Improving parameter priors for data-scarce estimation problems

Susana Almeida*, Nataliya Bulygina, Neil McIntyre, Thorsten Wagener, Wouter Buytaert

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)6090-6095
Number of pages6
JournalWater Resources Research
Volume49
Issue number9
DOIs
Publication statusPublished - Sep 2013

Keywords

  • rainfall-runoff modeling
  • ungauged catchments
  • uncertainty
  • Bayesian
  • noninformative prior distribution
  • regionalization
  • RAINFALL-RUNOFF MODEL
  • UNGAUGED CATCHMENTS
  • PREDICTIONS
  • DISTRIBUTIONS
  • INFERENCE
  • BASINS

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