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Landslides along Alabama highways are a relatively common occurrence in many regions of the state. These landslides can lead to damage to transportation infrastructure and significant traffic disruptions. The current practice identifies landslide locations primarily through maintenance personnel reports or motorist complaints. Once an unstable region is identified, the suspected slide area is commonly instrumented with inclinometers, which are then read at regular intervals to understand the slide plane location and identify changes in behavior. This inclinometer data has been collected at unstable sites across the state for many years and provides a unique dataset to understand how precipitation events influence landslide behavior along highways. Previously developed precipitation thresholds considering storm magnitude and duration were consistent with landslide events observed around the state, but there are many non-triggering events that fall above the thresholds (false positives). Approximately 70% of false positive storm events occurred during drier than average periods based on normalized soil moisture data from NASA’s SMAP instrument, while large movements occurred primarily during periods of average or above average soil moisture. This suggests that adding soil moisture data to landslide threshold predictions may help to reduce false positive events and to assess the likelihood of large movements occurring. These findings are now being used to develop improved warning thresholds that can highlight when landslides are likely to occur, allowing inspections and preventative maintenance to be prioritized.
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This content will become publicly available on July 28, 2026
Mixed Hydrometeorological Processes Explain Regional Landslide Potential
Abstract During December 2022–January 2023, nine atmospheric rivers (ARs) struck California consecutively, causing catastrophic flooding and 600+ landslides. The extensive footprints of landslide‐triggering storms and their diverse hydrometeorological forcings highlight the urgent need to incorporate regional‐scale hydrometeorology into landslide research. Here, using a meteorologically‐informed hydrologic model, we simulate the time‐evolving water budget during the nine‐AR event and identify hydrometeorological conditions that contributed to widespread landslide occurrences across California. Our analysis reveals that 89% of observed landslides occurred under excessively wet conditions, driven by precipitation exceeding the capacities of infiltration, storage, evapotranspiration, and soil drainage. Using K‐means clustering, we identify three distinct hydrometeorological pathways that increased landslide potential: intense precipitation‐induced runoff (∼32% of reported landslides), rain on pre‐wetted soils (∼53%), and snowmelt and soil ice thawing (∼15%). Our findings highlight the importance of constraining the compounding factors that influence slope stability over spatial scales consistent with landslide‐triggering weather systems.
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- Award ID(s):
- 1854951
- PAR ID:
- 10625339
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 52
- Issue:
- 14
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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