Comparative study of drought meteorological (SPI) and hydrological (SSI) indices based on the best cumulative distribution function for Urmia Basin

Document Type : Research/Original/Regular Article

Authors

1 Associate Professor/Renewable Energies and Environment Department, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran

2 M.Sc. Student/ Renewable Energies and Environment Department, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran

Abstract

Introduction
Atmospheric conditions such as climate change, ocean temperatures, changes in the jet stream, and changes in the local landscape are all factors that contribute to drought. Drought not only threatens agriculture but also causes a series of ecological, social, and economic damages. Therefore, it is necessary to have valid and appropriate indicators that can evaluate and measure drought efficiently. Drought monitoring by meteorological and hydrological indicators has always been of interest to researchers. Decreasing or increasing precipitation has a direct effect on river flow rate and groundwater level.
Materials and Methods
Drought monitoring was performed for 10 meteorological stations and 10 hydrometric stations in the western basin area of Urmia with different distribution functions to identify the best distribution function for fitting data based on standardized precipitation index (SPI) as meteorological drought index and standardized streamflow index (SSI) as hydrological drought index. SPI and SSI values were measured with four cumulative functions, i.e., Gamma, Weibull, logarithmic normal, and Gaussian normal functions. Some performance criteria such as MAE were used to evaluate the efficiency of the functions. This research has been conducted on an annual scale for 31 years from 1989 to 2019. Drought continuity zoning was done through the Inverse distance weighting (IDW) method based on drought continuity.
Results and Discussion
The results of the SSI showed that the cumulative gamma function had the best performance for fitting data for nine stations. Only at the Merakand station, the Weibull cumulative distribution function provide a better fit for the data. Babarood station with 99% correlation and MSE of 0.017 had the best fit among other stations. In order to understand the drought conditions of the region, according to the selected indicators and based on positive values for wet conditions and negative values for drought conditions, the stations have been studied. Finally, a zoning map of drought continuity is presented.
Conclusion
The SSI and SPI indicators are directly related to each other. The SPI is better for predicting the onset of drought and the SSI is better for measuring the severity and persistence of drought. During the last three decades (1998-2000), Urmia basin has witnessed the highest severity of hydrological drought. Based on the zoning map of the northwest region, especially the upper Chihriq station, it has always enjoyed more favorable conditions and has experienced a milder drought than other regions. However, the extent of severe drought changed from the western part in 1998 to the eastern part in 2000. the severity of drought in 1998 was less than in 2000. In 2000, the drought intensity reached the highest value just in the Merakand station.

Keywords

Main Subjects

References

Abramowitz, M., & Irene, S. (1964). Handbook of mathematical functions with formulas, graphs, and mathematical tables. National Bureau of Standards Applied Mathematics Series, 470 pages.
AghaKouchak, A. (2015). A multivariate approach for persistence-based drought prediction: Application to the 2010-2011 East Africa drought. Journal of Hydrology, 526, 127-135.
Barani Pesyan, V., Porakrami, M., Fotouhi Mehrbani, B., & Porakrami, S. (2017). The investigation of lake urmia drying trend and its important consequence on the surrounding settlements. Journal of Rural Research, 8(3), 438-453 (in Persian).
Bazrafshan, J., Nadi, M., & Ghorbani, K. (2015). Comparison of empirical copula–based joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) for drought monitoring in Iran. Water Resources Management, 29, 2027–2044.
Eghtedar Nezhad, M., Bazrafshan, O., & Sadeghi Lari, A. (2016). Adaptive evaluation of SPI, RDI and SDI indices in analyzing the meteorological and hydrological drought characteristics (Case study: Bam Plain). Journal of Water and Soil Science, 26(4.2), 69–81 (in Persian).
Hao, Z., & AghaKouchak, A. (2013). Multivariate standardized drought index: A parametric multi-index model. Advances in Water Resources, 57, 12-18.
Jahangir, M.H., & Mousavi, M. (2020). Comparative study of meteorological (SPI) and hydrological drought index (SSI) based on the best cumulative distribution function in Tehran Province. Iranian Journal of Watershed Management Science and Engineering, 14(48), 1-10 (in Persian).
Li, Y., Luo, L., Chang, J., Wang, Y., Guo, A., Fan, J., & Liu, Q. (2020). Hydrological drought evolution with a nonlinear joint index in regions with significant changes in underlying surface. Journal of Hydrology, 585, 124794.
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, Anaheim, Pp. 83-179.
MesbahZadeh, T., & Soleimani Sardoo, F. (2018). Temporal trend study of hydrological and meteorological drought in Karkheh watershed. Iranian Journal of Watershed Management Science and Engineering, 12(40), 105-114 (in Persian).
Mielby, S., & Henriksen, H.J. (2020). Hydrogeological studies integrating the climate, freshwater cycle, and catchment geography for the benefit of urban resilience and sustainability. Water, 12(12), 3324.
Mirakbari, M., Mortezaii, G., & Mohseni, M. (2018). Investigation of effect meteorological drought on surface and ground water resources by indices SPI, SPEI, SDI and GRI. Iranian Journal of Watershed Management Science and Engineering, 12(42), 70-80 (in Persian).
Moghadasi, M., Morid, S., Byon, H., Ghaemi, H., & Samani Jamal, M.V. (2004). Drought monitoring using deciles index, standardized precipitation index and effective drought index in Tehran Province, Iran. Iran Agricultural Research, 23(1), 95–110 (in Persian).
Mortezaii Frizhandi, G., & Mirakbari, M. (2018). Hydro logical drought monitoring using SDI and GRI indicators In the watershed of Azam Herat, Yazd province. Journal of Range and Watershed Management, 71(3), 775-785 (in Persian).
Nohegar, A., & Mahmoodabadi, S. (2012). Assessment of Some indicators of drought–regional and appointment of the most suitable indicator in the Kahorestan Zone. Geography (Regional Planning), 2(2), 89–98 (in Persian).
Pathak, A.A., & Dodamani, B.M. (2016). Comparison of two hydrological drought indices. Perspectives in Science, 8, 626-628.
Salimi, H., Asadi, E., & Darbandi, S. (2021). Meteorological and hydrological drought monitoring using several drought indices. Applied Water Science, 11(11), 1-10.
Sobral, B.S., de Oliveira-Junior, J.F., de Gois, G., Pereira-Júnior, E.R., de Bodas Terassi, P.M., Muniz-Júnior, J.G.R., Lyra G.B., & Zeri, M. (2019). Drought characterization for the state of Rio de Janeiro based on the annual SPI index: trends, statistical tests and its relation with ENSO. Atmospheric Research, 220, 141-154.
Soleimani Sardou, F., & Bahremand, A. (2014). Hydrological drought analysis using SDI index in Halilrud basin of Iran. Environmental Resources Research, 2(1), 48-56.
Telesca, L., Lovallo, M., Lopez-Moreno, I., & Vicente-Serrano, S. (2012). Investigation of scaling properties in monthly streamflow and Standardized Streamflow Index (SSI) time series in the Ebro basin (Spain). Physica A: Statistical Mechanics and its Applications, 391(4), 1662-1678.
Water and Soil Management and Modelling
Volume 2, Issue 4
December 2022
Pages 53-63

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History

  • Receive Date: 15 May 2022
  • Revise Date: 21 June 2022
  • Accept Date: 22 June 2022

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