A Practical, Objective and Robust Technique to Directly Estimate Time of Concentration

Giulia Giani*, Miguel A Rico-Ramirez, Ross A Woods

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)


There is a wide range of techniques to estimate time of concentration (Tc), but the resulting estimates can differ by up to 500% (Grimaldi et al., 2012). When it comes to practical applications (e.g. hydrograph design), it is challenging to assess which estimate is the most reliable or method most appropriate, as all the techniques suffer from significant uncertainties. Here we present a practical, objective, and robust method to estimate Tc based on hourly rainfall and streamflow timeseries only, which removes most of the sources of uncertainties arising from other methodologies by minimizing the conceptual hypothesis and the choices the user has to make to apply the method. The proposed method, used originally in the field of economics to assess the temporal correlation between two variables, has been adapted to be used for the first time in the field of hydrology. The method does not make any assumption about the rainfall-runoff transformation, does not require event selection or parameter estimation, and it is easily reproducible (Python and Matlab functions available from https://github.com/giuliagiani/Tc_DMCA). The proposed method agrees well with the traditionally used method to estimate Tc from observed hyetographs and hydrographs (Spearman rank correlation r=0.82). We also show that the proposed method gives robust results for relatively short records (for catchments responding in less than 20 hours, a rainfall-streamflow timeseries length of 5 years provides a reliable Tc), and works in presence of noise and bias in the timeseries.
Original languageEnglish
JournalWater Resources Research
Publication statusIn preparation - 18 Jun 2020

Fingerprint Dive into the research topics of 'A Practical, Objective and Robust Technique to Directly Estimate Time of Concentration'. Together they form a unique fingerprint.

Cite this