Predicting coastal cliff erosion using a Bayesian probabilistic model

This paper is not subject to U.S. copyright. The definitive version was published in Marine Geology 278 (2010): 140-149, doi:10.1016/j.margeo.2010.10.001.Regional coastal cliff retreat is difficult to model due to the episodic nature of failures and the along-shore variability of retreat events. There is a growing demand, however, for predictive models that can be used to forecast areas vulnerable to coastal erosion hazards. Increasingly, probabilistic models are being employed that require data sets of high temporal density to define the joint probability density function that relates forcing variables (e.g. wave conditions) and initial conditions (e.g. cliff geometry) to erosion events. In this study we use a multi-parameter Bayesian network to investigate correlations between key variables that control and influence variations in cliff retreat processes. The network uses Bayesian statistical methods to estimate event probabilities using existing observations. Within this framework, we forecast the spatial distribution of cliff retreat along two stretches of cliffed coast in Southern California. The input parameters are the height and slope of the cliff, a descriptor of material strength based on the dominant cliff-forming lithology, and the long-term cliff erosion rate that represents prior behavior. The model is forced using predicted wave impact hours. Results demonstrate that the Bayesian approach is well-suited to the forward modeling of coastal cliff retreat, with the correct outcomes forecast in 70–90% of the modeled transects. The model also performs well in identifying specific locations of high cliff erosion, thus providing a foundation for hazard mapping. This approach can be employed to predict cliff erosion at time-scales ranging from storm events to the impacts of sea-level rise at the century-scale

Review of Community Pharmacy Staff Educational Needs for Supporting Mental Health Consumers and Carers

Development of a mental health education package for community pharmacy staff should be informed by mental health consumers/carers’ needs, expectations and experiences, and staff knowledge, skills and attitudes. This review (1) explored research on community pharmacy practice and service provision for mental health consumers/carers, and (2) identified validated methods for assessing staff knowledge, skills and attitudes about mental illness to inform the development of a training questionnaire. A literature scan using key words knowledge, skills, attitudes, and beliefs combined with community pharmacy, pharmacist, and pharmacy support staff, and mental illness, depression, anxiety was conducted. A small number of studies were found that used reliable methods to assess pharmacists’ training needs regarding mental illness and treatment options. There was little published specifically in relation to depression and anxiety in community pharmacy practice. No studies assessed the training needs of pharmacy support staff. A systematic analysis of pharmacy staff learning needs is warranted