H23M:
Bayesian Methods and Multilevel Models for Hydroclimatic Applications Posters

Session ID#: 2778

Tuesday, 16 December 2014: 1:40 PM-6:00 PM
Not an Option (Moscone West)
Chairs:  Benjamin Renard, INRAE, RECOVER, Aix-en-Provence, France and Naresh Devineni, Department of Civil Engineering, The City College of New York; United Nations University Hub on Remote-Sensing and Sustainable Innovations for Resilient Urban Systems at The City College of New York; CUNY Remote Sensing Earth System Institute, Department of Civil Engineering, New York, United States
Primary Convener:  Naresh Devineni, Department of Civil Engineering, The City College of New York; United Nations University Hub on Remote-Sensing and Sustainable Innovations for Resilient Urban Systems at The City College of New York; CUNY Remote Sensing Earth System Institute, Department of Civil Engineering, New York, United States
Co-conveners:  Benjamin Renard, INRAE, RECOVER, Aix-en-Provence, France and Carlos Henrique Ribeiro Lima, Universidade de Brasilia, Brasilia, Brazil
OSPA Liaison:  Carlos Henrique Ribeiro Lima, Universidade de Brasilia, Brasilia, Brazil
Co-Sponsor(s):
  • NH - Natural Hazards
Index Terms:

1839 Hydrologic scaling [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1869 Stochastic hydrology [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]
Virtual Option?: No
Swirl Theme: Characterizing Uncertainty

Abstracts Submitted to this Session:

 
H23M-1050
Application of the Viterbi Algorithm in Hidden Markov Models for Exploring Irrigation Decision Series (4326)
Sanyogita Andriyas and Mac McKee, Utah State University, Logan, UT, United States
 
H23M-1051
The Iterative Research Cycle: Process-Based Model Evaluation (Invited) (5569)
Jasper A Vrugt, University of California Irvine, Irvine, CA, United States
 
H23M-1052
Bayesian Prediction and Projection of Sea Levels (5855)
Mark Berliner, Ohio State University, Columbus, OH, United States
 
H23M-1053
Climate-Informed Multi-Scale Stochastic (CIMSS) Hydrological Modeling: Incorporating Decadal-Scale Variability Using Paleo Data (Invited) (5875)
Mark Andrew Thyer, University of Adelaide, Adelaide, SA, Australia, Ben J Henley, University of Melbourne, Parkville, Australia and George A. Kuczera, The University of Newcastle, School of Engineering, Callaghan, NSW, Australia
 
H23M-1054
A Bayesian Hierarchical framework for identifying regional hydroclimate trends or climate effects from continental or global data (17975)
Xun Sun, Columbia Univ, New York, NY, United States and Upmanu Lall, Columbia University, Earth and Environmental Engineering, New York, United States
 
H23M-1055
Simulating the Effect of Uncertain Model Drivers on Hydrologic Predictions via an Approximate Bayesian Approach (20527)
Lucy Amanda Marshall, University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia and David J Nott, National University of Singapore, Singapore, Singapore
 
H23M-1056
Development of Hierarchical Bayesian Model Based on Regional Frequency Analysis and Its Application to Estimate Areal Rainfall in South Korea (21702)
JinYoung Kim and Hyun-han Kwon, Chonbuk National University, Jeonju, Korea, Republic of (South)
 
H23M-1057
Extreme Rainfall and Flood Events for the Hudson River Induced by Tropical Cyclones: a Statistical Forecast Model (21869)
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 University, Earth and Environmental Engineering, New York, United States, (4)Stevens Institute of Technology, Department of Civil, Environmental & Ocean Engineering, Hoboken, United States, (5)Jupiter, New York, United States
 
H23M-1058
Combining precipitation data from observed and numerical models to forecast precipitation characteristics in sparsely-gauged watersheds: an application to the Amazon River basin. (23006)
M. Chase Dwelle1, Valeriy Yu Ivanov2 and Veronica Berrocal1, (1)University of Michigan, Ann Arbor, MI, United States, (2)University of Michigan, Civil and Environmental Engineering, Ann Arbor, United States
 
H23M-1059
An Empirical Bayes Framework for Assessing Changes in the Hydrological Cycle (23319)
Linyin Cheng, University California Irvine, Irvine, CA, United States; NOAA, ESRL, Boulder, CO, United States and Amir AghaKouchak, University of California Irvine, Irvine, CA, United States
 
H23M-1060
Bayesian Assessment of the Uncertainties of Estimates of a Conceptual Rainfall-Runoff Model Parameters (24991)
Francisco Eustáquio Oliveira e Silva, Mauro Da Cunha Naghettini and Wilson Fernandes, UFMG Federal University of Minas Gerais, Belo Horizonte, Brazil
 
H23M-1061
Uncertainty Quantification of a Distributed Hydrology Model Using Bayesian Framework Across a Heterogeneous Watershed (25790)
Vidya 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)University of Illinois Urbana Champaign, Civil and Environmental Engineering, Urbana, United States
 
H23M-1062
Inferring Mountain Basin Precipitation from Streamflow Observations Using Bayesian Model Calibration (27902)
Brian M Henn, University of California San Diego, Scripps Institution of Oceanography, La Jolla, United States, Dmitri Kavetski, University of Adelaide, Adelaide, Australia, Martyn P Clark, University of Calgary, Schulich School of Engineering, Department of Civil Engineering, Calgary, AB, Canada and Jessica D Lundquist, University of Washington, Department of Civil and Environmental Engineering, Seattle, United States
 
H23M-1063
Uncertainty quantification of extreme precipitation projections including regional climate model interdependency and non-stationary bias (29104)
Maria Sunyer1, Henrik Madsen2, Dan Rosbjerg1 and Karsten Arnbjerg-Nielsen1, (1)DTU Environment, Kgs. Lyngby, Denmark, (2)DHI, Horsholm, Denmark
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