Hydrologic Analysis Tools
C++ Python Other



Author: Richard Barnes (rbarnes@umn.edu)

RichDEM is a set of digital elevation model (DEM) hydrologic analysis tools. RichDEM uses parallel processing and state of the art algorithms to quickly process even very large DEMs.

RichDEM can use both the D8 and D-infinite (Tarboton) flow metrics. It can resolve terrain depressions (or pits) either by filling or by channel carving. It can calculate contributing/up-slope areas, slopes, curvatures, and aspects.

Current State of the Repo

The branch oldmaster contains what was, until recently, the master branch. It is designed as a limited, simple set of programs. The current master branch recasts all of this code as a set of header-only libraries.

Unless otherwise noted, I consider the code available here as correct. However, documentation and testing efforts are on-going; functions may move between files. Users are encouraged to double-check results and feedback is very much welcome.

Improvements targeted at achieving the design philosophy below are underway.

Design Philosophy

The design of RichDEM is guided by these principles:

  • Algorithms will be well-tested. Every algorithm is verified by a rigorous testing procedure. See below.

  • Algorithms will be fast, without compromising safety and accuracy. The algorithms used in RichDEM are state of the art, permitting analyses that would take days on other systems to be performed in hours, or even minutes.

  • Algorithms will be available as libraries, whenever possible. RichDEM is designed as a set of header-only C++ libraries, making it easy to include in your projects and easy to incorporate into other programming languages. RichDEM also includes apps, which are simple wrappers around the algorithms, and a limited, but growing, set of algorithms which may have special requirements, like MPI, that make them unsuitable as libraries. These are available as programs.

  • Programs will have a command-line interface, not a GUI. Command-line interfaces are simple to use and offer extreme flexibility for both users and programmers. They are available on every type of operating system. RichDEM does not officially support any GUI. Per the above, encapsulating RichDEM in a high-level interface of your own is not difficult.

  • Algorithms and programs will be portable. Linux, Mac, and Windows should all be supported.

  • The code will be beautiful. RichDEM's code utilizes sensible variable names and reasonable abstractions to make it easy to understand, use, and design algorithms. The code contains extensive internal documentation which is DOxygen compatible.

  • Programs and algorithms will provide useful feedback. Progress bars will appear if desired and the output will be optimized for machine parsing.

  • **Analyses will be reproducible." Every time you run a RichDEM command that command is logged and timestamped in the output data, along with the version of the program you created the output with. Additionally, a history of all previous manipulations to the data is kept. Use rd_view_processing_history to see this.

Using It

Citing It

As of 883ea734e957, David A. Wheeler's SLOCCount estimates the value of RichDEM at $240,481 and 1.78 person-years of development effort. This value is yours to use, but citations are encouraged.

General usage of the library can be cited as:

Barnes, Richard. 2016. RichDEM: Terrain Analysis Software. http://github.com/r-barnes/richdem

An example BibTeX entry is:

  title        = {RichDEM: Terrain Analysis Software},
  author       = {Richard Barnes},
  year         = {2016},
  url          = {​http://github.com/r-barnes/richdem}, 

This information will be updated as versioned releases become available.

Users are also encouraged to cite the particular algorithms used. Citations to these will be printed whenever an app, program, or library function is run. Although I have written all of the code in this library, some of the algorithms were discovered or invented by others, and they deserve credit for their good work.

As A Library

Upon compilation, point your library search path to the include directory. Include various files using, e.g.

#include "richdem/common/Array2D.hpp"

All files include extensive documentation. At this stage the location of certain functions may be subject to change. This will be noted in the NEWS file. (TODO)

As A Handy Collection of Tools

Running make in the apps directory will produce a large number of useful scripts which are essentially wrappers around standard uses of the RichDEM libraries. The apps/README.md file and the apps themselves contain documentation explaining what they all do.

For Processing Large Datasets

The programs directory contains several programs which have not been converted to libraries. This is usually because their functionality is specific and they are unlikely to be useful as a library. Each directory contains a makefile and a readme explaining the purpose of the program.


