International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1537–1544, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1537-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1537–1544, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1537-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Aug 2020

14 Aug 2020

HYBRID MODELING: FUSION OF A DEEP LEARNING APPROACH AND A PHYSICS-BASED MODEL FOR GLOBAL HYDROLOGICAL MODELING

B. Kraft1,2, M. Jung1, M. Körner2, and M. Reichstein1 B. Kraft et al.
  • 1Department of Biogeochemical Integration, MPI for Biogeochemistry, Jena, Germany
  • 2Department of Aerospace and Geodesy, Technical University of Munich, Munich, Germany

Keywords: Hybrid Modeling, Deep Learning, Hydrology, Global Modeling, LSTM

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.