This large population-based study highlights the significant and independent effects of short-term air temperature changes (especially cold) in triggering out_OI. These findings represent the first step towards developing a geographically differentiated, operative outdoor-temperature-occupational-health warning system aimed at preventing outdoor work injuries.

During the last few decades, weather circulation type classifications (CTCs) have been widely used to gain insight into processes at the synoptic scale, but also for studying the relationship between atmospheric circulation and surface climate variability. This study aims to evaluate the best performing CTCs based on COST733 software for the stratification of daily ground-level precipitation and surface air temperature across Italy by means of four statistical metrics. Six classification methods belonging to the four COST733 groups (threshold-based, PCA-based, leader algorithms and optimization algorithms) were investigated on 32 and 26 data time series derived from Italian weather stations for daily mean temperature and daily mean precipitation, respectively. CTCs were computed using gridded mean sea level pressure and geopotential height at 500 hPa derived from the NCEP Reanalysis 2 dataset between 1979 and 2015 and tested on three different numbers of classes (8/9, 11/12 and 18 circulation types). Evaluation metrics showed an evident seasonal variability and high-spatial heterogeneity reflecting the geographical complexity of the Italian territory. The study points out that the best classification, both for temperature and precipitation, is strongly dependent on the classification variable (mean sea level pressure and geopotential height at 500 hPa) showing relevant differences between surface temperature and precipitation. A low number of circulation types (8/9) resulted as the most appropriate grouping for the Italian domain and the Principal Component Transversal and Simulated Annealing were the best performing classification procedures for ground-level precipitation and temperature stratification, respectively.

Extreme precipitation (EP) events are life‐threatening phenomena that are expected to continue to increase because of ongoing climate change. In the past decade, these events have been caused by important and well‐documented variations in large‐scale atmospheric circulation. Identifying the trends, dynamics, and related causes of EP could help in recognizing geographical areas that are at great risk and reducing their adverse impacts, particularly on a relatively small area such as the Italian peninsula. The relationships between large‐scale circulation types (CTs) and EP were investigated using a long time‐series (1979–2015) of meteorological data recorded by 46 weather stations in Italy. EP was defined as the number of days with accumulated precipitation above the 90th percentile (R90p). The seasonal trends of R90p were not homogeneous and showed significant increases primarily in winter and spring. Only a few CTs were significantly related to R90p, and this relationship was strongly dependent on latitude, orographic exposure, and season. Heterogeneous seasonal trends for daily CT occurrences were also observed. ‘Cyclonic’ CTs grouped together showed significant increasing trends in all seasons, whereas ‘Anticyclonic’ ones showed a generalized decreasing trend, explaining, only partially, the increase of R90p observed in some stations. Meanwhile, the R90p trends seem to be more influenced by the variations in the internal characteristics of CTs (i.e., the variation of some meteorological parameters that characterize them) observed over the past few decades than by changes in CT frequencies but still with high heterogeneity in Italy. The results of this and other similar studies can provide useful support for the implementation of mitigation and adaptation strategies to minimize the impacts of severe weather, particularly in complex areas such as the Mediterranean basin.

Soil erosion continues to be a threat to soil quality, impacting crop production and ecosystem services delivery. The quantitative assessment of soil erosion, both by water and by wind, is mostly carried out by modeling the phenomenon via remote sensing approaches. Several empirical and process-based physical models are used for erosion estimation worldwide, including USLE (or RUSLE), MMF, WEPP, PESERA, SWAT, etc. Furthermore, the amount of sediment produced by erosion phenomena is obtained by direct measurements carried out in experimental sites. Data collection for this purpose is very complex and expensive; in fact, we have few cases of measures distributed at the basin scale to monitor this phenomenon. In this work, we propose a methodology based on an expeditious way to monitor the volume of hilly lakes with GPS, sonar sensor and aquatic drone. The volume is obtained by means of an automatic GIS procedure based on the measurements of lake depth and surface area. Hilly lakes can be considered as sediment containers. Time-lapse measurements make it possible to estimate the silting rate of the lake. The volume of 12 hilly lakes in Tuscany was measured in 2010 and 2018, and the results in terms of silting rate were compared with the estimates of soil loss obtained by RUSLE and MMF. The analyses show that all the lakes measured are subject to silting phenomena. The sediment estimated by the measurements corresponds well to the amount of soil loss estimated with the models used. The relationships found are significant and promising for a distributed application of the methodology, which allows rapid estimation of erosion phenomena. Substantial differences in the proposed comparison (mainly found in two cases) can be justified by particular conditions found on site, which are difficult to predict from the models. The proposed approach allows for a monitoring of basin-scale erosion, which can be extended to larger domains which have hilly lakes, such as, for example, the Tuscany region, where there are more than 10,000 lakes.

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