Article

Journal of Agricultural, Biological, and Environmental Statistics

, Volume 11, Issue 2, pp 127-150

Spatial prediction on a river network

  • Noel CressieAffiliated withDepartment of Statistics, The Ohio State University Email author 
  • , Jesse FreyAffiliated withDepartment of Mathematical Sciences, Villanova University
  • , Bronwyn HarchAffiliated withCSIRO Mathematical & Information Sciences, Queensland Bioscience Precinct
  • , Mick SmithAffiliated withEnvironmental Branch, Maroochy Shire Council

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Abstract

This article develops methods for spatially predicting daily change of dissolved oxygen (Dochange) at both sampled locations (134 freshwater sites in 2002 and 2003) and other locations of interest throughout a river network in South East Queensland, Australia. In order to deal with the relative sparseness of the monitoring locations in comparison to the number of locations where one might want to make predictions, we make a classification of the river and stream locations. We then implement optimal spatial prediction (ordinary and constrained kriging) from geostatistics. Because of their directed-tree structure, rivers and streams offer special challenges. A complete approach to spatial prediction on a river network is given, with special attention paid to environmental exceedances. The methodology is used to produce a map of Dochange predictions for 2003. Dochange is one of the variables measured as part of the Ecosystem Health Monitoring Program conducted within the Moreton Bay Waterways and Catchments Partnership.

Key Words

Covariance-matching constrained kriging Dissolved oxygen Ordinary kriging Process-convolution model River monitoring network Spatial moving average