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Spatial Distribution Maps of Evapotranspiration Rate over Saudi Arabia
Jarbou Abdullah Bahrawi
Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz
University, Jeddah, Kingdom of Saudi Arabia [email protected]
Abstract. Estimation of daily evapotranspiration is the key issue in water resources management. In arid environments located countries, e.g. Saudi Arabia; water resources are scare and under pressure.
Agricultural practices in Saudi Arabia consume about 85% of national water budget. Therefore, national scale of daily evapotranspiration is national quest for better water resources management in the country.
Penman–Monteith equation used to estimate the potential daily evapotranspiration across the whole Saudi Arabia territory adjoined by the help of the meteorological stations distributed all over the country.
Potential daily evapotranspiration thematic maps were produced on monthly basis in addition to annual potential daily evapotranspiration map of the designated study area. Results pointed out that potential daily evapotranspiration could be divided into three categories according to the amount of the potential water evaporated.
Recommendations were made based on the findings of the current research for enhanced water resources rationalionalization.
Introduction
The water cycle starts with the evaporation of water from the surface of the water bodies as moist air. Moist air is cycled around the earth until it fallen on the earth surface as precipitation. This surface water flows along the surface of the ground, infiltrates into the ground surface and held in soil pore spaces and in the fractions of rock formation. Other part
of precipitation returns back as runoff, drains into lakes, rivers, streams, and moved back into the ocean, and the third part is returned and evaporated into the atmosphere, and water cycle goes on. Water is a scarce resource all over the world. It is needed in agriculture, and for human consumption, purposes, and most of the world population depend on water irrigated agricultural products (Lima, et al., 1999), so irrigated agriculture should well be designed and managed. Evapotranspiration represents the most important component of water cycle, and through it considerable amount of water is lost. Water lost from the soil by plants is through transpiration by its shoot system, and with augmentation of water evaporation from the soil surface, a significant amount of water in them lost to the atmosphere. Evapotranspiration estimation is a significant factor as regards water resources management in general and to agricultural irrigation water requirement in specific. During planning and designing of agricultural schemes particularly under arid conditions.
Currently evapotranspiration estimation using models at a regional scale which is based on local information from a meteorological station, and it demonstrates a comparatively insignificant area (Daughtry, et al., 1990).
This estimation is considered imprecise assessment due to discrepancies in many factors such as, soil moisture, surface and air temperature, and kind of vegetation and its outgrowth phase. Under the conditions of high evapotranspiration rate, scarce rainfall, limited amount of groundwater resource proper methods for determination of evapotranspiration are essential means for water conservation and utilization (Viessman and Lewis, 1996). Evapotranspiration measures can be determined by many methods, and specialties tend to use one or more of the different empirical formula methods which is suitable under arid land conditions.
Many evapotranspiration estimation methods have been developed, some of them are need of many weather parameters as inputs, and other need small number, and some were established incompletely in response of the data availability. There are several methods to estimate evapotranspiration like, Penman, Pan Evaporation, Penman-Monteith, Kimberly-Penman, Priestley-Taylor, Hargreaves, Samani-Hargreaves and Blaney-Criddle. In this study, the Penman-Monteith method was used for estimation of the evapotranspiration. This method is suitable for Saudi Arabia dry conditions with scarce water resources. It is thought that a knowledge will be obtained concerning the potential evapotranspiration rate and its distribution for clear decision making picture. Also it will provide a decision or adoption of an appropriate policy for management of water resources either for use in agriculture, where the agricultural sector
consumes around 90% of the water budget in Saudi Arabia (ElNesr, et al., 2010), drinking water,or domestic purposes. The study shall provide evapotranspiration map, and spatial distribution map of potential evapotranspiration rates.
Material and Methods Study area
The study area was The Kingdom of Saudi Arabia, which dry climate and lies between latitudes 16°21'58”'N and 32°9'57”'N and longitudes 34°33'48”'E and 55°41'29”E, as shown in Figure 1 (Wynbrandt, 2004).
The country falls in the subtropical desert region, with generally dry winds, high temperatures (Aquastat, 2014), with rainfall depths from 300 mm /year in the southwestern areas to zero in the eastern desert (Abdullah and Al Mazroui, 1998). Penman Monteith model (Allen, et al., 1998) was for estimation evapotranspiration using the requirement data (temperature, wind speed, solar radiation, relative humidity).
Data set of framework
Climate data: The meteorological data were collected from the Presidency of Meteorology and Environment in Kingdom of Saudi Arabia, and represents 22 meteorological stations, which cover 13 districts in Saudi Arabia. The data covers 29 years of daily information from most stations. (Table 1). Potential evapotranspiration was estimated using meteorological data and it’s generally estimated through either direct measurements (i.e., lysimeters, field plots, water balance, energy balance, etc.) or by using experiential methods built on climatic observations (i.e., Penman, Jensen-Haise, Hargreaves, Blaney-Criddle, etc.). The availability of direct measurements would simultaneously give the most adequate assessment of evapotranspiration; nevertheless inopportunely, such dimensions would most probably be scarce within most of these extremely arid areas.
