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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 articlepeer-review

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Event and model dependent rainfall adjustments to improve discharge predictions. / Andino, Diana Fuentes; Beven, Keith John; Kauffeldt, Anna; Xu, Chong-Yu; Halldin, Sven; Di Baldassarre, Giuliano.

In: Hydrological Sciences Journal, Vol. 62, No. 2, 01.2017, p. 232-245.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Andino, DF, Beven, KJ, Kauffeldt, A, Xu, C-Y, Halldin, S & Di Baldassarre, G 2017, 'Event and model dependent rainfall adjustments to improve discharge predictions', Hydrological Sciences Journal, vol. 62, no. 2, pp. 232-245. https://doi.org/10.1080/02626667.2016.1183775

APA

Andino, D. F., Beven, K. J., Kauffeldt, A., Xu, C-Y., Halldin, S., & Di Baldassarre, G. (2017). Event and model dependent rainfall adjustments to improve discharge predictions. Hydrological Sciences Journal, 62(2), 232-245. https://doi.org/10.1080/02626667.2016.1183775

Vancouver

Andino DF, Beven KJ, Kauffeldt A, Xu C-Y, Halldin S, Di Baldassarre G. Event and model dependent rainfall adjustments to improve discharge predictions. Hydrological Sciences Journal. 2017 Jan;62(2):232-245. https://doi.org/10.1080/02626667.2016.1183775

Author

Andino, Diana Fuentes ; Beven, Keith John ; Kauffeldt, Anna ; Xu, Chong-Yu ; Halldin, Sven ; Di Baldassarre, Giuliano. / Event and model dependent rainfall adjustments to improve discharge predictions. In: Hydrological Sciences Journal. 2017 ; Vol. 62, No. 2. pp. 232-245.

Bibtex

@article{c14660c994fd41c2a80566dbc4149666,
title = "Event and model dependent rainfall adjustments to improve discharge predictions",
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.",
keywords = "rainfall multiplier, rainfall input error, reliability of the predictions, precision of predictions, Topmodel, floods",
author = "Andino, {Diana Fuentes} and Beven, {Keith John} and Anna Kauffeldt and Chong-Yu Xu and Sven Halldin and {Di Baldassarre}, Giuliano",
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",
year = "2017",
month = jan,
doi = "10.1080/02626667.2016.1183775",
language = "English",
volume = "62",
pages = "232--245",
journal = "Hydrological Sciences Journal",
issn = "0262-6667",
publisher = "TAYLOR & FRANCIS LTD",
number = "2",

}

RIS

TY - JOUR

T1 - Event and model dependent rainfall adjustments to improve discharge predictions

AU - Andino, Diana Fuentes

AU - Beven, Keith John

AU - Kauffeldt, Anna

AU - Xu, Chong-Yu

AU - Halldin, Sven

AU - Di Baldassarre, Giuliano

N1 - 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

PY - 2017/1

Y1 - 2017/1

N2 - 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.

AB - 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.

KW - rainfall multiplier

KW - rainfall input error

KW - reliability of the predictions

KW - precision of predictions

KW - Topmodel

KW - floods

U2 - 10.1080/02626667.2016.1183775

DO - 10.1080/02626667.2016.1183775

M3 - Journal article

VL - 62

SP - 232

EP - 245

JO - Hydrological Sciences Journal

JF - Hydrological Sciences Journal

SN - 0262-6667

IS - 2

ER -