Environmental Sciences is an engine for economic and community development
in New Mexico, improving the lives of New Mexicans through academic, research, and Extension programs.
Koffi Djaman1, Michael O'Neill (ret.)1, Lamine Diop2, Ansoumana Bodian2, Samuel Allen1,
Komlan Koudahe3 and Kevin Lombard1
1 Department of Plant and Environmental Sciences, New Mexico State University, Farmington, NM, USA
2 Université Gaston Berger, Saint Louis, Sénégal
3 ADA Consulting Africa, Lomé, Togo
Acknowledgments
Appreciation is expressed to the dedicated staff who collected the long-term
weather data and to the NMSU Ag Science Centers for permission to use the data:
Agricultural Science Center at Tucumcari
Fabian Garcia Science Center, Las Cruces
Leyendecker Plant Science Research Center, Las Cruces
Sustainable Agriculture Science Center at Alcalde
Agricultural Science Center at Farmington

23. For more information on our ET studies, agronomic and horticultural trials,
hybrid poplar research, or Southwest Agroforestry Working Group initiative,
please contact:
Samuel C. Allen, Ph.D., NMSU Ag Science Center at Farmington
P.O. Box 1018, Farmington, NM 87499 USA; (505) 960-7757
samallen@nmsu.edu; http://farmingtonsc.nmsu.edu/
The College of Agricultural, Consumer and Environmental Sciences is an engine for
economic and community development in New Mexico, improving the lives of New
Mexicans through academic, research, and Extension programs.
New Mexico State University is an equal opportunity/affirmative action employer and
educator. NMSU and the U.S. Department of Agriculture cooperating.
Agricultural Science
Center at Farmington
hybrid poplar study
For More Info

24. Questions