Documentation is available at richdem.com. The documentation is auto-generated from the many README.md files throughout the codebase and the extensive comments in the source code.


Correctness is established via a number of methodologies building from code inspection in the simplest cases to output comparison between simple and complex implementations.

Correctness is noted in source code comments under @correctness sections. These are, in turn, printed to the Doxygen documentation output.

A master list of how correctness was established for each algorithm is available at tests/README.md.

Parsable Output

Every line of output from RichDEM begins with one of the following characters, making it easy to parse with a machine.

  • A: Algorithm name

  • a: Analysis command: the command line used to run the program

  • c: Configuration information: program version, input files, and command line options, &c.

  • C: Citation for algorithm

  • d: Debugging info

  • E: Indicates an error condition

  • i: I/O: Amount of data loaded from disk carrying on.

  • m: Miscallaneous counts

  • n: I/O: Amount of data transferred through a network

  • p: Progress information: inform the user to keep calm because we're

  • r: Amount of RAM used

  • t: Timing information: How long stuff took

  • W: Indicates a warning

All output data shall have the form:


The amount of whitespce may very for aesthetic purposes.

Specific Algorithms

Many of the algorithms used in RichDEM are documented in journal or conference publications. In the case of older algorithms by other authors, it is often possible to find the paper in the literature. Some of the newer algorithms I developed have not yet had a chance to be widely utilized. These algorithms are listed below.

Additionally, each publication has its own GitHub repository featuring easily-compiled, minimum working examples of the algorithms, along with examples and test data sets.

These are available as follows:

  • Flat-resolution algorithm. Link
  • Depression-filling algorithm. Link
  • Large dataset depression-filling algorithm. Link
  • Large dataset flow accumulation algorithm. Link


The algorithms used in RichDEM have been published in the following articles. Every algorithm/program will provide its relevant citation information when run.

  • Barnes, R., 2016. Non-divergent flow accumulation for trillion cell digital elevation models on desktops or clusters. In Review.

  • Barnes, R., 2016. Parallel priority-flood depression filling for trillion cell digital elevation models on desktops or clusters. Computers & Geosciences. doi:10.1016/j.cageo.2016.07.001

  • Zhou, G., Sun, Z., Fu, S., 2016. An efficient variant of the Priority-Flood algorithm for filling depressions in raster digital elevation models. Computers & Geosciences 90, Part A, 87 – 96. doi:http://dx.doi.org/10.1016/j.cageo.2016.02.021

  • Barnes, Lehman, Mulla. 2013. "An Efficient Assignment of Drainage Direction Over Flat Surfaces in Raster Digital Elevation Models". Computers & Geosciences. doi: 10.1016/j.cageo.2013.01.009

  • Barnes, Lehman, Mulla. 2013. "Priority-Flood: An Optimal Depression-Filling and Watershed-Labeling Algorithm for Digital Elevation Models". Computers & Geosciences. doi: 10.1016/j.cageo.2013.04.024

  • Mulla et al. including Barnes. 2012. "Strategic Planning for Minnesota’s Natural and Artificial Watersheds". Report to the Minnesota LCCMR.

  • Barnes, Lehman, Mulla. 2011. "Distributed Parallel D8 Up-Slope Area Calculation in Digital Elevation Models". Intn'l Conf. on Parallel & Distributed Processing Techniques & Applications. Link

  • Tarboton, D.G. 1997. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research. Vol. 33. pp 309-319.

  • Horn, B.K.P., 1981. Hill shading and the reflectance map. Proceedings of the IEEE 69, 14–47. doi:10.1109/PROC.1981.11918

  • Zevenbergen, L.W., Thorne, C.R., 1987. Quantitative analysis of land surface topography. Earth surface processes and landforms 12, 47–56.


If you see something, say something.

Users are encouraged to report any issues experienced with the code via Github's issue tracker. Feedback is also accepted via email (rbarnes@umn.edu), though this should be used only in cases wherein the issue tracker is not available.