Devils Hand Basin Garage Dissertation

Interview responses

Out of the 304 households interviewed, 72% (220) had experienced floods (corresponding with the actual high- and low-risk flood zones) with Table 4 indicating the flooding frequency. Table 4 also gives total financial losses, but the respondents’ estimates should be viewed with caution, partly due to a long time period since some of the flooding occurred and partly due to their unwillingness to fully discuss financial issues during the interviews. Concerning repeated flooding, 36% (109) of the households interviewed had experienced one flood, 28% (86) had experienced two floods, and 8% (25) had experienced at least three floods (the remaining households had not experienced flooding). Given the respondents’ reluctance to talk about financial issues, a further data limitation is possible in terms of the respondents not remembering or not fully reporting all the flooding that they had experienced.

From the households interviewed, 75% (227) were located on flat land, 22% (67) on moderate slopes, and 3% (10) on steep slopes. A percentage of 78 (236) have a cellar. Houses are constructed of various building materials: 67% (203) from fired bricks and 19% (57) from either non-fired bricks or a combination of fired and non-fired bricks. The other 14% (44) of houses are constructed from other materials, such as timber or breeze blocks. Seven per cent (20) of houses have a stone cellar that is an old, traditional flood risk reduction measure, because it is easy to clean after flood waters have receded; however, there are now modern dangers in the form of pesticides, fertilisers, and chemicals that often contaminate flood waters and leave a harmful residue.

Approximately half of the houses have a ground floor up to 1 m above the ground level, whereas 30% (91) have an elevated ground floor higher than 1 m. The 1-m height does not necessarily mean that house is protected against the Q100 flood, since Q is peak discharge rate, but it is a tool to evaluate household measures. In comparing the age of the houses with their ground floor elevation (Figure 2), the proportion of houses with elevated ground floors has substantially decreased over the past 20 years after peaking during Communist times. This decrease has occurred despite the frequent flooding. Moreover, the proportion of houses with elevated ground floors is similar for all risk zones. The developers and owners of new houses are following the fashionable or short-term lower cost choices of houses that are not raised, despite the flood risk.

Czech legislation recommends, rather than demands, that building authorities elevate the ground floor for new houses in the Q100 zone. Current regulations in the Czech Water Act No. 254/2001 forbid new houses in Q20 zones. In practice, monitoring and enforcement are not strict – especially when political and development interests simply ‘delay’ implementation. Yet the cost of elevating houses might nonetheless be acceptable, as shown by Botzen et al. (2013) in the Netherlands who found that 52% of those interviewed stated that they would be willing to pay a substantial investment of approximately €10 000 to elevate a new house to a level that would be deemed safe from flooding. The differences with the Czech Republic might be the level of affluence in the Netherlands alongside a culture highly aware of and respectful about flood dangers.

During our field work, we found another example of ‘house elevation’ through constructing an artificial mound that elevated the terrain on which the house is built by more than 1 m. Although the house was effectively at ground level, it stands on its own artificial hill, elevating it from flood waters. It is possible that this elevation was completed for the view rather than, or in addition to, flood risk reduction.

In terms of other measures, only one household (comprising two families) indicated that they decided to move as a result of flood risk. They did not migrate away from their home, but instead, with municipal support, they built a new house in the same community but on a hill. Another two households declined even this short move, despite the municipality offering them financial support. A strong connection to one's house, land, and place of birth is indicated by the reluctance to consider migration as an option – at least in this community, considering that rural communities around the Czech Republic display much more migration, mainly for economic reasons (Macours and Swinnen, 2005). A limitation of this analysis is that the financial support offered by the municipality might not have been deemed to be enough. Perhaps offering a substantial supplement to costs incurred would convince people to break their attachment to their land.

In terms of other flood risk reduction measures, the share of households that has purchased insurance for environmental hazards has gradually increased up to 95% in 2010 (see also Table 4). Yet a few respondents claimed that they could not obtain insurance, because the insurance companies refused to sell it to them or offered high premiums, claiming that the occurrence of floods in their area is more of a trend than of random events. One household gave up trying to restore their damaged ground floor and moved upstairs permanently. Even those with insurance stated that they usually did not receive enough of a pay-out to cover their financial losses.

Many factors could be at play here, including under-insuring due to inadequate pre-flood loss estimation or lack of affordability of higher coverage; underestimating the cost of repair and reconstruction; being subject to post-flood price gouging; financial priorities other than full insurance coverage or full reconstruction; or wishing to avoid the disruption entailed by full reconstruction – which could be dangerous for the occupants’ health and for the new materials and finishes if the property is not dried out properly after the flood. Because so many respondents were unwilling to discuss financial matters in great depth, it was hard to glean a deep understanding of these factors.

