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dc.contributor.editorLuo, Yiqi
dc.contributor.editorSmith, Benjamin
dc.date.accessioned2024-05-16T12:34:02Z
dc.date.available2024-05-16T12:34:02Z
dc.date.issued2024
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/90275
dc.description.abstractCarbon 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; and combining newly available machine learning techniques with process-based models to improve prediction of the land carbon cycle under climate change. This new edition includes seven new chapters: machine learning and its applications to carbon cycle research (five chapters); principles underlying carbon dioxide removal from the atmosphere, contemporary active research and management issues (one chapter); and community infrastructure for ecological forecasting (one chapter). Key Features Helps readers understand, implement, and criticize land carbon cycle models Offers a new theoretical framework to understand transient dynamics of the 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 the first edition with seven new chapters added Strives to balance theoretical considerations, technical details, and applications of ecosystem modeling for research, assessment, and crucial decision-makingen_US
dc.languageEnglishen_US
dc.subject.classificationthema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNA Agribusiness and primary industriesen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming::TVB Agricultural scienceen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PST Botany and plant sciencesen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technologyen_US
dc.subject.classificationthema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RB Earth sciences::RBG Geology, geomorphology and the lithosphere::RBGK Geochemistryen_US
dc.subject.classificationthema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RB Earth sciences::RBG Geology, geomorphology and the lithosphere::RBGB Sedimentology and pedologyen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSV Zoology and animal sciencesen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSP Hydrobiology::PSPF Freshwater biologyen_US
dc.subject.classificationthema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RN The environment::RNC Applied ecology::RNCB Biodiversityen_US
dc.subject.otherEcosystem Modeling;Data Assimilation in Modeling;Assessing Models;Types of Modelsen_US
dc.titleLand Carbon Cycle Modelingen_US
dc.title.alternativeMatrix Approach, Data Assimilation, Ecological Forecasting, and Machine Learning Second Editionen_US
dc.typebook
oapen.identifier.doi10.1201/9781032711126en_US
oapen.relation.isPublishedBy7b3c7b10-5b1e-40b3-860e-c6dd5197f0bben_US
oapen.relation.isbn9781032711126en_US
oapen.relation.isbn9781032698496en_US
oapen.relation.isbn9781040026311en_US
oapen.relation.isbn9781498737029en_US
oapen.imprintCRC Pressen_US
oapen.pages313en_US
oapen.remark.publicFunder name: Cornell University


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