Predicting the effect of climate change on temperate shallow lakes with the ecosystem model PCLake

Abstract

Global average surface temperatures are expected to rise by about 1.4-5.8°C from the present until the year 2100. This temperature increase will affect all ecosystems on earth. For shallow lakes-which can be either in a clear water or a turbid state-this climate change will expectedly negatively affect water transparency though the prediction is far from conclusive and experimental investigations elucidating the potential climatic effects on shallow lakes are still rare. The aim of this study was to further shape and sharpens hypotheses on the impact of climate change on shallow lakes by applying an existing and well-calibrated ecosystem model, PCLake. We focused on asymptotic model behaviour for a range of temperature and loading scenarios in a factorial design. We conclude that climate change will likely lead to decreased critical nutrient loadings. Combined with an expected increase in the external nutrient loading, this will increase the probability of a shift from a clear to a turbid state. As the model predicts a higher summer chlorophyll-a concentration, a stronger dominance of cyanobacteria during summer and a reduced zooplankton abundance due to climate change, the turbid state itself is likely to become even more severe.

Advancing projections of phytoplankton responses to climate change through ensemble modelling

“…by the year 2100) have previously been studied using models (e.g., Elliott et al, 2005;Mooij et al, 2007;Trolle et al, 2011) with conclusions similar to ours, that overall phytoplankton biomass is likely to increase, and cyanobacteria will become a more dominant feature of the phytoplankton species composition. In the model simulations, the effects on cyanobacteria are included both directly through influence of temperature on growth rate, and indirectly, e.g., through changes in water column stability and changes in nutrient transformation rates.…”

“…However, most studies that simulate future impacts on lake phytoplankton have utilised only a single mechanistic model (Mooij et al, 2007;Trolle et al, 2011;Elliott, 2012). Whilst such studies have merit, the advantage of applying multiple, independently developed models -i.e., an ensemble modelling approach -to a given lake system is that some of the inherent uncertainties in the individual model projections can be reduced by conveying the mean and range of the projections.…”

Challenges and opportunities for integrating lake ecosystem modelling approaches

“…Although this was not intended from the beginning, the fact that temperature dependencies of all processes are included allows preliminary simulations of the effects of temperature rise in studies on climate change, the results being mainly in agreement with observations that warming will decrease the critical loading levels ( Schep et al 2007;Mooij et al 2007). Mooij et al (2009) showed that the PCLake results were qualitatively comparable with those of a minimal dynamic model.…”

Environmental Impacts—Lake Ecosystems

“…Indications of such developments are based on long-term data of Danish and Dutch shallow lakes (van Donk et al 2003) as well as modelling studies (Mooij et al 2007). Moreover, a dominance of filamentous green algae rather than phytoplankton seems possible under elevated temperatures (Trochine et al 2011).…”

“…In attempting to account for these complex interactions, mechanistic numerical models continue to play a greater role in hypothesis testing (system understanding) and for predicting the future state of ecosystems given the projections of future climatic forcing according to climate models (Trolle et al 2012). Thus, the ability to link-and equally importantly to quantify-complex interactions Trolle, original) between physical, chemical and biological processes makes models one of the most important tools of modern science, and for the past decade, models have been used extensively, aiming to establish the potential effects of future climate on freshwater ecosystems (Mooij et al 2007;Trolle et al 2011;Elliott 2012).…”