1. 1
Fifth International Conference on Geotechnique, Construction Materials and
Environment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051
EXAMINING THE IMPACT OF THE 120-DAY WINDS ON
EVAPOTRANSPIRATION CONSIDERING PAN EVAPORATION
COEFFECIENT IN WEST REGION OF AFGHANISTAN
Homayoon Ganji1 Takamitsu Kajisa1 Masaaki Kondo1 Behroze Rostami1
1Graduate School of Bioresources, Mie University, 514-8507 Kurima-machiya-cho 1577, Tsu, Japan
ABSTRACT
Evapotranspiration (ET) as important applications in irrigation planning plays a significant role in regional
and global climate, through the hydrological cycle. ET adversely affected by the “120-day winds” in west region
of Afghanistan. In this study, by examining the impact of metrological factors on evaporation process, the
relationship of the wind speed (u2) and pan coefficient (Kp) with reference evapotranspiration (ET0) is examined.
Penman-Monteith Method is used to estimate (ET0-PM), whereas pan evaporation (Epan) data, which is measured
directly at the site, with experimental coefficient Kp is used to estimate (ETpan). Kp is presented as Kp-S, which is
obtained from Snyder (1992) equation and Kp-D, which is derived from ETpan and ET0-PM equations. As results,
by comparing ET0-PM with ETpan and Epan, it has been found that, in windy season from June until September,
the ET0-PM is lower than the Epan. Whereas, the ET0-PM is smaller when compare with ETpan. Contrary to what
is expected, determination coefficient (R2) of Epan shows higher value with ET0-PM than the ETpan. Furthermore,
standard error estimation (SEE) of Epan with ET0-PM is obtained larger than that of the ETpan. On the other hand,
by considering R2, effect of temperature is fond more significant on ETpan than the wind speed (u2) and relative
humidity (RH). Kp-S and Kp-D show similarity only in windy season from June until September. On the other
hand, by increasing u2, Kp-S decreases. Hence, the effect of strong wind on Kp-S is more than the RH in windy
area like Herat.
Keywords: Reference Evapotranspiration, Pan Evaporation,pan Evaporation Coefficient
INTRODUCTION
Afghanistan, as a dry country, is characterized by
extremes of climate and weather that can be counted
as continental climate [2].
The climate variation is observed arid in the
south and southwest and to semi-arid in the most
other parts of the county.
Agriculture as a main source of income in
Afghanistan has been facing challenges since
decades. Water scarcity is the extreme challenge
which threatening the agricultural production.
The main consumer of fresh water in
Afghanistan is Agricultural irrigation. There in
Afghanistan, both rain fed and irrigated cropping
agriculture is practiced. A land use survey was
conducted on 1990s which estimated 3.2 million ha
was irrigated of which 48 percent was intensively
irrigated and 52 percent was intermittently irrigated
with one or more crops [12].
Spatial distribution of water availability is not
equal among the regions in Afghanistan.
Western region consists of four provinces such
as Herat, Farah, Badghis and Ghour province, is
characterized with semi-arid climate that has low
precipitation, as the total precipitation was 345.6mm
in 2009.
Many various factors influence water availability
at the region as one of the main factor is strong
winds locally known as “120-day winds”.
The “120 day winds” usually begin in early July
and go on until late September with a great force
7m/s averagely [6]. This period is cover entire of the
summer season as this time is the main season of
crop growing. According to the data measured in
2009, the precipitation is almost zero at the windy
season and daily average temperature is high as
17.5 °C. The ET0 in Herat has the highest rate in
compare to the other cities in Afghanistan, as the
daily average value is more than 10 mm [5]. One of
the factors among the all other factors which
adversely affect the irrigation is the “120-day winds”.
The great impact of wind velocity is increasing ET
which can have profound implications for
hydrologic processes and agricultural crop
performance [3].
ET estimation has important applications in
irrigation planning as it plays a significant role in
regional and global climate, through the
hydrological cycle [9] [10] [11].
