Geographical information of the meteorological stations included in this study.
Recently, climate change is receiving much attention. Changes in the world’s climate have significant effect on water resources which affect the livelihood of people especially in hyper arid regions such as the Kingdom of Saudi Arabia (KSA). The KSA suffers an enduring water shortage problem, despite the fact that the agricultural activities consume up to 90% of the water amount in the Kingdom. Reference Evapotranspiration (ETo) is an agro-climatic property that involves temperature, humidity, solar radiation, and wind speed. Identifying changes in ETo can also help in future planning of agriculture-water projects and identify lower and higher ETo zones for proper planning and management of agricultural projects in arid regions.
1.1. Water resources and climate change
Water shortage is a swelling problem in the arid and semi-arid regions. Affected by its geographic location and its climate, the Kingdom of Saudi Arabia (KSA) suffers a severe water deficit. Even rain, which is the only renewable water source, comes in flash short duration storms of high intensity and most of it vanishes to evaporation. Thus, almost all agriculture of the kingdom is irrigated. Irrigation water, though, consumes 80 to 88 % of the total water consumption (Abu-Ghobar, 2000; Abderrahman, 2001). In addition to these water scarcity conditions, but it seems getting scary by the effects of climate change on the hydrological cycle and water supply. The quantity of irrigation water is determined initially by identifying the reference evapotranspiration (ETo). Several researches was conducted to detect climate changes, trends and variability in various parts of the world using some climate parameters such as air temperature, rainfall depth, ETo, and pan evapotranspiration ETp (Shwartz and Randall, 2003; Garbrecht, et al., 2004; Hegerl, et al., 2007; Fu, et al., 2009; Hakan, et al., 2010; Elnesr and Alazba, 2010; Elnesr et al., 2010a; and Elnesr et al. 2010b). The ETo parameter has a special importance because it combines changes in many other climate parameters including temperature, radiation, humidity, and wind speed. It has, however, direct influence on hydrologic water balance, irrigation and drainage canal design, reservoir operation, potentials for rain-fed agricultural production, and crop water requirements (Dinpashoh, 2006).
1.2. Climate change effect on evapotranspiration worldwide
Several studies conducted in North America have shown that some climate parameters are on the rise including ETo (Fehrman, 2007; Garbrecht et al., 2004; Szilagyi, 2001). Fehrman, 2007 found an increasing trend in ETo over the Mississippi area and that most of ETo increase can be attributed to the increase in July. He also found that the rate of ETo increase was 0.29 mm/years when his study period extended from 1940 to 1999 compared to 0.88 mm/year when the study period was limited to 1950 to 1999 records. The accelerated ET over North America is presumed to be due to a rise in temperature over the past century (Myeni et al. 1997, Milly and Dunne 2001). In the contrary ETo and pan evaporation has shown to decrease in China (Thomas, 2000, Liu et al., 2004) and at a rate of 1.19 mm/year (Song et al., 2010) despite the rise in maximum daily temperature. In the Tibetan Plateau ETo decreases as well at a rate of 1.31 mm/year or 2.0% of the annual total evapotranspiration (Shenbin et al., 2006). The decrease in ETo has been attributed to the decrease in wind speed and net radiation. In another study Gao et al., (2007) found that the actual evapotranspiration had a decreasing trend in most of the eastern part of china and there was an increasing trend in the western and the northern parts of northeast China and that the change in precipitation played a key role for the change of estimated actual evapotranspiration. Similar negative trends in pan evaporation were found in 24 out of 27 observation stations in a 19-year study in Thailand (Tebakari et al., 2005). In India, a significant decreasing trend was found in ETo all over the Indian plateau during the past 40 years, which was mainly caused by a significant increase in the relative humidity and a consistent significant decrease in the wind speed throughout the country (Bandyopadhyay et al., 2009). In Australia, Roderick and Farquhar (2004) found a decreasing trend in pan evaporation and conclude that Australia is becoming less arid. However, there is enough evidence now that a decrease in pan evaporation is an indicator to an increase in actual evaporation. This is what known now as the evaporation paradox (Hobbins et al., 2004).
