Science infrastructure planning

Building a strategic capability for the UK in environmental research, observation, data and modelling relies on the enduring partnership between UKRI-NERC and UKCEH and involves ongoing and substantial investments in capital infrastructure, skills and operating expenditure.

How we plan investments

The UK government and UK Research and Innovation operate a national research infrastructure roadmap, planning and investment process, which includes a vision and ambitions for the environment sector in UK and international contexts .  
In planning future investments in environmental research infrastructures, UKCEH will continue to work closely with BEIS-UKRI-NERC 

• To help set ambitious, national-scale, forward-looking strategic priorities for research infrastructure
• To meet capital funding principles and criteria
• To secure appropriate capital investment for future infrastructures with full life-cycle costs included
• To secure sustainable operating costs.

UKCEH contributes to national planning through our leadership and convening role for the science of land, water and air. We engage in horizon-scanning with UK researchers, partners and funders to identify future needs and priorities for environmental research and observation. We actively participate in important forums and networks that facilitate strategic planning, including: UK Research and Innovation (UKRI); the UK Environmental Observation Framework (UKEOF); the British Ecological Society (BES); the Partnership for European Environmental Research (PEER); and others.  We also engage bilaterally with key stakeholders such as Defra, its agencies, and devolved governments.

UKCEH Science Infrastructures continually innovate to meet today’s and future needs.  Some of the key drivers that inform our planning are:

Interlinked social-economic-environmental challenges. These challenges tend to be enduring and driven by humans, for example: climate and environmental change; biodiversity loss; and pollution.  They demand sustained research and observation to understand the problem, to co-design solutions, and to test and track the performance of interventions.

Integration and partnership.  The complexity and scale of interlinked social-economic-environmental challenges, and of environmental processes, demands integrated research and observation of the whole environment as an inter-connected system. This means improving our capability to simultaneously measure and model: multiple environmental, social and economic variables; across different land-uses (including urban, rural, industrial and agricultural) and scales (in space and time – especially landscape-scale); at higher resolution; in dense urban and less accessible places; in partnership with others.

Advances in science and technology.  Advances in technology offer new ways to research and observe our changing environment, and to solve environmental challenges.  Some examples of technology developments that will continue to transform the way we study the environment include:

• Autonomy and robotics, including: remote sensing (aerial drones and satellites); automated vehicles and sampling methods; miniaturization; low energy systems; mobile communications; and new sensors to observe and measure multiple environmental variables across landscapes.
• Artificial intelligence (AI) and machine learning: (a) for image and audio recognition of wildlife, biodiversity, habitats and land use; and (b) to analyse large volumes of data from varied sources, collection methods and quality. 
• Environmental DNA (eDNA) analysis to monitor species, biodiversity and ecosystem function in hard-to-see media such as soils and water bodies.
• Digital twins – creating digital models of the environment which combine environmental and weather process understanding with real-time data to track (nowcast) current events, forecast future behaviour and explore scenarios (eg climate change, flooding, pollution, environmental use and management practices).
• National investment in computational and e-infrastructure – including more powerful and distributed super-computing architectures, large-scale data storage, and high-capacity data networking – to enable environmental data collection, analysis and modelling.

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