Comparison of AWS-and AMOS-Based Intensity-Duration Thresholds and Predictive Performance for Shallow Landslides

Suhyeon Lee1*, Ji-Hyeok Park², Youngeun Kang¹, Seonghun Jeong3,4

(¹Department of Forest Resources, Gyeongsang National University,

²Kasuya Research Forest, Kyushu University,

3Department of Forest Environmental Resources, Gyeongsang National University,

4Institute of Agriculture and Life Science, Gyeongsang National University)

Abstract: Rainfall intensity–duration (I–D) thresholds are widely used to relate rainfall characteristics to shallow landslides and to support rainfall-based warning criteria. In the Republic of Korea, most domestic I–D threshold studies have used rainfall data from the Korea Meteorological Administration’s Automatic Weather System (AWS). However, AWS stations are often at lower elevations and relatively sparse in mountainous terrain, potentially under-representing rainfall conditions at shallow-landslide locations; the Automatic Mountain Observing System (AMOS) was designed to better capture mountain meteorology, yet direct AWS–AMOS comparisons for landslide-related rainfall remain limited. Therefore, this study compared I–D thresholds and their predictive performance using 598 reported shallow landslide analyzed 598 reported shallow landslides in Gyeongsangnam-do, Republic of Korea, associated with localized heavy rainfall in July 2025, pairing each landslide with its nearest AWS and AMOS stations (one-to-one) to extract rainfall data and compute event-scale mean intensity (I) and duration (D) for I–D datasets. Separate thresholds were derived for AWS and AMOS, and threshold exceedance was evaluated for both landslide-associated and non-triggering events; predictive performance was assessed using the Critical Success Index (CSI), and differences bewteen the two networks were tested with the Wilcoxon signed-rank test. The resulting I–D threshold equations were I=12.55×D−0.35 for the AWS and I=53.89×D−9.05 for the AMOS. CSI values were higher for AWS (0.31) than for AMOS (0.23), indicating better predictive performance for AWS network under the study conditions. The difference between the two types of meteorological networks was statistically significant (p < 0.05). These results may reflect a reporting bias toward lower elevations, and the triggering rainfall was dominated by a short, localized heavy-rainfall episode that limited the range of event types and durations. Therefore, further evaluation across multiple rainfall episodes and elevation-stratified landslides is warranted. Overall, the results indicate that both I–D threshold form and predictive performance can differ between AWS and AMOS, and that terrain-informed station selection and AWS–AMOS weighting may improve operational shallow-landslide forecasting and warnings.

Keyword: Rainfall characteristics, I-D Threshold, Landslide, Meteorological Station

Acknowledgement: This study was carried out with the support of ´R&D Program for Forest Science Technology (Project No. "RS-2025-02213493") provided by Korea Forest Service(Korea Forestry Promotion Institute).