Natural Hazards

, Volume 84, Issue 2, pp 1137–1160 | Cite as

Integrated assessment for hydrometeorological drought based on Markov chain model

  • Jie Yang
  • Yimin Wang
  • Jianxia Chang
  • Jun Yao
  • Qiang Huang
Original Paper


Drought assessment based on a single index cannot comprehensively reflect the characteristics of a drought affected by multiple factors. Therefore, the main purpose of this study is to accurately assess the drought by constructing an integrated drought assessment method (PDSI-SDI) that can combine the meteorological drought and hydrological drought at the same time. To better evaluate and forecast the drought, the Markov chain model is employed in this study to calculate the expected residence time, return period and transition probabilities of the drought. Furthermore, the Mann–Kendall method is adopted to predict the trend of the drought. The Weihe River Basin is selected as the study area, and according to the distribution characteristics of the water system, it is divided into five districts in order to better assess the drought. Results indicate that: (1) spatially, drought probabilities increase from south to north and west to east. (2) Temporally, probabilities of spring droughts are the highest, followed by summer droughts and autumn droughts, winter droughts have the lowest probabilities, extreme droughts are more likely to occur in autumn. (3) Drought preferentially transfers within the same scenario, except scenario 4 (meteorological drought with no hydrological drought) in autumn is prone to shift to scenario 1 (no meteorological drought with no hydrological drought). (4) There is a significant drying trend of the drought in the Weihe River Basin at the significance level of 95 %. The integrated drought assessment method and other methods adopted in this study can be applied in other regions as well.


Integrated drought assessment Palmer Drought Severity Index Streamflow drought index Markov chain model Mann–Kendall test method 



This research is supported by the National Natural Science Foundation of China (51190093), the high efficiency development and utilization of water resources key project of Ministry of Science and Technology (2016YFC0400906), the Key Innovation Group of Science and Technology of Shaanxi (2012KCT-10). Sincere gratitude is extended to the editor and the anonymous reviewers for their help.


