Improved simulation of carbon and water fluxes by assimilating multi-layer soil temperature and moisture into process-based biogeochemical model
Abstract Background Soil temperature and moisture are sensitive indicators in soil organic matter decomposition because they control global carbon and water cycles and their potential feedback to climatic variations. Although the Biome-Biogeochemical Cycles (Biome-BGC) model is broadly applied in si...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2019-04-01
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| Series: | Forest Ecosystems |
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| Online Access: | http://link.springer.com/article/10.1186/s40663-019-0171-5 |
| _version_ | 1797968195202580480 |
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| author | Min Yan Zengyuan Li Xin Tian Li Zhang Yu Zhou |
| author_facet | Min Yan Zengyuan Li Xin Tian Li Zhang Yu Zhou |
| author_sort | Min Yan |
| collection | DOAJ |
| description | Abstract Background Soil temperature and moisture are sensitive indicators in soil organic matter decomposition because they control global carbon and water cycles and their potential feedback to climatic variations. Although the Biome-Biogeochemical Cycles (Biome-BGC) model is broadly applied in simulating forest carbon and water fluxes, its single-layer soil module cannot represent vertical variations in soil moisture. This study introduces the Biome-BGC MuSo model, which is composed of a multi-layer soil module and new modules pertaining to phenology and management for simulations of carbon and water fluxes. Although this model considers soil processes among active layers, estimates of soil-related variables might be biased, leading to inaccurate estimates of carbon and water fluxes. Methods To improve the estimations of soil-related processes in Biome-BGC MuSo, this study assimilates ground-measured multi-layer daily soil temperature and moisture at the Changbai Mountains forest flux site by using the Ensemble Kalman Filter algorithm. The modeled estimates of water and carbon fluxes were evaluated with measurements using determination coefficient (R 2) and root mean square error (RMSE). The differences in the RMSEs from Biome-BGC MuSo and the assimilated Biome-BGC MuSo were calculated (ΔRMSE), and the relationships between ΔRMSE and the climatic and biophysical factors were analyzed. Results Compared with the original Biome-BGC model, Biome-BGC MuSo improved the simulations of ecosystem respiration (ER), net ecosystem exchange (NEE) and evapotranspiration (ET). Data assimilation of the soil-related variables into Biome-BGC MuSo in real time improved the accuracies of the simulated carbon and water fluxes (ET: R 2 = 0.81, RMSE = 0.70 mm·d− 1; ER: R 2 = 0.85, RMSE = 1.97 gC·m− 2·d− 1; NEE: R 2 = 0.70, RMSE = 1.16 gC·m− 2·d− 1). Conclusions This study proved that seasonal simulation of carbon and water fluxes are more accurate when using Biome-BGC MuSo with a multi-layer soil module than using Biome-BGC with a single-layer soil module. Moreover, assimilating the observed soil temperature and moisture data into Biome-BGC MuSo improved the modeled estimates of water and carbon fluxes via calibrated soil-related simulations. The assimilation strategy is applicable to various climatic and biophysical conditions, particularly densely forested areas, and for local or regional simulation. |
| first_indexed | 2024-04-11T02:43:06Z |
| format | Article |
| id | doaj.art-3f79c5fdb255491f9f273ac89f5e2254 |
| institution | Directory Open Access Journal |
| issn | 2197-5620 |
| language | English |
| last_indexed | 2024-04-11T02:43:06Z |
| publishDate | 2019-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Forest Ecosystems |
