Effects of model complexity on uncertainty estimates |
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| Affiliation: | 1. Kemakta Konsult AB, Box 12655, 112 93 Stockholm, Sweden;2. Imperial College of Science, Technology and Medicine, UK;3. Institute of Environmental Research and Engineering, Romania;4. Institute of Experimental Meteorology, Russia;5. Atomic Energy of Canada Ltd, Canada;6. Japan Atomic Energy Research Institute, Japan;7. Studiecentrum voor Kernenergie, Belgium;1. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, United States;2. Yukawa Institute for Theoretical Physics, Kyoto University, Kyoto 606-8502, Japan;3. RIKEN Nishina Center, Wako 351-0198, Japan;4. Center for Mathematics and Physics University of Aizu, Aizu Wakamatsu, Fukushima 965-0001, Japan;1. MJHS Institute for Innovation in Palliative Care, New York, NY, USA;2. Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA;3. Division of Neurosurgery, Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, Harquail Centre for Neuromodulation, University of Toronto, Toronto, ON, Canada;4. Department of Anesthesiology & Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA;5. Department of Neurology, Department of Physical Medicine & Rehabilitation, and Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA;6. Department of Anesthesiology, Pontificia Universidad Catolica de Chile, Santiago, Chile;7. Department of Anesthesiology, Seoul National University, Seoul, South Korea;8. Department of Anesthesiology and Department of Physical Medicine & Rehabilitation, Uniformed Services University of the Health Sciences, Bethesda, MD, USA;9. Department of Anesthesiology, Virginia Commonwealth University, Richmond, VA, USA;1. Department of Epidemiology and Biostatistics, University of South Carolina, United States of America;2. Department of Statistics, North Carolina State University, United States of America;3. Department of Medicine, Johns Hopkins University, School of Medicine, United States of America;4. Department of Epidemiology, Johns Hopkins Bloomberg, School of Public Health, United States of America;5. Department of Biostatistics, Johns Hopkins Bloomberg, School of Public Health, United States of America;1. School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China;2. School of the Environment, Nanjing University, Nanjing, 210023, China;3. School of Civil Engineering, Yantai University, Yantai, 264005, China;4. The Bartlett School of Construction and Project Management, University College London, London, WC1E 6BT, UK;1. Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, USA;2. Department of Statistics, University of California, Los Angeles, CA, USA |
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| Abstract: | In the Model Complexity working group of BIOMOVS II, models of varying complexity have been applied to a theoretical problem concerning downward transport of radionuclides in soils. The purpose was to study how uncertainty in model predictions varies with model complexity and how model simplifications can suitably be made. A scenario describing a case of surface contamination of a pasture soil was defined. Three different radionuclides with different environmental behavior and radioactive half-lives were considered: 137Cs, 90Sr and 129I. A detailed specification of the parameters required by different kinds of models was given, together with reasonable values for the parameter uncertainty. A total of seven modelling teams participated in the study using 13 different models. Four of the modelling groups performed uncertainty calculations using nine different modelling approaches. The models ranged in complexity from analytical solutions of a 2-box model using annual average data to numerical models coupling hydrology and transport using data varying on a daily basis. |
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