These instructions will take you through the steps needed to generate E*R* data for a landscape, and use that data to constrain the critical gradient following the techniques published in Hurst et al. (2012) and Hurst et al. (2013).
Here is a quick overview of how to set up and run the code, if you have done it before:
This driver file will combine several LSDRaster Functions in order to generate E*R* data for a landscape. This methodology matches the techniques published in Hurst et al. (2012) and Hurst et al. (2013) This code will produce:
This code is for research purposes and is under continuous development, so we cannot guarantee a bug-free experience!
See the other docs on this site for help with loading data into the correct format and general help with using this suite of software: Getting data into LSDTopoToolbox
The 4 input rasters are all generated using LH_Driver.cpp, and this code must be run before E*R* data can be generated. The files should be in the data directory in the following format:
The code is compiled using the provided makefile, E_STAR_R_STAR.make and the command:
make -f E_STAR_R_STAR.make
Which will create the binary file, E_STAR_R_STAR.out to be executed.
The driver is run with three arguments:
The syntax on a unix machine is as follows:
./E_STAR_R_STAR.out <path to data files> <Prefix> <Minimum patch area>
And a complete example (your path and filenames may vary):
./E_STAR_R_STAR.out /home/s0675405/DataStore/ER_tests/ Oregon 10
The final outputs are stored in two csv files and a raster file, which are written to the data folder supplied as an argument.
<prefix>_E_R_Star_Patch_Data.csv
This file contains all of the patch average values that are needed to calculate E*R* values The file is in the following format:
Final_ID,lh_means,lh_medians,lh_std_devs,lh_std_errs,cht_means,cht_medians,cht_std_devs,cht_std_errs,r_means,r_medians,r_std_devs,r_std_errs,s_means,s_medians,s_std_devs,s_std_errs
These files can be analysied using a series of python routines detailed below and located here.
<prefix>_E_R_Star_Raw_Data.csv
This file contains all of the hilltop data for every valid pixel in the landscape. The file is in the following format:
i,j,LH,CHT,Relief,Slope
These files can be analysied using a series of python routines detailed below and located here.
Output Raster Data
The code also writes a raster file of the hilltop patches, <prefix>_Patches.flt, to allow the user to inspect the spatial averaging which has been performed.
The latest version of the python scripts which accompany this analysis driver can be found here and provide a complete framework to go from the output data files to a series of figures utilizing the different methods for producing E*R* data, along with a best fit critical slope value for the nonlinear flux law proposed by Roering et al. (1999).
[explain python script here]
[explain python script here]
[explain python script here]
[explain python script here]
Choose how to constrain the fit: