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Asian Review of Environmental and Earth Sciences 
Vol. 7, No. 1, 26-34, 2020 

ISSN(E) 2313-8173/ ISSN(P) 2518-0134 
DOI: 10.20448/journal.506.2020.71.26.34 

© 2020 by the authors; licensee Asian Online Journal Publishing Group 

   
 

 
 
 
Assessment of Reference Evapotranspiration Estimation Models   

 
Olotu Yahaya1 
Iyawe Hope2 
Olatunde M.B3 
Olatunde F.O4 

 
 

( Corresponding Author) 
 
1Department of Agricultural & Bio-Envi Eng., Auchi Polytechnic, Auchi, Nigeria. 

 
2Department of Urban & Regional Planning, Auchi Polytechnic, Auchi, Nigeria. 

 
3,4Department of Surveying & Geoinformatics, Auchi Polytechnic, Auchi, Nigeria. 

 

 
Abstract 

Reference evapotranspiration (ETo) is an important factor in estimating crop water requirement, 
water resource management and designing irrigation scheduling. This study matches the outputs 
of open pans and Blaney Criddle (BC) to CROPWAT irrigation simulation software using 20-year 
daily weather data of Auchi and its environs were acquired from the Nigeria Meteorological 
Station (NMET) as baseline weather dataset. Thus, the baseline weather data was reproduced 
using a general circulation model for data re-analysis. The accuracy of each model was evaluated 
using various statistical metrics. ETo estimated from the four models were compared with the 
CROPWAT model, an irrigation software program used for solar radiation (RS), reference 
evapotranspiration (ETo) and crop water requirement (CWR) calculations. Validation outputs 
have the correlation values (R2) of 0.587, 0.536, 0.511 and 0.513 for EToBC-ETo CROPWAT, EToBT-
EToCROPWAT, ETo AUS-ETo CROPWAT, and EToUS-EToCROPWAT respectively. This result indicates a 
marginal good fit between the estimated Reference evapotranspiration (ETo) from each model and 
CROPWAT irrigation software. However, there were discrepancies between the simulated and 
computed reference evapotranspiration (ETo) for the model with Blaney Criddle and Australia 
pan having correlation (R2)) and bias value (AB) of 0.223; 0.241 and 2.30; 2.00 respectively. In 
conclusion, all the selected reference evapotranspiration (ETo) estimating models have strong 
capabilities of providing accurate outcome be useful for water resources and hydrological 
modeling. 

 
Keywords: Reference evapotranspiration, CROPWAT model, Water management. 

 
Citation | Olotu Yahaya; Iyawe Hope; Olatunde M.B; Olatunde F.O 
(2020). Assessment of Reference Evapotranspiration Estimation 
Models. Asian Review of Environmental and Earth Sciences, 7(1): 
26-34. 
History:  
Received: 18 November 2019 
Revised: 23 December 2019 
Accepted: 28 January 2020 
Published: 10 March 2020 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Acknowledgement: All authors contributed to the conception and design of 
the study. 
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 

1. Introduction ...................................................................................................................................................................................... 27 
2. Materials and Methods ................................................................................................................................................................... 27 
3. Results and Discussion ................................................................................................................................................................... 30 
4. Conclusion ......................................................................................................................................................................................... 31 
References .............................................................................................................................................................................................. 31 
 

 

 

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Contribution of this paper to the literature 
This study matches the outputs of open pans and Blaney Criddle (BC) to CROPWAT irrigation 
simulation software using 20-year daily weather data of Auchi and its environs were acquired 
from the Nigeria Meteorological Station (NMET) as baseline weather dataset. 

 

1. Introduction 
Severe water scarcity occurs in many countries particularly largely in Nigeria and agricultural water use is 

progressively becoming more limited in the light of growing water demand of various sectors [1]. Water 
management for crop suitability needs a comprehensive understanding of the climate, particularly, rainfall and 
evapotranspiration [2].  In developing nations like Nigeria, where there is a shortage of direct measurements of 
evapotranspiration, evapotranspiration can be estimated indirectly using climatological data [3]. The penman 
monteith FAO-56 equation is recommended for the estimation of reference evapotranspiration and provides a 
reliable reference evapotranspiration and provides a reliable reference evapotranspiration (ETo) value under 
different climate conditions. 

