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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 4 | Copyright
Hydrol. Earth Syst. Sci., 18, 1539-1548, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 29 Apr 2014

Research article | 29 Apr 2014

Climate information based streamflow and rainfall forecasts for Huai River basin using hierarchical Bayesian modeling

X. Chen1,6, Z. Hao1, N. Devineni2,3, and U. Lall4,5 X. Chen et al.
  • 1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
  • 2Department of Civil Engineering, The City University of New York (City College), New York, NY 10031, USA
  • 3NOAA-Cooperative Remote Sensing Science and Technology Center, The City University of New York (City College), New York, NY 10031, USA
  • 4Columbia Water Center, The Earth Institute, Columbia University, New York, NY 10027, USA
  • 5Department of Earth and Environmental Engineering, Columbia University, New York, NY 10027, USA
  • 6Bureau of Hydrology, Changjiang Water Resources Commission, Wuhan 430010, China

Abstract. A Hierarchal Bayesian model is presented for one season-ahead forecasts of summer rainfall and streamflow using exogenous climate variables for east central China. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multi-level structure with regression coefficients modeled from a common multi-variate normal distribution resulting in partial pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include receiver operating characteristic, reduction of error, coefficient of efficiency, rank probability skill scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast season-ahead regional summer rainfall and streamflow offers potential for developing adaptive water risk management strategies.

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