 Open Access
 Authors : Ayman G. Awadallah , Samar A. H. Ali , Nabil A. Awadallah
 Paper ID : IJERTV11IS040084
 Volume & Issue : Volume 11, Issue 04 (April 2022)
 Published (First Online): 21042022
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Improved Relationships for Peak Discharge Estimation at High Return Periods Using Geomorphological Characteristics: Case Study at Sultanate of Oman
Ayman G. Awadallap*, Samar A.H. Ali2 and Nabil A. Awadallap
1 Civil Engineering Dept., Faculty of Engineering, Fayoum University, Egypt
2 Ministry of Water Resources and Irrigation, Egypt.
Abstract Flash flooding can occur, even in hyperarid regions, due to relatively short, intense burst of rainfall such as during a thunderstorm. Even though flash floods are localized, they present a significant hazard because of their unpredictability and commonly very short duration. To estimate peak discharges of flash floods, morphometric analysis is used to understand the nature of the hydrological processes in a basin, in order to develop a relationship that enables estimation of peak discharge values, in terms of morphometric parameters. The study is conducted using 28 flow gauging stations, in the northern region of Sultanate of Oman, in two steps. The first step is the extraction of morphometric parameters from available Digital Elevation Models. The second step is to develop relationships to estimate peak discharges, at different return periods, using linear and nonlinear regression methods. The best obtained relationship is a nonlinear one in terms of the total number of streams across all orders, the relative relief ratio, and the effective rainfall, with a coefficient of determination ranging from 0.979 to 0.997 and mean percentage absolute errors from 26% to 45%, for examined return periods.
Keywords Flood, Morphometric Parameters, Peak discharge, Regression, Arid Region, Oman.

INTRODUCTION
Flash floods in arid regions have benefits and harms. The immediate impacts of flash flooding include loss of human life, damage to property, damage to power transmission and sometimes power generation, destruction of crops and loss of livestock. This triggered attention to many factors that affect floods such as topography, nature of the soil, and vegetated cover percentage to produce relationships to calculate the peak flow discharges at different return periods. Some of those relationships were expressed in terms of morphometric parameters, which result from the morphometric analysis of the drainage basins.
Several studies have explored the use of geomorphological characteristics. Developed relationships cover all climatic conditions. However, the current literature review focusses mainly on approaches to estimate flash floods and/or those developed in arid and semiarid regions. Angillieri [1] studied the geomorphological characteristics of a basin with a stream order of 5 and a parallel dendritic pattern. The drainage density was found to be the parameter that affects floods patterns most. Bhatt and Ahmed [2] utilized GIS to extract the morphometric parameters to assess which of them influence flooding hazards the most. The main identified parameters include the
ruggedness number () and the relief ratio (), the stream frequency (), the mean bifurcation ratio ( ), and the drainage density (). Six large basins were selected in Saudi
Arabia by Shi [3] to assess flood hazards. The morphometric
basins parameters of the six basins and their related 203 sub basins were combined to develop a relationship that predicts peak discharge values at different return periods provided.
In Oman, which is the study area of the current research, Al Rawas [4] considered parameters like the drainage density, the relief ratio, the basin relief, the form factor, and the total basin area. He has also shown that it is possible to improve predictions of peak flow discharges by introducing other factors such as the extent of urbanization and the percentage of the vegetated cover area, which resulted in a significant improvement of peak discharges estimation, especially at high return periods. The developed equation was in terms of basin relief, relief ratio, total basin area, and the vegetated cover. The Ministry of Transport & Communications [5], Directorate of Roads and Land Transport, Sultanate of Oman, proposed an equation for northern Oman to predict the peak discharges at various return periods in terms of the catchment area, the slope of the maximum stream length within the basin, the estimated rainfall depth at the same return period, the runoff to rainfall ratio which was calculated based on the Soil Conservation Service SCS curve number model.
Morphometric analysis was also used for prioritization of subwatersheds [6], extracting basin parameters using remote sensing [7,8,9,10,11], understand Paleo fluvial systems in the Kuwait [12], geohydrological studies [13], extract hydrologic indices [14], comparison between manual and automated delineation of basins [15], drainage basin asymmetry analysis [16], and many other applications.
The current research aims to investigate relationships between peak discharges and a large set of morphometric parameters, to identify the most influencing to be used in an arid region. Furthermore, to allow benefits in an operational context, these relationships have to produce accurate discharges and to show a robust behavior across return periods. The paper is organized as follows: After this introduction, the next two sections present the case study and the available data followed by the methodology. The results and discussion come next and finally the research conclusions and recommendations for future research.

