Hadjimichael-etal_2024_EarthsFuture

Scenario storyline discovery for planning in multi-actor human-natural systems confronting change

Antonia Hadjimichael^{1, 2*}, Patrick M. Reed ³, Julianne D. Quinn ⁴, Chris R. Vernon ⁵, Travis Thurber ⁵

¹ Department of Geosciences, The Pennsylvania State University, State College, PA, USA
² Earth and Environmental Systems Institute (EESI), The Pennsylvania State University, State College, PA, USA
³ School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA ⁴ Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA ⁵ Atmospheric Sciences & Global Change, Pacific Northwest National Laboratory, Richland, WA, USA

* corresponding author: [email protected]

Abstract

Scenarios have emerged as valuable tools in managing complex human-natural systems, but the traditional approach of limiting focus on a small number of predetermined scenarios can inadvertently miss consequential dynamics, extremes, and diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by exploring a wide range of possible futures and identifying those that yield consequential vulnerabilities. However, vulnerabilities are typically identified based on aggregate robustness measures that do not take full advantage of the richness of the underlying dynamics in the large ensembles of model simulations and can make it hard to identify key dynamics and/or storylines that can guide planning or further analyses. This study introduces the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced ``forensic''): a scenario discovery framework that addresses these challenges by organizing and investigating consequential scenarios using hierarchical classification of diverse outcomes across actors, sectors, and scales, while also aiding in the selection of scenario storylines, based on system dynamics that drive consequential outcomes. We present an application of this framework to the Upper Colorado River Basin, focusing on decadal droughts and their water scarcity implications for the basin’s diverse users and its obligations to downstream states through Lake Powell. We show how FRNSIC can explore alternative sets of impact metrics and drought dynamics and use them to identify drought scenario storylines, that can be used to inform future adaptation planning.

Journal reference

Hadjimichael, A., Reed, P.M., Quinn, J.D, Vernon, C.R., Thurber, T., Scenario storyline discovery for planning in multi-actor human-natural systems confronting change. Earth's Future (In Revision)

Data reference

Input data

Hadjimichael, A., Reed, P. M., Quinn, J. D., Vernon, C. R., & Thurber, T. (2023). Data for Hadjimichael et al. - Multi-actor, multi-impact scenario discovery (Version v1) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/2205512

To download this data, follow the instructions provided on the MSD-LIVE page.

Contributing modeling software

Model	Version	Repository Link	DOI
StateMod	15.0	https://github.com/OpenCDSS/cdss-app-statemod-fortran	-

Reproduce my experiment

1. Create new virtual environment and install all package dependencies using conda env create --file Hadjimichael-etal_2024_EarthsFuture.yml
2. Activate environment using conda activate Hadjimichael-etal_2024_EarthsFuture
3. Create directory /xdd_parquet_flow under /data
4. Download and install the supporting input data required to perform the analysis from Input data and save under ../data/xdd_parquet_flow
5. Go through the following notebooks in the workflow directory to re-create these results:

Script Name	Description	How to Run
classify_naturalized_streamflows-drought.ipynb	Script to perform drought classification for historic and synthetic streamflows (Fig. 3)	Execute notebook
streamflow_distribution_changes.ipynb	Script for drought classification across rolling windows (Figs. 4, S1-3)	Execute notebook
hive_plots_drought_impacts-history.ipynb	Script to generate hive plot and storyline for historically-informed conditions (Figs. 8-9)	Execute notebook
hive_plots_drought_impacts-non_stationary.ipynb	Script to generate hive plot and storyline for historically-informed conditions (Figs. 8, 10, 11, and 14)	Execute notebook

6. To recreate Fig. 12, analysis is performed in historic_analysis.xlsx
7. To recreate Fig. 13, analysis is performed in diversions_admin.xlsx