PRECIPITATION DATA IN THE NORTHERN PART
OF LIBYA
2020
Ph.D. THESIS
DEPARTMENT OF GEOGRAPHY
Mahmood Mohammed Mahmood SOLIMAN
Prof. Dr.Mücahit COŞKUN
TREND ANALYSIS OF TEMPERATURE AND PRECIPITATION IN
THE NORTHERN PART OF LIBYA
Mahmood Mohammed Mahmood SOLIMAN
T.C.
Karabuk University
Institute of Graduate Programs
Department of Geography
Prepared as
PhD Thesis
Prof. Dr. Mücahit COŞKUN
KARABUK
January, 2020
TABLE OF CONTENTS
TABLE OF
CONTENTS........................ 1
THESIS APPROVAL
PAGE................... 5
DECLARATION...................................... 6
ACKNOWLEDGEMENTS.................... 7
ABSTRACT............................................... 8
ÖZET.......................................................... 10
ARABIC
ABSTRACT...............................12
ARCHIVE RECORD
INFORMATION...................................... 14
ARŞİV KAYIT
BİLGİLERİ............................................... 15
ABBREVIATIONS.................................... 16
INTRODUCTION...................................... 18
I. SUBJECT OF THE
RESEARCH......... 18
II. PURPOSE AND SUB-AIMS OF THE
RESEARCH.... 21
III. SIGNIFICANCE AND LIMITATIONS OF
THE RESEARCH........... 22
IV. RESEARCH
HYPOTHESIS.......................................... 23
V. MATERIALS AND METHODS OF THE
RESEARCH................ 23
VI. PREVIOUS STUDIES........................... 38
I.
CHAPTER................................................. 41
CHARACTERISTICS OF PHYSICAL
GEOGRAPHY................ 41
1.1. Geological Properties of
Northern Libya............ 41
1.1.1.
Paleozoic................................................. 41
1.1.2.
Mesozoic.................................................. 41
1.1.3. Cenozoic (Tertiary and
Quaternary).... 42
1.2. Properties of
Geomorphology.................. 45
1.2.1. North
Mountains.................................... 45
1.2.1.1. Green
Mountain.................................. 45
1.2.1.2. Western's
Mountain............................ 46
1.2.2. Coastal Plains and Coastal
Landforms......... 46
1.2.2.1. Benghazi
Plain...................................... 46
1.2.2.2. Aljafarah
Plain..................................... 47
1.2.2.3. Sirte
Plain............................................ 47
1.2.3.
Plateaus.............................................. 47
1.2.3.1. Al-Butnan and
Defna........................... 47
1.2.3.2. Al-Hamada
Alhamra........................... 48
1.2.4. Karstic Land
Forms................... 48
1.2.5. Arid Region Land
Forms....................... 48
1.3. Climate of North
Libya.............................. 51
1.4. Properties of Hydrography........................ 51
1.4.1. Surface
Water.................................. 51
1.4.2.
Groundwater........................................... 51
1.5. Properties of Soils (Parent
Material and Components)...... 54
1.5.1. Zonal
Soils...................................... 54
1.5.1.1. Terra
Rossa................................... 54
1.5.1.2. Arid Steppes
Soil......................... 55
1.5.2. Intra-Zonal
Soils.................................... 55
1.5.3. Azonal
Soils..................................... 56
1.6. Properties of Vegetation in
North Libya.................... 59
II.CHAPTER...................................................... 62
CLIMATE
CHARACTERISTICS.................. 62
2.1. Effect of Physical Factors on
Climate...... 62
2.1.1. Planetary Factors....................... 62
2.1.1.1. Geographical
Location......................... 62
2.1.1.2. Air Masses and
Fronts............ 64
2.1.1.3.
Depressions............................. 65
2.1.2. Geographical
Factors........................ 68
2.1.2.1. Effect Mediterranean Sea
and Desert........ 68
2.1.2.2. Impact of Mountains and
Direction of Coastline.................. 68
2.1.2.3. Coastline
Direction........................... 69
2.1.2.4.
Aspects..................................... 71
2.2. Elements of
Climate................................... 78
2.2.1. Solar
Radiation................................. 78
2.2.2. Temperatures (Maximum,
Minimum and Average).................. 84
2.2.3. Atmospheric Pressure and
Wind.......... 95
2.2.3.1. Atmospheric
Pressure................. 95
2.2.3.2. Wind
Directions...................... 101
2.2.3.3. Wind
Speed.......................... 105
2.2.4. Humidity and
Precipitation............. 108
2.2.4.1. Relative
Humidity.......................... 108
2.2.4.2. Evaporation................................ 112
2.2.4.3.
Clouds.................................. 117
2.2.4.4.
Precipitation............................. 118
2.3. Some Classification of Climate
in Study Area.............. 131
2.3.1. SPI Index For Annual of
Rain.............. 131
2.3.2. Climate Classification of
Erinç............. 141
2.3.3.Climate Classification of L.
EMBERGER (Coefficient of Thermal – Rain) ... 149
2.3.4. Index of Johansson for
Continental Climate and Oceanity Climate... 151
2.3.5. Index of Kerner for
Continental Climate and Oceanity Climate...... 154
III.
CHAPTER.......................................... 157
DESCRIPTIVE STATISTICS AND
DISTRIBUTION OF TEMPERATURE AND PRECIPITATION
DATA....................... 157
3.1. Descriptive Statistics and Distributions
of Temperature Data.................. 158
3.2. Descriptive Statistics and
Distribution of Precipitation Data..................... 207
3.3. Statistical Investigation of
Temperatures and Precipitation Data............. 239
3.3.1. Investigation of Data
Distribution....... 239
3.3.2. Homogeneity of
Variances.................. 240
3.3.2.1.
Kruskal-Wallis...................... 240
3.3.2.2. Mann-Whitney
U..................... 241
3.3.3.Correlation Analysis
(Spearman's Rho, and Kendall's tau)................ 242
IV.
CHAPTER............................... 248
TREND ANALYSIS FOR TEMPERATURES AND
PRECIPITATION DATA... 248
4.1.Trend Analysis for Temperatures
Data....................................................... 249
4.1.1.Trend Analysis of
Mann-Kendall, Spearman, and Sen's slope........... 249
4.1.1.1. Evaluation on Maximum
Temperatures Results......................... 265
4.1.1.2. Evaluation of Minimum
Temperatures Results........................... 268
4.1.1.3. Evaluationof Average
Temperatures Results.............................. 272
4.1.2. Mann-Kendall Correlation
Coefficient U(t)-U'(t)............................... 276
4.1.2.1. Graphs of M-K U(t)-U'(t)
Results for Seasonally and Annual Maximum Temperatures (1971-2010)........................... 276
4.1.2.4. Evaluation for Results of
Mann-Kendall Embodiment Correlation U(t) -U (t') Tests of the Maximum, Minimum
and Average Temperatures Data......................... 336
4.1.3. Simple Linear Trend for
Temperatures Data.... 343
4.1.3.1. Simple Linear Trend of
MaximumTemperatures Data (1971-2010)... 343
4.1.3.2. Simple Linear Trend of
Minimum Temperatures Data (1971-2010) 352
4.1.3.3. Simple Linear Trend of
Average Temperatures Data (1971-2010) 362
4.2. Trend Analysis for Precipitation
Data..... 371
4.2.1. Trend Analysis of
Mann-Kendall, Spearman and Sen's slope........... 371
4.2.2. Relative Correlation of
Mann-Kendall U(t)-U'(t) Graphs for Precipitation Data (1971-2010)...383
4.2.3. Simple Linear Regression of
PrecipitationData (1971-2010).............. 405
4.2.3.1. Simple Linear Regression
for Seasonally Precipitation.............. 405
4.2.3.2. Simple Linear Regression
for Annual Precipitation Data (1971-2010) 413
4.2.4. Control's Models of Trend for
Annual Precipitation Data (1971-2010) 418
V.
CHAPTER.................................... 427
CONCLUSION............................................. 427
REFERENCES.............................................. 449
LIST OF
TABLES.......................................... 462
LIST OF
FIGURES....................................... 468
LIST OF
MAPS............................................... 486
CURRICULUM VITAE ...
488
The trend analysis is one of the important climate
studies for detecting climate change in the short and long term. Climate change
trend analysis can provide information on how climate has evolved to determine
the changes and trends in climate elements over time. The importance of climate
trend analysis studies is to estimate the risks of future climate changes based
on current data and to try to avoid drought and lack of water resources by
establishing sound scientific plans based on the results of these studies.
The scope of the research subject entitled “Trend
Analysis of Temperatures and Precipitation data in Northern part of Libya”
extends to analysis of 16 meteorological stations, 8 of which are coastal
stations, which are Agdabia, Benghazi, Derna, Misurata, Sirte, Tobruk, Tripoli,
Zwara. And 5 of them are desert stations, which are Ghadames, Ghariat, Hon,
Jaghboub, Jalo, and 3 mountain stations are Alfataiah, NALUT and SHAHAT. In
order to perform trend analysis, the data must be at least 30 years old. The
data for current analysis in most of the stations comprises of 40 years, from
1971 to 2010, except for Alfataiah Station (1981-2010) and Tobruk Station
(1984-2010).The necessary data comprising 40-year continuous data were
collected from Libyan National Meteorological center climate & climate
Change, Tripoli.
Microsoft Excel 2007 and IBM SPSS 23 program were used
to organize the climate and other data and to create tables, graphs and figures
for the data. Arc GIS 10.4 (Geography Information System) program was used to
make the maps. Trend Analysis and Windows software and IBM SPSS 23 program were
used to perform trends and other statistical analyzes. Descriptive statistics
of the data were made in the statistical methods used within the scope of the
study, Kolmogorov- Smirnov test for made normality distributions, and the data
were not distributed normally. After the Kruskal-Wallis H test was used to
determine the difference between the temperature and Precipitation, the
correlation was determined using the Spearman Rho method for correlation
analysis. For the trend analysis of 40-year temperature and precipitation data,
trends directions were determined by Mann-Kendall, Spearman Rho, Sen test,
Simple Linear Regression Analysis for temperatures.
In maximum temperatures, it is observed that there are
warming at western stations of the study area during the spring season and in
most stations in the summer, and Sirte, Agdabia, Jalo and Hon stations in
autumn. The winter season showed no significant trends in the maximum
temperatures. However, the general average temperatures of the maximum
temperatures have shown warming in all stations except for stations near Green
Mountain and Zwara station in the west. In minimum temperatures, it is observed
that there are warming at all stations in summer, except for Shahat station. In
spring, while in the autumn season, all stations showed warming except for
stations located on a high elevation, such as Nalut, Ghadames and Shahat. The
winter season has shown trends towards warming at stations of Jagboub, Alfataiah,
and Agdabia, as for the annual average, it showed trends towards warming in
some situations, such as Jaghboub, Alfataiah and Zwara. In average temperatures,
most stations showed a tendency to warming.
There were relationships between the indicators of the
North Atlantic Oscillation (positive and negative) and the increase trends in
the autumn season in several years the most important of which was in September
1993 with the value (-3.18) and its impact reached several stations such as
Al-FATAIAH, JAGHBOUB, JALO, TRIPOLI and DERNA in increasing and decreasing trends
in temperatures and precipitation in deferent years.
The El
Nino and La Nino phenomenon influenced temperature trends and precipitation in
the region. The El Niño effect (very strong) in the 1982-1983 season caused the
lowest temperatures in DERNA station and the maximum temperatures in ZWARA and
JALO stations while the phenomenon of La Niño affected the trends of
precipitation in some stations such as NALUT, SHAHAT, JAGHBOUB, TOBRUK, DERNA,
JALO, and JAGHBOUB.Based on results of this study, appropriate plans and
policies can be established to address current and future climate conditions,
by which areas threatened by drought and water shortages can be identified.
Keywords:
Temperature, Precipitation, Trend Analysis, Mann-Kendall, Spearman Trend Slope
Test, Physical Geography.
Trend analizi, uzun vadede iklim
değişikliğini tespit etmek için önemli iklim istatistik uygulamalarından
biridir. İklim elemanlarının zaman serileri içerisindeki değişikliklerini ve
eğilimlerini belirlemek iklimin nasıl değiştiği üzerine bir fikir verebilir.
Zaman serileri içerisindeki iklim değişkenliklerinin iklim parametrelerinde
artış ya da azalış eğilimi göstermesi, mevcut durumu tespit etmek kadar gelecek
ile ilgili öngörü yapılabilmesine de yardımcı olmaktadır.
Araştırmanın
kapsamını; “Kuzey Libya'da Sıcaklık ve Yağış Verilerinin Trend Analizi”
oluşturmaktadır. Tezin amacını ise, araştırmaya dâhil edilen istasyonların
(Agdabia, Bingazi, Derna, Misurata, Sirte, Tobruk, Trablus, Zwara, Ghadames,
Ghariat, Hon, Jaghboub, Jalo, Alfataiah, Nalut ve Shahat) sıcaklık ve yağış
verilerini değerlendirerek mevcut trendin eğilimlerini, boyutunu ve şiddetini
belirlemek meydana getirmekedir. Karma araştırma modelinin kullanıldığı bu
çalışmanın verileri Trablus’da bulunan Libya Ulusal Meteoroloji Merkezi İklim ve
İklim Değişikliği biriminden elde edilmiştir.
Verilerin analizinde
tablolar, grafikler ve şekiller oluşturmak için Microsoft Excel 2007, harita
yapımı ve analizi için Arc GIS 10.4 programı, Trend analizi ve diğer
istatistiksel işlemleri gerçekleştirmek için SPSS 23 programı kullanılmıştır.
Sıcaklık ve yağış arasındaki farkı belirlemek için Kruskal-Wallis H testi
uygulandıktan sonra korelasyon analizi için Spearman Rho yöntemi kullanılarak
korelasyon belirlenmiştir. 40 yıllık sıcaklık ve yağış verilerinin trend
analizi için eğilim yönleri Mann-Kendall, Spearman Rho, Sen testi, sıcaklıklar
için basit doğrusal regresyon analizi ile belirlenmiştir.
Maksimum
sıcaklıklarda, ilkbahar mevsiminde ve yaz aylarında çoğu istasyonda ve
sonbaharda Sirte, Agdabia, Jalove Hon istasyonlarında çalışma alanının batı
istasyonlarında ısınma olduğu görülmektedir. Kış mevsiminde maksimum
sıcaklıklarda önemli eğilimler göstermemiştir. Bununla birlikte, maksimum
sıcaklıkların genel ortalama sıcaklıkları, batıdaki Green Mountain ve Zwara
istasyonu yakınlarındaki istasyonlar hariç tüm istasyonlarda ısınmayı
göstermiştir.
Araştırmanın
sonucunda minimum sıcaklıklarda, Shahat istasyonu hariç, yaz aylarında tüm
istasyonlarda ısınma olduğu görülmektedir. İlkbaharda, tüm çöl istasyonları
ısınma eğilimlerini gösterirken, sonbahar mevsiminde tüm istasyonlar Nalut,
Ghadames ve Shahat gibi yükseltisi fazla olan istasyonlar dışında ısınma
eğilimi göstermemektedir. Kış mevsimi, Jagboub, Alfataiah ve Agdabia
istasyonlarında ısınmaya yönelik eğilimler göstermekte; yıllık ortalamada ise
Jaghboub, Alfataiah ve Zwara gibi bazı stasyonlar ısınma eğilimleri
göstermiştir. Ortalama sıcaklıklarda, çoğu istasyon her mevsim özellikle
(1998-2001) arasında ısınma eğilimi göstermiştir. Isınma eğilimi, Trablus, Misurata,
Bingazi gibi büyük nüfus merkezlerinin yakınında bulunan istasyonlarda ve
Agdabia, Tobruk ve Sirte gibi petrol rafinerilerinin yakınında bulunan
istasyonlarda görülmektedir.
