Using stratified Bayesian model averaging in probabilistic forecasts of precipitation over the middle and lower Yangtze River region

Qi, Haixia; Zhi, Xiefei; Peng, Tao; Bai, Yongqing; Lin, Chien-Chung; Chen, Wen

doi:10.1007/s00703-021-00799-w

Cited by 4 publications

(4 citation statements)

References 33 publications

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“…The MLYR experiences convergence of the summer monsoon and cold air from high latitudes, leading to the formation of Meiyu in June and July each year and resulting in a rainy summer season (Niu et al, 2021;Wang et al, 2021). The MLYR is known for its frequent drought and flood disasters, which are primarily caused by the uneven distribution of precipitation (Qi et al, 2021). Thus, it is of great significance to further study the spatial-temporal variation characteristics and the driving forces of extreme precipitation, which is possible to worsen the uneven temporal and spatial distribution of precipitation in the MLYR.…”

Section: Study Areamentioning

confidence: 99%

Changes of extreme precipitation in the middle and lower reaches of the Yangtze River and their correlation with atmospheric circulation

Yang

Huang

et al. 2023

Front. Earth Sci.

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Under the backdrop of climate change, the rising frequency and intensity of extreme precipitation events in the middle and lower Yangtze River (MLYR) region are causing serious consequences and economic losses. To assess regional risk and understand the impact of atmospheric circulation on extreme precipitation, it's crucial to analyze the spatial and temporal fluctuations of these events, focusing on the time-frequency phase relationship. Using the observed precipitation data from 1979 to 2015, nine extreme precipitation indices were calculated and a trend analysis and wavelet coherence analysis were performed to evaluate their variation. The results show that: 1) an overall increase in the frequency, intensity, and duration of precipitation in the MLYR, with significant rises in the wet daily precipitation and highest precipitation amount in 1-day period; 2) a higher likelihood of extreme precipitation events in the southeast of the MLYR, and an uneven spatial distribution; 3) the Western Pacific Subtropical High (WPSH), the South China Sea high (SCSH), and the East Asian westerly jet (EAJ) all play a role in precipitation changes in the MLYR, with the WPSH having the most significant impact, followed by the SCSH, and the EAJ being weaker. The results deepen our understanding of the various characteristics and causes of extreme precipitation in the MLYR.

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Section: Study Areamentioning

confidence: 99%

Changes of extreme precipitation in the middle and lower reaches of the Yangtze River and their correlation with atmospheric circulation

Yang

Huang

et al. 2023

Front. Earth Sci.

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“…Hence the assumption of normal distribution does not rigorously hold in this case. To explore the effect of conditional PDFs on the estimates of BMA parameters, it is assumed that the conditional probability p k ( y | f k , D ) follows a gamma distribution (PDF = 0 for y ≤ 0) with two parameters ( α and β ) (Qi et al., 2021; Vrugt & Robinson, 2007; Vrugt et al., 2008), and the log‐likelihood function is shown in Equation . The gamma distribution is not symmetric around the mean like the normal distribution.…”

Section: Methodsmentioning

confidence: 99%

Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling

Huang,

Merwade

2023

Water Resources Research

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As all kinds of numerical models are emerging in hydrologic and hydraulic engineering, Bayesian model averaging (BMA) is one of the popular multi‐model methods used to account for various uncertainties in the flood modeling process and generate robust ensemble predictions. The reliability of BMA parameters (weights and variances) determines the accuracy of BMA predictions. However, the uncertainty in BMA parameters with fixed values, which are usually obtained from Expectation‐Maximization (EM) algorithm, has not been adequately investigated in BMA‐related applications over the past few decades. Given the limitations of the default EM algorithm, Metropolis‐Hastings (M‐H) algorithm, which is one of the most widely used algorithms in Markov Chain Monte Carlo (MCMC), is proposed to estimate BMA parameters. Both numerical experiments and one‐dimensional Hydrologic Engineering Center‐River Analysis System models are employed to examine the applicability of M‐H algorithm with multiple independent Markov chains. The performances of EM and M‐H algorithms are compared based on the daily water stage predictions from 10 model members. Results show that BMA weights estimated from both algorithms are comparable, while BMA variances obtained from M‐H algorithm are closer to the given variances in the numerical experiment. Moreover, the normal proposal distribution used in M‐H algorithm can yield narrower distributions for BMA weights than those from the uniform proposal distribution. Overall, MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its performance is better than the default EM algorithm in terms of both deterministic and probabilistic evaluation metrics as well as algorithm flexibility.

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“…k k p y f D follows a gamma distribution (PDF = 0 for y ≤ 0) with two parameters (α and β) (Qi et al, 2021;Vrugt et al, 2008;Vrugt and Robinson, 2007), and the log-likelihood function is shown in Equation ( 4). The gamma distribution is not symmetric around the mean like the normal distribution.…”

Section: Bayesian Modeling Averaging (Bma) Analysismentioning

confidence: 99%

Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling

Huang

Merwade

2023

Preprint

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As all kinds of physics-based and data-driven models are emerging in hydrologic and hydraulic engineering, Bayesian model averaging (BMA) is one of the popular multi-model methods used to account for various uncertainty sources in the flood modeling process and generate robust ensemble predictions. The reliability of BMA parameters (weights and variances) determines the accuracy of BMA predictions. However, the uncertainty in BMA parameters with fixed values, which are usually obtained from Expectation-Maximization (EM) algorithm, has not been adequately investigated in BMA-related applications over the past few decades. Given the limitations of the commonly used EM algorithm, Metropolis-Hastings (M-H) algorithm, which is one of the most widely used algorithms in Markov Chain Monte Carlo (MCMC) method, is proposed to estimate BMA parameters. Both numerical experiments and one-dimensional HEC-RAS models are employed to examine the applicability of M-H algorithm with multiple independent Markov chains. The performances of EM and M-H algorithms are compared based on the daily water stage predictions from 10 model members. Results show that BMA weights estimated from both algorithms are comparable, while BMA variances obtained from M-H algorithm are closer to the given variances in the numerical experiment. Moreover, the normal proposal used in M-H algorithm can yield narrower distributions for BMA weights than those from the uniform proposal. Overall, MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its performance is better than the default EM algorithm in terms of multiple evaluation metrics as well as algorithm flexibility.

show abstract

Using stratified Bayesian model averaging in probabilistic forecasts of precipitation over the middle and lower Yangtze River region

Cited by 4 publications

References 33 publications

Changes of extreme precipitation in the middle and lower reaches of the Yangtze River and their correlation with atmospheric circulation

Changes of extreme precipitation in the middle and lower reaches of the Yangtze River and their correlation with atmospheric circulation

Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling

Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling

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