  A hybrid machine learning and process-based-modeling (PBM) approach is
proposed and evaluated at a handful of AmeriFlux sites to simulate the
top-layer soil moisture state. The Hybrid-PBM (HPBM) employed here uses the
Noah land-surface model integrated with Gaussian Processes. It is designed to
correct the model only in climatological situations similar to the training
data else it reverts to the PBM. In this way, our approach avoids bad
predictions in scenarios where similar training data is not available and
incorporates our physical understanding of the system. Here we assume an
autoregressive model and obtain out-of-sample results with upwards of a 3-fold
reduction in the RMSE using a one-year leave-one-out cross-validation at each
of the selected sites. A path is outlined for using hybrid modeling to build
global land-surface models with the potential to significantly outperform the
current state-of-the-art.
