The SLOSH (Sea Lake and Overland Surge from Hurricanes) model is an operational National Weather Service (NWS) tool often used by emergency managers in hurricane-prone coastal areas. It provides simulated storm surge information to determine coastal flooding from hurricanes, which is then used for planning evacuations and sheltering based on the projected Maximum Envelopes of Water (MEOWs) and Maximum of Maximums (MOMs). The model and the resulting data have been used by the NWS and emergency planners for many years. As emergency management and planning have matured, risk-based planning has become the norm. In the context of emergency management, risk is defined as a combination of severity and probability of some process or event, such as flooding or storm surge. However, although SLOSH has always given an indication of severity, i.e. showing how much water will flood an area due to hurricane driven storm surge, it does not explicitly offer any indication of probability. This project will address this issue by using existing SLOSH model runs to quantify flooding probability due to storm surge. To effectively utilize the probability and severity information in an emergency management context, a tool will be developed that will allow a user to determine the exceedance probability of a particular flood depth and visualize the hypothetical hurricane tracks that cause flooding at a given location. The risk-based planning tool will also allow for interactive assessment of storm surge and inundation in an operational setting, which is crucial for time-critical emergency management procedures. The tool is being developed as a prototype using the New Orleans SLOSH basin, with the expectation that the standard output structure and format of the SLOSH data will allow the tool development process to be applied to any SLOSH basin. This will be verified using the Vermilion Bay and Sabine Lake SLOSH basins (central and western Louisiana, respectively) in support of the State of Louisiana emergency management planning and operations.