Coastal vulnerability assessment using GIS-Based multicriteria analysis of Alexandria-northwestern Nile Delta, Egypt

Journal Pre-proof Coastal vulnerability assessment using GIS-Based multicriteria analysis of Alexandria-northwestern Nile Delta, Egypt Soha A. Mohamed PII:

S1464-343X(20)30002-9

DOI:

https://doi.org/10.1016/j.jafrearsci.2020.103751

Reference:

AES 103751

To appear in:

Journal of African Earth Sciences

Received Date: 30 March 2019 Revised Date:

29 November 2019

Accepted Date: 3 January 2020

Please cite this article as: Mohamed, S.A., Coastal vulnerability assessment using GIS-Based multicriteria analysis of Alexandria-northwestern Nile Delta, Egypt, Journal of African Earth Sciences (2020), doi: https://doi.org/10.1016/j.jafrearsci.2020.103751. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Coastal Vulnerability Assessment using GIS-Based Multicriteria Analysis of Alexandria-Northwestern Nile Delta, Egypt Soha A. Mohamed [email protected]

The Higher Institute of Tourism, Hotels and Computer (H.I.T.H.C.) The Ministry of Higher Education and Scientific Research (MHESR), Egypt

Abstract

This study intends to map the relative coastal vulnerability index (CVI) for the administrative governorates of Alexandria and adjacent Behera in the northwestern coastal margin of the Nile delta. In addition to other common environmental stresses, these governorates are under threat due to accelerated sea level rise induced from climate change. Of special interest is that the coastal margin of the study area is characterized by markedly constructing geology, morpho-dynamics and land surface topography that varies from low-lying (-3 m below MSL) to high land (∼20 m height). Therefore, nine physical and geological variables influencing the vulnerability of the coast are used in this study, including land elevation, seabed/beach composition, beach type (dissipative to reflective), relative sea level, historical shoreline change, tidal range, significant wave height, shore protection measures, and land cover. Results obtained from performing multi-criteria analysis of GIS indicates that about 16.58%, 15.45, 42.03%, 18.16 and 7.78% of the shoreline is under very high, high, moderate, low and very low vulnerability respectively. Of great concern is that although the low-lying broad depression (-1 to -3 m below MSL) east and southeast of Alexandria is protected now by a combination of natural shore-parallel elevated ridges (up to 10 m) and artificial shore-parallel detached structures, it is likely to be accidentally flooded by unexpected extreme storm or tsunami events at the lowest waterfront points.

Keyword: Satellite images; classification; ASTER; DEM; GIS; multi-criteria; coastal vulnerability; Alexandria-Northwestern Nile Delta; Egypt.

1. Introduction 1

The study area comprises the coastal margin of Alexandria (Egypt’s second largest city), and its adjacent eastern portion of the Nile delta on the Mediterranean Sea of Egypt. They together belong to the coastal governorates of Alexandria and Behera. These governorates are under threat due to natural and human-induced hazards, including events of storm surges, shoreline erosion, densely populated, intensive urbanization and tourism, saltwater intrusion and the threat of accelerated sea level rise induced from climate change. The coastal plain is markedly characterized by a distinctive variation in geology, geomorphology, topography, land cover and shoreline stability. One of the most prominent geographic features of the study governorates is that they are characterized by a wide diversity of backshore levels as a result of the existence of a low-lying remnant depression surrounded partially by a markedly high-elevated shore-parallel carbonate ridges. Vulnerability to accelerated sea-level rise due to climate change, flash floods, and storm surges adds additional pressure on the study coastal plain as illustrated in Figure 1. In winter, the lowest points at Alexandria are periodically experiencing sea flooding due to wave overtopping of severe storm surges. More lastly in 2003, 2010 and 2017, for an example, extreme-storm surges are accompanied by > 4 m wave height raised water level up to ~ one meter above MSL (field observations by the Coastal Research Institute staff at Alexandria). Calculation on storm surges indicate that high winds and low-pressure have acted to push winter waves toward the coastline and raising water level by ~ 40 cm above MSL in average (Hamed and El Gindy, 1988). Mitigating the effects of vulnerability resulting from these natural events require a detailed knowledge about vulnerability of the coastline. Figure 1. Storm surges of the study area in Winter Climate change-related issues in the region have greatest special attention that has resulted in a number of publications. These studies have investigated: evolution and composition of shoreline and shelf margin (Summerhayes et al., 1978; Frihy et al., 2010; Warne and Stanley, 1993), beach morpho-dynamic changes (Frihy et al. 2003; Frihy, 2017), coastal processes (Fanos et. al., 1991; Nafaa, 1995), rates of relative sea-lever rise and subsidence of the coastal margin (Frihy et al., 2010; Alam El-Din and Abdelrahman, 2010, El-Sayed, 2013, Wöppelmann et al., 2013), climate change assessment and consequences (El-Raey et al., 1999; Arnell et al., 2013 and 2016; Abdussalam et al., 2014; and Weaver et al., 2017). 2

