Journal of Environmental Studies

Journal of Environmental Studies

Assessment of the Application of the SSPI and SSPEI Drought Indices in Arid and Semi-Arid Regions: A Case Study of Iraq

Document Type : Research Paper

Authors
Hadi Razzaq Abed 1
Jafar Masoumpour Samakosh 1
Morteza Miri 2
Tayeb Raziei 2
1 Department of Geography, Faculty of Literature and Human Sciences, Razi University, Kermanshah, Iran.
2 Department of Drought and Climate Change, Soil Conservation and Watershed Management Research Institute, Agricultural Research Education and Extention Organization, Tehran, Iran.
10.22059/jes.2026.413392.1008689
Abstract
Objective: Climate change, anthropogenic pressures on water resources, and the sparse distribution of meteorological stations in Iraq present significant challenges for drought assessment. Furthermore, the country’s location within a global arid belt, leading to extreme temperature increases and erratic precipitation, introduces methodological complexities to drought studies. This research aims to enhance drought monitoring tools for water- and climate-related crisis management in Iraq by evaluating the performance of the Simplified Standardized Precipitation Index (SSPI) and Simplified Standardized Precipitation Evapotranspiration Index (SSPEI) against SPI and SPEI in arid and semi-arid climates of Iraq.
Method: The study utilized a 71-year (1950–2020) gridded dataset comprising precipitation, maximum and minimum air temperatures, derived from GPCC and CRU centers at a 0.25° resolution, to calculate SPI, SSPI, SPEI, and SSPEI drought indices at 3, 6, 9, 12, and 24-month time scales.
Results: Comparison of SSPI and SPI showed that the spatial and temporal patterns of the two indices were similar across most parts of Iraq, with their agreement increasing at longer time scales. At the 3-month timescale, greater differences between the two indices were observed in some areas of western, eastern, and central Iraq, whereas the degree of agreement increased at longer time scales. Correlation coefficients (R) ranged from 0.88 to 0.99, and coefficients of determination (R²) ranged from 0.79 to 1.00. The RMSE, Bias, and Slope values also indicated that the differences between the two indices were limited in most areas and generally decreased with increasing timescale. The comparison of SSPEI and SPEI likewise showed a very high degree of agreement across all timescales, with R and R² values mostly ranging from 0.97 to 0.99. The EF, RMSE, Bias, and Slope values further confirmed the close agreement between the two indices. Despite the observed high similarity, fitting a probability distribution to precipitation series with many zero values, or to water-balance series with extreme values, is not always feasible and may sometimes fail. In such cases, simplified alternatives like SSPI and SSPEI, derived using the Box–Cox transformation, are effective as they do not depend on a specific probability distribution.
Conclusions: Overall, the findings indicate that the modified SSPI and SSPEI indices exhibit patterns similar to those of SPI and SPEI across most parts of Iraq, with the degree of agreement increasing at longer time scales. The primary advantage of the SSPI and SSPEI indices is not that they produce significantly different results from conventional indices, but rather that they provide a more stable method for standardizing climatic series when the statistical characteristics of the data complicate the fitting of probability distributions used in traditional methods. Therefore, SSPI and SSPEI can be considered alternative and complementary approaches for drought monitoring in arid and semi-arid regions, particularly under conditions characterized by a high frequency of zero values, highly skewed series, or limitations in fitting probability distributions.
Keywords
Box-Cox transformation
drought
GPCC and CRU databases
standard normal distribution
statistical distribution fitting
Subjects
Aadhar, S., & Mishra, V. (2017). High-resolution near real-time drought monitoring in South Asia. Scientific Data, 4, 170145. https://doi.org/10.1038/sdata.2017.145
AghaKouchak, A., Farahmand, A., Melton, F.S., Teixeira, J., Anderson, M.C., Wardlow, B.D. & Hain, C.R. (2015). Remote sensing of drought: Progress, challenges and opportunities. Reviews of Geophysics, (53)2, 452-480. https://doi.org/10.1002/2014RG000456
Azizi, G., & Safarrad, T. (2010). Evaluation of Ordinary Kriging and Inverse Distance Weighting Methods for Estimating Drought and Wetness Values in Iran. The First National Conference on Geomatics in Natural Resources and Environmental Sciences, University of Tehran, Tehran. [in Persian] 
