FLASH FLOOD RISK ASSESSMENT APPLYING MULTI-CRITERIA ANALYSIS FOR SOME NORTHWESTERN COASTAL BASINS, EGYPT

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1 FLASH FLOOD RISK ASSESSMENT APPLYING MULTI-CRITERIA ANALYSIS FOR SOME NORTHWESTERN COASTAL BASINS, EGYPT GAD M. I., El-Shiekh A. E., Khalifa R. A., and Ahmed K. A. Desert Research Center, Cairo, Egypt ABSTRACT W ater resources are increasingly a constraint on economic and social development in Egypt. Coping with the water scarcity in the North Western Coastal zone Basins (NWCB) requires good management for the flash floods. This requires an accurate estimation for the hazard degrees and flood risk. Multi Criteria Analysis (MCA) describes any structured approach used to determine overall preferences among alternative options, where the options accomplish certain or several objectives. The maximizing of water use in arid zones, like NWCB, is a highly important issue due to the damage, danger and other hazards associated to it to human life, properties, and environment. MCA techniques were tested and evaluated for the purpose of flash flood risk assessment, hydromorphological parameters for sample catchments in NWCB, were used in this analysis. Drainage network and watershed boundaries of NWCB shape file was created using TOPAZ (Topographic Parameterization) technique from the 90 m Digital Elevation Model (DEMs). These data are used in Watershed Modeling System (WMS) package to automatically delineate basin boundaries and define stream networks. Thirty four basins in NWCB were delineated for the study of the risk assessment of flash floods. Cluster analysis, depending on 10 estimated hydro-morphological parameters, classifies the NWCB into three groups. Ten chosen hydro-morphological parameters have their direct effect on flash flooding were used for estimating hazard scale depending on the MCA procedures. The proposed risk scale assumed category three for the high Weighted Standardized Risk Factor (WSRF) of five basins, while the category two (moderate WSRF) represents the middle sector of NWCB (19 basins). The class one represents 10 basins (low WSRF). Field measurements are highly recommended to verify the results of MCA procedure used in this paper. KEY WORDS: Hydrology, Risk assessment, WMS software, MCA technique, Egypt EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 41

2 INTRODUCTION In the North Western Coastal Basins (NWCB), the rainfall considers the only local water resource for irrigation and domestic purposes. Surface runoff occurs in the form of flash floods through numerous basins dissecting the tableland plateau to the south of the coastal plain. Water use maximization from flash floods is an important item in almost all development projects and an integrated aspect of the detailed design of all rain fed systems is the underlying consideration of safety. Hazards associated with flash flooding may be controlled under presence of appropriate management system. Therefore, a great intention was made to have design criteria for flash flood protection in design manuals and codes of practice. Almost all of these manuals adopted the design recurrence interval as a measure for the safety level that will be considered during the design of flash flood protection system. This means that a flood event that may harm highly important element should have a design recurrence interval higher than that with less importance level (Stephen A. Nelson, 2004). This method of evaluating the flash flood risk level almost ignored the hydromorphological parameters of the catchments and the flash flood event itself. Site description Thirty four basins occupied the coastal area of north western coastal zone of Egypt were selected based on the available rainfall records. It occupies 50 km east of Marsa Matrouh City and 150 km to the west of the city, between longitudes & E and latitudes & N with total surface area of magnitude 2000 km 2 (Fig.1). Climatic conditions of the study area is characterized by a temperate Mediterranean climate. The mean monthly maximum air temperature value reaches 30 ºC at Aug., while the average minimum value reaches 9 ºC (at January) with mean annual value of 19 ºC. The average recorded value of pitch evaporation reaches 2863 mm/year. The recorded maximum relative humidity varies from 73% to 63% (in July and March respectively). The study area is characterized by short rainy season (Nov.-Feb.). December is the rainiest month (32 mm). The maximum annual rainfall was recorded in 1989/1990-season (275mm) while the annual mean value reaches 100 mm. Figure 1: Location map of the study area EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 42

