Articles | Volume 9, issue 11
https://doi.org/10.5194/gmd-9-4155-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-4155-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model evaluation paper
| 
21 Nov 2016
Model evaluation paper |
| 21 Nov 2016
A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 2: Model validation
Yuji Masutomi
CORRESPONDING AUTHOR
College of Agriculture, Ibaraki University, 3-21-1, Chuo, Ami, Inashiki, Ibaraki 300-0393, Japan
Keisuke Ono
Institute for Agro-Environmental Sciences, NARO, 3-1-3, Kannondai, Tsukuba, Ibaraki 305-8604, Japan
Takahiro Takimoto
Institute for Global Change Adaptation Science, Ibaraki University, 3-21-1, Chuo, Ami, Inashiki, Ibaraki 300-0393, Japan
Masayoshi Mano
Graduate School of Horticulture, Chiba University, 648 Matsudo, Matsudo-shi, Chiba 271-8510, Japan
Atsushi Maruyama
Institute for Agro-Environmental Sciences, NARO, 3-1-3, Kannondai, Tsukuba, Ibaraki 305-8604, Japan
Akira Miyata
Institute for Agro-Environmental Sciences, NARO, 3-1-3, Kannondai, Tsukuba, Ibaraki 305-8604, Japan
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Cited
21 citations as recorded by crossref.
- Aligning the harvesting year in global gridded crop model simulations with that in census reports is pivotal to national-level model performance evaluations for rice T. Iizumi et al. https://doi.org/10.1016/j.eja.2021.126367
- Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments T. Hasegawa et al. https://doi.org/10.1038/s41598-017-13582-y
- Impact assessment of climate change on the major rice cultivar Ciherang in Indonesia Y. KINOSE et al. https://doi.org/10.2480/agrmet.D-19-00045
- Development of Rice Paddy Model Based on Noah LSM: Consistent Parameterization of Subcanopy Resistance from the Ponded Water to Dense Rice Canopy H. Lim & Y. Lee https://doi.org/10.1007/s13143-024-00375-3
- Developing an eco-physiological process-based model of soybean growth and yield (MATCRO-Soy v.1): model calibration and evaluation A. Yusara et al. https://doi.org/10.5194/gmd-18-8801-2025
- Emerging research topics in agricultural meteorology and assessment of climate change adaptation T. IIZUMI et al. https://doi.org/10.2480/agrmet.D-17-00021
- Terrestrial ecosystem model studies and their contributions to AsiaFlux A. ITO & K. ICHII https://doi.org/10.2480/agrmet.D-20-00024
- The appropriate analytical solution for coupled leaf photosynthesis and stomatal conductance models for C3 plants Y. Masutomi https://doi.org/10.1016/j.ecolmodel.2023.110306
- Management practices and elevated atmospheric CO2 levels helped to sustain a high level of global rice production T. Lin & A. Jain https://doi.org/10.1038/s41598-026-55973-0
- Improvements in the Land and Crop Modeling Over Flooded Rice Fields by Incorporating the Shallow Paddy Water X. Xu et al. https://doi.org/10.1029/2022MS003248
- Improvement of simulating gross primary production and evapotranspiration in the land surface model based on parameter calibration H. Li et al. https://doi.org/10.3178/hrl.25-00033
- Ozone changes the linear relationship between photosynthesis and stomatal conductance and decreases water use efficiency in rice Y. Masutomi et al. https://doi.org/10.1016/j.scitotenv.2018.11.132
- A development of reduction scenarios of the short-lived climate pollutants (SLCPs) for mitigating global warming and environmental problems T. Nakajima et al. https://doi.org/10.1186/s40645-020-00351-1
- Development of the global maize yield model MATCRO-Maize version 1.0 M. Nagata et al. https://doi.org/10.5194/gmd-18-8927-2025
- Cropland expansion delays vegetation spring phenology according to satellite and in-situ observations G. Zhao et al. https://doi.org/10.1016/j.agee.2023.108651
- The Existence and Uniqueness of Solutions in the Leaf Photosynthesis‐Transpiration‐Stomatal Conductance Model for C3 Plants Y. Masutomi & K. Kobayashi https://doi.org/10.1111/gcb.70974
- Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape R. Mourad et al. https://doi.org/10.3390/rs12193121
- BESS-Rice: A remote sensing derived and biophysical process-based rice productivity simulation model Y. Huang et al. https://doi.org/10.1016/j.agrformet.2018.03.014
- Simulation of crop yield using the global hydrological model H08 (crp.v1) Z. Ai & N. Hanasaki https://doi.org/10.5194/gmd-16-3275-2023
- Improving the predictions of leaf photosynthesis during and after short‐term heat stress with current rice models T. Sun et al. https://doi.org/10.1111/pce.14683
- A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 1: Model description Y. Masutomi et al. https://doi.org/10.5194/gmd-9-4133-2016
21 citations as recorded by crossref.