Zwara, Trablus ve
Tobruk istasyonları gibi bazı istasyonlar, yıllık yağış ortalamasında azalma
eğilimleri gösterirken, diğer tüm istasyonların yağışında önemli bir eğilim
göstermemiştir.
Kuzey Atlantik
Salınımının göstergeleri (pozitif ve negatif) ile sonbahar mevsimindeki artış
eğilimleri arasında en önemlisi (-3.18) değeri ile Eylül 1993'te olan ilişkiler
bulunmaktadır ve onun etkisi AL-FATAIAH, JAGHBOUB, JALO, TRİPOLİ ve DERNA gibi
çeşitli istasyonlara ulaşmıştır.
El Nino ve La Nino
salınımları bölgedeki sıcaklık trendlerini ve yağışları etkilemiştir. 1982-1983
yıllarında El Niño etkisi (çok güçlü), DERNA istasyonundaki en düşük
sıcaklıklara ve ZWARA ve JALO istasyonlarındaki maksimum sıcaklıklara neden
olurken, la Niño salınımı NALUT, SHAHAT, JAGHBOUB, TOBRUK, DERNA, JALO ve
JAGHBOUB gibi bazı istasyonlarda yağış trendlerini etkilemiştir.
Araştırmanın
sonuçlarına göre Libya’daki iklim değişikliğinin olumsuz etkilerini ortadan
kaldırmak veya azaltmak için düşük yağış ve artan sıcaklıkları etkileyen bazı
tedbirler alınmalıdır. Bu tedbirlerin en önemlisi, su kaynakları yönetimi için
planlar yapılmalı ve uygulanmalıdır.
Anahtar Kelimeler: Sıcaklık, Yağış, Trend Analizi, Mann-Kendall, Spearman Trend Eğilim
Testi, Fiziki Coğrafya.
المُلخص
تعد دراسة
تحليل الاتجاهات واحدة من الدراسات المُناخية المهمة للكشف عن تغير المُناخ على المدى القريب
والبعيد ، ويمكن أن يوفر تحليل المتغيرات المُناخية معلومات عن كيفية تطور المُناخ
من خلال تحليل السلاسل الزمنية.
مجال البحث هو تحليل اتجاه درجات الحرارة وهطول الأمطار
في القسم الشمالي من ليبيا ، لـ 16 محطة أرصاد جوية، منها 8 محطات ساحلية وهي
أجدابيا وبنغازي ودرنة و مصراتة وسرت وطبرق وطرابلس و زوارة. و 5 منها محطات
صحراوية هي غدامس ، و القريات ، وهون ، و الجغبوب ، و جالو ، و 3 محطات جبلية هي
الفتائح و شحات و نالوت.
من أجل إجراء
تحليل الاتجاهات على نحو معتبر علمياً، يجب ألّا تقل البيانات المُناخية عن 30 عامًا. كانت السلاسل الزمنية في معظم المحطات (40 عامًا)
، من 1971 إلى 2010، باستثناء محطة الفتائح فقد كانت مدة بياناتها (30 عاماً) من1981
إلى 2001، ومحطة طبرق (27 عاماً) من 1984إلى 2010، لذلك، هناك العديد من المحطات في منطقة الدراسة
لم تُدرج في البحث، منها على سبيل المثال محطة ناصر و محطة يفرن و محطة الكفرة،
حيث لا توجد سلسلة بيانات منتظمة يمكن الاعتماد عليها في تحليل الاتجاهات.
الغرض من هذه
الدراسة هو تحليل اتجاهات بيانات درجات الحرارة و الأمطار للمحطات المشمولة في
البحث والتعرف على الزيادة أو النقصان في تلك المحطات، و ذلك للمساهمة في إثراء
المكتبة العلمية للمُناخ ، خاصة و إن دراسات تحليل اتجاهات المُناخ قليلة في
ليبيا، حيث تكمن أهمية دراسة تحليل اتجاه المُناخ في التنبؤ بمخاطر التغيرات المُناخية
المستقبلية، ومحاولة تجنب مشكلة الجفاف ونقص الموارد المائية و ترشيد استهلاكها، و يُعتقد
أن البحث سوف يسهم في تحديد المناطق الجافة في شمال ليبيا، من أجل إعطاء رؤية
مستقبلية لتطوير مشاريع المياه في ليبيا و التي من خلالها يمكن تحقيق التنمية
الاقتصادية في البلاد.
تم الحصول
على البيانات من المركز الوطني الليبي للأرصاد الجوية وتغير المُناخ، طرابلس. اُستخدم
برنامج Microsoft Excel 2007 و برنامج IBM SPSS 23 لتنظيم
البيانات المُناخية، ثم اُستخدم برنامج Arc GIS 10.4 (نظم المعلومات
الجغرافية) لعمل الخرائط الطبيعية و المُناخية.
اُستُخدم
برنامج IBM SPSS 23 لإجراء تحليلات اتجاهية وإحصائية أخرى. أُجريت
إحصاءات وصفية لبيانات الحرارة و المطر. أُجريت اختبارات التوزيعات الطبيعية
بواسطة اختبار Kolmogorov Smirnov حيث أشارت أغلب النتائج أن توزيع البيانات
لم يتم بشكل طبيعي. اُستخدم اختبار Kruskal-Wallisلتحديد الفرق بين درجة الحرارة وهطول الأمطار، حُددت
العلاقة بين البيانات باستخدام طريقة Spearman's
Rho and Kendall's tau لتحليل الارتباط. و لتحليل الاتجاهات اُستخدمت اختبارات: Mann-Kendall, Spearman, Sen slope Trend analysis
windows.
أعطت اختبارات تحليل الاتجاه عدة اتجاهات
مختلفة، حيث لوحظ وجود ارتفاع في درجات
الحرارة العظمى في المحطات الغربية لمنطقة الدراسة خلال فصل الربيع، وفي معظم
المحطات في فصل الصيف، و محطات سرت و أجدابيا و جالو وهون في الخريف، ولم يظهر
موسم الشتاء أي اتجاهات مهمة. ومع ذلك، فإن المعدل السنوي لدرجات الحرارة العظمى
أظهر اتجاهات نحو الزيادة في جميع المحطات باستثناء المحطات القريبة من الجبل
الأخضر بنغازي و الفتائح ومحطة زوارة في الغرب في درجات الحرارة الصغرى لوحظ
ارتفاع درجات الحرارة في جميع المحطات في فصل الصيف، باستثناء محطة شحات، في فصل
الربيع، أظهرت جميع المحطات الصحراوية اتجاهات نحو الاحترار، و كذلك الحال في فصل
الخريف أظهرت جميع المحطات زيادة في درجات الحرارة باستثناء المحطات الواقعة في مناطق
مرتفعة فوق مستوى سطح البحر، أظهر فصل الشتاء اتجاهات نحو الاحترار في محطات
الجغبوب و الفتائح و أجدابيا. في المعدل العام لدرجات الحرارة، أظهرت معظم المحطات
ميلًا إلى الاحترار في جميع الفصول وأيضاً في المعدل السنوي، خاصة بين (1998-2010)، ويمكن ملاحظة هذا الاحترار في المحطات القريبة من المراكز
السكانية الكبيرة مثل طرابلس و مصراتة و بنغازي. وكذلك المحطات الموجودة بالقرب من
مصافي تكرير النفط مثل أجدابيا و طبرق و سرت. لم تُظهر جميع المحطات أي اتجاهات
مهمة في هطول الأمطار، خاصة أن بعض المحطات أظهرت اتجاهات نحو الانخفاض في
المعدلات السنوية لكميات الأمطار، مثل محطات زوارة و طرابلس و طبرق.
كانت هناك علاقات بين مؤشرات تذبذب شمال
الأطلسي (إيجابية وسلبية) خاصة في موسم الخريف خلال عدة سنوات أهمها عام 1993،
عندما كان المؤشر في سبتمبر (-3.18)، و لقد وصل تأثيره إلى العديد من المحطات مثل
الفتائح و الجغبوب و جالو و طرابلس و درنة. و بالمقارنة بين نتائج الدراسة و نتائج
تذبذب شمال الأطلسي لوحظ أن هناك علاقة بين اتجاهات (الزيادة والنقصان) في العديد
من محطات منطقة الدراسة و يظهر الأثر واضحاً على درجات الحرارة وهطول الأمطار في
مواسم و سنوات مختلفة. و لظاهرة El
Niño تأثير ضعيف على
اتجاهات درجات الحرارة وهطول الأمطار في المنطقة في موسم 1982-1983 وصل تأثير
النينيو إلى منطقة الدراسة عندما كان (قوي جدًا) و اقتصر أثره فقط على درجات
الحرارة الصغرى في محطة درنة، و درجات الحرارة العظمى في محطات زوارة و جالو. في
حين أن ظاهرة La Niño كان لها تأثير على اتجاهات هطول الأمطار في بعض المحطات
مثل نالوت، و شحات، و الجغبوب، و طبرق، و درنة، و جالو.
استنادًا إلى التقييم الحقيقي للنتائج التي
تم الحصول عليها من تحليل الاتجاهات، يمكن إنشاء خطط وسياسات مناسبة لمواجهة
الظروف المُناخية الحالية والمستقبلية، من خلالها يمكن حصر المناطق المهددة بالجفاف
و العجز المائي.
الكلمات
الافتتاحية: درجة الحرارة،
التساقط، تحليل الاتجاه، مان كيندال، سبيرمان، المُناخ، الجغرافيا الطبيعية.
The
introduction part of thesis covers the subject of the research, purpose,
sub-objectives, justification, importance, limitations, materials and methods
and analysis of previous studies.
The
climate is one of the major physical elements on the earth that has become an
important area of scientific research. The governments of many countries in the
world have shown keen interest in climate change after the industrial
revolution of the 19th century. Besides, the issue of global warming
in recent decades has also increased the interest of scientists in climate
research.
In
general, climate change can be defined as a long-term and slow-developing
changes with significant global and local impacts on climatic conditions of the
world. These changes in climate during ice age and interglaciation period has
great impact on climatic patterns in various regions of the world that
influence the melting of glaciers, average temperature and amount of
precipitation (Türkeş, 2001).
This
implies that the portrayal of the climate patterns in a particular region must
include an analysis of mean conditions of the seasonal cycle of the probability
of extremes such as severe drought in the arid areas.
The
most recent assessment conducted by Intergovernmental Panel on Climate Change
(IPCC) has reported an increase in the mean surface temperature from 0.56 0C
in 1906 to 0.92 0C in 2005 (IPCC, 2007). This increase is larger
than what had been mentioned by previous IPCC report, which mentioned that
global mean surface temperature has increased by approximately 0.3 0C
to 0.6 0C from 1901-2000 (IPCC, 2001).
Climate
changes in the past were entirely due to natural causes. However, the share of
human activities in recent climate changes is much greater. The short-range
climate changes, which are suggested to be the result of more human activities,
are the subjects related to the phenomenon of global warming (Nişancı, 2007).
The
climate which has a dynamic structure shows continuous variation in temporal
and spatial scale, hence, a large part of the earth is subject to changes due
to the nature of the climate in short or long periods. On the other hand, the
idea of global climate change, which is expressed as the increase of global
temperature and the change of rainfall pattern, has started to be accepted by
the scientists due to the findings obtained in recent years (Karabulut, 2009).
Climate
change has both positive and negative effects. These impacts influence human
health and quality of life at greater extent with changes in water resources,
natural ecosystems, agriculture, forestry, and fishing activities. The negative
impact start dominating as the changes in the climate patterns increase
(Demirci, and Karakuyu, 2002; Türkeş, 2012).
The
elements of heat and precipitation are of the most important climatic elements
that affect directly or indirectly other air elements such as evaporation,
condensation, atmospheric pressure, etc. Any change in temperature or lack of
rain can cause a change in the general that affects regional or even local
climate. Temperature and precipitation that represents the main elements of
climate have both spatial and temporal variability.
The
oscillations in the temperature and precipitation parameters show evidence of
the general characteristics of the climate and they are of great importance.
Accordingly, recent studies on climate change have focused on the trend
analysis of temperature and precipitation parameters. The studies revealed that
during the period between 1850-2016, the year 2000 represented the hottest ten
years on a global scale (Cosun, 2009 andTürkeş, 2012).
This
definition of the climate as representative of conditions over several decades
should, of course, not mask the fact that climate can change rapidly.
Nevertheless, a substantial time interval is needed to observe a difference in
climate between any two periods. In general, the less the difference between
the two periods, the longer is the time needed to identify any changes in the
climate between them with confidence.
Following
the World Meteorological Organization (WMO), a period of 30 years is the
classical period for performing the statistics used to define climate. This is
well adopted for studying climate of recent decades since it requires a
reasonable amount of data while still providing a good sample of the different
types of weather that can occur in a particular area.
However,
when analyzing the most distant past, such as the last glacial maximum around
20.000 years ago, climatologists are often interested in varied characteristic
of longer time intervals. Consequently, the 30-year period proposed by the WMO
should be considered more as an indicator than a norm that must be followed in
all cases (Brohan, 2006).
The
analysis of previous literature has revealed that there is an absence of
scientific studies regarding trend analysis of temperature and precipitation in
Libya. Therefore, in present study the trend analysis was applied to
temperature and precipitation data of north Libya from 16 meteorological
stations for the period between 1971-2010, to identify general trends along with
present and future climate predictions in the study area. Besides, the
dimensions of climatic changes related to these climate parameters were
determined in order to make accurate inferences about future situations on the
climate. In this study, trend analysis was performed on temperature and
precipitation parameters instead of evaluating all climatic parameters which
best reflect the changes in climate with time.
The
study area is located in North Africa in Libya extending from 28.00 to 33.10
latitudes in north and 09.20 to 25.00 longitude in the east. Mediterranean sea
lies in the north, desert of Libya in the south, Tunisia and Algeria in the
west and Egypt in the east of the study area. There is about 1900 km long
coastline in the north of study area extending from Berdya gulf in the east to
Ras Ajdir in the west.
The study area covers an area of about 538,495 square kilometers comprising two different regions. First region comprises coastline area, Green Mountain in Northeast, and West Mountain in northwest, while the second one is southern region which is a dry desert area making the largest part of the study area. This diversity and extension in the surface features resulted in a variety of climatic conditions prevailing in the region from semi-humid climate in the north and semi-arid climate in the center to the very-arid climate in the south.
II.
PURPOSE AND SUB-AIMS OF THE RESEARCH
Present
research aims to identify the trend of temperature and precipitation data of
long period and find the direction and dimensions of these trends. For this
purpose, the following questions have been sought:
v What
are the planetary and local factors affecting the climate of northern Libya?
v In
which trends (increase/decrease) the trend has occurred in meteorological
stations (Desert stations or Coast stations)?
v Which
meteorological stations show strong trends and are these stations show changes
in temperature and precipitation data?
v Is
there any relationship between the temperatures and precipitationtrends in
Libya and global climate change and climatic phenomena such as Atlantic
oscillations and El Niño?
The study is based on one main axis to
answer these questions that is concerned with descriptive and statistical
analysis of temperatures and precipitation in the area.