3 Geomorphologically, the area of the selected North Western Coastal Basins (NWCB) is characterized by four geomorphic units; coastal plain, pediment plain, structural plain and hydrographic basins (Fig.2). The coastal plain stretches adjacent to the present Mediterranean shore line. It is generally narrow in width controlled by the southern elevated plateau. Foreshore dunes of oolitic sand composition characterize this plain. Also three coastal ridges enclosing three depressions describe the coastal plain. The piedmont plain is developed at the foot slope of the structural plateau. It is occupied by thick calcareous soils of fine alluvial deposits of many basins crossing the piedmont plain to the sea direction. The structural Plateau acts as major catchment area feeding the drainage basins during winter times. The plateau runs from the Qattara Depression southward to the piedmont plain northward with an elevation of 100 m at Fuka to 160 m at North Coast and reaches 250 m at Mersa Matrouh escarpment. The hydrographic basins form striking feature of the study area. They are of variable density and nature. They are of few numbers, shallow reaches and short lengths. Runoff occasionally occurs mainly in the lower part of the basins and on their benches which consist of low permeable, massive calcareous crust. Geologically, the area of NWCB has a sedimentary cover ranging in age from Quaternary to Lower Miocene of about 200 m thickness (Fig. 3). Many authors studied the geology of the area; of whom are; shata (1957), Said (1962), El-Shazly (1964), Hammad (1966, 1972), El-Shamy (1968) El-Senousi (1969), Selim (1969), Taha (1973), Misak (1974), Atwa (1979) Rizc (1982), Raslan (1995) and Saleh (2000). Based on these studies, the NWCB area is characterized by Quaternary, Pliocene and Miocene deposits. The Quaternary deposits are differentiated into Holocene and Pleistocene deposits. Holocene deposits are represented by variety of unconsolidated deposits including alluvial, eolian and beach deposits. The alluvial deposits are composed of quartz sand, silt, and clay of 40 m thickness. In lower altitudes, eolian deposits are developed as coastal dunes of carbonate nature or as inland dunes composed of quartz sand. Beach deposits are mainly composed of loose white carbonate sand grains intermixed with few quartz grains and fossil remains. Pleistocene deposits are formed of oolitic limestone deposits with dispersed quartz grains and fossil allochems. Miocene deposits exhibit great lateral facies variation. Three formations of Miocene rocks are well recognized; Moghra, Mamura and Marmarica Formations. Moghra Formation is a clastic fluviomarine delta front of Early Miocene age which grades laterally to the north and west into the marine Mamura Formation and to the south into the fluvial red beds of Gebel Kheshab (Said, 1962). It is composed of sandstone, siltstone, and calcareous shale with vertebrate remains and silicified tree trunks. Mamura Formation is a limestone and calcareous shale sequence. It forms the Marine equivalent of the Moghra Formation (Said, 1962). It rests above El Dabaa Formation and is conformably overlain by the Middle Miocene Marmarica Formation. In addition, three normal faults (NNW-SSE direction) belonging to the Late Miocene tectonics have affected the tableland producing the Marmarica structural plateau. The anticlines are represented by the headlands forming cliffs facing the sea (Ras Umm El-Rakham and Ras Abu Laho) while the synclines occupy the areas between the headlands. EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 43

4 Figure 2: Geomorphic and Orthographic map of NWCB Figure 3: Geological map of NWCB area (after CONOCO 1989) Hydrogeologically, the area of study is characterized by an aquifer system belonging to the Quaternary (Holocene and Pleistocene) and the Tertiary (Miocene). Holocene aquifer (alluvium aquifer) forms a limited groundwater aquifer (Sewidan, 1978). It occupies the main trunk and the deltas of some drainage basins. Precipitation forms the main sources of recharge. Pleistocene aquifer (Oolitic limestone aquifer) is composed of oolitic limestone interbedded with sandstone and clay. It is considered to be the most important aquifer dominating the whole coastal strip. The oolitic limestone aquifer extends southward from the coastal line to about 4 km. Groundwater generally flows from south to north following the general slope. Groundwater salinity ranges from brackish to high saline water according to rain fall quantity, over pumping and sea/salt water intrusion. Tertiary aquifer (Fissured limestone aquifer) is composed of limestone with little clay intercalations. Groundwater occurs in two forms; shallow groundwater (Perched groundwater) and deep groundwater. Rain water forms the main recharging source of this aquifer. Deep groundwater is formed when the rainwater is accumulated in the fractures, fissures solution plains of the limestone rock. This aquifer is generally low potential aquifer. Salinity is ranging from fairly fresh to hyper saline water. EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 44

