Gauge based precipitation estimation and associated model and product uncertainties

Quanxi Shao*, Julien Lerat, Heron Brink, Kerrie Tomkins, Ang Yang, Luk Peeters, Ming Li, Lu Zhang, Geoff Podger, Luigi J. Renzullo

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Estimating areal precipitation and quantifying the associated uncertainties are important for both hydrological research and water resource management. However, many, if not all, precipitation products provide only the precipitation at reasonable spatial scales without uncertainty attached. In this paper, we promote a double smoothing technique to derive the precipitation amounts at small grid size based on gauge observations and then propose a bootstrap method to quantify the rainfall model estimation uncertainty (the uncertainty of rainfall estimation by a given model; here our model is double smoothing) by the traditional bootstrap for parameter uncertainty and the rainfall product uncertainty in term of prediction. As the residuals by the direct use of smoothing approach are heterogeneous, making the direct use of bootstrapping method invalid, we use an empirical transformation to stabilise the residuals. Furthermore, by using bootstrapping method, we can easily upscale the precipitation and the associate uncertainty to any required scales. The product is easy to use in research and practice. We demonstrate our methods by applying it to Murray Darling Basin in the eastern Australia.

Original languageEnglish
Pages (from-to)100-112
Number of pages13
JournalJournal of Hydrology
Volume444-445
DOIs
Publication statusPublished - 11 Jun 2012
Externally publishedYes

Keywords

  • Bootstrap
  • Nonparametric kernel smoothing
  • Rainfall product
  • Uncertainty estimation
  • Upscaling

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