  1. Benitez JB, Domecq RM (2014) Analysis of meteorological drought episodes in Paraguay. Clim Change 127(1):15–25CrossRefGoogle Scholar
  2. Bierkens MEP, Wada Y, Wisser D et al (2013) Human water consumption intensifies hydrological drought worldwide. Environ Res Lett 8(3):6138Google Scholar
  3. Chang JX, Wang YM, Erkan I et al (2014) Impact of climate change and human activities on runoff in the Weihe River Basin, China. Quat Int 380–381:169–179Google Scholar
  4. Dai AG (2011a) Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008. J Geophys Res 116(D12):1248–1256CrossRefGoogle Scholar
  5. Dai AG (2011b) Erratum: Drought under global warming: a review. Wiley Interdiscip Rev Clim Change 2(1):45–65CrossRefGoogle Scholar
  6. Dai AG, Trenberth KE, Qian TT (2004) A global data set of Palmer Drought Severity Index for 1870–2002: relationship with soil moisture and effects of surface warming. J Hydrometeorol 5(6):1117–1130CrossRefGoogle Scholar
  7. Di BG, Montanari A (2009) Uncertainty in river discharge observations: a quantitative analysis. Hydrol Earth Syst Sci 13(6):913–921CrossRefGoogle Scholar
  8. Du J, Shi CX (2012) Effects of climatic factors and human activities on runoff of the Weihe River in recent decades. Quatern Int 28(1):58–65CrossRefGoogle Scholar
  9. Garen DC (1993) Revised surface-water supply index for Western United States. J Water Resour Plan Manag 119(4):437–554CrossRefGoogle Scholar
  10. Gocic M, Trajkovic S (2013) Analysis of changes in meteorological variables using Mann–Kendall and Sen’s slope estimator statistical tests in Serbia. Glob Planet Change 100(1):172–182CrossRefGoogle Scholar
  11. Hamed KH (2008) Trend detection in hydrologic data: the Mann–Kendall trend test under the scaling hypothesis. J Hydrol 349(3–4):350–363CrossRefGoogle Scholar
  12. Hao L, Zhang X, Liu S (2012) Risk assessment to China’s agricultural drought disaster in county unit. Nat Hazards 61(2):785–801CrossRefGoogle Scholar
  13. Hayes M, Svoboda M, Wall N et al (2011) The Lincoln declaration on drought indices: universal meteorological drought index recommended. Bull Am Meteorol Soc 92(4):485–488CrossRefGoogle Scholar
  14. Hong X, Guo S, Zhou Y, Xiong L (2014) Uncertainties in assessing hydrological drought using streamflow drought index for the upper Yangtze River basin. Stoch Env Res Risk Assess 29(4):1235–1247CrossRefGoogle Scholar
  15. Huang S, Chang J, Leng G et al (2015a) Integrated index for drought assessment based on variable fuzzy set theory: a case study in the Yellow River Basin, China. J Hydrol 527:608–618CrossRefGoogle Scholar
  16. Huang S, Huang Q, Chang J et al (2015b) The response of agricultural drought to meteorological drought and the influencing factors: a case study in the Wei River Basin, China. Agric Water Manag 159:45–54CrossRefGoogle Scholar
  17. Kendall MG (1975) Rank correlation methods. Nafner, New YorkGoogle Scholar
  18. Keshavarz M, Karami E, Vanclay F (2013) The social experience of drought in rural Iran. Land Use Policy 30(1):120–129CrossRefGoogle Scholar
  19. Mann HB (1945) Nonparametric test against trend. Econometrica 13(13):245–259CrossRefGoogle Scholar
  20. Mishra AM, Singh VP (2010) A review of drought concepts. J Hydrol 391(1):202–216CrossRefGoogle Scholar
  21. Nalbantis I, Tsakiris G (2009) Assessment of hydrological drought revisited. Water Resour Manag 23(5):881–897CrossRefGoogle Scholar
  22. Pai DS, Sridhar L, Guhathakurta P et al (2011) District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index. Nat Hazards 59(59):1797–1813CrossRefGoogle Scholar
  23. Palmer WC (1965) Meteorological drought. U.S Department of Commerce Weather Bureau Research Paper, WashingtonGoogle Scholar
  24. Paulo AA, Pereira LS (2007) Prediction of SPI drought class transitions using Markov chains. Water Resour Manag 21(10):1813–1827CrossRefGoogle Scholar
  25. Paulo AA, Ferreira E, Coelho C et al (2005) Drought class transition analysis through and loglinear models, an approach to early warning. Agric Water Manag 77(1):59–81CrossRefGoogle Scholar
  26. Ramos MH, Mathevet T, Thielen J et al (2010) Communicating uncertainty in hydro-meteorological forecasts: mission impossible? Meteorol Appl 17(2):223–235CrossRefGoogle Scholar
  27. Şen Z (1990) Critical drought analysis by second-order Markov chain. J Hydrol 120(1–4):183–202Google Scholar
  28. Sepulcre-Canto G, Horion S, Singleton A et al (2012) Development of a combined drought indicator to detect agricultural drought in Europe. Nat Hazards Earth Syst Sci 12(11):3519–3531CrossRefGoogle Scholar
  29. Shahid S, Behrawan H (2010) Drought risk assessment in the western part of Bangladesh. Nat Hazards 46(3):391–413CrossRefGoogle Scholar
  30. She DX, Xia J (2012) The spatial and temporal analysis of dry spells in the Yellow River Basin, China. Stoch Env Res Risk Assess 27(1):29–42CrossRefGoogle Scholar
  31. Sun L, Mitchell SW, Davidson A (2012) Multiple drought indices for agricultural drought risk assessment on the Canadian prairies. Int J Climatol 32(11):1628–1639CrossRefGoogle Scholar
  32. Tabari H, Nikbakht J, Talaee PH (2008) Hydrological drought assessment in Northwestern Iran based on streamflow drought index (SDI). Water Resour Manag 27(1):137–151CrossRefGoogle Scholar
  33. Tabari H, Zamani R, Rahmati H et al (2015) markov chains of different orders for streamflow drought analysis. Water Resour Manag 29(9):3441–3457CrossRefGoogle Scholar
  34. Wang Y, Zhou L (2005) Observed trends in extreme precipitation events in china during 1961–2001 and the associated changes in large-scale circulation. Geophys Res Lett 32(9):297–314CrossRefGoogle Scholar
  35. Zhao G, Mu X, Tian P et al (2013) Climate changes and their impacts on water resources in semiarid regions: a case study of the Wei River basin, China. Hydrol Process 27(26):3852–3863CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Jie Yang
    • 1
  • Yimin Wang
    • 1
  • Jianxia Chang
    • 1
  • Jun Yao
    • 2
  • Qiang Huang
    • 1
  1. 1.State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area, School of Water Resources and HydropowerXi’an University of TechnologyXi’anChina
  2. 2.Hanjiang - to - Weihe River Water Diversion Project Construction Co. Ltd.Xi’anChina

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