Nonetheless, Tables 5 and 6 show a trend of progress for implementing household measures for flood risk reduction. There are two provisos indicating data and interpretation limitations. First, people might not remember what they did several years ago or might not know what happened prior to their ownership of the house. Second, no one admitted to taking away or reducing measures but that might have happened. Irrespectively, Tables 5 and 6 show that households tend to prefer simple and cheap measures such as moving possessions upstairs or using mobile barriers, rather than changing their floor. In Table 6, the high uptake of hydro-isolation can be explained by it not usually being considered to be a special river flood risk reduction measure. Instead, it is a standard and basic way of avoiding dampness in the house from wet ground. Co-benefits from other measures are not so straightforward to identify, but the nature of the interviews in highlighting floods might have focused respondents on flood-related reason, even where other drivers dominated their decision to implement a measure.

Before the 1997 flood28512
1997–2006+22+12+12
2007–2010+16+1+13
After the 2010 flood+12+6+14
Total782451
Before the 1997 flood3045204
1997–2006+7+16+16+10
2007–2010+3+7+20
After the 2010 flood+6+13+6+8
Total46814422

Additionally, the number of measures adopted per household was limited. Fifty-nine per cent of households adopted one measure, 27% adopted two measures, 11% adopted three measures, and 4% adopted four measures. A pattern emerged of measures taken based on awareness. Table 7 shows that the higher the flood risk zone in which a house sits, the more measures the household tends to take. For comparison, Miceli et al. (2008) found in Italy that, the higher the flood risk perception, the higher the number of household measures taken to reduce flood risk. The factors creating the difference could be cultural or could be local, but none of the studies explored in depth with interviewees the reasons why flood risk perception did or did not influence flood risk reduction measures adopted. A table equivalent to Table 7 for internal measures is not entirely meaningful, since the main internal measures considered could be implemented and then easily reversed or changed multiple times suggesting that the number of households adopting a measure cannot be given accurately. That contrasts with the main external measures listed that are easy to observe as existing and that are not easily altered.

Very low risk (85 interviewees total)819125
Low risk (107 interviewees total)1324128
High risk (112 interviewees total)2538209

Despite the data in Table 7, and the increases over time in flood risk reduction measures taken in the Becva River Basin (Tables 5 and 6), it is hard to claim that flooding inevitably influenced household choices. Subject to the provisos mentioned above, the number of households adopting measures increases as a continuing trend over time, not simply immediately after floods. As well, some respondents mentioned that they implemented other measures; for example, applying plaster and other finishes that are water resistant; not applying any plaster or other finishes because frequent floods rendered it useless; building a private wall to keep flood water away from the property; and installing a pump and a mobile boiler.

Multi-scalar contexts – such as national culture, local affordability, and regulations and media at each governance level – influence flood risk reduction decision-making in all locations, with a cross-case study set of factors or contextual aspects rarely being identifiable (e.g. Parker et al., 2008, 2009; Kuhlicke et al., 2011). Factors that could be explored further include how a neighbour's or relative's measures taken influence a household's choice and how many measures, ostensibly for flood risk reduction, were undertaken as part of wider maintenance or renovation work. A cultural studies perspective, such as through focus groups or unstructured interviews, could glean insights into reasons underlying the observations of specific trends, but authors such as Parker et al. (2008, 2009) warn against trying to impose the transferability of conclusions from one context to another.

Regression

This section uses a probit model as a regression technique through the statistical software STATA, Data Analysis and Statistical Software, StataCorp LP, USA, Texas, to investigate the link among various factors and the probability of household flood risk reduction measures being applied. The equation used is:

The variables are:

yi

equals 1 if a household has undertaken any flood risk reduction measure and 0 otherwise.

X1

is a vector measuring the level and intensity of the household's exposure to floods, such as the total number of the floods experienced and the total financial losses from the floods.

X2

is a vector of dummy variables describing characteristics and the location of the house, such as having a cellar or an elevated ground floor.

X3

is a vector describing household characteristics, such as gender distribution, education, income, occupation, and family status.

X4

is a vector measuring individual perception of the household's flood risk and the flood risk reduction measures adopted by the local government.

Ƹi

is a stochastic error term that is assumed to be distributed normally, ƸiN(0,δ2).

We model the decision tree for regression as YES/NO for taking any flood risk reduction measure. If the decision is YES, then we distinguish between interior and exterior measures (Table 3). Table 8 shows the selection (probit) equation for the risk reduction measures.

floor2−0.6060.211a−0.1980.067a−0.0080.258−0.0010.049
floor3−0.2990.235−0.0980.0760.0570.3170.0110.060
Total floods0.1160.1320.0380.043−0.1770.164−0.0330.031
tot_loss0.0200.0790.0070.0260.1860.097c0.0350.018c
perc20.5880.187a0.1930.059a0.0130.2510.0020.047
perc30.7260.304b0.2380.098b−0.0900.368−0.0170.069
perc_mun20.4070.3060.1330.0991.1850.480b0.2230.087a
perc_mun30.4320.241c0.1410.078c0.5580.270b0.1050.050b
perc_mun40.3940.2650.1290.0860.8650.326a0.1630.060a
perc_mun50.3400.3350.1110.1090.246

Беккер глубоко вздохнул и перестал жаловаться на судьбу. Ему хотелось домой. Он посмотрел на дверь с номером 301. Там, за ней, его обратный билет.

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