
Academic Journal
Q2Environmental Data Science
About Environmental Data Science
Environmental Data Science is a scholarly journal published by Cambridge University Press. SCImago 2025 lists it in Q2, with an SJR of 0.652 and H-index of 11.
Coverage: 2022-2026. Research categories: Artificial Intelligence (Q2); Environmental Science (miscellaneous) (Q2); Statistics and Probability (Q2); Global and Planetary Change (Q3).
Open-access policies and author information
Reported in the official DOAJ public CSV snapshot (2026-09-01), downloaded 2026-10-03. Record updated 2026-03-17. This snapshot does not establish today’s listing status or fee quotation.
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Source-backed journal facts
Topics in published research
Meteorological Phenomena and Simulations; Climate variability and models; Hydrological Forecasting Using AI; Oceanographic and Atmospheric Processes; Energy Load and Power Forecasting; Atmospheric and Environmental Gas Dynamics.
OpenAlex classifies topics from published works. These topics are not the publisher’s official aims and scope.
Source: OpenAlex source record. Retrieved 2026-10-03. Source record updated 2026-10-02. OpenAlex metrics are different from SCImago metrics and the Clarivate Journal Impact Factor.
Journal Metrics
Quartile, SJR and the listed SCImago H-index use the 2025 imported SCImago dataset. A quartile may vary by subject category. Values without a source or reporting year are unverified historical entries. Verify the current Journal Impact Factor with Clarivate or the publisher before using it.
Aims & Scope
The publisher’s official aims and scope have not yet been verified for this profile. Use the journal website to check subject fit and accepted article types before submitting.
Recent Research Articles
Latest publications matched automatically by ISSN.
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Ahmet Benliay, Türkan Azeri
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2026 · DOI: 10.1017/eds.2026.10045Interpretable machine learning for CMIP6 multi-model ensembles
Siyi Wu, Steve M. Easterbrook
2026 · DOI: 10.1017/eds.2026.10044Explainable machine learning highlights the role of diffuse radiation in ecosystem carbon uptake of boreal forest
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Dominic Weisser, Chloe Hashimoto-Cullen, Benjamin Guedj
2026 · DOI: 10.1017/eds.2026.10055Convolutional neural network for the prediction of Sargassum seaweed beachings in Guadeloupe
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2026 · DOI: 10.1017/eds.2026.10053Calibrated conformal prediction intervals for microphysical process rates
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2026 · DOI: 10.1017/eds.2026.10036Joint bias correction and downscaling of subseasonal forecasts via diffusion models
Maria Pyrina, Adel Imamovic, Dominik Bueeler, Christoph Spirig et al.
2026 · DOI: 10.1017/eds.2026.10047Combined effects of site and model parameterization for soil respiration components in a Canadian wildfire chronosequence
John Zobitz, Xuan Zhou, Heidi Aaltonen, Egle Köster et al.
2026 · DOI: 10.1017/eds.2026.10034Assessing the risk of future Dunkelflaute events for Germany using generative deep learning
Felix Strnad, Jonathan Schmidt, Fabian Mockert, Philipp Hennig et al.
2026 · DOI: 10.1017/eds.2026.10038S ynoptic B ench : evaluating vision-language models on generating weather forecast discussions of the future
Timothy Higgins, Antonios Mamalakis, Chirag Agarwal
2026 · DOI: 10.1017/eds.2026.10050Generative unsupervised downscaling of climate models via domain alignment: Application to wind fields
Julie Keisler, Boutheina Oueslati, Anastase Charantonis, Yannig Goude et al.
2026 · DOI: 10.1017/eds.2026.10057CNN-based forecasting of early winter NAO using sea surface temperature
Elena Provenzano, Guillaume Gastineau, Carlos Mejia, Didier Swingedouw et al.
2026 · DOI: 10.1017/eds.2026.10059A machine learning approach to detecting environmental crimes in Brazil
Matthew C. Ingram
2026 · DOI: 10.1017/eds.2026.10060MoTiF: a self-supervised model for multi-source forecasting with application to tropical cyclones – CORRIGENDUM
Clément Dauvilliers, Claire Monteleoni
2026 · DOI: 10.1017/eds.2026.10032Human-centric skills are essential for the responsible and rigorous application of AI in ecology
Jessica Marielle Kendall-Bar, Allison R. Payne, Max F. Czapanskiy, Nathan Fox et al.
2026 · DOI: 10.1017/eds.2026.10042Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network
Lucas Howard, Aneesh C. Subramanian, Jithendra Raju Nadimpalli, Donata Giglio et al.
2026 · DOI: 10.1017/eds.2026.10033Precipitation nowcasting of satellite data using physically aligned neural networks
Antônio Catão, Leonardo Voltarelli, Melvin Poveda, Paulo Orenstein et al.
2026 · DOI: 10.1017/eds.2026.10041Reviews
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Version History
October 4, 2026 at 9:11 pm
October 2, 2026