Many different concepts are used for defending
the ET term, as one of them is reference
evapotranspiration (ET0). The evaporative demand

2. GEOMATE- Osaka, Nov. 16-18, 2015
2
of atmosphere apart from the crop type, crop
development and management practices is
introduced by ET0 [1]
For estimating the ET0, many different methods
have been developed based on their daily
performance under the given climatic condition
worldwide, of which six well known methods has
been examined by H. Ganji et al. (2015) to estimate
ET0 value for the west region. By considering pan
evaporation (Epan) as indicator, the Penman-
Monteith method is confirmed the most accurate
model for estimating the ET0 among the six well
known models in the west region of Afghanistan.
For estimating the ET0 using Epan value,
considering appropriate coefficient (KP) is important
in calculation.
The empirically derived coefficient Kp is a
correction factor which depends on the prevailing
upwind fetch distance, average daily wind speed,
and relative humidity conditions associated with the
sitting of the evaporation pan [4].
The KP is ranged from 0.35 to 0.85 depends on
deferent conditions [1]. Many various equations
have been presented for calculating the KP in the
world, but dose equations cannot cover the effective
environmental factors on Kp compatibly, as the local
estimation is necessary for estimating the accurate
value of ET0.
Among the all KP estimated equations that
presented till now, Snyder (1992) equation Eq. 3 is
used for estimating the ET0 in this study Table 1.
The proposed equation by Snyder (1992) is a
simpler equation for calculation daily KP as a
function of daily mean wind speed, measured at 2
meter height (Km/day), daily mean relative humidity
(RH) (%) and upwind distance fetch of low growing
vegetation (m).
In this paper, emphasize is focused on effect of
KP in estimation of the ETpan using Epan measured
data, especially in windy season. Estimating the
ETpan using Epan measured data is related to the Eq. 2
which requires the coefficient KP [1].
It’s hypothesized that, KP is effected more than
relative humidity as well as, ETpan is effected by the
wind speed more than the other required factors.
METHODS AND DATA
Site
The only official center where is appropriate for
researching is Urdu khan Regional Agricultural
Research Station. This station is the largest station in
the west region which was considered as study area
in this research. Urdu khan research center with a
total area of 225 hectares, located in a latitude of 39°
11' N and a longitude of 68° 13' E with an elevation
of 964 meters in Urdu khan village, at 5.8 kilometers
southeast of Herat city. The station is bounded by
Urdu khan right canal on the north and east canal on
the east. In 1968 approximately 15 hectares of the
total area were cultivated for the first time and
extended until now as a research center [11].
According to the measured data of climatic
factors in 2009, shown in Fig. 1 to 4, the maximum
mean annual temperature is around 37.5°C and
minimum mean temperature is 0.5°C, the total
precipitation is 345.6 mm, the daily average relative
humidity is 41.3 % and the daily average wind speed
is 271.7 km.
Metrological Data
Many different sources are used for data
collection Table 2 [5]. The metrological data is
collected for year 2009, which includes the daily
Epan, sunshine hours, daily values of precipitation,
maximum and minimum temperatures daily average
relative humidity and daily average wind speed.
Mostly the climatic data is provided by Urdu
Khan Research center where has not been equipped
with standard and modern measurement devices yet.
(I.e. the Epan is measured through a very simple pan
apart from standard A- class pan or etc.).
Furthermore, data missing is occurred sometimes as
there were some missing data for some days in 2009.
Thus, interpolation method is sued to cover missing
data.
Table 1 Accessible online database for irrigation
planning [5].
Name Features
CROPWAT (FAO) Data of mean ET0
CLIPWAT (FAO) Component data of ET0
NCDC (NOAA)
Air temperature, dew
point, and wind velocity
Weatherspark.com
Basic daily data. Cloud
cover, wind velocity, air
temperature and humidity
at the airport. Hourly data
may be available.
Urdu khan Research
center
Data of Epan,temperature,
sun shine and
precipitation
Calculation equations
The Equations that have been used in calculation
are listed in Table 2 as:
1. ET0 and ETpan have been estimated by using
Penman-Monteith methods and Epan data Eq.
(1and 2 respectively).
