|dc.description.abstract||An estimate of the risk associated with flood events is required to adequately design hydraulic structures and limit negative socio-economic impacts as a result of floods. The methods used to estimate design floods in South Africa are outdated and are in need of revision. A National Flood Studies Programme (NFSP) has recently been initiated by Smithers et al. (2016) to overhaul Design Flood Estimation (DFE) procedures in South Africa. One of the recommendations of the NFSP is development and assessment of a Continuous Simulation Modelling (CSM) approach to DFE. Consequently, the aim of this study is to further develop and assess the performance of an improved comprehensive CSM system, to consistently and reliably estimate design flood discharges in small catchments (0 - 100 km2) in South Africa using the ACRU model. In the development of the approach a strong emphasis has been placed on ease of use from a practitioner’s point of view. The aim is achieved through several specific objectives as summarised below.
The first objective was to review CSM approaches applied locally and internationally for DFE, in order to identify research gaps and guide the development of an improved national CSM system for DFE in South Africa. The review culminates with a list of recommendations and steps required to develop and adopt a CSM approach for DFE in practice. The first critical step identified and required was the development of a comprehensive CSM system using the ACRU model (Schulze, 1995). This included: the structure of the system and how to implement the system, an enhanced land cover and soils classification to apply with the system and default input information and databases to use with the system.
The second objective addresses the recommendations made from the literature review, where a comprehensive CSM system for DFE using the ACRU model is developed and described in detail. Based on similarities identified between the ACRU (Schulze, 1995) and SCS-SA models (Schmidt and Schulze, 1987a), as well as the fact that the SCS-SA model is relatively simple and widely applied in practice, the CSM system was adapted to be consistent with the land cover classification used in the SCS-SA model. This included the incorporation of a methodology and rules, developed by Rowe (2015), to represent land management practices and hydrological conditions within the ACRU model. The development of this comprehensive CSM system with default national scale inputs and land cover classifications contributes to new
knowledge on how to package a CSM system for DFE in South Africa.
The third objective focuses on the assessment and verification of the CSM system developed, using observed data. Through the verifications and assessments performed an inconsistency between daily simulated stormflow volumes and the volume of stormflow used in the daily stormflow peak discharge equation was identified. Therefore, a revision, which is more conceptually correct than the current assumption that all stormflow generated from an event contributes to the peak discharge on the day, was applied to the fraction of the simulated daily stormflow used in the peak discharge equation. This corrected the inconsistency and significantly improved the results, thereby providing an improved methodology to more accurately estimate peak discharges in the ACRU model than had hitherto been the case.
Despite the improvement in the results, a general over-simulation of peak discharges was still evident. Consequently, further investigation of the ACRU stormflow peak discharge computations was performed in order to identify which approach provides the most satisfactory results (Objective 4). This included a performance assessment of both the SCS single Unit Hydrograph (UH) approach and the incremental UH approach. The performance of each approach was assessed using both estimated parameters and parameters derived from observed data. These parameters include stormflow volumes, catchment lag times, and the distribution of daily rainfall, where applicable, to each approach. Comparison of the results from the two approaches indicated that more accurate results are obtained when applying the incremental UH approach, when using both estimated or observed parameter inputs. In terms of the incremental UH approach, it was identified that the approach is more sensitive to the use of synthetic daily rainfall distributions compared to estimated lag times. Based on the results obtained new knowledge and additional research gaps related to: (i) improved estimation of the distribution of daily rainfall within the ACRU model, (ii) links between the distribution of daily rainfall and catchment lag time, and (iii) the need to further verify and possibly recalibrate CNs for South Africa were identified.
The fifth objective addressed is an assessment of the impact of model configuration on the performance of the ACRU CSM system developed, in order to propose a final CSM system for DFE in South Africa. Results when using site-specific land cover and soils information are compared to those obtained when different sources of input information are used, such as the national land cover and soils maps developed for the entire country. The results when using
these default national datasets were not particularly good, however recommendations are made to improve on the results. In addition, the most appropriate current databases to use with the CSM system are defined, providing users with the most appropriate default information currently available to use in the absence of site-specific information.
The last objective addressed was a comparison of the performance of the final ACRU CSM system proposed in this study to that of the widely applied SCS-SA model and associated approaches, when using the same input information. Ultimately, the final ACRU CSM system proposed provides results that are superior to those from the SCS-SA model and associated approaches. In addition, several advantages of the ACRU CSM system over the traditional SCS-SA approaches were identified. Recommendations were, however, made to improve on the CSM system developed in this study and to use the results to update the SCS-SA model. New knowledge on the performance of the SCS-SA model and its associated approaches compared to that of the comprehensive CSM system developed for South Africa is therefore provided in this study.||en_US