Sea Level Impacts Input Dataset by Elevation, Region, and Scenario (SLIIDERS)

This repository hosts the code used to create the SLIIDERS-ECON and SLIIDERS-SLR datasets. The SLIIDERS datasets contain current and forecasted physical and socioeconomic metrics from 2000-2100 - organized by coastal segment, elevation slice, and scenario - for use as inputs to global coastal climate impacts research.

SLIIDERS-ECON contains socioeconomic variables, varying horizontally and vertically over space. SLIIDERS-SLR contains Monte Carlo projections of Local Sea Level Rise under different emissions and ice sheet dynamics assumptions, based on the outputs of LocalizeSL. Coastal segments in SLIIDERS-ECON can be matched to gridded LSLR projections in SLIIDERS-SLR via the SLR_site key.

All work utilizing this code or the resulting SLIIDERS datasets should cite Depsky, Bolliger et al. 2022 (in prep.). See License for details.

Installation

Most users will want to just use the datasets directly, accessible at the DOIs linked above. If you wish to recreate and/or modify the datasets, which we encourage, you will need to run the Jupyter notebooks in this repository. A collection of helper functions, organized into a Python package, is necessary to run the notebooks and can be found within the sliiders directory. A simple pip install will install this package

pip install -e sliiders

In addition, you will need to have Dask Gateway installed and configured to execute the parallel, Dask-backed workflows contained in this repo. Advanced users can use other Dask Cluster backends (including simply running Dask Distributed locally), but doing so will require modifying the cluster setup portion of notebooks that employ dask.

A Conda environment file better specifying a full environment needed to execute all of the workflows in this repo is in development and will be posted when complete.

Filepaths and other settings

All filepaths and settings for the notebooks can be found within settings.py. Before moving onto executing different parts of this repository, please adjust these settings to match your directory structure and desired values. Most values will not need to be updated unless you change a particular dataset. However, at minimum you should:

Update the DIR_DATA filepath within this file to point to the root directory within which all of the data consumed and generated by this workflow will live.
Update DASK_IMAGE to point to a Docker Image that you will use for Dask workers (advanced users not using Dask Gateway may not need this parameter).

Package Structure

sliiders: Contains .py files with essential settings and functions for the SLIIDERS workflow
- settings.py: Contains essential settings, including various parameters and data storage directories
- gcs.py: Contains functions related to the use of Google Cloud Storage (GCS). Users running workflows locally or on a different cloud provider are encouraged to contribute similar modules for other contexts.
- io.py: Contains various I/O-related functions
- spatial.py: Contains functions for executing spatial and geographic operations including those related to shapefiles, grid-cell level operations, and more.
- dask.py: Contains utility functions for working with dask clusters
- country_level_ypk.py: Contains functions for cleaning and working with country-level socioeconomic data, especially for the workflow in notebooks/country_level_ypk
notebooks: contains the workflows to create SLIIDERS-ECON and SLIIDERS-SLR.

Instructions (Generating SLIIDERS-ECON and SLIIDERS-SLR)

To generate SLIIDERS-ECON and SLIIDERS-SLR, please follow the directions in notebooks/README.md and other readme files in the subdirectories within notebooks to learn about how to execute the workflows.

The list and order of notebooks to run is reproduced in full here, along with any necessary manual steps. Click the docs link for each workflow to navigate to the relevant directory's page.

create-SLIIDERS-SLR (docs): Workflow to generate SLIIDERS-SLR
1. download-ifile-to-gcs.ipynb
2. convert-mat-version.ipynb
3. generate-projected-lsl.ipynb
4. retrieve-num-gcms.ipynb
5. process-localizesl-output.ipynb
create-SLIIDERS-ECON (docs): Workflow to generate SLIIDERS-ECON
1. download-sliiders-econ-input-data.ipynb
2. country_level_ypk (docs): Workflow for organizing and projecting GDP (Y), population (P), capital stock (K), and related variables for historical (1950-2020) and future (2010-2100) timelines.
  1. ypk1_prep_clean.ipynb
  2. ypk2_reorg_and_impute_ypk.ipynb
  3. ypk3_demo_ratios_historical_reg.ipynb
  4. ypk4_impute_hist_capital.ipynb
  5. ypk5_projected_yp.ipynb
  6. ypk6_projected_capital.ipynb
3. exposure (docs): Workflow to generate present-day exposure grid.
  1. 1-create-coastline-segments.ipynb
  2. 2-create-segment-regions.ipynb
  3. 3-fill_missing_litpop_with_geg.ipynb
  4. 4-vectorize-wetlands.ipynb
  5. 5-get_positive_elev_tiles.ipynb
  6. 6-generate_datum_conversion_grid.ipynb
  7. 7-create_dem_mss.ipynb
  8. 8-generate_protected_areas.ipynb
  9. 9-generate_exposure_tiles.ipynb
  10. 10-combine_exposure_tiles.ipynb
4. create-SLIIDERS-ECON.ipynb

The resulting datasets can be found at these paths, defined in settings.py:
SLIIDERS-ECON: PATH_SLIIDERS_ECON
SLIIDERS-SLR: PATH_SLIIDERS_SLR

Support

Please file an issue for any problems you encounter.

Contributing

We encourage community contributions. At the moment, we have no contribution template. Please fork the project and file a Merge Request to propose your addition. Clearly define the contribution that the Merge Request is making and, when any issues have been resolved, we will merge the new code.

Authors

The original authors of this code include:

Daniel Allen
Ian Bolliger
Junho Choi
Nicholas Depsky

License

This code is licensed under the MIT License. However, we request that wherever this code or the SLIIDERS datasets are used, that the underlying manuscript (Depsky et al. 2022) is cited. A citation guide will be posted once the manuscript preprint is available.

ClimateImpactLab / sliiders-archived