Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations

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Title: Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations
Authors: Mockler, Eva M.Chun, K. P.Sapriza-Azuri, G.Bruen, MichaelWheater, H. S.
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Date: Nov-2016
Online since: 2016-11-16T17:03:48Z
Abstract: Predictions of river flow dynamics provide vital information for many aspects of water management including water resource planning, climate adaptation, and flood and drought assessments. Many of the subjective choices that modellers make including model and criteria selection can have a significant impact on the magnitude and distribution of the output uncertainty. Hydrological modellers are tasked with understanding and minimising the uncertainty surrounding streamflow predictions before communicating the overall uncertainty to decision makers. Parameter uncertainty in conceptual rainfall-runoff models has been widely investigated, and model structural uncertainty and forcing data have been receiving increasing attention. This study aimed to assess uncertainties in streamflow predictions due to forcing data and the identification of behavioural parameter sets in 31 Irish catchments. By combining stochastic rainfall ensembles and multiple parameter sets for three conceptual rainfall-runoff models, an analysis of variance model was used to decompose the total uncertainty in streamflow simulations into contributions from (i) forcing data, (ii) identification of model parameters and (iii) interactions between the two. The analysis illustrates that, for our subjective choices, hydrological model selection had a greater contribution to overall uncertainty, while performance criteria selection influenced the relative intra-annual uncertainties in streamflow predictions. Uncertainties in streamflow predictions due to the method of determining parameters were relatively lower for wetter catchments, and more evenly distributed throughout the year when the Nash-Sutcliffe Efficiency of logarithmic values of flow (lnNSE) was the evaluation criterion.
Funding Details: Ireland Canada University Foundation (ICUF)
Irish Environmental Protection Agency
Global Institute for Water Security
Type of material: Journal Article
Publisher: Elsevier
Journal: Advances in Water Resources
Volume: 97
Start page: 299
End page: 313
Copyright (published version): 2016 the Authors
Keywords: UncertaintyHydrological modellingRainfall modellingModel parametersPerformance criteria
DOI: 10.1016/j.advwatres.2016.10.008
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License:
Appears in Collections:Centre for Water Resources Research Collection
Civil Engineering Research Collection

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