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LISFLOOD model python module

Project description

Lisflood OS

This repository hosts source code of LISFLOOD model. Go to Lisflood OS page for more information.

Other useful resources

Project Documentation Source code
Lisflood Model docs (this repository)
User guide
Lisvap Docs
Calibration tool Docs
Lisflood Utilities
Lisflood Usecases

Notes for Release 3.2.0

This release includes the following changes:

  • a new xarray reader for the water abstraction demand maps;
  • a unit test to verify the functioning of the reader for the water abstraction maps;
  • bug fixes and improvements to the caching function and the chunking function;
  • improvements of the management of latitude and longitude grids to allow maximum precision of the reference system;
  • improvements of the definition of the output variables.

All of the changes above improve the code capability to handle input and outputs. The modelling of the hydrological processes has not been changed.

IMPORTANT NOTE: the results of the unit tests of this release are different from the results of the unit tests of the release 3.1.1. The differences are due exclusively to a different use of of the optional moduels (e.g. groundwatersmooth) within the .xml setttings file of the unit tests. When using the same settings of .xml file, and the same dataset, v3.1.1 and v3.2.0 provide the same results.

Quick start

You can use conda environment to easily install dependencies.

  • Install miniconda
  • Create a conda env named "lisflood" and install dependencies:
conda create --name lisflood python=3.7 -c conda-forge
conda activate lisflood
conda install -c conda-forge pcraster
  • Install lisflood-model pypi package
pip install lisflood-model

Command above will also install the executable lisflood in the conda env, so that you can run LISFLOOD with the following:

lisflood /absolute_path/to/my/local/folder/TestCatchment/settings/cold_day_base.xml

You can also clone the repository which contains datasets to test the model. Follow this instruction for a basic test (included in this repository under tests/data/TestCatchment1)

  • Clone the master branch of this repository (you need to have git installed on your machine).
git clone --single-branch --branch master
  • Install requirements into a python 3 conda env
conda create --name lisflood python=3.7 -c conda-forge
conda activate lisflood
conda install -c conda-forge pcraster
cd lisflood-code
pip install -r requirements.txt

If you don't use conda but a plain virtualenv, you need to install PCRaster and GDAL by your own and include its python interface in PYTHONPATH environment variable. For details, please follow instruction on official docs.

  1. Compile the cython module kinematic_wave_parallel_tool

To compile this Cython module to enable OpenMP multithreading (parallel kinematic wave):

  • Delete the files *.so (if any) in directory hydrological-modules

  • Inside the hydrological_modules folder, execute "python build_ext --inplace"

Important: the module has to be compiled on the machine where the model is run - the resulting binary is not portable.

Then in the settings file the option "numberParallelThreadsKinematicWave" may take the following values: - "0" : auto-detection of the machine/node's number of CPUs (all CPUs are used minus 1) (do not set it if other simulations are running on the same machine/node) - "1" : serial execution (not parallel) - "2", "3", ... : manual setting of the number of parallel threads. (if exceeding the number of CPUs, the option is set to "0") -->

<textvar name="numCPUs_parallelKinematicWave" value="3"/>
  1. Run a cold run for the test catchment

Now your environment should be set up to run lisflood. Try with a prepared settings file for one of the two test catchments:

python src/ tests/data/TestCatchment/settings/cold_day_base.xml

If the command above successed without errors, producing into tests/data/TestCatchment1/outputs folder, your lisflood installation was correct.

Docker image

You can use the updated docker image to run lisflood, so without taking care to install dependencies on your system.

Pull image from repository:

docker pull jrce1/lisflood:latest

Run test catchments in image:

docker run -v /absolute_path/to/my/local/folder:/usecases jrce1/lisflood:latest usecases

After this command, you can find all files to run tests against catchments under the directory you mapped: /absolute_path/to/my/local/folder/


Now, you can run LISFLOOD as a docker container to test included catchments. Only thing you need to do is to map the test catchment folder to the container folder input, by using -v option.

In the XML settings file, all paths are adjusted to be relative to the very same settings file, so you don't need to edit paths, as long as you keep same folders structure.

Execute lisflood with a Docker image:

docker run -v /absolute_path/to/my/local/folder/LF_ETRS89_UseCase:/input jrce1/lisflood /input/settings/cold.xml

Once LISFLOOD finished, you find reported maps in /absolute_path/to/my/local/folder/LF_ETRS89_UseCase/out/ folder.

Pypi packaged LISFLOOD

LISFLOOD is also distributed as a standard python package. You can install the pip package in your Python 3 virtualenv:

pip install lisflood-model

Command above will also install the executable lisflood in the virtualenv, so that you can run LISFLOOD with the following:

lisflood /absolute_path/to/my/local/folder/TestCatchment/settings/cold_day_base.xml


Please note that there are known issues when installing the LISFLOOD code on Windows (source code and pypi package). We cannot provide Windows support and strongly recommend using LINUX to install the LISFLOOD code. Windows users are recommended to execute LISFLOOD with a Docker image.


If you find an issue in our code, please follow the GitHub flow to propose your changes (Fork, commit your changes and ask for a Pull Request). You are required to run unit tests during your development and before to propose a pull request.

To execute unit tests:

pytest tests/

Furthermore, before to propose a pull request, there are additional tests we ask you to execute:

pytest tests/ -m "slow

These tests could take 30 minutes or several hours, depending on your machine.

You can find full description and implementation details at Test documentation page.

Note: If yuor pull request is about a new feature you may want to integrate in LISFLOOD, ensure to include tests with good coverage for it.

For more info about pytest, see official website.

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