Abstract

This study delineated flash flood hazard zones leading to vast destruction to infrastructure, property, and loss of life. An integrated approach using remote sensing and geographic information systems was applied to reveal flash flood-prone zones. The study approach evaluated topographic, geologic, and hydrologic factors holistically to assess these hazard zones. The morphometric characteristics of Wadi Dahab sub-basins were supported by topographic, geologic, and hydrologic information. Data from Shuttle Radar Terrain Mission and Operational Land Imager imagery were analyzed to characterize hydrological morphometrics, lithology, soil types, and land use. A Natural Resources Conservation Service model was selected to calculate runoff depth at ungauged watersheds. A spatially distributed unit hydrograph was adopted to create the flow time and runoff velocity. The Flashflood Hazard Model was developed by spatial integration of all contributing factors. An analytical hierarchy process was adopted for the logic ranking of the effective factors. The flash flood hazard map classifies Wadi Dahab basin into five relative hazard zones: very high, high, moderate, low, and very low. The highly hazardous zones are distributed at the downstream of Wadi Dahab basin corresponding to steep topography and Precambrian rocks. The hazard map was validated using the flash flood markers defined from field observations.

INTRODUCTION

Geospatial prediction of flash flood hazard has become crucial in mountainous areas because of the socio-economic impact of flash floods and population growth (Abuzied et al. 2016c). Different studies around the world have supported the geospatial assessment of flash floods due to urbanization growth in mountainous regions (Youssef et al. 2011; Safaripour et al. 2012; Gioti et al. 2013). The Sinai Peninsula (Figure 1) has captured the Egyptian government's attention during the last decades in support of economic development. The Wadi Dahab area is considered one of the hazardous regions in the Sinai Peninsula due to its relatively high flash flooding range. Many researchers have considered Wadi Dahab as a strategic location for mitigation planning (Omran 2013; Selmi & Abdel-Raouf 2013). Therefore, the assessment of flash flood hazard in the Wadi Dahab area is extremely important to reduce catastrophic outcomes. This study reveals flash flood hazard zones through an integrated approach based on remote sensing and geographic information systems (GIS) techniques. Worldwide attention has focused on geospatial mapping of flash flood hazards using satellite images and geologic and topographic maps (Forte et al. 2006; Dawod et al. 2011; Clement 2013; Bajabaa et al. 2014; Abuzied et al. 2016c; Khosravi et al. 2018). However, few studies have conducted an integrated approach using remotely sensed, topographic, geologic, morphometric, and hydrologic data for spatial modeling of flash flood hazards (Elkhrachy 2015). Accordingly, this study adds an integrated multifactor analysis for a comprehensive assessment of flash flood hazard zones as the first comprehensive evaluation of flash flood hazards in Wadi Dahab basin, southeastern Sinai, Egypt.

Figure 1

The location map of Wadi Dahab basin, southeastern Sinai, Egypt is shown by Landsat ETM+ scene, ETM+, Enhanced Thematic Mapper Plus.

Figure 1

The location map of Wadi Dahab basin, southeastern Sinai, Egypt is shown by Landsat ETM+ scene, ETM+, Enhanced Thematic Mapper Plus.

The study included many remote sensing skills to examine the factors contributing to flash floods, such as Shuttle Radar Topography Mission (SRTM), Landsat 8 (OLI) satellite images, and geologic and topographic maps. Abuzied (2016) supported the use of Spot 5 and Landsat 7 to delineate lithology and land use in the mountainous region close to Wadi Dahab. SRTM has been used extensively in worldwide studies as a main source to extract several factors such as elevation, drainage network, slope, stream power index, and topographic wetness index (Pradhan & Lee 2010; Abuzied et al. 2016a, 2016d). Similar approaches were used in this study, utilizing OLI 8 to classify the lithological units, soil, and land use and SRTM to extract several factors such as elevation, drainage network, slope, stream power index, topographic wetness index, flow length, velocity, and flow time.

Drainage characterization plays an important role in evaluation of flash flood hazard (Dawod et al. 2012). Commonly, geomorphology has been essential to fluvial systems (Thorne 2002); hence, worldwide studies have characterized drainage networks in basins and sub-basins based on conventional geomorphologic methods (Horton 1945; Strahler 1964; Krishnamurthy et al. 1996). Watershed terrain analysis relies heavily on quantitative measures for drainage basins and their geometric characteristics (Abrahams 1984). Rationally, the morphometric variables of any basin can then reflect runoff routing, flood peaks, erosion estimates, sediment yields, and flood impact (Patton 1988; Gardiner 1990). Thus, this research adopted watershed terrain analysis to characterize the morphometric parameters of Wadi Dahab catchments and their drainage network.

The geospatial assessment of flash flood hazard in the Wadi Dahab area is challenging due to the rugged topography and lack of direct runoff data. Generally, flash floods are caused by complex interactions of topographical, geological, geomorphological, and hydrological effects (Abuzied et al. 2016c). For an integrated multifactor analysis, the Natural Resources Conservation Service (NRCS) rainfall-runoff model (SCS 1972) was selected in this study to evaluate hydrologic response at ungauged watersheds. The NRCS model predicts the runoff behavior in ungauged catchments better than more multifaceted models (Wagener & Wheater 2004), especially common in ephemeral catchments (Hedman 1970). Following NRCS approaches, several studies recognized relationships among basin geometric parameters and hydrological parameters (Fang et al. 2008; Masoud 2009). The spatially distributed unit hydrograph (SDUH) method was successfully used in several studies to estimate the hydrological parameters for highly hazardous sub-basins (Maidment 1993; Najjar 1999). The SDUH is generally used to evaluate the physical characteristics of the catchment and to parameterize a unit hydrograph in the absence of recorded rainfall and runoff (Cleveland et al. 2008). The geomorphological watershed characteristics of Dahab wadis are extremely variable but still definable, although synthetic hydrographs based on topographic characteristics have been adopted in this study to measure runoff velocity and flow time.

GIS is a standard technology to analyze, manipulate, and integrate the factors contributing to flash flood hazard with great accuracy and efficiency (Abuzied et al. 2016c). In this study, the causative factors for flash floods were constructed, analyzed, and integrated within the same georeferencing outline using GIS. Mapping of flash flood hazard requires ranking values corresponding to the importance of each factor and weights for its classes based on their contributions to flash flood hazard (Dawod et al. 2012). Several GIS models including logistic regression, bivariate statistical analysis, and analytical hierarchy process (AHP) have been used in different applications around the world to assign suitable ranks for any factor and suitable weights for its classes (Pradhan & Lee 2010; Moeinaddini et al. 2010; Regmi et al. 2014; Chapi et al. 2017). The AHP is an appropriate method to derive ranks for any factor corresponding to its relative importance based on a pairwise comparison analysis (Boroushaki & Malczewski 2008; Abuzied et al. 2016b). Therefore, the AHP was applied in this study to assign realistic ranks for the flash flood contributing factors. Our study goal is to explore a unique Flashflood Hazard Model (FHM) integrating multi-factor analysis. These factors are slope, stream power index, topographic wetness index, lithology, hydrological morphometrics, runoff depth, velocity, and flow time. The integrated approach adds to a more realistic flash flood hazard map and is a powerful tool for development actions in mountainous areas.

STUDY AREA

Wadi Dahab basin is located between the southwestern Gulf of Aqaba coast and the southeastern side of the Sinai rift zone (Figure 1). Dahab city is located on an alluvial fan extending from the Gulf of Aqaba to the main channel of Wadi Dahab, which represents one of the wadis with the greatest flash flood hazard on the Sinai Peninsula. The study area occupies 2,080 km2 and covers an area between latitude 28° 22′ 43.4″ and 28° 52′ 18.5″ N and longitude 33° 55′ 46.9″ and 34° 31′ 28.8″ E (Figure 1). The topography of Wadi Dahab basin differs gradually from steep mountains to gentle plain, sloping towards the Gulf of Aqaba. The relief varies in the study area from low zones to high rugged mountains on both sides of Wadi Dahab that range in elevation from 100 to 2,527 m, respectively (Figure S1, available with the online version of this paper). Several wadis represent separated drainage systems running through the study area. Various cycles of sedimentation created the wadis during Quaternary times and rainy periods (Gilboa 1980). Numerous active wadis drain through the study area, causing a huge amount of flood flow, such as Wadi Zaghraa, Wadi Nasb, and Wadi Saal. The main tributaries of these wadis play an important role in the erosional processes that occur in the southwestern coastal zone because the wadis are fed by seasonal floods (Omran 2013).

Wadi Dahab basin is an extremely arid climate area in which winters are cold with intense rain, and summers are hot and dry. The maximum temperature at Wadi Dahab is recorded as 37 °C, while the lowest temperature may reach −3 °C in the highland areas such as Saint Katherine. The amount of precipitation increases in the western Wadi Dahab, where the average annual precipitation is about 64 mm (Figure 2). Occasionally, the precipitation exists as snow on the mountains peaks. In southern Sinai, the probability of active runoff can be produced from storms of more than 10 mm (Geriesh 1998). In the Middle East, literature on flash floods (Moore et al. 1991) recommended that a wet month could be characterized by one or two rainy days, alternating from 10 mm in the low coastal regions to 50 mm in the highland regions. The flash floods take place seasonally in the study area due to convective rains (Dayan & Abramski 1983). The hydrographical basins on the western side of Wadi Dahab are responsible for runoff and debris flow, because the western branches of their steep sloping channels drain from the highlands where high rates of rainfall prevail (Figures 2 and 3). Several flash floods have occurred along Wadi Dahab in the past years. The last destructive one occurred in 1994, in which the water level at the wadi exit increased up to more than 2.5 m, leading to huge damage to infrastructure and threatening life and property (WRRI 2006).