Evapotranspiration (ET) is considered to be the dominant component of the hydrologic cycle due to the fact 
that about 60% of annual precipitation falling over the land surface is returned to atmosphere as ET [4]. 
Evapotranspiration and transpiration process take place simultaneously in any catchment area, and therefore, 
cannot be separated in the analysis and evaluation of water budget for a district [5]. The estimation of 
evapotranspiration is important for irrigation engineering applications and for studies relating to hydrologic water 
balance, water resource planning and management and land use planning. 

The estimate of reference evapotranspiration is important in Crop water requirement and development of 
irrigation scheduling. The general knowledge of the spatial distribution of reference evapotranspiration (ET0) is 
still unclear despite its importance for global ecosystem research [6]. One reason is that ET0 is difficult to observe 
directly as it depends on several meteorological parameters which are observed only at major stations [7]. Field 
measurement of meteorological variables is a critical part of evapotranspiration estimation process. Measurement 
and recording errors in field variable result in evapotranspiration estimation errors [6]. There have been 
significant processes in the capability of near surface meteorological variable measurement such as temperature, 
precipitation, wind speed, solar radiation and humidity using automated climate stations [7]. This has the effects of 
simplifying evapotranspiration model usage. Crop evapotranspiration is estimated by multiplying the reference 
evapotranspiration by crop specific crop coefficient (Kc) values at different crop growth stages. Moreover, different 
reference evapotranspiration methods have been developed over the years range from direct measurement from a 
reference surface such as alfalfa grass [7, 8] or can be computed from weather data based methods i.e. (a) 
temperature based [7, 9] (b) radiation based [7]. 

Therefore, in this research study Open Pans Evaporation (US Class A, Australian Pan and British Tank) and 
empirical method (Blaney Criddle) have been analysed and compared with the CROPWAT model for estimation of 
ETo for current period of 15-year (1996-2010) in Auchi, Edo-State, Nigeria. 
 

2. Materials and Methods 
2.1. Study Area 

The study area comprises of Auchi district, which is one of the centrally located district of Etsako west LGA 
with 2338.9 sq km geographical area. The topography of the study area is undulating in nature. According to 
survey of Nigeria the latitude and longitude are 7.07o 04’00”N and 6.27o 16’00’E respectively, and it has an average 
elevation of 188 meters above mean sea level (wikipedia). In Auchi, the season is warm, oppressive and overcast and 
the dry season is hot, muggy and partly cloudy. The recent population census of Auchi shows that the community 
inhabits about 104,540 people. Auchi has two distinct climatic seasons (wet and dry season). The wet season spread 
from April to October, while the dry season is from November to March [10]. In Auchi, the average annual 
temperature is 25.9 °C and precipitation averages of 1389 mm. The precipitation varies 249 mm between the driest 
month and the wettest month. The variation in temperatures throughout the year is 3.8 °C. The driest month is 
January. There is 11 mm of precipitation in January. The greatest amount of precipitation occurs in September, 
with an average of 260 mm. The aerial view of Auchi is shown in plate 1 and location map in Figure 2 and Figure 3. 
Figure 4 shows the satellite imagery showing the existing built area of Auchi. Computed average climate data for 
three decades (30 years) for Auchi is shown in Table 1. 
 

 
Figure-1. Areal view of Auchi, Nigeria. 

Source: Ministry of lands, surveying and housing, Edo State, 2008. 



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Figure-2. Map of Nigeria indicating Etsako west local govt area and Auchi. 
                       Source: Ministry of lands, surveying and housing, Edo State, 2008. 

                         

 
Figure-3. Base map of Auchi, 

                      Source: Ministry of land & survey, Benin City, 2017. 
 



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Figure-4. Satellite imagery showing the existing built up area of Auchi community Edo-State. 

Source: Ministry of Land & Survey, Benin City, 2017. 

