Harnessing big data to rethink land heterogeneity in Earth system models
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 complexi...
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doaj-c98a4de42016434abaee1fe163d141222020-11-24T22:35:56ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382018-06-01223311333010.5194/hess-22-3311-2018Harnessing big data to rethink land heterogeneity in Earth system modelsN. W. Chaney0M. H. J. Van Huijgevoort1E. Shevliakova2S. Malyshev3P. C. D. Milly4P. P. G. Gauthier5B. N. Sulman6Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey, USAProgram in Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey, USANOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USANOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USAUS Geological Survey, Princeton, New Jersey, USADepartment of Geosciences, Princeton University, Princeton, New Jersey, USASierra Nevada Research Institute, University of California, Merced, California, USAThe 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.https://www.hydrol-earth-syst-sci.net/22/3311/2018/hess-22-3311-2018.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
N. W. Chaney M. H. J. Van Huijgevoort E. Shevliakova S. Malyshev P. C. D. Milly P. P. G. Gauthier B. N. Sulman |
spellingShingle |
N. W. Chaney M. H. J. Van Huijgevoort E. Shevliakova S. Malyshev P. C. D. Milly P. P. G. Gauthier B. N. Sulman Harnessing big data to rethink land heterogeneity in Earth system models Hydrology and Earth System Sciences |
author_facet |
N. W. Chaney M. H. J. Van Huijgevoort E. Shevliakova S. Malyshev P. C. D. Milly P. P. G. Gauthier B. N. Sulman |
author_sort |
N. W. Chaney |
title |
Harnessing big data to rethink land heterogeneity in Earth system models |
title_short |
Harnessing big data to rethink land heterogeneity in Earth system models |
title_full |
Harnessing big data to rethink land heterogeneity in Earth system models |
title_fullStr |
Harnessing big data to rethink land heterogeneity in Earth system models |
title_full_unstemmed |
Harnessing big data to rethink land heterogeneity in Earth system models |
title_sort |
harnessing big data to rethink land heterogeneity in earth system models |
publisher |
Copernicus Publications |
series |
Hydrology and Earth System Sciences |
issn |
1027-5606 1607-7938 |
publishDate |
2018-06-01 |
description |
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. |
url |
https://www.hydrol-earth-syst-sci.net/22/3311/2018/hess-22-3311-2018.pdf |
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