H43D:
Diagnostics, Sensitivity, and Uncertainty Analysis of Earth and Environmental Models Posters


Submit an Abstract to this Session


Session ID#: 44187

Session Description:
Environmental models are key monitoring and prediction tools used in science and engineering. These complex models not only have uncertain inputs and structure, which propagate into uncertain model outputs, but also continually increase in computational demand. Sensitivity and Uncertainty Analyses (SA/UA) are used to understand model structure, identify informative model parameters, characterize uncertainties, and reduce the computational load of demanding applications such as model optimizations. This session invites contributions on both theory and application of model diagnostics as well as sensitivity and uncertainty analysis. We further welcome contributions focusing on aspects of the model-data interface, which plays an important role in uncertainty quantification.

Particular topics of interest include (but are not limited to):

  • Methods for better understanding and characterization of "uncertainty" and "sensitivity";
  • Strategies for more comprehensive SA/UA (e.g., multi-criteria SA);
  • Input data uncertainty quantification;
  • Methods for improving computational efficiency of SA/UA;
  • Combining models and data to make better predictions.
Primary Convener:  Juliane Mai, University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada
Conveners:  Mary C Hill, University of Kansas, Geology, Lawrence, KS, United States, Saman Razavi, University of Saskatchewan, Saskatoon, SK, Canada and Matthias Cuntz, INRA Institut National de la Recherche Agronomique, Paris Cedex 07, France
Primary Liaison:  Juliane Mai, University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada
Chairs:  Juliane Mai, University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada, Mary C Hill, University of Kansas, Geology, Lawrence, KS, United States and Saman Razavi, University of Saskatchewan, Saskatoon, SK, Canada
OSPA Liaison:  Juliane Mai, University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada

Cross-Listed:
  • B - Biogeosciences
  • EP - Earth and Planetary Surface Processes
  • GC - Global Environmental Change
  • 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:

