A model-independent data assimilation (MIDA) module and its applications in ecology

Huang, Xin; Lu, Dan; Ricciuto, Daniel M.; Hanson, Paul J.; Richardson, Andrew D.; Lu, Xuehe; Weng, Ensheng; Nie, Sheng; Jiang, Lifen; Hou, Enqing; Steinmacher, Igor F.; Luo, Yiqi

doi:10.5194/gmd-14-5217-2021

Title: A model-independent data assimilation (MIDA) module and its applications in ecology

Journal Article · 19 August 2021 · Geoscientific Model Development (Online)

DOI: https://doi.org/10.5194/gmd-14-5217-2021 · OSTI ID:1814549

Huang, Xin; Lu, Dan;

;

; Lu, Xuehe;

; Nie, Sheng; Jiang, Lifen;

; Steinmacher, Igor F.;

Abstract. Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1814549

Journal Information:: Geoscientific Model Development (Online), Journal Name: Geoscientific Model Development (Online) Journal Issue: 8 Vol. 14; ISSN 1991-9603

Publisher:: Copernicus GmbHCopyright Statement

Country of Publication:: Germany

Language:: English

References (64)

Evaluation and improvement of a global land model against soil carbon data using a Bayesian Markov chain Monte Carlo method: Calibration of a carbon cycle model Hararuk, Oleksandra; Xia, Jianyang; Luo, Yiqi Journal of Geophysical Research: Biogeosciences, Vol. 119, Issue 3 https://doi.org/10.1002/2013JG002535	journal	March 2014
Data-Constrained Projections of Methane Fluxes in a Northern Minnesota Peatland in Response to Elevated CO ₂ and Warming : Data-Constrained Forecast of CH Ma, Shuang; Jiang, Jiang; Huang, Yuanyuan Journal of Geophysical Research: Biogeosciences, Vol. 122, Issue 11 https://doi.org/10.1002/2017JG003932	journal	November 2017
Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO ₂ and a Gradient of Experimental Warming Jiang, Jiang; Huang, Yuanyuan; Ma, Shuang Journal of Geophysical Research: Biogeosciences, Vol. 123, Issue 3 https://doi.org/10.1002/2017JG004040	journal	March 2018
The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter Journal of Advances in Modeling Earth Systems, Vol. 10, Issue 2 https://doi.org/10.1002/2017MS000962	journal	February 2018
Calibration of the E3SM Land Model Using Surrogate-Based Global Optimization Lu, Dan; Ricciuto, Daniel; Stoyanov, Miroslav Journal of Advances in Modeling Earth Systems, Vol. 10, Issue 6 https://doi.org/10.1002/2017MS001134	journal	June 2018
Bayesian Inference in Statistical Analysis Box, George E. P.; Tiao, George C. John Wiley & Sons https://doi.org/10.1002/9781118033197	book	January 1992
Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints Richardson, Andrew D.; Williams, Mathew; Hollinger, David Y. Oecologia, Vol. 164, Issue 1 https://doi.org/10.1007/s00442-010-1628-y	journal	April 2010
A primer for data assimilation with ecological models using Markov Chain Monte Carlo (MCMC) Zobitz, J. M.; Desai, A. R.; Moore, D. J. P. Oecologia, Vol. 167, Issue 3 https://doi.org/10.1007/s00442-011-2107-9	journal	August 2011
The Ensemble Kalman Filter: theoretical formulation and practical implementation Evensen, Geir Ocean Dynamics, Vol. 53, Issue 4 https://doi.org/10.1007/s10236-003-0036-9	journal	November 2003
The global-scale temperature and moisture dependencies of soil organic carbon decomposition: an analysis using a mechanistic decomposition model Ise, Takeshi; Moorcroft, Paul R. Biogeochemistry, Vol. 80, Issue 3 https://doi.org/10.1007/s10533-006-9019-5	journal	June 2006
The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data Fox, Andrew; Williams, Mathew; Richardson, Andrew D. Agricultural and Forest Meteorology, Vol. 149, Issue 10 https://doi.org/10.1016/j.agrformet.2009.05.002	journal	October 2009
A review of applications of model–data fusion to studies of terrestrial carbon fluxes at different scales Wang, Ying-Ping; Trudinger, Cathy M.; Enting, Ian G. Agricultural and Forest Meteorology, Vol. 149, Issue 11 https://doi.org/10.1016/j.agrformet.2009.07.009	journal	November 2009
