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Team name: Lucky 13

Predicting Water Impacts from Current and Future Mining in the Black Hills, SD

(w/ a Digital Twin framework)

Sacred Sites in the Black Hills region, SD

Team (lucky) 13:

Lucky 13 Project Workflow 2 (made in Claude Cowork) Lucky 13 Project Workflow 1 (made in Claude Cowork)

Data Dashboard

Dashboard Screenshot (link to work soon) Dashboard Screenshot (link to work soon)

People

Name Affiliation Contact Github
Naupaka Zimmerman University of Kansas naupaka@gmail.com naupaka
Lala Kounta Michigan State University kountalala@msu.edu lkounta
Lilly Jones CIRES Earth Lab, CU Boulder lijo8146@colorado.edu daearconsulting
Joni Tobacco Salish Kootenai College jktobacco@gmail.com jonit12324
Elisha Yellow Thunder Oglala Lakota College/SDSU eyellowthunder@olc.edu wakinyanzi
C. Jason Tinant Oglala Lakota College jtinant@olc.edu cjtinant
Chong Seok Choi Cornell University cc3226@cornell.edu chongseokchoi
Sankung Fatty -- fattysankung9@gmail.com --
Al Kaushkis -- akuslikis@gmail.com --
Max Cook Colorado State University maxwell.cook@colostate.edu maxwellCcook
Erica Laundreaux -- erica.lx32@gmail.com --

Team Photo

Team photo

Team Norms and Decision Making

Our team norms:

  • Define everything:
  • Goals:
  • To predict water impacts from current and future mining in the Black Hills, SD.
  • Raise awareness on Tribal issues of mining impacts.

  • Grow and share our data science knowledge.

  • transparency and processes

  • Include only open data

  • Centralize in repository

  • How we will keep a team log

  • How we protect others intellectual property
  • What is our AI authorship agreement
  • What are our Possible pitfalls
  • What is our decision making strategy:

...

Our product(s) 📣

Short term:

  • Acquire mining permit information and geospatial locations of proposed and current mine sites
  • Set up agentic AI to scrape and extract information from mining permits
  • Gather and harmonize (agentic LLM) input data including:
    • Topography (DEM)
  • Climate (PRISM, Temp/Precip)
  • Geologic Maps
  • Land Use / Land Cover
  • Stremflow data (USGS)
    • streamstats tool
  • OpenET for Evapotranspiration data
  • Snow cover
  • SSURGO (soils)

Long term:

  • Integrate two models for water quality monitoring:
    • SWAT (Soil and Water Assessment Tool)
  • Stream Vulnerability Index

Day 2 morning whiteboard or notes photo

Morning whiteboard or notes showing the question, hypotheses, and context we used to start Day 2.

Our question(s) 📣

Our working question:

How are existing and proposed mine sites in the Black Hills region of South Dakota impacting water resources downstream? How are sacred sites in Black Hills geogrpahically related to proposed mining exploration?

What would count as progress:

Progress might look like a framework for a Digital Twin for the Black Hills region that can integrate water impact models (e.g., SWAT). A case study integrating mining permits and water modeling would be ideal! Eventually, we want a dashboard to allow users to explore potential impacts at a gievn location.

Hypotheses/Intentions [Optional: probably not relevant if you are creating an educational tool]

Why this matters (the “upshot”) 📣

This matters because:

...

People who could use this:

...

Data sources we’re exploring 📣

!!! note "data exploration" Provide a snapshot showing some initial data patterns.

Add 2-4 promising data sources (links +1-line notes)

Exploration figure

Snapshot showing initial data patterns.

Promising data sources:

Methods/technologies we’re testing 📣

!!! note "methods" Add 2-4 methods/technologies we're testing (stats, models, viz).

  • LLM/agentic harmonization of input data (topography, weather, streamflow, geology, land use, etc.)
  • LLM-based PDF scraping of mine permit information (location, type, etc.)
  • SWAT (Soil and Water Assessment Tool)
  • Stream Vulnerability Index

View shared code{ .md-button }

Methods/technologies we are testing:

Method or technology What we tested Early note
... ... ...
... ... ...
... ... ...
... ... ...

Challenges identified

  • Finding mine permits and extracting geographic information
  • Water quality data is sparse

Visuals

Method or workflow visual

Next Steps

Short term:

Long term:

!!! note "Day 3 Tasks" Sythesis: highlight 2-3 visuals that tell the story; keep text crisp. Practice a 6-minute walkthrough of the homepage. Why -> Questions -> Data/Methods -> Findings -> Next

[Edit content below here in Markdown](https://github.com/CU-ESIIL/Summit_group_2026_13/edit/main/docs/index.md?plain=1#L203){ .md-button target="_blank" rel="noopener" }

Team members and collaborators who contributed to this project.

Findings at a glance 📣

Headline 1 — what, where, how much

...

Headline 2 — change/trend/contrast

...

Headline 3 — implication for practice or policy

...

Visuals that tell a story 📣

Story visual

Visual 1: the main pattern or output we want people to remember.

What’s next? 📣

Short term:

  • ...

Long term:

  • ...

Who should see this next

  • ...

Cite & Reuse

If you use these materials, please cite:

Summit Team. (2026). Summit Group 2026 Team 13 — Innovation Summit 2026. https://github.com/CU-ESIIL/Summit_group_2026_13

License: CC-BY-4.0 unless noted.