 
2.2. Data Collection  

In order to calculate reference evapotranspiration (ETo), the respective daily climate data collected from 
Nigeria Meteorological Agency from the time periods of 1995-2010. The climate data are as follows: 

i. Maximum temperature ( oC). 
ii. Minimum temperature (oC). 
iii. Relative humidity       (%). 
iv. Wind speed                   (km/h). 
v. Sunshine hour             (hours). 
vi. Precipitation             (mm). 
vii. Evaporation depth (mm). 
These data were used as input parameters in the estimation of reference evapotranspiration (ETo) different 

estimation approaches. Three Pan Evaporation methods (US Class A, Australia pan and British tank), empirically-
based method (Blaney-Criddle) and CROPWAT model (using inbuilt modified Penman-Moniteth equation) were 
applied to compute ETo. The results generated from the Pans evaporation and Blaney-Criddle were compared with 
the CROPWAT reference evapotranspiration (ETo) estimation. Table 1 shows a set of equations and expression 
used in estimating reference evapotranspiration (ETo). 
 

Table-1. Methods of computation. 

S/No Empirical equations Methods of  computation 

1. EToUS                    = kp.Epan 
EToAUSTRALIA  = kp.Epan 
ETObritish          = kp.Epan 

 Evaporation pan 

2. ETo =  p(0.46Tmean+8.13) Blaney – Crriddle  
3. 

ETo =  

Inbuilt in CROPWAT MODEL 

FAO – 56 Penman-Monteith  

         Source: Allen, et al. [1]. 
  
      Where: ETo – Reference evapotranspiration (mm/day). 
 Rn – Net Radiation at crop surface (mjm2/day). 
 G – Soil heat flux density (mjm-2/day-1)  
 T – The mean Daily air temperature at 2m height (oC)  
 U2 – The wind speed at 2m height (m/s-1)  
 ea – The saturation vapor pressure (kpa) 
 ed – The actual vapor pressure (kpa) 
 (ea-ed) – The saturation vapor pressure deficit (kpa) 

   - The slope vapour pressure curve (kpaoc-1) 

 r = The psychometric constant (kpaoc-1) 
 Tmean = the mean daily temperature (oC)  
 P = Percentage of sunshine hour 
 Kp = Pan coefficient (US Class A = 0.7; Australia = 0.9 and British Tank = 0.85) 
 Epan = Evaporation depth (mm) 
 

2.3. Model Validation  
 Prediction error statistics, The performance evaluation of the different method was undertaken by comparing 
the values obtained by CROPWAT equation by the following statistical method to obtain the prediction error [5].  
The Average Bias (AB) of the evaluated methods was calculated using the equation (1). 
  



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                              (1) 

Where: 
O, is the reference evapotranspiration (ETo) estimated by the considered method (mm day-1). 
N, is the total number of observations. 
P, is the simulated reference evapotranspiration  
The errors of the evaluated methods were calculated using equations (2-4). 

RMSE =                       (2) 

 

MAE =   

                                        (3) 
The Estimated Standard Error (ESE) was calculated using the equation: 

 ESE =                                    (4) 

 

2.4. Statistical Analysis 
 Generated outputs were subjected to critical analysis using statistical metrics such as determination of 
correlation (R2), last significant deference (LSD), Root mean square. Package such as (SSPS) and MATSET were 
applied to model the computed variable.  
 

3. Results and Discussion 
3.1. Estimation of Reference Evapotranspiration (ETo) 

The vales of reference evapotranspiration (ETo) estimated from five approaches for 15-year (1996-2010) in 
Auchi as shown in Table 2-5. The estimated highest values (ETo) was obtained from CROPWAT model, which the 
empirically-based Blandey-Criddle gave the least value for all the simulations. In Table 2, ETo value of 3.3 mm/day 
was estimated from CROPWAT model, while 2.0 mm/day was estimated using US Class A Pan and Australia Pan, 
British Tank and Blanley Criddle produced ETo values of 2.5 mm/day, 2.3 mm/day and 1.0 mm/day respectively. 
There is gradual decrease in the rate of evapotranspiration in the month of July, August, September and October. 
This period is classified as wet season in Auchi and its environs. During this period, the region usually experiences 
reduction in temperature (minimum and maximum) which is the main driver of evapotranspiration.  However, the 
rate of ETo starts to raise from November to the beginning of June. This period is categorized as dry season with 
little rainfall depth and increase temperature. This trend is continuous for the 15-year of experimental iterations.  
 

