H23M:
Bayesian Methods and Multilevel Models for Hydroclimatic Applications Posters

Tuesday, 16 December 2014: 1:40 PM-6:00 PM
Chairs:  Benjamin Renard, Irstea, Lyon, France and Naresh Devineni, CUNY City College, New York, NY, United States
Primary Conveners:  Naresh Devineni, CUNY City College, New York, NY, United States
Co-conveners:  Benjamin Renard, Irstea, Lyon, France and Carlos Henrique Ribeiro Lima, Universidade de Brasilia, Brasilia, Brazil
OSPA Liaisons:  Carlos Henrique Ribeiro Lima, Universidade de Brasilia, Brasilia, Brazil

Abstracts Submitted to this Session:

1050
 
Application of the Viterbi Algorithm in Hidden Markov Models for Exploring Irrigation Decision Series
Sanyogita Andriyas and Mac McKee, Utah State University, Logan, UT, United States
1051
 
The Iterative Research Cycle: Process-Based Model Evaluation
Jasper A Vrugt, University of California Irvine, Irvine, CA, United States
1052
 
Bayesian Prediction and Projection of Sea Levels
Mark Berliner, Ohio State University, Columbus, OH, United States
1053
 
Climate-Informed Multi-Scale Stochastic (CIMSS) Hydrological Modeling: Incorporating Decadal-Scale Variability Using Paleo Data
Mark Andrew Thyer, University of Adelaide, Adelaide, Australia, Ben J Henley, University of Melbourne, Parkville, Australia and George A. Kuczera, University of Newcastle, Callaghan, Australia
1054
 
A Bayesian Hierarchical framework for identifying regional hydroclimate trends or climate effects from continental or global data
Xun Sun and Upmanu Lall, Columbia Univ, New York, NY, United States
1055
 
Simulating the Effect of Uncertain Model Drivers on Hydrologic Predictions via an Approximate Bayesian Approach
Lucy Amanda Marshall, University of New South Wales, Sydney, Australia and David Nott, National University of Singapore, Singapore, Singapore
1056
 
Development of Hierarchical Bayesian Model Based on Regional Frequency Analysis and Its Application to Estimate Areal Rainfall in South Korea
JinYoung Kim and Hyun-han Kwon, Chonbuk National University, Jeonju, South Korea
1057
 
Extreme Rainfall and Flood Events for the Hudson River Induced by Tropical Cyclones: a Statistical Forecast Model
Federico Conticello1, Timothy M Hall2, Upmanu Lall3, Philip M Orton4, Francesco Cioffi1 and Nickitas Georgas5, (1)Sapienza University of Rome, DICEA, Rome, Italy, (2)NASA Goddard Institute for Space Studies, New York, NY, United States, (3)Columbia Univ, New York, NY, United States, (4)Stevens Inst of Tech, Hoboken, NJ, United States, (5)Stevens Institute of Tech., Hoboken, NJ, United States
1058
 
Combining precipitation data from observed and numerical models to forecast precipitation characteristics in sparsely-gauged watersheds: an application to the Amazon River basin.
M. Chase Dwelle1, Valeriy Yu Ivanov2 and Veronica Berrocal1, (1)University of Michigan, Ann Arbor, MI, United States, (2)University of Michigan, Department of Civil and Environmental Engineering, Ann Arbor, MI, United States
1059
 
An Empirical Bayes Framework for Assessing Changes in the Hydrological Cycle
Linyin Cheng, University California Irvine, Irvine, CA, United States; Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States and Amir AghaKouchak, University of California Irvine, Irvine, CA, United States
1060
 
Bayesian Assessment of the Uncertainties of Estimates of a Conceptual Rainfall-Runoff Model Parameters
Francisco Eustáquio Oliveira e Silva, Mauro Da Cunha Naghettini and Wilson Fernandes, UFMG Federal University of Minas Gerais, Belo Horizonte, Brazil
1061
 
Uncertainty Quantification of a Distributed Hydrology Model Using Bayesian Framework Across a Heterogeneous Watershed
S Samadi1, Alexis Atlani2, Michael Meadows1 and Ana Paula Barros3, (1)University of South Carolina, Civil and Environmental Engineering, Columbia, SC, United States, (2)École des Ponts ParisTech, Champs sur Marne, élève-ingénieur, Île-de-France, France, (3)Duke University, Civil and Environmental Engineering, Durham, NC, United States
1062
 
Inferring Mountain Basin Precipitation from Streamflow Observations Using Bayesian Model Calibration
Brian M Henn, University of Washington Seattle Campus, Civil and Environmental Engineering, Seattle, WA, United States, Dmitri Kavetski, University of Adelaide, Adelaide, Australia, Martyn P Clark, NCAR, Boulder, CO, United States and Jessica D Lundquist, University of Washington, Seattle, WA, United States
1063
 
Uncertainty quantification of extreme precipitation projections including regional climate model interdependency and non-stationary bias
Maria Sunyer1, Henrik Madsen2, Dan Rosbjerg1 and Karsten Arnbjerg-Nielsen1, (1)DTU Environment, Kgs. Lyngby, Denmark, (2)DHI, Horsholm, Denmark
 
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