| spelling | doaj.art-3f79c5fdb255491f9f273ac89f5e22542023-01-02T18:36:22ZengKeAi Communications Co., Ltd.Forest Ecosystems2197-56202019-04-016111510.1186/s40663-019-0171-5Improved simulation of carbon and water fluxes by assimilating multi-layer soil temperature and moisture into process-based biogeochemical modelMin Yan0Zengyuan Li1Xin Tian2Li Zhang3Yu Zhou4Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesInstitute of Forest Resource Information Techniques, Chinese Academy of ForestryInstitute of Forest Resource Information Techniques, Chinese Academy of ForestryInstitute of Remote Sensing and Digital Earth, Chinese Academy of SciencesGraduate School of Geography, Clark UniversityAbstract Background Soil temperature and moisture are sensitive indicators in soil organic matter decomposition because they control global carbon and water cycles and their potential feedback to climatic variations. Although the Biome-Biogeochemical Cycles (Biome-BGC) model is broadly applied in simulating forest carbon and water fluxes, its single-layer soil module cannot represent vertical variations in soil moisture. This study introduces the Biome-BGC MuSo model, which is composed of a multi-layer soil module and new modules pertaining to phenology and management for simulations of carbon and water fluxes. Although this model considers soil processes among active layers, estimates of soil-related variables might be biased, leading to inaccurate estimates of carbon and water fluxes. Methods To improve the estimations of soil-related processes in Biome-BGC MuSo, this study assimilates ground-measured multi-layer daily soil temperature and moisture at the Changbai Mountains forest flux site by using the Ensemble Kalman Filter algorithm. The modeled estimates of water and carbon fluxes were evaluated with measurements using determination coefficient (R 2) and root mean square error (RMSE). The differences in the RMSEs from Biome-BGC MuSo and the assimilated Biome-BGC MuSo were calculated (ΔRMSE), and the relationships between ΔRMSE and the climatic and biophysical factors were analyzed. Results Compared with the original Biome-BGC model, Biome-BGC MuSo improved the simulations of ecosystem respiration (ER), net ecosystem exchange (NEE) and evapotranspiration (ET). Data assimilation of the soil-related variables into Biome-BGC MuSo in real time improved the accuracies of the simulated carbon and water fluxes (ET: R 2 = 0.81, RMSE = 0.70 mm·d− 1; ER: R 2 = 0.85, RMSE = 1.97 gC·m− 2·d− 1; NEE: R 2 = 0.70, RMSE = 1.16 gC·m− 2·d− 1). Conclusions This study proved that seasonal simulation of carbon and water fluxes are more accurate when using Biome-BGC MuSo with a multi-layer soil module than using Biome-BGC with a single-layer soil module. Moreover, assimilating the observed soil temperature and moisture data into Biome-BGC MuSo improved the modeled estimates of water and carbon fluxes via calibrated soil-related simulations. The assimilation strategy is applicable to various climatic and biophysical conditions, particularly densely forested areas, and for local or regional simulation.http://link.springer.com/article/10.1186/s40663-019-0171-5Biome-BGC MuSoSoil temperatureSoil moistureEnsemble Kalman filterData assimilation |
| spellingShingle | Min Yan Zengyuan Li Xin Tian Li Zhang Yu Zhou Improved simulation of carbon and water fluxes by assimilating multi-layer soil temperature and moisture into process-based biogeochemical model Forest Ecosystems Biome-BGC MuSo Soil temperature Soil moisture Ensemble Kalman filter Data assimilation |
| title | Improved simulation of carbon and water fluxes by assimilating multi-layer soil temperature and moisture into process-based biogeochemical model |
| title_full | Improved simulation of carbon and water fluxes by assimilating multi-layer soil temperature and moisture into process-based biogeochemical model |
| title_fullStr | Improved simulation of carbon and water fluxes by assimilating multi-layer soil temperature and moisture into process-based biogeochemical model |
| title_full_unstemmed | Improved simulation of carbon and water fluxes by assimilating multi-layer soil temperature and moisture into process-based biogeochemical model |
| title_short | Improved simulation of carbon and water fluxes by assimilating multi-layer soil temperature and moisture into process-based biogeochemical model |
| title_sort | improved simulation of carbon and water fluxes by assimilating multi layer soil temperature and moisture into process based biogeochemical model |
| topic | Biome-BGC MuSo Soil temperature Soil moisture Ensemble Kalman filter Data assimilation |
| url | http://link.springer.com/article/10.1186/s40663-019-0171-5 |
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