International journal of engineering and geosciences vol 1 issue 1 by selcuk university

International Journal of Engineering and Geosciences (IJEG), Vol; 1 , Issue; 01, pp. 1-7, December, 2016, ISSN 2548-0960, Turkey, DOI: Your DOI number

INVESTIGATION OF THREE-DIMENSIONAL MODELLING AVAILABILITY TAKEN PHOTOGRAPH OF THE UNMANNED AERIAL VEHICLE; SAMPLE OF KANLIDIVANE CHURCH Ulvi, A., 1* Toprak, A.S.,2 1

SelcukUniversity, Hadim Vocational Schools of Higher Education, Konya, Turkey (aliulvi@selcuk.edu.tr) 2 KOP

Administration , Konya, Turkey (ahmetsuadtoprak@gmail.com)

*Corresponding Author, Received: 01/06/2016, Accepted: 15/07/2016 ABSTRACT: Cultural heritages due to have different natural characteristics, have different sizes, and heir complicated structure should be measured and requires a more sophisticated measurement tools and techniques to documentation. One of them aerial photos taken by UAV pictures to use in architectural photogrammetry.In this study, Photogrammetric study was conducted in the ancient church next to the pothole. The study was completed with photographs taken from the air with UAV and close range photogrammetry. The images obtained from both methods adjusted in photogrammetric software and obtained a three-dimensional model of the church. Photography by UAV has proved to be a technical supporters of close range photogrammetry. Also coordinates of the reference points on the images obtained through photogrammetric software and compared with terrain coordinates. Point position accuracy of points mxyz = 2.1 cm were found. In order to protect the world heritage of cultural heritage IHA help to be sensitive enough to measure derived from aerial photographs taken, can be used as a base to work from different professional disciplines, The UAV was concluded in anywhere near the height can be used for photogrammetric. Keywords: UAV Photogrammetry, Precision,3D model

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 1-7, December, 2016,

located 3 km north of Ayas. The first settlement on the ruins of the old name Kanytelleis BC It was built in the late 3rd century. A tower of the period to the Hellenistic city has kept its existence until the 11th century."KanlıBloody Divane", the pothole within the city, former offenders, among the people because of the belief that gnawed by wild animals, as is known. The church is next to the pothole (Figure 1).

1. INTRODUCTION In many of cultural heritage documentation work could not be registered for reasons such as the lack of, cost, technology insufficieny, qualified staff and time constraints. (Hunt et al from., 2014).Although Turkey is an important country in terms of archaeological and cultural heritage, the lack of efficient work in the field work or be very limited,of the studies can not always be carried out with sufficient accuracy and lack of documentation however, due to a misunderstanding of the scope,not being able pass on to future generations is in danger. This troubled situation of new technologies in order to produce solutions in this area in order to prove the availability of terrestrial laser scanning technology and Unmanned Aerial Vehicle (UAV) is used in this context. Cultural heritage of different natural features, are required to have a different size and detail can be measured due to the complex and sophisticated measuring tools and techniques to document. One of them is the Unmanned Aerial Vehicle (UAV) technology.

Figure 1. Kanlıdivane Church overview

1.1 Unmanned aerial vehicles (UAV) UAV's can be defined as fixed and rotary wing aircraft which is on the fly without a human being (Eroglu, 2013). These vehicles by remote control, semiautomatic, automatic, or have all of these capabilities (EISENBEISS, 2009). The academic resources were analyzed, we come across dozens of similar statements about the UAV. They are also considering unmanned aerial vehicles (UAVs) could make the definition as follows: Which can be controlled from the ground, flight planning capabilities which, with fixed or rotary wing, military and used in civilian areas on a pilot system for non Unmanned Aerial Vehicle (UAV) is called

3.MATERIAL METHOD 3.1. Pre-work preparation Used in the application H (Figure 2), digital camera (figure 3), total station (Figure 4) and a ground control plates used in the evaluation of the photograph obtained from the UAV (Figure 5), paper targets affixed to the wall (Figure 6) and the image transmission system, (Figure 7) are provided.

1.2 UAV Benefits Against manned system,The biggest advantage of UAVs; UAV in risky situations without risking human life and inaccessible areas of the low-altitude flight profile and it is close to the ends of the object and can not be used in place manned system. For example, natural disaster areas, mountainous and volcanic areas, flood plains, earthquake and accident scenes and desert areas, areas that are difficult to enter, are used. (Ulvi A., 2015)

Figure 2. DJI Phantom UAV

In addition to these advantages, the mapping activities and architectural applications, and are also used frequently in archaeological sites. 2.STUDY AREA Kanlıdivane is in the rural area of Erdemli district, which is a part of Mersin Province. It is 18 km (11 mi) to Erdemli and 55 km (34 mi) to Mersin. Its altitude is approximately 230 m (750 ft). It is close to the town Kumkuyu at the coast and just few hundred meters to Çanakçı rock tombs.Kanytelleis-Kanlıdivane ruins Mersin-Silifke highway since the 50th km, the resort is

Figure 3. Canon PowerShot A810

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 1-7, December, 2016,

3.2. Field study Ground control points have been established as a homogeneous field in the application (Figure 8).In this application 8 Ground control points has been installed.While for establishment of ground control points, point of care has been taken to distribute to completely cover the work area and to see each other.

Figure 4.Total Station Topcon GPT 3007

Figure 8. Distribution of ground control points in field Georeferencing operation of ground control points were made by Topcon GPT 3007 reflectorless total station (Figure 9). Around of applications, making closed traverse was calculated coordinates of ground control points.ground control and coordinates of the feature point is measured in the local system (Figure 10).

Figure 5.Graund Control Point

Figure 6.Target

Figure 9. Surveying with Topcon GPT 3007

Figure 7. FatShark

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 1-7, December, 2016,

Figure 10. Measuring point operations, and UAV Ground Control Points overview

Figure 13. Flying with UAV After the flight the UAV, the aerial photography work has been completed. Aerial photographs obtained is exemplified below (Figure 14-15).

After measurement and rectification process, UAV flight madefinal checks have been completed (Figure 11-1213).

Figure 14.Taken photograph with UAV Figure 11. UAV, FPV control and image transfer system

Figure 15.Taken photograph with UAV Figure 12. performed the final pre-flight checks After this process is completed, work has started on the office using data obtained..

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 1-7, December, 2016,

After the drawing, the sensitivity study was conducted on thirty points determined on the church. Coordinates obtained from the land of this point, been accepted as absolute coordinates. The coordinates of the reference point on the adjusted images, obtained through photogrammetry software and compared with terrain coordinates.

3.3.Office work The coordinates of the traverse used for field measurement is shown in Table 1. Table 1. traverse coordinates N.N.

P.1

1000.000

P.2

1000.000

1021.225

1001.583

P.3

1035.587

1027.236

1005.679

P.4

1052.584

1014.728

1002.668

P.5

1026.608

1007.357

1001.568

P.6

1028.650

1019.729

1001.710

Point position accuracy of points mxyz = 2.1 cm were found (Table 2).

4. RESULTS Especially in the field of architecture photogrammetry, UAV usage began to hold an important place, increasingly widespread, providing significant advantages in terms of cost time,and effort. for users. In order to protect the world heritage of cultural heritage, with the help of UAV Aerial photographs taken, the measurement obtained

Photos taken from the ground, and obtained from the UAV photos was combined in Photomodeler photogrammetry software and adjustedand made ready for drawing (Figure 16).

-sufficient accuracy as in, -can be used as a base to work from different professional disciplines, -UAVs can be used for photogrammetric anywhere near heights It concluded has been reached. For documenting the historical and cultural heritage, the use of close range photogrammetry together with the UAV is considered to give a new impetus to the work done in this area. In addition, this model of UAV 's help made using photogrammetric techniques, photographing opportunities increase, and with reason, allows documentation to be more comprehensive and realistic (Toprak AS, 2014)

Figure 16. Regulation of photographs obtained in the field and to be ready drawings 3D drawing of the church through adjusted images is completed (Figure 17).

Figure 17. 3D drawing of church

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 1-7, December, 2016,

Table 2. Comparison of point coordinates and differences Araziden Elde edilen

Resimlerden elde edilen

(Kesin Koordinatlar)

Koordinatlar

Farklar

Vy(cm)

Vx(cm)

Vz(cm)

VyVy(cm)

VxVx(cm)

VzVz(cm)

1.4

0.9

1.4

2.0

0.8

2.0

1.1

1.2

Y(m)

X(m)

Z(m)

1.2

0.2

0.7

1.4

0.0

0.5

1022.155

1012.437

1002.439

1022.141

1012.428

1002.425

0.014

0.009

0.014

1.0

1.2

1.3

1.0

1.4

1.7

1023.585

1024.625

1003.644

1023.574

1024.614

1003.633

0.011

0.8

-0.9

-1.1

0.6

0.8

1.2

1005.794

1025.177

1001.568

1005.782

1025.175

1001.561

0.012

0.002

0.007

0.4

-1.3

1.5

0.2

1.7

2.2

1013.200

1003.236

1002.686

1013.190

1003.224

1002.673

0.010

0.012

0.013

-0.7

-0.6

1.2

0.5

0.4

1.4

1013.426

1011.582

1001.702

1013.418

1011.591

1001.713

0.008

-0.009

-0.011

0.7

1.1

-1.2

0.5

1.2

1.4

1011.285

1010.104

1001.703

1011.281

1010.117

1001.688

0.004

-0.013

0.015

0.9

1.3

0.8

1.7

1014.052

1024.595

1001.635

1014.059

1024.601

1001.623

-0.007

-0.006

0.012

2.0

1.3

1.2

4.0

1.7

1.4

1012.413

1028.761

1001.638

1012.406

1028.750

1001.650

0.007

0.011

-0.012

1.3

-1.2

1.5

1.7

1.4

2.2

1008.660

1036.831

1001.754

1008.651

1036.818

1001.741

0.009

0.013

1.4

0.8

-1.3

2.0

0.6

1.7

1013.754

1036.155

1001.723

1013.734

1036.142

1001.711

0.020

0.013

0.012

1.1

-0.7

1.4

1.2

0.5

2.0

1010.803

1042.134

1001.667

1010.790

1042.146

1001.652

0.013

-0.012

0.015

1.3

-1.2

0.8

1.7

1.4

0.6

1018.076

1029.970

1001.713

1018.062

1029.962

1001.726

0.014

0.008

-0.013 -1.4

0.7

1.1

2.0

0.5

1.2

1018.132

1047.474

1003.755

1018.121

1047.481

1003.741

0.011

-0.007

0.014 1.4

1.2

-1.2

2.0

1.4

1013.834

1019.238

1003.808

1013.821

1019.250

1003.800

0.013

-0.012

0.008 1.8

1.2

-1.6

3.2

1.4

2.6

1018.445

1019.955

1003.266

1018.459

1019.948

1003.255

-0.014

0.007

0.011 -1.1

-1.6

0.7

1.2

2.6

0.5

1026.290

1041.783

1005.373

1026.276

1041.771

1005.385

0.014

0.012

-0.012 1.0

1.2

-1.4

1.0

1.4

2.0

1005.509

1018.912

1001.218

1005.491

1018.900

1001.234

0.018

0.012

-0.016 1.1

1.4

0.5

1.2

2.0

0.2

300

1017.743

1013.358

1009.448

1017.754

1013.374

1009.441

-0.011

-0.016

0.007 1.4

-1.3

1.2

2.0

1.7

1.4

301

1013.415

1014.301

1009.440

1013.405

1014.289

1009.454

0.010

0.012

-0.014 0.9

-0.9

1.5

0.8

2.2

302

1013.518

1018.706

1011.609

1013.507

1018.692

1011.604

0.011

0.014

0.005 1.1

-1.4

1.6

1.2

2.0

2.6

303

1013.137

1018.776

1007.152

1013.123

1018.789

1007.140

0.014

-0.013

0.012 1.6

-0.5

1.6

2.6

0.3

2.6

304

1007.322

1019.947

1007.128

1007.313

1019.956

1007.113

0.009

-0.009

0.015 1.2

0.2

1.0

1.4

0.0

1.0

305

1009.922

1019.425

1004.038

1009.911

1019.439

1004.022

0.011

-0.014

0.016 -1.1

-0.3

1.3

1.2

0.1

1.7

306

1011.838

1019.024

1004.046

1011.822

1019.029

1004.030

0.016

-0.005

0.016 -1.0

1.4

1.0

2.0

307

1015.085

1018.335

1003.434

1015.073

1018.333

1003.424

0.012

0.002

0.010

308

1016.427

1018.039

1003.436

1016.438

1018.042

1003.423

-0.011

-0.003

0.013

1.1

1.2

-1.7

1.2

1.4

2.9

309

1014.619

1018.466

1008.562

1014.629

1018.452

1008.548

-0.010

0.014

1.3

-1.2

1.3

1.7

1.4

1.7

310

1015.557

1018.242

1008.565

1015.546

1018.230

1008.582

0.011

0.012

-0.017

1.0

1.3

1.2

1.0

1.7

1.4

311

1014.587

1018.444

1006.723

1014.574

1018.456

1006.710

0.013

-0.012

0.013

[VV]=

43.4

35.7

48.7

312

1015.556

1018.242

1006.739

1015.546

1018.229

1006.727

0.010

0.013

0.012

N.N

1.2

mxyz =

2.1

1.1

1.3

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 1-7, December, 2016,

REFERENCES Avdan, U., Gülşen, F. F., Ergincan, F. veÇömert, R. (2014). Arkeolojik Alanlarda Taş Planlarının Çıkarılmasında İnsansız Hava Araçlarının Kullanılması (AnavarzaÖrneği). Mühendislik Ölçmeleri Sempozyumu, 15-17 Ekim 2014, HititÜniversitesi, Çorum.

UlviA.,2015, Metrik Olmayan Dijital Kameraların Hava Fotogrametrisinde Yakın Resim Çalışmalarda (Yere Yakın Yüksekliklerde) Kullanılabilirliği Üzerine Bir Çalışma”, DoktoraTezi

Eisenbeiss, H., 2009, “UAV Photogrammetry” Doctor of

Toprak A.S.,2014, “Fotogrametrik Tekniklerin İnsansız Hava Araçları İle Mühendislik Projelerinde Kullanılabilirliğinin Araştırılması” Yüksek LisansTezi.

