Simulation of climatic series with nonstationary trends and periodicities

A Gibbs sampling technique was recently proposed to model nonstationary trend and periodic components in monthly climatic series (). This paper describes the development of new techniques for simulating trends and periodicities estimated by the Gibbs procedure from observed rainfall data. The objective is to provide possible future climatic scenarios for planning, design and operation of water resource systems. In particular, trend is considered as an important indicator of climate change. Historical series of rainfall observations from Italy and Switzerland are used in this study. Support is found for the approach from the results of Mann–Kendall and associated tests for trend which do not provide evidence of long-term trends. Analysis of amplitudes and periodicities of waves in trend curves from Gibbs sampling highlights the diversity of the procedure.