Journal of Advanced Informatics in Water, Soil, and Structure

Journal of Advanced Informatics in Water, Soil, and Structure

Drought Monitoring and Prediction in Kashmar County Using SPI Index and Markov Chain

Document Type : Research Article

Authors
Mehdi Bashiri
Gholam-Reza Esmaeily
Department of Nature Engineering and Medicinal Plants, University of Torbat Heydarieh, Torbat Heydarieh, Iran.
10.22048/wss.2024.432881.1005
Abstract
Drought is a natural and mysterious creeping phenomenon, and many believe that it has a complex mechanism and is less known than other natural disasters. Studying drought events is very important for natural and water resource management planning. One strategy to manage drought is to predict drought conditions using a probabilistic tool. The study aimed to predict the probability and severity of meteorological drought in Kashmar. To this aim, the monthly rainfall data of the Kashmar Synoptic Station was used to analyze a 30-year period (2017-1987). The drought status of Kashmar County was considered by drought duration of The Standardized Precipitation Index (SPI) at 1, 3, 6, 9, 12, 18, 24, and 48-month timescales. Next, using the Markov Chain, the transition probability matrix for the study area was carried out, and the probability of meteorological droughts was predicted for severity. The results of this study showed that the severest drought in Kashmar occurred in 2000 and 2009, with an SPI coefficient of greater than -3, while the highest precipitation occurred in 1993, with an SPI coefficient of 2.8. Then, the Markov chain model was used to calculate the balance probabilities for dry, wet, and normal periods at different time scales. The results showed that, on average, the stationary probability of dry, normal, and wet periods is 29, 30, and 41 percent, respectively. Consequently, it means that the region's climate conditions are often normal. As a result, given the critical situation of Kashmar County, the opportunity to reduce water stress and aquifer discharge can be exploited.
Keywords
Climate Change
Razavi-Khorasan
Rainfall
Standardized Precipitation Index
Trend

