[DSRP Evidence](https://dsrpevidence.org/)

# Modeling the combined effects of the 2023 Türkiye–Syria earthquake and an Atmospheric River event on landslide hazard

Jimenez et al., 2026, Natural Hazards and Earth System Sciences — Geology

Patterns: [Distinctions](https://dsrpevidence.org/pattern/distinctions), [Systems](https://dsrpevidence.org/pattern/systems), [Relationships](https://dsrpevidence.org/pattern/relationships)

## In short

Landslide hazard is not a sum of two independent risk factors but an emergent property of how the earthquake and rainfall subsystems relate in time — their sequencing shifts the critical slope-angle boundary separating safe from hazardous terrain.

## What they found (results)

The team found that when a major rainfall event follows earthquake shaking, ground already weakened by the quake fails at slopes up to 7-13 degrees lower than normal, making combined seismic-hydrological hazard substantially higher and more widespread than either driver modeled alone.

## Abstract

The 6 February 2023 Türkiye–Syria earthquake doublet triggered thousands of coseismic landslides. On 14–15 March, an Atmospheric River (AR) delivered up to 183 mm of rainfall across the seismically weakened region, triggering hundreds of additional landslides and debris flows, causing extensive downstream flooding and damage. Existing regional hazard assessment tools typically model seismic- and rainfall-triggered landslides separately, and rarely incorporate post-seismic hillslope weakening into rainfall-based predictions. Here, we develop a rapidly deployable regional hazard model that integrates seismic and rainfall drivers with post-seismic legacy effects to map landslide probability using the open-source Landlab framework and global gridded datasets. Incorporating legacy effects improves model performance under the AR forcing, yields more realistic probabilities, and lowers the critical slope for landslide initiation by up to 13° relative to the same model without the legacy effect. Analysing event sequencing, we find that this AR event preceding or coincident with the earthquake produces the greatest hazard extent, with median critical slopes in high-probability areas (P(F)≥0.6) up to 7° lower than when the AR follows the seismic event. Finally, we develop a pre-event forecasting method that uses historical extreme rainfall records to map post-seismic landslide hazard and show that it closely replicates the hazard map derived from the 14–15 March AR event. The proposed model supports disaster preparedness, early warning, and risk reduction before and after earthquakes in landslide-prone regions.

These researchers were not testing DSRP. The finding is theirs; the correspondence to DSRP is drawn by this site.

[Source](https://doi.org/10.5194/nhess-26-3815-2026)