Yağış Zaman Serilerindeki Kısa Dönemli Eğilimlerin İzlenmesinde Çeşitli Yöntemlerin Karşılaştırılması

Abstract

Yağıştaki eğilimler, büyük ölçekli iklimsel olaylar veya döngüsel hareketler ile ilişkilidir. Kısa veri kayıtlarında bu davranışın incelenmesi sıkıntılı olabilmektedir. Bu çalışmada, Ocak 2005 - Ekim 2018 tarihleri arasında Bursa ilinde ölçülen aylık yağış zaman serileri kullanılmıştır. Bölgede en uzun zaman kaydına ve en az veri kaybına sahip istasyon verileri analizlerde kullanılmak üzere seçilmiştir. Bu amaca yönelik olarak, Osmangazi, Keleş, Uludağ, Gemlik, İznik, Karacabey ve Mustafakemalpaşa istasyonları seçilmiştir. Seçilen istasyonların yağış zaman serilerindeki kısa dönemli eğilimlerini tespit etmek için doğrusal eğilim (LT), hareketli ortalama (MA), Mann-Kendall (MK), dönüm noktaları (TP), Spearman sıralı korelasyon (SROC), yenilikçi Şen (IS), yenilikçi eğilim analizi (ITA), dağılım fonksiyonundaki değişiklikler (CD) ve standart yağış indis (SPI) yöntemleri kullanılmıştır. Uygulanan analizler neticesinde, önceki çalışmaların ortaya koymuş olduğu eğilimlerin tek başına LT, MK, SROC, IS, ITA ve MA yöntemleriyle tespit edilemediğini göstermektedir. Buna karşın, SPI-48, CD ve SPI yöntemleri ise eğilimleri tek başına belirgin bir şekilde tespit edebilmektedir. Aykırı değerlere sahip olan verilerde kısa dönemli eğilimleri tespit etmek zordur. Aykırı değerler barındıran verilerde kısa dönemli eğilimlerin değerlendirilmesinde CD, ITA ve IS yöntemlerinin birlikte kullanılması önerilmektedir.

Keywords

• Bursa,kısa dönem eğilim,istatistiksel analiz,yağış

A COMPARISON OF SEVERAL METHODS IN TRACKING SHORT-TERM TRENDS ASSOCIATED WITH THE PRECIPITATION TIME SERIES

Abstract

Precipitation trends can be linked to large-scale climatic events or cyclic behaviors. However, exploring these patterns in short data records can be problematic. In this study, the monthly precipitation time series recorded across the Bursa district in Turkey were addressed between January 2005 to October 2018. Stations with the minimum data loss and the longest time records in the region were selected for the analysis. Therefore, Osmangazi, Keles, Uludag, Gemlik, Iznik, Karacabey, and Mustafakemalpasa stations were selected. Linear trend (LT), moving average (MA), Mann-Kendall (MK), turn points (TP), Spearman rank-order correlation (SROC), innovative Şen (IS), innovative trend analysis (ITA), changes in distribution (CD), and standardized precipitation index (SPI) methods were used to detect short-term trends in the precipitation time series. Results indicated that the trends, reported by the previous studies could not be reproduced at a monthly scale when using LT, MK, SROC, IS, ITA, and MA. However, the trends observed by the SPI-48 were also tracked down using CD, and SPI methods. It is concluded that the detection of the short-term trends is problematic whilst the outliers deviate results of the analysis. Hence, a combination of CD, ITA, and IS methods is a key in evaluation of the short-term trends within a data run.

