This repository holds a set of Jupyter notebooks for the interactive evaluation of river management measures, the RiverScape Python package and necessary input data.
For a detailed description of the concepts, models and study area we refer to the reference publications Straatsma and Kleinhans (2018), Straatsma et al. (2017) and Straatsma et al. (2019).
A few steps are required to run the Jupyter notebooks:
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You will need a working Python environment, we recommend to install Miniconda. Follow their instructions given at:
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Download the requirements file and use it to install all modules required to run the Jupyter notebooks:
conda env create -f requirements.yaml
The requirements file will create a environment named riverscape using Python 3.8. In case you prefer a different name or Python version you need to edit the requirements.yaml file.
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Activate the environment in a command prompt:
conda activate riverscape
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Clone or download this repository:
git clone https://github.com/UU-Hydro/RiverScape.git
This will clone RiverScape into the current working directory.
General information on Jupyter notebooks and manuals can be found here. The user guide and short reference on Conda can be found here.
Activate the environment in a command prompt:
conda activate riverscape
Change to the RiverScape scripts directory. You can start Jupyter from the command prompt and afterwards select a notebook in your browser:
jupyter-notebook
You can also open individual notebooks directly by specifying the filename, e.g. the intervention planning with:
jupyter-notebook intervent_parameter.ipynb
The following Jupyter notebooks are provided:
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Intervention planning: intervent_parameter.ipynb
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Evaluation of biodiversity: biodiversity_evaluation.ipynb
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Evaluation of implementation costs: cost_evaluation.ipynb
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Evaluation of landowner involvement: landowner_evaluation.ipynb
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Evaluation of an ensemble of pre-defined measures: measure_evaluation.ipynb
In case you want to run the evaluation notebooks without explicitly defining your own set of measures first you can load output data from a pre-defined set of measures. A single measure is included in the output folder. An ensemble of measures is provided as compressed file. Extract the file example_measures_waal.zip in the outputs folder. You'll get a example_measures_waal subfolder containing outputs of 17 measures.