Natural Hazards

, Volume 91, Issue 1, pp 155–178 | Cite as

Spatiotemporal variation of hydrological drought based on the Optimal Standardized Streamflow Index in Luanhe River basin, China

  • Xu Chen
  • Fa-wen Li
  • Ping Feng
Original Paper


To establish the drought index objectively and reasonably and evaluate the hydrological drought accurately, firstly, the optimal distribution was selected from nine distributions (normal, lognormal, exponential, gamma, general extreme value, inverse Gaussian, logistic, log-logistic and Weibull), then the Optimal Standardized Streamflow Index (OSSI) was calculated based on the optimal distribution, and last, the spatiotemporal evolution of hydrological drought based on the OSSI series was investigated through the monthly streamflow data of seven hydrological stations during the period 1961–2011 in Luanhe River basin, China. Results suggest: (1) the general extreme value and log-logistic distributions performed prominently in fitting the monthly streamflow of Luanhe River basin. (2) The main periods of hydrological drought in Luanhe River basin were 148–169, 75–80, 42–45, 14–19 and 8–9 months. (3) The hydrological drought had an aggravating trend over the past 51 year and with the increase in timescale, the aggravating trend was more serious. (4) The lower the drought grade was, the broader the coverage area. As for the Luanhe River basin, the whole basin suffered the mild and more serious drought, while the severe and more serious drought only cover some areas. (5) With the increase in time step, the frequency distribution of mild droughts across the basin tended to be concentrated, the frequency of extreme droughts in middle and upper reaches tended to increase and the frequency in downstream tends to decrease. This research can provide powerful references for water resources planning and management and drought mitigation.


Hydrological drought OSSI Period and trend Drought duration and severity Drought frequency 



The authors sincerely acknowledge the insightful comments and corrections of editors and reviewers. This investigation is supported by the Natural Science Foundation of China (Nos. 51579169, 51279123, 51179117) and the National Key Research and Development Plan (Grant No. 2016YFC0401407).


  1. Awange JL, Khandu SM et al (2016) Exploring hydro-meteorological drought patterns over the Greater Horn of Africa (1979–2014) using remote sensing and reanalysis products. Adv Water Resour 94:45–59CrossRefGoogle Scholar
  2. Hamed KH, Rao AR (1998) A modified Mann–Kendall trend test for autocorrelated data. J Hydrol 204(1):182–196CrossRefGoogle Scholar
  3. Hayes M, Wilhite DA, Svoboda M et al (1999) Monitoring the 1996 drought using the standardized precipitation index. Bull Am Meteor Soc 80:429–438CrossRefGoogle Scholar
  4. Hong X, Guo S, Zhou Y et al (2014) Uncertainties in assessing hydrological drought using Streamflow Drought Index for the upper Yangtze River basin. Stoch Environ Res Risk Assess 29(4):1235–1247CrossRefGoogle Scholar
  5. Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis. Philos Trans R Soc 454:903–995Google Scholar
  6. Kang S, Lin H (2007) Wavelet analysis of hydrological and water quality signals in an agricultural watershed. J Hydrol 338(1–2):1–14CrossRefGoogle Scholar
  7. Kendall M (1975) Rank correlation methods. Charles Griffin, LondonGoogle Scholar
  8. Lin Q et al (2017) Correlation between hydrological drought, climatic factors, reservoir operation, and vegetation cover in the Xijiang Basin, South China. J Hydrol 549:512–524CrossRefGoogle Scholar
  9. Liu Z, Xia X, Zhou W (2015) A test for equality of two distributions via jackknife empirical likelihood and characteristic functions. Comput Stat Data Anal 92:97–114CrossRefGoogle Scholar
  10. Lu WX, Chen SM, Luo JN (2013) Meteorological drought characteristics research of western Jilin Province based on wavelet analyses. Appl Mech Mater 295–298:2121–2126CrossRefGoogle Scholar
  11. Mann HB (1945) Nonparametric tests against trend. Econometrica 13(3):245–259CrossRefGoogle Scholar
  12. McMahon TA, Pegram GGS, Vogel RM et al (2007) Revisiting reservoir storage–yield relationships using a global streamflow database. Adv Water Resour 30(8):1858–1872CrossRefGoogle Scholar
  13. Nalbantis I, Tsakiris G (2008) Assessment of hydrological drought revisited. Water Resour Manag 23(5):881–897CrossRefGoogle Scholar
  14. Panagiotis A, Fotios M, Nikos K et al (2012) Computation of drought index SPI with alternative distribution functions. Water Resour Manag 26:2453–2473CrossRefGoogle Scholar
  15. Peel MC, Wang QJ, Vogel RM et al (2001) The utility of L-moment ratio diagrams for selecting a regional probability distribution. Hydrol Sci J 46(1):147–155CrossRefGoogle Scholar
  16. Sang YF, Wang Z, Liu C (2014) Comparison of the MK test and EMD method for trend identification in hydrological time series. J Hydrol 510:293–298CrossRefGoogle Scholar
  17. Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought. Geophys Res Lett 35(2):226–236CrossRefGoogle Scholar
  18. Spinoni J, Naumann G, Vogt JV et al (2015) The biggest drought events in Europe from 1950 to 2012. J Hydrol 3:509–524Google Scholar
  19. Tabari H, Nikbakht J, Hosseinzadeh Talaee P (2012) Hydrological drought assessment in northwestern iran based on Streamflow Drought Index (SDI). Water Resour Manag 27(1):137–151CrossRefGoogle Scholar
  20. Thomas J, Prasannakumar V (2016) Temporal analysis of rainfall (1871–2012) and drought characteristics over a tropical monsoon-dominated State (Kerala) of India. J Hydrol 534:266–280CrossRefGoogle Scholar
  21. Trinh T, Ishida K, Kavvas ML et al (2017) Assessment of 21st century drought conditions at Shasta Dam based on dynamically projected water supply conditions by a regional climate model coupled with a physically-based hydrology model. Sci Total Environ 586:197–205CrossRefGoogle Scholar
  22. Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23(7):1696–1718CrossRefGoogle Scholar
  23. Wu Z, Lin Q, Lu G et al (2015) Analysis of hydrological drought frequency for the Xijiang River Basin in South China using observed streamflow data. Nat Hazards 77(3):1655–1677CrossRefGoogle Scholar
  24. Yue S, Wang CY (2002) Power of the Mann–Whitney test for detecting a shift in median or mean of hydro-meteorological data. Stoch Environ Res Risk Assess 16(4):307–323CrossRefGoogle Scholar

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© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.State Key Laboratory of Hydraulic Engineering Simulation and SafetyTianjin UniversityTianjinPeople’s Republic of China
  2. 2.Water Conservancy Project, College of Water Resources Science and EngineeringTaiyuan University of TechnologyTaiyuanPeople’s Republic of China
  3. 3.Hydrology and Water ResourcesHohai UniversityNanjingPeople’s Republic of China

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