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

Abstract

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.

Relationships of total phosphorus and chlorophyll in lakes worldwide

“…These unproductive lakes could have low nutrient concentrations from low nitrogen export in unvegetated watersheds in these regions (Wurtsbaugh et al 1985; Morris and Lewis Jr 1988; Stuchlík et al 2006), or due to lower rates of algal production at colder water temperatures (Butterwick et al 2005) with a shorter ice‐free growing season (Whiteford et al 2016). These findings support a recent study that alpine lakes have nonlinear patterns in Chl a and TP along an elevation gradient (Kärcher et al 2020). At high TP concentrations representing hypereutrophic conditions, with high algal productivity and biomass, further TP increases may not result in further increases in Chl a concentrations because light limitations due to shading from dense algal blooms (Agusti et al 1990; Brothers et al 2014).…”

“…However, as the range in phosphorus becomes greater, the relationship to Chl a becomes sigmoidal (McCauley et al 1989; Chow‐Fraser et al 1994; Brown et al 2000; Filstrup et al 2014). At low concentrations of TP, lags (i.e., asymptotes) in Chl a values have been found in ultra‐oligotrophic lakes, which is potentially due to large proportions of TP being biologically unavailable (Chow‐Fraser et al 1994; Kärcher et al 2020). At high concentrations of TP, chlorophyll plateaus as limitation from other nutrients begins to occur when phosphorus availability is high (Søndergaard et al 2017; Filstrup and Downing 2017).…”

Long-Term Water Quality Patterns in an Estuarine Reservoir and the Functional Changes in Relations of Trophic State Variables Depending on the Construction of Serial Weirs in Upstream Reaches

“…Most studies based on ambient concentrations suggested that CHL-a was more strongly associated with total phosphorus (TP) than with total nitrogen (TN). This supports the view that phosphorus is an important regulator of algal growth in water bodies [ 22 , 23 , 24 ]. However, the linear relationships between TP and CHL-a in systems can be differed by latitudinal variation, spatial heterogeneities of physicochemical factors, and land-use pattern [ 16 , 25 , 26 , 27 ].…”

Long‐Term Trends in Productivity Across Intermountain West Lakes Provide No Evidence of Widespread Eutrophication

“…Most notably, surface water temperature was the second most important predictor variable of our trophic state model and could be important for a wide range of remote sensing based water quality models. Water temperature has proven to be an important predictor of chlorophyll‐a across inland lakes (Karcher et al., 2020; Liu et al., 2019) as well as oceans (Dunstan et al., 2018). However, applied remote sensing models that predict chlorophyll‐a are often limited to strictly optical predictors such as band‐ratio (blue‐green) models.…”