Güneş ışınımı, güneşlenme süresi ve sıcaklık verilerinin trend analizi
Trend analysis of solar radiation, sunshine duration and temperature data
Yazar: ERCAN AKSAY
Danışman: DOÇ. DR. YAVUZ SELİM GÜÇLÜ
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / İnşaat Mühendisliği Ana Bilim Dalı / Hidrolik ve Su Kaynakları Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: İnşaat Mühendisliği = Civil Engineering

Özet:
Dünya genelinde fosil yakıt kullanımının artması atmosfere daha fazla sera gazının salınmasına neden olmaktadır. Bu durum Güneşten gelen ışınların atmosfere geri dönmesini engelleyerek küresel ısınmayı tetiklemektedir. Küresel ısınma dünyadaki hidrolojik döngüde ve iklimin şekillenmesinde olumsuz bir etkiye sahiptir. Bu nedenle güneşlenme süresi, güneş ışınımı ve sıcaklık gibi meteorolojik verilerin incelenmesi ve analiz edilmesi; küresel ısınmanın sebep olduğu iklim değişikliğinin izlenmesi, su kaynakları yönetimi ve planlanması için önemli bir rol oynamaktadır. Güneş ile alakalı meteorolojik verilerin incelenmesi ve analiz edilmesinde trend analiz yöntemlerinin kullanılması, verilerin trendlerinin artan veya azalan yönde olduğunun belirlenmesini sağlamaktadır. Çalışma kapsamında Türkiye'nin belirli noktalarından elde edilen günlük güneş ışınımı, güneşlenme süresi ve sıcaklık verilerinin trend analizi yapılmıştır. Trend analiz yöntemi olarak Şen'in yaklaşımı Yenilikçi Trend Analizi (YTA) ve Güçlü'nün yaklaşımı Kıyaslamalı Yenilikçi Trend Analizi (K-YTA) kullanılmıştır. YTA ile verilerin trendleri ayrı grafiklerde incelenebilirken, K-YTA yönteminde trendler tek bir grafikte karşılaştırmalı olarak incelenebilmektedir. K-YTA, YTA yöntemine ek olarak verileri boyutsuzlaştırarak üç farklı veri trendinin birbirleriyle ilişkisinin incelenmesine imkan sunmaktadır. Bu sayede veri trendlerindeki eğilimler tek bir grafikte incelenerek veriler arasındaki ilişkiyi açık biçimde göstermektedir. Bu çalışma güneş ile alakalı verilerde trend analizi ve incelemesi yapmak isteyenlere yardımcı olacaktır. Bu çalışmada Türkiye'nin dokuz farklı istasyonundan 1981-2010 yılları arasındaki günlük güneş ışınımı, güneşlenme süresi ve sıcaklık verileri kullanılmıştır. Bu veriler Meteoroloji Genel Müdürlüğü (MGM) tarafından elde edilmiştir.
YTA ve K-YTA yöntemleri kullanılarak verilerin trend analizleri yapılmıştır. Analizlerin sonucunda tüm istasyonlardaki sıcaklık verilerinde bütüncül olarak artan trend tespit edilmiştir. Bazı istasyonlarda güneş ışınımı artan trende sahipken bazı istasyonlarda trend azalan yöndedir. İstasyonların genelinde güneşlenme süresinin trendinin olmadığı gözlemlenmiştir. Bu çalışma, K-YTA ile verilerin trendlerinin tek grafik üzerinde karşılaştırılması sonucu güneşlenme süresi değişmezken sıcaklıkların arttığını göstermiştir. Bu sonuç ile birlikte verileri kullanılan bölgelerin küresel ısınmanın etkisinde olduğu söylenebilmektedir. Küresel ısınma sonucu artan sıcaklıklar ve değişimlerin, su kaynaklarını ve iklimi olumsuz etkileyebileceğinden dolayı ülkemiz için bu konuda araştırmalar yapılması ve önlemler alınması gerektiği düşünülmektedir.

