The Guyana coastal system is characterized by very thick deposits of Amazon mud and high mud concentrations in its coastal waters. The mud deposits can be quite soft and may liquefy under incoming waves. Subsequently, the liquefied mud damps the incoming waves effectively. This paper presents a simple model to predict wave attenuation over soft (fluid) mud beds. This model is based on the two-layer approach by Gade [Gade, H.G., 1958, Effects of a non-rigid, impermeable bottom on plane surface waves in shallow water, Journal of Marine Research, 16 (2) 61-82.] which is implemented in the standard version of the state-of-the-art wave-prediction model SWAN. Input to the mud wave damping module consists of the extension, thickness, density and viscosity of the liquefied (fluid) mud layer. The model is validated against small-scale wave attenuation measurements carried out in a laboratory wave flume. The model predictions agree favourably with the experimental data. Next, the model is applied to predict wave height and wave attenuation in the Guyana coastal system. Extension and thickness of the liquefiable layer could be assessed from dual-frequency echo soundings. In the absence of field data, the density of the liquefied mud layer is obtained from literature, whereas the value of the mud's viscosity had to be established by trial and error - the selected value, though, is in the range of literature values. The model predicts significant wave attenuation. The computed changes in wave energy spectrum agree qualitatively with measurements in Surinam, whereas the decrease in significant wave height agrees more or less with historic observations along the Guyana coast.@en

Quantitative Risk Analysis (QRA) is a standard tool in some high-risk industries (such as the on- and offshore exploration and production and chemical industry). Presently, existing knowledge concerning human error likelihood and human reliability assessment is insufficiently represented in QRAs. In this paper we attempt to implement the quantification of the human factors in a QRA, which we call QRA+. We analysed a specific incident scenario: the risk of overfilling chemical storage tanks that operate at atmospheric pressure. This scenario was chosen because it is a relevant example of a high-risk scenario in the chemical industry. We identified relevant technological and human parameters within this scenario through on-site visits and interviews with site-experts. The quantitative knowledge concerning the technological parameters was obtained from officially documented SIL statistics, whereas the Standardized Plant Analysis Risk-Human Reliability analysis (SPAR-H) was used to quantify the human factors. Beta distributions were used to model failure probability distributions to account for the uncertainty inherent in dealing with human reliability. For seamless integration of existing qualitative and quantitative knowledge, we made use of a Bayesian Belief Network. The resulting model provides an integrated and more accurate estimation of the failure probabilities for both technological and human factors and the uncertainty surrounding such probability estimates. Furthermore, it gives insight in where these failure probabilities originate and how they interact. This will allow companies to identify those parameters they need to influence to get optimal results concerning their management of risk.@en