Sciences. Eroğlu O., 2013, “İnsansız Hava Araçlarında Arazi Verilerine Dayalı UçuşYönü Sınırlamasız Konumlandırma Sistemi Benzetim Çalışması” YüksekLisansTezi.

International Journal of Engineering and Geosciences (IJEG), Vol; 1 , Issue; 01, pp. 8-17, December, 2016, ISSN 2548-0960, Turkey, DOI: Your DOI number

THE PROPOSAL OF THE BUILDING APPLICATION FOR MORE BENEFITING FROM SOLAR LIGHT Erdem, N., 1* Ince, H.,2 ¹ Osmaniye Korkut Ata University, Engineering Faculty, Department of Geomatic Engineering, 80000 Osmaniye, Turkey, (nurierdem@osmaniye.edu.tr); ² Trakya University, Edirne Technical Sciences Vocational School, 22020 Edirne, Turkey, hince@trakya.edu.tr *Corresponding Author, Received: 08/06/2016, Accepted: 09/07/2016 ABSTRACT: There is a proverb, that emphasizes the importance of sunlight for human health, which is “Where the sun does not enter the physician enters”. It is one of the most important elements to see the sunlight of the buildings for both healthy life and energy saving. The positioning may be desirable to take the advantage of the morning and evening sunlight of the buildings to be constructed in the housing area. Indeed, in 1985 constructed blocks of buildings in Eskisehir Yenikent in public housing projects, designed and applicated according to the this principle. This study was made with the purpose of the application and to be designed to see the sunlight during the day of the blocks will be the method of discrete structures in accordance with the development plan will be built the individual or collective housing project in the Eastern Mediterranean. At the beginning of this study, the azimuth angles were calculated in the sunrise and sunset in four provinces forming region, baseline, throughout the year and annual sun graphics arranged, by meteorological data help received from meteorological stations in the region. Information of sun tanning about the province of Adana was found sufficient to represent the region according to the results of the evaluation. It describes the inning information building design and application in accordance with the principles for provinces in the region at the end of the study. Keywords: Solar Energy, Urban Design, the Building Application.

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 8-17, December, 2016,

energy, we don’t benefit enough from solar energy. In terms of the solar energy potential due to its geographical location, our country is very fortunate compared to other countries. Monthly average solar potential of Turkey is given in Table 1 (URL-3).

1. INTRODUCTİON As a necessary factor of production and an essential of public welfare, energy is one of the basic inputs of economical and sociological growth. To meet the human needs and to maintain its sustainable and healthy development, energy is needed and it has many areas of usage like industry, buildings, transport and agriculture (Koç, 2008).

In Turkey, Southeastern Anatolia Region has the maximum field of solar energy, followed by Mediterranean Region. Regional distribution of solar energy potential of Turkey and sunshine durations are given in Table 2.

What kind of a residence we live in, in what kind of structures we continue our lives? What is the cost of this living to us, to our region, our country? What are the construction and utilization methods that make our lives easier, increase our quality of life and production, and allow us to save energy? For example; a housing in Ankara requires “4.5 times” more energy to live as against a housing in Berlin (Erengezgin, 2016).

Table 1. Monthly Average Solar Potential of Turkey (URL-3).

Months January February March April May June July August Septembe r October Novembe r December Total

In this study, information about the sunshine durations, solar energy potential and solar angles of our provinces in Eastern Mediterranean Region have been given, and azimuth angles of every month of the year and several days have been calculated. Taking the advantage of these solar angles; the azimuth angle needed for positioning the structure determined and in the fields, it is examined how to applique these angles to the corners of the building that will be made, application made on the subject, and the findings have been presented in the results section. 2. HEALTH BENEFITS OF THE SUN The effects of weather conditions and other factors on which life depends, on human psychology is very large, especially sunlight. Sunshine also effects our mental health. If there is no sun, there is depression. Experts warn those who want to get a home: "Prefer sun-exposed buildings." Indicating that living in a dark environment makes it easier to fall into depression, the darkness narrowing the imaginary world and people without sun couldn’t look forward to life and could become more aggressive and angry, experts add "Sunlessness reduce people’s energy, leading them to introversion, making them unhappy." (URL-1).

Monthly Total Solar Energy (Kcal/cm (kWh/m22 -month) month) 4,45 51,75 5,44 63,27 8,31 96,65 10,51 122,23 13,23 153,86 14,51 168,75 15,08 175,38 13,62 158,40

Average

Sunshine Duration (hours /month) 103,0 115,0 165,0 197,0 273,0 325,0 365,0 343,0

10,60

123,28

280,0

7,73

89,90

214,0

5,23

60,82

157,0

4,03 112,74 308,0 kcal/cm2day

46,87 1311 3,6 kWh/m2day

103,0 2640 7,2 hours/day

However, it was found through later studies that these values are lower than the true potential of Turkey. Since 1992 EİE and DMİ make evaluations in order to get more healthy results about solar energy. As a result of these evaluations in progress, these values of solar energy potential of Turkey are expected to increase further 20-25% more than the old results (URL-3). Table 2. Regional Distribution of Solar Energy Potential of Turkey (URL-3).

"Hazelnut-sized pineal gland in our brain produces the hormone melatonin. In a dark environment, the gland increases the production of its hormones. The hormone melatonin slows the physical movement of people, which makes them sleepy and exhausted. Being able to fall asleep in the dark is an indicator of this. Light reduces the production of melatonin and contrary symptoms begin to format. Person starts to cheer up and becomes more active." According to researches, suicidal tendencies are at an increasingly higher rate and people become more depressed in the Nordic countries (i.e. Sweden, Norway) (URL-2).

Region Southeaster n Anatolia Mediterrane an Eastern Anatolia Central Anatolia Aegean Marmara Black Sea

3. SOLAR ENERGY POTENTIAL IN TURKEY Nowadays, it is known that solar radiation energy has many benefits such as heating, hot water and air conditioning (Akıncıtürk, 1999a). Although our country is located in a region called sunbelt which is rich in solar

Total Solar Energy (kWh/m2-year)

Sunshine Duration (hours/year)

1460

2993

1390

2956

1365

2664

1314

2628

1304 1168 1120

2738 2409 1971

In our day, applications are performing to make the best of the sunshine in the buildings that will be constructed

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 8-17, December, 2016,

in collective housing areas. Thus in Eskişehir Yenikent district, building blocks that are located in the development area which has been constructed in 1985 for 1860 dwellers, are positioned to take advantage of sunlight during the day. As in this Eskişehir Yenikent housing estate example, “it is necessary to position buildings in housing estates according to the sunlight to save energy, to produce hot water and heating” in our country (Akıncıtürk, 1999b; İnce, 2005). 4. AZIMUTH While positioning the structures to benefit from sunlight, incidence direction angles of sun rays (azimuth angles) being utilized. Azimuth angle of the sun consist of the latitude of the construction site (), the declination angle of the sun for a particular day of the year () and the angle of the sunrise and sunset according to local noon. These angles are called azimuth (Deriş, 1975; Aksoy, 1975; Kılıç ve Öztürk, 1980).

Figure 1. Schematic of the Sun Zenith Angle, Elevation and Azimuth Angle, at the Spherical Triangle from the Point on Earth of N (İnce, 2005).

Latitude (angle)  : It is the angle of the line which combines the aboveground N point to the centrosphere, with the equatorial plane. It is marked with a (+) from the Equator to north and with a (-) to south (İnce, 2005).

Zenith angle (z): It is the angle of direct sun rays with the normal of horizontal plane (Figure 2). At sunrise and sunset z=90o. Zenith angle is obtained from the below formula (Aksoy, 1975). cos z= cos  cos  cos h + sin  sin  (2)

Hour Angle (h) : It is the angle between the line which combines the longitude of the taken into account point on earth with the centrosphere and the longitude indicated by the sun rays. Hour angle is calculated from “sun noon”, when the longitude of the sun and the longitude of the point which is being taken into account are the same. The difference is marked with a (–) for before the local noon, and with a (+) for after the local noon. Every one hour time difference is considered as a hour angle of 15o (İnce, 2005).

Solar elevation angle (y): It is the angle of the horizontal rays of the sun. As seen on Figure 1, z+y =90o. Solar elevation angle is obtained from the formula (İnce, 2005): y=90 - z. Solar azimuth angle () : This angle represents the deviation of sun rays rotation compared to the clockwise direction of north (Figure 1).  as follows (İnce, 2005);

Declination Angle () : It is the angle of the sun rays to the equatorial plane (Figure 1). This angle results from 23o 27’ degree which is between the rotational axis of the world and the normal of the orbital plane. Absolute value in solstices is maximum (June 21 summer solstice = +23O.45, December 22 winter solstice = -23o,45). Declination angle is obtained from the equation of (İnce, 2005):  = 23o,45 sin (360  n  284  )

 365 

Before the local noon (in degrees) =180o -o, (or in grade =200 -G ) (3) After the local noon (in degrees) =180o +o, (or in grade =200+G ) (4)

(1)

cos= cos  cos  cosh  sin  cos 

(5)

cos y

Here, n is the number of days.

5. PRESENTATION OF APPLICATION AREA Eastern Mediterranean Region containing Adana, Mersin, Hatay and Osmaniye provinces have been chosen for application. Sunshine duration is the time between the sunrise and the sunset on sunny days. From the relevant researches, in the provinces of Eastern Mediterranean Region (Figure 2) , sunshine duration by months, number of overcast and foggy days have been investigated and given as an abstract below.

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 8-17, December, 2016,

of Meteorology, annual average temperature is 18.8˚C, highest temperature is 45.6 ˚C, and lowest temperature is –8.4˚C. Due to the climatic conditions of this region, sun can be seen in every season (URL-5).

Figure 2. Locations of Hatay, Adana, Mersin and Osmaniye provinces which are located in Eastern Mediterranean Region (URL-4). 5.1. Hatay Province The annual sunshine duration of Hatay and its surrounding areas is around 2600-3000 hours. The annual monthly average sunshine duration has been determined as 7.15 hours/day (Figure 3). Annual temperature average is of 18°C. Maximum temperature recorded in August as 34.0, the minimum is recorded in December and January as of 2,0°C. Lowest temperature ranges between -4,-10°C below zero in high and inlands. The annual average temperature of Hatay and its districts are determined as 16°C and more. Average relative humidity is around 67%. Annual average of foggy days is between 1-50 days (Erarslan, 2012).

Figure 4. Sunshine duration of Adana Province (URL3).

Figure 3. Sunshine duration of Hatay Province (URL-3).

Figure 5. Sunshine duration of Mersin Province (URL3).

5.3. Mersin Province According to the measurements, annual number of days with overcast weather is 25,3. Majority of the year passes sunny and with some clouds. Mersin is one of the provinces with the highest sunshine duration in our country (Figure 5). Average daily sunshine duration is 7,4 hours and this can change up to 8-10 hours on summer days (URL-3).

5.2. Adana Province

The average annual temperature in Mersin is 18,7°C. Being detected in the 50-years long observation, the highest temperature is 40°C (21.06.1942), and the lowest temperature is -6,6°C (06.02.1950). Average temperature of summer days ranges between 25- 33°C. In winter the average temperature ranges between 9– 15°C. Some years, the temperature goes below 0°C. Snowfall can not be seen in coastal areas. However, there are varying amounts of snow at the Toros Mountains piedmonts in winter days. Annual average of relative humidity is 69%, and ranges between 65% 75% through months. As a result of 50-years long

The number of summer days which the temperature rises to or above 25˚C is 179. Adana has fair weather. The annual average of sunshine duration in 2015 is 7.13 hours (Figure 4), and the annual average of daily sunshine duration is determined as 345.92 cal/cm²/min. Days of the overcast weather is 49,2. Lowland and seaside daily average of sunshine duration is 8,60 hours. Sunshine duration is at its maximum at July, and it is at its minimum at December and January. Relative humidity average is around 65%. According to the 67 years of annual measurements by Regional Directorate

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 8-17, December, 2016,

1- As declination angle of the sun – for a perscrutation – values of 1. and 15. days of every months and June 21 and December 22 are calculated with relation no (1).

observations, there were snow cover of 2 cm thickness only in 01.01.1950 in city centrum. In the last 10-year period, a total of 27 days with fog incident have occured. The annual average number of foggy days is approximately 2 days (URL-6).

2- Eastern Mediterranean Region is between 32o 56’ – 36o 42’ longitudes and 35o 52’ – 38o 00’ latitudes. 36o, 37o and 38o considered as latitudes, on an examination of an example (Table 11), it has been seen that the acquired solar azimuth angles are in a close range, thereby the mean for the region used as =37o.

5.4. Osmaniye Province Since there aren’t any researches on annual average solar radiation and the annual total amount of solar energy have been made in this province, State Meteorology General Directorate and Osmaniye Directorate of Meteorology Station were not able to grant us any information. Highest temperature in 2007 was recorded in May 29 is 42.5 (°C), the lowest temperature is recorded in December 31 is -4.0°C. Maximum daily temperature difference is not recorded. Annual average of sunshine duration is 7.45 hours (Figure 6). Only the measured ones of the meteorological elements like numbers of snowy, snow covered, foggy, frosty days and the highest snow cover thickness in 2013 have been examined (Dolgun et al., 2013).

3- When sun is on the longitude of the considered point, in other words in local noon, hour angle is zero. The differences between the local noon and the sunrise and sunset are calculated as hour angles at sunrise and sunset (İnce, 2005). Information about hour angles of sunrise and sunset of Eastern Mediterranean Region provinces (Adana, Hatay, Osmaniye and Mersin) are obtained from the calendars showing the relevant time. In Table 3, the time of sunrise, sunset and noon in Adana, Hatay, Osmaniye and Mersin at June 21 and the hour angles and solar azimuth angles calculated from them have been given. Table 3. The time of sunrise, sunset and noon in Adana, Hatay, Osmaniye and Mersin at June 21 and the hour angles and solar azimuth angles.