The coastal hazards are studied using the integration of remote sensing and GIS in terms of physical and social variables. The physical variables include sea level rise, slope, land cover, soil, geomorphology, and geology (Murali et al., 2013; Choudhary et al., 2018; Mohamed, 2019; Mohamed and El-Raey, 2019). Murali et al. (2013) developed a Physical Vulnerability Index (PVI) in India using the Analytical Hierarchical Process (AHP). Mohamed and El-Raey (2019) developed a composite vulnerability index in El-Arish city in Egypt. Jana and Bhattacharya (2013); Andres et al. (2015); Mohmed (2019) investigated the social variables such as the total population and population density. Although such previous studies, there is much less information on combining different variables to assess severity of coastal vulnerability to inundation and erosion induced from sea-level rise in the study area. The present study differs greatly from previous studies in which we combine significant conclusive parameters such as measured georeferenced elevation base data and the most recent protection structures. The overall purpose of this study, however, is to quantitatively assess the degree of vulnerability to the expected beach erosion and/or inundation that would likely induced from sea level rise, by means of vulnerability index CVI, of various coastal sectors along the study area based on several key parameters. Attention is paid to the low-lying broad depression that related to historical evolution of the former Mareotis and Abu Qir lakes and the high elevated carbonate ridges in the region. The CVI Index provides a simple numerical basis for ranking sections of coastline in terms of their potential for change that can be used by managers to identify regions where risks may be relatively high. Many worldwide attempts have been made to measure coastal vulnerability and estimate risk intensities to sea-level rise considering physical and/or socioeconomic variables (Diez et al., 2007; Doukakis, 2005; Gornitz, 1991; Pendleton et al., 2004; Thieler and Hammer-Klose, 1999). The majority of such studies have categorized the vulnerability of different coastal environments relatively, using basic information or diverse variables on coastal geomorphology, rate of sea level rise, past shoreline evolution, coastal slope, mean tidal range, and mean wave height, protective structures, population, land use ... etc.

2. Study area The administrative coastal margins of Alexandria and Behera governorates are diverted at Abu Qir headland point into two oriented western and eastern coastlines for a total distance of 187.16 km and extend ~25.0 km inland. The western shore of Alexandria (west of Abu Qi headland) is typically marine environment dominated with high carbonate content (El-Wakeel and El-Sayed, 1978). The coast east of El-Agami headland, consists of narrow resort beaches alternating between small embayment, pocked beaches separated by headland points and cliffs; the coast is interrupted by commercial harbors (eastern, western and El-Diekhila harbors). In contrast, the coastline further west of El-Agami headland is characterized by straight, carbonate3

