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https://github.com/r-barnes/richdem

High-performance Terrain and Hydrology Analysis
https://github.com/r-barnes/richdem

big-data digital-elevation-model geosciences geospatial hydrologic-modeling hydrology

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High-performance Terrain and Hydrology Analysis

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README 

        
RichDEM

=======



[![ReadTheDocs](https://readthedocs.org/projects/richdem/badge/?version=latest)](https://richdem.readthedocs.io/)

[![Travis](https://travis-ci.org/r-barnes/richdem.svg?branch=master)](https://travis-ci.org/r-barnes/richdem)

[![DOI](https://zenodo.org/badge/7469158.svg)](https://zenodo.org/badge/latestdoi/7469158)



Author: Richard Barnes ([email protected])



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 offers a variety of flow metrics, such as D8 and D∞. It can flood or

breach depressions. It can calculate flow accumulation, slops, curvatures, &c.



RichDEM is available as a performant C++ library, a low-dependency Python

package, and a set of command-line tools.



Please cite RichDEM (see below).



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 as they provide justification of continued

development.



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:



    @manual{RichDEM,

      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.



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. Citations to particular algorithms will be printed whenever an app,

program, or library function is run. Such citations are prefixed by the

character `C` and look like:



    C Barnes, R., Lehman, C., Mulla, D., 2014. Priority-flood: An optimal depression-filling and watershed-labeling algorithm for digital elevation models. Computers & Geosciences 62, 117–127. doi:10.1016/j.cageo.2013.04.024



A typical academic citation might read as follows:



 > We performed hydrological corrections on our DEM using the Zhou (2016) algorithm implemented in RichDEM (Barnes 2016).



Compilation

-----------



To compile, first acquire the source code:



    git clone --recursive https://github.com/r-barnes/richdem



You will need a C++ compiler, *cmake*, and, optionally, *libgdal*. If you are

using *conda*, you can install these requirements in a new environment with

the following commands:



    $ conda create -n richdem

    $ conda activate richdem

    $ conda install --file=requirements.txt -c conda-forge



Then compile using the standard `cmake` sequence:



    $ mkdir build

    $ cd build

    $ cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo ..

    $ make -j 6    # Adjust to use more or fewer processors



If you do not want to build *richdem* with *gdal*, use the `-DUSE_GDAL=OFF`

option. To install *richdem*:



    $ cmake --install . --prefix /my/install/prefix



If you are using *conda*, `/my/install/prefix` should be `$CONDA_PREFIX`.



RichDEM includes some programs to process extremely large digital elevation

models, as well as for handling many DEM formats. Special prerequisites,

automatically detected by cmake, are needed for these to compile: GDAL, MPI, and

Boost. To install these on a Debian machine use:



    sudo apt install openmpi-bin libgdal-dev libopenmpi-dev libboost-iostreams-dev



As A Python Package

-------------------



### From source



After installing the *richdem* library (as described above), you can build

*richdem* as a Python package. To do this, you will of course need Python

(3.7+), which you can install with *conda*,



    $ conda install python



You can now build the *richdem* Python package,



    $ cd wrappers/pyrichdem

    $ pip install pybind11

    $ python3 setup.py install --user



### From conda



To install a pre-built version of *richdem* using *conda*,



    $ conda install richdem -c conda-forge



### From Google Colabs



You can compile and run RichDEM from source in Google Colabs by placing these

commands into a cell and running it.



    !pip install pybind11

    !git clone --recursive https://github.com/r-barnes/richdem.git ghrichdem

    !cd ghrichdem/wrappers/pyrichdem && pip install .



### From PyPI using pip



Get the package with:



    $ pip install richdem



And use:



    >>> import richdem



The command:



    >>> help(richdem)



provides all the relevant documentation.



As A Command-line Tool

----------------------



To get the command-line tools, install the Python package with:



    pip3 install richdem



The command-line tools are all named `rd_*`, so typing `rd_` on your command-

line and hitting tab a few times should give you the full list of what's

available.



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](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

-------------



Documentation is available at [richdem.com](https://richdem.com/).

The documentation is auto-generated from the many `README.md` files throughout

the codebase and the extensive comments in the source code.



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.**



Testing Methodology

===================



Simple algorithms are shown to be correct through visual inspection and

comparison against hand-crafted examples. Correctness for more complex

algorithms is often "boot-strapped" by comparing the results of simple

algorithms versus the complex algorithms on a large number of randomly-generated

datasets.



This is a work in progress. TODO



Correctness

===========



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](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



 * **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

          carrying on.



 * **r**: Amount of RAM used



 * **t**: Timing information: How long stuff took



 * **W**: Indicates a warning



All output data shall have the form:



      [=  [UNIT]]



The amount of whitespace 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](https://github.com/r-barnes/Barnes2013-FlatSurfaces)

 * Depression-filling algorithm. [Link](https://github.com/r-barnes/Barnes2013-Depressions)

 * Large dataset depression-filling algorithm. [Link](https://github.com/r-barnes/Barnes2016-ParallelPriorityFlood)

 * Large dataset flow accumulation algorithm. [Link](https://github.com/r-barnes/Barnes2016-ParallelFlowAccum)



Publications

============

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., 2017. Parallel non-divergent flow accumulation for trillion cell digital elevation models on desktops or clusters. Environmental Modelling & Software 92, 202–212. doi:[10.1016/j.envsoft.2017.02.022](https://doi.org/10.1016/j.envsoft.2017.02.022)



* Barnes, R., 2016. Parallel priority-flood depression filling for trillion cell digital elevation models on desktops or clusters. Computers & Geosciences 96, 56–68. doi:[10.1016/j.cageo.2016.07.001](https://doi.org/10.1016/j.cageo.2016.07.001)



* Barnes, R., Lehman, C., Mulla, D., 2014a. An efficient assignment of drainage direction over flat surfaces in raster digital elevation models. Computers & Geosciences 62, 128–135. doi:[10.1016/j.cageo.2013.01.009](https://doi.org/10.1016/j.cageo.2013.01.009)



* Barnes, R., Lehman, C., Mulla, D., 2014b. Priority-flood: An optimal depression-filling and watershed-labeling algorithm for digital elevation models. Computers & Geosciences 62, 117–127. doi:[10.1016/j.cageo.2013.04.024](https://doi.org/10.1016/j.cageo.2013.04.024)



* 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](http://rbarnes.org/section/sci/2011_barnes_distributed.pdf)



* Horn, B.K.P., 1981. Hill shading and the reflectance map. Proceedings of the IEEE 69, 14–47. doi:[10.1109/PROC.1981.11918](http://dx.doi.org/10.1109/PROC.1981.11918)



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



* 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.



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



* 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:[10.1016/j.cageo.2016.02.021](http://dx.doi.org/10.1016/j.cageo.2016.02.021)



Credits

=======



RichDEM has been developed and tested using computational resources provided by

the [Minnesota Supercomputing Institute](https://www.msi.umn.edu/) (MSI) and the

U.S. National Science Foundation's [XSEDE](https://www.xsede.org/).



Funding for the development of RichDEM has been provided by the [Legislative-Citizen Commission on Minnesota Resources](http://www.lccmr.leg.mn/) (LCCMR), the U.S. National Science

Foundation [Graduate Research Fellowship](https://www.nsfgrfp.org/), and the U.S. Department of Energy

[Computational Science Graduate Fellowship](https://www.krellinst.org/csgf/).



Feedback

========



_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 ([email protected]), though

this is highly discouraged as it does not provide a resource for others.