Abstract. Surface waterblooms of toxic cyanobacteria (scums) interfere with the use of lakes, for instance in the production of drinking water or for recreation. Routine monitoring data are not sufficient for early warning due to the large temporal and spatial variability in the occurrence of surface waterblooms, and the time lag between the formation of the scum and the availability of relevant information for risk management. We combined a ''traditional'' dynamic simulation model based upon differential equations with fuzzy logic to describe the three main conditions governing surface waterbloom formation: (1) a preexisting population of cyanobacteria, (2) buoyancy of the cells, and (3) stability of the water column. The attributes and membership functions of the fuzzy model were based on earlier field studies of diel changes in buoyancy and vertical distribution of cyanobacteria. The model was applied without further calibration to the large lake IJsselmeer (1200 km 2 ) in the Netherlands, and we validated the model output using 12 years of NOAA-AVHRR (National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometers) satellite images on which surface blooms are discernible as an enhanced vegetation index or increased surface water temperature. Existing surface blooms were predicted with high accuracy, but additional blooms were also predicted. A statistical test (Cohen's Kappa) showed that correct predictions of the absence or presence of surface blooms were highly unlikely to have occurred by chance only. The model can be used to predict the occurrence of surface waterblooms in advance on the basis of the long-term weather forecast, leaving time for appropriate management of the problem. The lake management has expressed interest in converting the present model into a fully operationalonline-early warning system.

The set-up, application and validation of a generic ecological model (GEM) for estuaries and coastal waters is presented. This model is a comprehensive ecological model of the bottom of the foodweb, consisting of a set of modules, representing specific water quality processes and primary production that can be combined with any transport model to create a dedicated model for a specific ecosystem. GEM links different physical, chemical and ecological model components into one generic and flexible modelling tool that allows for variable sized, curvilinear grids to accomodate both the requirements for local accuracy while maintaining a relatively short model run-time. The GEM model describes the behaviour of nutrients, organic matter and primary producers in estuaries and coastal waters, incorporating dynamic process modules for dissolved oxygen, nutrients and phytoplankton. GEM integrates the best aspects of existing Dutch estuarine models that were mostly dedicated to only one type of ecosystem, geographic area or subset of processes. Particular strengths of GEM include its generic applicability and the integration and interaction of biological, chemical and physical processes into one predictive tool. The model offers flexibility in choosing which processes to include, and the ability to integrate results from different processes modelled simultaneously with different temporal resolutions. The generic applicability of the model is illustrated using a number of representative examples from case studies in which the GEM model was successfully applied. Validation of these examples was carried out using the 'cost function' to compare model results with field observations. The validation results demonstrated consistent accuracy of the GEM model for various key parameters in both spatial dimensions (horizontally and vertically) as well as temporal dimensions (seasonally and across years) for a variety of water systems without the need for major reparameterisation.

Dispersal of eggs and larvae of herring, plaice and sole in the southern North Sea was studied by modelling using real-time hydrodynamic forcing (with wind, air pressure and river discharge) and species-specific knowledge of larval behaviour (incorporating salinity triggers), temperature-dependent growth and spawning characteristics. Larval transport was simulated using a finitevolume advection-diffusion model (Delft3D-WAQ) coupled to a 3-dimensional hydrodynamic model (Delft3D-FLOW). Model parameter settings were refined following a sensitivity analysis. Validation of modelled hydrodynamics and larval distribution patterns showed broad agreement with field data. Differences in model results for larval distribution, transport success and timing of arrival at nursery grounds between baseline conditions and a scenario that incorporated a proposed 1000 ha coastal reclamation (protruding 6 to 7 km from the Dutch coastline) for the expansion of the Port of Rotterdam (Maasvlakte-2) were insignificant in comparison to the interannual variability in larval dispersal for these species. Results suggest that effects of the proposed coastal reclamation on the transport success of fish larvae (flatfish and herring), an issue over which public stakeholders had expressed concern, will be negligible.