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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 6 | Copyright
Hydrol. Earth Syst. Sci., 22, 3311-3330, 2018
https://doi.org/10.5194/hess-22-3311-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 14 Jun 2018

Research article | 14 Jun 2018

Harnessing big data to rethink land heterogeneity in Earth system models

Nathaniel W. Chaney1, Marjolein H. J. Van Huijgevoort1, Elena Shevliakova2, Sergey Malyshev2, Paul C. D. Milly3, Paul P. G. Gauthier4, and Benjamin N. Sulman5 Nathaniel W. Chaney et al.
  • 1Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey, USA
  • 2NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
  • 3US Geological Survey, Princeton, New Jersey, USA
  • 4Department of Geosciences, Princeton University, Princeton, New Jersey, USA
  • 5Sierra Nevada Research Institute, University of California, Merced, California, USA

Abstract. The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth system models – a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and computational constraints, these data are underused for global applications. As a proof of concept, this study explores how to effectively and efficiently harness these data in Earth system models over a 1/4° ( ∼ 25km) grid cell in the western foothills of the Sierra Nevada in central California. First, a novel hierarchical multivariate clustering approach (HMC) is introduced that summarizes the high-dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this clustering approach impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled using HMC. The results over the test domain show that (1) the observed similarity over the landscape makes it possible to converge on the macroscale response of the fully distributed model with around 300 sub-grid land model tiles; (2) assembling the sub-grid tile configuration from available environmental data can have a large impact on the macroscale states and fluxes of the water, energy, and carbon cycles; for example, the defined subsurface connections between the tiles lead to a dampening of macroscale extremes; (3) connecting the fine-scale grid to the model tiles via HMC enables circumvention of the classic scale discrepancies between the macroscale and field-scale estimates; this has potentially significant implications for the evaluation and application of Earth system models.

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The petabytes of existing global environmental data provide an invaluable asset to improve the characterization of land heterogeneity in Earth system models. This study introduces a clustering algorithm that summarizes a domain’s heterogeneity through spatially interconnected clusters. A series of land model simulations in central California using this approach illustrate the critical role that multi-scale heterogeneity can have on the macroscale water, energy, and carbon cycles.
The petabytes of existing global environmental data provide an invaluable asset to improve the...
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