Identification of Trend in Spatial and Temporal Dry and Wet Periods in Northwest of Iran Based on SPI and RAI Indices

Document Type : Research Article

Authors

• Majid Montaseri ¹
• Babak Amirataee ²
• Keyvan Khalili ¹

¹ Urmia University

² Urmia university

https://doi.org/10.22067/jsw.v30i2.39679

Abstract

Introduction: Droughts are natural extreme phenomena, which frequently occur around the world. This phenomenon can occur in any region, but its effects will be more severe in arid and semi-arid regions. Several studies have highlighted the increasing of droughts trend around the world. The majority of studies in assessing the trend of time series are based on basic Mann-Kendall or Spearman's methods and no serious attention has been paid to the impact of autocorrelation coefficient on time series. However, limited numbers of studies have included the lag-1 autocorrelation coefficient and its impacts on the time series trend. The aim of this study was to investigate the trend of dry and wet periods in northwest of Iran using Mann-Kendall trend test with removing all significant autocorrelations coefficients based on SPI and RAI drought indices.
Materials and Methods: Study area has a region of 334,000 square kilometers, with wet, arid and semiarid climate, located in the northwest of Iran. The rainfall data were collected from 39 synoptic stations with average rainfall of 146 mm as the minimum of Gom station, and the highest annual rainfall of 1687 mm, in the Bandaranzali station. In this study, Standardized Precipitation Index (SPI) and Rainfall Anomaly Index (RAI) were used for trend analysis of dry and wet periods. SPI was developed by McKee et al. in 1993 to determine and monitor droughts. This index is able to determine the wet and dry situations for a specific time scale for each location using rainfall data. RAI index was developed by Van Rooy in 1965 to calculate the deviation of rainfall from the normal amount of rainfall and it evaluates monthly or annual rainfall on a linear scale resulting from a data series. Then, correlation coefficients of time series of these drought indices with different lags were determined for check the dependence or independence of the SPI and RAI values. Finally, based on dependence or independence of the time series values, trend analysis of wet and dry periods was conducted in different stations using one of the basic or modified Mann-Kendall tests. Also, the magnitude of the trends was derived from the Theil- Sen’s slope estimator.
Results and Discussion: Time series of SPI and RAI drought indices for a given annual rainfall as an example for three stations of Marivan, Gom and Maku show that during 1991 to 1994 and from 2002 to 2007 are in wet period and during 1987 to 1990 and 1998 to 2001 are in the dry period. It is clearly show that, dry and wet periods in RAI index are more severe than SPI. Comparison the correlation between Lag-1 autocorrelation coefficients values of SPI and RAI time series and Lag-1 autocorrelation coefficients of annual rainfall data indicate that these correlations are high and about 0.97 and 0.99, respectively. This difference is due to the different classification of SPI and RAI drought indices. The results of trend analysis indicate a decreasing trend in most of stations. Also, Mann-Kendall statistic has been declining while eliminating the effect of all significant correlation coefficients of dry and wet periods. This result in both SPI and RAI indices are similar and have a high correlation with R = 0.99. According to results, west of the study area have a significant decreasing (negative) trend. The spatial distribution of dry and wet periods showed that the difference between Mann-Kendall statistics of SPI and RAI indices is minimal. Also, The results show that, the slope of the trend line based on the SPI and RAI drought indices is negative in most of stations and correlation between these two indices in determining the slope of the trend line is high. But, this correlation compared with the trend statistics of SPI and RAI time series is less.
Conclusions: In this study, first the time series of SPI and RAI time series based on annual precipitation and common quantitative classification of mentioned two drought indices were determined. Then, trends of dry and wet periods of selected stations in northwest of Iran were evaluated based on these indices using the Mann-Kendall trend test with removing all significant autocorrelation coefficients. The results from this study indicate that using Mann-Kendall test with removing all significant autocorrelation coefficients effects are essential in assessing trend in time series. Although, according to various studies available in the literature, SPI is known as more accurate than RAI in drought mitigation, but according the results of this study, can solely be used both RAI and SPI index for trend detection.

