Tectonic uplift, soil production, soil depth, and rock strength at the Dragon's Back Pressure Ridge, Carrizo Plain, California

Tectonic uplift, soil production, soil depth, and rock strength at the Dragon's Back Pressure Ridge, Carrizo Plain, California

Supporting data for “Landscape transience reveals a bottom-up control on soil production”

Emily C. Geyman*, David A. Paige, Michael P. Lamb

*Corresponding author: Emily C. Geyman, [e-mail]

Last updated: July 3, 2024

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Dataset overview.

This dataset contains:

1. Georeferenced TIFF files of the (i) LiDAR-derived surface elevation, (ii) geological map (based on the mapping from Dibblee (1973) and Arrowsmith (1995)), (iii) reconstructed cumulative uplift, and (iv) reconstructed uplift rate at the Dragon’s Back Pressure Ridge, Carrizo Plain, California.

2. Raw and processed ground penetrating radar (GPR) observations of soil thickness.

3. Geomorphic properties: (i) hilltop erosion rate, (ii) hilltop soil production rate, (iii) hilltop saprolite weakness (based on cone penetrometer observations), and (iv) hilltop soil thickness.

4. Raw and processed observations from the cone penetrometer (used to compute the saprolite weakness).

5. Soil pit observations.

6. Matlab code used to perform the MCMC inversion to generate the uplift reconstructions (item (1) above).

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Dataset details.

See the publication: “Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” for details on the field methodology and data analysis. Details about each data product also are provided below. 

1. Geotiffs.

We provide georeferenced TIFF files of the (i) surface elevation, (ii) geological map, (iii) cumulative uplift, and (iv) uplift rate at Dragon’s Back Pressure Ridge, Carrizo Plain, California. The coordinate system for the geotiffs is WGS84 / UTM Zone 11 N (EPSG:32611). All geotiffs are provided at 0.5 m x 0.5 m spatial resolution. Details about each dataset are provided below.

(i) Surface elevation. We use LiDAR data from the 2005 B4 Lidar Project, acquired and processed by the National Center for Airborne Laser Mapping (NCALM). The full LiDAR dataset is available for download from OpenTopography (https://portal.opentopography.org/datasetMetadata?otCollectionID=OT.032018.32611.1). We convert the LiDAR point cloud to a 0.5 m gridded bare earth digital elevation model (DEM). 

(ii) Geological map. The original geological map of Dibblee (1973, 1999)  is available from the United States Geological Survey (USGS) at https://pubs.usgs.gov/of/1999/of99-014/. This mapping was refined by Arrowsmith (1995) and Hilley & Arrowsmith (2008). We modify the geological map using high-resolution satellite imagery (from Google, ESRI, and Bing mosaics), as well as high-resolution imagery from the National Agriculture Imagery Program (NAIP), in order to follow the contacts of the Pink, Tan, and Gray members of the Paso Robles Formation. The units on the geological map are coded as:

1 - Pink Member, Paso Robles Formation

2 - Tan Member, Paso Robles Formation

3 - Gray Member, Paso Robles Formation

4 - Undifferentiated Paso Robles Formation

5 - Quaternary alluvium (older)

6 - Quaternary alluvium (younger)

7-8 - Quaternary landslides and terraces

(iii) Cumulative uplift. We follow the general approach of Hilley & Arrowsmith (2008) to reconstruct the cumulative uplift at Dragon’s Back Pressure Ridge based on the observed positions and elevations of the stratigraphic contacts between the Pink, Tan, and Gray members of the Paso Robles Formation. Put simply, since the Pink, Tan, and Gray members of the Paso Robles Formation are initially flat-lying, the progressive increase in elevation of the contacts between these members from the start to the middle of the Dragon’s Back Pressure Ridge records the cumulative tectonic uplift. We perform a Markov Chain Monte Carlo (MCMC) inversion to reconstruct the uplift history that can best explain our geological observations (i.e., the positions and elevations of the Pink, Tan, and Gray members of the Paso Robles Formation). See section 6 for the Matlab code used to perform the MCMC inversion.

Dataset A: “cumulative_uplift_mean.tif” -- the mean reconstructed cumulative uplift (units: meters).

Dataset B: “cumulative_uplift_uncertainty_IQR.tif” -- the uncertainty of the reconstructed cumulative uplift (units: meters), documented as the inter-quartile range (IQR), the difference between the 75th percentile and the 25th percentile of the MCMC cumulative uplift estimates.