Bigas, H. (2012). The global water crisis: Addressing an urgent security issue. United Nations University-Institute for Water, Environment and Health. https://bdd.pseau.org/outils/biblio/resume.php?d=5075
Box, G. E. P., & Cox, D. R. (1964). An Analysis of Transformations. Journal of the Royal Statistical Society: Series B (Methodological), 26: 211-43. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x
Dai, A. (2011). Drought under global warming: A review. Wiley interdisciplinary reviews: Climate Change, 2, 45–65. https://doi.org/10.1002/wcc.81
Eklund, L., & Seaquist, J. (2015). Meteorological, agricultural and socioeconomic drought in the Duhok Governorate, Iraqi Kurdistan. Natural Hazards, 76, 421–441. https://doi.org/10.1007/s11069-014-1504-x
Guttman, N. B. (1998). Comparing the Palmer drought index and the standardized precipitation index. Journal of the American Water Resources Association, 34(1), 113–121. https://doi.org/10.1111/j.1752-1688. 1998.tb05964.x
Keyantash, J.A., & Dracup, J.A. (2004). An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resources Research, 40, 1–14. https://doi.org/10.1029/2003WR002610
Kim, J.-B., So, J.M., & Bae, D.H. (2020). Global Warming Impacts on Severe Drought Characteristics in Asia Monsoon Region. Water, 12, 1360. https://doi.org/10.3390/w12051360
Kogan, F.N. (1997). Global Drought Watch from Space. Bulletin of the American Meteorological Society, 4, 621-636. https://doi.org/10.1175/1520-0477
Lee, J.H., Kwon, H.H., Jang, H.W., & Kim, T.W. (2016). Future Changes in Drought Characteristics under Extreme Climate Change Over South Korea. Advances in Meteorology,1–19. https://doi.org/10.1155/ 2016/9164265
Mail, A.S.M. (2017). Assessment of Drought Conditions and their Impacts on the Environment of the Udhaim River Basin, Iraq, University of Warsaw, Faculty of Geography and Regional Studies.
McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, American Meteorological Society. 179–184.
Miri, M., Azizi, G., Khoshakhlagh, F., & Rahimi, M. (2017). Evaluation Statistically of Temperature and Precipitation Datasets with Observed Data in Iran. Iran-Watershed Management Science & Engineering, 10(35), 39-51. http://jwmsei.ir/article-1-587-fa.html [in Persian] 
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900. https://doi.org/10.13031/2013.23153
Raziei, T. (2021a). Performance evaluation of different probability distribution functions for computing Standardized Precipitation Index over diverse climates of Iran. International Journal of Climatology, 41,3352–3373. https://doi.org/10.1002/joc.7023
Raziei, T. (2021b). Revisiting the rainfall anomaly index to serve as a simplified standardized precipitation index. Journal of Hydrology, 602,126761. https://doi.org/10.1016/j.jhydrol.2021.126761
Raziei, T. (2022). Improving the normalization procedure of the simplified standardized precipitation index (SSPI) using Box–Cox transformation. Stochastic Environmental Research and Risk Assessment. 37, 925–951. https://doi.org/10.1007/s00477-022-02317-9
Raziei, T., & Miri, M. (2023). An alternative approach for computing the standardized precipitation-evapotranspiration index (SPEI). Water Resources Management, 37:4123–4141. https://doi.org/ 10.1007/s11269-023-03542-9
Razzaq Abed, H., Masoompour Samakosh, J., Miri, M., & Raziei, T. (2025). Evaluation of spatial and temporal accuracy of GPCC and CRU datasets against observational data. Journal of Arid Regions Geographic Studies, 16(59): 1-13. https://doi:10.22034/jargs.2024.475185.1134 [in Persian] 
Robaa, S.M., & Al-Barazanji, Z.J. (2013). Trends of annual mean surface air temperature over Iraq. Natural Sciences, 11, 138–145. https://doi.org/10.7537/marsnsj111213.21
Subash, N., Ram Mohanb, H.S., & Banukumar, K. (2011). Comparing water-vegetative indices for rice (Oryza sativa L.) wheat (Triticum aestivum L.) drought assessment. Computers and Electronics in Agriculture, 77(2),175-187. https://doi.org/10.1016/j.compag.2011.05.001
Vicente-Serrano, S. M., & Beguería, S. (2016). Comment on “Candidate distributions for climatological drought indices (SPI and SPEI)” by James H. Stagge et al. International Journal of Climatology, 36(4), 2120–2131. https://doi.org/10.1002/joc.4474
Vicente-Serrano, S.M., Beguería, S., & López-Moreno, J.I. (2010). A multiscalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index. Journal of Climate, 23(7), 1696–1718. https://doi.org/10.1175/2009JCLI2909.1
WMO. (2012). Standardized Precipitation Index User Guide. World Meteorological Organization. CH-1211 Geneva 2, Switzerland. https://library.wmo.int/viewer/39629#page=2&viewer
Journal of Environmental Studies
Volume 52, Issue 2
Summer 2026
Pages 273-294