Trend and Oscillation Analysis of Water Vapor Pressure in South and South West of Iran

Document Type : مقاله پژوهشی

Authors

• Hasan Lashkari ¹
• Aliakbar Matkan ¹
• Hosein Asakareh ²
• Younes Khosravi ¹

¹ University of Shahid Beheshti

² University of Zanjan

10.22067/geo.v4i1.40438

Abstract

Introduction
Identification and analysis of time-series behavior of the climatic elements and processes upon them are the basic factors of climatology science and has an important role in management and environmental planning. In general, the behavior of climatic elements are observed in 3 form: Trend, Oscillation and Fluctuation. These behaviors will help us to identify more the climate change. Today by changing more the trend of climatic elements, climate change has become more important than previous years. But in between the climatic elements, it has been noticed less than the other elements such as water vapor pressure. The importance of water vapor is that this climatic parameter is has an important role in explaining the climate change, because of 1) It is the main source of rainfall in all weather systems, 2) It supplies the latent heat in this process and controls the heat in the troposphere and 3) It is the intensify severe storms. Given the significant role of atmospheric moisture in the Earth's energy equilibrium, it can be helpful in explaining climate change. Therefore, this article tries to survey two time series behaviors of climate data; the trends and fluctuations in water vapor pressure. The Mann-Kendall test is one of the popular nonparametric techniques for explaining the trend analyzing in hydrological and climatic data. The spectrum analysis is one of the best methods for extracting and analyzing visible and invisible climatic oscillations with different wavelength. In fact, spectrum show the distribution of variance along the entire range of wavelengths in time series. So in this article, we want to analyze the trend by using the Mann-Kendall test and detect and identify the fluctuations and cycles of water vapor pressure time series by using the spectrum analysis.

Study area
The study area, with about 360,200 km2 area, is located in the south and southwest of Iran and approximately between 25° 00'N and 34° 25'N latitudes and between 45° 38'E and 59° 17'E longitudes. Southern and southwestern parts of the study area is located in sides of two massive sources of moisture, Persian Gulf and Oman Sea.
Materials and methods
In this study, water vapor pressure data from 28 synoptic stations, during a period of more than 20 years in south and southwest of Iran were used that were collected by the Iranian Meteorological data website (http://www.weather.ir). It should be noted that to survey the Mann-Kendall test and Spectral analysis, the stations with 44-years period (2010-1967) and stations that have data since their establishment were used, respectively. The methodology of this study is detecting trends and oscillation of water vapor pressure in south and southwest of Iran by Mann-Kendal test and Spectral analysis, respectively based on ground station. Tests for the detection of significant trends in climatological time series can be classified as parametric and non-parametric methods. Parametric trend tests require data to be independent and normally distributed, while non-parametric trend tests require only that the data be independent. In this study, a non-parametric method, Mann-Kendal, was used to detect trends. To analyze the Oscillation of water vapor pressure, the spectral analysis has been applied. In this function, frequency and cycles indicate the time scale and amount of variance in the time scale, respectively. After extracting all waves, in second step the proportion of each waves in explaining the total variance are specified and Finally, the significance of each waves are tested. For performing the Mann-Kendall test and spectrum analysis technique, the capability of programming in MATLAB software was used.

Result and discussion
The results showed significant trend in most of the stations. As the results of this article, the downward significant trend in the stations of Ahwaz, Bandarabas, Shahrekord, Boroujen, Abadeh and Khorramabad, the upward significant trend in Lengeh, Bushehr, Dezful and non-significant trend in Abadan, Fasa and Shiraz was seen. By applying the spectrum analyzing in 95% confidence level on the water vapor pressure data, the multiple cycles were obtained; So that the significant sinusoidal cycles 3-2, 4 and 15-7 years were more common. Results show that the cycles of 3-2 years was most repeated in the water vapor pressure time series. 4-year cycle was the other cycle of events occurred in the study area that researchers have attributed this cycle to ENSO. The survey showed that more cycles are traceable in the southern part of the Zagros. Another cycle in the region was 15-7 year cycle that most scientists have attributed this cycle to sunspot activity. The final survey showed that the plurality of cycles in the West and South West of the study area.

Conclusion
In general it can be concluded that the downward significant trend of water vapor pressure was observed in the Zagros and the upward significant trend was observed in nearby the Persian Gulf. By applying the spectrum analyzing on the water vapor pressure data, the multiple cycles were obtained. Results of spectral analysis showed that the cycles of 3-2 years was most repeated in the water vapor pressure time series. The spatial distribution of this cycle was observed in the near of Persian Gulf coasts. Most scientists have attributed this cycle as a result of El Niño - Southern Oscillation (ENSO) and Quasi-biennial oscillation (QBO).

Keywords

• Water Vapor Pressure
• Trend, Oscillation
• Mann-Kendal
• Spectrum Analysis

---

References

تقوی، فرحناز؛ ناصری، محسن؛ بیات، بردیا؛ متولیان، سید ساجد؛ آزادی فرد، داوود؛1390. تعیین الگوهای رفتار اقلیم در مناطق مختلف ایران بر اساس تحلیل طیفی و خوشه‌بندی مقادیر حدی بارش و دما. پژوهش‌های جغرافیای طبیعی. شماره 77. صص 124-109.

