Development of High Resolution Precipitation Extreme Dataset Using Spatial Interpolation Methods and Geostatistics in South Sulawesi, Indonesia
AbstractConsecutive Dry Days (CDD) is one of several precipitation extreme parameter suggested by Expert Team on Climate Change Detection and Indices (ETCDDI) to give a brief overview about climatic condition, especially related to climate change and drought occurrences. Daily precipitation data from observed Indonesia Agency for Meteorology, Climatology and Geophysics (BMKG) station network and Climate Hazzard group Infra-Red Precipitation with Station (CHIRPS) during 35 years (1981 – 2015) in South Sulawesi was used for CDD calculation. Three approaches were used for developing high resolution gridded (0.05o x 0.05o) precipitation extreme index over ungauged area, i.e. i) CHIRPS standardization with observed CDD (Std_CHIRPS); ii) spatial interpolation using nearest neighbours (NN) and invers distance weighted (IDW); and iii) geostatistics method using ordinary kriging (OK) and regression kriging (RK).Spatial and temporal assessments for each interpolation performance were done by applying leave one out cross validation method with actual CDD from observed station data. This study found that spatial and temporal distribution of CDD over the region are described by almost all interpolation methods, except Std_CHIRPS. Interpolation performance was reduced during La Niña periods or after El Niño years. Highest correlation coefficient value with lowest RMSE obtained by OK and RK. Nevertheless, RK shows closest standard deviation value compared to observation data. Better interpolation performance achieved by RK compare to another method for CDD in South Sulawesi.
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