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

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...

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Veröffentlicht in:Limnology and oceanography letters Jg. 5; H. 2; S. 228 - 235
Hauptverfasser: Wagner, Tyler, Lottig, Noah R., Bartley, Meridith L., Hanks, Ephraim M., Schliep, Erin M., Wikle, Nathan B., King, Katelyn B. S., McCullough, Ian, Stachelek, Joseph, Cheruvelil, Kendra S., Filstrup, Christopher T., Lapierre, Jean Francois, Liu, Boyang, Soranno, Patricia A., Tan, Pang‐Ning, Wang, Qi, Webster, Katherine, Zhou, Jiayu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 01.04.2020
Wiley
Schlagworte:
Algae
Biomass
Chlorophyll
Climate change
Climate effects
Eutrophication
Lakes
Land use
Nutrient concentrations
Nutrients
Phosphorus
Population
Productivity
Variables
Water quality
United States > US
ISSN:2378-2242, 2378-2242
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2378-2242
2378-2242
DOI:10.1002/lol2.10134