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.
Highlights
Water resources development and management is heavily dependent on hydrological and meteorological data
Standard Normal Homogeneity Test (SNHT) & Pettitt test were chosen to identify any sudden shifts in the mean of the data sets thereby enabling the identification of change points
A critical probability level of 80% was chosen for acceptance of significant change points in the Pettitt test whereas critical confidence level of 90% was used in the Standard Normal Homogeneity test (SNHT) [3]
Summary
Water resources development and management is heavily dependent on hydrological and meteorological data. In hydrologic analysis it is customary to search for long datasets since such data ensures that the sample taken represents the system performance. Longer the time series the greater are the chances that the data series is neither stationary, consistent nor homogeneous. It is necessary to identify the spatial representation of the data used in an analysis. Spatial distribution of rain gauges is often non-representative since they are mostly located in the valleys where easy access is the main criteria. It has been identified that in many mountainous catchments, the higher elevations receive more precipitation than the regions in the valley [2]. Prior to a responsible hydrological analysis, a suitable spatial and temporal analysis of data needs to be carried out through an efficient screening procedure
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