Advancing projections of phytoplankton responses to climate change through ensemble modelling☆
Introduction
Ecological processes in lakes and reservoirs are highly sensitive to environmental change (Williamson et al., 2008). Phytoplankton, being an integral component of lake food webs, has proven to be particularly responsive to changes in factors influenced by global change such as nutrients (Reynolds, 1984, Lampert and Sommer, 1997) and climate (Huber et al., 2008, Jöhnk et al., 2008). This sensitivity is of particular concern to lake managers around the world and, when coupled with the related health hazard of cyanobacterial blooms (Chorus and Bartram, 1999, Paerl and Huisman, 2008), makes understanding their ecology and projecting their biomass of paramount importance.
Projecting cyanobacterial blooms has proved challenging and has motivated the development of numerous computer models that have attempted to simulate the seasonal development of lake phytoplankton (Mooij et al., 2010, Trolle et al., 2012). Nevertheless, with the additional pressures that freshwater ecosystems are subject to with a changing climate, the need for the predictive ability of such models has never been more important than now (Dale et al., 2006, Oliver et al., 2012). 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. Nevertheless, no ensemble modelling studies have yet been carried out for projection of lake phytoplankton dynamics, perhaps due to constraints associated with both the considerable resources and the availability of expertise needed to model these aquatic ecosystems.
In this study we take advantage of the expertise available within an international network of modelling experts and apply three autonomously developed models to the same freshwater lake system. We do not intend to provide a comprehensive review of the conceptual differences between these models (for that, we refer to the bulk of literature already available, e.g., Mooij et al., 2010, Trolle et al., 2012), but rather take advantage of these differences in evaluating the diversity of simulated signals they provide. We further simulate a range of future climate change scenarios, represented by a 1.5, 3 and 5 °C warming scenario and two increased nutrient load scenarios, so that the different model projections and likely impacts of warming can be assessed. We test our hypothesis that, as for weather forecast models, the ensemble model mean (derived as the daily average of output from the three individual models) can provide a better predictive working model compared with any individual model (Gneiting and Raftery, 2005.), whilst also allowing statistical uncertainties to be expressed where otherwise they would not be, i.e., with the more common approach of applying a single mechanistic model. We test this method on Lake Engelsholm (Denmark) which, like many other lowland lakes worldwide, has been undergoing eutrophication as a result of decades of anthropogenic impacts from both point and diffuse sources of nutrient pollution.
Section snippets
Study site
Lake Engelsholm is a typical small (surface area of 0.44 km2) and shallow (maximum depth of 6.1 m, mean depth of 2.4 m) lake in Denmark. It is currently in a eutrophic state caused by high external nutrient loads from the surrounding catchment, resulting in an annual average Secchi depth of approx. 2 m, annual average chlorophyll a concentrations of approx. 25–30 mg m−3 (data from 1999 to 2001), and occasional occurrences of cyanobacterial blooms during summers. The catchment area (15.2 km2)
Performance of the individual aquatic ecosystem models and the ensemble mean simulation
The three individual models generally performed to a similar level achieved in other peer-reviewed studies in terms of the r2 and relative absolute error (RE) values between model simulations and observations of chlorophyll a (e.g., review by Arhonditsis and Brett, 2004, where median r2 was 0.48 and RE was 44% for simulation of phytoplankton biomass across 153 individual modelling studies). The variation explained (r2) by the models increased considerably by use of monthly means (Table 2),
Perspectives of the ensemble modelling approach
Our study is the first to apply several individual complex dynamic lake models to the same aquatic ecosystem. The ensemble modelling approach has been used for a number of years for weather forecasts and global circulation models (GCMs), and is common practise when, for example, the IPCC reports on the potential effects of anthropogenic activities on future climate. An ensemble modelling approach can be applied either as a single-model ensemble, where multiple parameter combinations or multiple
Conclusions
We examined the performance of multiple aquatic ecosystem models in terms of their ability to reproduce phytoplankton biomass in a typical lowland temperate lake. The suite of models was subsequently used to project the effects of climate warming on phytoplankton biomass, and thus the potential future implications for water users. We found that; 1) using the mean of all models generally was superior to any individual model in reproducing observed phytoplankton dynamics; 2) in a typical lowland
Acknowledgements
The study was supported by the EU project REFRESH (Adaptive strategies to mitigate the impacts of climate change on European freshwater ecosystems, Env. 2009.2.1.2.1). D.T. and E.J. were also supported by CLEAR (a Villum Kann Rasmussen Foundation, Centre of Excellence project), CRES, E.J. by CIRCE and ARC and D.H. by the New Zealand Ministry of Business, Innovation and Employment (UOWX0505). Author contributions: DT developed the hypotheses and carried out statistical tests. DT and JAE
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