| Popis: |
In this thesis, a riverflow modelling framework developed for monthly riverflow prediction in the 400,000 km2 Volta Basin of West Africa is presented. By analysing available catchment rainfall, runoff and potential evapotranspiration series in the basin using methods such as correlation plots, autoregressive (AR) and autoregressive with exogenous input (ARX) modelling, it is shown that the monthly catchment rainfall-runoff process is better characterised by non-linear models. First, a spatio-temporal linear dynamic model employing the Kalman smoother and the Expectation-Maximisation (EM) algorithm was developed and applied to filling in short gaps, of up to one month, in daily riverflow series in the basin. This model was found to be a very good and powerful tool for filling in such data gaps. Then, two non-linear modelling frameworks - a non-linear autoregressive and moving average with exogenous input (NARMAX) polynomial and a data-based mechanistic (DBM) modelling framework - were developed and applied to the monthly rainfall-runoff series in the basin for river catchment runoff prediction. Both methods predicted monthly runoff adequately, with the DBM framework also providing physically interpretable results. This indicates that data-driven approaches are appropriate for riverflow modelling in the data-poor Volta Basin |
