Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources

Interpolating climatic variables such as rainfall is challenging due to the highly variable nature of meteorological processes, the effects of terrain and geography, and the difficulty in establishing a representative network of stations. While interpolation models are being adapted to include these effects, often the rainfall data contain significant gaps in coverage. In this paper, we evaluated rainfall data from an agro-ecological monitoring network for producing maps of total monthly rainfall in Sri Lanka. We compared four spatial interpolation techniques: inverse distance weighting, thin-plate splines, ordinary kriging, and Bayesian kriging. Error metrics were used to validate interpolations against independent data. Satellite data were used to assess the spatial pattern of rainfall. Results indicated that Bayesian kriging and splines performed best in low and high rainfall, respectively. Rainfall maps generated from the agro-ecological network were found to have accuracies consistent with previous studies in Sri Lanka. Rainfall data from an agro-ecological monitoring network were evaluated for producing maps of monthly rainfall in Sri Lanka.Inverse distance weighting, thin-plate splines, ordinary kriging, and Bayesian kriging were compared.Error metrics and the structural similarity index were employed to validate interpolations against independent data.Bayesian kriging and splines predicted the most accurately in low and high rainfall conditions, respectively.Interpolated rainfall predictions were found to be as accurate as previous studies in Sri Lanka.