Temporal variability of damage potential on roads as a conceptual contribution towards a short-term avalanche risk simulation

The fatality risk caused by avalanches on road networks can be analysed using a long-term approach, resulting in a mean value of risk, and with emphasis on short-term fluctuations due to the temporal variability of both, the hazard potential and the damage potential. In this study, the approach for analysing the long-term fatality risk has been adapted by modelling the highly variable short-term risk. The emphasis was on the temporal variability of the damage potential and the related risk peaks. For defined hazard scenarios resulting from classified amounts of snow accumulation, the fatality risk was calculated by modelling the hazard potential and observing the traffic volume. The avalanche occurrence probability was calculated using a statistical relationship between new snow height and observed avalanche releases. The number of persons at risk was determined from the recorded traffic density. The method resulted in a value for the fatality risk within the observed time frame for the studied road segment. The long-term fatality risk due to snow avalanches as well as the short-term fatality risk was compared to the average fatality risk due to traffic accidents. The application of the method had shown that the long-term avalanche risk is lower than the fatality risk due to traffic accidents. The analyses of short-term avalanche-induced fatality risk provided risk peaks that were 50 times higher than the statistical accident risk. Apart from situations with high hazard level and high traffic density, risk peaks result from both, a high hazard level combined with a low traffic density and a high traffic density combined with a low hazard level. This provided evidence for the importance of the temporal variability of the damage potential for risk simulations on road networks. The assumed dependence of the risk calculation on the sum of precipitation within three days is a simplified model. Thus, further research is needed for an improved determination of the diurnal avalanche probability. Nevertheless, the presented approach may contribute as a conceptual step towards a risk-based decision-making in risk management.

Risk-conscious Reconstruction in Pakistan-administered Kashmir. A Case Study of the Chakhama Valley

The Chakhama Valley, a remote area in Pakistan-administered Kashmir, was badly damaged by the 7.6-magnitude earthquake that struck India and Pakistan on 8 October 2005. More than 5% of the population lost their lives, and about 90% of the existing housing was irreparably damaged or completely destroyed. In early 2006, the Aga Khan Development Network (AKDN) initiated a multisector, community-driven reconstruction program in the Chakhama Valley on the premise that the scale of the disaster required a response that would address all aspects of people's lives. One important aspect covered the promotion of disaster risk management for sustainable recovery in a safe environment. Accordingly, prevailing hazards (rockfalls, landslides, and debris flow, in addition to earthquake hazards) and existing risks were thoroughly assessed, and the information was incorporated into the main planning processes. Hazard maps, detailed site investigations, and proposals for precautionary measures assisted engineers in supporting the reconstruction of private homes in safe locations to render investments disaster resilient. The information was also used for community-based land use decisions and disaster mitigation and preparedness. The work revealed three main problems: (1) thorough assessment of hazards and incorporation of this assessment into planning processes is time consuming and often little understood by the population directly affected, but it pays off in the long run; (2) relocating people out of dangerous places is a highly sensitive issue that requires the support of clear and forceful government policies; and (3) the involvement of local communities is essential for the success of mitigation and preparedness.

Managing Debris Flow Risks

Debris flows represent a widespread threat to villages and small towns in the Swiss Alps. For many centuries people “managed” such risks by trying to avoid hazardous areas. However, major debris flow and flood events in the last 25 years have revealed that the degree of freedom to engage in this type of risk management has substantially decreased. This became especially evident during the 1999 disasters in a number of places in Switzerland. The winter of that year was unusually wet. In February heavy snowfall triggered destructive avalanches. In May high temperatures caused heavy snowmelt, with excessive rainfall contributing more water to the already saturated soils. Landslides, debris flows and floods were triggered in many locations, including Sörenberg. Hazard prevention and disaster management have a long tradition in Switzerland, although an integrated approach to risk management is rather new. Only in recent years have methods and tools been developed to assess hazards, define protection goals, and implement disaster reduction measures. The case of Sörenberg serves as an example of how today's approaches to disaster reduction are implemented at the local level.

Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning

Prediction of psychosis in patients at clinical high risk (CHR) has become a mainstream focus of clinical and research interest worldwide. When using CHR instruments for clinical purposes, the predicted outcome is but only a probability; and, consequently, any therapeutic action following the assessment is based on probabilistic prognostic reasoning. Yet, probabilistic reasoning makes considerable demands on the clinicians. We provide here a scholarly practical guide summarising the key concepts to support clinicians with probabilistic prognostic reasoning in the CHR state. We review risk or cumulative incidence of psychosis in, person-time rate of psychosis, Kaplan-Meier estimates of psychosis risk, measures of prognostic accuracy, sensitivity and specificity in receiver operator characteristic curves, positive and negative predictive values, Bayes’ theorem, likelihood ratios, potentials and limits of real-life applications of prognostic probabilistic reasoning in the CHR state. Understanding basic measures used for prognostic probabilistic reasoning is a prerequisite for successfully implementing the early detection and prevention of psychosis in clinical practice. Future refinement of these measures for CHR patients may actually influence risk management, especially as regards initiating or withholding treatment.