On the predictability of daily rainfall during rainy season over the Huaihe River Basin
(2019) In Water 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.
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- author
- Cao, Qing ; Hao, Zhenchun ; Yuan, Feifei LU ; Berndtsson, Ronny LU ; Xu, Shijie ; Gao, Huibin and Hao, Jie
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Rainy-season precipitation prediction, The Bayesian-NHMM, The Huaihe River Basin
- in
- Water
- volume
- 11
- issue
- 5
- article number
- 916
- 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
- 2023-09-23 05:42:04
@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>}}, author = {{Cao, Qing and Hao, Zhenchun and Yuan, Feifei and Berndtsson, Ronny and Xu, Shijie and Gao, Huibin and Hao, Jie}}, issn = {{2073-4441}}, keywords = {{Rainy-season precipitation prediction; The Bayesian-NHMM; The Huaihe River Basin}}, language = {{eng}}, number = {{5}}, publisher = {{MDPI AG}}, series = {{Water}}, title = {{On the predictability of daily rainfall during rainy season over the Huaihe River Basin}}, url = {{http://dx.doi.org/10.3390/w11050916}}, doi = {{10.3390/w11050916}}, volume = {{11}}, year = {{2019}}, }