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Vyacheslav (Hrsg.) Lyubchich

Evaluating Climate Change Impacts

Ebook (EPUB Format)

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem, social, and infrastructure resilience, given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies.This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts… Mehr

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Produktdetails


Weitere Autoren: Gel, Yulia (Hrsg.) / Kilbourne, K. Halimeda (Hrsg.) / Miller, Thomas James (Hrsg.) / Newlands, Nathaniel K. (Hrsg.) / Smith, A. (Hrsg.)
  • ISBN: 978-1-351-19081-7
  • EAN: 9781351190817
  • Produktnummer: 33884702
  • Verlag: Taylor & Francis Ltd.
  • Sprache: Englisch
  • Erscheinungsjahr: 2020
  • Seitenangabe: 394 S.
  • Plattform: EPUB
  • Masse: 13'748 KB
  • Auflage: 1. Auflage

Über den Autor


The book is an interdisciplinary initiative of statisticians, climatologists, ecologists and oceanographers whose research addresses development and implementation of analytical methodology for assessing climate change impacts. The team includes statisticians V. Lyubchich and Y. R. Gel (statistical and machine learning methods for quantification of the climate-induced risk), climatologist K. H. Kilbourne (paleoclimatology, geochemistry, assessment of the causes of climate variability), fisheries scientist T. J. Miller (effects of ocean acidification on blue crab, recruitment issues in menhaden and striped bass), disaster expert A. B. Smith (analysis of economic and societal impacts of extreme events and natural hazards), and research scientist in food-water-energy nexus N. K. Newlands (sustainability, precision agriculture and risk analysis using machine learning and integrated modeling).

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