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

Authors

Vincent Garreta, CEREGE, UMR 6635, CNRS, Universit´e Aix-Marseille, Europˆole de l’Arbois, 13545 Aix-en-Provence, France
Pascal Monestiez, INRA, Unit´e de Biostatistique et Processus spatiaux, Domaine St Paul, Site Agroparc, 84914 Avignon Cedex 9, France
Jay M. Ver Hoef, NOAA National Marine Mammal Lab, Alaska Fisheries Science Center

Date of this Version

2009

Comments

Published in Environmetrics (2009).

Abstract

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.