Figure 2

The Isohyet map of Wadi Dahab basin is created by maximum daily rainfall data.

Figure 2

The Isohyet map of Wadi Dahab basin is created by maximum daily rainfall data.

Figure 3

The slope map of Wadi Dahab basin is generated by using SRTM DEM with a 29-m grid cell size.

Figure 3

The slope map of Wadi Dahab basin is generated by using SRTM DEM with a 29-m grid cell size.

Geologically, Wadi Dahab represents the eastern extension of the Precambrian Arabian-Nubian shield that occupies the area extending from western Saudi Arabia to southern Sinai. Several lithological units exist with diverse hydrological properties (Figure 4). The lithological units consist of Precambrian basement rocks, sedimentary succession, and Quaternary wadi deposits (Said 1962). The Precambrian basement rocks are classified into metamorphic and igneous rock units. The Precambrian igneous rocks cover approximately 63% of the area, and the metamorphic rocks cover 7% of a small part of Wadi Dahab basin, especially in Wadi Nasb, Wadi Saal, and Wadi Zaghraa (Figure 4). The sedimentary rocks cover 13% at the northern part of the Wadi Dahab representing Cambrian to Upper Cretaceous rocks. The recent deposits occupy 17% of the area, including alluvial fans, terraces, and wadi deposits. Several wadis are responsible for shaping the Quaternary alluvial fans when the stream velocity reduces quickly (Figure 4). Commonly, the Precambrian basement rocks are extremely weathered, and exist on hillslopes as fragmented blocks vulnerable to transport when the shear strength decreases. The Precambrian basement rocks follow the steep hills along the main wadis, and the steep slopes accelerate weathering products into the wadis.

Figure 4

The geologic map of Wadi Dahab basin shows different lithological units and the trends of major faults.

Figure 4

The geologic map of Wadi Dahab basin shows different lithological units and the trends of major faults.

The fault system present in the study area is greatly influenced by Red Sea rifting trending mainly in NW–SE (Gulf of Suez direction) and NNE–SSW (Gulf of Aqaba direction) (Figure 4). Wadi Nasb and Wadi Ghaieb faults originated with the formation of Gulf of Suez faults, while the majority of NE trending faults are in the direction of the Gulf of Aqaba and are younger, of Pleistocene age (Said 1962). Geologic structures, mainly faults and joints, have captured the interest of many hydrogeologists, as they act as good groundwater conduits, representing zones of high infiltration and groundwater potentiality. Most of these geologic structures are found in the basement complex with negligible abundance in softer rocks. Accordingly, there are two main types of aquifers present – alluvial and basement aquifers. The groundwater runs laterally and downwards via open fractures to recharge the alluvial deposits of the main wadis, i.e., Wadi Nasb, Wadi Saal, and Wadi El-Ghaib (El Rayes 1992). Therefore, these alluvial deposits receive large amounts of water recharge through the fractures in the basement rocks, producing good-quality reservoirs. Most of the groundwater wells in the study area are dug in these alluvial deposits, reflecting the high availability of groundwater. The alluvial aquifers have good hydraulic parameters with high storage capacity and good water quality with increasing thickness towards the downstream reaches (Shendi et al. 1997).

DATA AND METHODS

Wadi Dahab basin is extremely vulnerable to flash floods due to the cumulative effects of its physical environmental characteristics. Therefore, the holistic approach was considered to assess flash flood hazard based on topographical, geological, geomorphological, and hydrological factors. The assessment workflow is illustrated by Figure 5, which shows the data extracted from different sources.

Figure 5

Flow chart shows all data and methods that were used to assess flash flood hazards.

Figure 5

Flow chart shows all data and methods that were used to assess flash flood hazards.

Spatial database

This study suggests eight factors that contribute to the catastrophic impacts of flash floods. The following factors were selected carefully to develop a unique FHM: slope, stream power index, topographic wetness index, lithology, hydrological morphometrics, runoff depth, runoff velocity, and flow time. The preparation methods of these factors are briefly described in the following paragraphs.

Topographic parameters

SRTM data were adopted and processed to generate a Digital Elevation Model (DEM) at a spatial resolution of 29 m (Figure S1, available with the online version of this paper). The DEM is considered an essential data source to derive information on different topographic factors inducing flash flooding, such as slope, stream power index, and topographic wetness index. Slope is one of the key factors with respect to the possibility of existing flash flood hazards. We generated the slope map using SRTM DEM at a spatial resolution of 29 m, divided equally into six classes (Figure 3).

The stream power index (SPI) is also recommended as a flash flood contributing factor, because it refers to the rate of the erosive power in the drainage network (Regmi et al. 2014). The SPI can be calculated using Equation (1) according to the assumption that discharge (Q) is directly proportional to specific catchment area (A) (Moore et al. 1991). 
formula
(1)
where A is the sub-basin area and β is the slope gradient in degrees. The SPI map of Wadi Dahab was produced using Map Algebra in ArcMap 10.4 and classified into five classes with equal intervals (Figure 6).
Figure 6

The stream power index map (SPI) of Wadi Dahab basin, southeastern Sinai, Egypt.

Figure 6

The stream power index map (SPI) of Wadi Dahab basin, southeastern Sinai, Egypt.

The topographic wetness index (TWI) can also be suggested in the current study as a flash flood causative factor, because it reflects the topography responses on the size and location of saturated zones created by runoff (Pradhan & Lee 2010). The TWI can be obtained using Equation (2) using the relation between the slope gradient (in degrees) of the topographic heights (β) and the catchment area of each cell (A) (Moore & Burch 1986). 
formula
(2)

The TWI map of Wadi Dahab was also generated using Map Algebra in ArcMap 10.4 and classified to five classes with equal intervals (Figure 7).

Figure 7

The topographic wetness index (TWI) map of Wadi Dahab basin, southeastern Sinai, Egypt.

Figure 7

The topographic wetness index (TWI) map of Wadi Dahab basin, southeastern Sinai, Egypt.

Lithology

The lithological units in Wadi Dahab were defined using remote sensing techniques and information from previous studies and geologic maps (Said 1962; Conoco 1987; EGSMA 1994). The United States Geological Survey (USGS) website was used to download two scenes of the OLI Landsat 8 satellite. The scenes were pre-processed to decrease the haze effects before mosaicking and sub-setting using ENVI 5.3. To obtain the highest contrast on lithological units, the combination of processing techniques was carried out to bands from each spectral zone, such as mid-infrared, short-wave infrared II, and visible. Decorrelation stretch and Intensity-Hue-Saturation transformation were used to enhance bands 6, 7, and 4 (Abuzied & Alrefaee 2017). Moreover, principal component analysis was used for all bands to evaluate the principal component (PC) containing the most information. The best information was depicted from bands 6, 7, and 4. A PC combination (3, 4, and 5) was created to enhance different rock units and compared with band ratio combinations (Abuzied & Alrefaee 2018). Algebra combinations and permutation were also considered to create band ratios. The contrast improved gradually with using bands from various spectral zones such as bands 6, 7, and 4. The combination of 4/3, 7/3, and 6/2 was considered as the suitable input to classify lithological units.

The processing techniques produced 17 classes, which were created by supervised image classification followed by post-classification smoothing and vectorization of the raster layer (Figure 4). The same especial rock units are validated and tested by the lithological map, which was added by EGSMA (Egyptian Geological Survey and Mining Authority 1994). The processing techniques classify Wadi Dahab basin into different rock units (Figure 4), including Precambrian igneous rocks (such as Granodiorite, Diorite, Alkaline granite, Monzogranite, Catherina volcanics, and ring dyke), Precambrian metamorphic rocks (such as Metagabbro, basic Meta-volcanic, acidic Meta-volcanic, Metasediments, and phyillite), and Phanerozoic succession (such as Firani group, Cambrian formation, lower Cretaceous formations, upper Cretaceous formation, and Quaternary deposits and recent wadi deposits). Most of the flash flood damage zones could be observed in association with the Precambrian rocks.

