Optimization of a similarity measure for estimating ungauged streamflow

Abstract

One approach to predicting streamflow in an ungauged catchment is to select an ensemble of hydrological models previously identified for similar gauged catchments, where the similarity is based on some combination of important physical catchment attributes. The focus of this paper is the identification of catchment attributes and optimization of a similarity measure to produce the best possible ungauged streamflow predictions given a data set and a conceptual model structure. As a case study, the SimHyd rainfall-runoff model is applied to simulate monthly streamflow in 184 Australian catchments. Initial results show that none of 27 catchment attributes can be safely said to consistently give a better ensemble of models than random selection when used independently of other attributes. This is contrary to prior expectations and indicates the sparseness of information within our database of catchments, the importance in this case of prior knowledge for defining important attributes, and the potential importance of combining multiple attributes in order to usefully gauge similarity. Seven relatively independent attributes are then selected on the basis of prior knowledge. The weight with which each of these attributes contributes to the similarity measure is optimized to maximize streamflow prediction performance across a set of 95 catchments. The other 89 catchments are used to independently test the accuracy of streamflow predictions. Using the optimal set of weights led to marked improvement in the accuracy of predictions, showing that the method, while inferior to local calibration, is superior to alternative methods of model regionalization based on regression and spatial proximity. However, there is evidence of nonuniqueness in the optimal solution and the possibility that the attribute weights are somewhat dependent on the catchments used. Copyright 2009 by the American Geophysical Union.