As 2025 begins, Indonesia is again plagued by floods, a recurring disaster that has become an annual routine. Despite the country’s relatively uniform rainfall distribution, many areas have been hit by severe flooding and landslides, resulting in significant economic, health, and human losses.
According to various sources, several regions in Indonesia have been severely affected by the floods, including Tebingtinggi in North Sumatra, Bandar Lampung in Lampung, Cirebon in West Java, Pekalongan in Central Java, Bima in West Nusa Tenggara, Banjarmasin in South Kalimantan, Morowali Utara in Central Sulawesi, Seram Barat in Maluku, Sorong in West Papua, and Jayapura in Papua.
The floods have not only caused widespread damage but also resulted in loss of life and subsequent disasters like landslides. Bandar Lampung, Morowali Utara, Sorong, Jayapura, and Pekalongan are among the cities and regencies that have reported fatalities, with Pekalongan being the most severely affected, recording 25 deaths due to landslides.
The intense rainfall and flooding phenomenon at the beginning of the year is attributed to the Madden-Julian Oscillation (MJO). According to the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG), the formation of rain clouds in January was triggered by the MJO, which caused non-seasonal wave movements from west to east, resulting in widespread and intense rainfall in tropical regions like Indonesia. While the MJO phenomenon is a common occurrence, climate change has exacerbated its anomalies, making flood disasters increasingly difficult to monitor and predict.
In analyzing flood disasters, the OpenStreetMap (OSM) platform has proven to be a valuable tool. As an open-source mapping platform, OSM contains a vast amount of data that can be used to assess exposure to disasters like floods and landslides. By utilizing building and highway algorithms, along with additional keys like amenity and landuse, we can obtain exposure data from flood disasters that can be further analyzed.
In the case of the Indonesian flood disaster, Bandar Lampung and Pekalongan were among the most severely affected regions. The exposure data from OSM revealed that Bandar Lampung had a flooded area of 16.43 km2, accounting for approximately 10% of the province’s total area, with 12,533 buildings inundated and 146 km of roads affected.
Pekalongan, on the other hand, had a larger flooded area of 94.5 km2, accounting for around 12% of the regency’s total area, with 6,970 buildings affected and over 150 km of roads cut off. The situation in Pekalongan was further exacerbated by landslides, which affected an estimated 14.7 km2 of the area, as per picture below:
OSM data also can be used to visually analyze the causes of landslides. By extracting data on river networks, landuse, and land cover, it can be concluded that the landslide-affected areas in Pekalongan were mostly dominated by river headwaters, specifically the Surabayan River, which, combined with the loose soil conditions and agricultural landuse (key:value = landuse:farmland), disrupted soil stability and led to intense soil erosion, ultimately triggering landslides when accumulated with high rainfall.
In conclusion, the early 2025 floods in Indonesia have once again highlighted the country’s vulnerability to natural disasters. The severe flooding and landslides have resulted in significant human, economic, and environmental losses. The use of OpenStreetMap (OSM) data has proven to be a valuable tool in analyzing and understanding the impact of flood disasters. By leveraging OSM data, policymakers and disaster management agencies can better prepare for and respond to future flood disasters, ultimately reducing the risk of loss of life and property. Furthermore, addressing the root causes of flooding, such as deforestation, land degradation, and climate change, is crucial to mitigating the impact of future flood disasters.
