STATISTICAL DOWNSCALING MODEL WITH PRINCIPAL COMPONENT REGRESSION AND LATENT ROOT REGRESSION TO FORECAST RAINFALL IN PANGKEP REGENCY

Abstract

Climate information, especially rainfall, is needed by various sectors in Indonesia, including the marine and fisheries sectors. Estimating high-resolution climate models continues to develop by involving global-scale climate variables, one of which is the global circulation model (GCM) output precipitation. Statistical downscaling (SD) relates global scale climate variables to local scales. Principal component regression (PCR) and latent root regression (LRR) techniques are statistical methods used in the SD model to overcome the high correlation between GCM data grids. PCR focuses on the variability in the predictor variables, while the LRR focuses on the variability between the response variables and predictors. This method was applied to Pangkep Regency rainfall data as a local scale response variable and GCM precipitation as a predictor variable (January 1999 to December 2020). This study aimed to obtain the number of principal component (PC) in the SD model and the forecast value of the 2020 rainfall data. In addition, the dummy variable resulting from K-means was used as a predictor variable in PCR and LRR. The result is that using the first 11-15 PC has a cumulative diversity proportion of 98%. Furthermore, by using the data for the 1999-2019 period, adding a dummy variable to the PCR can increase the accuracy of the model (the coefficient of determination is 92.27%-92.43%). However, LRR with and without dummy variables produces relatively the same model accuracy. In general, the LRR model is better at explaining the diversity of the Pangkep District rainfall data than the PCR model. The prediction of rainfall for the 2020 period at LRR with 13 PC is an accurate prediction based on the highest correlation value (0.97) and the lowest root mean square error prediction (75.17).