Subjects

  1. Alizadeh, A., & Ashgar-Toosi, Sh. (2008). Development of a Model for Monitoring and forecasting drought (case study: Khorasan Razavi province). Journal of agricultural sciences and industries, water and soil, 22(1), 223-234.
  2. Anonymous. (2005). Report of hydrogeology studies of Kashmar plain. Consulting engineers for water and sustainable development, 265 pp.
  3. Asiayi, M., (2006). Drought Indicators. Sokhan Gostar Publications, first edition. 176 pp.
  4. Bashari, M., & Vafakhah, M. (2011). Comparison of different time series analysis methods for forecasting monthly discharge in Karkheh watershed. Irrigation and Water Engineering, 1 (2),75-86.
  5. Ben-Gai, Y., Bitan, A., Manes, A., Alpert, P., & Rubin, S. (1998). Spatial and temporal changes in rainfall frequency distribution patterns in Israel. Theoretical and Applied Climatology, Vol. 61, 177-190.
  6. Bonaccorso, B., Cancelliere, A., & Rossi, G. (2015). Probabilistic forecasting of drought class transitions in Sicily (Italy) using standardized precipitation index and North Atlantic oscillation index. Journal of Hydrology, 526, 136-150.
  7. Capra, A., & Scicolone, B. (2012). Spatiotemporal variability of drought on a short–medium time scale in the Calabria Region (Southern Italy). Theoretical and applied climatology, 110(3), 471-488.
  8. Chu, P. S., Nash, A. J., & Porter, F. Y. (1993). Diagnostic studies of two contrasting rainfall episodes in Hawaii: Dry 1981 and wet 1982. Journal of Climate, 6(7), 1457-1462.
  9. Delghandi, M., Joorablou, S., & Ganji Nowroozi, Z. (2023). The impact of climate change on severity, duration, and magnitude of drought using SPI and RDI in the Semnan region. Journal of drought and climate change research, 1(1 (1) ), 1-18.
  10. Eshghi, P. (2014). Investigating the drought situation using different indicators and its effect on the groundwater level. Master's Seminar in Watershed Management, Faculty of Agriculture and Natural Resources, Torbat Heydarieh University, 82 pp.
  11. Gabriel, K. R., & Neumann, J. (1962). A Markov chain model for daily rainfall occurrence at Tel Aviv. Quarterly Journal of the Royal Meteorological Society, 88(375), 90-95.
  12. Ghamghami, M., & Bazrafshan, J. (2012). Prediction of meteorological drought conditions in Iran using Markov chain model. Water and soil resources conservation, 1(3), 1-12.
  13. Gutiérrez-García, K., Avilés, A., & Nauditt, A. (2023). Evaluating Markov chains and Bayesian networks as probabilistic meteorological drought forecasting tools in the seasonally dry tropics of Costa Rica. Theoretical Applied Climatology, 154, 1291–1307.
  14. Habibi, B., Meddi, M., Torfs, P. J., Remaoun, M., & Van Lanen, H. A. (2018). Characterization and prediction of meteorological drought using stochastic models in the semi-arid Chéliff–Zahrez basin (Algeria). Journal of Hydrology: Regional Studies, 16, 15-31.
  15. Hashemi Dovin, M., & Ahangarzadeh, Z. (2013). Meteorological Drought Monitoring of North Khorasan Province in GIS environment. First National Conference on Hydrology and Meteorology, Kerman, 13-26.
  16. Helmi, M., & Shahidi, A. (2023). The using of SPI and SPEI indices in evaluating the effect of drought on quality of surface water resources (Case study: Kashafroud river). Journal of Drought and Climate change Research, 1(1), 83-96.
  17. Hosseini, A., Baghban, P., & Cheharara, T. (2018). Drought monitoring of Lake Urmia basin using SPI standardized precipitation index. The third national conference on soil protection and watershed management, 29-30 June, Tehran.
  18. Jahangir, M. H., Khoshmashraban, M., & Yousefi, H. (2015). Drought monitoring with Standard Precipitation Index (SPI) and drought forecasting with multi-layers perceptron (Case study: Tehran and Alborz Provinces). Iranian journal of Ecohydrology, 2(4), 417-428.
  19. Khalighi Sigaroudi, Sh., Sadeghi Sangdehi, S.A., Awsati, Kh., & GHavidel Rahimi, Y. (2009). The Study of Drought and Wet Year Assessment models for Stations in Mazandaran province. Iranian journal of Range and Desert Research, 16 (1), 44-54.
  20. Mahmoudi, P., &  Rigi, A. (2023). Probabilistic Prediction of Drought in Iran Using Homogenous and Nonhomogeneous Markov Chains. Journal of Hydrologic Engineering, 28(5), 1-15.
  21. 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 (Vol. 17, No. 22, pp. 179-183). Boston, MA: American Meteorological Society.
  22. McMahon, T.A., & Srikanthan, R. (2001). Stochastic generation of annual, monthly and daily climate data. Hydrology and earth system science, 5(4), 653-670.
