Analysis of drought dynamics using SPI and SARIMA models: A case study of the Rostov Region, Russia

Abstract

Based on precipitation data from six weather stations covering the period 1960–2024, this study presents a retrospective analysis of drought dynamics in the Rostov Region, Russia, and evaluates the potential of the SARIMA model for forecasting moisture regime fluctuations. The Standardized Precipitation Index (SPI) was employed as the primary drought indicator. Two key phases of crop development were analyzed: the vegetation initiation period (March–May), assessed using the three-month SPI of May (SPI-3), and the full active growing season (April–September), assessed using the six-month SPI of September (SPI-6). The Mann-Kendall test revealed a non-significant positive trend in SPI-3 across all stations, while SPI-6 trends were non-significant and varied in direction. The highest frequency of drought events, based on both SPI-3 and SPI-6, occurred during 1960–1969, with a general decline in subsequent decades. The lowest drought frequency was observed during 2010–2019. Notably, the frequency of extreme droughts has shown an increasing trend, posing significant risks to agricultural productivity. Although SARIMA modeling proved useful for short-term forecasting, its application was limited by unrealistic long-term projections and deviations from climatic norms. Consequently, drought forecasts were restricted to a two-year horizon. Nonetheless, the SARIMA approach remains a valuable supplementary tool for anticipating precipitation dynamics and drought events.

Keywords

• Drought forecasting
• Standardized Precipitation Index (SPI)
• SARIMA model
• Vegetation Dynamics
• Precipitation variability

References

1. Ashabokov, B.A., Beytuganov, M.N., Tashilova, A.A., Fedchenko, L.M., Shapovalov, A.V., 2018. Changes of temperature and precipitation regimes in the south of European Russia in 1961-2015. Mausam 69(4): 553–562.
2. Bouaziz, M., Medhioub, E., Csaplovisc, E., 2021. A machine learning model for drought tracking and forecasting using remote precipitation data and a standardized precipitation index from arid regions. Journal of Arid Environments 189: 104478.
3. Ding, J., Tarokh, V., Yang, Y., 2017. Bridging AIC and BIC: a new criterion for autoregression. IEEE Transactions on Information Theory 64(6): 4024–4043.
4. Docheshmeh Gorgij, A., Alizamir, M., Kisi, O. Elshafie, A., 2022. Drought modelling by standard precipitation index (SPI) in a semi-arid climate using deep learning method: long short-term memory. Neural Computing and Applications 34: 2425–2442.
5. Gudko, V., Usatov, A., Ioshpa, A., Denisenko, Y., Shevtsova, V., Azarin, K., 2021. Agro-climatic conditions of the Southern Federal District of Russia in the context of climate change. Theoretical and Applied Climatology 145(3): 989–1006.
6. Gudko, V., Usatov, A., Denisenko, Y., Duplii, N., Azarin, K., 2022. Dependence of maize yield on hydrothermal factors in various agro-climatic zones of the Rostov region of Russia in the context of climate change. International Journal of Biometeorology 66(7): 1461-1472.
7. Gudko, V., Usatov, A., Minkina, T., Duplii, N., Azarin, K., Tatarinova, T.V., Denisenko, Y., 2024. Dependence of the pea grain yield on climatic factors under semi-arid conditions. Agronomy 14(1): 133.
8. Gudko, V., Usatov, A., Minkina, T., Tarigholizadeh, S., Azarin, K., Sushkova, S., Dmitrieva, A., 2024. Annual and seasonal precipitation dynamics in the South of Russia in the context of climate change. Theoretical and Applied Climatology 155(7): 6177–6193.

9. Holgate, C. M., Van Dijk, A.I.J., M., Evans, J.P., Pitman, A.J., 2020. Local and remote drivers of Southeast Australian drought. Geophysical Research Letters 47(18): e2020GL090238.
10. Huang, C., Petukhina, A., 2022. Applied time series analysis and forecasting with Python. Springer, Switzerland. 372 p.
11. Kattsov, V., Govorkova, V., Meleshko, V., Pavlova, T., Shkolnik, I., 2008. Climate change projections and impacts in Russian Federation and central Asia states. North Eurasia Climate Centre, Saint Petersburg, Russia. Available at [Access date: 18.11.2024]: http://neacc.meteoinfo.ru/research/climate-change-projections
12. Lukyanets, A.S., Bragin, A.S., 2021. The impact of climate risks on Russia’s economic development: Example of the North Caucasian Federal District. RUDN Journal of Economics 29(2): 439-450.
13. McKee, T.B., Doesken, N.J., Kleist, J., 1993. The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology 17(22): 179–183.
14. Ottom, M.A., Al-Shibli, F., Atoum, M.S., 2023. The future of data storytelling for precipitation prediction in the Dead-Sea-Jordan using SARIMA model. International Journal of Membrane Science and Technology 10(1): 1159–1169.
15. Panigrahi, S., Vidyarthi, V.K., 2024. Assessing the suitability of McKee et al. (1993) drought severity classification across India. Natural Hazards 120(14): 13543–13572.
16. Ray, S., Das, S.S., Mishra, P., Al Khatib, A.M.G., 2021. Time series SARIMA modelling and forecasting of monthly rainfall and temperature in the South Asian countries. Earth Systems and Environment 5: 531–546.
17. RRIHI-WDS, 2025. Russian Research Institute of Hydrometeorological Information – World Data Center (RRIHI-WDS), Available at [Access date 03.02.2025]: http://aisori-m.meteo.ru [in Russian]
18. Salmin, A.S., Asaulyak, I.F., Belolyubtsev A.I., 2021. Analysing time series of standardized precipitation index (SPI). Advances in Current Natural Sciences 5: 101–109.
19. Svoboda, M., Hayes, M., Wood, D. 2012. Standardized precipitation index: user guide. World Meteorological Organization, Geneva.
20. Van Ginkel, M., Biradar, C., 2021. Drought early warning in agri-food systems. Climate 9(9): 134.
21. Wu, G., Chen, J., Shi, X., Kim, J.S., Xia, J., Zhang, L, 2022. Impacts of global climate warming on meteorological and hydrological droughts and their propagations. Earth's Future 10(3): e2021EF002542.