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Environmental Data Science
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Open access
ISSN:
2634-4602 (Online)
Editor:
Julien Brajard
Nansen Center (NERSC), Norway
Editorial board
Environmental Data Science
(EDS) is an open-access transdisciplinary journal dedicated to advances in data-driven methods to understand and predict environmental processes and impacts, and their patterns in space and time. The methodological scope is defined broadly to encompass artificial intelligence (including machine learning, deep learning, and computer vision), statistics, data mining, and econometrics, as well as hybrid approaches that combine data-driven methods with physical process-based modeling.
EDS is a venue for topics relating to the geosphere (the solid earth and its processes), cryosphere (e.g. ice, snow, permafrost and tundra), biosphere (biodiversity and ecosystems), hydrosphere (oceans and fresh water, including the water cycle) and atmosphere (e.g. meteorology, climatology). It also welcomes work that shows how data science can inform societal responses to environmental problems, such as climate change, air quality, energy, natural resources and land use. Papers in EDS can address a range of data types from in-situ observations, to remote sensing, as well as data simulated by physical models, and reanalysis products.
EDS promotes open research by encouraging authors to
share their data and code
and by
publishing reviews
alongside accepted articles. For more details about the types of article published in the journal, see the
Instructions for Authors
Latest articles
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Article
How reliable are retrieval-augmented and standard ChatGPT models to support flood susceptibility mapping?
Ali Pourzangbar
Mário J. Franca
Environmental Data Science
Volume
Article
Detecting unique wind field features in hurricane Sandy from topological data maps
Justin Hoffmeier
Environmental Data Science
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Data-driven discovery of meteotsunami patterns from sparse observations
Ardiansyah Fauzi
Emiliano Renzi
Frederic Dias
Daniel Santiago Pelaez-Zapata
Tatjana Kokina
Environmental Data Science
Volume
Article
Combined effects of site and model parameterization for soil respiration components in a Canadian wildfire chronosequence
John Zobitz
Xuan Zhou
Heidi Aaltonen
Egle Köster
Frank Berninger
Jukka Pumpanen
Kajar Köster
Environmental Data Science
Volume
Article
Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network
Lucas Howard
Aneesh C. Subramanian
Jithendra Raju Nadimpalli
Donata Giglio
Ibrahim Hoteit
Environmental Data Science
Volume
Article
MoTiF: a self-supervised model for multi-source forecasting with application to tropical cyclones – CORRIGENDUM
Clément Dauvilliers
Claire Monteleoni
Environmental Data Science
Volume
Article
Utilization of artificial intelligence and thermal cameras in material analysis for hot-summer Mediterranean climates
Ahmet Benliay
Türkan Azeri
Environmental Data Science
Volume
Article
Which meteorological parameters influence extreme wind speed in a wind farm? A heterogeneous Granger causality approach
Kateřina Hlaváčková-Schindler
Rainer Wöss
Irene Schicker
Claudia Plant
Environmental Data Science
Volume
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EDS Blog
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Climate Informatics 2025: Exploring Climate Science and Data Science in the Global South
20 November 2025,
Ricardo Barros Lourenço
As many readers will know, COP30, the UN climate conference, got underway in Belém, Brazil this month. In this blog post, we’re pleased to report on another...
Join Us at the Upcoming Climate Informatics Conference in Cambridge!
11 April 2023,
Ricardo Barros Lourenço
We are excited to announce the upcoming Climate Informatics Conference, taking place at the historic Trinity Hall at the University of Cambridge, in Cambridge,...
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1.7
2024 Impact Factor:
304
out of
376
Environmental Sciences
2024 Journal Citation Reports
© Clarivate Analytics
Most read
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Article
FloodGNN-GRU: a spatio-temporal graph neural network for flood prediction
Arnold Kazadi
James Doss-Gollin
Antonia Sebastian
Arlei Silva
Environmental Data Science
Volume
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Most cited
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Article
Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science
Amy McGovern
Imme Ebert-Uphoff
David John Gagne II
Ann Bostrom
Environmental Data Science
Volume
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