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Risk communication, where scientists inform policy-makersor the populace of the probability and magnitude of possibledisasters, is essential to disaster management – enablingpeople to make better decisions regarding preventative steps,evacuations, etc. Psychological research, however, hasidentified multiple biases that can affect people’sinterpretation of probabilities and thus risk. For example,availability (Tversky & Kahneman, 1973) is known toconfound probability estimates while the description-experience gap (D-E Gap) (Hertwig & Erev, 2009) shows lowprobability events being over-weighted when described andunder-weighted when learnt from laboratory tasks. This paperexamines how probability descriptions interact with realworld experience of events. Responses from 294 participantsacross 8 conditions showed that people’s responses, given thesame described probabilities and consequences, were alteredby their familiarity with the disaster (bushfire vs earthquake)and its salience to them personally. The implications of thisfor risk communication are discussed.

Author(s): John McCloskey (corresponding author) [1]; Suleyman S. Nalbant [1]; Sandy Steacy [1] Following the massive loss of life caused by the Sumatra-Andaman earthquake in Indonesia and its tsunami, the [...]

Forecasting the spatial and temporal distribution of aftershocks is of great importance to earthquake scientists, civil protection authorities and the general public as these events cause disproportionate damage and consternation relative to their size. At present, there are two main approaches to such forecasts—purely statistical methods based on observations of the initial portions of aftershock sequences and a physics-based approach based on Coulomb stress changes caused by the main shock. Here we develop a new method which combines the spatial constraints from the Coulomb model with the statistical power of the STEP (short-term earthquake probability) approach. We test this pseudo prospectively and retrospectively on the Canterbury sequence against the STEP model and a Coulomb rate–state method, using data from the first 10 d following each main event to forecast the rate of M ≥ 4 events in the following 100 d. We find that in retrospective tests the new model outperforms STEP for two events in the sequence but this is not the case for pseudo-prospective tests. Further, the Coulomb rate–state approach never performs better than STEP. Our results suggest that incorporating the physical constraints from Coulomb stress changes can increase the forecasting power of statistical models and clearly show the importance of good data quality if prospective forecasts are to be implemented in practice. [ABSTRACT FROM PUBLISHER]

The Canterbury earthquake sequence, which includes the devastating Christchurch event of 2011 February, has to date led to losses of around 40 billion NZ dollars. The location and severity of the earthquakes was a surprise to most inhabitants as the seismic hazard model was dominated by an expected Mw > 8 earthquake on the Alpine fault and an Mw 7.5 earthquake on the Porters Pass fault, 150 and 80 km to the west of Christchurch. The sequence to date has included an Mw = 7.1 earthquake and 3 Mw ≥ 5.9 events which migrated from west to east. Here we investigate whether the later events are consistent with stress triggering and whether a simple stress map produced shortly after the first earthquake would have accurately indicated the regions where the subsequent activity occurred. We find that 100 per cent of M > 5.5 earthquakes occurred in positive stress areas computed using a slip model for the first event that was available within 10 d of its occurrence. We further find that the stress changes at the starting points of major slip patches of post-Darfield main events are consistent with triggering although this is not always true at the hypocentral locations. Our results suggest that Coulomb stress changes contributed to the evolution of the Canterbury sequence and we note additional areas of increased stress in the Christchurch region and on the Porters Pass fault. [ABSTRACT FROM AUTHOR]