Simulation of maize evapotranspiration: An inter-comparison among 29 maize models
Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006–2013) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first “blind” phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET.
Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
---|---|
Format: | article biblioteca |
Langue: | eng |
Sujets: | F60 - Physiologie et biochimie végétale, U10 - Informatique, mathématiques et statistiques, maïs, Zea mays, évapotranspiration, modèle mathématique, besoin en eau, http://aims.fao.org/aos/agrovoc/c_12332, http://aims.fao.org/aos/agrovoc/c_8504, http://aims.fao.org/aos/agrovoc/c_2741, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_8323, |
Accès en ligne: | http://agritrop.cirad.fr/592866/ http://agritrop.cirad.fr/592866/1/Simulation%20of%20maize%20evapotranspiration%20An%20inter-comparison%20among%2029%20Models.pdf |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
id |
dig-cirad-fr-592866 |
---|---|
record_format |
koha |
institution |
CIRAD FR |
collection |
DSpace |
country |
Francia |
countrycode |
FR |
component |
Bibliográfico |
access |
En linea |
databasecode |
dig-cirad-fr |
tag |
biblioteca |
region |
Europa del Oeste |
libraryname |
Biblioteca del CIRAD Francia |
language |
eng |
topic |
F60 - Physiologie et biochimie végétale U10 - Informatique, mathématiques et statistiques maïs Zea mays évapotranspiration modèle mathématique besoin en eau http://aims.fao.org/aos/agrovoc/c_12332 http://aims.fao.org/aos/agrovoc/c_8504 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_8323 F60 - Physiologie et biochimie végétale U10 - Informatique, mathématiques et statistiques maïs Zea mays évapotranspiration modèle mathématique besoin en eau http://aims.fao.org/aos/agrovoc/c_12332 http://aims.fao.org/aos/agrovoc/c_8504 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_8323 |
spellingShingle |
F60 - Physiologie et biochimie végétale U10 - Informatique, mathématiques et statistiques maïs Zea mays évapotranspiration modèle mathématique besoin en eau http://aims.fao.org/aos/agrovoc/c_12332 http://aims.fao.org/aos/agrovoc/c_8504 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_8323 F60 - Physiologie et biochimie végétale U10 - Informatique, mathématiques et statistiques maïs Zea mays évapotranspiration modèle mathématique besoin en eau http://aims.fao.org/aos/agrovoc/c_12332 http://aims.fao.org/aos/agrovoc/c_8504 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_8323 Kimball, Bruce A. Boote, Kenneth J. Hatfield, Jerry L. Ahuja, Lajpat R. Stöckle, Claudio Archontoulis, Sotirios Baron, Christian Basso, Bruno Bertuzzi, Patrick Constantin, Julie Deryng, Delphine Dumont, Benjamin Durand, Jean-Louis Ewert, Franck Gaiser, Thomas Gayler, Sebastian Hoffmann, Munir P. Jiang, Qianjing Kim, Soo-Hyung Lizaso, Jon Moulin, Sophie Nendel, Claas Parker, Philip Palosuo, Taru Priesack, Eckart Qi, Zhiming Srivastava, Amit Kumar Stella, Tommaso Tao, Fulu Thorp, Kelly R. Timlin, Dennis Twine, Tracy E. Webber, Heidi Willaume, Magali Williams, Karina E. Simulation of maize evapotranspiration: An inter-comparison among 29 maize models |
description |
Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006–2013) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first “blind” phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET. |
format |
article |
topic_facet |
F60 - Physiologie et biochimie végétale U10 - Informatique, mathématiques et statistiques maïs Zea mays évapotranspiration modèle mathématique besoin en eau http://aims.fao.org/aos/agrovoc/c_12332 http://aims.fao.org/aos/agrovoc/c_8504 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_8323 |
author |
Kimball, Bruce A. Boote, Kenneth J. Hatfield, Jerry L. Ahuja, Lajpat R. Stöckle, Claudio Archontoulis, Sotirios Baron, Christian Basso, Bruno Bertuzzi, Patrick Constantin, Julie Deryng, Delphine Dumont, Benjamin Durand, Jean-Louis Ewert, Franck Gaiser, Thomas Gayler, Sebastian Hoffmann, Munir P. Jiang, Qianjing Kim, Soo-Hyung Lizaso, Jon Moulin, Sophie Nendel, Claas Parker, Philip Palosuo, Taru Priesack, Eckart Qi, Zhiming Srivastava, Amit Kumar Stella, Tommaso Tao, Fulu Thorp, Kelly R. Timlin, Dennis Twine, Tracy E. Webber, Heidi Willaume, Magali Williams, Karina E. |
author_facet |
Kimball, Bruce A. Boote, Kenneth J. Hatfield, Jerry L. Ahuja, Lajpat R. Stöckle, Claudio Archontoulis, Sotirios Baron, Christian Basso, Bruno Bertuzzi, Patrick Constantin, Julie Deryng, Delphine Dumont, Benjamin Durand, Jean-Louis Ewert, Franck Gaiser, Thomas Gayler, Sebastian Hoffmann, Munir P. Jiang, Qianjing Kim, Soo-Hyung Lizaso, Jon Moulin, Sophie Nendel, Claas Parker, Philip Palosuo, Taru Priesack, Eckart Qi, Zhiming Srivastava, Amit Kumar Stella, Tommaso Tao, Fulu Thorp, Kelly R. Timlin, Dennis Twine, Tracy E. Webber, Heidi Willaume, Magali Williams, Karina E. |
author_sort |
Kimball, Bruce A. |
title |
Simulation of maize evapotranspiration: An inter-comparison among 29 maize models |
title_short |
Simulation of maize evapotranspiration: An inter-comparison among 29 maize models |
title_full |
Simulation of maize evapotranspiration: An inter-comparison among 29 maize models |
title_fullStr |
Simulation of maize evapotranspiration: An inter-comparison among 29 maize models |
title_full_unstemmed |
Simulation of maize evapotranspiration: An inter-comparison among 29 maize models |