A
number of studies have recommended the study of trends for different climate
variables (Nalley, 2012) to understand the relationship between human
activities and climate. In the light of this, the main objective of this
research is to analyze the trends that may exist in the time series of climatic
variables. These parameters serve as good indicators of how the climate has
evolved as the studies on climate change indicate an increase in patterns of
temperature and precipitation experienced in different parts of the world.
Therefore, information about the impacts of climate change on the
spatiotemporal characteristics of temperature and precipitation is required to
understand the effects of climate change on scarcity of water resources in
region.
III.
SIGNIFICANCE AND LIMITATIONS OF THE RESEARCH
The
subject of the study is important because it is the first study that provides a
clearer understanding of climatic trends in Libya with such extent the obtained
results are thought to provide an important contribution to the development of
climate plans and policies.
Climatic
characteristics, climate change and trends have a very decisive influence on
human activities and ecosystems. These effects are mostly manifested by
temperature and precipitation elements. This issue demonstrates the necessity
of the subject taken in the study and shows the importance of taking the basis
of temperature and precipitation data for trend analysis. Besides, it is also
important to analyze the trends of heat and rain in the region to identify the
causes of drought and how to treat them by sound scientific methods as the
study area suffers from the problem of water shortage and the spread of drought
in agricultural land.
During
the course of research, some difficulties were encountered in terms of accuracy
of the results of the study. In order to carry out the trend analysis which
constitutes the subject of the research, there is a need for at least 30 years
of temperature and precipitation data continuously. While the number of
meteorological stations in North Libya is more than the number of stations
included in the study, all of these stations were not included in the study.
This is because some stations did not have long-year data and data sets
containing long years did not show uninterrupted measurements and in some cases
the data for 5 years or more were not taken consecutively in these stations.
Besides, the most important difficulties faced during the research were the
lack of sources and references of climate.
The hypotheses created within the scope of
the research are as follows:
H0:
Observation sequences of North of Libya temperatures (mean, average maximum and
average minimum) show trends.
Ha:
Observation sequences of the North of Libya temperatures (mean, average maximum
and average minimum) do not show any trend.
H0:
Observation sequences for the precipitation of the North of Libya show a trend.
Ha:
Observation sequences of the precipitation of the North of Libya do not show
any trend.
H0:
There is a relationship between Temperatures and Precipitation trends, Atlantic
oscillations, and El Niño.
Ha:
There isn't a relationship between Temperatures and Precipitation trends,
Atlantic oscillations, and El Niño.
V.
MATERIALS AND METHODS OF THE RESEARCH
Within
the scope of the research, a detailed literature review was conducted including
local and international sources of information. In light of the literature,
features and characteristics of physical geography were determined as they have
an influence on climatic properties and necessary preliminary works were made
about the evaluation processes of the programs, methods, and analyses used in
the trend analysis. Following actions were taken after collection of secondary
data of climate obtained from the Libyan National Meteorological Office, The
steps regarding the organization and evaluation of the scattered data are given
below:
v Monthly
and long-year average and total values of temperature, solar duration,
humidity, precipitation, pressure and wind velocity were calculated with
Microsoft Excel 2007 program. Tables and graphs were created through this
program to visualize these numerical data including solar radiation,
temperature, atmospheric pressure, wind directions, humidity, evaporation,
clouds, precipitation, SPI index, and all classifications of climate.
v The
Excel (QI Macros) was used to extract the standard deviations of temperature
and precipitation variables and graphs were made.
v IBM
SPSS 20 program was used in creating selective statistics, analysis of data
distribution, determination of differences and correlation analysis. In the
determination of the distribution of data Kolmogorov-Smirnov, Mann-Whitney U
for the difference and Spearman Rho test for correlation analysis were used.
v SPSS
20 program was used to determine the homogeneity of variances on temperature
and precipitation data and Kruskal-Wallis H test was preferred.
v Trend
values of temperature and precipitation data were determined using Trend
Analysis for Windows program. Microsoft Word 2007 program was used to table
these values.
v Simple
linear regression analyses for temperature and precipitation data were
performed on the IBM SPSS 20 program and results were visualized by graphs on
Microsoft Excel 2007 program.
v Mann-Kendall-rank
correlation graphs were created on the Microsoft Excel 2007 program using the
u(t)-u'(t) values obtained from the Trend Analysis for Windows software.
v App
of XmR Trend Control Chart in Excel (QI Macros) was used to identify the trends
of maximum and minor temperatures and general averages for seasons, and years,
and also was used to identify the monthly and annual precipitation trends.
v The
tabulation of the values obtained from these formulas
and the creation was carried out via Microsoft Excel 2007.
v Mapping was done through GIS program (Arc map 10.3).
* Climate Data, consists of a groups of time series, a
simple model of data in a time series is to view each observation as being the
realization of a random variable made up of a trend through time, (one or more)
seasonal effects, and remaining effects that are not a function of time (Tamra
et al., 2013).
Climatic data for 16
synoptic stations across Libya were collected from the Libyan National Meteorological
Centre (LNMC), for periods of 1971-2010 from 16 stations except for ALFATAIAH
station 1981-2010 and TOBRUK station 1984-2010.
as most of the meteorological stations of the study area stopped working
at the beginning of 2011 due to the political circumstances of the country. The
following table 1 and map2 show the
geographical characteristics of the selected stations.
Table
1. Geographical Information about
Meteorological Stations
|
NO |
Station Code |
Station Name |
North Latitude |
East Longitude |
Elevation |
Distance from sea |
Monitoring Period |
|
1 |
62055 |
AGDABIA |
30.43 |
20.10 |
0.7 |
21 |
1971 - 2010 |
|
2 |
62115 |
ALFATAIAH |
32.45 |
22.38 |
263 |
15 |
1981 - 2010 |
|
3 |
62053 |
BENGHAZI |
32.05 |
20.16 |
129 |
13 |
1971 - 2010 |
|
4 |
62059 |
DERNA |
32.47 |
22.35 |
26 |
0.6 |
1971 - 2010 |
|
5 |
62103 |
GHADAMES |
30.08 |
09.30 |
357 |
400 |
1971 - 2010 |
|
6 |
62120 |
GHARIAT |
30.23 |
13.35 |
497 |
254 |
1971 - 2010 |
|
7 |
62131 |
HON |
29.07 |
15.57 |
263 |
230 |
1971 - 2010 |
|
8 |
62176 |
JAGHBOUB |
29.45 |
24.32 |
0.2 |
247 |
1971 - 2010 |
|
9 |
62161 |
JALO |
29.01 |
21.34 |
60 |
206 |
1971 - 2010 |
|
10 |
62016 |
MISURATA |
32.19 |
15.03 |
32 |
0.2 |
1971 - 2010 |
|
11 |
62002 |
NALUT |
31.52 |
10.59 |
640 |
160 |
1971 - 2010 |
|
12 |
62056 |
SHAHAT |
32.49 |
21.51 |
621 |
11 |
1971 - 2010 |
|
13 |
62019 |
SIRTE |
31.12 |
16.35 |
13 |
0.3 |
1971 - 2010 |
|
14 |
62062 |
TOBRUK |
32.06 |
23.56 |
50 |
0.2 |
1984 - 2010 |
|
15 |
62010 |
TRIPOLI |
32.40 |
13.09 |
81 |
3.2 |
1971 - 2010 |
|
16 |
62007 |
ZWARA |
32.53 |
12.05 |
0.3 |
1.2 |
1971 - 2010 |
Source:
Unpublished data from General Administration of Meteorology and Climate,
Tripoli, Libya, 2012.
v
Methods
The
following are the stages for the regulation and evaluation of the data. The
longest observational periods of meteorological stations were first determined
and the data sets were evaluated in this context.
§ Applied Statistical Methods
Various statistical methods were used in
the research. Firstly, descriptive statistics of the data were determined and
then the distribution of the data was examined with the Kolmogorov-Smirnov
test. Following this process, the data were subjected to the Kruskal Wallis H
test to determine the homogeneity of the variances and whether there was a
difference between the variables.
The difference between the average
temperature and precipitation data was considered separately and the Mann
Whitney U test was used for this. Limiting the number of groups to two required
the use of the Mann Whitney test instead of Kruskal Wallis test. Correlation
analysis was performed with Spearman Rho test on temperature and precipitation
data before the trend analysis.
The normal distribution of the data and the
lack of homogeneity necessitate the use of this method. Mann-Kendall and
Spearman's Rho tests were used for the analysis of temperature and
precipitation data. In the determination of trend start year, Mann-Kendall
Order Correlation test statistic and Trend Linear Regression and Sen's Trend
Slope tests were used (Yılmaz, 2018).
In the analysis of trend analysis, it is
very important to obtain data containing long-term measurements and to
determine the increase or decrease in trend of time series in terms of
parametric and nonparametric methods (Gümüş, 2006).
It is possible to obtain better results
with the use of parametric tests in cases where the data have a normal
distribution and the variances are homogeneous. In non-parametric tests, the
time series generally shows a non-normal distribution. Therefore, The use of
nonparametric methods in the determination of the trend in the climatic data
where continuous variables such as temperature and precipitation are concerned
provides more accurate results than parametric methods (Yılmaz, 2018).
Within the scope of this study, descriptive
statistics were performed primarily on average temperature and precipitation
data. After descriptive statistics, Kolmogorov-Smirnov test was used to
investigate the distribution of the data. Then, temperature and precipitation
data were tested for Kruskal-Wallis H correlation analysis by Spearman Rho for
the homogeneity of variance. The determination of the normal distribution of
the data required the use of these methods. As a result of these stages,
Mann-Kendall, Spearman's Rho, Sen-Trends and Mann-Kendall Correlation tests and
Simple Linear regression analysis were applied to the data.
§ Kolmogorov-Smirnov
It is a method used to investigate the
normality of data. It is preferred when the group size is greater than 30. The
evaluation of the test is carried out by comparing the obtained p value with
the desired level of significance. If the group size is less than 30, the
Shapiro-Walks test is suitable (Yılmaz, 2018).
§ Kruskal Wallis H Test
In studies of climatic change, change types
such as the form of sudden or step climatic fluctuation or periodicity, strong
tendency and change in atmospheric or hydrological series, constitute the main
alternatives of homogeneity. Some of the aforementioned types of change may
reflect non-homogeneity. The observation records obtained from the applications
performed on the data are often not homogeneous (Yılmaz, 2018; Türkeş, 2013).
In scientific studies, the data to be
included in the research must be absolutely reliable. One of the ways to
provide valid information on the reliability of the data is the quality control
and homogeneity analysis for meteorological measurements. Climatologically data
are not always homogeneous. Such data show occasional non-homogenous
characteristics depending on urbanization, measuring instrument problems and
station location change factors. However, when the discontinuities and values
observed do not have a normal distribution, the precipitation parameter differs
from many other meteorological parameters. Depending on the nature of the
precipitation parameter, it is inconvenient to use the one-way variance test
(ANOVA) in the homogeneity analysis of precipitation data. One-way variance
test is applicable for cases where normal distribution is seen and the
variances of the series are accepted as equal (Sönmez, 2007).
One
way variance test is a parametric test. The Kruskal Wallis H test refers to the
non-parametric response of the one-way analysis of variance where the samples
exhibit independent irrelevant characteristics. An alternative non-parametric
Kruskal Wallis H test is preferred when one-way analysis of variance is not
possible. The assumptions adopted by the ANOVA test are invalid and when the
number of groups to be compared exceeds two, the Kruskal Wallis test is
applied. In the Kruskal Wallis analysis, first the values are sorted, the
difference between the independent tools of the ranking values is compared and
the difference between them is tested (Büyüköztürk, 2016).
The Kruskal Wallis H homogeneity test is
performed on averages and variances to demonstrate the homogeneity of these
values and reflects a non-parametric effective homogeneity test. In this
homogeneity test, it is not possible to use original observations containing
each of the analyzed sequences. In the Kruskal Wallis H homogeneity test, the
sequence numbers of the total sequential sequence formed by ordering the
original observations from small to large are used (Türkeş, 2013).
The following hypotheses were considered to
test the homogeneity of the mean:
H0:
Group variances are homogeneous.
Ha:
Group variances are not homogeneous.
§ Mann Whitney U-Test
Mann-Whitney U test is used to determine
whether the data of two non-related samples differ significantly from each
other. In order to be able to apply this test, the dependent variable must be
at least on the ranking scale and the observation results must be independent
from each other. In the evaluation of the analysis, the total number of the
values of the two groups is taken as the basis (Büyüköztürk, 2016 and İçel, 2009).
§ Mann-Kendall Test
The statistical methods used to determine
the degree and direction of the relationship between variables and the
dependent and independent variables of the variables are defined as
correlation. In this respect, Mann-Kendall and Spearman Rho tests are applied
when the parametric Pearson Correlation test is not possible to use (Norrant, 2006;Karmeshu,
2012;Gozalan, 2019 andÖzbunar, 2019).
The Mann-Kendall test is one of the
commonly used methods for determining the trend occurring in time series for
study areas such as climatology and hydrology(Salamiet al., 2016). The
Mann-Kendall test is a statistical method proposed by the World Meteorological
Organization (WMO). In many studies on trend analysis, this method has demonstrated
superiority among other methods used (Pielke, 2002 and Hendricks, 2015). The
Mann-Kendall test statistic (S statistic) is formulated with the following
equation (Bulutet al., 2006; Al-Tahir et al., 2010;Bonfils, 2012 and Khomsiet
al., 2016).
The S value in the equation shows almost
normal distribution with the mean and variance values stated below in cases
where n is greater than or equal to eight (n> 8). The value of n corresponds
to the data length in the equation in years. If the value of n is greater than
or equal to thirty (n ≥ 30) the z test approaches
the t-test. The sign function is indicated by a sign data test performed on an
(xj) data set, which is sorted by a set of (xi) and (xj)data as specified in
the following equation (Özfidaner, 2007).
The variance determination of S is determined by the following equation:
In the above equation, the numbers of the
relative groups in the data set are denoted by the value of p and the connected
observations in a series i are indicated by the value of t. The use of the
collection term shown in the equation only occurs if there is observation in
the data.
The
Z value that denotes the standardized Mann-Kendall test statistic can be
calculated by the following equation (9) and under the assumption that there is
no course in the series corresponding to the null hypothesis (H0),
it shows the standard normal distribution with a mean of zero and variance
(Özfidaner, 2007).
Null-hypothesis is accepted when the 1-α/2
condition is satisfied, while Z is indicated by the plus (+). The Z value
indicated by minus (-) reflects the decrease. Depending on the results of the
Mann-Kendall test the corresponding value of Z value was determined as 1.96 for
the two-way 95% confidence interval.
§ Mann-Kendall Correlation Coefficient
The sequential analysis of the Mann-Kendall
u (t) test sample value was used to determine the beginning of the trend in the
observation sequences with significant trend and the point or points of change
in the observation sequence (Türkeş, 2013 Yılmaz, 2018 and Gozalan 2019) and test results were shown
graphically.
Test sample value t:
The average of distribution function E
(t):
and
has variance (t):
Equations are given. Test sample value u (t):
As shown in the equation, the null
hypothesis is rejected for the large values of u
(t) according to the bi-lateral shape. If the obtained u (t) value is
significant at 95% or 99%, trend can be determined depending on whether u (t)
is greater than or equal to 0. If u (t)> 0, the trend is increasing, if u
(t) <0, the trend is decreasing. The determination of the u(t') test sample
value was also determined in a similar way to u(t') by applying the inverse of
the current process performed in series (Yılmaz, 2018).