Keywords

• Autocorrelation Coefficient
• Mann-Kendall
• RAI
• SPI
• Trend

20.1001.1.20084757.1395.30.2.2.4

References

1- Adeloye A.J., and Montaseri M. 2002. Preliminary streamflow data analyses prior to water resources planning study. Hydrological Science, 47(5): 679-692.

2- Ahani H., Kherad M., Kousari M.R., Roosmalen L.V., Aryanfar R., and Hosseini S.M. 2012. Non-parametric trend analysis of the aridity index for three large arid and semi-arid basins in Iran, Theoretical and Applied Climatology, 112(3-4): 553-564.

3- Amirataee B., Montaseri M., and Yasi M. 2013. Comparison of inherent performance of seven drought indices in drought mitigation using a monte carlo simulation approach. Journal of Civil and Environmental Engineering, 43(1): 25-39. (in Persian)

4- AMS (American Meteorological Society). 2004. Statement on meteorological drought, Bulletin of American Meteorological Society, 85: 771-773.

5- Bars R.L. 1990. Hydrology: An Introduction to Hydrologic Science, Addison-Wesley Publishing Co., New York, USA.

6- Bhalme H.N., and Mooley D.A. 1980. Large-scale drought/floods and monsoon circulation, Monthly Weather Review, 108:1197–1211.

7- Bonaccorso B., Bordi I., Cancelliere A., Rossi G., and Sutera A. 2003. Spatial variability of drought: an analysis of the SPI in Sicily, Water Resources Management, 17:273–296.

8- Bordi I., Fraedrich K., Gerstengarbe F.W., Werner P.C., and Sutera A. 2004. Potential predictability of dry and wet periods: Sicily and Elbe-Basin (Germany). Theoretical and Applied Climatology, 77:125–138.

9- Bradley R.S., Dı´az H.F., Eischeid J.K., Jones P.D., Kelly P.M., and Goodess C.M. 1987. Precipitation fluctuations over northern hemisphere land areas since the mid-19th century, Science, 237:171–175.

10- Brunetti M., Brunetti M., Maugeri M., Nanni T., Navarra A., Maugeri M., Nanni T., and Navarra A. 2002. Droughts and extreme events in regional daily Italian precipitation series, International Journal of Climatology, 22: 509–621.

11- Daneshvar Vousoughi F., Dinpashoh Y., Aalami M.T., and Jhajharia D. 2013. Trend analysis of groundwater using non-parametric methods (case study: Ardabil plain), Stochastic Environmental Research and Risk Assessment, 27: 547–559.

12- Edwards D.C., and Mckee T.B. 1997. Characteristics of 20th Century Drought in the United State at Multiple Time Scales. Journal of the Atmospheric Sciences, 634:1–30.

13- Gibbs W.J., and Maher J.V. 1967. Rainfall deciles as drought indicators, Bureau of Meteorology Bulletin No. 48, Commonwealth of Australia, Melbourne.

14- Hamed K.H., and Rao A.R. 1998. A modified Mann–Kendall trend test for autocorrelated data, Journal of Hydrology, 204: 182–196.

15- Hayes M.J., Svoboda M.D., Wilhite D.A., and Vanyarkho O.V. 1999. Monitoring the 1996 drought using the standardized precipitation index, Bulletin of the American Meteorological Society, 80(3): 429-438.

16- Herschy W.R. 2002. The world’s maximum observed floods. Flow Measurement and Instrumentation, 13:231–235.

17- Hulme M. 1996. Recent climatic change in the world’s drylands, Geophysical Research Letters, 23:61–64.

18- Kampata J.M., Parida B.P., and Moalafhi D.B. 2008. Trend analysis of rainfall in the headstreams of the Zambezi River Basin in Zambia, Physics and Chemistry of the Earth, 33.

19- Kendall M.G. 1975. Rank Correlation Measures, Charles Griffin Inc, London.

