The role of data science in environmental digital twins: in praise of the arrows

Blair, Gordon S. ORCID: https://orcid.org/0000-0001-6212-1906; Henrys, Peter A. ORCID: https://orcid.org/0000-0003-4758-1482. 2023 The role of data science in environmental digital twins: in praise of the arrows [in special issue: Environmental data science: part 2] Environmetrics, 34 (2), e2789. 6, pp. https://doi.org/10.1002/env.2789

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Official URL: http://dx.doi.org/10.1002/env.2789

Abstract/Summary

Digital twins are increasingly important in many domains, including for understanding and managing the natural environment. Digital twins of the natural environment are fueled by the unprecedented amounts of environmental data now available from a variety of sources from remote sensing to potentially dense deployment of earth-based sensors. Because of this, data science techniques inevitably have a crucial role to play in making sense of this complex, highly heterogeneous data. This short article reflects on the role of data science in digital twins of the natural environment, with particular attention on how resultant data models can work alongside the rich legacy of process models that exist in this domain. We seek to unpick the complex two-way relationship between data and process understanding. By focusing on the interactions, we end up with a template for digital twins that incorporates a rich, highly dynamic learning process with the potential to handle the complexities and emergent behaviors of this important area.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/env.2789
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-) Pollution (Science Area 2017-)
ISSN:	1180-4009
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	adaptive modeling, adaptive sampling, complex systems, digital twins, environmental data science, environmental modelling
NORA Subject Terms:	Ecology and Environment Computer Science Data and Information
Date made live:	03 Nov 2023 09:59 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/535378

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1002/env.2789

UKCEH and CEH Sections/Science Areas:

Soils and Land Use (Science Area 2017-)
Pollution (Science Area 2017-)

ISSN:

1180-4009

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

adaptive modeling, adaptive sampling, complex systems, digital twins, environmental data science, environmental modelling