Streamflow variability and reconstruction for the Colorado River Basin

This paper focuses on the relationship between streamflow in the Colorado River and climate indicators such as the Multivariate ENSO Index (MEI) and tree-ring data. Improved procedures for streamflow reconstruction using tree-ring information are presented. Streamflow reconstruction is important for identification of historic severe-sustained droughts. Traditionally, Principal Components Analysis (PCA) and stepwise regression are used to form a transfer function (i.e., tree-ring information to reconstruct streamflow. However, PCA has several procedural choices that may result in very different reconstructions. This study assesses the different procedures in PCA-based regression and suggests alternative procedures for selection of variables and principal components. Cross validation statistics are presented as an alternative for independently testing and identifying the optimal model. The results show that a parsimonious model with a low mean square error can be obtained by using strict rules for principal component selection and cross validation statistics. Additionally, the procedure suggested in this study results in a model that is physically consistent with the relationship between the predictand and the predictor. The alternative PCA-based regression models presented here are applied to the reconstruction of the upper Colorado River Basin streamflow and compared with results of a previous reconstruction using traditional procedures. The results have implications for determining the worst scenario to be used for planning and the allocation of water supply in the Colorado River basin during a severe-sustained drought