Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data

Tyler Wagner, Noah R. Lottig, Meridith L. Bartley, Ephraim M. Hanks, Erin M. Schliep, Nathan B. Wikle, Katelyn B.S. King, Ian McCullough, Jemma Stachelek, Kendra S. Cheruvelil, Christopher T. Filstrup, Jean Francois Lapierre, Boyang Liu, Patricia A. Soranno, Pang Ning Tan, Qi Wang, Katherine Webster, Jiayu Zhou

Research output: Contribution to journalLetterpeer-review

2 Scopus citations

Abstract

Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land-use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint-nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll a on observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty.

Original languageEnglish (US)
Pages (from-to)228-235
Number of pages8
JournalLimnology And Oceanography Letters
Volume5
Issue number2
DOIs
StatePublished - Apr 2020

All Science Journal Classification (ASJC) codes

  • Aquatic Science
  • Oceanography

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