CASE STUDY AND AVAILABLE DATA
The study focusses on the northern region facing the Arabian Gulf of Sultanate of Oman, which occupies the southeastern part of the Arabian Peninsula. The area of Oman
is close to 309 500 2 with most of it being an arid zone,
subject to many flash floods that occurred in the years 1987,
1989, 1997, 2002, 2003, 2005, 2007, 2010, 2015 and 2020. The
Mean Annual Rainfall () is 51 mm for the entire country, ranging from less than 20 mm in its deserts to over 350 mm in
its rugged mountains. The study area consists of 10 basins monitored by 28 streamflow gauging stations (Figure 1).
The streamflow and rainfall frequency analysis results are obtained from the hydrology section report, issued by the Ministry of Transport and Communications [5], Directorate of roads and Land Transport, Oman, in the framework of the elaboration of the Highway Design Manual. Table S1 (in the supplementary material) summarizes the frequency analysis results of the flow gauging stations and the areal average of the daily rainfall depths at the same return periods.

Basins identification and preprocessing
Using 30m DEM, whose source was the Shuttle Radar Topography Mission (SRTM) version 3 Plus [17], ten major basins are identified englobing all flow gauging stations. The extraction of the morphometric parameters of 28 drainage basins is created via ARC Hydro extension in ArcMap 10.4. These morphometric parameters are subdivided into four categories: drainage network, geometry, drainage texture, and basin relief parameters. Every category contains a group of parameters that describe the characteristics of the drainage basins.

Calculation of morphometric parameters

Drainage network parameters
In this category, the Stream Order (), is determined based on the topdown Strahler method [18]. The Stream Number
() defined as the number of streams in each order, the Total Numbers of Streams across all orders ( ), and the Stream Length (), which is the total length of individual stream
segments of each order, are alsocalculated [19]. Finally, the
Bifurcation Ratio () is calculated, as per Equation (1) [20].
=
+1

Geometry parameters
(1)
Geometry parameters include the areal and linear
characteristics, such as the total surface area (), the total basin perimeter (), and the basin length (), defined as the
maximum dimension of the basin in the direction of the main
drainage channel [21]. It encompasses also the form parameters that describe the shape of the basin, such as the
Form Factor ( ) [22], the Elongation Ratio () [21], defined
by equations 2 and 3, respectively.
=
2
(2)
=
(3)
where is the diameter of the circle with the same area as
that of the basin.

Drainage texture parameters
These parameters include the Drainage density (), defined as the ratio between the summation of all streams
length in a drainage basin to the area of the same drainage
basin [23], the Stream frequency (), which is the ratio between the number of streams in one basin to the basin area,
and the Constant of channel maintenance (), which is the
inverse of the drainage density [21].
=
(4)
Fig. 1. Study area and location of available flow gauging stations.



METHODOLOGY
The methodology of this study consists of two stages: (i) Preparing input data through Basins identification and
=
=
1
(5)
(6)
preprocessing then the extraction of morphometric parameters
from the digital elevation model (DEM) and through determining the effective rainfall; (ii) developing an equation to estimate the peak discharges at different return periods using linear and nonlinear regression analyses.

Basin relief parameters
These parameters include the Total Basin Relief (), which is the difference between the maximum height of the basin and
the height of the outlet for the same basin, the Relief Ratio
(), which is the ratio of the total basin relief to the basin length, the Relative Relief Ratio (), which is the ratio between the total basin relief () and total basin perimeter ()
[24], to finally obtain the value of the Ruggedness Number(), which is the product of by [25].