20- Keyantash J., and Dracup J.A. 2004. An aggregate drought index: assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resources Research, 40(9):W09304.

21- Keyantash, J., and Dracup, J.A. 2002. The quantification of drought: An evaluation of drought indices. Bulletin of the American Meteorological Society, 83: 1167– 1180.

22- Khaliq M.N., Ouarda T.B.M.J., and Gachon P. 2009. Identification of temporal trends in annual and seasonal low flows occurring in Canadian rivers: The effect of short- and long-term persistence, Journal of Hydrology, 369: 183–197.

23- Loucks D.P., Stedinger, J.R. and Haith, D.A. 1981. Water Resource systems planning and analysis. Prentice-Hall, Englewood cliffs, N.J.

24- Mann H.B. 1945. Non-parametric test against trend, Econometrica, 13: 245-259.

25- Marofi S., Soleymani S., Salarijazi M., and Marofi H. 2012. Watershed-wide trend analysis of temperature characteristics, Theoretical and Applied Climatology, 110:311–320.

26- McKee T.B., Doesken N.J., and Kleist J. 1993. The relation of drought frequency and duration to time scales, p. 179–184. In Proceedings of the Eighth Conference on Applied Climatology, American Meteorological Society, Boston.

27- McKee T.B., Doesken N.J., and Kleist J. 1995. Drought monitoring with multiple time scales, p. 233–236. Proceedings of the Ninth Conference on Applied Climatology. American Meteorological Society, Boston.

28- Mirza M.M.Q. 2002. Global warming and changes in the probability of occurrence of floods in Bangladesh and implications, Global Environmental Change, 12: 127–138.

29- Mishra A.K., Singh V.P., and Desai V.R. 2009. Drought characterization: a probabilistic approach, Stochastic Environmental Research and Risk Assessment, 23(1): 41–55.

30- Moreira E.E., Paulo A.A., Pereira L.S., and Mexia J.T. 2006. Analysis of SPI drought class transitions using loglinear models, Journal of Hydrology, 331: 349–359.

31- New M., Todd M., Hulme M., and Jones P. 2001. Precipitation measurements and trends in the twentieth century, International Journal of Climatology, 21: 1899–1922.

32- Nikbakht J., Tabari H. and Hosseinzadeh Talaee, P. 2013. Streamflow drought severity analysis by percent of normal index (PNI) in northwest Iran, Theoretical and Applied Climatology, 112(3-4): 565-573.

33- Nitzche M.H., Silva B.B., and Martinez A.S. 1985. Indicativo de ano Seco e Chuvoso. Sociedade Brasileira de Agrometeorologia, Londrina-PR, Brazil, 307-314.

34- Oladipo E.O. 1985. A comparative performance analysis of three meteorological drought indices. Journal of Climatology, 5: 655 – 664.

35- Palmer W.C. 1965. Meteorological drought, Research Paper No. 45, U.S. Department of Commerce Weather Bureau, Washington, DC.

36- Piccarreta M., Capolongo D., and Boenzi F. 2004. Trend analysis of precipitation and drought in Basilicata from 1923 to 2000 within a Southern Italy context, International Journal of Climatology, 24:907–922.

37- Piri H., Rahdari V., Maleki S. 2013. Study and compare performance of four meteorological drought index in the risk management droughts in Sistan and Baluchestan province. Irrigation & Water Engineering, 11:96-114. (in Persian)

38- Rebetez M. 1999. Twentieth century trends in droughts in southern Switzerland, Geophysical Research Letters, 26(6): 755–758.

39- Sen P.K. 1968. Estimates of the regression coefficient based on Kendall’s tau, Journal of the American Statistical Association, 63: 1379–1389.

40- Shafer B.A., and Dezman L.E. 1982. Development of a Surface Water Supply Index (SWSI) to assess the severity of drought conditions in snowpack runoff areas, p. 164–175. In Proceedings of the Western Snow Conference, Fort Collins, CO.

41- Silva Y., Takahashi K., and Chavez R. 2007. Dry and wet rainy seasons in the Mantaro River basin (central Peruvian Andes), Advances in Geosciences, 14: 1–4.