title_sort |
simulation of maize evapotranspiration: an inter-comparison among 29 maize models |
url |
http://agritrop.cirad.fr/592866/ http://agritrop.cirad.fr/592866/1/Simulation%20of%20maize%20evapotranspiration%20An%20inter-comparison%20among%2029%20Models.pdf |
work_keys_str_mv |
AT kimballbrucea simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT bootekennethj simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT hatfieldjerryl simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT ahujalajpatr simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT stockleclaudio simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT archontoulissotirios simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT baronchristian simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT bassobruno simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT bertuzzipatrick simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT constantinjulie simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT deryngdelphine simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT dumontbenjamin simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT durandjeanlouis simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT ewertfranck simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT gaiserthomas simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT gaylersebastian simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT hoffmannmunirp simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT jiangqianjing simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT kimsoohyung simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT lizasojon simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT moulinsophie simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT nendelclaas simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT parkerphilip simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT palosuotaru simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT priesackeckart simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT qizhiming simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT srivastavaamitkumar simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT stellatommaso simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT taofulu simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT thorpkellyr simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT timlindennis simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT twinetracye simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT webberheidi simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT willaumemagali simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels AT williamskarinae simulationofmaizeevapotranspirationanintercomparisonamong29maizemodels |
_version_ |
1792499765176434688 |
spelling |
dig-cirad-fr-5928662024-01-29T02:04:47Z http://agritrop.cirad.fr/592866/ http://agritrop.cirad.fr/592866/ Simulation of maize evapotranspiration: An inter-comparison among 29 maize models. Kimball Bruce A., Boote Kenneth J., Hatfield Jerry L., Ahuja Lajpat R., Stöckle Claudio, Archontoulis Sotirios, Baron Christian, Basso Bruno, Bertuzzi Patrick, Constantin Julie, Deryng Delphine, Dumont Benjamin, Durand Jean-Louis, Ewert Franck, Gaiser Thomas, Gayler Sebastian, Hoffmann Munir P., Jiang Qianjing, Kim Soo-Hyung, Lizaso Jon, Moulin Sophie, Nendel Claas, Parker Philip, Palosuo Taru, Priesack Eckart, Qi Zhiming, Srivastava Amit Kumar, Stella Tommaso, Tao Fulu, Thorp Kelly R., Timlin Dennis, Twine Tracy E., Webber Heidi, Willaume Magali, Williams Karina E.. 2019. Agricultural and Forest Meteorology, 271 : 264-284.https://doi.org/10.1016/j.agrformet.2019.02.037 <https://doi.org/10.1016/j.agrformet.2019.02.037> Simulation of maize evapotranspiration: An inter-comparison among 29 maize models Kimball, Bruce A. Boote, Kenneth J. Hatfield, Jerry L. Ahuja, Lajpat R. Stöckle, Claudio Archontoulis, Sotirios Baron, Christian Basso, Bruno Bertuzzi, Patrick Constantin, Julie Deryng, Delphine Dumont, Benjamin Durand, Jean-Louis Ewert, Franck Gaiser, Thomas Gayler, Sebastian Hoffmann, Munir P. Jiang, Qianjing Kim, Soo-Hyung Lizaso, Jon Moulin, Sophie Nendel, Claas Parker, Philip Palosuo, Taru Priesack, Eckart Qi, Zhiming Srivastava, Amit Kumar Stella, Tommaso Tao, Fulu Thorp, Kelly R. Timlin, Dennis Twine, Tracy E. Webber, Heidi Willaume, Magali Williams, Karina E. eng 2019 Agricultural and Forest Meteorology F60 - Physiologie et biochimie végétale U10 - Informatique, mathématiques et statistiques maïs Zea mays évapotranspiration modèle mathématique besoin en eau http://aims.fao.org/aos/agrovoc/c_12332 http://aims.fao.org/aos/agrovoc/c_8504 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_8323 Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006–2013) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first “blind” phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/592866/1/Simulation%20of%20maize%20evapotranspiration%20An%20inter-comparison%20among%2029%20Models.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1016/j.agrformet.2019.02.037 10.1016/j.agrformet.2019.02.037 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.agrformet.2019.02.037 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.agrformet.2019.02.037 info:eu-repo/semantics/reference/purl/https://doi.org/10.18167/DVN1/JSAHFB |