Hybrid state-space model and adjusting procedure based on Bayesian approaches for spatio-temporal rainfall disaggregation

Disaggregation is the transforming process from highlevel scale data into low-level which preserves the consistency of the high-level statistic characteristics. This process, considering the dependence between spatial and temporal, is known as the spatio-temporal disaggregation. In general, this method is divided into two stages, namely the data modeling and preserving of consistency the high level scale statistic characteristics. This study proposes a hybrid model that combines a state-space model and adjusting procedure to disaggregate spatio-temporal rainfall through Bayesian approach using WinBUGS. The results show that the generated hourly rainfall data are consistent with the observed daily rainfall data at some locations which have only the daily rainfall data in the watershed Sampean, Bondowoso, Indonesia.