Drainage network and morphometric parameters

A topographic map at the scale of 1:50,000 was selected to delineate the main drainage network. The delineated stream network was used to guide the hydrological feature extraction in terrain processing analysis. SRTM elevation data (29-m grid cell size) was processed additionally to extract drainage networks and watersheds (Figure 8) using the ArcHydro tool (Maidment & Morehouse 2002). For catchment definition, a threshold of 80,000 29 × 29 m cells (an area of 2,080 km2) was accepted as an appropriate threshold to delineate 43 catchments within Wadi Dahab basin (Figure 8). The catchments' number shows little difference from the catchments' number described in the literature (Masoud 2009; Omran 2013). In addition, this threshold represents a suitable choice to extract the major known streams in Wadi Dahab basin. The morphometric parameters could be easily calculated from the drainage networks and watersheds (Table 1 and Figure 9). The morphometric parameters were grouped according to basin dimensions, basin shape, basin surface, and drainage network (Table 1 and Figure 9). The morphometric parameters have different indications reflecting the flash floods' impact. For example, the values of some morphometric parameters have direct proportions with the flash flood impact, in which a high value relates to high flash flood hazards, such as Relief ratio (Rr), Relative relief ratio (Rv), Ruggedness index (RI), Drainage density (D), Texture ratio (Tr), Frequency (Fq), Circulation ratio (Cr), and Elongation ratio (Er) (El Maghraby et al. 2014). However, the values of some morphometric parameters have inverse proportions with the flash flood impact, in which a high value relates to low flash flood hazard, such as Shape factor (Sf), Hypsometric index (HI), and Bifurcation ratio (Br). In the current study, we calculated these morphometric parameters using different equations (Table 1) to derive one layer representing the morphometric contribution of Wadi Dahab catchments in the flash flood hazard map. Based on the relation between the morphometric parameters and their impacts on flooding, the weight for each morphometric parameter was assigned using the following equations: 
formula
(3)
 
formula
(4)
where (Xmin) and (Xmax) are minimum and maximum values of morphometric parameters in all Wadi Dahab catchments. Equation (3) was used to normalize the values of all parameters (X) having a direct relation with flooding, while Equation (4) was used to normalize the values of all parameters (X) having an inverse relation with flooding. The different normalized values for all morphometric parameters were summed to calculate the morphometric contribution of each sub-basin to flash flood hazard occurring (Figure 9(l)). The Morphometric Hazard Index (MHI) in each sub-basin was classified using equal intervals into five classes (very low, low, moderate, high, and very high) to evaluate the most critical sub-basins for flooding (Figure 9(l)).
Table 1

Methodology adopted for computations of morphometric parameters (modified after Masoud 2016)

Parameter Formula Reference 
The basin dimensions category 
 Area (AArcHydro analysis Schumn (1956)  
 Perimeter (PArcHydro analysis Schumn (1956)  
 Length (LbArcHydro analysis Schumn (1956)  
 Width (WArcHydro analysis Schumn (1956)  
The basin shape category 
 Circulation ratio (Cr4πA/P Miller (1953)  
 Elongation ratio (Er1.128 × A0.5/Lb Schumn (1956)  
 Shape factor (SfLb2/A Horton (1932)  
The basin surface category 
 Relief ratio (RrR/Lb Schumn (1956)  
 Relative relief ratio (RVR/P Melton (1957)  
 Ruggedness index (RIR×D Schumn (1956)  
 Hypsometric index (HI(Emean – Emin)/ (Emax – Emin) Pike & Wilson (1971)  
The drainage network category 
 Total stream length (TSLGIS software analysis Horton (1945)  
 Total stream number (TSNGIS software analysis Strahler (1952)  
 Stream order (SrOHierarchial rank Strahler (1964)  
 Texture ratio (TrTSN/P Horton (1945)  
 Drainage density (DTSL/A Horton (1945)  
 Stream frequency (FqTSN/A Horton (1945)  
 Bifurcation ratio (BrNu/Nu+1 Schumn (1956)  
Parameter Formula Reference 
The basin dimensions category 
 Area (AArcHydro analysis Schumn (1956)  
 Perimeter (PArcHydro analysis Schumn (1956)  
 Length (LbArcHydro analysis Schumn (1956)  
 Width (WArcHydro analysis Schumn (1956)  
The basin shape category 
 Circulation ratio (Cr4πA/P Miller (1953)  
 Elongation ratio (Er1.128 × A0.5/Lb Schumn (1956)  
 Shape factor (SfLb2/A Horton (1932)  
The basin surface category 
 Relief ratio (RrR/Lb Schumn (1956)  
 Relative relief ratio (RVR/P Melton (1957)  
 Ruggedness index (RIR×D Schumn (1956)  
 Hypsometric index (HI(Emean – Emin)/ (Emax – Emin) Pike & Wilson (1971)  
The drainage network category 
 Total stream length (TSLGIS software analysis Horton (1945)  
 Total stream number (TSNGIS software analysis Strahler (1952)  
 Stream order (SrOHierarchial rank Strahler (1964)  
 Texture ratio (TrTSN/P Horton (1945)  
 Drainage density (DTSL/A Horton (1945)  
 Stream frequency (FqTSN/A Horton (1945)  
 Bifurcation ratio (BrNu/Nu+1 Schumn (1956)  

Note:R is the relief, E is the elevation in km, Nu is the total number of stream segments in order u, and Nu+1 is the total number of segments of the next higher order.

Figure 8

The watershed map shows 43 catchments and six stream orders within Wadi Dahab basin.

Figure 8

The watershed map shows 43 catchments and six stream orders within Wadi Dahab basin.

Figure 9

(a)–(k) The calculated morphometric parameters for Wadi Dahab catchments and their drainage networks. (l) The morphometric hazard map for Wadi Dahab basin, southeastern Sinai, Egypt. (Continued.)

Figure 9

(a)–(k) The calculated morphometric parameters for Wadi Dahab catchments and their drainage networks. (l) The morphometric hazard map for Wadi Dahab basin, southeastern Sinai, Egypt. (Continued.)

Hydrological parameters

To add a confident FHM, responses of surface hydrological processes are crucial tasks. These hydrological responses are essentially associated with spatial and temporal distributions of water flows and their accumulations, including runoff depth, velocity, and flow time. The surface runoff was evaluated from maximum daily rainfall data using the NRCS rainfall-runoff model (SCS 1972). Historical data from eight stations of the Egyptian Meteorological Authority (1990 to 2016) were contributed to create an Isohyet map (Figure 2) and to anticipate the runoff behavior. According to these data, the average daily rainfall in the Dahab area is small, but it might rise intensely within 1 day, resulting in a catastrophic runoff. The most disastrous storm might be recurrent several times through the next period of storms (Abuzied et al. 2016c). Accordingly, the maximum rainfall per day was suggested in this study to evaluate the runoff behavior and to assess flash flood hazard.

The synthetic hydrographs were adopted in the study using the raster GIS functions through ArcMap10.4. The climate data, topographic data, soil, and land use were prepared to estimate the runoff scenario. The highest storm values occurred in Saint Katherine station from 1934 to 2004 (76.2 mm/day) in November 1937. The Saint Katherine station is located at the west of Wadi Dahab basin and is characterized by having the highest elevation and slope gradient in the area. Therefore, the rainfall amount from this station represents a good record (WRRI 2006). Commonly, runoff happens when the rainfall intensity is more than the infiltration capacity at a location (Yair & Lavee 1985). Initial losses are affected by surface and subsurface storage, soil type, infiltration, land use, and evapotranspiration. Thus, initial losses take place before runoff approaches the drainage networks in the watersheds, whereas transmission losses take place as rainfall flows through the stream network. The initial abstractions considered in the study are the infiltration losses only as the result of arid conditions, sparse vegetation, and topography ruggedness of the Dahab area.

Figure 10

The runoff depth map of Wadi Dahab basin was extracted using SCS rainfall-runoff model. SCS, Soil Conservation Service.

Figure 10

The runoff depth map of Wadi Dahab basin was extracted using SCS rainfall-runoff model. SCS, Soil Conservation Service.

In short, the runoff depths in all Wadi Dahab sub-basins (Figure 10) were calculated using the NRCS method (Q in mm) (Equations (5)–(7)) as a function of the maximum retention (S), initial loss (Ia), and maximum daily rainfall depth (P in mm). We calculated the maximum retention (S) using soil and rock properties. The hydrologic soil-cover coefficient (CN) was recommenced in NRCS methods to evaluate the maximum potential retention (S) (Equation (7)). 
formula
(5)
 
formula
(6)
 
formula
(7)
The hydrologic soil-cover coefficient (CN) can be experimentally determined using a function of soil type, land use, and antecedent moisture condition (AMC). AMC can be estimated using a function of the total precipitation during a 5-day period before a storm. The AMC has been assumed to be low in some literature applied to the arid regions of Egypt because the rainy months are limited (Gheith & Sultan 2002). Thus, the AMC has been supposed to be Level I in this study because rain events are rare in Wadi Dahab basin. Accordingly, CN value was extracted based on the relation between soil groups and land use to predict the runoff behavior in Wadi Dahab.

OLI Landsat 8 satellite images were processed to classify land use and soil types using unsupervised and maximum likelihood classification (Figures S2 and S3, available with the online version of this paper). Soil types were classified mainly based on hydrological and physical properties. Based on the infiltration capacity, the soil types were categorized into four main groups, including quaternary and recent deposits, soil containing clastic rocks and fragments, soil containing calcareous carbonate rocks, and soil containing hard rocks (Figure S2). Quaternary and recent deposits are deep, very well-drained gravel and sand producing high infiltration capacity and low runoff. These deposits were characterized as A-type group with infiltration capacity >0.76 cm/h. The clastic rocks were characterized as B-type group with infiltration capacity (0.38 to 0.76 cm/h) due to their fine to coarse texture and well-drained properties. Thus, low curve number (70.62 to 77.87) was assigned to Quaternary and recent deposits and the clastic rocks. The carbonate rocks were characterized as C-type group due to their infiltration characteristics. The Precambrian basement rocks were characterized as D-type group due to their texture and drained properties. The bedrock surface on the basement and the carbonate rocks is lacking soil cover. Hence, the infiltration rate is low in these areas, producing high runoff. A high curve number was assigned to both of the rock types (84.5 to 92.02). The different hydrologic soil groups were tabulated in each catchment to determine CN values. To estimate the rainfall excess, a single CN value was assigned at the outlet of all Wadi Dahab sub-basins with only one soil type, whereas area-weighted CN values were applied to sub-basins having different soil types. An area-weighted average method was applied to calculate CN values in the sub-basins having different soil types.

