Mapping Terrain Characteristic in the Northern Gulf of Suez.

(1)

Mapping Terrain Characteristic in the Northern Gulf of Suez.

By

Said Abd El-Rahman Awad Allah Heikal

Advisor:

Prof. Dr. Maged M.L.H. El Rakaiby

JUNE 2013

(2)

(3)

Abstract

The demand for timely and accurate geo-information has been increased for the management of the earth related disciplines. In response to this demand, geo -information are being producing in map form as well as in digital form. Updating the existing geo-information for the use of their development activities become more necessary

Current terrain-information capturing procedures are mostly based on conventional photogrammetric methods. These procedures are slow and data acquisition is expensive. Developing countries cannot afford the time and cost required to capture terrain-information for updating their topographic databases The satellite data, has wide application potential in terrain-information production. Economical and practical constraints prompt the need of obtaining terrain characteristics information for development of desert areas with reduced field effort. The fusion of multi- sensor satellite data is an effective mean of exploiting the complimentary nature of different data types. This technique allows fusion of spectral information of multi source data with high accuracy.

The analysis of digital surface models from hilly terrain data becomes very useful for sloped landscapes (as the scarps facing the Gulf of Suez

(4)

from both sides). The first task to study satellite data is to separate ground areas and object lineaments as drainage lines for object classification.

The main objective of this study is to investigation the use of satellite imagery (Land-sat, and SPOT), as well as SRTM data and GIS techniques for updating terrain information databases and geomorphological mapping.

In this study, fusion of satellite data such as Shuttle Radar Topography Mission (SRTM) and SPOT-4 data was applied to test the potentially of this technique in mapping terrain characteristics of the northern part of Gulf of Suez area, Egypt. Generally, satellite data (remotely sensed data) and GIS techniques are useful as an aid in geomorphological mapping because of the ease with which land forms units and individual forms can be outlined even if their genesis and age sometimes remain obscure.

(5)

Acknowledgment

I would like to thank the UNIGIS staff at the faculty of computers and informatics, Zagazig University. And special thanks for my supervisor, prof.

Dr. Maged M.L.H. El Rakaiby for his patience and support.

In addition, I would like to thank my family and friends for them constant support and motivation in bringing this dissertation to completion.

Thanks are extended to all who helped me during

the different stages of this study.

(6)

List of Figures

Figure

Number Title

Page (s)

1 Space image mosaic of Egypt showing the study area 14

2 The drainage basins of the studied area 15

3 The watershed, and the border of the studied area 15

4 Topographic map of the studied area 16

5 Mosaic of topographic sheets 17

6 Mosaic of 2 SPOT-4 colored (XS) satellite images and resolution merge with panchromatic SPOT-4 image

18

7 Flow-chart shown the methodology of the study 22

8 Snap shot shown the structure of Db for the studied area 24

9 TINs models structure 29

10 Slope function description 30

11 The direction the cell's slope faces 30

12 Shadow and light shades 32

13 The slope or angle of the illumination source above the horizon 32

14 The use of hill-shade for display 32

15 The use of hill-shade for analysis 33

16 Creates contours or iso-lines from DEM 34

17 The study area Precipitations 36

18 The study area air temperature 37

19 The study area wind 38

20 Flow chart of DEM hydrology analysis methodology 40

21 Working of flow direction analysis 43

22 Flow direction map of the studied area 44

23 Flow accumulation of the studied area 46

24 Stream order of studied area basins 46

25 The drainage basins of the studied area 47

26 Geological map of the studied area 53

27 Contour map of studied area 76

28 Slope map of the studied area 76

29 Aspect (Slope Direction) map of the studied area 77

30 Hill-shade map of the studied area 77

31 TIN model map of the studied area 78

32 Faults map of the studied area 80

33 Rose ofdirection and length of faults in the study area ⁹⁰

(10)

List of Tables

Table

Number Title

Page (s) 1 Orbit and Acquisition Characteristics of landsat 19 2 Radiometric Characteristics of landsat 20 3 Descriptions of DEM hydrology analysis tools 41 4 Morphometric parameter for the studied area 48

5 Description of geological symbol 53

6 Direction and length of faults in the study area 90

(11)

List of Abbreviations

GIS Geographic Information System

RS Remote Sensing

Db Database

MSS Multi Spectral Scanner

TM Thematic Mapper

ETM+ Enhanced Thematic Mapper + DEM Digital Elevation Model

SRTM Shuttle Radar Topography Mission

Km Kilo Meter

M Meter

GLCF Global Land Cover Facilities

µM Micro Meters

TIN Triangular Irregular Networks UTM Universal Transverse Mercator

(12)

Chapter One

INTRODUCTION

(13)

This part will include the study objective, expected result, problem description, intended audience, study area, methodology, literature review, and thesis Structure.

1-1 Study objective and expected result:

The main objective of this study is to investigation the use of satellite imagery (Land-sat, and SPOT), as well as SRTM data or remotely sensed data and GIS techniques for updating terrain information databases and geomorphological mapping. In this study, fusion of satellite data such as Shuttle Radar Topography Mission (SRTM) and SPOT-4 data was applied to test the potentially of this technique in using terrain characteristics for geomorphological mapping of the northern part of Gulf of Suez area.

