A simple taxonomy for describing the spatio-temporal structure of environmental modelling data

https://doi.org/10.1016/j.envsoft.2020.104810Get rights and content

Highlights

  • A simple taxonomy for describing the spatio-temporal structure of environmental numerical modelling data.

  • Motivations for the design of the taxonomy.

  • Structured derivation of the taxonomy.

  • Use case examples of application in a variety of modelling domains.

Abstract

Environmental modelling practitioners are now seeking to move forwards together and build standards and technologies with more universal applicability and integration. With so many overlapping environmental modelling technologies and infrastructures being offered and with so many relevant supporting technologies contributing on the periphery, there is considerable scope to articulate and utilise underlying concepts which draw them together. As such, this paper offers the Spatio-temporal Modelling Taxonomy, a simple taxonomy for describing the spatio-temporal structure of environmental numerical modelling data used as input to, or produced as output from environmental numerical models. The taxonomy is motivated from common spatial structures, a set of feature-types to describe observed environmental data and the implementation of the OpenMI integrated modelling standard. It serves as a natural evolution of terminology that is in common use in environmental numerical modelling and is designed to strike the right balance between complexity and utility. It implements a structured, theoretical framework, whilst being essentially practical in nature to apply to the ‘real world’ facing numerical modellers and those seeking to integrate environmental numerical models and the data supporting them.

Section snippets

Software availability

An xml encoding of the full Spatio-temporal Modelling Taxonomy is given in the Spatio-temporal Modelling Taxonomy github repository: https://github.com/QHarpham/Spatio-temporal-Modelling-Taxonomy.

Motivation

Three independent technical innovations help to motivate and shape the derivation of the taxonomy for describing the spatio-temporal structure of the data supporting environmental numerical modelling: common spatial structures, the Climate Science Modelling Language (CSML) and usage of adaptors in OpenMI.

Derivation

The three motivations discussed above (common spatial structures, CSML and OpenMI) adopt an implementation of Point, Polyline and Polygon in some form. The common spatial structures and OpenMI refer to these directly (with grids constructed from polygons) and CSML implements Points, Trajectories (including a time dimension to the structure) and Grids. We therefore begin with purely spatial structures in 2-dimensions with these three, adopting the terminology ‘Point’, ‘Polyline’ and ‘Polygon’,

Examples and use cases

The Spatio-temporal Modelling Taxonomy has been derived from a set of principles and has a logical structure and symmetry. However, the overriding objective is for it to relate to real-world situations and be of practical use. It may be pleasing in theory, but is it applicable in practice? Indeed, a simpler or a more complex and holistic taxonomy may be possible, but any such structured approach must be appropriately balanced between utility and completeness. Its very existence, not least any

Discussion and conclusions

This narrative has so far shown that a taxonomy built around a common pattern (see Fig. 8) can be created based on established principles and formalising commonly used terms. It has also been demonstrated to be relevant to a wide variety of environmental modelling use cases. This is not surprising since the vast majority (if not all) environmental modelling data can be said to be applicable in some way to both time and space. Basing the taxonomy around spatio-temporal structures will inevitably

Declaration of competing interest

I confirm that there is no conflict of interest concerning this publication.

Acknowledgements

This work is a further development of PhD studies undertaken at and funded by HR Wallingford as an associated research centre of the Open University (Harpham, 2019). Much of the understanding and examples used in this paper originate from the development and application of the OpenMI 2.0 standard. This is one of the results of over a decade of research by a wide variety of individuals. The projects include: HarmonIT (EC contract: EVK1-CT-2001-00090) and OpenMI-Life (Grant agreement number

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