Provinces and Latitudes

Sunrise

Hour Angle (h) 12.48Adana 5.12=7.36 37 o -114. 00 12.47Osmaniye 5.09=7.38 o 37 -114,50 12.48Hatay 5.12=7.36 o 36 -114.00 12.54Mersin 5.16=7.38 o 36 -114.50

Figure 6. Sunshine duration of Osmaniye Province (URL-3). It is concluded that in Eastern Mediterranean Region annual sunshine duration is at its maximum on summer months, at its minimum on winter months, number of foggy days is rather high in November, December, January and February, number of the days with overcast weather is in its minimum in July and August.

Azimuth Angle (β) 57 g,6796

59 g,4092

58 g,5893

59 g,0572

Sunset Hour Angle (h) 20.1112.48=7.23 110 o.75 20.0712.47=7.20 110 o,00 20.0512.48=7.17 109 o,25 20.1312.54=7.19 109 o,75

Azimuth Angle (β) 335g,1951

335 g,9738

335 g,4981

336 g,0029

In the examination at Table 3, it has been seen that there is only a 5 or 6 minutes of difference with every other four provinces sunrise, sunset and noon times, and it has not effected the solar azimuth calculation results significantly. In this respect, to easily calculate the solar azimuth angle for Eastern Mediterranean Region; angles of sunset and sunrise of Adana -which is located in the middle of the region- have been used and the obtained results have been given in Figure 7.

6. CALCULATING THE SOLAR AZIMUTH ANGLE IN EASTERN MEDITERRANEAN REGION AND DETERMINING THE SUITABLE LOCATION FOR THE HOUSINGS 6.1- Calculating the Solar Azimuth Angle in Eastern Mediterranean Region Solar azimuth angle consists of the declination angle of the sun (), latitude of the place (), hour angle of the sun (h) and elevation angle of the sun (y). These factors that is being used to determine the solar azimuth angle are obtained through;

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 8-17, December, 2016,

Figure 7. Solar azimuth angles calculated with sunset and sunrise hour angles for Eastern Mediterranean Region in some days of the year (it is arranged from İnce, 2005).

Figure 8. Location of a building between its solar azimuth angles of maximum sunrise and minimum sunset (İnce, 2005).

6.2. Determining the Suitable Location for the Housings

If a building is located as its C corner is applicated to the ground in compliance with AB and AD, the building would benefit from the sunlight to the utmost. If corners of the building will be applicated through polar method (Figure 8), application elements, netcad, eghas, geocad... etc. can be obtained with computer-aided mapping programs. According to the application elements that have been calculated with rectangular coordinate method, building corners should be sketched with computer-assisted cartography to the related building blocks and thereby the coordinates of these corner points should be obtained. From the building corner points that have been calculated with benchmark, polar applique elements of the building should be calculated and polar application should be done (İnce, 2005).

For a housing in Eastern Mediterranean Region to benefit from solar energy through sunrise to sunset all year long at maximum, the maximum value of azimuth angle at sunrise, and the minimum value at sunset should be taken into consideration. If we are to examine Figure 7; we can see that on December 22, sunrise is at its maximum azimuth angle (133G,4796), and sunset is at its minimum azimuth angle value (264G,4051). A building that has been considered with a rectangular default structure can be located as shown in Figure 5; between its maximum sunrise azimuth angle value (GD) and minimum sunset azimuth angle value (GB). In Figure 7, December 22 GD=Sunrise azimuth angle (133G,4796), GB =Sunset azimuth angle (264G,4051).  between GB and GD obtained from the formula (İnce, 2005): =GB - GD =130G,9255

In Eastern Mediterranean Region, before the application of the buildings to the property, in the related building block; it is determined how many buildings will be constructed in the field considering the size of the area, determined building type, size of the buildings, TAKS, KAKS and the garden distances (Yıldız, 1999). And related building corner application elements calculated with rectangular coordination method (Tüdeş, 1995).

(6)

In Figure 8, between the facades that are crossing at the corner A of the building, there is usually a right angle (=90o=100G). If the AB jamb of the building diverted from the left of GD direction, and the AD jamb of the building diverted from the right of GB with an angle of , considering , the azimuth angles of AB and AD is (İnce, 2005): =( - )/2=15G,4628, (AB)= GD +=148G,9424, (AD)= GB -=248G,9423 Approximately, (AB)=150G, (AD)=250G considered to ease the application.

6.3-Calculation of the Application Elements that are Necessary for the Application of the Building Corners to the Field In Eastern Mediterranean Region, before the application of the buildings to the property, in the related building block; it is determined how many buildings will be constructed in the field considering the size of the area, determined building type, size of the buildings, TAKS, KAKS and the garden distances (Yıldız, 1999). Later, related building corner application elements calculated with rectangular coordination method (Figure 9) or polar method (Tüdeş, 1995).

(7) (8) (9) can

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HC’=YBM+D’C’ C’B’=CB”=CB*cos(α+50G) B”B=CB*sin(α+50G) B’B=ÖBM+B”B HB’=HC’+C’B’

(19) (20) (21) (22) (23)

Obtained with above relations. If the application of the building corner points with polar method is wanted; first, the coordinations of corner points: YA=YH+HA’*sin(HJ)+A’A*sin[(HJ)+300G] XA=XH+HA’*cos(HJ)+A’A*cos[(HJ)+300G] YB=YH+HB’*sin(HJ)+B’B*sin[(HJ)+300G] XB=XH+HB’*cos(HJ)+B’B*cos[(HJ)+300G] YC=YH+HC’*sin(HJ)+C’C*sin[(HJ)+300G] XC=XH+HC’*cos(HJ)+C’C*cos[(HJ)+300G] YD=YH+HD’*sin(HJ)+D’D*sin[(HJ)+300G] XD =XH+HD’*cos(HJ)+D’D*cos[(HJ)+300G]

are obtained with above relations (İnce, 2000), then H point being the station, J being the junction, application elements of building corner points from H, considering the coordinates of the points, calculated with geodetic basic relations (Table 4).

Figure 9. The application of building corner points to a building block with prismatic method (İnce, 2005). Necessary application elements for application of a building to a block corner point with prismatic method is calculated with below relations (Figure 6).

Table 4. Necessary application elements for a building’s corner points that will be located to benefit from sunlight at most

In Figure 9; CC’=Front Yard Distance (ÖBM), DD”=Side Yard Distance (YBM). First, the azimuth angle (HJ) is calculated using H and J coordinates (YH, XH; YJ, XJ) with this relation:

D.N.

J A B C D

YH YJ YA YB YC YD

XH XJ XA XB XC XD

(HJ)=arctan(ΔYHJ/ΔXHJ) In Figure 6; α and θ angles; (HJ)>150G α=(HJ)-100G θ=100G-(50G+α)

Horizontal Distance

Azimuth Angle

Horizontal Angle

HJ HA HB HC HD

(HJ) (HA) (HB) (HC) (HD)

0.0000 (HA)-(HJ) (HB)-(HJ) (HC)-(HJ) (HD)-(HJ)

7. QUANTITATIVE APPLICATION

stated as per above, then using DAA”, DCD” and CBB” right-angled triangles, frontage of the building (CD=AB) and the depth (DA=CB) taken into consideration, elements needed for the application of the building corner points with prismatic method;

An empty building block from Osmaniye Municipality’s building site that has not yet any constructions made has been chosen for quantitative application (Figure 10). According to the zoning data from the zoning plan, for the buildings that will be made in this building block in the order of discrete structures; TAKS=0.35, KAKS=1.75. According to the Planned Area Regulations which is in use for the building areas in our country; In this building block, minimum facades should be 9 m, side yard distance should be 5.00 m, front yard distance should be 5.00 m, total building floor =KAKS/TAKS = 5, adjacent yard distance should be 3.50 m, back yard distance=building eaves height/2= (0.50+5*3)/2=7.75 m. obtained.

A’A=Right extent of the building’s A corner B’B=Right extent of the building’s B corner C’C=Right extent of the building’s C corner =ÖBM D’D=Right extent of the building’s D corner HA’=Right-foot length of the building’s A corner HB’=Right-foot length of the building’s B corner HC’=Right-foot length of the building’s C corner HD’=Right-foot length of the building’s D corner D”C=D’C’=CD*cosθ DD”=CD*sinθ HC’=YBM+D”C D’D=C’C+D”D=ÖBM+D”D DA”=DA*cosθ A’A=D’A”=D’D+DA” AA”=D’A’=DA*sinθ HA’=YBM+D’A’ HD’=D’’’D=YBM

B.N.

(10) (11) (12) (13) (14) (15) (16) (17) (18)

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 8-17, December, 2016,

HA’=AA”=5.00 m HD’=HA’+A’D’=6.737 m HC’HD’+D’C’=26.433 m HB’=HC’-CC”’=24.696 m values are obtained. Because the building’s prismatic applique elements of A, B, C, D corners and the coordinate of H is located in the left side of the HJ azimuth angle; (P’P)=(HJ)+300=61.1134 values taken into consideration; YA=YH+5.00*sin(HJ)+14.848*sin61.1134=520785.888 m XA=XH+5.00*cos(HJ)+14.848*cos61.1134=4103406.67 9m YB=YH+24.696*sin(HJ)+18.322*sin61.1134=520800.03 0m XB=XH+24.696*cos(HJ)+18.322*cos61.1134=4103392. 537 m

J Figure 10. Osmaniye Municipality construction plan example.

YC=YH+26.433*sin(HJ)+8.474*sin61.1134=520792.959 m XC=XH+26.433*cos(HJ)+8.474*cos61.1134=4103385.4 66 m

Since the terminal points (H, J) of south-west side of the building block are not apparent, frontal lines of the indicated building block has been made by crossing the lines and quantitative results of the coordinates have been given in Table 5.

YD=YH+6.737*sin(HJ)+5.000*sin61.1134=520778.817 m XD=XH+6.737*cos(HJ)+5.000*cos61.1134=4103399.60 8m

Point Y X H 520770.857 4103402.259 J 520820.798 4103330.930 Table 5. Coordinates of H and J points.

And for the calculation of corner parcel E, F and G coordinates;

Availing from the coordinates of the H and J points, with geodetic basic relations, azimuth angle (HJ) is

HE=HC’+3.50=29.933 m. Assuming the EF is perpendicular to HJ, considering the distance of the backyard, with the values of EF=BB’+7.75=32.446 m ve HG=EF, these results were obtained:

(HJ)=161.1134. With a building that will be constructed as two departments on the base, for every department 100 m2 space and facade DC=AB= 20 m., depth of building=DA=BC=10 m. designed.

YE=YH+29.933*sin(HJ)=520788.025 m XE=XH+29.933*cos(HJ)=4103377.739 m

According to the design in Figure 6, for the building that will be applicated in the corner parcel of building block,

YF=YH+29.933*sin(HJ)+32.446*sin61.1134=520814.60 4m XF=XH+29.933*cos(HJ)+32.446*cos61.1134=4103396. 348 m

α=(HJ)-100G=61G.1134 θ=(HJ)-150G=11G.1134

YG=YH+0.00*sin(HJ)+32.446*sin61.1134=520797.436 m XG=XH+0.00*cos(HJ)+32.446*cos61.1134=4103420.86 8m

above values have been found, then as prismatic application elements; AA”=YBM=5.00 m DD’=ÖBM=5.00 m

For the applique of the corners of the building that will be constructed in the HGFE block corner point parcel with polar method; considering H as the station and the J as the junction point, necessary applique elements are calculated with geodetic basic linkage pursuant to the coordinates, the results have been given in Table 6.

AD”=BC”’=10.00*cosθ=9.848 m DD”=A’D’=10.00*sinθ=1.737 m DC”=D’C’=20.00*cosθ=19.696 m CC”=20.00*sinθ=3.474 m AA’=ÖBM+AD”=14.848 m CC’=ÖBM+CC”=8.474 m BB’=CC’+BC”’=18.322 m

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 8-17, December, 2016,

Table 6. Application elements calculated from the H station point, for the applique of A, B, C, D, E, F, G points with polar method. D.N .

B.N .

Horizont al Distance

Azimut h Angle

north side of the building and the corner which is located to its right side corner point should be 150G, and the azimuth angle of the line that connects the top corner to its left side corner point should be 250G. The building can be applicated after the calculation of the applique elements that belongs to the building corner points has been done using rectangular coordinate method or polar coordinate method considering the garden distances of the building blocks, frontage length and depth of the building. 6- If the corners of the building will be applicated using the polar method, necessary applique elements should be gathered after their sketching on housings with computer, or after the calculation of coordinates of the building corners. 7- For the buildings that will be constructed in the area, if the front of the development blocks planned as north-west or south-east, it will be much more easier to applique the parcels and the corners of the buildings that will be constructed on. Thereby the south side of the building can benefit from the sunlight all year long much more efficiently. It is suitable to locate active working areas and living areas in south. 8- Because the solar energy is not taken into consideration, buildings facing to all directions started to emerge while we create our cities. This is why while creating the new city blocks, new systems should be designed to make an efficient use of solar energy. 9- Annual air conditioning expenses can be lowered in new housing areas with this research in Eastern Mediterranean Region with its mean latitude of 37o and with a high solar energy potential. Also, it will provide a big economical gain, and the effects of the combustion of typical fossil fuel gases and other waste materials would be minimized. 10- Humankind’s biological clock is adjusted to the sunlight. Inadequate natural lighting in an environment of working and living can cause: Drowsiness, laziness and depressive feelings. The solution is to direct the buildings to the sun while constructing them.

