Development and assessment of a daily time-step continuous simulation modelling approach for design flood estimation at ungauged locations: ACRU model and Thukela Catchment case study

The estimation of design floods is necessary for the design of hydraulic structures. Commonly used event-based approaches to design flood estimation have several limitations, which include the estimation of antecedent soil moisture conditions and the assumption that the exceedance probability of the design flood is the same as the exceedance probability of the design rainfall. Many of the limitations of event-based approaches may be overcome by the use of continuous simulation modelling for design flood estimation. This paper contains a brief summary on the development and assessment of a continuous simulationmodelling system for design flood estimation in ungauged catchments. These developments include an investigation into the appropriate spatial scale of model configuration for optimum performance of the system, the temporal disaggregation of daily rainfall for hydrograph generation, flood routing in ungauged catchments and the use of radar information and rain-gauge data to improve the estimation of catchment rainfall. Results from the application of the system for design flood estimation in the Thukela Catchment in South Africa are presented and discussed. The results from the study highlight the challenges of hydrological modelling in an operational catchment and the need for reliable rainfall and runoff data. From the results obtained, it is concluded that reasonable and consistent estimates of design floods in the Thukela Catchment, particularly in smaller sub-catchments, can be obtained using the ACRU model.Keywords: Design flood estimation; continuous simulation modelling, Thukela Catchmen

Assessment of satellite-derived rainfall and its use in the ACRU agro-hydrological model

Unfortunately, for various reasons, in-situ rain gauge networks are diminishing, especially in southern Africa, resulting in sparse networks whose records give a poor representation of rainfall occurrence, patterns and magnitudes. Hydrological models are used to inform decision making; however, model performance is directly linked to the quality of input data, such as rainfall. Therefore, the use of satellite-derived rainfall is being increasingly advocated as a viable alternative or supplement. The aim of this study was to evaluate the representativeness of satellite-derived rainfall and its utility in the ACRU agro-hydrological model to simulate streamflow magnitudes, distributions and patterns. The satellite-derived rainfall products selected for use in this study were TRMM3B42, FEWSARC2.0, FEWSRFE2.0, TAMSAT 3.0 and GPM-IMERG4. The satellite rainfall products were validated against available historical observed records and then were used to drive simulations using the ACRU agro-hydrological model in the upper uMngeni, upper uThukela and upper and central Breede catchments in South Africa. At the daily timescale, satellite-derived and observed rainfall were poorly correlated and variable among locations. However, monthly, seasonal and yearly rainfall totals and simulated streamflow volumes were in closer agreement with historical observations than the daily correlations; more so in the upper uMngeni and uThukela than in the upper and central Breede (e.g. FEWSARC2.0 and FEWSRFE2.0, producing relative volume errors of 3.18%, 4.63%, −5.07% and 2.54%, 9.54%, −1.67%, respectively, at Gauges V2E002, 0268883 and 02396985). Therefore, the satellite-derived rainfall shows promise for use in applications operating at coarser temporal scales than at finer daily ones. Complex topographical rainfall generation and varying weather systems, e.g. frontal rainfall, affected the accuracy of satellite-derived product estimates. This study focused on utilising the wealth of available raw satellite data; however, it is clear that the raw satellite data need to be corrected for bias and/or downscaled to provide more accurate results

Assessment of satellite-derived rainfall and its use in the ACRU agro-hydrological model

Unfortunately, for various reasons, in-situ rain gauge networks are diminishing, especially in southern Africa, resulting in sparse networks whose records give a poor representation of rainfall occurrence, patterns and magnitudes. Hydrological models are used to inform decision making; however, model performance is directly linked to the quality of input data, such as rainfall. Therefore, the use of satellite-derived rainfall is being increasingly advocated as a viable alternative or supplement. The aim of this study was to evaluate the representativeness of satellite-derived rainfall and its utility in the ACRU agro-hydrological model to simulate streamflow magnitudes, distributions and patterns. The satellite-derived rainfall products selected for use in this study were TRMM3B42, FEWSARC2.0, FEWSRFE2.0, TAMSAT 3.0 and GPM-IMERG4. The satellite rainfall products were validated against available historical observed records and then were used to drive simulations using the ACRU agro-hydrological model in the upper uMngeni, upper uThukela and upper and central Breede catchments in South Africa. At the daily timescale, satellite-derived and observed rainfall were poorly correlated and variable among locations. However, monthly, seasonal and yearly rainfall totals and simulated streamflow volumes were in closer agreement with historical observations than the daily correlations; more so in the upper uMngeni and uThukela than in the upper and central Breede (e.g. FEWSARC2.0 and FEWSRFE2.0, producing relative volume errors of 3.18%, 4.63%, −5.07% and 2.54%, 9.54%, −1.67%, respectively, at Gauges V2E002, 0268883 and 02396985). Therefore, the satellite-derived rainfall shows promise for use in applications operating at coarser temporal scales than at finer daily ones. Complex topographical rainfall generation and varying weather systems, e.g. frontal rainfall, affected the accuracy of satellite-derived product estimates. This study focused on utilising the wealth of available raw satellite data; however, it is clear that the raw satellite data need to be corrected for bias and/or downscaled to provide more accurate results.</jats:p