Flood Hazard Assessment in Wadi Dahab, Egypt Based on Basin Morphometry Using GIS Techniques

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1 1 Flood Hazard Assessment in Wadi Dahab, Egypt Based on Basin Morphometry Using GIS Techniques Adel OMRAN, Dietrich SCHRÖDER, Ahmed EL RAYES and Mohammed GERIESH The GI_Forum Program Committee accepted this paper as reviewed full paper. Abstract The presented work is aimed at using GIS techniques to produce a potential flood hazard map based on geomorphic parameters and to estimate the risk degree of individual subbasin by combining normalized values of the parameters. The study area Dahab basin is one of the most important basins in South Sinai, which drains into the Gulf of Aqaba, Egypt. It covers an area of about 2080 km 2. Although it is located in an arid region, the basin could receive a huge amount of rainwater during one of the rare storm events. The maximum recorded value reached up to 150 million m 3 /storm. Approximately 50% of the expected rainfall could turn into flash floods to the outlet of the basin, causing catastrophic effects on existing infrastructure and surrounding environments. Based on the Aster Digital Elevation Model, the drainage pattern of the Wadi s sub-basins were delineated and compared, with topographic map sheets of 1:50,000 scale as a reference. The sub-basins were extracted and morphometrically analysed to assess flash flood susceptibility. The morphometric parameters were measured by stream number and stream length. The parameters were computed using ESRI s ArcGIS 9.3, enriched by some VB code to compute stream numbers according to Strahler Theory. The case study results show that more than 35% of the sub-basins have high susceptibility of flooding and about 60% have medium susceptibility. Basins of high and moderate flood risk require detailed studies to implement actions to protect these areas against flood hazard. 1 Introduction Flash floods are among the catastrophic natural hazards in the world causing the largest amount of deaths and property damage (CEOS 2003). Floods can have an impact on many aspects of human life due to their destructive effect, and can create significant expenses through mitigation efforts. There have been many studies on flood hazard and risk mapping using remote sensing data and GIS tools. Radar remote sensing data have been extensively used for flood monitoring across the globe (HESS et. al. 1995, LE TOAN et al. 1997), and many of these studies have applied probabilistic methods (HORRITT & BATES 2002, PRADHAN & SHAFIE 2009, PRADHAN 2010, BHUYIAN et al. 2009). Hydrological and stochastic rainfall methods for flood susceptibility mapping have been employed in other areas (HAENG et. al. 2001, Car, A., Griesebner, G. & Strobl, J. (Eds.) (2011): Geospatial GI_Forum '11. Herbert Wichmann Verlag, VDE VERLAG GMBH, Berlin/Offenbach. ISBN This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (

2 2 O. Adel, D. Schröder, A. El Rayes and M. Geriesh CUNDERLIK & BURN 2002). Flood susceptibility mapping using GIS and neural network methods have been applied in various case studies (SANYAL & LU 2005, ZERGER 2002). Drainage characteristics of hydrographic basins and sub-basins in many areas of the world have been studied using conventional geomorphologic approaches (HORTON 1945, STRAH- LER 1964, RUDRIAIAH, M. et. al. 2008; NAGESWARARAO, K. et. al and AL SAUD 2009). GARDINER (1990) indicated that in some studies, the morphometric characteristics of basins have been used to predict and describe flood peaks and estimation of erosion rate, underling the importance of such studies. The application of geomorphic principles to flood potential or flood risk has led to a noteworthy amount of researches, attempting to identify the relationships between basin morphometry and flooding impact (PATTON 1988). Identification of drainage networks within basins or sub-basins can be achieved using traditional methods such as field observations and topographic maps, or alternatively with advanced methods using remote sensing and Digital Elevation Models (MACKA 2001, MAIDMENT 2002). Many authors have pointed out that it is difficult to examine all drainage networks from field observations due to their extent throughout rough terrain over vast areas. Morphometric studies include the evaluation of streams through measurement of stream network properties, which are calculated based on such characteristics as drainage density, bifurcation ratio, stream frequency and overland flow (RUDRIAIAH. et al. 2008), as well as sub-basin characteristics, in particular parameters of form. It is difficult to measure the details of drainage elements in the field, which is needed for such studies. In many surveys, the analysis of drainage networks was manually conducted via tracing the stream network from the topographic maps, which is both time consuming and cumbersome. For this task it is better to use a DEM within a GIS environment, and then compare the results with topographic maps. GIS techniques have proven to be effective tools in the extraction of stream networks and in the assessment of flood risk and hazard management. It is well know that the extraction of stream networks based on GIS can be done easily, while it is still difficult to count stream segments and measure their lengths of different order. Moreover, the workflow to extract the information needed in particular for larger areas is still time consuming, as many manual procedures are involved. In the current study, the morphometric analyses have been used to estimate the flash flood risk levels of sub-watersheds within the watershed. Both the drainage network and drainage watershed were analysed. Each characteristic was captured by a set of network and basin parameters that were relevant to the flash flood risk. This paper describes how GIS techniques were used to both extract stream network parameters, with emphasis on ordering and measuring the length of each stream segment using an automated workflow, and also to produce a flood hazard risk map based on the results of the morphometric analysis. 2 The Study Area The Wadi Dahab region is home to many tourism destinations; with tourism as the major source of Egypt s national income, this location is critical for maintaining a healthy econ-

