Impacts of uncertain river flow data on rainfall-runoff model calibration and discharge predictions

Hilary McMillan, Jim Freer, Florian Pappenberger, Tobias Krueger, Martyn Clark

Research output: Contribution to journalArticle

169 Citations (Scopus)

Abstract

Abstract 10.1002/hyp.7587.abs In order to quantify total error affecting hydrological models and predictions, we must explicitly recognize errors in input data, model structure, model parameters and validation data. This paper tackles the last of these: errors in discharge measurements used to calibrate a rainfall-runoff model, caused by stage–discharge rating-curve uncertainty. This uncertainty may be due to several combined sources, including errors in stage and velocity measurements during individual gaugings, assumptions regarding a particular form of stage–discharge relationship, extrapolation of the stage–discharge relationship beyond the maximum gauging, and cross-section change due to vegetation growth and/or bed movement. A methodology is presented to systematically assess and quantify the uncertainty in discharge measurements due to all of these sources. For a given stage measurement, a complete PDF of true discharge is estimated. Consequently, new model calibration techniques can be introduced to explicitly account for the discharge error distribution. The method is demonstrated for a gravel-bed river in New Zealand, where all the above uncertainty sources can be identified, including significant uncertainty in cross-section form due to scour and re-deposition of sediment. Results show that rigorous consideration of uncertainty in flow data results in significant improvement of the model's ability to predict the observed flow. Copyright © 2010 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)1270-1284
Number of pages15
JournalHydrological Processes
Volume24
Issue number10
DOIs
Publication statusPublished - 2010

Keywords

  • gravel-bed river
  • River flow
  • rating curve
  • stage–discharge
  • model calibration
  • uncertainty

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