Abstract
Integrated hydrologic models can simulate coupled surface and subsurface
processes but are computationally expensive to run at high resolutions
over large domains. Here we develop a novel deep learning model to
emulate continental-scale subsurface flows simulated by the integrated
ParFlow-CLM model. We compare convolutional neural networks like ResNet
and UNet run autoregressively against our novel architecture called the
Forced SpatioTemporal RNN (FSTR). The FSTR model incorporates separate
encoding of initial conditions, static parameters, and meteorological
forcings, which are fused in a recurrent loop to produce spatiotemporal
predictions of groundwater. We evaluate the model architectures on their
ability to reproduce 4D pressure heads, water table depths, and surface
soil moisture over the contiguous US at 1km resolution and daily time
steps over the course of a full water year. The FSTR model shows
superior performance to the baseline models, producing stable
simulations that capture both seasonal and event-scale dynamics across a
wide array of hydroclimatic regimes. The emulators provide over 1000x
speedup compared to the original physical model, which will enable new
capabilities like uncertainty quantification and data assimilation for
integrated hydrologic modeling that were not previously possible. Our
results demonstrate the promise of using specialized deep learning
architectures like FSTR for emulating complex process-based models
without sacrificing fidelity.