RESULTS AND DISCUSSION
A. Morphometric characteristics of the 28 drainage basins
=
()
(7)
We present hereafter the major morphometric
characteristics of the 28 drainage basins. Starting by the
= () (8)
= Ã— (9)

Calculating the effective rainfall depth
The effective rainfall, which is also used as a predictor of peak discharges, is calculated using the wellacknowledged Soil Conservation Service Curve Number (SCSCN) [26].
2
drainage networks characteristics, five basins are of second
order, twelve basins are of thirdorder, nine basins are of fourth order, and two basins area of fifthorder. On the other hand, the values of bifurcation ratios range between 2 and 7 and higher bifurcation ratio values are observed at lower stream orders in the mountains, while lower bifurcation ratio values are observed at higher stream orders where the area is characterized as flat. As for the average bifurcation ratio values, the
= ( 0.2)
+ 0.8
(10)
maximum value is 6 for station No. 5, while the minimum value
is 2.56 for station No. 1.
Where is the effective rainfall depth (mm) corresponding to the rainfall depths at return period , is the rainfall depth (mm) at return period , is the potential storage of the soil (mm), which takes into account the , calculated as follows:
1000
= ( 10) Ã— 25.4 (11)
The relies on determining the hydrologic soil group and
the land cover. Hydrologic Soil Groups are obtained from the
Global Hydrologic Soil Groups (HYSOGs250m) dataset and hence the areal averages of the Curve Number for each basin are determined assuming a desert shrub cover of poor condition [27].

Developing relationships to estimate peak discharge using morphometric parameters
Relationships are developed to estimate peak discharges at various return periods equation. Linear and nonlinear regression equations are tested. The nonlinear equation is of the form:
= 1 Ã— 2 Ã— Ã— (12)
where:
is the peak discharge at return period n.
1, 2, , are input variables.
, , , are the regression coefficients.
A stepwise regression method is used, via the Statistical
Package for Social Sciences (SPSS) software [28], to select the most influential morphometric variables affecting the discharges. Beside the verification of the statistical significance of the regression coefficients to be included and the overall significance of the developed relationships, three performance criteria are calculated to assess the goodness of
fit: 2 (the adjusted coefficient of determination), the Mean Absolute Percentage Error () and the Roof Mean Square Error () defined as follows:
As for the geometrical and the drainage texture characteristics, two of the studied basins are circular, while the remaining basins are elongated with the possibility of low peak discharges. It is also found that the drainage densities are relatively low, ranging from 0.53 to 0.30, with an average value of 0.40. Investigating the stream frequencies, it is found that the frequency values are also low, where the maximum value is
0.10 and the minimum is 0.05 with an average value of 0.08. On the other hand, the constant channel maintenance values are high, which reflects strong control of lithology, where the range is between 3.36 and 1.89 with an average value of 2.55.
The drainage texture values are less than 2, which indicates that the surface of the basins is very coarse. As for the infiltration number values, they range from 0.05 to 0.02 with an average value of 0.03. The lengths of overland flow range from
1.68 km to 0.95 km. These high values indicate decreased values of drainage density and surface runoff with weak development of the drainage density.
For the relief characteristics, the maximum ruggedness number is 0.98 and the minimum number is 0.18 while the average is equal to 0.56, where these weak values express that the study region has weak dissection and erosion.
B. Developing relationships to estimate peak discharge
To develop an equation to estimate peak discharges at 2, 5, 10, 25, 50 and 100year return periods, linear regression is first explored. The application of the linear regression with a constant term shows that the most influential variables are the area, the rainfall depths at the 100year return period, the effective rainfall depths corresponding to the rainfall depths at the 100year return period, and the total lengths of streams orders. By verifying the parameters coefficients resulting from the linear regression analysis, it is noticed that the coefficient of the intercept is not statistically significant. Deleting the
= 100
1
 
