Cloud and cluster computing in uncertainty analysis of integrated flood models

Abstract

There is an increased awareness of the importance of flood management aimed at preventing human and material losses. A wide variety of numerical modelling tools have been developed in order to make decision-making more efficient, and to better target management actions. Hydroinformatics assumes the holistic integrated approach to managing the information propagating through models, and analysis of uncertainty propagation through models is an important part of such studies. Many popular approaches to uncertainty analysis typically involve various strategies of Monte Carlo sampling of uncertain variables and/or parameters and running a model a large number of times, so that in the case of complex river systems this procedure becomes very time-consuming. In this study the popular modelling systems HEC-HMS, HEC-RAS and Sobek1D2D were applied to modelling the hydraulics of the Timis–Bega basin in Romania. We considered the problem of studying how the flood inundation is influenced by uncertainties in water levels of the reservoirs in the catchment, and uncertainties in the digital elevation model (DEM) used in the 2D hydraulic model. For this we used cloud computing (Amazon Elastic Compute Cloud platform) and cluster computing on the basis of a number of office desktop computers, and were able to show their efficiency, leading to a considerable reduction of the required computer time for uncertainty analysis of complex models. The conducted experiments allowed us to associate probabilities to various areas prone to flooding. This study allows us to draw a conclusion that cloud and cluster computing offer an effective and efficient technology that makes uncertainty-aware modelling a practical possibility even when using complex models.