Statistical Based Regional Flood Frequency Estimation Study for South Africa Using Systematic, Historical and Palaeoflood Data Pilot Study – Catchment Management Area 15 by D van Bladeren, P K Zawada and D Mahlangu SRK Consulting & Council for Geoscience Report to the Water Research Commission on the project “Statistical Based Regional Flood Frequency Estimation Study for South Africa using Systematic, Historical and Palaeoflood Data” WRC Report No 1260/1/07 ISBN 078-1-77005-537-7 March 2007 DISCLAIMER This report has been reviewed by the Water Research Commission (WRC) and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the WRC, nor does mention of trade names or commercial products constitute endorsement or recommendation for use EXECUTIVE SUMMARY INTRODUCTION During the past 10 years South Africa has experienced several devastating flood events that highlighted the need for more accurate and reasonable flood estimation. The most notable events were those of 1995/96 in KwaZulu-Natal and north eastern areas, the November 1996 floods in the Southern Cape Region, the floods of February to March 2000 in the Limpopo, Mpumalanga and Eastern Cape provinces and the recent floods in March 2003 in Montagu in the Western Cape. These events emphasized the need for a standard approach to estimate flood probabilities before developments are initiated or existing developments evaluated for flood hazards. The flood peak magnitudes and probabilities of occurrence or return period required for flood lines are often overlooked, ignored or dealt with in a casual way with devastating effects. The National Disaster and new Water Act and the rapid rate at which developments are being planned will require the near mass production of flood peak probabilities across the country that should be consistent, realistic and reliable. At present the methodologies for flood frequency analysis in South Africa consists of three basic approaches, all of which have certain validity limits (Kovacs, 1993): Deterministic methods (Rational, Synthetic unit hydrograph, Direct runoff hydrograph, SCS, etc.) Statistical methods such as the LP3, GEV and Log-normal (annual maximum flood series data) Empirical methods (Midgley-Pitman, HRU 1/71, CAPA and RMF) Experience in the Department of Water Affairs and Forestry (DWAF) has shown that these methods often give vastly different results and unless a certain amount of judgement and experience can be used, the selection of final values may be inconsistent and subjective. This pilot study provides a basis to develop simple and consistent methodologies for the rest of the country to estimate flood peaks and their associated probabilities for use by authorities, consultants and planners. Since the last extensive review of the methodologies in 1970’s to early 1980’s the following should be noted that justify a review of the methodologies: The period of observation has been extended by a further 25 to 30 years i.e. more data, The number of sites are significantly more, South Africa has had several extreme flood events that added to the extreme flood peak data base, The technology regarding the statistical analysis of flood data has improved, The gathering of historical data by DWAF (van Bladeren, 1992) has in many areas increased the period of observation to between 100 to 150 years, and i By including the modelling and dating of palaeofloods, where possible, this period may be extended to more than 200 years, which is the upper limit of the normally requested design flood peaks used in design and planning. From the above it was clear that the revision and updating of the methods was long overdue and this project could be seen as one way to resuscitate the neglected field of flood research in South Africa. The inclusion of palaeofloods is based on the success of previous projects completed for the WRC by the Council for Geo-Science and DWAF. That project demonstrated the value that the inclusion of palaeoflood data in flood frequency studies (Zawada et al., 1996). This project integrates systematic, historical and palaeoflood data to provide flood growth curves that are scaled using an index flood to provide estimates of flood peaks and their associated probabilities. The development is motivated by the stated desire of the Committee of State Road Authorities in their guidelines for the hydraulic design and maintenance of river crossings (TRH 25, 1994) and Alexander (1990) who identified the lack of regional growth curves for southern Africa as a serious drawback when selecting applicable flood probabilities. This report of the pilot study is an attempt to develop a robust and reliable method of estimating the full range of usually requested flood peaks used in design, based on index floods and regional growth curves that are obtained from the analysis of observed data. The project integrates systematic, historical and palaeoflood data to derive