The successive analysis of the Mann-Kendall
rank correlation test was used to graphically show the trends detected in the
temperature and precipitation from the u(t) and u(t’) test sample values. While
the curves of u(t) and u(t') trend values on
the charts intersect at one point, they provide evidence for the existence of a
significant trend. The intersection of u(t) and u(t') several times indicates
that there are no trends in the sequence. In order to determine the starting
date of the trends in the direction of increase or decrease, attention is paid
to the points where the u (t) and u (t’) curves overlap (Papadimitrio, 1991).
§ Spearman's Rho Test
The Rho test of Spearman is one of the
tests to detect the correlation between two observation sequences. It is used
because it has fast and simple application in determining the Simple Linear
trend in observation series. The determination of Rxy which
represents the sequence statistics, takes place from small to large, or from
large to small. The rs correlation coefficient symbolizes Spearman's
Rho test statistic (Gümüş, 2006, Zarei, 2017 and Gozalan
2019).
Spearman's
Relationship Coefficient:
The
rsdistribution approach is normal when n> 30. Accordingly, normal
distribution tables were used to determine rs test statistic. The Z
value corresponding to the test statistic of rs is calculated by the
following equation (Gümüş, 2006).
The higher Z value obtained as a result of
the equation compared to the Zα / 2
value shown in the standard normal distribution tables at the α
significance level is the evidence for the existence of a trend towards
increase or decrease.
In order to determine whether there is any
tendency in the observation series included in the study, it is examined
whether the sample value is significant or not. If the sample value is
significant then the trend is calculated to be increased if rs> 0
while trend decreases in the direction of decreasing if rs<0 (Türkeş,
2013).
§ Sen's Trend Slope Method
Sen's trend slope method is a nonparametric
statistical method which is used to determine the change of the slope in unit
time in the case of determination of Simple Linear trend presence for time
series. The lack of data in the time series does not constitute an obstacle to
the implementation of this test. The size of the trend observed in the time
series is calculated by the following formulas in the following order (Demirci,
2008, Karpouzos, 2010, Aziz, 2017 and Ay, 2017).
The Qi value corresponds to the data
between xi and xk. (xj), and (j)are values
that were determined at the time, (xk), and (k) are the time
determined value, and (j) is the next time for the (k) time. N value is
calculated by N= n(n - 1)/2 equation and the median values of N and Qiare
used to determine the trend of Sen (Polat, 2017 and Saplıoğlu, 2013).The N
values of the Qi are primarily sorted from small to large, and then
the trend curve of Sen is calculated by the following equation:
And if N is an even number, the
amount of change of the slope in time series is calculated using the following
formula:
§ Simple Linear Regression Analysis
Simple linear regression analysis is a
parametric method, assuming that the data is normally distributed. With this
test, the relationship between the variables determined as X and Y is tried to
be analyzed whether there is a linear trend (Cosun, 2008 and Şen, 2013).
The simple linear regression equation is
established as y = ax + b, where a is constant in the simple linear regression
equation that indicates the direction and amount of change. A positive value
shows change in the direction of change, a negative value indicates that the
change occurred in the direction of change. The fact that a is not far from
zero shows that there is no change (Bulutet al., 2006).
In the regression analysis, the
cause-effect relationship between two or more variables is examined in order to
make predictions or to make inferences and this relationship is characterized
by a regression model which is a mathematical modeling model (Yılmaz, 2018). It
can be expressed in the model using the equation (Agresti, 2011):
where E indicates a mean; Y |x, which
indicates possible values of Y when x is restricted to some single value; B0,
is the intercept parameter, and B1 is the slope parameter.
§ Statisticswithin the Constraints of ExcelURL:1and Analyze data using the
XmR Trend Control Chart in Excel with (QI Macros)URL:2.
In
the application of (QI Macros) run chart is used to study collected data for
trends or patterns over a specific period of time. A run chart will help:
v Monitor
data over time to detect trends.
v The
Figures (Charts) is a record of a process over time.
v The
vertical axis represents the process being measured precipitation. The
horizontal axis represents the time units during which measurements are made.
v
|
The Charts calculates the fit of the
data to the trend, As follows: -R² gives the fit of the line
to the trend. Greater than 0.80 is a good fit. -Ryx is the linear correlation
coefficient. Compare to probability for df = n -2. -The slope is the positive or
negative slope of the trend. -Sigma is the estimated
standard deviation of R bar/d2. -Probability is a critical
value for Ryx. If Ryx > Probability then a statistically significant
correlation exists. |
UCL: mt + b + A2R
CL:
mt + b
LCL: mt + b – A2R
This sample format explains the above codes:
Figure 1. Explaining
How to Extract Results from Trend Control Chart
Control Limits are used to determine if a
data is stable or not. Control limits are the "key ingredient" that
distinguish control charts from a simple line graph to the charts of trend
analysis and are calculated from input data. The years and stations that did
not show a trend through the upper and lower limit line of the precipitation
are identified by positive control line and the negative control line (two red
lines).
These four equations were applied to annual Precipitation Data, and equations are as follows:
(McKeeet al., 1993) developed the
Standardized Precipitation Index (SPI) which can be calculated at different
time scales to monitor droughts in the different usable water resources. Due to
its robustness it has already been widely used to study droughts in different
regions like USA, (Hayes,1999), Italy (Bonaccorso,et al., 2003), and Turkey
(Sönmezet al.,2005). Present study tries to identify the droughts locations in
the northern half of Libya as lack of rainfall and its fluctuation has had a
significant impact on the region.
The computation of SPI requires long-term
data on precipitation to determine the probability distribution function which
is then transformed to a normal distribution with mean zero and standard
deviation of one. Thus, the values of SPI are expressed in standard deviations,
positive SPI indicating greater than median precipitation and negative values
indicating less than median precipitation (McKee et al., 1993).
The Standardized Precipitation Index (SPI)
method converts the precipitation parameter into a single numerical value in
order to define the drought of regions with different climates. This method is
obtained by the following equation 1 by dividing the difference from the mean
(Xi) to the standard deviation (σ) in
a selected time period.
|
Table 2.Standardized Precipitation Index (SPI)
Values
|
§ Climate Classification of ERINÇ
Evaluating average temperatures when a
region is considered wet or dry leads to incorrect results. For this reason, it
is necessary to base the precipitation activity on the average maximum
temperatures rather than the average temperatures. In the determination of
Erinç precipitation activity, temperatures below 0 degree are excluded due to
the fact that evaporation has not occurred.
It is possible to reach the right
conclusions on Turkey's climatic conditions by using Erinç precipitation
activity index (Erinç, 1996). For this reason, this index was utilized when
the climate classification for North of Libya stations was carried out.
Turkish climate scientist Erinç developed
the equation in 1965 to identify simple Bora drought index is as follows:
1. The annual drought index:
Table 3.Determining
Drought Indicators in Erinç's Equation
|
Index value |
Climate class |
vegetation |
|
From 8 small |
Very Arid |
Desert |
|
8- 15 |
Arid |
Desert Steppe |
|
15- 23 |
Semi Arid |
Steppe |
|
23- 40 |
Semi Humidity |
Dry Forest |
|
40- 55 |
Humidity |
Humidity Forest |
|
From 55 upper |
Very Humidity |
Very Humidity Forest |
Source: URL:2.
§ Climate Classification of L. EMBERGER (Coefficient of Thermal – Rain)
|
|
P = Total annual rain.
M = Average maximum temperature.
m = Average minimum temperature.
2000 = Constant No.
The index of equation as follows:
Q = < 20 and P = < 300 mm = Very arid Mediterranean climate.
Q = 20–32 and P = 300- 400 mm = Arid Mediterranean climate.
Q = 32–63 and P = 400 – 600 mm = Semi arid Mediterranean climate.
Q = 63–98 and P = 600 – 800 mm = Mediterranean climate little rain.
Q = > 98 and P = > 1000 mm = Mediterranean rainy climate.
§ Index of JOHANSSON for Continental Climate and Oceanity
Climate
The climatic equations of the Continental and Oceanity are important in the study area. The study area comprises of two geographically different regions. One region has continental climate (Desert) while the other region possesses marine climate that include the coastal area of the Mediterranean Sea. Therefore, the Johnson equation will be used to determine the difference between Continental and Oceanity areas.
The Johansson Continentality Index is used
for the climatic classification between continental and oceanic climates. The
index is calculated by the following formula (Toroset al., 2008).
The index of equation as follows:
O - 33 Oceanity climate.
34 - 66 Continental climate.
67 - 100 very Continental climate.
Where E is the annual range of monthly mean
air temperatures, in (°C), (difference between the maximum and minimum monthly
mean air temperatures) and Sin ƒ is the latitude of the stations. The value of
the annual difference of maximum and minimum air temperature is used to
determine the Continentality of the climate. The climate is characterized as
marine when k varies between 0 and 33, as continental when k varies between 34
and 66, and exceptionally continental when k varies between 67 and 100.
§ Index of KERNER for Continental Climate and Oceanity
Climate
Kerner was motivated by the fact that in
marine climates the spring months are colder than the autumn months which led
to formulation of the Thermodynamic fraction (Baltas, 2007).
Where To and Ta are the October and April mean values of air temperatures, respectively. E is the annual range of monthly mean air Temperatures in (°C). Small or negative values of k1 imply a continental climate, while larger ones imply Oceanity, More specifically, in the present study, when the Kerner Oceanity is higher than 10 the climate is characterized as an oceanic. The following table and graph shows the branches of the equation and the values in Study areas.
Some
related studies to the subject of research are given below:
Table 4. Some Studies in the Scope of Trend Analysis
|
Researcher Name and Year |
The Topic |
Method |
|
|
Papadimitrio(1991) |
Some
Statistical Characteristics of Air Temperature Variations at Four
Mediterranean Stations |
Mann-Kendall
Pattern Correlation Test |
|
|
Pielke (2002) |
Evaluating
regional and local trends in temperature: an example from eastern Colorado,
USA. |
Mann-Kendall's
test |
|
|
Çiçek (2003) |
The
Statistical Analysis of Precipitation in Ankara |
Wald-Wolfowitz
Serial Correlation, and Mann-Kendall |
|
|
Norrant (2006) |
Monthly
and Daily Precipitation Trends in The Mediterranean (1950-2000) |
Mann-Kendall's
test |
|
|
Cosun (2008) |
Climate
Change Trend Analysis in Kahramanmaraş Province |
Mann-
Kendall Test and Linear Regression test |
|
|
İçel(2009) |
Trend
Analysis for Temperatures and Precipitation in Eastern Coast of Mediterranean
in Turkey |
ANOVA, and Mann-Whitney U |
|
|
Karpouzos(2010) |
Trend
Analysis of Precipitation Data in Pieria Region (Greece) |
Mann-Kendall
and Sen's Slope |
|
|
Al-Tahir(2010) |
Statistical
properties of the Temperature, Relative Humidity, and Solar Radiation in the
Blue Nile-Eastern Sudan Region |
Mann-Kendall's
test |
|
|
Bahadır (2011) |
Determination
of Seasonal Changes of Rainfall in Trabzon and Rize with Marginal and Matrix
Methods and Trend Analysis |
ARIMA |
|
|
Nalley (2012) |
Trend
Analysis of Temperatures and Precipitation Data Over Southern Ontario and
Quebec Using The Discrete Wavelet Transform, Master Thesis, McGill
University, France. |
Mann-Kendall
Trend test, and Discrete Wavelet Transform (DWT) Applications on Different
Time Series. |
|
|
Karabulut(2012) |
Trend
Analysis of Extreme Maximum and Minimum Temperatures in the Eastern
Mediterranean |
Mann-Kendall
and Linear Regression Analysis |
|
|
Karmeshu (2012) |
Trend
Detection in Annual Temperature & Precipitation Using the Mann Kendall
test – A Case Study to Assess Climate Change on Select States in the
Northeastern United States. |
Mann-Kendall'
test |
|
|
Researcher Name and Year |
The Topic |
Method |
|
|
Bonfils (2012) |
Trend
Analysis of the Mean Annual Temperature in Rwanda during the Last Fifty Years |
Mann-Kendall'
test |
|
|
Al-Kenawy (2012) |
Trend
and Variability of Surface Air Temperature in North-Eastern Spain (1920–2006) |
Mann-Kendall'
test |
|
|
Şen (2013) |
Trend
Analysis of Temperatures Precipitation Data in Isparta, in Turkey, Master
Thesis, in Suleyman Demirel University, Turkey. |
Mann-Kendall,
Spearman's Rho, and Simple Linear Regression Analysis |
|
|
Ageena (2013) |
Trend
and Patterns in the climate of Libya |
Standard
Error Bars, Mann-Kendall, Sen’s slope |
|
|
Saplıoğlu (2013) |
Trend
Analysis of Black Sea Region Rainfall Series |
Mann-Kendall
test, Sen's Slope test |
|
|
Hendricks (2015) |
Spatial
Precipitation Trends and Effects of Climate Change on Hawaiian Aquifer |
The
Mann-Kendall and Sen’s Slope test |
|
|
Oliveira (2015) |
Trend
Analysis of Extreme Precipitation in Sub Regions of Northeast Brazil |
Mann-Kendall'
test |
|
|
Dwayne (2016) |
Long-Term
Trend Analysis of Precipitation and Air temperature for Kentucky, United
States, Department of Biology and Agricultural Engineering, University of
Kentucky, Lexington, USA. |
Mann-Kendall
Trend test, and The test statistic tn1, tn2 |
|
|
Khomsi(2016) |
Regional
Impacts of Global change: seasonal Trends in Extreme Rainfall, run-off, and
Temperature in two contrasting regions of Morocco |
Mann-Kendall's
test |
|
|
Salami (2016) |
Trend
Analysis of Hydro-meteorological variables in the Coastal Area of Lagos
"using Mann-Kendall's test and Standard Anomaly Index Methods" |
Mann-Kendall's
test, and Standard Anomaly Index Methods (SAI) |
|
|
Aziz (2017) |
Trend
analysis in observed and projected precipitation and mean Temperature over
the Black Volta Basin, West Africa |
Mann-Kendall's
test, and Sen's Slope |
|
|
Researcher Name and Year |
The Topic |
Method |
|
Zarei (2017) |
Impact
of Land Use Change on Precipitation and Temperature Trends in an Arid
Environments |
Mann-Kendall,
and Spearman's Rho tests |
|
Ay (2017) |
Trend
Analysis of Monthly Total Rainfall and Monthly Mean Air Temperature Variables
of Yozgat in Turkey |
Mann-Kendall's
test, and Sen's Slope |
|
Polat (2017) |
Climate
Characteristics and Trend Analysis of Long-Term Temperature and Precipitation
Data in Rize in Turkey. |
Mann-Kendall
and Sen's Slope tests |
|
Yılmaz (2018) |
Trend
Analysis of Temperature and Precipitation Data in Western Black Sea |
Mann-Kendall,
Spearman's Rho, and Sen's Slope tests |
|
Nia(2018) |
Trend
in Temperatures average as A parameter to Quantify in Climatology in North of
Algeria (1973- 2015) |
Mann-Kendall's
Test |
|
Gedefaw(2018) |
Innovative
Trend Analysis of Annual and Seasonal Rainfall Variability in Amhara Regional
State, Ethiopia |
Mann-Kendall'
test, and Innovative Trend Analysis Method (ITAM) |
|
Gozalan (2019)
|
Comparative
trend analysis of temperature and humidity parameters at surface, 850, 700
and 500 hPa pressure levels: Case of Turkey |
Mann-Kendall,
Spearman's Rho, and Sen's Slope tests |
|
Özbunar (2019) |
Trend
analysis of Temperature Parameters of Florya, Sarıyer, Kumköy and Şile
(Istanbul) Stations |
Mann-Kendall,
Spearman's Rho, and Sen's Slope tests |
|
Nashwan(2019) |
Unidirectional
trends in annual and seasonal climate and extremes in Egypt |
Mann-Kendall's
Test |
I.
