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Inter-model agreement on projected shifts in California hydroclimate characteristics critical to water management

Author

Listed:
  • Geeta G. Persad

    (Union of Concerned Scientists
    University of Texas at Austin)

  • Daniel L. Swain

    (University of California, Los Angeles
    National Center for Atmospheric Research
    The Nature Conservancy of California)

  • Claire Kouba

    (University of California, Davis)

  • J. Pablo Ortiz-Partida

    (Union of Concerned Scientists)

Abstract

Shifts away from the historical hydroclimate in populated regions can have dire consequences for water management. Regions like the state of California—where highly engineered, geographically interconnected, and inflexible water management systems are predicated on particular spatiotemporal patterns of water availability—are particularly vulnerable to hydroclimate shifts. However, much of the analysis of hydroclimate sensitivity to anthropogenic climate change has focused on gross metrics like annual mean precipitation, which is highly uncertain at the regional scale. This perceived uncertainty has deterred adaptation investments and quantitative integration of climate projection data into regional water management. Here, we assess projected future shifts in the state of California in a range of hydroclimate metrics critical to water management, using data from 10 statistically downscaled global climate model and two emissions scenarios currently used by the state. We find substantial inter-model agreement under both emissions scenarios—and > 80% inter-model agreement under the more severe climate change scenario—across metrics that collectively point toward an increasingly volatile, temporally concentrated, and extreme precipitation future for the state. We show, via hydrologic and operations modeling, that accounting for shifts in these more nuanced metrics reduces the projected reliability and sustainability of current water management practices to a greater degree than would be inferred from changes in total annual precipitation alone. These results highlight both the viability and critical importance of incorporating climate change projections quantitatively into water management decisions in California and other regions vulnerable to hydroclimate shifts, and underscore the need to develop integrated climate-hydrologic-operations models and decision-making protocols capable of accounting for all projected hydroclimate shifts.

Suggested Citation

  • Geeta G. Persad & Daniel L. Swain & Claire Kouba & J. Pablo Ortiz-Partida, 2020. "Inter-model agreement on projected shifts in California hydroclimate characteristics critical to water management," Climatic Change, Springer, vol. 162(3), pages 1493-1513, October.
  • Handle: RePEc:spr:climat:v:162:y:2020:i:3:d:10.1007_s10584-020-02882-4
    DOI: 10.1007/s10584-020-02882-4
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    References listed on IDEAS

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    1. John C. Fyfe & Chris Derksen & Lawrence Mudryk & Gregory M. Flato & Benjamin D. Santer & Neil C. Swart & Noah P. Molotch & Xuebin Zhang & Hui Wan & Vivek K. Arora & John Scinocca & Yanjun Jiao, 2017. "Large near-term projected snowpack loss over the western United States," Nature Communications, Nature, vol. 8(1), pages 1-7, April.
    2. Hyung-Il Eum & A. Vasan & Slobodan Simonovic, 2012. "Integrated Reservoir Management System for Flood Risk Assessment Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(13), pages 3785-3802, October.
    3. Sarfaraz Alam & Mekonnen Gebremichael & Ruopu Li & Jeff Dozier & Dennis P. Lettenmaier, 2019. "Climate change impacts on groundwater storage in the Central Valley, California," Climatic Change, Springer, vol. 157(3), pages 387-406, December.
    4. Detlef Vuuren & Jae Edmonds & Mikiko Kainuma & Keywan Riahi & Allison Thomson & Kathy Hibbard & George Hurtt & Tom Kram & Volker Krey & Jean-Francois Lamarque & Toshihiko Masui & Malte Meinshausen & N, 2011. "The representative concentration pathways: an overview," Climatic Change, Springer, vol. 109(1), pages 5-31, November.
    5. Piers M. Forster & Amanda C. Maycock & Christine M. McKenna & Christopher J. Smith, 2020. "Latest climate models confirm need for urgent mitigation," Nature Climate Change, Nature, vol. 10(1), pages 7-10, January.
    6. Patrick Ray & Sungwook Wi & Andrew Schwarz & Matthew Correa & Minxue He & Casey Brown, 2020. "Vulnerability and risk: climate change and water supply from California’s Central Valley water system," Climatic Change, Springer, vol. 161(1), pages 177-199, July.
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