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Precipitation is a highly important and a key input for many hydrological and ecological models, including water resource management, recharge assessment, irrigation scheduling, vegetation, and crop production models. However, rainfall records are often incomplete due to the missing rainfall data in the measured period or insufficient stations in the study region. The spatial interpolation method is one of the most common technique, and it is frequently used to estimate values of meteorological parameters in locations where they are not measured or missing. The use of different interpolation schemes in the same catchment may cause significant differences and deviations from the actual spatial distribution of rainfall. It is needed to compare the performances of each interpolation method to get excellent interpolation technique. GIS is offering the vast rage range of statistical methods to interpolate precipitation based on data recorded at several irregularly spaced gauges. In this analysis, five methods were used to predict monthly precipitation, and each technique was compared.
Über den Autor / die Autorin
Ajith L. K. Wijemannage, MSc in Meteorology in University of the Philippines, MSc in Geoinformatics in University of Colombo, Sri Lanka, Director, Department of Meteorology, Sri Lanka. Manjula Ranagalage, (BA, MA, MSc in GIS & Remote Sensing) Senior Lecturer, Rajarata University of Sri Lanka, Mihintale, Sri Lanka.
