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Bayesian calibration of groundwater models with input data uncertainty

Published:2017-04 Issue:4 Volume:53 Page:3224-3245
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Xu Tianfang¹²^ORCID,Valocchi Albert J.¹^ORCID,Ye Ming³^ORCID,Liang Feng⁴,Lin Yu-Feng⁵^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering; University of Illinois at Urbana-Champaign; Urbana Illinois USA

2. Now at Department of Earth and Environmental Sciences; Michigan State University; East Lansing Michigan USA

3. Department of Scientific Computing; Florida State University; Tallahassee Florida USA

4. Department of Statistics; University of Illinois at Urbana-Champaign; Urbana Illinois USA

5. Illinois State Geological Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign; Urbana Illinois USA

Funder

National Science Foundation Hydrologic Science Program

Computational Science and Engineering Fellowship, College of Engineering, University of Illinois

NSF-EAR

U.S. Department of Energy

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/am-pdf/10.1002%2F2016WR019512

Reference52 articles.

1. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction;Ajami;Water Resour. Res.,2007

2. Reply to comment by B. Renard et al. on “An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction;Ajami;Water Resour. Res.,2009

3. A review of surrogate models and their application to groundwater modeling;Asher;Water Resour. Res.,2015

4. Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections;Bosshard;Water Resour. Res.,2013

5. Bromiley , P. A. 2014

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