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

Baronetti, Alice; Acquaotta, Fiorella; Fratianni, Simona

doi:10.3354/cr01517

Cited by 24 publications

(16 citation statements)

References 74 publications

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“…Several study developed by the Earth Science Department of Turin show a decrease of consecutive rainy days in the Piedmont plain and hills, a general increase of the annual precipitation, and a positive tendency in the density of precipitation and in the numbers of days. These positive trends make the region particularly prone to severe erosion, with serious consequences on agriculture and grapevine cultivation (Baronetti et al, 2018).…”

Section: Study Areamentioning

confidence: 99%

Estimation of rainfall erosivity in Piedmont (Northwestern Italy) by using 10-minute fixed-interval rainfall data

Acquaotta¹,

Baronetti²,

Bentivenga³

et al. 2019

Időjárás

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⎯ Rainfall erosivity index (EI 30) is widely used in soil erosion models for predicting soil loss. This index consists in the product between the maximum intensity of 30-min rainfall and the total kinetic energy of a precipitation event. The main goal of this study was to characterize the soil erosion in Piedmont (Northwestern Italy), studying the magnitude, frequency, and trends of rainfall erosivity. Rainfall erosivity for twelve stations well distributed over the whole region were firstly computed on the basis of 10-min timeresolution rainfall data using a continuous 17-year series of daily rainfall events. For each station the equation to predict EI 30 from daily rainfall data was calculated, and, using the Nash and Sutcliffe (1970) model-efficiency, the relationships between real EI 30 and modeled EI 30 was validated. The rainfall erosivity model was applied to the long term daily rainfall series of the selected stations, to create annual and seasonal erosivity time series for the climate normal period 1986-2015. Afterwards, the Mann-Kendall non-parametric test statistic to detect time trends in the rainfall erosivity time series was applied. The results have led to the conclusion that the annual rainfall erosivity should have experienced mixed trends in most of the study area, although more than half of the stations did not show a statistical trend.

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Section: Study Areamentioning

confidence: 99%

Estimation of rainfall erosivity in Piedmont (Northwestern Italy) by using 10-minute fixed-interval rainfall data

Acquaotta¹,

Baronetti²,

Bentivenga³

et al. 2019

Időjárás

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show abstract

“…Adequate random points (5,000 for the entire study area and 1,550 for Alpine region) were generated in GIS (ESRI ArcGIS) and used to sample the raster data values from ERIs, rainfall erosivity, and hillslope erosion for the baseline and future periods. These randomly sampled data were used for statistical analyses (linear regression, coefficient of efficiency, root mean square error [RMSE], and Kolmogorov–Smirnov test) and analysis of the relationship between rainfall extremes with erosivity for all the periods (Acquaotta et al ., ; Baronetti et al ., ). The RMSE has been calculated as a standard statistical metric (Chai and Draxler, ; Du et al ., ) to measure model performance in this study.…”

Section: Methodsmentioning

confidence: 97%

Extreme rainfall, rainfall erosivity, and hillslope erosion in Australian Alpine region and their future changes

Zhu

Yang

et al. 2019

Intl Journal of Climatology

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The Australian Alpine region is highly vulnerable to extreme climate events such as heavy rainfall and snow falls, these events subsequently impact rainfall erosivity and hillslope erosion in the region. In this study, the relationship between extreme rainfall indices (ERIs) and rainfall erosivity was examined across the Alpine region in New South Wales (NSW) and Australian Capital Territory (ACT) and the surrounding areas including Murray and Murrumbidgee and South East and Tablelands (SET). Rainfall erosivity, hillslope erosion, and their changes were estimated in the future periods using the revised universal soil loss equation and the NSW/ACT Regional Climate Modeling (NARCliM) projections. Results from the study demonstrate a good relationship between ERIs (especially Rx5Day) and rainfall erosivity. The rainfall erosivity and hillslope erosion are projected to increase about 2 and 8% for the near future (2020–2039), further increase to 8 and 18% for the far future (2060–2079) in the Alpine region assuming the groundcover is maintained at the current condition. The change in rainfall erosivity and erosion risk is highly uneven in space and in season with the highest erosion risk in summer with an increase about 33% in the next 50 years. The highest erosion risk area is predicted within SET (maximum rate 19.95 Mg ha−1 year−1), but on average, the ACT has the highest erosion rate, which is above 1.36 Mg ha−1 year−1 in all periods. The snowmelt in spring in the Alpine region is estimated to increase the rainfall erosivity by 13% in the baseline period, up to 24% in the near future, but far less (about 1%) in the far future due to predicted temperature rise and less snow available in the Alpine region in the next 50 years.

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“…In literature are described two different type of clustering: hierarchical and non‐hierarchical. In the present study, a hierarchical agglomerative CA was performed for the monthly rain, temperature, and snow depth using the Ward.D2 method in the R pvclust package (Baronetti et al, ). Ünal et al .…”

Section: Methodsmentioning

confidence: 99%

“…In the present study, a hierarchical agglomerative CA was performed for the monthly rain, temperature, and snow depth using the Ward. D2 method in the R pvclust package (Baronetti et al, 2018). Ünal et al (2003) evidenced that the hierarchical clustering is more suitable for climatological data, where their distribution is not a priori assumed.…”

Section: Cluster Analysismentioning

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

Cited by 24 publications

References 74 publications

Estimation of rainfall erosivity in Piedmont (Northwestern Italy) by using 10-minute fixed-interval rainfall data

Estimation of rainfall erosivity in Piedmont (Northwestern Italy) by using 10-minute fixed-interval rainfall data

Extreme rainfall, rainfall erosivity, and hillslope erosion in Australian Alpine region and their future changes

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

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