H045
Diagnostics, Sensitivity, and Uncertainty Analysis of Earth and Environmental Models

Session ID#: 157055

Session Description:
As science and engineering are used to understand, plan for, and resolve societal problems, process-based, AI/ML, and hybrid models are key simulation and prediction tools. For all models, inputs and structure tend to be complex and have errors, which propagate into uncertain model outputs. In addition, models continually increase in computational demand with the pursuit of greater realism and integration of more and different types of data. Sensitivity and Uncertainty Analyses (SA/UA) are used to understand the implications of choices around model structure, identify important and unimportant model parameters and data correlations, and characterize uncertainties. This session invites contributions on both theory and application of model diagnostics, sensitivity, and uncertainty analysis. We further welcome contributions that explore the model-data interface, which plays an important role in uncertainty quantification. We also highly welcome applications to real world problems such as studies that are focusing on water scarcity, contamination, food-energy-earth systems, and potential environmental impacts on and of pandemics such as covid.

Particular topics of interest include:

  • AI/ML techniques for exploring extensive data sets
  • Explainable AI and other methods for integrating domain science into AI/ML
  • Strategies for more comprehensive SA/UA
  • Input data error quantification: can model fit be too good?
  • Understanding more faster: improving computational efficiency of SA/UA
  • Parameterization alternatives: the dance of simple concepts and data fitting given the complexity of environmental systems and related societal interactions
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, Mary C Hill, University of Kansas, Geology, Lawrence, KS, United States and Saman Razavi, University of Saskatchewan, Saskatoon, SK, Canada
 
The quest to find physically meaningful parameters in environmental models (1051237)
Luis Samaniego1, Oldrich Rakovec2, Rohini Kumar3, Robert Schweppe4 and Stephan Thober2, (1)Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany, (2)Helmholtz Centre for Environmental Research - UFZ, Computational Hydrosystems, Leipzig, Germany, (3)Helmholtz Centre for Environmental Research GmbH – UFZ, Computational Hydrosystems, Leipzig, Germany, (4)Helmholtz Centre for Environmental Research UFZ Leipzig, Computational Hydrosystems, Leipzig, Germany
 
Does model calibration with soil moisture seasonality as objective metrics improve the reproducibility of streamflow seasonality? (1054084)
Ryoko Araki, San Diego State University, Department of Geography, San Diego, CA, United States and Hilary K McMillan, San Diego State University, San Diego, CA, United States
 
Uncertainty in Predictions of Flood Inundation Caused by Model Grid Sampling (1070532)
Martin Nguyen1, Matthew Wilson1, Emily Lane2 and James Brasington3, (1)University of Canterbury, School of Earth and Environment - Geospatial Research Institute, Christchurch, New Zealand, (2)NIWA National Institute of Water and Atmospheric Research, Christchurch, New Zealand, (3)University of Canterbury, Waterways Centre, Christchurch, New Zealand
 
Investigating the Model Hypothesis Space. What can we learn from Benchmarking Automatic Model Structure Identification with a Large Model Ensemble? (1072293)
Diana Spieler, Dresden University of Technology, Department of Hydro Sciences, Dresden, Germany and Niels Schütze, Dresden University of Technology, Department of Hydroscience, Dresden, Germany
 
Sensitivity Analysis of Sum-of-Echoes Model Using Logging NMR Data in Fractured Granite (1081031)
Uttam Singh, Indian Institute of Technology Roorkee, Roorkee, India, Pramod Kumar Sharma, Associate Professor, Indian Institute of Technology Roorkee, Civil Engineering Department, Roorkee, India and Brijesh Kumar Yadav, Indian Institute of Technology Roorkee, Hydrology, Roorkee, India
 
An Integrated Evaluation Framework based on Generalized Likelihood Uncertainty Estimation for Quantifying Uncertainty in Flood Modeling (1082617)
Tao Huang and Venkatesh Merwade, Purdue University, West Lafayette, IN, United States
 
Global sensitivity analysis of a one-dimensional ocean biogeochemical model (1089342)
Nabir Mamnun, Alfred Wegener Institute Helmholtz-Center for Polar and Marine Research Bremerhaven, Bremerhaven, Germany, Christoph D Voelker, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany, Sebastian Krumscheid, Chair of Mathematics for Uncertainty Quantification, RWTH Aachen University, Aachen, Germany, Mihalis Vrekoussis, University of Bremen, Institute of Environmental Physics + MARUM, Bremen, Germany and Lars Nerger, Alfred Wegener Institute, Bremerhaven, Germany, Germany
 
Uncertainty and Sensitivity Analyses of a Semi-Distributed Non-Parsimonious Hydrologic Model Across Spatial and Temporal Scales Using New and Traditional Global Sensitivity Analysis Approaches (1092310)
Juan Sebastian Acero Triana, University of California Riverside, Riverside, United States, Hoori Ajami, University of California Riverside, Riverside, CA, United States and Saman Razavi, University of Saskatchewan, Saskatoon, SK, Canada
 
