تحليل روند تغييرات بلندمدت شاخص تراکم بارش (PCI) روزانه به عنوان شاخصي از تغيير اقليم در حوضه آبريز خليج فارس و درياي عمان
محورهای موضوعی : خشکسالی در هواشناسی و کشاورزی
پیمان محمودی
1
(دانشيار، گروه جغرافياي طبيعي، دانشگاه سيستان و بلوچستان، زاهدان، ايران.)
ابراهیم فتاحی
2
(دانشيار، پژوهشگاه هواشناسي و علوم جو، تهران، ايران)
محسن حیدری
3
(دانش آموخته کارشناسي ارشد مهندسي کشاورزي، اداره کل هواشناسي استان سيستان و بلوچستان، زاهدان، ايران. )
اله بخش ریگی
4
(دانش آموخته کارشناسي ارشد رياضي، اداره کل هواشناسي استان سيستان و بلوچستان، زاهدان، ايران.)
علیرضا قائمی
5
(دانش آموخته دکتراي مهندسي عمران-مديريت منابع آب، گروه مهندسي عمران، دانشگاه سيستان و بلوچستان، زاهدان، ايران.)
جبار رضایی
6
(دانشجوي دکتراي اقليم شناسي، گروه جغرافيا، دانشگاه زنجان، زنجان، ايران.)
کلید واژه: شاخص تراکم بارش (PCI), تخمينگر شيب سن, تغيير اقليم, بارش, روند,
چکیده مقاله :
زمينه و هدف: حوضه آبريز خليج فارس و درياي عمان به دليل موقعيت خاص جغرافيايي- اقليمي و قرارگرفتن در پهنه خشک و نيمه خشک کره زمين، در مواجه با ناهنجاريهاي اقليمي به شدت آسيب‌پذير است به طوري که يکي از مهمترين عناصر اقليمي آن يعني بارش، تغيير پذيري شديدي از خود نشان مي‌دهد. به طوريکه در جايي سبب بروز خشکسالي‌هاي ممتد و در مکان ديگر مسبب سيل و طغيان رودخانه‌ها مي‌شود. لذا لزوم بررسي و پيش بيني تغييرات اقليمي در اين حوضه آبريز، جهت کاهش خطرات و زيان‌هاي احتمالي در سطوح مختلف اجتماعي، اقتصادي و زيست محيطي از اهميت خاصي برخوردار است.
روش پژوهش: در اين پژوهش از دادههاي بارش روزانه 47 ايستگاه هواشناسي براي يک بازه زماني 30 ساله (2022-1993) که از سازمان هواشناسي ايران دريافت شد، استفاده گرديد. بعد از اخذ داده‌ها و تشکيل بانک اطلاعاتي آنها، شاخص تراکم بارش (PCI) روزانه براي تمامي ايستگاه‌هاي مورد مطالعه محاسبه شد. مقدار شاخص تراکم بارش (PCI) روزانه عددي در بازه صفر و يک است. هر چقدر مقدار شاخص تراکم بارش (PCI) به عدد يک نزديک‌تر باشد نشان دهنده تمرکز مقدار زياد بارندگي در تعداد روزهاي محدود است که احتمال وقوع سيل و بارندگي‌هاي شديد در اين مناطق بيشتر مي‌شود. در نهايت با استفاده از تخمينگر شيب سن روند تغييرات بلندمدت آنها مورد تحليل قرار گرفتند.
يافته‌ها: ميانگين بلندمدت مقادير شاخص تراکم بارش (PCI) روزانه براي حوضه آبريز خليج فارس و درياي عمان نشان از بالا بودن اين شاخص در حوضه آبريز مورد مطالعه دارد. بالاترين مقادير اين شاخص متعلق به نوار ساحلي جنوب تا جنوب شرق از بوشهر تا چابهار و پايين‌ترين آن نيز در غرب و جنوب غرب حوضه آبريز از آبادان تا پيرانشهر مشاهده مي‌شود. نتايج تحليل روند اين شاخص نشان داد که کل حوضه آبريز به استثناي چند ايستگاه محدود (بروجن، جاسک و مسجد سليمان) که روند آنها منفي مي‌باشد بقيه ايستگاه‌هاي واقع در حوضه آبريز تحت سيطره روندهاي افزايشي بوده‌اند. لذا شيب روند تغييرات افزايشي حاکي از تمرکز بارش‌ها در تعداد روزهاي بارشي کمتر است است که اين مي‌تواند باعث افزايش روزهاي همراه با بارش‌هاي سنگين و سيل آسا در اين حوضه آبريز باشد.
