H11C:
Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Systems Models I

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Session ID#: 16878

Session Description:
Proper characterization of uncertainty remains a major challenge, and is inherent to many aspects of modelling such as parameter estimation and model prediction. To address this challenge, methods for “uncertainty analysis” (UA) that try to quantify uncertainty and how it propagates through models, and the closely-related methods for “sensitivity analysis” (SA) that facilitate understanding the role and significance of uncertain factors in the functioning of models, have proved to be very helpful.

This session invites contributions on both theory or application of SA/UA methods, including those related to parameter estimation, input data, and initial and boundary conditions. Particular topics of interest include:

  1. Methods for effective characterization of “uncertainty” and “sensitivity”
  2. Single- versus Multi-criteria SA/UA
  3. Metric specification for model evaluations
  4. The role of data error in model SA/UA
  5. Methods for improving computational efficiency of SA/UA (e.g. surrogate modelling)
  6. Spatial and/or temporal variation of SA/UA
Primary Convener:  Amin Haghnegahdar, University of Saskatchewan, Global Institute for Water Security, Saskatoon, SK, Canada
Conveners:  Saman Razavi, University of Saskatchewan, Global Institute for Water Security, Saskatoon, SK, Canada, Hoshin Vijai Gupta, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States and Mary C Hill
Chairs:  Amin Haghnegahdar, University of Saskatchewan, Global Institute for Water Security, Saskatoon, SK, Canada and Saman Razavi, University of Saskatchewan, Global Institute for Water Security, Saskatoon, SK, Canada
OSPA Liaison:  Amin Haghnegahdar, University of Saskatchewan, Global Institute for Water Security, Saskatoon, SK, Canada

Cross-Listed:
  • PA - Public Affairs
Index Terms:

1805 Computational hydrology [HYDROLOGY]
1846 Model calibration [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]

Abstracts Submitted to this Session:

Using Global Sensitivity Analysis to Understand the Implications of Epistemic Uncertainty in Earth Systems Modelling (185141)
Thorsten Wagener1, Francesca Pianosi2, Susana Almeida2 and Elizabeth Holcombe3, (1)University of Potsdam, Potsdam, Germany, (2)University of Bristol, Civil Engineering, Bristol, United Kingdom, (3)University of Bristol, Bristol, United Kingdom
Understanding parameter sensitivities in continental-domain hydrologic modeling (138433)
Andrew James Newman1, Martyn P Clark1, Naoki Mizukami2, Andrew W Wood3 and Pablo A. Mendoza3, (1)National Center for Atmospheric Research, Boulder, United States, (2)NSF National Center for Atmospheric Research, Boulder, United States, (3)National Center for Atmospheric Research, Boulder, CO, United States
Hydrologic climate change impacts in the Columbia River Basin and their sensitivity to methodological choices (132356)
Oriana Chegwidden1, Bart Nijssen2, Yixin Mao2 and David E Rupp3, (1)CarbonPlan, San Francisco, United States, (2)University of Washington Seattle Campus, Seattle, WA, United States, (3)Oregon Climate Change Research Institute, CEOAS/OSU, Corvallis, OR, United States
Understanding identifiability as a crucial step in uncertainty assessment (Invited) (120563)
Anthony John Jakeman, Australian National University, Institute for Water Futures and Integrated Catchment Assessment and Management Centre, Canberra, ACT, Australia, Joseph H.A. Guillaume, Aalto University, Water and Development Research Group, Aalto, Finland, Mary C Hill and Lynn Seo, Australian National University, Integrated Catchment Assessment and Management Centre, Canberra, Australia
The Difference between Uncertainty and Information, and Why This Matters (187096)
Dr. Grey Stephen Nearing, PHD, Upstream Tech, Tuscaloosa, United States
Making Steppingstones out of Stumbling Blocks: A New Bayesian Model Evidence Estimator with Application to Groundwater Model Selection (152378)
Ming Ye, Florida State University, Earth, Ocean, and Atmospheric Science, Tallahassee, United States, Ahmed S. Elshall, Florida State University, Department of Earth, Ocean, and Atmospheric Science, Tallahassee, FL, United States, Guoping Tang, Oak Ridge National Laboratory, Climate Change Science Institute, Environmental Science Division, Oak Ridge, TN, United States and Saeedeh Samani, Florida State University, Scientific Computing, Tallahassee, FL, United States
A Hierarchical Approach to Modeling Errors in Dynamic Hydrologic Systems (168893)
Lucy Amanda Marshall, University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia
A Fast Surrogate-facilitated Data-driven Bayesian Approach to Uncertainty Quantification of a Regional Groundwater Flow Model with Structural Error (162539)
Tianfang Xu, Michigan State University, Department of Earth and Environmental Sciences, East Lansing, United States, Albert J Valocchi, University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering, Urbana, United States, Ming Ye, Florida State University, Earth, Ocean, and Atmospheric Science, Tallahassee, United States and Feng Liang, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, IL, China

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