3 Flood Hazard Assessment in Wadi Dahab, Egypt 3 omy. The Dahab basin lies at the south eastern part of Sinai Peninsula and is bounded by latitude and N and longitude and E. It occupies an area of about 2080 km 2 (Fig. 1). Fig. 1: (A) Location map of Wadi Dahab, (B) Basin Catchment area The study area has generally an arid climate with quite significant rainfall intensity. Severe floods from Wadi Dahab could cause significant damage and loss of lives at the City of Dahab and along the main streams. Due to the meteorological conditions of South Sinai, flood events are not frequent, but severe. The affected areas include tourist locations, as well as residences of local inhabitants. Fig. 2: Lithological Map of Wadi Dahab Hydrographic Basin

4 4 O. Adel, D. Schröder, A. El Rayes and M. Geriesh The maximum intensity of rainfall which has been recorded for a storm event of 24 hours is 76 mm which means that the area has received about 159 million m 3 with a return period of 100 years. Based on the climate conditions and the geology of the area, it can be concluded that about 50% of the precipitation is lost via evapotranspiration and infiltration, so that only half of the rainfall contributes to runoff. However, this is still enough to cause potential catastrophic effects. Wadi Dahab catchments are covered mainly with Cretaceous to Pre-Cambrian rocks. Sedimentary rocks of Cambrian to Cretaceous ages are located in the north of the basin, while the rest of the basin is covered with Pre-Cambrian rocks (igneous and metamorphic rocks), in addition to recent Wadi deposits which cover the valley floors of the main streams (Fig. 2). 3 Methods Criteria and steps used to extract the morphometric parameters will be thoroughly illustrated, in the following way. The workflow was based on tools available in the ArcGIS 9.3 toolbox; in particular tools from the Spatial Analyst extension were used. The individual tools were combined using the ArcGIS ModelBuilder for batch processing. As the Model- Builder is still weak in implementing loops, the models have been partly exported to scripts and enriched by Python code. These work steps are indicated as follows. The first step was the extraction of the stream network from the Digital Elevation Model (DEM). Here, the well-known workflow of hydrological analysis was followed as shown in (Fig. 3). The final result of this step was the stream network map of the Dahab basin. Using the ordering system of Strahler, the Dahab basin is of the eighth order. The obtained results were confirmed and calibrated by comparing the extracted parameters with a georeferenced topographic map (1:50,000). Fig. 3: ModelBuilder diagram showing the workflow to extract the drainage pattern from the Digital Elevation Model

5 Flood Hazard Assessment in Wadi Dahab, Egypt 5 The second step was the delineation of basin boundaries by identifying the ridge lines (water divide) between sub-basins. By calibrating the results of the first step with the topographic map, pour points were identified and digitized. The snap pour point tool was used to ensure the selection of points of high accumulated flow during delineation drainage basins using the watershed tool. As in the first step, it is important not to use the simplification option while converting the raster map to vector features. Otherwise, the delineated stream network and basins will not match exactly, which is a prerequisite of the following steps. The result of this step was the delineation of 178 sub-basins in Wadi Dahab, which were then classified based on their stream order and basin size into ten classes. For instance, Dahab Mega basin had a class of one with the highest stream order of eight, while subbasins of classes nine and ten were of order four (Fig. 4). Fig. 4: (A) Model to build Watershed of sub basins, (B) Basin classification map In the Third and final step the morphometric parameters including the total length and number of segments for each stream order of each sub-basin were extracted. To count the correct number of stream segments, the polylines extracted as stream lines in the first step had to be reorganized, as the definition of stream segments of Strahler does not match the definition of segments in ArcGIS. Fig. 5: Strahler ordering system

