CHAPTER 6 FLOOD MODELLING
6. Regional Maximum Flood Peak (RMF)
1.4. Discussion
The disadvantage of this approach is the loss of topographic detail, particularly along the river bed area. Where contour data with different intervals were compared, it was found that valley slopes with higher gradients showed marginal variation in detail between the 5 m /20 m and 1 m/2 m data. Broad valleys with shallow gradient and wide floodplains show greater variation in detail. It was also found that the fit between the modelled results and the design flood estimates improved with larger return periods. As larger return periods result in increased flood elevations, the improved fit is attributed to less variation between the various contour intervals on valleys slopes compared to the greater variation on the floodplains and river beds. The spatial similarity between the modelled results and the control data show that existing contour data can be used for flood hazard models. Contour data with the smallest intervals will improve the results.
Synthetic drainage lines produced by ArcHYDRO Tools® have two drawbacks that render them unusable in this process. Where the valleys are broad and the floodplains are flat, the tool that generates these lines will often produce parallel drainage lines that do not follow the correct river course. The second problem with the synthetic drainage lines is that they do not extend to the source point of the river in the sub-catchments. HEC-GeoRAS® uses river centrelines to calculate flow direction and cross-section intervals as part of the flood modelling process and if no river data are available, that portion of the sub-catchment cannot be modelled. This is resolved by using the Surveyor General river data that coincided with the synthetic drainage lines and incising these river courses into the DEM using an ArcHYDRO Tools® function.
Highly detailed river centrelines are not required for any part of the flood routing calculations, making the SG 1:50 000 river feature class adequate for this application. Water body data from the SG 1:50 000 GIS data was modified where necessary. The smaller farm dam footprints generally matched satellite imagery. Dams constructed after the SG data were created, were captured. The large dam footprint did not always match the satellite imagery and these were recaptured.
Using the Regional Maximum Flood method to estimate discharges has many advantages for this modelling approach. Regional Maximum Flood only requires a geographic position from which the upstream catchment area is calculated. Once the area factor was adjusted, the Kovács (1988) Regional Maximum Flood formulae were used to calculate estimated discharges for a given quaternary catchment, the only input is the combined primary upstream and secondary
quaternary catchments areas. As Regional Maximum Flood was derived from gauge data, climatological and catchment characteristics do not need to be considered. A key component of this model is the assumption that flood deposits mapped in the field equate to Regional Maximum Flood. This correlation is very difficult to do with estimated return periods because successive floods have reworked sediments (Charlton 2007). Where the smaller flood deposits are identified relating them to a specific flood can only be done using age dating techniques.
Acknowledging that there is a level of uncertainty around the reliability of return period discharges derived from the Regional Maximum Flood, the unadjusted Kovács (1988) formulae were used because the modelled results closely matched the field observations and the control 1:100 year return period design floodline estimates when applying the same calibration factor. If the derived 1:100 year return discharge estimate was over reported by 20% (Görgens 2002), the QRMF modelled results would not matched the field observations, the derived 1:100 year return modelled result would consistently plot above the control 1:100 year design floodline estimates, which was not the case. This issue may be accommodated by the calibration factors or averaged out due to the modelling process.
Estimating Manning n values or using Chezy or Darcy-Weisbach formulae to calculate roughness coefficients for cross-section across a quaternary catchment is a resource intensive task. Replacing the roughness coefficients with a calibration factor, which represents a best fit Manning based average value across a reach and associated slope, eliminates the need to determine roughness coefficients. The calibration factors also provide a means to raise or lower modelled results to match the control data. Application of the calibration factors across 15 quaternary catchments that produced consistently high R2 fit coefficients for both the field data/QMRMF relationship and QM100 derived discharge/design 1:100 floodline estimates shows that they are independent of geographic location. Similarly, these quaternary catchments cover a range of Regional Maximum Flood K envelopes indicating that the calibration factors are also independent of these. Any quaternary catchment’s flood zones could then be desktop modelled, as demonstrated for U20M.
When comparing the modelled flood extents for QMRMF and QM100 (Figure 6.27) it can be seen that where the valley slopes is moderate to steep there is very little difference between the two
flood surfaces. Where slopes are moderate to shallow the difference between the surfaces are greater because small differences in elevation result in larger horizontal variation. For delineating flood risk zones the QMRMF flood elevation surface can be used as a conservative estimate. Should there be a need to generate 1:100 or 1:200 year Regional Maximum Flood return period derived estimates then it is recommended to use the Kovács (1988) formulae as is, and use it as a conservative estimate (Görgens 2002). Apart from the applications already described, the Flood Zone Model estimates can be used for pre-screening of developments and to highlight areas where design flood estimates should be carried out.
The advantages of the Regional Maximum Flood- and Modelling- based approach described here are thus:
Existing GIS datasets produce sufficiently accurate DEM surfaces to create sub- catchments and reach drainage lines;
Design peak discharges can be estimated for any quaternary catchment’s location within the Regional Maximum Flood regions and applied to the sub-catchments under consideration;
Can be applied to gauged and ungauged catchments;
Reach slopes can be calculated by a GIS system and the defined river reach calibration factor values are comparable to the use of Manning’s n-values used in HEC-RAS;
Overall processing time for a quaternary catchment, depending on size and complexity of the drainage pattern is 20 – 25 hours;
No or limited field work or detailed surveys are required to implement this approach;
and
The disadvantages are:
The model results are not as accurate as design floodline estimates and cannot be used for construction purposes.
The technique requires advanced GIS software such as Spatial Analyst® and 3D Analyst®.
If field verification is necessary, skilled personnel such as earth scientists or hydrologists are required.