Due to the wide variation of geomorphological and topographical characteristics, a SDUH method was selected to estimate the hydrological parameters for the most hazardous catchments in Wadi Dahab (Maidment 1993). We could evaluate the physical characteristics of Wadi Dahab basin and parameterize a unit hydrograph in the absence of recorded rainfall and runoff measurements. Several other hydrological parameters, including runoff distance, runoff velocity, and flow time, were estimated in this study for that purpose (Cleveland et al. 2008). To estimate the flow time across the catchment, at least one parameter was necessary, such as average velocity. The average flow velocities (V) were derived based on Manning's flow equation (Manning 1891) (Equation (8)): 
formula
(8)
where Kst is the inverse of the Manning value and indicates the surface roughness, R is the hydraulic radius, and S is the land slope. The land use layer can be used for assigning expressive averaged roughness values represented by Kst. Two Kst values were assigned, depending on land use map for the sheet flow, which were 90 m1/3/s for the bare soil without vegetation and 10 m1/3/s for the cultivated soils, and the flow radius for both was assumed to be 0.03 m (Haestad Methods; Dyhouse et al. 2003). Kst of 33 m1/3/s was assigned to the study channels which are normal, clean, straight, full stage channels containing no deep pools, according to Chow (1959). R values were assigned according to the six stream orders present in the study area, as only a part of a pixel is really covered by the channel using Haestad Methods (Dyhouse et al. 2003) (Table 2). The final Kst R2/3 raster for combined sheet and gully flow (Figure 11) is then used with the land slope raster in the raster calculator tool in ArcMap 10.4 to create the velocity map (Figure 12). The travel time to the outlet was evaluated according to the runoff conduit and its travel time through each grid cell of DEM, applying flow length function in ArcMap 10.4 using the inverse velocity as a weight factor (Figure 13). The time–area diagram collected for Dahab watershed by GIS was used to derive SDUH (Maidment 1993). The spatial pattern of excess rainfall was varied by isochrone zones in the watershed, creating a uniform pattern over the whole watershed. The time–area analysis is extensively used to derive the discharge hydrograph due to the availability of rainfall hyetograph excess (Muzik 1996). The storage variable was ignored in this analysis; then the watershed was divided into sub-zones separated by isochrones (Figure 13). The isochrone has a maximum flow time to the outlet, which is considered the time of concentration (tc), and is sometimes known as the time of equilibrium (Maidment 1993). The unit hydrograph of Dahab watershed could be generated from the time–area diagram according to the following equations given by (Maidment 1993): 
formula
(9)
 
formula
(10)
 
formula
(11)
 
formula
(12)
where U(i) is the unit hydrograph ordinates in isochrone zone i, P is excess rainfall in meters, Δt is discrete time points, Q is the corresponding discharge rate at the watershed outlet (m3/s), and Pij is the average excess rainfall over all cells in isochrone zone i during time interval j.
Table 2

The stream orders used to assign the Kst R2/3 and R values

Stream order Kst R2/3(m2/3/s) R(m) 
11 0.19 
16 0.33 
20 0.48 
24 0.62 
28 0.76 
31 0.90 
Stream order Kst R2/3(m2/3/s) R(m) 
11 0.19 
16 0.33 
20 0.48 
24 0.62 
28 0.76 
31 0.90 
Figure 11

The final Kst R2/3 raster for combined sheet and gully flow in Wadi Dahab basin, southeastern Sinai, Egypt.

Figure 11

The final Kst R2/3 raster for combined sheet and gully flow in Wadi Dahab basin, southeastern Sinai, Egypt.

Figure 12

The velocity map shows different rates for runoff traveling through Wadi Dahab basin, Egypt.

Figure 12

The velocity map shows different rates for runoff traveling through Wadi Dahab basin, Egypt.

Figure 13

The flow time map shows different rates for runoff traveling through Wadi Dahab basin, southeastern Sinai, Egypt.

Figure 13

The flow time map shows different rates for runoff traveling through Wadi Dahab basin, southeastern Sinai, Egypt.

Multi-criteria evaluation

The multi-criteria evaluation represents a necessary analysis for modeling hazards due to flash floods. Three main steps were achieved to define the flash flood hazard index, including standardization of the criterion weights, estimation of each criterion rank, and summation of the criteria (Tables 3 and 4).

Table 3

The resulting weights are based on the principal Eigenvector of the decision matrix

 SPI MHI Slope Lithology TWI 
T 1 
V 1 
SPI 0.33 0.5 1 
Q 0.25 0.33 0.5 1 
MHI 0.20 0.25 0.33 0.5 1 
Slope 0.12 0.14 0.25 0.33 0.5 1 0.5 
Lithology 0.14 0.12 0.20 0.5 0.33 1 
TWI 0.11 0.11 0.17 0.25 0.25 0.33 0.5 1 
Sum 3.15 3.45 7.45 11.58 16.08 27.33 27 38 
Rank 0.309 0.271 0.155 0.092 0.071 0.041 0.037 0.023 
 SPI MHI Slope Lithology TWI 
T 1 
V 1 
SPI 0.33 0.5 1 
Q 0.25 0.33 0.5 1 
MHI 0.20 0.25 0.33 0.5 1 
Slope 0.12 0.14 0.25 0.33 0.5 1 0.5 
Lithology 0.14 0.12 0.20 0.5 0.33 1 
TWI 0.11 0.11 0.17 0.25 0.25 0.33 0.5 1 
Sum 3.15 3.45 7.45 11.58 16.08 27.33 27 38 
Rank 0.309 0.271 0.155 0.092 0.071 0.041 0.037 0.023 

Number of comparisons = 28, consistency index = 0.027, maximum Eigen value = 8.269, Eigenvector solution: 4 iterations, delta = 3.5 × 10−8.

Note:T is flow time, V is runoff velocity, SPI is stream power index, Q is runoff depth, MHI is morphometric hazard index, and TWI is topographic wetness index.

Table 4

The rating scheme reveals the ranks of effective factors and weights of their classes based on their relative impacts on flash flood hazards

Thematic layer Classes Flash flood potentiality Rank Weight 
Flow time (T in h) >4 Very high 0.309 
4–8 High 0.67 
8–12 Moderate 0.33 
12–16 Low 0.25 
16–20 Very low 
Runoff velocity (V in m/sec) 0.223–4.417 Very low 0.271 
4.417–8.612 Low 0.25 
8.612–12.808 Moderate 0.33 
12.808–17.003 High 0.67 
17.003–21.199 Very high 
Stream power index (SPI−13.81– − 6.05 Very low 0.155 
−6.05–1.72 Low 0.25 
1.72–9.48 Moderate 0.33 
9.48–17.25 High 0.67 
17.25–25.011 Very high 
Runoff depth (mm) 12.02–23.71 Very low 0.092 
23.71–35.40 Low 0.25 
35.40–47.09 Moderate 0.33 
47.09–58.78 High 0.67 
58.78–70.47 Very high 
Morphometric hazard index (MHI2.68–4.06 Very low 0.071 
4.06–5.45 Low 0.25 
5.45–6.83 Moderate 0.33 
6.83–8.21 High 0.67 
8.21–9.59 Very high 
Slope (degrees) 0–20 Very low 0.041 
20–30 Low 0.25 
30–40 Moderate 0.33 
40–50 High 0.67 
50–58.5 Very high 
Lithology Quaternary and recent wadi deposits Very low 0.037 
Phanerozoic succession (lower Cretaceous and upper Cretaceous rocks) Low 0.25 
Phanerozoic succession (Firani group and Cambrian rocks) Moderate 0.33 
Precambrian metamorphic rocks High 0.67 
Precambrian igneous rocks Very high 
Topographic wetness index (TWI−6.5–0.47 Very high 0.023 
0.47–7.30 High 0.67 
7.30–14.13 Moderate 0.33 
14.13–20.96 Low 0.25 
20.96–27.78 Very low 
Thematic layer Classes Flash flood potentiality Rank Weight 
Flow time (T in h) >4 Very high 0.309 
4–8 High 0.67 
8–12 Moderate 0.33 
12–16 Low 0.25 
16–20 Very low 
Runoff velocity (V in m/sec) 0.223–4.417 Very low 0.271 
4.417–8.612 Low 0.25 
8.612–12.808 Moderate 0.33 
12.808–17.003 High 0.67 
17.003–21.199 Very high 
Stream power index (SPI−13.81– − 6.05 Very low 0.155 
−6.05–1.72 Low 0.25 
1.72–9.48 Moderate 0.33 
9.48–17.25 High 0.67 
17.25–25.011 Very high 
Runoff depth (mm) 12.02–23.71 Very low 0.092 
23.71–35.40 Low 0.25 
35.40–47.09 Moderate 0.33 
47.09–58.78 High 0.67 
58.78–70.47 Very high 
Morphometric hazard index (MHI2.68–4.06 Very low 0.071 
4.06–5.45 Low 0.25 
5.45–6.83 Moderate 0.33 
6.83–8.21 High 0.67 
8.21–9.59 Very high 
Slope (degrees) 0–20 Very low 0.041 
20–30 Low 0.25 
30–40 Moderate 0.33 
40–50 High 0.67 
50–58.5 Very high 
Lithology Quaternary and recent wadi deposits Very low 0.037 
Phanerozoic succession (lower Cretaceous and upper Cretaceous rocks) Low 0.25 
Phanerozoic succession (Firani group and Cambrian rocks) Moderate 0.33 
Precambrian metamorphic rocks High 0.67 
Precambrian igneous rocks Very high 
Topographic wetness index (TWI−6.5–0.47 Very high 0.023 
0.47–7.30 High 0.67 
7.30–14.13 Moderate 0.33 
14.13–20.96 Low 0.25 
20.96–27.78 Very low 

Standardization of the criterion weights

All factors increasing the severity impacts of flash flood hazard were derived in this study on different scales. Consequently, a linear scaling analysis is necessary to standardize the weight of each criterion, because it is the simplest procedure for standardization (Equations (3) and (4)). All classes of the effective criteria were given scores from 0 to 1, in which 0 represents less severity for flash flooding and 1 represents the highest severity for flash flooding (Table 4).

