Abstract Understanding economic water use remains challenging due to limited data availability and confidentiality constraints that restrict the robust assessment of sectoral patterns. This study presents the first publicly available national dataset for Scotland that integrates abstraction and network-supplied water volumes across 81 economic sectors, enabling consistent sectoral water-use assessment and ranking of major contributors to national demand. An integrated, diagnostic analytical approach was applied to capture both short- and long-term water-use behaviour. Short-term analysis captures monthly water-use dynamics, including recurring seasonal patterns and responses to extreme events, while long-term results describe sectoral developments and the drivers shaping them. Sectoral profiles combining these observations with qualitative evidence reveal three dominant driver types: hydro-climatic, economic-structural, and behavioural-operational. These profiles were used to derive indicative sectoral estimates for 2025, reflecting heterogeneous and evolving influences over time. Gross Value Added (GVA) provides contextual information but has only limited explanatory power, underscoring the need for sector-specific interpretation. Differentiated approaches therefore provide a more realistic basis for water-use assessment than uniform assumptions, particularly where demand is heterogeneous. The study delivers a sector-level overview of economic water use in Scotland, supporting more transparent monitoring and more robust interpretation of future demand. The dataset further offers insights into water-intensive activities common across many economies. This driver-based classification offers transferable analytical logic for regions with fragmented water-use data and evolving economic structures, supporting more targeted monitoring and realistic demand assessment.
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Maria Clemens
Scott J. McGrane
Christopher J. White
Water Resources Management
Stanford University
University of Strathclyde
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Clemens et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69df2ba0e4eeef8a2a6b09c3 — DOI: https://doi.org/10.1007/s11269-026-04574-7