Abstract

Machine learning techniques have proven useful at predicting many variables of hydrologic interest, and often out-perform traditional models for univariate predictions. However, demonstration of multivariate output deep learning models has not had the same success as the univariate case in the hydrologic sciences. Multivariate prediction is a clear area where machine learning still lags behind traditional processed based modeling efforts. Reasons for this include the lack of coincident data from multiple variables, which make it difficult to train multivariate deep-learning models, as well as the need to capture inter-variable covariances and satisfy physical constraints. For these reasons process-based hydrologic models are still used to simulate and make predictions for entire hydrologic systems. Therefore, we anticipate that future state of the art hydrologic models will couple machine learning with process based representations in a way that satisfies physical constraints and allows for a blending of theoretical and data driven approaches as they are most appropriate. In this presentation we will demonstrate that it is possible to train deep learning models to represent individual processes, forming an effective process-parameterization, that can be directly coupled with a physically based hydrologic model. We will develop a deep-learning representation of latent heat and couple it to a mass and energy balance conserving hydrologic model. We will demonstrate its performance characteristics compared to traditional methods of predicting latent heat. We will also compare how incorporation of this deep learning representation affects other major states and fluxes internal to the hydrologic model.