H53D:
Metamodeling and Surrogate Modeling: Addressing Model Uncertainty and Support for Decision Making II Posters


Session ID#: 8347

Session Description:
Recent progress in surrogate- and meta-modeling provides opportunities to gain insight from complicated mathematical models in a fraction of the time. Model emulation can help identify sources of model uncertainty and leverage the ever-increasing level of detail inherent in modern models on a timescale meeting the practical needs of decision makers. In some cases, real-time operation and management guided by numerical models becomes possible. Statistical learning, artificial intelligence, model simplification, and other runtime mitigation accomplish these goals. Another value to surrogate/meta-modeling is coupling multiple processes more efficiently than iteratively linking. For example, climate and socioeconomic models, or groundwater and surface water models can be linked capturing the insights of each through surrogates.  We encourage contributions exploring new techniques and management endpoints for model simplification, emulation, metamodeling, and surrogate modeling of environmental processes. Techniques that propagate uncertainty from raw data, through modeling errors, to decision-support predictions and forecasts, are particularly encouraged.
Primary Convener:  Michael N Fienen, U.S. Geological Survey, Upper Midwest Water Science Center, Madison, United States
Conveners:  Anthony John Jakeman, Australian National University, Institute for Water Futures and Integrated Catchment Assessment and Management Centre, Canberra, ACT, Australia, Andrea Castelletti, Politecnico di Milano, Milano, 20133, Italy and Saman Razavi, University of Saskatchewan, Saskatoon, SK, Canada
Chairs:  Andrea Castelletti, Politecnico di Milano, Department of Electronics, Information, and Bioengineering, Milano, Italy and Saman Razavi, University of Saskatchewan, Saskatoon, SK, Canada
OSPA Liaison:  Andrea Castelletti, Politecnico di Milano, Department of Electronics, Information, and Bioengineering, Milano, Italy

Cross-Listed:
  • A - Atmospheric Sciences
  • GC - Global Environmental Change
  • OS - Ocean Sciences
  • SI - Societal Impacts and Policy Sciences
Index Terms:

1839 Hydrologic scaling [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]
6309 Decision making under uncertainty [POLICY SCIENCES]

Abstracts Submitted to this Session:

Surrogate Data Generation By Gradual Wavelet Reconstruction (GWR): A General Method with Applications to Simulation, Hypothesis Testing and Uncertainty Analysis (48308)
Christopher J Keylock, University of Loughborough, Loughborough, United Kingdom
Estimation of Model’s Marginal likelihood Using Adaptive Sparse Grid Surrogates in Bayesian Model Averaging (64681)
Xiankui Zeng, Nanjing University, Nanjing, China
Challenges to Applying a Metamodel for Groundwater Flow Beyond Underlying Numerical Model Boundaries (69309)
Howard W Reeves, US Geological Survey, Lansing, MI, United States, Michael N Fienen, U.S. Geological Survey, Upper Midwest Water Science Center, Madison, United States and Daniel Feinstein, USGS Wisconsin Water Science Center, Middleton, WI, United States
A Combined Approach to Model Reduction for Nonlinear Groundwater Flow and Solute Transport Simulations Using POD and DEIM. (71336)
Zachary Stanko1, Scott E Boyce2 and William W-G Yeh1, (1)University of California Los Angeles, Los Angeles, CA, United States, (2)USGS California Water Science Center San Diego, San Diego, CA, United States
High-Performance Integrated Control of water quality and quantity in urban water reservoirs by dynamic emulation and model predictive control (71928)
Andrea Castelletti, Politecnico di Milano, Department of Electroinics, Information and Bioengineering, Milano, Italy, Stefano Galelli, Organization Not Listed, Washington, DC, United States and Albert Goedbloed, National University of Singapore, Singapore, Singapore
Adaptive probabilistic collocation based Kalman filter for unsaturated flow problem (72728)
Jun Man1, Weixuan Li2, Lingzao Zeng1 and Laosheng Wu3, (1)Zhejiang University, College of Environmental and Resource Sciences, Hangzhou, China, (2)Pacific Northwest National Laboratory, Richland, United States, (3)University of California, Department of Environmental Sciences, Riverside, CA, United States
Short-Term Stochastic Optimization of Hydropower Production and Marketing under Meteorological Uncertainty (69001)
Dirk Schwanenberg, Deltares, Delft, Netherlands; University of Duisburg-Essen, Essen, Germany, Steffi Naumann, Fraunhofer IOSB-AST, Department of Surface Water and Maritime Systems, Ilmenau, Germany and Christopher Allen, Bonneville Power Administration, Portland, OR, United States
Multi-Fidelity Surrogates of Groundwater Flow (75463)
Michael Asher1, John Jakeman2 and Anthony John Jakeman1, (1)Australian National University, Canberra, ACT, Australia, (2)Sandia National Laboratories, Albuquerque, NM, United States
Incorporating approximation error in surrogate based Bayesian inversion (76887)
Jiangjiang Zhang1, Lingzao Zeng2, Weixuan Li3 and Laosheng Wu1,4, (1)Zhejiang University, Hangzhou, China, (2)Zhejiang University, College of Environmental and Resource Sciences, Hangzhou, China, (3)Pacific Northwest National Laboratory, Richland, WA, United States, (4)University of California, Department of Environmental Sciences, Riverside, CA, United States
Efficient Bayesian parameter estimation with implicit sampling and surrogate modeling for a vadose zone hydrological problem (80144)
Yaning Liu, George Shu Heng Pau and Stefan Finsterle, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
Quantifying Uncertainty in Distributed Flash Flood Forecasting for a Semiarid Region (83071)
Mohsen Pourreza Bilondi, University of Birjand, Water Engineering, Birjand, Iran, Vidya Samadi, Clemson University, Department of Agricultural Sciences, Clemson, SC, United States, Bijan Ghahraman, Ferdowsi University of Mashhad, Mashhad, Iran and Ali-Mohammad Akhoond-Ali, Shahid Chamran University Of Ahvaz, Ahvaz, Iran

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