The floods in December and

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1 technical GIS data models for regional flood risk determination by Riaan Botes, Dr. Alan Smith and Prof. Ron Uken, University of KwaZulu-Natal Traditionally flood hydrology has concentrated on design parameters which are important for structures but the methods do not necessarily allow for cost effective production of flood risk maps for large areas such as district municipalities. By utilising existing GIS data, a GIS platform and components of a Hydro Data Model, combined with HEC-RAS for the river analysis, this methodology provides a rapid, cost effective alternative for flood risk modelling. The floods in December and January 2010/2011 affected 33 municipalities country wide (The Water Wheel, 2011) highlighted again that South Africa is a flood prone country and that its preparedness and ability to deal with this type of natural disaster is often limited. One of the problems facing authorities is the lack of flood line data and by extension, the lack of data to guide settlement planning and proactive management of existing settlements and infrastructure in flood risk areas. Using the province of KwaZulu-Natal (KZN) as an example, which has a network of drainage lines approximately km in extent, only 1% of this is populated with flood lines. These are limited to larger urban centres such as Durban and Richards Bay or areas that have a long history of repeated flooding such as Ladysmith and Umzimkhulu. Taking into account other urban areas, informal settlements and traditional settlement areas, about km of the drainage network urgently requires the determination of flood lines. According to the National Water Act, 36 of 1998, a 1:100 year flood line must be determined for any township development and information on flooding or where floods are likely to occur must be made available to the public. Engineering standard 1:100 year flood lines are expensive and time consuming due to the detailed survey information required, making the cost to produce data for km in the billions of rands. A number of factors are making the need for flood lines more urgent. Firstly, the rapid growth of informal peri-urban areas, has led to, and continues to result in settlement in unsuitable areas such Fig. 1. Extract of Quaternary Catchment V12G (Ladysmith). (A) Hydrological DEM background with 5 m (green lines), 20 m (red lines), SG rivers (light blue lines) and the cleaned drainage lines (blue lines). (B) Derived sub catchment boundaries (coloured polygons). (C) Derived sub catchments (red lines) with cross-sections (black lines) overlain on the modelled 1:100 year flood risk area. PositionIT October

2 technical Fig. 2: Modelled 1:100 year flood risk area displayed as depth of flow. The engineering 1:100 year flood line is overlain for comparison. as flood plains. Increased population growth and constraints on available land exasperates this problem. Secondly, there is a general perception that floods are a rare occurrence and the 1:100 year concept is often misconceived as meaning that there will not be another flood event for another 100 years. The historic data clearly shows that this is not the case (Van Bladeren, 1992; Botes et.al, 2010). Thirdly, field observations and historic data show that adherence to a 1:100 year flood line for all areas is not always applicable and that a 1:100 year flood may occur in one catchment while the neighbouring catchment experienced a 1:200 year flood during the same event (Van Bladeren, 1992). Fourthly, the effect of climate change is a reality that needs to be taken into account especially for areas considered outside the risk zone, which may well become at risk as the effects of climate change deepen. This article discusses an alternative approach to the conventional engineering standard flood line calculations. The methods involve a geographic information system (GIS) platform, associated river modelling software and existing GIS datasets to rapidly delineate flood risk areas accounting for 1:100 and 1:200 year flood scenarios. Also included in the models are maximum probable flood levels and the associated additional effects of climate change. GIS and data models A number of data modelling tools have been developed that run on, or interface with GIS platforms. ArcGIS and its extensions Spatial Analyst and 3D Analyst are used as the primary GIS platform. These software packages are used to manipulate and process the overall spatial data. ArcHydro (Archydrotools), HECRAS and HEC-Georas and are used for the hydrological modelling. The data processing can be divided into four groups each with specific outputs: ArcGIS is used to view, manipulate and process the various geographic data components. Spatial Analyst and 3D Analyst is used to create the digital elevation models (DEM), manage the raster data and produce triangulated irregular networks (TIN). These extensions are also used by the Archydrotools data model and HEC-Georas interface. Archydrotools is the toolset used to create and manage a hydrological