TY - JOUR
T1 - Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA
AU - Sihi, Debjani
AU - Davidson, Eric A.
AU - Chen, Min
AU - Savage, Kathleen E.
AU - Richardson, Andrew D.
AU - Keenan, Trevor F.
AU - Hollinger, David Y.
N1 - Funding Information:
The project was supported by USDA grant # 2014-67003-22073 . The trenching experiment at the Harvard Forest was supported by US Department of Energy, Office of Science, Terrestrial Ecosystem Science program award DE-SC0006741 . Research at Harvard Forest is supported by the National Science Foundation’s LTER program ( DEB-1237491 ). Howland Forest is supported by the Office of Science (BER), US Department of Energy, and the USDA Forest Service's Northern Research Station . TFK was supported by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy under Contract DE-AC02-05CH11231 as part of the RGCM BGC-Climate Feedbacks SFA. The authors would like to acknowledge William Munger for providing the code for soil moisture bucket model. FORTRAN codes used for this modeling exercise and soil R data (and associated metadata) from Howland Forest are made available in the GitHub repository ( https://github.com/trevorkeenan/FoBAAR-DAMM ). Howland Field Crew (Holly Hughes and John Lee) assisted in ancillary measurements at the Howland Forest. Biometric data for the Harvard site is archived in the Harvard Forest Data Repository ( http://harvardforest.fas.harvard.edu/harvard-forest-data-archive ). Tower-based eddy-covariance data, as well as soil respiration data (both control and trench plots) used for this study, can be obtained from the AmeriFlux website ( http://ameriflux.lbl.gov/ ). Soil R data (and associated metadata) from Harvard Forest are freely available in the Harvard Forest Data Archive ( http://harvardforest.fas.harvard.edu:8080/exist/apps/datasets/showData.html?id=hf006 , ID: HF006). The computations in this paper were run on the Odyssey cluster supported by the FAS Division of Science, Research Computing Group at Harvard University . We thank the guest editor and two anonymous reviewers for their careful insights which immensely helped to improve the quality of the article.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/4/15
Y1 - 2018/4/15
N2 - Heterotrophic respiration (Rh), microbial processing of soil organic matter to carbon dioxide (CO2), is a major, yet highly uncertain, carbon (C) flux from terrestrial systems to the atmosphere. Temperature sensitivity of Rh is often represented with a simple Q10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed as a way to disentangle the confounding factors of apparent temperature sensitivity of Rh and improve the performance of ecosystem models and ESMs. The objective of this work was to insert into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh and evaluate the model performance in terms of soil and ecosystem respiration. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen, soluble C substrates, and extracellular enzymes to the enzymatic reaction site. Here, we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FöBAAR (Forest Biomass, Assimilation, Allocation and Respiration). We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FöBAAR model better captured the seasonal and inter-annual dynamics of soil respiration (Soil R) compared to the FöBAAR-only model for the Harvard Forest, where higher frequency and duration of drying events significantly regulate substrate supply to heterotrophs. However, DAMM-FöBAAR showed improvement over FöBAAR-only at the boreal transition Howland Forest only in unusually dry years. The frequency of synoptic-scale dry periods is lower at Howland, resulting in only brief water limitation of Rh in some years. At both sites, the declining trend of soil R during drying events was captured by the DAMM-FöBAAR model; however, model performance was also contingent on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q10 function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.
AB - Heterotrophic respiration (Rh), microbial processing of soil organic matter to carbon dioxide (CO2), is a major, yet highly uncertain, carbon (C) flux from terrestrial systems to the atmosphere. Temperature sensitivity of Rh is often represented with a simple Q10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed as a way to disentangle the confounding factors of apparent temperature sensitivity of Rh and improve the performance of ecosystem models and ESMs. The objective of this work was to insert into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh and evaluate the model performance in terms of soil and ecosystem respiration. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen, soluble C substrates, and extracellular enzymes to the enzymatic reaction site. Here, we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FöBAAR (Forest Biomass, Assimilation, Allocation and Respiration). We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FöBAAR model better captured the seasonal and inter-annual dynamics of soil respiration (Soil R) compared to the FöBAAR-only model for the Harvard Forest, where higher frequency and duration of drying events significantly regulate substrate supply to heterotrophs. However, DAMM-FöBAAR showed improvement over FöBAAR-only at the boreal transition Howland Forest only in unusually dry years. The frequency of synoptic-scale dry periods is lower at Howland, resulting in only brief water limitation of Rh in some years. At both sites, the declining trend of soil R during drying events was captured by the DAMM-FöBAAR model; however, model performance was also contingent on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q10 function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.
KW - Climate change
KW - DAMM
KW - FöBAAR
KW - Q
KW - Soil carbon
KW - Soil respiration
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U2 - 10.1016/j.agrformet.2018.01.026
DO - 10.1016/j.agrformet.2018.01.026
M3 - Article
AN - SCOPUS:85041471015
SN - 0168-1923
VL - 252
SP - 155
EP - 166
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
ER -