Horizont al Angle

H J A

15.667

30.750

27.758

8.390

29.933

44.144

32.446

161.113 4 81.7928 120.478 7 141.363 8 120.466 5 161.113 4 108.550 1 61.1134

0.0000 320.6794 359.3653 380.2504 359.3531 0.0000 347.4367 300.0000

8. CONCLUSION AND RECOMMENDATIONS The contribution of buildings being able to see the sun to our country’s economy would be much higher than annuity value of the land that the building in question be found. In the study, it has been seen that the Eastern Mediterranean Region is located between the latitudes of 36o-38o, while it is not making major differences in solar azimuth angle calculations, calculations have been made with mean latitude value (37o). At the summer solstice of the year, the effect of the local noon and sunrise (and sunset) time differences (thus hour angles) of Adana, Hatay, Osmaniye and Mersin provinces are not important in the context of azimuth angle calculation, thereby the calculations have been made with the hour angles of Adana for the region. Overall assessment of the research results are summarized as follows: 1- To be focused on the sun should not be considered only as an energy based requirement. Because the sun is not only an energy source, it is the source of the life itself, unlike other energy sources. 2- To make the housings in Eastern Mediterranean Region benefit from the solar energy preeminently; the values of solar azimuth angle should be at its maximum at sunrise, and its minimum at sunset. 3- The researches about the sunshine duration in Eastern Mediterranean Region have showed us that the annual sunshine duration is at its maximum when it is summer, and at its minimum when is winter. And the number of sunny days in a year is approximately 90%. 4- The hour angle in solar azimuth angle calculation is zero at local noon time. Information about hour angles at sunrise and sunset are all obtained from the calendars that indicates the time of noon and the time of the sunrise and sunset (İnce, 2005). 5- To make a building that will be constructed in Eastern Mediterranean Region benefit from sunlight all year long, it is determined that the azimuth angle of the line that connects the upper

REFERENCES Akıncıtürk, N., 1999a. Güneş Işınımlarının Yapıdaki Yararlı Etkilerinin İncelenmesi, YTÜ, Yapı Fiziği Fiziksel Çevre Denetimi Kongresi, 18-19 Kasım, İstanbul. Akıncıtürk, N., 1999b. Konutlarda Isı Kaybının Yalıtımla Azaltılmasının Enerji Tüketimindeki Olumlu Etkilerinin İncelenmesi, Enerji ve Tabii Kaynaklar Bakanlığı, 17. Enerji Haftası Etkinlikleri, Enerji Sempozyumu Kitabı, YTÜ, 917 Ocak, İstanbul. Aksoy, A., 1975. Jeodezik Astronominin Temel Bilgileri (Küresel Astronomi), M. T. Basımevi, İstanbul. Deriş, N., 1979. Güneş Enerjisi Sıcak Su ile Isıtma Tekniği, Sermet Matbaası, İstanbul. Dolgun, A., ve diğerleri, 2013, Osmaniye İl Çevre Durum Raporu, T.C. Osmaniye Valiliği İl Çevre ve Orman Müdürlüğü, Osmaniye.

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Erarslan, C., 2012. Hatay İl Çevre Durum Raporu, T.C. Hatay Valiliği İl Çevre ve Orman Müdürlüğü, http://www.hartay-cevreorman.gov.tr, Hatay. Erengezgin, Ç., Güneş Evi, http://www.mmo.org.tr/resimler/dosya_ekler/fcce0 621b49c983_ek.pdf, Ağustos, 2016. Kılıç, A., Öztürk, A., 1980. Güneş Enerjisi, İTÜ Mak. Fak., Kipaş Dağıtımcılık, İstanbul. Koç, E., 2008. Osmaniye’nin Sosyo-Ekonomik ve Kültürel Yapısı, ISBN: 978-9944-0426, Mart, Osmaniye. İnce, H., 2000. Küçük Nokta Hesabında Yeni Bir Yöntem, TÜ, Bilimsel Araştırmalar Dergisi Fen Bilimleri B Serisi, s.18-22, Edirne. İnce, H., 2005, Trakya Bölgesinde Toplu Konut Alanlarında Yapılacak Binaların Gün Boyu Güneş Işığından Yaralanması İçin Bir Tasarım Önerisi, “4. Planlı Dönemde Trakya’da Sanayileşme ve Çevre Sempozyumu” TMMOB Makine Mühendisleri Odası Bildiriler Kitabı, Sayfa 371380, 14-15 Ekim, Edirne. Tüdeş, T., 1995. Aplikasyon, KTÜ Müh. Mim. Fak. Yayını, Trabzon. URL-1; Memory Center, http://www.mcaturk.com, Temmuz, 2016.

URL-2; http://www.tavsiyeediyorum.com/psikolog_4683_t unc_ tataker. htm, Nisan 2008. URL-3; http://www.eie.gov.tr/index.html, Mayıs 2016. URL-4; http://www.hgk.mil.tr, Mayıs 2016. URL-5; Adana İl Çevre ve Orman Müdürlüğü, İl Çevre Durum Raporları Rehberi, T.C. Çevre ve Orman Bakanlığı, http://www.adana-cevreorman.gov.tr, 2012, Adana. URL-6; 2013 Yılı Mersin İl Çevre Durum Raporu, T.C. Mersin Valiliği İl Çevre ve Orman Müdürlüğü, Mersin. Yıldız, F., 1999. İmar Bilgisi Planlama Uygulama Mevzuat, Nobel Yayın Dağıtım, Ankara.

International Journal of Engineering and Geosciences (IJEG), Vol; 1 , Issue; 01, pp. 18-23, December, 2016, ISSN 2548-0960, Turkey, DOI: Your DOI number

EVALUATION OF RECENT GLOBAL GEOPOTENTIAL MODELS BY GNSS/LEVELLING DATA: INTERNAL AEGEAN REGION Yilmaz, M., 1* Turgut, B., 1 Gullu, M., 1 Yilmaz, I., 1 1

Afyon Kocatepe University, Engineering Faculty, Department of Geomatic Engineering, TR-03200 Afyonkarahisar, Turkey (mustafayilmaz, bturgut, mgullu, iyilmaz@aku.edu.tr) *Corresponding Author, Received: 11/06/2016, Accepted: 18/07/2016

ABSTRACT: Global geopotential models of spherical harmonic coefficients are used to determine the external gravitational field of the Earth. These coefficients are derived from satellite orbit perturbations, terrestrial gravity anomalies and altimeter data. Hundreds of thousands of coefficients and standard deviation values for these coefficients are estimated from millions of observation. Measurement amount, homogenous distribution of the measurements of global scale, different measurement types reflecting the different frequencies of the gravity signal and measuringassessment techniques affect the model accuracy directly. Starting from 1960â&#x20AC;&#x2122;s and lasts to the present day and also gaining new acceleration with the satellite gravity field missions, every outcome of the studies related to the determination of the global Geopotential model is experienced by a series of validation tests. Accuracy of the model can either be determined from the estimated error degree variances concerning the coefficients (interior validation) or comparison of geoid heights, gravity anomalies, gravity disturbances and components of vertical deflection calculated from the model with terrestrial measurements directly (outer validation). In this paper, recent global geopotential models are primarily explained. Global geopotential models are compared with GNSS/levelling data of the study area. The objective of this comparison is to determine the best fit global geopotential model which will contribute to the study of Turkish geoid determination. Keywords: Geoid, Geopotential model, GNSS/levelling.

International Journal of Engineering and Geosciences (IJEG), Vol; 1 , Issue; 01, pp. 18-23, December, 2016,

Internal Aegean Region study area were used to quantify the GGMs’ accuracy in order to find the geopotential model that best fits the study area for further geoid determination at regional and national scales.

1. INTRODUCTION The geoid surface serves as a reference for most applications that require a datum for determining topographic heights or ocean depths. The improvements derived from recent satellite gravity missions have significantly improved earth gravity field knowledge, such that global geopotential models (GGMs) representing the Earth's gravity field have acquired greater importance to the geosciences.

2. THEORETICAL BACKGROUND 2.1. GNSS/Levelling Global Navigation Satellite System (GNSS)-derived ellipsoidal heights refer to a reference ellipsoid, while orthometric heights refer to an equipotential reference surface determined through levelling. When these heights are collocated at the same benchmark, their difference can be used to determine geoid height through a geometrical approach. The GNSS/Levelling geoid heights are computed by the following equation (Heiskanen and Moritz, 1967): (1) N hH

The technological and scientific developments in satellite techniques and computation algorithms provide significant improvements in the determination of the global gravity field models. Since the launch of the CHAllenging Minisatellite Payload (CHAMP), Gravity Recovery And Climate Experiment (GRACE), and Gravity field and steady-state Ocean Circulation Explorer (GOCE) missions (2000, 2002, and 2009 respectively), numerous GGMs have become available to the scientific community through the public domain (http://icgem.gfz-potsdam.de/ICGEM). Especially, the releases of the Earth Gravitational Model 2008 (EGM2008) by the US National Geospatial Intelligence Agency (Pavlis et al., 2008) and European Improved Gravity model of the Earth by New techniques (EIGEN5C) by the GFZ-GRGS cooperation (Förste et al., 2008) are significant achievements in the determination of the Earth’s mean gravity field. These high-degree models lead to significant improvement of our knowledge of the long wavelength part of the Earth’s static gravitational field, and thereby of the long wavelengths of the geoid. Therefore, corresponding improvements are expected for precise regional geoid model determination because regional geoid models typically include a GGM as underlying geopotential representation (Erol et al., 2009).

where N is the geoid height, h is the ellipsoidal height computed from GNSS and H is the orthometric height computed from levelling (Fig. 1). Geoid heights have been computed based on the known ellipsoidal and orthometric heights (Banarjee et al., 1999). Eq. (1) is not exact due to the ignorance of the deflection of the vertical (). Nevertheless, it is accurate enough for most practical applications, because  has a negligible influence (sub mm-order) on the orthometric height (Tenzer et al., 2005).

The geodesy community engaged in comprehensive efforts for the comparison and validation of GGMs using several techniques and independent data sets that were not used for the development and evaluation of these GGMs. To improve local geoid models, it is essential to select the best GGM for the studied area. In the selection of a GGM for geoid determination, published error estimates for GGMs are frequently not used to judge which GGM is best for a certain region. This is because the published quality estimates may be too optimistic and/or presented as global averages and thus not necessarily representative of the performance of the GGM in a particular region. Hence, the user of a GGM should perform his own accuracy and precision verifications (Kiamehr and Sjöberg, 2005).

Figure 1. The relationship between the height systems 2.2. Global Geopotential Model For a better determination of orbits and height systems in science and engineering, it is necessary to significantly improve our knowledge of the gravity field of the Earth, both in terms of accuracy and spatial resolution (Rummel et al., 2002). The GGM is used to determine the long wavelength part of the earth’s gravity field and comprises a set of fully-normalized, spherical harmonic coefficients that are obtained from geopotential solutions (Mainville et al., 1992). These coefficients are determined from the incorporation of satellite observations, land and ship-track gravity data, marine gravity anomalies derived from satellite radar altimetry and airborne gravity data (Rapp, 1997).

Continuous developments in the acquisition, modelling and processing of GPS data have provided geodesists with highly reliable and precise external control to evaluate global and regional models for the Earth’s gravity field (Kotsakis, 2008). The main objective of this study is comparing EGM2008, EIGEN-6C4, GOCE and EGM2008 COmbined model (GECO), The Combined Gravity Models (GGM05C and GOCO05C). Geoid heights determined from GNSS/Levelling over the

The geoid height (N) can be represented by a set of spherical harmonic coefficients (in spherical

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approximation) with the following equation (Heiskanen and Moritz, 1967):

where (θ, λ) co-latitude and longitude of the computation point, R is the mean radius of the Earth,

Ellipsoidal heights at 87 points have been determined using dual-frequency GNSS receivers and antennas with respect to TNFGN (aligned to ITRF96) (reference epoch 2005.00) and orthometric heights at these points have been determined through spirit levelling with respect to the Turkish National Vertical Control Network (fixed to local mean sea level of the Antalya tide gauge). Geoid heights at 87 TNFGN points have been computed according to the Eq. (1) based on the known ellipsoidal and orthometric heights above.

Pm is the associated Legendre polynomials, C m and

3.2. GGMs and Model Evaluation

N ( ,  )  R

  P lm (sin  )C lm cos m  S lm sin m 

lmax

l  2 m 0

(2)

S m are the spherical harmonic coefficients for degree l and order m, respectively.

Earth Gravitational Model 2008 EGM2008 is a spherical harmonic model of the Earth’s gravitational potential, developed by a least squares combination of the ITG-GRACE03S gravitational model and its associated error covariance matrix, with the gravitational information obtained from a global set of area-mean free-air gravity anomalies defined on a 5 arcminute equiangular grid. This grid was formed by merging terrestrial, altimetry-derived, and airborne gravity data. Over areas where only lower resolution gravity data were available, their spectral content was supplemented with gravitational information implied by the topography. EGM2008 is complete to degree and order 2159, and contains additional coefficients up to degree 2190 and order 2159 (Pavlis et al., 2012). The national geoid model for Turkish territory, Turkish Hybrid Geoid 2009 (THG-09) (Kilicoglu et al., 2011) was computed depending on EGM2008

3. STUDY AREA, DATA ACQUISITION, AND GGMS 3.1. Study Area and Source Data The area is located in the internal Aegean region of Turkey within the geographical boundaries: 370.3083 N ≤ φ ≤ 400.4417 N; 280.4833 E ≤ λ ≤ 320.7167 E defining a total area of  133000 km2 (350 km x 380 km) with a rough topography (Fig. 2). All our GGM evaluation tests based on geoid height refer to the 87 points that belong to Turkish National Fundamental GPS Network (TNFGN) (Fig. 3).

The Latest Combined Global Gravity Field Model Including GOCE Data up to Degree and Order 2190 EIGEN-6C4 is a static global combined gravity field model up to degree and order 2190. It has been elaborated jointly by GFZ Potsdam and GRGS Toulouse and contains the following satellite and ground data: - LAGEOS (degree 2 - 30): 1985 - 2010 - GRACE RL03 GRGS (degree 2 - 130): ten years 2003 - 2012 - GOCE-SGG data. - DTU12 ocean geoid data and an EGM2008 geoid height grid for the continents (max degree 370). The combination of these different satellite and surface data sets has been done by a band-limited combination of normal equations (to max degree 370), which are generated from observation equations for the spherical harmonic coefficients. The resulted solution to degree and order 370 has been extended to degree and order 2190 by a block diagonal solution using the DTU10 global gravity anomaly data grid (Förste et al., 2015).

Figure 2. The topography of the study area

The Global Gravity Model by Locally Combining GOCE Data and EGM2008 GECO is a global gravity model, computed by incorporating the GOCE-only TIM R5 solution into EGM2008. The input data of GECO: - EGM2008 spherical harmonic coefficients and corresponding error standard deviations - EGM2008 global grid of geoid error standard deviations (5' x 5' resolution) - GOCE TIM R5 spherical harmonic coefficients - GOCE TIM R5 block-diagonal coefficient error covariance matrix.

