Multivariate rainfall disaggregation at a fine timescale

Abstract

A methodology for spatial-temporal disaggregation of rainfall is proposed. The methodology involves the combination of several univariate and multivariate rainfall models operating at different timescales, in a disaggregation framework that can appropriately modify outputs of finer timescale models so as to become consistent with given coarser timescale series. Potential hydrologic applications include enhancement of historical data series and generation of simulated data series. Specifically, the methodology can be applied to derive spatially consistent hourly rainfall series in rain gages where only daily data are available. In addition, in a simulation framework the methodology provides a way to take simulations of multivariate daily rainfall (incorporating spatial and temporal nonstationarity) and generate multivariate fields at fine temporal resolution. The methodology is tested via a case study dealing with the disaggregation of daily historical data of five rain gages into hourly series. Comparisons show that the methodology results in good preservation of important properties of the hourly rainfall process such as marginal moments, temporal and spatial correlations, and proportions and lengths of dry intervals as well as a good reproduction of the actual hyetographs.