F. Lombardo, E. Volpi, and D. Koutsoyiannis, Temporal disaggregation of rainfall, IDRA 2014 – XXXIV Conference of Hydraulics and Hydraulic Engineering, Bari, Italy, doi:10.13140/RG.2.2.32878.61768, 2014.
Temporal disaggregation models of rainfall aim at generating finer scale time series of rainfall that are fully consistent with any given coarse-scale totals. In this work, we present a disaggregation method that initially retains the formalism, the parameter set, and the generation routine of the downscaling model described by Lombardo et al (2012), which generates time series with Hurst-Kolmogorov (HK) dependence structure. Then it uses an adjusting procedure to achieve the full consistency of lower-level and higher-level variables without affecting the stochastic structure implied by the original downscaling model. Furthermore, we investigate how our simple and parsimonious model may account for rainfall intermittency, because the capability of disaggregation models to reproduce rainfall intermittency is a fundamental requirement in simulation. Intermittency is quantified by the probability that a time interval is dry . Here we focus on a modelling approach of a mixed type, with a discrete description of intermittency and a continuous description of rainfall. In other words, we model the intermittent rainfall process as the product of the following two stochastic processes: (i) The rainfall occurrence process, which is described by a binary valued stochastic process, with the values 0 and 1 representing dry and wet conditions, respectively; (ii) The non-zero rainfall process, which is given by our disaggregation model. We study the rainfall process as intermittent with both independent (Bernoullian) and dependent (Markovian) occurrences, where dependence is quantified by the probability that two consecutive time intervals are dry . In either case, we provide the analytical formulations of the main statistics of our mixed-type disaggregation model and show their clear accordance with Monte Carlo simulations.
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