Calibration of Rainfall-Stream flow Relationship for Assessing and Forecasting Hydrological Drought in Kavir-e Lut Basin, Iran

Author

• Hamid Nazaripour

Abstract

Hydrological drought is defined as a significant decrease in the availability of water in all its forms appearing in the land phase of the hydrological cycle. These forms are reflected in various hydrological variables such as stream flow (including snowmelt and spring flow), lake and reservoir level, and groundwater level. A variety of indices and methods for characterizing hydrological drought have been devised. In this study, an index called Streamflow Drought Index (SDI) have been used for characterizing the severity of hydrological droughts. Investigation of the forecasting possibility of hydrological droughts has been carried out by two approaches. First, when the appropriate historical data are available, Markov chain method have been used. The main output of the methodology is the matrix of state transition frequency for a selected pair of reference periods under the hypothesis of a Markov chain for the underlying state process. In other words, the output is a single value of drought state while the probabilities of remaining in the same state or passing to other states in the next reference period are withdrawn from tables which have been obtained off-line. Since, in general, streamflow data are difficult to obtain in real-time, the possibility of using a meteorological drought index was investigated. More specifically, a linear function of SPI was found to predict SDI to an accuracy level which is sufficient for characterizing drought severity. This involves prior calibration of a simple regression equation with modified SPI as the explanatory variable and SDI as the explained variable. The methodology is validated using reliable data from the Nesa and Fashkoh rivers basin in the southwestern margin of Kavir-e Lut (Iran). The results indicate that calibration of rainfall and streamflow relations provides a good opportunity to forecasting of hydrological droughts states in the lack of river flow data. A key consideration is that in this basin, a high degree of successful prediction is observed for the wet period of October to March. In addition, due to lack of storage snow in this basin, which can compensate the deficit rainfall in dry period, drought states predicting for other periods is also possible by using Markov chain methodology.

Keywords

• Hydrological drought
• Streamflow Drought Index
• Drought prediction
• Markov chain
• Kavir-e Lut basin
• Iran

20.1001.1.20086377.1395.9.31.7.4

---

References

1)                   drought in Israel. J Hydrol 92(1–2):179–191.

2)                   Bonaccorso, B., Bordi, I., Cancelliere, A., Rossi, G., Sutera, A. 2003. Spatial variability of drought: an analysis of the SPI in Sicily. Water Resources Management 17: 272-296.

3)                   Clausen B, Pearson CP (1995) Regional frequency analysis of annual maximum streamflow drought.J Hydrol 173:111–130.

4)                   Cordery I, McCall M (2000) A model for forecasting drought from teleconnections. Water Resour Res 36:763–768.

5)                   Correia FN, Santos MA, Rodrigues R (1987) Engineering risk in regional drought studies. In: Duckstein L, Plate EJ (Eds) Engineering, reliability and risk in water resources. Proc. of ASI Tucson Arizona USA 1985 Martinus Ninjhoff Pub.

6)                   Domonkos P (2003) Recent precipitation trends in Hungary in the context of larger scale climatic changes. Nat Hazards 29:255–271.

7)                   Dracup JA, Lee KS, Paulson EG. 1980. On the statistical characteristics of drought events. Water Resour Res 16:289–296.

8)                   Guttman, N.B., 1998. Comparing the Palmer Drought Index and the Standardized Precipitation Index. J. Am. Water Resour. Assoc. 34 (1), 113–121.

9)                   Hayes M, Wilhite DA, Svoboda M, Vanyarkho O (1999) Monitoring the 1996 drought using the standardized precipitation index. Bull Am Meteorol Soc 80:429–438.

10)               Hayes, M.J., 2000. Drought Indices. National Drought Mitigation Center, University of Nebraska, Lincoln, Nebraska, USA.

11)               Lohani VK, Loganathan GV (1997) An early warning system for drought management using the Palmer drought index. J Am Water Resour Assoc 33(6):1375–1386.

12)               Lohani VK, Loganathan GV, Mostaghimi S (1998) Long-term analysis and short-term forecasting of dry spells by the Palmer drought severity index. Nord Hydrol 29(1):21–40.

13)               McKee TB, Doeskin NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the eighth conference on applied climatology, Anaheim, CA, January17–23, 1993. American Meteorological Society Boston MA, pp 179–184.

