Probabilistic flood forecasting by a sampling-based Bayesian model

Shuili Fadian Xuebao/Journal of Hydroelectric Engineering

Volumn 30, Issue 3, 2011, Pages 27-33

  Li, Mingliang ^a   Yang, Dawen ^a   Chen, Jinsong ^b  

^a TSINGHUA UNIVERSITY   (China)

^b LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

Author keywords

Bayesian method; Flood control engineering; Input uncertainty; Markov chain Monte Carlo; Parameter uncertainty; Probabilistic forecasting; Xin'anjiang model

Indexed keywords

BAYESIAN METHODS; FLOOD CONTROL ENGINEERING; INPUT UNCERTAINTY; MARKOV CHAIN MONTE CARLO; PARAMETER UNCERTAINTY; PROBABILISTIC FORECASTING; XIN'ANJIANG MODELS;

BAYESIAN NETWORKS; CATCHMENTS; DISTRIBUTION FUNCTIONS; FLOOD CONTROL; FLOODS; MARKOV PROCESSES; MEASUREMENT ERRORS; PROBABILITY DISTRIBUTIONS; RAIN; UNCERTAINTY ANALYSIS;

WEATHER FORECASTING;

EID: 79960070000     PISSN: 10031243     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (9)

1. Renard B, Kavetski D, Kuczera G, et al. Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors[J]. Water Resources Research, 2010.
2. XING Zhengxiang, RUI Xiaofang, CUI Haiyan, et al. Bayesian probabilistic flood forecasting model based on adaptive Metropolis-MCMC algorithm[J]. Journal of Hydraulic Engineering, 2007(12): 94-100. (in Chinese)
3. Vrugt J A, Diks C G H, Gupta H V, et al. Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation[J]. Water Resources Research, 2005 (1).
4. Thyer M, Renard B, Kavetski D, et al. Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis[J]. Water Resources Research, 2009.
5. Kendall W S, Liang F, Wang J S. Markov chain Monte Carlo: Innovations and applications[M]. World Scientific Publishing Co., 2005.
6. Haario H, Laine M, Mira A, et al. DRAM: Efficient adaptive MCMC[J]. Statistics and Computing, 2006 (4): 339-354.
7. Gelman A, Rubin D. Inference from iterative simulation using multiple sequences[J]. Statistical Science, 1992 (4): 457-472.
8. ZHAI Jiarui. Practical algorithms and related codes for hydrological predictions[M]. Zhengzhou: Yellow River Water Conservancy Press, 1995. (in Chinese)
9. Gneiting T, Westveld A, Raferty A, et al. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation[R]. Department of Statistics, University of Washington, 2004.