information system via the Arc Hydro data model and is described as...hydrological information system that synthesises geospatial and temporal water resource data to support hydrologic analysis and modelling (Maidment, 2002). It is not the purpose of this research to build a hydrological information and management system. However Archydrotools contains useful tools to manipulate and produce the hydrological surfaces and from this, the catchment and sub-catchment delineations. HEC-RAS is used for hydrological processing using data extracted via HEC-Georas (river flow paths, cross-section xyz profiles) together with the flow volumes. The modelled elevation surfaces are imported via HEC-Georas to calculate the water elevation hydrological surface subtraction to produce the final flood elevation surface as a GIS shape file. GIS and particularly the Esri suite of products, ArcInfo and ArcView has become a widely used tool to manage and process spatial data (Maidment, 2002). The GIS Water Resources Consortium, a collaboration between Esri and the Centre for Research in Water Resources, have developed the ArcGIS Hydro Data Model. This was aimed at using GIS to represent surface water elements (hydrography) and integrating these features with hydraulic and hydrological simulation models (Maidment, 2002). The advancement that allowed the development of data models in ArcGIS was the ability to process both layer and feature orientated operations (Maidment, 2002; Esri, 2009). In a layer only orientated system data is represented by type (point, line, polygons) and the relationship between features of different types cannot not be related directly. The ArcGIS relation database structure has geospatial coordinate data that is stored as a field in a relational data table (geodatabase) instead of a standalone spatial database (Esri, 2009). The permanent relationship between spatial features and attributes in data tables allows for feature to feature connections. What this means is that the interaction between individual GIS features in different data layers can be computed to model the flow of water to emulate a natural system (Maidment, 2002). The ArcHydro framework data structure used for storing the geospatial data to describe the water features of a system are (Maidment et. al; 2002, Esri, 2009a): Geodatabase: the relational database that stores all the water related information used is a model. Feature dataset: stores the feature classes, projections and coordinate systems. Geometric network: stores the 58 PositionIT October 2012

3 news topological information connections between water features. Feature class: Stores information about the individual geographic features. The geospatial data comprises five feature classes: - HydroEdges: Network of rivers and streams defining flow path centrelines. - HydroJunctions: Points located at the end of flow segments or other strategic points along a flow path. - Water bodies: Natural and man-made water bodies. - Watershed: The land area that contributes water flow over the ground surface to a drainage system - Monitoring points: Points representing water flow monitoring positions (gauge stations) Relationship: Stores relationships between features (e.g. which river is located in which watershed). The overall data model comprises five components: Networks: A geometric network is the core of the ArcHydro data model and consists of HydroEdges and HydroJunctions and their topological connections that allow the tracing of water movement downstream through the network (Olivera et. al., 2002). Drainage systems: The topography directs the flow of water across the ground surface to accumulate along a line of lowest elevation (HydroEdge). This HydroEdge is connected to other HydroEdges at lower elevations via HydroJunctions. Drainage areas are defined by watersheds delineating the direction of flow and accumulation of water along individual drainage lines. digital elevation models (DEM), are used to define the watersheds and drainage patterns (Olivera et.al. 2002). Channels: To delineate a flood plain and calculate the water elevation surface. The river morphology needs to be defined. This is done using a series of cross-sections (ideally surveyed data). The cross-section lines can extract ground elevation information from the DEM. These 3D cross-sections are used in hydraulic modelling to route water flow along a drainage line to produce water elevation surfaces (Noman and Nelson, 2002). Hydrography: This is the map representation of the water surface elements. These may be point line or polygon features, using attributes to define its function or cartographic classification. Tabular datasets can be linked to these from other sources such as gauge station records or dam level records (Davis et. al., 2002). Time series data: Typically rainfall and gauge station data records, this data reflects temporal data acquired at positions in a hydro network. The relational database allows this data to be integrated into the ArcHydro model where the data can be graphed (river flow hydrographs) or