Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

Abstract

Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of models. One of multi modelling approaches called "committee modelling" is one of the topics in part of this study. Special attention is given to the so-called “fuzzy committee” approach to hydrological modelling. The comparative interpretation of the resulting uncertainty statistics from different sampling schemes (MCS, GLUE, MCMC, SCEMUA, DREAM, PSO, and ACCO) for uncertainty estimations of hydrological model is presented. The uncertainty statistics are considerably depending on the sampling method used. Another aspect of uncertainty analysis relates to predicting uncertainty (rather than its analysis). Machine learning techniques were proposed to build model of probability distribution function as predictive uncertainty models, which allows adequate uncertainty estimation for hydrological models. In flood modelling hydrological models are typically used in combination with hydraulic models forming a cascade, often supported by geospatial processing. SWAT hydrological and SOBEK hydrodynamic models are integrated for uncertainty analysis of flood inundation modelling of the Nzoia catchment (Kenya), and the parametric uncertainty of the hydrological model is allowed to propagate through the model cascade using Monte Carlo simulations, leading to the generation of the probabilistic flood maps. Due to the high computational complexity of these experiments, the high performance (cluster) computing framework is designed and used. Overall, this thesis presents research efforts in: (i) committee modelling of hydrological models, (ii) hybrid committee hydrological models, (iii) influence of sampling strategies on prediction uncertainty of hydrological models, (iv) uncertainty prediction using machine learning techniques, (v) committee of predictive uncertainty models and (vi) uncertainty in flood inundation extent. This study refined a number of hydroinformatics techniques, thus enhancing uncertainty-based hydrological and integrated modelling.