Improving parameter priors for data-scarce estimation problems

Almeida, Susana, Bulygina, Nataliya, McIntyre, Neil, Wagener, Thorsten and Buytaert, Wouter (2013) Improving parameter priors for data-scarce estimation problems. Water Resources Research, 49 9: 6090-6095. doi:10.1002/wrcr.20437


Author Almeida, Susana
Bulygina, Nataliya
McIntyre, Neil
Wagener, Thorsten
Buytaert, Wouter
Title Improving parameter priors for data-scarce estimation problems
Journal name Water Resources Research   Check publisher's open access policy
ISSN 0043-1397
1944-7973
Publication date 2013-09
Year available 2013
Sub-type Article (original research)
DOI 10.1002/wrcr.20437
Volume 49
Issue 9
Start page 6090
End page 6095
Total pages 6
Place of publication Hoboken NJ USA
Publisher Wiley-Blackwell Publishing
Collection year 2014
Language eng
Abstract Runoff prediction in ungauged catchments is a recurrent problem in hydrology. Conceptual models are usually calibrated by defining a feasible parameter range and then conditioning parameter sets on observed system responses, e.g., streamflow. In ungauged catchments, several studies condition models on regionalized response signatures, such as runoff ratio or base flow index, using a Bayesian procedure. In this technical note, the Model Parameter Estimation Experiment (MOPEX) data set is used to explore the impact on model performance of assumptions made about the prior distribution. In particular, the common assumption of uniform prior on parameters is shown to be unsuitable. This is because the uniform prior on parameters maps onto skewed response signature priors that can counteract the valuable information gained from the regionalization. To address this issue, we test a methodological development based on an initial transformation of the uniform prior on parameters into a prior that maps to a uniform response signature distribution. We demonstrate that this method contributes to improved estimation of the response signatures.
Keyword rainfall-runoff modeling
ungauged catchments
uncertainty
Bayesian
noninformative prior distribution
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Centre for Water in the Minerals Industry
Official 2014 Collection
 
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