(13)
intercept term from the regression equation, the most influential
= 1

parameters are the number of stream order 4, the length of
stream order 1, the length of stream order 4, the total surface
Where:
=
1
( )2
=1
(14)
area, the rainfall depths at 100year return period, and the effective rainfall depths corresponding to the rainfall depths at 100year return period. The linear equation for the 100year
, is the observed peak dscharge;
is the estimated peak discharge;
is the average value for the observed series; and
is the number of data points.
return period can be written as follows.
100 = 448.724 0.0151 0.0414 + 5.43
8.14100 + 14.95100 (15)
where: 100 is the peak discharge at 100year return period, 4 is the stream number of 4th stream order, 1 is the length of streams of the 1st order, 4 is the length of streams of the 4th
order, is the Area, 100 is the rainfall depths at the 100year
100
To obtain an improved relationship, the nonlinear option is
return period and
is the effective rainfall depths
explored and is transformed to the linear form using natural
corresponding to the rainfall depths at the 100year return
period. This equation produces a of 347.4 m3/s, a
logarithms. The obtained relationship is described by equation
16.
of 40.78% (which is rather high) with an adjusted 2 of 0.95.
Tables I to III provide the results of the analysis, while Figure
2 presents a plot of the predicted vs. the observed 100.
Relationships for other return periods are also explored;
however, for 2year and 5year return periods, no satisfactory equation, with statistically significant coefficients, is found.
Fig. 2. Predicted vs. Observed 100 using linear regression TABLE I.
Model
Adjusted
StdError of the Estimate
6
0.975
0.951
0.938
391.7322402
TABLE II. MODEL SUMMARY FOR LINEAR REGRESSION WITHOUT INTERCEPT TERM
ln 100 = 1.022 ln + 0.594 ln + 0.868 ln 100 (16) where 100 is the Peak discharge at the 100year return period, is the total numbers of streams across all orders, Rp is the Relative Relief Ratio and 100 is the effective rainfall
depths corresponding to the rainfall depths at the 100year
return period.
The obtained relationship is simpler, yet it produces a lower
(compared to the linear equation with a larger number of variables) of 32.37% (calculated in the original scale of
variables), a of 335m3/s, with an adjusted 2 of 0.995.
Tables IV to VI provide the full results of the analysis
(calculated in the natural logarithm scale), while Figure 3
presents a plot of the predicted vs. the observed 100. Relationships for all return periods are also explored. All
coefficients are found statistically significant.
TABLE V. MODEL SUMMARY FOR LINEAR REGRESSION OF NATURAL LOGARITHMS OF VARIABLES
Model
Adjusted
StdError of the Estimate
1
0.996
0.995
0.994
0.4025221
TABLE VI. ANOVA TABLE FOR SECOND SCENARIO LINEAR REGRESSION OF NATURAL LOGARITHMS OF VARIABLES
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
1279.617
3
426.539
2632.565609
0.000
Residual
4.051
25
0.162
Total
1283.667
28
TABLE III. ANOVA TABLE FOR SECOND SCENARIO LINEAR WITHOUT INTERCEPT TERM
Model
Sum of Squares
Df
Mean Square
F
Sig.
6
Regression
66144088.756
6
11024014.793
71.839
0.000
Residual
3375991.257
22
153454.148
Total
69520080.013d
28
TABLE VII. COEFFICIENTS TABLE FOR LINEAR REGRESSION OF NATURAL LOGARITHMS OF VARIABLES
Model
Unstandardized Coefficients
Standardized Coefficients
t
sig.
B
Std. Error
Beta
6
ln
1.022
0.081
0.485
12.593
0.000
ln
0.594
0.201
0.40
2.960
0.007
100
ln
0.868
0.063
0.530
13.839
0.000
TABLE IV.
COEFFICIENTS TABLE FOR LINEAR REGRESSION WITHOUT INTERCEPT TERM
Model
Unstandardized Coefficients
Standardized Coefficients
t
sig.
B
Std. Error
Beta
6
5.427
1.103
2.194
4.921
0.000
100
14.946
2.875
0.746
5.198
0.000
100
8.143
1.894
0.694
4.299
0.000
4
0.041
0.012
0.345
3.333
0.003
4
448.721
168.938
0.222
2.656
0.014
1
0.015
0.006
1.125
2.566
0.018
Fig. 3. Predicted vs. Observed 100 using nonlinear regression
By applying the same nonlinear form, for of streams of the 50, 25, 10, 5, and 2year return periods, the following
equations (17 to 21) are obtained:
50
the rainfall depths at 100year return period. As for the nonlinear regression analysis, the developed relationship is in terms of the total numbers of stream across all orders, the
ln 50 = 1.046 ln + 0.581 ln + 0.852 ln ln 25 = 1.1 ln + 0.603 ln + 0.809 ln 25
10
(17)
(18)
relative relief ratio, and the effective rainfall depths
corresponding to the rainfall depths at the 100year return
period. The linear equation produces a of 40.78% with
ln 10 = 1.247 ln + 0.733 ln + 0.662 ln (19)
5
an adjusted 2 of 0.95 but the nonlinear equation (which has a
ln 5 = 1.419 ln + 0.931 ln + 0.414 ln ln 2 = 1.409 ln + 0.837 ln + 0.128 ln 2
(20)
(21)
fewer number of input variables) shows better performance and
produces a of 32.37% with an adjusted 2 of 0.995.
The for 50, 25, 10, 5 and 2 are 26.1%,
27.83%, 37.31%, 44.44% and 45.13%, respectively with
adjusted 2 of 0.997, 0.996, 0.993, 0.986 and 0.979,
respectively. Figures 4 and 5 show plots between the relative
errors for various return periods and the total No. of streams across all orders and the relative relief ratio values.
Fig. 4. Relative errors for various return periods vs.
Fig. 5. Relative errors for various return periods vs.


