On the predictability of daily rainfall during rainy season over the Huaihe River Basin

Research output: Contribution to journalArticle


In terms of climate change and precipitation, there is large interest in how large-scale climatic features affect regional rainfall amount and rainfall occurrence. Large-scale climate elements need to be downscaled to the regional level for hydrologic applications. Here, a new Nonhomogeneous Hidden Markov Model (NHMM) called the Bayesian-NHMM is presented for downscaling and predicting of multisite daily rainfall during rainy season over the Huaihe River Basin (HRB). The Bayesian-NHMM provides a Bayesian method for parameters estimation. The model avoids the risk to have no solutions for parameter estimation, which often occurs in the traditional NHMM that uses point estimates of parameters. The Bayesian-NHMM accurately captures seasonality and interannual variability of rainfall amount and wet days during the rainy season. The model establishes a link between large-scale meteorological characteristics and local precipitation patterns. It also provides a more stable and efficient method to estimate parameters in the model. These results suggest that prediction of daily precipitation could be improved by the suggested new Bayesian-NHMM method, which can be helpful for water resources management and research on climate change.


External organisations
  • Hohai University
  • Nanjing Hydraulic Research Institute
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Oceanography, Hydrology, Water Resources


  • Rainy-season precipitation prediction, The Bayesian-NHMM, The Huaihe River Basin
Original languageEnglish
Article number916
JournalWater (Switzerland)
Issue number5
Publication statusPublished - 2019
Publication categoryResearch