Update After First-Round Peer Review

The revised manuscript includes additional modeling components for Topographic Wetness Index (TWI) and Soil Water Repellency (SWR). As a result, the main simulation script has been updated.

Please use Main_SF_para_prob_eff_revisedpaper.m instead of Main_SF_para_prob_eff.m to reproduce the results reported in the revised manuscript.

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First Submission

Code for Probabilistic Post-Fire Shallow Landslide Susceptibility Modeling Considering Spatiotemporal Land Cover Uncertainties: A Case of the January 2025 Palisades Wildfire in Southern California.

Probabilistic, physics-based modeling of post-fire shallow-landslide susceptibility and its uncertainty.

Core scripts

• RFHydrorealization.py — generates random fields of post-fire hydraulic conductivity
• RFroot.py — generates random fields of post-fire root cohesion
• Main_SF_para_prob_eff.m — MATLAB model for shallow-landslide susceptibility (factor of safety and failure probability)

Inputs

• Place the required geospatial inputs in RasterT_Palisad4_SpatialJoin6_TableToExcel.xlsx (derived from ArcGIS Pro).

Quick start

1. Generate hydraulic conductivity random fields
   • Run RFHydrorealization.py to produce ensembles of post-fire hydraulic conductivity (or multipliers) over the study grid.
2. Generate root cohesion random fields
   • Run RFroot.py to produce ensembles of post-fire root cohesion (or multipliers) that evolve over time.
3. Run the physical model in MATLAB
   • Open Main_SF_para_prob_eff.m and set the required paths.

Outputs

• FS maps for each time step and ensemble member