Review of The Search for a Common Language: Environmental Writing and Education Edited by Melody Graulich and Paul Crumbley

This edited collection grew out of a symposium held at Utah State University in Logan in 2002. According to the editors, the symposium's purpose was to "publicly explore the particular ways environmental writing educates the public through a fusion of science and literary expression." The Search for a Common Language achieves that purpose by including short prose pieces-ranging from memoirs, essays on specific locations, and scientific papers - as well as poetry on natural themes. The range of topics and genres and the inclusion of poetry provide a variety of ways to talk about the environment and reach out to different audiences to educate them about the natural world.

Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.