- Aligning the harvesting year in global gridded crop model simulations with that in census reports is pivotal to national-level model performance evaluations for rice T. Iizumi et al. https://doi.org/10.1016/j.eja.2021.126367
- Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments T. Hasegawa et al. https://doi.org/10.1038/s41598-017-13582-y
- Impact assessment of climate change on the major rice cultivar Ciherang in Indonesia Y. KINOSE et al. https://doi.org/10.2480/agrmet.D-19-00045
- Development of Rice Paddy Model Based on Noah LSM: Consistent Parameterization of Subcanopy Resistance from the Ponded Water to Dense Rice Canopy H. Lim & Y. Lee https://doi.org/10.1007/s13143-024-00375-3
- Developing an eco-physiological process-based model of soybean growth and yield (MATCRO-Soy v.1): model calibration and evaluation A. Yusara et al. https://doi.org/10.5194/gmd-18-8801-2025
- Emerging research topics in agricultural meteorology and assessment of climate change adaptation T. IIZUMI et al. https://doi.org/10.2480/agrmet.D-17-00021
- Terrestrial ecosystem model studies and their contributions to AsiaFlux A. ITO & K. ICHII https://doi.org/10.2480/agrmet.D-20-00024
- The appropriate analytical solution for coupled leaf photosynthesis and stomatal conductance models for C3 plants Y. Masutomi https://doi.org/10.1016/j.ecolmodel.2023.110306
- Management practices and elevated atmospheric CO2 levels helped to sustain a high level of global rice production T. Lin & A. Jain https://doi.org/10.1038/s41598-026-55973-0
- Improvements in the Land and Crop Modeling Over Flooded Rice Fields by Incorporating the Shallow Paddy Water X. Xu et al. https://doi.org/10.1029/2022MS003248
- Improvement of simulating gross primary production and evapotranspiration in the land surface model based on parameter calibration H. Li et al. https://doi.org/10.3178/hrl.25-00033
- Ozone changes the linear relationship between photosynthesis and stomatal conductance and decreases water use efficiency in rice Y. Masutomi et al. https://doi.org/10.1016/j.scitotenv.2018.11.132
- A development of reduction scenarios of the short-lived climate pollutants (SLCPs) for mitigating global warming and environmental problems T. Nakajima et al. https://doi.org/10.1186/s40645-020-00351-1
- Development of the global maize yield model MATCRO-Maize version 1.0 M. Nagata et al. https://doi.org/10.5194/gmd-18-8927-2025
- Cropland expansion delays vegetation spring phenology according to satellite and in-situ observations G. Zhao et al. https://doi.org/10.1016/j.agee.2023.108651
- The Existence and Uniqueness of Solutions in the Leaf Photosynthesis‐Transpiration‐Stomatal Conductance Model for C3 Plants Y. Masutomi & K. Kobayashi https://doi.org/10.1111/gcb.70974
- Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape R. Mourad et al. https://doi.org/10.3390/rs12193121
- BESS-Rice: A remote sensing derived and biophysical process-based rice productivity simulation model Y. Huang et al. https://doi.org/10.1016/j.agrformet.2018.03.014
- Simulation of crop yield using the global hydrological model H08 (crp.v1) Z. Ai & N. Hanasaki https://doi.org/10.5194/gmd-16-3275-2023
- Improving the predictions of leaf photosynthesis during and after short‐term heat stress with current rice models T. Sun et al. https://doi.org/10.1111/pce.14683
- A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 1: Model description Y. Masutomi et al. https://doi.org/10.5194/gmd-9-4133-2016
Saved (final revised paper)
Latest update: 01 Nov 2026
Short summary
We conducted two types of validation for the simulations by MATCRO-Rice developed by Masutomi et al. (2016). The results of the validation indicate that MATCRO-Rice has a high ability to accurately and consistently simulate latent heat flux, sensible heat flux, net carbon uptake by crops, and crop yield.
We conducted two types of validation for the simulations by MATCRO-Rice developed by Masutomi et...
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