spatial depicted (rainfall distribution) (Maidment et. al., 2002). The ArcHydro data model can be used for a range of levels of detail, from basic analysis to building complex integrated systems using real-time monitoring systems. For this research the Archydrotools components of the ArcHydro data model is used. In practise ArcHydro tools uses a base DEM and performs a series of manipulations on this data to produce a hydrological DEM flow surface. The Spatial Analyst s topo to raster interpolation does not take into account the water body surface. The first process that is imposed on the water body surface DEM is to level out the underlying cells to the same elevation. When ArcHydro tools models the stream flow lines, these do not always follow the correct river course, especially in low relief areas. There is a tendency for the model to calculate the shortest route or to produce multiple parallel drainage lines across this type of terrain. To prevent this, the existing river drainage lines are used to deeply incise the DEM surface forcing the model to calculate flow along the correct drainage line path (Hellweger, 1997). Manipulation of the DEM surface can create sinks i.e. holes into which water will flow in but never out. A final processing step removes these sinks. Where sinks occur naturally in limestone areas (karst topography) special rules are available to deal with this in the model. The sink filled DEM surface is defined as the hydrological surface and the balance of the ArcHydro tools modelling is based on this DEM. A further series of processing steps are applied to calculate the lowest point of accumulated flow and hence the drainage lines. The first step is to calculate the flow direction of the water. This is done by the software examining each cell in the hydrological surface and its relative elevation to the surrounding cells. The cell with the lowest elevation defines the flow direction. A similar process is applied to define the flow accumulation grid and by summing the unit flow from the cells until there is no cell available at a lower elevation. A stream grid is created by selecting the cells along a line with the highest accumulation. River density is based on the user defined catchment area so that a single line of the highest accumulation is defined per area. The final processing step in this series links the stream grid cell assigning unique cell values to each drainage line. The data from the stream link grid is outputted as a vector drainage line. Watershed areas are calculated from the stream link grid and flow direct grid as sub-catchment areas (Esri, 2009). These are outputted as vector polygon features. In the overall process ArcHydro tools provides the hydrological surface and the sub-catchment boundaries. The hydrological surface is converted to a triangulated irregular network (TIN) surface for use in HEC-GeoRAS. HEC-GeoRAS HEC-GeoRAS is effectively a data management interface between ArcGIS and HEC-RAS. River reach (river segment between junctions), cross-section and other related data are stored in a geodatabase file. The river and cross-section data layers are created with predefined attribute tables which are manually populated in the case of the river and reach names while all other attributes are automatically calculated by HEC-GeoRAS. This interface extracts the data in an.xml format that is imported into HEC-RAS (Cameron and Ackerman, 2005). HEC-RAS Hydraulic calculations are carried out in separate software. For this task, the software developed by the Hydrological Engineering Centre is used. It has advantages in that it interfaces with ArcGIS via a HEC-GeoRAS module and has a wide range of capabilities it can perform flow routing using only 3D cross-sections, flow paths, water volumes and friction values as primary input. PositionIT October

4 news HEC-RAS performs one dimensional hydraulic calculation for a network of natural and constructed channels (Brunner, 2010). The Steady Flow Water Surface Profiles module is used for calculating water surface profiles for steady, gradually varied flow using supercritical, subcritical and mixed flow regimes (Brunner, 2010). The programme solves an energy loss equation between two cross-sections using friction and contract/expansion coefficients (Brunner, 2010). The Froude Number (Fr) is used to defined open channel flow as supercritical (Fr >1), generally high velocity shallow flows on steep gradients or subcritical (Fr <1) which tends towards deeper and slower flow velocities with shallow gradients. Critical flow (Fr =1) is the division between these conditions (Bridge, 2003). HEC-RAS consists of a number of editors tasked to deal with different functions in the modelling process. For this application only the Geometric, Steady Flow Data, Cross-section and Steady Flow Simulation Editors are used. The.xml file exported from HEC-GeoRAS is imported into the Geometric Editor which is a Graphical User Interface (GUI) that is used to manage the geographic data. In this editor the Manning friction values are entered for