Fifth International Conference on Geotechnique, Construction Materials andEnvironment, 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 of atmosphere apart from the crop type, crop development and management practices is

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GEOMATE- Osaka, Nov. 16-18, 2015

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

SiteThe 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 FeaturesCROPWAT (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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GEOMATE- Osaka, Nov. 16-18, 2015

Table 2 Calculation equations

Model Equation No

Penman-Monteith (ET0 -PM) ET 0−PM=0.408 ( Rn−G )+ 900

T+273u2(es−ea)

+(1+0.34 u2 )

1

ETpan ET Pan=K p× Epan2

Derived Pan Coefficient Kp-D¿ ET 0−PM ÷ E pan 3

Snyder Pan Coefficient K p−S=0.0482+[0.24 ln (F ) ]−0.000376 u2+¿0.0045 RH4

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”.

1-Jan

14-Jan

27-Jan 9-F

eb22-

Feb7-M

ar

20-Mar

2-Apr

15-Apr

28-Apr

11-May

24-May

6-Jun

19-Jun 2-J

ul15-

Jul28-

Jul

10-Aug

23-Aug

5-Sep18-

Sep1-O

ct14-

Oct27-

Oct9-N

ov

22-Nov

5-Dec18-

Dec31-

Dec

0

100

200

300

400

500

600

700

800

Wind Speed (u2)

Km

/day

Windy Season

Fig. 1 Daily average wind speed, 2009

1-Jan13-

Jan25-

Jan6-Feb18-

Feb2-M

ar

14-Mar

26-Mar

7-Apr

19-Apr

1-May

13-May

25-May

6-Jun18-

Jun30-

Jun12-

Jul24-

Jul5-A

ug

17-Aug

29-Aug

10-Sep

22-Sep

4-Oct16-

Oct28-

Oct9-N

ov

21-Nov

3-Dec15-

Dec27-

Dec0

5

10

15

20

25

30

35

40

Temperature (T)

°C

Windy Season

Fig. 2 Daily average Temperature, 2009

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GEOMATE- Osaka, Nov. 16-18, 2015

1-Jan13-

Jan25-

Jan6-Feb18-

Feb2-M

ar

14-Mar

26-Mar

7-Apr

19-Apr

1-May

13-May

25-May

6-Jun18-

Jun30-

Jun12-

Jul24-

Jul5-A

ug

17-Aug

29-Aug

10-Sep

22-Sep

4-Oct16-

Oct28-

Oct9-N

ov

21-Nov

3-Dec15-

Dec27-

Dec

0

10

20

30

40

50

60

70

80

90

100

Relative Humididty (RH)

%

Windy Season

Fig. 3 Daily average relative humidity, 2009

1-Jan

13-Ja

n25

-Jan

6-Feb

18-Fe

b2-M

ar14

-Mar

26-M

ar

7-Apr

19-Apr

1-May

13-M

ay

25-M

ay

6-Jun

18-Ju

n30

-Jun

12-Ju

l24

-Jul

5-Aug

17-Aug

29-Aug

10-Se

p22

-Sep4-O

ct16

-Oct28

-Oct9-N

ov21

-Nov3-D

ec15

-Dec27

-Dec

0

5

10

15

20

25

30

35

40

45

50

Precipitation (PRCP)

mm

/day

Windy Season

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.

1-Jan12-Jan23-Jan3-F

eb14-Feb25-Feb8-M

ar19-Mar30-Mar10-Apr21-Apr2-M

ay13-May24-May4-Ju

n15-Jun26-Jun7-Ju

l18-Jul29-Jul9-Aug20-Aug31-Aug11-Sep22-Sep3-O

ct14-Oct25-Oct5-N

ov16-Nov27-Nov8-D

ec19-Dec30-Dec

0

5

10

15

20

Epan Penman-Montieth

(mm

/day

)

EpanET0-PM

Windy Season

Fig. 5 Daily average Epan and ET0 PM, 2009

1-Jan13-

Jan25-

Jan6-Feb18-

Feb2-M

ar

14-Mar

26-Mar

7-Apr

19-Apr

1-May

13-May

25-May

6-Jun18-

Jun30-

Jun12-

Jul24-

Jul5-A

ug

17-Aug

29-Aug

10-Sep

22-Sep

4-Oct16-

Oct28-

Oct9-N

ov

21-Nov

3-Dec15-

Dec27-

Dec

0

5

10

15

20

ET0-PM ETpan

(mm

/day

)

ET0-PM ETpan

Windy Season

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.

