Principles in damage evaluation

Flood damage evaluation is increasingly significant for decision making (Penning-Rowsell et al., 2003; Messner et al., 2007). The need to assess all benefits and costs of flood risk management policy, such as PLFP, is necessary in order to (Messner et al., 2007):

 Specify the risk situation (size of area or number of properties at risk).

 Determine the potentials of risk reduction and their respective costs.

 Compare the benefits and costs of risk reduction in terms of the benefit-cost ratio and/or the net.

 Compare the benefit-cost ratios of several policy fields dealing with risk reduction in order to decide where the tax money should be spent first.

In assessing the tangible or direct flood impacts, Meyer and Messner (2005) outlined some basic steps in the overall process. These principles include the selection of an appropriate approach. The decision of which method to use depends on a number of factors including the spatial level of the study (either national or local), the objective of the study, and the availability of resources and pre-existing data (Meyer and Messner, 2005). It is acknowledged that the flood damage evaluation process can be a laborious and time-consuming task, especially on a national scale. The next step of the evaluation principle involves the determination of the type of damage to be assessed, whether tangible or indirect damage (Meyer and Messner, 2005; Messner et al., 2007), and here it advisable that more attention is given to the damage which has greatest impact on the total cost. Thirdly, the necessary information is collected for the evaluation, such as inundation characteristics of the study area (Messner et al., 2007). As indicated earlier, flood depth information is the most vital parameter used to derive a damage function,

and in the UK such depth-damage functions are further differentiated by flood duration as was shown in Figure 2.7. Elsewhere in the Netherlands, velocity is usually included in damage functions for residential properties (Kok et al., 2004). The last stage of evaluation process involves bringing all necessary information together to calculate the expected damage (Meyer and Messner, 2005), which also implies using the appropriate methodology to achieve the objective of the study.

Several examples of existing flood damage database are well known in Europe, which can be used for assessments. These include the HOWAS database in Germany (IWK 1999; Merz et al., 2004), which contains information on damages which occurred during nine events in the past, with around 3600 individual damages to buildings (IWK, 1999; Buck, 2004). The evaluation of damage associated with this data is by insurance damage adjustment and equates to replacement costs. The MCM, as previously mentioned, is a well known damage data for flood appraisal in the UK, developed by the Flood Hazard Research Centre (FHRC, 2010). This database is not derived from real flood data but has been synthetically generated; it provides absolute depth-damage function for 100 residential and more than ten non-residential property types (Penning-Rowsell et al., 2003; Messner et al., 2007). Prior to the current MCM, predecessor data including the Blue, Red and Yellow manuals have all been used for flood damage evaluation in the UK (Penning-Rowsell and Chatterton, 1977; Parker et al., 1987;

Penning-Rowsell et al., 1992). The MCM database will be further explored in this study to assess PLFP schemes.

In flood damage appraisal, the MCM advises the use of the Weighted Annual Average Damage (WAAD) approach, especially where the appraiser has no information on the flood return period and depth distribution (FHRC, 2010), and it involves annualising flood damages using a range of flood frequencies and depth data for the study area. The driving data for the WAAD calculations in the MCM is based on the information in Figure 2.12, which was developed given the constraint of getting detailed data for flood appraisal (Penning-Rowsell et al., 2005; Messner et al., 2007). This figure shows the percentages of properties inundated at different depth bands for a range of flood frequencies for the case studies. For instance, the 5 year flood event shows very high proportion of properties which are flooded at low depths than the subsequent flood events. Such flood frequency depth distribution is essential for the WAAD method, and hence will be discussed further in the study.

Figure 2.12: Property flood depth distribution by return period (Source: Penning-Rowsell et al., 2005; Messner et al., 2007)

The MCM flood depth data (Figure 2.12) was first developed by FHRC in association with Entec UK, who attempted to derive an algorithm to estimate the weighted average property damage for all property, irrespective of frequency and depth of flooding. This was a vital step, as it removes the need for property levels and flooding threshold levels in the broad scale evaluation of annual average damages. Building on this, John Chatterton Associates improved the sample base of the weighted depth-damage data to some 9,000 properties, from 14 flood plain locations (Penning-Rowsell et al., 2005;

Messner et al., 2007). This data, although limited to the English Midlands, broadly represents the average for UK, and can be used to generate WAAD for related studies given some assumptions on flood depths and return periods. For example, a minimum of three return periods is often required in estimating flood damage by this approach and the MCM handbook gives further guidance in using this method (FHRC, 2010).

Based on the information shown in Figure 2.12, the MCM has derived WAAD for different standards of protection at the property threshold (FHRC, 2010). For example, the WAAD for a residential property with no protection and no flood warning is £5393 (FHRC, 2010). This figure was derived based on seven flood events which ranged from 2 to 200 year return period, and the damages with respect to two warning lead-time (i.e.

less than or greater than 8 hours ) are relatively lower.