Figure 3. Geographical distribution of 87 TNFGN points

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EGM2008 geoid undulations are computed on a global spherical grid of resolution 0.5° x 0.5° by making a synthesis from EGM2008 coefficients up to degree 359. The GOCE geoid on the same grid are computed by making a synthesis from the TIM R5 coefficients up to degree 250. Two geoid grids are merged by leastsquares adjustment. Finally, the GECO spherical harmonic coefficients are computed by making an analysis of the combined global geoid grid. The analysis is performed up to degree 359 (consistently with the 0.5° x 0.5° resolution). From degree 360 to degree 2190 the GECO coefficients are the same of EGM2008. The GECO coefficient errors are computed as a weighted average of the coefficient errors of EGM2008 and the TIM R5 solution (Gilardoni et al., 2016).

In GGM evaluation, geoid heights based on GNSSderived ellipsoidal heights and spirit levelled orthometric heights at discrete points provide an estimated accuracy of the GGM’s. The usual and accepted practice is to adopt for a reference model that GGM that is a best fit to the geoid height point estimates determined from the GNSS/levelling. The evaluation of GGMs focuses on the correspondent geoid height differences between the GGMs and GNSS/levelling using the equation below: N  N GNSS / Lev  N GGM (3) where ∆N is the geoid height residual, NGNSS/Lev is the geoid height estimated from GNSS/levelling , and NGGM is the geoid height estimated from GGMs. For the statistical analysis of geoid height differences, minimum and maximum values of ∆N are determined and the overall performance of GGMs is assessed through RMSE accuracy measure defined by:

The Combined Gravity Model GGM05C GGM05C was estimated to spherical harmonic degree and order 360 from a combination of GRACE and GOCE gravity information (based on GGM05G) and surface gravity anomalies from DTU13. The 2 minute resolution anomalies were used, assuming that they were classical gravity anomalies (i.e., defined on the ellipsoid). The first step was a low pass filter applied to the DTU13 global anomaly field. This was followed by a spherical harmonic analysis of the gravity anomaly set on the ellipsoid, where the coefficients were analytically transformed to degree 540, but only the coefficients up to degree 360 were used. Rather than reprocess the surface gravity data, the full covariance from GGM03C was adopted as apriori. The covariance was then modified so that, below degree 240, the terrestrial information was severely downweighted in order to preserve the accuracy of the GRACE and GOCE gravity contribution. This artificial covariance was used to combine the surface gravity information with GGM05G to obtain the GGM05C solution (Ries et al., 2016).

RMSE 

Degree 2190 2190

GECO GGM05C GOCO05C

2015 2016 2016

2190 360 720

(4)

k 1

4. CASE STUDY For the evaluation process, the geoid heights based on GGMs are interpolated from the closest grid points using software obtained from International Centre for Global Earth Models (ICGEM) web page <http://icgem.gfzpotsdam.de/ICGEM> using the Kriging interpolation method and refer to the reference system World Geodetic System 1984 (WGS84). The differences between GNSS/levelling based geoid heights and GGM-based geoid heights may be affected by datum inconsistencies. In order to minimize these offsets (i.e. bias and tilt) a 4-parameter transformation is used. The geoid heights obtained from GGMs are compared with discrete geoid heights based on GNSS/levelling data after fitting the tilt. The statistical values of the height data sets that were used for GGM evaluation are given in Table 2. Height

GGMs that are compared over the study area are listed in Table 1 with model characteristics. Year 2008 2014

 ( N ) 2

where n is the number of the points used for the accuracy verification and k refers to the residual sequence.

The Combined Gravity Model GOCO05C GOCO05C is a static global combined gravity field model up to degree and order 720 based on full normal equation systems (more than 500000 parameters). It has been elaborated by the Gravity Observation Combination (GOCO) Group. GOCO05C is a combination model based on the satellite-only gravity field model GOCO05S and several gravity anomaly datasets (Arctic, Australia, Canada, Europe, Oceans, South America, USA), constituting a global 15'x15' data grid. For the remaining land areas (Central America, Asia, Africa, Antarctica) fill-in datasets (NIMA96, GOCO05S, RWI_TOIS2012) were used (Fecher et al., 2016).

Model EGM2008 EIGEN-6C4

1 n

Data S (GRACE), G, A S (GOCE, GRACE, LAGEOS), G, A S (Goce), EGM2008 S (GRACE, GOCE), G, A S, G, A

Min.

Max.

Mean

Std. Dev. 308.9833 308.8989 1.3207 2.1706 2.1619 2.1506 2.1600 2.1605

203.7893 1865.7583 1040.7602 h 168.5663 1827.3193 1003.7240 H 32.1204 38.9270 37.0362 NGNSS/Lev 27.8353 40.1786 36.2804 NEGM2008 27.8693 40.2083 36.2854 NEIGEN6C4 27.8477 39.9013 36.2882 NGGM05C 27.9391 40.0077 36.2864 NGOCO05C 27.9349 40.1837 36.2879 NGECO Table 2. Statistics of height datasets over the study area (units in m.)

Table 1. GGMs used for the evaluation (S: Satellite tracking, G: Gravity, A: Altimetry)

The graphical representations have been adopted for the comparative evaluation of GGMs by producing a residual map for each GGM (Fig. 4-8) that indicates the occurrence and magnitude of geoid height differences. The residual maps are produced by the Surfer  13 software before fitting the tilt.

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Figure 8. GOCO05C residual map (height differences in m.)

Figure 4. EGM2008 residual map (height differences in m.)

COMPARATIVE RESULTS AND CONCLUSIONS The visual analysis of the geoid height residual maps shows that EGM2008 has a better terrain approximation than the other GGMs. It is visible from Fig.4 that the deviation of EGM2008 based geoid heights from GNSS/levelling based geoid heights is reduced for most parts of the study area ( -0.8 m. before fitting the tilt). Global statistics of geoid height residuals based on GGMs are presented in Table 3. When the statistics summarized in Table 2 are evaluated, the following conclusions can be drawn based on this study: (i) EGM2008, EIGEN6C4, and GECO has better results because of their higher frequency content. (ii) EGM2008 provides more accurate results than other GGMs.

Figure 5. EIGEN-6C4 residual map (height differences in m.)

From the statistical values of NGNSS/Lev – NGGM, RMSEs were used to infer the best fit of the GGMs to the GNSS/levelling data for model evaluating because any gravimetric determination of the geoid is deficient in the zero and first-degree terms. Obviously, EGM2008 fit the GPS/levelling data better than other GGMs over the study area. Table 3. Statistics of NGNSS/Lev - NGGM over the study area after fitting the tilt (units in m.) Model Min. Max. Range Mean

Figure 6. GECO residual map (height differences in m.)

EGM2008 EIGEN6C4 GECO GGM05C GOCO05C

-0.7735 -0.7837 -0.7963 -0.9326 -1.4371

-0.1703 -0.1762 -0.1563 0.0282 -0.6374

0.6032 0.6075 0.6400 0.9608 0.7997

-0.3050 -0.3228 -0.3251 -0.3479 -0.3509

The results of GGM evaluation in this study have indicated the outstanding of EGM2008 to the other GGMs. EGM2008 better statistics than the other GGMs and fits best to the THG-09 at ± 0.2803 m. agreement despite the coefficient errors and GNSS/levelling dataset that can not be considered as an entirely errorless. From our GGM evaluation results we can conclude that EGM2008 can be used as a reference earth geopotential model for further geoid determinations at regional and national scales.

Figure 7. GGM05C residual map (height differences in m.)

Due to advancements and improvements in instrumentation, software, processes, applications, and understanding, high resolution GGMs (e.g. up to degree and order 2190) are major steps to represent the gravity field of the Earth with a high accuracy. Nowadays global

RMSE 0.2803 0.3282 0.3318 0.3677 0.3501

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gravity field models, mainly derived from satellite measurements, become more and more detailed and accurate. These gravity field models should be combined with terrestrial gravity anomalies) and GNSS/levellingderived or altimetry-derived geoid heights. Furthermore, an important task of geodesy is to make the gravity field functionals available to other geosciences. For all these purposes, it is necessary to calculate the corresponding functionals as accurately as possible or, at least, with a well-defined accuracy from a given global gravity field model. Therefore, in order to achieve major improvements for the future high-accuracy gravimetric geoid models in Turkey, further and future analysis of high resolution GGMs (e.g. GOCE-based GGMs) will be needed.

Kiamehr, R., Sjöberg, L.E. (2005). Comparison of the qualities of recent global and local gravimetric geoid models in Iran. Studia Geophysica et Geodaetica, 49, 289-304. Kilicoglu, A., Direnc, A., Yildiz, H., Bolme, M., Aktug, B., Simav, M., Lenk, O. (2011). Regional gravimetric quasi-geoid model and transformation surface to national height system for Turkey (THG-09). Studia Geophysica et Geodaetica, 55, 557-578. Kotsakis, C. (2008). Transforming ellipsoidal heights and geoid undulations between different geodetic reference frames. Journal of Geodesy, 82, 249-260. Mainville, A., Forsberg, R., Sideris, M.G. (1992). Global positioning system testing of geoids computed from geopotential model and local gravity data: a case study. Journal of Geophysical Research, 97 (B7), 11137-11147.

ACKNOWLEDGEMENTS This study was supported by Afyon Kocatepe University Scientific Research Projects Coordination Department (Project No: 15.HIZ.DES.91). REFERENCES

Pavlis, N.K., Holmes, S.A., Kenyon S.C., Factor J.K. (2008). An earth gravitational model to degree 2160: EGM2008. General Assembly of the European Geosciences Union, 13-18 April, Vienna, Austria.

Banarjee, P., Foulger, G.R., Satyaprakash, Dabral, C.P. (1999). Geoid undulation modelling and interpretation at Ladak, NW Himalaya using GPS and levelling data. Journal of Geodesy, 73, 79-86.

Pavlis, N.K., Holmes, S.A., Kenyon, S.C., Factor, J.K. (2012). The development and evaluation of the Earth Gravitational Model 2008 (EGM2008). Journal of Geophysical Research, 117, B04406, doi: 10.1029/2011JB008916.

Erol, B., Sideris, M.G., Celik, R.N. (2009). Comparison of global geopotential models from the champ and grace missions for regional geoid modelling in Turkey. Studia Geophysica et Geodaetica, 53, 419-441.

Rapp, R.H. (1997). Past and future developments in geopotential modelling, in: Forsberg, R, Feissl, M., Dietrich, R. (Eds.) Geodesy on the Move, Springer, Berlin, pp. 58-78.

Fecher, T., Pail, R., Gruber, T. and the GOCO Project Team. (2016). The combined satellite gravity field model GOCO05C. EGU General Assembly, 17-22 April, Vienna, Austria, Geophysical Research Abstracts, 18 (EGU2016-7696).

Ries, J., Bettadpur, S., Eanes, R., Kang, Z., Ko, U., McCullough, C., Nagel, P., Pie, N., Poole, S., Richter, T., Save, H., Tapley, B. (2016). The combined gravity model GGM05C. http://dx.doi.org/10.5880/icgem.2016.002.

Förste, C., Flechtner, F., Schmidt, R., Stubenvoll, R., Rothacher, M., Kusche, J., Neumayer, K.H., Biancale, R., Lemoine, J.-M., Barthelmes, F., Bruinsma, S., König, R., Meyer, U. (2008). EIGEN-GL05C - A new global combined high-resolution GRACE-based gravity field model of the GFZ-GRGS cooperation. Geophysical Research Abstracts, Vol. 10, EGU2008-A-03426, SRefID: 1607-7962/gra/EGU2008-A-03426, 2008.

Rummel, R., Balmino, G., Johannessen, ., Visser, P., Woodworth, P. (2002). Dedicated gravity field missionsprinciples and aims. Journal of Geodynamics, 33, 3-20. Tenzer, R., Vanicek, P., Santos, M., Featherstone, W.E., Kuhn, M. (2005). The rigorous determination of orthometric heights. Journal of Geodesy, 79, 82-92.

Förste, C., Bruinsma, S.L., Abrikosov, O., Lemoine, J.M., Marty, J.C., Flechtner, F., Balmino, G., Barthelmes, F., Biancale, R. (2015). EIGEN-6C4 The latest combined global gravity field model including GOCE data up to degree and order 2190 of GFZ Potsdam and GRGS Toulouse. http://dx.doi.org/10.5880/icgem.2015.1.

Gilardoni, M., Reguzzoni, M., Sampietro, D. (2016). GECO: a global gravity model by locally combining GOCE data and EGM2008. Studia Geophysica et Geodaetica, 60, 228-247. Heiskanen, W.A., Moritz, H. (1967). Geodesy.W.H. Freeman, San Francisco.

Physical

International Journal of Engineering and Geosciences (IJEG), Vol; 1 , Issue; 01, pp. 24-33, December, 2016, ISSN 2548-0960, Turkey, DOI: Your DOI number

VARIABILITY AND DECADAL EVOLUTION OF TEMPERATURE AND SALINITY IN THE MEDITERRANEAN SEA SURFACE Nacef, L., 1* Bachari, N.E.I.,1 Bouda, A.,2 and Boubnia, R.,2 1Selçuk

University, Engineering Faculty, Department of Geomatic Engineering, Konya, Turkey (yakar/etusat/orhanosman@selcuk.edu.tr);

Department of Ecology and Environment, Faculty of Biological Sciences, Houari Boumediene University of Science and Technology (USTHB), B.P 32 El-Alia 16111, Algiers, Algeria. (nacef_l, bachari10@yahoo.fr); École Nationale Supérieure des Sciences de la Mer et de l'Aménagement du Littoral (ENSSMAL), Campus universitaire de Delly Ibrahim, Bois des Cars, B.P. 19, 16320, Algiers, Algeria. (abderbouda, rboubnia@yahoo.fr) 2

*Corresponding Author, Received: 08/06/2016, Accepted: 20/07/2016 ABSTRACT: Based on the Med-Atlas 20002 database data at Mediterranean Sea surface, analysis of spatial and temporal variations of temperature and salinity, as well as, the search of its possible trends are the main goals of this work. The used statistical techniques allowed us to obtain various climatological fields of temperature and salinity, on a period of 45 years (1955-1999). Spatial and temporal analysis of those fields shows that the north-south gradient is weaker than the east-west gradient. The strongest variability in both mean fields is sharper in downwelling areas than anywhere else, showing the colder and less saline surface waters. Warmer and saltiest water surface are located in southeast of the Levantine basin. The eastern Mediterranean Sea is generally more saline than the western basin. The temperature seasonal cycle is more marked than the salinity seasonal cycle. The summer-winter thermal and saline fields are completely contrasted, especially in the northern Adriatic Sea. The largest positive peak of inter-annual temperature variability is encountered in 1994, the largest negative peak in 1992. Whereas those related to salinity observed in 1983 and in 1997 respectively. The decadal variations indicate a cooling of Mediterranean Sea surface in 1970s and a northward warming since 1980s that accelerated in 1990s. The eastern Mediterranean Sea exhibits a higher warming rate as compared to the western basin, but the average increase is about 0.2 °C/decade. The Salinity rising corresponds to the cooling periods and the decreasing is associated with the warming ones. Keywords: Mediterranean Sea surface, Temperature, Salinity, Spatio-temporal/Interannual/decadal variability.

