Seasonal Variability of Snow Density in the Spanish Pyrenees
Spanish latitudes and meteorological conditions cause the snow phenomena to mainly take place in mountainous areas, playing a key role in water resource management, with the Pyrenees as one of the most important and best monitored areas. Based on the most significant dataset of snow density (SDEN) i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/11/1598 |
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| author | Eduardo Lastrada Guillermo Cobos Julio Garzón-Roca F. Javier Torrijo |
| author_facet | Eduardo Lastrada Guillermo Cobos Julio Garzón-Roca F. Javier Torrijo |
| author_sort | Eduardo Lastrada |
| collection | DOAJ |
| description | Spanish latitudes and meteorological conditions cause the snow phenomena to mainly take place in mountainous areas, playing a key role in water resource management, with the Pyrenees as one of the most important and best monitored areas. Based on the most significant dataset of snow density (SDEN) in the Spanish Pyrenees for on-site manual samples and automatic measurements, in this study, single and multiple linear regression models are evaluated that relate SDEN with intra-annual time dependence and other drivers such as the seasonal accumulated precipitation, 7-day average temperatures, snow depth (SD) and elevation. The seasonal accumulated precipitation presented a more dominant influence than daily precipitation, usually being the second most dominant SDEN driver, followed by temperature. Average temperatures showed the best fitting to SDEN. The results showed similar densification rates ranging widely from 0.7 × 10<sup>3</sup> kg/L/day to 2 × 10<sup>3</sup> kg/L/day without showing a spatial pattern. The densification rate for the set of manual samples was set to 1.2 kg/L/day, very similar to the set of automatic measurements (1.3 kg/L/day). The results increase knowledge on SDEN in the Pyrenees. The SDEN regression models that are given in this work may allow us, in the future, to estimate SDEN, and consequently Snow Water Equivalent (SWE), using an economical and extensive SD and meteorological network, although the high spatial variability that has been found must be regarded. Estimating a relationship between SDEN and several climate drivers enables us to take into account the impact of climate variability on SDEN. |
| first_indexed | 2024-03-10T10:40:21Z |
| format | Article |
| id | doaj.art-bde561cd9e994c5aacb3492c69aefd52 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T10:40:21Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-bde561cd9e994c5aacb3492c69aefd522023-11-21T23:02:03ZengMDPI AGWater2073-44412021-06-011311159810.3390/w13111598Seasonal Variability of Snow Density in the Spanish PyreneesEduardo Lastrada0Guillermo Cobos1Julio Garzón-Roca2F. Javier Torrijo3Department of Geological and Geotechnical Engineering, Universitat Politècnica de València, 46014 Valencia, SpainDepartment of Geological and Geotechnical Engineering, Universitat Politècnica de València, 46014 Valencia, SpainIndependent Researcher, 46014 Valencia, SpainDepartment of Geological and Geotechnical Engineering, Universitat Politècnica de València, 46014 Valencia, SpainSpanish latitudes and meteorological conditions cause the snow phenomena to mainly take place in mountainous areas, playing a key role in water resource management, with the Pyrenees as one of the most important and best monitored areas. Based on the most significant dataset of snow density (SDEN) in the Spanish Pyrenees for on-site manual samples and automatic measurements, in this study, single and multiple linear regression models are evaluated that relate SDEN with intra-annual time dependence and other drivers such as the seasonal accumulated precipitation, 7-day average temperatures, snow depth (SD) and elevation. The seasonal accumulated precipitation presented a more dominant influence than daily precipitation, usually being the second most dominant SDEN driver, followed by temperature. Average temperatures showed the best fitting to SDEN. The results showed similar densification rates ranging widely from 0.7 × 10<sup>3</sup> kg/L/day to 2 × 10<sup>3</sup> kg/L/day without showing a spatial pattern. The densification rate for the set of manual samples was set to 1.2 kg/L/day, very similar to the set of automatic measurements (1.3 kg/L/day). The results increase knowledge on SDEN in the Pyrenees. The SDEN regression models that are given in this work may allow us, in the future, to estimate SDEN, and consequently Snow Water Equivalent (SWE), using an economical and extensive SD and meteorological network, although the high spatial variability that has been found must be regarded. Estimating a relationship between SDEN and several climate drivers enables us to take into account the impact of climate variability on SDEN.https://www.mdpi.com/2073-4441/13/11/1598snow densitySpanish PyreneesERHIN programwater equivalentclimate variability |
| spellingShingle | Eduardo Lastrada Guillermo Cobos Julio Garzón-Roca F. Javier Torrijo Seasonal Variability of Snow Density in the Spanish Pyrenees Water snow density Spanish Pyrenees ERHIN program water equivalent climate variability |
| title | Seasonal Variability of Snow Density in the Spanish Pyrenees |
| title_full | Seasonal Variability of Snow Density in the Spanish Pyrenees |
| title_fullStr | Seasonal Variability of Snow Density in the Spanish Pyrenees |
| title_full_unstemmed | Seasonal Variability of Snow Density in the Spanish Pyrenees |
| title_short | Seasonal Variability of Snow Density in the Spanish Pyrenees |
| title_sort | seasonal variability of snow density in the spanish pyrenees |
| topic | snow density Spanish Pyrenees ERHIN program water equivalent climate variability |
| url | https://www.mdpi.com/2073-4441/13/11/1598 |
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