High-resolution snow water equivalent estimation: a data-driven method for localized downscaling of climate data.
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| Title: | High-resolution snow water equivalent estimation: a data-driven method for localized downscaling of climate data. |
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| Authors: | Zakeri, Fatemeh1,2 (AUTHOR) fatemeh.zakeri@unil.ch, Mariethoz, Gregoire2 (AUTHOR), Girotto, Manuela1 (AUTHOR) |
| Source: | Hydrology & Earth System Sciences. 2025, Vol. 29 Issue 23, p6935-6958. 24p. |
| Subject Terms: | *Snow-water equivalent, *Downscaling (Climatology), *Comparative studies, *K-nearest neighbor classification, *Atmospheric models |
| Geographic Terms: | California, Colorado, United States |
| Abstract: | Estimating high-resolution daily snow water equivalent (SWE) in mountainous regions is challenging due to geographical complexity and the irregular availability of high-resolution meteorological data. This study introduces a method for downscaling SWE based on low-resolution climate models. It is based on the dependence between climate estimators and SWE. Although SWE changes rapidly, patterns often repeat under similar meteorological conditions. We implement this principle to downscale SWE to a 500 m resolution using a k -nearest neighbor algorithm with a customized distance metric. To evaluate the performance of our approach, we conducted tests for California's Sierra Nevada and Colorado's Upper Colorado River Basin in the western United States using different low-resolution climate models (ec-earth3-veg, mpi-esm1-2, and cnrm-esm2-1) at both 100 and 9 km scales. We performed a cross-validation analysis and compared our results with commonly used gridded SWE datasets, as well as with point-scale time series. The results demonstrate that our approach enables the generation of downscaled SWE, which closely matches reanalysis data in terms of statistical properties. The outputs demonstrate that, for each region, performance depends on the choice and accuracy of the climate model inputs, such as precipitation and temperature data. Overall, the cnrm-esm2-1 model demonstrates superior accuracy in Colorado, outperforming other models at both the 100 and 9 km resolutions. Conversely, the ec-earth3-veg model achieves the best performance in California with 9 km resolution climate data. Across models, a 9 km resolution typically provides slightly better accuracy compared to a 100 km resolution. This opens up possibilities for applications in regions with limited in situ or meteorological measurements. The approach also has the potential to recreate unmeasured historical SWE values. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 190253598 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: High-resolution snow water equivalent estimation: a data-driven method for localized downscaling of climate data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zakeri%2C+Fatemeh%22">Zakeri, Fatemeh</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> fatemeh.zakeri@unil.ch</i><br /><searchLink fieldCode="AR" term="%22Mariethoz%2C+Gregoire%22">Mariethoz, Gregoire</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Girotto%2C+Manuela%22">Girotto, Manuela</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Hydrology+%26+Earth+System+Sciences%22">Hydrology & Earth System Sciences</searchLink>. 2025, Vol. 29 Issue 23, p6935-6958. 24p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Snow-water+equivalent%22">Snow-water equivalent</searchLink><br />*<searchLink fieldCode="DE" term="%22Downscaling+%28Climatology%29%22">Downscaling (Climatology)</searchLink><br />*<searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br />*<searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br />*<searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22California%22">California</searchLink><br /><searchLink fieldCode="DE" term="%22Colorado%22">Colorado</searchLink><br /><searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Estimating high-resolution daily snow water equivalent (SWE) in mountainous regions is challenging due to geographical complexity and the irregular availability of high-resolution meteorological data. This study introduces a method for downscaling SWE based on low-resolution climate models. It is based on the dependence between climate estimators and SWE. Although SWE changes rapidly, patterns often repeat under similar meteorological conditions. We implement this principle to downscale SWE to a 500 m resolution using a k -nearest neighbor algorithm with a customized distance metric. To evaluate the performance of our approach, we conducted tests for California's Sierra Nevada and Colorado's Upper Colorado River Basin in the western United States using different low-resolution climate models (ec-earth3-veg, mpi-esm1-2, and cnrm-esm2-1) at both 100 and 9 km scales. We performed a cross-validation analysis and compared our results with commonly used gridded SWE datasets, as well as with point-scale time series. The results demonstrate that our approach enables the generation of downscaled SWE, which closely matches reanalysis data in terms of statistical properties. The outputs demonstrate that, for each region, performance depends on the choice and accuracy of the climate model inputs, such as precipitation and temperature data. Overall, the cnrm-esm2-1 model demonstrates superior accuracy in Colorado, outperforming other models at both the 100 and 9 km resolutions. Conversely, the ec-earth3-veg model achieves the best performance in California with 9 km resolution climate data. Across models, a 9 km resolution typically provides slightly better accuracy compared to a 100 km resolution. This opens up possibilities for applications in regions with limited in situ or meteorological measurements. The approach also has the potential to recreate unmeasured historical SWE values. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=190253598 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.5194/hess-29-6935-2025 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 6935 Subjects: – SubjectFull: Snow-water equivalent Type: general – SubjectFull: Downscaling (Climatology) Type: general – SubjectFull: Comparative studies Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Atmospheric models Type: general – SubjectFull: California Type: general – SubjectFull: Colorado Type: general – SubjectFull: United States Type: general Titles: – TitleFull: High-resolution snow water equivalent estimation: a data-driven method for localized downscaling of climate data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zakeri, Fatemeh – PersonEntity: Name: NameFull: Mariethoz, Gregoire – PersonEntity: Name: NameFull: Girotto, Manuela IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10275606 Numbering: – Type: volume Value: 29 – Type: issue Value: 23 Titles: – TitleFull: Hydrology & Earth System Sciences Type: main |
| ResultId | 1 |
