Trend Analysis of Precipitation Extreme Indices in Iran Based on Quantile Regression Model
Document Type : Research Paper
Authors
- 1 Ph.D. Scholar, Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran
- 2 Assoc. Professor, Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran
- 3 Assoc. Professor, Department of Geography, Faculty of Literature and Humanities, Urmia University, Urmia, Iran
- 4 Assoc. Professor, Department of Civil and Environmental Engineering, Western University, London, Canada
Abstract
The incidence of climate events such as droughts and floods in any given region is intricately tied to the temporal and spatial distribution of precipitation. In hydrological modeling, precise analyses of precipitation trends and extreme precipitation indices hold significant importance. This study aims to examine the trends in annual precipitation averages and extreme precipitation indices using a quantile regression (QR) model across 39 synoptic stations in Iran over a 50-year statistical period (1972-2021). Iran experienced its highest annual precipitation average in 1982, reaching 491.6 mm, while the lowest was recorded in 2021 at 218.3 mm. The quantile regression model analysis revealed a downward trend in Iran's annual precipitation averages across the 0.05, 0.5, and 0.95 quantiles, with significance levels of 0.1, 0.05, and 0.01, respectively. Extreme precipitation indices in the northern and western parts of Iran were notably higher than in other regions. The R10 and R20 indices also represent the number of days with at least 10 mm and 20 mm of precipitation, respectively. They show a decreasing trend in northern and northwestern Iran at significance levels of 0.1, 0.05, and 0.01. These trend analyses offer valuable insights into annual precipitation averages and extreme indices, aiding water resources.
Keywords
Subjects
Hydrology
Abbas, S. A., Xuan, Y., & Song, X. (2019). Quantile regression-based methods for investigating rainfall trends associated with flooding and drought conditions. Water. Resour. Manag. 33(12), 4249–4264. DOI: 10.1007/s11269-019-02362-0.
Akbary, M. (2015). Combinatory Mediterranean-Sudanese systems role in the occurrence of heavy rainfalls (case study: South west of Iran. Meteorol. Atmospher. Phys. 127, 675–683. DOI: 10.1007/s00703-015-0389-x.
Babaei, O., Ghasemi, E., & Fatahi, F. (2014). Assessment of climate change effects on extreme precipitation indices over Iran. J. Spat. Assess. Nat. Hazard. 1(3), 85–103.
Bazrafshan, O., Zamani, H., & Shekari, M. A. (2020). copula-based index for drought analysis in arid and semi-arid regions of Iran. Nat. Resour. Mode. 33(1). DOI: 10.1111/nrm.12237.
Caloiero, T., Coscarelli, R., & Ferrari, E. (2018). Application of the Innovative Trend Analysis Method for the Trend Analysis of Rainfall Anomalies in Southern Italy. Water. Resour. Manag. 32(15), 4971–4983. DOI: 10.1007/s11269-018-2117-z.
Cheng, Q., Gao, L., Zuo, X., & Zhong, F. (2019). Statistical analyses of spatial and temporal variabilities in total, daytime, and nighttime precipitation indices and of extreme dry/wet association with large-scale circulations of Southwest China, 1961–2016. Atmos. Res. 219: 166–182. DOI: 10.1016/j.atmosres.2018.12.033.
Dabanlı, İ., Şen, Z., Yeleğen, M., Şişman, E., Selek, B., & Güçlü, Y. (2016). Trend assessment by the innovative-Şen method. Water. Resour. Manag. 30(14), 5193–5203. DOI: 10.1007/s11269-016-1478-4.
Dinpashoh, Y., Mirabbasi, R., Jhajharia, D., Abianeh, H. Z., & Mostafaeipour, A. (2014). Effect of short-termandlong term persistence on identification of temporal trends. J. Hydrol. Eng. 19(3), 617–625. DOI: 10.1061/(ASCE)HE.1943-5584.00008.
Fan, L. (2014). Quantile trends in temperature extremes in China. Atmos. Ocean. Sci. Lett. 7(4), 304–308. DOI: 10.1080/16742834.2014.11447180
Fathian, F., Ghadami, M., Haghighi, P., Amini, M., Naderi, S., & Ghaedi, Z. (2020). Assessment of changes in climate extremes of temperature and precipitation over Iran. Theor. Appl. Climatol .141, 1119–1133. DOI: 10.1007/s00704-020-03269-2.
Ghasemi, A. R., & Khalili, D. (2008). The association between regional and global atmospheric patterns and winter precipitation in Iran. Atmos. Res. 88(2), 116–133. DOI: 10.1016/j.atmosres.2007.10.009.
Greenville, A. C., Glenda, M., Wardle, G. M., & Dickman, C. R. (2012). Extreme climatic events drive mammal irruptions: regression analysis of 100-year trends in desert rainfall and temperature. Ecol. Evol. 2(11), 2645–2658. DOI: 10.1002/ece3.377.
Hedjazizadeh, Z., Halabian, A. H., Karbalaee, A., & Toulabi, M. (2020). Detection of extreme values variations of precipitation over Iran. JNEH. 9(23), 135-150. DOI: 10.22111/JNEH.2019.29874.1519.
International Panel on Climate Change, IPCC (1996). Climate Change 1995: The Science of Climate. Cambridge University Press. 588 pp. Available online at: https://www.ipcc.ch/site/assets/uploads/2018/02/, Accessed 7, April 2024.
Jamali, M., Gohari, A., Motamedi, A., & Haghighi, A. T. (2022). Spatiotemporal changes in air temperature and precipitation extremes over Iran. Water. 14(21), 3465. DOI: 10.3390/w14213465.