A Bayesian framework for model calibration, comparison and analysis: Application to four models for the biogeochemistry of a Norway spruce forest van Oijen, M.; Cameron, D. R.; Butterbach-Bahl, K. Agricultural and Forest Meteorology, Vol. 151, Issue 12 https://doi.org/10.1016/j.agrformet.2011.06.017	journal	December 2011
Software for ensemble-based data assimilation systems—Implementation strategies and scalability Nerger, Lars; Hiller, Wolfgang Computers & Geosciences, Vol. 55 https://doi.org/10.1016/j.cageo.2012.03.026	journal	June 2013
Bayesian comparison of six different temperature-based budburst models for four temperate tree species Fu, Yongshuo H.; Campioli, Matteo; Van Oijen, Marcel Ecological Modelling, Vol. 230 https://doi.org/10.1016/j.ecolmodel.2012.01.010	journal	April 2012
Can a multi-model ensemble improve phenology predictions for climate change studies? Yun, Kyungdahm; Hsiao, Jennifer; Jung, Myung-Pyo Ecological Modelling, Vol. 362 https://doi.org/10.1016/j.ecolmodel.2017.08.003	journal	October 2017
Data assimilation framework: Linking an open data assimilation library (OpenDA) to a widely adopted model interface (OpenMI) Ridler, Marc E.; van Velzen, Nils; Hummel, Stef Environmental Modelling & Software, Vol. 57 https://doi.org/10.1016/j.envsoft.2014.02.008	journal	July 2014
Climate Change and Terrestrial Ecosystem Modeling Bonan, Gordon Cambridge University Press https://doi.org/10.1017/9781107339217	book	January 2019
Carbon and Other Biogeochemical Cycles Climate Change 2013 – The Physical Science Basis https://doi.org/10.1017/CBO9781107415324.015	book	March 2014
Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS): CARBON FLUXES FROM DATA ASSIMILATION SYSTEM Rayner, P. J.; Scholze, M.; Knorr, W. Global Biogeochemical Cycles, Vol. 19, Issue 2 https://doi.org/10.1029/2004GB002254	journal	June 2005
Probabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction: PROBABILISTIC INVERSION OF TERRESTRIAL ECOSYSTEM Xu, Tao; White, Luther; Hui, Dafeng Global Biogeochemical Cycles, Vol. 20, Issue 2 https://doi.org/10.1029/2005GB002468	journal	May 2006
OptIC project: An intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models Trudinger, Cathy M.; Raupach, Michael R.; Rayner, Peter J. Journal of Geophysical Research, Vol. 112, Issue G2 https://doi.org/10.1029/2006JG000367	journal	January 2007
Propagating uncertainty through prognostic carbon cycle data assimilation system simulations Scholze, M.; Kaminski, T.; Rayner, P. Journal of Geophysical Research, Vol. 112, Issue D17 https://doi.org/10.1029/2007JD008642	journal	January 2007
Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length Ricciuto, Daniel M.; King, Anthony W.; Dragoni, D. Journal of Geophysical Research, Vol. 116, Issue G1 https://doi.org/10.1029/2010JG001400	journal	January 2011
Quantifying Soil Phosphorus Dynamics: A Data Assimilation Approach Hou, Enqing; Lu, Xingjie; Jiang, Lifen Journal of Geophysical Research: Biogeosciences, Vol. 124, Issue 7 https://doi.org/10.1029/2018JG004903	journal	July 2019
Evaluation of a Data Assimilation System for Land Surface Models Using CLM4.5 Fox, Andrew M.; Hoar, Timothy J.; Anderson, Jeffrey L. Journal of Advances in Modeling Earth Systems, Vol. 10, Issue 10 https://doi.org/10.1029/2018MS001362	journal	October 2018
The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty Lawrence, David M.; Fisher, Rosie A.; Koven, Charles D. Journal of Advances in Modeling Earth Systems, Vol. 11, Issue 12 https://doi.org/10.1029/2018MS001583	journal	December 2019
Model structures amplify uncertainty in predicted soil carbon responses to climate change Shi, Zheng; Crowell, Sean; Luo, Yiqi Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-04526-9	journal	June 2018
More replenishment than priming loss of soil organic carbon with additional carbon input Liang, Junyi; Zhou, Zhenghu; Huo, Changfu Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05667-7	journal	August 2018
Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery Richardson, Andrew D.; Hufkens, Koen; Milliman, Tom Scientific Data, Vol. 5, Issue 1 https://doi.org/10.1038/sdata.2018.28	journal	March 2018