CONCLUSIONS AND RECOMMENDATIONS
The aim of this research is to develop relationships that estimate peak flow discharge at various return periods. The study area is located in the northern region of the Sultanate of Oman, which consists of 10 basins monitored by 28 peak flow discharges stations. Morphometric parameters are extracted and analyzed to shed light on the basins underlying hydrologic processes and their eventual response to floods.
Using a stepwise regression approach, linear and nonlinear relationships are developed between selected morphometric parameters and peak discharges at various return periods. A linear equation is developed in terms of the length of streams for order 4, the number of streams for order 4, the length of streams for order 1, the area, the rainfall depths at 100year return period, and the effective rainfall depths corresponding to
Recommendations for future research include to extend the
study to more gauged basins in arid regions, to relate the morphological parameters to the time of concentration of the basins calculated via calibration of the observed flows, and to extend the study to produce relationships to estimate average runoff values and not only peak discharges.
REFERENCES
[1] M. Y. E. Angillieri, Morphometric analysis of ColangÃ¼il river basin and flash flood hazard, San Juan, Argentina, 1st ed., vol. 55. Environmental Geology, 2008, pp. 107111. [2] S. Bhatt, and S. A. Ahmed, Morphometric analysis to determine floods in the Upper Krishna basin using Cartosat DEM, 8th ed., vol. 29. Geocarto international, 2014, pp. 878894. [3] Q. Shi, Flood hazard assessment along the Western Regions of Saudi Arabia using GISbased morphometry and remote sensing techniques, MSc Thesis, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia, 2014. [4] G. A. A. AlRawas, Flash flood modeling in Oman wadis, OhD Thesis, Department of Civil Engineering, University of Calgary, 2009. [5] Highway design manual of Oman hydrology section, Directorate of Roads and Land Transport, Ministry of Transport and Communications, Sultanate of Oman, 2010. [6] S. Biswas, S. Sudhakar, and V. R. Desai, Prioritization of subwatersheds based on morphometric analysis of drainage basin: A remote sensing and GIS approach, 3rd ed., vol. 27. Journal of the Indian society of remote sensing,1999, pp. 155166. [7] R. Chopra, R.D. Dhiman, and P.K. Sharma, Morphometric analysis of subwatersheds in Gurdaspur district, Punjab using remote sensing and GIS techniques, 4th ed., vol. 33. Journal of the Indian Society of Remote Sensing, 2005, pp. 531539. [8] M. Kamala, and M. Samynathan, Morphometric Analysis of Drainage Basin Using GIS Techniques: A Case Study of Amaravathi River Basin, Tamilnadu, 7th(F) ed., vol. 9. International Journal of Recent Scientific Research, 2018, pp. 2814228147. [9] D. Mishra, B. N. Singh, and D. K. Behera, Morphometric Analysis of Bara Tehsil of Allahabad District Through Cartosat1 DEM Data, 1st ed., vol. 6. International Journal of Creative Research Thoughts (IJCRT), 2018, pp. 18971907. [10] P. D. Sreedevi, S. H. H. K. Owais, H. H. Khan, and S. Ahmed, Morphometric analysis of a watershed of South India using SRTM data and GIS, 4th ed., vol. 73. Journal of the geological society of India, 2009, pp. 543552. [11] S. S. Vittala, S. Govindaiah, and H. H. Gowda, Morphometric analysis of subwatersheds in the Pavagada area of Tumkur district, South India using remote sensing and GIS techniques, 4th ed., vol.32. Journal of the Indian Society of Remote Sensing, 2004, pp. 351362.
