The Application of Wavelet Analysis to Identify the Periodic Behavior of Droughts in Iran

جغرافیا و مخاطرات محیطی

Article 5, Volume 7, Issue 1 - Serial Number 25, January 2018, Pages 153-168   

Document Type: Research Article

DOI: 10.22067/geo.v7i1.60717

Authors

Peyman Mahmoudi^* ; Hojatolah Daneshmand

University of Sistan and Baluchestan

Abstract

1. Introduction
Variability of climatic phenomena is manifested in three intervals in the form of trend, oscillation, and fluctuation. Trend refers to the long-term behavior of time series, and fluctuation reflects the unique and unrepeatable behaviors. However, the distinctive aspect of oscillation is its recurring pattern. In this case, after a while the climatic system presents a configuration “similar” to the previous pattern with a slight difference. Many climate oscillations in a fixed time period (e.g., one year or less) are marked with monthly or seasonal periods. These cycles are called apparent cycles or periods. Many other climatic cycles are not manifested immediately, but rather occur gradually over time. This type of cycles is known as latent cycles.
Drought as a climatic phenomenon is considered as one of the most important natural disasters due to its special vastness and short-term and long-term economic, social, and environmental consequences. Thus, studies conducted to reveal the spatiotemporal behavior of this phenomenon and particularly periodicities can be very useful for predicting this phenomenon in the future. In addition, these studies can be used to identify the causes of drought.
Iran, a vast country with precipitation about one third of global precipitation, is located in arid and semi-arid areas of the world, and aridness is considered as its main natural and climatic characteristic. However, in recent years, precipitation anomalies are increased in different regions of Iran due to reasons often related to global climate changes, and also severe temporal and spatial fluctuations of drought have imposed huge damage to the economy of the country, especially in recent decades.

2. Materials and Methods
Iran is a geographically uneven country with the average height of the sea level of about 1250 meters. Owing to its geographical situation, climate of Iran is dependent on some elements of the general air circulation active in a special spatial and temporal range. Meanwhile, the subtropical high pressure system is the dominant phenomenon of warm period of the year in Iran which brings under its domination most parts of the country in the southern mountains of Alborz. Of course, the role of roughness height and latitude in the continuation of its dominance in warm period of the year appears important as well. In general, Iran’s temperature increases from north to south and west to east. The reason for this feature is the existence of highlands in north and west of the country as well as the gradual decrease of radiation angle towards more northern latitudes.
This study, attempts to identify the periodic behavior of droughts in Iran; therefore, a step can be taken toward more accurate prediction of this climate risk in future based on the knowledge achieved. To achieve this goal, the monthly precipitation data for a period of 41 synoptic stations for a period of 31 years (1983-2013) received from Iran meteorological organization was used. Then, to identify different degrees of drought, effective drought index (EDI) was used. The main purpose of this index is to assign a numerical value to each precipitation incident in monthly scale to be able to compare the areas with different climates. Then, to identify the dominant periods in drought time series of stations, wavelet transform is used. In this study, the frequency is shown as period per month. In other words, the period or cycle is measured in terms of months. Low frequencies represent the large periodic cycles, and high frequencies represent the small periodic cycles.

3. Results and Discussion
The results showed that the dominant periods in time series of effective drought index (EDI) in different stations of Iran vary significantly and include the period of 24-28 months to 210-220 months. Moreover, in a number of stations, dominant periodic intervals are seen, including the two period intervals of 108-120 and 60-80 months. However, in general, this behavior is not observed at all stations; in fact, we are faced with periodic variability in most stations. Except for the semi-northern half of the central Iran, the periodic interval is increasing in other parts. In other words, the periodic interval is opening. In the early periods, short-term periodic intervals are observed. However, periodic intervals are approaching to large ranges during the period and at the end of the series. Thus, throughout the country, it can be concluded that periodic intervals re oriented towards long-term periods with very mild slope. This can indicate that large-scale factors play an important role in the creation of periodic intervals, and the role of the regional factors is declining.

4. Conclusions
The results showed that periodic intervals were shorter at the beginning but longer at the end of time series. This suggests that the probability of the incidence of drought in Iran is increasing. Another result of this study showed that the interval between occurrence of droughts were longer at the beginning and then shorter at the end of times series. That is, the occurrence of droughts in Iran has long-term periods of return at the beginning and shorter ones at the end.

Keywords

drought; Wavelet transform; Frequency; Periodic interval

References

تقوی، فرناز؛ نیستانی، ابوافضل؛ محمدی، حسین؛ رستمی جلیلیان، شیما؛ 1390. کاربرد تحلیل موجک در شناسایی رفتار بارش در مناطق غربی ایران. مجله ژئوفیزیک ایران، شماره 2، 30-13.

جهانبخش، سعید؛ ساری صراف، بهروز؛ قائمی، هوشنگ؛ پوراصغر، فرناز؛ 1390. بررسی تأثیر پدیده دو قطبی دمایی اقیانوس هند بر تغییرپذیری بارش‌های فصلی استان‌های جنوبی کشور. فصلنامه تحقیقات جغرافیایی، شماره 4 (پیاپی 103)، 46-27.

