Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models
Abstract
The topography, and the biotic and abiotic parameters are typically upscaled to make watershed-scale hydrologic-biogeochemical models computationally tractable. However, upscaling procedure can produce biases when nonlinear interactions between different processes are not fully captured at coarse resolutions. Here, we applied the Proper Orthogonal Decomposition Mapping Method (PODMM) to downscale the field solutions from a coarse (7 km) resolution grid to a fine (220 m) resolution grid. PODMM trains a reduced-order model (ROM) with coarse-resolution and fine-resolution solutions, here obtained using PAWS+CLM, a quasi-3-D watershed processes model that has been validated for many temperate watersheds. Subsequent fine-resolution solutions were approximated based only on coarse-resolution solutions and the ROM. The approximation errors were efficiently quantified using an error estimator. By jointly estimating correlated variables and temporally varying the ROM parameters, we further reduced the approximation errors by up to 20%. We also improved the method's robustness by constructing multiple ROMs using different set of variables, and selecting the best approximation based on the error estimator. The ROMs produced accurate downscaling of soil moisture, latent heat flux, and net primary production with O(1000) reduction in computational cost. The subgrid distributions were also nearly indistinguishable from the ones obtained using the fine-resolution model. Comparedmore »
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Pennsylvania State Univ., University Park, PA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1471004
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Water Resources Research
- Additional Journal Information:
- Journal Volume: 52; Journal Issue: 2; Journal ID: ISSN 0043-1397
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES
Citation Formats
Pau, George Shu Heng, Shen, Chaopeng, Riley, William J., and Liu, Yaning. Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models. United States: N. p., 2016.
Web. doi:10.1002/2015WR017782.
Pau, George Shu Heng, Shen, Chaopeng, Riley, William J., & Liu, Yaning. Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models. United States. https://doi.org/10.1002/2015WR017782
Pau, George Shu Heng, Shen, Chaopeng, Riley, William J., and Liu, Yaning. Wed .
"Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models". United States. https://doi.org/10.1002/2015WR017782. https://www.osti.gov/servlets/purl/1471004.
@article{osti_1471004,
title = {Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models},
author = {Pau, George Shu Heng and Shen, Chaopeng and Riley, William J. and Liu, Yaning},
abstractNote = {The topography, and the biotic and abiotic parameters are typically upscaled to make watershed-scale hydrologic-biogeochemical models computationally tractable. However, upscaling procedure can produce biases when nonlinear interactions between different processes are not fully captured at coarse resolutions. Here, we applied the Proper Orthogonal Decomposition Mapping Method (PODMM) to downscale the field solutions from a coarse (7 km) resolution grid to a fine (220 m) resolution grid. PODMM trains a reduced-order model (ROM) with coarse-resolution and fine-resolution solutions, here obtained using PAWS+CLM, a quasi-3-D watershed processes model that has been validated for many temperate watersheds. Subsequent fine-resolution solutions were approximated based only on coarse-resolution solutions and the ROM. The approximation errors were efficiently quantified using an error estimator. By jointly estimating correlated variables and temporally varying the ROM parameters, we further reduced the approximation errors by up to 20%. We also improved the method's robustness by constructing multiple ROMs using different set of variables, and selecting the best approximation based on the error estimator. The ROMs produced accurate downscaling of soil moisture, latent heat flux, and net primary production with O(1000) reduction in computational cost. The subgrid distributions were also nearly indistinguishable from the ones obtained using the fine-resolution model. Compared to coarse-resolution solutions, biases in upscaled ROM solutions were reduced by up to 80%. This method has the potential to help address the long-standing spatial scaling problem in hydrology and enable long-time integration, parameter estimation, and stochastic uncertainty analysis while accurately representing the heterogeneities.},
doi = {10.1002/2015WR017782},
journal = {Water Resources Research},
number = 2,
volume = 52,
place = {United States},
year = {Wed Feb 10 00:00:00 EST 2016},
month = {Wed Feb 10 00:00:00 EST 2016}
}
Web of Science
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