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Comparing Model Representations of Physiological Limits on Transpiration at a Semi‐Arid Ponderosa Pine Site
Author(s) -
Hawkins Linnia R.,
Bassouni Maoya,
Anderegg William R. L.,
Venturas Martin D.,
Good Stephen P.,
Kwon Hyojung J.,
Hanson Chad V.,
Fiorella Richard P.,
Bowen Gabriel J.,
Still Christopher J.
Publication year - 2022
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2021ms002927
Subject(s) - transpiration , environmental science , canopy , biogeochemical cycle , arid , vapour pressure deficit , evapotranspiration , ecosystem , atmospheric sciences , computer science , ecology , geology , botany , photosynthesis , biology
Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi‐empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi‐arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big‐leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations.

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