5 MATERIALS AND METHODS The materials used in this paper were collected through carrying out 12 field trips in NWCB during the period through the program of Wadi development in the north western coastal zone funded by Desert Research Center (DRC). These field trips were achieved with the team work of the hydrology Dept., DRC for selected sites. The basic hydrologic data of these sites were obtained during these field trips. In addition, the archival data such as long term rainfall records were collected from the DRC library beside the recent rainfall records from the 15 installed rain gauge stations and monitoring monthly periodic records. These rainfall records were used in estimating the recurrence period and rainfall event distribution in NWCB according to Weibull, (1932) ranking method and Raghunath, (1990). The statistical analysis of the rainfall records during the period 1947/ /97 (50 seasons), the recurrence period T and the probability of exceedence P r, was estimated based on the following relations (Bennett & Doyle 1997); T = (N + 1) / M... (1) P r = 100*M / (N + 1).....(2) Where M is the descending order rank (dimensionless) and N is the total number of records (dimensionless). The methodological approach used in this paper is based on the mathematical modeling techniques applying Watershed Modeling System (WMS, version 7.1) and STATISTICA version 7 computer programs. As any area under development that is subjected to flash flood hazards had to be protected against flood events, these events are estimated based on a certain recurrence interval. However, some basins may be subject to more danger than other sub-basins. This is why a risk assessment from the flash flood event point of view, has to be carried out prior the design or proposing the storm protection scheme (USDT, 1996). As a result, the high-risk locations will receive more intention than sub-basins with lower risk or even their protection works may be designed with a higher recurrence interval. The criteria adopted in this study for risk analysis was based on hydro-morphological parameters that may result in more loss in surface water and damage to the crossing locations. These selected parameters are the basin drainage area (A), basin slope (BS), average overland flow (AOFD), basin length (L), basin shape factor (Shape), basin sinuosity factor (Sin), basin average elevation above mean sea level (AVEL), basin maximum stream length (MSL), basin maximum stream slop (MSS) and basin centroid stream distance (CSD). To estimate these selected ten dependent parameters required for the flash flood risk assessment, delineation of the watershed boundaries and their characteristics was carried out through the use of a Digital Elevation Model (DEM) file (Fig. 4) and the Topographic Parameterization program technique (TOPAZ) (Martz and Garbrecht 1993). Figure 4: Digital Elevation Model (DEM) file of the study area EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 45

