Crop physiology calibration in the CLM

Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.

doi:10.5194/gmd-8-1071-2015

Title: Crop physiology calibration in the CLM

Journal Article · Wed Apr 15 00:00:00 EDT 2015 · Geoscientific Model Development (Online)

DOI:https://doi.org/10.5194/gmd-8-1071-2015· OSTI ID:1208895

Bilionis, I. ^[1]; Drewniak, B. A. ^[2]; Constantinescu, E. M. ^[1]

Argonne National Lab., IL (United States). Mathematics and Computer Science Div.
Argonne National Lab., IL (United States). Environmental Science Div.

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurements of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

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

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1208895

Alternate ID(s):: OSTI ID: 1392545

Journal Information:: Geoscientific Model Development (Online), Vol. 8, Issue 4; ISSN 1991-9603

Publisher:: European Geosciences UnionCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 24 works

Citation information provided by
Web of Science

References (29)

Inverse Problem Theory and Methods for Model Parameter Estimation Tarantola, Albert https://doi.org/10.1137/1.9780898717921	book	January 2005
Interactive Crop Management in the Community Earth System Model (CESM1): Seasonal Influences on Land–Atmosphere Fluxes Levis, Samuel; Bonan, Gordon B.; Kluzek, Erik Journal of Climate, Vol. 25, Issue 14 https://doi.org/10.1175/JCLI-D-11-00446.1	journal	July 2012
LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection Hourdin, Frédéric; Grandpeix, Jean-Yves; Rio, Catherine Climate Dynamics, Vol. 40, Issue 9-10 https://doi.org/10.1007/s00382-012-1343-y	journal	April 2012
Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems Verma, Shashi B.; Dobermann, Achim; Cassman, Kenneth G. Agricultural and Forest Meteorology, Vol. 131, Issue 1-2 https://doi.org/10.1016/j.agrformet.2005.05.003	journal	July 2005
Integrated BIosphere Simulator (IBIS) Yield and Nitrate Loss Predictions for Wisconsin Maize Receiving Varied Amounts of Nitrogen Fertilizer Kucharik, Christopher J.; Brye, Kristofor R. Journal of Environment Quality, Vol. 32, Issue 1 https://doi.org/10.2134/jeq2003.0247	journal	January 2003
Bayesian calibration of computer models Kennedy, Marc C.; O'Hagan, Anthony Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 3 https://doi.org/10.1111/1467-9868.00294	journal	August 2001
Solution of inverse problems with limited forward solver evaluations: a Bayesian perspective Bilionis, I.; Zabaras, N. Inverse Problems, Vol. 30, Issue 1 https://doi.org/10.1088/0266-5611/30/1/015004	journal	December 2013
Variational inference for Dirichlet process mixtures Blei, David M.; Jordan, Michael I. Bayesian Analysis, Vol. 1, Issue 1 https://doi.org/10.1214/06-BA104	journal	March 2006
On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm Reichstein, Markus; Falge, Eva; Baldocchi, Dennis Global Change Biology, Vol. 11, Issue 9 https://doi.org/10.1111/j.1365-2486.2005.001002.x	journal	September 2005
Tuning the climate of a global model: TUNING THE CLIMATE OF A GLOBAL MODEL Mauritsen, Thorsten; Stevens, Bjorn; Roeckner, Erich Journal of Advances in Modeling Earth Systems, Vol. 4, Issue 3 https://doi.org/10.1029/2012MS000154	journal	March 2012
Integrated BIosphere Simulator (IBIS) Yield and Nitrate Loss Predictions for Wisconsin Maize Receiving Varied Amounts of Nitrogen Fertilizer Kucharik, Christopher J.; Brye, Kristofor R. Journal of Environment Quality, Vol. 32, Issue 1 https://doi.org/10.2134/jeq2003.2470	journal	January 2003
An assessment of storage terms in the surface energy balance of maize and soybean Meyers, T. Agricultural and Forest Meteorology, Vol. 125, Issue 1-2 https://doi.org/10.1016/j.agrformet.2004.03.001	journal	September 2004
An efficient climate forecasting method using an intermediate complexity Earth System Model and the ensemble Kalman filter Hargreaves, J. C.; Annan, J. D.; Edwards, N. R. Climate Dynamics, Vol. 23, Issue 7-8 https://doi.org/10.1007/s00382-004-0471-4	journal	August 2004
The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4 Koven, C. D.; Riley, W. J.; Subin, Z. M. Biogeosciences, Vol. 10, Issue 11 https://doi.org/10.5194/bg-10-7109-2013	journal	January 2013
Free energy computations by minimization of Kullback–Leibler divergence: An efficient adaptive biasing potential method for sparse representations Bilionis, I.; Koutsourelakis, P. S. Journal of Computational Physics, Vol. 231, Issue 9 https://doi.org/10.1016/j.jcp.2012.01.033	journal	May 2012
The CCSM4 Land Simulation, 1850–2005: Assessment of Surface Climate and New Capabilities Lawrence, David M.; Oleson, Keith W.; Flanner, Mark G. Journal of Climate, Vol. 25, Issue 7 https://doi.org/10.1175/JCLI-D-11-00103.1	journal	April 2012
A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters Koutsourelakis, P. S. Journal of Computational Physics, Vol. 228, Issue 17 https://doi.org/10.1016/j.jcp.2009.05.016	journal	September 2009
A note on the effect of GCM tuning on climate sensitivity Bender, F. A-M Environmental Research Letters, Vol. 3, Issue 1 https://doi.org/10.1088/1748-9326/3/1/014001	journal	January 2008
Modeling agriculture in the Community Land Model Drewniak, B.; Song, J.; Prell, J. Geoscientific Model Development, Vol. 6, Issue 2 https://doi.org/10.5194/gmd-6-495-2013	journal	January 2013
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
Optimization of a coupled hydrology-crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter: ASSIMILATION OF LAI AND SOIL MOISTURE Pauwels, Valentijn R. N.; Verhoest, Niko E. C.; De Lannoy, Gabriëlle J. M. Water Resources Research, Vol. 43, Issue 4 https://doi.org/10.1029/2006WR004942	journal	April 2007
Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model Yang, B.; Qian, Y.; Lin, G. Atmospheric Chemistry and Physics, Vol. 12, Issue 5 https://doi.org/10.5194/acp-12-2409-2012	journal	January 2012
Data Assimilation Evensen, Geir https://doi.org/10.1007/978-3-642-03711-5	book	January 2009
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
Sequential Monte Carlo samplers Del Moral, Pierre; Doucet, Arnaud; Jasra, Ajay Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 68, Issue 3 https://doi.org/10.1111/j.1467-9868.2006.00553.x	journal	June 2006
Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter Annan, J. D.; Hargreaves, J. C.; Edwards, N. R. Ocean Modelling, Vol. 8, Issue 1-2 https://doi.org/10.1016/j.ocemod.2003.12.004	journal	January 2005
Sequential data assimilation Evensen, Geir Data Assimilation https://doi.org/10.1007/978-3-642-03711-5_4	book	January 2009
An efficient climate forecasting method using an intermediate complexity Earth System Model and the ensemble Kalman filter Hargreaves, J. C.; Annan, J. D.; Edwards, N. R. Springer-Verlag https://doi.org/10.48350/158498	text	January 2004
Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter Annan, J. D.; Hargreaves, J. C.; Edwards, N. R. Elsevier https://doi.org/10.48350/158570	text	January 2005

