H54A:
Computational Methods and Tools for Model–Data Integration I


Submit an Abstract to this Session


Session ID#: 61964

Session Description:
The widening accessibility of computational resources and expanding volumes of Earth science data are fueling the rise in data-driven research and discovery in Earth system science. A growing number of open source tools are being developed and used for model–data fusion, data assimilation, visualization and visual analytics, model benchmarking, and multi-model ensemble prediction experiments. Such Earth science models and supporting languages, tools, and analytics software are being deployed on large workstations, clusters, dynamically provisioned cloud services, and supercomputers. This session highlights innovative computational methods, open source software tools and related cyberinfrastructure for model-data integration and their application to address research challenges in hydrology and biogeosciences. Examples might include methodology development for model-data integration (e.g., data assimilation, uncertainty quantification, and model-informed experimental design), application of machine learning methods, assimilation of remote sensing data, language features, data management standards, user interface and visualization capabilities, and novel use cases for Open Science applications.
Primary Convener:  Dr. Forrest M Hoffman, PhD, Oak Ridge National Laboratory, Oak Ridge, TN, United States
Conveners:  Xingyuan Chen, Pacific Northwest National Laboratory, Richland, United States, Tianfang Xu, Arizona State University, School of Sustainable Engineering and the Built Environment, Tempe, United States and Hyungjun Kim, University of Tokyo, Bunkyo-ku, Japan
Primary Liaison:  Dr. Forrest M Hoffman, PhD, Oak Ridge National Laboratory, Oak Ridge, TN, United States
Chairs:  Dr. Forrest M Hoffman, PhD, Oak Ridge National Laboratory, Computational Sciences & Engineering Division, Oak Ridge, United States, Xingyuan Chen, Pacific Northwest National Laboratory, Richland, United States, Tianfang Xu, UIUC-Civil Engineering, Urbana, United States and Hyungjun Kim, University of Tokyo, Bunkyo-ku, Japan
OSPA Liaison:  Dr. Forrest M Hoffman, PhD, Oak Ridge National Laboratory, Computational Sciences & Engineering Division, Oak Ridge, United States
Co-Organized with:
Hydrology, and Biogeosciences

Proposed Co-Organized Session with:
  • B - Biogeosciences
Index Terms:

0430 Computational methods and data processing [BIOGEOSCIENCES]
0466 Modeling [BIOGEOSCIENCES]
1849 Numerical approximations and analysis [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]

Abstracts Submitted to this Session:

A Bayesian Approach to Soil Biogeochemical Model Comparison (443582)
Hua W Xie, University of California Irvine, Irvine, CA, United States and Steven D Allison, University of California Irvine, Ecology and Evolutionary Biology, Irvine, United States
Efficient Surrogate Modeling Methods to Advance Model-Data Integration (Invited) (360533)
Dan Lu, Oak Ridge National Laboratory, Oak Ridge, TN, United States and Daniel M Ricciuto, Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, TN, United States
Environmental classification at scale to support global farming decisions: (467364)
Patricio Salvatore La Rosa1, Michael Martinez2, E Morris3, Brett Lord Castillo1, Alexander H Sheridan4, Reza Madankan5, Mackenzie Kolbet1 and Adrian Cartier6, (1)Bayer Crop Science Division, Saint Louis, MO, United States, (2)Bayer Crop Science Division, IT, Products and Engineering, Saint Louis, MO, United States, (3)Monsanto Company, St. Louis, MO, United States, (4)Bayer Crop Science Division, Production Research, Saint Louis, MO, United States, (5)Bayer Crop Science Division, Global IT Analytics, Saint Louis, MO, United States, (6)Monsanto Company, Information Technology, St. Louis, MO, United States
Generating improved estimates of streamflow using model averaging of downscaled runoff products under uncertainty (406133)
Marko Kalevi Kallio1, Joseph H.A. Guillaume2, Matti Kummu1 and Kirsi Virrantaus3, (1)Aalto University, Water and Development Research Group, Aalto, Finland, (2)Australian National University, Canberra, ACT, Australia, (3)Aalto University School of Engineering, Department of Built Environment, Espoo, Finland
Machine Learning Application on Closing Data Gaps in Groundwater Measurements (421219)
Huiying Ren1, Erol Cromwell1 and Xingyuan Chen2, (1)Pacific Northwest National Laboratory, Richland, WA, United States, (2)Pacific Northwest National Laboratory, Richland, United States
Towards improved standardisation of model evaluation using modelevaluation.org (Invited) (376302)
Gab Abramowitz, ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, Australia
Multi-site critical zone process understanding through standardized and automated data ingestion and model-data coupling (432306)
Roelof Versteeg1, Erek Alper2, Baptiste Dafflon3, Nicola Falco3, Doug Val Johnson2, Satish Karra4, Peter C Lichtner5, Anastasia Rodzianko1, Reza Soltanian6, Haruko M Wainwright7, Mike van der Werf8, Ye Zhang9 and Haiyan Zhou1, (1)Subsurface Insights, Hanover, NH, United States, (2)Subsurface Insights, Hanover, United States, (3)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (4)Los Alamos National Laboratory, Los Alamos, United States, (5)OFM Research, Redmond, CA, United States, (6)Wright State University Main Campus, Dayton, United States, (7)Lawrence Berkeley National Laboratory, Department of Nuclear Science and Engineering, Berkeley, United States, (8)Deltares, Delft, Netherlands, (9)Univ of Wyoming, Geology and Geophysics, Laramie, WY, United States
Using Sensitivity Analysis as a Tool to Determine the Need for Regeneration of Hydrological and Biogeochemical Predictions (422493)
Bhavna Arora1, Boris Faybishenko2 and Deb Agarwal2, (1)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, CA, United States, (2)Lawrence Berkeley National Laboratory, Berkeley, CA, United States

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