DOI: 10.5061/dryad.tht76hfb0
title: Data from: Role of tectonic rock damage in erosional processes: A global analysis

description: The role of active faults in driving rock uplift is well-known, but their
 influence on rock damage and erosional efficiency remains unclear
 globally. Using 1744 10Be-derived erosion rates, we show that erosional
 efficiency is elevated on average within ~15 km of a fault trace and
 decreases with distance, up to ~100 km. Reverse faults, and those longer
 than 140 km, show the strongest effects. This length scale of decay
 suggests tectonic damage extends beyond fault-core pulverization on
 primary faults, possibly including fracturing or grain-to-grain contact
 weakening due to seismic shaking and distributed deformation on complex
 fault networks. Machine learning identifies fault proximity as a dominant
 control on erosional efficiency, exceeding precipitation and lithology,
 particularly when a measure of seismic shaking is included. These findings
 indicate that active tectonics are associated with erosion not only
 through uplift but also by enhancing erosional efficiency through
 long-range rock damage.
description: # Data from: Role of tectonic rock damage in erosional processes: A global
 analysis Dataset DOI: [10.5061/dryad.tht76hfb0](10.5061/dryad.tht76hfb0)
 ## Description of the data and file structure **Repository for *Role of
 Fault-Induced Rock Damage in Erosional Processes: A Global Analysis***
 **Boontigan Kuhasubpasin, Seulgi Moon, Carolina Lithgow-Bertelloni** This
 repository contains the reproducible workflow and source code used to
 generate all figures and results presented in the associated manuscript.
 The code encompasses data preprocessing, statistical analyses, and
 visualization routines related to fault proximity, erosion efficiency,
 seismic shaking, and landscape metrics. The directory structure mirrors
 the organization of the manuscript and its supplementary materials. All
 code cells are annotated to facilitate transparency, reproducibility, and
 ease of interpretation. ## Required Files ### 1.
 2025_Kuhusubpasin_FaultRockDamage_211125.ipynb A fully documented Python
 Jupyter notebook containing the complete reproducible workflow used in the
 study. This notebook includes all data processing steps, statistical
 analyses, and figure-generation code required to reproduce the
 visualizations in the manuscript. ### 2. Grid_E_20km_factors_weight.zip
 Equal-area (20 × 20 km) global grid dataset generated from
 E_OctopusV24_FaultRockDamage_1117.zip. This dataset contains
 basin-area–weighted interpolations of key physical variables for spatially
 uniform comparison. | Field Name | Description | |
 :------------------------- |
 :---------------------------------------------------------------------- |
 | `lon`, `lat` | Longitude and latitude of grid cell center. | | `PGA`,
 `PGA_STD` | Basin area-weighted mean and standard deviation of PGA. | |
 `MAP`, `MAP_STD` | Basin area-weighted mean and standard deviation of MAP.
 | | `Dist_km`, `Dist_km_ST` | Basin area-weighted mean and standard
 deviation of fault distance. | | `F_reg`, `F_length` | Basin area-weighted
 mean fault parameters. | | `CstRks`, `SedRks`, `Seds` | Basin
 area-weighted lithologic proportions. | | `K`, `K_STD` | Basin
 area-weighted mean and standard deviation of K. | | `Kq`, `Kq_STD` | Basin
 area-weighted mean and standard deviation of Kq. | | `Eq_n`, `Eq_n_STD` |
 Basin area-weighted earthquake counts and standard deviation. | |
 `Sum_Mw`, `Sum_Mw_STD` | Basin area-weighted cumulative moment magnitude
 and standard deviation. | | `N_POLYS` | Number of basins intersecting each
 grid cell. | | `geometry` | 20 × 20 km grid cell polygon. | 
 
 ### 3. E_OctopusV24_FaultRockDamage_1117.zip (hosted by Zenodo) Primary
 global basin-scale dataset used in the fault-related rock damage and
 erosion-efficiency analysis. This archive contains processed observations
 including erosion rates, channel steepness metrics, lithology, fault
 attributes, seismicity, and climatic factors. | Field Name | Description |
 | :----------------------------------------- |
