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dc.contributor.advisorPegram, G. G. S.
dc.creatorMkwananzi, Nokuphumula.
dc.date.accessioned2011-10-12T07:46:56Z
dc.date.available2011-10-12T07:46:56Z
dc.date.created2003
dc.date.issued2003
dc.identifier.urihttp://hdl.handle.net/10413/3767
dc.descriptionThesis (M.Sc.)-University of Natal, Durban,2003.en
dc.description.abstractThe research project described in this dissertation studies the modelling techniques employed for the Mlazi River in the context of flood analysis and flood forecasting in order to model flood inundation. These techniques are applicable to an environment where there is uncertainty due to a lack of historical input data for calibration and validation purposes. This uncertainty is best explained by understanding the process and data required to model flood inundation. In order to model flood inundation in real time, forecasted flood flows would be required as input to a hydraulic river model used for simulating flood inundation levels. During this process, forecasted flood flows would be obtained from a flood-forecasting model that would need to be calibrated and validated. The calibration process would require historical rainfall data correlating with streamflow data and subsequently, the validation process would require real time streamflow data. In the context of the Mlazi Catchment, there are only two stream gauges located in the upper subcatchments. Although these stream gauges have recorded data for 20 years, the streamflow data does not correlate with disaggregated daily rainfall data, of which there are records for at least 40 years. Therefore it would be difficult to develop the forecasting model based on the rainfall and streamflow data available. In this instance, a more realistic approach to modelling flood inundation involved the integration of GIS technology, a physically based hydrological model for flood analysis, a conceptual forecasting model for real time forecasting and a hydraulic model for computation of inundation levels. The integration of modelling techniques are better explained by categorising the process into three phases: Phase 1 Desktop catchment modelling: A continuous, physically based simulation model (HEC-HMS Model) was set up using GIS technology. The model applied the SCS-UH method for the estimation of peak discharges. Synthetic hyetographs for various recurrence intervals were used as input to the model. A sensitivity analysis was implemented and subsequently the HEC-HMS model was calibrated against output SCS-UH method and peak discharges simulated. The synthetic hyetographs together with results from the HEC-HMS model were used for validation of the Mlazi Meta Model (MMM) used for real time flood forecasting. Phase 2 Implementation of the Inundation Model: The hydraulic model (HEC-RAS) was created using a Digital Elevation Model (DEM). A field survey was conducted for the purpose of capturing the roughness coefficients and hydraulic structures, which were incorporated into the model and also for the confirmation of the terrain cross sections from the DEM. Flow data for the computation of levels of inundation were obtained from the HEC-HMS model. The levels of inundation for the natural channel of Mlazi River were simulated using the one dimensional steady state analysis, whereas for the canal overbank areas, simulation was conducted for unsteady state conditions. Phase 3 Creation of the Mlazi Meta Model (MMM): The MMM used for real time flood forecasting is a linear catchment model which consists of a semi-distributed three reservoir cell model (Pegram and Sinclair, 2002). The MMM parameters were initially adjusted using the HEC-HMS model so that it became representative of the Mlazi catchment. This approach sounds unreasonable because a model is being validated by another model but it gave the best initial estimate of the parameters rather than using trial and error. The MMM will be further updated using record radar data and streamflow data once all structures have been put in place. The confidence in the applicability of the HEC-HMS model is based on the intensive efforts applied in setting it up. Furthermore, the output results from the calibrated HEC-HMS model were compared with other reliable methods of computing design peak discharges and also validated with frequency analysis conducted on one of the subcatchments.en
dc.language.isoenen
dc.subjectFlood control.en
dc.subjectFlood forecasting.en
dc.subjectHydrologic models.en
dc.subjectUmlaas River (KwaZulu-Natal)en
dc.subjectTheses--Civil engineering.en
dc.titleModelling flood inundation in the Mlazi river under uncertainty.en
dc.typeThesisen


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