نیوار

نیوار

کاربرد تحلیل طیفی برای تعیین بسامد غالب بارش روزانه در حوضه آبریز دریاچه ارومیه

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری ، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه شهرکرد
2 استادیار گروه مهندسی آب، دانشکده کشاورزی، دانشگاه شهرکرد
3 دانشیار،گروه مهندسی آب، دانشکده کشاورزی، دانشگاه شهرکرد
10.30467/nivar.2026.530926.1341
چکیده
شناخت رفتار بارش در بلندمدت به‌عنوان یکی از متغیرترین عناصر اقلیمی، نقش کلیدی در مدیریت منابع آب و کاهش آسیب‌پذیری بخش‌های کشاورزی و منابع طبیعی دارد. از آنجا که تغییرات زمانی و مکانی بارش می‌تواند پیامدهای اقتصادی، اجتماعی و زیست‌محیطی گسترده‌ای به همراه داشته باشد، به‌کارگیری روش‌های نوین مانند تحلیل طیفی برای شناسایی الگوها و چرخه‌های بارش ضرورت دارد. در این پژوهش، با استفاده از تحلیل طیفی، فرکانس‌های غالب بارش روزانه در حوضه آبریز دریاچه ارومیه بررسی شد. داده‌های روزانه 11 ایستگاه منتخب در این حوضه برای یک دوره آماری حداقل بیست ‌ساله (1970 تا 2022) تحلیل و به صورت جزئی‌تر در سه ایستگاه تبریز، کهریز و مهاباد (دارای پراکندگی مناسب در اطراف دریاچه ارومیه) رفتار بارشی در سه وضعیت نرمال، خشکسالی و ترسالی به تفصیل مورد بررسی قرار گرفت. روش تحقیق مبتنی بر تحلیل طیفی با استفاده از سری فوریه است که بر روی داده‌های بارش روزانه 11 ایستگاه منتخب با دوره آماری حداقل 20 سال اعمال شد. داده‌ها پس از گردآوری از سازمان هواشناسی، کنترل کیفی و انتخاب سال‌های تر و خشک بر اساس شاخص SPI، مورد تجزیه و تحلیل قرار گرفتند. نتایج نشان داد چرخه‌های سالانه و نیم‌سالانه در بیشتر ایستگاه‌ها نقش اصلی را در توزیع زمانی بارش دارند، اما شدت و پایداری آن‌ها بسته به موقعیت جغرافیایی متفاوت است. در شرایط نرمال، ایستگاه‌ها بارش‌های منظم و متمرکز دارند، در حالی که در خشکسالی چرخه‌ها پراکنده و تضعیف می‌شوند و در ترسالی چرخه‌های بلندمدت با دامنه قوی‌تر ظاهر شده و انسجام بارش افزایش می‌یابد. بررسی دوره‌های بازگشت نشان داد که خشکسالی در اکثر 11 ایستگاه به‌صورت سالانه تکرار می‌شود، در حالی که ترسالی‌ها دارای الگوی زمانی متنوع‌تری هستند و می‌توانند حتی در بازه‌های کوتاه چندروزه نیز ظاهر شوند. این تفاوت در توزیع زمانی، به‌ویژه در ایستگاه‌هایی که چرخه‌های کوتاه‌تر دارند، نشانه‌ای از اقلیم ناپایدارتر و حساسیت بیشتر به تغییرات اقلیمی است. نتایج این مطالعه بر ضرورت مدیریت منابع آب متناسب با ویژگی‌های فرکانسی هر ناحیه تأکید دارد و نشان می‌دهد که تحلیل طیفی می‌تواند به عنوان ابزار تصمیم‌یار در طراحی استراتژی‌های مقابله با خشکسالی و بهینه‌سازی ذخیره‌سازی آب به‌کار رود.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Application of Spectral Analysis for Determination of Daily Rainfall Frequency in the Urmia Lake Basin

نویسندگان English

Shima Kabiri 1
Mohammadali Nasresfahani 2
Ahmadreza Ghasemi Dastgerdi 3
1 PhD student, Department of Water Engineering, Faculty of Agriculture, Shahrekord University
2 Assistant Professor, Department of Water Engineering, Faculty of Agriculture, Shahrekord University
3 Associate Professor, Department of Water Engineering, Faculty of Agriculture, Shahrekord University
چکیده English