the reach cross-sections. Water flow volumes are entered into the Steady Flow Data Editor. This editor extracts the river and reach data from the geometric editor. The surface water elevation to be calculated is managed here, e.g. 1:100 and or 1:200 profiles. For the software to compute the water surfaces it needs to know the starting water level at the start and end of reaches that are not connected at junctions to other reaches (boundary conditions). For a steady flow analysis, four boundary conditions are available, namely "known water surface", "critical depth", "normal depth" and "rating curve". Except for the "critical depth" option, the other boundary conditions require information not available, e.g. what the water depth is in every reach of the area being modelled. When "critical depth" is selected the software will calculate the critical flow depth for the first cross-section along a reach from the cross-section profile and water volumes from the first two cross-sections using the Froude formula. HEC-RAS will then compute a theoretical initial water elevation to start the computational process. This is a very useful function in that no other data needs to be collected before the simulation model can be processed. The Steady Flow Analysis Editor performs the simulation based on the data provided in the other editors. At this point error warnings may be listed that prevent the simulation to continue and requires user intervention. Common errors are missing data such as boundary conditions or Manning friction values. In some cases there may be issues with individual cross-sections for which the cross-section editor is used to correct these. Once the simulation is completed the elevation surfaces can be viewed in the geometric editor. In older versions of HEC-RAS, the water surface profiles were exported and imported into ArcGIS using the HEC-GeoRAS module. From here the water surface profiles were used to construct TIN models. The original hydrological TIN model was subtracted for the water elevation surface TIN, converted to raster grids and the extracted as polygons shape files. The latest release of HEC-RAS (4.1) allows the user to carry out this process in HEC-RAS with fewer processing steps. Converting the hydrological raster surface to a floating point TIN, which is read directly into the HEC-RAS RAS Mapper tool, the simulation water surface profiles can be selected and directly output as polygon shapes files. Subtracting the original hydrological surface from the water elevation surface defines the area of inundation for a particular set of input parameters. Climate change Accommodating the impact of climate change is an important component that needs to be addressed. Even extant flood line calculations have not incorporated this aspect. This is a difficult issue to quantify as the global climate change model s (GCM) predictions for the medium term ( ) are usually given as percentages and each CGM predicts different outcomes. What the climate change models cannot necessarily show is whether the percentage change will be over a year, season or a series of concentrated events. Indications are that we can expect storm events of greater intensity (Knoessen et.al, 2009). Information on the predicted climate change for the pilot areas (Knoessen et.al, 2009; Golder and Associates 2010 a,b) can be calculated and added to the water elevation surfaces and processed as new surfaces. Data application The output from any data model is dependent on the accuracy and detail of the input data (Noman and Nelson, 2002). Ideally a detailed survey of a river course with 1 m contours, preferably with a gauge station, is required. However, as previously discussed, the cost to produce such data for a large area is not viable and hence existing GIS datasets must be relied on to provide the input data. In modelling large geographic areas it is useful to work in discrete data blocks that can easily be processed. An added advantage is if these data blocks can conform to an existing structure. The Department of Water Affairs (DWA) Quaternary Catchment data layer provides a well established geographic area that is generally of suitable extent to allow the processing of raster grids within the capabilities of high end desktop computers. Data from the V12G Quaternary Catchment is used as an example of the model process. Areas surrounding the town of Ladysmith have a long history of flooding (Bell and Mason, 1998). Ladysmith itself falls in the V12G Quaternary catchment, but water from six other catchments feed their flow into the Klip River at Regional Maximum Flood (RMF) conditions equating to about 9800 m 3 /s. Recently an attenuation dam was constructed to trap flow from the above catchments and restrict flow to 400 m 3 /s. Topographically the area is characterised by low relief underlain by Vryheid Formation shales with interspaced remnant flat lying sandstone hills of the Vryheid Formation. The result is broad, low relief drainage areas susceptible to wide overbank flow during flood conditions. The shale derived soils are clay rich with low absorption potential meaning that most of the rainfall is transmitted as surface run-off. To build a basic ArcHydro model the input data required are the hydrographic features and a DEM. DEM surfaces are primarily created using 60 PositionIT October 2012

