Advancing integrated continental-scale hydrologic forecasting through democratized data and ML-accelerated modeling

Today water and resource managers face a significant challenge managing systems that are rapidly evolving in a warming climate, where historical observations are no longer a reliable guide. Capturing interactions from bedrock to treetops is important to understand water stresses and is a critical gap in our current models. Simulations with integrated hydrology models (that solve the 3D Richards' equation and 2D shallow water equations in a globally-implicit manner) provide robust results out to continental scales, yet are computationally expensive. Groundwater-surface water are tightly coupled and can have a large impact on watershed dynamics, yet are challenging for all models to accurately resolve.

We have developed a hybrid physics-based, machine learning digital twin over the entire continental US (CONUS). This proof-of-concept forecast system runs operationally, providing all hydrologic states and fluxes from bedrock to the top of the canopy at hourly timesteps and greater than 1km resolution. Automated comparison to observations is enabled through the HydroData platform, supporting continuous evaluation and model improvement. This talk will highlight the technical challenges of combining integrated hydrologic modeling with machine learning in a national forecast system, including physics-based approaches that improve solver performance by more than an order of magnitude for continental-scale simulations. Machine learning emulators embedded within integrated hydrology models can also drastically reduce computational burden and provide 30m spatial resolution for groundwater and surface water. We advance a vision that deploys these models and openly available forcing and parameter datasets to understand future water challenges from local to continental scales.