Cited By (4)

Computationally Efficient Variational Approximations for Bayesian Inverse Problems Tsilifis, Panagiotis; Bilionis, Ilias; Katsounaros, Ioannis Journal of Verification, Validation and Uncertainty Quantification, Vol. 1, Issue 3 https://doi.org/10.1115/1.4034102	journal	July 2016
Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques Lu, Dan; Ricciuto, Daniel Geoscientific Model Development, Vol. 12, Issue 5 https://doi.org/10.5194/gmd-12-1791-2019	journal	January 2019
Representing agriculture in Earth System Models: Approaches and priorities for development: AGRICULTURE IN ESMS McDermid, S. S.; Mearns, L. O.; Ruane, A. C. Journal of Advances in Modeling Earth Systems, Vol. 9, Issue 5 https://doi.org/10.1002/2016ms000749	journal	September 2017
Impact assessment of climate change on rice yields using the ORYZA model in the Sichuan Basin, China Xu, Chenchen; Wu, Wenxiang; Ge, Quansheng International Journal of Climatology, Vol. 38, Issue 7 https://doi.org/10.1002/joc.5473	journal	March 2018

Similar Records

Improving maize growth processes in the community land model: Implementation and evaluation

Journal Article · Thu Mar 15 00:00:00 EDT 2018 · Agricultural and Forest Meteorology · OSTI ID:1208895

Peng, Bin; Guan, Kaiyu; Chen, Min; +5 more

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

Journal Article · Fri Jun 03 00:00:00 EDT 2016 · Biogeochemistry · OSTI ID:1208895

Lokupitiya, E.; Denning, A. S.; Schaefer, K.; +33 more