Accurate and Efficient Prediction of Fine-Resolution Hydrologic and Carbon Dynamic Simulations from Coarse-Resolution Models

TitleAccurate and Efficient Prediction of Fine-Resolution Hydrologic and Carbon Dynamic Simulations from Coarse-Resolution Models
Publication TypeJournal Article
Year of Publication2016
AuthorsPau, George S. H., Shen Chaopeng, Riley William J., and Liu Yaning
JournalWater Resources Research
Date Published01/2016
Abstract / Summary

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- and fine-resolution solutions, here obtained using PAWS+CLM, a quasi-3D 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. This article is protected by copyright. All rights reserved.

URLhttp://onlinelibrary.wiley.com/doi/10.1002/2015WR017782/full
DOI10.1002/2015WR017782
Journal: Water Resources Research
Year of Publication: 2016
Date Published: 01/2016

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- and fine-resolution solutions, here obtained using PAWS+CLM, a quasi-3D 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. This article is protected by copyright. All rights reserved.

DOI: 10.1002/2015WR017782
Citation:
Pau, GS, C Shen, WJ Riley, and Y Liu.  2016.  "Accurate and Efficient Prediction of Fine-Resolution Hydrologic and Carbon Dynamic Simulations from Coarse-Resolution Models."  Water Resources Research.  https://doi.org/10.1002/2015WR017782.