Building a Wildfire and Landslide Digital Twin by Conversation
How RenoTwin, a 3D digital twin of the Reno region for wildfire spread and post-fire debris flows, was built by talking to an AI coding agent: every prompt, what the agent did, and what fire scientists, hydrologists and engineers would still need to verify.
RenoTwin is an interactive 3D digital twin of a 51 × 113 km region around Reno, Nevada, for studying wildfire spread and post-fire debris flows on a desktop or a Meta Quest 3 headset. The first version, which simulated the 2026 Hawk Fire on Peavine Peak, took about one day. It went from a single prompt to real terrain from USGS elevation data, the fire's mapped perimeter, burn severity from satellite imagery, and a Rothermel surface fire spread model over LANDFIRE fuels. After calibrating one spread factor, the simulated fire fills the mapped perimeter in the real 106 hours. The rest was added intermittently over the following three weeks, as time allowed:
- a debris-flow likelihood model after Staley et al. (2017), with rainstorms and snow
- about 237,000 OpenStreetMap buildings and a detailed downtown
- the Floriston Fire, modelled while it was still burning (it has since been put out), from the live National Weather Service observations of the time
The human wrote 51 prompts and no code. The agent wrote the Python data pipeline, the Unity 6 application, the shaders, and the tools for testing without opening Unity. This playbook records how the conversation steered the work: what the human had to know, what the agent could and could not do, where it was wrong, and how those errors were caught. It also lists the parts that fire scientists, hydrologists and geotechnical engineers would need to verify before anyone relied on the results. No domain expert has reviewed this work.