sand beaches with limited hard engineering structures built for recreation purposes and not for shore protection (Frihy, 2009). The study area is shown in Figure 2. Figure 2. Location map of the study area In contrast to Alexandria region, the deltaic shore from Abu Qir headland to further east of the Rosetta promontory, 146.13 km long, is characterized by broad beaches composed of very fine to medium quartz sand. Abu Qir bay is positioned to the east of Abu Qir headland and is bounded to the east by the Rosetta promontory which has been experienced dramatic erosion during the 20th century generally due to the combined effect of the construction of the high Aswan Dam and climatic factors (Frihy and Khafagy, 1991). The bay is backed by Idku Lake, sand dunes cultivated lands and prohibited by natural gas related industry at Idku coast. The study coastline is backed by two shallow brackish water bodes (maximum of ~2 m water depth), Maryut Lake (200 Km2 in 2016) and Idku lagoon (~120 km2). Unlike the Idku lagoon, the modern (Maryut Lake) and its former Mareotis Lake has no direct connection to the sea except at El-Max bay. Until as late as the early nineteenth century, former Maryut Lake extended alongshore to further east and south east of Alexandria (Jacotin, 1809; El-Falaki, 1872; El Rafeay, 1948; Warne and Stanley, 1993). These previous studies have also confirmed the existence of former Abu Qir lagoon (~105 km2) just east of Alexandria. Over time, the eastern extension of the former low lying Maryut and the former Abu Qir water bodies had been dried up and intensely cultivated at present. Both the high-elevated topographic elements and the identified low-lying areas or depressions are considered herein when calculating CVI of the study region. Egypt’s population has increased to approximately 92 million, with a growth rate of ~2.0%/yr. Of this number, about 11,092,178 million lives in the study area (CAPMAS, 2017). This number includes the 4,989,756 million and 6,102,422 million living in Alexandria and Behera, respectively. Based on the available data, spatial distribution of human population in the study area is classified in each district as shown in Figure 3. As seen in this figure, the distribution pattern of population is found to be rather uneven within different districts of the study area and varies greatly from 23.6 x103 to 1.2x106 persons per 200 m2, being relatively dense near the coastline. The study area copes with its rapidly growing population, increasing urbanization and industrialization which could worsen the coastal hazards in the study area. Figure 3. Spatial distribution of population in the study area according to CAPMAS (2016)

3. Data Collection Multiple data types are used to accomplish this study, Landsat images with 30 meters spatial resolution are used to identify and extract the land cover in the study area. The satellite 4

images are downloaded freely from the United States Geological survey website (https://earthexplorer.usgs.gov/). ASTER DEM image with a 30-meters spatial resolution is used to extract the DEM map and downloaded from (https://gdex.cr.usgs.gov/gdex/). Additional information about the collected data types are presented in Table 1. Table (1): Data Description

4.

Methodology

Calculating CVI for the study area is composed of several complementary steps. These steps include: (1) generating a recent and high-resolution digital elevation map by combining ASTER data with elevation points and contours digitized form maps, (2) creating land cover classes using Landsat satellite image acquired in 2019, (3) collecting and updating information on various coastal vulnerability variables required for this research and finally (4) develop the CVI for the study area. Figure 4 manifests in detail a flowchart of the data collection and methodology steps. Figure 4. Methodology flowchart

4.1. Coastal vulnerability parameters Nine parameters are used in this study to identify the coastal vulnerability of the study area as presented below. 4.1.1. Land cover An unsupervised classification is performed using the ISODATA (Iterative Self-Organizing Data Analysis Technique) clustering algorithm, where 100 classes with their signatures are generated. Mean plot and divergence matrix are two separability analysis techniques applied to the signatures to test if they totally separable in the bands are being studied or not. Sixty polygons are drawn as training areas for available land cover. This process is performed for all land cover classes and saved as a supervised signature file. The final step in image classification is merging and appending signatures created from both supervised and unsupervised training areas. Maximum likelihood classification (MLC) approach is used for land cover mapping. The generated classified land cover map is verified using ground truth data and Google earth. An accuracy assessment for the supervised classification is done using 130 reference points that are generated randomly. All the randomly generated points are identified and assigned in different classes and then are considered as reference points. The correctly identified points are considered as classified values. 4.1.2. Backshore elevation 5

