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Which observations are most useful for reducing uncertainty in a scientific model? Michael N. Fienen, Laura A. Schachter and Randall J. Hunt describe a protocol for evaluating the value of observation data in relation to the forecasts that a model is intended to support.
The report introduces the mathematical background needed to connect observations, parameters and predictions, including distributions, local sensitivity, the Jacobian matrix and forecast sensitivity. It considers parameter estimation and the way information affects uncertainty, then presents a workflow using available software tools and an applied example. Equations, diagrams and a study-area map accompany the explanation. This complete 2025 technical report preserves the original mathematical notation, example, references, software discussion and all map attribution notices.
Map data ©OpenStreetMap contributors, available under the Open Database License (ODbL): https://www.openstreetmap.org/copyright. Original map attribution retained.
Michael N. Fienen; Laura A. Schachter; Randall J. Hunt. A Model Uncertainty Quantification Protocol for Evaluating the Value of Observation Data. U.S. Geological Survey, 2025. https://doi.org/10.3133/sir20255007. U.S. Government text is public domain; credited OpenStreetMap map data are available under ODbL. Original agency, author and photograph credits retained.