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Cyanobacteria blooms are a worldwide concern for water bodies and may be promoted by eutrophication and climate change. The prediction of cyanobacterial blooms and identification of the main triggering factors are of paramount importance for water management. In this study, we analyzed a comprehensive dataset including ten-years measurements collected at Lake Varese, an eutrophic lake in Northern Italy. Microscopic analysis of the water samples was performed to characterize the community distribution and dynamics along the years. We observed that cyanobacteria represented a significant fraction of the phytoplankton community, up to 60% as biovolume, and a shift in the phytoplankton community distribution towards cyanobacteria dominance onwards 2010 was detected. The relationships between cyanobacteria biovolume, nutrients, and environmental parameters were investigated through simple and multiple linear regressions. We found that 14-days average air temperature together with total phosphorus may only partly explain the cyanobacteria biovolume variance at Lake Varese. However, weather forecasts can be used to predict an algal outbreak two weeks in advance and, eventually, to adopt management actions. The prediction of cyanobacteria algal blooms remains challenging and more frequent samplings, combined with the microscopy analysis and the metagenomics technique, would allow a more conclusive analysis.

Abstract. 13Pristine coastal shallow systems are usually dominated by extensive meadows of seagrass species, 14 which are assumed to take advantage of nutrient supply from sediment. An increasing nutrient input is 15 thought to favour phytoplankton, epiphytic microalgae, as well as opportunistic ephemeral macroalgae 16 that coexist with seagrasses. The primary cause of shifts and succession in the macrophyte community 17 is the increase of nutrient load to water; however temperature plays also an important role. A 18 competition model between rooted seagrass (Zostera marina), macroalgae (Ulva sp), and 19 phytoplankton has been developed to analyse the succession of primary producer communities in these 20 systems. Successions of dominance states, with different resilience characteristics, are found when 21 modifying the input of nutrients and the seasonal temperature and light intensity forcing. 22 23

Within the activities of the Rural, Water and Ecosystem resources (RWER) Unit of the IES during 2004-2009, a special attention was devoted to the development of a pan-European GIS based model of fate and transport of contaminants. Such model was designed for the purpose of providing spatially explicit assessment of the continental-scale trends in the environmental distribution of contaminants, capitalizing on the increasing wealth of geographic information on landscape and climate parameters which are drivers of the fate of chemicals. The spatially explicit model allows in principle the prediction of expected concentrations of chemicals at a given geographically defined location. Most of the times, however, the information on chemical emissions to the environment is not sufficiently accurate to enable reliable point-wise predictions. Under such circumstances, however, the use of spatially explicit models may be useful to derive frequency distributions of chemical concentrations, reflecting the variability of environmental parameters and emissions. In this case, maps should not be seen as geographies of contamination, but rather their histograms should be examined and presented for communication of chemical trends to the public and stakeholders. Current procedures for the authorization of chemicals in Europe rely on chemical risk assessment that is made in non-spatial terms. The procedures aim at preventing unacceptable risks from an individual emission, under "worst case scenario" assumptions. Sometimes, however, risks may arise from a combination of multiple chemicals, from multiple points or areas of emission, and they may end up affecting people or ecosystems at different locations in space. For these reasons, it may be sometimes useful to use spatially explicit models and take into account not "scenario" emissions, but "realistic" representations of actual emissions in space. Another good reason for the use of spatially explicit models is that, very often, chemical emissions are not known, while chemical concentrations are monitored at specific locations in the environment. In such cases, inverse modeling of emissions from concentrations may prove to be a very valuable exercise, and needs use of spatially explicit models to be extended. This report does not suggest that spatially explicit models should always be used, nor promotes their inclusion in current and prospective authorization procedures. Spatially explicit models are rather to be seen as assessment tools to be used wisely and having well in mind the limitations of our current understanding of chemical fate for the vast majority of substances. Recently Hollander et al., 2008, and Hollander et al., 2009, have examined in more detail the importance of both landscape/climate variability and emission variability on model results, concluding that the latter is usually much more important. Compared to the variability of physico-chemical properties among different substances, these studies highlight that landscape and climate parameters are usually less im...