H044
Diagnostics, Sensitivity, and Uncertainty Analysis of Earth, Hydrologic and Environmental Models, Including Multiple Hypotheses and Extremes
Session ID#: 85481
Session Description:
Environmental models are key monitoring and prediction tools used in science and engineering. These models are increasingly being used to inform risks associated with extreme events. However, these complex models not only have uncertain inputs and structure, which propagate into uncertain model outputs, but also continually increase in computational demand with the pursuit of greater realism. Sensitivity and Uncertainty Analyses (SA/UA), including methods using machine learning are used to understand the consequences of model structure, identify important model parameters, and assess uncertainties. This session invites contributions exploring uncertainty quantification and sensitivity analysis topics including (but not limited to): Strategies for more comprehensive and computationally efficient SA/UA, Input data error quantification, Multiple hypotheses and parameterization alternatives, Highly parametrized models enabled by AI/ML techniques, SA/UA in agent-based models, Assessment of uncertainty and applications at extremes, Modeling of aleatory and epistemic uncertainties, Applied studies in any area of earth, hydrologic or environmental modeling.
Index Terms:
0430 Computational methods and data processing [BIOGEOSCIENCES]
1805 Computational hydrology [HYDROLOGY]
1846 Model calibration [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]
Primary Convener: Juliane Mai, University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada
Conveners: Lucy Amanda Marshall, University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia, Meredith L Carr, U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, Rockville, MD, United States and Hoshin Vijai Gupta, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States
Primary Liaison: Juliane Mai, Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany
CPU-efficient Bayesian Uncertainty Quantification of Highly Parameterized Hydrologic Models with a Kalman Jump Distribution (487559)
Jiangjiang Zhang, Zhejiang University, Hangzhou, China, Jasper A Vrugt, UC-Irvine, Irvine, CA, United States and Lingzao Zeng, Zhejiang University, College of Environmental and Resource Sciences, Hangzhou, China
Dynamic process connectivity for model diagnostics, evaluation, and intercomparison (504651)
Andrew Bennett1, Bart Nijssen1 and Grey Stephen Nearing2, (1)University of Washington Seattle Campus, Seattle, WA, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States
Automated geological uncertainty quantification with borehole data using Bayesian Evidential Learning (508296)
David Zhen Yin1, Maisha L Amaru2 and Jef Caers1, (1)Stanford University, Department of Geological Sciences, Stanford, CA, United States, (2)Chevron Corporation Houston, Houston, TX, United States
Bayesian Calibration of an Ecohydrological Model to inform Spatial Water Quality Risk Assessment and Green Infrastructure Siting (517607)
Jared David Smith1, Julianne Quinn1, Lawrence E Band2 and Laurence Lin3, (1)University of Virginia, Charlottesville, VA, United States, (2)University of Virginia, Engineering Systems and Environment, Charlottesville, VA, United States, (3)University of North Carolina at Chapel Hill, Institute for the Environment, Chapel Hill, NC, United States
Discretization strategies for distributed models of mountainous watersheds (518983)
Hongli Liu1, Genevieve Brown2, James R Craig2, Bryan Tolson2, Andrew James Newman1 and Andy Wood1, (1)National Center for Atmospheric Research, Boulder, CO, United States, (2)University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada
Quantifying conceptual model structure uncertainty and assessing relative model performance: findings from a 36-model, 559-catchment comparison study (563212)
Wouter Knoben, University of Bristol, Bristol, BS8, United Kingdom, Ross A Woods, University of Bristol, Bristol, United Kingdom, Jim E Freer, University of Bristol, Geographical Sciences, Bristol, United Kingdom, Murray Cameron Peel, The University of Melbourne, Parkville, VIC, Australia and Keirnan Fowler, University of Melbourne, Department of Infrastructure Engineering, Parkville, VIC, Australia
Sensitivity Analysis and Multi-objective Optimization of an Earth System Model of Intermediate Complexity (564453)
Wei Gong1, Qingyun Duan1 and Yuhan Shi2,3, (1)Beijing Normal University, Institute of Land Surface System and Sustainable Development, Faculty of Geographical Sciences, Beijing, China, (2)Maumelle, AR, United States, (3)Beijing Normal University, Beijing, China
A novel Bayesian inference framework for improved calibration of hydrological models: a case study in the Renhe River basin in China (579933)
Song Liu1, Dunxian She2, Liping Zhang2, Jun Xia3, Senlin Chen1, Lianghua Chen4 and Bo Li4, (1)School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, China, (2)Wuhan University, School of Water Resources and Hydropower Engineering, Wuhan, China, (3)Wuhan University, State Key Laboratory of Water Resources and Hydro Power Engineering Sciences, Wuhan, China, (4)China Yangtze Power Co., Ltd, Wuhan, China
On the Relationship Between Classification, Optimization, and Regionalization (582282)
Cassio Rampinelli1, Tyler J Smith1 and Lucy Amanda Marshall2, (1)Clarkson University, Department of Civil & Environmental Engineering, Potsdam, NY, United States, (2)University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia
Uncertainty Quantification for E3SM Land Component using Low-Rank Surrogate Models (582367)
Cosmin Safta1, Khachik Sargsyan1, Alex Gorodetsky2, John Jakeman1 and Daniel M Ricciuto3, (1)Sandia National Laboratories, Albuquerque, NM, United States, (2)University of Michigan, Ann Arbor, United States, (3)Oak Ridge National Laboratory, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge, TN, United States
Separating Aleatory and Epistemic uncertainties in evaluation of hydraulic loading including dam failures with HEC-WAT (586772)
William Lehman1, Meredith L Carr2, Joseph Kanney3, Matthew Fleming4, Brennan Beam4 and Leila Ostadrahimi4, (1)U.S. Army Corps of Engineers, West Sacramento, CA, United States, (2)CRREL, Hanover, NH, United States, (3)Nuclear Regulatory Commission, Washington, MD, United States, (4)USACE, HEC, Davis, CA, United States
Quantifying uncertainty in flood inundation mapping using Bayesian Model Averaging (BMA) and Hierarchical BMA (Invited) (622021)
Venkatesh Merwade, Purdue University, West Lafayette, IN, United States and Zhu Liu, University of California-Davis, Land, Air and Water Resource, Davis, CA, United States
PARAMETER GROUPING FOR THE ROBUST CALIBRATION OF COMPLEX HYDROLOGIC MODELS (624532)
Shahram Sahraei1, Masoud Asadzadeh1, Fisaha Unduche2 and Tricia Stadnyk3, (1)University of Manitoba, Department of Civil Engineering, Winnipeg, MB, Canada, (2)Hydrologic Forecast Centre, Manitoba Infrastructure, Winnipeg, Canada, (3)University of Calgary, Department of Geography, Calgary, AB, Canada