5 news the Surveyor General SG 5 m contour dataset and infilled with the 20 m contour where necessary. The base DEM (10 x 10 m grid spacing) surface is created from the contour data using Spatial Analyst. River and water body data is taken from the (SG) 1: map vector data. Water body features need to be checked to ensure that all large dams are captured in the dataset. In its current form, the river dataset is unsuitable, as the river polylines do not cross over water bodies and also tend to be split at stream/river confluences. This can require extensive user intervention to meet the modelling data standard. ArcHydro requires that the main drainage line through a catchment is continuous and that the vector points in the downstream direction. By running the ArcHydro tools process, without the river incision step, the modelled drainage lines produced are overlain onto the SG river data and only the coincidental SG river data is cleaned. Fig. 1A shows an example of a hydrological DEM with the 5 m and 20 m contours, SG rivers and the cleaned river dataset. Once the rivers have been cleaned the complete ArcHydro tools process is repeated. In defining the drainage density, ArcHydro tools recommends a 1% catchment but this leads to quaternary catchments with sub catchments based on different areas. For a regional analysis it was deemed preferable to use a constant area value and calculations showed that 2 km² was around 1% for most quaternary catchments in KZN. In the overall process ArcHydro tools provides the hydrological surface and the sub-catchment boundaries. In Fig. 1B the derived sub-catchments are overlain on the DEM. The hydrological surface is converted to a TIN surface for use in HEC-GeoRAS. The sub-catchment boundaries are checked against the rivers to ensure that each sub-catchment only contains one reach (river segment between junctions). In the HEC-GeoRAS interface a geodatabase is generated to store the river reach and cross-section data. River data is copied into the HEC-GeoRAS river layer and each reach is given a unique ID code. The river layer provides the ID data for the referencing of the cross-sections and is used in HEC-RAS to provide the link for the water flow volumes. A flow path layer is created from the river layer and Fig. 3: Derived flood risk area surfaces. Flood terraces and the recent January 2011 flood extents are shown. defines the relationship between the line of flow and direction and stationing (distance between cross-sections on the flow path) for the cross-sections. The concept of the flow path is a very important function especially when using existing data, in that the flow path does not necessarily represent the river thalweg (lowest part of a river course). This means that changes in river position on a floodplain, since the data was captured or minor positional errors in the river data, does not affect the cross-sectional water volume calculations. Cross-section lines are drawn across reaches so that these extend across the potential flood plain and are perpendicular to the flow direction targeting locations such as change in slope, valley shape and in-line features such as bridges, weirs and dams. An important point to note is that the only elevation data extracted is that of the cross-sections and the HEC-RAS software assumes a constant slope and shape between cross-sections. Ideally as many cross-sections as possible will enhance the valley shape but this is impractical as it adds dramatically to the time it takes to carry out the task and there is a data limit beyond which HEC-GeoRAS cannot extract the data. Where this occurs the data has to be split into two or more files and exported separately and rejoined in HEC-RAS. Cross-section placement is done using a two pass process. The first pass places cross-sections to deal with the geographic features as listed above, including water bodies and wetlands. After a simulation run, a second pass is used to check that cross-sections do not truncate the generated flood areas and the settlement information is used to densify the cross-sections to provide greater accuracy over these areas. HEC-GeoRAS automatically populates the required attribute data such as reach length, connectivity of junctions (nodes), cross-section lengths and stations positions along reaches. The elevation data for the cross-sections are extracted from the previously prepared TIN model. HEC-GeoRAS extracts the river reach, flow path and cross-section data into an.xml format that is imported into HEC-RAS (Fig. 1C). Regional maximum flood peak To determine flood water surface extents from flow modelling, the amount of rainfall within a catchment or sub-catchment needs to be calculated, expressed as cubic metres per second m 3 /s commonly referred to as cumecs. There are a number of ways to calculate this data each with its own advantages and disadvantages. These methods were evaluated against the primary concept of being able to 62 PositionIT October 2012

