TY  - JOUR
A1  - Kärcher, Oskar
A1  - Filstrup, Christopher T.
A1  - Brauns, Mario
A1  - Tasevska, Orhideja
A1  - Patceva, Suzana
A1  - Hellwig, Niels
A1  - Walz, Ariane
A1  - Frank, Karin
A1  - Markovic, Danijela
T1  - Chlorophyll a relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions
JF  - Inland Waters
N2  - 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.
KW  - chlorophyll a
KW  - nutrients
KW  - Ohrid-Prespa region
KW  - perialpine lakes
KW  - water temperature
Y1  - 2020
U6  - https://doi.org/10.1080/20442041.2019.1689768
SN  - 2044-2041
SN  - 2044-205X
VL  - 10
IS  - 1
SP  - 29
EP  - 41
PB  - Taylor & Francis
CY  - London
ER  - 
TY  - GEN
A1  - Kärcher, Oskar
A1  - Filstrup, Christopher T.
A1  - Brauns, Mario
A1  - Tasevska, Orhideja
A1  - Patceva, Suzana
A1  - Hellwig, Niels
A1  - Walz, Ariane
A1  - Frank, Karin
A1  - Markovic, Danijela
T1  - Chlorophyll a relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions
T2  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - 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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1443 
KW  - chlorophyll a
KW  - nutrients
KW  - Ohrid-Prespa region
KW  - perialpine lakes
KW  - water temperature
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-515271
SN  - 1866-8372
IS  - 1
ER  -