Creating the digital elevation model in this study is based mainly on the digital elevation data collected from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) launched in 1999. ASTER elevation data with 30 meters spatial resolution is acquired in 2019 from http://gdex.cr.usgs.gov/gdex. DEM is the digital representation of the land surface elevation above or below mean sea level at regularly spaced horizontal intervals. DEM in this study is created using the inverse distance weighted (IDW) interpolation technique to integrate the Aster elevation data, the digitized elevation points and contours from maps of scale 1:50,000 for the study area. Shoreline data 4.1.3. Shoreline composition / seabed type

4.1.4. Beach type Beaches can be characterized as ranging from dissipative to reflective (Wright and Short 1983, 1984), depending on the grain size, beach slope, and wave parameters. Accordingly, Alexandria beaches have been classified as reflected surf zones, and as dissipative in the Nile delta (Nafaa and Frihy 1993). The vulnerability of the reflective beaches is much risky than the dissipative. Coastal areas having steep slope seabed are considered as highly vulnerable relative to genteelly slope zones of low vulnerability (Jana and Hegde, 2016). 4.1.5. Relative sea level change rate (RSLR) Relative sea-level rise statistically estimated from tide-gauge records at Alexandria (1944–2006) and Rosetta estuary of the Nile delta (1969- 2008) indicate rates of 1.8 and 4.9 mm/year, respectively. Unlike the Nile delta, previous studies indicated that Alexandria coastal region is considered stable or undergoing moderate subsidence partially due to effects of recent neotectonics lowering and earthquakes (Frihy et al., 2010; Alam El-Din and Abdelrahman 2010, Wöppelmann et al., 2013). In contrast, higher rates of relative sea-level are estimated at the Nile delta (4-7mm/year) as a partial response to rise in eustatic (world) sea level, plus the added effects of land lowering (subsidence) triggered by neotectonics of the northern delta and adjacent seafloor (Stanley, 1990; Stanley, 2017). Subsiding rate in the northern Nile delta increases markedly eastward to a maximum of about 0.5 cm/year in the Port Said-Manzala lagoon region due to natural compaction and dewatering of the thick Holocene deltaic sediments. 4.1.6. Shoreline change rate (erosion, accretion or stable) The coastal vulnerability of the accreted coasts is considered as less vulnerable than those of eroded areas. The sandy shorelines are always subjected to changes due to the prevailing coastal processes, which are essentially controlled by wave characteristics and wave-induced littoral currents. From the coastal vulnerability point of view, coasts subjected to accretion are considered as less vulnerable compared with those experienced erosion (Kumar et al., 2010). 6

Several studies have been undertaken to determine beach changes of the study coastline, among these studies, including: El-Wakeel and El-Sayed (1980); Frihy et al. (1994) and Frihy (2017). 4.1.7. Tidal range The tidal range is the vertical difference between the highest high tide and the lowest low tide. The coastal areas with high tidal range that generate strong tidal currents are considered as highly vulnerable, while those with low tidal range as less vulnerable (Jana and Hegde, 2016). The study coast is characterized by semi-diurnal micro-tidal with a mean range of approximately 0.4 meters (UNESCO/UNDP, 1978). 4.1.8. Average wave height As the Mediterranean Sea of Egypt is a wave-dominated coastline, wave-induced longshore currents are the main coastal process acting to cause morphologic changes in the study coast (Manohar, 1981 and Coleman et al., 1981). Longshore current is generated in the surf zone when waves approach the coast at an oblique angle. On average, the wave height and period are 1.2 meters and 5.6 seconds, respectively (Nafaa et al., 1991). Waves generated from the NW and N transport the sediment in a uniformly easterly direction along the Nile delta coastline which is generally east-west oriented, In contrast and owing to the SW-NE shoreline orientation of Alexandria (57o from the north), two main wave exposures are responsible for generating opposing SW and NE long-shore sediment transport, but the net littoral drift is to the NE (Frihy et al., 2010). 4.1.9. Shore protection structures Multiple shoreline protection measures have been constructed along the Nile delta coast, mostly since the past half-century following closure in 1965 of the Aswan High Dam, including seawalls, detached breakwaters, revetment, groins, and harbor/ estuary entrance jetties. In the study area, the west of Abu Qir bay, are now protected by a 1.2-km-long shore-parallel seawall (known as Mohamed Aly or El-Tarh Seawall). Further east, a 5 km long seawall is built on the outer margin of the promontory together with 15 groins along the eastern and western sides of that wall. In Alexandria, a series of submerged breakwaters have been built east of Alexandria to protect the coastline from Miami to Montaza, 5 km long, to dissipate wave energy before reaching the coast particularly in winter. In addition to the existing protective structures, the prominent morphodynamic features such accreting coastlines within the concave shores at Abu Qir bay, Abu Khashaba and the high-elevated submerged and emerged shore-parallel carbonate ridges act effectively as a natural defense and adaptive measure against beach erosion and sea inundation (Frihy and El-Sayed, 2013). 4.1.10.