CHAPTER
CHARACTERISTICS
OF PHYSICAL GEOGRAPHY
1.1.
Geological Properties of Northern Libya
The geological
regions of northern Libya consist of several sedimentary basins and high
plateaus interspersed with several cracks, Most part of the study area consists
of the Sahara desert which has old geological properties with the exception of
the narrow Mediterranean coastal strip and mountain ranges to the south
(Al-meselaty, 1995). The sedimentary layer that forms the surface of the desert
is found everywhere on the bedrock of continent of Africa. The bedrock appears
on the surface in some places due to the removal of sedimentary formations by
erosion.
Chemical sedimentary
rocks are found in humid coastal areas such as Green Mountain and Benghazi plain,
where chemical sedimentary rocks are either dissolved in water directly, with
calcium bicarbonate, ferrous, dolomite, etc. It occurs when the substances
precipitate or when these dissolved substances change and replace others. The
saturation of the water to the solution it carries causes the solution to
precipitate. Some organisms also contribute to sedimentation (Coşkun, 2019).
The areas associated
to different geological periods can be classified as follows:
Most of the early Paleozoic formations are
concentrated in the south of Libya that are found in the south-west and
southern edge of the study area in Hamada Hemra. Some of the Nubian sandstone
formations belong to this early Paleozoic period. At the beginning of Silurian
and Carboniferous periods, the sea covered large parts of the southern boundary
of the study area (Sharaf, 1971), and sandstone rocks are widely visible in
those areas (Al-meselaty, 1995).
In the northwest of Libya, the Mesozoic
formations of Triassic and Jurassic periods are concentrated in limited places.
Triassic formations are found at the base of Western Mountain which are
limestone rocks of amorphous or thick and deep in form. Triassic and Jurassic
rocks has disappeared in northern Libya under the layers of the newer rocks,
however, re-emerging in Tunisia on a large scale (Sharaf, 1971). In the east,
the Cretaceous rocks appear in many areas of the Green Mountain, especially
Marawah, and in deep valleys such as the Bakur Valley near Tukera.
The Cretaceous rocks appear on the surface
only in limited patches on the surface of the Green Mountain in the region of
JARDES and ALMEHAJIR located southwest of ALBIDA. Although these formations
disappear in the eastern part of the study area, they reappear widely in the
Egyptian Western Desert around Libya-Egypt border. Moreover, there are a lot of
limestone of the Eocene near south-east (Sharaf, 1971).
1.1.3.
Cenozoic
(Tertiary and Quaternary)
The
rocks of Miocene are the basis of the bedrock of the Green Mountain and
plateaus that are extending south of the green mountain. These rocks appear in
the deep valleys, which are white limestone rocks and mixed in the Sirte plains
with clay and sand rocks and sometimes mixed with Oligocene and Eocenen rocks
south of areas Sirte and Agdabia. Most of the Miocene rocks Include
north-eastern plateaus in the study area as Albutnan plateau. These rocks
continue to extend westward forming the vast areas of the steppes south of the
SIRTE plains and disappear completely in the northwestern regions of Libya
(Sharaf, 1971).
Quaternary
formations include sedimentation of Quaternary formations include sedimentation
of alluriel, colluriel, some dune formations near coastline area, and
Mediterranean sea which is still accumulating at present. The most important of
these formations is the sedimentary soil in the valleys of the mountains and
their estuaries such as the red soil formations found in the valleys of the
Green Mountain and the western mountains during Pleistocene period.
It
is known that sometimes in the Quaternary period, the deserts of Libya had
received a lot of rain and were rich by river valleys. However, sand and quartz
are covering vast areas of Libya desert like the rest of Great Desert in
present configurations of quaternary period (Sharaf, 1971). The coastal sand
dunes and rocky hills were formed by the cohesion of mostly limestone sand
along the Libyan coast. During this period, the saline soils formed that
accumulated in the Marshes areas near the oases such as the JAGHBOUB oasis
(Sharaf, 1971).
Generally, Paleozoic rocks and Mesozoic continental
deposits occupy the greater part of southern Libya south of lat 28° N. Mesozoic
sedimentary rocks form the Hamada al l;Iamra' plateau of northwest Libya and
are largely covered by a thin veneer of early Tertiary sedimentary rocks. Other
Tertiary rocks occupy almost all the central and northeastern part of the
country and smaller areas in south-central Libya. The narrow coastal plains arc
generally mantled by Quaternary deposits; a third of the country is covered by
sand dunes and gravel plains (William, 1970).
The
following table 5 summarizes the geological time scale experienced by the study
area and the excavations that confirm this in its areas:
Table 5.The
Geological Time Scale in Northern Libya
|
Era |
Period |
Epoch |
The important of
Configurations |
Millions Years |
|
Cenozoic |
Quaternary |
Holocene |
Continenetal sands in Desert, Beach
sands, Solonchaks |
0.2 - 1 |
|
Pleistocene |
Dunes Sands, Red Soil,Saline soil in
oases |
1.8- 3 |
||
|
Tertiary |
Miocene |
Limestone rocksin most of study
area, and some times mixed with Clay
Sand rocks in Sirte |
11- 23 |
|
|
Oligocene |
Rough Limestone rocks in center and,
and southwest of study area |
24-33 |
||
|
Eocene |
Precise white limestone rocks in most
east of study area in Green Mountain and Albutnan |
41- 50 |
||
|
Misozoic |
Cretaceous |
Limestone rocks with Lignite in West
Mountain |
100-150 |
|
|
Jurassic |
Limestone rocks with crystal in West
Mountain and Green Mountain, |
160-200 |
||
|
Triassic |
Limestone contains a Flint in West
Mountain and some areas in east , and south of Sirte gulf |
206-248 |
||
|
Paleozoic |
Carboniferous |
Vocanic rocks in south center of study
area |
300-350 |
|
|
Silurian |
Mudstone and Dolomite in Southwest of
study area |
420 |
||
Based
on: (Sharaf, 1971 and Al-meselaty, 1995).
Map 3. Geological
Map for Northern of Libya
Source: Geological Ages: U.S. Geological Survey, 2002 (Arc Shape File),
Projection: World Robinson. See Meta Data for additional information, And
Geological Map in (Sharaf, 1971), p. 18.
1.2.
Properties of Geomorphology
Looking at the Physical map, the region can
be divided into three main types of the surface:
The northern highlands are divided into two
main sections separated by the Gulf of Sirte, which are as follows:
It is a plateau with a length of 300
kilometers from east to west that comprises mostly limestone and in many places
this plateau descends toward the sea. The Green Mountain consists of three
terraces one of which is 280 meters high and represented by the forest covered
city. The second one is 600 meters high which is represented by the village of
Sidi al-Hamri the south of Al-Bayda city. The third one is 860 meters high where the Al-Bida city is located. The Green
Mountain has main edges:
*First edge is the longest and lowest in
altitude. It comprised of the area between Sosa town and Al-Hilal head with an
average elevation of 300 m. The first edge begins directly after the end of the
narrow coastal plain.
*Second edge begins after the end of the
first edge which has the highest altitude with an elevation ranging between
420-600 meters above sea level. The surface of this edge appears in the form of
simple hills.
*Third edge is the highest top in the Green
Mountain with the highest elevation at Sidi al-Hamri area that is about 860 m
above sea level (Alheram, 1995).
The limestone formations that make up the
Green Mountain are reflected in the general shape of the water drainage
network. From Green Mountain, a large group of river valleys descend to the
north and flow into the sea (Johnson, 1973)
such as Kouf, Mahboul, Atharoun, Naqa, Derna valleys.
In the south, however, after the Water Splitting Line, there are many valleys that are poured into the desert, ending in swamps which are called Blatat, such as Samalus valley, Ramle valley, and others.
This mountain has several names such as
Nefusa Mountain, Geryan Mountain and
TerhonaMountain.The peaks of the western mountain extend from the
borders of Tunisia in the west to Alkums city in the east, and from A-Jufara
plain in the north, to Alhamad plateau in the south. The maximum elevation of
the West Mountain at the city of Geryan is 750 m above sea level.
Most of the western mountain consists of
limestone rocks. However, there are some small areas, such as the Abu Qanush
area, basaltic rocks are also present along with limestone rocks that cover
most of this mountain,. In the north of West Mountain, there are a large
network of valleys. These valleys descend in different directions due to the
general decline in gradient that include Ghazwan valley, Jarjir valley, Maimon
valley, Bani Walid valley and other small valleys (Alheram, 1995).
1.2.2.
Coastal Plains and Coastal Landforms
A group of coastal plains is also found
between the mountains and the sea or between the desert and the sea. In
general, these plains represent a very small area in Libya that does not exceed
more than 5% of total area of the country. These plains run parallel to the
Mediterranean shores in the form of narrow strip and exceed towards the
northern mountainous region of the Mediterranean sea. These plains are in the
form of a tighten place between the Albutnan plateau and Mediterranean sea and
many areas in Green Mountain. The most important of these plains are Benghazi
plain, Sirte plains, Aljafarah plain and eastern lowlands. These plains have
many coastal valleys through which the water is poured into the sea during
rainy season in the winter., These plains can be briefly described as follows:
The plain of Benghazi appears in the shape of
a triangle whose head is located in the north widening in the south and
gradually interfering with the Sirte plains in the west.The Benghazi plain’s
elevation isn't exceeding 100 meters until the western slopes of Green Mountain
appear where the plain meets those slopes at the river valleys that flow into
the sea such as the Bakour Valley in the north.The plain is covered with unique
Quaternary sediments consisting of red sedimentary soils which are transported
by river valleys of Green Mountain to Benghazi plain (Alheram, 1995).
In Benghazi plain, there are many large
marshes, such as Bu Jarrar, and Birses marshes which are separated along the
coast by white sands.Some rocky beaches appear in the plain of Benghazi
especially in the areas near of the Green Mountain.
It is one of the largest coastal plains in
Libya with an area of more than 17,000 square kilometers (Alheram, 1995). It
extends from the Alkoms city in the east to the West Mountain in the west.
The coastline of the plain is characterized
by a straight line with the exception of some estuaries of valleys where the
Jafara plain cuts many valleys pouring their water into the sea during the
winter including Al-Majinin, Al-Hira and other valleys. In some areas, the
rocky hills rise abruptly causing a difference in the degree of the slope
towards the sea (Alheram, 1995).
This area includes all the plains surrounding
the Gulf of Sirte between the Alburj head in the west and the town of
Al-Zwaitina in the east. However, it is difficult to determine the southern
boundary of the Sirte plain because the surface of the plain gradually
increases as it moves away from the coast due to absence of any natural
obstacles. It is just like a large arc of about 750 km in length and extends
south to 30°S latitude, the Sirte plains are characterized by their low sandy
beaches. As a result of the expansion of the surface of the plain and its low
altitude from the surrounding areas, it became a huge basin where many large
valleys such as Zmzm Valley are found. Besides, many salt marshes such as
Tawargha marsh are also found in the Sirte plain (Alheram, 1995).
The most important plateaus which can be
explained are the plateau of Al-Butnan and Defna and Al-Hamada:
It is a rectangular plateau with an average
elevation of 200 meters above sea level stretching, from the Bumba Gulf in the
west to the Libyan-Egyptian border in the east at a distance about 220 km to
the northern border of the eastern Libyan desert. Many of the valleys that flow
into the Mediterranean Sea descend from this plateau.
However, the southern side of this plateau
gradually descends towards the desert. Most of the plateau formations consist
of limestone (Sharaf, 1971).
This is located in the southwest of the study
area and extends to Tunisian and Algerian lands in the west while bordered to
the east by the Jufra oasis. The surface of the plateau consists of sandy
rocks. A number of river valleys emerged from this plateau heading towards the
Gulf of Sirte, Ghadames area and some valleys are heading Alshatiy valley in
the south of the study area. Moreover, some shallow basins are also found in
the Hamada plateau, which receives floods from the northern river valleys
during the rainy season (Alheram, 1995).
The karst phenomenon is widespread in many
places of the study area, the most important of which are the limestone caves,
karst drilling, the corridors and the red limestone soil on the surface of the
green mountain. Besides, large carbon formations and multi-directional cracks
serve as large reservoir for rainwater that falls heavily in the winter. The
surface karstic drilling spread in the plain of Benghazi. The limestone base on
the surface is directly separated by a thin layer of red soil resulting from
karst erosion (Alheram, 1995).
In general, the desert of Libya represents
about 90% of the total area of the lands in the country. The desert stretches
from the mountainous regions and Sirte Gulf in the north to the southern
boundary of the country.
It is the part of Great Desert Sahara in the
North Africa which is considered as the world's drier region. The Libyan desert
contains large underground plains covered with sand at different heights.
Besides, small hills are also found over the surface of the desert in different forms such as Shahid Sahrawi. These small hills are found isolated or in groups in different shapes and forms like cones and columns. These different forms and shapes are associated to the erosion of ancient plateaus. Many of these forms exist in the Jaghboub and Jalo oasis.
v
Great Sand Sea
The sand covers a vast area in the south of
the study area which constitutes flat surface areas. The sand in the south-east
of the study area is called the Great Sand Sea, a vast area that lies between
the latitudes of 26°to 28.5°N and 24°to 30°Wlongitudes. It is characterized
with big sand dunes which correspond to the direction of wind (Alheram, 1995).
There are many oases as Jaghboub, Jalo, Hon, Ghadames and others which are located within the boundaries of the study area in the south. These oases are depressions extending along latitude 29°N and characterized by the abundance of shallow lakes, salt marshes, isolated hills, and dry valleys. The reason for the emergence of these oases is related to various erosion factors that have been exposed through its long geological history (Sharaf, 1971).
Map 4. The Physical Map of Northern Libya
Since the topic of the thesis is related to
climatology, the subject of climate is not mentioned in this section. The
climate of North Libya Sea Department is given in the next chapter.
1.4.
Properties of Hydrography
The surface water depends entirely on
rainwater that flows on the surface of the earth in the valley and coastal
plains or seeps down into the pores of the surface soils where the plants live
directly. The average annual runoff of the seasonal river valleys in the coast
of Libya is about 285 million m3/year; of which about 110 millionm3of
water is borne by the valleys sloping north of the western mountain, 20 million
m3/year in central region of Sirte and Aljefara, about 90 million m3/year
in Green Mountain area and 65 million m3/year is distributed over
other coastal areas such as Al-Betnan plateau and West Coast (General
Information Authority, Statistical Book, 2007).
Renewable groundwater is concentrated in the
plain areas such as the Jaffara plain, the Benghazi plain and the Misurata
region. This water has been produced by collecting rainwater through dams that
were established on the coastal valleys such as Al-Qatara valley dam, Derna
valley dam, Kaam dam, Al-Mjinin dam, and others. Non-renewable groundwater is found stable in
the desert in the south of the study area. it is a large underground reservoir
that has been storing water for millions of years. Besides, karst water
reservoirs are also found in many areas of Green Mountain. These are aquifers
which are called karstic groundwater.
The characteristic of water is dense in
limestone rocks as the limestone increases the proportion of salt and calcium
carbonate. The water dissolves the rock and makes water channels, karst caves,
lagoons and tributary tables inside the surface. These features are filled with
water as whole or in part resulting in the formation of waterfalls in many areas of the Green
Mountain region, such as the Derna waterfall, Ain Dabbousia, Ain Marara and
others.
