Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling does
Abstract 20) across 31 western USA watersheds affected by snow, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May among our sites, with warmer basins having earlier snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R 2 = 0.85), suggesting that a 1 d earlier DOS20 corresponds with a 1 d earlier DOQ25 and 0.7 d earlier DOQ50. Empirical projections of future DOS20 based on a stepwise multiple linear regression across sites and years under the RCP8.5 scenario for the late 21st century show that DOS20 will occur on average 11 ± 4 d earlier per 1 ∘ C of warming. However, DOS20 in colder watersheds (mean November–February air temperature, T NDJF < - 8 ∘ C) is on average 70 % more sensitive to climate change than in warmer watersheds (T NDJF > 0 ∘ C). Moreover, empirical projections of DOQ25 and DOQ50 based on DOS20 are about four and two times more sensitive to climate change, respectively, than those simulated by a state-of-the-art land surface model (NoahMP-WRF) under the same scenario. Given the importance of changes in streamflow timing for water resources, and the significant discrepancies found in projected streamflow sensitivity, snowmelt detection methods such as DOS20 based on diel streamflow cycles may help to constrain model parameters, improve hydrological predictions, and inform process understanding.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling does ; volume:26 ; number:13 ; year:2022 ; pages:3393-3417 ; extent:25
Hydrology and earth system sciences ; 26, Heft 13 (2022), 3393-3417 (gesamt 25)
- Creator
- DOI
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10.5194/hess-26-3393-2022
- URN
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urn:nbn:de:101:1-2022070705163429247132
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:35 AM CEST
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Krogh, Sebastian A.
- Scaff, Lucia
- Kirchner, James
- Gordon, Beatrice
- Sterle, Gary
- Harpold, Adrian