Understanding long-term precipitation behavior, as one of the most variable climatic elements, plays a crucial role in water resource management and in reducing the vulnerability of agricultural and natural resource sectors. Since temporal and spatial variations in precipitation can lead to wide-ranging economic, social, and environmental consequences, the application of modern approaches such as spectral analysis to identify rainfall patterns and cycles is essential. In this study, spectral analysis was employed to investigate the dominant frequencies of daily precipitation within the Lake Urmia basin. Daily data from 11 selected meteorological stations across the basin, covering a minimum 20-year statistical period (1970–2022), were analyzed. Furthermore, three representative stations—Tabriz, Kahriz, and Mahabad—located at different positions around Lake Urmia, were examined in greater detail to assess precipitation behavior under normal, drought, and wet conditions. The research methodology was based on spectral analysis using the Fourier series, applied to daily precipitation data from the 11 stations with at least 20 years of records. After data collection from the Meteorological Organization, quality control procedures were carried out, and wet and dry years were identified using the Standardized Precipitation Index (SPI). The results revealed that annual and semi-annual cycles play a primary role in the temporal distribution of rainfall across most stations; however, their intensity and persistence vary depending on geographical location. Under normal conditions, stations exhibit relatively regular and concentrated precipitation patterns. During drought periods, cycles become weakened and more scattered, whereas in wet years, stronger long-term cycles emerge, leading to more coherent precipitation behavior. Return period analysis indicated that drought tends to recur annually in most of the 11 stations, while wet conditions display more diverse temporal patterns, sometimes appearing over short multi-day intervals. This variability in temporal distribution, particularly in stations with shorter cycles, reflects a more unstable climate and greater sensitivity to climatic fluctuations. The findings highlight the importance of tailoring water resource management strategies to the specific frequency characteristics of each region and demonstrate that spectral analysis can serve as a valuable decision-support tool for drought mitigation and optimizing water storage planning.

