Land carbon cycle modeling : matrix approach, data assimilation, & ecological forecasting /: matrix approach, data assimilation, & ecological forecasting. (2022)

• Record Type: Book
• Further Information: Note: Edited by Yiqi Luo and Benjamin Smith.
• Editors: Luo, Yiqi; Smith, Benjamin
• Contents: Unit 1: Fundamentals of carbon cycle modeling. Chapter 1: Theoretical foundation of the land carbon cycle and matrix approach. Yiqi Luo. Chapter 2: Introduction to modeling. Benjamin Smith. Chapter 3: Flow diagrams and balance equations of land carbon models. Yuanyuan Huang. Chapter 4: Practice 1, Developing carbon flow diagrams and balance equations. Yuanyuan Huang. Unit 2 : Matrix representation of carbon balance. Chapter 5: Developing matrix representation of land carbon models. Yuanyuan Huang. Chapter 6: Coupled carbon-nitrogen matrix models. Zheng Shi and Xingjie Lu. Chapter 7: Compartmental systems. Carlos Sierra. Chapter 8: Practice 2, Matrix representation of carbon balance equations and coding. Yuanyuan Huang. Unit 3 : Carbon cycle diagnostics for uncertainty analysis. Chapter 9: Unified diagnostic system for uncertainty analysis. Yiqi Luo. Chapter 10: Sensitivity analysis with matrix equations: A case study with ORCHIDEE. Yuanyuan Huang. Chapter 11: Matrix phosphorus model and data assimilation. Enqing Hou. Chapter 12: Practice 3, Diagnostic variables in matrix models. Xingjie Lu. Unit 4 : Semi-analytic spin-up (SASU). Chapter 13: Non-autonomous ODE system solver and stability analysis. Ying Wang. Chapter 14: Semi-Analytic Spin-Up (SASU) of coupled carbon-nitrogen cycle models. Xingjie Lu and Jianyang Xia. Chapter 15: Time characteristics of compartmental systems. Carlos Sierra. Chapter 16: Practice 4, Efficiency and convergence of semi-analytic spin-up (SASU) in TECO. Xingjie Lu. Unit 5 : Traceability and benchmark analysis. Chapter 17: Overview of traceability analysis. Jianyang Xia. Chapter 18: Applications of the transient traceability framework. Lifen Jiang. Chapter 19: Benchmark analysis. Yiqi Luo & Forrest M. Hoffman. Chapter 20: Practice 5, Traceability analysis for evaluating terrestrial carbon cycle models. Jianyang Xia & Jian Zhou. Unit 6 : Introduction to data assimilation. Chapter 21: Data assimilation: Introduction, procedure, and applications. Yiqi Luo. Chapter 22: Bayesian statistics and Markov chain Monte Carlo method in data assimilation. Feng Tao. Chapter 23: Application of data assimilation to soil incubation data. Junyi Liang & Jiang Jiang. Chapter 24: Practice 6, The seven-step procedure for data assimilation. Xin Huang. Unit 7 : Data assimilation with field measurements and satellite data. Chapter 25: Model-data integration at the SPRUCE experiment. Daniel Ricciuto. Chapter 26: Application of data assimilation to a peatland methane study. Shuang Ma. Chapter 27: Global data assimilation using earth observation – the CARDAMOM approach. Mathew Williams. Chapter 28: Practice 7, Data assimilation at the SPRUCE site. Shuang Ma. Unit 8 : Value of data to constrain models and their predictions. Chapter 29: Information contents of different types of data sets to constrain parameters and predictions. Enqing Hou. Chapter 30: Using data assimilation to identify mechanisms controlling lake carbon dynamics. Oleksandra (Sasha) Hararuk. Chapter 31: Data-constrained uncertainty analysis in global soil carbon models. Zheng Shi. Chapter 32: Practice 8, Information contents of land carbon pool and flux measurements to constrain a land carbon model. Enqing Hou. Unit 9 : Ecological forecasting with EcoPAD. Chapter 33: Introduction to ecological forecasting. Yiqi Luo. Chapter 34: Ecological Platform for Assimilating Data (EcoPAD) for ecological forecasting. Yuanyuan Huang. Chapter 35: Practice 9, Ecological forecasting at the SPRUCE site. Jiang Jiang. Unit 10 : Process-based machine learning and data-driven modeling (PRODA). Chapter 36: Introduction to machine learning and neural networks. Toby Dylan Hocking. Chapter 37: PROcess-guided deep learning and DAta-driven modeling (PRODA). Feng Tao & Yiqi Luo. Chapter 38: Practice 10, Deep learning to optimize parametrization of CLM5. Feng Tao. Appendices. Appendix 1: Matrix algebra in land carbon cycle modeling. Ye Chen. Appendix 2: Introduction to programming in Python. Xin Huang. Appendix 3: CarboTrain user guide. Yuan Gao
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2022
• Extent: 1 online resource, illustrations (black and white, and colour)
• Subjects: 577.144; Carbon cycle (Biogeochemistry); Carbon
• Languages: English
• ISBNs: 9780429531309; 9781498737029; 9780429155659
• Related ISBNs: 9781498737012
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
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