There are some other objectives and expected result for this study such as:

1. Produce various maps of study area.

2. Under standing of the shape of the study area surface, this is useful for many fields, such as regional planning, and agriculture…etc.

3. Understanding of how water flows across an area and how changes in that area may affect that flow using hydrological analysis.

1-2 Problem Description:

How we can using the GIS and Remote Sensing to Infer Geomorphological mapping in the Northern Gulf of Suez Coastal Plain?

1-3 Intended audience:

GIS users include:

 State, governmental departments and local authorities.

 Non-governmental organizations.

 Environmental and engineering consultants.

(14)

 Universities and institutes.

(Langer, 1995) confirms that GIS is now common place across many sectors. GIS had wide spread use in issues dealt with by local authorities;

however, its use is often limited to simple map overlay and visualization techniques.

1-4 Study Area:

The area under investigation is located in the northern part of the Eastern Desert. It lies between latitudes 29º30´ and 30º00´ N and longitudes 32º02´ and 32º50´ E covering an area of about 10704.7 Km² (fig. 1).

Figure (1): Space image mosaic of Egypt showing the study area.

Source: Landsat ETM+ satellite images, obtained from GLCF web site.

A number of large hydrographic basins dissect the area under consideration (fig. 2); these wadis drain the high terrain to the Gulf of Suez.

They all have their outlets in the Gulf of Suez. The study of the drainage

(15)

basins of the area under consideration necessitates the tracing of the drainage lines to the watershed areas and this will be the study area (fig. 3 and 4).

Figure (2): The drainage basins of the studied area.

Source: Obtained from DEM and topographic sheets.

Figure (3): The watershed, and the border of the studied area.

(16)

Figure (4): Topographic map of the studied area.

Source: Obtained from topographic sheets and updated from SPOT-4 Image.

(17)

1-5 Available data:

1-5-1 Topographic sheets:

A topographic maps scale 1 : 100,000 of the military surveying authority, Egypt, were used for the present study (fig. 5).

Figure (5): Mosaic of topographic sheets.

Source: Military survey Authority, Egypt.

1-5-2 SPOT-4 data:

SPOT-4 is a French earth observation satellite that was launched in March 1998 at an altitude of 810 Km. SPOT-4 scenes are typically 60 by 60 Km. (vertical viewing) or 60 Km. by up to 80 Km. for oblique viewing.

SPOT-4 is characterized by multi-spectral data representation by 4 bands covering green, red, near infra-red and short wave infra-red portions of spectrum with 20 M. spatial resolution, in addition to a single panchromatic band acquired in the wave length region from 0.61 to 0.68 µM. with 10 M.

spatial resolution. The optical imaging instruments (HRVs) on board SPOT-4 satellite are steering able to either side of the ground track by up to

(18)

30 degrees. Stereo-images can therefore be acquired, which are ideal for topographic mapping. (M. M. Abdeen, et al, 2009). The study area is covered by two SPOT-4 scenes (fig. 6).

Figure (6): Mosaic of 2 SPOT-4 colored (XS) satellite images and resolution merge with panchromatic SPOT-4 image.

Source: National Authority for remote sensing and space science, Egypt.

1-5-3 Landsat Data:

Landsat is an American Earth observation satellite. Imagery from the Landsat satellites has been acquired since 1972, with a variety of characteristics to consider. There have been six operational Landsat satellites, with three different useful sensors, all of which are available

(19)

through the GLCF web site. The MSS sensor provides the oldest and lowest quality Landsat data, from 1972- present. The TM sensor has improved quality and is available from 1984 present. The ETM+ sensor on the Landsat 7 satellite was the best quality of all, until a mechanical anomaly occurred on the sensor in May, 2003 (Landsat user hand book web site).

Landsat 7 imagery is still being collected, even with this unfortunate defect. Landsat satellites acquire imagery in a regular, tiled fashion, following the world reference system (WRS1 for MSS, WRS2 for TM and ETM+). The Landsat satellites follow a repetitive, circular, and sun- synchronous, near earth orbit.

Table (1): Orbit and Acquisition Characteristics of landsat:

Satellite Sensor Swath (Km) Scene Size (Km) Altitude (Km)

L 1-5 MSS 180 180×170 917

L 4-5 TM 185 170×183 705

L7 ETM+ 185 170×183 705

Source: GLCF web-site.

Landsat imagery is acquired in a very precise manner; to better emphasized particular land cover aspects. Some of the parameters of this precision involve a scenes radiometry, providing distinct characteristics to components of the image scene. These measures help determine what the images are good for, from a science perspective. For example, Band 1, 2 and 3 are used together to approximate how the real world appears. Band 4, 5 or 7 from ETM+ are used in combination with 1, 2 or 3 to demonstrate vegetation condition. It is sometimes necessary to convert the radiometric values from the initial at-sensor measures, to compensate for atmospheric interference. (Landsat user hand book web site).

