Uncertainty and Bias in Global to Regional Scale Assessments of Current and Future Coastal Flood Risk.

This study provides a literature-based comparative assessment of uncertainties and biases in global to world-regional scale assessments of current and future coastal flood risks, considering mean and extreme sea-level hazards, the propagation of these into the floodplain, people and coastal assets exposed, and their vulnerability. Globally, by far the largest bias is introduced by not considering human adaptation, which can lead to an overestimation of coastal flood risk in 2100 by up to factor 1300. But even when considering adaptation, uncertainties in how coastal societies will adapt to sea-level rise dominate with a factor of up to 27 all other uncertainties. Other large uncertainties that have been quantified globally are associated with socio-economic development (factors 2.3-5.8), digital elevation data (factors 1.2-3.8), ice sheet models (factor 1.6-3.8) and greenhouse gas emissions (factors 1.6-2.1). Local uncertainties that stand out but have not been quantified globally, relate to depth-damage functions, defense failure mechanisms, surge and wave heights in areas affected by tropical cyclones (in particular for large return periods), as well as nearshore interactions between mean sea-levels, storm surges, tides and waves. Advancing the state-of-the-art requires analyzing and reporting more comprehensively on underlying uncertainties, including those in data, methods and adaptation scenarios. Epistemic uncertainties in digital elevation, coastal protection levels and depth-damage functions would be best reduced through open community-based efforts, in which many scholars work together in collecting and validating these data.

The adsorption of dabigatran is as efficient as addition of idarucizumab to neutralize the drug in routine coagulation assays.

INTRODUCTION: The direct thrombin inhibitor dabigatran interferes with thrombophilia screening and with the diagnosis of hemostasis disorders that develop during treatment with the anticoagulant. In vitro addition of idarucizumab, a humanized antibody fragment that binds dabigatran, to plasma samples containing dabigatran fully neutralizes the drug. This study was carried out to determine whether binding of dabigatran on selected insoluble commercial adsorbent material, DOAC-STOPR , was as efficient as idarucizumab to neutralize the anticoagulant activity of the drug in vitro. METHODS: Coagulation assays sensitive to dabigatran were carried out with patient and control plasma samples spiked with dabigatran and supplemented with idarucizumab or incubated with adsorbent material. RESULTS: In samples containing upto 10 000 ng/mL dabigatran, the adsorption procedure was at least as efficient as the addition of idarucizumab to neutralize the activity of the anticoagulant drug. Neither the adsorption procedure nor the addition of idarucizumab did impair routine coagulation assays carried out with plasma devoid of dabigatran, such as the activated partial thromboplastin time, prothrombin time, fibrinogen Clauss, and the thrombophilia screening assays used to detect antiphospholipid antibodies or activated protein C resistance. In addition, the adsorption procedure did not interfere with the detection of lupus anticoagulant samples. CONCLUSIONS: Adsorption of dabigatran in plasma samples containing the drug neutralizes its activity as efficiently as the addition of idarucizumab. This method allows the evaluation of thrombophilia markers without interruption of anticoagulation therapy or the detection of hemostasis disorders in patients treated with the drug.

Application of a Bayesian approach to stochastic delineation of capture zones.

This paper presents a Bayesian Monte Carlo method for evaluating the uncertainty in the delineation of well capture zones and its application to a wellfield in a heterogeneous, multiaquifer system. In the method presented, Bayes' rule is used to update prior distributions for the unknown parameters of the stochastic model for the hydraulic conductivity, and to calculate probability-based weights for parameter realizations using head residuals. These weights are then assigned to the corresponding capture zones obtained using forward particle tracking. Statistical analysis of the set of weighted protection zones results in a probability distribution for the capture zones. The suitability of the Bayesian stochastic method for a multilayered system is investigated, using the wellfield Het Rot at Nieuwrode, Belgium, located in a three-layered aquifer system, as an example. The hydraulic conductivity of the production aquifer is modeled as a spatially correlated random function with uncertain parameters. The aquitard and overlying unconfined aquifer are assigned random, homogeneous conductivities. The stochastic results are compared with deterministic capture zones obtained with a calibrated model for the area. The predictions of the stochastic approach are more conservative and indicate that parameter uncertainty should be taken into account in the delineation of well capture zones.