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efficient tool that can define the accuracy requirements, determine the sampling conditions and the precision with which the parameter can be determined. Thus, improving our knowledge of Mediterranean water masses characteristics, particularly the surface temperature and salinity, and their variability is a daunting challenge for the scientific community in the field of measurements and modelling in Mediterranean region. Obtain accurate estimates of these parameters is important for understanding the circulation and Mediterranean climate as well as their evolution in the context of climate change. Furthermore, systematic assessments of variability of basic hydrological parameters (temperature and salinity) in relation to climate and to changes in biogeochemical processes and biodiversity are crucial to understand the mode of response/functioning of the marine ecosystem.

1. INTRODUCTION The climate of the earth is extremely complex due to interactions between the atmosphere, hydrosphere, geosphere, biosphere and cryosphere. Moreover, the exchange of energy, of matter and moisture between these environmental spheres are the main mechanisms that govern the climate system (Simmons & Bengtsson, 1984; Beniston, 2004; IPCC, 2007). The global atmospheric and oceanic circulations are strongly interdependent and the Sea Surface Temperature (SST) is the link between the two (Gill, 1982). Among others, ocean circulation depends on characteristics of the different water masses. A water mass acquires its basic characteristics that are the SST and the Sea Surface Salinity (SSS) under the effect of exchanges with the atmosphere. The thermohaline circulation assigned particularly by surface salinity that will determine the threshold in temperature beneath which surface water will plunge. In the same way as temperature and pressure, salinity modulates the distribution of heat in the oceans and affects the density of seawater that is a fundamental parameter ocean circulation (Pickard & Emery, 1964). Changes in temperature and salinity can reduce the deep-water masses formation, affecting the duration, frequency and intensity of this process. This could ultimately have an impact on biodiversity, due to the local climate change (Pusceddu et al., 2010).

In this study, we took advantage of the MEDAR/MEDATLAS 2002 database release to build different climatologies of temperature and salinity at various spatial and temporal scales. Use these climatologies to assess the spatio-temporal variability and to infer the decadal change in the fields of temperature and salinity at Mediterranean Sea surface. We focus our effort on study of variations and decadal trends. We will be expecting that this quantitative description improves measurement accuracy and increases our understanding of the Mediterranean processes. 2. MATERIAL AND METHODS

The Mediterranean Sea behaves as a downscaled ocean where the main processes that affect the global ocean are present (Béthoux et al., 1999; CIESM, 2002; Lejeusne et al., 2010). Considered as a thermodynamic machine that exchange water with Atlantic Ocean through the Gibraltar Strait and heat with atmosphere through its surface. Mediterranean basin shows an excess of evaporation over the freshwater input and heat loss through the ocean-atmosphere interactions. These losses of fresh water and heat are compensated by two exchange layers in the Strait of Gibraltar: a top layer, towards the Mediterranean including a relatively warm and fresh water inflow and underlying relatively colder and saltier layer towards the Atlantic (Bryden et al., 1994; Tsimplis & Bryden, 2000).

2.1. Data description Data from 1889 to 2000 of temperature and salinity profiles, contained in MEDAR/MEDATLAS (Fichaut et al., 2003; Medar group, 2002), are the main source of data in this study. On the 1900–2000 period, we chose to extract only data collected with bottles and Conductivity-Temperature-Depth "CTD", data collected with Mechanical-Bathy-Thermographs "MBT" and eXpendable Bathy-Thermographs "XBT" are inherently less accurate. The quality control procedures of the data already made by the MEDAR/MEDATLAS group allow us to extract the data at Mediterranean Sea surface level that have successfully passed the tests.

Mediterranean Sea circulation is determined by heat exchanges, through the SST and freshwater, that depend on weather conditions and ocean characteristics (Tsimplis et al., 2005). The water flux and SST are crucial in dense water formation, and thus in the Mediterranean thermohaline circulation (Béthoux et al., 1999; Barnier et al., 2006). Consequently, they affect the characteristics of Mediterranean water masses (temperature, salinity and density) and can potentially affect the Atlantic Ocean circulation by changing the Mediterranean outgoing waters properties (Potter & Lozier, 2004; Tsimplis et al., 2005; Millot et al., 2006). SST in the Mediterranean can also influence the atmosphere properties at lower levels and occurrence of episodes of heavy coastal rainfall (Castellari et al., 1998; Li, 2006; Lebeaupin et al., 2006). In the Mediterranean, oceanographic and physical aspects of climate change are described in many reports and scientific studies, but there are still uncertainties about the extent of physical and chemical expected changes at regional and local scales (Lionello, 2012).

On the investigated period, temporal and spatial distribution of extracted data shows that the largest number of data refers to the 1946–2000 period. Density of observations is weaker in winter months. The highest density cover the gulf of Lyons, the Ligurian Sea, the North Adriatic Sea and the Alboran Sea. Southern Levantine basin and the coasts of Tunisia and Libya suffer from a severe lack of data. This heterogeneity is the result of weather conditions and the tendency to make more investigations for specific processes (e.g., in the Gulf of Lions for tracking the deep waters formation). Several Mediterranean areas, mainly its southern parts, are widely under-sampled due to historical-political difficulties, as well as to financial and logistic constraints. This is a serious limit which makes the interpretation of data more difficult (Millot & Briand, 2002) and prevents reliable trend detection (Millot & Taupier-Letage, 2005). 2.2. Data processing

Despite a great progress in measuring and understanding data errors, the performed SST and SSS climatologies from different data sets in the Mediterranean show a wide range of discrepancies that we are still struggling to reduce them (Hewitt & Griggs, 2004; Sanchez Gomez et al., 2008, 2009). To overcome some of these gaps, the SST and SSS variability is an

For achieving different climatologies at various spatial and temporal scales, we needed to construct complete data series where data availability is the first limitation one can expect. Therefore, the data processing aims at interpolating the available information on the required time and space scales.

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changes heavily in the range of 13 to 27 °C and increases from north to south and from west to east. While monthly salinity changes more softly with values between 36 and 39 and increases eastwards, except for of Adriatic Sea surface.

Here, we describe the main steps in our approach. As a first step, we adopted the 29 boxes defined by MEDAR/MEDATLAS 2002. The choice of this zonation is supported by the use of such areas in the quality control and regional parameterization of MEDATLAS data. These areas represent the spatial grid with which one can filter the most details of ocean circulation characteristics and properties of the Mediterranean surface. We applied a variance-based analysis to the temperature and salinity data from these 29 boxes. This analysis helps to aggregate the dependant areas. The results of this analysis allowed us to identify 18 independent zones (Fig. 1). This grid is used for subsequent binning of the data and in remaining processing and analysis. Data for each variable are then averaged monthly for all 18 zones for the period running from January 1955 to December 1999. We note that before 1955 and during 2000, the data are too scarce to be interpolated.

Mean annual values of SST and SSS averaged spatially for the eighteen zones and corresponding limits are illustrated in figure 2. It shows that the mean surface temperature of Mediterranean waters ranges from 18 °C (Z01: Western Alboran in the western basin) to over 22.1 °C (Z18: Levantine Basin in eastern basin). The warmest area is the Levantine Basin, with 22.1°C. The coolest areas are the Gulf of Lions, the Ligurian Sea and northern Adriatic Sea, with a mean SST of 17.5 °C (Fig. 2a). The surface salinity of the Mediterranean Sea softly increases from Gibraltar to Ligurian Sea (from about 37.4 in the western Alboran to 38.5 in Ligurian Sea) with a decline in the Gulf of Lions (about 37.2). From Sardinia Strait to northern Ionian Sea, salinity shows a relative stability around 38.3. It strongly decreases in the Adriatic Sea (about 36.8) and then rises to 39 and upwards in the eastern basin (Fig. 2b). Furthermore, figure 2 shows that these fields vary reasonably between the superior and inferior regional limits. Figure 3 shows the SST and SSS annual cycles for Mediterranean Sea and these three sub-basins (WMS, CMS and EMS). Annual cycles of temperature showed a clear seasonal cycle at all spatial scales, from minimum values in the range of 13.8-16.4 °C during February to the maximum values 22.8-25.6 °C during August. The WMS and EMS are in the same magnitude order, from December to April. However, difference grows from April to reach its maximum (about 2.5 °C) in August.

Figure 1. Study area, defined by a grid of 18 spatial points (each point represents a subregion of the Mediterranean Sea surface). For spatial interpolation, we used linear regressions between a variable in a given zone and the same variable located in adjacent zones to fill in missing values. Only regressions with a high correlation coefficient (r ≥ 0.9) were applied in this process. For temporal interpolation, we used the binned and spatially interpolated database to construct 3- and 5-month moving averages. When the one-binned datum is missing, the 3or 5-binned one replaces it. At this stage, our database is a monthly climatology of each variable over the 18 zones and covering the period from January 1955 to December 1999. We constructed the average SST and SSS monthly climatologies at 18 zones scale to compare with the upper and lower regional limits. We constructed the average annual and their standard deviation fields to characterize spatial variations and detect areas of high variability. The mean seasonal contrast fields (summer–winter) to study seasonal variations. The mean annual cycles on a global scale (whole Mediterranean Sea) and regional (three sub-basins: occidental "WMS", Central "CMS" and Oriental "EMS") in order to analyse typical annual cycle and establish regional differences. The annual anomalies series to describe inter-annual variations. In order to detect trends in fields, we constructed the average for 4 decades (1958-1969, 1970-1979, 1980-1989 and 1990-1999).

3. RESULTS AND DISCUSSION 3.1. Annual variability

Figure 2. Mean annual SST (a) and SSS (b) at eighteen zones and corresponding limits.

Monthly climatologies spatially averaged over the whole Mediterranean Sea surface show that the monthly temperature

Annual cycles of salinity show a minimum between two peaks values, with salinities are subject to almost linear

International Journal of Engineering and Geosciences (IJEG), Vol; 1 , Issue; 01, pp. 24-33, December, 2016,

increase/decrease between the periods of these extreme values. The two salinity peaks values observed in November and February, while in the WMS basin observed in August and February. The minimum salinity values are observed in June, except that the EMS exhibits this minimum in April. The lowest salinities generally confined at 37.1-37.4 interval and the highest ones confined at 37.6-37.9 interval, except that the EMS exhibits higher salinities (about 1). The EMS is generally more saline than the WMS, by about 1 (Fig. 3b). The surface salinity increase is in phase with the temperature decrease during February. Figure 4. Spatial distribution of temperatures (a) and salinities (b) seasonal contrasts in Mediterranean Sea surface, averaged over 1955-1999 period. 3.2. Spatial variability Figure 5 depicts the spatial variability of surface temperature at Mediterranean Sea based on the mean annual field and its field of average standard deviations. It shows that the north-south temperature gradient (3 °C) is weaker than the west-east gradient (4 °C). The highest SST variability observed in the Gulf of Lions, south of the Aegean Sea. These areas own also the coldest surface water. By cons, the warmer surface waters are in the southeast Levantine Basin. The minimum variability observed in the Alboran Sea and in the central part of the basin, particularly the Sicilia strait and the southern Tyrrhenian Sea. The WMS surface shows an average temperature of 18 °C. The north-south thermal gradient is slightly stronger than west-east gradient. The coldest water surfaces are located in the northwest (minimum of 17.3 °C in the Gulf of Lions) and warmer waters are in the southern Algeria basin with a maximum of 18.8 °C. Areas with strong variability and lower temperatures occupy the northwest, whereas the most stable areas and warmer occupy the southern part of this basin. The CMS surface shows an average temperature of 18.9 °C, except that the Adriatic Sea surface exhibits an average temperature of 17.7 °C. The northsouth thermal gradient is stronger than the west-east gradient. Surface temperatures decrease southeastern (20.5 °C in the Gulf of Syrte) to the northwesterly (17.5 °C in the north Adriatic). The SST field is relatively stable. Areas with strong variability and lower temperatures occupy the northern Adriatic. The southern Ionian Sea records the strongest surface temperatures. The EMS surface shows an average temperature of over 20 °C and can reaches 22.2 °C in the extreme southeast, except that the Aegean Sea surface shows an average temperature varies between 19 and 20 °C. Three sectors are distinguished: the first is the Aegean Sea that shows a clear positive southward gradient. The second is the Crete passage and area located between Crete and Cyprus that shows an increase of SST from west to east. The third one is the southern Cyprus that confines the highest temperatures of whole Mediterranean Sea. To north of Crete, the strong and low variability varies in the same sense. The highest spatial thermal variability and the lowest temperatures occupy the surface of the southern Aegean Sea.

Figure 3. Mean annual cycle of SST (a) and SSS (b) over whole Mediterranean Sea and these three basins, for 1955-1999 period. The mean seasonal thermal contrast field at Mediterranean surface (Fig. 4a) shows that it undergoes higher summer (JuneAugust) temperatures than winter (December-February). The lowest seasonal gap is located in western Alboran Sea and the Gulf of Lions while the strongest difference is located in the Adriatic notably its northern part. The mean seasonal differences field in salinity (Fig. 4b) shows that the surface waters of the northern boundaries of the Mediterranean are less salty in summer compared to winter, notably in the northern Adriatic.

