Rainfall forecasting with climate change detection and its pattern relationship to rice production

Rainfall as one of main factor of climate change, has a significant effect to agriculture. Prediction modelling of rainfall intensity is used to identify and to provide rainfall future description as a reference of agriculture planning regarding to food security. This study aims to provide the modelling of rainfall intensity using Seasonal Autoregressive Integrated Moving Average. Climate change in the rainfall intensity is detected using the existence of heavy-tail phenomena and therefore a Generalized Pareto Distribution is used to model it. To know the relationship between the rainfall intensity and its impact to rice production, Copula method is used as the extension. The study found: SARIMA ([1; 2; 6]; 1; 0)(0; 1; 1)12 is the best model of the rainfall forecasting, climate change indication is detected but it occurs not significantly in area of Tabanan, Bali, and the relationship of the rainfall and the rice production indicates that a decreasing of rainfall intensity will lead to a decreasing rice production.