0 2 4 6 8 10 12 14 16 180

5

10

15

20f(x) = 1.26670075527673 x − 0.127761111613586R² = 0.882172858251059

ET0-PM

Epan

Fig. 7 Regression between Epan and ET0-PM, 2009

0 2 4 6 8 10 12 14 16 180

5

10

15

20

f(x) = 0.915170121777147 x + 0.876830927891528R² = 0.809072482407383

ET0-PM

ETpa

n

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

Modelscoefficients

SEE mm/dayR2 a

ETpan0.8091 0.915 2.0

Epan0.8822 1.267 2.7

3) In this study, Kp is presented as Kp-S,

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GEOMATE- Osaka, Nov. 16-18, 2015

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.

1-Jan11-Ja

n21-Ja

n31-Ja

n10-Fe

b20-Fe

b2-M

ar

12-Mar

22-Mar

1-Apr11-Apr

21-Apr1-M

ay

11-May

21-May

31-May10-Ju

n20-Ju

n30-Ju

n10-Ju

l20-Ju

l30-Ju

l9-Aug

19-Aug29-Aug

8-Sep18-Se

p28-Se

p8-Oct

18-Oct28-Oct

7-Nov

17-Nov

27-Nov7-Dec

17-Dec27-Dec

0

1

2

3

4

5

6

7

8

Kp-D Kp-SKp-D Kp-S

Windy Season

Fig. 9 Daily Kp-D and Kp-S, 2009According 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).

0 1 2 3 4 5 6 7 80

1

2

f(x) = 0.0220410702081283 x + 0.929672191617692R² = 0.0108449101096562

Kp- D

Kp-S

Fig. 10 Correlation between Kp-D and Kp-S

1-Jan12-Jan23-Jan3-Feb

14-Feb25-Feb8-Mar

19-Mar30-M

ar10-Apr

21-Apr2-May

13-May24-M

ay4-Jun15-Ju

n26-Ju

n7-Jul18-Ju

l29-Ju

l9-Aug

20-Aug31-Aug

11-Sep22-Se

p3-Oct

14-Oct25-Oct

5-Nov16-Nov

27-Nov8-Dec

19-Dec30-Dec

10

100

1000

0

1

10

100

u2 (km/day) RH (%) Kp-S

Km/da

y

u2 (km/d) RH(%) KP-S

Windy Season

Fig. 11Daily average of u2, RH and Kp-S in 2009

0 100 200 300 400 500 600 700 8000

1

2

f(x) = − 0.000758129754077016 x + 1.15781897028856R² = 0.59856297770726

u2 (Km/day)

Kp-S

Fig. 12 Correlation between Kp-S and u2, 2009

1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec-10

0

10

20

30

40

50

60

0.000376u2-0.0045RH 0.0045/0.000376

Fig. 13 Comparing of u2 and RH, 2009

0 100 200 300 400 500 600 700 8000

5

10

15

20

f(x) = 0.0179021295037181 x + 1.39745046241363R² = 0.363077289018263

u2 (Km/day)

ET

pan

(mm

/day

)

Fig. 14 Correlation between ETpan and u2, 2009

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GEOMATE- Osaka, Nov. 16-18, 2015

0 5 10 15 20 25 30 35 400

2

4

6

8

10

12

14

16

f(x) = 0.442439849952573 x − 1.4950635818666R² = 0.798051891000757

T (°C)

ET

pan

(mm

/day

)

Fig. 15 Correlation between ETpan and T, 2009

0 10 20 30 40 50 60 70 80 90 1000

2

4

6

8

10

12

14

16

18

20

f(x) = − 0.143660038819117 x + 12.1907992404574R² = 0.613673329927303

RH (%)

ETp

an (m

m/d

ay)

Fig. 16 Correlation between ETpan and T, 2009

CONCLUSIONET 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 from spectral 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.

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