Given that much of the world is ungauged and that even within the United States, we have entire regions with sparse data, we need more insight into future river flows on many different time horizons. At the AGU Fall Meeting 2022, we presented some challenges and successes of building an operational, deep-learning hydrological modeling system to scale commercially and globally to guide water management efficiently.
The HydroForecast model is a theory-guided, statistical prediction model that sources globally and publicly accessible data inputs from remote-sensing observations and meteorological datasets. In our presentation, we shared how we use an operational neural network distributed modal to make predictions in data-sparse regions and construct historical reanalysis streamflow records in ungauged, human-altered basins.