Equation of State Calculations by Fast Computing Machines Metropolis, Nicholas; Rosenbluth, Arianna W.; Rosenbluth, Marshall N. The Journal of Chemical Physics, Vol. 21, Issue 6 https://doi.org/10.1063/1.1699114	journal	June 1953
The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times Bloom, A. Anthony; Exbrayat, Jean-François; van der Velde, Ivar R. Proceedings of the National Academy of Sciences, Vol. 113, Issue 5 https://doi.org/10.1073/pnas.1515160113	journal	January 2016
A method for the solution of certain non-linear problems in least squares Levenberg, Kenneth Quarterly of Applied Mathematics, Vol. 2, Issue 2 https://doi.org/10.1090/qam/10666	journal	January 1944
Monte Carlo sampling methods using Markov chains and their applications Hastings, W. K. Biometrika, Vol. 57, Issue 1 https://doi.org/10.1093/biomet/57.1.97	journal	April 1970
Microbial models with data-driven parameters predict stronger soil carbon responses to climate change Hararuk, Oleksandra; Smith, Matthew J.; Luo, Yiqi Global Change Biology, Vol. 21, Issue 6 https://doi.org/10.1111/gcb.12827	journal	March 2015
Challenging terrestrial biosphere models with data from the long‐term multifactor Prairie Heating and CO ₂ Enrichment experiment De Kauwe, Martin G.; Medlyn, Belinda E.; Walker, Anthony P. Global Change Biology, Vol. 23, Issue 9 https://doi.org/10.1111/gcb.13643	journal	March 2017
TRY plant trait database – enhanced coverage and open access Kattge, Jens; Bönisch, Gerhard; Díaz, Sandra Global Change Biology, Vol. 26, Issue 1 https://doi.org/10.1111/gcb.14904	journal	December 2019
Model parameterization to represent processes at unresolved scales and changing properties of evolving systems Luo, Yiqi; Schuur, Edward A. G. Global Change Biology, Vol. 26, Issue 3 https://doi.org/10.1111/gcb.14939	journal	January 2020
An improved analysis of forest carbon dynamics using data assimilation Williams, Mathew; Schwarz, Paul A.; Law, Beverly E. Global Change Biology, Vol. 11, Issue 1 https://doi.org/10.1111/j.1365-2486.2004.00891.x	journal	January 2005
Model-data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications Raupach, M. R.; Rayner, P. J.; Barrett, D. J. Global Change Biology, Vol. 11, Issue 3 https://doi.org/10.1111/j.1365-2486.2005.00917.x	journal	March 2005
Using model-data fusion to interpret past trends, and quantify uncertainties in future projections, of terrestrial ecosystem carbon cycling Keenan, Trevor F.; Davidson, Eric; Moffat, Antje M. Global Change Biology, Vol. 18, Issue 8 https://doi.org/10.1111/j.1365-2486.2012.02684.x	journal	April 2012
A global Fine-Root Ecology Database to address below-ground challenges in plant ecology Iversen, Colleen M.; McCormack, M. Luke; Powell, A. Shafer New Phytologist, Vol. 215, Issue 1, p. 15-26 https://doi.org/10.1111/nph.14486	journal	February 2017
The Data Assimilation Research Testbed: A Community Facility Anderson, Jeffrey; Hoar, Tim; Raeder, Kevin Bulletin of the American Meteorological Society, Vol. 90, Issue 9 https://doi.org/10.1175/2009BAMS2618.1	journal	September 2009
DART/CAM: An Ensemble Data Assimilation System for CESM Atmospheric Models Raeder, Kevin; Anderson, Jeffrey L.; Collins, Nancy Journal of Climate, Vol. 25, Issue 18 https://doi.org/10.1175/JCLI-D-11-00395.1	journal	September 2012
Climate–Carbon Cycle Feedback Analysis: Results from the C ⁴ MIP Model Intercomparison Friedlingstein, P.; Cox, P.; Betts, R. Journal of Climate, Vol. 19, Issue 14 https://doi.org/10.1175/JCLI3800.1	journal	July 2006
Weak convergence and optimal scaling of random walk Metropolis algorithms Roberts, G. O.; Gelman, A.; Gilks, W. R. The Annals of Applied Probability, Vol. 7, Issue 1 https://doi.org/10.1214/aoap/1034625254	journal	February 1997
Inference from Iterative Simulation Using Multiple Sequences Gelman, Andrew; Rubin, Donald B. Statistical Science, Vol. 7, Issue 4 https://doi.org/10.1214/ss/1177011136	journal	November 1992
