HYBRID MODELING: FUSION of A DEEP LEARNING APPROACH and A PHYSICS-BASED MODEL for GLOBAL HYDROLOGICAL MODELING

Abstract

Process-based models of complex environmental systems incorporate expert knowledge which is often incomplete and uncertain. With the growing amount of Earth observation data and advances in machine learning, a new paradigm is promising to synergize the advantages of deep learning in terms of data adaptiveness and performance for poorly understood processes with the advantages of process-based modeling in terms of interpretability and theoretical foundations: hybrid modeling. Here, we present such an end-to-end hybrid modeling approach that learns and predicts spatial-temporal variations of observed and unobserved (latent) hydrological variables globally. The model combines a dynamic neural network and a conceptual water balance model, constrained by the water cycle observational products of evapotranspiration, runoff, snow-water equivalent, and terrestrial water storage variations. We show that the model reproduces observed water cycle variations very well and that the emergent relations of runoff-generating processes are qualitatively consistent with our understanding. The presented model is - to our knowledge - the first of its kind and may contribute new insights about the dynamics of the global hydrological system.