Summer School 2023: Convergence Science in Action

Summer School 2023: Convergence Science in Action

UCAR Campus in Boulder, Colorado · August 7-11, 2023

The Institute for Geospatial Understanding through an Integrated Discovery Environment, I-GUIDE, led a week-long Summer School in August 2023 on Convergence Science in Action. Certain complex and compelling societal problems require a convergent research approach, when knowledge, tools, and modes of thinking from multiple disciplines are strategically integrated and merged. 35 graduate students and early career scholars collaborated with project members of I-GUIDE to develop novel solutions to complex problems that rely on computation- or data-intensive geospatial data science. The participants experienced the collaborative and professional interactions that are key to comprehensively working on convergence science problems, including geoethics, geo-enabling reproducible and open science, geovisualization, and geoAI and spatial data science via cloud and high-performance computing.

This year our application areas included topics such as climate change, biodiversity, water security, sustainable development, and implications of these as studied via social science data.

View the playlist of all 2023 Summer School presentations on the I-GUIDE YouTube channel.

Organizers


Summer School 2023 Team Projects and Presentation Recordings

Characterizing the Spatiotemporal Changes of Wildland Urban Interface in the United States

Using Google Earth Engine, this team mapped and analyzed  the changing extent of the wildland-urban interface from 2011-present across the continental US and characterized risk and socioeconomic factors using NASA SEDAC’s Social Vulnerability Index and LANDFIRE’s Wildfire Risk to Communities data to evaluate areas at greatest risk to wildfire.

Team Lead: Eric Jensen, Desert Research Institute

Team Members


Margins of Adaptation to Human Heat Stress: Local, National, and Global Socioeconomic Responses

With a grid-resolving economic model (SIMPLE-G), this team simulated how heat stress affects labor productivity in the agricultural sector and generates cascading impacts on farming for the continental US at fine spatial scales.

This team was selected as the winner of the 2023 Audience Choice Award at the 2023 Summer School and was invited to present at the 2023 I-GUIDE Forum at Columbia University, New York City in October 2023.

Team Lead: Zhan Wang, Purdue University

Team Members


Improving the 3D Representation of Rivers in Digital Elevation Models (DEM)

This team demonstrated how more accurate modeling of stream and river banks makes for important improvements in estimating flooding impacts and other hydrological concerns.

Team Lead: Sayan Dey, Saint Louis University

Team Members


Investigating Hydrological Responses and Shrinkage Patterns of the Great Salt Lake

Over the last decade, the Great Salt Lake has experienced a significant loss of water due to a combination of climate and anthropogenic changes, and this group evaluated the effectiveness of the National Water Model’s retrospective simulations in capturing changes in inflows to this terminal lake.

This team was selected as the winner of the Judges Choice Award at the 2023 Summer School and was invited to present at the 2023 I-GUIDE Forum at Columbia University, New York City in October 2023.

Team Lead: Irene Garousi-Nejad, CUAHSI

Team Members


International Supply Metacoupling in a Meta-Coupled World

Measuring how natural and social disruptive factors have impacts far and wide (the focus of tele- and metacoupling) was demonstrated by this group with a focus on the global environmental and socioeconomic linkages in the medical supply trade chains.

Team Lead: Min Gon Chung, Michigan State University

Team Members


GeoAI Model Transformation

New foundation GeoAI models, recently released by NASA and IBM, were the focus of this team as they imported these into I-GUIDE’s platform and managed to fine-tune a model with a novel application for estimating built-up areas.

Team Leads:

Team Members

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