A data-conditioned stochastic parameterization of temporal plant trait variability in an ecohydrological model and the potential for plasticity

Shaoqing Liu; Gene-Hua C Ng

doi:10.1016/j.agrformet.2019.05.005

A data-conditioned stochastic parameterization of temporal plant trait variability in an ecohydrological model and the potential for plasticity

Shaoqing Liu, Gene-Hua C Ng

Earth and Environmental Sciences-Twin Cities

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

Recent studies have begun to incorporate spatially variable plant traits into ecohydrological models, but temporal trait variability remains under-studied. Because of its potential to influence ecosystem function, representing stress-induced temporal trait variability into models should be a research priority. We present a new data-model integration approach to identify temporal variability in plant traits and generate stochastic-in-time model parameterizations. The data-conditioned stochastic parameterization was developed within the CLM 4.5 model utilizing global trait data as prior information and tested for a desert shrubland site. A synthetic experiment demonstrated that the framework successfully uncovered time-varying trait values. Using in-situ ecohydrological observations, we found the specific leaf area (SLA) for a common broadleaf-evergreen-shrub to be temporally dynamic and significantly correlated with seasonal water availability. We constructed a regression model based on the data-conditioned SLA estimates and soil wetness and used it to generate stochastic SLA parameters for a 40-year hindcast simulation. The stochastic-in-time SLA parameters resulted in greater productivity and water use efficiency than a standard static parameter. Our stochastic-in-time method can help evaluate stress-induced trait plasticity that extends our understanding beyond sparse spatial plant trait database and improve our ability to simulate carbon and water fluxes under global change.

Original language	English (US)
Pages (from-to)	184-194
Number of pages	11
Journal	Agricultural and Forest Meteorology
Volume	274
DOIs	https://doi.org/10.1016/j.agrformet.2019.05.005
State	Published - Aug 15 2019

Bibliographical note

Funding Information:
This study was supported by funding from NSF (NSF-1724781). Supercomputing resources were provided by the Minnesota Supercomputing Institute (MSI) at University of Minnesota-Twin Cities and the Cheyenne cluster at NCAR. The authors thank Peter Reich (University of Minnesota) for valuable conversations about the TRY database and trait variability at the inception of the project; Ethan Butler (University of Minnesota) also provided insightful suggestions about model interpretations. Two anonymous reviewers contributed comments that helped with the clarity of this paper. This study utilized data from the TRY initiative on plant traits (http://www.try-db.org). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Bönisch (Max Planck Institute for Biogeochemistry, Jena, Germany).

Funding Information:
This study was supported by funding from NSF ( NSF-1724781 ). Supercomputing resources were provided by the Minnesota Supercomputing Institute (MSI) at University of Minnesota-Twin Cities and the Cheyenne cluster at NCAR. The authors thank Peter Reich (University of Minnesota) for valuable conversations about the TRY database and trait variability at the inception of the project; Ethan Butler (University of Minnesota) also provided insightful suggestions about model interpretations. Two anonymous reviewers contributed comments that helped with the clarity of this paper. This study utilized data from the TRY initiative on plant traits ( http://www.try-db.org ). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Bönisch (Max Planck Institute for Biogeochemistry, Jena, Germany).

Publisher Copyright:
© 2019 Elsevier B.V.

Keywords

Data-model integration
Ecohydrological models
Plant trait
Stochastic parameterization
Temporal trait variability

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10.1016/j.agrformet.2019.05.005

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title = "A data-conditioned stochastic parameterization of temporal plant trait variability in an ecohydrological model and the potential for plasticity",

abstract = "Recent studies have begun to incorporate spatially variable plant traits into ecohydrological models, but temporal trait variability remains under-studied. Because of its potential to influence ecosystem function, representing stress-induced temporal trait variability into models should be a research priority. We present a new data-model integration approach to identify temporal variability in plant traits and generate stochastic-in-time model parameterizations. The data-conditioned stochastic parameterization was developed within the CLM 4.5 model utilizing global trait data as prior information and tested for a desert shrubland site. A synthetic experiment demonstrated that the framework successfully uncovered time-varying trait values. Using in-situ ecohydrological observations, we found the specific leaf area (SLA) for a common broadleaf-evergreen-shrub to be temporally dynamic and significantly correlated with seasonal water availability. We constructed a regression model based on the data-conditioned SLA estimates and soil wetness and used it to generate stochastic SLA parameters for a 40-year hindcast simulation. The stochastic-in-time SLA parameters resulted in greater productivity and water use efficiency than a standard static parameter. Our stochastic-in-time method can help evaluate stress-induced trait plasticity that extends our understanding beyond sparse spatial plant trait database and improve our ability to simulate carbon and water fluxes under global change.",

keywords = "Data-model integration, Ecohydrological models, Plant trait, Stochastic parameterization, Temporal trait variability",

