Rainfall estimation using raingages and radar - A Bayesian approach: 2. An application

D. J. Seo, James A. Smith

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

In Seo and Smith (this issue), a set of estimators was built in a Bayesian framework to estimate rainfall depth at an ungaged location using raingage measurements and radar rainfall data. The estimators are equivalent to lognormal co-kriging (simple co-kriging in the Gaussian domain) with uncertain mean and variance of gage rainfall. In this paper, the estimators are evaluated via cross-validation using hourly radar rainfall data and simulated hourly raingage data. Generation of raingage data is based on sample statistics of actual raingage measurements and radar rainfall data. The estimators are compared with lognormal co-kriging and nonparametric estimators. The Bayesian estimators are shown to provide some improvement over lognormal co-kriging under the criteria of mean error, root mean square error, and standardized mean square error. It is shown that, if the prior could be assessed more accurately, the margin of improvement in predicting estimation variance could be larger. In updating the uncertain mean and variance of gage rainfall, inclusion of radar rainfall data is seen to provide little improvement over using raingage data only.

Original languageEnglish (US)
Pages (from-to)31-44
Number of pages14
JournalStochastic Hydrology and Hydraulics
Volume5
Issue number1
DOIs
StatePublished - Mar 1 1991

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Environmental Chemistry
  • Modeling and Simulation
  • Water Science and Technology
  • Safety, Risk, Reliability and Quality
  • Ocean Engineering
  • Environmental Science(all)
  • Mechanical Engineering

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