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Chlorophyll a relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions

  • Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables.Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a-nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic-eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.show moreshow less

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Author details:Oskar KärcherORCiDGND, Christopher T. Filstrup, Mario BraunsORCiDGND, Orhideja TasevskaORCiD, Suzana Patceva, Niels HellwigORCiDGND, Ariane WalzORCiDGND, Karin FrankORCiDGND, Danijela Markovic
DOI:https://doi.org/10.1080/20442041.2019.1689768
ISSN:2044-2041
ISSN:2044-205X
Title of parent work (English):Inland Waters
Publisher:Taylor & Francis
Place of publishing:London
Publication type:Article
Language:English
Date of first publication:2020/01/29
Publication year:2020
Release date:2024/03/11
Tag:Ohrid-Prespa region; chlorophyll a; nutrients; perialpine lakes; water temperature
Volume:10
Issue:1
Number of pages:13
First page:29
Last Page:41
Funding institution:FG Grant MA 6593/2-1 and the EU funded ECOPOTENTIAL (Horizon 2020 ref. 641762) project
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Umweltwissenschaften und Geographie
DDC classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Peer review:Referiert
Publishing method:Open Access / Hybrid Open-Access
License (German):License LogoCC-BY-NC-ND - Namensnennung, nicht kommerziell, keine Bearbeitungen 4.0 International
External remark:Zweitveröffentlichung in der Schriftenreihe Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 1443
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