Land cover

7

4.2.

Calculation of CVI

The study coastline is divided into several zones or grid templates of 2 km by 2 km; these grids are used to store, analyze data to finally develop the CVI using the software package ArcGIS version 10.4.1. In the present study, nine risk variables are considered for calculating the CVI, including land cover, backshore relief, seabed and beach composition, beach type, rate of relative sea level change, historical rate of shoreline change (erosion or accretion), mean tidal range, significant wave height, and protection measures. These variables are used earlier by others to calculate CVI, including Gornitz (1991), Thieler and Hammer-Klose (1999) and Doukakis (2005). The CVI is calculated as the square root of the product of the ranked variables divided by the total number of variables:

CVI =

1. 2. 3. 4. 5. 6. 7. 8. 9 9

Equation 1

Where; a1 = shore relief; a2 = seabed and beach composition; a3 = beach type; a4 = RSL change; a5 = shoreline erosion or accretion; a6=mean tidal range, a7 = mean wave height, a8= coastal protective structures, and a9 = land cover types. The selected nine vulnerability variables are assigned a vulnerability weights and ranks based on value ranges contributing to five vulnerability classes; namely: very low, low, medium, high, and very high. The ranking scores of the variables, adopted from Gornitz (1991). The final step is to display results of the CVI values on a map to highlight regions of coastal “hot-spots” as well as areas of relatively lower risk.

5. Results and discussion The results of CVI for the study area and a description of the selected variables and their corresponding ranking will be presented here after.

5.1.

Coastal vulnerability

Considering the nine variables affecting coastal vulnerability of the study area, the coast is divided every 2 km into segments as seen in Figure 4. Each segment is assigned a rank from one to five according to the ranking scheme given in Table 2. Then, all the nine vulnerability maps are depicted as thick colored shore-parallel line to calculate the CVI at each grid point as presented in Figure 6. Table 2. Ranking of CVI variables

8

Based on satellite image classification and the in-situ observation coupled with topographic maps, google earth and digital globe, the main land cover that exists in the study area are the water surface water, sabkha, bare soil, vegetation, urban and built-up as shown in Figure 5. Deep water and shallow water represent about 43% and 1.77% respectively. Sabkha is found mainly around the fish farms, Mallahat Maryut, Maryut and Idku Lakes and constitutes 73.47 km2 of the study area. Bare soil represents about 9.9% of the study area. Vegetation, urban and built-up areas constitutes 25% and 19.2 % respectively. Overall accuracy is calculated from the error matrix and found to be 92.2% which is good enough to map land cover in the study area. Figure 5. Prominent land cover map of the study area Coastal vulnerability analysis of land cover variable showed that 19.97% and 12.33% are in very low and low zones. 30.72 km (14.76%) has moderate vulnerability, while 43.88 km (21.09%) and 66.28 km (31.85%) have high and very high coastal vulnerability respectively.