نتايج: نتايج تحليل روند اين شاخص نشان داد که بيشتر مساحت حوضه آبريز مورد مطالعه داراي روند افزايشي بوده‌اند يعني بارش‌ها در تعداد روزهاي بارشي کمتري تمرکز پيدا کرده‌اند. اين روند افزايشي که مي‌تواند ناشي از افزايش خشکسالي‌ها در اين حوضه آبريز باشد مي‌تواند وقوع بارش‌هاي سيل‌آسا را در داخل اين حوضه آبريز تشديد کند. لذا تغييرات اين شاخص در زير حوضه‌هاي بندرعباس-سديج، بلوچستان جنوبي، کارون
Background and Aim: The Persian Gulf and Gulf of Oman basin, due to its specific geographic-climatic position and location in the arid and semi-arid regions of the globe, is highly vulnerable to climatic anomalies, such that one of its most important climatic elements, precipitation, exhibits severe variability. This leads to prolonged droughts in some areas and causes floods and river overflows in other locations. Therefore, the study and prediction of climatic changes in this basin is crucial for reducing potential hazards and damages at various social, economic, and environmental levels.
Method: In this research, daily precipitation data from 47 meteorological stations over 30 years (1993-2022) were obtained from the Iranian Meteorological Organization. After acquiring the data and creating a database, the daily Precipitation Concentration Index (PCI) was calculated for all studied stations. The PCI value is a number between zero and one. The closer the PCI is to one, the higher the concentration of precipitation within a limited number of days, increasing the likelihood of floods and heavy rainfall events in those areas. Ultimately, the long-term trends of the PCI were analyzed using Sen's slope estimator.
Results: The long-term average of the daily PCI values for the Persian Gulf and Gulf of Oman basin indicates a high level of this index in the studied basin. The highest values of this index are observed along the southern to southeastern coastal strip from Bushehr to Chabahar, while the lowest values are found in the western and southwestern parts of the basin from Abadan to Piranshahr. The trend analysis results showed that, except for a few stations) Brujen, Jusk, Masjed Soleiman) with negative trends, the entire basin has been dominated by increasing trends. The increasing trend slope indicates that precipitation is concentrated within fewer rainy days, which could lead to an increase in heavy rainfall and flood events within this basin.
Conclusion: The PCI trend analysis results showed that most of the studied basin area has experienced increasing trends, meaning that precipitation has become concentrated within fewer rainy days. This increasing trend, which could be due to an increase in droughts in this basin, may exacerbate the occurrence of flood-like rainfall events within the basin. Therefore, the changes of this index in the sub-basins of Bandar Abbas-Sadij, South Baluchistan, Karun and Western Marzi have been statistically significant. This trend highlights the need for serious attention to flood, drought, and other hydroclimatological hazard management in this basin.
Alijani, B., Mahmoudi, P., & Chogan, A. J. (2012) A Study of annual and seasonal precipitation trends in Iran using a nonparametric method (Sen’s slope estimator). Journal of Climate Research, 3(6), 23–42. [In Persian]
Asakereh, H., Tarkarani, F., & Soltani, S. (2013). On Tempo-Spatial Characters of Extreme Daily Precipitation of Northwest of Iran. Iran-Water Resources Research, 8(3): 39-53. [In Persian] ]
Bhaga, T. D., Dube, T., Shekede, M. D., & Shoko C. (2020). Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review. Remote Sensing, 12(24): 4184. https://doi.org/10.3390/rs12244184]
Choobeh, S., Abghari, H., & Erfanian, M. (2023). Investigating and Classifying Temporal-Spatial Characteristics of Iran’s Annual Precipitation Using Maximal Overlap Discrete Wavelet Transform and Multiscale Entropy. Desert Ecosystem Engineering, 12(38): 11-26. [In Persian] ]
Cortesi, N., Gonzalez-Hidalgo, J. C., Brunetti, M., & Martin-Vide, J. (2012). Daily precipitation concentration across Europe 1971–2010. Natural Hazards and Earth System Sciences, 12: 2799–2810. https:// doi.org/ 10. 5194/nhess- 12- 2799- 2012]
Darand, M., & Pazhoh, F. (2022). Spatiotemporal changes in precipitation concentration over Iran during 1962–2019. Climatic Change, 173: 25. https://doi.org/10.1007/s10584-022-03421-z]
De Luis, M., González-Hidalgo, J. C., Brunetti, M., & Longares, L. A. (2011). Precipitation concentration changes in Spain 1946–2005. Natural Hazards and Earth System Sciences, 11: 1259–1265. https://doi.org/10.5194/nhess-11-1259-2011, 2011.
Guo, E., Wang, Y., Jirigala, B., & Jin, E. (2020). Spatiotemporal variations of precipitation concentration and their potential links to drought in mainland China. Journal of Cleaner Production, 267: 122004. https://doi.org/10.1016/j.jclepro.2020.122004
Halabian, A. H. (2022). Assesment of Spatial- Temporal Changes of Precipitation in Iran. Desert Ecosystem Engineering, 5(13): 101-116. [In Persian]
IPCC. (2014). Climate Change 2014. Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.
