Generating synthetic high resolution rainfall time series at sites with only daily rainfall using a master-target scaling approach

Abstract

Australia is typical of many countries in that it has an extensive network of rainfall recording stations which record rainfall data in various forms ranging from a daily time step down to 6-min resolution. However, while the length of historical daily records is often large, there are very few 6-min (pluviograph) records available of significant length. Not only does this lack of significant short time scale data impose a major obstacle in the application of a Monte Carlo approach to risk estimation, it also inhibits the application of rainfall simulation models that use this data for direct calibration. While the advent of numerous stochastic rainfall models provides methods for extending historical rainfall records, without adequate historical rainfall data available for calibration their accuracy is questionable.This paper describes the development of a new technique which significantly extends the applicability of stochastic point rainfall models that require historical data for calibration. The technique uses a new 'master-target' scaling relationship. A model calibration is undertaken at a 'master' site with a long pluviograph record, which is then scaled to the 'target' site using the information from the target site in form of either a short pluviograph or a daily rainfall record. This approach removes the need for significant pluviograph data at the 'target' site and enables the stochastic rainfall model to be applied at sites with either short pluviograph or daily rainfall records. The master-target scaling technique is demonstrated using an existing high-resolution point rainfall model based on wet-dry alternating storm events. Extensive testing using numerous pairs of Australian sites demonstrates its validity. © 2010 Elsevier B.V.