Advanced

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

Cao, Qing; Hao, Zhenchun; Yuan, Feifei LU ; Berndtsson, Ronny LU ; Xu, Shijie; Gao, Huibin and Hao, Jie (2019) In Water (Switzerland) 11(5).
Abstract

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... (More)

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.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Rainy-season precipitation prediction, The Bayesian-NHMM, The Huaihe River Basin
in
Water (Switzerland)
volume
11
issue
5
publisher
MDPI AG
external identifiers
  • scopus:85066327783
ISSN
2073-4441
DOI
10.3390/w11050916
language
English
LU publication?
yes
id
df9661fc-5f1d-4de3-aec0-f004ce146ab0
date added to LUP
2019-06-12 14:37:07
date last changed
2019-07-02 04:47:56
@article{df9661fc-5f1d-4de3-aec0-f004ce146ab0,
  abstract     = {<p>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.</p>},
  articleno    = {916},
  author       = {Cao, Qing and Hao, Zhenchun and Yuan, Feifei and Berndtsson, Ronny and Xu, Shijie and Gao, Huibin and Hao, Jie},
  issn         = {2073-4441},
  keyword      = {Rainy-season precipitation prediction,The Bayesian-NHMM,The Huaihe River Basin},
  language     = {eng},
  number       = {5},
  publisher    = {MDPI AG},
  series       = {Water (Switzerland)},
  title        = {On the predictability of daily rainfall during rainy season over the Huaihe River Basin},
  url          = {http://dx.doi.org/10.3390/w11050916},
  volume       = {11},
  year         = {2019},
}