Meteorological drought analysis with SPI correction index in arid climate(Case study of central, southern and eastern Iran)

Document Type : Full length article


1 PhD student in Climatology, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Professor of Climatology, Physical Geography Department, Tehran University, Tehran, Iran

3 Associate Professor of Climatology, Ahwaz Branch, Islamic Azad University, Ahwaz, Iran

4 Assistant Professor, Geography Department, Islamic Azad University, Science and Research Branch, Tehran, Iran


Extended abstract


Iran is located in an arid region, so planning to adaptably cope with drought consequences in this arid climate regions is essential and a constitutes important water-resources/water-demands managerial program at country level. In order to compile a drought plan, we need to carefully study the historic rainfall period using a appropriate drought indices. Drought is a climatic phenomenon that is statistically random and is studied by several indicators. One of these indicators that has triggered a major paradigm change is the one proposed by McKee et al. (1993) known as Standardized Precipitation Index (SPI). The index was developed to define and monitor drought and determine rainfall intensity anomalies in time scales of 3 to 48 months in Colorado. In the calculation of SPI, the total precipitation of a cumulation period consisting of several consecutive months are compared with its counterpart during the studied years. Based on the appropriate statistical distribution, the probability of similar precipitation is calculated. The index number is obtained after normalizing the corresponding cumulative probability distribution function value using standard normal distribution. In this method, the rainfall of different months is included in the calculation without considering the distance or proximity to the present time and using a uniform weight. It has been observed that extreme monthly rainfall events, several times heavier than long-term a verge keeps the index value high for several consecutive months. The dry periods that follow the such a wet month are hidden from the SPI, and this index mistakenly show that period as wet, even though we know that these precipitations, especially in arid areas, are mostly in the form of showers and are quickly out of reach and will not have much effect on the wet season.

Materials and methods

In this study, SPI drought index in12 provinces with26 synoptic stations located in the center, south and east of Iran with a dry climate (determined by Domarten method) has been calculated and analyzed over a period of 34 years (1364-1397) for a cumulation period of twelve months. The most abundant and the least precipitated stations were Safiabad-Dezful and Nehbandan, respectively. To calculate the SPI index, gamma distribution was used to fit long-term precipitation data. Since the SPI index does not take into account the effect of precipitation timing by definition, it is likely that in the period under review to estimate unreal wet period. Therefore, in this study, for the first time, the a new EP-SPI correction index is presented, in which the effective precipitation data obtained by damping the effect of precipitation with the time factor, as used as input to SPI Generator software, developed in2018 by The National Drought Mitigation Center was used. In this way, the effect of precipitation in the first month close to the event is 12 times, in the second month 11 times and also in the previous months compared to 12 and the farthest month. As far as the twelfth month is concerned, in practice it has not had much effect on the current drought, and only once it has been included in the calculation in the ratio of 1/12. Thus, the effect of older rainfall is reduced and the index provides a better estimate of drought.

Results and discussion

Since the two methods used have the same logic, the regression of EP-SPI versus SPI values shows a significant correlation at the5% level with a coefficient of determination of 0/673 and a slope of 0/82. To compare the two methods SPI and EP-SPI, we need to perform statistical tests. If the data has a normal distribution, it is possible to use a parametric test, otherwise we must use a non-parametric test. Using the Kolmogorov-Smirnov test, we determine the normality of the data distribution. According to the result of this test with error α=0.05, the distribution of normal SPI and EP-SPI values and the difference between the two methods were not normal. In reviewing the studied stations, out of 9895 events, the cases in which SPI was positive and EP_SPI negative included 880 cases, of which 277 were differences in the values of SPI and EP-SPI index more than 1. In 67 cases, the degree of drought was 2 to 4 levels of difference with SPI, which indicates the presence of drought in the study areas. Three acute incidents occurred in Qom, Mehrabad and Omidiyeh stations in January and February 1996 and December 1998. In these three stations at least 4 levels of drought have been shifted. According to the study, the lowest number of disputes was related to Safiabad station (Dezful) in Khuzestan province with 6 incidents and the highest number was related to Omidieh (Aghajari) stations in Khuzestan province and Minab in Hormozgan province with 18 incidents. it is arrived 86% of the incidents were seen with a difference of more than one in the months of November to February and the highest difference between the two methods was equivalent to four levels of drought index shift from very severe to mild drought. Also, in terms of average, the difference between the two methods was more severe in December and January.