Estimation of each criterion rank

Ranks of all factors were assigned based on their contribution to increasing flash flood hazard. The factors and their ranks were adopted according to the knowledge of experts in the field of modeling flash flood hazard (Maidment 1993; Gheith & Sultan 2002) and field observations for the reasons extending the severity of flash floods. The AHP was used to support the estimation of each criterion rank, because it is an appropriate procedure to define the relative importance of each factor and then compute the relative rank score (Saaty 1990). The AHP analysis depends on the pairwise comparison matrix (PCM). The PCM was attained with the following aspects: aii = 1 and aij = 1/aji. The relative importance of each factor was established using pairwise comparisons of all related criteria (Table 3). The importance of each paired comparison was estimated based on their influence on the higher hierarchy. The ranks of all factors were calculated and their classes were normalized (Table 4). The AHP analysis for the predictive FHM allows minor inconsistency in judgment and provides a powerful theoretical framework to evaluate multi-criteria, because the consistency index is achieved from the principal Eigen value and the ratio scales are achieved from the principal Eigenvectors (Saaty 1990). The consistency ratio (CR) was computed using Equations (13) and (14) to estimate by how much each factor is more important than the other. The acceptable and reliable score for CR is normally less than 0.1 (10%) (Saaty 1990): 
formula
(13)
 
formula
(14)
where RI represents random consistency index based on the number of criteria (Saaty 1990), CI represents consistency index, n symbolizes the number of criteria, and λmax represents the principal Eigen value.

Summation of the criteria

Index overlay analysis was suggested as the simplest GIS method to integrate spatially all criteria, in which each class within each criterion was assigned a weight based on its relative contribution to the flash flood hazards. A knowledge-driven approach was adopted to derive the ranks (R) of all criteria and the weights (W) for the criteria classes (Tables 3 and 4). To find the flash flood hazard zones, the flash flood hazard index (FHI) was achieved using Equation (15), where Rn denotes the rank for factor n and Wni denotes the weight of class i of factor n (Figure 14(a)). 
formula
(15)
Figure 14

The flash flood hazard maps show different rates of hazard zones and different damage locations in Wadi Dahab basin, Egypt: (a) the predicted flash flood-prone zones and (b) the flash flood markers and the effects of catastrophic runoff on infrastructure in Wadi Dahab basin, southeastern Sinai, Egypt.

Figure 14

The flash flood hazard maps show different rates of hazard zones and different damage locations in Wadi Dahab basin, Egypt: (a) the predicted flash flood-prone zones and (b) the flash flood markers and the effects of catastrophic runoff on infrastructure in Wadi Dahab basin, southeastern Sinai, Egypt.

RESULTS

The geospatial hazard modeling of flash flood in Wadi Dahab basin was achieved by integrating the previously interpreted criteria through ranking and weighting processes. Based on the calculated weights and ranks, the influence levels of contributing factors and their alternatives were created. The hydrological factors, such as flow time and runoff velocity, and the SPI represent the most influential factors on the flash flood hazard ranking: 0.309, 0.271, and 0.155, respectively (Table 4). The different classes of all causative factors were assigned scores from 0 to 1 according to their relative contributions to occurring hazards. Hence, the shortest flow time, the highest runoff velocity, and the highest SPI were given scores of 1, representing the maximum severity for flash flooding (Table 4); while the longest flow time, the lowest runoff velocity, and the lowest SPI were given scores of 0, representing the least severity for flash flooding (Table 4). The runoff depth and morphometric index of Wadi Dahab catchments represent the following influence factors that cause flash flood hazard and have ranks of 0.092 and 0.071, respectively (Table 4). The highest runoff depth and the highest morphometric index were assigned scores of 1, representing the maximum contributing classes to occurring hazards; while the lowest runoff depth and the lowest morphometric index were assigned scores of 0, representing the least contributing classes to the occurring hazards. The slope, lithology, and TWI represent the least influential factors on the flash flood hazard, assigned 0.041, 0.037, and 0.023, respectively (Table 4). Steep slope, the Precambrian basement rocks, and the lowest TWI were assigned scores of 1, representing the maximum severity for flash flooding; while gentle slope, Quaternary and recent deposits, and the highest TWI were assigned scores of 0, representing the least severity for flash flooding.

The flash flood hazard map classifies Wadi Dahab basin relatively into different hazard zones. The resulting hazard map consists of five main classes varying from very low to very high (Figure 14(a)). The interpreted hazard map refers to the most vulnerable zones to flash flooding. The most vulnerable zones are mainly distributed in the eastern side of the mapped area, where steep topography to downhill lands and Precambrian basement rocks exist, which cause high runoff velocity and short time for water accumulation. The very high and high hazard zones are located along several wadis and tributaries in the study area, such as El-Ghaib, Zaghraa, Nasb, Khasheib, Rimthy, and Saal (Figure 14(a)). Most of these wadis are characterized by rugged topography and widely spread fault actions controlling the drainage distribution.

The flash flood hazard map was tested with the flash flood markers that were recorded from field survey to validate the final hazard map (Figure 14(a) and 14(b)). Based on the correlation analysis, all flash flood markers are associated with very high and high hazard zones. The flash flood hazard map indicates that 1.59% of Wadi Dahab's total area is very high hazard zone (Figure 14(a) and 14(b)). The very high hazard zones occupy an area of 33.07 km2, whereas the very low hazard zones occupy an area about 550.16 km2 (26.45% of the total mapped area). The moderate flash flood hazard zones are distributed heterogeneously, covering 24.75% of the mapped area. The high hazard zones occupy 261.87 km2, representing 12.59% of the Wadi Dahab area, while the low hazard zones occupy 720,096 km2, representing 34.62% of the Wadi Dahab area (Figure 14(a) and 14(b)).

DISCUSSION

Geospatial mapping of flash flood hazards in Wadi Dahab was derived from multi-analyses including the SDUH method, NRCS rainfall-runoff model, morphometric analysis, topographic and geologic estimations, and field studies. The predicted hazard zones could be achieved from the findings of these analyses, which include multi-factors controlling hazard severity (Figure 14(a)). The factors were spatially integrated to provide comprehensive evaluation for flash flood hazards in Wadi Dahab basin. These factors include slope, stream power index, topographic wetness index, lithology, hydrological morphometrics, runoff depth, runoff velocity, and flow time. The influence of factors on occurring flash flood hazards varies spatially from one zone to another in Wadi Dahab. The ranks and weights describe more precisely and quantitatively the variation in the influence of factors on increasing the severity of flash floods (Table 4). The integration of hydrological data adds the holistic nature of the approach to reveal the flash flood-prone zones in mountainous regions (Figure 14(a) and 14(b)).

Spatially distributed unit hydrograph

The SDUH method was applied successfully to estimate runoff velocity and flow time (Figures 12 and 13). As mentioned previously, the maximum storm hyetograph developed by WRRI (2006) of the Saint Katherine area was used to evaluate the scenario of the maximum storm in Wadi Dahab (Figure 15). The excess rainfall indicates direct runoff and represents the rain amount which falls at intensities exceeding the infiltration capacity. In this case, an infiltration capacity of 1.66 mm/h was calculated by the NRCS method. The flow velocity was calculated for the channel and overland flow. The complexity of the geometrical and hydraulic characteristics is too great for the Wadi Dahab channels. Furthermore, the overland flow velocity has more influence than channel flow velocity in Wadi Dahab basin. The velocity grid generally indicates the time needed for water passing through each cell of the grid. The flow velocity in Wadi Dahab basin ranges from 0.223 to 21.199 m/s during the maximum storm (Figure 12). The high velocities occur in Wadi Saal, Wadi Nasb, Wadi Rimthy, Wadi Khasheib, and Wadi El Ghaieb, ranging between 12.808 and 21.199 m/s (Figure 12). The high velocities in these wadis are affected by steep slope, high topographic elevations, and Precambrian basement cover (Figure S1; Figures 3 and 4). The runoff velocity decreases at the northern side of Wadi Dahab because of gentle slope, low elevations, and sedimentary rock cover (Figure S1; Figures 3 and 4).

Figure 15

Wadi Dahab hydrograph for the maximum storm.

Figure 15

Wadi Dahab hydrograph for the maximum storm.