3.2. Model Performance  
Figures 5-12 show the variation of ET0 calculated by different methods. The simulation is run for every month 

of the year. The monthly performance of each of the models (US Class A (EToUS), Australia Pan (EToAUS), British 
Tank (EToBT) and  Blaney Criddle(EToBC)) was compared with generated output from CROPWAT model. In 
evaluating monthly performance of the models against the CROPWAT software, EToUS, EToAUS and EToBT 
were estimated using the pan coefficient of 0.7, 0.9 and 0.85 respectively. Mean temperature and percentage of 
sunshine hours were used to determine reference evapotranspiration for Blaney Criddle approach at Auchi.There 
are differences between the monthly reference evapotranspiration ETo from Claas A pan, Australia pan, British 
Tank and Blaney Criddle model, and CROPWAT model. In the most of January, February March, November and 
December in 1996, CROPWAT software shown had the best performance over other models Figure 5. 
Evapotranspiration values were highly underestimated using EToBC and EToUS, however better estimation was 
obtained in EToAUS Figure 5. This indicates that it is only CROPWAT model that could effectively capture the 
increase in the minimum and maximum during the dry season.  However, all the ETo estimation models were to 
perform better during the wet season (May- October) except CROPWAT model which produced higher reference 
evapotranspiration Figure 5. This observation is applicable to the ETo estimations for the current period’s 1997 
and 1998 as depicted in Figure 6-7. 
 As shown in Figure 8, in Auchi for 1999, CROPWAT and Blandey-Criddle models provided a good estimate of 
ETo values of 3.4 mm/day and  1.1 mm/day in January , while  0.7 mm/day, 0.9 mm/day and 0.8 mm/day were 
estimated using US Class A, Australia Pan and British Tank respectively Table 5. However, ETo estimated from 
Blandey-Criddle is the lowest in November (0.6 mm/day) and December (0.5 mm/day), while the three open 
evaporation pans (Class A, Australia and British) produced higher ETo values of 1.8 mm/day, 2.3 mm/day and 2,1 
mm/day and CROPWAT model simulated highest value of 3.4 mm/day in November and 3.2 mm/day in 
December respectively Table 5 and Figure 8. These results could be due to higher wind speed, sunshine and the 
increase in minimum and maximum temperature. 
 Estimation of reference evapotranspiration with the use of CROPWAT model using average climate (minimum 
and maximum temperature, sunshine hour, relative humidity, wind speed) data in Auchi, showed variations in 
monthly estimated ETo using CROPWAT model and other models (US Class A, Australia and British Tank). 
Significant underestimation of ETo throughout the year (wet and dry) was obtained in all the models except 
CROPWAT software which produced realistic estimate of ETo during the dry season and overestimated during the 
wet season. In order to compute accurate and precise crop water requirement (ETc) which is the product of 
reference evapotranspiration (ETo) and crop coefficient (Kc), great caution should be applied should be applied in 
selecting most appropriate approach of estimating of ETo for different seasons. If the ETo is underestimated, this 
leads to incorrect estimation of crop water requirement and poor irrigation scheduling system. However, over 
estimation of ETo will create incorrect variable and if this value is used to compute crop water requirement (ETc), 
it can lead to under-irrigation. Generally, accurate estimation of reference evapotranspiration (ETo) is imperative to 
developing sound and sustainable water resources management mostly in arid regions. Highest annual average bias 
values were obtained in 2001 with 2.8 mm, 2.8 mm, 2.8 mm and 2.2 mm, whereas lowest values were estimated in 



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1997 with biases of 1.7 mm, 1.3 mm, 1.5 mm and 2.6 mm for ETo(US), ETo(AUS), ETo(BT) and ETo(BC) Table 2-5. This 
observation is likely due to decrease in minimum temperature and increase in number of raining and rain depth in 
1997. Increase in other parameters influencing evapotranspiration such as temperature, wind speed, sunshine hours 
and solar radiation may be responsible for higher estimation of ETo using CROPWAT software. 
 

