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Partitioning model uncertainty in multi-model ensemble river flow projections

Author

Listed:
  • Gordon Aitken

    (Water Resilient Cities, Institute for Infrastructure and Environment, Heriot-Watt University)

  • Lindsay Beevers

    (Water Resilient Cities, Institute for Infrastructure and Environment, Heriot-Watt University
    Institute for Infrastructure and Environment, University of Edinburgh)

  • Simon Parry

    (UK Centre for Ecology & Hydrology)

  • Katie Facer-Childs

    (UK Centre for Ecology & Hydrology)

Abstract

Floods are the largest natural disaster currently facing the UK, whilst the incidents of droughts have increased in recent years. Floods and droughts can have devastating consequences on society, resulting in significant financial damage to the economy. Climate models suggest that precipitation and temperature changes will exacerbate future hydrological extremes (i.e., floods and droughts). Such events are likely to become more frequent and intense in the future; thus to develop adaptation plans climate model projections feed hydrological models to provide future water resource projections. ‘eFLaG’ is one set of future river flow projections produced for the UK driven by UKCP18 climate projections from the UK Met Office. The UKCP18-derived eFLaG dataset provides state-of-the-art projections for a single GCM driven by RCP 8.5 across the entire UK. A QE-ANOVA approach has been used to partition contributing sources of uncertainty for two flow quantiles (Q5 high flows and Q95 low flows), at near and far future time scales, for each of the 186 GB catchments in the eFLaG dataset. Results suggest a larger hydrological model uncertainty associated with low flows and greater regional climate model uncertainty for high flows which remains stationary between flow indicators. Total uncertainty increases from near to far future and highly uncertain catchments have been identified with a high concentration in South-East England.

Suggested Citation

  • Gordon Aitken & Lindsay Beevers & Simon Parry & Katie Facer-Childs, 2023. "Partitioning model uncertainty in multi-model ensemble river flow projections," Climatic Change, Springer, vol. 176(11), pages 1-28, November.
    • Handle: RePEc:spr:climat:v:176:y:2023:i:11:d:10.1007_s10584-023-03621-1
    DOI: 10.1007/s10584-023-03621-1
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    References listed on IDEAS

    as
    1. Lindsay Beevers & Lila Collet & Gordon Aitken & Claire Maravat & Annie Visser, 2020. "The influence of climate model uncertainty on fluvial flood hazard estimation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(3), pages 2489-2510, December.
    2. Jan Niel & E. Uytven & P. Willems, 2019. "Uncertainty Analysis of Climate Change Impact on River Flow Extremes Based on a Large Multi-Model Ensemble," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4319-4333, September.
    3. Tobias Vetter & Julia Reinhardt & Martina Flörke & Ann Griensven & Fred Hattermann & Shaochun Huang & Hagen Koch & Ilias G. Pechlivanidis & Stefan Plötner & Ousmane Seidou & Buda Su & R. Willem Vervoo, 2017. "Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins," Climatic Change, Springer, vol. 141(3), pages 419-433, April.
    4. A. Kay & H. Davies & V. Bell & R. Jones, 2009. "Comparison of uncertainty sources for climate change impacts: flood frequency in England," Climatic Change, Springer, vol. 92(1), pages 41-63, January.
    5. Alison C. Rudd & A. L. Kay & V. A. Bell, 2019. "National-scale analysis of future river flow and soil moisture droughts: potential changes in drought characteristics," Climatic Change, Springer, vol. 156(3), pages 323-340, October.
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    Keywords

    UKCP18; Regional climate model; QE-ANOVA;
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