Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

XING Zhenxiang; RUI Xiaofang; FU Qiang

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Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

XING Zhenxiang, RUI Xiaofang, FU Qiang

文章导航 > Chinese Geographical Science > 2011 > 21(1): 74-83

XING Zhenxiang, RUI Xiaofang, FU Qiang. Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting[J]. 中国地理科学, 2011, 21(1): 74-83.

引用本文:

XING Zhenxiang, RUI Xiaofang, FU Qiang. Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting[J]. 中国地理科学, 2011, 21(1): 74-83.

XING Zhenxiang, RUI Xiaofang, FU Qiang. Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting[J]. Chinese Geographical Science, 2011, 21(1): 74-83.

Citation:

XING Zhenxiang, RUI Xiaofang, FU Qiang. Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting[J]. Chinese Geographical Science, 2011, 21(1): 74-83.

Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

1. Department of Water Conservancy & Civil Engineering, Northeast Agriculture University, Harbin 150030, China;2. Key Laboratory for Water-saving Agriculture of Universities in Heilongjiang Province, Harbin 150030, China;3. Department of Hydrology, Hohai University, Nanjing 210098, China

基金项目: 中国博士后科学基金（面上资助）;中国科学院中乌白专项2010;黑龙江省博士后科学基金;黑龙江省教育厅科学技术研究项目

详细信息

通讯作者:
XING Zhenxiang

Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

1. Department of Water Conservancy & Civil Engineering, Northeast Agriculture University, Harbin 150030, China;2. Key Laboratory for Water-saving Agriculture of Universities in Heilongjiang Province, Harbin 150030, China;3. Department of Hydrology, Hohai University, Nanjing 210098, China

Funds: The National Natural Science Foundation of China;Special Project of CAS-Russia, Ukraine and Belarus (2010);The Postdoctoral Foundation of Heilongjiang province;Foundation of Heilongjiang Province Educational Committee

More Information

Corresponding author: XING Zhenxiang

摘要: A hydrologic model consists of several parameters that are usually considered certain. Hydrologic model parameters are calibrated through observed hydrologic processes whose uncertainty result in uncertainty of hydrologic model parameters, so hydrologic forecasting is uncertain. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo based Adaptive Metropolis method (AM-MCMC) is used to research uncertainty of parameters of Nash model, while the probabilistic flood forecasting being made with the simulated samples of parameters of Nash model in this paper. Study case showed that the AM-MCMC based on BFS proposed in the paper can obtain the posterior distribution of parameters of Nash model according to known information of parameters. Worked with Nash model, AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as get predictands of mean and variance of flood discharge, which may be used to estimate risk of flood control decision.
- Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting
Abstract: A hydrologic model consists of several parameters that are usually considered certain. Hydrologic model parameters are calibrated through observed hydrologic processes whose uncertainty result in uncertainty of hydrologic model parameters, so hydrologic forecasting is uncertain. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo based Adaptive Metropolis method (AM-MCMC) is used to research uncertainty of parameters of Nash model, while the probabilistic flood forecasting being made with the simulated samples of parameters of Nash model in this paper. Study case showed that the AM-MCMC based on BFS proposed in the paper can obtain the posterior distribution of parameters of Nash model according to known information of parameters. Worked with Nash model, AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as get predictands of mean and variance of flood discharge, which may be used to estimate risk of flood control decision.
- Bayesian Forecasting System

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Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

1. Department of Water Conservancy & Civil Engineering, Northeast Agriculture University, Harbin 150030, China;2. Key Laboratory for Water-saving Agriculture of Universities in Heilongjiang Province, Harbin 150030, China;3. Department of Hydrology, Hohai University, Nanjing 210098, China

基金项目: 中国博士后科学基金（面上资助）;中国科学院中乌白专项2010;黑龙江省博士后科学基金;黑龙江省教育厅科学技术研究项目

通讯作者: XING Zhenxiang

收稿日期: 2010-03-24
修回日期: 2010-06-08
刊出日期: 2011-01-15

关键词:

Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

摘要: A hydrologic model consists of several parameters that are usually considered certain. Hydrologic model parameters are calibrated through observed hydrologic processes whose uncertainty result in uncertainty of hydrologic model parameters, so hydrologic forecasting is uncertain. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo based Adaptive Metropolis method (AM-MCMC) is used to research uncertainty of parameters of Nash model, while the probabilistic flood forecasting being made with the simulated samples of parameters of Nash model in this paper. Study case showed that the AM-MCMC based on BFS proposed in the paper can obtain the posterior distribution of parameters of Nash model according to known information of parameters. Worked with Nash model, AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as get predictands of mean and variance of flood discharge, which may be used to estimate risk of flood control decision.

English Abstract

Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

1. Department of Water Conservancy & Civil Engineering, Northeast Agriculture University, Harbin 150030, China;2. Key Laboratory for Water-saving Agriculture of Universities in Heilongjiang Province, Harbin 150030, China;3. Department of Hydrology, Hohai University, Nanjing 210098, China

Corresponding author: XING Zhenxiang

Received Date: 2010-03-24
Rev Recd Date: 2010-06-08
Publish Date: 2011-01-15

Keywords:

Bayesian Forecasting System

Abstract: A hydrologic model consists of several parameters that are usually considered certain. Hydrologic model parameters are calibrated through observed hydrologic processes whose uncertainty result in uncertainty of hydrologic model parameters, so hydrologic forecasting is uncertain. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo based Adaptive Metropolis method (AM-MCMC) is used to research uncertainty of parameters of Nash model, while the probabilistic flood forecasting being made with the simulated samples of parameters of Nash model in this paper. Study case showed that the AM-MCMC based on BFS proposed in the paper can obtain the posterior distribution of parameters of Nash model according to known information of parameters. Worked with Nash model, AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as get predictands of mean and variance of flood discharge, which may be used to estimate risk of flood control decision.

XING Zhenxiang, RUI Xiaofang, FU Qiang. Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting[J]. 中国地理科学, 2011, 21(1): 74-83.

引用本文:

XING Zhenxiang, RUI Xiaofang, FU Qiang. Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting[J]. 中国地理科学, 2011, 21(1): 74-83.

XING Zhenxiang, RUI Xiaofang, FU Qiang. Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting[J]. Chinese Geographical Science, 2011, 21(1): 74-83.

Citation:

XING Zhenxiang, RUI Xiaofang, FU Qiang. Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting[J]. Chinese Geographical Science, 2011, 21(1): 74-83.

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Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

通讯作者: XING Zhenxiang

Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

Corresponding author: XING Zhenxiang

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出版历程

Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

1. Department of Water Conservancy & Civil Engineering, Northeast Agriculture University, Harbin 150030, China;2. Key Laboratory for Water-saving Agriculture of Universities in Heilongjiang Province, Harbin 150030, China;3. Department of Hydrology, Hohai University, Nanjing 210098, China

通讯作者: XING Zhenxiang

English Abstract

Parameters Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

1. Department of Water Conservancy & Civil Engineering, Northeast Agriculture University, Harbin 150030, China;2. Key Laboratory for Water-saving Agriculture of Universities in Heilongjiang Province, Harbin 150030, China;3. Department of Hydrology, Hohai University, Nanjing 210098, China

Corresponding author: XING Zhenxiang

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通讯作者:
XING Zhenxiang