6 In order to obtain these selected basin characteristics, Watershed Modeling System (WMS) package had been used. WMS is a comprehensive environment for hydrologic analysis. It was developed by the Environmental Modeling Research Laboratory of Brigham Young University in cooperation with the U.S.A. Army Corps of Engineers Waterways Experiment Station. It was used to delineate the catchment and basin streams (Nelson et al 2000). The input data to WMS model were obtained from SRTM3 (Shuttle Radar Topography Mission). SRTM3 data gave the elevations in reference to the mean sea level in the center of a grid of 90mx90m spacing. Moreover, SRTM3 data are used to trace and convert the drainage network and basin boundaries to lines and polygons by WMS drainage coverage (Nelson et al, 2000). These hydro-morphological parameters of the different selected basins in the NWCB were statistically analyzed by using Pearson's correlation coefficient in order to differentiate and confirm the interpretation of them. The Pearson's correlation coefficient is the most applicable one of the most multivariate correlation (John, C. Davis, 1986). By using these ten hydro-morphological variables, basic statistics and correlation matrix of the transformed data input of these different variables are obtained. Moreover, the cluster analysis was carried out on the non transformed input data matrix of 34 selected basins with ten hydro-morphological parameters applying STATISTICA software V.7.1. The results are given as R-mode and Q-mode dendrograms with amalgamation rule of single linkage and Euclidean distance with and without (1-Pearson r) method. In the other side, Multi Criteria Analysis (MCA) was used for statistical analysis after standardization of the selected ten hydro-morphological parameters applying STATISTICA software (v.7.1). MCA was appeared in the 1960s as a decision-making tool. It was used to make a comparative assessment of alternatives or heterogeneous measures. With this technique, several criteria can be taken into account simultaneously in a complex situation. The method is designed to help decision-makers to integrate the different options, reflecting different factors of the addressed problems, into a prospective or retrospective framework. The results are usually directed at providing advice or recommendations for future activities. MCA describes any structured approach used to determine overall preferences among alternative options, where the options accomplish certain or several objectives. In MCA, desirable objectives are specified and corresponding attributes or indicators are identified. The actual measurement of indicators need not be in monetary terms, but are often based on the quantitative analysis (through scoring, ranking and weighting) of a wide range of qualitative impact categories and criteria (Baptista et al., 2007). MCA provides techniques for comparing and ranking different outcomes, even though a variety of indictors are used. Standardization of hydro-morphological Parameters The selected ten hydro-morphological parameters obtained for each watershed are expressed in different units. It is therefore difficult to compare across criteria. For many of the arithmetic MCA techniques, it is necessary to reduce the scores to the same unit. This is called standardization. The difference between the actual parameter and that of the lowest value is divided by the difference between the parameters of the highest value and that of the lowest value. This led to standardized factors that reflect the degree of risk for each parameter compared to the same parameter in the other sheds (Heun, 2008 and Baptista et al., 2007). The following relations show the mathematical equation by which each morphological parameter is defined. EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 46

7 Basin Drainage Area Standardized Risk Factor (ASRF) = A A Min A Max A Min Basin Slope Standardized Risk Factor (BS SRF) = BS BS Min BS Max BS Min Basin Average Overland Flow Standardized Risk Factor (AOFD SRF) = AOFD AOFD Min AOFD Max AOFD Min Basin Length Standardized Risk Factor (L SRF) = L L Min L Max L Min Basin Shape Ratio Standardized Risk Factor (Shape SRF) = Shape Shape Min Shape Max Shape Min Basin Sinuosity Ratio Standardized Risk Factor (Sin SRF) = Sin Sin Min Sin Max Sin Min Basin Average Elevation above mean sea level Risk Factor (AVEL SRF) = AVEL AVEL Min AVEL Max AVEL Min Basin Maximum Stream Length Risk Factor (MSL SRF) = MSL MSL Min MSL Max MSL Min Basin Maximum Stream Slop Risk Factor (MSS SRF) = MSS MSS Min MSS Max MSS Min Basin Ccentroid Stream Distance Risk Factor (CSD SRF) = CSD CSD Min CSD Max CSD Min (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Where; Max. refers to the maximum value of the mentioned parameter and Min. refers to the minimum value of the mentioned parameter. The weighted sum was then applied to standardized parameters. The principle is that the standardized parameters for the individual criteria are added up, leading to a single factor. And to express the importance of certain parameter compared to others the individual standardized factors were multiplied by a weight coefficient (W), that was assume in this study constant for all factors and equal to 1/(No. of parameters) for simplification, before being added up. The resulted sum is the Weighted Standardized Risk Factor (WSRF). W SRF =Wx(A SRF +BS SRF + AOFD SRF +L SFR +Shape SRF +Sin SRF +AVEL SRF + MSL SRF + MSS SRF + CSD SRF ) (13) In addition, Box plot technique is useful to display differences between populations without making any assumptions of the underlying statistical distribution. It is non-parametric. Spacing between the different parts of the box help indicate the degree of dispersion (spread) and skewness in the data, and identify outliers. The box plot technique was applied to test all the data for values that are extremely high outlier. An outlier is an observation that is numerically distant from the rest of the data which may lead to biased results. The mild and extreme higher outlier was calculated for each data set and all watersheds that have their parameters values above the extreme higher outlier were considered as the highest risk watersheds. Mild higher outlier = UQ IQR (14) Extreme higher outlier = UQ + 3 IQR...(15) IQR = UQ LQ....(16) Where; UQ is the upper quartile, LQ is the lower quartile and IQR is the inter-quartile range for each data set. Then the extreme higher outlier was considered as the highest parameter value when calculating WSRF. This technique was adopted for all other parameters and the W SRF for each of them was recalculated and their risk level was estimated based on the new results. EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 47

