Development of Technology to Predict and Respond to Secondary Damage in Wildfire-Affected Areas from Extreme Rainfall

A technology has been developed to predict the risk of debris flow transition caused by landslides in wildfire-affected areas, where secondary damage from extreme rainfall is a concern, and to evaluate the effectiveness of disaster prevention structures such as check dams. Wildfire-affected areas are prone to landslides and debris flow disasters due to weakened ground conditions. This highlights the importance of establishing more precise disaster response measures.

Last year, a landslide caused by heavy rain occurred in Shinsang-ri, Jojong-myeon, Gapyeong-gun, Gyeonggi Province, causing damage to residential houses. The photo is unrelated to the article. Source=Yonhap News Agency

The Korea Institute of Geoscience and Mineral Resources (KIGAM) announced on April 9 that its Geological Hazard Research Laboratory at the National Land Safety Research Headquarters has developed an assessment technology that precisely analyzes the risk range of debris flow triggered by landslides after extreme rainfall, enabling effective deployment of disaster prevention facilities according to the impact on rocks and driftwood.

Extreme rainfall refers to localized heavy downpours that meet the criteria for disaster alerts, such as more than 50mm per hour and at least 90mm within three hours, or more than 72mm within an hour. Debris flow is a natural disaster where soil, rocks, and trees generated by landslides flow downstream with water.

Wildfire-affected areas are particularly vulnerable to extreme rainfall. When heavy rain falls on regions where vegetation has been destroyed by wildfire, soil stability is weakened, increasing the risk of landslides.

In particular, sediment, rocks, and driftwood generated by extreme rainfall can move downstream with water, transition into debris flow, and significantly increase the scale of damage. Previously, it was difficult to physically analyze the complex behavior of wildfire-affected areas under extreme rainfall conditions in an integrated manner.

The 2D debris flow model 'KIGAM-DF (KIGAM-Debris Flow)' developed by the research team improves on these limitations by enabling the prediction of debris flow spread and the establishment of response strategies in mountain environments weakened by wildfires or logging. As such, it is expected to advance the forest disaster response system.

Soil Changes Before and After Wildfire in the Wildfire-Affected Area (AI Generated). Korea Institute of Geoscience and Mineral Resources

KIGAM-DF is designed to comprehensively analyze the entire process from debris flow generation to movement and deposition, and also incorporates the generation, movement, and accumulation of driftwood.

The core of the model is its ability to simulate not only the fluid characteristics of debris flow but also the impact of sediment, rocks, and trees that can directly cause damage downstream. It enables the prediction of damage and the establishment of response scenarios with minimal data input.

When KIGAM-DF was applied to the debris flow source area during the 2011 Umyeonsan landslide and to the Yecheon-gun area, where large-scale debris flow occurred in 2023, prediction accuracy reached the 85-90% range.

Based on these results, the research team is currently applying this technology to assess debris flow risk in the Yeongnam region, which experienced a major wildfire last year, and in the Sancheong-gun area, which saw casualties.

Kim Minseok, head of the Geological Hazard Research Laboratory, said, "KIGAM-DF is expected to play a key role in disaster prevention for vulnerable areas, as it can quantitatively predict the risk range of complex disasters where landslides transition into debris flow."

Meanwhile, the results of this study have recently been published in the international journals 'Environmental Modelling and Software' and 'CATENA.'

This content was produced with the assistance of AI translation services.