growth curves for floods and using selected catchment characteristics to develop an index flood estimation methodology. The pilot area selected for the study was Catchment Management Area (CMA) 15 in the Eastern Cape. CMA 15 includes drainage regions K7-9, L, M, N, P and Q. Drainage regions J, K3-6, R and S were also included to provide fringe information. REVIEW OF FLOOD AND REGIONAL FLOOD HYDROLOGY IN SOUTH AFRICA Flood hydrology in South Africa (Kovacs, 1993) is generally classified as deterministic, statistical or empirical. Several regional flood studies have been undertaken in South Africa using systematic data. Most of the previous regional studies in South Africa defined regions based on climate or drainage regions. Data used to develop the methods for the previous studies only used gauged records and are thus based on relatively short periods of observation. The only study that included historical data is the recently developed SDF (Alexander, 2002) and the previous attempt by van Bladeren (1993). From the review, the Catchment Parameter (CAPA) method developed by McPherson (1983) to estimate the mean annual flood was selected as the basis to estimate the index flood for this study. The CAPA method uses several catchment variables to estimate a lumped parameter and is site specific. Alexander (2002) and van Bladeren (1993) have evaluated several distributions in various studies throughout South Africa and ii have concluded that the log-Pearson type III distribution is the most appropriate for South Africa. Another contender is the general extreme value distribution. REGIONAL FLOOD STUDY CMA 15 The regional flood study for CMA 15 consisted of the gathering of annual maximum flood (AMF) peak data, determination and estimation of relevant catchment characteristics, the development of a methodology to estimate the index flood (Qi), the development of flood peak growth curves and the comparison or verification of the results obtained using the proposed methodology against those obtained, using other methods and actual observed flood data. The 348 sites identified on the Hydrological Information System (HIS) of DWAF for the study area was reduced to 112 useable sites after assessment of the sites. All the sites originally identified are shown in Appendix A and the sites finally used and their AMF, historical and palaeoflood data are listed in Appendix B. A break-down of the data used is summarised in the table below. Summary of Flood Peak Data Sets (Appendix B) Drainage Period of Observation (years) Region Systematic Data Including Historical Data Including Palaeoflood Data Sites Average Maximum Sites Average Maximum Sites Average Maximum J 26 46 90 5 109 152 2 2996 3000 K 15 39 42 - - - - - - L 11 55 83 4 100 154 1 1901 1901 M 3 49 76 1 148 148 - - - N 5 69 97 2 130 135 2 7996 8001 P 3 37 45 2 111 113 - - - Q 20 63 96 6 141 182 - - - R 17 61 79 6 141 154 - - - S 12 40 54 1 127 127 - - - Average - 51 - - 125 - - 4777 - Total 112 5766 - 27 3394 - 5 23885 - The average period of observation for the systematic data is 51 years. With inclusion of the historical data the period of observation is 125 years and when the palaeoflood data is included, the period of observation is extended to 4777 years. The applicability of the combined data sets may thus be taken as 10000 years (twice the period of observation). The development of the index flood used catchment characteristics generated by a GIS database using the WR90 data and topographical data derived from the 1:50000 mapping series supplied by DWAF. The index flood development used the same methodology as the CAPA method (McPherson, 1983) but during the iii analysis it was found that some of the variables used to calculate the lumped parameter M, used in the CAPA method, should be replaced and this resulted in a new lumped parameter M’ being proposed. This new methodology is presently referred to as NCAPA and the parameter M’ and is defined as; Where; A=Catchment Area (km2) MAP=Mean Annual Precipitation (mm) s=Standard River Slope – 1085 (m/m) and L=Longest Stream Length (km) The land-use or catchment characteristics defined by soils, geology, vegetation and MAP is taken into account by a regionalisation that is based on previous studies, MAP and vegetation. This regionalisation resulted in regional boundaries that are very similar to those shown for the veld types as shown in the HRU 1/72 report. The HRU 1/72 veld type zones were thus used for the classification of the regions identified in the study and is shown below. iv The base used for the derivation of the index flood is the mean annual flood obtained from the analysis of the log transformed data and is referred to as the log derived mean annual flood (Qml) in the report. The Qml proved to be the most robust parameter and is least affected by period of observation and outliers than either the mean annual or median annual flood estimates.