Predictive mapping of the natural flow regimes of France

Abstract

Hydrologic variability is important in sustaining a variety of ecological processes in streams and rivers. Natural flow regime classifications group streams and rivers that are relatively homogeneous with respect to flow variability and have been promoted as a method of defining units for management of river flows. Although there has been considerable interest in classifying natural flow regimes, there has been less emphasis given to developing accurate methods of extrapolating these classifications to locations without flow data. We developed a method of mapping flow regime classes using boosted regression trees (BRT) that automatically fits non-linear functions and interactions between explanatory variables of flow regimes, both of which can be expected when comparing responses between complex systems such as watersheds. A natural flow regimes classification of continental France was developed from cluster analysis of 157 hydrological indices derived from 763 gauging stations representing unmodified flows. BRT models were used to predict the likelihood of gauging stations belonging to each class based on the watershed characteristics. These models were used to extrapolate the natural flow regime classification to all segments of a national river network. The performance of the BRT models were compared with other methods of assigning locations to flow regime classes, including the use of geographically contiguous regions, linear discriminant analysis (LDA) and classification and regression trees (CART). The “fitted” misclassification rate (associated with model fits) for assignment based on the BRT models was 13% whereas the fitted misclassification rates for geographically contiguous regions, LDA and CART were 52%, 44% and 39% respectively. A “predictive” misclassification rate (calculated for new cases) was estimated for assignments based on the BRT, LDA and CART models using cross validation analysis. For assignment based on the BRT models, the mean predictive misclassification rate was 37% whereas for LDA and CART it was 45% and 64% respectively. Our method of mapping flow regimes could increase the confidence in decisions associated with setting environmental flows and the ability to undertake broad-scale eco-hydrological research.