Kim et al. (2026) Quantitative risk assessment for the compound drought-wildfire disaster

Identification

• Journal: Scientific Reports
• Year: 2026
• Date: 2026-02-03
• Authors: Kyunghun Kim, Hung Soo Kim
• DOI: 10.1038/s41598-026-36589-w

Research Groups

• Institute of Water Resources System, INHA University, Incheon, Republic of Korea
• Department of Civil Engineering, INHA University, Incheon, Republic of Korea

Short Summary

This study develops a novel methodology for the quantitative risk assessment of compound drought-wildfire (CDW) disasters in South Korea, demonstrating that drought conditions can amplify wildfire risk by approximately three times compared to normal conditions.

Objective

• To develop a quantifiable methodology for assessing compound drought-wildfire (CDW) disaster risk by simulating drought-induced weather conditions and quantifying potential wildfire spread and severity using the Fire Weather Index (FWI).

Study Configuration

• Spatial Scale: 107 watersheds across the Korean Peninsula.
• Temporal Scale: Daily meteorological data from January 1, 1981, to December 31, 2024 (44 years). Drought scenarios were based on a 12-month drought with a 30-year return period.

Methodology and Data

• Models used: Fire Weather Index (FWI), Bivariate frequency analysis (using copula functions) for drought scenarios, Hidden Markov Model (for daily precipitation sequences), Artificial Neural Network (ANN) model (for relative humidity estimation).
• Data sources: Water Management Information System (WAMIS) for area-averaged daily precipitation; Korea Meteorological Administration (KMA) Automated Synoptic Observing System (ASOS) stations (54 stations) for daily mean temperature, mean wind speed, and mean relative humidity; Korea Forest Service (KFS) annual reports for wildfire statistics.

Main Results

• The defined drought scenario (12-month duration, 30-year return period) resulted in an average annual precipitation of 685.64 mm, representing approximately 52% of the 1,319 mm/year observed under normal conditions.
• The mean Drought Fire Weather Index (DFWI) was 5.19 (standard deviation 1.87), while the mean Normal Fire Weather Index (NFWI) was 1.71 (standard deviation 0.33).
• Drought conditions intensified wildfire risk by approximately 3.03 times across the entire study area compared to normal conditions.
• Monthly DFWI calculations indicated that the risk of CDW disasters is highest in April and May, and lowest from June to September.
• Spatial analysis revealed that watersheds in the southeastern part of the Korean Peninsula consistently face a high risk of CDW disasters, attributed to mountain-shielded topography and a dry continental climate.
• Validation with four historical CDW events showed FWI increases ranging from 1.23 to 3.85 times (mean 2.59 times) compared to non-CDW years, quantitatively consistent with the scenario-based findings.

Contributions

• This is the first study to propose a methodology for the quantitative risk assessment of compound drought-wildfire (CDW) disasters.
• It establishes a novel framework for quantifying CDW risks by explicitly incorporating the interaction between drought and wildfire.
• The study provides essential, data-driven indicators for developing effective and evidence-based CDW disaster response strategies.
• It numerically quantifies the impact of drought on wildfire risk, moving beyond theoretical discussions.
• The findings emphasize the critical need for integrated drought and wildfire response management.

Funding

• National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C2091773).

Citation

@article{Kim2026Quantitative,
  author = {Kim, Kyunghun and Kim, Hung Soo},
  title = {Quantitative risk assessment for the compound drought-wildfire disaster},
  journal = {Scientific Reports},
  year = {2026},
  doi = {10.1038/s41598-026-36589-w},
  url = {https://doi.org/10.1038/s41598-026-36589-w}
}