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

Oskar Kärcher, Christopher T. Filstrup, Mario Brauns, Orhideja Tasevska, Suzana Patceva, Niels Hellwig, Ariane Walz, Karin Frank, Danijela Markovic

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)29-41
Number of pages13
JournalInland Waters
Issue number1
StatePublished - Jan 2 2020

Bibliographical note

Funding Information:
Current research is funded by the DFG Grant MA 6593/2-1 and the EU funded ECOPOTENTIAL (Horizon 2020 ref. 641762) project.

Publisher Copyright:
© 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.


  • Ohrid-Prespa region
  • chlorophyll a
  • nutrients
  • perialpine lakes
  • water temperature


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