Assessment of flood damages and benefits of remedial actions: "What are the weak links?"; with application to the Loire

Flood damage models are used to determine the impact of measures to reduce damage due to river flooding. Such models are characterized by uncertainty. This uncertainty may affect the decisions made on the basis of the model outcomes. To reduce uncertainty effectively, the most important sources of uncertainty must be found. Uncertainty analysis serves this purpose.\ud \ud By way of a questionnaire experts were asked about their judgment of the significance of uncertainty sources in flood damage assessment. The results of this questionnaire are compared to an uncertainty analysis by Monte Carlo Simulation, which Torterotot (1993) applied to the French model CIFLUPEDE.\ud \ud The paper concludes that the role of uncertainty in flood damage assessment is highly significant and cannot be neglected. Both the experts and the analysis on the flood damage assessment model indicate the hydrologic relations ‘frequence of occurrence — river discharge — river water level’ and the damage estimates as the most important uncertainty sources. For embanked rivers dike breach is the most significant uncertainty source.\ud \ud A question which appears is, taking into account these uncertainties, to what level of precision can flood damage assessment models predict the expected annual flood damage and the costs and revenues of flood alleviation measures? It is of importance to explore the boundaries of flood damage modeling and to try to find ways to move these boundaries. The uncertainty analysis presented in this paper can be seen as one more step on the way to this goal

Are geometric morphometric analyses replicable? Evaluating landmark measurement error and its impact on extant and fossil Microtus classification.

Geometric morphometric analyses are frequently employed to quantify biological shape and shape variation. Despite the popularity of this technique, quantification of measurement error in geometric morphometric datasets and its impact on statistical results is seldom assessed in the literature. Here, we evaluate error on 2D landmark coordinate configurations of the lower first molar of five North American Microtus (vole) species. We acquired data from the same specimens several times to quantify error from four data acquisition sources: specimen presentation, imaging devices, interobserver variation, and intraobserver variation. We then evaluated the impact of those errors on linear discriminant analysis-based classifications of the five species using recent specimens of known species affinity and fossil specimens of unknown species affinity. Results indicate that data acquisition error can be substantial, sometimes explaining >30% of the total variation among datasets. Comparisons of datasets digitized by different individuals exhibit the greatest discrepancies in landmark precision, and comparison of datasets photographed from different presentation angles yields the greatest discrepancies in species classification results. All error sources impact statistical classification to some extent. For example, no two landmark dataset replicates exhibit the same predicted group memberships of recent or fossil specimens. Our findings emphasize the need to mitigate error as much as possible during geometric morphometric data collection. Though the impact of measurement error on statistical fidelity is likely analysis-specific, we recommend that all geometric morphometric studies standardize specimen imaging equipment, specimen presentations (if analyses are 2D), and landmark digitizers to reduce error and subsequent analytical misinterpretations

Molecular evolution in protobiological systems Final report, Dec. 1, 1961 - Nov. 30, 1964

Arc discharge of irradiated mixtures of simple organic and inorganic compounds for development of theory of molecular evolution in protobiological system