4. Conclusion 
Reference evapotranspiration (ETo) was estimated using open pan (US Class A, Australia pan, and British 

tank) and empirically-based approach (Blane-Criddle) using 15-year baseline climate parameters. The output of 
each of the approach was compared with the CROPWAT model. The result of the analysis shows that the open pan 
method (ETo US, ETo AUS and ETo BT) underestimated reference evapotranspiration during the dry season, 
while the CROPWAT model performs better. Blaney Criddle (BC) estimates better evapotranspiration values in 
the dry season than the open pan approach. Open pan and Blaney criddle (BC) models give more accurate estimate 
of reference evapotranspiration (ETo) than CROPWAT model during the wet season. This observation indicates 
weather-sensitivity of different models for estimating reference evapotranspiration (ETo). Calibration of reference 
evapotranspiration (ETo) measured from the open pans and Blaney Criddle in comparison to CROPWAT model 
were estimated. Generally, application of more than one models is very important in estimating reference 
evapotranspiration (ETo) for better estimation of crop water requirement and irrigation scheduling. 
 

References 
[1] R. L. Allen, L. A. Pereira, D. Raes, and M. Smith, "Crop evapotranspiration guidelines for computing crop water requirements. 

FAO irrigation and drainage Paper No. 56," Food and Agriculture Organization, vol. 13, pp. 98-123, 2009. 
[2] R. G. Allen, W. O. Pruitt, J. Wright, T. A. Howell, and F. Ventura, "A recommendation on standardized surface resistance for 

hourly calculation of reference ETo by the FAO56 Penman–Monteith method," Agricultural Water Management, vol. 81, pp. 1-22, 
2006.Available at: https://doi.org/10.1016/j.agwat.2005.03.007. 

[3] P. W. D. Burnett, "Water resources of Kerala’ public work department. Kerala," Resources Newsletter, vol. 127, pp. 16-18, 2007. 
[4] FAO, "State of the world’s forests. Retrieved from: ftp.fao.org/docrep/fao/005/y7581," 2003. 
[5] R. G. Allen, L. Pereira, D. Raes, and M. Smith, "Crop evapotranspiration (guidelines for computing crop water requirements)," 

Irrigation and Drainage, Paper No. 56 FAO, Rome1998. 
[6] A. Patel, R. Sharda, S. Patel, and P. Meena, "Reference evapotranspiration estimation using CROPWAT model at Ludhiana district 

(Punjab)," International Journal of Science, Environment and Technology, vol. 6, pp. 620-629, 2017. 
[7] J. Doorenbos and W. O. Pruitt, "Crop water requirements (Land and Water Development Division)," Irrigation and Drainage Paper 

No. 24.  FAO, Rome, 1977. 
[8] I. Watson and A. D. Burnett, Hydrology: An environmental approach. Boca Raton, FL: CRC Press, 1995. 
[9] C. W. Thornthwaite, "An approach towards a rational classification of climate," Geogr Revue, p. 38, 1948. 
[10] Y. Olotu, O. I. Galadima, and L. O. Alumi, "Climate change on discharge and sedimentation of River Awara," Nigeria. Article, vol. 

90, pp. 2-15, 2010. 

 
Table-2. Estimated reference evapotranspiration (ETo) using four approaches in 1996. 

Months ETo (US) ETo (AUS) ETo (BT) ETo (BC) ETo (CROPWAT) 

Jan 2.0 2.5 2.3 1.0 3.3 
Feb 2.0 2.5 2.3 0.7 3.6 

March 2.2 2.9 2.6 0.7 3.8 
April 2.0 2.6 2.4 0.6 3.5 
May 1.8 2.3 2.1 1.0 3.7 
June 2.0 2.6 2.4 0.9 3.3 
July 0.4 0.5 0.4 0.6 3.9 

August 0.9 1.2 1.1 0.6 3.0 
Sept 0.7 0.9 0.8 0.6 3.1 

October 0.8 1.0 0.9 0.8 3.3 
Nov 1.7 2.2 1.9 1.2 3.6 
Dec 1.9 2.5 2.3 1.2 3.4 

Source: Field study, 2016. 
 
