Assessing flood risk at the global scale: model setup, results, and sensitivity

Globally, economic losses from flooding exceeded $19 billion in 2012, and are rising rapidly. Hence, there is an increasing need for global-scale flood risk assessments, also within the context of integrated global assessments. We have developed and validated a model cascade for producing global flood risk maps, based on numerous flood return-periods. Validation results indicate that the model simulates interannual fluctuations in flood impacts well. The cascade involves: hydrological and hydraulic modelling; extreme value statistics; inundation modelling; flood impact modelling; and estimating annual expected impacts. The initial results estimate global impacts for several indicators, for example annual expected exposed population (169 million); and annual expected exposed GDP ($1383 billion). These results are relatively insensitive to the extreme value distribution employed to estimate low frequency flood volumes. However, they are extremely sensitive to the assumed flood protection standard; developing a database of such standards should be a research priority. Also, results are sensitive to the use of two different climate forcing datasets. The impact model can easily accommodate new, user-defined, impact indicators. We envisage several applications, for example: identifying risk hotspots; calculating macro-scale risk for the insurance industry and large companies; and assessing potential benefits (and costs) of adaptation measures

Model fit after pairwise maximum likelihood

Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log--likelihoods of the bivariate response patterns is maximized instead. Little is yet known about how to assess model fit when the analysis is based on such a pairwise maximum likelihood (PML) of two--way contingency tables. We propose new fit criteria for the PML method and conduct a simulation study to evaluate their performance in model selection. With large sample sizes (500 or more), PML performs as well the robust weighted least squares analysis of polychoric correlations

Secondary production and energetics of the shrimp Caridina nilotica in Lake Victoria, East Africa: model development and application

Measurements of body mass, carbon content, respiration, growth, and egestion are combined in a model of secondary production by the tropical freshwater shrimp Caridina . The model is developed to permit its direct application to empirical data for abundances and size frequency distributions of field populations. Model calculations combined with population data for offshore Lake Victoria over a period of two years indicate that Caridina consume the equivalent of 2.2% of annual lake primary production. Present net annual secondary production by the shrimp is an order of magnitude greater than the present fishery yield of the lake. Detritus-fed experimental organisms evidently had assimilation efficiencies as low as 10% by model calculation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42892/1/10750_2004_Article_BF00031923.pd