کلیدواژه‌ها English

Spectral Analysis
Urmia Lake
Draught
Wet Spell
Rainfall
1.       Ahmadi O, Alamdari P, Servati M, Khoshzaman T, Shahbaee kootenaee A. Assessment and Analysis of Temperature and Precipitation Return Periods Using Spectral Analysis and its Effect on Land Management (A Case Study: Khodaafarin Area, East Azerbaijan). 2019. jwss; 23 (1) :223-235. (In Persian)
2.       Amir Ahmadi A., Abbasnia M. (2022). Regionalization of Climate at Isfahan Province by Using New Statistical Techniques. Journal of Arid Regions Geographic Studies. 1(1): 53-68. (In Persian)
3.       Bahoushi A., Shayan S. (2010). Analyzing the intensity, duration, frequency, and extent of droughts in the Lake Urmia basin using the standard precipitation index. National Conference on Water Crisis in Agriculture and Natural Resources،Tehran. (In Persian)
4.       Alijani, B., Bayat, A., Dostkamyan, M., & Bolyani, Y. (2011). Spectral Analysis of Iran's Annual Precipitation Time Series. Journal of Physical Geography Research, 43(75), 1-18. (In Persian)
5.       Asgari, A., & Rahimzadeh, F. (2003). The Prominence of Precipitation Fluctuations in the Country Relative to its Trend and Shift. In The Third Regional Conference on Climate Change. (In Persian)
6.       Daneshmand, H., & Mahmoudi, P. (2016). Spectral Analysis of Droughts in Iran. Journal of Climate Research, 7(25), 1-16. (In Persian)
7.       Fanai, Z. (2017). Investigating the Effects of Lake Urmia Drought on the Vulnerability of the Natural and Human Environment of the Surrounding Area. Environmental and Trans-Sectoral Development, (2), 1-16. (In Persian)
8.       Iran Meteorological Organization. (2016). Daily Precipitation Statistics of Lake Urmia Basin Stations. Tehran: Iran Meteorological Organization Publications. (In Persian)
9.       Iran Water Resources Management Company, West Azerbaijan Regional Water Authority. (2019). Report on the Status of Water Resources in Lake Urmia Basin. Urmia: West Azerbaijan Regional Water Authority Publications. (In Persian)
10.   Jalili, S. (2010). Spectral Analysis of Lake Urmia Water Level Time Series and the Impact of Climatic and Hydrological Variables on It (Doctoral dissertation). Tarbiat Modares University, Tehran, Iran. (In Persian)
11.   Karimi, V., & Akbari Nodehi, D. (2019). Comparison of Meteorological Drought Indices in Mazandaran Province. Nivar, 43 (107), 79-91. (In Persian)
12.   Mirmousavi, S. H., Dostkamyan, M., & Sotoudeh, F. (2016). Investigation and Analysis of the Spatial Pattern of Decadal Changes in Heavy and Very Heavy Rainfall in Iran. Geography and Environmental Planning Journal, (3), 85-87. (In Persian)
13.   Moghbel, M., Davoodi, M., Naeistani, A., & Taghavi, F. (2011). Detection of Precipitation Regime Changes in Iran in Recent Decades. Nivar, 35 (73), 55-66. (In Persian)
14.   National Drought and Crisis Management Center. (2018). Drought Analysis Report of Lake Urmia Basin. Tehran: Ministry of Energy. (In Persian)
15.   Razmi Janayar, T., & Talshm Kael, L. (2012). Investigation of Probability Distribution and Return Period of Maximum 24-Hour Precipitation and Peak Discharge in the Southeastern Region of Lake Urmia. The First National Conference on Tourism and Ecotourism of Iran, Hamadan, Iran. (In Persian)
16.   Rezaei Banafsheh, M., Najafi, M. S., Naghizadeh, H., & Abkharabat, S. (2014). Analysis of Extreme Precipitation Behavior in Relation to Factors Affecting Precipitation in the West and Northwest of Iran. Geography and Development, 4 (1), 1-20. (In Persian)
17.   Taghavi, F., Naeistani, A., Mohammadi, H., & Rostami Jalilian, S. (2012). Application of Wavelet Analysis in Identifying Precipitation Behavior in Western Regions of Iran. Iranian Journal of Geophysics, *6*(2), 1-15. (In Persian)
18.   Water Research Institute. (2017). Hydro-Climatic Analysis of Lake Urmia Basin. Tehran: Ministry of Energy. (In Persian)
19.   West Azerbaijan Provincial Management and Planning Organization. (2020). Comprehensive Plan for Water Resources Management of Lake Urmia Basin. Urmia: West Azerbaijan Governorate Publications. (In Persian)
20.   Zolghadr, M., Sanaee, B., & Ghaffarian, P. (2013). Investigating the Relationship Between Dry and Wet Periods in Lake Urmia Basin with the Teleconnection Pattern of the North Atlantic Oscillation. Journal of Oceanography, 4 (13), 23-34. (In Persian)
21.   Ahmadi, O., Alamdari, P., Servati, M., Khoshzaman, T., & Shahbaee Kootenaee, A. (2019). Assessment and Analysis of Temperature and Precipitation Return Periods Using Spectral Analysis and its Effect on Land Management (A Case Study: Khodaafarin Area, East Azerbaijan). Journal of Water and Soil Science, 23, 223-235.
22.   Akter, N., Islam, M. R., Karim, M. A., Miah, M. G., & Rahman, M. M. (2023). Spatiotemporal rainfall variability and its relationship to flash flood risk in Northeastern Sylhet Haor of Bangladesh. Journal of Water and Climate Change, 14(11), 3985-3999.
23.   Ddou, R., Hanchane, M., Krakauer, N. Y., Kessabi, R., Obda, K., Souab, M., & Achir, I. E. (2023). Wavelet Analysis for Studying Rainfall Variability and Regionalizing Data: An Applied Study of the Moulouya Watershed in Morocco. Applied Sciences, 13, 3841.
24.   Deng, Y., Wang, X., Ruan, H., Lin, J., Chen, X., Chen, Y., ... & Deng, H. (2024). The magnitude and frequency of detected precipitation determine the accuracy performance of precipitation data sets in the high mountains of Asia. Scientific Reports, 14(1), 17251.
25.   Ilyés, C., Mohammed, M. A., Szabó, N. P., & Szűcs, P. (2025). A hybrid approach to exploring the spatiotemporal patterns of precipitation in Sudan: Insights from neural network clustering and Fourier-wavelet transform analysis. Water Cycle.
26.   Jahangiri, E., Motamedvaziri, B., & Kiadaliri, H. (2024). Investigating the Impact of Climate Change on Drought with SPI and SPEI Indices (Case Study of Karun 3 Watershed). Iranian Journal of Watershed Management Science & Engineering, 18, 85-97.
27.   Jou, P. H., & Mirhashemi, S. H. (2023). Frequency analysis of extreme daily rainfall over an arid zone of Iran using Fourier series method. Applied Water Science, 13(1), 16.
28.   Koutsoyiannis, D., Kozonis, D., & Manetas, A. (1998). A mathematical framework for studying rainfall intensity-duration-frequency relationships. Journal of Hydrology, 206, 118-135.
29.   Mckee, T. B., Doesken, N. J., & Kleist, J. R. (1993). The Relationship of Drought Frequency and Duration to Time Scales. In Proceedings of the 8th Conference on Applied Climatology (pp. 179-184). Anaheim, CA: American Meteorological Society.
30.   Moustakis, Y., Onof, C. J., & Paschalis, A. (2020). Atmospheric convection, dynamics and topography shape the scaling pattern of hourly rainfall extremes with temperature globally. Communications Earth & Environment, 1(1), 11.
31.   Nazeri Tahroudi, M. (2025). Comprehensive global assessment of precipitation trend and pattern variability considering their distribution dynamics. Scientific Reports, 15(1), 22458.
32.   Thomas, E., Joseph, I., & Abraham, N. (2023). Wavelet analysis of annual rainfall over Kerala and sunspot number. New Astronomy, 98, 101944.
33.   Zakaria, S. M., Huma, S., Hafeez, M., & Nadeem, S. (2022). Identification of precipitation zones in Pakistan by Global Wavelet Power Spectrum. Journal of Earth and Space Science, 9(3), 124-138.

مقالات آماده انتشار، پذیرفته شده
انتشار آنلاین از 20 بهمن 1404

  • تاریخ دریافت 01 تیر 1404
  • تاریخ بازنگری 15 دی 1404
  • تاریخ پذیرش 20 بهمن 1404
  • تاریخ انتشار 20 بهمن 1404