Cities increasingly maintain a live digital twin: a 3D model of streets, drainage and terrain fed by real-time rainfall and river-level sensors. The twin predicts which low-lying blocks will flood next and helps planners decide where a limited resilience budget buys the most protection. This scene models a small city in a river valley — buildings on lower ground flood first, exactly as in a real hydrology model.
Real municipal digital twins (e.g. in Rotterdam, Singapore and Hull) combine hundreds of IoT rain/river gauges with hydraulic models refreshed every few minutes, letting officials simulate "what if it rains 40mm in an hour" scenarios before the storm even arrives.
A live 3D digital twin of a river-valley city: IoT sensors report which blocks are flooding, drainage capacity and storm intensity set the water level, and a resilience budget funds flood barriers at the highest-risk locations first.
Flood depth depends on both terrain elevation and drainage capacity, not rainfall alone. Investment is most effective when it targets blocks pre-scored as highest-risk against a reference storm — exactly how real capital-planning models prioritise limited budgets.
Raise storm intensity and watch water rise fastest in the river valley. Increase drainage capacity to offset it. Add resilience budget to fund gold flood-barrier rings at the riskiest blocks and watch their sensors turn green even as the water keeps rising.
Cities such as Rotterdam and Singapore run hydraulic digital twins refreshed every few minutes from hundreds of rain and river-level sensors, letting planners test "what if" storm scenarios before they happen.