Models in the implementation of the Water Framework Directive - benchmarking as part of the modelling proccess

The Water Framework Directive (WFD; European Commission 2000) is a framework for European environmental legislation that aims at improving water quality by using an integrated approach to implement the necessary societal and technical measures. Assessments to guide, support, monitor and evaluate policies, such as the WFD, require scientific approaches which integrate biophysical and human aspects of ecological systems and their interactions, as outlined by the International Council for Science (2002). These assessments need to be based on sound scientific principles and address the environmental problems in a holistic way. End-users need help to select the most appropriate methods and models. Advice on the selection and use of a wide range of water quality models has been developed within the project Benchmark Models for the Water Framework Directive (BMW). In this article, the authors summarise the role of benchmarking in the modelling process and explain how such an archive of validated models can be used to support the implementation of the WFD.

A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example

We present a method to integrate environmental time series into stock assessment models and to test the significance of correlations between population processes and the environmental time series. Parameters that relate the environmental time series to population processes are included in the stock assessment model, and likelihood ratio tests are used to determine if the parameters improve the fit to the data significantly. Two approaches are considered to integrate the environmental relationship. In the environmental model, the population dynamics process (e.g. recruitment) is proportional to the environmental variable, whereas in the environmental model with process error it is proportional to the environmental variable, but the model allows an additional temporal variation (process error) constrained by a log-normal distribution. The methods are tested by using simulation analysis and compared to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. In the traditional method, the estimates of recruitment were provided by a model that allowed the recruitment only to have a temporal variation constrained by a log-normal distribution. We illustrate the methods by applying them to test the statistical significance of the correlation between sea-surface temperature (SST) and recruitment to the snapper (Pagrus auratus) stock in the Hauraki Gulf–Bay of Plenty, New Zealand. Simulation analyses indicated that the integrated approach with additional process error is superior to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. The results suggest that, for the snapper stock, recruitment is positively correlated with SST at the time of spawning.