This paper discusses the result of the development of a hydro-meteorological 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 3-hour 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.

Flash flood is one of the hydro-meteorological hazards that often occurs in Indonesia. Some major factors affecting this hazard are high topography and weak soil structure. Every incident of flash flood has caused losses in the form of damage to infrastructure, victims, and the other losses. Therefore, flash flood hazard areas should be mapped so that community and local government can anticipate the event during heavy rainfall. This study developed a flash flood hazard index using a decision-making model, with approach of the Analytic Hierarchy Process (AHP). With this method, four parameters were calculated, namely the distance to the stream channels, slope, cover and soil types. The weight and rank values were assigned to the layers and classes of each layer respectively. A hazard map was obtained using an algorithm that combines factors in weighted linear combinations. The index of flash flood hazard zone with rainfall factor was analysed with sensitivity analysis shows better result since 13 of 16 flash flood points (81.25%) are in high vulnerability to flooding.

This study investigates the dynamics of water resources in the Citarum watershed during periods of weak La Niña, normal, and weak El Niño conditions occurring sequentially. The Citarum watershed serves various purposes, being utilized not only by seven (7) districts and two (2) cities in West Java, Indonesia but also as a source of raw water for drinking in the City of Jakarta. Using a time-series analysis of surface water data, data-driven (machine learning) methods, and statistical analysis methods, spatiotemporal predictions of surface water have been made. The surface water time series data (2017–2021), obtained from in situ instruments, are used to assess water resources, predict groundwater recharge, and analyze seasonal patterns. The results indicate that surface water follows a seasonal pattern, particularly during the monsoon season, corresponding to the groundwater recharge pattern. In upstream areas, water resources exhibit an increasing trend during both weak La Nina and weak El Niño, except for Jatiluhur Dam, where a decline is observed in both seasons. Machine learning predictions suggest that water levels and groundwater recharge tend to decrease in both upstream and downstream areas.

Hydrometeorological hazard early warning system has been developed in order to support public society in anticipating the extreme weather in Indonesia. Meanwhile, the early warning system in Indonesia still produced locally and need arduous human resources to process the enormous size data to have a good quality early warning. Therefore, in order to increase the effectivity, efficiency, and efficacy of the early warning system to produce the desired result, the automatic process was needed. This study will explain further the mechanism of process automation from the weather forecast to the output of warning displayed in an information system. The automation process combines several scripting in Python and batch script for GIS processing, as well as process scheduling using task scheduler. The output of extreme weather warning is in form of JSON files stored in organized folder directory. The automated process proven to be more useful in increasing the effectivity, efficiency, and efficacy of the early warning system to produce the desired result and later the automated process could be enhanced further to boost the lead time and reducing the arduous task in producing the early warning system.

In this study, model of deforestation is expressed as a function of the direct causes, each of these expressed as a function of the indirect causes. The socio-economic growth variables, such as population and GDP growth, were used as indirect causes of deforestation. The model assumed that deforestation was caused by wood consumption, forest product export, conversion to cropland, and forest fire. This study also calculated the elasticity of forest fire deforestation with respect to the population based on forest fire data between 1991 and 2000. Finally, it analysed the carbon emissions due to deforestation rate in Indonesian forest.

The 1257 AD Mt. Samalas eruption (Lombok, Indonesia) is one of the major volcanic eruption that causes global climate change and ejected thick layers (50 m) of pyroclastic density current (PDC). In this study, this PDC deposits from two different locations, namely Luk and Jugil, in the northern coast of Lombok were subjected to series of magnetic measurements that include mass-specific magnetic susceptibility, isothermal remanent magnetization (IRM) saturation, anhysteretic remanent magnetization (ARM) decay as well as hysteresis parameter. Prior to magnetic measurements PDC sample were sieved through to divide the samples into five different grain sizes, i.e., clay, silt, fine sand, coarse sand, and granule. Magnetic measurements were carried out for each grain size. The results show that the predominant magnetic mineral in all samples is PSD (pseudo-single domain) and MD (multidomain) magnetite. However, the mass-specific magnetic susceptibility of clay-size samples from Luk (~ 501 × 10⁻⁸ m³/kg) differ significantly from that of Jugil (~ 848 × 10⁻⁸ m³/kg) suggesting that although the deposits from Luk and Jugil were of the same sources, they likely to came from different eruptions events. So far, the concentration-dependent magnetic parameter, i.e., mass-specific magnetic susceptibility is still the best parameter to identified PDC deposits of Mt. Samalas based on their eruptive events.