Abstract
This paper is the second of two in the current issue that presents a framework for simulating continuous (uninterrupted) rainfall sequences at both gaged and ungaged locations. The ultimate objective of the papers is to present a methodology for stochastically generating continuous subdaily rainfall sequences at any location such that the statistics at a range of aggregation scales are preserved. In this paper we complete the regionalized algorithm by adopting a rationale for generating daily sequences at any location by sampling daily rainfall records from “nearby” gages with statistically similar rainfall sequences.The approach consists of two distinct steps: first the identification of a set of locations with daily rainfall sequences that are statistically similar to the location of interest, and second the development of an algorithm to sample daily rainfall from those locations. In the first step, the similarity between all bivariate combinations of 2708 daily rainfall records across Australia were considered, and a logistic regression model was formulated to predict the similarity between stations as a function of a number of physiographic covariates. Based on the model results, a number of nearby locations with adequate daily rainfall records are identified for any ungaged location of interest (the “target” location), and then used as the basis for stochastically generating the daily rainfall sequences. The continuous simulation algorithm was tested at five locations where long historical daily rainfall records are available for comparison, and found to perform well in representing the distributional and dependence attributes of the observed daily record. These daily sequences were then used to disaggregate to a subdaily time step using the rainfall state‐based disaggregation approach described in the first paper, and found to provide a good representation of the continuous rainfall sequences at the location of interest.
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