Forecasting Hydrological Regime Based on Rainfall Regime Using Two-dimensional Markov Chain in Anzali Watershed

Document Type : Research Article

Authors

1 Graduated Ph.D. Student, Water Engineering Department, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran

2 Professor of Water Engineering Department, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran

3 Assistant professor, Agricultural Research, Education and Extension Organization, Soil Conservation and Watershed Management Research Institute, Tehran, Iran

Abstract

Despite the need to predict the flow regime in rivers, especially in hydrological drought conditions, little progress has been made in this area; so meteorological situation of basins in general, and meteorological drought in particular, have been used for these purposes. In this study, the prediction of hydrological conditions in rivers based on the basin condition in terms of rainfall is done using Markov chain concepts for the case study of Anzali Wetland catchment with 9 meteorological stations and 20 hydrometric stations over the years. The data from 1985 to 2015 have been used. Two-dimensional Markov probability matrix was used to predict the hydrological status of the rivers by observing the meteorological condition of the basin. The two-dimensional transition probability matrix is different from what has been customary so far, using two different phases of moisture flow in hydrometeorological processes in a matrix so that its columns represent the meteorological state and its rows represent the hydrological conditions. Evaluation of Anzali catchment conditions showed that meteorological and hydrological conditions are the same in each month by the probability of higher than 50%. Based on the results of the 2D matrix of the probability of transfer, the probability of being in normal, wet and dry hydrologic conditions, if these conditions occur in a step ahead in the meteorological phase, is 80, 50 and 40%, respectively. Based on the achieved results, with the expected value of the pertinent discharges in the forecasted hydrological condition concerning the former step climatological state, the probable discharge in the river is predicted.

Keywords

References

[1]. Cao C. Time serials of rainfall and their stochastic simulation. Urban storm drainage; Italy 25-28 July 1993, 45-62.
[2]. Hess G D. Operational, short term prediction of rainfall using a cycled Markov chain method. Australian meteorological magazine. 1990; 38(3): 201-205.
[3]. Martin- Vide Javier, Gomez Lnda. Regionalization of peninsular Spain based on the length of dry spells. Int. J. Climatol. 1989; 19: 537-555.
[4]. Haghighatjou P, Shahmohammadi Heydari Z. Markov Chain Usage To Investigation Of Drought And Wet Possibilities Of The Sistan Region Due To Hirmand River Discharge. Proceedings Of The First National Conference On Solution To Coping With Water Crisis, Zabol University, Zabol. 2001. [Persian]
[5]. AshgarToosi Sh, Alizadeh A, Javanmard S. Probability of Drought Forecasting In Khorasan Province. Geographical research Quarterly. 2003; 70: 119-128. [Persian]
[6]. Raziei T, Daneshkar Arasteh P, Akhtari R and Shaghafian B. Investigation of Meteorological Droughts in the Sistan and Balouchestan Province, Using the Standardized Precipitation Index and Markov Chain Model. Journal of Iranian Water Resources Research. 2003; 3(1): 25-35. [Persian]
[7]. Asakereh H, Mazini F. Investigating the Probability of Dry Days in Golestan Province Using Markov Chain. Geography and Development. 2010; 17: 29-44. [Persian]
[8]. Yousei N, Hejam S, Irannejad P. Estimating Drought and Wetland Probabilities Using Markov Chain and Probability Distribution, Case Study: Qazvin. Geographical Research. 2007; 39(8): - [Persian]
[9]. Rahimian M, Ghane A. Monitoring and Forecasting of Semnan Drought Using SPI Index and Markov Chain. Proceedings of the First International Conference on Water Resources Management, Shahroud University of Technology, Shahroud. 2009. [Persian]
[10]. MofidiPour N, BardiSheikh V, Ounagh M, Sa`adoddin A. Meteorological and Hydrological Drought Survey in Atrak Watershed. Watershed Management. 2012; 3(5): 16-26. [Persian]
[11]. Alijani B, Mahmudi P, Rigi Chahi A, Khosravi P. Evaluation of Glacial Period Continuity in Iran Using Markov Chain Model. Natural Geography Research. 2010; 3: 1-20. [Persian]
[12]. Mahavar Pour Z. Probability of daily precipitation in Iran and its prediction by the Markov chain model. Geographical Research. 2014; 29(4): 229-240. [Persian]
[13]. Eslami A. Development of a mathematical model for hydroclimatological drought forecasting. Ph.D. dissertation.Universiti Putra Malaysia (UPM), Serdang, Malaysia. 2012.
[14]. Teymoori M, Fathzadeh A. Surface Water Resources Index Monitoring Using Modified SWSI Index, And Markov Chain Model, Case Study: Atrak Basin. Journal of Geography and Development. 2014; 34(1): 99-108. [Persian]
[15]. Nalbantis I. Evaluation of hydrological drought index. Journal of European water. 2008; 23(24): 67-77.
[16]. Abhishek A, Channaveerappa Dodamani B M. Comparison of two hydrological drought indices. Perspectives in science. 2016; 8: 626-628.
[17]. Shukla S, wood A. Use of standardized runoff index for characterizing hydrologic drought. Geophysical research letters. 2008; 35(2): L02405.
[18]. SoleimaniSardouf, Bahramand A. Hydrological drought analysis using SDI index in Halilrud Basin of Iran. International journal of environment resources research. 2014; 1(3): 279-288. [Persian]
[19]. Azareh A, Rahdari M R, Sardoii E R, Moghadam F A. Investigate the relationship between hydrological and meteorological drought in Karaj dam basin. European journal of experimental biology. 2014; 4(3): 102-107.
[20]. McKee TB, Doesken NJ, Kleist J. Drought monitoring with multiple timescales. 9th conference on Applied Climatology, TX. USA. 1994; 233-236.
[21]. Nalbantis I, Tsakiris G. Assessment of hydrological drought revisited. Water resources management. 2009; 23(5): 881-897.
[22]. DrinC Software: Drought Indices Calculator. Tigkas D, Sakiris G T, Vangelis H, Pangalou D. National University Athenes. 2005.
[23]. Razi F, Shokoohi A. Determining and Estimating the Lag time between Meteorological and Hydrological Drought Using a Water Balance Model. Watershed engineering and management. 2019; 13(1). [Persian]
Iranian journal of Ecohydrology
Volume 7, Issue 3
October 2020
Pages 663-674

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History

  • Receive Date: 02 March 2020
  • Revise Date: 15 June 2020
  • Accept Date: 15 June 2020

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