Abstract
Penman-Monteith formülü, referans (çim) evapotranspirasyon (ET0) değerlerinin hesaplanmasında tutarlı
ve sağlıklı sonuçlar vermesi sebebiyle literatürde en çok kullanılan eşitliktir. Penman-Monteith formülünde
referans evapotranspirasyon değerini etkileyen bağımsız değişkenler olarak solar radyasyon (Rs), hava
sıcaklığı (T), rüzgâr hızı (U2), ve bağıl nem (RH) gibi meteorolojik veriler kullanılmaktadır. PenmanMonteith
eşitliğiyle evapotranspirasyon değerinin hesaplanmasının biraz karışık olmasından ve bu eşitliğin
fazla sayıda tanımlayıcı meteorolojik veriye ihtiyaç duymasından dolayı, yeterli veri olmadığı durumlarda
onun yerine Hargreaves-Samani eşitliği kullanılmaktadır. Bu çalışmada nem oranının yüksek olduğu
Akdeniz Bölgesine ait 38 adet meteoroloji istasyonunda ölçülmüş maksimum sıcaklık, minimum sıcaklık,
maksimum nem ve minimum nem verileri kullanılarak Hargreaves-Samani eşitliği kalibre edilmiştir. Bu
çalışmanın sonucunda maksimum sıcaklık, minimum sıcaklık, maksimum nem ve minimum nem verileriyle
kalibre edilmiş eşitlikler ile elde edilen evapotranspirasyon değerlerinin Penman-Monteith formülü ile
hesaplanan değerlere yakın sonuçlar verdiği tespit edilmiştir. Akdeniz Bölgesindeki bitki su ihtiyacının
kalibre edilmiş Hargreaves-Samani eşitliği ile hesaplanmasında maksimum sıcaklık verisinin diğer
meteorolojik verilere kıyasla daha etkili olduğu görülmüştür
Abstract
Penman-Monteith equation is the most commonly
used model in the literature for estimation of
reference (grass) evapotranspiration (ET0) because
of the fact that it gives more accurate and consistent
estimates. Despite its higher accuracy potential,
Penman-Monteith equation requires quite a few
meteorological quantities as explanatory variables
affecting the value of evapotranspiration such as
solar radiation, air temperature, wind velocity, and
relative humidity. Because it needs so many
meteorological variables and partly due to its
cumbersome analytical form, the Penman-Monteith
(PM) equation is not easily applicable for all cases
for calculating the reference evapotranspiration,
and therefore the Hargreaves-Samani (HS) equation
is necessarily used instead of it for regions at which
the pertinent meteorological data may not be
available.
Originally, the HS equation was developed for
semiarid environments, therefore, its results are less
accurate as compared to the FAO-56 PM formula
for different climate conditions. For this reason,
many researchers tried to calibrate the HS equation
based on the FAO-56 PM with different methods.
Generally, the main approach for the calibration of
the HS equation is linear regression with PM ET0 or
lysimeter values. . Alternatively, some researchers
were calibrated the HS equation by adjusting the
constant values of the HS equation. In addition to
these methods, different numerous attempts were
done for improving the estimation capability of the
HS equation with the help of additional climatic
variables such as wind speed (WS), relative humidity
(RH). Various studies have shown that this method
overestimates ET0 in warm humid areas.
In this study, first, Hargreaves-Samani equation is
calibrated using maximum temperature, minimum
temperature, maximum humidity and minimum
humidity data measured at 38 stations in the highly
humid Mediterranean Region of Turkey. Using the
monthly average values of the relevant
meteorological variables and ET0,PM values over the
period: 1975–2010 were used for the calibration
(training phase) and validation (testing phase) of the
HS equation. With the help of Excel Solver, suitable
magnitudes for a and b coefficients of the HS
equation for different meteorological variables
(RHmax, RHmin, Tmax and Tmin) were computed based
on the ET0 values computed by the FAO-56 PM
equation. The predictive abilities of the HS equation
calibration in such manner were quantitatively
evaluated by the common test statistics.
For reliability assessment of calibrated HS
equations, 3 weather stations data were used for
regions. The results of the original and calibrated
HS equations data were compared. A reduction in
the mean absolute relative error (MARE), root mean
square error (RMSE) and mean absolute error
(MAE) statistics are obtained with calibrated HS
equations. The scatter diagram of the FAO-56 PM
equation versus original and calibrated HS
equations show that the calibrated HS equations
estimates are closer to the exact line (1:1 line). The
calibrated HS equation reduces the MARE statistics
almost 3% and 10% for the training and testing data
sets, respectively. The best estimations obtained
from calibration with the maximum temperature,
which provided the smallest values of MAE (0.849
and 0.704 mm), MARE (14.76 and 10.56%) and
RMSE (1.143 and 0.904 mm) according to training
and testing datasets, respectively.
In conclusion, it is observed that the calculated
evapotranspiration values using calibrated
Hargreaves-Samani equation are close to those
calculated by Penman-Monteith equation. The
maximum temperatures are observed to be more
effective than the other meteorological variables on
the evapotranspiration values calculated by the
calibrated Hargreaves-Samani equation for crop
water requirements in the Mediterranean Region.
Details
| Other ID | JA26ZK56CH |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | December 1, 2016 |
| Submission Date | December 1, 2016 |
| Published in Issue | Year 2016 Volume: 7 Issue: 2 |