PREDICTION OF DROUGHT IMPACT ON RICE PADDIES IN WEST JAVA USING ANALOGUE DOWNSCALING METHOD
Surmaini, Elza; Indonesian Agroclimate and Hydrology Research Institute
Hadi, Tri Wahyu; Faculty of Earth Sciences and Technology, Bandung Institute of Technology Jalan Ganesa No. 11, Bandung 40132, West Java, Indonesia
Subagyono, Kasdi; Indonesian Agency for Agricultural Research and Development Jalan Ragunan No. 29 Pasarminggu, South Jakarta 12540, Indonesia
Puspito, Nanang Tyasbudi; Faculty of Earth Sciences and Technology, Bandung Institute of Technology Jalan Ganesa No. 11, Bandung 40132, West Java, Indonesia
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Indonesia consistently experiences dry climatic conditions and droughts during El Niño, with significant consequences for rice production. To mitigate the impacts of such droughts, robust, simple and timely rainfall forecast is critically important for predicting drought prior to planting time over rice growing areas in Indonesia. The main objective of this study was to predict drought in rice growing areas using ensemble seasonal prediction. The skill of National Oceanic and Atmospheric Administration’s (NOAA’s) seasonal prediction model Climate Forecast System version 2 (CFSv2) for predicting rice drought in West Java was investigated in a series of hindcast experiments in 1989-2010. The Constructed Analogue (CA) method was employed to produce downscaled local rainfall prediction with stream function (y) and velocity potential (c) at 850 hPa as predictors and observed rainfall as predictant. We used forty two rain gauges in northern part of West Java in Indramayu, Cirebon, Sumedang and Majalengka Districts. To be able to quantify the uncertainties, a multi-window scheme for predictors was applied to obtain ensemble rainfall prediction. Drought events in dry season planting were predicted by rainfall thresholds. The skill of downscaled rainfall prediction was assessed using Relative Operating Characteristics (ROC) method. Results of the study showed that the skills of the probabilistic seasonal prediction for early detection of rice area drought were found to range from 62% to 82% with an improved lead time of 2-4 months. The lead time of 2-4 months provided sufficient time for practical policy makers, extension workers and farmers to cope with drought by preparing suitable farming practices and equipments.