CHAPTER 4 METHODOLOGY
4. Introduction
4.5. Flood methodology
This thesis focuses on developing its own model for flooding, hereafter termed the Flood Zone Model (FZM) (as described in Botes et al. 2010; Botes et al. 2011). This has been developed as a planning and disaster management modelling tool to estimate regional maximum and 1:100
Table 4.1. Description of the five representative quaternary catchments used for the model development.
Municipal area/s The Msunduzi and Richmond
Emnambithi/
Ladysmith
Umzimkhulu;
Umzumbe and Ubuhlebezwe
Hibiscus Coast and Ezingoleni
Mbonambi;
Hlabisa and Mtubatuba Quaternary
catchment U20H V12G T52D T40G W23A
Quaternary
catchment area 220 km² 509 km² 530 km² 300 km² 413 km²
Upstream
catchment area No upstream
catchments 1 643 km² 3 640 km² No upstream
catchments 8 840 km² RMF estimated
discharge 1 931 m³/s 4 639 m³/s 7 926 m³/s 2 855 m³/s 16 792 m³/s
Homestead estimate
17 000 homesteads 13 100 homesteads (Excluding formal area of Ladysmith)
9 300 homesteads 11 500 homesteads (Excluding formal area of Port Shepstone)
8 800 homesteads
Estimated homesteads within flood zone
653 500 400 600 500
Land use Formal urbanisation and industry. Large portion is dense low cost development and informal and traditional settlement.
Predominantly commercial agriculture with a mix of urban and rural settlement.
Mainly a mixture of traditional settlement and agriculture.
The area has a mix of formal urban settlement and low cost housing development, agriculture and traditional settlement.
Predominantly traditional settlement with minor urban settlement and agriculture
Selection criteria Area has a history of flooding. The Edendale area is under pressure for further
development and settlement densification with associated encroachment into flood risk areas.
This site is selected to test the model in a dense urban/peri-urban environment.
There is a long flood history. This site is selected to test the model against engineering flood line data and to measure its performance in broad, low relief valleys. Also tests within an informal densification area.
There are recent flood line calculations available to test against. This site is selected to test the model in predominantly hilly terrain.
There are some recent engineering flood line calculations available to test against. This is also a coastal catchment. This area is selected to test the model on a developed coastal catchment and the inland settlement densification in progress.
The site is selected to test the model along a major drainage line, contrasting traditional settlement areas situated on undulating hills and settlement development around Kwamsane. The area has an extensive flooding history.
year return period flood elevations. The Flood Zone Model aims to provide flood information as a guide for areas without design flood data and to serve as a means to determine target areas for detailed design flood studies. This is a rapid and cost effective approach to delineate flood zones at quaternary catchment level using limited resources. The hydraulic component of the Flood Zone Model utilizes existing GIS data (1:50 000 Surveyor General (SG) 5 m and 20 m contour data) to construct 10 m digital elevation models for a quaternary catchment. Digital elevation models are reprocessed using ArcHYDRO Tools® (Maidment 2002) to level out areas for water bodies, enforce drainage paths and eliminate sinks (holes in the digital elevation model surface where water can flow into but not out of) to produce a final hydrological drainage surface. The hydrological digital elevation model is used as a base to determine water flow direction and accumulation to delineate 2 km² sub-catchments and river reaches (river segment between tributaries). Cross-sections placed at approximately 50 m intervals or less, depending on slope and topographic features, are used to extract valley profiles from the hydrological digital elevation models.
The hydrological component of the Flood Zone Model is derived from the Regional Maximum Flood peak (RMF) as discussed in depth in Chapter 6. Kovács (1988) applied this method to southern Africa where he defined eight hydrologically homogeneous zones. By calculating the upstream catchment area of the position to be determined, and applying the appropriate Kovács formulae, the maximum flood peak discharge can be estimated. Kovács (1988) also produced theoretical probabilistic distributions allowing the estimation of design 1:100 year return periods. In the Flood Zone Model, sub-catchment cross-section profiles and reach data are extracted from the GIS and imported into HEC-RAS©, a hydraulic river analysis flood routing software package where it is combined with the estimated sub-catchment peak discharges to produce flood elevation model simulations.
As the Regional Maximum Flood peak is derived from the measured maximum peak discharges from long-rain flood events, the equivalent field evidence in the form of flood deposits, erosion marks and debris line can be mapped and correlated to this. A series of calibration factors (CF) have been developed based on reach slope that are used to adjust the modelled flood elevations to the mapped flood deposits and existing 1:100 year design flood estimates. The calibration factors (CF) are based on an averaged reach Manning roughness coefficient. By increasing or decreasing the calibration factor value, it has the effect of increasing/decreasing energy losses
from interaction of the water with the channel substrate, affecting the travel time and elevation of the water moving through the drainage system.