[12] R. Mohammad, Geographical Information System Based Analysis of Paleo fluvial Systems in the Kuwait Region, MSc Thesis, Department of Geology, School of Arts and Sciences, University of Pittsburgh, 2008. [13] R. A. Hajam, A. Hamid, and S. Bhat, Application of morphometric analysis for geohydrological studies using geo spatial technologya case study of Vishav Drainage Basin, 3rd ed., vol.4. Hydrology Current Research, 2003, pp. 112. [14] S. Karalis, et al. Assessment of the relationships among catchments morphometric parameters and hydrologic indices, 5th ed., vol.13. International Journal of Geosciences, 2014, pp. 1571. [15] M. Y. E. Angillieri, and O. M. FernÃ¡ndez, Morphometric analysis of river basins using GIS and remote sensing of an Andean section of Route 150, Argentina. A comparison between manual and automated delineation of basins, 2nd ed., vol. 34. Revista mexicana de ciencias geolÃ³gicas, 2017, pp. 150156. [16] D. Baioni, (2016). Analysis of Drainage Basin Asymmetry in the Ventana River, Northern Apennines (Central Italy), 1st ed., vol.121. International Journal of Earth & Environmental Sciences, 2016, pp. 15. [17] NASA, Shuttle Radar Topography Mission (SRTM) version 3 Plus, Void free dataset, https://lpdaac.usgs.gov/products/srtmimgrv003/, 2015. [18] A.H. Strahler, Dynamic Basis of Geomorphology, vol. 63.Geological Society of America Bulletin, 1952, pp. 923938.
[19] K.G. Smith, (1950). Standards for grading texture of erosional topography, 9th ed., vol.248. American Journal of Science, 1950, pp. 655668. [20] A.H. Strahler, Quantitative Geomorphology of Drainage Basins and Channel Networks, In: Chow, V., Ed., Handbook of Applied Hydrology, McGraw Hill, New York, pp. 439476, 1964. [21] S.A. Schumm, Evolution of drainage systems and slopes in badlands at Perth Amboy, 5th ed., vol. 67. Geological Society of America Bulletin. New Jersey, 1956, pp. 597646. [22] R.E. Horton, Drainagebasin characteristics, Eos Trans. AGU, vol. 13, pp. 350361, 1932. [23] R.E. Horton, Erosional development of streams and their drainage basins: Hydrophysical approach to quantitative morphology, Geol. Soc. Amer. Bull., vol. 56, pp. 275360, 1945. [24] M.A. Melton, An analysis of the relations among elements of climate, surface properties, and geomorphology, Columbia University, New York, 1957. [25] A.H. Strahler, and A. Strahler, Introducing physical geography, New York: Wiley, 2013. [26] USDA (United States Department of Agriculture), Urban hydrology for small watersheds, Technical release, no 55 (TR55). Soil Conservation Service (SCS), Washington, DC, 1986. [27] C.W. Ross, et al. Global Hydrologic Soil Groups for Curve NumberBased Runoff Modeling (HYSOGs250m), ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1566, 2018. [28] IBM Statistical Package for Social Sciences, last accessed, https://www.ibm.com/products/spssstatistics, April 2022.Published by : http://www.ijert.org
International Journal of Engineering Research & Technology (IJERT)
ISSN: 22780181
Vol. 11 Issue 04, April2022
TABLE S1. FREQUENCY ANALYSIS RESULTS OF THE STREAMFLOW AT THE GAUGING STATIONS IN NORTH OMAN
Return Periods (years) Return Periods (years) 