42- Smith J.B., Huq S., Lenhart S., Mata L.J., Nemesova I., and Toure S. 1996. Vulnerability and adaptation to climate change, p. 161-181. Interim results from the U. S. country studies program, U.S. Country Studies Program, Washington, DC, USA.

43- Tabari H., Abghari H., and Hosseinzadeh Talaee P. 2012a. Temporal trends and spatial characteristics of drought and rainfall in arid and semiarid regions of Iran, Hydrological Processes, 26:3351–3361.

44- Tabari H., Hosseinzadeh Talaee P., Ezani A., and Shifteh Some’e B. 2012b. Shift changes and monotonic trends in autocorrelated temperature series over Iran, Theoretical and Applied Climatology, 109:95–108.

45- Theil H. 1950. A rank-invariant method of linear and polynomial regression analysis, Part 3. Proceedings of Koninalijke Nederlandse Akademie van Weinenschatpen A53:1397–1412.

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

47- Vicente-Serrano S.M., and Cuadrat-Prats J.M. 2007. Trends in drought intensity and variability in the middle Ebro valley (NE of the Iberian peninsula) during the second half of the twentieth century, Theoretical and Applied Climatology, 88:247–258.

48- Vicente-Serrano S.M., Gonzalez-Hidalgo J.C., de Luis M., and Raventos J. 2004. Drought patterns in the Mediterranean area: the Valencia region (eastern Spain), Climate Research, 26: 5–15.

49- Von Storch H. 1995. Misuses of Statistical Analysis in Climate Research, Analysis of Climate Variability: Applications of Statistical Techniques, Berlin, Springer, 11–26.

50- Watson R.T., Zinyowera M.C., and Moss R.H. 1997. The Regional Impacts of Climate Change: An Assessment of Vulnerability. Cambridge University Press: Cambridge; 517.

51- Wilhite D.A. 2000. Drought: A Global Assessment, Rutledge Press, London and New York, Volume I.

52- Wilhite D.A., and Glantz M.H. 1985. Understanding the drought phenomenon: the role of definitions, Water International, 10: 111-120.

53- World Meteorological Organization (WMO). 2003. Statement on the Status of Global Climate in 2003, WMO Publ. no. 966, WMO, Geneva.

54- Worrall F., Burt T.P., and Adamson J.K. 2006. Trends in drought frequency– the fate of doc export from British peatlands, Climatic Change, 76: 339–359.

55- Xu C.Y., and Singh V.P. 2004. Review on regional water resources assessment models under stationary and changing climate, Water Resource Management 18: 591–612.

56- Xu Z.X., Takeuchi K., and Ishidaira H. 2003. Monotonic trend and step changes in Japanese precipitation, Journal of Hydrology, 279:144–150.

57- Yue S., and Hashino M. 2003. Temperature trends in Japan: 1900–1996, Theoretical and Applied Climatology, 75: 15–27.

58- Yue S., and Wang C.Y. 2002. The influence of serial correlation on the Mann– Whitney test for detecting a shift in median, Advances in Water Resources, 25: 325–333.

59- Yue S., Pilon P., and Phinney B. 2003. Canadian streamflow trend detection: impacts of serial and cross-correlation, Hydrological Science Journal 48(1):51–63.

60- Zhang Q., Singh V.P., Li J., and Chen X. 2011. Analysis of the periods of maximum consecutive wet days in China, Journal of Geophysical Research, 116 (D23106).

61- Zhang Q., Xu C.Y., and Zhang Z. 2009. Observed changes of drought/wetness episodes in the Pearl River basin, China, using the standardized precipitation index and aridity index, Theoretical and Applied Climatology, 98: 89–99.

62- Zhang Q., Xu C.Y., Gemmer M., Chen Y.D., and Liu C.L. 2008. Changing properties of precipitation concentration in the Pearl River basin, China, Stochastic Environmental Research and Risk Assessment, 23(3): 377-385.