چتفیلد، سی؛1381. مقدمه ای بر تجزیه‌وتحلیل سری‌های زمانی. ترجمه حسنعلی نیرومند و ابوالقاسم بزرگ نیا. انتشارات فردوسی مشهد.

عساکره، حسین؛1388. تحلیل طیفی سری های زمانی دمای سالانه تبریز. تحقیقات جغرافیایی. شماره 94. صص 55-33.

عساکره، حسین؛1391. تحلیل تغییرات مؤلفه های مبنای نمایه های فرین بارش شهر زنجان. فصلنامه تحقیقات جغرافیایی. سال 27. شماره 105. صص 18041-18059.

عساکره، حسین؛ رزمی، رباب؛1391. تحلیل تغییرات بارش سالانه شمال غرب ایران. جغرافیا و برنامه‌ریزی. شماره 3. صص 162-147.

میرزایی، سید یحیی؛ چیت سازان، منوچهر؛ چینی پرداز، رحیم؛1383. استفاده از تحلیل سری زمانی در تعیین زمان تأخیر رواناب در حوضه جهان بین. آب و فاضلاب. شماره 51. صص 59-53.

Abraham, J. P., J. M. Gorman, F. Reseghetti, K. E. Trenberth, and W. J. Minkowycz., 2011. A new method of calculating ocean temperatures using expendable bathythermographs.Energy and Environment Research1(1), 12– 11.

Asakereh, H., 2009. The Spectralanalysisof time seriesof annualtemperaturein Tabriz. Geographical Research Journal 94, 33-55.

Asakereh, H and Razmi, R., 2012. The analysis of annual precipitation changes in North West of Iran. Geography and Plannling 3, 147-162.

Asakereh, H., 2012. The analysis ofPCAchanges of extreme indices ofprecipitationin Zanjan. Geographical Research 105, 18041-18095.

Azad, S and T. S. Vigneshb and R. Narasimha., 2010. Periodicities in Indian monsoon rainfall over spectrally homogeneous regions. International Journal of Climatology 30, 2289 - 2298.

Bihart, O and Mehmetcik, B., 2003. The power of statistical tests for trend detection.Turkish Journal of Engineering& Environmental Sciences 27, 247-251.

Brighitte, E.J., and Hendry, M. J., 2003. Application of Harmonic Analysis of Water Levels to Determine Vertical Hydraulic Conductivities. Ground Water 41 (40), 514-522.

Brooks, J., Oxley, D., Vedlitz, A., Zahran, S., & Lindsey, C., 2014. Abnormal Daily Temperature and Concern about Climate Change across the United States. Review of Policy Research, 31(3), 199-217.

Cazelles, B., Chavez, M., McMichael, A,J and Hales, S., 2005. Non-stationary Influence of El Niño on the Synchronous Dengue Epidemics in Thailand. PLoS Medicine 2 (4), 313-318.

Chatfield, C., 1975. The analysis of time series: Theory and practice, Chapman and hill. London, 263.

Chatfield, C., 2002. The analysis of time series: an introduction, translated by: Niroumand, H.A and Bozorgnia, A, Ferdowsi University publication.

Christopher, M. M., Geoffrey,O. S, Donald,T. R and David M. A., 2003. Variability of El Niño/Southern Oscillation activity at millennial timescales during the Holocene epoch. Journal of Nature 420, 162-165.

Fasullo, J and Lesley, S., and Trenberth, KE., 2005. Trends and variability in column-integratedatmospheric water vapor. Climate Dynamic 24, 741-758.

Fayram, A. H., Tober Griffin, J. D., & Wendel, J. L., 2014. Effects of localized temperature and precipitation on historic Walleye recruitment in Wisconsin, USA with implications for climate change. Aquatic Ecosystem Health & Management, 17(2), 115-121.

Hartmann, S. Becker, and L. King., 2008. Quasi-Periodicities in Chinese Precipitation Time Series. Theoretical and Applied Climatology 92, 155–163.

Hameed .S., Yeh W.M, Li.M.T, Cess.R. and Wang.W.C., 1983.An Analysis of Periodicities in the 1470, Beijing Precipitation Record, Geophysics Research Letters 10, 436-439.

Hoinka, KP., 1998. Mean global surface pressure series evaluated from ECMWF reanalysis data. Quarterly Journal of the Royal Meteorological Society 124(551), 2291–2297.

Horrocks, M., Augustinus, P., Deng, Y., Shane, P., Andersson, S., 2005. Holocene vegetation, environment, and tephras recorded from Lake Pupuke, Auckland, New Zealand. New Zealand Journal of Geology and Geophysics 48, 85- 94.

Kaiser, DP., 2000. Decreasing cloudiness over China: an updated analysis examining additional variables. Geophysical Research Letters 27, 2193–2196.