6 news carry out this modelling using existing data that can be calibrated from field data. Deterministic approaches such as the Rational Formula (Alexander, 2002, Pegram and Parak, 2004) are limited to smaller catchments < 15 km² in size and use rainfall data and catchment characteristic variables such as soil porosity and land cover information that affect the precipitation run-off. Statistical methods use rainfall and gauge data creating probability curves for the annual maximum flood peak (Smithers and Schulze, 2002). Rainfall data for ungauged catchments are inferred from these curves. These methods have three major constraints for this application in that they rely on information that does not exist at a regional level; rainfall data that may not necessarily have recorded the highest possible event and target return period flood levels that cannot be field verified. Alternatively an empirical approach is used that is constructed from recorded flood peaks. Known as the Regional Maximum Flood peak (RMF) method, the original work by Francou-Rodier (1967) and its application to southern Africa was investigated by Kovacs (1988). More recently Alexander (2002) and Pegram and Parak (2004) recommended its application to South Africa. Francou-Rodier (1967) found that when the world's flood peaks were plotted against catchment size, hydrological homogenous regional zones could be defined (catchments > 100 km²). Kovacs (1988) applied this process to southern Africa defining eight hydrological homogenous regions. The region value of a catchment is determined by its location with respect to these regions. Using the surface area of a catchment or sub-catchment and its region value and applying the various Francou-Rodier Equations (see Kovacs 1988); the RMFMAX for any catchment (gauged or ungauged) can be calculated. This process gives a peak flow value in m 3 /s for each catchment. The statistical 1:100 and 1:200 year flood peaks (RMF100) and (RMF200) are a derived subset of RMFMAX using Log-Pearson 3 probability tables (Kovacs, 1988). In a regional investigation RMF has two major advantages. The maximum derived flood peak can be correlated with Quaternary Catchment Location Geomorphology T52D Umzimkhulu area Existing flood lines, recurring flood history, broken hilly terrain with steep narrow valleys. T40G Port Shepstone area Coastal interface. U20H Edendale area Recurring flood history. Undulating terrain with wide valleys. V12G Ladysmith area Existing flood lines, recurring flood history. Low relief terrain. W23A Mtubatuba area Recorded the highest flood elevation on record in KZN during Cyclone Domoina (1984). U10M Umkomaas area As a desktop calibration test in comparison to the Durban 1:100 year flood lines. Table 1: List of pilot quaternary catchments. flood deposits in the field. Hydrological regions already account for catchment characteristics and as the flood peak is based on actual run-off as measured in the river flow during flood conditions, the need for detailed catchment variables and modelled rainfall data can be ignored. The RMF is applied by using the quaternary catchment area and calculating the RMFMAX flow, on the assumption that each sub catchment in a quaternary catchment will contribute equally to the overall flow according to their areas. The RMFMAX values for each sub-catchment are calculated by the ratio of their areas to that of the quaternary catchment. Water flow calculations As each sub-catchment contributes a finite flow volume to the system, the upstream sub-catchments need to be added to the downstream sub-catchments progressively so that the total sub-catchment flow matches the overall quaternary catchment flow. This is done by assigning flow groupings to the sub-catchments. Two models have been built in ArcGIS Model Builder that sums the flow volumes for each targeted surface per flow group and then appends this data to a single database table. The data from this table is entered into the Steady Flow Data Editor in HEC-RAS. Model calibration To calibrate the model, five quaternary catchments were selected for a pilot study. The selection was based on the area having existing flood lines or exhibiting a diverse range of geographic conditions that are representative of KZN in general (Table 1). In these pilots areas, where access could be gained to the rivers, river terraces, flood deposits and debris marks were captured using GPS using a differential system providing sub-metre accuracy. A combination of main rivers, streams and tributaries were inspected to target as representative a sample as possible. The core hypotheses is that the RMFMAX water elevation surface model simulations should closely coincide with the field data, showing the maximum flood extent on the ground and that a calibration factor may be needed to match the model with the field data. HEC-RAS produces a simulation model based on three