Climate variations in a high altitude Alpine basin and their effects on a glacial environment (Italian Western Alps)

Giaccone, Elisa; Colombo, Nicola; Acquaotta, Fiorella; Paro, Luca; Fratianni, Simona

doi:10.1016/s0187-6236(15)30004-7

Cited by 7 publications

(6 citation statements)

References 32 publications

(21 reference statements)

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“…Two main types of factors determine the thermalpluviometric characteristics of Piedmont. The orography, which is the most important internal factor, controls Piedmont's climate, while the atmospheric circulation, with dry continental air flowing in from the Po Valley in the east and relatively moist Mediterranean and Atlantic air coming from the northwest, is the most important external factor (Fratianni et al 2009, Giaccone et al 2015. The an nual distribution of precipitation in Piedmont is characterised by a bimodal trend, with 2 maxima (spring and autumn) and 2 minima (winter and summer).…”

Section: Study Areamentioning

confidence: 99%

Rainfall variability from a dense rain gauge network in north-western Italy

Baronetti¹,

Acquaotta²,

Fratianni³

2018

Clim. Res.

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The aim of this study was to investigate the spatial and temporal distribution of rainfall in Piedmont, a region in northwestern Italy, in order to evaluate the high intensity precipitation events that occurred in the 2004−2016 period. A daily precipitation series of 211 ground stations, belonging to 2 different meteorological monitoring networks, were analysed. As at first step, a quality control was performed on the daily precipitation series to evaluate the homogeneity of the series. The annual rainfall events were spatialised, using the ordinary kriging method, considering the whole set of weather stations. Moreover, 5 climatic areas were identified through a cluster analysis method. In order to better understand the extreme rainfall events, the main climatic precipitation indices were calculated, using ClimPACT2 software, and the thresholds by percentile were calculated for each cluster on a daily scale to identify the different precipitation types (weak, medium, heavy, very heavy [R95p]). Non-parametric (Kolmogorov-Smirnov and Wilcoxon) and parametric (Student's t-test) tests were applied to the annual and seasonal number of events observed for each rainfall class in order to study the statistical relationship between the clusters. The results lead to the conclusion that the investigated area is characterised by an increase in precipitation. Considering the extreme events, this methodology shows that even though the north sector is the wettest, central Piedmont is the area in which the highest number of extreme events was recorded.

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Section: Study Areamentioning

confidence: 99%

Rainfall variability from a dense rain gauge network in north-western Italy

Baronetti¹,

Acquaotta²,

Fratianni³

2018

Clim. Res.

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“…The major internal factor that controls the climate in Piedmont is its orography. The external factor is the circulation of relatively dry continental air flowing in from the Po Valley in the east and relatively moist Mediterranean and Atlantic air Biancotti et al, 1998. coming from the northwest Giaccone et al, 2015). The annual precipitation cycle in Piedmont shows a bimodal pattern with two maxima, one in autumn and one in spring, and two minima, in winter and in summer.…”

Section: Geography Of the Piedmont Regionmentioning

confidence: 99%

Section: Geography Of the Piedmont Regionmentioning

confidence: 99%

Assessment of parallel precipitation measurements networks in Piedmont, Italy

Acquaotta

Fratianni

Venema

2016

Intl Journal of Climatology

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Long historical climate records usually contain non-climatic changes that can influence the observed behaviour of meteorological variables. The availability of parallel measurements offers an ideal occasion to study these discontinuities as they record the same climate. The transition from manual to automatic measurements has been analysed in this study. The dataset has been obtained from two independent climate networks in the Piedmont region, in Northwest Italy. From this dataset, 17 pairs of stations with up to 17 years of overlapping have been identified. On average the overlapping period is 12 years with 8760 daily data matched for a pair of stations. This dataset has proven valuable because it has made it possible to study a set of independently managed pairs of standard-quality stations, while most previous studies in this field analysed data from well-maintained instruments with the same operator, often at meteorological offices. The transition between two networks has highlighted important differences in the amount of precipitation and in the number of rainy days. On average, the automatic network has measured 12% less precipitation and has recorded 9% more rainy days (8 days) per year than the manual network. Both of these differences produce a spurious change in the precipitation of the analysed area, thus showing the importance of having a homogeneous dataset to identify real climate variations.

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“…Daily snow depth values inferior to 1 cm were deleted in order to prevent any errors due to the sensitivity of the instrumentation (Wang et al, 2010). In addition, the continuity of the selected daily series was checked and the monthly snow depths were calculated, and months with fewer than 80% of records were deleted (Isotta et al, 2014;Giaccone et al, 2015). Subsequently, daily series of temperature and rain were checked.…”

Section: Dataset and Preliminary Controlmentioning

confidence: 99%

A quality control approach to better characterize the spatial distribution of snow depth over New Brunswick, Canada

Baronetti

Fratianni

Acquaotta

et al. 2019

Intl Journal of Climatology

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The aim of this work is to develop, from a high‐resolution climate analysis, a quality control standard methodology applied to manual snow cover (HS) series managed by the Snow Survey Database in New Brunswick (Canada). The database collected snow depth data biweekly starting at the end of January until the end of April. A 30‐year (1981–2010) analysis of 60 weather stations belonging to two independent meteorological networks was performed. A quality control of the climatic series was performed to evaluate the homogeneity. Three snow depth climatic areas were defined by means of two geostatistical methods (Kriging and Cluster analyses) applied on monthly snow depth, precipitation and temperature data series. Then, for each cluster, the climatological thresholds that characterize a snow fall event during the cold months were detected. Subsequently, a quality control on the daily snow depth series recorded during the January to April period was performed. For each daily series, outlier values were identified by checking both the sudden day‐to‐day changes and extreme thresholds (95th percentile). The quality control was then carried out to the manual series and the observed doubtful events were compared with the snow depth values recorded in the nearby stations. The results show that for the daily snow depth series, the highest number of suspect events was recorded during the months of March and April, and the analysis also shows that there are rain‐on‐snow events. As for the manual records, questionable snow depth errors randomly distributed in the series were highlighted. Finally, in order to improve the spatial distribution of stations located in the Canadian territory, the results give evidence that, thanks to the high‐resolution climatic analysis, the proposed approach provides all the benchmarks required to conduct a quality control of snow depth series in absence of other auxiliary variables.

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Climate variations in a high altitude Alpine basin and their effects on a glacial environment (Italian Western Alps)

Cited by 7 publications

References 32 publications

Rainfall variability from a dense rain gauge network in north-western Italy

Rainfall variability from a dense rain gauge network in north-western Italy

Assessment of parallel precipitation measurements networks in Piedmont, Italy

A quality control approach to better characterize the spatial distribution of snow depth over New Brunswick, Canada

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