The monthly pattern of more than one difference between SPI and EP_SPI twelve months in the rainy months of December to February and low rainfall in June to October was in accordance with the rainfall regime in the region. In general, the difference in precipitation from year to year, more at the beginning or end of the rainy season, greatly affects the SPI values, while in the EP-SPI method, their effect dampens over time and a better understanding of the real state of drought/wet period has provided in the area. In this study, it was found that8.9% of the twelve-month drought events in dry climate were hidden from the SPI index. This was consistent with a comparison of NDVI maps that were typically received from NOAA satellite AVHRR sensors on5 dates. In these 5 dates, the difference between SPI and EP-SPI index has been observed in more than ten stations. Therefore, it can be said that the efficiency of EP-SPI method for detecting droughts in a period of twelve months is confirmed. Although no similar study has been found to date, Nadi and Sheikhi Soghanloo (2020) observed that the difference between SPImod and SPI indices is more obvious in stations with drier climates. Therefore, they concluded that using SPImod instead of SPI provides more accurate results due to the elimination of seasonal rainfall effects. It can be concluded that the results of this study were consistent with the result of this study.

Keywords: Correction Index EP-SPI, arid zone, Drought, SPI.


Main Subjects

بابایی فینی، ا. و علیجانی، ب. (1392). تحلیل فضایی خشکسالی‏‏های بلندمدت ایران، پژوهشهای جغرافیای طبیعی، شمارة 3، ص 1-12.
پیروزنیک، مهرناز (1399). سند توسعة پایدار 2030 سازمان ملل متحد با عنوان دگرگون ساختن جهان ما، بند 33.
حمصی، ملیحهسادات؛ یاراحمدی، داریوش؛ اونق، مجید و شمسیپور، علیاکبر (1398). ارزیابی تغییر اقلیم و کاربری زمین و ارائة برنامة پیشنهادی آمایش کم کربن در حوضة آبخیز دشت کاشان، پژوهش های جغرافیای طبیعی, 51(4): 613-632.
رضایی، حسین؛ خانمحمدی، ندا؛ منتصری، مجید. و بهمنش، جواد (1397). ارزیابی انتخاب تابع توزیع احتماالتی مناسب در استفاده از شاخصهای خشکسالی SPI و RDI، نشریة دانش آب و خاک، جلد 28، شمارة 1، ص 29-40.
شکوهی، ع. (1391). مقایسة شاخصهای RDI و SPI برای تحلیل خشکسالی کشاورزی (مطالعة موردی: قزوین و تاکستان)، نشریة مهندسی آبیاری و آب ایران، دورة 3، شمارة 9، ص 111-122.
طرح جامع آب کشور، شناخت اقلیم ایران. (1370). بخش اول: بررسی‏های بنیادین بارندگی در ایران، مهندسین مشاور جاماب، وزارت نیرو، ص 184-193.
فاضل دهکردی، ل.؛ آذرنیوند، ح.؛ زارع چاهوکی، م.؛ محمودی کهن، ف. و خلیقی سیگارودی، ش. (1395). پایش خشکسالی با استفاده از شاخص پوشش گیاهی NDVI (مطالعة موردی: مراتع استان ایلام)، منابع طبیعی ایران، شمارة 1، ص 141-154.
مجردی، برات؛ میرمیری، جواد و علیزاده، حسین (1399). ارزیابی شاخص وضعیت پوشش گیاهی VCI با استفاده از شاخص بارش استاندارد اصلاحشدة MSPI بهمنظور پایش و پهنهبندی خشکسالی، مهندسی و مدیریت آبخیز، 12(3): 725-736.
مجیدی، ع. ا.؛ رادفر، م.؛ میرعباسی نجفآبادی، ر. و معروفی، ص. (1394). ارزیابی خشکسالی هواشناسی قهاوند- دشت رزن بر اساس شاخصهای خشکسالی، نهمین همایش ملی روز جهانی محیط زیست، دانشگاه تهران، اردیبهشت 1394، تهران، ایران.
مجیدی، ع.ا.؛ رادفر، م.؛ میرعباسی نجفآبادی، ر. و معروفی، ص. (۱۳۹۷). گزارش فنی تحلیل روند خصوصیات خشکسالی‏های هواشناسی استان همدان، پ‍‍ژوهشنامة مدیریت حوزة آبخیز، ۹(۱۷):  295-305.
محمودی، ا. و میرعباسی نجف‌آبادی، ر. (1393). مقایسة روشهای SPI و SPI اصلاحشده در تشخیص خشکسالی (مطالعة موردی: شهر دهدشت، کهکلویه، و بویراحمد)، دومین کنفرانس ملی بحران آب، دانشگاه شهرکرد، شهریور 1393، شهرکرد، ایران.
میرعباسی نجف‌آبادی، ر. و دینپژوه، ی. (1389). تحلیل روند جریان جریان در شمال غربی ایران در سه دهه اخیر. مجله آب و خاک، شماره 24، ص 768-757.
نادی، مهدی و شیوخی سوغانلو، سعید (1399). مقایسة نمایههای SPI و SPImod در پایش خشکسالی چند نمونة اقلیمی ایران، پژوهشنامة مدیریت حوزة آبخیز، سال یازدهم، شماره 12، ص 108-118.