primary variables, water volume, river topography from a cross-section of the hydrological surface, and the Manning value used to calculate energy loss from channel friction. The calculated water volume data has been tested against published data from Kovacs (1988) and is comparable. Cross-section data produced from the best available contour data could not be improved unless more detailed data such as high resolution lidar becomes available. This makes the Manning value the only variable that can be modified to calibrate the flood simulation surfaces to the field data. Whitmore and Landström (2010) describe manipulation of the Manning value as a convenient way of calibrating simulated data with observed data in modelling software. Manning values are widely published, guiding hydrologists on the channel and overbank conditions that should be applied. In this application, Manning values are used as a calibration variable and may not relate PositionIT October

7 technical to published values especially as a single average value is applied across a reach and across cross-section profiles without differentiation between channel friction and overbank/floodplain conditions. From the initial pilot areas it was found that sub-catchments with larger rivers, with associated deeper flows under flood conditions, match field observations with realistic Manning values of 0,05, while upstream sub-catchments progressively produced lower water surfaces than that seen in the field data. Various catchment parameters were investigated in an attempt to quantify this. It was found that there appeared to be a direct relationship to reach slope in that reaches with slopes < 1:0,01 were larger rivers that accumulated the most run-off and hence exhibited the deepest flow while reaches with slopes > 1:0,025 tended to be upper catchment tributaries that only accumulated flow from its associated sub catchment and hence were relatively shallow flows. An intermediate category serves as a bridge between these two categories. The higher sloped reaches are probably functioning initially in subcritical flow conditions with a relatively shallow water depth where there is a greater frictional interaction from the river bed creating more turbulent flow higher into the water column. This effectively reduces the rate of flow backing up the water creating higher flood elevations. In the low slope sub-catchments, the flow is supercritical with a much deeper water column where the friction effect from the river bed has less effect. By using a calibration value of 1 for slopes >1: 0,025 and 0,05 for slopes < 1: 0,01 and 0,2 for the intermediate slopes, all reaches across the tested quaternary catchments match the field data and are comparable with existing 1:100 year flood lines. Discussion and conclusion Fig. 2 shows a comparison between the engineering 1:100 year flood line and the modelled 1:100 year flood risk area (FRA) for Ladysmith, KZN. The FRA is displayed as flow depths. As can be seen at points A, there is good correlation between the two water surface elevations and extents. However, at points B the modelled data extends further. The difference in water depth is approximately 1 m higher for the FRA than the flood line and the variation is due to the very low relief of the area where small differences in elevation have a large effect on the horizontal reach. Since the calibration values used are aimed to be a best fit for all catchments, some catchments will have higher and some lower water surfaces compared to the base data. These data are intended as a planning tool and wherever possible the data have been calibrated to err on the conservative side so that it equals or exceeds the base data water surface elevations. Inspection of the homestead positions in Fig. 2 shows that a substantial problem already exists in the urban and traditional settled areas. While at risk, homesteads at point C can be delineated from the existing 1:100 year flood line data, the at risk homesteads at points D can be delineated from the FRA data. RMF modelling can produce a number of water elevation surfaces. Examples of these are shown in Fig. 3. It should be noted that the horizontal difference between a 1:100 and maximum projected flood are relatively small unless the terrain has a very low relief. The positions of river terraces are also shown. Ancient terraces represent old erosion features when the overall terrain was at a higher elevation. The extent of the flood waters from the January 2011 flood event are shown and clearly indicate that the recent floods did not reach the 1:100 year extent but the flow was still sufficient to damage bridges at points A (Fig. 3). At point B, a number of new structures have been erected within the last year. The recent flood water came to within 5 m of these structures and recent flood deposits are clearly visible as far uphill as terrace 3 which equates to the maximum projected flood elevation. The recent flood events highlighted the impact that even relatively minor flooding can have on people and infrastructure. Socio-economic