Penman–Monteith
Monteith (1965) derived a further term for the Penman equation so that actual evaporation from a vegetated surface could be estimated.
Evapotranspiration was estimated using the FAO56 Penman-Monteith equation for daily ETo in m d−1 after Allen, et al., (1998):
( ) ( ) ( )
(
2)
2
34 . 0 1
273 408 900
. 0
U
e e T U
G R ET
a s a
n
+ + Δ
+ − +
−
Ο = γ
γ
(1) Where:
Rn : The net radiation at the crop surface [ MJ /m2/day]
G : The soil heat flux density [MJ / m2 / day]
Ta : The mean daily air temperature at 2 m height [ΟC] U2 : The wind speed at 2 m height [ m/s]
es : Saturation vapor pressure [kPa] ea : Actual vapor pressure [kPa]
∆ : The slop of vapour pressure curve [ kPa /ΟC]
The measured meteorological data available were Ta, Relative Humidity (RH) and U2 whereas soil heat flux (G) was taken equal o zero, G = 0, (Allen et al., 2005). The slope of the saturation vapour pressure curve (∆) is computed by the following equation as in Murray (1967) :
[ ]
(
4098+237.3)
2=
Δ Ο
a
a
T
T
e (2)
Where ,eΟ
[ ]
Ta is calculated according to (Tetens,1930):[ ]
= + Ο 237.3
27 . exp 17 611 .
0 T
T T
e a (3)
The net radiation Rn was estimated as the difference between the net short wave incoming radiation Rns and the net long wave outgoing radiation Rnl. The calculation of Rns and Rnl, followed the procedures outlined in Allen, et al., (1998) and Doorenbos and Pruitt (1975). All radiation was computed in daily energy flux units (MJ / m2 / day). Allen, et al., (1998) stated a validated equation to calculate the incoming solar radiation Rs from air temperature difference:
n x a
s cR T T
R = − (4)
Where:
Ra = Extraterrestrial radiation [MJ / m2 / day],
c = An adjustment coefficient = 0.19 for coastal stations and 0.16 for inland stations
Tn = Minimum dry bulb air temperature [°C]
Tx = Maximum dry bulb air temperature [°C].
The psychometric constant γ is evaluated as:
γ =0.00163λP (5) Where:
P = Atmospheric pressure [kPa]
λ = Latent heat flux [ MJ kg−1]
The atmospheric pressure is expressed as in Burman et al. (1987)
26 . 5
293 0065 . 0 3 293 .
101
−
= z
P (6)
Where, z: altitude [m]. The latent heat λ depends on the average temperature, Eq. (7), while it can be taken as an approximate value of 2.45 for Ta = 20°C. In the current study, we chose to calculate the latent heat using Eq. (7).
Ta
00236 . 0 5 . 2 −
λ = (7) The saturation vapor pressure, es and actual vapour pressure, ea, are calculated according to
Allen et al. (2005) as:
[ ] [ ]
(
n x)
s e T e T
e =0.5 Ο + Ο (8)
[ ] [ ]
(
x n n x)
a RH e T RH e T
e =0.005 Ο + Ο (9)
Where:
RHx = Maximum relative humidity [%]
RHn = Minimum relative humidity [%]
The average daily ETo in a specific month was calculated by taking the arithmetic average of the daily values in that month. The summation of all ETo daily values in a year for a station will give the total annual ETo
for that station.
Fig. 1. Station locations over the Kingdom of Saudi Arabia.
Results and Discussion
Potential evapotranspiration maps were produced on monthly basis to cover the entire area of the Kingdom of Saudi Arabia. The Penman- Monteith method was implemented with the aid of meteorological data to estimate the potential evapotranspiration that covers the designated study area.
Table 1. Geographical information of the meteorological stations included in this study.
Name Long (o) Lat (o) Alt (m)
SULAYEL 45.61 20.46 614 TAIF 40.55 21.48 1454 TURAIF 38.66 31.68 818 JEDDAH-I.E 39.2 21.4 10 RIYADH 46.71 24.71 612 YENBO 38.06 24.15 6 MADINAH 39.7 24.55 636 DHAHRAN 50.15 26.26 17 QATIF 50 26.5 5 GIZAN 42.58 16.9 3 GASSIM 43.76 26.3 650 WEJH 36.46 26.2 21 HAIL 41.68 27.43 1013 NAJRAN 44.43 17.61 1210 ABHA 42.65 18.23 2093 KHAMIS-MUSHAIT 42.8 18.3 2054
HAFR AL BATIN 46.11 28.33 360
TABUK 36.63 28.36 776 AL JOUF 40.1 29.78 689
KWASH 41.88 19 350 BISHA 42.61 19.98 1163 RAFHA 43.48 29.63 447
Potential evapotranspiration maps were illustrated in Figures (2, 3a, 3b). Annual mean of potential evapotranspiration was demonstrated in Figure 2. Classifying the final evapotranspiration maps were based on Jenks rule of classification, where the output classes were based on
natural groupings innate in the data (ESRI, 2008). Jenks rule identifies break points by picking the class breaks that best group similar values and heighten the differences between classes.
