2022 Early Hearing Detection & Intervention Virtual Conference

March 13 - 15, 2022

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5/20/2019  |   2:30 PM - 2:45 PM   |  A PROMISING APPROACH TO SOLVE LIGHT-ASSOCIATED MISSPECIFICATIONS IN RIVER METABOLISM MODELS   |  250 AB

A PROMISING APPROACH TO SOLVE LIGHT-ASSOCIATED MISSPECIFICATIONS IN RIVER METABOLISM MODELS

Light drives variation in riverine gross primary productivity (GPP), but characterizing light regimes at reach scales remains challenging. Additionally, we do not know how ecosystem-scale GPP responds to light; stream scientists use metabolism models assuming either linear or saturating relationships between GPP and light without testing the actual relationship. These misspecifications can cause large errors in GPP estimates. Here we developed a new modeling approach that accounts for process error (PE) during the day (versus generic PE during both day and night) to allow a flexible means of addressing light saturation and to account for misspecified light inputs into metabolism models. We compared three models (i.e., linear GPP-Light & generic PE; saturated GPP-Light & generic PE; and linear GPP-Light & daytime PE) by simulating [O2] data under different light scenarios and evaluating each model’s success in recovering the simulation parameter values. Results showed that only shallow rivers (mean depth ~0.20 m) with low light attenuation (~0.80 m-1) might be light saturated. Across all scenarios, daytime PE had the greatest accuracy and least bias of the three model variants, suggesting it is a promising approach to improve estimates of river metabolism.

  • Modeling
  • Primary Production
  • Light

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Presenters/Authors

Maite Arroita (), University of the Basque Country, maite.arroita@ehu.eus;


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Charles Yackulic (), USGS Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, cyackulic@usgs.gov;


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Alison Appling (), US Geological Survey, alison.appling@gmail.com;


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Robert O. Hall (), Flathead Lake Biological Station, University of Montana, bob.hall@flbs.umt.edu;


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Arturo Elosegi (), University of the Basque Country (UPV/EHU), arturo.elosegi@ehu.eus;


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