An Algorithm with Improved Computational Efficiency for Estimating Parameter Uncertainty and its Impact on Model Prediction Uncertainty (421710)
Jeffrey Y Jung, Colorado State University, Fort Collins, CO, United States, Jeffrey D Niemann, Colorado State University, Department of Civil and Environmental Engineering, Fort Collins, United States and Blair P Greimann, Bureau of Reclamation Denver, Denver, CO, United States
Evaluation of surface temperature-based precipitation correction procedure to improve the 3- and 6-M SPI forecasts at near real-time by improving the sample size of observations (447284)
Bala Narapusetty, Climate Prediction Center College Park, College Park, MD, United States, Dan C Collins, Climate Prediction Center College Park, College Park, United States, Raghuram G Murtugudde, Univ of MD--ESSIC, College Park, United States, Christa D Peters-Lidard, NASA GSFC, Greenbelt, United States and Jon Gottschalck, NOAA, Climate Prediction Center, Washington, DC, United States
Investigating Parameter Sensitivity in WRF-Hydro for Streamflow Modeling over the North-Western Himalayan Basin (428047)
Ankur Dixit, Indian Institute of Technology Delhi, Centre for Atmospheric Sciences, New Delhi, India and Sandeep Sahany, Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, India
Streamflow drought with the calibrated WRF-Hydro in Korean Peninsula (427439)
Jaehyeong Lee and Yeonjoo Kim, Yonsei University, Department of Civil and Environmental Engineering, Seoul, Korea, Republic of (South)
Assessment and Reduction of Physical Parameterization Uncertainty for Noah-MP Land Surface Model (425049)
Yanjun Gan, Chinese Academy of Meteorological Sciences, State Key Laboratory of Severe Weather, Beijing, China, Xin-Zhong Liang, University of Maryland, College Park, Department of Atmospheric and Oceanic Science & Earth System Science Interdisciplinary Center, College Park, United States and Qingyun Duan, Hohai University, Nanjing, China
Characterizing Sediment Transport in a Watershed Using CCHE1D Sediment Transport Model (372413)
Kezhen (Jenny) Wang1, Ali Ercan2, Toan Q Trinh2 and ML Levent Kavvas3, (1)Cornell University, Department of Biological and Environmental Engineering, Ithaca, NY, United States, (2)University of California Davis, Davis, CA, United States, (3)Univ California Davis, Davis, CA, United States
Exploiting Model Structure for Global Sensitivity Analysis in E3SM Land Model (448956)
Cosmin Safta1, Khachik Sargsyan1, Daniel M Ricciuto2, John Jakeman1 and Alex Gorodetsky3, (1)Sandia National Laboratories, Albuquerque, NM, United States, (2)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (3)University of Michigan, Ann Arbor, United States
Global CO2 Flux Models with Machine Learning: Sensitivity Analysis (380559)
Brianna Bonner1, Donovan Murphy1, Dr. Grey Stephen Nearing, PHD2, Craig Pelissier3 and Milton Halem4, (1)University of Alabama, Tuscaloosa, AL, United States, (2)Upstream Tech, Tuscaloosa, United States, (3)NASA Goddard Space Flight Center, Computational and Information Science & Technology Office, Greenbelt, MD, United States, (4)University of Maryland Baltimore County, Computer Science, Baltimore, MD, United States
Quantifying Data Uncertainty and Bias in a Bayesian Model for Large Lake Systems (396734)
Joeseph Smith, University of Michigan, Cooperative Institute for Great Lakes Research, School of Environment and Sustainability, Ann Arbor, MI, United States, Andrew Gronewold, University of Michigan, School for Environment and Sustainability, Ann Arbor, United States and Scott Steinschneider, Cornell University, Ithaca, United States
Using Sensitivity Analysis and Data Assimilation to Improve Process Understanding of Hydrologic Exchange Flows in Dynamic River Corridors from km- to Reach Scales (454812)
Xingyuan Chen1, Pin Shuai2, Xuehang Song3, Heng Dai4, Glenn E Hammond5, Tim C Johnson1 and John M Zachara6, (1)Pacific Northwest National Laboratory, Richland, United States, (2)Texas A&M University College Station, College Station, TX, United States, (3)Pacific Northwest National Laboratory, Richland, WA, United States, (4)Jinan University, Guangzhou, China, (5)Sandia National Laboratories, Applied Systems Analysis & Research, Albuquerque, United States, (6)Pacific Northwest Natl Lab, Richland, WA, United States
Approximation Bayesian computation for calibration of water quality models considering the impact of observation uncertainty (432738)
Xia Wu, Lucy Amanda Marshall and Ashish Sharma, University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia
Choosing hydrologic signatures for model evaluation and catchment description: an approach based on ecosystem services theory (367945)
Hilary K McMillan, San Diego State University, Department of Geography, San Diego, CA, United States and Ida Westerberg, IVL Swedish Environmental Research Institute, Stockholm, Sweden
VARS-TOOL: A Novel Toolbox for Comprehensive and Efficient Global Sensitivity Analysis (450992)
Saman Razavi, University of Saskatchewan, Global Institute for Water Security, Saskatoon, SK, Canada, Razi Sheikholeslami, Sharif University of Technology, Department of Civil Engineering, Tehran, Iran, Hoshin Vijai Gupta, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States and Amin Haghnegahdar, Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada; University of Saskatchewan, Saskatoon, SK, Canada
Strategies for Handling Simulation Model Crashes in Global Sensitivity Analysis (443059)
Razi Sheikholeslami1, Amin Haghnegahdar2,3 and Saman Razavi3, (1)Sharif University of Technology, Department of Civil Engineering, Tehran, Iran, (2)Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada, (3)University of Saskatchewan, Saskatoon, SK, Canada
Integrating MT-DREAMzs and nested sampling algorithms to estimate marginal likelihood and comparison with several other methods (346227)
Tongtong Cao1, Xiankui Zeng1, Jichun Wu1, Dong WANG2 and Yuanyuan Sun3, (1)Nanjing University, Nanjing, China, (2)Nanjing University, Department of Hydrosciences, Nanjing, China, (3)School of Earth Sciences and Engineering, Nanjing University, Nanjing, China, Nanjing, China
Effect of Parameter Uncertainty on Surface Water Depletion Caused by Groundwater Pumping (382350)
Hai V Pham, Desert Research Institute Las Vegas, Division of Hydrologic Sciences, Las Vegas, NV, United States, Rosemary W H Carroll, Desert Research Institute, Division of Hydrologic Sciences, Reno, United States, Greg Pohll, Desert Research Institute Reno, Division of Hydrologic Sciences, Reno, NV, United States, Kip Allander, US Geological Survey, Carson City, NV, United States and Cara Nadler, USGS Nevada Water Science Center, Carson City, NV, United States
Uncertainty analysis for CO2 injection scenarios considering wellbore and reservoir flow (388458)
Jize Piao, Yonsei University, Department of Earth System Sciences, Seoul, Korea, Republic of (South), Weonshik Han, Yonsei university, Department of Earth System Sciences, Seoul, South Korea, Sungwook Choung, KBSI Korea Basic Science Institute, Earth and Environmental Sciences, Seoul, Korea, Republic of (South) and Kue-Young Kim, KIGAM Korea Institute of Geoscience and Mineral Resources, Daejeon, South Korea
Modeling Groundwater Vulnerability to Contamination from Unconventional Oil and Gas Development: Uncertainty Analysis Using Linear-based Methods (458196)
Mario Soriano Jr1, Erica Barth-Naftilan2, Kristina Gutchess2, Nicole C Deziel3 and James E Saiers4, (1)Yale University, School of Forestry and Environmental Studies, New Haven, United States, (2)Yale University, School of Forestry and Environmental Studies, New Haven, CT, United States, (3)Yale University, School of Public Health, New Haven, CT, United States, (4)Yale University, School of the Environment, New Haven, CT, United States
Multi-Model Analysis to Understand the Sensitivity of Rainfall-Runoff Model Structure to Potential Evapotranspiration Inputs. (466442)
Dilhani Ishanka Jayathilake, Clarkson University, Potsdam, NY, United States and Tyler J Smith, Clarkson University, Department of Civil & Environmental Engineering, Potsdam, NY, United States
Prediction of Natural CO2Migration and Distribution in Little Grand Wash and Salt Wash Faults System (403439)
Gidon Han, Yonsei University, Department of Earth System Science, Seoul, Korea, Republic of (South), Weon Shik Han, Yonsei University, Department of Earth System Science, Seoul, South Korea and Kue-Young Kim, KIGAM Korea Institute of Geoscience and Mineral Resources, Daejeon, South Korea
Runoff model structure impacts on simulated streamflow uncertainties (380779)
Dongmei Feng, Northeastern University, Department of Civil and Environmental Engineering, Boston, MA, United States and Edward Beighley, Northeastern University, Civil and Environmental Engineering, Boston, United States
Study on parameter uncertainty of hydrological model based on factorial analysis and variance decomposition method (362022)
Ziyan Li, Dengfeng Liu, Qiang Huang, Tao Bai, Shuai Zhou and Lianpeng Zhang, Xi'an University of Technology, School of Water Resources and Hydropower, Xi'an, China
Uncertainty in Streamflow Records in the U.S.: Estimates from Data Mining (467009)
Julie E. Kiang, US Geological Survey, Reston, United States and Robert R Mason Jr, U.S. Geological Survey, Reston, VA, United States

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