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Abstract. By showing the uncertainty surrounding a prediction, probabilistic forecasts can give an earlier indication of potential upcoming floods, increasing the amount of time available to prepare. However, making a decision based on probabilistic information is challenging. As part of the UK-wide policy's move towards forecast-based flood risk management, the Environment Agency (EA), responsible for managing risks of flooding in England, is transitioning towards the use of probabilistic fluvial forecasts for flood early warning. While science and decision-making are both individually progressing, there is still a lack of an ideal framework for the incorporation of new and probabilistic science into decision-making practices, and, respectively, the uptake of decision-makers' perspectives in the design of scientific practice. To address this, interviews were carried out with EA decision-makers (i.e. Duty Officers), key players in the EA's flood warning decision-making process, to understand how they perceive this transition might impact on their decision-making. The interviews highlight the complex landscape in which EA Duty Officers operate and the breadth of factors that inform their decisions, in addition to the forecast. Although EA Duty Officers already account for uncertainty and communicate their confidence in the forecast they currently use, the interviews revealed a decision-making process which is still very binary and linear to an extent, which appears at odds with probabilistic forecasting. Based on the interview results, we make recommendations to support a successful transition to probabilistic forecasting for flood early warning in England. These recommendations include the new system's co-design together with Duty Officers, the preparation of clear guidelines on how probabilistic forecast should be used for decision-making in practice, EA communication with all players in the decision-making chain (internal and external) that this transition will become operational practice and the documentation of this transition to help other institutes yet to face a similar challenge. We believe that this paper is of wide interest for a range of sectors at the intersection between geoscience and society. A glossary of technical terms is highlighted by asterisks in the text and included in Appendix A.

The data generated by environmental research infrastructures (ENV RIs) are key to analysing the quality of general living standards and the conditions of development of environmentally sensitive economic activities: monitoring the atmosphere and ocean is increasingly and critically important in a context marked by the risks caused by global warming.Given the cost of ENV RIs, their benefits to society, in terms of economic impacts, must be assessed and demonstrated. The primary objective of this article is to review the main tools used to assess the economic impacts of ENV RIs and to propose a methodological framework. The latter classifies the impacts into three categories: (1) upstream impacts on equipment suppliers; (2) downstream impacts on the performance and quality of observational data, monitoring services and forecasts; and (3) feedback impacts in terms of improved knowledge about the environment to the benefit of economic activities. In this framework, the entire data and service supply chain is considered for the assessment of impacts.An ocean-related case study serves as a practical example: Argo, a global in situ ocean observing system, provides an understanding of the supply chain from upstream suppliers of ENV RIs to primary and processed ocean data providers. It highlights the methodological issues involved in assessing the different categories of impacts.The article gives precedence to tried and tested methods. It concludes that further work and more data are needed to improve assessment methods. International audience