A shortage of water sources is observed
despite the large extent of study area. Even if it is available in some areas,
the scarcity prevails because of natural factors like lack of rainfall, high
temperatures, and high evaporation rates. Besides, the mountainous areas don't
benefit from the amount of rain falling in the winter because most of the
rainwater seeps into the ground through the cracks of the karst and some part
penetrates deep into the ground while very little water appears in the form of
small waterfalls. It is concluded from the previous map which shows the dry and
seasonal waterways in northern Libya that these river valleys do not contain
any water for many years. Sometimes, sudden rain falls around which produces
strong floods of water going directly towards the Mediterranean sea. However,
this water cannot be used and often causes Environmental and Economic problems
in the areas from which they originate.
Map 5. Hydrography of North Libya
1.5.
Properties of Soils (Parent Material and Components)
Soil formation reveals not only the geographic properties
such as climate, topography, parent material and vegetation but also geomorphic
process like erosion and sedimentation conditions of an area. Paleosols are indicator
of past climate changes and formed long periods ago that do not have
relationship in their chemical and physical characteristics to the present-day
climate or vegetation (Atalay, 2013/a).
The systems for classification of the field have been formed
by developed coun-tries in order to make the best of existing natural sources
against increasing population with Industrial Revolution. For instance; local
planning made by America of 1930s, the studies of “Land Use Survey” made by
England in 1922, the report of “SCOTT” published in 1941 explained the use of
natural environ-ment and how it should be used (Coskun 2016).In general, soils
in northern Libya are divided into two main sections which are the coastal
soils and the desert soils. There are many types of classifications of soils
around the world including biological, physical, chemical, and other
classifications (Eswaran, 2002).
The soils in the study area can be classified into three
main classes depending on the Factor-genetic of classification that Zonal,
Intra-zonal, and Azonal soils.
These soils result from the maximum effect of climate and
living vegetation upon parent material in areas extremes of weathering
prevails. and where the landscape and climate have been stable for long time
(Eswaran, 2002).
Red soil covers a large part of the green
mountain in the region of Al-Marj and region of Al-Fataiah, and Benghazi Plain,
it is muddy, calcareous, with calcium carbonate and its high ability to
conserve water, filtration rate is 4 centimeter/ hour (Gefli, 1972).
This spreads in most of the southern regions
of Green Mountain and Benghazi plain, Western Mountain, and Albutnan plateau.
Besides, this soilis also found in the transitional area between the mountains
and the desert where very little amounts of rainfall happens.
In the more arid parts of the Libya Desert,
surfaces of some soils are covered by a layer of small stones interlocked, it
is characterized by poverty in the necessary nutrients such as nitrogen,
phosphorous and potassium, as well as its low ability to retain moisture, it is
Incoherent soil, mixed with gravel and stones, and often exposed to air
erosion.
These soils cover large areas of northern
Libya especially in the desert and semi-desert areas which are composed of
different originals under a dry-hot climate that leads to a lack of vegetation
cover except desert grasses or shrubs. Besides, the amounts of rain are also
not enough to wash dissolved salts, gypsum and calcium carbonate. This type of
soil is widely distributed in the northern, central and eastern region, it is
characterized by a low percentage of organic matter and nitrogen, as well as
high carbonates, salinity, and alkalinity, and this soil suffers from the
problem of dismantlement by winds.
1.5.1.5.
Reddish Brown Arid Soil
It is a widespread soil in the Jufara plain
and in Benghazi plain. The origin of this soil is due to the desert sands with
a limited effect of silt, and the carbonate with a percentage of salt and
gypsum (Export, 1978).
Reflect the dominance of single local factor, such as parent rock or extreme of drainage, as they are not related to general climatic controls, they are not found in zones (Nalley, 2002), including the following types:
1.5.2.1.Saline
Soils, (Marshy soils)
This is widespread in many areas on the
coastline, and is particularly prevalent between the Libyan-Tunisian border in
the west and the Sirte plains in the east, between Taourgha and Sirte, and
continues in some northern parts of the Benghazi plain (Export, 1978). Another
type of saline soil is located to the south of the Green Mountain where
rainwater accumulates, such as the Al-Belet region south of the Green Mountain,
where evaporation increases due to high temperature, which helps to raise the
poetic water to the surface of the soil, which it evaporates and leaves salts
in the surface layer of soil, to form a salt crust (Abu-Khashim, 1995).
Is a black soil whose spread is related to
the wet climate in regions Al-Bida, Al-Quba, Labraq, and the southern part of
Al-Marj plain, these is characterized by muddy soil, poor drainage, and cracks
in the dry season and little Calcium carbonate and nitrogen, and the ratio of
acidity between the simple and the average and despite the lack of nutrients,
but it compensates using phosphoric fertilizers (Export, 1978).
1.5.3.
Azonal
Soils
Incomplete soils, have a more recent origin
and occur where soil-forming processes have had insufficient time to operate
fully, as a consequence, these soils usually showing just the characteristics
of their origin, there is two types in the study area, it spreads in all
coastal valleys and some and in some desert basins.
1.5.3.1. Sedimentary
Soil (Fluvisols)
This type is prevalent in valleys, and its
distribution is related to the water drainage system. It is spread in different
parts of the study area, especially in the valleys that cut the Green Mountain,
and Western Mountain. and Al-Sagayef area in Al-Batnan in eastern of the study
area, and mixed with gravel and rocks, and contain sufficient amounts of
potassium, and a little of phosphorus (Abu-Khashim, 1995).
1.5.3.2. Desert
Sedimentary
(Oasis Soil)
Located in the bottoms of the southern
valleys (Oasis) it is caused by the deposition of materials by the wind at
different periods of time. They contain varying amounts of gravel, consisting
of sand and gravel mixed with clay, and spread in Jaghboub, Jalo, Ghadames
areas.
This type includes sand dunes that is
disassembled sand moving from one place to other by wind. , This type of soil
is characterized by high salinity and poor essential nutrients with inability
to retain moisture. This type of soil is home to some plants that circumvent
the drought such as cactus. It also includes Continental Sands covering vast
areas of the study area, especially the southern regions such as Hamada
Alhamra. It also includes sedimentary soils in the lowlands, such as oases and
southern valleys Areas, as Aljufra, Ghariat, and Hon.
Map 6. Soil Map of North Libya
1.6.
Properties of Vegetation in North Libya
Except
for some northern regions like Green Mountain or Western Mountain. Or some
transitional areas between the mountains and the desert and between the coast
and the desert. There are oak trees, peat and pine in the Green Mountain due to
amount of rainfall exceeding (500 mm).
On
other side are the transition areas between the coast and the mountain, on the
other the deserts are very common. The dry desert climate is characterized by
drought-tolerant plants and thorny weeds, such as cacti. But it disappears when
you see the sand. These are some plant species growing in different regions of
Libya are as follows:Arbutupavarii,Ceratonia, silique, Cistus
salvifolius, Juniperusphoenicea, Cupressus sempervirens, Myrtuscommunis,
Pinus halepensis, Pistacialentiscus andQuercus coccifera(Ali,
2015). In general, the number of natural plant species in Libya is estimated at
1750 species (Jafri, 1972 and Elgadi, 1986).
Some
important plant species will be addressed in the study area, and important
vegetation in Green Mountain and other areas, and it is worth mentioning that
the western area of the study area is completely devoid of forests due to
topographic and climatic factors (Sharaf, 1971).
v Cupressus sempervirens, the
most important areas of growth are the Lemloda area, and around city of
Al-Bayda and Al-Kouf Valley, It is the highest mountain area and the most rainy
about (400 -600 mm) in the years.
v Pinus halepensis, it grows in the
northeast of the Green Mountain, on the valleys of the coastal region between
Karsa in the east and Sousa in the west. The best places to grow this species
are naturally protected areas from strong winds.
v Arbutus unedo,in Libya local name is
(Shemary) these trees are characterized by their fruits, which resemble red
berries. They are short shrubs that grow in many parts of the Green Mountain,
where rain is abundant and often mixed with cypress trees.
v Quercus sp.it broad-leafed trees that
grow in many deep valleys of the Green Mountain, as Al-Loleb valley, Zaza
Valley, and ZaweatMesaud, and it requires deep soils and available water.
v Olea europaea(Olives), natural
olive, it is due to ancient Greek or Roman origins, and there is the largest
gatherings of this kind in the region of Ghraib, and it grow in the region of
Mertoba and Um-Alrezem in east of Green Mountain.
v Ceratoniasiliqua,carob trees grow in
many area in Green Mountain, including the middle plateau of the mountain,
along the middle coast, where the soil is replenished and water is available.
It is usually found in the form of scattered trees, and is rarely found in
large gatherings, which is an evergreen plant, the height of the tree ranges
between (10 - 20 meters).
v Pistaciaatlantica,the local name is
Al-Batoum, where the Al-Batoum forests are located in most parts of Green
Mountain and to the north of the city of Benghazi until to Tukera, and this tree
help used to protect the soil.
And grow in oases some natural plant like
TamarixAphylla that grows in the desert oases (Abu-Khasim, 1995), a tree with
an existing trunk, sometimes branching from the base near the surface of the
earth. This shrub is spread in the oasis of Jalo, and is particularly grows in
sandy and saline soil, and like other oases in the deserts of the world, the
Libyan oases are famous for palm trees, where natural palm trees grow on the
edges of the salt lakes as Almelfa Lake in Jaghboub oasis.
In different areas of the study area, there
are many types of grasses, which depend on the amount of rain and the
distribution of soil, including seasonal growth during the spring, and what
permanent growth and flourishes during the rainy season, there is no doubt that
weeds are one of the most widespread plants in Libya, especially the seasonal
grasses that grow after the fall of rain, there are different species, the most
important of which is the grass that grows in disassembled soil, as Albelooz
that is spread in the northwest plain of the study area in Al-Shahal.
In the agricultural areas, grasses that grow next to agricultural crops such as Zaghlil, Chrysanthemum, and Anemones are scattered. It is worth mentioning that these weeds grow next to agricultural fields that are planted on rain water, such as wheat and barley. There are also grasses has different names, such as Shiyh, Alhlfaa, Mothnan, Aljell and kandul, and other species with many local names, in the west and east of study area.
*Human Impact on Vegetation:
Many of human activities occurred in Green Mountaın
area as result of increase of development activities and growth of population.
The investigations were many searces out to study the flora and vegetation
composition of coastal region of AL-jabal AL-akhdar area and the effects of
human impacts on the vegetation composition.
The results in (Al-shatshat, 2014) showed that 104
plant species belonging to 37 families were found. On the family level, both
Fabaceae and Asteraceae were the major plant families in the area with 16 and
15 species, respectively. The annuals form the huge number of the
plants(64.4%), while other life forms of the biological spectrum appeared in
different percentages. Negative interactions between human activities (land
abuse, charcoal burning, overgrazing…etc.) and vegetation were noticed among
the study site.
Forest
ecosystems that exist within the terrestrial ecosystems are one of the
seriously important resources for the life of living organisms. Natural,
cultural and economic functions of forest products and services are inarguable.
As well as these functions, natural foods that are obtained from forests like
fruit, mushrooms and leaves pose a supplementary food especially for poor
sections of the society living in rural areas.
Countries
have to produce serious policies in order to prevent rural population living in
the forest areas from migrating to urban areas, and to increase the income
resources. Agricultural forest activities are among important policies that are
made for the people living in rural areas. Opening proper forest areas for
agricultural activities without damaging the natural tissue is in fact the
activity that will make the existing areas of a country become more economic
and functional (Coşkun al., 2017).
5.1.General Comments on Results
Present study has analyzed the trends of
temperature and precipitation in selected locations of North Libya. The trend
analysis of temperature data obtained from the 16 meteorological stations
selected from almost all of North Libya over long period of time has been
conducted in this research. Variability of maximum, minimum and mean average
temperature as well as the extremes of temperature have been studied for the
period between the years 1971 and 2010. Graphical distribution of data,
measures of association and statistical trend detection methods have been applied
to examine the long-term changes and trends of temperature in the study area (Chapter
3).
The results of temperature analysis for
the period of 40 years (1971-2010) have revealed significant increases in
maximum, minimum and mean average temperature. A significant annual change in
temperature towards strong warming has been observed in the study area after
the mid-1990s.Besides, rapid increases in temperature, almost doubling from the
previous figure, during the period of last 12 years (1998-2010) have also been
indicated (Tables 33 to 48).
The findings of present study, discussed
in Chapter 3, have revealed that minimum temperature has rapidly increased at
most of stations with a much higher rate (0.9 to1.3 °C) during the period of
last 12 years (1998-2010). This is considerably faster than the (IPCC, 2007)
global mean temperature increase of (0.74 °C) over the last 100 years.
Seasonal temperature data indicates that
rapid increases in maximum temperature took place in autumn, summer and spring
while the most rapid warming is observed in the summer and autumn. The results
have revealed that minimum temperature, particularly mean seasonal average in
minimum temperature, is characterized byhigher rates of increase compared to
the maximum temperature with the highest rates of increase in the warm season
(April-September). These trends in maximum, minimum and mean average
temperature are in line with global, regional and local temperature trends
since the late nineteenth century which show the most rapid increase since the
mid-1970s (IPCC, 2007 and URL6).
The results of the study of variability
in temporal and spatial precipitation in Libya for the period 1971-2010are
presented in Chapter3. The data are analyzed using several graphical
techniques, trend analysis, measures of association and statistical trend
detection methods for sites in the study area.
The Green Mountain and Benghazi plain in
the northeast, outside of the coastal region, have received some of the highest
levels of precipitation in north of Libya. It is found that 15 % of the study
area has received annual averages precipitation of 265 - 538 mm whereas about
85% of the study area at inland stations (Sahara) has received<50 mm of
precipitation.
Northern region of Libya is frequently
affected by low pressure (depressions) associated with a branch of the westerly
jet stream (Amgailey, 1995; Zikrey 1998). These depressions are more frequent
in winter with only 28% of the total annual depressions resulting in effective
precipitation over Libya, seasonal and annual precipitation trends in Libya
seem to be strongly affected by local factors including topography and land
use. The coastal region is mainly affected by Mediterranean convection
processes, apart from central coast region.
5.2. Comments on the Results of Planetary
and Local Factors Affecting the Climate of Northern Libya
Chapter II discusses the relationships
between planetary, regional, and local factors on the climate of the study area.
The study area is affected by the dry continental air coming from central Africa
which increases the temperatures in the summer. On the other hand, the cold air
coming from Europe is responsible for the decrease in temperatures and rainfall
in most coastal stations in the winter.
The coefficient of correlation between
the general mean temperature and elevation of sea level is negative (-0.502), while
the coefficient of correlation between temperature and distance from the sea is
positive (0.541). Besides, the coefficient of correlation between the annual
rate of rainfall and elevation shows a positive but relatively weak trend with
the value of (0.185), while the impact of the distance from the sea on the
amounts of rainfall shows very negative trend with a value of (-0.735).
The trends of temperature and
precipitation are linked to the mathematical location (latitude) in some
stations; however the effect of elevation and distance from the Mediterranean seems
much stronger which is noticed in figure (2). For example, the stations located
within the latitude 29,30°N shows an increase in the general rates of
temperature and decrease in rain, while in some stations, there is no rain. These
characteristics are found in the stations of JALO, JAGHBOUB, HON, GHADAMES,
GHARIAT, and AGDABIA station. It is observed that some stations are located at
the same latitude, but they differ in climatic characteristics due to the
elevation, distance from the sea, and this is evident in the stations of
SHAHAT, ZWARA and DERNA.
