The main goal of this paper is to develop a flood management model that takes into account the specifics of catastrophic risk management: highly mutually dependent losses, the lack of information, the need for long-term perspectives and explicit analyses of spatial and temporal heterogeneities of various agents such as individuals, governments, and insurers. We use modified data from a pilot region of the Upper Tisza river, Hungary, to illustrate the evaluation of a public multipillar flood loss-spreading program involving partial compensation to flood victims by the central government, the pooling of risks through a mandatory public catastrophe insurance on the basis of location-specific exposures, and the demand for a contingent ex-ante credit to reinsure the insurances liabilities. GIS-based catastrophe models and stochastic optimization methods are used to guide policy analysis with respect to location-specific risk exposures. We use economically sound risk indicators leading to convex stochastic optimization problems strongly connected with nonconvex insolvency constraint, VaR and CVaR
