H22D:
Diagnostics, Sensitivity, and Uncertainty Analysis of Earth and Environmental Models II

Submit an Abstract to this Session



Session ID#: 34907

Session Description:
Environmental models are key monitoring and prediction tools used in science and engineering. Complex models not only have uncertain inputs and structure, which propagate into uncertain model outputs, but also continually increase in computation demand. Sensitivity and Uncertainty Analyses (SA/UA) are hence used to understand model structure, characterize uncertainties, and reduce the computational load of such models. This session invites contributions on both theory and application of SA/UA methods, that may include but are not limited to parameter estimation, input data uncertainty quantification, algorithmic improvements, dimensionality reductions, approximation techniques, surrogate models, or surrogate assisted methods.

Particular topics of interest include (but are not limited to):

  • Methods for better understanding and characterization of “uncertainty” and “sensitivity”
  • Strategies for a more comprehensive SA/UA (e.g., multi-criteria SA)
  • Role of data and epistemic errors in model SA/UA
  • Methods for improving computational efficiency of SA/UA (e.g. surrogate modeling)
  • Spatial and/or temporal variation of SA/UA
Primary Convener:  Juliane Mai, University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada
Conveners:  Kimberly C Brumble, University of Calgary, Department of Philosophy, Calgary, AB, Canada, Amin Haghnegahdar, University of Saskatchewan, Global Institute for Water Security, Saskatoon, SK, Canada and Bryan Tolson, University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada
Chairs:  Bryan Tolson1, Amin Haghnegahdar2, Juliane Mai1 and Kimberly C Brumble3, (1)University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada(2)University of Saskatchewan, Global Institute for Water Security, Saskatoon, SK, Canada(3)University of Calgary, Department of Philosophy, Calgary, AB, Canada
OSPA Liaison:  Mary C Hill, University of Kansas, Geology, Lawrence, KS, United States

Cross-Listed:
  • B - Biogeosciences
  • EP - Earth and Planetary Surface Processes
  • GC - Global Environmental Change
  • NG - Nonlinear Geophysics
Index Terms:

0430 Computational methods and data processing [BIOGEOSCIENCES]
1805 Computational hydrology [HYDROLOGY]
1846 Model calibration [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]

Abstracts Submitted to this Session:

Calibration of hydrological models using streamflow signatures in a Bayesian framework. (Invited) (279400)
Fabrizio Fenicia1, Dmitri Kavetski2, Carlo Albert1 and Peter Reichert1, (1)EAWAG Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland, (2)School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, South Australia, Australia
Revisiting the PLUMBER Experiments from a Process-Diagnostics Perspective (257157)
Dr. Grey Stephen Nearing, PHD, University of Alabama, Geological Sciences, Tuscaloosa, United States, Benjamin L Ruddell, Arizona State University, Tempe, AZ, United States, Martyn P Clark, Hydrometeorological Applications Program, Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, United States, Bart Nijssen, University of Washington, Seattle, WA, United States and Christa D Peters-Lidard, NASA Goddard Space Flight Center, Greenbelt, MD, United States
What Makes Hydrologic Models Differ? Using SUMMA to Systematically Explore Model Uncertainty and Error (226038)
Andrew Bennett, University of Arizona, Department of Hydrology & Atmospheric Sciences, Tucson, AZ, United States, Bart Nijssen, University of Washington, Seattle, WA, United States, Oriana Chegwidden, University of Washington, Department of Civil and Environmental Engineering, Seattle, WA, United States, Andrew W Wood, National Center for Atmospheric Research, Boulder, CO, United States and Martyn P Clark, Hydrometeorological Applications Program, Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, United States
A Novel Uncertainty Framework for Improving Discharge Data Quality Using Hydraulic Modelling. (240637)
Valentin Mansanarez1, Ida Westerberg2, Steve W Lyon3 and Norris Lam1, (1)Stockholm University, Stockholm, Sweden, (2)IVL Swedish Environmental Research Institute, Stockholm, Sweden, (3)Bolin Centre for Climate Research, Stockholm, Sweden
Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling (249465)
Reynold Chow1,2, Jeremy Bennett1, Jürnjakob Dugge1, Thomas Wöhling1,3 and Wolfgang Nowak1,4, (1)University of Tübingen, Center for Applied Geoscience, Tübingen, Germany, (2)University of Stuttgart, Stochastic Simulation and Safety Research for Hydrosystems, Stuttgart, Germany, (3)Dresden University of Technology, Chair of Hydrology, Dresden, Germany, (4)University of Stuttgart, Stochastic Simulation and Safety Research for Hydrosystems (IWS/SC SimTech), Stuttgart, Germany
Multi-objective Uncertainty Analysis of Ecohydrological Systems: a Bayesian Approach (294449)
Lucy Amanda Marshall1, Yating Tang1, Hoori Ajami2 and Ashish Sharma1, (1)University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia, (2)University of California Riverside, Riverside, CA, United States

See more of: Hydrology