(20)

Table (2): Radiometric Characteristics of landsat:

Satellite Spectral Resolution (µM) Band Spatial Resolution (M.) Landsat 1-3 MSS

Band 4: 0.50-0.60 Band 5: 0.60-0.70 Band 6: 0.70-0.80 Band 7: 0.80-1.10

Green Red Near IR Near IR

79 79 79 79 MSS

Band 4: 0.50-0.60 Band 5: 0.60-0.70 Band 6: 0.70-0.80 Band 7: 0.80-1.10

Green Red Near IR Near IR

82 82 82 82 Landsat 4-5

TM

Band 1: 0.45-0.52 Band 2: 0.52-0.60 Band 3: 0.63-0.69 Band 4: 0.76-0.90 Band 5: 1.55-1.75 Band 6: 10.4-12.5 Band 7: 2.08-2.35

Blue Green Red Near IR Mid IR Thermal Mid IR

30 30 30 30 30 120 30 ETM+

Band 1: 0.450-0.515 Band 2: 0.525-0.605 Band 3: 0.630-0.690 Band 4: 0.760-0.900 Band 5: 1.550-1.750 Band 6*: 10.40-12.5 Band 7: 2.080-2.35

Blue Green Red Near IR Mid IR Thermal Mid IR

30 30 30 30 30 60 30 Landsat 7

Band 8: 0.52-0.92 Pan 15

* Band 6 on Landsat 7 is divided into 2 bands, high and low gain.

Source: Landsat data user handbook web-site.

1-5-4 DEM:

A DEM is a raster representation of a continuous surface, usually referencing the surface of the earth. The accuracy of this data is determined primarily by the resolution (the distance between sample points). Other factors affecting accuracy are data type (integer or floating point) and the

(21)

actual sampling of the surface when creating the original DEM. (ESRI, Arc-GIS 9.3 help data).

Errors in DEMs are usually classified as either sinks or peaks. A sink is an area surrounded by higher elevation values and is also referred to as a depression or pit. This is an area of internal drainage. Some of these may be natural, particularly in glacial or karst areas (Mark, 1988), although many sinks are imperfections in the DEM. Likewise, a spike or peak is an area surrounded by cells of lower value. These are more commonly natural features and are less detrimental to the calculation of flow direction.

Errors such as these, especially sinks, should be removed before attempting to derive any surface information. Sinks, being areas of internal drainage, prevent down slope flow routing of water. (ESRI, Arc-GIS 9.3 help data).

The number of sinks in a given DEM is normally higher for coarser resolution DEMs. Another common cause of sinks results from storing the elevation data as an integer number. This can be particularly troublesome in areas of low vertical relief. It is not uncommon to find 1 percent of the cells in a 30-meter-resolution DEM to be sinks. This can increase as much as 5 percent for a 3 arc–second DEM. DEMs may also contain noticeable horizontal striping, a result of systematic sampling errors when creating the DEM. Again, this is most noticeable on integer data in flat areas. (ESRI, Arc-GIS 9.3 help data).

The hydrologic analysis functions described here are designed to model the convergence of flow across a natural terrain surface. There is an assumption that the surface contains sufficient vertical relief that a flow path can be determined. The functions assume that water can flow in from many cells but out through only one cell. (ESRI, Arc-GIS 9.3 help data).

(22)

DEM is often used to create perspective maps and for various types of geological interpretation. In the present study the SRTM data can be used to produce DEM with a spatial resolution of 90 M × 90 M. (M. M.

Abdeen, etal, 2009) 1-6 Methodology:

Remote sensing techniques and GIS techniques have been integrated together, in order to get the geomorphological map of the study area (fig.

7).

Figure (7): Flow-chart shown the methodology of the study.

1-6-1 Tools:

The present study performs a GIS analysis using Arc Info-Arc GIS 9.3, which represented as a set of processes, such as Raster calculation, Slope, Aspect, TIN, Hill-shade, and hydrological analysis in addition to facilities of Db and working with it.

(23)

1-6-2 GIS:

Has been used to:

 Building geometric spatial information.

 Identify the spatial source using for geometric correction.

 Vector data geometric correction.

 Building topology.

 Making raster data.

 Data formats conversion (raster and vector).

 Save attributes.

 Cartographic representation for spatial information.

 Spatial analysis.

 Modelization.

GIS Data Capture Steps:

1- Scan the analog maps.

2- Geo-referencing the scanning maps.

3- Create Db (fig. 8).

4- Digitizing the data.

5- Apply topology.

6- Data format conversion (Vector and Raster).

7- Save attributes / Data entry (Tables, Census… etc).

(24)

Figure (8): Snap shot shown the structure of Db for the studied area.

Source: Capture from Arc-map 9.3 Interface.

(25)

1-7 Literature review:

No detailed and integrated geomorphological investigation work using GIS and RS of the present area was carried out. The geology of parts of area was studied by very few authors. No published hydrological work is available.