 :----------------------------------------------------------------------------------------------- | | `OBSID1` | Observation ID of the basin (Codilean et al., 2018). | | `EBE_MMYR`, `EBE_MMYRE` | ^10^Be-derived erosion rate (mm/yr) and associated uncertainty (Codilean et al., 2018). | | `ksn045` | Channel steepness index (concavity = 0.45). | | `r045`, `std045` | Correlation coefficient and standard deviation for channel steepness fits. | | `ksnq045` | Rainfall-weighted channel steepness index. | | `rq045`, `stdq045` | Correlation coefficient and standard deviation for rainfall-weighted fits. | | `PGA`, `PGA_STD` | Peak ground acceleration (g) with 10% exceedance probability in 50 years and standard deviation. | | `MAP`, `MAP_STD` | Mean annual precipitation (mm/yr) and standard deviation. | | `Dist_km` | Distance to nearest mapped active fault (km), calculated in this study. | | `F_reg`, `F_length`, `F_sliprate`, `F_dip` | Fault region and geometric/slip parameters. | | `Lithology` | Dominant lithologic class. | | `CstRks`, `SedRks`, `Seds` | Proportions of crystalline rock, sedimentary rock, and unconsolidated sediments. | | `K`, `Kq` | Erosional efficiency coefficients (uniform and rainfall-weighted). | | `log_D` | Log~10~-transformed fault distance. | | `Eq_n` | Number of earthquakes (Mw &gt; 4) within 1° of basin centroid. | | `Sum_M0`, `Sum_Mw` | Cumulative seismic moment and moment magnitude within 1° of basin centroid. | | `geometry` | Basin centroid geometry used in this study (derived from Codilean et al., 2018). | ### 4. SoCal_Database.zip: Southern California Subset (hosted by Zenodo) Regional dataset for Southern California including basin-scale variables and additional region-specific data. #### 4a. E_OctopusV24_FaultRockDamage_socal.shp Southern California subset of the basin-scale dataset. All fields are identical to the global dataset, with one additional variable: | Field Name | Description | | :--------- | :------------------------------------------------------------------------------------------------------------------- | | `Vs_SoCal` | Shear-wave velocity (V~s~) in km/s at 500 m depth from Rayleigh-wave ambient noise tomography (Zigone et al., 2015). | #### 4b. Socal_Eq.csv Derived earthquake dataset used for regional seismic-distance analysis. | Field Name | Description | | :--------- | :----------------------------------------------------------------------------------------------- | | `Magn` | Earthquake moment magnitude (Mw) obtained from the GCMT. | | `Dist_km` | Distance (km) from each earthquake to the nearest mapped active fault, calculated in this study. | #### 4c. vs_socal_fault.csv Shear-wave velocity sampling dataset used in regional fault-distance analysis. | Field Name | Description | | :--------- | :------------------------------------------------------------------------------------------------------------------- | | `Vs_SoCal` | Shear-wave velocity (V~s~) in km/s at 500 m depth from Rayleigh-wave ambient noise tomography (Zigone et al., 2015). | | `Dist_km` | Distance (km) from each Vs sampling location to the nearest mapped active fault, calculated in this study. | ## Code/software All analyses were performed using **Python.** ## Access information Data were derived from the following publicly available repositories: * Basin-averaged erosion rates from Octopus ([https://octopusdata.org](https://octopusdata.org): Codilean et al., 2018) * Active fault map from GEM global active faults ([https://www.globalquakemodel.org/product/active-faults-database](https://www.globalquakemodel.org/product/active-faults-database): Styron et sl., 2020) * Digital elevation model from HydroSHEDs ([https://www.hydrosheds.org/products/hydrosheds](https://www.hydrosheds.org/products/hydrosheds): Lehner et al., 2008) * Mean annual precipitation from World Clim ([https://www.worldclim.org/data/worldclim21.html](https://www.worldclim.org/data/worldclim21.html): Fick et al., 2017) * Lithologic map from GLiM ([https://www.geo.uni-hamburg.de/en/geologie/forschung/aquatische-geochemie/glim.html](https://www.geo.uni-hamburg.de/en/geologie/forschung/aquatische-geochemie/glim.html): Hartmann et al., 2012) * Peak ground acceleration from GEM global hazard map ([https://www.globalquakemodel.org/product/global-seismic-hazard-map](https://www.globalquakemodel.org/product/global-seismic-hazard-map): Johnson et al., 2023) * Earthquake catalog from ISC ([https://www.isc.ac.uk/event_bibliography/eventindex.php](https://www.isc.ac.uk/event_bibliography/eventindex.php): Di et al., 2014) * Earthquake magnitudes were obtained from the GCMT ([https://www.globalcmt.org/CMTcite.html](https://www.globalcmt.org/CMTcite.html): Dziewonski et al.,1981 and Ekstrom et al., 2012) * Shear-wave velocity from the Southern California ambient noise tomography model ([https://doi.org/10.1007/s00024-014-0872-1](https://doi.org/10.1007/s00024-014-0872-1): Zigone et al., 2015)