Abstract:
Fossil fuels have started to be used extensively around the world, causing more greenhouse gases to be released into the atmosphere. This situation prevents the rays from the Sun from returning to the atmosphere and initiates global warming. Global warming has a negative impact on the hydrological cycle and the climate in the world. The estimation and analysis of solar radiation, sunshine duration and temperature data has a separate importance for the management and planning of the world's water resources. Hydraulic and Water Resources Engineering, which is one of the important areas of expertise of civil engineering, carries out studies on planning and managing water resources, climate change and its effects. Therefore, the study and analysis of meteorological data such as sunshine duration, solar radiation and temperature plays an important role for monitoring climate change caused by global warming, water resource management and planning. The use of trend analysis methods in the examination and analysis of meteorological data related to the sun allows determining whether the trends of the data are in an increasing or decreasing direction. Within the scope of the study, trend analysis of daily sunshine duration, solar radiation and temperature data obtained from certain residential areas of Turkey was performed. As a trend analysis method, Şen's approach Innovative Trend Analysis (ITA) and Güçlü's approach Comparative Innovative Trend Analysis (C-ITA) were used. To be able to do trend analysis, data sets need to be de-dimensionalized in order to become comparable and scrutinizable. The two methods that can be used for dimensionalization are standardization and normalization. The standardization method provides the opportunity to compare the data together. We can briefly explain the application steps as follows. Firstly, the average and standard deviation of the data to be analyzed are calculated. Then, the average value of the data is extracted from each data and divided by the standard deviation value. In the normalization method, the following steps are applied. Firstly, the data set is sorted from small to large. Minimum and maximum values are determined in the sorted data set. The minimum value is subtracted from the x value to be dimensionalized and divided by the difference between the maximum and minimum value. In C-ITA, the data are dimensionalized and brought to the same scale. Thus, different data types can be compared and analyzed on the same scale. The data generated in standardization can be a negative value, but in normalization, all values occur between 0 and 1. The absence of a negative value provides an advantage for analysis. In addition to the innovative trend analysis method proposed by Şen, the data reached up to a maximum of 1 and were examined by using normalization in the comparative innovative trend analysis method proposed by Güçlü. The innovative trend analysis method (ITA), introduced to the literature by Şen, is able to perform trend analysis without relying on any assumptions, unlike classical methods. With the innovative trend analysis, it is possible to identify increasing and decreasing trends that are monotonic or non-monotonic. In addition, the intensity of the trend in the form of low, medium and high can be seen. It is possible to explain the application steps of innovative trend analysis as follows. Firstly, the data required for trend analysis are divided into two equal parts. Data sequences divided into two equal parts are sorted from small to large. A 1:1 line is drawn on the graph and a scattering graph is created by placing the data sorted from small to large in the horizontal and vertical planes. In the chart, it is understood that there is no trend in the data scattering remaining on the 1:1 line, there is a decreasing trend in the part remaining below it, and an increasing trend in the part remaining above it. Güçlü proposed the comparative innovative trend analysis (C-ITA) method. The first steps of the application of the method are the same as the steps of the innovative trend analysis proposed by Şen. Unlike Şen's method, three different data series related to each other can be seen in the graph of this method. In this way, these data can be compared with each other. In order to be able to make a comparison on the same graph, it is important to de-dimensionalize the data with normalization. Therefore, the data is translated into values from 0 to 1. The application steps of the method are as follows. Firstly, three different data sequences related to each other are divided into two equal parts. Data sets formed by dividing three different data into two equal parts are sorted from small to large. Dimensionalization is performed by applying normalization to the resulting data sequences. Thus, the data sets sorted from small to large are converted to values from 0 to 1 using the formula. The first half of the desizated data sets are placed on the graph so that the horizontal axis and the second half are placed on the vertical axis. With the resulting chart, trend analysis can be performed in such a way that three different data types are simultaneously, separately and together. While the trends of the data can be examined in separate graphs with the ITA, the trends can be examined comparatively in a single graph using the C-ITA method. In addition to the ITA method, C-ITA provides the opportunity to examine the relationship of three different data trends to each other by de-dimensionalizing the data. In this way, the trends in data trends are analyzed in a single graph and clearly show the ratio between the data. This study will help those who want to conduct trend analysis and analysis of data related to the sun. In this study, daily sunshine duration, solar radiation and temperature data between 1981 and 2010 from nine different stations of Turkey were used. These data were obtained by the Turkish State Meteorological Service (TSMS). According to the ITA results, the trend of solar radiation data at Gaziantep, İskenderun, Elbistan and Birecik stations is in a decreasing direction. At Adıyaman, Şanlıurfa, Silifke and Adana stations, the trend of solar radiation data is in an increasing direction. On the other hand, there is no trend of solar radiation data at Siverek station. The trend of sunshine duration data of Gaziantep, Adıyaman, Şanlıurfa, Silifke, Adana and Elbistan stations tends to decrease. The trend of the sunshine duration data of Birecik station is on an increasing trend. There is no trend of Siverek station's sunshine duration data. According to the results of the temperature data, the trend of all stations is in an increasing direction. After these results, the data were analyzed according to the C-ITA method. With the advantage of the method, the trends of the three data types were examined and interpreted in a single chart. With the method showing the results in a single graph, it was possible to see the relationship of the data with each other and their effect on each other. As a result, the temperature tends to increase at all stations, regardless of whether the solar radiation and sunshine duration data are on an increasing or decreasing trend. According to this situation, it can be said that the station regions are under the influence of global warming. Since global warming has negative effects on the climate, it can be said that the regions under study carry risks in terms of water resources.