(iv) Cumulative uplift rate. The uplift rate dataset is constructed by taking the spatial derivative of the cumulative uplift dataset (iii) in the along-strike direction of the San Andreas Fault, and then converting from space to time using the long-term slip rate on the San Andreas Fault of approximately 33 mm/yr. This is the same approach as used in Hilley & Arrowsmith (2008).

Dataset A: “uplift_rate_mean.tif” -- the mean reconstructed uplift rate (units: mm/yr).

Dataset B: “uplift_rate_uncertainty_IQR.tif” -- the uncertainty of the reconstructed uplift rate (units: mm/yr), documented as the inter-quartile range (IQR), the difference between the 75th percentile and the 25th percentile of the MCMC estimates.

2. Ground penetrating radar (GPR).

The GPR data were acquired with a MALA HDR GPR system with a 450 MHz shielded antenna. Data were acquired every 4 cm, tracked by a survey wheel for precise relative positioning. The GPR survey covered approximately 19 km of ridgeline and included 21 short (approximately 10 m) ridgetop profiles with cone penetrometer observations that serve as ground-truth for the depth of the soil-saprolite boundary inferred from the GPR data. The GPR data were processed using the open-source GPRPy software (Plattner, 2020). We constrained sub-surface velocities by fitting 364 diffraction hyperbolas in the GPR transects. The hyperbola fitting supports a spatially-uniform velocity of approximately 0.11 m/ns. The locations and fitted velocities of the individual hyperbolas used to construct this velocity model are included in the file “GPR_velocities.csv.”

The folder “Radar450MHz_raw” includes the raw radar data. The folder “Radar450MHz_GPS” includes the GPS data associated with each radar dataset (saved as .cor files). The GPS observations are aggregated in the spreadsheet “GPS_all” in that folder. The shapefile folder includes the final processed GPR-derived soil thickness estimates (soil thickness reported in units of meters) as a .shp file. The coordinate system for the shapefile is WGS84 / UTM Zone 11 N. 

3. Geomorphic properties.

These are the datasets plotted in Figures 3 and 4 of “Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” 

4. Cone penetrometer observations.

This folder contains 3 files:

1. ConePenetrometerSummaryTable_Overview.csv: A summary of the 212 cone penetrometer stations. For each station, there is metadata about the location (GPS coordinates), the stratigraphic unit (Pink, Tan, or Gray Member of the Paso Robles Formation), the side of the ridge, (southeast = SW, center = C, or northwest = NW), and the saprolite weakness, calculated as the cone penetrometer ease of penetration [cm/strike] at the position of the soil-saprolite boundary. 

2. ConePenetrometerSummaryTable_Data.csv: All of the raw observations from the cone penetrometer. The raw observations are the cumulative strike number vs. the cumulative depth of penetration into the ground.

3. ConePenetrometerSummaryTableFinal.xlsx: An Excel spreadsheet with the same data from items (1) and (2) above as separate sheets ("Overview") and ("Data").

5. Soil pit observations.

This folder contains 2 files:

1. soil_pit_summary: A summary table containing the soil pit locations and the inferred depth to the soil-saprolite boundary.

2. soil_pit_layers: Simplified stratigraphic columns providing grain sizes and classifications (soil vs. saprolite) of the layers identified in each soil pit.

6. Matlab code. 

This folder contains 2 primary Matlab scripts, with supporting data files and helper functions.

1. DBPR_uplift: code to reconstruct the tectonic uplift at DBPR based on the positions and elevations of the stratigraphic contacts.

1. soil_depth_vs_strength: code to reconstruct Fig. 4 Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” 

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References

Arrowsmith, J. R. Coupled tectonic deformation and geomorphic degradation along the San Andreas Fault System. Ph.D. thesis, Stanford University (1995).

Dibblee Jr, T. Regional geologic map of San Andreas Fault and related faults and Carrizo Plain, Temblor, Caliente, and La Panza Ranges and vicinity, California. US Geological Survey Miscellaneous Geological Investigations, Map I-757, scale 1:125,000 (1973). 

Dibblee, T. W. et al. Regional geologic map of San Andreas and related faults in Carrizo Plain, Temblor, Caliente and La Panza Ranges and vicinity, California: A digital database. Tech. Rep., US Geological Survey (1999). 

Hilley, G. E. & Arrowsmith, J. R. Geomorphic response to uplift along the Dragon’s Back pressure ridge, Carrizo Plain, California. Geology, 36, 367–370 (2008). 

Plattner, A. M. GPRPy: Open-source ground-penetrating radar processing and visualization software. The Leading Edge 39, 332–337 (2020).