In addition, the time of concentration was determined by the SDUH method, in which the time defines the maximum flow time to the catchments' outlet. Generally, time of concentration is the time required by the entire drainage area to cause the runoff. It can be estimated as time from the start of excess rainfall to the inflection point on the recession limb (Figure 15). Hence, the shorter time of concentration represents the higher peak discharge and greater flash flood hazard in the same rainfall condition. The shortest flow time was calculated in Wadi Zaghraa, Wadi Khasheib, and Wadi El Ghaib, ranging from 2 to 4 hours during the maximum storm (Figure 13). The longest flow time was calculated in Wadi Saal, Wadi El-Genah, and western parts of Wadi Nasb and Wadi Rimthy, ranging from 12 to 20 hours during the maximum storm (Figure 13). Many factors essentially control flow time through Wadi Dahab catchments, such as the basin length, runoff velocity, and slope gradients. The lengths of Wadi Khasheib sub-basin and Wadi Rimthy sub-basin are short, whereas the lengths of Wadi Nasb sub-basin and Wadi El Ghaieb sub-basin are long. In addition, the runoff velocity is high in Wadi Rimthy and in Wadi Khasheib. The values of flow time are relatively decreasing with increasing amounts of storm and rainfall excess in all Wadi Dahab sub-basins. In short, there is an inverse relation between rainfall excess and time of concentration, which is the longest travel time for runoff (Saghafian et al. 2008). Therefore, the flow time and runoff velocity deserved the highest ranks in FHM, and their classes were assigned weights corresponding to the influence of the respective classes on occurring flash flood hazards (Table 4).

From the obtained Dahab hydrograph for the maximum storm (Figure 15), the peak discharge (Qp) was 743.5 m3/s and was achieved after 42 h (time to peak Tp). The time of concentration (Tc), which is the maximum time of flow to the basin outlet, was about 52 h. The time between maximum rainfall and peak discharge (lag time Tl) was 33 h. The runoff volume coming through the outlet was calculated to be 63.4 million m3. The base flow hydrograph was separated from the direct runoff hydrograph by joining the first sign of the hydrograph rise and the point of inflection on the falling limb (McCuen 1989). The shape of the maximum storm hydrograph indicated that Dahab basin has a very high hazard from flash floods, with high peak discharge and runoff volume, steep curve flanks and short time to peak caused by the steep slope, the sparse vegetation, impermeable rock types and high drainage density of the basin. Another indication of its hazardousness is the shorter lag time compared with time to peak and the nearly low constant base flow.

NRCS rainfall-runoff model

The analysis of runoff characteristics in Wadi Dahab drainage network was applied using NRCS empirical equations. The runoff depth was calculated in each Wadi Dahab sub-basin to assess streams' likelihood of flash floods (Table 4). Precipitation and infiltration play basic roles in runoff production. Commonly, a smaller infiltration amount causes a higher runoff behavior and a greater flash flood hazard. In Wadi Dahab, most lithological units are Precambrian rocks, which are characterized by low infiltration rates (Figure 4). In addition, urban area and highway pavement are characterized by smaller infiltration and thus produce greater runoff. The highest values of runoff depths are distributed at the northwestern and central parts of Wadi Dahab, especially close to Saint Katherine, ranging from 47.09 to 70.47 mm (Figure 10); while the low values of runoff depth are distributed at the downstream of Wadi Dahab, ranging from 12.02 to 35.4 mm (Figure 10). However, the downstream of Wadi Dahab represents the most hazardous zone because of physiographic characteristics such as basin geometry, slope, and topographic elevation. These characteristics support the travel and accumulation of surface runoff from Saint Katherine to Dahab city (Figure S1 and Figure 3). The temporal and spatial distributions of runoff depths control the flooding characteristics and thus produce hazards in some zones in Wadi Dahab. Hence, the runoff depth and its classes deserved the given rank and weights in FHM corresponding to its actual influence on occurring flash flood hazards in the study area (Table 4).

Morphometric analysis

The morphometric analysis of Wadi Dahab catchments and their stream network resulted in the MHI (Figure 9(l)). The morphometric parameters were estimated in all 43 catchments, and their stream orders (Figure 9(a)9(k)). The parameters of catchment shape including elongation ratio, circulation ratio, and shape factor mainly affect the stream efficiency and thus the resulting flash flood hazard (Figure 9(l)). Surface runoff travels the same distances in a circular catchment and probably arrives at the catchment outlet at the same time, resulting in a high flood peak. However, a surface runoff spreads out over time in an elliptical catchment because of its outlet at one end of the major axis, resulting in low flood peak (Schumn 1956). The values of elongation ratios increase in proportion to decreasing elongation shape of the catchments, and thus high values indicate high flash flood hazard. The high values of elongation ratio range from 0.459 to 0.643 distributed in several zones at Wadi Khasheib and Wadi Rimthy (Figure 9(a)). These catchments are characterized by steep slope and high relief, resulting in a high tendency for flash flood hazards. The high values of circulation ratio range from 0.24 to 0.64 and are distributed also in several zones at Wadi Khasheib and Wadi Rimthy (Figure 9(b)). Surface runoff flows through the same distances in these catchments and accordingly reaches the outlet at the same time, causing a high flash flood hazard. The low values of shape factor range from 0.0032 to 0.011, distributed likely at Wadi Khasheib and Wadi Rimthy (Figure 9(c)). The shape factor is directly proportional to the catchment length, and thus, the low values of shape factor indicate the rapid travel of runoff through the catchment, resulting in a high tendency for runoff hazards.

The parameters of catchment surface comprise relief ratio, relative relief ratio, ruggedness, and hypsometric index control hydrological responses in sub-basins (Schumn 1956). Generally, higher relief and steeper slope support the fast runoff through catchments, producing shorter flow time, higher flow velocity, less time for infiltration and thus higher possibility for flash flooding. The high values of relief ratio range from 5.38 to 9.29, distributed in several zones at Wadi Khasheib and Wadi Rimthy (Figure 9(d)). The high values of relative relief ratio range from 0.024 to 0.045, located in several zones upstream of Wadi Khasheib and Wadi Rimthy (Figure 9(e)). The high values of ruggedness ratio range from 0.71 to 1.32, distributed in several zones at Wadi Zaghraa, Wadi Khasheib, Wadi Nasb, and Wadi Rimthy (Figure 9(f)). The low values of hypsometric index range from 0.24 to 0.41, existing mostly at Wadi Zaghraa, Wadi Khasheib, Wadi Nasb, and Wadi Rimthy (Figure 9(g)).

The parameters of drainage network, comprising drainage density, stream frequency, texture ratio, and bifurcation ratio, indicate runoff behavior in each catchment. The values of these parameters reflect the topographic and geologic characteristics of any terrain, including lithology, infiltration capacity, slope, and relief. For example, the high values of drainage density, stream frequency, and texture ratio generate a high flash flood hazard. Generally, the physical characteristics of underlying terrain, such as impermeable sub-surface rocks and sparse vegetation, control drainage density, stream frequency, and texture ratio. A high drainage density indicates probably a low infiltration rate and high runoff potential. The high drainage density range is mostly from 0.9 to 1.34 and exists at Wadi Khasheib and Wadi Rimthy (Figure 9(h)). The high stream frequency range from 0.52 to 0.78 occurs at Wadi Khasheib and upstream of Wadi Rimthy (Figure 9(i)). The high texture ratio range from 0.48 to 0.69 is distributed at Wadi Khasheib and Wadi Rimthy (Figure 9(j)). Additionally, the low bifurcation ratio results in high flash flood hazard because the precipitation probably accumulates in one channel rather than dispersing freely. The low bifurcation ratio range from 2 to 3.7 is located at Wadi Zaghraa, Wadi Khasheib, and Wadi Rimthy (Figure 9(k)).

The MHI for Wadi Dahab catchments was estimated based on the total normalized values of the morphometric parameters in each catchment (Figure 9(l)). The summation of the normalized values adds a suitable layer to evaluate relatively the high or low flood-prone catchments in Wadi Dahab. The morphometric analysis suggests that the upstream of Wadi Khasheib and Wadi Rimthy are the most hazardous wadis, especially at the catchments numbered 3, 4, 10, 12, 13, 14, 17, 29, and 38 (Figure 9(l)). These catchments are associated with high relief topography, steep slope hills, Precambrian basement rocks, and draining of higher order wadis. These catchments' characteristics indicate the high potentiality for flash flooding and low change for groundwater recharge. Therefore, the maximum weight was assigned to the highest value of MHI, while the minimum weight was assigned to the lowest value of MHI (Table 4).

Topographic and geologic investigations

The lithological units represent an essential indicator to evaluate flash flood-prone zones. Most of the Precambrian basement rocks in Wadi Dahab have been exposed to an extended period of weathering, producing very vulnerable rocks for fracturing and sliding. In addition, the structural features in Wadi Dahab cause different degrees of stress, producing some weak zones in the weathered rocks. The weathered disintegrated rocks in the vicinity of faults/structural lineaments cause excessive water flow along the drainage channels, mainly on the steep slopes, and during the rains. Hence, any water moving along fault planes promotes erosional processes. Generally, the Precambrian basement rocks are distinguished by low infiltration capacity, steep slope, and high relief, resulting in a high possibility of runoff hazards. Therefore, the Precambrian basement rocks, especially Dokhan volcanics and old granitoids, have the highest weight in FHM, while the sedimentary succession has the lowest weight in the model. Consequently, the zones close to Precambrian rocks are the most vulnerable to flash flooding.

