| Title | Predicting new snow density in the Italian Alps: A variability analysis based on 10 years of measurements |
| Abstract | Despite its strong impact on the time evolution of the snowpack, current estimation of new snow density (?hn) is usually accomplished either by using local empirical techniques or by assuming a constant snow density. Faced with the lack of an estimation model of ?hn valid for a wide spatial scale and supported by a suitable number of observations, this study aims to develop simple monthly linear regression models at scale of the entire Italian Alpine chain based on 12,112 snowfall observations at 122 stations, using only air temperature as predictor. Moreover, the remaining variance is investigated in both time and space, also considering some qualitative features of the snowfall events. The daily ?hn measurements present a mean value of 115 kg m-3 (105 and 159 kg m-3 for dry and wet conditions, respectively). The mean air temperature of the 24 hr preceding the snowfall event has been found to be the best predictor of the ?hn, within 31% of uncertainty. The analysis of associated residues allows supporting the idea that the adoption of a more local approach than the one analysed here is not able to substantially increase the predictive capabilities of the model. In fact, the main factor explaining the remaining variance over the air temperature is the wind, but in a complex orography, as mountain regions are, supplying realistic local wind fields is particularly challenging. Therefore, we conclude that using only the daily mean temperature as predictor is a good choice for estimating daily new snow density at scale of Italian Alpine chain, as well as at more regional scale. |
| Source | Hydrological processes (Online), pp. 1–14 |
| Keywords | analysis of varianceItalian Alpsmountain chain scalenew snow densityobserved variability |
| Journal | Hydrological processes (Online) |
| Editor | Wiley, [Chichester, Sussex, England], Regno Unito |
| Year | 2018 |
| Type | Articolo in rivista |
| DOI | 10.1002/hyp.13249 |
| Authors | Mauro Valt, Nicolas Guyennon, Franco Salerno, Anna B. Petrangeli, Rosamaria Salvatori, Paola Cianfarra, Emanuele Romano |
| Text | 390718 2018 10.1002/hyp.13249 Scopus 2 s2.0 85052811723 ISI Web of Science WOS WOS 000444948700007 analysis of variance Italian Alps mountain chain scale new snow density observed variability Predicting new snow density in the Italian Alps A variability analysis based on 10 years of measurements Mauro Valt, Nicolas Guyennon, Franco Salerno, Anna B. Petrangeli, Rosamaria Salvatori, Paola Cianfarra, Emanuele Romano DRST Centro Valanghe di Arabba, ARPA Veneto, Arabba, Italy National Research Council, Water Research Institute IRSA CNR , Rome, Italy National Research Council, Water Research Institute IRSA CNR , Brugherio, Italy National Research Council, Water Research Institute IRSA CNR , Rome, Italy National Research Council, Institute for Atmospheric Pollution IIA CNR , Rome, Italy Dipartimento di Scienze, Universita degli Studi Roma Tre, Rome, Italy National Research Council, Water Research Institute IRSA CNR , Rome, Italy Despite its strong impact on the time evolution of the snowpack, current estimation of new snow density hn is usually accomplished either by using local empirical techniques or by assuming a constant snow density. Faced with the lack of an estimation model of hn valid for a wide spatial scale and supported by a suitable number of observations, this study aims to develop simple monthly linear regression models at scale of the entire Italian Alpine chain based on 12,112 snowfall observations at 122 stations, using only air temperature as predictor. Moreover, the remaining variance is investigated in both time and space, also considering some qualitative features of the snowfall events. The daily hn measurements present a mean value of 115 kg m 3 105 and 159 kg m 3 for dry and wet conditions, respectively . The mean air temperature of the 24 hr preceding the snowfall event has been found to be the best predictor of the hn, within 31% of uncertainty. The analysis of associated residues allows supporting the idea that the adoption of a more local approach than the one analysed here is not able to substantially increase the predictive capabilities of the model. In fact, the main factor explaining the remaining variance over the air temperature is the wind, but in a complex orography, as mountain regions are, supplying realistic local wind fields is particularly challenging. Therefore, we conclude that using only the daily mean temperature as predictor is a good choice for estimating daily new snow density at scale of Italian Alpine chain, as well as at more regional scale. Published version https //onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.13249 Articolo in rivista Wiley 1099 1085 Hydrological processes Online Hydrological processes Online Hydrol. process. Online Hydrological processes Online rosamaria.salvatori SALVATORI ROSAMARIA annabruna.petrangeli PETRANGELI ANNA BRUNA franco.salerno SALERNO FRANCO emanuele.romano ROMANO EMANUELE nicolasdominique.guyennon GUYENNON NICOLAS DOMINIQUE |
