Application of Consecutive Dry Days (CDD) Multi-Model Ensemble (MME) Prediction to Support Agricultural Sector in South Sulawesi Rice Production Centers

Abstract

Abstract Sufficient water availability during the vegetative, reproductive, and early ripening phases of the rice plants is essential. Information on drought, such as Consecutive Dry Days (CDD) predictions in this period, became very crucial and had an important role in maintaining rice production stability. The aim of this study is to investigate the performance of CDD Multi-Model Ensemble prediction, which is applied to South Sulawesi rice production centers. CDD observation was calculated using high resolution gridded precipitation blending data, obtained from BMKG precipitation network stations and the daily-improved Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) version 2.0. The North American Multi-Model Ensemble (NMME) monthly precipitation hindcast data during 1982 – 2010 periods from each nine individual global climate models were used to develop seasonal CDD predictions. World Meteorological Organization (WMO) Standard Verification for Long Range Forecast (SVS-LRF) method applied to describe this CDD prediction performance on four different seasons. Investigation of model performance during strong El Niño event in 1997 also conducted in order to get general skill overview regarding extreme climate event. Best performance of CDD prediction generally occurred during JJA and DJF period. MME CDD prediction shows better performance compared to individual model performance for almost all season. Spatial coherence between prediction and observation over rice production centers during 1997 El Niño confirms the skill of CDD predictions. The application of this prediction on agricultural sector will be very useful in order to support rice production sustainability and food security. Further analysis result can be found on full paper.