نبیزاده بلخکانلو، عادل؛ ضیائیان فیروزآبادی، پرویز، و خدمتزاده، علی (1399). بررسی اثرات خشکسالی کشاورزی بر تراکم پوشش گیاهی با استفاده از سنجش از دور (مطالعۀ موردی: حوضۀ آبریز سیمینهرود)، پژوهش‏های جغرافیای طبیعی، 52(3): 395-408.
Attafi, R.; Darvishi Boloorani, A.; M. Fadhil Al-Quraishi, A. and Amiraslani, F. (2021). Comparative analysis of NDVI and CHIRPS-based SPI to assess drought impacts on crop yield in Basrah Governorate, Iraq, Caspian Journal of Environmental Sciences, 19(3): 547-557.
Babaei Fini, A. and Alijani, B. (2012). Spatial analysis of long-term droughts in Iran, Natural Geography Research, No. 3: 1-12.
Bonsal, B.R.; Aider, R. and Gachon, P. (2012). An assessment of Canadian prairie drought: past, present, and future, Climate Dynamics, No. 41: 501-516.
Bonsal, B.R.; Wheaton, E.E.; Chipanshi, A.C.; Lin, C.; Sauchyn, D.J. and Wen, L. (2011). Drought Research in Canada: A Review, Atmosphere-Ocean, 49(4): 303-319.
Byun, H.R. and Wilhite, D.A. (1999). Objective quantification of drought severity and duration, Journal of Climate, 12(9): 2747-2756.
Cheval, S. (2015). The Standardized Precipitation Index – an overview, Romanian journal of meteorology, Vol. 12, Issues 1-2: 35-37.
Comprehensive water plan of the country, knowledge of Iran's climate. (1991). Part 1: Fundamental rainfall studies in Iran, Jamab Consulting Engineers, Ministry of Energy, 184-193.
Fazel Dehkordi, L.; Azarnivand, H.; Zare Chahooki, M.; Mahmoodi Kohan, F. and Khalighi Sigarudi, Sh. (2016). Drought monitoring using NDVI vegetation index (Case study: Rangelands of Ilam province), Natural Resources of Iran, No. 1: 154-141.
Hayes, M.; Svoboda, M.; Wall, N. and Widham, M. (2011). The Lincoln Declaration on Drought Indices: Universal meteorological Drought Index Recommended, Bulletin of the American Meteorological Society, No. 92(4): 485-488.
Hayes, M.J. (2000). Revisiting the SPI: Clarifying the Process, Drought Network News (Newsletter of IDIC and NDMC), Vol. 12, No. 1: 13-14.
Homsi, Maliha Sadat.; Yarahmadi, Dariush.; Oneq, Majid. and Shamsipoor, Ali Akbar. (2019). Assessing climate change and land use and presenting a proposed low car0bon management program in the Kashan plain watershed. Natural Geography Research, 51(4): 613. Meteorological Society 92(4): 485-488, doi: 10.1175/2010BAMS3103.1
Kao, S.C. and Govindaraju, R.S. (2010). A copula-based joint deficit index for droughts, Journal of Hydrology, No. 380: 121-134.
Karavitis, C.A.; Alexandris, S.; Tsesmelis, D.E. and Athanasopoulos, G. (2011). Application of the Standardized Precipitation Index (SPI) in Greece, Water, No. 3: 787-805.
Koutroulis, A.G.; Vrohidou, A.E.K. and Tsanis, I.K. (2010). Spatiotemporal Characteristics of Meteorological Drought for the Island of Crete, Journal of Hydrometeorology, No. 206 , Vol. 12.
Lloyd-Hughes, B. and Saunders, M.A. (2002). Seasonal prediction of European spring precipitation from El Niño-southern oscillation and local sea-surface temperatures, International Journal of Climatology, No. 22: 1-14.
Mahmoodi, A. and Mirabbasi Najafabadi, R. (2014). Comparison of the SPI, and SPI modified methods in diagnosis of drought occurred (Case Study: Dehdasht city, Kohkeloye and Bouyer Ahmad), 2th National Conference of water crisis, university of shahrekord, September 2014, Shahrekord, Iran.
Majidi, A.A.; Radfar, M.; Mirabbasi Najafabadi, R. and Marofi, S. (2015). Assessment meteorological drought Ghahavand- Razan plain by drought indices, 9th National Conference on World Environment Day, University of Tehran, Mey 2015, Tehran, Iran.
Majidi, A.A.; Radfer, M.; Mir Abbasi Najafabadi, R. and Marofi, S. (2018). Technical Report, Analysis of the trend of meteorological drought characteristics of Hamadan province, Watershed Management Research Journal, 9(17): 295-305.