8 RESULTS AND DISCUSSION Based on the statistical analysis of the long term period of rainfall (50 seasons) (Table 1 and Fig.5), it is noticed that the rainfall depth of 11.5 mm more than the mean value of initial abstraction recurs every 3 years with probability 33.3%. During this period the maximum daily rainfall is 29.6 mm/day and the seasonal rainfall is mm/year. In addition, the rainfall amount of 16.5 mm/hour recurs after 15 years, where the maximum daily rainfall is 70.6mm/day and the seasonal reaches 274.6mm with probability 6.6%. The monthly rainfall amount of 147 mm recurs after 50 years, where the maximum annual rainfall reaches 276.8mm with probability 1.96%. Table 1: The estimated recurrence periods and probability of exceedence of rainfall events based on the records of the interval in the selected NWCB (Gad et al, 2002) Return Period (T) 14 years base-period ( ) Max. rainfall depth (mm) One hour Daily Annually Probability (%) 50 years base period ( ) Max. rainfall depth (mm) Annually Monthly Probability (%) Recurrence interval T (year) Annual Rainfall depth (mm) Probability of exceedance P (%) Figure 5: Probability of exceedance P (%) and the recurrence period T (years) of the annual rainfall in the NWCB area during the period Jan Jan (after Gad et In addition, the drainage characteristics of terrain surfaces of the thirty four selected basins in NWCB required for flash flood risk assessment, which automatically computed applying WMS, reflect great tendency of these catchments to receive flash floods with peak runoff as a result of weathered and fractured nature of the Marmarica plateau bedrock (Gad, 2009 and Morad et al., 2014). Their estimated hydromorphological parameters include basin area (A), basin slope (BS), average overland flow (AOFD), basin length (L), shape factor (Shape), sinuosity factor (Sin), mean basin elevation (AVEL), maximum stream length (MSL), maximum stream slope (MSS) and centroid stream slope (CSD) (Table 2 and Fig. 6). EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 48

9 Table 2: The results of the selected hydro-morphological parameters of NWCB extracted from DEM by WMS software (v. 7.1) Basin No Basin name Location A BS AOFD L Shape Sin AVEL MSL MSS CSD B1 Makman Ras El-Hekma area B2 Baghoush B3 Abu Grof B4 Agourah B5 Baghoush-west B6 Khrigah B7 Al-Madwar B8 Al-Qasabah B9 Grawlla Marsa Matrouh area B10 Kharoubah B11 El-Raml B12 Maged B13 Um Ashtaan B14 Al-Sammed B15 Um Al-Rakhm B16 Hash B17 Megwah B18 Al-Hagn B19 Al-Abeteer B20 Al-Halazin B21 Qanourah B22 El-Washka Sidi Barrani area B23 El-Sanab B24 El-Negeila B25 Abu Nafla B26 El-Sabbaba B27 El-Sabbaba-west B28 El-Harika B29 El-Labda B30 El-Basbos B31 El-Kabash B32 El-Ziba B33 El-Tharan B34 Hamad A=Basin area (km 2 ), BS=Basin slope, AOFD=Average overland flow (m), L=Basin length (m), Shape=Shape factor, Sin=Sinuosity factor, AVEL=Mean basin elevation (m.amsl), MSL=Maximum stream length (m), MSS=Maximum stream slope and CSD=Centroid stream slope. EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 49

