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Why does snowmelt-driven streamflow response to warming vary? A data-driven review and predictive framework
Indexado
WoS WOS:000787042500001
Scopus SCOPUS_ID:85129941779
DOI 10.1088/1748-9326/AC64B4
Año 2022
Tipo revisión

Citas Totales

Autores Afiliación Chile

Instituciones Chile

% Participación
Internacional

Autores
Afiliación Extranjera

Instituciones
Extranjeras


Abstract



Climate change is altering the seasonal accumulation and ablation of snow across mid-latitude mountainous regions in the Northern Hemisphere with profound implications for the water resources available to downstream communities and environments. Despite decades of empirical and model-based research on snowmelt-driven streamflow, our ability to predict whether streamflow will increase or decrease in a changing climate remains limited by two factors. First, predictions are fundamentally hampered by high spatial and temporal variability in the processes that control net snow accumulation and ablation across mountainous environments. Second, we lack a consistent and testable framework to coordinate research to determine which dominant mechanisms influencing seasonal snow dynamics are most and least important for streamflow generation in different basins. Our data-driven review marks a step towards the development of such a framework. We first conduct a systematic literature review that synthesizes knowledge about seasonal snowmelt-driven streamflow and how it is altered by climate change, highlighting unsettled questions about how annual streamflow volume is shaped by changing snow dynamics. Drawing from literature, we then propose a framework comprised of three testable, inter-related mechanisms-snow season mass and energy exchanges, the intensity of snow season liquid water inputs, and the synchrony of energy and water availability. Using data for 537 catchments in the United States, we demonstrate the utility of each mechanism and suggest that streamflow prediction will be more challenging in regions with multiple interacting mechanisms. This framework is intended to inform the research community and improve management predictions as it is tested and refined.

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Disciplinas de Investigación



WOS
Environmental Sciences
Meteorology & Atmospheric Sciences
Scopus
Sin Disciplinas
SciELO
Sin Disciplinas

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Publicaciones WoS (Ediciones: ISSHP, ISTP, AHCI, SSCI, SCI), Scopus, SciELO Chile.

Colaboración Institucional



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Autores - Afiliación



Ord. Autor Género Institución - País
1 Gordon, Beatrice L. Mujer Univ Nevada - Estados Unidos
University of Nevada, Reno - Estados Unidos
2 Brooks, Paul D. Hombre Univ Utah - Estados Unidos
University of Utah, College of Mines and Earth Sciences - Estados Unidos
3 Krogh, Sebastian A. Hombre Universidad de Concepción - Chile
4 Boisrame, Gabrielle F. S. Mujer Desert Res Inst - Estados Unidos
Desert Research Institute - Estados Unidos
5 Carroll, Rosemary W. H. Mujer Desert Res Inst - Estados Unidos
Desert Research Institute - Estados Unidos
6 McNamara, James P. Hombre Boise State Univ - Estados Unidos
Boise State University - Estados Unidos
7 Harpold, Adrian A. Hombre Univ Nevada - Estados Unidos
University of Nevada, Reno - Estados Unidos

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Financiamiento



Fuente
USDA NIFA
Lincoln Institute
Lincoln Institute's Babbitt Dissertation Fellowship Program

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Agradecimientos



Agradecimiento
We are very grateful to Dr Ross A Woods and Ms Lina Wang for their thoughtful and constructive feedback on our mechanisms, figures, and methods. We would also like to thank the two anonymous referees for their helpful and detailed feedback on this manuscript as well as the editorial and administrative team at ERL for their generous assistance. Support for this research is provided the USDA NIFA (Project #NEVW-201708812) and the Lincoln Institute's Babbitt Dissertation Fellowship Program. The data products used in this study are freely available to the public: streamflow, Daymet precipitation, NLDAS precipitation, modeled snowmelt and rain, catchment shapefiles, and attributes can be retrieved from https://ral.ucar.edu/solutions/products/camels (last access: 20 July 2021) (Newman et al 2015, Addor et al 2017). NCA-LDAS data were accessed at https://disc.gsfc.nasa.gov/(last access: 10 July 2020) (Kumar et al 2019).
We are very grateful to Dr Ross A Woods and Ms Lina Wang for their thoughtful and constructive feedback on our mechanisms, figures, and methods. We would also like to thank the two anonymous referees for their helpful and detailed feedback on this manuscript as well as the editorial and administrative team at ERL for their generous assistance. Support for this research is provided the USDA NIFA (Project # NEVW-2017-08812) and the Lincoln Institute’s Babbitt Dissertation Fellowship Program.

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