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

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)184-194
Number of pages11
JournalAgricultural and Forest Meteorology
Volume274
DOIs
StatePublished - Aug 15 2019

Fingerprint

plasticity
parameterization
leaf area
broadleaved evergreens
shrubland
ecosystem function
water use efficiency
water availability
global change
shrub
shrublands
desert
deserts
shrubs
water
productivity
carbon
ecosystems
simulation
parameter

Keywords

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

Cite this

@article{a86c5ac6e81244d5a425c4a33851d8fa,
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}",
year = "2019",
month = "8",
day = "15",
doi = "10.1016/j.agrformet.2019.05.005",
language = "English (US)",
volume = "274",
pages = "184--194",
journal = "Agricultural and Forest Meteorology",
issn = "0168-1923",
publisher = "Elsevier",

}

TY - JOUR

T1 - A data-conditioned stochastic parameterization of temporal plant trait variability in an ecohydrological model and the potential for plasticity

AU - Liu, Shaoqing

AU - Ng, Gene-Hua C

PY - 2019/8/15

Y1 - 2019/8/15

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.

KW - Data-model integration

KW - Ecohydrological models

KW - Plant trait

KW - Stochastic parameterization

KW - Temporal trait variability

UR - http://www.scopus.com/inward/record.url?scp=85065721756&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85065721756&partnerID=8YFLogxK

U2 - 10.1016/j.agrformet.2019.05.005

DO - 10.1016/j.agrformet.2019.05.005

M3 - Article

VL - 274

SP - 184

EP - 194

JO - Agricultural and Forest Meteorology

JF - Agricultural and Forest Meteorology

SN - 0168-1923

ER -