Javari, M. (2016). Trend and homogeneity analysis of precipitation in Iran. Clim. 4(3), 44. DOI: 10.3390/cli4030044.
Klein Tank, A. M. G., Zwiers, F. W., & Zhang, X. (2009). Guidelines on Analysis of Extremes in a Changing Climate in Support of Informed Decisions for Adaptation, WMO-TD; No. 1500/WCDMP-No. 72; World Meteorological Organization: Geneva, Switzerland. p. 52.
Koenker, R. (2005). Quantile regression. Cambridge University Press, Cambridge.
Koenker, R., $ Bassett, G. (1978). Regression quantiles. Econometrica. 46, 33–50. DOI: 10.2307/1913643.
Lee, K., Baek, H. J., & Cho, C. (2013). Analysis of changes in extreme temperatures using quantile regression. Asia. Pac. J. Atmos. Sci. 49(3): 313–323. DOI: 10.1007/s13143-013-0030-1.
Lucas, E. W. M., de Sousa, F. D. A. S., dos Santos Silva, F. D., da Rocha Júnior, R. L., Pinto, D. D. C., & da Silva, V.D.P.R. (2021). Trends in climate extreme indices assessed in the Xingu River basin-Brazilian Amazon. Weather. Clim. Extrem. 31. 100306. DOI: 10.1016/j.wace.2021.100306.
Mohammadi, H., Azizi, Gh., khoshahklagh, F., & Ranjbar, F. (2017). Analysis of Daily Precipitation Extreme Indices Trend in Iran. Phys. Geogr. 49(1), 21-37. DOI: 10.22059/JPHGR.2017.61577.
Mohan, P., Srinivas, C., Yesubabu, V, Baskaran, R., & Venkatraman, B. (2018). Simulation of a heavy rainfall event over Chennai in Southeast India using WRF: Sensitivity to microphysics parameterization. Atmos. Res. 210, 83–99. DOI: 10.1016/j.atmosres.2018.04.005.
Pandey, B. K., & Khare, D. (2018). Identification of trend in long term precipitation and reference evapotranspiration over Narmada River basin (India). Glob. Planet. Change. 161, 172–182. DOI: 10.1016/j.gloplacha.2017.12.017.
Qian, Z., Wang, L., Chen, X., Zhang, H., & Li, Z. (2022). Heteroscedastic Characteristics of Precipitation with Climate Changes in China. Atmosphere. 13, 2116. DOI: 10.3390/atmos13122116.
Rahimzadeh, F., Asgari, A., & Fattahi, E. (2009). Variability of extreme temperature and precipitation in Iran during recent decades. Int. J. Climatol. 29(3), 329–343. DOI: 10.1002/joc.1739.
Rasooli, A. A., Roshani, R., & Ghasemi, A. R. (2013). Analyzing the Spatial-Temporal Changes of Annual Precipitation of Iran. GeoRes. 28(1), 205-224.
Salehi, S., Dehghani, M., Mortazavi, S.M., & Singh, V.P. (2020). Trend analysis and change point detection of seasonal and annual precipitation in Iran. Int. J. Climatol. 40(1), 308-323. DOI: 10.1002/joc.6211.
Sanikhani, H., Kisi, O., Mirabbasi, R., & Meshram, S. G. (2018). Trend analysis of rainfall pattern over the Central India during 1901–2010. Arab. J. Geosci. 11(15), 437. DOI: 10.1007/s12517-018-3800-3.
Sharafi, S., & Ghaleni, M. M. (2022). Spatial assessment of drought features over different climates and seasons across Iran. Theor. Appl. Climatol. 147(1), 941–957. DOI: 10.1007/s00704-021-03853-0.
Shi, X., & Xu, X. (2008). Interdecadal trend turning of global terrestrial temperature and precipitation during 1951– 2002. Prog. Nat. Sci. 18(11), 1383–1393. DOI: 10.1016/j.pnsc.2008.06.002.
Shifteh Some'e, B., Ezani, A., & Tabari, H. (2012). Spatiotemporal trends and change point of precipitation in Iran. Atmos. Res.113, 1-12. DOI: 10.1016/j.atmosres.2012.04.016.
Tareghian, R. M., & Rasmussen, P. (2013). Statistical downscaling of precipitation using quantile regression. J. Hydrol. 487, 122–135. DOI: 10.1016/j.jhydrol.2013.02.029.
Wang, R., Chen, J., Chen, & X., Wang, Y. (2017). Variability of precipitation extremes and dryness/wetness over the southeast coastal region of China, 1960–2014. Int. J. Climatol. 37(13), 4656-4669. DOI: 10.1002/joc.5113.
Wang, Y., Xu, Y., Tabari, H., Wang, J., Wang, Q., Song, S., & Hu, Z. (2020). Innovative trend analysis of annual and seasonal rainfall in the Yangtze River Delta, eastern China. Atmos. Res. 231(3), 104673. DOI: 10.1016/j.atmosres.2019.104673.
Xu, Y., Xu, Y., Wang, Y., Wu, L., Li, G., & Song, S. (2016). Spatial and temporal trends of reference crop evapotranspiration and its influential variables in Yangtze River Delta, eastern China. Theor. Appl. Climatol. 130(3–4), 1–14. DOI: 10.1016/j.atmosres.2019.104673.
Yang, H., Xiao, H., Guo, C., & Sun, Y. (2019). Spatial-temporal analysis of precipitation variability in Qinghai Province, China. Atmos. Res. 228(1), 242–260. DOI: 10.1016/j.atmosres.2019.06.005.
Zarrin, A., & Dadashi-Roudbari, A. (2021). Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theor. Appl. Climatol. 144(1-2): 643–660. DOI: 10.1007/s00704-021-03568-2.
)