SCIENTIA GEOGRAPHICA SINICA ›› 2016, Vol. 36 ›› Issue (9): 1437-1444.doi: 10.13249/j.cnki.sgs.2016.09.017

• Orginal Article • Previous Articles    

Bayesian Probabilistic Forecasting of Seasonal Hydrological Drought Based on Copula Function

Yuhu Zhang1,2(), Liu Xiang1,2(), Qing Sun3, Qiuhua Chen3   

  1. 1. College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
    2. Beijing Laboratory of Water Resource Security, Beijing 100048, China
    3. School of Mathematical Sciences, Capital Normal University, Beijing 100048, China
  • Received:2016-01-04 Revised:2016-03-28 Online:2016-09-20 Published:2016-09-20
  • Supported by:
    Key Technology Research and Development Program(2013BAC10B01, 2012BAC19B0305), Scientific Research Project of Beijing Educational Committee (KZ201410028030).


Forecasting of hydrological drought plays an important role in the decision-making process of water resources management. Bayesian networks provide an elegant tool to reflect the autocorrelation in the runoff record and develop the conditional probabilities, furnishing a framework for various types of probabilistic drought forecasting. This study presents a Bayesian probabilistic forecasting model based on best-fitted first-order copula functions. Standardized runoff index (SRI) is used to characterize the historical hydrological droughts and forecast probabilistic drought by season runoff correlations of a target season with the previous seasons in future. We used the Xidaqiao hydrological station in the Aksu River, sub-basin of the Trim River Basin of Xinjiang as a case, and apply the Bayesian probabilistic forecasting model to forecast the probability of autumn drought during the period 2000-2010 based on data from the previous summer, and testing the accuracy of the model. The results show that the probability of an autumn drought in the Aksu River Basin during 2001-2009 was low (24%-38%), with mainly abnormal and moderate droughts, whereas drought was very likely to occur in 2010 (95%), with the probability of occurrence of an exceptional drought being as high as 81%. The model is reliable and can forecast hydrological drought in the next season when current hydrological conditions are known. And the model can quantitatively express the uncertainty of hydrological drought and then improve its prediction accuracy. It does not require the linear assumption of normality and has a wide range of applications. The model provides an useful tool for uncertainty modeling through a probabilistic representation of model parameter uncertainty, developing conditional probabilities for given forecast variables, and returning the highest probable forecast along with an assessment of the uncertainty around that value. However, this study only selects the highest seasonal correlation as a condition, and further studies of hydrological drought forecasting are needed using high-dimensional copula functions. Furthermore, it’s a very urgent task to use more hydrological sites to forecast regional hydrological drought.

Key words: drought, forecast, Bayesian, Copula function, the Aksu River

CLC Number: 

  • K903