This paper discusses the result of the development of a hydrometeorological hazard early warning system (H-MHEWS) that combines weather prediction from Weather Research and Forecasting (WRF) and the hydrometeorological hazard index from the National Disaster Management Authority (BNPB), Indonesia. In its current development phase, the hazards that H-MHEWS predicts are floods, landslides, and extreme weather events. Potential hazard indices are obtained by using an overlay approach and resampling so that the data have a 100-m spatial resolution. All indices are classified into 4 status categories: "No alert", "Advisory", "Watch", and "Warning". Flood potential is produced by overlaying rainfall prediction at 3hour intervals with the flood index. Landslide potential is produced by overlaying rainfall prediction with the landslide index. Extreme weather potential is divided into 3 categories, i.e. heavy rain, strong winds, and extreme ocean waves. The whole prediction is dynamic, following weather predictions at 3-hour intervals. The hazard prediction results will trigger a 'Warning' alert in case of emergency status. This alert will be set up in a notification system to make it easier for the user to identify the most dangerous hydrometeorological hazard events.

Aquacrop is free-licensed Food and Agricultural Organization’s crop modelling that requires minimum inputs of climate variables namely rainfall, maximum temperature, minimum temperature variables and geographic information of the area to be simulated (longitude, latitude, altitude). This study aims to measure the difference in irrigated and rainfed rice productivity from the Aquacrop crop modelling simulation to the influence of climate pattern variations in Java Island, Indonesia. The k-means clustering method applied to the rainfall, maximum, and minimum temperature variables from the bias-corrected MERRA2 data resulted in two climate regions. The principal component analysis result showed that the maximum and minimum temperature variables are the variables that most contribute to the determination of the clustering area using the k-means method compared to the rainfall variable. This study has calculated the probability of the irrigated and rainfed rice productivity resulting from the Aquacrop simulation in those climate regions during La Nina [El Nino] years that will be higher [smaller] than the mean value of rice productivity during neutral years. However, the validation between the actual irrigated and rainfed rice productivity with the Aquacrop simulation results from 2001-2014 showed low correlation values that vary between negative and positive values in all climate regions. Meanwhile, the validation on the El Nino composite years generally showed positive correlation values. In addition, the neutral and La Nina composite years resulted in varying correlation values between negative and positive correlation.

In our paper we will compare grid-connected PV systems with fossil fuel based electricity within electricity infrastructures at Indonesian islands. Our approach is based on geographic mapping of the irradiance potential (kWh/m 2 /year), electrification rates on islands (%), performance prediction of grid-connected PV systems (kWh/kW p ) and the CO 2 reduction potential (g/kWh) of grid-connected PV systems in the Indonesian archipelago in the year 2011. So far, extensive studies on gridconnected PV systems for island electrification in Indonesia are lacking, and as such the results will be relevant for the realization of a pilot grid-connected photovoltaic system of 35kWp in Jayapura, situated in the Indonesian province of Papua, since in this province the electrical energy demand is growing. In this study we presented a method to determine the potential of grid connected PV in Indonesia. The total potential is about 94 TWh/year and the required installed capacity is around 80 GWp, based on PV modules with an efficiency of 15%. By using the full potential of grid connected PV 3.0 Mt CO 2 emissions can be saved.

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