1-8 Thesis Structure:

1-8-1 Chapter one – Introduction.

1-8-1-1 Study objective and expected result.

1-8-1-2 Problem description.

1-8-1-3 Intended audience.

1-8-1-4 Study area.

1-8-1-5 Available data.

1-8-1-6 Methodology.

1-8-1-7 Literature review.

1-8-1-8 Thesis structure.

1-8-2 Chapter Two – DEM analysis.

1-8-2-1 TIN.

1-8-2-2 Slope.

1-8-2-3 Aspect.

1-8-2-4 Hill-shade.

1-8-2-5 Elevation and contour

1-8-3 Chapter Three – Hydrology of the Northern Gulf of Suez 1-8-3-1 Climatic condition.

1-8-3-2 Drainage net-works.

1-8-3-3 Qualitative and quantitative drainage investigation.

(26)

1-8-4 Chapter Four – Geology of the northern Gulf of Suez.

1-8-4-1 Litho-stratigraphy.

1-8-4-2 Geological history.

1-8-5 Chapter Five – Geomorphology of the northern Gulf of Suez.

1-8-5-1 Topography.

1-8-5-2 Structural setting.

1-8-6 Conclusion

(27)

Chapter Two

DEM Analysis

1- TIN.

2- Slope.

3- Aspect (Slope Direction).

4- Hill-shade.

5- Elevation and Contour.

(28)

2-1 TIN

Triangular Irregular Networks (TIN) has been used by the GIS community for many years and is a digital means to represent surface morphology. TINs are a form of vector based digital geographic data and are constructed by triangulating a set of vertices (points). The vertices are connected with a series of edges to form a network of triangles. There are different methods of interpolation to form these triangles, such as Delaunay triangulation or distance ordering. Arc-GIS support the Delaunay triangulation method. (ESRI, Arc-GIS 9.3 help data).

The resulting triangulation satisfies the Delaunay triangle criterion, which ensures that no vertex lies within the interior of any of the circumcircles of the triangles in the network. If the Delaunay criterion is satisfied everywhere on the TIN, the minimum interior angle of all triangles is maximized. The result is that long, thin triangles are avoided as much as possible. (ESRI, Arc-GIS 9.3 help data).

The edges of TINs form contiguous, non overlapping triangular facets and can be used to capture the position of linear features that play an important role in a surface, such as ridgelines or stream courses. The graphics below show the nodes and edges of a TIN (left) and the nodes, edges, and faces of a TIN (right). (ESRI, Arc-GIS 9.3 help data).

Because nodes can be placed irregularly over a surface, TINs can have a higher resolution in areas where a surface is highly variable or where more detail is desired and a lower resolution in areas that are less variable. (ESRI, Arc-GIS 9.3 help data).

The input features used to create a TIN remain in the same position as the nodes or edges in the TIN. This allows a TIN to preserve all the precision of the input data while simultaneously modeling the values between known points. You can include precisely located features on a

(29)

surface—such as mountain peaks, roads, and streams—by using them as input features to the TIN nodes. (ESRI, Arc-GIS 9.3 help data).

A TIN expects units to be in feet or meters, not decimal degrees.

Delaunay triangulations are not valid when constructed using angular coordinates from geographic coordinate systems. (ESRI, Arc-GIS 9.3 help data).

TIN models (fig. 9) are less widely available than raster surface models and tend to be more expensive to build and process. The cost of obtaining good source data can be high, and processing TINs tends to be less efficient than processing raster data because of the complex data structure. (ESRI, Arc-GIS 9.3 help data).

TINs are typically used for high-precision modeling of smaller areas, such as in Applied Geo-morphological applications, where they are useful because they allow calculations of planimetric area, surface area, and volume. (ESRI, Arc-GIS 9.3 help data).

The maximum allowable size of a TIN varies relative to free, contiguous, memory resources. 10 to 15 million nodes represent the largest size achievable under normal operating conditions with Win32. Regardless, it's strongly recommended to cap the size at a few million for the sake of usability and performance. Anything larger than this is best represented using a terrain dataset. (ESRI, Arc-GIS 9.3 help data).

Figure (9): TINs models structure.

Source: Arc-GIS 9.3 help data.

(30)

2-2 Slope

Identifies the rate of maximum change in z-value from each cell (fig.

10). (ESRI, Arc-GIS 9.3 help data).

Figure (10): Slope function description.

2-3 Aspect

Derives aspect from a raster surface. Aspect identifies the down slope direction of the maximum rate of change in value from each cell to its neighbors. Aspect can be thought of as the slope direction. The values of the output raster will be the compass direction of the aspect. (ESRI, Arc- GIS 9.3 help data).

Aspect identifies the steepest down slope direction from each cell to its neighbors. It can be thought of as slope direction or the compass direction a hill faces. Aspect is measured clockwise in degrees from 0, due north, to 360, again due north, coming full circle. The value of each cell in an aspect dataset indicates the direction the cell's slope faces (fig. 11). Flat areas having no down slope direction are given a value of -1. (ESRI, Arc- GIS 9.3 help data).

