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Revisiting “An Exercise in Groundwater Model Calibration and Prediction” After 30 Years: Insights and New Directions

In 1988, an important publication moved model calibration and forecasting beyond case studies and theoretical analysis. It reported on a somewhat idyllic graduate student modeling exercise where many of the system properties were known; the primary forecasts of interest were heads in pumping wells a...

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Autores principales: Hunt, Randall J., Fienen, Michael N., White, Jeremy T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064931/
https://www.ncbi.nlm.nih.gov/pubmed/31115917
http://dx.doi.org/10.1111/gwat.12907
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author Hunt, Randall J.
Fienen, Michael N.
White, Jeremy T.
author_facet Hunt, Randall J.
Fienen, Michael N.
White, Jeremy T.
author_sort Hunt, Randall J.
collection PubMed
description In 1988, an important publication moved model calibration and forecasting beyond case studies and theoretical analysis. It reported on a somewhat idyllic graduate student modeling exercise where many of the system properties were known; the primary forecasts of interest were heads in pumping wells after a river was modified. The model was calibrated using manual trial‐and‐error approaches where a model's forecast quality was not related to how well it was calibrated. Here, we investigate whether tools widely available today obviate the shortcomings identified 30 years ago. A reconstructed version of the 1988 true model was tested using increasing parameter estimation sophistication. The parameter estimation demonstrated the inverse problem was non‐unique because only head data were available for calibration. When a flux observation was included, current parameter estimation approaches were able to overcome all calibration and forecast issues noted in 1988. The best forecasts were obtained from a highly parameterized model that used pilot points for hydraulic conductivity and was constrained with soft knowledge. Like the 1988 results, however, the best calibrated model did not produce the best forecasts due to parameter overfitting. Finally, a computationally frugal linear uncertainty analysis demonstrated that the single‐zone model was oversimplified, with only half of the forecasts falling within the calculated uncertainty bounds. Uncertainties from the highly parameterized models had all six forecasts within the calculated uncertainty. The current results outperformed those of the 1988 effort, demonstrating the value of quantitative parameter estimation and uncertainty analysis methods.
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spelling pubmed-70649312020-03-16 Revisiting “An Exercise in Groundwater Model Calibration and Prediction” After 30 Years: Insights and New Directions Hunt, Randall J. Fienen, Michael N. White, Jeremy T. Ground Water Research Papers/ In 1988, an important publication moved model calibration and forecasting beyond case studies and theoretical analysis. It reported on a somewhat idyllic graduate student modeling exercise where many of the system properties were known; the primary forecasts of interest were heads in pumping wells after a river was modified. The model was calibrated using manual trial‐and‐error approaches where a model's forecast quality was not related to how well it was calibrated. Here, we investigate whether tools widely available today obviate the shortcomings identified 30 years ago. A reconstructed version of the 1988 true model was tested using increasing parameter estimation sophistication. The parameter estimation demonstrated the inverse problem was non‐unique because only head data were available for calibration. When a flux observation was included, current parameter estimation approaches were able to overcome all calibration and forecast issues noted in 1988. The best forecasts were obtained from a highly parameterized model that used pilot points for hydraulic conductivity and was constrained with soft knowledge. Like the 1988 results, however, the best calibrated model did not produce the best forecasts due to parameter overfitting. Finally, a computationally frugal linear uncertainty analysis demonstrated that the single‐zone model was oversimplified, with only half of the forecasts falling within the calculated uncertainty bounds. Uncertainties from the highly parameterized models had all six forecasts within the calculated uncertainty. The current results outperformed those of the 1988 effort, demonstrating the value of quantitative parameter estimation and uncertainty analysis methods. Blackwell Publishing Ltd 2019-07-02 2020 /pmc/articles/PMC7064931/ /pubmed/31115917 http://dx.doi.org/10.1111/gwat.12907 Text en © 2019 The Authors. Groundwater published by Wiley Periodicals, Inc. on behalf of National Ground Water Association. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Papers/
Hunt, Randall J.
Fienen, Michael N.
White, Jeremy T.
Revisiting “An Exercise in Groundwater Model Calibration and Prediction” After 30 Years: Insights and New Directions
title Revisiting “An Exercise in Groundwater Model Calibration and Prediction” After 30 Years: Insights and New Directions
title_full Revisiting “An Exercise in Groundwater Model Calibration and Prediction” After 30 Years: Insights and New Directions
title_fullStr Revisiting “An Exercise in Groundwater Model Calibration and Prediction” After 30 Years: Insights and New Directions
title_full_unstemmed Revisiting “An Exercise in Groundwater Model Calibration and Prediction” After 30 Years: Insights and New Directions
title_short Revisiting “An Exercise in Groundwater Model Calibration and Prediction” After 30 Years: Insights and New Directions
title_sort revisiting “an exercise in groundwater model calibration and prediction” after 30 years: insights and new directions
topic Research Papers/
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064931/
https://www.ncbi.nlm.nih.gov/pubmed/31115917
http://dx.doi.org/10.1111/gwat.12907
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