factors are leading to settlement development in areas with a high flood risk. Costs to provide information for use by planners and officials to effectively manage this situation are beyond the budget of most local authorities. The methodology presented here has shown that by using existing GIS data, a GIS data model and river analysis software, flood risk areas can be generated and calibrated to provide a cost effective spatial planning tool to guide future planning, determine homesteads and infrastructure at risk and assist disaster management practitioners to proactively plan ahead for future flood events. Processing times for a quaternary catchment average about 20 hours. A third of the time is consumed by data cleaning and preparation with the balance for the modelling. One of the key elements in this process is the ability to calibrate the simulation data. Initial results show that the calibration values used can be consistently applied across a range of quaternary catchment conditions. Ongoing data collection will improve the calibration of the model. Catchment variables such as geology, catchment topographic profiles and orographic effects are also being investigated to determine their effect on the calibration variable. Acknowledgment This paper was presented at AfricaGEO 2011 and is republished here with permission. References [1] W J R Alexander: The Standard Design Flood - theory and practise: Journal of the South African Institution of Civil Engineering, 44(1), p26-30, [2] F G Bell and T R Mason: The problem of flooding in Ladysmith, Natal, South Africa. Engineering Geology, (July 2010), pp.3-10, [3] Z A Botes, A M Smith, R. Uken, A New Spatial Planning Tool for the Delineation of Flood Risk Areas. Planning Africa 2010 Conference. South African Planning Institute, [4] J S Bridge: Rivers and floodplains: forms, processes, and sedimentary record, Malden, USA: Wiley-Blackwell. p491, [5] G W Brunner: HEC-RAS: River Analysis System; Hydraulic Reference Manual, Version 4: US Army Corps of Engineers: Hydrological Engineering Centre, CPD-69. p411, [6] G W Brunner: HEC-RAS: River Analysis System; User's Manual, Version 4. s.l.: US Army Corps of Engineers: Hydrological Engineering Centre, 2008a. CPD-68. p747, 2008a. [7] T Cameron and P E Ackerman: HEC-GeoRAS: GIS Tools for support of HEC-RAS using ArcGIS; User's Manual, Version 4: US Army Corps of Engineers: Hydrological Engineering Centre, CPD-83. p204, PositionIT October 2012

8 technical [8] K Davis, J Furnans, D R Maidment, V Samuels, K Schneider: Hydrography. [Ed] DR Maidment Arc Hydro: GIS for Water Resources: ESRI Press, p55-86, [9] Esri: ArcGIS9: Using ArcGIS Spatial Analyst. ESRI Press. p238, [10] Esri, (2009). Arc Hydro Tools Overview: Environmental Systems Research Institute, Inc., Integrated Software Manual. [11] Esri: ArcGIS User's Manual: Environmental Systems Research Institute, Inc., 2009a. Integrated Software Manual, 2009a. [12] Francou-Rodier. (1967) in Kovacs [13] Golder Associates. (2010). KZN Flood Risk Project: Climate Change Assessment p51, [14] F Hellweger: AGREE - DEM Surface Reconditioning System. GISHYDRO/ferdi/research/agree/agree. html#part4. [Online] [15] D Knoesen, R Schulze, C. Pringle, M Summerton, C Dickens, and R Kunz: Water for the Future: Impacts of climate change on water resources in the Orange-Senqu River basin. Report to New Water, a project funded under the Sixth Research Framework of the European Union. Institute of Natural resources, Pietermaritzburg, South Africa, [16] Z Kovacs: Regional Maximum Flood Peaks in Southern Africa: Department of Water Affairs, TR137. p91, [17] D R Maidment: Why Arc Hydro? Arc Hydro: GIS for Water Resources: ESRI Press, p203, [18] D R Maidment, S Morehouse, S Grise: Arc Hydro Framework. [Ed] D R Maidment Arc Hydro: GIS for Water Resources: ESRI Press, p55-86, [19] D R Maidment, V Merwade, T Whiteaker, M Blongewicz, D Arctur: Time Series. [Ed] D R Maidment Arc Hydro: GIS for Water Resources: ESRI Press, p55-86, [20] National Water Act 36 of 1998: p113, [21] N Noman, and J Nelson: River Channels. [Ed] D R Maidment Arc Hydro: GIS for Water Resources: ESRI Press, p55-86, [22] F Olivera, J Furnans, D R Maidment, D Djokic, and Y Zichuan: Drainage Systems. [Ed] D R Maidment Arc Hydro: GIS for Water Resources: ESRI Press, p55-86, [23] F Olivera, D R Maidment, D Honeycutt: Hydro Networks. [Ed] DR Maidment Arc Hydro: GIS for Water Resources: ESRI Press, p55-86, [24] J C Smithers, and R. Schulze: Design rainfall and flood estimation in South Africa, Water Research Commission. (WRC K5/1060), [25] The Water Wheel: Natural Disasters, SA mops up following floods. The Water Wheel, 10(2), pp.22-25, [26] D van Bladeren: Historical Flood Documentation Series. No.1: Natal and Transkei, : Department of Water Affairs and Forestry, TR 147. p85, [27] J C Warner, G W Brunner, C Wolfe, and S Piper: HEC-RAS: River Analysis System; Applications Guide, Version 4: US Army Corps of Engineers: Hydrological Engineering Centre, CPD-70. p351, Contact Riaan Botes, Tel , riaanb@iafrica.com PositionIT October