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Figure 6. Spatial distribution of salinities and its mean standard deviations in Mediterranean Sea surface, averaged over 19551999 period. 3.3. Inter-annual/decadal variability The annual SST anomalies chronology (Fig. 7a) shows that the largest positive peak of inter-annual SST variability is encountered in 1994, the largest negative peak in 1992. Whereas those related to salinity are observed in 1983 for the largest positive and in 1997 for the largest negative peak (Fig. 7b). The solid line (Fig. 7a) represents the 5-year running mean, which highlights trends. It indicates a general cooling trend over 1963â&#x20AC;&#x201C;1971 period and a general warming trend over 1972â&#x20AC;&#x201C;1999 period. A more thorough examination of the time series reveals a very small SST cooling trend in the early nineties (1992-1993) and then a strong warming trend throughout the rest of the record. Related to salinity (Fig. 7b), it shows a general rising trend in salinity over the whole 1963-1985 period then a strong decreasing trend throughout the rest of the record. The rising trend period is disconnected by a decreasing trend during the 1974-1979 period.

Figure 5. Spatial distribution of temperatures and its mean standard deviations in Mediterranean Sea surface, averaged over 1955-1999 period. Concerning the salinity, Figure 6 shows that the continuous increase from Alboran Sea (37.2) to the southeast of Levantine basin (38.9) is the more remarkable characteristic, except that the northern Adriatic Sea exhibits an averaged salinity of 36.5. The north-south gradient (0.3) is weaker than the west-east gradient (1.7). The relative increase shaped of an anticyclonic cell (38) covers the southwestern Ligurian Sea and the northwest Tyrrhenian Sea. The less salty waters occupy the north Adriatic surface, with a net cyclonic cell (36.5). The southern Aegean Sea surface maintains a salinity level exceeding the 38, with a configuration relatively similar to that of SST. The less salty WMS surface waters are located in western Alboran Sea and in the Gulf of Lions. This latter area also exhibits the strongest variability. The most salty surface waters and less variable occupy the southwest of Ligurian Sea. In this basin, the north-south gradient is weaker than the west east one. In CMS surface, the north-south salt gradient is stronger than the west-east one. Saltiest surface waters occupy the Gulf of Syrte. By cons, the less salty waters are located in the northern Adriatic. The northern Adriatic holds also strongest variability. The saltiest EMS surface waters and most stable are in the southern Levantine basin. While, surface waters less salty and strongest variables are located in the southern Aegean Sea, with a maximum of 1.7. The north-south gradient is weaker than the west-east one, exception for the northern Crete.

Figure 7. Annual anomalies chronology of SST (a) and SSS (b) in whole Mediterranean Sea, over the 1958-1999 period. Solid lines represents the 5-year running mean.

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a no stationary picture of the basin dynamic and significant biogeochemical differences between regions that have consequences on the local dynamic.

The right panels of figure 8 depicts the decadal SST averages from the 1960s (Fig. 8a) to the 1990s (Fig. 8d). It shows that the Mediterranean Sea surface sustained a cooling in the 1970s and an acceleration of the warming in the 1990s. This is particularly true for Gulf of Lions, the Ligurian Sea, the Adriatic and the Aegean Sea where SST increase is northwards. The average increase in sea surface temperature is about 0.2 °C/decade, although the higher warming rate in the eastern basin. During 1960s (Fig.8a), the warmest area is the Levantine Basin with SST values between 21.4 and 22.2°C. The coolest area is the Gulf of Lions with SST values less than 17.4°C. SST decreases clearly from the south to the north in WMS. While in EMS, it increases from the west to the east, especially in Levantine Basin. The basin-average SST decrease during the 1970s (Fig. 8b) which is particularly pronounced in the northern parts of WMS, of Adriatic and of Aegean Sea. During 1980s (Fig. 8c), there is a gradual increase of SST but with a higher warming trend in the western basin than in the Levantine basin. During this decade, only the northernmost Adriatic Sea surface remaining with temperature of 17.5 ° C. During the 1990s (Fig. 8d), there is an acceleration of the warming in the western basin and the SST warming rate becomes larger in the eastern than in the western sub-basin. During this decade, temperature is higher than 17 °C in the entire Mediterranean Sea surface, with the SST increasing northward in the WMS and eastward in the EMS.

The various climatologies constructed indicate some main features that deserve to be discussed. Surface thermal field reveals a clear north-to-south and the west-to-east gradients. Salinity field increases eastwards from Gibraltar to eastern basin (Fig. 2). The warmest and saltiest area is Levantine Basin. While, the coolest and less salty areas are the Gulf of Lions and the Ligurian Sea. The principal atmospheric and ocean circulation forcing mainly support those gradients, regional features such as Atlantic Ocean or river run-off influence on the occidental basin. Temperature seasonal cycle is more marked with respect to the salinity seasonal cycle (Fig. 3) which related especially to the solar radiation seasonal cycle. The EMS exhibited higher temperatures (≈ 2 °C) than those at WMS, especially in winter (Fig 3a). The EMS is generally more saline than the WMS (≈ 1) that is related to the climate induced by the intense evaporation from the eastern basin resulting in Levantine Intermediate Water formation. At the surface, three major factors driven salinity dynamics: the input in marginal seas (terrestrial input from the Po and other Italian rivers and from the Dardanelles), the effect of evaporation in the eastern basin and the influx of the low-salinity Atlantic water. The thermohaline basin-wide circulation modulates the intensity and the patterns of the spatial gradients. The average seasonal thermal contrast field (Fig. 4a) indicates a weaker contrast located in western Alboran Sea and the Gulf of Lions. In the Gulf of Lions, this contrast can be explained by the upwelling phenomenon, most active during the spring that reduces the temperature deviation between summer and winter in this zone. In the Alboran Sea, the low contrast can be explained by the nature of surface waters which originated from the Atlantic and that have a relatively homogeneous temperature throughout the year, by the air flux from the southwest established by the displacement to the south of the Azores anticyclone, during the summer. This flux brings relatively cool air masses from Atlantic that reduce the surface temperature in the Alboran Sea surface, during this season. The strongest seasonal contrast is located in the Adriatic Sea, notably its northern part. It can be explained by the cold winter winds (the Bora, Tramontane) that cool surface waters in these regions. In the rest of Mediterranean surfaces, we can explain this thermal contrast by the displacement of Hadley Cell northwards during summer, by the Sirocco in the southern Mediterranean and by the Etesian in the Aegean Sea. These factors further may warm surface waters in these regions and, therefore, amplify the summer-winter temperature contrast in the Mediterranean. The average seasonal contrast in salinity (Fig. 4b) indicates that the surface waters of Mediterranean northern boundaries are less salty in summer compared to winter, notably in the northern Adriatic.

The left panels of figure 8 illustrates the decadal salinity averages from the 1960s (Fig. 8e) to the 1990s (Fig. 8h). It indicates that there is a discernible trend of increased salinity corresponds to the cooling periods. Conversely, the decreased salinity is associated with the warming periods; this is particularly true for the western part of WMS, for Adriatic Sea and for Levantine basin during 1990s. During 1970s, surface salinity shows a general increasing trend, except that for the northern Adriatic and the western of Alboran that exhibit a decreasing (Fig. 8f). The inverted situation during 1980s, except that for the northern Adriatic Sea that continued in its decreasing salinity trend (Fig. 8g). During this decade, increasing trend concerns all Mediterranean surfaces located south of 40 degrees. During 1990s (Fig. 8h), surface salinity shows an acceleration of decreasing trend for all Mediterranean surfaces, except for Levantine Basin that exhibits a relatively increasing trend. 3.4. Discussion In the present study, we used 18 identified zones (Fig. 1). This partition does not take into account the biogeochemistry and ecology features of the Mediterranean Sea. More recently, several studies have attempted to partition the basin either using abiotic parameters (Gabrié et al., 2012) or biotic parameters (D’Ortenzio & D'Alacala, 2009). These studies have confirmed

Figure 8. Averaged SST (left panels) and SSS (right panels) in the Mediterranean Sea over the (a – e) 1960s, (b – f) 1970s, (c – g) 1980s, and (d – h) 1990s.

International Journal of Engineering and Geosciences (IJEG), Vol; 1 , Issue; 01, pp. 24-33, December, 2016, In the Adriatic and the northwestern Mediterranean Sea, the surface salinity contrast can be explained by the freshwater inputs from rivers (Rhone, Po) after the late spring merger of the reliefs snow (Pyrenees, Alps, Apennines). In the Aegean Sea, it can be explained by the water exchange between Black Sea and Aegean Sea, also by the evaporation increases in summer. The increase in surface salinity is in phase with the decrease in temperature during February that explains the downwelling of surface waters denser in late winter. Thus, the seasonal differences in salinity and surface temperature clearly reflected the forcing that induces the vertically circulation in Mediterranean Sea. These findings are consistent with observations and modelling studies of the Mediterranean climate variability (Coll et al., 2010; Woodruff et al., 2008; Somot, 2005; Levitus et al., 2005, 2009). Spatial distribution of both fields reveals a weaker northsouth gradient than the east-west one and the strongest variability is sharper in downwelling areas than anywhere else. The external surface water masses input mainly determines this spatial variability, also the spatial variability of air-sea heat fluxes and the upward vertical transports of intermediate water due to turbulent mixing and/or upwelling processes (Mariotti, 2010). Our results highlight favourable areas for the existence of convective processes in the Mediterranean Sea and the production of dense water masses with low temperature and high salinity characteristics, and are consistent with the existing knowledge and major experiments that led to the discovery of the sub-basin scale circulation and its mesoscale features (Bergamasco & Malanotte-Rizzoli, 2010). The observed increasing warming of the Mediterranean Sea surface can be related to the East Atlantic pattern index variability that seems to follow the global warming trend, accelerated after the early 1990s. The SST yearly anomalies can be related to the Atlantic Multidecadal Oscillation (AMO) index yearly mean variability. Our results are consistent with the study of Mariotti and Dell’Aquila (2011) who found a high correlation between AMO and the Mediterranean SST throughout the year over the period 1870–2009. The brief surface cooling period occurred during the early 1990s can be associated with a more prolonged cooling in the Atlantic area west of Gibraltar. After the early 1990s, the SST warming rate becomes larger in the eastern than in the western basin. This behaviour can be explained by the local ocean processes such as the heat horizontal advection (from the western basin) and/or vertical mixing which probably control the accelerated surface warming in the eastern basin. The enhanced variability shift observed in the Mediterranean SST in the early 1990s may be explained partly by the North Atlantic Oscillation (NAO) index variations. The high positive NAO phase is associated with warmer conditions over the western sub-basin and cooler conditions over the eastern sub-basin, while the contrary occurs during negative NAO phases. Atmospheric conditions seem to control the net air–sea heat flux and SST variations. In particular, the large SST increase in the western basin during the late 1980s seems to follow closely both NAO index and the net air– sea heat flux increase. In the early 1990s, a large atmospheric variability shift occurred as evidenced by

the abrupt decrease of both. This NAO variability probably contributed to the change in the SST spatiotemporal variability pattern and it is associated with the change in the direction of the SST spatial increasing tendency in the Mediterranean Sea (i.e. from the westward to the eastward direction). The observed increase, since 1980s, in surface temperature is generally higher in northern than in southern Mediterranean Sea, with a rapid warming since the mid-1990s. Surface cooling along with the combined salt content increase of the Eastern Mediterranean induced by increased net evaporation during this period may gradually decrease stratification in the Aegean Sea resulting in larger deep-water formation rates favouring the Eastern Mediterranean Transient (EMT). Those findings are consistent with recent observations (Russo et al., 2002) and the Atmosphere–Ocean Regional Climate Models (Somot et al., 2008; Skliris et al., 2007). The strong negative anomaly salinity decrease observed during 1996-1997 period, can probably reduce the Aegean Dense Water Formation (DWF) efficiency and led to a long-term decay phase for the EMT. In general, the temperature has fluctuated from cold conditions through the 1960s and 1970s to recent warming that commenced in the 1980s. These events are thought to be predominantly a consequence of climate change (Solomon et al., 2007; Hoegh-Guldberg & Bruno, 2010). The long-term surface warming may have an impact on the future thermohaline circulation of the Mediterranean Sea. A significant increase of SST may diminish the dense water formation rates in the various deep/intermediate water formation sites of the basin and thus may slow down the thermohaline circulation. Changes in the temperature and salinity of the outflowing Mediterranean water through the Gibraltar strait may influence the general circulation in the North Atlantic, which is a major site of deep-water formation controlling the global thermohaline circulation.

4. CONCLUSION Thanks to MEDATLAS 2002 database release, which we used to construct the various climatological fields of temperature and salinity at Mediterranean Sea surface. These climatology fields served as tools for spatiotemporal. The climatological characteristics can serve as benchmark for carrying out further analysis of the changes in the oceanographic properties that may have occurred during the last years in the Mediterranean Sea. Climatologies thus realized can contribute to improve ocean-atmosphere interactions at local scales, understanding the mechanisms causing interannual downwelling variations in the Mediterranean and improving the constraints for the ocean-atmosphere coupled models. The Mediterranean Sea surface hydrology characteristics (temperature, salinity) show a high interannual variability as well as long-term trends over the last decades. During 1980–1990, there is a significant SST rise in the western basin following a large warming of the inflowing surface Atlantic waters and a long-term increase of the NAO index, whereas SST slowly increased in the eastern basin. After a brief cooling

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period in the early 1990s for both sub-basins and a change from a high positive to a low NAO phase, the Mediterranean mean warming rate accelerated while its spatial increasing tendency changed from the westward to the eastward direction. The increasing trend in temperature encountered in the MEDAR/MEDATLAS 2002 data from the 1960s to the 1990s has to be confirmed by other datasets and/or if more recent data become available (e.g. 2000s and 2010s). If this trend persisted, it would most likely have had an impact on the future thermohaline circulation of the Mediterranean Sea and a significant effect on the Mediterranean Sea marine ecosystems, as well as on the marine biogeochemical cycles. The study identified a clear effect of sea surface warming on marine ecosystems favouring and accelerating the settlement of new alien species at an unexpectedly rapid rate. The rate of entrance of the new invaders is greater than the temperature rate itself, presenting an important warning for the future of Mediterranean Sea biodiversity. This might have profound consequences for marine biodiversity, restructuring the whole ecosystem with potential consequences for ecosystem functioning and services. From the preceding analysis, a number of research needs can be identified. These include the identification and further analysis of historical data to describe past Mediterranean climate and ecosystem development, and a better understanding of biogeochemical fluxes, food web dynamics and ecosystem functioning, including feedback mechanisms, under current predictions of climate change.

