Quantifying data worth toward reducing predictive uncertainty

Alyssa Dausman, John Doherty, Christian Langevin, Michael Sukop

    Research output: Contribution to journalArticle

    62 Citations (Scopus)

    Abstract

    The present study demonstrates a methodology for optimization of environmental data acquisition. Based on the premise that the worth of data increases in proportion to its ability to reduce the uncertainty of key model predictions, the methodology can be used to compare the worth of different data types, gathered at different locations within study areas of arbitrary complexity. The method is applied to a hypothetical nonlinear, variable density numerical model of salt and heat transport. The relative utilities of temperature and concentration measurements at different locations within the model domain are assessed in terms of their ability to reduce the uncertainty associated with predictions of movement of the salt water interface in response to a decrease in fresh water recharge. In order to test the sensitivity of the method to nonlinear model behavior, analyses were repeated for multiple realizations of system properties. Rankings of observation worth were similar for all realizations, indicating robust performance of the methodology when employed in conjunction with a highly nonlinear model. The analysis showed that while concentration and temperature measurements can both aid in the prediction of interface movement, concentration measurements, especially when taken in proximity to the interface at locations where the interface is expected to move, are of greater worth than temperature measurements. Nevertheless, it was also demonstrated that pairs of temperature measurements, taken in strategic locations with respect to the interface, can also lead to more precise predictions of interface movement. Journal compilation

    Original languageEnglish
    Pages (from-to)729-740
    Number of pages12
    JournalGroundwater
    Volume48
    Issue number5
    DOIs
    Publication statusPublished - Sep 2010

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    Dausman, A., Doherty, J., Langevin, C., & Sukop, M. (2010). Quantifying data worth toward reducing predictive uncertainty. Groundwater, 48(5), 729-740. https://doi.org/10.1111/j.1745-6584.2010.00679.x