Map 7 shows the effect of the direction
of the mountains towards the coast on the annual rate of rainfall. The
direction of the coast in parallel to the rain winds reduces the chances of
rainfall, like in the stations of TOBRUK, ZWARA and SIRTE, while the stations
facing to the rainy winds like BENGHAZI, MISRATA and SHAHAT receive a larger amount
of rain.
5.3. Comments on the Results of Trend
Analysis
In which trends (increase/decrease) the
trend has occurred at meteorological stations (Desert stations or Coast
stations) and which meteorological stations show strong trends and are these
stations show changes in temperature and precipitation data?To answer the study
questions, comments will be made on the results of the tests used in the third
chapter, as follows:
*Mann-Kendall and Spearman
Maximum temperatures, an
upward trend of heat at the station of MISURATA, TRIPOLI and ZWARA is observed
in spring season. This trend is assumed to be linked with the heating due to
the blowing of the southern wind in the forefront of the air depressions that
prevail in the late spring along the Libyan coast and presence of high-rise
buildings in TRIPOLI and MISURATA.
A significant increase at most stations
is indicated in the seasons of summer and autumn while a poor weak trend is
identified in winter. The increase in the trend of temperature manystations
such as MISURATA, AGDABIA, SIRTE, TRIPOLI, ZWARA associated to urban expansion
and sprawl towards the natural margins of cities resulting in low wind speed
and absorption of higher solar radiation. Moreover, an increase trend in the
annual average temperature is also observed at most stations, except ALFATAIAH,
BENGHAZI, DERNA, SHAHAT and ZWARA. This appears to be associated with observed
increase in temperature during the last 12 years of monitoring period.
BENGHAZI and SHAHAT are the only two
stations that have shown insignificant trends towards decreasing. The absolute
and geographical along with the elevation of these stations are among the
important factors that affect the trend since the station of SHAHAT is located
at a height of 621 meters.
The findings
obtained from the U(t)-U'(t) graphs have indicated that , many stations have
shown positive trends in the summer starting from the year 1993.The stations
such as ALFATAIAH, JAGHBOUB, JALO and MISURATA have started to show an increase
in temperature during the 1990s. Addition , many stations such as AGDABIA,
JAGHBOUB, SIRTE, TOBRUK, TRIPOLI and ZWARA have shown a positive trend in the
autumn during 1980.Besides, most of the station have been observed to have
positive trend in annual average temperature in 1990s.No trend is found in the
seasons of spring and winter except at JAGBOUB station with a negative trend in
2002at JALO station with positive trend in 1989.
Minimum Temperatures,the
annual rate of minimum temperature has shown a tendency to increase in the
summer and autumn seasons while a weak trend is observed in spring and winter.
Only the AGDABIA and JAGHBOUB stations have shown an upward trend at all the
seasons. ALFATAIAH, DERNA, MISURATA and ZWARA station have shown an increase in
trend during autumn. Most of the stations have shown no significant trend in
the annual average of the minimum temperature except DERNA, JAGHBOUB and
TRIPOLI stations.
The U(t)-U'(t) graphs show that there
were positive trends towards the increase during the spring season at DERNA
Station which started in 1999 followed by HON in 1998, JAGHBOUB in 2006, TOBRUK
in 2004 and ZWARA in 1998. In the summer, only two stations have shown a
positive trend which are HON in 2003 and TRIPOLI in 1994. Besides,some coastal
stations have shown a positive trend in the autumn starting in 1986 at TRIPOLI
Station, in 1989 at MISURATA Station, in 1992 at DERNA Station and in 2000 at
TOBRUK Station. In the winter, only one station has shown a trend towards the
increase starting in 2004. The annual average temperature has shown no trend in
most of the stations except a positive trend at DERNA in 1983, addition JAGHBOUB in 2002 and in
TOBRUK in 2004.
Average temperatures,general
increasing trend has been observed at the stations of MISURATA, SIRTE, TRIPOLI
and ZWARA. This increasing trends linked with urbanization factors such as
expansion of buildings at the expense of green spaces. These areas have turned
into hot islands leading to an increase in the trends in the annual average
temperature. Desert stations such as GHADAMES, HON, JAGHBOUB and JALO have
shown an increasing trend the average annual temperature. This upward trend is
associated to the increase in temperature during the summer in dry regions.
Moreover, the mountain stations such as ALFATAIAH, SHAHAT and NALUT have also
shown an increasing trend. This increase is thought to be associated with large
amounts of solar radiation during the summer and lack of sufficient amount of
cold air in the summer due to the altitude factor.
It is observed in U(t)-U'(t) graphs that
most of the stations have not shown any trends in spring season except ZWARA
station with a positive trend in 1988.In summer, JALO and ZWARA stations only
have shown positive trend in 1993. In autumn, some stations have shown positive
trend in 1980s. However, in winter, only BENGHAZI station has shown positive
trend in 2010. Besides, four stations have also shown positive trend in average
annual temperatures in 1990s that are AGDABIA, DERNA.
Precipitation, it
has been observed that most of the stations have shown no trend towards the
increase of precipitation in spring except GHADAMES station. This is, because
of the fall of a large amount of rain in a short period leading to an increase
in the quarterly average which results in an increase in trend. Besides, most
of the stations has shown no or very weak trend in the summer except for is
Sirte station. However, in autumn, a decreased trend has been observed in the
stations of HON, SIRTE and TRIPOLI. Besides one station has shown a very
negligible downward trend. The winter season has shown insignificant trends
towards the increase and decrease in some stations such as TRIPOLI TOBRUK and
BENGHAZI while some desert stations such as JALO, GHADAMES and HON have shown
an insignificant upward trend.
Almost all of the stations have not
shown any significant trend in the general average of precipitation except the upward
trend shown only at GHADAMES station.
However, some stations have shown a significant decreasing trend like
TRIPOLI, ZWARA, TOBRUK, SIRTE and NALUT.
It is noteworthy
that Mann-Kendall and Spearman tests of trend are not feasible to measure the
rainfall trends in the desert stations because of the seasonal and annual
scarcity and fluctuation of rainfall. In the desert climate, a large amount of
rain may fall in one day and the rain may not fall for many years.
*Sen's Trend Slope Test
Maximum Temperatures,it
has been observed that in most of the stations there is a trend towards
increase of rainfall in the except BENGHAZI, DERNA, JAGHBOUB, JALO, SHAHAT and
TOBRUK. The remaining stations have not shown any significant trends in the
other seasons.
Minimum Temperatures, the
trend is found increasing in the spring at stations of GHADAMES, HON, MISURATA,
NALUT, TOBRUK and ZWARA, while other stations have not shown any meaningful
trends. AGDABIA station has shown the strongest trend in the summer probably
because of the presence of oil refineries and petrochemical industries located
to the west of the city of AGDABIA in the regions of Al-Briga and Ras-Lanuf.
The other stations have shown trends towards increase in minimum temperature
such as BENGHAZI, GHARIAT and NALUT. In the autumn, all stations have shown
trends towards increase except BENGHAZI, SHAHAT, SIRTE, TRIPOLI and ZWARA.
Almost all stations have not shown any trend in winter except four stations of
GHARIAT, HON, MISURATA and ZWARA. Only ZWARA station has shown a trend in
annual minimum temperatures. Some stations have shown trends towards increase
in annual minimum temperatures such as AGDABIA, HON, MISURATA, TOBRUK and
ZWARA.
Average Temperatures,atrend
towards increase in the temperature is observed at MISURATA station perhaps due
to the presence of some heavy industries such as the iron and steel industry.
Some of the stations have not shown any trend in any season while some stations
have shown trends in the summer and autumn such as(HON, JAGHBOUB, JALO, TOBRUK
and TRIPOLI. There is a trend towards increase at NALUT station during the
spring and autumn.
Precipitation, MISURATA
and SIRTE have shown important trends towards increase in the spring. The rest
of the stations have not shown any important trends. Some stations have shown
negative trends such as stations of TRIPOLI, TOBRUK and ZWARA, it should be
noted that the trends observed in the seasonal and annual rainfall data are not
as important as the temperature data. The basis of this argument is that the
trends of precipitation are short term as opposed to temperature trends that
are long-term.
* Simple
Linear Regression Results
Maximum Temperatures, it
is found that almost all stations have shown no significant trends when
examining the trends that have occurred over the years in average maximum
temperature values on a seasonal basis except NALUT and ZWARA stations.
Besides, it is also observed that the results in the rest of the stations are
approaching zero either increasing or decreasing during spring. In summer, only
ZWARA station has shown an upward trend and no other station shown an increase
or decrease in trend. Autumn is considered as the most important in increased
trends during which8 stations have shown an upward trend that are GHADAMES,
GHARIAT, HON, MISURATA, SIRTE, TRIPOLI and ZWARA. Looking at the previous trend
tests, it will be noted that the autumn season is characterized by the
increasing trend the maximum temperature data for most of the stations. It is
observed through the results that in the winter, the general average annual
maximum temperature have not shown any trend (increase or decrease).
Minimum Temperatures, it
has been observed that only five stations have shown a trend towards an
increase in the spring while examining the trends in minimum temperature values
over many years on a seasonal basis that are GHADAMES, HON, MISURATA, TOBRUK and
ZWARA. Other stations have not shown any trends indicating the stability of the
minimum temperatures in most of the stations in spring season.
In summer season, HON station where the
increase is highest, although there is an increase in most stations except for
three stations of ALFATAIAH, BENGHAZI and NALUT.DERNA station has shown an
increasing trend along with GHADAMES, GHARIAT, JAGHBOUB, JALO, MISURATA,
SHAHAT, SIRTE, TOBRUK, TRIPOLI and ZWARA. These results indicate a tendency
towards warming in most stations and the trend is also found increasing in
desert stations except for BENGHAZI station. The autumn season is characterized
by an increase in trend in all stations. The winter season has not shown any
trend in all stations except for four stations of GHARIAT, HON, MISURATA and
ZWARA with trend towards increase.
Many stations have shown a trend towards
an increase in the annual average temperature data. HON station has shown the
strongest trend, followed by ZWARA, TOBRUK, AGDABIA, GHADAMES, GHARIAT and DERNA.
Average Temperatures, the
evaluation of the simple linear regression for the average of the minimum
temperatures in the spring has shown no trend in almost all stations except for
two stations of NALUT and ZWARA that show a tendency to increase. In summer,
many stations have shown a trend toward increase. ZWARA station has shown the
strongest followed by AGDABIA, MISURATA, HON, JAGHBOUB and JALO stations.
Almost all the stations in the autumn have shown a trend towards increase except
ALFATAIAH, BENGHAZI, DERNA and SHAHAT. The strongest trend towards increase is
found at ZWARA, HON, GHADAMES, MISURATA, GHARIAT, TOBRUK and AGDABIA stations
followed by SIRTE, TRIPOLI, JAGHBOUB and JALO. The winter season has not shown
a trend in all stations. The average annual temperature has shown a trend in
three stations of HON, MISURATA and ZWARA.
Precipitation, most of the stations have shown a decreasing trend in annual average precipitation in most of the seasons except for winter. The most important results deducted from the simple linear regression equation are the results of the annual rainfall data as the large fluctuation in the amounts of rainfall in the stations of the study area have shown a significant difference in trends between coastal stations and desert stations. This reflects the importance of prevailing natural factors of geographical location and proximity or distance from the sea. There are only four stations that show an increasing trend which are ALFATAIAH, DERNA, GHADAMES and GHARIAT. Most of the stations have shown a decreasing trend such as TRIPOLI station with large trend towards decreasing followed by ZWARA, TOBRUK, NALUT, BENGHAZI, SIRTE, SHAHAT, MISURATA, HON and JAGHBOUB stations. Only two of AJDABIA and JALO have shown no trend.
*Results of Charts Control's Models for Annual Rainfall
There
no observed trend of increase or decrease in most stations except for AGDABIA, ALFATAIAH,
BENGHAZI and SIRTE in 1991, GHADAMES in 1976, HON in 1990, JAGHBOUB in 1988 and
JALO in 2006. This increase in trends may be due to the large amounts of
rainfall during a short period of time in the year in which the increase
occurred.
5.4.
Relationships between Results of Thesis and (NAO and El Niño) and
Causes of increases Temperature and potential causes of variability in northern
Libyan Precipitation
Is there any
relationship between the temperatures and precipitation trends in Libya and
global climate change and climatic phenomena such as North Atlantic Oscillations
(NAO) and El Niño?
The Pacific El
Nino-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) phenomena
play an important role in interannual temperature variability in many regions
throughout the world (Philander, 1990 and Hurrell, 1995). Significant effects
are observed on the anomaly variability patterns of precipitation over the arid
and semiarid regions of North Africa by the North Atlantic Oscillation (NAO),
while the El-Nino of the Southern Oscillation (ENSO) is significantly affecting
the variability over some regions in North Africa (Djomouet
al., 2013).
*Relationships
between Results of Thesis and (NAO)
The North
Atlantic Oscillation (NAO) is a large-scale mode of natural climate variability
governing the path of Atlantic mid-latitude storm tracks and precipitation
regimes in the Atlantic and Mediterranean sectors (Küçüket
al., 2009).
Temperature
variability can be associated to variations in large-scale atmospheric patterns
represented by Eastern Atlantic and the Western Mediterranean Oscillations,
resulting from increases in atmospheric circulation and anticyclone conditions
in recent decades, which seem to play a significant role in explaining spatial
and temporal variability of temperatures in the Mediterranean basin. Therefore,
regional and local temperature trends can be strongly influenced by regional
variability and changes in the climate system. In addition, It is believed that
the volcanic eruptions can be responsible for changes (fall) in global annual
average temperature by less than (1.0 °C) (Pidwirny,2004); this may explain why
1992 and 1993 were the coldest years in the 1990s with mean annual temperature
in Libya potentially affected by the eruption of Mount Pinatubo.
To reveal the
relation between Arctic Oscillation, North Atlantic Oscillation and
temperature, precipitation regime of north Libya, With looking at values NAO in
period (1971-2010) There was a weak relationship between the two variables in
terms of results, except
for three of the stations that showed a trend towards increasing the maximum
temperatures during 1993, which are JAGBOUB, JALO and MISURATA stations, this
corresponds to the positive indicator that was shown North Atlantic Oscillation
in 1993.Table 103 and figure 300 show the relationships between the trends
(increase and decrease) of maximum, minimum and average temperatures and the
North Atlantic Volatility Index (negative and positive).
Table 106. The Relationship Between North Atlantic Oscillation Indicator and Trend Analysis Results for Seasonal and Annual
Source, student’s work depending on URL4.
Following conclusions can be reached through the table (106) and the figure (285):
·
Positive Indicators
1. The effect of North Atlantic oscillation
(NAO) on the increase in the trend of precipitation at SHAHAT station is
observed in the summer 1976.
2.
In autumn 1982, the effect on the increase in average
temperature at GHADAMES and an increase in maximum temperature at ZWARA station
was observed.
3.
In autumn 1986, the effect was observed only on minimum
temperatures in TRIPOLI station.
4.
In autumn 1989,the effect on the maximum temperature was observed
at AGDABIA, JAGHBOUB, SIRTE and TOBRUK stations, while on minimum temperatures
at MISURATA station and there is an effect on the increased trend of
precipitation at AGDABIA station in autumn.
5.
In summer 1994, NAO affected only TRIPOLI station on minimum
temperature.
