Model choice

As discussed in the literature review, the flood hazard has been measured in a variety of ways notably by hydraulic modelling, remote sensing techniques and community based

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approaches; often with the integration of GIS. Each of these methods has advantages and disadvantages already discussed.

The present analysis used hydraulic modelling for flood hazard determination.

Hydraulic modelling was considered appropriate given estimation of flood hazardousness in this study was based on a historic flood event, i.e. the 2008 flood event. The choice of the 2008 flood event follows the availability of hydrological data in basin which was mostly up to 2008 at the time of the research. The hydraulic model approach also allows objective quantification of the hazard in terms of flood depths, a difficult aspect to achieve if satellite imagery or community based approaches were to be used.

Hydraulic models can be data intensive and quite complex requiring expert skill, requirements often not met in developing countries. A parsimonious hydraulic model is therefore paramount for the Lower Shire floodplain. A number of flood inundation models are available. These include one dimensional models such as MIKE II (DHI, 1993), ISIS (ISIS, 1995) , HEC-RAS (HEC-RAS, 2002). These models consider flow to be longitudinal, thus approximating the domain as a series of cross sections perpendicular to flow. They are computationally very efficient (Hunter et al., 2007) and well suited to parameterization using traditional field surveys (Bates and De Roo, 2000;

Horritt and Bates, 2001). However, these models fail to approximate the domain as a surface but rather use a series of cross section. Hence, they are also unable to simulate the lateral diffusion of the flood wave (Hunter et al., 2007). Areas between cross section are not explicitly represented (Bates and De Roo, 2000). Besides, the cross sections are under the subjectivity of location and orientation (Hunter et al., 2007).

On the other extreme end are the 2D full Saint Venant equations- based models e.g.

TELEMAC-2D (Bates and Anderson, 1993), MIKE 21 (DHI, 1996). These offer a better approximation of known hydraulic processes and require no secondary treatment to determine flood inundation as they are integrated for use with available satellite imagery. Despite these advantages, their computational costs and data requirements are high (Bates and De Roo, 2000).

Models from simplified full Saint Venant equation that neglect different aspects of the momentum equations have increasingly been used. Given typically available non-error free data used in model construction and validation, mathematically rigorous models may not be justified (Hunter et al., 2007). Such models include the volume spreading models such as the Rapid Flood Spreading Method (RFSM) (Gouldby et al., 2008) and dynamic models such as JFLOW (Bradbrook et al., 2004) and RFSM-EDA (Jamieson et al., 2012)

Lisflood-FP (Bates and De Roo, 2000) is also one of the simplified versions of full Saint Venant equations. It simulates channel flow with kinematic or diffuse approximation of one dimensional St Venant equations while floodplain inundation is approximated with a 2D diffusion wave using Manning’s law and a storage cell concept applied over the raster grid (Bates and De Roo, 2000). A mathematical description of the model as given by Horritt and Bates (2001) is outlined below.

Channel flow in Lisflood-FP is described by continuity and momentum equations:

Continuity ^{t q}

where Q is the volumetric flow rate in the channel, A the cross-sectional area of the flow,

x

is the distance between cross sections, q the flow into the channel from other sources (i.e. from the floodplain or possibly tributary channels), S_othe down-slope of the bed,

n

is the Manning friction coefficient distinguished as n_c for channels and n_fp for floodplains, P is the wetted perimeter of the flow, and h is the flow depth. The term in brackets is the diffusion term, which according to Horritt and Bates (2001) forces the flow to respond to both the bed slope and the free surface slope, and can be switched on and off in the model, to enable both kinematic and diffusive wave approximations to be tested. The channel parameters required to run the model are its

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width, bed slope, depth (for linking to floodplain flows) and Manning’s n value and these can be varied spatially along the reach.

Over the floodplain, flows are also derived from the continuity and momentum equations applied to a grid of square cells. A cellular approximation is given by:

y

From the various flow equations available that would be equally applicable, Lisflood-FP uses Manning’s equation.

Qyis treated analogous to Q_x. The flow depth, h_flow represents the depth through which water can flow between two cells, and is defined as the difference between the highest water free surface in the two cells and the highest bed elevation. The model simulates the time evolution of water depth in each model grid cell at each time step in response to main channel flood waves and represents the simplest physical representation capable of simulating dynamic floodplain inundation (Wilson et al., 2007)

A number of advantages have been associated with Lisflood-FP. It models floods with minimum representation of floodplain hydraulics often demanded in typical hydraulic models. Yet results from Lisflood-FP have been found to be comparable with those from other models including full St Venant based models. On their application on the 35km stretch of the River Meuse in The Netherlands, for example, Bates and De Roob (2000), found that Lisflood-FP outperformed both the simpler planar approximation to the free surface based on a linear interpolation of maximum water surface elevations recorded at two gauge stations, and the steady state simulation with a two dimensional finite element model. Lisflood-FP and TELEMAC-2D also achieved similar results in

terms of fit and mass balance error over a 40km stretch on River Thames, UK with Lisflood-FP further emerging computationally efficient on speed (Horritt and Bates, 2001). Fewtrell et al.(2009) compared Lisflood-FP with SOBEK, a fully 2D hydrodynamic model, in the prediction of the flood inundation on the River Thames in Greenwich, UK. Flood extent from the two models was comparable.

In addition, Lisflood-FP results are easily compared to typically available hydraulic data such as flow, stage data and satellite imagery; it requires relatively little hydraulic modelling expertise and is more computationally efficient than full St Venant models (Bates and De Roo, 2000; Fewtrell et al., 2009; Neal et al., 2012).

Furthermore, Lisflood-FP has been applied to a diverse set of conditions. It is applicable to fluvial, coastal and estuarine flooding and is executable in 1D, coupled 1D/2D and 2D (Neal et al., 2012). It has been extensively tested including in topographically complex regions, very large catchments and data scarce regions e.g. in the Amazon (Wilson et al., 2007), the Niger River (Neal et al., 2012) and the Ob (Biancamaria et al., 2009).

While JFLOW is very similar to Lisflood-FP (Bradbrook et al., 2004), Lisflood-FP is used in this study owing to its wider applicability in African catchments (Coulthard et al., 2013; Phanthuwongpakde, 2011; Zahera et al., 2011) where data availability and quality pose a bigger challenge. For a detailed discussion of Lisflood-FP, one is referred to Bates and De Roob (2000); Neal et al. (2012) amongst others.

In document Contemporary disaster management framework quantification of flood risk in rural Lower Shire Valley, Malawi (Page 98-102)