Computational uncertainty management for coastal flood prevention system

Multivariate and progressive uncertainty is the main factor of accuracy in simulation systems. It can be a critical issue for systems that forecast and prevent extreme events and related risks. To deal with this problem, computational uncertainty management strategies should be used. This paper aims to demonstrate an adaptation of the computational uncertainty management strategy in the framework of a system for prediction and prevention of such natural disasters as coastal floods. The main goal of the chosen strategy is to highlight the most significant ways of uncertainty propagation and to collocate blocks of action with procedures for reduction or evaluation of uncertainty in a way that catches the major part of model error. Blocks of action involve several procedures: calibration of models, data assimilation, ensemble forecasts, and various techniques for residual uncertainty evaluation (including risk evaluation). The strategy described in this paper was tested and proved based on a case study of the coastal flood prevention system in St. Petersburg.