2. Kp-S and Kp-D were calculated by using Eq. (3
and 4 respectively)
3. SEE is calculated by sing Eq. (5).

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Table 2 Calculation equations
Model Equation No
Penman-Monteith (ET0 -PM) 𝑬𝑻 𝟎 − 𝐏𝐌 =
𝟎. 𝟒𝟎𝟖( 𝑹 𝒏 − 𝑮) + 
𝟗𝟎𝟎
𝑻 + 𝟐𝟕𝟑
𝒖 𝟐 (𝒆 𝒔 − 𝒆 𝒂 )
 + ( 𝟏 + 𝟎. 𝟑𝟒𝒖 𝟐
)
1
ETpan
𝑬𝑻 𝑷𝒂𝒏 = 𝑲 𝒑 × 𝑬 𝒑𝒂𝒏
2
Derived Pan Coefficient
Kp-D= 𝑬𝑻 𝟎 − 𝐏𝐌 ÷ 𝑬 𝒑𝒂𝒏 3
Snyder Pan Coefficient
𝑲 𝒑 − 𝐒 = 𝟎. 𝟎𝟒𝟖𝟐 + [ 𝟎. 𝟐𝟒𝒍𝒏( 𝑭)] − 𝟎. 𝟎𝟎𝟎𝟑𝟕𝟔𝒖 𝟐 +0.0045 RH
4
Standard Error Estimation (SEE) 𝛔 𝒆𝒔𝒕 = √
∑(𝒀 − 𝒀′) 𝟐
𝑵
5
Where:
ET0 is grass reference evapotranspiration (mm day-1),
Rn is net radiation (MJ m-2 day-1), G is soil heat flux
(MJ m-2 day-1), γ is the psychometric constant (kPa
°C-1), es is the saturation vapor pressure (kPa), ea is
the actual vapor pressure (kPa), ∆ is the slope of the
saturation vapor pressure-temperature curve (kPa °C-
1), T is the average daily air temperature (°C), u2 is
the mean daily wind speed at 2 m (m s-1 except in
Eq. 4 is Km/day) [2] [14]. T is the mean monthly
temperature (°C), KP-S and Kp-D is the pan
coefficient, Epan is the pan evaporation (mm day-1), F
is the fetch (m), RH is the daily average humidity
(%), σest is the standard error of the estimate, Y is an
actual score (ETpan), Y' is a predicted score (ET0-
PM), and N is the number of days.
RESULT AND DESCUSSION
1) daily variation of metrological variables for
year 2009 is shown in (Fig. 1 to 4).
Wind speed is seen high from around June until
end of September, as the peak occurred in August
more than 500 km/day Fig. 1. This period is known
as the “120-day winds” in the west region of
Afghanistan,especially in Herat province.
Temperature increases from January on, until
medal of August, as the peak is seen over 30 °C.
From August on, the temperature decreases as the
lowest rate is seen in December and January less
than 5 °C Fig 2.
Relative humidity is one of the requirements
factor for Kp calculation. Humidity decreases from
April on, as the lowest daily average rate is seen in
July less than 20 %. The peak occurred in December
more than 80 % Fig. 3. Furthermore, Precipitation
(PRCP) occurs mostly from December until May as
the peak is seen in March more than 8 mm/day, but
from May on, there is no any PRCP Fig. 4. This
period coincides with windy season “120-day
winds”.
Fig. 1 Daily average wind speed,2009
Fig. 2 Daily average Temperature, 2009
Fig. 3 Daily average relative humidity, 2009
0
100
200
300
400
500
600
700
800Km/day
WindSpeed (u2)
Windy Season
0
5
10
15
20
25
30
35
40
°C
Temperature (T)
Windy Season
0
10
20
30
40
50
60
70
80
90
100
%
Relative Humididty (RH)
Windy Season

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Fig. 4 Daily average precipitation, 2009
2) Compression of Epan and ETpan with
ET0-PM.
By comparing them, it has been found that, in the
windy season, form June until September, the ET0-
PM is lower than the Epan, which was measured
directly at the site Fig. 5, whereas the difference is
smaller when ET0-PM compared with ETpan Fig. 6.
The reason refers to the Kp application. ETpan is
estimated by using the Eq. 2. Kp as a correction
factor is recommended for accurate estimation of
ETpan value.