Keywords

• Precipitation,short term trend,statistical analysis,Bursa

References

1. 1. Adeloye, A. J., and Montaseri, M. (2002) Preliminary streamflow data analyses prior to water resources planning study/analyses préliminaires des données de débit en vue d'une étude de planification des ressources en eau, Hydrological Sciences Journal, 47(5), 679-692. doi: 10.1080/02626660209492973
2. 2. Akkoyunlu, B. O., Baltaci, H., and Tayanc, M. (2019) Atmospheric conditions of extreme precipitation events in western Turkey for the period 2006–2015, Natural Hazards and Earth System Sciences, 19(1), 107-119. doi: 10.5194/nhess-19-107-2019
3. 3. Bacanli, U. G., and Kargi, P. G. (2019) Uzun ve Kısa Süreli Periyotlarda Kuraklık Analizi: Bursa Örneği, Doğal Afetler ve Çevre Dergisi, 5(1), 166-174. doi: 10.21324/dacd.429391
4. 4. Caliskan, O., Cicek, I., and Matzarakis, A. (2012) The climate and bioclimate of Bursa (Turkey) from the perspective of tourism, Theoretical and Applied Climatology, 107(3-4), 417-425. doi: 10.1007/s00704-011-0489-6
5. 5. Dabanli, I., Şen, Z., Yelegen, M. O., Sisman, E., Selek, B., and Guclu, Y. S. (2016) Trend assessment by the Innovative-Şen method, Water resources management, 30(14), 5193-5203. doi: 10.1007/s11269-016-1478-4
6. 6. Demir, I., Kilic, G., Coskun, M., and Sumer, U. M. (2008) Türkiye’de maksimum, minimum ve ortalama hava sıcaklıkları ile yağış dizilerinde gözlenen değişiklikler ve eğilimler, TMMOB İklim Değişimi Sempozyumu, Bildiriler Kitabı, 69-84. (in Turkish)
7. 7. Gulten, I. C. E. L., and Ataol, M. (2014) Türkiye’de Yıllık Ortalama Sıcaklıklar İle Yağışlarda Eğilimler Ve Nao Arasındaki İlişkileri (1975-2009), Coğrafya Dergisi, (28), 55-68. (in Turkish)
8. 8. Hurrell, J. W. (1996) Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation, Oceanographic Literature Review, 2(43), 116.

