“…For the past, the reconstruction of long timeseries of climate extremes supports that naturally climatic processes are governing the occurrence of erosive rainfall and, thus, help us to understand present processes and to improve future projections 42 . Attempts have been made to relate weather types or climate indices, to temporally coincident multi-secular trends in hydrological processes 43,44 . In this way, the predictive performance of the trends leaves a small potential for long-term predictability in rainfall 45 .…”
Erosive storms constitute a major natural hazard. They are frequently a source of erosional processes impacting the natural landscape with considerable economic consequences. Understanding the aggressiveness of storms (or rainfall erosivity) is essential for the awareness of environmental hazards as well as for knowledge of how to potentially control them. Reconstructing historical changes in rainfall erosivity is challenging as it requires continuous time-series of short-term rainfall events. Here, we present the first homogeneous environmental (1500–2019 CE) record, with the annual resolution, of storm aggressiveness for the Po River region, northern Italy, which is to date also the longest such time-series of erosivity in the world. To generate the annual erosivity time-series, we developed a model consistent with a sample (for 1981–2015 CE) of detailed Revised Universal Soil Loss Erosion-based data obtained for the study region. The modelled data show a noticeable descending trend in rainfall erosivity together with a limited inter-annual variability until ~1708, followed by a slowly increasing erosivity trend. This trend has continued until the present day, along with a larger inter-annual variability, likely associated with an increased occurrence of short-term, cyclone-related, extreme rainfall events. These findings call for the need of strengthening the environmental support capacity of the Po River landscape and beyond in the face of predicted future changing erosive storm patterns.
“…For the past, the reconstruction of long timeseries of climate extremes supports that naturally climatic processes are governing the occurrence of erosive rainfall and, thus, help us to understand present processes and to improve future projections 42 . Attempts have been made to relate weather types or climate indices, to temporally coincident multi-secular trends in hydrological processes 43,44 . In this way, the predictive performance of the trends leaves a small potential for long-term predictability in rainfall 45 .…”
Erosive storms constitute a major natural hazard. They are frequently a source of erosional processes impacting the natural landscape with considerable economic consequences. Understanding the aggressiveness of storms (or rainfall erosivity) is essential for the awareness of environmental hazards as well as for knowledge of how to potentially control them. Reconstructing historical changes in rainfall erosivity is challenging as it requires continuous time-series of short-term rainfall events. Here, we present the first homogeneous environmental (1500–2019 CE) record, with the annual resolution, of storm aggressiveness for the Po River region, northern Italy, which is to date also the longest such time-series of erosivity in the world. To generate the annual erosivity time-series, we developed a model consistent with a sample (for 1981–2015 CE) of detailed Revised Universal Soil Loss Erosion-based data obtained for the study region. The modelled data show a noticeable descending trend in rainfall erosivity together with a limited inter-annual variability until ~1708, followed by a slowly increasing erosivity trend. This trend has continued until the present day, along with a larger inter-annual variability, likely associated with an increased occurrence of short-term, cyclone-related, extreme rainfall events. These findings call for the need of strengthening the environmental support capacity of the Po River landscape and beyond in the face of predicted future changing erosive storm patterns.
“… Figure 7 Monthly precipitation (98th percentiles) and frequency of floods and flash-floods (bars) driven by dominating frontal, convective or advective-convective rainfall events over the Mediterranean basin. The number of monthly floods (blue bars) and flash-floods (black bars) is from Guzzetti et al 101 , Gaume et al 102 and Diodato et al 103 for the period 1950–2006. For the same period, monthly percentiles (prc) were extracted from the CRU Global Climate Dataset 104 .…”
Rainfall erosivity and its derivative, erosivity density (ED, i.e., the erosivity per unit of rain), is a main driver of considerable environmental damages and economic losses worldwide. This study is the first to investigate the interannual variability, and return periods, of both rainfall erosivity and ED over the Mediterranean for the period 1680–2019. By capturing the relationship between seasonal rainfall, its variability, and recorded hydrological extremes in documentary data consistent with a sample (1981–2015) of detailed Revised Universal Soil Loss Erosion-based data, we show a noticeable decreasing trend of rainfall erosivity since about 1838. However, the 30-year return period of ED values indicates a positive long-term trend, in tandem with the resurgence of very wet days (> 95th percentile) and the erosive activity of rains during the past two decades. A possible fingerprint of recent warming is the occurrence of prolonged wet spells in apparently more erratic and unexpected ways.
“…The spatial extent and pattern of wood distribution have a great influence on the microclimate, pedogenesis and hydrology, including soil erosion of both aquatic and terrestrial ecosystems (Diodato and Bellocchi, 2008b;Diodato et al, 2017), linking up to regional and global climatic aspects (Gaillard et al, 2018;Morin et al, 2018).…”
The prediction of forest productivity is essential for sustainable forest management, particularly in countries, like Italy, where forest is an important part of many protected areas. A spatial predictive probability model for forest productivity rates in Italy was developed over the period 1961-1990, based on 135 annually-resolved records of site productivity and auxiliary variables measured at 219 stations. Our analysis shows that the probability of finding high (> 7.3 m 3 ha −1 yr −1) and low (< 5.8 m 3 ha −1 yr −1) productivity rates changes across different regions of Italy. The generated spatial patterns contribute to a better understanding of the factors structuring the distribution of forest productivity in Italy because they reflect the dependence of temperature and water availability conditions on the latitudinal and altitudinal location of the study areas. We observed that the temperature control dominates forest productivity at high elevations and latitudes, whereas low-elevation sites in central and southern Italy are more sensitive to water availability. The proposed spatial probability modelling should be further assessed for its possible incorporation into forest management plans.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
