Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/75420
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Type: Journal article
Title: Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models
Author: McMillan, H.
Jackson, B.
Clark, M.
Kavetski, D.
Woods, R.
Citation: Journal of Hydrology, 2011; 400(1-2):83-94
Publisher: Elsevier Science BV
Issue Date: 2011
ISSN: 0022-1694
1879-2707
Statement of
Responsibility: 
Hilary McMillan, Bethanna Jackson, Martyn Clark, Dmitri Kavetski, Ross Woods
Abstract: This paper presents an investigation of rainfall error models used in hydrological model calibration and prediction. Traditional calibration methods assume input error to be negligible: an assumption which can lead to bias in parameter estimation and compromise model predictions. In response, a growing number of studies now specify an error model for rainfall input, usually simple in form due to both difficulties in understanding sampling errors in rainfall, and to computational constraints during parameter estimation. Such rainfall error models have not typically been validated against experimental evidence. In this study we use data from a dense gauge/radar network in the Mahurangi catchment (New Zealand) to directly evaluate the form of basic statistical rainfall error models. For this catchment, our results confirm the suitability of a multiplicative error formulation for correcting mean catchment rainfall values during high-rainfall periods (e.g., intensities over 1. mm/h); or for longer timesteps at any rainfall intensity (timestep 1. day or greater). We show that the popular lognormal multiplier distribution provides a relatively close approximation to the true error characteristics but does not capture the distribution tails, especially during heavy rainfall where input errors would have important consequences for runoff prediction. Our research highlights the dependency of rainfall error structure on the data timestep. © 2011 Elsevier B.V.
Keywords: Hydrology
Rainfall
Radar
Error models
Input uncertainty
Rights: Copyright © 2011 Elsevier B.V. All rights reserved.
DOI: 10.1016/j.jhydrol.2011.01.026
Appears in Collections:Aurora harvest 4
Civil and Environmental Engineering publications
Environment Institute publications

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