Some researchers developed a hypothetical scenario to study the effect of possible increase on temperature over the ETo and subsequently on water supply. A study conducted by Abderrahman et al. (1991) concluded that in the KSA, a 1℃ increase in temperature would increase ETo from 1 to 4.5%. In another study, that includes selected cities in KSA, United Arab Emirates and Kuwait, Abderahman and Al-Harazin (2003) concluded that an increase in temperature by 1℃ would increase ETo over these area by a maximum of 20%. In general, studies involving ETo calculation seemed to be more limited worldwide compared to other climate parameters. In the other hand, regarding other climatic parameters, Hakan et al. (2010) reported an increasing trend in temperature and ETo in most of stations they analyzed in Turkey using Mann-Kendall analysis. Cohen and Stanhill, (1996) studied rainfall changes in the Jordan Valley/Jordan and found a tangible but insignificant decrease at a rate of -0.47 and -0.16 mm/year for two different stations. Similar conclusions were observed by Al-Ansari et al (1999) who observed a general decrease in rainfall intensity. Smadi (2006), and Smadi and Zghoul (2006) found a prompt shift in rainfall and temperature in Jordan. ElNesr et al (2010b) concluded that the Saudi Arabia and the Arabian Peninsula are suffering from a considerable warming trend form year 1980 to 2008. Still, Elnesr et al. (2010a) concluded that the percentage land area with annual
1.3. Objective of the study
This study aims to trace the ETo values over time throughout all the area of the Saudi Arabia, then to quantify the future of water demand according to the ETo trends
2. Material and methods
2.1. Geography of the Saudi Arabia
Saudi Arabia is the largest country of the Arabian Peninsula; it occupies about 80% of its area (Wynbrandt, 2004). The country lies between latitudes 16°21'58''N, and 32°9'57''N, and longitudes 34°33'48''E and 55°41'29''E, as illustrated in Fig. 1. Saudi Arabia has a desert dry climate with high temperatures in most of the country. However, the country falls in the tropical and subtropical desert region. Winds reaching the country are generally dry, and almost all the area is arid. Because of the aridity and the relatively cloudless skies, there are great extremes in temperature, but there are also wide variations between the seasons and regions (AQUASTAT, 2008).
2.2. Evapotranspiration calculation
Evapotranspiration was calculated using Food and Agricultural Organization (FAO) Penman- Monteith (PM) procedure, FAO 56 method, presented by Allen et al. (1998). In this method, ETo is expressed as follows:
The measured meteorological data available were T
whereis calculated according to (Tetens, 1930):
The net radiation R
where P: atmospheric pressure [kPa], λ: latent heat flux [ MJ kg-1]. The atmospheric pressure is expressed as in Burman et al. (1987)
where z: altitude [m]. The latent heat λ depends on the average temperature, Eqn(7), while it can be taken as an approximate value of 2.45 as reported by Harrison (1963) for T
The saturation vapour pressure, e
The average daily ET
2.3. Climatic data source and description
Basic climatic data were taken from the Presidency of Meteorology and Environment in KSA, the official climate agency in the country. The data set is the most accurate one in KSA and used by all other governmental and academic agencies for climate research and prediction. Weather stations are equipped with up-to-date monitoring devices and subjected to regular inspection and replacement for defected devices (personal communication with the Presidency of Meteorology and Environment). Data represents 29 meteorological stations as shown in Fig. 1. These stations represent all the 13 districts of the KSA. The data covers 29 years of daily meteorological records for 20 stations, 24 years for 6 stations, and 3 stations with less than 20 years as shown in Table 1. All of the data ends in 2008 and started at 1980 and 1985 for the 29 and 24 years logging.
|Deg. N.||Deg. East||m|
2.4. Data grouping and contouring
After correction the data sets, daily ETo values were calculated for each station, then aggregated to annual and monthly values. Annual ETo value (mm/year) for each station was calculated by summation of the daily ETo for the entire year. On the other hand, the monthly average ETo value was calculated by taking the average of the daily ETo values during each month.