For slope angles, the highest weight was assigned to the slopes ranging from 40° to 50°, while the lowest weight was assigned to very gentle slopes ranging from 0° to 20° (Table 4). Generally, the flash flood hazards increase with the increase of slope gradient up to a particular extent, and then decrease (Abuzied et al. 2016c). In the case of SPI, the maximum weight was assigned to the highest value of SPI (11.6–25.01) indicating a high probability of flash flooding; while the minimum weight was assigned to the lowest value for SPI (−13.8–6.7) indicating a low probability of runoff hazards (Table 4). The erosive power of water flow depends mainly on the assumption that discharge increases with the increase of catchment area and slope gradient (Moore et al. 1991). Therefore, the high values of SPI cause a high probability of runoff hazards. The erosional and torrential activities are usually associated with drainage, and thus, SPI deserves its rank in FHM. In addition, the maximum weight of TWI was assigned to the lowest value class (−6.3–0.10). However, the minimum weight of TWI was assigned to the highest value class (22.69–27.78). TWI refers to the topography impact on the location of saturated areas by runoff (Moore et al. 1991). Commonly, the sediment transportation increases with the increase of catchment area and slope gradient. Hence, the higher slope gradient relatively gives lower TWI and higher runoff probabilities.

Flashflood hazard model

The FHM was holistically developed using spatial integration of several analyses including SDUH, NRCS rainfall-runoff model, morphometric analysis, topographic and geologic estimations, and field studies. Flash flood markers with GPS locations were recorded in the field study for mapping flash flood hazard zones. These hazard markers include highway undercutting, damage to traffic signs and electrical lines, and movement of large boulders and debris onto asphaltic roads (Figure 14(b)). The flash flood signs and damage locations were added to test and validate the flash flood hazard map (Figure 14(a) and 14(b)). The recorded damage locations match high and very high hazard zones on the flash flood hazard map. The flash flood hazard map refers to several hazard zones at Wadi El-Ghaib, Wadi Zaghraa, Wadi Nasb, Wadi Khasheib, Wadi Rimthy, and Wadi Saal (Figure 14(a) and 14(b)). These wadis have rugged topography, steep slope terrain, and Precambrian basement rocks which create high runoff velocity and short flow time for water accumulation. In short, the hard rocks in Wadi Dahab cover the steep hills along the main wadis, accelerating runoff accumulation into the wadis and creating several vulnerable zones for flash flood hazards (Figure 14(a) and 14(b)).

According to the final hazard map, some trails were made to decrease the destructive power of Dahab flash floods on the main infrastructure connecting Dahab city with Saint Katherine city. Eight culvert crossings made of circular pipe sections reinforced with concrete were suggested to be put under this main road to allow the flow of flash flood waters, preserving the road. The culvert locations were proposed based on the intersection between the highway and the flash flood hazard (Figure 16). Another trial to create a benefit from the flash flood water was by proposing new groundwater well locations. The locations were chosen based on the intersection of the flash flood hazard with the areas of high TWI and fault buffer zones of 300 m (Figure 16). As mentioned before, most of the study area is covered by fractured and faulted Precambrian rocks, which are considered to be good conduits and storage for groundwater in the area. The high TWI also gives indications of high groundwater potentiality in terms of high flow accumulation and gentle slope which allows water to percolate underground. The TWI map was classified into ten classes, with the highest class including all wells and dams present in the area (Figure 16), as they represent locations of high groundwater availability. The locations of the 26 new proposed wells were suggested in Wadi Nasb and Wadi Rimthy, as these high hazard zones allow the water to stay much longer on the gentle land surface, increasing the groundwater recharge. The areas with low flash flood hazards, especially the northern areas of Wadi Dahab with flat land surface, are considered suitable for new land use plans. However, flash flood hazards should be considered during planning, as these areas have high fault densities.

Figure 16

Management trials recommended in this study to decrease the destructive power of Dahab flash floods on the main infrastructures.

Figure 16

Management trials recommended in this study to decrease the destructive power of Dahab flash floods on the main infrastructures.

CONCLUSIONS

The development of a predictive model for flash floods in Wadi Dahab is the main outcome of this study. The FHM was built based on eight causative factors indicating flash flood hazards. The factors include slope, SPI, TWI, lithology, hydrological morphometrics, runoff depth, runoff velocity, and flow time. The ranks and the weights for the contributing factors and their classes were assigned based on AHP. Therefore, the holistic approach based on topographical, geological, geomorphological, and hydrological factors was used to delineate hazard zones. The selected factors were spatially integrated to calculate the FHI for each 29 × 29 m cell.

A flash flood hazard map was created in which 1.59% of Wadi Dahab total area represents very high hazard zones for flash flooding. The high hazard zones occupy 12.59% of the Wadi Dahab area. All the highly hazardous zones are located in structurally controlled channels, rugged topography, and Precambrian rocks. The very high and high hazard zones are distributed along several wadis in the study area, such as El-Ghaib, Zaghraa, Nasb, Khasheib, Rimthy, and Saal. The moderate, low, and very low hazard zones occupy 24.75%, 34.62%, and 26.45% of the total Wadi Dahab area, respectively. Several flash flood markers were described from field observations to test and validate the flash flood hazard map. Most damage locations were recorded in association with rugged topography, steep slope terrain, and Precambrian rocks.

In short, most of the highly hazardous zones are distributed in different locations around built-up communities. Hence, the development actions and management plans should consider these zones in order to reduce and compensate for greater hazard potential. The results of this study provide new locations which are susceptible to flash flooding. Briefly, this study recommends that decision-makers apply mitigation efforts in Wadi Dahab basin to avoid the socio-economic impacts of flash flood hazards.

ACKNOWLEDGEMENTS

The authors wish to express their appreciation to the Editor of Journal of Hydroinformatics and three anonymous reviewers, for constructive and fruitful criticism on an earlier draft of the manuscript.

