2022 Early Hearing Detection & Intervention Virtual Conference

March 13 - 15, 2022

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5/24/2018  |   11:00 AM - 11:15 AM   |  ASSESSMENT OF CONFIDENCE IN A MAUMEE RIVER WATERSHED SWAT MODEL   |  330 B

ASSESSMENT OF CONFIDENCE IN A MAUMEE RIVER WATERSHED SWAT MODEL

The Great Lakes Water Quality Agreement sets reducing phosphorus as a strategy for limiting occurrences of harmful algal blooms in Lake Erie, and assessing compliance with phosphorus reduction targets can be done using watershed models. This case study highlights performance upstream of the calibration site in a Soil & Water Assessment Tool (SWAT) model of the Maumee River watershed – a watershed which accounts for half of the total phosphorus to the western Lake Erie basin. We compiled all available measurements for average daily flow and phosphorus concentration data in the watershed, and sorted this database based on appropriateness for comparison with SWAT outputs. We found consistent over- and under-prediction of sites within our model, but a greater number of sites with over-prediction. Using a semi-quantitative approach, we explored the influence of factors specific to the monitoring data that we hypothesized would influence model validation, such as accuracy of flow prediction, drainage area of the gauge, and the quality and type of measured data. Results highlight the importance for the appropriate use of model outputs upstream of the calibration site, and the potential for model improvement through multi-site calibration.

  • Modeling
  • Phosphorous
  • Conservation

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Presenters/Authors

Haley Kujawa (), The Ohio State University, kujawa.21@osu.edu;


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Margaret Kalcic (), The Ohio State University, kalcic.4@osu.edu;


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Jay Martin (), The Ohio State University, martin.1130@osu.edu;


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Anna Apostel (), The Ohio State University, apostel.4@osu.edu;


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Jeffrey Kast (), The Ohio State University, kast.14@osu.edu;


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Noel Aloysius (), University of Missouri, aloysiusn@missouri.edu;


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Awoke Teshager (), University of Michigan, awoke@umich.edu;


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Yu-Chen Wang (), University of Michigan, yuchenw@umich.edu;


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Rebecca Muenich (), Arizona State University, rebecca.muenich@asu.edu;


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