Figure (11): The direction the cell's slope faces.

(31)

Reasons to use the aspect function:

There are many different reasons to use the aspect function. For instance(ESRI, Arc-GIS 9.3 help data), you may want to:

 Find all north facing slopes on a mountain as part of a search for the best slopes for ski runs.

 Calculate the solar illumination for each location in a region as part of a study to determine the diversity of life at each site.

 Find all southerly slopes in a mountainous region to identify locations where the snow is likely to melt first as part of a study to identify those residential locations that are likely to be hit by runoff first.

 Identify areas of flat land to find an area for a plane to land in case of emergency.

2-4 Hill-shade:

The Hill-shade tool obtains the hypothetical illumination of a surface by determining illumination values for each cell in a raster. It does this by setting a position for a hypothetical light source and calculating the illumination values of each cell in relation to neighboring cells. It can greatly enhance the visualization of a surface for analysis or graphical display, especially when using transparency. (ESRI, Arc-GIS 9.3 help data).

By default, shadow and light are shades of gray associated with integers from 0 to 255 (increasing from black to white). The azimuth is the angular direction of the sun, measured from north in clockwise degrees from 0 to 360. An azimuth of 90 is east. The default is 315 (NW) (fig. 12).

(ESRI, Arc-GIS 9.3 help data).

(32)

Figure (12): Shadow and light shades.

The altitude is the slope or angle of the illumination source above the horizon. The units are in degrees, from 0 (on the horizon) to 90 (overhead).

The default is 45 degrees (fig. 13). (ESRI, Arc-GIS 9.3 help data).

Figure (13): The slope or angle of the illumination source above the horizon.

2-4-1 Using hill-shade for display

By placing an elevation raster on top of a created hill-shade and making the elevation raster transparent, you can create realistic images of the landscape. Add other layers, such as roads, streams, or vegetation, to further increase the informational content in the display (fig. 14). (ESRI, Arc-GIS 9.3 help data).

Figure (14): The use of hill-shade for display.

(33)

2-4-2 Using hill-shade in analysis

By modeling shade (the default option), you can calculate the local illumination and whether the cell falls in a shadow or not. And can identify those cells that will be in the shadow of another cell at a particular time of day. Cells that are in the shadow of another cell are coded 0; all other cells are coded with integers from 1 to 255. You can reclassify all values greater than 1 to 1, producing a binary output raster. In the example below, the black areas are in shadow. The azimuth is the same in each image, but the sun angle (altitude) has been modified (fig. 15). (ESRI, Arc-GIS 9.3 help data).

Figure (15): The use of hill-shade for analysis.

2-5 Elevation and Contour:

Contours are polylines that connect points of equal value, such as elevation, temperature, precipitation, pollution, or atmospheric pressure.

The distribution of the polylines shows how values change across a surface.

Where there is little change in a value, the polylines are spaced farther apart. Where the values rise or fall rapidly, the polylines are closer together. (ESRI, Arc-GIS 9.3 help data).

(34)

Why create contours?

By following the polyline of a particular contour, you can identify which locations have the same value. Contours are also a useful surface representation because they allow simultaneous visualization of flat and steep areas (distance between contours) and ridges and valleys (converging and diverging polylines). The example below (fig. 10) shows an input elevation dataset and the output contour dataset. The areas where the contours are closer together indicate the steeper locations. They correspond with the areas of higher elevation (shown in white on the input elevation dataset). The contour attribute table contains an elevation attribute for each contour polyline. (ESRI, Arc-GIS 9.3 help data).

In this study we can Creates contours or iso-lines from a raster surface (DEM) (fig. 16).

Figure (16): Creates contours or iso-lines from DEM.

Source: After Arc-GIS 9.3 help data.

(35)

Chapter Three

Hydrology of the Northern Gulf of Suez I- Climatic Condition.

II- Drainage Networks.

III- Hydrology.

(36)

In order to understand the hydrologic cycle of the studied area, we can the following function:

Precipitation= Evapotranspiration + Runoff + Infiltration (weyman, 1975).

3-1 Climatic conditions:

The climatic conditions prevailing in the area are typically arid, where the mean annual rainfall 20.2 mm per year.

3-1-1 Precipitation:

Generally speaking, the study area which is apart of the northern gulf of Suez, receives small amounts of rainfall mainly in autumn, winter and spring.

In the Suez station, the rainy period starts in October and ends in may, with a maximum recorded value of 5 mm in October. The average annual precipitation is 20.2 mm the months, June through September are dry (fig. 17).

Figure (17): The study area Precipitations.

Source: Data of stations from national authority for aerial observation, Egypt.

(37)

3-1-2 Air temperature:

The mean annual values of air temperature are 22.2 °С at the Suez station. In the winter months, the average ranges between 12 °С and 18 °С and in summer months it ranges between 25 °С and 29 °С (fig. 18).

Figure (18): The study area air temperature.