Kalaycı Serdar , M. Cagatay Karabork , Ercan Kahya., 2004. Analysis of EL NINO signals on Turkish streamflow and precipitation pattern using spectral analydsis. Fresenius Environmental Bulletin 13(8), 719-725

Kane, R. P. and Teixeira. N R., 1991. Power Spectrum Analysis of the Annual Rainfall Series for Massachusetts (NE. U.S.A). Climatic Change 18, 89-94.

Kaufmann, R. K., Kauppi, H., Mann, M. L., & Stock, J. H., 2011. Reconciling anthropogenic climate change with observed temperature 1998–2008. Proceedings of the National Academy of Sciences 108(29), 11790-11793.

Kendall, M.G ., 1975. Rank Correlation Methods, Charles Griffin, London.

Kuttler, W. Weber, S. Schonnefeld, J and Hesselschwerdt, A., 2007. Urban/rural atmospheric water vapour pressure differences and urban moisture excess in Krefeld, Germany. International Journal of Climatology 27, 2005 – 2015.

Lamb, H., 1972. Climate: present, past and future. Fundamentals and Climate Now. Methuen: London, 240–250.

Livada, I., G. Charambous, and M.N. Assimakopoulos., 2008. Spatial and Temporal study of Precipitation Charactristics over Greece. Theoretical and Applied Climatology 93, 45-55.

Mann, H.B., 1945. Nonparametric Tests Against Trend, Econometrica 13, 245-259.

Mirzaee, S.Y.,Chitsazan, M and Chinipardaz, R., 2005. Application of Time Series Analysis in Determination of Lag Time in Jahanbin Basin. Water and Wastewater 15(3) - 53-59.

Mitchell, J. M., Jr., Chairman, B. Dzerdzeevskii, H. Flohn, W. L. Hofmeyr, H. H. Lmb. K. N. Rao and C .C. Wallen., 1966. Climatic Change- report of a working group of the commission for climatology. WMO Tech. Note 79, Geneva, 79.

Schickedanz, Paul, T and Bowen. E. G., 1977. The Computation of Climatological Power Spectra. Journal of Applied Meteorology 16, 359-367.

Selwam and R.Joshi., 1995. Universal Spectrum for Interannual Variability in Coads Global Air and Sea-Surface Temperature. International. J. Climatology 15, 613-623.

Shih, D.C.-F., Lee, C.-D., Chiou, K.-F., Tsai, S.-M., 2007. Spectral analysis of tidal fluctuations in ground water level. Journal of the American Water Resources Association 36, 1087 –1099.

Serrano, A. Mateos, V.L and Garcia, J.A., 1999. Trends analysis of monthly precipitation over the Iberian Peninsula for the period 1921-1995, phys. Chem. Earth (B), 24, 84-90.

Sorte, C. J., Jones, S. J., & Miller, L. P., 2011. Geographic variation in temperature tolerance as an indicator of potential population responses to climate change. Journal of Experimental Marine Biology and Ecology 400(1), 209-217.

Srinivas, V, V., 2013. Regionalization of Precipitation in India—A Review. Journal of the Indian Institute of Science 93(2), 153-162.

Stainforth, D. A., Chapman, S. C., & Watkins, N. W., 2013. Mapping climate change in European temperature distributions. Environmental Research Letters 8(3), 31-34.

Stephens, T., Atkin, D., Augustinus, P., Shane, P., Lorrey, A., Shane, P., Snowball, I., 2012. A late glacial Antarctic climate teleconnection and variable Holocene seasonality at Lake Pupuke, Auckland, New Zealand. Journal of Pale limnology 48, 785-800.

Sun Li, Sheen Banish, and Sui Bo., 2010. A Study on Water Vapor Transport and Budget of Heavy Rain in Northeast China.Advances in atmospheric sciences 27(6), 1399–1414.

Taghavi, F., M Nasseri,M., Bayat, B., Motevallian,S.S., Azadifard, D., 2012. The Identification of Climatic Patterns of Iran Based on Spectral Analysis and Clustering of Precipitation and Temperature Extreme Values. Physical Geography Research Physical Geography Research 43, 109-124.

Telesca, L; Vicente-Serrano, S. M and Lo´pez-Moreno. J. I., 2013. Power spectral characteristics of drought indices in the Ebro river basin at different temporal scales. Stoch Environ Res Risk Assess 27, 1155–1170.

Telemeco, R. S., Abbott, K. C., & Janzen, F. J., 2013. Modeling the Effects of Climate Change–Induced Shifts in Reproductive Phenology on Temperature-Dependent Traits. The American Naturalist 181(5), 637-648.

Tonkaz, T. Çetin, M. Şimşek, M., 2003. Observed changes of some climatic parameters of Şanlıurfa Province. Journal of Agricultural Science 18, 29–38.

Ware, D.M and Thomson, R, E., 2000, Interannual to Multidecadal Timescale Climate Variations in the Northeast Pacific, Journal of Climate 13, 3209-3220.

Yun-Ju. J, and Lee. J.Y., 2010. Time series analysis of hydrologic data obtained from a man-made undersea LPG cavern. Engineering Geology 113, 70–80.