Hydrograph matching method for measuring model performance

Despite all the progress made over the years on developing automatic methods for analysing hydrographs and measuring the performance of rainfall-runoff models, automatic methods cannot yet match the power and flexibility of the human eye and brain. Very simple approaches are therefore being developed that mimic the way hydrologists inspect and interpret hydrographs, including the way that patterns are recognised, links are made by eye, and hydrological responses and errors are studied and remembered. In this paper, a dynamic programming algorithm originally designed for use in data mining is customised for use with hydrographs. It generates sets of rays that are analogous to the visual links made by the hydrologist's eye when linking features or times in one hydrograph to the corresponding features or times in another hydrograph. One outcome from this work is a new family of performance measures called visual performance measures. These can measure differences in amplitude and timing, including the timing errors between simulated and observed hydrographs in model calibration. To demonstrate this, two visual performance measures, one based on the Nash-Sutcliffe Efficiency and the other on the mean absolute error, are used in a total of 34 split-sample calibration-validation tests for two rainfall-runoff models applied to the Hodder catchment, northwest England. The customised algorithm, called the Hydrograph Matching Algorithm, is very simple to apply; it is given in a few lines of pseudocode. (C) 2011 Elsevier ay. All rights reserved.