REFERENCES

REFERENCES
Abrahams
A.
1984
Channel networks: a geomorphological perspective
.
Water Resour. Res.
20
,
161
168
.
Abuzied
S. M.
2016
Groundwater potential zone assessment in Wadi Watir area, Egypt using radar data and GIS
.
Arab. J. Geosci.
9
(
7
),
1
20
.
doi:10.1007/s12517-016-2519-2
.
Abuzied
S. M.
&
Alrefaee
H. A.
2018
Spatial prediction of landslide susceptible zones in El-Qaá area, Egypt, using an integrated approach based on GIS statistical analysis
.
Bull. Eng. Geol. Environ.
1
27
.
https://doi.org/10.1007/s10064-018-1302-x
.
Abuzied
S. M.
,
Ibrahim
S. K.
,
Kaiser
M. F.
&
Saleem
T. A.
2016a
Geospatial susceptibility mapping of earthquake-induced landslides in Nuweiba area, Gulf of Aqaba, Egypt
.
J. Mt. Sci.
13
(
7
),
1286
1303
.
doi:10.1007/s11629-015-3441-x
.
Abuzied
S. M.
,
Ibrahim
S. K.
,
Kaiser
M. F.
&
Seleem
T. A.
2016b
Application of remote sensing and spatial data integrations for mapping porphyry copper zones in Nuweiba area, Egypt
.
Int. J. Signal Process. Syst.
4
(
2
),
102
108
.
doi:10.12720/ijsps.4.2.102-108
.
Abuzied
S. M.
,
Yuan
M.
,
Ibrahim
S. K.
,
Kaiser
M. F.
&
Saleem
T. A.
2016c
Geospatial risk assessment of flash floods in Nuweiba area, Egypt
.
J. Arid Environ.
133
,
54
72
.
http://dx.doi.org/10.1016/j.jaridenv.2016.06.004
.
Abuzied
S. M.
,
Yuan
M.
,
Ibrahim
S. K.
,
Kaiser
M. F.
&
Seleem
T. A.
2016d
Delineation of groundwater potential zones in Nuweiba area (Egypt) using remote sensing and GIS techniques
.
Int. J. Signal Process. Syst.
4
(
2
),
109
117
.
doi:10.12720/ijsps.4.2.109-117
.
Bajabaa
S.
,
Masoud
M.
&
Al-Amri
N.
2014
Flash flood hazard mapping based on quantitative hydrology, geomorphology and GIS techniques (case study of Wadi Al Lith, Saudi Arabia)
.
Arab. J. Geosci.
7
(
6
),
2469
2481
.
https://doi.org/10.1007/s12517-013-0941-2
.
Chapi
K.
,
Singh
V. P.
,
Shirzadi
A.
,
Shahabi
H.
,
Bui
D. T.
,
Pham
B. T.
&
Khosravi
K.
2017
A novel hybrid artificial intelligence approach for flood susceptibility assessment
.
Environ. Modell. Softw.
95
,
229
245
.
Chow
V. T.
1959
Open-channel Hydraulics
.
McGraw-Hill
,
New York
, pp.
168
175
.
Clement
A. R.
2013
An application of Geographic Information System in mapping flood risk zones in a north central city in Nigeria
.
Afric. J. Environ. Sci. Tech.
7
(
6
),
365
371
.
http://dx.doi.org/10.5897/AJEST12.182
.
Cleveland
T. G.
,
Thompson
D. B.
,
Fang
X.
&
He
X.
2008
Synthesis of unit hydrographs from a digital elevation model
.
J. Irrig. Drain. Eng.
134
(
2
),
212
221
.
Conoco
C.
1987
Stratigraphic Lexicon and Explanatory Notes to the Geological Map of Egypt (scale 1: 500,000)
.
Conoco Inc. in collaboration with Egyptian General Authority for Petroleum (UNESCO Joint Map Project), 20 Sheets
,
Cairo
,
Egypt
.
Dawod
G. M.
,
Mirza
M. N.
&
Al-Ghamdi
K. A.
2011
GIS-based spatial mapping of flash flood hazard in Makkah City, Saudi Arabia
.
J. Geog. Info. Sys.
3
(
3
),
225
231
.
Dawod
G. M.
,
Mirza
M. N.
&
Al-Ghamdi
K. A.
2012
GIS-based estimation of flood hazard impacts on road network in Makkah city, Saudi Arabia
.
Environ. Earth Sci.
67
(
8
),
2205
2215
.
Dayan
U.
&
Abramski
R.
1983
Heavy rain in the Middle East related to unusual jet stream properties
.
Bull. Am. Meteor. Soc.
64
(
10
),
1138
1140
.
EGSMA
1994
Egyptian Geological Survey and Mining Authority. Geologic map of Sinai, Arab Republic of Egypt. Sheet No.1, Scale 1:250.000
.
El Maghraby
M.
,
Masoud
M.
&
Niyazi
B.
2014
Assessment of surface runoff in arid, data scarce regions; an approach applied in Wadi Al Hamd, Al Madinal al Munawarah, Saudi Arabia
.
Life Sci. J.
11
(
4
),
271
289
.
El Rayes
A.
1992
Hydrogeological Studies of Saint Katherine Area, South Sinai, Egypt
.
MSc Thesis
,
Suez Canal University
,
Ismailia
,
Egypt
.
Elkhrachy
I.
2015
Flash flood hazard mapping using satellite images and GIS tools: a case study of Najran City, Kingdom of Saudi Arabia (KSA)
.
Egypt J. Remote Sens. Space Sci.
18
(
2
),
261
278
.
https://doi.org/10.1016/j.ejrs.2015.06.007
.
Fang
X.
,
Thompson
D. B.
,
Cleveland
T. G.
,
Pradhan
P.
&
Malla
R.
2008
Time of concentration estimated using watershed parameters determined by automated and manual methods
.
J. Irrig. Drain. Eng.
134
(
2
),
202
211
.
Gardiner
V.
1990
Drainage basin morphometry
. In:
Geomorphological Techniques
(
Goudie
A. S.
, ed.).
Unwin Hyman
,
London
, pp.
71
81
.
Geriesh
M. H.
1998
Artificial recharge as an effective tool for augmenting the natural groundwater resources in Saint Katherine area, South Sinai, Egypt
. In:
Proceedings of the 5th Conference on Geology and Sinai Development
,
Suez Canal University
,
Ismailia
, pp.
47
64
.
Gilboa
Y.
1980
Post Eocene clastics distribution along the El-Qaà plain, southern Sinai
.
J. Earth Sci.
29
,
197
206
.
Gioti
E.
,
Riga
C.
,
Kalogeropoulos
K.
&
Chalkias
C.
2013
A GIS-based flash flood runoff model using high resolution DEM and meteorological data
.
EARSeL eProc.
12
(
1
),
33
43
.
Haestad Methods
;
Dyhouse
G.
,
Hatchett
J.
&
Benn
J.
2003
Floodplain Modeling Using HEC-RAS
,
1st edn
.
Haestad Press
,
Waterbury, CT
,
USA
.
Hedman
E.
1970
Mean Annual Runoff as Related to Channel Geometry of Selected Streams in California
.
US Government Printing Office
,
Washington, DC
.
Horton
R.
1932
Drainage-basin characteristics
.
Trans. Am. Geophys. Union.
13
,
350
361
.
Khosravi
K.
,
Pham
B. T.
,
Chapi
K.
,
Shirzadi
A.
,
Shahabi
H.
,
Revhaug
I.
,
Prakash
I.
&
Bui
D. T.
2018
A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran
.
Sci. Total Environ.
627
,
744
755
.
Krishnamurthy
J.
,
Srinivas
G.
,
Jayaraman
V.
&
Candrasekhar
M.
1996
Influence of rock types and structures in the development of drainage networks in typical hardrock terrain
.
ITC J.
3
,
252
259
.
Maidment
D. R.
1993
Handbook of Hydrology (1)
.
McGraw-Hill
,
New York
.
Maidment
D. R.
&
Morehouse
S.
2002
Arc Hydro: GIS for Water Resources, (1)
.
ESRI, Inc.
,
Redlands, CA, USA
.
Manning
R.
1891
On the flow of water in open channels and pipes
.
Trans. Inst. Civil Eng. Ireland.
20
,
161
207
.
McCuen
R. H.
1989
Hydrologic Analysis and Design
.
Prentice-Hall
,
Englewood Cliffs, NJ
.
Melton
M. A.
1957
An analysis of the relations among elements of climate, surface properties, and geomorphology (No. CU-TR-11)
.
Columbia University
,
New York
.
Miller
V.
1953
A quantitative geomorphic study of drainage basin characteristics in the Clinch Mountain area Virginia and Tennessee: Columbia University Geology Department, N.Y. Report no. 3, Project NR 389-042, Office of Naval Research, Geography Branch, 30 p
.
Moore
I. D.
&
Burch
G. J.
1986
Sediment transport capacity of sheet and rill flow: application of unit stream power theory
.
Water. Resour. Res.
22
(
8
),
1350
1360
.
doi:10.1029/WR022i008p01350
.
Muzik
I.
1996
GIS-derived distributed unit hydrograph
. In:
Application of Geographic Information Systems in Hydrology and Water Resources
(
Kovar
K.
&
Nachtnebel
H. P.
, eds).
IAHS Publ. no. 235
, pp.
453
460
.
Omran
A.
2013
Application of GIS and Remote Sensing for water resource management in arid area - Wadi Dahab basin, South eastern Sinai, Egypt (Case study). PhD Dissertation, der Mathematisch-Naturwissenschaftlichen Fakultät, der Eberhard Karls Universität Tübingen
.
Patton
P.
1988
Drainage basin morphometry and floods
. In:
Flood Geomorphology
(
Baker
V.
,
Kochel
R.
&
Patton
P.
, eds).
John Wiley & Sons
,
New York
, pp.
51
64
.
Regmi
A. D.
,
Devkota
K. C.
,
Yoshida
K.
,
Pradhan
B.
,
Pourghasemi
H. R.
,
Kumamoto
T.
&
Akgun
A.
2014
Application of frequency ratio, statistical index, and weights of evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya
.
Arab. J. Geosci.
7
(
2
),
725
742
.
doi:10.1007/s12517-012-0807-z
.
Safaripour
M.
,
Monavari
M.
,
Zare
M.
,
Abedi
Z.
&
Gharagozlou
A.
2012
Flood risk assessment using GIS (case study: Golestan province, Iran)
.
Pol. J. Environ. Stud.
21
,
1817
1824
.
Saghafian
B.
,
Farazjoo
H.
,
Bozorgy
B.
&
Yazdandoost
F.
2008
Flood intensification due to changes in land use
.
Water Resour. Manage.
22
(
8
),
1051
1067
.
Said
R.
1962
The Geology of Egypt
.
Elsevier
,
Amsterdam
,
377
p.
Selmi
E.
&
Abdel-Raouf
O.
2013
The use of magnetic and geo-electrical data to delineate the subsurface structures and groundwater potentiality in Southeastern Sinai, Egypt
.
Environ. Earth Sci.
70
(
4
),
1479
1494
.
https://doi.org/10.1007/s12665-013-2234-1
.
Shendi
E. H.
,
Geriesh
M. H.
&
Mousa
M. M.
1997
Geophysical and hydrogeological studies on Wadi Saal Basin, Southern Sinai, Egypt
.
Egypt. J. Geol.
41
(
2B
),
871
908
.
Soil Conservation Service (SCS)
1972
Estimation of Direct Runoff From Storm Rainfall
.
National Engineering Handbook NEH Notice V Mockus
, pp.
4
102
.
Strahler
A. N.
1952
Hypsometric (area-altitude) analysis of erosional topography
.
Geol. Soc. Am. Bull.
63
(
11
),
1117
1142
.
Thorne
C. R.
2002
Geomorphic analysis of large alluvial rivers
.
Geomorph.
44
(
3–4
),
203
219
.
https://doi.org/10.1016/S0169-555X(01)00175-1
.
Wagener
T.
&
Wheater
H.
2004
Rainfall-runoff Modelling in Gauged and Ungauged Catchments
.
Imperial College Press
,
London
.
Water Resources Researches Institute (WRRI)
.
2006
Architectural and Engineering Services for the Flood Protection of the City of Dahab in South Sinai, Arab Republic of Egypt
.
Report, EuropeAid/122288/D/SV/EG
.
Yair
A.
,
Lavee
H.
1985
Runoff generation in arid and semi-arid zones
. In:
Hydrological Forecasting
(
Anderson
M. G.
&
Burt
T. P.
, eds).
John Wiley and Sons
.
Youssef
A. M.
,
Pradhan
B.
&
Hassan
A. M.
2011
Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery
.
Environ. Earth Sci.
62
(
3
),
611
623
.
https://doi.org/10.1007/s12665-010-0551-1
.

Supplementary data