Development of a Global Sensitivity Analysis Tool for a Fully Distributed Conceptual Hydrological Model (1101879)
Krishna Mondal1, Kallem Sushanth1, Pranesh Paul2 and Rajendra Singh1, (1)Indian Institute of Technology Kharagpur, Agricultural and Food Engineering Department, Kharagpur, India, (2)University of Guelph, School of Engineering Guelph, Ontario, Canada
 
A data-agnostic method to investigate dominant controls across Earth system observations and models (1112424)
Robert Reinecke1, Francesca Pianosi2 and Thorsten Wagener1, (1)University of Potsdam, Institute of Environmental Science and Geography, Potsdam, Germany, (2)University of Bristol, Civil Engineering, Bristol, United Kingdom
 
Uncertainty Quantification and Apportionment of Water Quality Index in South Florida Watershed (1124101)
Farshad Jahangiri, Sharif University of Technology, Tehran, Iran and Razi Sheikholeslami, University of Oxford, School of Geography and the Environment, Oxford, United Kingdom
 
Quantifying Uncertainty in Hydrological Simulations and Forecasts using Bayesian and Sparse Bayesian Extreme Learning Machine Models (1128105)
Mohammad Sina Jahangir, John Quilty, John You, David Hah and Henry Hughes, University of Waterloo, Civil and Environmental Engineering, Waterloo, ON, Canada
 
Diagnostics and postprocessing of the National Hydrological Model product (1137146)
Edom Moges, University of California Berkeley, Berkeley, CA, United States, Jessica M Driscoll, University of Arizona, Tucson, United States, Liang Zhang, University of California, Berkeley, Department of Civil and Environmental Engineering, Berkeley, CA, United States, Benjamin L Ruddell, Northern Arizona University, School of Informatics, Computing, and Cyber Systems, Flagstaff, AZ, United States and Laurel Larsen, University of California, Department of Geography, Department of Civil and Environmental Engineering, Berkeley, United States
 
Sensitivity of Subsidence Data in the Northeastern Netherlands to Geological Complexities (1151872)
Marius Wouters1, Ramon F Hanssen2 and Rob Govers1, (1)Utrecht University, Faculty of Geosciences, Department of Earth Sciences, Utrecht, Netherlands, (2)Delft University of Technology, Faculty Of Civil Engineering and Geosciences, Department of Geoscience and Remote Sensing, Delft, Netherlands
 
The impact of meteorological forcing uncertainty on hydrological modeling in representative cryosphere basins on the global scale (1167830)
Guoqiang Tang1, Martyn P Clark1, Wouter Knoben2, Hongli Liu1, Shervan Gharari3, Louise Arnal4, Hylke Beck5, Andy Wood6, Andrew James Newman7 and Simon Michael Papalexiou8, (1)University of Saskatchewan, Centre for Hydrology, Saskatoon, SK, Canada, (2)University of Saskatchewan, Centre for Hydrology, Canmore, AB, Canada, (3)University of Saskatchewan, Centre for Hydrology, Saskatoon, Canada, (4)University of Saskatchewan, Centre for Hydrology, Canmore, Canada, (5)Joint Research Center Ispra, Ispra, Italy, (6)National Center for Atmospheric Research, Hydrometeorological Applications Program, Research Applications Laboratory, Boulder, United States, (7)National Center for Atmospheric Research, Boulder, United States, (8)University of Calgary, Civil Engineering, Calgary, Canada
 
Dominant source of uncertainty in CLM simulations of hydrological signatures: a CONUS-scale comparison between forcing and parametric uncertainty (1181816)
Hisham Eldardiry1, Ning Sun2, Hongxiang Yan1, Patrick M Reed3, Rohini Gupta3, Andrew D Jones4 and Deeksha Rastogi5, (1)Pacific Northwest National Laboratory, Energy and Environment Directorate, Richland, WA, United States, (2)Pacific Northwest National Laboratory, Richland, WA, United States, (3)Cornell University, School of Civil and Environmental Engineering, Ithaca, NY, United States, (4)Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Berkeley, CA, United States, (5)Oak Ridge National Laboratory, Computational Sciences and Engineering Division, Oak Ridge, United States
 
Diagnostic Bayes: Merging the Strengths of Bayesian Inference and Diagnostic Model Evaluation (1182235)
Debora Yumi de Oliveira, University of California Irvine, Irvine, CA, United States and Jasper A Vrugt, UC-Irvine, Irvine, CA, United States
 
Can hydrologists improve model performance by calibrating their models to discontinuous data? (1199143)
Hongren Shen, Bryan Tolson and Juliane Mai, University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada
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