Geology Reference
In-Depth Information
10
−
3
,
Other coef
cients are identi
ed as C
0
= 0.1396, C
1
=
3.019
×
−
10
−
3
, C
3
= 1.626
10
−
5
, C
4
= 8.224
10
−
5
, C
5
= 0.1842,
C
2
=
1.2109
×
×
×
−
10
−
3
, C
7
= 3.655
10
−
3
, and C
8
=
10
−
3
.
C
6
=
1.095
×
×
4.442
×
−
−
7.4.1.5
Copais Approach
for Daily Time Steps
''
''
Alexandris et al. [
4
] also developed another equation for estimating ET
0
on a daily
basis (mm d
−
1
):
ET
0
¼
m
1
þ
C
2
m
2
þ
C
1
m
3
þ
m
4
C
1
C
2
ð
7
:
23
Þ
where m
1
= 0.057, m
2
= 0.227, m
3
= 0.643, and m
4
= 0.0124, and C
1
and C
2
can be
estimated from the following equations:
C
1
¼
a
1
þ
a
2
RH
þ
a
3
R
s
þ
a
4
R
s
RH
ð
7
:
24
Þ
where a
1
= 0.6416, a
2
=
0.00784, a
3
= 0.372, and a
4
=
0.00264 and
−
−
C
2
¼
b
1
þ
b
2
T
þ
b
3
R
s
þ
b
4
TR
s
ð
7
:
25
Þ
where b1 =
0.0033, b2 = 0.00812, b3 = 0.101, and b4 = 0.00584. In these
equations C
1
and C
2
represent evapotranspiration in mm day
−
1
. The units of the
parameters a
1
,a
2
, and b
1
are mm day
−
1
,a
3
,a
4
,b
3
, are 10
6
−
mm
3
MJ, m
1
are
mm day
−
1
,m
2
and m
3
are dimensionless, and m
4
are day mm
−
1
.
×
7.4.2 Model Performance Analysis Relative to FAO56-PM
The meteorological hourly data of 5 years from the Hydrological Radar Experiment
(HYREX) based at the British Atmospheric Data Centre (BADC) covering the
period January 1995 to December 1999 and meteorological daily data of Santa
Monica Station at California for 3 years covering the period January 2000 to
December 2002 were analyzed for calculating evapotranspiration by different
methods. In this study, the FAO-56 PM method was considered as the benchmark
method and the performance of three other ET
0
estimation methods (ASCE PM,
CIMIS PM, and the Copais Approach) were compared. A MATLAB-based pro-
gram module was developed for use in this study, which uses climate variables and
calculates hourly and daily ET
0
by the aforementioned empirical equations. Vari-
ations in performance of the other equations compared with the FAO-56 PM
method were estimated using graphics, simple linear regression, and statistical
factors such as the Standard Error of Estimate (SEE) statistic, Root Mean Squared
Differences, Mean Bias Error (MBE), variance of the distribution of differences,
coef
cient of determination R
2
, and slope S of a linear regression
t (through the