The detailed topographic map generated in this study is used to determine the backshore elevation in the study area. According to the generated DEM and Table 2, the study backshore elevation vulnerability is shown in Figure 7a. The results revealed that nearly 10.03 km (5.34%) of the coast has low coastal and located in, while 41.36 km (22.02%) has moderate coastal vulnerability and 70.12 km (37.34%) and 66.27 km (35.29%) have high and very high respectively. The soft sandy beaches of Abu Qir bay offer least resistance, and thereby are relatively vulnerable to sea-level rise relative to those of Alexandria shores which are less vulnerable due to their rocky nature. Therefore, the study shores are classified into two main risk levels, moderate (104.7 km equivalent to 55.79%) and very high (83.02 km equivalent to 44.2%) as seen in Figure 7b. Beach type Alexandria and the Nile delta beaches are ranked as very high and very low risk, respectively as presented in Figure 7c. Very low 83.015 km 44.2% Very high 104.78 km 55.79% Relative sea level change rate (RSLR) According to the published estimates of relative sea-level rates, the study coast is divided into two main categories, namely moderate and highly vulnerable level (Figure 7d). The seawall (1820–1830) is recently elevated to be 3.5 above mean sea level to combat the likely SLR and wave overtopping (Frihy and El-Sayed, 2013). High = 83.01 km (44.2%) - Moderate 104.78 km (55.79%) 9

Shoreline change rate (erosion, accretion or stable) Based on these studies, Figure 7e is generated; it shows low to moderate risk levels at Alexandria coast, and very low and low along the accreting central part of Abu Qir and Abu Khashaba coast. High risk level of erosion prevails on both sides of the Rosetta promontory. Tidal range Accordingly, the entire study coast is classified into very low vulnerable coast. Tidal range vulnerability variable is presented in Figure 7f. Average wave height Therefore, the entire coastline is designated as very low vulnerability class as seen in Figure 7g. Shore protection structures Figure 6h shows the presence or not of the coastal protective structures. Emerged structures such as El-Tarha and the Rosetta seawalls are raked as very low, while submerged breakwaters in Alexandria or groins on both sides of Rosetta promontory are ranked as moderate vulnerability. Coastal vulnerability index (CVI) The CVI is calculated for all coastal segments along the study coastline ranges between 1.4 and 1.7; Results of CVI are standardized into four classes: very low (CVI = 1), low (CVI = 2), moderate (CVI = 3); and very high (CVI = 4). Figure 7 shows the combined CVI for the study area. Results of the analysis have been mapped through a GIS system, thus enabling the identification of the most vulnerable areas at fine spatial scales. The CVI classification indicates that 17.52% of the study coast is under the very high-vulnerability category mostly along the unprotected coastal sectors of the Nile delta. In contrast, the low and very low vulnerable coastlines are backed by: (a) high elevated carbonate ridges (number I, II and III) along the coastline of Alexandria; (b) protective structures (at El-Tarh and Rosetta seawall-groin field); and (c) accreted shores at the central part of Abu Qir bay and Abu Kashaba. These three measures are effective to mitigate possible sea flooding of the densely populated low-lying depression (-1 to -3 m below MSL) which occupy east and southeast of Alexandria and part of Behera governorate. Such measures are likely to be unable to protect the region from drowning in the event of disasters such as tsunami. 6. Conclusions and Recommendations The present study is an attempt to develop a coastal vulnerability index (CVI) for the coastal area of Alexandria and Behera governorates on the Nile delta using nine relative 10

