IDEAS home Printed from https://ideas.repec.org/a/spr/climat/v121y2013i2p317-329.html
   My bibliography  Save this article

Quantification of uncertainty sources in a probabilistic climate change assessment of future water shortages

Author

Listed:
  • C. Harris
  • A. Quinn
  • J. Bridgeman

Abstract

As the incorporation of probabilistic climate change information into UK water resource management gathers apace, understanding the relative scales of the uncertainty sources in projections of future water shortage metrics is necessary for the resultant information to be understood and used effectively. Utilising modified UKCP09 weather generator data and a multi-model approach, this paper represents a first attempt at extending an uncertainty assessment of future stream flows under forced climates to consider metrics of water shortage based on the triggering of reservoir control curves. It is found that the perturbed physics ensemble uncertainty, which describes climate model parameter error uncertainty, is the cause of a far greater proportion of both the overall flow and water shortage per year probability uncertainty than that caused by SRES emissions scenario choice in the 2080s. The methodology for producing metrics of future water shortage risk from UKCP09 weather generator information described here acts as the basis of a robustness analysis of the North Staffordshire WRZ to climate change, which provides an alternative approach for making decisions despite large uncertainties, which will follow. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • C. Harris & A. Quinn & J. Bridgeman, 2013. "Quantification of uncertainty sources in a probabilistic climate change assessment of future water shortages," Climatic Change, Springer, vol. 121(2), pages 317-329, November.
    • Handle: RePEc:spr:climat:v:121:y:2013:i:2:p:317-329
    DOI: 10.1007/s10584-013-0871-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10584-013-0871-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10584-013-0871-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Julia Hall & Conor Murphy, 2010. "Vulnerability Analysis of Future Public Water Supply Under Changing Climate Conditions: A Study of the Moy Catchment, Western Ireland," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(13), pages 3527-3545, October.
    2. 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.
    3. Helge Bormann, 2011. "Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations," Climatic Change, Springer, vol. 104(3), pages 729-753, February.
    4. Simon Gosling & Glenn McGregor & Jason Lowe, 2012. "The benefits of quantifying climate model uncertainty in climate change impacts assessment: an example with heat-related mortality change estimates," Climatic Change, Springer, vol. 112(2), pages 217-231, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michael J. Scott & Don S. Daly & Mohamad I. Hejazi & G. Page Kyle & Lu Liu & Haewon C. McJeon & Anupriya Mundra & Pralit L. Patel & Jennie S. Rice & Nathalie Voisin, 2016. "Sensitivity of future U.S. Water shortages to socioeconomic and climate drivers: a case study in Georgia using an integrated human-earth system modeling framework," Climatic Change, Springer, vol. 136(2), pages 233-246, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nima Fayaz & Laura E. Condon & David G. Chandler, 2020. "Evaluating the Sensitivity of Projected Reservoir Reliability to the Choice of Climate Projection: A Case Study of Bull Run Watershed, Portland, Oregon," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 1991-2009, April.
    2. Indira Pokhrel & Ajay Kalra & Md Mafuzur Rahaman & Ranjeet Thakali, 2020. "Forecasting of Future Flooding and Risk Assessment under CMIP6 Climate Projection in Neuse River, North Carolina," Forecasting, MDPI, vol. 2(3), pages 1-23, August.
    3. Carolina Natel Moura & Sílvio Luís Rafaeli Neto & Claudia Guimarães Camargo Campos & Eder Alexandre Schatz Sá, 2020. "Hydrological Impacts of Climate Change in a Well-preserved Upland Watershed," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(8), pages 2255-2267, June.
    4. Yi Yang & Jianping Tang, 2023. "Downscaling and uncertainty analysis of future concurrent long-duration dry and hot events in China," Climatic Change, Springer, vol. 176(2), pages 1-25, February.