6 6 O. Adel, D. Schröder, A. El Rayes and M. Geriesh A stream segment in ArcGIS is defined from a source or confluence of a tributary to the next confluence, while according to Strahler, a tributary of lower order will not split a stream segment of higher order. For instance, as shown in figure 5, there will be only two segments of order two according to the Strahler definition, whereas ArcGIS will define five different segments of the same order. The attribute table of the stream lines contains all the information needed, i.e. the order number and the topology in the correct downstream order, given by the two attributes from node and to node. Thus by a simple algorithm a dissolve attribute was added to the segments attribute table. In a second step, the polylines were merged in their correct order. In figure 6, the algorithm is given in a simplified pseudo code, which has been implemented in Visual basic (VB). Fig. 6: Pseudo-code for dissolving stream segments according to Strahler ordering system Next, the number of streams in each sub-basin had to be calculated. Here, a spatial join of the sub-basin with the dissolved stream network using the spatial operator contains had to be performed. Due to the independent vectorization of the stream network and the subbasins, there might be a shift of the point of confluence by one pixel of the raster layer (for example, tributaries of order n might be situated in a sub-basin of order n+1). Thus, before applying the spatial join, the stream network had to be pre-processed again. After intersecting the stream network with the sub-basins, all small segments with a length of less than one pixel were deleted. As the calculation had to be done for each stream order, a Python modified model was used. In particular, a loop over all the orders had been added to the original model (Fig.7). Finally, by repeating the spatial join, the total length for all stream orders of each sub-basin was added. In this process, the final attribute table gave the total length in each sub-basin in addition to the total number for each order.

7 Flood Hazard Assessment in Wadi Dahab, Egypt 7 Fig. 7: Python code snippet for counting stream segments of each Strahler order Now the morphometric parameters were calculated, and are summarized in Table 1. Evaluation of morphometric parameters could be calculated from the analysis of various drainage parameters, such as ordering of the various streams and basin areas, perimeter and length of drainage channels, drainage density, stream frequency, bifurcation ratio and texture ratio (KUMAR. et al. 2000). Locating high risk spots in the study area was then possible after the evaluation of data from all the 178 sub-basins.

8 8 O. Adel, D. Schröder, A. El Rayes and M. Geriesh Table 1: Morphometric Parameters Serial No. Morphometric Parameter Formula References 1 Bifurcation ratio (R b ) R b = N u / (N u + 1) N u = Total no. of stream segments of order 'u' For sub-basin with several R b the average will be used 2 Drainage density (D) D = L u / A L u = Total stream length of all orders A = Area of the basin (km 2 ) 3 Stream frequency (F s ) F s = N / A N = Total no. of streams of all orders A = Area of the basin (km 2 ) 4 Circularity ratio (R c ) R c = 4 π A / P 2 A = Area of the basin (km 2 ) P= perimeter of basin 5 Elongation ratio (R e ) R e = 4A/ π / L b A = Area of the basin (km 2 ) L b = Basin length SCHUMM (1956) HORTON (1932) HORTON (1932) MILLER (1953) SCHUMM (1956) Based upon the relationship between the parameter values and the risk of flash flood, the analysis of each parameter was calculated using a simple statistical method (YOUSSEF et al. 2009, PRADHAN 2010). Each parameter is classified into three classes based on the morphometric characteristics and their relations with their potential degree of risk. The equation used is (Max Min)/3. Then the values for each parameter are classified into three intervals. To come up with a final assessment of flood hazards, different scores on a scale according to their importance to flood risk were assigned. An overlay operation would evaluate the intersected regions by a summation of scores, so that each region is characterized by a score measure. To present a readable final map, the result was divided into three categories (low, medium and high risk) using equal intervals. The results were scored on a 1 to 3 scale, in ascending order of hazard significance. The raw score for each feature was normalized using (equation 1) taking into account the different signs of parameters as well. The normalized scores are ranged from (0 to 1) to minimize the value of total flood risk after summation of all the parameters (equation 2). X j = (R j R min )/ (R max R min ) (1) Where: X j : is the normalized score R j : is raw score R min : is the minimum score R max : is the maximum score Basins of high frequency and high density values tend to collect more runoff water, which increases the rate of flow discharge out of basin, resulting in a high risk value. In such basins, the rate of downward infiltration is expected to be low, while the basin bifurcation ratio will be also low, reflecting a high flooding risk. Basins with higher circularity and elongation ratios (R c and R e ) tend to have higher flash flood potentiality. Circular basins are also more susceptible to flash floods. The final flooding risk of the study area according to morphometric parameters of the Dahab basin is estimated as:

