Stochastic modelling of river morphodynamics

Abstract

Modern river management has to reconcile a number of functions, such as protection against floods and provision of safe and efficient navigation, floodplain agriculture, ecology and recreation. Knowledge on uncertainty in fluvial processes is important to make this possible, to design effective river engineering works, for operational forecasting and for the maintenance of the river system. In this research the focus is in particular on the quantification of uncertainty in river morphodynamics. Morphological changes can cause flood safety problems, navigation problems, problems with the water distribution over different river branches and stability or functioning problems with hydraulic structures. They may also influence the groundwater level, which may on its turn affect other functions, such as ecology and agriculture. With respect to large-scale engineering projects, such as the project Room for the River in the Netherlands, the social relevance of decisions in river management practice becomes more and more important. River systems are of a dynamic and stochastic nature and the underlying processes are not completely understood. An imperfect description of physical processes, along with the inability to accurately quantify the model inputs and parameters, leads to uncertainty in morphodynamic predictions. For this reason, identifying the uncertainty sources and assessing their contribution to the overall uncertainty in morphodynamic predictions is necessary in order to come to grips with system behaviour. This calls for a stochastic method that enables us to indicate ranges of possible morphodynamic states, their probability of occurrence and the estimation of undesired morphological effects. Stochastic modelling of river morphology and its potential in present-day river management practice is the topic of this thesis. In summary, this thesis shows how to analyse the stochastic nature of river morphology by means of Monte Carlo Simulation. It provides insight into the uncertainty sources that contribute most to the stochastic morphodynamic river behaviour. Furthermore, three applications illustrate the potential of a stochastic model approach in river management practice. The conclusion can be drawn that the use of this 'computation-intensive' approach adds value to river engineering and management practice.