  23. Mosaedi, A., Mohammadi-Moghaddam, S., & Kavakebi, Gh. (2017). Drought characteristics based on Reconnaissance Drought Index and its variations in different time periods and regions of Iran. Journal of Water and Soil Conservation, 23(6), 27-52.
  24. Muller, J. C. Y. (2014). Adapting to climate change and addressing drought–learning from the Red Cross Red Crescent experiences in the Horn of Africa. Weather and Climate Extremes, 3, 31-36.
  25. Ojaklou-Shahabi, R. (2014). Investigation of meteorological drought indicators in the synoptic stations of West Azerbaijan province. Master's seminar on watershed management, Torbat Heydarieh University, Faculty of Agriculture and Natural Resources.
  26. Rahmat, S. N., Jayasuriya, N., & Bhuiyan, M. A. (2017). Short-term droughts forecast using Markov chain model in Victoria, Australia. Theoretical and Applied Climatology, 129(1-2), 445-457.
  27. Raziei, T. (2016). Drought forecasting in eastern and central arid and semi-arid regions of Iran using time series and Markov chain models. Watershed Engineering and Management, 8(4), 454-477.
  28. Sadeghinia, A., Hejazizadeh, Z., Hamidianpour, M., & Poursarbandan, R. (2013). Estimating drought and drought probabilities using SPI index and Markov chain model (Case study: Tehran). Scientific-Research Quarterly of Geographical Space, 13(43), 65-81.
  29. Samadian, M., Asadi, E., Ghorbani, M. A., & Ahmadi, F. (2023). Joint analysis of meteorological drought characteristics based on SPI and CRU scenario. Water and Irrigation Management, 13(1), 189-207. 
  30. Shah, R., Bharadiya, N., & Manekar, V. (2015). Drought index computation using standardized precipitation index (SPI) method for Surat District, Gujarat. Aquatic Procedia, 4, 1243-1249.
  31. Shokrikochak, S., & Behnia, A. (2013). Monitoring and Prediction of Khuzestan Province, Iran Drought Using SPI drought Index and Markov Chain. Irrigation Sciences and Engineering, 36(3), 1-12.
  32. Siasar, H., & Salari, A. (2023). Predicting the probability of droughts using SPI drought index based on Markov chain model (Case study: Villages of Sistan and Baluchistan province). Rural Development Strategies, 10(3), 387-402.
  33. Siasar, H., & Shahr-Derazi, F., (2015). Drought monitoring and forecasting using SPI drought index and Markov chain (case study: Iranshahr county). 3rd International Conference on New Achievements in Engineering and Basic Sciences, Zamin-Kav Research Center, 29 Dec, 1-11.
  34. Szilagyi, J., Balint, G., & Csik, A. (2006). Hybrid, Markov chain-based model for daily streamflow generation at multiple catchment sites. Journal of Hydrologic Engineering, 11(3), 245-256.
  35. Szinell, G.S., Bussay, A., & Szentimerg, T. (1998). Drought tendencies in Hungary. International Journal of Climatology, 18, 1479-1491.
  36. Taheri, M., & Panahi, M., 2014. Short-term prediction of drought using standardized precipitation index and Markov probability chain (case study: Qazvin synoptic station). Journal of Agriculture and Natural Resources Research, 4(18), 21-28.
  37. Thompson, S.A. (1999). Hydrology for water management. Balkema, Rotterdam, The Netherlands, 476pp.
  38. Vafakhah, M., & bashari, M. (2012). Probability study of hydrological drought and wet periods occurrence using markov chain in kashafrood watershed. Watershed management researches (pajouhesh-va-sazandegi), 25(1 (94)), 1-9. 
  39. Van Huijgevoort, M. H. J., Van Lanen, H. A. J., Teuling, A. J., & Uijlenhoet, R. (2014). Identification of changes in hydrological drought characteristics from a multi-GCM driven ensemble constrained by observed discharge. Journal of Hydrology, 512, 421-434.
  40. Wilks, D. S. (2011). Statistical methods in the atmospheric sciences. Academic.
  41. WMO, (2006). Drought Monitoring and Early Warning: Concepts, Progress and Future Challenges, WMO-No. 1006. World Meteorological Organization, Geneva, Switzerland. 26 pp.
  42. WMO, (2012). Standardized Precipitation Index User Guide. In: Svoboda, M., Hayes, M., Wood, M. (Eds.), World Meteorological Organization Report WMO-No. 1090, Geneva, Switzerland.
  43. Yan-jun, Li., Xiao-ding, Z., Fan, Lu., & Jing, M. (2012). Analysis of drought evolvement characteristics based on standardized precipitation index in the Huaihe river basin. Procedia Engineering, 28, 434-437.
  44. Zarei, A. R. (2018). Evaluation of drought condition in arid and semi-arid regions, using RDI Index. Water Resources Management, 32(5), 1689-1711.
Journal of Advanced Informatics in Water, Soil, and Structure
Volume 1, Issue 1 - Serial Number 1
January 2025
Pages 61-74

  • Receive Date 28 December 2023
  • Revise Date 05 June 2024
  • Accept Date 05 June 2024
  • First Publish Date 01 January 2025
  • Publish Date 01 January 2025