9 Flood Hazard Assessment in Wadi Dahab, Egypt 9 Flooding risk = Normalized Average Bifurcation Ratio (R b ) + Normalized Frequency (F s ) + Normalized Density (D) + (2) Normalized Circularity (R c ) + Normalized Elongation (R e ) According to this equation, the study area can be divided into three classes of flood hazard susceptibility, as shown in the final map (Fig.8). Up to now no field measurements are available, thus the map should be considered as primary results. To complete the work, other parameters such as runoff calculation, rainfall statistics and evaporation calculations should be included. Fig. 8: Flood hazard risk map based on morphometric parameter for Dahab Basin 4 Discussion and Conclusion The flood hazard risk map of Dahab Mega-basin (Fig. 8) shows that 34% of the total subbasins in the Dahab basin have a high flooding risk. Most of these sub-basins drain into basins of higher order as Wadi Nasab, Wadi Ramthy, Wadi Ghaieb, and Wadi saal. 60% of all sub-basins have medium flooding risk, which includes the Dahab mega-basin. Only a few of the sub-basins have a low risk flooding susceptibility.

10 10 O. Adel, D. Schröder, A. El Rayes and M. Geriesh Flood risk maps which depend on the analysis of morphometric parameter may be considered as a first step to evaluate the flooding risk of hydrographic basins. The preliminary results reflect the need to conduct a more detailed study, which would take other factors into consideration, such as the amount of surface discharge, rainfall statistics, infiltration and evaporation at locations of high risk in Wadi Dahab basin. In conclusion, the GIS technique has been proven as a useful tool for creating flood risk maps. In particular, it is useful for drawing and measuring linear objects, such as stream length, in order to build up the stream network, which is the main morphometric element needed for the creation of such maps. The GIS models help us to enhance the automatic measuring process of stream network parameters, such as ordering and length estimation, without manual coding, editing, or any time consuming processes. References AL SAUD, M. (2009), Morphmetric Analysis of Wadi Aurnah Drainage System Western Arbian Peninsula. The open hydrology Journal, 3: ARCGIS (2009), GIS software, version 9.3, Environmental Systems Research Institute (ESRI), New York. BHUYIAN, C., SINGH, R.P. & FLÜGEL W. A. (2009), Modelling of ground water rechargepotential in the hard-rock Aravalli terrain, India: a GIS approach. Environ Earth Sci., 59 (4): CEOS (2003), The use of earth observing satellites for hazard support: assessments and scenarios, final report of the CEOS Disaster Management Support Group (DMSG). Helen M. Wood, Chair. National Oceanic and Atmospheric Administration (NOAA) United States Department of Commerce. CUNDERLIK, J. M. & BURN, D. H. (2002), Analysis of the linkage between rain and flood regime and its application to regional flood frequency estimation. J Hydrol, 261 (1-4): GARDINER, V. (1990), Drainage basin morphometry. In: GOUDIE, A. (Ed.), Geomorphological techniques. London: Unwin Hyman, pp HAENG HEO-J., SALAS, J. D. & BOES, D. C. (2001), Regional flood frequency analysis based on a Weibull model. Part 2 Simulations and applications. J Hydrol., 242 (3-4): HESS, L. L., MELACK, J., FILOSO, S. & WANG, Y. (1995), Delineation of inundated area and vegetation along the Amazon floodplain with the SIR-C Synthetic Aperture Radar. IEEE T Geosci. Remote, 33: HORRITT, M. S. & BATES, P. D. (2002), Evaluation of 1D and 2D numerical models for predicting river flood inundation. J Hydrol., 268: HORTON, R. E. (1932): Drainage basin characteristics. Trans. Amer. Geophys. Union, 13: HORTON, R. E. (1945), Erosional development of streams and their drainage basins: hydrophysical approach to quantitative morphology. Bull. Geol. Soc. Amer.,5: KUMAR, R., KUMAR, S., LOHANI, A. K., NEMA, R. K. & SINGH, R. D. (2000), Evaluation of geomorphological characteristics of a catchment using GIS. GIS India, 9 (3): LE TOAN, T., RIBBES, F., WANGE, L. F., FLOURY, N., DING, N. & KONG, K. H. (1997), Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE T Geosci. Remote, 35:

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