3-1-3 Relative Humidity:

The humidity is relatively higher in winter than in summer at Suez, it reaches its maximum value of 57% in November, whereas it reaches its minimum value of 43% in May.

3-1-4 Wind:

The direction of the prevailing wind in winter months is from the northwest and is generally cool. The area however is subjected during the spring months to the khamasien severe storms which blow from the southeast. These storms are hot and are considered to be an important degradation factor affecting the geomorphic configuration of the study area

(38)

(fig. 19). During the rest of the year, the direction of wind is generally from the north with an average speed of 15 km/h (Salem, 1988).

Figure (19): The study area wind.

3-1-5 Evapo-transpiration:

To determine the evapo-transpiration, we must have the required climatic data, these are: mean air temperature, mean relative humidity, total wind run, mean sunshine duration in hour/day and mean radiation.

The daily potential evapo-transpiration at wadi ghweibba (western side of the Gulf of Suez) ranging monthly in mm/day in between 2.37 mm/day in December to 7.9 mm/day in June (Heikal, 1985).

3-2 Drainage Networks:

To determine the volume of runoff at the studied area the following equation is used:

V= 750*A (ps-8) Where: v: is the average runoff volume per storm.

(39)

A: is the catchments area km².

Ps: is the average rainfall ≥ 10mm. per storm (Ball, 1937).

When the volume of runoff is calculated to every channel order and sub basin in the studied area, the relation between the runoff volume and both the channel order and sub basin area is direct proportion.

3-2-1 Drainage Basin Analyses:

Quantitative description of the drainage pattern was discussed in different works: Horton, 1945 and later elaborated or modified by Strahler, 1957 and Shreve, 1970 (Gregory and walling, 1973).

3-2-2 Hydrology Model:

An understanding of the shape of the earth's surface is useful for many fields, such as regional planning, and agriculture …etc. These fields require an understanding of how water flows across an area and how changes in that area may affect that flow.

When modeling the flow of water, you may want to know where the water came from and where it is going. The following topics explain how to use the hydrologic analysis functions to help model the movement of water across a surface, the concepts and key terms regarding drainage systems and surface processes, how the tools can be used to extract hydrologic information from a digital elevation model (DEM), and sample hydrologic analysis (fig. 20).

(40)

Figure (20): Flow chart of DEM hydrology analysis methodology.

Source: The Arc-GIS 9.3 help data.

The hydrologic modeling functions in Arc-GIS Spatial Analyst provide methods for describing the physical components of a surface. The hydrologic tools allow you to identify sinks, determine flow direction, calculate flow accumulation, delineate watersheds, and create stream networks. The image below is of a resulting stream network derived from an elevation model.

Using an elevation raster or digital elevation model (DEM) as input, it is possible to automatically delineate a drainage system and quantify the characteristics of the system. The following graphics illustrate the steps involved in calculating a watershed and stream network from a DEM.

Using the DEM as input into the Flow Direction tool, the direction in which water would flow out of each cell is determined.

With the Sink function, any sinks in the original DEM are identified. A sink is usually an incorrect value lower than the values of its surroundings. The depressions shown in the graphic above (the scattered colored points) are problematic because any water that flows into them

(41)

cannot flow out. To ensure proper drainage mapping, these depressions can be filled using the Fill tool.

To create a stream network, use the Flow Accumulation tool to calculate the number of upslope cells flowing to a location. The output of the Flow Direction tool from above is used as input.

A threshold can be specified on the raster derived from the Flow Accumulation tool; the initial stage is defining the stream network system.

This task can be accomplished with the Con tool or using Map Algebra. An example of Con is newraster = con (accum > 100, 1). All cells with more than 100 cells flowing into them will be part of the stream network. Apply the Stream Order tool to represent the order of each of the segments in a network. The available methods for ordering are the Shreve and Strahler techniques.

Table (3): Descriptions of DEM hydrology analysis tools.

Source: The Arc-GIS 9.3 help data.

(42)

3-2-3 Determining flow direction

One of the keys to deriving hydrologic characteristics about a surface is the ability to determine the direction of flow from every cell in the raster.

This is done with the Flow Direction function.

This function takes a surface as input and outputs a raster showing the direction of flow out of each cell. If the output drop raster option is chosen, an output raster is created showing a ratio of the maximum change in elevation from each cell along the direction of flow to the path length between centers of cells and is expressed in percentages. If the force all edge cells to flow outward option is chosen, all cells at the edge of the surface raster will flow outward from the surface raster.

There are eight valid output directions relating to the eight adjacent cells into which flow could travel. This approach is commonly referred to as an eight direction (D8) flow model and follows an approach presented in (Jensen and Domingue, 1988).

The direction of flow is determined by finding the direction of steepest descent, or maximum drop, from each cell. This is calculated as:

Maximum drop = change in z-value / distance.

The distance is determined between cell centers. Therefore if the cell size is one, the distance between two orthogonal cells is one and the distance between two diagonal cells is 1.414216, the square root of two. If the maximum descent to several cells is the same, the neighborhood is enlarged until the steepest descent is found.