Fig. 2. Spatial distribution of Mean annual ETo (mm) over the Kingdom of Saudi Arabia.
The features were divided into classes whose boundaries were set where there fairly big jumps in the data values. According to the potential evapotranspiration value, there were three classes demonstrated in Table 2.
Based on potential evapotranspiration classes listed in Table 2, there are three different indicative parameters need to be considered for interactive water resources management plan. Indicative parameters are:
1- Crop water demand, 2- Crop spatial distribution and 3- Crop rotation.
For water demanding crops, different crops with relatively long cultivation span (120 days) are preferred to be planted across the study expect the southern region. Corp rotation shall starts from November till February (winter crops) where potential evapotranspiration is less than 6 mm/day (Figures 3b of Nov, Dec, Figure 3a of January and February).
On the other hand, crops with low water requirements but with similar cultivation span of 120 days will be favored to the cultivation in May and last till August; where potential evapotranspiration may reach 14 mm/day. Suitable areas for summer crops will be across the study area except the northern region (Figures 3a of May, June, Figures 3b July and August).
Table 2. Values of potential evapotranspiration classes.
Low Medium High 4-6 mm 6-10 mm 10-14 mm
Jan Mar May Feb Apr June Nov Sep July Dec Oct Aug
Short cultivation crops with less than 60 days of life span will be favored to be planted in March and April and once again in September
Fig. 3a. Spatial distribution of Mean monthly ETo (mm) over the Kingdom of Saudi Arabia.
Fig. 3b. Spatial distribution of Mean monthly ETo (mm) over the Kingdom of Saudi Arabia.
and October preferred area located in both eastern and western regions of the study area (Figures 3a of March, April. Figures 3b of September and October).
Valuable information could be extracted from annual mean daily evapotranspiration map only when the limitation conditions are taken into account. Limiting conditions for the application of the Penman- Monteith method may rely mainly on meteorological parameterization (Allen, et al., 2005), the Planetary Boundary Layer (Su, et al., 2003), and the type of land use (Li, et al., 2009). Due to the fact that the study area has relatively large surface relief the application of the Penman-Monteith method is more consistent over the agricultural lands (Hayes, et al., 1999; Su, et al., 2003; Psilovikos and Elhag, 2013). Under such conditions, Penman-Monteith method relies basically on meteorological data conducted from various weather stations located in the study area, and on the corresponding cloud conditions at the time of data acquisition (Elhag, et al., 2011). According to Jin, et al. (2005), the aforementioned parameters are in agreement with the results of this study.
Conclusion
Spatial distribution of potential evapotranspiration is the key stone component in water resources management. The consistent distribution of the potential daily evapotranspiration supports decision makers to review the current land use practices in terms of water management, while enabling them to recommend accurate land use changes.
Meteorological parameters are used in conjunction with crop rotation and crop water requirements to overcome difficulties in finding potential daily evapotranspiration measurements on a regional scale. The applicability and the accuracy of Penman-Monteith method in estimation of potential daily evapotranspiration over the study area will benefit the national strategy of rational use of water in agriculture sector.
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ﻲﻓ ﺢﺗﻧﻟا تﻻدﻌﻣﻟ ﻲﻧﺎﻛﻣﻟا ﻊﻳزوﺗﻟا طﺋارﺧ ﺔﻳﺑرﻌﻟا ﺔﻛﻠﻣﻣﻟا
ﺔﻳدوﻌﺳﻟا
دﺑﻋ عوﺑرﺟ يوارﺣﺑ ﷲا
ﻩﺎﻳﻣﻟا دراوﻣ ةرادٕاو موﻠﻋ مﺳﻗ
، ﺔﻋارزو ﺔﺋﻳﺑﻟاو دﺎﺻرﻷا ﺔﻳﻠﻛ ﺔﻓﺎﺟﻟا قطﺎﻧﻣﻟا
، ﺔﻌﻣﺎﺟ
زﻳزﻌﻟادﺑﻋ كﻠﻣﻟا ﺔﻳدوﻌﺳﻟا ﺔﻳﺑرﻌﻟا ﺔﻛﻠﻣﻣﻟا ،ةدﺟ ،
صﻠﺧﺗﺳﻣﻟا .