Kaboli, S., Hekmatzadeh, A., & Torabihaghighi, A. (2020). Determination of spatio-temporal pattern of daily precipitation concentration index over Iran. Water Resources Engineering, 13(46): 57-68. [In Persian]
Kabir, M., Habiba, U. E., Khan, W., Shah, A., Rahim, S., De los Rios-Escalante, P. R., Farooqi, Z. U. R., Ali, L., & Shafiq, M. (2023). Climate change due to increasing concentration of carbon dioxide and its impacts on environment in 21st century; a mini review. Journal of King Saud University – Science, 35(5): 102693. https://doi.org/10.1016/j.jksus.2023.102693
Khalili, K., Nazeri Tahrudi, M., Mirabbasi Najaf Abadi, R., & Ahmadi, F. (2016). Investigation the Concentration and Trend of Winter Precipitation of Iranian Border Stations over the Last Half Century. Water and Soil, 30(4): 1309-1321. [In Persian]
Khezerluei Mohammadyar, Z., & Alijani, B. (2024). An Analysis of precipitation Periods trend in Iran. Journal of Applied researches in Geographical Sciences, 24 (73): 521-539. [In Persian]
Lu, Y., Jiang, S., Ren, L., Zhang, L., Wang, M., Liu, R., & Wei, L. (2019). Spatial and Temporal Variability in Precipitation Concentration over Mainland China, 1961–2017. Water, 11(5): 881. https://doi.org/10.3390/w11050881
Martin-Vide, J. (2004). Spatial distribution of a daily precipitation concentration index in peninsular Spain. International Journal of Climatology, 24(8): 955–971. https://doi.org/10. 1002/ joc. 1030
Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of hydrology, 391(1–2): 202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012
Mondol, A. H., Mamun, A., Iqbal, I., & Jang, D. (2018). Precipitation concentration in Bangladesh over different temporal periods. Advances Meteorology, 2018: 1–18. https://doi.org/ 10.1155/ 2018/ 18490 50
Nazeri, M., Khalili, K., & Behmanesh, J. (2016). Investigating Changes of Seasonal Precipitation Concentration of Iran in Recent Half-Century. Water and Soil Science, 26(2-2): 111-123. [In Persian]
Royé, D., & Martin-Vide, J. (2017). Concentration of daily precipitation in the contiguous United States. Atmospheric Research, 196: 237-247. https://doi.org/10.1016/j.atmosres.2017.06.011
Rui, Z., Aoqi, L., Taotao, C., Guimin, X., Qi, W., & Daocai, C. (2020). Analysis of precipitation concentration degree changes and its spatial evolution in the western plain of Jilin Province. Mausam, 71(2): 291–298. https://doi.org/10.54302/mausam.v71i2.27.
Sanaei, M., Barati, G., & Shakiba, A. (2024). Spatial change analysis, Analysis of Spatial patterns changes in rainy season over Iran. Journal of Applied researches in Geographical Sciences, 24 (72): 19. [In Persian]
Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324): 1379-1389.
Serrano-Notivoli, R., Martín-Vide, J., Saz, M., Longares, L., Beguería, S., Sarricolea, P., Meseguer-Ruiz, O., & de Luis, M. (2018). Spatio-temporal variability of daily precipitation concentration in Spain based on a high-resolution gridded data set. International Journal of Climatology, 38(51): e518–e530. https://doi.org/10.1002/joc.5387
Suhaila, J., & Jemain, A. A. (2012). Spatial analysis of daily rainfall intensity and concentration index in Peninsular Malaysia. Theoretical and Applied Climatology, 108(1-2): 235-245. https://doi.org/10.1007/s00704-011-0529-2
Tabari, H. (2020). Climate change impact on flood and extreme precipitation increases with water availability. Scientific Reports, 10: 13768. https://doi.org/10.1038/s41598-020-70816-2
Theil, H. (1950). A rank-invariant method of linear and polynomial regression analysis. Indagationes Mathematicae, 1(2): 85–89.
Tolika, K. (2019). On the analysis of the temporal precipitation distribution over Greece using the Precipitation Concentration Index (PCI): annual, seasonal, monthly analysis and association with the atmospheric circulation. Theoretical and Applied Climatology, 137: 2303–2319. https://doi.org/10.1007/s00704-018-2736-6
Vyshkvarkova, E., Voskresenskaya, E., & Martin-Vide, J. (2018). Spatial distribution of the daily precipitation concentration index in Southern Russia. Atmospheric research, 203: 36-43. https://doi.org/10.1016/j.atmosres.2017.12.003
Yang, X., Liao, X., Di, D., & Shi, W. (2023). A Review of Drought Disturbance on Socioeconomic Development. Water, 15(22): 3912. https://doi.org/10.3390/w15223912
Zhang, L. J., & Qian, Y. P. (2003). Annual distribution features of precipitation in China and their interannual variations. Journal of Meteorological Research, 17(2): 146–163.
Zhang, K., Yao, Y., Qian, X., Wang, J. (2019). Various characteristics of precipitation concentration index and its cause analysis in China between 1960 and 2016. International Journal of Climatology, 39: 4648–4658. https://doi.org/10.1002/joc.6092