vulnerability variables. Vulnerability is referred to the effect of inundation as expected from accelerated relative sea-level rise. Vulnerability is expressed as a function in nine environmental variables. Most of the vulnerability variables are dynamic in nature and include: shore relief (elevation), seabed/ beach composition, beach type, shoreline change rate, mean tidal range, mean wave height, and coastal protective structures. These variables are compiled from published sources except for the land topography which has been generated herein from an analysis of extensive georeferenced levels data obtained from topographic maps, field measurements and ASTER GDEM. Zones of vulnerability of different magnitude (very low, low, moderate and very high are identified and depicted on a map. The updated high-resolution contour map created in this study served to identify prominent topographic features which are significantly influencing coastal vulnerability. These features reflect differences in the land elevations which vary from low-lying (<1.0 m below MSL) to high-elevated land (∼20 m height), associated with zones of intermediate elevation. Contrary to what is published earlier, the results of this study confirmed that Alexandria and the western coastline of the Nile delta seem to be lower in vulnerability except at few local lowest points which are now being mitigated using artificially protective measures. These points are represented by the densely inhabited low-lying broad area east and southeast of Alexandria; these areas probably would be inundated in the absence of such mitigation. Such inundation is likely to happen if a disaster such as a tsunami occurs accidently to this densely populated lowlying depression. Deltaic areas when investigating the influence of land topography on the effect of sea inundation due to future increase in relative sea-level. The approach used in this study allows us to identify shoreline hotspot vulnerable sectors. Results of this study are essential for future coastal planning purposes and could be used to help decision makers enact appropriate adaptation measures to alleviate future change due to possible rise in sea-level. 7. References 1. Ahmed, S., (2017): “Environmental Risk Assessment in Alexandria Governorate using Remote Sensing Techniques and GIS”. PhD Thesis, Department of Environmental Studies, Institute of Graduate Studies and Research, University of Alexandria, Egypt. 2. Alam El-Din K., A., and Abdelrahman, S., M., (2010): “Is the rate of sea level rise accelerating along the Egyptian Coast?”. 1st International Conference on Coastal Zone Management of River Deltas and Low Land Coastlines, Alexandria, Egypt, pp. 127-144. 3. Andres, J., Ignacio, F., Cruz, G. T., Nardi, F. and Henry, S., (2015): “Assessing the effectiveness of a social vulnerability index in predicting heterogeneity in the impacts of natural hazards: Case study of the Tropical Storm Ishi flood in the Philippines”. Vienna Yearbook of Population Research, Special issue on Demographic differential vulnerability to climate-related disasters, JSTOR, 13: pp. 91-129, www.jstor.org/stable/24770027. 11

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Table 1. Description of the collected data Acquiring Date

Data Scale

Topographic maps

1996

1:50,000

ASTER

2019

30 meters

Landsat-8 OLI/TIRS

2019

30 meters

Coastal variables (Tabular data)

2018

-

Data Type

Source of Data Egyptian General Survey Authority http://www.esa.gov.eg/ https://gdex.cr.usgs.gov/gdex/ https://earthexplorer.usgs.gov/ Coastal Research Institute in Alexandria https://www.gfar.net/organizations/coastal-research-institute

Table 2. Ranking of CVI variables used for determination of the coastal vulnerability index (CVI) of the study area according to Gornitz (1991) Vulnerability Rank Variable Very low Low Moderate High Very High 1 2 3 4 5 1

Land cover

2

Backshore relief/ elevation (meter)

3

Shoreline/ seabed type (composition)

4

Beach types

5

Relative sea level change (mm/year)

6

Shoreline stability erosion/accretion (meter/year)

7

Mean tidal range (meter)

8

Mean wave height (meter)

