]> 2026-05-06T06:23:56-06:00 Model:DrEICH algorithm 3002 en Edinburgh Active development and maintenance of the code has moved to GitHub and been incorporated within broader LSDTopoTools software package: https://github.com/LSDtopotools/LSDTopoTools2 United Kingdom Simon David University of Edinburgh Mudd Milodowski +44 (0)131 650 2535 +44 (0)131 650 9170 Rm 1.05d Geography Building Geography Building Drummond Street EH8 9XP Model developer No but possible 160 Edinburgh 1 No but possible No but possible Single Processor United Kingdom Yes Topographic analysis; no calibration required. Two test DEMs are included in the repository, both from Wayne National Forest, Ohio. Two example driver files, one for each DEM, are also included. These DEMs are the same as are used in the associated manuscript (http://onlinelibrary.wiley.com/doi/10.1002/2013WR015167/full) so that the resulting channel networks can be compared with figures from the paper. Any 1m resolution digital elevation model. The input file is a DEM in .flt format. A driver text file is also required which contains the parameters used for the extraction. Information on the parameters needed in the driver file is available in the documentation (http://www.geos.ed.ac.uk/~smudd/LSDTT_docs/html/channel_heads.html). There are two user-defined parameters which need to be defined in the model. 1) The m/n value. This parameter is in the steady state stream power equation for channel slope: dz/dx = (U/K)^1/n * A(x)^(-m/n), where U is rock uplift rate, K is an erodibility coefficient, A is drainage area, and m and n are constants. The best fit m/n value for each landscape must be determined using the chi analysis toolkit (https://csdms.colorado.edu/wiki/Model:Chi_analysis_tools) before the DrEICH algorithm can be run. The routines in the chi analysis toolkit provide a statistical method of identifying the m/n value for the landscape. 2) The number of linked pixels with a contour curvature > 0.1 m^-1. The first stage in the DrEICH algorithm is identifying valleys with positive curvature in which to run the model. A valley is selected to contain a channel head if there are more than a defined number of pixels in that valley with a contour curvature greater than 0.1. This parameter does not affect the location of the channel head within each valley, but does affect how many valleys will be selected. A default value of 10 is suggested, but this may need to vary depending on the relief of the landscape (a lower value of 5 may be more appropriate for lower-relief landscapes). This algorithm attempts to identify channel head, which are features present on a metre to sub-metre scale. Therefore, the accuracy of the prediction will decrease as the DEM resolution becomes coarser. 1 to 2m resolution DEMs are suggested as appropriate for use with this tool. With larger DEMs this tool will take longer to run - it takes around half an hour to run with a 1m DEM with 23000 rows and 18000 columns. Documentation is available for guidance. A chi analysis of the landscape must first be performed to get the correct m/n value for the landscape. This can be done using the chi analysis toolkit available on CSDMS (https://csdms.colorado.edu/wiki/Model:Chi_analysis_tools). This tool works under the assumption that the channels incise approximately based on the stream power law. It identifies the channel head as the upstream limit of fluvial incision based on the chi profile of the channel. No time resolution constraints as this software performs topographic analysis. As is, no updates are provided 2020 Yes This tool uses chi river profile analysis to predict channel head locations across a landscape and therefore allow the extraction of river networks. It is most suitable for use with high resolution LiDAR (1m) DEMs. The model requires an input DEM in float format and will output the extracted channel heads and networks, also in float format. For detailed information about how to use this tool please refer to the documentation (http://www.geos.ed.ac.uk/~smudd/LSDTT_docs/html/channel_heads.html) and to the associated paper (http://onlinelibrary.wiley.com/doi/10.1002/2013WR015167/full). Fiona 3 float University of Edinburgh Clubb 0.25 Yes Varies depending on the input DEM size Terrestrial Hydrology Channel extraction tool As code geomorphology hydrological bedrock channel erosion Tool http://www.geos.ed.ac.uk/~smudd/LSDTT_docs/html/channel_heads.html algorithm data chi river profile river profile analysis predict channel head channel head locations chi river river profile profile analysis analysis to predict river networks predict channel head locations extraction of river river extracted channel heads high resolution lidar geomorphology hydrological bedrock channel erosion dreich algorithm 3 Algorithm for extracting channel networks from high resolution topographic data float +44 (0)131 650 9170 false Geography Building Drummond Street EH8 9XP true GPL v2 C++ Around 10 minutes to half an hour Through CSDMS repository https://github.com/csdms-contrib/dreich_algorithm 2D Landscape-Scale 2012 Unix Linux Model developer false 2014-05-27T10:34:47Z 2456804.9408218 2020-09-17T02:15:51Z 2459109.5943403 DrEICH algorithm 3002 0 plainlist 1 [[:Model:DrEICH algorithm]] DrEICH algorithm# QUERY9caec148b70b85cdffe41c4a77d05614 3002 1 plainlist 5 [[User:+]] [[First name member::Fiona]] [[Last name member::Clubb]] DrEICH algorithm# QUERY4044f4a8cfb7e4253794c048f35a8bfc 3002 1 ul 5 [[Model:+]] [[First name::Fiona]] [[Last name::Clubb]] DrEICH algorithm# QUERY1abc3434f8656d51b099ba0e7919856e