2 
5 
10 
25 
50 
100 
2 
5 
10 
25 
50 
100 

Station ID 
Rainfall (mm) 
Flow (m3/sec) 

Al Haju 
25.5266 
45.9267 
61.7977 
85.6321 
106.8273 
131.7804 
63.1 
149.3 
223.1 
340.9 
450.3 
581.9 
Al Khawd 
28.0086 
45.0199 
57.8313 
77.3542 
95.7721 
119.5013 
182.2 
458.7 
722.2 
1,187.20 
<>1,662.40 
2,282.90 
Al Qabil 
25.5543 
45.3459 
60.4363 
82.5873 
102.0995 
125.0799 
257.1 
466.9 
601.3 
766.2 
885.1 
1,000.30 
Aswad 
29.0159 
51.4752 
68.2597 
92.1047 
111.9861 
133.8655 
56.9 
170.2 
305.8 
602.8 
974.3 
1,552.20 
AzZahir 1 
27.3975 
45.6224 
58.2137 
74.7786 
88.8173 
105.6090 
49.2 
105.9 
155.6 
236.9 
314 
408.6 
Bayda 
28.9517 
49.0594 
64.3791 
86.5181 
105.2802 
126.1991 
53.9 
136.7 
218.9 
369.9 
530.3 
746.6 
Dasir 
28.0344 
44.0919 
55.5522 
71.8504 
86.0520 
102.9803 
77.5 
147.7 
207.9 
304.2 
393.8 
501.7 
Fulayj 
29.5455 
48.7032 
63.4086 
85.3456 
104.9945 
128.3451 
75.7 
191.6 
310.2 
534.4 
778.5 
1,115.20 
Ghuzayn 
25.8687 
42.4364 
55.2441 
74.2861 
91.1553 
110.8873 
118.3 
291.9 
469.2 
803.1 
1,166.00 
1,665.60 
Hajir 1 
22.2801 
42.4666 
59.6023 
87.5287 
114.5510 
148.8266 
21.6 
69.2 
128.9 
265.9 
444.7 
733.2 
Hajir 2 
19.1242 
39.0019 
56.8761 
87.3439 
117.9233 
157.6807 
21.1 
54.5 
84.8 
136.1 
186.5 
250.1 
Hajir 3 
21.0202 
47.6535 
74.0773 
123.2076 
176.4796 
250.1670 
50.55 
112.18 
159.26 
226.99 
283.96 
346.88 
Hammam 
22.9344 
44.2485 
62.2564 
91.5553 
120.1050 
156.7727 
44.8 
101.8 
166.4 
300.6 
460.4 
698.5 
Hayl 
28.7206 
46.5384 
59.6603 
78.4386 
94.4864 
112.7616 
300.6 
578.8 
792.3 
1100.9 
1361.5 
1650.4 
Houqain 
31.9282 
47.8533 
58.1182 
70.8629 
80.1957 
89.3757 
145.91 
326.14 
453.22 
623.19 
756.45 
895.08 
Ibra 
43.7961 
67.5225 
83.3288 
103.9365 
119.9982 
136.8623 
184.21 
464.112 
723.35 
1169.03 
1613.32 
2181.05 
Lihban 
27.8773 
42.6513 
53.8911 
70.6816 
86.0750 
104.8581 
112.8 
228.5 
331.3 
501.5 
665.1 
867.8 
Maul 
21.9370 
46.8962 
70.5411 
113.0987 
158.1655 
219.5834 
47.4 
114.6 
170.6 
257.9 
337.2 
430.7 
Mazara 1 
25.4710 
45.5658 
61.3427 
85.5396 
107.7388 
134.7869 
483.6 
1,192.90 
1,878.50 
3,104.90 
4,374.40 
6,050.10 
Mulayinah 
28.0036 
46.5979 
60.6756 
80.9737 
98.1891 
117.4469 
234.9 
461.4 
627 
856.2 
1,041.80 
1,240.30 
Mutarid 
25.3417 
44.5111 
58.8649 
79.7141 
98.0799 
119.8371 
91.2 
179.7 
242.1 
325.7 
391.1 
459.3 
Qalhat 
22.6451 
37.3307 
47.3589 
60.3282 
71.0200 
83.4860 
60.5 
191.9 
340.4 
647.9 
1,013.40 
1,556.90 
Riqqah 
28.2156 
45.8042 
58.5863 
76.4321 
91.1606 
107.2898 
69.6 
133.6 
192.7 
293.7 
394.1 
521.8 
Sabakh 
28.2540 
48.0942 
62.8274 
83.6653 
100.9762 
119.9733 
77.9 
164.1 
241.3 
369.8 
494.1 
648.9 
Sur 
25.4959 
44.8019 
59.7473 
79.8306 
98.8334 
125.1173 
150.9 
341.5 
524.4 
849.2 
1,183.10 
1,621.40 
Yanbu 
27.7224 
45.4225 
58.4640 
76.8554 
92.1386 
108.9188 
47.5 
91.8 
121.1 
158.4 
186.1 
213.6 
Al Bih Near Salhad 
45.7548 
74.2834 
92.6443 
116.1033 
134.1467 
152.9760 
52.1 
112.6 
162.9 
241.3 
312.4 
396.2 
Khasab Near Khasab 
27.8773 
42.6513 
53.8911 
70.6816 
86.0750 
104.8581 
81.8 
191.9 
296.5 
480.9 
668.9 
913.9 
IJERTV11IS040084
www.ijert.org
(This work is licensed under a Creative Commons Attribution 4.0 International License.)
145