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Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios

Grabs, T. and Xu, C. Y. and Seibert, Jan and Reynolds, J. E. and Halldin, S. (2020). Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios. Hydrological Sciences Journal. 65 , 1512-1524
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Abstract

Discharge observations and reliable rainfall forecasts are essential for flood prediction but their availability and accuracy are often limited. However, even scarce data may still allow adequate flood forecasts to be made. Here, we explored how far using limited discharge calibration data and uncertain forcing data would affect the performance of a bucket-type hydrological model for simulating floods in a tropical basin. Three events above thresholds with a high and a low frequency of occurrence were used in calibration and 81 rainfall scenarios with different degrees of uncertainty were used as input to assess their effects on flood predictions. Relatively similar model performance was found when using calibrated parameters based on a few events above different thresholds. Flood predictions were sensitive to rainfall errors, but those related to volume had a larger impact. The results of this study indicate that a limited number of events can be useful for predicting floods given uncertain rainfall forecasts.

Authors/Creators:Grabs, T. and Xu, C. Y. and Seibert, Jan and Reynolds, J. E. and Halldin, S.
Title:Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios
Year of publishing :2020
Volume:65
Page range:1512-1524
Number of Pages:13
Publisher:Taylor & Francis
ISSN:0262-6667
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 105 Earth and Related Environmental Sciences > Oceanography, Hydrology, Water Resources
Keywords:floods, rainfall forecasts, rainfall-runoff modelling, event-based calibration, ungauged basins, value of information
URN:NBN:urn:nbn:se:slu:epsilon-p-105769
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-105769
Additional ID:
Type of IDID
DOI10.1080/02626667.2020.1747619
Web of Science (WoS)000532251900001
ID Code:17248
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Aquatic Sciences and Assessment
Deposited By: SLUpub Connector
Deposited On:10 Jul 2020 10:44
Metadata Last Modified:10 Jul 2020 10:44

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