H31M:
Predictions, Models, and Hydrological Information: How Much Certainty Should We Expect in an Uncertain World? I


Session ID#: 10615

Session Description:
We can express uncertainty in predictions and processes including aleatory and epistemic errors, but what techniques allow us to increase our certainty? Can we both improve and increase our prediction certainties using qualitative reasoning; comparative hydrology; hydrological signatures; data fusion; and statistical techniques. In exploring the above approaches how sensitive are our outcomes to the assumptions applied? What therefore is the right balance between certainty, reliability, model inadequacy, and precision if we are trying to understand catchment processes, or predict the future, or predict extremes? We invite contributions related to novel theoretical development, new computational techniques, and real-world applications that recognize inherent uncertainties but focus on their reduction.  We also particularly invite contributions that directly introduce empirical process understanding to help constrain the modeling process that can include inductive and deductive reasoning.
Primary Convener:  Jim E Freer, University of Saskatchewan Coldwater Laboratory, Canmore, AB, Canada
Conveners:  Tianfang Xu, Michigan State University, Department of Earth and Environmental Sciences, East Lansing, United States, Doerthe Tetzlaff, University of Aberdeen, Northern Rivers Institute, Aberdeen, United Kingdom and Dr. Grey Stephen Nearing, PHD, Upstream Tech, Tuscaloosa, United States
Chairs:  Jim E Freer, University of Saskatchewan Coldwater Laboratory, Canmore, AB, Canada and Dr. Grey Stephen Nearing, PHD, Upstream Tech, Tuscaloosa, United States
OSPA Liaison:  Doerthe Tetzlaff, University of Aberdeen, Northern Rivers Institute, Aberdeen, United Kingdom
Index Terms:

1804 Catchment [HYDROLOGY]
1846 Model calibration [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]

Abstracts Submitted to this Session:

Channelling information flows from observation to decision; or how to increase certainty (Invited) (60401)
Steven V Weijs, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland; University of British Columbia, Civil Engineering, Vancouver, BC, Canada
On the Usefulness of Hydrologic Landscapes for Hydrologic Model Calibration and Selection (82314)
Keith A Sawicz, Environmental Protection Agency Corvallis, Corvallis, OR, United States, Scott G Leibowitz, U.S. Environmental Protection Agency (retired), Corvallis, OR, United States, Randy L Comeleo, US EPA, Pacific Ecological Systems Division, Corvallis, United States, Chas Jones Jr, University of Alaska Fairbanks, Fairbanks, AK, United States and Parker J Wigington Jr, Environmental Protection Agency San Francisco, San Francisco, CA, United States
The Scientific Method, Diagnostic Bayes, and How to Detect Epistemic Errors (86581)
Jasper A Vrugt, University of California Irvine, Civil and Environmental Engineering, Irvine, CA, United States
On the Impact of Uncertainty in Initial Conditions of Hydrologic Models on Prediction (81930)
Razi Sheikholeslami, Sharif University of Technology, Department of Civil Engineering, Tehran, Iran and Saman Razavi, University of Saskatchewan, Saskatoon, SK, Canada
Hydrological Uncertainty in Practice: Reasons to be Cheerful (Invited) (59540)
Hilary K McMillan, National Institute of Water and Atmospheric Research (NIWA), Christchurch, New Zealand
An Approach for Including Uncertainty in Integrated Water Resources Assessments within Large River Basins of Southern Africa. (71673)
Denis Hughes, Rhodes University, Grahamstown, South Africa
Using High Resolution Tracer Data to Constrain Storage and Flux Estimates in a Spatially Distributed Rainfall-runoff Model (63907)
Marjolein Van Huijgevoort1, Doerthe Tetzlaff2, Edwin Sutanudjaja3 and Chris Soulsby1, (1)University of Aberdeen, Aberdeen, United Kingdom, (2)University of Aberdeen, Northern Rivers Institute, Aberdeen, United Kingdom, (3)Utrecht University, Physical Geography, Utrecht, Netherlands
Information, Predictability and Fitness for Purpose (Invited) (60283)
Keith Beven, Lancaster University, Lancaster Environment Centre, Lancaster, United Kingdom; Uppsala University, Department of Earth Sciences, Uppsala, Sweden

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