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  • Event and model dependent rainfall adjustments to improve discharge predictions

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Hydrological Sciences Journal on 28/04/2016, available online: http://www.tandfonline.com/10.1080/02626667.2016.1183775

    Accepted author manuscript, 921 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Event and model dependent rainfall adjustments to improve discharge predictions

Research output: Contribution to journalJournal article

Published
  • Diana Fuentes Andino
  • Keith John Beven
  • Anna Kauffeldt
  • Chong-Yu Xu
  • Sven Halldin
  • Giuliano Di Baldassarre
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<mark>Journal publication date</mark>01/2017
<mark>Journal</mark>Hydrological Sciences Journal
Issue number2
Volume62
Number of pages14
Pages (from-to)232-245
Publication statusPublished
Early online date28/04/16
Original languageEnglish

Abstract

Most conceptual rainfall–runoff models use as input spatially averaged rainfall fields which are typically associated with significant errors that affect the model outcome. In this study, it is hypothesized that a simple spatially and temporally averaged event–dependent rainfall multiplier can account for errors in the rainfall input. The potentials and limitations of this lumped multiplier approach are explored by evaluating the effects of multipliers on the accuracy and precision of the predictive distributions. Parameter sets found to be behavioural across a range of different flood events were assumed to be a good representation of the catchment dynamics and were used to identify rainfall multipliers for each of the individual events. An effect of the parameter sets on identified multipliers was found, however it was small compared to the differences between events. Accounting for event–dependent multipliers improved the reliability of the predictions. At the cost of a small decrease in precision, the distribution of identified multipliers for past events can be used to account for possible rainfall errors when predicting future events. By using behavioural parameter sets to identify rainfall multipliers, the method offers a simple and computationally efficient way to address rainfall errors in hydrological modelling.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in Hydrological Sciences Journal on 28/04/2016, available online: http://www.tandfonline.com/10.1080/02626667.2016.1183775