Stochastic simulators can effectively generate the intrinsic variability of the rainfall process,which is an important issue in the analysis of the projections uncertainties. In this paper, a procedurefor stochastic modeling of precipitation at monthly scale is proposed. The model adopts variabletransformations, which are finalized to the deseasonalization and the Gaussianization of the monthlyrainfall process, and includes a procedure for testing the autocorrelation. The model was appliedto a homogeneous database of monthly rainfall values registered in 12 rain gauges in the region ofCalabria (Southern Italy). After the estimation of the model parameters, a set of 10^4 years of monthlyrainfall for each rain gauge was generated by means of a Monte Carlo technique. Then, dry andwet periods were analyzed through the application of the standardized precipitation index (SPI).Some results, confirmed through the application of the drought severity index (DSI), showed thatthe proposed model provided a good representation of the monthly rainfall for the considered raingauges. Moreover, the results of the SPI application indicate a greater probability of dry conditionsthan wet conditions, especially when long-term precipitation patterns are considered.
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|Titolo:||An Analysis of the Occurrence Probabilities of Wet and Dry Periods through a Stochastic Monthly Rainfall Model|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||1.1 Articolo in rivista|