GAUGECAST++: A Physics-Informed Latent Forecasting System for Localized Flood Prediction

Sparse-gauge flood forecasting is challenging because river-level dynamics combine transport, storage, rainfall forcing, and sharp event-scale phase shifts. We introduce GaugeCast++, a physics-informed latent forecasting framework that learns a coordinate system in which flood dynamics become easier to evolve. Its key component is a gauge-conditioned pullback operator, which reparameterizes the forecast field and suppresses residual transport, yielding a more stable latent evolution than direct prediction in observation space. To address delayed or shifted flood peaks, GaugeCast++ adds semigroup-consistent phase-transport learning, enforcing agreement between long-horizon forecasts and composed short-horizon rollouts in both amplitude and event phase. Finally, a decoupled inverse rainfall ambiguity module estimates uncertainty caused by imperfect future rainfall forcing. Experiments across six UK river-gauge catchments show consistent gains in event-level MSE, NSE, peak timing, and peak magnitude against strong neural forecasting baselines.