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Land Carbon Cycle Modeling: Matrix Approach, Data Assimilation, Ecological Forecasting, and Machine Learning
Luo Yiqi,Smith Benjamin (Autor)
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Crc Press
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Land Carbon Cycle Modeling: Matrix Approach, Data Assimilation, Ecological Forecasting, and Machine Learning - Luo Yiqi,Smith Benjamin
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Reseña del libro "Land Carbon Cycle Modeling: Matrix Approach, Data Assimilation, Ecological Forecasting, and Machine Learning"
Carbon moves through the atmosphere, through the oceans, onto land, and into ecosystems. This cycling has a large effect on climate - changing geographic patterns of rainfall and the frequency of extreme weather - and is altered as the use of fossil fuels adds carbon to the cycle. The dynamics of this global carbon cycling are largely predicted over broad spatial scales and long periods of time by Earth system models. This book addresses the crucial question of how to assess, evaluate, and estimate the potential impact of the additional carbon to the land carbon cycle. The contributors describe a set of new approaches to land carbon cycle modeling for better exploring ecological questions regarding changes in carbon cycling; employing data assimilation techniques for model improvement; doing real- or near-time ecological forecasting for decision support; combining newly available machine learning techniques with process-based models to improve prediction of land carbon cycle under climate change. This new edition includes 7 new chapters on machine learning and its applications to carbon cycle research (5 chapters). on principles underlying carbon dioxide removal from the atmosphere (1 chapter), a contemporary active research and management issue, and on community infrastructure for ecological forecasting. Key Features Helps readers understand, implement, and criticize land carbon cycle models Offers a new theoretical framework to understand transient dynamics of land carbon cycle Describes a suite of modeling skills - matrix approach to represent land carbon, nitrogen, and phosphorus cycles; data assimilation and machine learning to improve parameterization; and workflow systems to facilitate ecological forecasting Introduces a new set of techniques, such as semi-analytic spin-up (SASU), unified diagnostic system with a 1-3-5 scheme, traceability analysis, and benchmark analysis, and PROcess-guided machine learning and DAta-driven modeling (PRODA) for model evaluation and improvement Reorganized from 1st edition and seven new chapters added Strives to balance theoretical considerations, technical details, and applications of ecosystem modeling for research, assessment, and crucial decision making