10 Figure 6: Drainage network of the selected 34 basins extracted from DEM file by WMS software The basic statistics of the selected hydro-morphological parameters show that the drainage area (A) of the studied basins ranges from to km 2 (Abu Nafla and Khrigah basins respectively) with mean value of km 2 and standard deviation of (Table 3). Otherwise, the basin slope (BS) ranges from (El-Sabbaba basin) to (Agourah basin) with mean value 0.01 and standard deviation The high BS value characterizing Agourah basin reflects high tendency to generate great runoff and sediment load yields (Gad and Abdel-Latif, 2003). The basin length of overland flow (AOFD) can be described as the length of flow of water over the surface before it becomes concentrated in definite stream channels (Krishnamurthy et al. 1996). It ranges between and km with mean value and standard deviation (El-Sanab and El-Raml basins respectively). The minimum value of basin length factor (L) reaches m (Maged basin) and the maximum value reaches m (Abu Nafla basin) with mean and standard deviation The basin shape factor (Shape) ranges between 2.16 and 9.1 (Baghoush and Hash basins respectively) and mean value of 5.2 while the standard deviation reaches The basin sinuosity factor (Sin) ranges from 1.11 (Al-Halazin basin) to 1.8 (Makman basin) with mean value 1.36 and standard deviation 0.19 reflecting lithological and structural control. The mean basin elevation (AVEL) ranges from 58.01m (Khrigah basin) to 188.1m (Abu Nafla basin) with mean value and standard deviation The basin maximum stream length (MSL) ranges from m (Grawlla basin) to m (Hamad basin) with mean value and standard deviation The basin maximum stream slope (MSS) ranges from (Hamad basin) to (Makman basin) with mean value 0.01 and standard deviation The basin centroid stream slope (CSD) of the 34 extracted basins ranges from to m (Makman and Hamad basin respectively) with average value of m and standard deviation Moreover, the close inspection of correlation matrix was useful because it can point out associations between variables that can show the overall coherence of the data set and indicate the participation of the individual hydro-morphological parameters in several influence factors, a fact which commonly occurred in NWCB (Gad, 2009). Pearson correlation analysis between the different hydro-morphological parameters (Table 4) shows that the marked correlations are significant at probability less than It is clear that the basin catchment area (A) is direct positively correlated with L, Sin, AVEL, MSL and CSD (0.9, 0.46, 0.53, 0.92 & 0.93 respectively). EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 50

11 Variable Table 3: The basic statistics of non-parametric hydro-morphological variables of NWCB Mean Median Min. Max. 25th Percentile 75th Percentile G.M. H. M. Std. Dev. Variance Sk. Kurt. A BS AOFD L Shape Sin AVEL MSL MSS CSD Min.=Minimum, Max. = Maximum, G.M.= Geometric mean, H.M.= Harmonic mean, Std.Dev.= Standard Deviation, Sk=Skewness, Kurt = Kurtosis The Basin Slope (BS) is direct positively correlated with AOFD and MSS (0.41 & 0.58 respectively) and reverse correlated with A, L, Shape, Sin, AVEL, MSL and CSD (-0.3, -0.46, -0.16, -0.35, -0.55, and respectively). The Basin length factor (L) is direct positively correlated with A, Sin, AVEL, MSL and CSD (0.9, 0.52, 0.7, 0.96 & 0.96 respectively) while Sin factor is direct positively correlated with Area, L, AVEL, MSL and CSD (0.46, 0.52, 0.37, 0.63 & 0.55 Respectively). The Basin Shape factor (Shape) is direct positively correlated with MSS (0.21) while Sin factor is direct positively correlated with Area, L, AVEL, MSL and CSD (0.46, 0.52, 0.37, 0.63 & 0.55 Respectively). Moreover, the correlation coefficient of 0.98 characterized to the relation between basin Max Stream Length (MSL) and Centroid Stream Distance (CSD) reflects the effect of the geological structures of these streams to form peak flow and receives flash floods (Gad, 2001). Table 4: The correlation coefficients between the selected variables of the studied NWCB Variable A BS AOFD L Shape Sin AVEL MSL MSS CSD A BS AOFD L Shape Sin AVEL MSL MSS CSD EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 51

