H11D:
Efficient Diagnostics, Sensitivity, and Uncertainty Analysis of Complex Environmental Models I Posters


Session ID#: 8519

Session Description:
Environmental models are key monitoring and prediction tools used in applied sciences. Modern environmental models incorporate multiple physical processes, have high-dimensional input/output spaces and tend to increase continually in computation time. Many-query applications such as sensitivity analysis, uncertainty quantification as well as model calibration usually require a large number of model evaluations leading to a high computational demand. This makes rigorous analyses often impractical.

In this session we invite contributions that deal with the efficient handling of parameter identifiability, sensitivity analysis, uncertainty quantification, model calibration, or optimisation. We encourage reports of all kind of efficiency improvements that may include but are not limited to algorithmic improvements, dimensionality reductions, approximation techniques, surrogate models, or surrogate assisted methods. We further invite original works that exploit efficient methods on different types of data.

Primary Convener:  Matthias Cuntz, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
Conveners:  Juliane Mai, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany, Dmitri Kavetski, School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, South Australia, Australia and Rafael Rosolem, University of Bristol, Civil Engineering, Bristol, United Kingdom
Chairs:  Juliane Mai, Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany and Rafael Rosolem, University of Bristol, Civil Engineering, Bristol, United Kingdom
OSPA Liaison:  Juliane Mai, Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany
Co-Organized with:
Hydrology, and Biogeosciences

Cross-Listed:
  • B - Biogeosciences
  • 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:

pyNSMC: A Python Module for Null-Space Monte Carlo Uncertainty Analysis (60915)
Jeremy White, USGS Texas Water Science Center, Austin, TX, United States and Linzy Kay Foster, USGS Oklahoma-Texas Water Science Center, Austin, TX, United States
Spatially-distributed Calibration of Two Macroscale Hydrologic Models Across the Columbia River Basin (62025)
Oriana Chegwidden, CarbonPlan, San Francisco, United States, Mu Xiao, Arizona State University, Tempe, AZ, United States, David E Rupp, Oregon Climate Change Research Institute, CEOAS/OSU, Corvallis, OR, United States, Matt R Stumbaugh, University of Washington, Department of Civil and Environmental Engineering, Seattle, WA, United States, Joseph Hamman, Applied Physics Laboratory University of Washington, Seattle, WA, United States, Ming Pan, Scripps Institution of Oceanography, Center for Western Weather and Water Extremes (CW3E), La Jolla, CA, United States and Bart Nijssen, University of Washington, Seattle, WA, United States
Comparison of Two Global Sensitivity Analysis Methods for Hydrologic Modeling over the Columbia River Basin (62793)
Maysoun Hameed1, Mehmet Cuneyd Demirel2 and Hamid Moradkhani1, (1)Portland State University, Portland, OR, United States, (2)Geological Survey of Denmark and Greenland, København K, Denmark
Conceptual and computational challenges in continental-domain hydrologic model parameter estimation: VIC applications over the contiguous USA (71139)
Naoki Mizukami1, Martyn P Clark1, Andrew James Newman2, Bart Nijssen3, Luis Samaniego4, Levi D Brekke5, Jeffrey Richard Arnold6 and Andrew W Wood7, (1)National Center for Atmospheric Research, Boulder, United States, (2)University Corporation for Atmospheric Research, Boulder, CO, United States, (3)University of Washington Seattle Campus, Seattle, WA, United States, (4)Helmholtz Centre for Environmental Research UFZ Leipzig, Computation Hydrosystems, Leipzig, Germany, (5)Bureau of Reclamation, Research and Development, Denver, United States, (6)US Army Corps of Engineers, Jacksonville, FL, United States, (7)National Center for Atmospheric Research, Boulder, CO, United States
Parameter Identification and Uncertainty Analysis for Visual MODFLOW based Groundwater Flow Model in a Small River Basin, Eastern India (72597)
Suraj Jena, Ph.D. Scholar, School of Infrastructure, Bhubaneswar, India
Efficient Calibration of Distributed Catchment Models Using Perceptual Understanding and Hydrologic Signatures (72710)
Christopher Hutton, Wessex Water Services Ltd, Bath, United Kingdom, Thorsten Wagener, University of Bristol, Civil Engineering, Bristol, United Kingdom, Jim E Freer, University of Saskatchewan Coldwater Laboratory, Canmore, AB, Canada, Christopher Duffy, Penn State University, University Park, PA, United States and Dawei Han, University of Bristol, Department of Civil Engineering, Bristol, United Kingdom
Variogram Analysis of Response surfaces (VARS): A New Framework for Global Sensitivity Analysis of Earth and Environmental Systems Models (73347)
Saman Razavi, University of Saskatchewan, Saskatoon, SK, Canada and Hoshin Vijai Gupta, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States
Quantifying the Sensitivity of Energy Fluxes to Land Surface Parameter Selection Using the Active Subspace Method (74575)
Jennifer Jefferson1, James M Gilbert1, Reed M Maxwell1 and Paul G Constantine2, (1)Colorado School of Mines, Hydrologic Science and Engineering Program and Department of Geology and Geological Engineering, Golden, CO, United States, (2)Colorado School of Mines, Applied Mathematics and Statistics, Golden, CO, United States
Advanced Simulation Capability for Environmental Management (ASCEM): Developments in Uncertainty Quantification and Sensitivity Analysis. (83108)
Shane Wesley McKinney, Los Alamos National Laboratory, EES-16, Los Alamos, NM, United States
Hydrograph structure informed calibration in the frequency domain with time localization (84614)
Karthik Kumarasamy and Patrick Belmont, Utah State University, Department of Watershed Sciences, Logan, UT, United States
Management of groundwater in-situ bioremediation system using reactive transport modelling under parametric uncertainty: field scale application (85132)
Elicia Verardo, University of Bordeaux 1, Talence, France, Olivier Atteia, Institut Polytechnique de Bordeaux, ENSEGID, Pessac, France and Laurent Rouvreau, Geological survey of France (BRGM), Orleans, France

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