14)               Min SK, Kwon WT, Park EH, Choi Y (2003) Spatial and temporal comparisons of droughts over Korea with East Asia. Int J Climatol 23:223–233.

15)               Mishra, A. K., & Desai, V. R. (2005). Drought forecasting using stochastic models. Stochastic Environmental Research and Risk Assessment, 19(5), 326-339.

16)               Nalbantis I (1995) Use of multiple-time-step information in rainfall-runoff modelling. J Hydrol 165:135–159.

17)               Nalbantis, I. (2008). Evaluation of a hydrological drought index. European Water, 23(24), 67-77.

18)               Nalbantis, I., & Tsakiris, G. (2009) Assessment of hydrological drought revisited. Water Resources Management, 23(5), 881-897.

19)               Nalbantis, I., & Tsakiris, G. (2009). Assessment of hydrological drought revisited. Water Resources Management, 23(5), 881-897.

20)               Nasrabadi, E, Masoodian, A, Asakereh, H (2013) Comparison of gridded precipitation time series data in APHRODITE and Asfazari databases within Iran’s territory. Atmos Clim Sci 3: pp. 235.

21)               NtaleHK, Gan T (2003) Drought indices and their application to EastAfrica. Int J Climatol 23:1335–1357.

22)               OcholaWO, Kerkides P (2003) AMarkov chain simulation model for predicting critical wet and dry spells in Kenya: analysing rainfall events in the Kano plains. Irrig Drain 52(4):327–342.

23)               Paulo AA, Pereira LS (2006) Drought concepts and characterization. Comparing drought indices applied at local and regional scales. Water Int 31(1):37–49.

24)               Paulo AA, Pereira LS (2007) Prediction of SPI drought class transitions usingMarkov chains.Water Resour Manag 21(10):1813–1827.

25)               Paulo AA, Pereira LS, Matias PG (2003) Analysis of local and regional droughts in southern Portugal using the theory of runs and the Standardised Precipitation Index. In: Rossi G, Cancelliere A, Pereira LS, Oweis T, ShatanawiM(2003) Tools for drought mitigation inMediterranean regions. Kluwer, Dordrecht, pp 147–157.

26)               Rossi G, Benedini M, Tsakiris G, Giakoumakis S. 1992. On regional drought estimation and analysis. Water Resour Management. 6:249–277.

27)               RouaultM, Richard Y (2003) Intensity and spatial extension of droughts in South Africa at different time scales.Water SA 29:489–500.

28)               Tigkas, d. 2008. Drought Identification in Greek Regions. In the Prpceeding of the International Symposium:Water Shortage Management; Tsakiris G.(ED.), 20 June 2008, Athens-Greeece. pp.121-131.

29)               Tsakiris G, Pangalou D, Vangelis H (2006) Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resour Manag 21(5):821–833.

30)               Tsakiris G, Vangelis H (2004) Towards a drought watch system based on spatial SPI. Water Resour Manag 18:1–12.

31)               Tsakiris G, Vangelis H (2005) Establishing a drought index incorporating evapotranspiration. European Water 9/10:3–11.

32)               Tsakiris G, Vangelis H (2005) Establishing a drought index incorporating evapotranspiration. European Water 9/10:3–11.

33)               Tsakiris, G., Tigkas, d., Vangelis, H., Pangalou, D. 2007. Regional Drought Indentification and Assessment- Case Study in Crete. In Methods and Tools for Drought Analysis and Management, Rossi et al.(EDS.). Springer, The Netherlands.169-191.

34)               Vogt JV, Somma F (eds) (2000) Drought and drought mitigation in Europe. Kluwer, Dordrecht, The Netherlands, p 336.

35)               Whilhite, D. A., & Glantz, M. H. (1985). Understanding the drought phenomenon: The role of definitions. Water Int, 10, 111-120.

36)               Wilhite DA, Hayes MJ, Svoboda MD (2000) Drought monitoring and assessment: status and trends in the United States. In: Vogt JV, Somma F (eds) Drought and drought mitigation in Europe. Kluwer, Dordrecht, pp 149–160.

37)               Zarch, M. A. A., Malekinezhad, H., Mobin, M. H., Dastorani, M. T., & Kousari, M. R. (2011). Drought monitoring by reconnaissance drought index (RDI) in Iran. Water resources management, 25(13), 3485-3504.

38)              Zelenhasic E, Salvai A (1987) A method of streamflow drought analysis. Water Resour Res 23(1):156–168.