author = "Shaoqing Liu and Ng, {Gene-Hua C}",

note = "Funding Information: This study was supported by funding from NSF (NSF-1724781). Supercomputing resources were provided by the Minnesota Supercomputing Institute (MSI) at University of Minnesota-Twin Cities and the Cheyenne cluster at NCAR. The authors thank Peter Reich (University of Minnesota) for valuable conversations about the TRY database and trait variability at the inception of the project; Ethan Butler (University of Minnesota) also provided insightful suggestions about model interpretations. Two anonymous reviewers contributed comments that helped with the clarity of this paper. This study utilized data from the TRY initiative on plant traits (http://www.try-db.org). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. B{\"o}nisch (Max Planck Institute for Biogeochemistry, Jena, Germany). Funding Information: This study was supported by funding from NSF ( NSF-1724781 ). Supercomputing resources were provided by the Minnesota Supercomputing Institute (MSI) at University of Minnesota-Twin Cities and the Cheyenne cluster at NCAR. The authors thank Peter Reich (University of Minnesota) for valuable conversations about the TRY database and trait variability at the inception of the project; Ethan Butler (University of Minnesota) also provided insightful suggestions about model interpretations. Two anonymous reviewers contributed comments that helped with the clarity of this paper. This study utilized data from the TRY initiative on plant traits ( http://www.try-db.org ). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. B{\"o}nisch (Max Planck Institute for Biogeochemistry, Jena, Germany). Publisher Copyright: {\textcopyright} 2019 Elsevier B.V.",

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doi = "10.1016/j.agrformet.2019.05.005",

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AU - Liu, Shaoqing

AU - Ng, Gene-Hua C

N1 - Funding Information: This study was supported by funding from NSF (NSF-1724781). Supercomputing resources were provided by the Minnesota Supercomputing Institute (MSI) at University of Minnesota-Twin Cities and the Cheyenne cluster at NCAR. The authors thank Peter Reich (University of Minnesota) for valuable conversations about the TRY database and trait variability at the inception of the project; Ethan Butler (University of Minnesota) also provided insightful suggestions about model interpretations. Two anonymous reviewers contributed comments that helped with the clarity of this paper. This study utilized data from the TRY initiative on plant traits (http://www.try-db.org). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Bönisch (Max Planck Institute for Biogeochemistry, Jena, Germany). Funding Information: This study was supported by funding from NSF ( NSF-1724781 ). Supercomputing resources were provided by the Minnesota Supercomputing Institute (MSI) at University of Minnesota-Twin Cities and the Cheyenne cluster at NCAR. The authors thank Peter Reich (University of Minnesota) for valuable conversations about the TRY database and trait variability at the inception of the project; Ethan Butler (University of Minnesota) also provided insightful suggestions about model interpretations. Two anonymous reviewers contributed comments that helped with the clarity of this paper. This study utilized data from the TRY initiative on plant traits ( http://www.try-db.org ). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Bönisch (Max Planck Institute for Biogeochemistry, Jena, Germany). Publisher Copyright: © 2019 Elsevier B.V.

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N2 - Recent studies have begun to incorporate spatially variable plant traits into ecohydrological models, but temporal trait variability remains under-studied. Because of its potential to influence ecosystem function, representing stress-induced temporal trait variability into models should be a research priority. We present a new data-model integration approach to identify temporal variability in plant traits and generate stochastic-in-time model parameterizations. The data-conditioned stochastic parameterization was developed within the CLM 4.5 model utilizing global trait data as prior information and tested for a desert shrubland site. A synthetic experiment demonstrated that the framework successfully uncovered time-varying trait values. Using in-situ ecohydrological observations, we found the specific leaf area (SLA) for a common broadleaf-evergreen-shrub to be temporally dynamic and significantly correlated with seasonal water availability. We constructed a regression model based on the data-conditioned SLA estimates and soil wetness and used it to generate stochastic SLA parameters for a 40-year hindcast simulation. The stochastic-in-time SLA parameters resulted in greater productivity and water use efficiency than a standard static parameter. Our stochastic-in-time method can help evaluate stress-induced trait plasticity that extends our understanding beyond sparse spatial plant trait database and improve our ability to simulate carbon and water fluxes under global change.

AB - Recent studies have begun to incorporate spatially variable plant traits into ecohydrological models, but temporal trait variability remains under-studied. Because of its potential to influence ecosystem function, representing stress-induced temporal trait variability into models should be a research priority. We present a new data-model integration approach to identify temporal variability in plant traits and generate stochastic-in-time model parameterizations. The data-conditioned stochastic parameterization was developed within the CLM 4.5 model utilizing global trait data as prior information and tested for a desert shrubland site. A synthetic experiment demonstrated that the framework successfully uncovered time-varying trait values. Using in-situ ecohydrological observations, we found the specific leaf area (SLA) for a common broadleaf-evergreen-shrub to be temporally dynamic and significantly correlated with seasonal water availability. We constructed a regression model based on the data-conditioned SLA estimates and soil wetness and used it to generate stochastic SLA parameters for a 40-year hindcast simulation. The stochastic-in-time SLA parameters resulted in greater productivity and water use efficiency than a standard static parameter. Our stochastic-in-time method can help evaluate stress-induced trait plasticity that extends our understanding beyond sparse spatial plant trait database and improve our ability to simulate carbon and water fluxes under global change.

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KW - Ecohydrological models

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KW - Stochastic parameterization

KW - Temporal trait variability

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ER -

A data-conditioned stochastic parameterization of temporal plant trait variability in an ecohydrological model and the potential for plasticity

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