Hydrologic Data Uncertainty—Evident and Ignored

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Hydrologic Data Uncertainty—Evident and Ignored

Ali Naghibi, Ph.D., P.Eng.1 1 Environmental Resources Management (ERM) Canada, 1111 West Hastings St., Vancouver, BC, Canada V6E 2J3. E-mail: ali.naghibi@erm.com

Abstract Measured and estimated hydrologic data are key to development of watershed assessment tools including hydrologic, water quality, and water balance models. Although, it is recognized that uncertainty in hydrologic data undermines the reliability of such models, water resources practitioners still do not or cannot adequately incorporate data uncertainty in model calibration and validation. Disconnected and decoupled data collection, analysis, and modeling processes are the primary reason why data uncertainty is neglected during calibration and validation. This work investigates the impacts of data uncertainty on hydrologic analysis and modeling results. An example project is used to demonstrate how the characterization of data uncertainty can inform the data collection, data analysis, and modeling processes. Main sources of data uncertainty (e.g., field measurements, rating curve development, and time-series development) are reviewed, and a discussion is provided about how these uncertainties can be characterized. This work acknowledges that sources of data uncertainty and appropriate methodologies to characterize and reduce uncertainty are case-specific and therefore does not recommend one methodology over another. However, it provides a list of considerations for identifying and characterizing the most critical sources of uncertainty.

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