4.5.1. Flash flood hydrology
HEC-RAS© is capable of processing multiple simulations concurrently. As the flood routing parameters for the sub-catchments of a quaternary catchment are already established for the Flood Zone Model, peak discharges estimated from a model better suited for small catchments can be used to model flash flood inundation zones. In estimating peak discharges and extracting soil, topographic and land cover data from existing GIS datasets, and lumped at sub-catchment level, a semi-distributed flash flood modelling (Flash Flood Model - FFM) approach can be applied.
To determine the peak discharges, the Rational Formula (RF) (Alexander 2002; Pegram &
Parak 2004) (Eq1) was selected because it is widely applied in the Republic of South Africa (Alexander 2002; Pegram & Parak 2004; Parak & Pegram 2006) for calculating discharges in small catchments. Additionally, the input parameters could be derived from existing GIS datasets in keeping with the Flood Zone Model methodology approach:
Q = icA/3.6 ………. [Eq1]
Where:
Q = discharge (m3/s) i = rainfall intensity (mm/h)
c = Run-off coefficient dimensionless value between 0-1 A = Area of catchment (km2)
Design rainfall depths for 20, 50, 100 and 200 years (probabilistically based estimate of rainfall depths and duration)(Smithers & Schulze 2003) for the area were extracted from Hydrorisk (Smithers & Schulze 2003). This calculates design rainfall return periods using a Regional
Linear Moment Algorithm and Scale Invariant approach (RLM&SI) (Smithers & Schulze 2003;
Gericke & Du Plessis 2011).
Since rainfall intensity (i) is a function of time of concentration and recurrence interval, intensity – duration – frequency (IDF) tables are used to establish intensities (Parak & Pegram 2006). Parak and Pegram (2006) fitted power law curves to 29 catchments and computed parameters for calculating intensity (Eq2):
i=ad-c ………. [Eq2]
Where:
i = rainfall intensity (mm/h)
a = return period rainfall depth (mm) d = time of concentration (hours)
-c = power law parameters
Time of concentration (Tc) values was calculated using the Bransby-Williams formula (WSUD 2012) (Eq3):
Tc = 91 L/A0.1 S0.2 ………. [Eq3]
Where:
Tc= Time of concentration (minutes) L = main reach length (km)
A = Area (km2)
S = reach slope (m/km)
The run-off coefficient (c) is a subjective estimate of the factors such as soil conditions and land cover, contributing to water (rainfall) retention in a catchment thereby reducing the water volume that will reach a drainage line and contribute to channel flow (Pegram & Parak 2004).
Published values (Anquetin et al. 2010; Mark and Marek 2011) relate to larger catchments and
may not apply to small sub-catchments. Considering the rainfall intensities involved, Hortonian flow(Stomph et al. 2002; Beven 2004; Lee et al. 1991) may also be a factor in that there is insufficient time for absorption and most of the precipitation converts to run-off. The method set out by Mark and Marek (2011), which sums estimations of relief, soil infiltration, vegetation cover and surface conditions with a weighted factor for return periods, was used to determine a run-off coefficient. Slope data were derived from the hydrological DEM and the average slope per sub-catchment was determined and categorized (Mark & Marek 2011). Soil permeability and depth for the sub-catchments were extracted from the AGIS (Schoeman et al. 2002) soil data and averaged for each sub-catchment. Land cover data were taken from the Ezemvelo KZN Wildlife land cover dataset and the predominant land cover type was extracted for each sub- catchment.
Variables were calculated for each sub-catchment and substituted into the relevant formulas.
The peak discharges estimated using the rational formula for the 20, 50, 100 and 200 years design rainfall (RF20, RF50, RF100, RF200) was processed in HEC-RAS© to produce flash flood elevation surfaces.
4.5.2. Calibration
Calibrations of the Flash Flood Model results are based on field observations which consisted of mapped flood deposits with a differential GPS to establish the highest flood elevations, together with first hand reports documented in the homestead surveys. Calibration of the Flash Flood Model results had to be approached differently to that of the Flood Zone Model. In the Flood Zone Model the maximum flood elevation surfaces could be directly related and calibrated to well-established peak discharges using the Regional Maximum Flood. In the Flash Flood Model, the maximum flood elevation surfaces (inundation levels) were established from field observations and homestead surveys (control data), but the rainfall depths that resulted in the peak discharges to achieve these inundation levels were unknown. The flash flood elevation surfaces from HEC-RAS© were overlain on the control data and the one that was the closest match to the control data was selected. Initial results were based on the Flood Zone Model calibration factors. An iterative process was applied using a series of calibration factors until the selected flash flood elevation surface produced a best fit to the control data.