Optimized Gross Primary Productivity Over the Croplands Within the BEPS Particle Filtering Data Assimilation System (BEPS_PF v1.0)
Abstract Agricultural ecosystems play an important role in modulating the global carbon balance by taking up atmospheric carbon dioxide, while large differences and uncertainties exist in the estimated crop gross primary productivity (GPP) by terrestrial ecosystem models (TEMs). With the aim of redu...
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| Language: | English |
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American Geophysical Union (AGU)
2025-01-01
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| Series: | Journal of Advances in Modeling Earth Systems |
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| Online Access: | https://doi.org/10.1029/2024MS004412 |
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| author | Xiuli Xing Mousong Wu Huajie Zhu Wenzhuo Duan Weimin Ju Xiaorong Wang Youhua Ran Yongguang Zhang Fei Jiang |
| author_facet | Xiuli Xing Mousong Wu Huajie Zhu Wenzhuo Duan Weimin Ju Xiaorong Wang Youhua Ran Yongguang Zhang Fei Jiang |
| author_sort | Xiuli Xing |
| collection | DOAJ |
| description | Abstract Agricultural ecosystems play an important role in modulating the global carbon balance by taking up atmospheric carbon dioxide, while large differences and uncertainties exist in the estimated crop gross primary productivity (GPP) by terrestrial ecosystem models (TEMs). With the aim of reducing the parameter uncertainty in TEMs for crop GPP simulation, we developed a particle filtering data assimilation (DA) system based on the ecosystem model BEPS (Biosphere Exchange Process Simulator), that is, the BEPS_PF (v1.0). We investigated the feasibility of BEPS_PF on the multiple parameters optimization across typical crops (wheat, rice, soybean and corn) and on reducing the uncertainty of GPP over 32 cropland eddy covariance sites globally. With BEPS_PF DA, the average R2 between GPP and observed data at the hourly scale has been efficiently improved by 0.36 and root mean square error reduced by 0.18 gC m−2 hr−1. The DA system has successfully corrected the GPP from the irrigated croplands which was severely underestimated by the model's prior parameters. We found that the maximum carboxylation rate at 25°C (Vcmax25) as well as the leaf nitrogen content (Nleaf) were co‐varied with strong seasonal variations. The optimized Vcmax25 showed large differences among different crop types with ranges 27.07–62.95, 42.17–93.32, 31.89–105.81, and 38.34–89.29 μmol m−2 s−1 for corn, soybean, wheat, and rice respectively. We demonstrated that the BEPS_PF is an efficient tool for optimizing different processes in the ecosystems, and with the satellite data it can be extended to regional and global scales for more accurate estimation of carbon fluxes. |
| format | Article |
| id | doaj-art-a33ce850a60e4e1a8a7d35909dc27b46 |
| institution | Kabale University |
| issn | 1942-2466 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj-art-a33ce850a60e4e1a8a7d35909dc27b462025-01-28T13:21:09ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662025-01-01171n/an/a10.1029/2024MS004412Optimized Gross Primary Productivity Over the Croplands Within the BEPS Particle Filtering Data Assimilation System (BEPS_PF v1.0)Xiuli Xing0Mousong Wu1Huajie Zhu2Wenzhuo Duan3Weimin Ju4Xiaorong Wang5Youhua Ran6Yongguang Zhang7Fei Jiang8International Institute for Earth System Science Nanjing University Nanjing ChinaInternational Institute for Earth System Science Nanjing University Nanjing ChinaInternational Institute for Earth System Science Nanjing University Nanjing ChinaInternational Institute for Earth System Science Nanjing University Nanjing ChinaInternational Institute for Earth System Science Nanjing University Nanjing ChinaSchool of Biological Sciences The University of Hong Kong Hong Kong ChinaHeihe Remote Sensing Experimental Research Station Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou ChinaInternational Institute for Earth System Science Nanjing University Nanjing ChinaInternational Institute for Earth System Science Nanjing University Nanjing ChinaAbstract Agricultural ecosystems play an important role in modulating the global carbon balance by taking up atmospheric carbon dioxide, while large differences and uncertainties exist in the estimated crop gross primary productivity (GPP) by terrestrial ecosystem models (TEMs). With the aim of reducing the parameter uncertainty in TEMs for crop GPP simulation, we developed a particle filtering data assimilation (DA) system based on the ecosystem model BEPS (Biosphere Exchange Process Simulator), that is, the BEPS_PF (v1.0). We investigated the feasibility of BEPS_PF on the multiple parameters optimization across typical crops (wheat, rice, soybean and corn) and on reducing the uncertainty of GPP over 32 cropland eddy covariance sites globally. With BEPS_PF DA, the average R2 between GPP and observed data at the hourly scale has been efficiently improved by 0.36 and root mean square error reduced by 0.18 gC m−2 hr−1. The DA system has successfully corrected the GPP from the irrigated croplands which was severely underestimated by the model's prior parameters. We found that the maximum carboxylation rate at 25°C (Vcmax25) as well as the leaf nitrogen content (Nleaf) were co‐varied with strong seasonal variations. The optimized Vcmax25 showed large differences among different crop types with ranges 27.07–62.95, 42.17–93.32, 31.89–105.81, and 38.34–89.29 μmol m−2 s−1 for corn, soybean, wheat, and rice respectively. We demonstrated that the BEPS_PF is an efficient tool for optimizing different processes in the ecosystems, and with the satellite data it can be extended to regional and global scales for more accurate estimation of carbon fluxes.https://doi.org/10.1029/2024MS004412particle filterdata assimilationgross primary productivityBEPS modelagricultural ecosystems |
| spellingShingle | Xiuli Xing Mousong Wu Huajie Zhu Wenzhuo Duan Weimin Ju Xiaorong Wang Youhua Ran Yongguang Zhang Fei Jiang Optimized Gross Primary Productivity Over the Croplands Within the BEPS Particle Filtering Data Assimilation System (BEPS_PF v1.0) Journal of Advances in Modeling Earth Systems particle filter data assimilation gross primary productivity BEPS model agricultural ecosystems |
| title | Optimized Gross Primary Productivity Over the Croplands Within the BEPS Particle Filtering Data Assimilation System (BEPS_PF v1.0) |
| title_full | Optimized Gross Primary Productivity Over the Croplands Within the BEPS Particle Filtering Data Assimilation System (BEPS_PF v1.0) |
| title_fullStr | Optimized Gross Primary Productivity Over the Croplands Within the BEPS Particle Filtering Data Assimilation System (BEPS_PF v1.0) |
| title_full_unstemmed | Optimized Gross Primary Productivity Over the Croplands Within the BEPS Particle Filtering Data Assimilation System (BEPS_PF v1.0) |
| title_short | Optimized Gross Primary Productivity Over the Croplands Within the BEPS Particle Filtering Data Assimilation System (BEPS_PF v1.0) |
| title_sort | optimized gross primary productivity over the croplands within the beps particle filtering data assimilation system beps pf v1 0 |
| topic | particle filter data assimilation gross primary productivity BEPS model agricultural ecosystems |
| url | https://doi.org/10.1029/2024MS004412 |
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