ELEVATED CO ₂ DIFFERENTIATES ECOSYSTEM CARBON PROCESSES: DECONVOLUTION ANALYSIS OF DUKE FOREST FACE DATA Luo, Yiqi; Wu, Lianhai; Andrews, Jeffrey A. Ecological Monographs, Vol. 71, Issue 3 https://doi.org/10.1890/0012-9615(2001)071[0357:ECDECP]2.0.CO;2	journal	August 2001
Scaling from Trees to Forests: Tractable Macroscopic Equations for Forest Dynamics Strigul, Nikolay; Pristinski, Denis; Purves, Drew Ecological Monographs, Vol. 78, Issue 4 https://doi.org/10.1890/08-0082.1	journal	November 2008
Assimilation of multiple data sets with the ensemble Kalman filter to improve forecasts of forest carbon dynamics Gao, Chao; Wang, Han; Weng, Ensheng Ecological Applications, Vol. 21, Issue 5 https://doi.org/10.1890/09-1234.1	journal	July 2011
Ecological forecasting and data assimilation in a data-rich era Luo, Yiqi; Ogle, Kiona; Tucker, Colin Ecological Applications, Vol. 21, Issue 5 https://doi.org/10.1890/09-1275.1	journal	July 2011
Relative information contributions of model vs. data to short- and long-term forecasts of forest carbon dynamics Weng, Ensheng; Luo, Yiqi Ecological Applications, Vol. 21, Issue 5 https://doi.org/10.1890/09-1394.1	journal	July 2011
Facilitating feedbacks between field measurements and ecosystem models LeBauer, David S.; Wang, Dan; Richter, Katherine T. Ecological Monographs, Vol. 83, Issue 2 https://doi.org/10.1890/12-0137.1	journal	May 2013
Rate my data: quantifying the value of ecological data for the development of models of the terrestrial carbon cycle Keenan, Trevor F.; Davidson, Eric A.; Munger, J. William Ecological Applications, Vol. 23, Issue 1 https://doi.org/10.1890/12-0747.1	journal	January 2013
Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States Tao, Feng; Zhou, Zhenghu; Huang, Yuanyuan Frontiers in Big Data, Vol. 3 https://doi.org/10.3389/fdata.2020.00017	journal	June 2020
An Ensemble Kalman Filter with a complex marine ecosystem model: hindcasting phytoplankton in the Cretan Sea Allen, J. I.; Eknes, M.; Evensen, G. Annales Geophysicae, Vol. 21, Issue 1 https://doi.org/10.5194/angeo-21-399-2003	journal	January 2003
The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties Smith, M. J.; Purves, D. W.; Vanderwel, M. C. Biogeosciences, Vol. 10, Issue 1 https://doi.org/10.5194/bg-10-583-2013	journal	January 2013
Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods Lu, Dan; Ricciuto, Daniel; Walker, Anthony Biogeosciences, Vol. 14, Issue 18 https://doi.org/10.5194/bg-14-4295-2017	journal	January 2017
Attaining whole-ecosystem warming using air and deep-soil heating methods with an elevated CO ₂ atmosphere Hanson, Paul J.; Riggs, Jeffery S.; Nettles, W. Robert Biogeosciences, Vol. 14, Issue 4 https://doi.org/10.5194/bg-14-861-2017	journal	January 2017
Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation Fer, Istem; Kelly, Ryan; Moorcroft, Paul R. Biogeosciences, Vol. 15, Issue 19 https://doi.org/10.5194/bg-15-5801-2018	journal	January 2018
Leaf area index identified as a major source of variability in modeled CO₂ fertilization Li, Qianyu; Lu, Xingjie; Wang, Yingping Biogeosciences, Vol. 15, Issue 22 https://doi.org/10.5194/bg-15-6909-2018	journal	January 2018
Competition alters predicted forest carbon cycle responses to nitrogen availability and elevated CO₂: simulations using an explicitly competitive, game-theoretic vegetation demographic model Weng, Ensheng; Dybzinski, Ray; Farrior, Caroline E. Biogeosciences, Vol. 16, Issue 23 https://doi.org/10.5194/bg-16-4577-2019	journal	January 2019
Improving land surface models with FLUXNET data Williams, M.; Richardson, A. D.; Reichstein, M. Biogeosciences, Vol. 6, Issue 7 https://doi.org/10.5194/bg-6-1341-2009	journal	January 2009
Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model Safta, C.; Ricciuto, D. M.; Sargsyan, K. Geoscientific Model Development, Vol. 8, Issue 7 https://doi.org/10.5194/gmd-8-1899-2015	journal	January 2015

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