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Harnessing big data to rethink land heterogeneity in Earth system models

Publication ,  Journal Article
Chaney, NW; Van Huijgevoort, MHJ; Shevliakova, E; Malyshev, S; Milly, PCD; Gauthier, PPG; Sulman, BN
Published in: Hydrology and Earth System Sciences
June 14, 2018

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° (∼ 25-km) 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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Published In

Hydrology and Earth System Sciences

DOI

EISSN

1607-7938

ISSN

1027-5606

Publication Date

June 14, 2018

Volume

22

Issue

6

Start / End Page

3311 / 3330

Related Subject Headings

  • Environmental Engineering
  • 4013 Geomatic engineering
  • 3709 Physical geography and environmental geoscience
  • 3707 Hydrology
  • 0907 Environmental Engineering
  • 0905 Civil Engineering
  • 0406 Physical Geography and Environmental Geoscience
 

Citation

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Chaney, N. W., Van Huijgevoort, M. H. J., Shevliakova, E., Malyshev, S., Milly, P. C. D., Gauthier, P. P. G., & Sulman, B. N. (2018). Harnessing big data to rethink land heterogeneity in Earth system models. Hydrology and Earth System Sciences, 22(6), 3311–3330. https://doi.org/10.5194/hess-22-3311-2018
Chaney, N. W., M. H. J. Van Huijgevoort, E. Shevliakova, S. Malyshev, P. C. D. Milly, P. P. G. Gauthier, and B. N. Sulman. “Harnessing big data to rethink land heterogeneity in Earth system models.” Hydrology and Earth System Sciences 22, no. 6 (June 14, 2018): 3311–30. https://doi.org/10.5194/hess-22-3311-2018.
Chaney NW, Van Huijgevoort MHJ, Shevliakova E, Malyshev S, Milly PCD, Gauthier PPG, et al. Harnessing big data to rethink land heterogeneity in Earth system models. Hydrology and Earth System Sciences. 2018 Jun 14;22(6):3311–30.
Chaney, N. W., et al. “Harnessing big data to rethink land heterogeneity in Earth system models.” Hydrology and Earth System Sciences, vol. 22, no. 6, June 2018, pp. 3311–30. Scopus, doi:10.5194/hess-22-3311-2018.
Chaney NW, Van Huijgevoort MHJ, Shevliakova E, Malyshev S, Milly PCD, Gauthier PPG, Sulman BN. Harnessing big data to rethink land heterogeneity in Earth system models. Hydrology and Earth System Sciences. 2018 Jun 14;22(6):3311–3330.

Published In

Hydrology and Earth System Sciences

DOI

EISSN

1607-7938

ISSN

1027-5606

Publication Date

June 14, 2018

Volume

22

Issue

6

Start / End Page

3311 / 3330

Related Subject Headings

  • Environmental Engineering
  • 4013 Geomatic engineering
  • 3709 Physical geography and environmental geoscience
  • 3707 Hydrology
  • 0907 Environmental Engineering
  • 0905 Civil Engineering
  • 0406 Physical Geography and Environmental Geoscience