Abstract:
Inconsistencies and non-homogeneities in the hydrological and meteorological time series could be identified by incorporating statistical tests that detect trends and change points. Inconsistency which reflects systematic errors during recording and the non homogeneity that arises from either natural or man made changes to the gauging environment are both important for adequate time series analysis. It has also been identified that statistical tests together with physical or historical evidence and justifications from metadata need to be incorporated for a very detailed study. A case study was carried out for the rainfall data of Attanagalu Oya basin in the western province of Sri Lanka with a data set consisting of six stations having daily rainfall data for 30 years. According to Pettitt test, a significant change around 1977 & 1985 at Karasnagala and Pasyala could be found. However Pasyala is the most significant station for the change of rainfall pattern, which was confirmed by t-test. Knowledge of Meta data was found very important in order to make necessary corrections to shifts identified through Double Mass Analysis. This paper shows that statistical tests and rational judgements would enable suitable corrections even though it is common to find that most of the hydrological and meteorological data are either flagged for quality or poorly documented.