إ ن رﻳدﻘﺗ ﺢﺗﻧﻟا دﺣأ وﻫ ﻲﻣوﻳﻟا ﻲﻓ ﺔﺳﻳﺋرﻟا رﺻﻧﻌﻟا
ةرادإ
ﺔﻳﺋﺎﻣﻟا دراوﻣﻟا .
إ ن تﺎﺋﻳﺑﻟا ﻲﻓ ﺔﻌﻗاوﻟا ﻝودﻟا ﻰﻠﻋ ،ﺔﻓﺎﺟﻟاو ﺔﻠﺣﺎﻘﻟا
ﻝﺎﺛﻣﻟا ﻝﻳﺑﺳ ﻟا ﺔﻳﺑرﻌﻟا ﺔﻛﻠﻣﻣﻟا
نوﻛﺗ ؛ﺔﻳدوﻌﺳ ﺔﻳﺋﺎﻣﻟا دراوﻣﻟا
ﻲﻓ كﻠﺗ
تﺎﺋﻳﺑﻟا ﺔﺣﻳﺣﺷ دراوﻣﻟا كﻠﺗﻟ ﻲﻓازﻧﺗﺳا طﻐﺿ تﺣﺗو .
تﺎﻛﻼﻬﺗﺳﻻا
ﺔﻳﻋارزﻟا ﻟا ﻲﻓ
وﻌﺳﻟا ﺔﻳﺑرﻌﻟا ﺔﻛﻠﻣﻣ ﺔﻳد
فزﻧﺗﺳﺗ ﻲﻟاوﺣ 85 نﻣ ٪
دراوﻣﻟا ﺔﻳﺋﺎﻣﻟا ﻰﻠﻋ ﻲﻣوﻳﻟا ﺢﺗﻧﻟا دﻳدﺣﺗ نﺈﻓ اذﻟ قﺎطﻧ
ﻲﻧطو وﻫ
ﻲﻌﺳﻟا ةرادﻹ ﺔﻳﺋﺎﻣﻟا دراوﻣﻠﻟ ﻝﺿﻓأ دﻼﺑﻟا ﻲﻓ
. مﺗ ﺔﺳاردﻟا ﻩذﻫ ﻲﻓ
مادﺧﺗﺳا ﺔﻟدﺎﻌﻣ
نﺎﻣﻧﺑ رﻳدﻘﺗﻟ ثﻳﺗﻧوﻣ
ﺢﺗﻧﻟا رﺑﻋ ﻲﻣوﻳﻟا ﻲﺿارأ
ﺔﻛﻠﻣﻣﻟا ﺔﻳدوﻌﺳﻟا ﺔﻳﺑرﻌﻟا
، ﻣ كﻟذو ن تﺎطﺣﻣﺑ ﺔﻧﺎﻌﺗﺳﻻا ﻝﻼﺧ
دﺎﺻرﻷا ﺟﻟا
ﺔﻳو ﺔﻋزوﻣﻟا
،دﻼﺑﻟا ءﺎﺣﻧأ ﻊﻳﻣﺟ ﻲﻓ مﺗ ﻪﻳﻠﻋو
ءﺎﺷﻧإ
طﺋارﺧ ﻲﻣوﻳﻟا ﺢﺗﻧﻟا
ﻣﻟا ﻰﻠﻋ نﻛﻣ يرﻬﺷ سﺎﺳأ
ﻰﻟإ ﺔﻓﺎﺿﻹﺎﺑ إ
ءﺎﺷﻧ
طﺋارﺧ نﻛﻣﻣﻟا ﻲﻣوﻳﻟا ﺢﺗﻧﻟا ﻰﻠﻋ
أ سﺎﺳ يوﻧﺳ ﺔﻘطﻧﻣﻟ ﺔﺳاردﻟا .
ﺔﺳاردﻟا ﺞﺋﺎﺗﻧ رﻳﺷﺗو نأ ﻰﻟإ
ﺢﺗﻧﻟا تﻻدﻌﻣ ﻲﻣوﻳﻟا
ﻰﻟإ ﺎﻬﻣﻳﺳﻘﺗ نﻛﻣﻳ
ثﻼﺛ
ًﻘﻓوتﺎﺋﻓ ﺎ ﺔﻳﻣﻛﻟ ﻩﺎﻳﻣﻟا ﺢﺗﻧﻟا ﺔﻳﻠﻣﻋ نﻣ ةدوﻘﻔﻣﻟا .
تﻣدﻗ دﻘﻟو
تﺎﻳﺻوﺗ
ًدﺎﻧﺗﺳا ﻰﻟإ ا ﺞﺋﺎﺗﻧ ثﺣﺑﻟا ﺔﻳﻟﺎﺣﻟا ﺔﻳﺋﺎﻣﻟا دراوﻣﻟا زﻳزﻌﺗﻟ
ﺔﻳﻟﺎﺣﻟا .
