This repository is for a research project, in partnership with the WWF (Jeff Opperman, Natalie Sharbol), focused on understanding and examining the impact of dams on the Connectivity Status Index of Free Flowing Rivers on a global scale.

Only a third of the world’s large rivers still have their free-flowing status and dams are the primary cause of this loss in river connectivity. However, we have a poor understanding of whether different dam purposes (e.g., hydroelectric, water supply) affect river connectivity differently and whether these relationships vary across regional settings.

For this project, we seek to examine patterns among dam purpose and metrics of river connectivity across the globe. We will compile global datasets on dam infrastructure and river hydro-geometric and connectivity properties to quantify relationships of dam impacts on river connectivity. We originally conducted a pilot study examining relationships in Africa and were able to develop the project methodologies and workflow to the globe.

The attribute join of Global Reservoir and Dam dataset (GRanD) and World’s Free-Flowing Rivers datasets (FFR)

This repository could be used for future projections on how dams might change a river or how dam removal may benefit a river.

• numpy
• pandas
• matplotlib
• geopandas
• seaborn
• zipfile
• earthpy

GRanD Dataset

• “Global River and Dam Database” (GRanD) Lehner, B., C. R. Liermann, C. Revenga, and others. 2011. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Frontiers in Ecology and the Environment 9: 494–502. doi:10.1890/100125 *Provides vector point locations for 7320 total recorded dams and reservoirs around the world as point shapefiles.

FFR Dataset

• “Mapping the world's free-flowing rivers Geodatabase” (FFR) Grill, G., B. Lehner, M. Thieme, and others. 2019. Mapping the world’s free-flowing rivers. Nature 569: 215–221. doi:10.1038/s41586-019-1111-9
• The dataset consists of a geometric network of the global river system and associated attributes, such as hydro-geometric properties, as well as connectivity metrics calculated using the methodologies described in the research article.

GRanD-FFR Dataset Merge

• The dataset we created that merged the GRanD and FFR databases. It is a csv file that was joined by the GOID attribute

See Zenodo record for version-specific citation

• .gitignore: file containing the appropriate lines to ignore .ipynb checkpoints for Jupyter Notebook
• environment.yml: instructions to set up the environment used in the repository
• LICENSE: allows others to be able to use, change and distribute as an open source
• main.sh: contains bash commands
• README.md: contains project description, how to run the workflow and example usage
• notebooks directory containing
  • 01_grand_ffr_merge_global_data.ipynb: Jupyter Notebook containing code to merge GRanD and FFR datasets
  • 02_dam_river_flow_analysis_blog.ipynb:Jupyter Notebook containing the code and markdown to analyze global trends of dams river function
  • 03_dam_river_flow_analysis_blog.html: Hides code of Jupyter Notebook so only markdown and visuals are displayed
  • data_merge_fxn.py: stores functions needed to run GRAND_FFR_merge_global_data.ipynb
  • plot_functions.py: stores functions need to run blog.ipynb

1. Clone the repository https://github.com/krantz23/ffr-dam-impact, or download and decompress the zip file (see the green button for Clone or download).
2. Open a terminal and change directories to this directory (cd ffr-dam-impact).
3. Follow steps in Required Installations (below)
4. Launch Jupyter Notebook and open desired exploration or final notebook from the notebooks sub-directory.
5. If you wish to see how the merge was done you can run the "01_grand_ffr_merge_global_data.ipynb" notebook. This is very data rich and will take time and lots of processing power.
6. The data we got from the merging of the datasets is available on figshare and is accessed through earthpy in our notebook "02_dam_river_flow_analysis_blog.ipynb".
7. In the notebook, Run All Cells.

• In order to run all packages, we used the Earth Data Analytics python environment.

Or, run Bash script

1. Activate workflow environment $ conda activate ffr-dam-env
2. Make sure you are in the correct directory $ cd ffr-dam-impact
3. Run bash script $ .main.sh

Install the environment.yml on your Local Computer. About Conda Environments: https://conda.io/docs/user-guide/tasks/manage-environments.html

To install the environment in your Anaconda installation, run the following in the Terminal:

conda env create -f environment.yml

Note that it takes a bit of time to run this setup. Also note that for the code above to work, you need to be in the directory where the environment.yml file lives.

To activate this environment once it has been installed, run the following in the Terminal: On Windows, Mac, and Linux: conda activate environment

Others can use the commands in this repository to link the GRanD and FFR datasets to look at other relationships among dam attributes (e.g., dam height, dam age) and river connectivity characteristics. The code is specific to the GRanD and FFR geospatial shapefile datasets, however, if these datasets are updated over time or dam and river data are available from other sources it would be possible to adapt the notebooks to perform similar analyses. One could also feasibly use other vector point file dam location datasets along with hydrology datasets to run similar analysis.