chowdhury-etal_2023_hydropower

This repository contains Jupyter Notebooks and instructions to reproduce the results of the following paper:

Hydropower Expansion in Eco-Sensitive River Basins under Global Energy-Economic Change

AFM Kamal Chowdhury^1*, Thomas Wild², Ying Zhang², Matthew Binsted², Gokul Iyer², Son H. Kim², and Jonathan Lamontagne³

¹ Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, United States

² Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD 20740, United States

³ Department of Civil and Environmental Engineering, Tufts University, Medford, MA 02155, United States

* corresponding author: kchy@umd.edu

Citation: Chowdhury, A.F.M.K., Wild, T., Zhang, Y. et al. Hydropower expansion in eco-sensitive river basins under global energy-economic change. Nat Sustain 7, 213–222 (2024). https://doi.org/10.1038/s41893-023-01260-z

Summary

In this study, we investigate how rapid economic growth and transition to low-carbon energy may impact hydropower development, with potential countervailing effects of increasingly cost-competitive variable renewable energy (VRE). We explore the effects of these forces on hydropower expansion in the world's 20 most eco-sensitive river basins, that have substantial untapped hydropower potential and ecological richness. Our investigation is based on the Global Change Analysis Model (GCAM), an integrated model of global energy-water-economy dynamics. The Jupyter Notebooks provided in this repository, in combination with the GCAM outputs and other data provided in this Zenodo repository, can be used to conduct our key analysis, and reproduce the relevant results.

Data

The following data, provided in this Zenodo repository, will be required to reproduce our results.

Folder name	Contents
other_data	hydro_fish_stats_by_basins.csv : Basin-scale untapped hydropower potential, planned hydropower capacity, fish richness, and fish catch.
	eco_sensitivity_rank_by_basins.csv : Eco-sensitivity ranks of the global basins, an output file based on our analysis of the above-mentioned four hydropower-fish parameters.
	L227.SmthRenewRsrcCurves_hydro.csv : Basin-region-scale exploitable hydropower potential and resource supply curves.
	basin_shapefile : Shapefile of the GCAM basin-regions.
gcam_outputs	scenario_names.csv : Names of the eight scenarios used in this study.
	scenario_outputs : GCAM outputs for each scenario that are used in our analysis. These outputs are generated using GCAM version 5.3 with an endogenous representation of hydropower, as described in Zhang et al. (2022). The instructions on running GCAM scenarios are available here.

Jupyter Notebooks

The analysis and plotting will require the following Jupyter Notebooks, written in Python 3.8.

• plot_fig1_selection_of_eco_sensitive_basins.ipynb : Define eco-sensitivity rank of the global river basins based on four hydropower-fish parameters: untapped hydropower potential, planned hydropower capacity, fish richness, and fish catch. Also, plot the parameters on a scatter plot and identify the world's top 20 eco-sensitive river basins.

• plot_fig2_overall level_of_hydropower_deployment.ipynb : Estimate and plot the overall level of hydropower deployment in the world's top 20 eco-sensitive river basins for 2015 and 2050 under eight scenarios.

• plot_fig3_basin_region_scale_hydropower_deployment.ipynb : Estimate and plot the basin-region-scale level of hydropower deployment in the world's top 20 eco-sensitive river basins for 2015 and 2050 under eight scenarios.

• plot_fig4_uncertainty_in_basin_scale_mid_century_deployment.ipynb : Estimate and plot the min-max range of basin-scale hydropower deployment (as share of exploitable potential) in 2050 across the eight scenarios for the world's top 20 eco-sensitive river basins.

Reproduce Our Results

Our results can be reproduced in the following steps:

• Install required Python packages, including geopandas and plotly, preferably in a dedicated virtual environment.

• Download gcam_outputs and other_data from the Zenodo repository and save them in the data folder.

• Run the Jupyter Notebooks to obtain the results as mentioned above.

• In the Notebooks, use save_outputs = 'yes' to save the output plots in the figures folder.