A press database on natural risks and its application in the study of floods in Northeastern Spain

The aim of this work is to introduce a systematic press database on natural hazards and climate change in Catalonia (NE of Spain) and to analyze its potential application to social-impact studies. For this reason, a review of the concepts of risk, hazard, vulnerability and social perception is also included. This database has been built for the period 1982¿2007 and contains all the news related with those issues published by the oldest still-active newspaper in Catalonia. Some parameters are registered for each article and for each event, including criteria that enable us to determine the importance accorded to it by the newspaper, and a compilation of information about it. This ACCESS data base allows each article to be classified on the basis of the seven defined topics and key words, as well as summary information about the format and structuring of the new itself, the social impact of the event and data about the magnitude or intensity of the event. The coverage given to this type of news has been assessed because of its influence on construction of the social perception of natural risk and climate change, and as a potential source of information about them. The treatment accorded by the press to different risks is also considered. More than 14 000 press articles have been classified. Results show that the largest number of news items for the period 1982¿2007 relates to forest fires and droughts, followed by floods and heavy rainfalls, although floods are the major risk in the region of study. Two flood events recorded in 2002 have been analyzed in order to show an example of the role of the press information as indicator of risk perception.

Scale effect in hazard assessment - application to daily rainfall

Daily precipitation is recorded as the total amount of water collected by a rain-gauge in 24h. Events are modelled as a Poisson process and the 24h precipitation by a Generalized Pareto Distribution (GPD) of excesses. Hazard assessment is complete when estimates of the Poisson rate and the distribution parameters, together with a measure of their uncertainty, are obtained. The shape parameter of the GPD determines the support of the variable: Weibull domain of attraction (DA) corresponds to finite support variables, as should be for natural phenomena. However, Fréchet DA has been reported for daily precipitation, which implies an infinite support and a heavy-tailed distribution. We use the fact that a log-scale is better suited to the type of variable analyzed to overcome this inconsistency, thus showing that using the appropriate natural scale can be extremely important for proper hazard assessment. The approach is illustrated with precipitation data from the Eastern coast of the Iberian Peninsula affected by severe convective precipitation. The estimation is carried out by using Bayesian techniques

The effect of scale in daily precipitation hazard assessment

Daily precipitation is recorded as the total amount of water collected by a rain-gauge in 24 h. Events are modelled as a Poisson process and the 24 h precipitation by a Generalised Pareto Distribution (GPD) of excesses. Hazard assessment is complete when estimates of the Poisson rate and the distribution parameters, together with a measure of their uncertainty, are obtained. The shape parameter of the GPD determines the support of the variable: Weibull domain of attraction (DA) corresponds to finite support variables as should be for natural phenomena. However, Fréchet DA has been reported for daily precipitation, which implies an infinite support and a heavy-tailed distribution. Bayesian techniques are used to estimate the parameters. The approach is illustrated with precipitation data from the Eastern coast of the Iberian Peninsula affected by severe convective precipitation. The estimated GPD is mainly in the Fréchet DA, something incompatible with the common sense assumption of that precipitation is a bounded phenomenon. The bounded character of precipitation is then taken as a priori hypothesis. Consistency of this hypothesis with the data is checked in two cases: using the raw-data (in mm) and using log-transformed data. As expected, a Bayesian model checking clearly rejects the model in the raw-data case. However, log-transformed data seem to be consistent with the model. This fact may be due to the adequacy of the log-scale to represent positive measurements for which differences are better relative than absolute