Fig. 5 Daily average Epan and ET0 PM, 2009
Fig. 6 Daily average ETpan and ET0 PM, 2009
It was expected that, the ETpan has higher
correlation with ET0-PM than the Epan, but contrary
to what is expected, determination coefficient (R2)
of Epan shows higher value with ET0-pM (Fig 7 and
8). Furthermore, by using the Eq. 5, SEE of Epan is
obtained less than the SEE of ET0-PM Table 3.
Fig. 7 Regression between Epan and ET0-PM, 2009
Fig. 8 Regression between ETpan and ET0-PM, 2009
Table 3 Correlated coefficient of Epan and ETpan with
ET0-PM including standard error estimation value
Models
coefficients
SEE mm/day
R2 a
ETpan
0.8091 0.915 2.0
Epan
0.8822 1.267 2.7
3) In this study, Kp is presented as Kp-S,
obtained by using Eq.3, which is proposed by
Snyder (1992) and Kp-D, which is calculated by
using Eq. 3. Both Kp are compared between each
other, as they show similarity only in windy season
from June until September Fig. 9. By using
correlation method, it has been found that, the
coefficient R2 is 0.0108 Fig. 9.
Fig. 9 Daily Kp-D and Kp-S, 2009
0
10
20
30
40
50
mm/day
Precipitation (PRCP)
Windy Season
0
5
10
15
20
(mm/day)
Epan Penman-MontiethEpan ET0-PM
Windy Season
0
5
10
15
20
(mm/day)
ET0-PM ETpanET0-PM ETpan
Windy Season
y = 1.2667x - 0.1278
R² = 0.8822
0
5
10
15
20
0 5 10 15 20
Epan
ET0-PM
y = 0.9152x + 0.8768
R² = 0.8091
0
5
10
15
20
0 5 10 15 20
ETpan
ET0-PM
0
2
4
6
8
Kp-D Kp-SKp-D Kp-S
Windy Season

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According to the Allen et al. (1998), Kp is ranged
from 0.35 to 0.85 depends on deferent conditions,
but the Kp-S has been found ranged from 0.66 to
1.25. On the other hand, as shown in Fig. 11, by
increasing u2 Kp-S and RH decrease. Therefore, the
relationship between Kp-S and u2 is shown in Fig. 12,
as the R2 value is 0.5986.
Effect of strong wind on Kp-S is more than the
RH in windy area like Herat. As it is seen in Fig. 13,
at the windy season, impact of wind factor is larger
than RH.
It was expected that, the effect of u2 on ETpan is
more than the effect of T and RH, but contrary to
what is expected, by correlating them with ETpan, it
was found that, T has highest R2 which is 0.7981 and
RH has the second R2 value which is 0.6137,
Whereas the u2 has the lowest R2 value which is
0.3631(Fig. 14 to 16).
Fig. 10 Correlation between Kp-D and Kp-S
Fig. 11Daily average of u2, RH and Kp-S in 2009
Fig. 12 Correlation between Kp-S and u2,2009
Fig. 13 Comparing of u2 and RH, 2009
Fig. 14 Correlation between ETpan and u2,2009
Fig. 15 Correlation between ETpan and T, 2009
y = 0.022x + 0.9297
R² = 0.0108
0
1
2
0 2 4 6 8
Kp-S
Kp- D
0
1
10
100
1
10
100
1000
1-Jan
1-Feb
1-Mar
1-Apr
1-May
1-Jun
1-Jul
1-Aug
1-Sep
1-Oct
1-Nov
1-Dec
Km/day
u2 (km/day) RH (%) Kp-Su2 (km/d) RH(%) KP-S
Windy Season
y = -0.0008x + 1.1578
R² = 0.5986
0
1
2
0 200 400 600 800
Kp-S
u2 (Km/day)
-10
0
10
20
30
40
50
60
0.000376u2-0.0045RH
0.0045/0.000376
y = 0.0179x + 1.3975
R² = 0.3631
0
5
10
15
20
0 200 400 600 800
ETpan(mm/day)
u2 (Km/day)
y = 0.4424x - 1.4951
R² = 0.7981
0
5
10
15
20
0 10 20 30 40
ETpan(mm/day)
T (°C)

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Fig. 16 Correlation between ETpan and T, 2009
CONCLUSION
ET adversely affected by strong wind “120-day
winds” as the daily average ET0 is more than 15 mm.
the reason refers to the climate condition as in the
summer season, the wind speed is strong high, the
humidity is low as less than 20%, Temperature is
high more than 30 °C and the precipitation is almost
zero.
