The use of radar and hydrological models for flash flood evaluation and prediction

• Main Author: Benjamin, Michael Richard
• Format: Others
• Language: en
• Published: 2016
• Subjects: Radar; Hydrological models; Floods forecasting; Flood control
• Online Access: http://hdl.handle.net/10539/21077

Dissertation Submitted for the degree of Master of Science in Geography at the University of the Witwatersrand FEBRUARY 08, 2016 === A flash flood is a flood which occurs within 6 hours from the start of a particular rainfall event. The ability to accurately evaluate and forecast flash floods could help in mitigating their harmful effects by helping communities plan their settlements outside of high risk areas and by providing information for the formulation and implementation of early warning systems. The overall aim of the study is to evaluate the use of RADAR data and hydrological models for flash flood evaluation and prediction. This is done by initialising both a lumped hydrological model (NAM) and a distributed hydrological model (MikeSHE) with both RADAR and raingauge derived precipitation estimates for the Jukskei river catchment located in Gauteng South Africa. The results of the model simulations are compared with each other and with actual streamflow data using various statistical techniques. The hydrometeorological characteristics of flash floods in the study catchment are also evaluated on a case by case basis. A fast response time and short duration are noted as the resounding characteristics of floods in the study catchment. All the model runs failed to correlate with streamflow (with any significant statistical certainty). The models also failed to significantly predict streamflow when using the pair sampled t-test. This highlights the difficulty in using rainfall estimates and hydrological models for discharge prediction. Although it is expected that the more advanced distributed model would fare better when predicting the variables associated with high flow events, it was only marginally better when simulating event timing. The lumped model did, however, fare better when correlating with stream flow, number of high flow events, peak flow, as well as total duration and volume === MT2016