Around the world, areas with unique ecosystems are prone to harmful factors deteriorating their environment. Management of these areas need knowledge on the ecological status of these areas, to make decisions for future problems. They need easily accessible information on indicators describing the conditions of these areas. Obtaining this information can be done using numerical models. The use of models however will introduce uncertainty in the information, which should be taken into account when using model output for the making of decisions. When model output is known together with its uncertainty, it can be efficiently visualized in a toolbox to make it commonly available for users. In this way the use of uncertainty can be incorporated in management purposes. In this research, an uncertainty analysis is conducted and a visualization of model output together with uncertainty is made for the Wadden Sea area. In this area eutrophication and consequently algal bloom causes deterioration of the water quality. Due to Suspended Particulate Matter (SPM) in the water column, the incoming light is reduced, decreasing the amount of algal bloom. With a Delft3D-WAQ model, using the GEM/BLOOM module, the chlorophyll-a concentration can be modelled, which is an indicator of the amount of algae in the water column. The model Delft3D-WAQ Sediments is used to calculate the SPM concentrations, which is used as a forcing for the GEM/BLOOM model. Therefore, they are a main driving force and uncertainty source in the setup for this project. The main research question is formulated as: How can uncertainty from a SPM model as a driving force for a GEM/BLOOM model be identified, quantified and visualized to help decision makers? Using a literature review the uncertainty sources within the input files are identified. To quantify the uncertainty, first a sensitivity analysis and consequently an uncertainty analysis is used. This sensitivity analysis is done by varying the values of certain input files and assessing the variability it creates in the model output, resulting in the most influential input. The uncertainty analysis is used to obtain the magnitude of the uncertainty coming from this most influential input. This analysis is a Monte Carlo simulation, where different input is assessed by giving these a Probability Density Function (PDF) simulating the uncertainty in the input. From these distributions, samples are drawn to create different experiments to assess the influence of the uncertainty. Using a Latin Hypercube Sampling with Dependence instead of a random sampling, the amount of model runs is reduced from thousands to 188. The dependencies between the input parameters are taken into account. Afterwards, the output is estimated with a PDF from which the PDF characteristics mode and spreading are used to describe the SPM concentration and its uncertainty for each segment in time and space. A toolbox is developed for a 3D visualization of the model output and its uncertainty. A cubic shaped marker is placed in this environment for each segment by its x-, y- and z-coordinates. The SPM concentrations are visualized by coloring the markers and the uncertainty is incorporated using a white hue. The parameters in the erosion and deposition fluxes in the model equations are identified to be most influential and are therefore assessed on uncertainty. The SPM results are validated by a comparison against measurement data from Rijkswaterstaat and data used in earlier. The model gives a good approximation of the SPM concentrations. Some areas indicate a very high uncertainty, mainly where the SPM values are unrealistically high. Overall, the values are within the same order of magnitude as the validation data. The uncertainty in the model output is mainly present in critical areas, where the influence of different factors is significant, such as the effect of a river outflow, stratification and tidal influence. This indicates that either the bed load module of the model or the hydrodynamic input is not completely optimized yet. For the quantification of the uncertainty a log-normal distribution is used to estimate the output values at every location and time step. The characteristics of this distribution, the mode and the spreading, are used to quantify the concentrations of SPM and the uncertainty into values that can be used in the visualization. To answer the research question: the uncertainties are identified with a literature review resulting in the sources of uncertainty, quantified with the uncertainty analysis into a mode and a spreading and visualized in a toolbox with a 3D environment using a marker for each segment and using color for displaying the concentrations and a white hue to visualize the uncertainty. This toolbox than helps decision makers to easily access the data and according uncertainty. The SPM model has some high uncertainties, but is a good estimation to be used as a driving force for the GEM/BLOOM model.