When a direction of steepest descent is found, the output cell is coded with the value representing that direction.

If all neighbors are higher than the processing cell, the processing cell is a sink and has an undefined flow direction. Cells with undefined flow direction can be flagged as sinks using the Sink function. To obtain an

(43)

accurate representation of flow direction across a surface, the sinks should be filled.

Figure (21): Working of flow direction analysis.

Source: The Arc-GIS help data.

How Flow Direction works

The direction of flow is determined by the direction of steepest descent from each cell. This is calculated as:

Change in z-value / distance * 100.

The distance is calculated between cell centers. Therefore, if the cell size is 1, the distance between two orthogonal cells is 1, and the distance between two diagonal cells is 1.414.

If the descent to all adjacent cells is the same, the neighborhood is enlarged until a steepest descent is found.

If all neighbors are higher than the processing cell, it will be considered noise, filled to the lowest value of its neighbors, and have a flow direction toward this cell. However, if a one-cell sink is next to the physical edge of the raster or has at least one No Data cell as a neighbor, then it is not filled due to insufficient neighbor information. To be considered a true one-cell sink, all neighbor information must be present.

(44)

If two cells flow to each other, they are sinks and have an undefined flow direction. This method of deriving flow direction from a digital elevation model (DEM) is presented in Jenson and Domingue (1988).

Figure (22): Flow direction map of the studied area.

Source: Derived from DEM of the studied area.

3-2-4 Flow Accumulation:

A sample usage of the Flow Accumulation tool with the {in_weight_raster} might be to determine how much rain has fallen within a given watershed. In such a case, the {in_weight_raster} may be a continuous raster representing average rainfall during a given storm. The output of Flow Accumulation would then represent the amount of rain that would flow through each cell, assuming that all rain became runoff and there was no interception, evapo-transpiration, or loss to groundwater. This could also be viewed as the amount of rain that fell on the surface, upslope from each cell.

(45)

The results of Flow Accumulation can be used to create a stream network by applying a threshold value to select cells with a high accumulated flow. For example, the procedure to create a raster where the value one represents the stream network on a background of No Data could use one of the following:

 Perform a Con operation in which the input conditional raster is

"Flowacc", the input true raster a constant "1", and the expression "> 100".

 Alternatively, perform a Set Null in which the input conditional raster is

"Flowacc", the input true raster a constant "1", and the expression "< 100".

In both examples, all cells that have more than 100 cells flowing into them are assigned one; all other cells are assigned No Data. For future processing, it is important that the stream network, a set of raster linear features, be represented as values on a background of No Data.

The resulting stream network can be used as input to the Stream Order, Stream Line, and Stream Link tools.

This method of deriving accumulated flow from a DEM is presented in Jenson and Domingue (1988). An analytical method for determining an appropriate threshold value for stream network delineation is presented in Tarboton et al. (1991).

(46)

Figure (23): Flow accumulation of the studied area.

Source: Derived from flow direction map of the studied area.

Figure (24): Stream order of studied area basins.

Source: Derived from flow accumulation map, flow direction map and DEM of the studied area.

(47)

3-3 Qualitative and quantitative drainage investigation:

Figure (25): The drainage basins of the studied area.

Source: Obtained from DEM and topographic sheets.

(48)

Straihler analysis of drainage system is used here, generally table (4) shown morphometric parameter for the studied area (based on basin number in fig. 25):

1st Order 2nd Order 3rd Order 4th Order 5th Order 6 th Order

Basin no.

Area Km squre

parameter Km

Basin length Km

Basin Wides Km

High altitude

M

Low

altitude M Number Length Km Number Length Km Number Length Km Number Length Km Number Length Km Number Length Km