 

Table-3. Estimated reference evapotranspiration (ETo) using four approaches in 1997. 

Months ETo (US) ETo (AUS) ETo (BT) ETo (BC) ETo (CROPWAT) 

Jan 2.0 2.6 2.4 1.1 3.7 

Feb 2.2 2.7 2.4 1.0 3.8 

March 2.0 2.5 2.5 0.8 3.5 

April 2.0 2.4 2.3 0.5 3.7 

May 1.9 2.5 2.2 0.9 3.3 

June 2.0 0.5 2.3 0.5 2.8 

July 0.4 1.3 2.4 0.5 2.8 

August 0.5 1.1 1.1 0.6 2.8 

Sept 0.8 1.7 1.0 0.6 2.9 

October 0.9 2.3 1.1 0.8 3.1 

Nov 1.8 2.3 2.0 1.0 3.4 

Dec 2.0 2.6 2.4 0.5 3.4 
Source: Field study, 2016. 
 
 
 
 
 



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Table-4. Estimated reference evapotranspiration (ETo) using four approaches in 1998. 

Months ETo (US) ETo (AUS) ETo (BT) ETo (BC) ETo (CROPWAT) 

Jan 1.3 1.6 1.5 1.0 3.3 
Feb 1.5 1.9 1.7 1.1 3.3 

March 1.4 1.8 1.6 0.8 3.6 
April 1.2 2.5 1.4 1.0 3.4 
May 0.9 1.2 1.1 0.9 3.6 
June 0.7 0.9 0.8 0.6 3.2 
July 0.5 0.6 0.6 0.6 2.8 

August 0.6 1.7 0.7 0.6 2.8 
Sept 0.5 0.6 0.6 0.8 3.3 

October 0.7 0.9 0.8 0.6 3.2 
Nov 1.1 1.4 1.2 0.6 3.5 
Dec 1.0 1.5 1.2 0.6 3.4 

Source: Field study, 2016. 
 

Table-5. Estimated reference evapotranspiration (ETo) using four approaches in 1999. 

Months ETo (US) ETo (AUS) ETo (BT) ETo (BC) ETo (CROPWAT) 

Jan 0.7 0.9 0.8 2.0 3.4 
Feb 0.8 1.0 0.9 1.1 3.1 

March 1.1 1.4 1.2 1.0 3.6 
April 0.8 1.1 1.0 0.8 3.5 

May 0.6 0.6 0.7 1.0 3.6 
June 0.7 0.9 0.8 0.9 3.3 
July 0.5 0.6 0.6 0.6 2.8 

August 0.6 1.7 0.7 0.6 2.9 
Sept 0.4 0.5 0.5 0.6 2.9 

October 0.5 0.6 0.6 0.8 3.1 
Nov 1.1 1.4 1.2 0.6 3.4 
Dec 1.8 2.3 2.1 0.5 3.2 

Source: Field study, 2016. 

 

 
Figure-5. Variation of of references evapotranspiration (ETo) estimated from five methods in 1996. 

                                   Source: Field study, 2016. 
 

 
Figure-6. Variation of of references evapotranspiration (ETo) estimated from five methods in 1997. 

                                    Source: Field study, 2016. 

 



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Figure-7. Variation of references evapotranspiration (ETo) estimated from five approaches. 

                                        Source: Field study, 2016. 
 

 
Figure-8. Variation of of references evapotranspiration (ETo) estimated from five approaches in 1999. 

                                  Source: Field study, 2016. 
 

 
Figure-9. Variation of  reference evapotranspiration (ETo) estimated from five approaches in 2000. 

                                    Source: Field study, 2016. 
 

 
Figure-10.Variation of of references evapotranspiration (ETo) estimated from five approaches in 2001. 

                                 Source: Field study, 2016. 
 



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Figure-11.Variation of of references evapotranspiration (ETo) estimated from five approaches in 2002. 

                                 Source: Field study, 2016. 
 

 
Figure-12. Variation of of references evapotranspiration (ETo) estimated from five approaches in 2003. 

                                 Source: Field study, 2016. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

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