9. 9. Hurrell, J. W. (1995) Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation, Science, 269(5224), 676-679. doi: 10.1126/science.269.5224.676
10. 10. Katip, A. (2018) Meteorological Drought Analysis Using Artificial Neural Networks for Bursa City, Turkey, Applied Ecology and Environmental Research, 16(3), 3315-3332. doi:10.15666/aeer/1603_33153332
11. 11. Kendall, M. G. (1975) Rank correlation methods, 4th edn, Charles Griffin, London.
12. 12. Kendall, M., and Stewart A. (1976) The advanced theory of statistic, vol. 3, Design and Analysis, and Time Series, Charles Griffin & Co Ltd, London, UK.
13. 13. Kwarteng, A. Y., Dorvlo, A. S., and Vijaya Kumar, G. T. (2009) Analysis of a 27‐year rainfall data (1977–2003) in the Sultanate of Oman, International Journal of Climatology: A Journal of the Royal Meteorological Society, 29(4), 605-617. doi: 10.1002/joc.1727
14. 14. Li, W., He, X., Scaioni, M., Yao, D., Mi, C., Zhao, J., Chen, Y., Zhang K., Gao, J., and Li, X. (2019) Annual precipitation and daily extreme precipitation distribution: possible trends from 1960 to 2010 in urban areas of China, Geomatics, Natural Hazards and Risk, 10(1), 1694-1711. doi:10.1080/19475705.2019.1609604
15. 15. Mann, H. B. (1945) Nonparametric tests against trend, Econometrica: Journal of the Econometric Society, 245-259. doi: 10.2307/1907187
16. 16. McGhee, J. W. (1985) Introductory Statistics, West Publishing Co, New York, USA.
17. 17. Ozturk, M. (2010) Uludağ (Zirve) ve Bursa Meteoroloji İstasyonlarının Karşılaştırmalı İklimi, Türk Coğrafya Dergisi, (55), 13-24. (in Turkish)
18. 18. Pandey, B. K., Tiwari, H., and Khare, D. (2017) Trend analysis using discrete wavelet transform (DWT) for long-term precipitation (1851–2006) over India, Hydrological sciences journal, 62(13), 2187-2208. doi: 10.1080/02626667.2017.1371849
19. 19. Petrie, M. D., Collins, S. L., Gutzler, D. S., and Moore, D. M. (2014) Regional trends and local variability in monsoon precipitation in the northern Chihuahuan Desert, USA, Journal of Arid Environments, 103, 63-70. doi: 10.1016/j.jaridenv.2014.01.005
20. 20. Rubel, F., and Kottek, M. (2010) Observed and projected climate shifts 1901–2100 depicted by world maps of the Köppen-Geiger climate classification, Meteorologische Zeitschrift, 19(2), 135-141. doi: 10.1127/0941-2948/2010/0430
21. 21. Salehi, S., Dehghani, M., Mortazavi, S.M., and Singh, V. P. (2019) Trend analysis and change point detection of seasonal and annual precipitation in Iran, International Journal of Climatology, 1-16. doi: 10.1002/joc.6211
22. 22. Şen, Z. (2017) Innovative trend significance test and applications, Theoretical and applied climatology, 127(3-4), 939-947. doi: 10.1007/s00704-015-1681-x
23. 23. Şen, Z. (2013) Square diagonal trend test procedure, 6th International Perspective on Water Resources & The Environment, January, 07-09. Izmir, Turkey.
24. 24. Şen, Z. (2011) Innovative trend analysis methodology, Journal of Hydrologic Engineering, 17(9), 1042-1046. doi: 10.1061/(ASCE)HE.1943-5584.0000556
25. 25. Sezen, C., and Partal, T. (2019) The impacts of Arctic oscillation and the North Sea Caspian pattern on the temperature and precipitation regime in Turkey, Meteorology and Atmospheric Physics, 1-20. doi:10.1007/s00703-019-00665-w
26. 26. Sirdaş, S., and Şen, Z. (2003) Spatio-temporal drought analysis in the Trakya region, Turkey, Hydrological Sciences Journal, 48(5), 809-820. doi: 10.1623/hysj.48.5.809.51458
27. 27. Simisek, O., and Cakmak, B. (2010) Drought analysis for 2007-2008 agricultural year of Turkey, Tekirdağ Ziraat Fakültesi Dergisi, 7(3), 99-109.
28. 28. Tatli, H. (2015) Downscaling standardized precipitation index via model output statistics, Atmósfera, 28(2), 83-98. doi: 10.1016/S0187-6236(15)30002-3
29. 29. Turkes, M., Telat, K. O. C., and Saris, F. (2007) Türkiye’nin Yağiş Toplami ve Yoğunluğu Dizilerindeki Değişikliklerin ve Eğilimlerin Zamansal ve Alansal Çözümlemesi, Cografi Bilimler Dergisi, 5(1), 57-73. (in Turkish)
30. 30. Vaheddoost, B. (2019) Evaluation of Monthly Drought Using Standardized Precipitation Index in Bursa, Turkey, 4th Eurasian Conference on Civil and Environmental Engineering (ECOCEE), June 17-18, DoubleTree by Hilton, Istanbul, Turkey.
31. 31. Vaheddoost, B., and Aksoy, H. (2017) Structural characteristics of annual precipitation in Lake Urmia basin, Theoretical and Applied Climatology, 128(3-4), 919-932. doi: 10.1007/s00704-016-1748-3
32. 32. Van Beusekom, A. E., González, G., and Rivera, M. M. (2015) Short-term precipitation and temperature trends along an elevation gradient in northeastern Puerto Rico, Earth Interactions, 19(3), 1-33. doi: 10.1175/EI-D-14-0023.1
33. 33. Verma, P., and Ghosh, S. K. (2019) Trend analysis of Climatic Research Unit temperature dataset for Gangotri glacier, India, Dynamics of Atmospheres and Oceans, 85, 83-97. doi:10.1016/j.dynatmoce.2019.01.002
34. 34. World Meteorological Organization (WMO). (2012) Standardized precipitation index user guide, ISBN 978-92-63-11091-6, Geneva, Switzerland.
35. 35. Wu, P., Ding, Y., Liu, Y., and Li, X. (2019) The Characteristics of moisture recycling and its impact on regional precipitation against the background of climate warming over Northwest China, International Journal of Climatology, 1-15. doi:10.1002/joc.6136
36. 36. Wu, H., and Qian, H. (2017) Innovative trend analysis of annual and seasonal rainfall and extreme values in Shaanxi, China, since the 1950s, International Journal of Climatology, 37(5), 2582-2592. doi:10.1002/joc.4866
37. 37. Yilmaz, E. (2018) Türkiye'de aylık yağış eğilimleri, yağış kaymaları ve yağış Eğilim Rejimleri (1971-2010) (Monthly Precipitation Trends, Precipitation Temporal Shifts and Precipitation Trends Regimes in Turkey (1971–2010)), Journal of Human Sciences, 15(4), 2066-2091. doi:10.2139/ssrn.3419380
38. 38. Zhang, X., Vincent, L. A., Hogg, W. D., and Niitsoo, A. (2000) Temperature and precipitation trends in Canada during the 20th century, Atmosphere-Ocean, 38(3), 395-429. doi:10.1080/07055900.2000.9649654
39. 39. Zhang, X., Zwiers, F. W., Hegerl, G. C., Lambert, F. H., Gillett, N. P., Solomon, S., Stott, P. A., and Nozawa, T. (2007) Detection of human influence on twentieth-century precipitation trends, Nature, 448(7152), 461. doi:10.1038/nature06025
40. 40. Menezes, Z., McLaren, C. H., von Sanden, N., Zhang, X., and Black, M. (2006) Timely detection of turning points: Should I use the seasonally adjusted or trend estimates. Proceedings of Conference on seasonality, seasonal adjustment and their implications for short-term analysis and forecasting. May 10-12, Luxembourg.
41. 41. Zuo, B., Li J., Sun, C., and Zhou, X. (2019) A new statistical method for detecting trend turning, Theoretical and Applied Climatology, 1-13. doi:10.1007/s00704-019-02817-9