Evapotranspiration data were graphically represented by contour maps irrespective of stations altitude. Analysis of ET variations with stations’ altitude for each of the 30 years under study revealed no trends. Other researchers have also found no correlation between ET and station altitude in China (Thomas, 2000). Contour maps present clearly zones of common ET values as well as clarify vividly ET differences between zones and viability a long months or years. This approach has also been adopted by other researchers to study ET variability in China (Thomas, 2000; Shenbin et al., 2006).
Data was arranged in three columns format namely, longitude, latitude, and ETo. Each set of data was gridded separately using the ordinary point-Kriging method which estimates the values of the points at the grid nodes (Abramowitz and Stegun, 1972, and Isaaks and Srivastava, 1989). This procedure is used by SURFER™ Software which has been used in our calculations. The resulted grid was blanked outside the political borders of the KSA. The political borders’ information of the KSA was grabbed from electronic map of NIMA (2003). The electronic map was digitized and converted to DMS geographic coordinate system. The blanked grid was plotted as a contour map using Surfer™ 8.0 software (Surfer, 2002). Sample plots for the average daily ET
All of the data are daily values, the obtained climatic data records were carefully inspected for missing and erroneous reading. Very few errors were found, (median value of 00.45%). Errors were classified into four categories: Errors because of mistaken extreme values such as a relative humidity exceeds 100% or below 0%. Illogical errors such as the recorded maximum daily temperature (T
2.5. Non-parametric trend analysis methods
2.5.1. Mann-kendall test
The Mann-Kendall test is a non-parametric test used for identifying trends in time series data. The test compares the relative magnitudes of sample data rather than the data values themselves Both Kendall tau coefficient (τ) and Mann-Kendall coefficient (s) are nonparametric statistics used to find rank correlation. Kendall (τ) is a ratio between the actual rating score of correlation, to the maximum possible score. To obtain the rating score for a time series, the dataset is sorted in ascending order according to time, and then the following formula is applied:
where s: the rating score (also called the Mann-Kendall sum); x: the data value; i and j: counters; n: number of data values in the series; Sign is a function having values of +1, 0, or -1 if (x
Hence, the Kendall (τ) is calculated as:
A positive value of s or τ is an indicator of an increasing trend, and a negative value indicates a decreasing trend. However, it is necessary to compute the probability associated with s or τ and the sample size, n, to quantify the significance of the trend statistically. Kendall and Gibbons (1990) introduced a normal-approximation test that could be applied on datasets of more than ten values with s variance (σ2):
where CFR: repetition correction factor, to fix the effect of tied groups of data (when some of the data values appear more than one time in the dataset, this group of values are called a tied group); g: number of tied groups; k: a counter; m: number of data values in each tied group. Then normal distribution parameter (called the Mann-Kendall statistic, Z) is calculated as follows:
The last step is to find the minimum probability level at which the parameter Z is significant, this could be found using two-tailed t statistical Tables or as mentioned by Abramowitz and Stegun (1972):
where αmin: Minimum level of significance; q: counter; bx: constants: b0= 0.3989, b1= 0.3194, b2= -0.3566, b3= 1.7814, b4= -1.8213, b5= 1.3303, b6= 0.2316, ABS(Z): the absolute value of Z. Kendall tau is considered significant when alpha min is less than a specified alpha value, i.e 0.05.
2.5.2. Sen-slope estimator test
Sen’s statistic is the median slope of each point-pair slope in a dataset (Sen, 1968). To perform the complete Sen’s test, several rules and conditions should be satisfied; the time series should be equally spaced, i.e. the interval between data points should be equal. However, Sen’s method considers missing data. The data should be sorted ascending according to time, and then apply the following formula to calculate Sen’s slope estimator (Q) as the median of Sen’s matrix members.