12 In addition, cluster analysis comprises of a series of multivariate methods which are used to find true groups of data or stations. In clustering, the objects are grouped such that similar objects fall into the same class (Danielsson et al., 1999). The hierarchical method of cluster analysis, which is used in this study, has the advantage of not demanding any of prior knowledge of the number of clusters, which the nonhierarchical method does. A review by Sharma 1996 suggests Ward's clustering procedure to be the best, because it yields a larger proportion of correct classified observations than do most other methods. Hence, Ward's clustering procedure is used in this study. As a distance measure, the squared Euclidean distance was used, which is one of the most commonly adopted measures (Fovell and Fovell 1994). The output of the R- mode cluster analysis is given as a dendrogram (Fig. 7). There are two major clusters as shown in Fig.7. The first cluster domains, the hydro-morphological parameters of Sin, Shape and AVEL with basin area (A) as independent variable. This cluster reflects the impact of both Sin and AVEL to generate peak flow (Abulohom and Gad, 2011). The second cluster domains the parameters L and CSD with MSL as independent variable. Figure 7: Horizontal icicle plot of the studied 10 hydro-morphological parameters (R- mode) based on Euclidean distance (left map) and 1-Pearson r method (right map) Moreover, the output of the Q-mode cluster analysis is given as a dendrogram (Fig. 8). It is noticed that there are fifth major clusters when interpreted at similarity level with a distance The first cluster domains the basins Abu Grof, Aghourah, Baghoush-west, Al-Halazin, Khrigah, Megwah, Al-Hagn and Al- Abeteer which exhibit the minimum values of A (less than 70km 2 ) and maximum values of BS (more than 0.01) and reflecting a tendency to form flash floods. The second cluster involves the basins Al-Madwar, El- Raml, Um Ashtaan, Grawlla, El-Harika, Al-Sammed, Hash, Kharoubah, Um Al-Rakhm, El-Labda, El- Washka and El-Sabbaba-west. EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 52

13 Figure 8: Vertical icicle plot of the studied 34 basins (Q- mode) This cluster is characterized by small tendency to form flash floods and high tendency to recharge the shallow groundwater aquifers. The third cluster domains the basins Qanourah, El-Tharan, El-Sanab, Al- Qasabah and Hamad. This cluster is characterized by its moderate potentiality to form flash flood. The fourth cluster domains Baghoush, El-Ziba, El-Negeila and El-Kabash basins. The fifth cluster includes El- Sabbaba and El-Basbos. The independent cases involve Makman and Abu Nafla basins. Their independence may attribute to the effect of geologic structure since the fault systems in the Qattara Depression affect the northern Marmarica plateau (, Galal and Gad 2010). In the other side, Table 5 and (Fig. 9) represent the results of applying the MCA technique for the watersheds of the selected NWCB. The estimated weighted standardized risk factor (W SRF ) was used to classify NWCB into 3 categories on a quartile basis (Low, Moderate and High classes). As a general, the W SRF values of the studied basins exhibit high risk class for basins B25, B26, B30, B32 and B34 (Abu Nafla, El-Sabbaba, El-Bassos, El-Ziba and Hamad basins) (Table 5). In addition, Moderate risk class (category two) represents 56% of the studied basins (19 basins) and low risk class includes the rest of the studied basins (10 basins with ratio 29%). EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 53

14 Table 5: The flash flood risk classes of the studied NWCB based on MCA technique Basin No. A SRF BS SRF AOFD SRF L SRF Shape SRF Sin SRF AVEL SRF MSL SRF MSS SRF CSD SRF W SRF Degree B L B M B L B M B M B M B L B M B M B M B M B L B M B M B M B M B M B M B M B M B L B L B L B M B H B H B M B L B L B H B M B H B L B H EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 54