    5. Alison Kay, 2022. "Differences in hydrological impacts using regional climate model and nested convection-permitting model data," Climatic Change, Springer, vol. 173(1), pages 1-19, July.
    6. S. Camici & L. Brocca & T. Moramarco, 2017. "Accuracy versus variability of climate projections for flood assessment in central Italy," Climatic Change, Springer, vol. 141(2), pages 273-286, March.
    7. Shirin Karimi & Bahman Jabbarian Amiri & Arash Malekian, 2019. "Similarity Metrics-Based Uncertainty Analysis of River Water Quality Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 1927-1945, April.
    8. Eun-Sung Chung & Kwangjae Won & Yeonjoo Kim & Hosun Lee, 2014. "Water Resource Vulnerability Characteristics by District’s Population Size in a Changing Climate Using Subjective and Objective Weights," Sustainability, MDPI, vol. 6(9), pages 1-17, September.
    9. Florence Habets & Julien Boé & Michel Déqué & Agnès Ducharne & Simon Gascoin & Ali Hachour & Eric Martin & Christian Pagé & Eric Sauquet & Laurent Terray & Dominique Thiéry & Ludovic Oudin & Pascal Vi, 2013. "Impact of climate change on the hydrogeology of two basins in northern France," Climatic Change, Springer, vol. 121(4), pages 771-785, December.
    10. Richard Arsenault & François Brissette & Jean-Stéphane Malo & Marie Minville & Robert Leconte, 2013. "Structural and Non-Structural Climate Change Adaptation Strategies for the Péribonka Water Resource System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2075-2087, May.
    11. Ye Tian & Yue-Ping Xu & Xu-Jie Zhang, 2013. "Assessment of Climate Change Impacts on River High Flows through Comparative Use of GR4J, HBV and Xinanjiang Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2871-2888, June.
    12. Michelle Vliet & Stephen Blenkinsop & Aidan Burton & Colin Harpham & Hans Broers & Hayley Fowler, 2012. "A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe," Climatic Change, Springer, vol. 111(2), pages 249-277, March.
    13. Zigeng Niu & Lan Feng & Xinxin Chen & Xiuping Yi, 2021. "Evaluation and Future Projection of Extreme Climate Events in the Yellow River Basin and Yangtze River Basin in China Using Ensembled CMIP5 Models Data," IJERPH, MDPI, vol. 18(11), pages 1-26, June.
    14. Lauren M. Cook & Seth McGinnis & Constantine Samaras, 2020. "The effect of modeling choices on updating intensity-duration-frequency curves and stormwater infrastructure designs for climate change," Climatic Change, Springer, vol. 159(2), pages 289-308, March.
    15. Xiang, Keyu & Li, Yi & Horton, Robert & Feng, Hao, 2020. "Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review," Agricultural Water Management, Elsevier, vol. 232(C).
    16. Sogol Moradian & Ali Torabi Haghighi & Maryam Asadi & Seyed Ahmad Mirbagheri, 2023. "Future Changes in Precipitation Over Northern Europe Based on a Multi-model Ensemble from CMIP6: Focus on Tana River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2447-2463, May.
    17. Qi Cui & Tariq Ali & Wei Xie & Jikun Huang & Jinxia Wang, 2022. "The uncertainty of climate change impacts on China’s agricultural economy based on an integrated assessment approach," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(3), pages 1-22, March.
    18. Lüliu Liu & Hongmei Xu & Yong Wang & Tong Jiang, 2017. "Impacts of 1.5 and 2 °C global warming on water availability and extreme hydrological events in Yiluo and Beijiang River catchments in China," Climatic Change, Springer, vol. 145(1), pages 145-158, November.
    19. Zhe Yuan & Jijun Xu & Yongqiang Wang, 2018. "Projection of Future Extreme Precipitation and Flood Changes of the Jinsha River Basin in China Based on CMIP5 Climate Models," IJERPH, MDPI, vol. 15(11), pages 1-17, November.
    20. Seyed Morteza Seyedian & Ozgur Kisi & Abbas Parsaie & Mojtaba Kashani, 2024. "Improving the Reliability of Compound Channel Discharge Prediction Using Machine Learning Techniques and Resampling Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4685-4709, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:climat:v:121:y:2013:i:2:p:317-329. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.