Freshwater lakes are biologically sensitive to changes in the surrounding environment and the impacts that such changes have on their water quality are of considerable ecological, recreational and economic importance. In this study the phytoplankton community model, PROTECH, was used to experiment with the effects of elevated temperatures and increased nutrient load on phytoplankton succession and productivity. The response of a phytoplankton community to combined incremental changes in these drivers was analysed, in order to elucidate the resulting ecological changes. Annual mean phytoplankton biomass increased with increases in temperature and nutrient loading, although the latter had the larger effect. The phenology of the dominant phytoplankton taxa changed with increasing water temperature; the three spring blooming species all peaked earlier in the year. The simulated summer bloom of Anabaena became earlier in the year and the Chlorella bloom later. The increased phytoplankton biomass was largely dominated by the cyanobacterium Anabaena, which was especially prevalent during the summer bloom. This resulted in a progressive loss of phytoplankton biodiversity with increasing water temperature and nutrient supply. Model experimentation showed that whilst both factors greatly affected the community, the changes to nutrient loading generally had the greater effect and that at low nutrient levels the effect of water temperature change was reduced considerably. Finally, the model predicted that cyanobacteria have the potential to dominate the phytoplankton community, with clear consequences for water quality, and that this dominance was at its greatest when high water temperatures were combined with high nutrient loads.

There is increasing evidence that recent changes in climate have had an effect on lake 10 phytoplankton communities and it has been suggested that it is likely that Cyanobacteria will 11 increase in relative abundance under the predicted future climate. However, testing such a 12 qualitative prediction is challenging and usually requires some form of numerical computer 13 model. Therefore, the lake modelling literature was reviewed for studies that examined the 14 impact of climate change upon Cyanobacteria. These studies, taken collectively, generally 15show an increase in relative Cyanobacteria abundance with increasing water temperature, 16 decreased flushing rate and increased nutrient loads. Furthermore, they suggest that whilst 17 the direct effects of climate change on the lakes can change the timing of bloom events and 18Cyanobacteria abundance, the amount of phytoplankton biomass produced over a year is not 19 enhanced directly by these changes. Also, warmer waters in the spring increased nutrient 20 consumption by the phytoplankton community which in some lakes caused nitrogen 21 limitation later in the year to the advantage of some nitrogen-fixing Cyanobacteria. Finally, it 22 is also possible that an increase in Cyanobacteria dominance of the phytoplankton biomass 23 will lead to poorer energy flow to higher trophic levels due to their relatively poor edibility 24 for zooplankton. 25 26 KEYWORDS: 27 lake modelling, nitrogen limitation, phenology, water quality, eutrophication 28 3 29

Climate change is expected to modify rainfall, temperature and catchment hydrological responses across the world, and adapting to these water-related changes is a pressing challenge. This paper reviews the impact of anthropogenic climate change on water in the UK and looks at projections of future change. The natural variability of the UK climate makes change hard to detect; only historical increases in air temperature can be attributed to anthropogenic climate forcing, but over the last 50 years more winter rainfall has been falling in intense events. Future changes in rainfall and evapotranspiration could lead to changed flow regimes and impacts on water quality, aquatic ecosystems and water availability. Summer flows may decrease on average, but floods may become larger and more frequent. River and lake water quality may decline as a result of higher water temperatures, lower river flows and increased algal blooms in summer, and because of higher flows in the winter. In communicating this important work, researchers should pay particular attention to explaining confidence and uncertainty clearly. Much of the relevant research is either global or highly localized: decision-makers would benefit from more studies that address water and climate change at a spatial and temporal scale appropriate for the decisions they make.

A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: superindividual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.

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Highlights  The General Lake Model (GLM) is stress tested against 32 globally distributed lakes.  There was low correlation between input data uncertainty and model performance.  Model performance related to lake-morphometry, light extinction and flow regime; deep, clear lakes with high residence times had the lowest model error.

The phytoplankton lake community model PROTECH (Phytoplankton RespOnses To Environmental CHange) was applied to the eutrophic lake, Esthwaite Water (United Kingdom). It was validated against monitoring data from 2003 and simulated well the seasonal pattern of total chlorophyll, diatom chlorophyll and Cyanobacteria chlorophyll with respective R 2 -values calculated between observed and simulated of 0.68, 0.72 and 0.77 (all Po0.01). This simulation was then rerun through various combinations of factorized changes covering a range of half to double the flushing rate and from À1 to 1 4 1C changes in water temperature. Their effect on the phytoplankton was measured as annual, spring, summer and autumn means of the total and species chlorophyll concentrations. In addition, Cyanobacteria mean percentage abundance (%Cb) and maximum percentage abundance (Max %Cb) was recorded, as were the number of days that Cyanobacteria chlorophyll concentration exceed two World Health Organization (WHO) derived risk thresholds (10 and 50 mg m À3 ). The phytoplankton community was dominated in the year by three of the eight phytoplankton simulated. The vernal bloom of the diatom Asterionella showed little annual or seasonal response to the changing drivers but this was not the case for the two Cyanobacteria that also dominated, Anabaena and Aphanizomenon. These Cyanobacteria showed enhanced abundance, community dominance and increased duration above the highest WHO risk threshold with increasing water temperature and decreasing flushing rate: this effect was greatest in the summer period. However, the response was ultimately controlled by the availability of nutrients, particularly phosphorus and nitrogen, with occasional declines in the latter's concentration helping the dominance of these nitrogen-fixing phytoplankton.