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International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 34-38,Decemberh, 2016, ISSN 2548-0960, Turkey, DOI: Your DOI number

ACCURACY OF 3D (THREE-DIMENSIONAL) TERRAIN MODELS IN SIMULATIONS Yemenicioglu, C., 1* Kaya, S.,1 Seker, D.Z.,1 1Istanbul

Technical University, Civil Engineering Faculty, Department of Geomatics Engineering, 34469, Istanbul, Turkey (yolcucanan@gmail.com, kayasina@itu.edu.tr, seker@itu.edu.tr) *Corresponding Author, Received: 05/06/2016, Accepted: 023/07/2016

ABSTRACT: The usage of realistic three-dimensional (3D) polygon terrain models with multiple levels of detail (LOD) is becoming widespread in popular applications like computer games or simulations, as it offers many advantages. These models, which represent an actual location in the world, are essential for the simulation-based training of military vehicles like planes, helicopters or tanks. Because training scenarios on this kind of simulations are used to observe or to hit a target on the modeled location. In addition to that, driving the behavior of terrestrial vehicles is influenced by the terrain properties like slopes, ramps, hitches, etc. because of the direct interaction with the ground. For this reason, the terrain models in the simulation scene should not only display the textures realistically, but also represent an accurate morphology; meaning the terrain altitudes should be modeled as correct as possible. Such terrain representations can be created by using Digital Terrain Model (DTM) for the geometry and satellite images for texturing. The geometry models are in the form of polygonal meshes through the triangulation methods. However, the accuracy is influenced by some parameters. Using insufficient (under-refined) triangles during the 3D modeling causes missing of some altitude vertices. That means these points will not be present in the model. Consequently, it can be thought that the number of triangles should be increased for a better geometrical fidelity. Nevertheless, it is not always correct as the usage of too much (overrefined) triangles can also cause errors, especially in terrains with almost vertical faces (like cliffs). In addition to that, the performance of the system deteriorates drastically through the increase in the number of triangles, as the computational complexity is also getting higher. Keywords: 3D, Accuracy, Simulation Model, Digital Terrain Model, Real Time Rendering

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 34-38,Decemberh, 2016,

1. INTRODUCTION UTM projection coordinates have been selected for the outputs and WGS 84 ellipsoid has been taken as the horizontal reference, therefore all data has been converted to this system before utilizing.

The convergence to the reality of 3D terrain models, which could be created quickly with the help of satellite images and DTMs, depends on not only the graphical quality but also the geometrical detail level of the surface. The altitude information in the DTMS is used in order to create triangular surface geometries during the 3D modeling with software tools (Smelik et al., 2009). The terrain model can be produced with several submodels with different quality levels, which are called LODs (Level of Detail). The visual scene is switched between these LODs to ease the representation of the graphical environment (Pregasis, 2016a). There are different numbers of triangles in every LOD. The number and form of the triangles influence the surface structure, so the geometrical accuracy is affected by them (Tariq, 2009).

The models have been created in three LODS. The number of the triangles for the LODs are given in Table1. Table 1. Number of triangles for LODs

To display these influences, an example terrain from Istanbul Bosporus area has been modeled as 3D using three different level of details. In the study, ASTER DTM with a 15m resolution for the altitude data and Quickbird satellite images for surface texturing have been used. Ground control points are selected for 3D model and DTM data and the altitude differences are measured in order to calculate “Root Mean Squared Error (RMSE)” of 3D model LODs. The least error measurement is gathered in the middle level of detail (2. LOD). The interpretation of the error sources at every level has been provided at the conclusion. Presagis Terra Vista (Pregasis, 2016b), Creator (Pregasis, 2016a) and Global Mapper (Blue Marble Geographics, 2016) software tools have been used for the modeling and analysis in this study.

Level of Detail (LOD)

Triangle amount

Low (1) Middle (2) High (3)

121 527 7250

Figure 1. ASTER DTM data from the selected model surface and modeling process in Terra Vista software tool

In the study, realistic 3D terrain models with three LODs in a simulation scene are examined and accuracy of altitudes and root mean squared errors (RMSE) are calculated for every detail level. After the examination of the relationship between the triangle amount and RMSE, it was seen that the lowest inaccuracy (best representation) occurs in the intermediate detail level (2.LOD). In conclusion, two methods are introduced to determine the amount of the triangles. The first one is the comparison of the altitudes with the real values after the interpolation, which is the traditional way. The second method is to compare the vertical areas between the vertices instead of altitudes. In this study, software tools, Presagis-Terra Vista for modeling applications and Global Mapper for GIS applications, are used.

Figure 2. Different perspective views of the resulting model. 11 ground control points, which are spread in different positions on the 3D model, have been selected for the RMSE of the LODs Hata! Başvuru kaynağı bulunamadı.). They are chosen from the most and least sloping positions to show the triangulation errors as a result of the modelling.

2. METHODOLGY AND APPLICATION For the application, the modeling has been done with ASTER DTM data in 15m resolution and texturing has been applied to the models from Quickbird satellite images. The surface of the model between the coordinates 41° 9.43977' K - 41° 10.87128' K and 29° 5.22732' D - 29° 6.29880' D has been examined for the error analysis (Figure 1). The models have been produced with Terra Vista software tool of Presagis (Pregasis, 2016b) (Figure 2).

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 34-38,Decemberh, 2016,

Figure 4. Altitude values of ground control points in LODs and DTM (lower right) The RMSE results of the LODS in the model has been given in Table 2.Hata! Başvuru kaynağı bulunamadı. As seen in the table above, the least error has been gathered from the middle LOD (2.LOD). The error sources, which are different at every level, has been discussed in the next section. Hata! Başvuru kaynağı bulunamadı.

Figure 3. The distribution of ground control points The measurements of the altitude for ground control points for each of the LODs in the model and their corresponding points on the DTM has been shown in Figure 4.

Table 2. RMSE results of the LODs in the 3D model

Source Aster DTM Z (m)

3D Model Error 1 LOD 1DTM (m)

3D Model Error 2 LOD 2DTM (m)

3D Model Error 3 LOD 3DTM (m)

66.28 163.26 40.46 180.9 163.505 78.32 168.77 134.62 55.78 33.94 91.36

42,85 -109,41 -9,26 -133,63 -145 -5,46 -3,93 16,37 -14,48 -6,65 -38,36

-12,64 -9 -7,26 -5,5 -8 3,48 -8,25 -2,62 -6,69 -8,64 -3,37

-12,64 -9 -9,26 7,42 3 -6,52 -1,92 -6,62 -6,98 -9,44 -13,36

∑ (Ln –Dn) ^ 2

54669,70 603

612,17 1125

795,78 6425

∑ (Ln –Dn) ^2 /n

4969,97 3275

RMSE (√ (∑ (Ln-Dn) ^2 /n )

70,5 (m)

Ground control points

3D Model LOD2 Z

3D Model LOD3 Z

N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11

109.13 53.85 31.20 47.27 19.10 72.86 164.84 150.99 41.30 27.29 53.00

53.64 154.26 33.20 175.40 155.10 81.80 160.52 132.00 49.09 25.30 87.99

53.64 154.26 31.20 188.32 166.31 71.80 166.85 128.00 48.80 24.50 78.00

55,65 19205

7,5 (m)

72,34 422045

8,5 (m)

from the corresponding points on the DTM during the automatic triangulation. Other than that, the amount of triangles influences the error amount as explained in the following.

3. RESULTS AND RECOMMENDATIONS Each of the error amounts can be associated with different causes. Essentially, the vertical correctness in the terrain models is related closely with the DTM resolution, because altitudes of vertices are gathered

3.1. Under-refined triangles/polygons

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 34-38,Decemberh, 2016,

Under-refined triangles are one of the sources in the low level of detail (1.LOD). Terrain projection algorithms, which instantiate the data on the height map, encounters this problem at most. The number of triangles/polygons to model the terrain should be increased to gather better quality, however, the vertex selections are defined in similar height values. For example, if high-frequency height maps (raster format DTM) are considered, the difference of the altitudes for two adjacent points should be calculated. It is not possible to obtain any quality improvement when these points have the same height value. However, there might be some points on the surface which are not regarded (Blue Marble Geographics, 2016).

Figure 6. Division of the edge between v0 and v1 points As the problem takes place on the meshes of a lowresolution height map, the best solution is to abort the optimization process as no new data can be gathered. In every loop of the calculation, the resolution of the surface mesh is doubled, because the distance between vertices is halved. When đ??Ťđ?? is the resolution of the height map and đ??Ťđ??Ś is the initial resolution of the mesh, the number of calculation loops can be found as;

The surface, which lays on the red line in the figure, cannot be represented on the terrain as the peak point represented by the bright white pixel was not taken into account, and the calculation assumes no height difference in the model. A pre-scanning on the height map can be made to check the frequencies and mesh resolution can be adjusted to decrease this kind of error. It is also mostly enough to select the triangle vertices carefully aware of this problem. Like if one of the vertices was selected in the middle of the v0-v1 line in Figure 5, such problem would not occur.

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If the number of optimization loops is more than the n number, the resolution of the mesh in the model is more than the source resolution and it is not necessary to continue with the optimization. However, it is not always trivial to avoid over-refined triangulation. Especially, this problem occurs on cliff-like sloping terrain structures and other similar almost vertical faces. Every vertex assignment on the edge should be done considering the error increase. Considering the position function đ?&#x2019;&#x2018;: â&#x201E;?đ?&#x;? â&#x2020;Ś â&#x201E;?đ?&#x;&#x2018; " for the vertices on the global coordinates, the equation can be written for two vertex points đ?&#x2019;&#x2014;đ?&#x;&#x17D; , đ?&#x2019;&#x2014;đ?&#x;? â&#x2C6;&#x2C6; â&#x201E;?đ?&#x;? and a constant height error đ?&#x153;ş:

Figure 5. The surface between v0 and v1, which is not regarded. 3.2. Over-refined triangles/polygons Over-refined triangles are the reason of the error in the high LOD (3.LOD). This error happens during the elimination of extreme height differences in the DTM. When optimization algorithm (automatic triangulation) try to divide an edge, the situation in takes place.

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If the assigned vertex point does not decrease the convergence error more than đ?&#x153;ş value, it is not necessary to make the assignment. But in a situation like in Figure 7, it is not possible to correct assignment points with the interpolation. New vertices are created in every optimization loop as a result, which results in a continuous execution without an end and distorted surfaces correspondingly.

The optimization algorithm in the figure above tries to assign new vertices on the edge between v0-v1 points to improve the accuracy of the modeling for the height differences. In this example, the resolution of the height map is relatively low, there is no new data to gather and optimization process continues till the same height value is gathered from the pixel on the height map (Figure 6).

Figure 7. Over-refined triangle error on keen vertical faces

International Journal of Engineering and Geosciences (IJEG), Vol; 1; , Issue; 01, pp. 34-38,Decemberh, 2016,

There are two ways to avoid such surface defections. The first solution is to measure the resulting area after the optimization instead of the height difference. In Figure 7, the dashed surface shows area before and after assignment of the vertex đ?&#x2019;&#x2014;â&#x20AC;˛đ?&#x2019;&#x201E; . Vertex assignment reduces the area, so the error lessens. However, this method increases the amount of calculations and affects the projection performance drastically.

height maps are also an essential factor. Further researches might be useful to find out the influence of such elements. REFERENCES Blue Marble Geographics, 2016. Global Mapper TM, http://www.bluemarblegeo.com/products/globalmapper.php. (Accessed 30 April 2016).

If performance is significant for the application, reducing the error constant đ?&#x153;ş and limiting the number of optimization loops provide a good solution, as it is the second way to reduce the error (Schmiade, 2008).

Presagis, 2016a. Creator 15, http://www.presagis.com/products_services/products/mo deling-simulation/content_creation/creator/#features. (Accessed 15 April 2016).

4. CONCLUSION Presagis, 2016b. Overview, http://www.presagis.com/products_services/products/mo deling-simulation/visualization/vega_prime#overview. (Accessed 2016 April 5).

The terrain models in simulation applications should display the textures realistically, and represent accurate morphology, as these properties are essential for user perception and success of the simulation training. The realistic visualization of 3D terrain models, which are generated through satellite images and DTMs, is based on the graphical quality and the geometrical detail level of the surface (LOD). There are different numbers of triangles in every LOD.

Schmiade, T., 2008. Adaptive GPU-based terrain rendering, U. o. Siegen, Ed., Master's Thesis Computer Graphics Group. Smelik R. M., De Kraker, K. J., Tutenel, T., Bidarra R. and S. A. Groenewegen, S.A., 2009. A survey of procedural methods for terrain modelling, Proceedings of the CASA Workshop on 3D Advanced Media In Gaming And Simulation (3AMIGAS).

A terrain model from the coordinates 41Â° 9.43977' K 41Â° 10.87128' K and 29Â° 5.22732' D - 29Â° 6.29880' D has been examined to analyze the effect of LOD to the accuracy. The RMSE results of the LODS in the model have shown that the best results have been gathered from the 2. LOD (medium quality). There are two causes for that. The rough modelling has the problem of under-refined triangulation, and the fine modelling is affected by the phenomena of the over-refined triangulation. These effects should be taken into consideration for successful modelling.

Tariq, S., 2009. D3D11 tessellation, in Game Developers Conference. Session: Advanced Visual Effects with Direct3D for PC.

This paper has primarily researched the triangulationrelated issues affecting the quality of the terrain models. It should be forgotten that that is not the only parameter for the realistic representation. For example, source