ETpan is found closer to the ET0-PM than that of
Epan. Hence, SEE of ETpan with ET0-PM is smaller
than that of the Epan.
For daily accurate estimating of ETpan in the west
region of Afghanistan,appropriate Kp as a correction
factor is needed. In this study, Kp-S, which is
calculated through Snyder proposed equation, is
confirmed appropriate coefficient for ETpan
calculation as it ranges from 0.66 to 1.25. it has been
found that, effect of u2 on Kp-S is more than RH in
windy condition like Herat. Furthermore, Kp-S
decreases in windy season when the wind speed is
strong.
ACKNOWLEDGEMENTS
Access to the accurate data is still a challenge in
Afghanistan due to the luck of modern stations, but
recently, the obtained data is more accurate than the
last decades.
REFERENCES
[1] Allen, Richard G., et al. "Crop
evapotranspiration-Guidelines for computing
crop water requirements-FAO Irrigation and
drainage paper 56" FAO, Rome , Vol. 300(9),
year 1998.
[2] A Qureshi, Asad Sarwar. Water resources
management in Afghanistan: The issues and
options.Vol. 49. IWMI, 2002.
[3] Bandyopadhyay, A., et al. "Temporal trends in
estimates of reference evapotranspiration over
India." Journal of Hydrologic Engineering 14.5
(2009): 508-515.
[4] Doorenbos, Jan. "Guidelines for predicting crop
water requirements." FAO irrigation and
drainage paper 24, Revised 1977, pp. 15-29.
[5] Ganji H, Rahmany Ab. S, Kajisa T, Kondo M,
Hajime N, “Effect of the 120-day winds” on
Agriculture and Environmental Condition in
Herat, Afghanistan”. Brisbane, Australia. 4th Int.
Conf. on International Conference on
Geotechnique, Construction Materials and
Environment, 2014, pp. 500.
[6] Ganji H, Rahmany Ab. S, Kajisa T, Kondo M,
Hajime N, “Comparison of the Crop Water
Need between Actual Wind Condition and Zero
Wind Simulation; Wind Velocity within 24-
Hour Interval in Herat, Afghanistan”. In Tokyo
University of Agriculture. 20th Int. Conf. on
ISSAAS, 2014, pp. 91.
[7] Ganji H, Kajisa T, Kondo M, Rostami B,
“Examining the six well known equations for
estimating reference evapotranspiration in Herat
Afghanistan”. In Tsu City, Japan. 1th Int. Conf.
on Science & Environment, 2014.
[8] J. M. Norman, 1996: Use of remote sensing for
evapotranspiration monitoring over land
surfaces. Hydrol. Sci.—J. Sci. Hydrol., 41, 495–
516.
[9] Kalma, J. D., and I. R. Calder, “Land surface
processes in large scale hydrology”, World
Meteorological Organization Operational
Hydrology Rep, 1994, pp. 40, 60.
[10] Kustas, W. P., and C. S. T. Daughtry, 1990:
Estimation of the soil heat flux/net radiation
ratio fromspectral data. Agric. For. Meteor., 49,
205–223.
[11] Osmanzai, M. 2009. Annual Report. CIMMYT,
Afghanistan.
[12] Rout, Bob, and Cynthia Lee. How the water
flows: a typology of irrigation systems in
Afghanistan. Kabul: Afghanistan Research and
Evaluation Unit, 2008, pp 13.
[13] Snyder, Richard L. "Equation for evaporation
pan to evapotranspiration conversions", J. of
Irrigation and Drainage Engineering, Vol. 118.6.
1992, pp. 977-980.
y = -0.1437x + 12.191
R² = 0.6137
0
5
10
15
20
0 20 40 60 80 100
ETpan(mm/day)
RH (% )