1 5.9 12.6 4.2 2.4 45 10 3 3.97 1 2.1

2 9.3 14.7 5 3.2 52 18 4 8.54 1 1.32

3 122.9 65.3 20.7 8.3 505 8 55 74.86 14 33.53 2 18.1 1 15.28

4 65.7 56.2 22.3 4.9 1128 10 28 43.93 6 13.1 1 27.67

5 112.4 62.6 23.2 10 1239 15 50 68 11 37.81 2 19.44 1 10.64

6 12 17.6 6.7 2.4 172 17 6 8.89 1 5.13

7 9.5 14.3 5.6 2.7 145 24 4 7.6 1 3.02

8 11.8 24.3 9.9 2.1 290 35 6 8.12 1 5.67

9 26 31 11.9 3.8 602 35 8 17.23 2 9.37 1 3.92

10 18 30.7 12.2 2.8 891 25 10 10.23 3 4.6 1 9.65

11 7.6 15.8 6.7 1.9 193 23 4 3.37 2 4.76 1 1.46

12 13.4 20.7 7.7 3.9 707 31 6 8.03 2 0.944 1 7.14

13 8.7 15 5.3 3.2 679 26 3 4.42 1 5.41

14 5.4 12.1 4.7 2.2 702 6 2 3.03 1 1.61

15 72.6 39.7 12.1 8.6 1100 23 41 41.52 12 19.75 2 6.34 1 7.27

16 67.4 38.4 12 10.8 956 10 38 34.18 5 14.51 2 10.56 1 4.81

17 4.6 9.4 3.1 2.1 769 21 3 4.35 1 0.61

18 8.5 15.3 4.6 2.8 863 8 5 6.23 1 3.07

19 54.3 40.4 12.3 8.2 918 20 23 26.53 5 8.25 2 3.7 1 10.65

20 8.5 13.5 4.7 3 538 9 4 6.4 2 1.4 1 0.4

21 35.49 30.32 11.21 6.9 886 9 13 18.93 6 10.18 2 4.3 1 6.3

22 35.49 30.32 9.54 5.2 918 11 15 2.1 5 2.5 2 0.32 1 0.1

23 2954.2 324.3 65.8 80.1 1182 10 1310 1519.9 246 800.27 81 432.8 14 256.23 5 64.85 1 56.82

24 34.4 32.4 10 4.8 107 10 16 14.88 4 20.37 1 4.18

25 643.5 143 47.9 28.8 452 13 289 326.51 76 182.74 21 95.67 3 42.42 1 30.24

26 21.5 29.1 9.5 3.6 109 11 12 13.7 2 3.67 1 9.24

27 29.9 35.5 13.9 3.5 136 14 13 19.76 2 14.79 1 0.085

28 379.1 109 32.3 22.2 867 18 175 217.64 45 95.67 12 55.15 2 38.04 1 15.91

(49)

29 9.1 18.5 7 2.1 90 25 4 4.48 1 4.43

30 94.8 49.5 16.7 11.9 645 41 45 48.4 14 40.82 2 27 1 3.02

31 18.3 30 11.5 2.9 567 55 5 9.25 2 9.21 1 5.08

32 2.9 7.4 2.8 1.7 120 64 2 1.72 1 0.91

33 3.4 10.3 3.7 1.4 204 71 2 1.99 1 1.94

34 10.9 15.5 6.1 3.2 526 81 7 6.88 1 4.21

35 7.3 12.3 4.9 2.2 527 172 3 3.31 1 2.18

36 8.8 13 3.3 3.9 613 64 6 6.07 1 3.88

37 6.9 11.8 3.8 2.7 631 67 4 6.66 1 0.89

38 30 29.8 7.9 6.6 856 40 12 22.37 2 2.52 1 8.3

39 361.6 101.6 36 14.6 846 16 179 219.44 92 141.7 43 48.08 1 35.42

40 3.5 9.3 2.9 1.5 33 10 3 2.22 1 1.33

41 19.8 39.8 14.8 2.9 101 8 8 11.81 2 13.24 1 3.79

42 16.7 30.7 10.3 1.8 75 13 7 9.95 2 6.18 1 4.64

43 19.8 3.98 15.1 3.49 102 10 8 1.2 2 1.3 1 0.4

44 16.66 3.11 11.37 1.8 69 11 7 1 2 0.62 1 0.47

45 76.24 5.19 17.44 7.2 68 5 34 3.8 8 2.2 3 1.54 1 0.2

46 1.33 0.5 1.64 0.89 8 4 2 0.2 1 0.02

47 6.29 1.13 4.73 1.89 23 7 3 0.5 1 0.23

48 260.1 9.24 35.1 11.45 279 4 121 15.14 50 6.33 5 5.2 1 2.1

49 50.7 3.27 11.25 5.59 92 7 24 3.7 8 2.1 2 0.7 1 0.2

50 53.13 5.47 23 4.19 240 3 19 3.9 4 0.22 2 2.24 1 0.005

51 263.1 9.18 33.1 14.69 503 0 109 16.1 59 9.9 7 3.44 2 3.1 1 0.1

52 20.4 27.9 9.3 2.8 51 4 12 15.3 3 10.96 1 1.58

53 445.9 160.5 46.7 23.8 766 6 195 208.89 49 110.82 10 65.21 2 24.38 1 45.69

54 20.2 29.9 12.8 2.6 94 5 8 10.27 2 9.08 1 3.94

55 4.2 9.7 2.9 2.7 47 1 3 2.47 1 1.94

56 81.9 69.5 25.7 5.4 525 4 32 51.91 8 15.86 2 1.92 1 23.78

57 179.9 90.8 30.2 10.4 699 5 77 92.37 12 40.97 4 44.31 2 6.46 1 12.19

58 11.5 15.8 5.5 2.4 32 1 5 10.17 1 2.43

59 31.6 29.2 10.5 4 85 7 16 19.96 4 11.01 1 5.06

60 7.5 17.9 6.2 2.3 35 8 4 6.22 1 2.3

61 79.2 49 18.1 7.9 236 1 36 29.76 8 27.86 2 19 1 2.87

62 7.3 14.2 5.1 2.4 52 4 4 4.1 1 2.1

63 24.4 27.3 11 3.2 142 6 10 19.33 2 8.62 1 4.44

Mapping Terrain Characteristic in the Northern Gulf of Suez.