McKee, T.B.; Doesken, N.J. and Kleist, J. (1993). The re‏‏‏‏‏‏‏lationship of drought frequency and duration to time scales, Proceedings of the 8th Conference on Applied Climatology, American Meteorological Society, Boston,179-184.
Mirabbasi Najafabadi, R. and Dinpashoh, Y. (2010). Trend analysis of stream flow across the north west of iran in recent three decades, Journal of Water and Soil, No. 24: 757-768.
Mojaradi, Bayat; Mirmiri, Javad and Alizadeh, Hossein (2020). Evaluation of VCI Vegetation Status Index Using MSPI Modified Standard Precipitation Index for Drought Monitoring and Zoning, Watershed Engineering and Management, 12 (3): 725-736.
Nabizadeh Balkhanlu, Adel; Derek Ziaian Firoozabadi, Parviz and Fahm Khidmatzadeh, Ali (2020). Investigation of the effect of agricultural drought on vegetation density using remote sensing (Case study: Siminehroud catchment), Natural Geography Research, 52(3): 395-408.
Nadi, Mehdi and Sheikhi Soghanloo, Saeed (2020). Comparison of SPI and SPImod indices in drought monitoring of some climatic samples of Iran, Journal of Watershed Management, 11th year, No. 12: 108-118.
National Drought Mitigation Center, [cited 2019 Aug], Available from:
Precipitation and temperature data of synoptic stations of Meteorological Organization of Iran (IRIMO).
Redmond, K.T. (2002). The depiction of drought: A commentary, Bulletin of the American Meteorological Society, No. 83: 1143-1147.
Rezaei, Hossein; Khan Mohammadi, Neda; Montaseri, Majid and Behmanesh, Javad (2018). Evaluation of selecting the appropriate probability distribution function in using SPI and RDI drought indices. Journal of Soil and Water Knowledge, Vol. 28, No. 1: 29-40.
Shokouhi, A. (2012). Comparison of RDI and SPI indices for agricultural drought analysis (Case study: Qazvin and Takestan), Iranian Journal of Irrigation and Water Engineering, Vol. 3, No. 9: 111-122.
Spinoni, J.; Antofie, T.; Barbosa, P.; Bihari, Z.; Lakatos, M.; Szalai, S.; Szentimrey, T. and Vogt, J.V. (2013). An overview of drought events in the Carpathian Region in 1961–2010, Adv. Sci. Res., No. 10(1): 21- 32.
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150.
United Nations (2015). General Assembly Resolution A/RES/70/1, Transforming Our World, the 2030 Agenda for Sustainable Development, [cited 2016 Feb 10], Available from: http://
United Nations/ World Bank High Level Panel on Water (2018). Outcome Document, Making every Drop Count, An Agenda for Water Action, 34.
Vermote, Ric; Justice, Chris; Csiszar, Ivan; Eidenshink, Jeff; Myneni, Ranga; Baret, Frederic; Masuoka, Ed; Wolfe, Robert and Claverie, Martin (2014). NOAA CDR Program, NOAA Climate Data Record (CDR) of Normalized Difference Vegetation Index (NDVI), Version 4. [indicate subset used]. NOAA National Climatic Data Center. doi:10.7289/V5PZ56R6.
Vicente-Serrano, S.M.; Gonzalez-Hidalgo, J.C.; De Luis, M. and Raventos, J. (2004). Drought patterns in the Mediterranean area: the Valencia region (eastern Spain), Climate Research, No. 26: 5-15.
World Health Organization, health-topics, drought, [cited 2021 Jun]. Available from:
Yaseen, Z.M.; Ali, M. and Sharafati, A. et al. (2021). Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh, Sci Rep, 11: 34-35.
Zhai, L. and Feng, Q. (2009). Spatial and temporal pattern of precipitation and drought in Gansu Province, Northwest China. Natural Hazards 49: 1-24, doi: 10.1007/s11069-008-9274-y.
Zuo, Dongdong.; Wei Hou, Hao Wu.; Pengcheng, Yan. and Qiang, Zhang. (2021). Feasibility of Calculating Standardized Precipitation Index with Short-Term Precipitation Data in China, Atmosphere, Vol. 12, No. 5: 603.
Volume 53, Issue 4
February 2022
Pages 465-485
  • Receive Date: 30 June 2021
  • Revise Date: 31 December 2021
  • Accept Date: 01 January 2022
  • First Publish Date: 02 January 2022