Identification of inhomogeneities in precipitation time series using stochastic simulation

Accurate quantification of observed precipitation variability is required for a number of purposes. However, high quality data seldom exist because in reality many types of non-climatic factors can cause time series discontinuities which may hide the true climatic signal and patterns, and thus potentially bias the conclusions of climate and hydrological studies. We propose the direct sequential simulation (DSS) approach for inhomogeneities detection in precipitation time series. Local probability density functions, calculated at known monitoring stations locations, by using spatial and temporal neighbourhood observations, are used for detection and classification of inhomogeneities. This stochastic approach was applied to four precipitation series using data from 62 surrounding stations located in the southern region of Portugal (1980-2001). Among other tests, three well established statistical tests were also applied: the Standard normal homogeneity test (SNHT) for a single break, the Buishand range test and the Pettit test. The inhomogeneities detection methodology is detailed, and the results from the testing procedures are compared and discussed.