15 Figure 9: The risk classes of the selected NWCB based on the MCA technique Worth to mention that, from the results in Table 5, it was found that all catchments with large drainage area have high W SRF value, and as a result, it causes skewness to the resulted W SRF values for all the other sheds. Therefore, almost all of watersheds have a low to moderate flood risk factor (category 1&2). The drainage area (A), as a main parameter directly affecting the value of flood peak flow, was plotted to test it for extreme high values that may affect the results (Fig. 10). Figure 10: The hydro-morphological variable A (left chart) and BS (right chart) of the selected NWCB From the plot, it was noticed that one main basin area (B25) is extremely high ( km 2 ) and three other basins (B2, B26 and B30) have an area more than 500 km 2 while all the other values fall below 300 km 2. In addition, the Basin Slope (BS), as another main parameter directly affecting the value of flood peak flow, was noticed from (Fig.10) that seven basins (B4, B5, B6, B9, B13, B33 & B34) are extremely high slope (more than 0.015) while all the other slope values fall below In this case, Box plot technique (Fig. 11) is useful to display differences between populations without making any assumptions of the underlying statistical distribution. Spacing between the different parts of the box helps to indicate the degree of dispersion (spread) and skewness in the data, and identify outliers (Table 3 and Fig.11). EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 55

16 Figure 11: Box plot of non-parametric hydro-morphological variables (extremely high basin area A showing its skewness in upper chart) and the relation between A and BS (lower chart) of the studied NWCB Based on the Box Plot results of non-parametric variables and the neglecting of the extremely high values of the hydro-morphological variables A and BS (Fig. 12), the W SRF values were recalculated and their risk level was estimated based on the new results (Table 6 and Fig.13). The new calculated W SRF values affect the risk classes of the basins Nos. B17, B19, B20, B21, B23, B24 and B28 (Megwah, Al-Abeteer, Al- Halazin, Qanourah, El-Sanab, El-Negeila and El-Harika basins). The risk class of the Megwah, Al-Abeteer and El-Harika basins changed from Medium to Low risk, while the opposite change was related to the basins Al-Halazin, Qanourah and El-Sanab which changed from Low risk class to Medium risk class. Moreover, there is only one basin which changed from Medium to High risk class (El-Negeila basin). EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 56

17 Figure 12: Plot of hydro-morphological outliers values of A (left chart) and BS (right chart) of the studied NWCB Table 6: The flash flood risk classes of the studied NWCB based on parametric MCA Basin A BS AOFD L Shape Sin AVEL MSL MSS CSD W SRF Class B Min Max L B Min L B M B Max M B Min Min L B Max M B M B Max M B L B18 Min M B L B Min Max Min L B M B L B Min M B Min Max Max Min H B M B L B L B34 Max Max Max H EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 57

18 Figure 13: The results of risk classes of the selected NWCB after re-calculated W SRF values based on the MCA technique CONCLUSION Flash flood protection measurements depending solely on recurrence interval have been adopted for long time without giving weight to the hydro morphological parameters of the watersheds that cause such floods. The paper presented the use of multi criteria analysis technique to use these parameters when defining the design flash flood events. It was noticed during the analysis that the drainage basin area and basin slope have great effect on the floods generated at its outlet while other factors have less effect than the drainage area and basin slope such as the shape factor and sinuosity factor. During the analysis, a higher limit for all the parameters values was adopted based on the sample that was concerned during the analysis to calculate the standardized factors. The box plot test represented a very useful, easy to use and quick tool when trying to exclude extremely high parameter that may lead to unrealistic risk factor especially for small parameter values. However, using regression techniques, a maximum values can be calculated/estimated for any region for the purpose of defining the upper limit of each parameter depending on the meteorological characteristics of this region. The weighted standardized risk factor obtained can be used during the design of flash flood protection measurements and/or the calculation of design of peak flows for crossing structure. This may lead to more economic design procedure that can be adopted in drainage design guidelines and manuals. However, further studies should be made concerning the environmental hazard of the flash flood events and special intention should be made when trying to control floods to keep the environment. Field measurements are highly recommended to verify the results of MCA procedure used in this work. Acknowledgements The data of this work were presented from the Wadi development program in the northwestern coastal zone. Assistance from program team work is gratefully acknowledged. We thank Prof Dr M. A. Wassif and the program team for the use of their data and their support and cooperation. EUROPEAN JOURNAL OF BUSINESS AND SOCIAL SCIENCES 58

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