·
Negative Indicators
1. In autumn 1981,the effect of NAO at GHARIAT
station on average temperature, at NALUT station on minimum temperature and at
TRIPOLI station on maximum temperature was observed.
2. Autumn 1992 found the effect at DERNA station
on minimum temperature.
3. There was a strong relationship between the
(NAO) index and the maximum and average temperatures in several stations in
summer 1993 including ALFATAIAH, JAGHBOUB, JALO and MISURATAon maximum temperatures, JALO and TRIPOLI on average
temperature. It is worth noting that the index of (NAO) had reached (-3.18)
this year.
4. In summer 2009, the effect was observed on
the decreasing trend of precipitation in SIRTE station.
5. In summer 2010,theeffectwas observed on
increasing average temperature at BENGHAZI station.
*Relationships between Results of Thesis and El Niño
The Pacific El Nino-Southern Oscillation
(ENSO) phenomena play a role in interannual temperature variability in many
regions through the world (Hurrell, 1995),the El-Nino of the Southern
Oscillation (ENSO) significantly affecting the variability over some regions in
North Africa (Djomouet al., 2013).Events are
defined as 5 consecutive overlapping 3-month periods at or above the +0.5o
anomaly for warm (El Niño) events and at or below the -0.5 anomaly for cold (La
Niña) events. The threshold is further broken down into Weak (with a 0.5 to 0.9
SST anomaly), Moderate (1.0 to 1.4), Strong (1.5 to 1.9) and Very Strong (≥ 2.0) events. For the
purpose of this report for an event to be categorized as weak, moderate, strong
or very strong (URL7, 2020).
· El
Niño
1- In the season of 1972
– 1973, there was a very strong effect of El Nino, but it doesn’t affect the
results of the study.
2- In the season of 1982
– 1983,avery strong effect was observed that might be related to the increasing
trend in the minimum temperature in the autumn at DERNA station and on the
maximum temperature in autumn at ZWARA station.
3- In the season of 1987
– 1988,a strong effect was observed that might be related to the increasing
trend in maximum temperature in the autumn at HON station and increase in
average temperature in spring at ZWARA station.
4- In the season of 1997
– 1998, a very strong effect was observed that might be related to the
increasing trend in annual average temperatures in JALO and ZWARA stations.
5- In the season of 1997
– 1998, a very strong effect was observed that might be related to the
increasing trend in annual average temperatures in JALO and ZWARA stations.
6- In the season of 2008 – 2009, a strong effect is observed but was not found associated with the results of the study.
·
La Niño
1. In the season of 1973
– 1974, a strong effect was observed, which might be related to the increasing
trend in the precipitation at NALUT station in summer.
2. In the season of 1975
– 1976,a strong effect was observed that might be related to the increase in
precipitation trend at SHAHAT station in summer.
3. In the season of 1988
– 1989, a strong effect was observed that might be related with the increasing
trend in maximum temperature in the autumn at AGDABIA, HON, JAGHBOUB, SIRTE and
TOBRUK stations and on the minimum temperature in autumn at MISURATA station,
while increase in precipitation trend in AGDABIA station in autumn.
4. In the season 1998 –
1999, the El Nino index was very strong and the same result was found at DERNA,
HON and ZWARA stations in the minimum temperatures during the spring season.
5. In the season 1999 –
2000, a strong effect was observed that might be related to the increasing
trend in minimum temperatures in autumn in DERNA and TOBRUK station.
6. In the season of 2007
– 2008,a strong effect was observed that was related to the trend towards an
increase in maximum temperatures in winter at JALO station.
7. In the season 2010 –
2011, there was only a trend towards an increase in average temperatures in
winter at BENGHAZI station.
It is noted that in some
years the El Nino indicators were strong but did not affect the trends of many
stations and conversely, some stations showed strong trends in some years in
which the El Nino indicators were weak (Tables 92, 93, 94, 106 and Figure 286).
*Another
Causes of increases Temperature and variability Precipitation in northern
Libyan
Increases Temperature,the
interannual variability of temperature in the North Africa region is complex
and controlled by many factors (Balas, 2007).Changes in the state of the
climate system can occur due to natural reasons that are external (e.g.
variation in the solar out puts and suns pots) and/or internal (e.g. atmospheric
compositions, atmospheric-oceanic oscillations and volcanic activity).
(Houghtonet al., 2001) has suggested that natural climate change factors
probably increased during the first half of the 20th century. The
reconstructions of climate during the 20th century indicate that the direct
effect of variations in solar radiation over the last 10 decades was about
20-25% of the observed change while the rest were resulting from increases in
greenhouse gases. (Pidwirny, 2004) has suggested that 1% change of solar output
constant caused a change in equilibrium temperature of about (0.6 °C). (Joneset
al., 2001) showed that the relationship between global annual temperature and
sunspot number data over the 20th century is varied with changes in temperature
which was higher during the first half of the 20th century (1901-1950) relative
to the second half (1951-2000).
Overgrazing in semi-arid areas leads to
increased rates of albedo (shortwave radiation as the fraction of the solar
energy reflected from the earth back into space). The uncovered land have a
greater irradiative heat loss than adjacent vegetated areas as it reflects more
sunlight compared to the vegetated land in the forms of crops, grasses and
trees (Barry, 1977). This situation leads to changes in climate which may
further influence climatic parameters (temperature, precipitation).
According to the Technical Centre of
Environmental Protection (TCEP, 1998), about half million ha were cleared
during the period 1980-2000 in different regions in north of Libya (particularly
around TRIPOLI, MISURATA and Green Mountain) for seasonal irrigated
plantations. Regional urbanization and industrialization are also believed to
be more influential on regional temperature than the global warming from
1951-2000 (Chung, 2004). However, the effects of urban heat islands on
temperature and precipitation are found very weak in Libya.
Latitude, altitude and land–sea distribution
are the main physical and geographical factors controlling temperature and they
seem to play a noticeable role to explaining variability of temperature in
Libya (Al-Jadide, 1985 and Ageena, 2002). The changing composition of the
atmosphere, including greenhouse gases and aerosol content, is a major internal
forcing mechanism of climate change.
In developed countries, carbon derivatives,
sulfur derivatives, nitrogen derivatives and non methane volatile organic
compounds emitted to the atmosphere by between years 2000 and 2007 showed a
decline in the general total (Coşkun, 2011). Despite that, these countries
still suffer from breakthroughs in the proportion of carbon dioxide, as is the
case in the study area, the amounts of aerosols in the atmosphere produced by
human activities can change the climate through changing the chemical and
microphysical properties of clouds which absorb solar and infrared radiation.
Total emissions of carbon dioxide (CO2) have sharply increased in Libya,
particularly during the last 30 years (1980-2010), with an increase from
83,214,246 tons in 1980 to 133,452,660 tons in 2009, a probable function of the
expanding petrochemical and oil production in the country over the period. A
positive (high) correlation is found between emissions of CO2 and mean annual
temperatures for the different regions, with values ranging from 0.36 to 0.84,
with six cases exceeding 0.50. Some of the most rapid increases are observed in
the sites near large oil fields and exports centers e.g. AGDABIA and ZWARA
(Ageena, 2013).
No clear evidence about the relationships
between population increase and temperature is observed through trend analysis.
The study area includes the most populated cites, where 40% of Libya’s people
resided, with the total population of TRIPOLI (1,063,571), BENGHAZI (674,951)
and MISURATA (543,129). Increasing emissions of CO2 since the early
1970s have had a pronounced effect on temperature increases at some stations
near oil refineries and petrochemical industries such as AGDABIA, SIRTE, and
ZWARA.
Variability of Precipitation, global
precipitation has increased significantly by approximately 2% during the 20th
century (Folland, 2002). (Mosmannet al., 2004) haveidentified rates of increase
between 7% and 12% for the areas lying between 30–85° N latitude and by about
2% for the areas lying between 0–55° S. On the other hand, a pattern of
continuous aridity since the late 1960s has been observed over the western
parts of North Africa and South of the Sahara since the 1980s which includes a
large area of Libya (Folland, 2001 and Ageena, 2013). Is generally
characterized by high temporal and spatial variability, which can partly be
explained by changes in atmospheric circulation (Xoplakiet al., 2003). It is
expected to affect fluctuations in the hydrological cycle including
increases/decreases in precipitation, geographical distribution of
precipitation and droughts. Relationships between precipitation indices for the
different stations in North Africa (Morocco, Algeria, Tunisia, Libya and Egypt)
and large scale atmospheric circulation patterns including the North Atlantic
Oscillation (NAO), Western Mediterranean Oscillation (WEMO), Mediterranean
Oscillation (MO), El Nino and Southern Oscillation (Tramblayet al., 2013andAgeena,
2013). He has identified decreases in total precipitation and wet days with an
increase in the duration of dry periods (Meddi, 2010), has also identified a
decrease in total annual precipitation in northwest Algeria after 1970 and
related this to the El Nino Southern Oscillation (ENSO) index.
These findings are supported by (Al-Hamlyet
al., 1998) as he has observed an increase in dry years after 1970 in Morocco
and identified a relationship with positive NAO phases. The relationship
between North Atlantic Oscillation (NAO), El Niño Southern Oscillation (ENSO),
Southern Oscillation Index (SOI) and local precipitation are well studied
(Philander, 1990; Hurrell, 1995; Jones et al., 1997; Houghton et al., 2001; McCarthy
et al., 2001, andDjomou et al., 2013).
In general, changes of ENSO in recent decades
are replicated in precipitation variations throughout the world, particularly
over the tropics and sub-tropics regions (Houghton et al., 2001). (McCarthy et
al., 2001) suggested that the NAO is the most responsible factor for
inter-annual fluctuation in precipitation over the Northern Africa (Ageena,
2013). with high correlations for a few cities across Libya. In comparison,
weakly negative links between SOI and annual and seasonal precipitation has
been found at three cities in Libya (Ageena, 2013).Changes of temperature and
its effect on air masses-movements and air pressure circulation are one of the
important impacts on changes in global precipitation (Ritter, 2003). The study
has revealed a number of results; which following are the most important No
clear links between precipitation and temperature in north of Libya are
identified with a mixture of negative and positive correlations while negative
correlations only found at a small number of stations with 95% confidence level(Ageena,
2013).
The results are also compared with the
findings of previous studies over the last half of the 20th century which
examined spatial and temporal variability in local and regional precipitation
in Mediterranean sea and North Africa.
Analysis of mean average temperature data of
40 years (1971-2010) has shown significant increases (0.9 °C) with a doubling
of the warming rate (1.7 °C) at most stations for the last 12 years (1998-2010
Tables from (33 to 48). As a result a rapid increase in annual maximum and
minimum temperature is observed and the overall mean value shows upward trend.
This finding agree with that of (Al-Kenawy, 2012) who has found that mean
average temperature in north-eastern Spain is mostly increased during the
period 1920-2006 resulted from the increase in maximum temperatures, especially
in spring and summer.
Also study of (Philandraset al., 2008)
identified decreasing trends since the early 1960s until the mid-1970s across
Greece, they found that contribution of summer in increase annual averages
temperatures is unquestionable, also (Domroes, 2005) have found negative trends
in maximum temperature observed across Egypt for period (1971–2000), whine
examination of annual average temperature for the study period (1971- 2010) an
increase is identified which is statistically significant (95% confidence
level) at most stations, this corroborates the concerns of rapid global warming
and its impacts in this region. These results are consistent with previous
regional and local research (Ben-Gaiet al., 1999 andDomroes,2005), which showed
a significant increase in temperature of different regions across the
Mediterranean basin.
In study in North of Algeria for period
(1973- 2015), in most of the eight stations the study has brought to light an
increase in average temperature monthly, seasonal, and annual average
temperatures (Nia, 2018). and the study did not show the results of the trends
of minimum and maximum temperatures, this increase is caused by an increase in
the maximum temperature, and looking at the results of previous studies in that
region, can be noted that increase in annual rate results from the increase in
the maximum temperatures, and this is what is shown by a study (Ramos, 2006 and
Al-Kenawy et al., 2012), who has found that temperatures in north-eastern Spain
is mostly increased during the period 1920-2006 resulted from the increase in
maximum temperature (North-eastern Spain).
(Nashwan, 2019),the results showed a large
difference between the trends obtained using Mann Kendall test. the test showed
increasing trends in maximum temperature and a number of minimum temperatureat
stations of Egypt, but almost no change in rainfall and rainfall extremes.
(Maged, 2016), in Egypt, magnitude of climate
change i s illustrated to increase over the period 1970– 2010, i.e. annual mean
temperature increased from (0.98 °C) at Alexandria to (1.68°C) at Cairo.
Seasonally, the highest warming trends were observed for summer temperatures
and also increasing temperature trends detected with different magnitude in the
remainder seasons. This research is the closest to the results of the study,
because the time series was (1970 - 2010), as researchers used (Mann-Kendall)
to trend analysis. Also at (Cosun, 2008), in Kahramanmaraş, Turkey,
Mediterranean Sea region, have found most increase in maximum temperature, and
average temperatures. However the results are not statistically significant
because (p) value is higher than (Alfa) level.
(Fontaine, 2011), this study have
identified a slight general increase in North Africa since the mid-90s with
significant northward migrations of rainfall amounts. The findings are supported
by (Yosefet al., 2009) who found an increased trend in total annual rainfall
across Israel for the period 1950-2003. However, Al-Tantawi, 2005 identified
positive trends (0.170 mm) in annual precipitation computed at most of study
stations observed during the period 1971-2000, this result is not consistent
with the results of study of (Meddi et al., 2010) which as they have observed
decrease in total annual rainfall for the north-eastern Algeria after 1970.
In Europe (Moberg, 2005) they identified
significant increasing precipitation trends over the 20th century in winter,
based on about 80 stations situated in Central and Western Europe during the
period 1901-1999, with low precipitation trends in summer.Afterwards (Boccolari,
2013) have found decreases in precipitation trend (-6.33 mm) over Modena in
Italy during the 1831-2010. A general decreasing trend (-2.32) in mean annual
precipitation for the 26 years (1979-2004) with increase in trend in parts of
central and northern Greece have been identified by (Hatzianastassiou, 2008).
Moreover, the (IPCC, 2007) report illustrated that the linear trends of
rainfall decreases for 1900 to 2005 in western Africa.These findings are
supported by (Meddi et al., 2010) as they have observed decrease in total
annual rainfall for the north-eastern Algeria after 1970.
Negative precipitation evolution in the
western parts of Northern Africa with no significant precipitation trends have
been observed in central Tunisia Mediterranean , and the Mediterranean parts of
Libya and Egypt, during the last decades (Schillinget al., 2012). During the
20th century, general decrease in total annual rainfall has been found in
Turkey, and apparently during the 1930-1993, none of the decreasing trends in
precipitation were found significant. About 19% of total stations showed a
significant trend with majority of these trends are downward especially in
Mediterranean Sea stations (Türkeş, 1996).
*Work to increase the number
synoptic stations, and improvements to data completeness, standard of stations
and operating procedures along with regular training provision to operational
station meteorologists is necessary to raise the efficiency and performance of
Libyan meteorological stations.
*The statistical tests used in the study show that during the 40-year period, winter and autumn rains have a decreasing trend in coastal stations. Considering that rain waters are used for storage in soil and agricultural irrigation in dry seasons, the decrease in winter rainfall may cause problems in terms of agricultural area which are decreasing in the long run. Therefore, some measures should be taken to eliminate or reduce the negative impacts of climate change on our country which is reflected by the low rainfall, and increased temperatures. The most important of these measures is the establishment and implementation of plans for water resources management.
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