Combining Parametric Land Surface Models with Machine Learning
This incremental approach improves soil moisture prediction for land surface modeling, potentially enhancing global models.
The authors tackled the problem of simulating top-layer soil moisture by combining a parametric land surface model with Gaussian Processes, achieving up to a 3-fold reduction in RMSE in out-of-sample tests at selected AmeriFlux sites.
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.