Abstract
Empirical Bayes theory, adapted to a hydrologic context, is used to develop procedures for inferring hydrologic quantities by combining site‐specific and regional information. It ‘borrows strength’ from ‘similar’ basins to improve upon inference at a particular basin. The superpopulation is a key concept in the empirical Bayes approach. It is a probability distribution from which basin parameters are randomly assigned, a conceptualization closely related to regionalization models. It is inferred from observable regional data and expresses the degree of basin ‘similarity’ in a region. This approach treats regionalized estimators as a special case and leads to procedures similar to James‐Stein estimators. Empirical Bayes procedures can lead to substantial improvements in performance over site‐specific procedures. However, for basins which are very different from the majority, site‐specific procedures may perform better. The method of moments approach to inferring the superpopulation is considered in detail. Finally, two examples in flood frequency analysis are presented to illustrate various facets of empirical Bayes procedures.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
