Modeling, Monitoring and Prediction of Drought in Iran

Document Type : Original Article

Authors

1 Department of physical Geography, Climatology, Faculty of Literature and Humanities, University of Mohaghegh Ardabili, Ardabil, Iran

2 Desert Studies Faculty, Semnan University, Semnan, Iran

Abstract

The drought phenomenon is not specific to the region and it affects different parts of the world. One of these areas is Iran in Southwest Asia, which suffered from this phenomenon in recent years. The purpose of this study is to model, analyze and predict the drought in Iran. To do this, climatic parameters (precipitation, temperature, sunshine, minimum relative humidity and wind speed) were used at 30 stations in the period of 29 years (1990-2018). For modeling of TIBI fuzzy index, first, four indicators (SET, SPI, SEB, MCZI) were fuzzy in Matlab software. Then the indices were compared and Topsis model were used for prioritizing areas involved with drought. Finally, Anfis adaptive artificial neural network model was used to predict. Results showed that the new fuzzy index TIBI for classifying drought reflected four of the above indicators with high accuracy. Among these five climatic parameters used in this study, the temperature and precipitation parameters had the most influential effect on the fluctuation of drought severity. The severity of drought was more based on a 6-month scale modeling than 12 months. The highest percentage of drought occurrence was at Bandar Abbas station with a value of 24.3 on a 12-month scale and the lowest was in Shahrekord station with a percentage of 0.36% on a six-month scale. Based on Anfis model and TIBI fuzzy index, Bandar Abbas, Bushehr and Zahedan stations were more exposed to drought due to the TIBI index of 0.62, 0.96 and 0.97, respectively. According to the results in both 6 and 12 months scale, the southern regions of Iran were more severely affected by drought, which requires suitable water management in these areas.

Keywords

References

  1. Shamsniya, S.A., Pirmoradian, N. and Amiri, S.N. (2009). Drought Modeling in Fars Province Using Time Series Analysis, Journal of Geography and Planning,14(28), pp. 165-189. [In Persian]
  2. Mirzaie Nodoushan, F., Araghinejad, S., Bozorghaddad, O., (2015). Development of WEAP Integrated Water Model Model for Drought Condition Modeling,  Watershed Engineering and Management, 7(1), pp.85-97. [In Persian]
  3. Samidianfard, S. and Asadi, E., (2018). Prediction of spi drought index using support vector and multiple linear regressions. Journal of Soil and Water Resources Conservation, 6(4), pp.1-16. [In Persian]
  4. Zeinali, B. and Safarian Zengir, V., (2017), Drought Monitoring in Urmia Lake Basin Using Fuzzy Index,  Journal of Natural Environment Hazards, 6(12), pp.37-61. [In Persian]
  5. Haddadi, H. and Heidari, H. (2015). Detection of the effect of precipitation fluctuations on surface water flood in Lake Urmia catchment basin, Geography and Environmental Planning, 57(1), pp.247-262. [In Persian]
  6. Sobhani, B., GhafariGilandeh, A. and Golvost, A. (2015). Drought monitoring in Ardabil province by means of SEPI fuzzy index developed based on the fuzzy logic, Journal of Geographical Sciences, 15(36), pp.51-72. [In Persian]
  7. Alizadeh, S., Mohammadi, H. and Kardavani, P., 2017. Dispersion modeling drought caused by climate change in Iran using system dynamics. Town and Country Planning, 9(1), pp.169-188 [In Persian]
  8. Huang, S., Huang, Q., Chang, J., Zhu, Y., Leng, G. and Xing, L., 2015. Drought structure based on a nonparametric multivariate standardized drought index across the Yellow River basin, China. Journal of Hydrology, 530, pp.127-136.
  9. Spinoni, J., Naumann, G., Vogt, J.V. and Barbosa, P., 2015. The biggest drought events in Europe from 1950 to 2012. Journal of Hydrology: Regional Studies, 3, pp.509-524.
  10. Komasi, M., Alami, M.T. and Nourani, V., 2013. Drought Forecasting by SPI Index and ANFIS Model Using Fuzzy C-mean Clustering. Water and Wastewater, 24(4), pp.90-102.
  11. Byzedi, M., 2018. Evaluation of drought in western synoptic stations using herbst and neuro-fuzzy method. Iran-Water Resources Research, 14(1), pp.278-284.
  12. Anumalla, S., Ramamurthy, B., Gosselin, D.C. and Burbach, M., 2005, May. Ground water monitoring using smart sensors. In IEEE International Conference on Electro Information Technology, IEEE, pp.1-6.
  13. Ekhtiari, S. and Dinpashoh, Y., 2019. Application of effective drought index (EDI) in characterizing drought periods (case study: Tabriz, Bandar-e Anzali and Zahedan stations). Sustainable Water Resources Management, pp.1-7.
  14. Zeleke, T.T., Giorgi, F., Diro, G.T. and Zaitchik, B.F., 2017. Trend and periodicity of drought over Ethiopia. International Journal of Climatology, 37(13), pp.4733-4748.
  15. Quesada-Montano, B., Di Baldassarre, G., Rangecroft, S. and Van Loon, A.F., 2018. Hydrological change: Towards a consistent approach to assess changes on both floods and droughts. Advances in Water Resources, 111, pp.31-35.
  16. Um, M.J., Kim, Y. and Kim, J., 2017. Evaluating historical drought characteristics simulated in CORDEX East Asia against observations. International Journal of Climatology, 37(13), pp.4643-4655.
  17. Modaresirad, A., Ghahraman, B., Khalili, D., Ghahremani, Z. and Ahmadi, S., 2017. Integrated meteorological and hydrological drought model: a management tool for proactive water resources planning of semi-arid regions. Advances in Water Resources, 107, pp.336-353.
  18. Kis, A., Pongrácz, R. and Bartholy, J., 2017. Multi‐model analysis of regional dry and wet conditions for the Carpathian Region. International Journal of Climatology, 37(13), pp.4543-4560.
  19. Lawal, D.U., Matori, A.N. and Balogun, A.L., 2011. A geographic information system and multi-criteria decision analysis in proposing new recreational park sites in universiti teknologi malaysia. Modern Applied Science, 5(3), pp.39-55.
  20. Fathi-Zadeh, H., Gholami-nia, A., Mobin, M. and Soodyzizadeh, H. (2017). Investigating the Relationship between Meteorological Drought and Solar Variables in Some Iranian Standards. Journal of Natural Environment Hazards, 17(6): 63-87. [In Persian]
  21. ParsaMehr, A. H. and Khosravani, Z. (2017). Determination of drought determination using multi-criteria decision making based on TOPSIS. Iranian Journal of Range and Desert Research, 24(1), pp. 16-29. [In Persian]
Iranica Journal of Energy & Environment
Volume 10, Issue 3
September 2019
Pages 216-224

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