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
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -