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El Nino events have been frequently marked by drought occurrences with severe consequences for agricultural production in Indonesia. Paddy drought occurs almost every year and extends during El Nino phenomena. The Nino 3.4 index is commonly used as an important tool for managing a food security policy. However, there are no details regarding the impact of El Nino on drought-induced paddy damage. We developed the Paddy Drought Impact Index (PDII), which is the ratio of drought-induced paddy damaged area to the total paddy area planted in order to investigate the impact of drought on paddies among 335 districts in Indonesia. Unlike other agricultural drought indices, this index represents real-life percentage of drought-induced paddy damage to indicate each district’s relative severity to drought, which can be easily understood by practical users. The connection between the Nino 3.4 index and PDII was assessed using cross correlation analysis. Scatter plots of best lag time Nino 3.4 index against PDII were examined. The findings show that with 2 months lag of Nino 3.4 prior to PDII, March and June Nino 3.4 indices can be used to predict May–July and August–October PDII, respectively. Critical thresholds of the March Nino 3.4 index were found to range from 0.0 to 0.5 °C, which is associated with a 0.57 probability of weak El Nino occurrence during the subsequent 5 months. On the other hand, a higher probability of 0.67 for occurrences of moderate El Nino is associated with the critical thresholds of June Nino 3.4 index, which ranges from 0.5–1.0 °C. This study has found that the potential impact of drought due to the weak and moderate El Nino occurrences in Indonesia is such that yields are reduced by about 40 % in average. We also found that the most drought-prone areas are located in West Java for both May–July and August–October and in South Sulawesi for August–October.

Droughts have severe consequences for rice crops in Indonesia, as these occur annually and increase during El Niño phenomena. Accordingly, paddy drought assessment is necessary for developing adaptation strategies for successful crop production. We conducted a detailed assessment of paddy drought-climate indices in Indonesia. This was done by looking at the onset and trends of the Standardized Precipitation Index (SPI) in a three-month time scale (SPI-3) and exploring their relationship with paddy drought-affected areas. The Cartesian quadrant was used to illustrate the combination of SPI-3 onset and trend on the paddy drought affected area. This gave four different drought risk levels: low, moderate, high, and very high. The hit rate (HR) as the proportion of drought occurrences were correctly hindcast and percent of correct (PC) as the total number of correct hindcast divided by the total number of hindcast was used to verify the accuracy of drought effect prediction on paddy rice. The results demonstrate that the highest accuracy of paddy drought predictions occurred in the peak of dry season in July, while the accuracy of drought and non-drought occurrences (PC) was higher in the non-peak months, April through September, excluding July.

Droughts and floods due to extreme climate events has caused yield loss in various regions of Indonesia, including the Provinces of Aceh and North Sumatra. An early detection model needs to be developed to anticipate the negative impacts of extreme climate event. The model may describe the association of surplus and rainfall deficits with paddy damage due to drought and flood. We used Standardized Precipitation Index (SPI) to explore drought and flood characteristics in period 1989-2016. The study aimed: (i) to analyze the relationship between SPI and paddy damage due to drought and flood events, (ii) to analyze the critical value of the duration and intensity of SPI which causes paddy damage, and (iii) to determine which districts were prone to drought and flood in the Provinces of Aceh and North Sumatra. The results concluded that SPI-3 and -6 months can better describe the frequency of drought and rice flooding. In addition, drought on paddy occured mostly if the SPI was smaller than -1 which took place within 4-5 months, whereas flood occured if the SPI was greater than 1. Short duration drought (2-3 months) were observed in five districts in Aceh (2) and North Sumatra (3). On other hand, more flood districts were identified (9 districts).

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.