An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction-Reference-Cited by-同舟云学术

An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction

Published:2007-01 Issue:1 Volume:43 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Ajami Newsha K.¹,Duan Qingyun²,Sorooshian Soroosh³

Affiliation:

1. Berkeley Water Center, Department of Civil and Environmental Engineering; University of California, Berkeley; Berkeley California USA

2. Lawrence Livermore National Laboratory; Livermore California USA

3. Department of Civil and Environmental Engineering; University of California, Irvine; Irvine California USA

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1029/2005WR004745/fullpdf

Reference49 articles.

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2. Ajami , N. K. Q. Duan H. Moradkhani S. Sorooshian 2005 Recursive Bayesian model combination for streamflow forecasting American Meteorological Society meeting San Diego, Calif.

3. Multi-model combination techniques for hydrological forecasting: Application to Distributed Model Intercomparison Project results;Ajami;J. Hydrometeorol.,2006

4. Water quality modeling: Review of the analysis uncertainty;Beck;Water Resour. Res.,1987

5. The future of distributed models: Model calibration and uncertainty prediction;Beven;Hydrol. Processes,1992

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