The aim of this research is to identify and characterize, in terms of length, intensity, and spatial propagation, the main drought events which took place in the Po Valley (Italy) from 1965 to 2017. Two drought indices were applied, the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI). Daily precipitation and temperature series belonging to the National System for the Collection, Processing, and Dissemination of Climatic Data of Environmental Interest (SCIA) database were collected. Subsequent to an accurate quality control, the converted weekly climatic values were spatialized on a 20 × 20 km cell grid, and for each index, weekly severe and extreme drought episodes at a 12-, 24-, and 36-month time scale were calculated. Results showed that the application of two indices is fundamental in the study of drought episodes, and that different triggering factors act over time. Especially, since the 2001 drought, episodes have become stronger in terms of frequency and length, and they seem to be mostly related to changes in the intra-annual precipitation distribution. An analysis of the spatial propagation also indicates that two spatial gradients follow each other during the analysed period.
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.
We analyse the expected characteristics of drought events in northern Italy for baseline (1971–2000), near (2021–2050), and far (2071–2100) future conditions, estimating the drought spatial extent and duration, the percentage of affected area, and the frequency of drought episodes. To this end, daily ensembles of precipitation and temperature records from Global Climate Models (GCMs) and Regional Climate Models (RCMs) pairs, extracted from EURO-CORDEX and MED-CORDEX for the RCP 4.5 and 8.5 scenarios, are collected at spatial resolution of 0.11 degrees. Before the analysis, model outputs are validated on daily weather station time series, and scaling factors for possible use in bias correction are identified. Annual temperature and precipitation anomalies for near and far future conditions are investigated; drought events are identified by the standardized precipitation evapotranspiration index and standardized precipitation index at the 12-, 24-, and 36-month timescales. This study highlights the importance of using multiple drought indicators in the detection of drought events, since the comparison reveals that evapotranspiration anomaly is the main triggering factor. For both scenarios, the results indicate an intensification of droughts in northern Italy for the period 2071–2100, with the Alpine chain being especially affected by an increase of drought severity. A North-to-South spatial gradient of drought duration is also observed.
⎯ 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.
Societies can be better prepared to face hydrological extremes (e.g. flash floods) by understanding the trends and variability of rainfall aggressiveness and its derivative, erosivity density (ED). Estimating extended time series of ED is, however, scientifically challenging because of the paucity of long-term high-resolution pluviometric observations. This research presents the longest ED time series reconstruction (1701-2019) in northwest Italy (Piedmont region) to date, which is analysed to identify damaging hydrological periods. With this aim, we developed a model consistent with a sample of detailed novel Revised Universal Soil Loss Erosion-based high-resolution data and documentary hydrological extreme records. The modelled data show a noticeable rising trend in ED from 1897 onwards, together with an increase of extreme values for return periods of 10 and 50 years, consistent with the Clausius-Clapeyron scaling of extreme rainfall. We also suggest the North Atlantic Oscillation and Atlantic Multidecadal Oscillation may be associated with rainfall extremes in Piedmont.
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.
<p>The increase in drought conditions is one of the main consequences of climatic change, that affects both natural and socioeconomic systems. Northern Italy is historically rich in water resources, and one of the most fertile areas in Italy. However, in the last decades drought events increased also here, affecting the hydrological behaviour of the Po River and vegetation growth.</p> <p>This study aims to quantify the spatial distributions of drought events and identify their effects on vegetation greenness in northern Italy during the 2000-2020 period using MODIS images at 1 km spatial resolution. For this purpose, correlation maps between fields of bi-weekly vegetation indices (NDVI and EVI) and drought indices (SPI and SPEI) were estimated.</p> <p>The NDVI and EVI indices were extracted from the atmospherically corrected MODIS images and vegetation trends were investigated by mean on the Mann-Kendall test. To analyze drought events, 150 daily precipitation ground station series were collected, aggregated at bi-weekly scale, reconstructed, homogenised and spatialised at 1km resolution by mean of the Universal Kriging with auxiliary variables. Land Surface Temperature (LST), assumed as air temperature, was collected from MODIS images. Pixels with clouds were removed, and the accuracy was determined against the high resolution gridded temperature dataset available for northern Italy. The NDVI-LST space was investigated at yearly scale exploring the link between NDVI and LST for 6000 random points in the study area. Evapotranspiration was estimated by means of the Hargreaves equation and severe and extreme drought episodes were detected by means of drought indices (SPI and SPEI) calculated at 12-, 24- and 36-months aggregation time. Trends were analysed and the main drought events were characterised, identifying the percentage of area under drought, and the magnitude, duration and frequency of droughts. Each pixel was analysed to investigate the impacts of severe and extreme drought events on vegetation properties, and the Pearson&#8217;s correlation between NDVI/EVI and SPEI/SPI at different time scales was estimated. Finally, on the basis of the correlation maps and on the CORINE Land Cover 2020, drought impacts on different vegetation communities at medium (12 months) and long (24 and 36 months) time scales were detected as the percentage of vegetation under drought stress.</p> <p>The study highlights the importance of applying multiple indices to study droughts, since even though positive temperature trends were recorded in northern Italy, in the last two decades the main trigger of droughts is the lack of precipitation.&#160;Moreover the western portion of northern Italy was mostly interested by drought intensification. The investigation on drought duration revealed that the longest extreme drought events were detected in the Po Valley, where the strongest negative impacts on vegetation were detected. The results also indicated that first droughts interested herbaceous vegetation, while subsequently affecting also sparse and open forests.</p>
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