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Estimation of Extreme Design Rainfall Events in South Africa

This presentation provides an overview of the estimation of extreme design rainfall events in South Africa, taking into account non-stationary data and climate change impacts. The study aims to update design rainfall estimates for all durations and extend to longer return periods, as well as update Probable Maximum Precipitation (PMP) estimates. The methodology includes data collation and the development of a method to account for non-stationary data on extreme rainfall estimation.

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Estimation of Extreme Design Rainfall Events in South Africa

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  1. Estimation of Extreme Design Rainfall Events and Accounting for Non-stationary Data in Design Rainfall Estimation in South Africa 18 SEPTEMBER 2018 Katelyn Johnson School Of EngineeringUniversity Of KwaZulu-Natal Supervisor: Prof. JC Smithers, Co- Supervisors: Prof. RE Schulze & Dr. TR Kjeldsen 19thSANCIAHS Symposium

  2. Overview of Presentation Introduction Literature review Objectives Data Methodology Preliminary results

  3. Introduction Estimating extreme design rainfall events Dealing with non-stationary data e.g. climate change Extreme rainfall and flooding in KZN in 2016 and 2017. (ENCA, 2016; SABC, 2017)

  4. Design Rainfall Estimation - South Africa • Regional L-moment Algorithm and Scale Invariance (RLMA&SI) approach • Estimates T = 2 to 200 years, 5 mins to 7 days • More than a decade of new data available • Impacts of extended record length • Design of large dams - recommended T >200 years • Need to update and extend Smithers and Schulze (2000a; 2000b; 2003)

  5. Probable Maximum Precipitation What is PMP? “the greatest depth of precipitation for a given duration meteorologically possible for a given storm area at a particular location at a particular time of year, with no allowance made for long-term climatic trends.” World Meteorological Organisation (2009)

  6. Probable Maximum Precipitation Used to estimate Probable Maximum Flood High-hazard hydraulic structures Maximise safety and reliability Picture…… Shaw (1994); DWAF (2017)

  7. PMP in South Africa Hydrological Research Unit (HRU) of the University of Witwatersrand, 1972 Approx. 30 years of rainfall data (1932 to 1961) Severe storms exceeded HRU PMP HRU (1972), Görgenset al.(2007)

  8. PMP in South Africa May no longer represent upper limit of extreme rainfall in many regions May be subject to 25 % error Need to be updated Example of PMP curves (HRU, 1972) HRU (1972), Görgenset al.(2007)

  9. Climate Change IPPC (2007)

  10. Climate Change Impacts in South Africa Mean annual temperature increased Frequency of extreme events increased in parts Annual maximum rainfall increasing Changes predicted to continue in future Ndiritu (2005), Midgely (2011), Schulze et al. (2011), IPPC (2014), Ziervogelet al. (2014)

  11. Climate Change and Non-Stationarity • Previous studies assume stationary climate – not realistic • Evidence of CC – impacts extreme events • Need methods to account for trends in extreme rainfall events in non-stationary environment

  12. Objectives of the Study • Update design rainfall estimates for all durations and extend to T > 200 years • Update PMP estimates • Develop and assess the performance of a method to account for the impacts of non-stationary data on extreme design rainfall estimation, including PMP

  13. Data Collation • Data from previous studies up to 2000 • Update data record: SASRI, SAWS • Problems: • Not many SASRI stations can be used to update • Many SAWS stations no longer operating • Limited short duration data records available • Difficulty acquiring data from SAWS

  14. Methodology

  15. High-return period rainfall

  16. PMP

  17. Non-stationarity

  18. Non-stationarity

  19. Preliminary Results

  20. Thank You Any Questions ?

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