Its sign reflect the trend’s direction, while its value reflects how steep the trend is. To determine whether the median slope is statistically different than zero, the variance is calculated using Eqn. (4), to obtain the confidence interval of Q at a specific probability level, e.g 95%. The area (Z) under two-tailed normal distribution curve is calculated at the level (1-α/2), where α=1-confidence level. For example, for a confidence level of 95%, Z should be evaluated at 0.975, hence Z= 1.96. Next, the parameter C
The upper and lower confidence boundaries for Q are then calculated as follows:
where int() represents the integer value; M
3. Results and discussion
The daily ET
The variation of ET
The changes in ET
Due to some restrictions in Man-Kendall and Sen’s methods, two stations out of the 29 stations were omitted from calculations due to the small number of years they had (less than 10 years); those were stations #3 (Guraiat) and # 13 (Dammam). Mann-Kendall and Sen Slope statistics were performed on the rest 27 stations on monthly basis to confirm trends direction and test its significance. Two parameters were calculated namely Kendall τ and Sen Slope Q and their confidence limits at 95% and 99% probability level as described in Materials and Methods. A group of selected results is shown in Figure 6 where the parameters of Mann-Kendall and Sen Slope and their significant tests are presented. The Figure represents ET trends in January for four stations, Tabuk, Sharurrah Yenbo, and Hail, showing possible combinations of Mann-Kendall (MK) and Sen Statistic, in addition to their significance under increasing or decreasing ET
The two tests gave similar results in all of these cases but Sen Slope test were found to be more conservative. A positive sign in τ or Q indicates an increasing trend, Figure 6 C and D while a negative value indicates a decreasing trend, Figure 6 A and B. The significance of τ was tested by comparing the calculated α
Analysis of monthly average ET
Figure 6A-D represents four possible cases of Q and τ and their significance. In Tabuk and Sharurrah, both Q (-0.020, -0.028) and τ (-0.365, -0.281), respectively, were negative indicating a decreasing trend for ET
Both Yenbo and Hail have increasing ET
The previous analyses shown in Figure 6 were carried out for the 27 stations out of 29 under study. Man-Kendall, and Sen’s methods’ can deal with data series with 10 or more data points. However, Gurrayat and Dammam have less than 10 years of data and they were excluded from trend analysis. Average ET
The tests were carried out for maximum, minimum and average monthly ET
Fourteen stations have a positive Q and τ, for at least 10 months in a year therefore an uptrend in ETo namely; Turaif, Arar, Al jouf, Hafr Al-Baten, Hail, Gassim, Dhahran, Riyadh (old), Yenbo, Taif, W-dawaser, Bisha, Abha, and khamis Mushait. Another six stations showed a negative or zero Q during the whole year, therefore a downtrend in ET
However, the up or down trends or downtrends in ET
The number of stations with a decreasing trend is far less than those with increasing ET
The numbers of stations with increasing or decreasing trend are shown in the last two rows of Table 2 and 3. Figure 7, as well, illustrates the number of stations with significant/non-significant increasing/decreasing trend of ET throughout the studied areas. At least 15 stations or higher showed an increasing trend for the entire year except in January at which 14 stations showed a decreasing trend. March, April and June showed the highest number of stations with increasing ET
Further inspection on the location of stations with increasing trends in ET
To have an aerial graph for the regions with a decreasing or increasing trend in ET
Water scarcity problem can be solved by proper management of water usage. Most of the depleted water in KSA is consumed through agriculture. Identifying the ET
The authors wish to express their deep thanks and gratitude to “Shaikh Mohammad Bin Husain Alamoudi” for his kind financial support to the King Saud University, through the research chair “Alamoudi Chair for Water Researches” (AWC), where this paper is part of the AWC chair activities. Thanks should also be expressed to the Presidency of Meteorology and Environment in Riyadh, KSA, who kindly support this research my meteorological data.