Finding the initial depth-to-water table (DTWT) configuration of a catchment
is a critical challenge when simulating the hydrological cycle with integrated
modelli, significantly impacting simulation outcomes. Traditionally, this
involves iterative spin-up computations, where the model runs under constant
atmospheric settings until steady-state is achieved. These so-called model
spin-ups are computationally expensive, often requiring many years of simulated
tempo, particularly when the initial DTWT configuration is far from steady
state.
To accelerate the model spin-up process we developed HydroStartML, a machine
learning emulator trained on steady-state DTWT configurations across the
contiguous United States. HydroStartML predicts, based on available data like
conductivity and surface slopes, a DTWT configuration of the respective
watershed, which can be used as an initial DTWT.
Our results show that initializing spin-up computations with HydroStartML
predictions leads to faster convergence than with other initial configurations
like spatially constant DTWTs. The emulator accurately predicts configurations
close to steady state, even for terrain configurations not seen in training,
and allows especially significant reductions in computational spin-up effort in
regions with deep DTWTs. This work opens the door for hybrid approaches that
blend machine learning and traditional simulation, enhancing predictive
accuracy and efficiency in hydrology for improving water resource management
and understanding complex environmental interactions.

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