Usage of normalized soil moisture for improving the performance of rainfall thresholds for landslides along transportation corridors

Abstract. Landslides along transportation corridors pose significant risks to infrastructure and public safety, necessitating accurate prediction and mitigation strategies. Many early warning systems for landslides are based on rainfall thresholds derived from historical data that distinguish landslide triggering from non-triggering events. However, it is widely recognized that antecedent moisture conditions have a major impact on the likelihood of a particular rainfall event leading to a landslide. We aim to improve existing rainfall thresholds for landslides along highways by incorporating antecedent soil moisture conditions. The landslide inventory was compiled using data from inclinometers at suspected landslide sites and from landslide reports following major storm events along Alabama highways. This inventory was combined with precipitation data from the National Oceanic and Atmospheric Administration (NOAA) and soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) satellite. We explored the accuracy of rainfall thresholds from previous studies for forecasting landslides along the highways of Alabama. Additionally, we investigated the potential of reducing the number of non-landslide events that exceed the thresholds (false positives) by utilizing soil moisture data derived from SMAP. This study demonstrates that sites with multiple inclinometers in a landslide region produce more robust datasets compared to those with a single inclinometer, enabling more effective differentiation between landslide and non-landslide events. Furthermore, using normalized soil moisture in the development of rainfall thresholds shows potential for reducing false positives, as approximately 75 percent of the false positive cases in this study occurred when the soil moisture was at or below average conditions. Our proposed normalized soil moisture-dependent thresholds will support decision-making systems by enabling users to weigh the tradeoffs between potential false alarms and missed alarms, depending on the relative cost or risk of each for a given project. The findings will aid transportation authorities and civil engineers in making informed decisions about possible interventions or preventative maintenance in the future.