9

Protective structures

Deep water

Bare soil

Sabkha

Shallow water

Urban/Vegetation

≥ 20

10 - 20

5 - 10

2-5

0-2

Sandstone

Most sedimentary rocks

Coarse unconsolidated sediments

Fine unconsolidated sediments

Rhythmic bar beach

Transverse bar rip

Low tide terrace

Reflective

< -1.1

- 1.1 : 1

1:<2 Eustatic rise

2 : < 4.0

≥4

>+2 accretion

2 : < -1 Stable

-1 : < -1.1 Erosion

-1.1 : < -2 Erosion

≤ -2 Erosion

< 0.99 Microtidal 0 : < 2.9

0.99 : < 1.9 Microtidal 2.9 : < 4.9

4:<6 Mesotidal 5.9 : < 6.9

≥6 Macrotidal ≥ 6.9

Emerged seawall

-

1.9 : < 4 Mesotidal 4.9 : < 5.9 Submerged breakwaters and groins

-

No structure

High-medium grade metamorphic Dissipative Longshore bar trough

Table 3. Statistical calculation of CVI Rank #

Rank Name

Rank Boundaries

1

Very Low

1.4 : < 1.46

2

Low

3

Moderate

1.52 : < 1.58

4

High

1.58 : < 1.64

5

Very High

1.64 : 1.7

1.46

: < 1.52

a. Storm surges

b. Coastal flooding Figure 1. Storm surges of the study area in Winter

1

Figure 2. The Northwestern coast of the Nile Delta, Egypt

2

Figure 3. Methodology flowchart

3

Figure 4. Prominent land cover map of the study area

4

Figure 5. 2-km boxes along the coast the study area

5

a. Land cover

b. Backshore elevation

c. Shoreline composition

d. Beach type

6

e. Relative sea level change rate

f.

g. Tidal range

Shoreline change rate

h. Average wave height 7

i. Shore protection structures Figure 6. CVI variables in the Western coast of the Egyptian Nile Delta

8

Figure 7. Coastal vulnerability map for the Western Egyptian Nile Delta coast 1: El-Agami, 2: Dekhaila Harbor, 3: Western Harbor, 4: El-Max Bay, 5: El-Gomrok Harbor, 6: Eastern Harbor, 7: El- Silcila, 8: Asafra, 9: Mandara, 10: Abu Qir, 11: El-Tarh Seawall, 12: Idku inlet, 13: Rosetta Promontory, 14: Abu Khashaba, 15: Rashid

9

Highlights: 1. The Western coast of the Nile Delta has lower vulnerability contrary to what was published before 2. The present study developed a coastal vulnerability index (CVI) for the coastal area of Alexandria and Behera Egyptian governorates. 3. Nine physical variables are used to calculate the CVI of the study area. 4. The CVI results indicates that about 17.52%, 4.12, 45.37%, and 32.98% of the shoreline is under high, moderate, low and very low coastal vulnerability respectively.

Conflict of Interest Declaration and Author Agreement

Title of Paper: “Coastal Vulnerability Assessment using GIS-Based Multicriteria Analysis of Alexandria-Northwestern Nile Delta, Egypt” The author declares that there is no conflict of interests regarding the publication of this paper. This statement is to certify that the author has seen and approved the manuscript being submitted. I warranted that the article is the author's original work. I’m the corresponding author and I shall bear the full responsibility for the submission. I have read, understood and complied with Elsevier’s ethical guidelines: http://www.elsevier.com/wps/find/intro.cws_home/publishing. My manuscript is my own work and the content of my paper has not been copied from elsewhere; my manuscript is unpublished and is not currently under review with another journal; all data measurements are genuine results and have not been manipulated. I’d like to declare that there are no funding sources and I have not received any funding support. I’ve followed all Elsevier’s guidelines for defining authors according to: http://www.elsevier.com/wps/find/editorshome.editors/ethicsglossary I accept the Editor-in-Chief's decisions over acceptance or rejection or in the event of any breach of the Principles of Ethical Publishing in the Journal of Africa Earth being discovered of retraction are final. No additional authors will be added post submission unless editors receive agreement from all authors and detailed information is supplied as to why the author list should be amended.

Author: Soha A. Mohamed The Higher Institute of Tourism, Hotels, and Computer (H.I.T.H.C) http://www.seyouf.org/EN/index.php

The Ministry of Higher Education and Scientific Research (MHESR), Egypt http://portal.mohesr.gov.eg/ar-eg/Pages/default.aspx