جهانبخش، سعید؛ عدالت دوست، معصومه؛ 1387. تأثیر فعالیت‌های خورشیدی بر تغییرات بارندگی‌های سالانه ایران. فصلنامه تحقیقات جغرافیایی، شماره 1 (پیاپی 88)، 24-3.

حسن زاده، یوسف؛ عبدی کردانی، امین؛ فاخری فرد، احمد؛ 1391. پیش بینی خشکسالی با استفاده از الگوریتم ژنتیک و مدل ترکیبی شبکه عصبی-موجکی. نشریه آب و فاضلاب، شماره 3 (مسلسل 83)، 59-48.

خسروی، محمود؛ میردیلمی، سیده سمیرا؛ 1392. تأثیر فعالیت لکه‌های خورشیدی بر بارش استان گلستان (دوره آماری 1961 تا 2005. فصلنامه جغرافیا و برنامه ریزی محیطی، شماره 4 (پیاپی 52)، 46-33.

عراقی، علیرضا؛ موسوی بایگی، محمد؛ هاشمی نیا، سید مجید؛ 1393. به کارگیری تبدیل موجک گسسته برای تحلیل همبستگی بسامدی رطوبت نسبی. نشریه آبیاری و زهکشی ایران، شماره 2، 257-265.

مرتینز، آ؛ 1380. آنالیز سیگنال ویولت، فیلتر بانک، تبدیل‌های زمان فرکانس و کاربرد آنها. ترجمه محمد حسن مرادی، انتشارات دانشگاه صنعتی امیرکبیر.

نوشیروانی، نوشیروان؛ 1393. مطالعه تغییرات روزانه شدت خشکسالی در ایران. پایان نامه کارشناسی ارشد علوم محیط زیست، دانشکده علوم، دانشگاه زنجان، 98 ص.

Akhtari, R., Morid, S., Mahdian, MH., Smakhtin, V., 2009. Assessment of areal interpolation methods for spatial analysis of SPI and EDI drought indices. International Journal of Climatology 29: 135–145.

Byun, HR., Wilhite, DA., 1999. Objective quantification of drought severity and duration. Journal of Climate 12: 2747–2756.

Drago, AF., Boxall, SR., 2002. Use of the wavelet transform on hydro-meteorological data. Physics and Chemistry of the Earth, Parts A,B,C 27 (32-34): 1387-1399.

Emery, WJ., Thomson, RE., 2004. Data analysis method in physical oceanography. Elsevier, UK,638 pp.

Gamage, N., Blumen, W., 1993. Comparative analysis of low level cold fronts, Wavelet, Fourier, and empirical orthogonal function decompositions. Monthly Weather Review 121: 2867-2878.

Kalamaras, N., Michalopoulou, H., Byun, HR., 2010. Detection of drought events in Greece using daily precipitation. Hydrological Research 41 (2): 126–133.

Kulkarni, JR., 2000. Wavelet analysis of the association between the Southern oscillation and the Indian summer monsoon. International Journal of Climatology 20: 89-104.

Kuo, CC., Gan, TY., Yu, PS., 2010. Wavelet analysis on the variability, teleconnectivity, and predictability of the seasonal rainfall of Taiwan. Monthly Weather Review 138: 162-175.

Labat, D., Ababou, R., Mangin, A., 2000. Rainfall-runoff relations for karstic sorings, Part II: Continuous wavelet and discrete orthogonal multiresolution analyses. Journal of Hydrology 238: 149-178.

Morid, S., Smakhtin, V., Bagherzadeh, K., 2007. Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology 27: 2103–2111

Nakkan, M., 1999. Wavelet analysis of rainfall-runoff variability isolating climatic from anthropogenic patterns. Environmental Modelling and software 14: 283-295.

Pandey, RP., Dash, BB., Mishra, SK., Singh, R., 2008. Study of indices for drought characterization in KBK districts in Orissa (India). Hydrolological Process 22: 1895–1907.

Penalba, OC., Vargas, WM. 2004. Interdecadal and interannual variations of annual and extreme precipitation over central-northeastern Argentina. International Journal of Climatology 24: 1565-1580.

Roudier, P., Mahe, G., 2010. Study of water stress and droughts with indicators using daily data on the Bani river (Niger basin, Mali). International Journal of Climatology 30: 1689–1705

Smith LC., Turcotte DL., Isacks BL., 1998. Stream flow characterization and feature detection using a discrete wavelet transform. Hydrological process 23: 233-249.

Torrence C., Compo GP., 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorology Society 79: 61-78.

Toshie Kayano M., Sansigolo C., 2009. Interannual to decadal variations of precipitation and daily maximum and daily minimum temperature in Southern Brazil. Theoretical and Applied Climatology 97: 81-90.

Xingang D., Ping W., Jifan C., 2003. Multiscale characteristics of the rainy season rainfall and interdecadal decaying of summer monsoon in North China. Chinese Science Bulletin: 48: 2730-2734.

Yan Z, Tsimplis MN., Woolf D., 2004. Analysis of the relationship between the North Atlantic oscillation and sea level changes in northeast Europ. International Journal of Climatology 24: 743-758.