Building a machine learning surrogate model for wildfire activities within a global Earth system model
Abstract
Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire modelmore »
- 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:
- 1853837
- Alternate Identifier(s):
- OSTI ID: 1885141
- Grant/Contract Number:
- Energy Exascale Earth System Modeling (E3SM, https://e3sm.org/) Project; Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Scientific Focus Area; AC02-05CH11231
- Resource Type:
- Published Article
- Journal Name:
- Geoscientific Model Development (Online)
- Additional Journal Information:
- Journal Name: Geoscientific Model Development (Online) Journal Volume: 15 Journal Issue: 5; Journal ID: ISSN 1991-9603
- Publisher:
- Copernicus GmbH
- Country of Publication:
- Germany
- Language:
- English
- Subject:
- 58 GEOSCIENCES
Citation Formats
Zhu, Qing, Li, Fa, Riley, William J., Xu, Li, Zhao, Lei, Yuan, Kunxiaojia, Wu, Huayi, Gong, Jianya, and Randerson, James. Building a machine learning surrogate model for wildfire activities within a global Earth system model. Germany: N. p., 2022.
Web. doi:10.5194/gmd-15-1899-2022.
Zhu, Qing, Li, Fa, Riley, William J., Xu, Li, Zhao, Lei, Yuan, Kunxiaojia, Wu, Huayi, Gong, Jianya, & Randerson, James. Building a machine learning surrogate model for wildfire activities within a global Earth system model. Germany. https://doi.org/10.5194/gmd-15-1899-2022
Zhu, Qing, Li, Fa, Riley, William J., Xu, Li, Zhao, Lei, Yuan, Kunxiaojia, Wu, Huayi, Gong, Jianya, and Randerson, James. Tue .
"Building a machine learning surrogate model for wildfire activities within a global Earth system model". Germany. https://doi.org/10.5194/gmd-15-1899-2022.
@article{osti_1853837,
title = {Building a machine learning surrogate model for wildfire activities within a global Earth system model},
author = {Zhu, Qing and Li, Fa and Riley, William J. and Xu, Li and Zhao, Lei and Yuan, Kunxiaojia and Wu, Huayi and Gong, Jianya and Randerson, James},
abstractNote = {Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.},
doi = {10.5194/gmd-15-1899-2022},
journal = {Geoscientific Model Development (Online)},
number = 5,
volume = 15,
place = {Germany},
year = {Tue Mar 08 00:00:00 EST 2022},
month = {Tue Mar 08 00:00:00 EST 2022}
}
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