Reconstruction of erosivity density in northwest Italy since 1701

Diodato, Nazzareno; Gómara, Íñigo; Baronetti, Alice; Fratianni, Simona; Bellocchi, Gianni

doi:10.1080/02626667.2021.1918696

Cited by 10 publications

(6 citation statements)

References 87 publications

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“…The two values correspond, respectively, to the median and the 3 rd quartile of the distribution of the RED data (Fig 4A). These values also mark the range of critical RED values identified by Diodato et al [8] with a 50-year return period in north-western Italy. The advantage of using RED threshold values compared to other rainfall aggressiveness indicators are [54]: a) greater propensity to classify geomorphological hazards; b) greater stability, obtained with a shorter and heterogeneous period of recording; and c) easier mapping over large areas due to its independence on altitude up to 3000 m a.s.l.…”

Section: Plos Onesupporting

confidence: 76%

“…Rainfall erosivity density (RED in MJ hm -2 h -1 , equivalent to the most common MJ ha -1 h -1 ), i.e. rainfall erosivity per unit of rainfall, is an important climatic indicator of floods and soil erosion [ 7 , 8 ]. RED effects are apparent in catastrophic weather events, due to the intensification of daily storms in the northern Hemisphere ( Fig 1A ) and erosive precipitation in other parts of the world ( Fig 1B ).…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, in many parts of Africa, where the warning state is expected to be exceeded over a wide area, soil erosion is becoming a major problem due to the high sediment production in tropical mountain streams[83]. The Mediterranean also has a high probability of reaching a warning state, where the aggressiveness of rainfall[8], in tandem with RED, seems to show a propensity to increase in recent decades[18]. However, in addition to the frequent high erosivity of precipitation regimes and human disturbances, it can be argued that much of the Mediterranean landscape is naturally vulnerable to soil erosion processes[84,85].Fig 7B, on the other hand, shows the map of the hydrological hazard-prone areas at alert state, highlighting the fact that ~19% of the world's land area has a greater than 50% chance of exceeding the z k threshold (RED > 3.0 MJ hm -2 h -1 ).…”

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confidence: 99%

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Global assessment of storm disaster-prone areas

Diodato¹,

Borrelli

Panagos

et al. 2022

PLoS ONE

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Background Advances in climate change research contribute to improved forecasts of hydrological extremes with potentially severe impacts on human societies and natural landscapes. Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm-2 h-1 yr-1) per rainfall unit (mm), is a measure of rainstorm aggressiveness and a proxy indicator of damaging hydrological events. Methods and findings Here, using downscaled RED data from 3,625 raingauges worldwide and log-normal ordinary kriging with probability mapping, we identify damaging hydrological hazard-prone areas that exceed warning and alert thresholds (1.5 and 3.0 MJ hm-2 h-1, respectively). Applying exceedance probabilities in a geographical information system shows that, under current climate conditions, hazard-prone areas exceeding a 50% probability cover ~31% and ~19% of the world’s land at warning and alert states, respectively. Conclusion RED is identified as a key driver behind the spatial growth of environmental disruption worldwide (with tropical Latin America, South Africa, India and the Indian Archipelago most affected).

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Section: Plos Onesupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Global assessment of storm disaster-prone areas

Diodato¹,

Borrelli

Panagos

et al. 2022

PLoS ONE

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“…This study provided, for the first time, drought spatial distribution maps for the whole of northern Italy. The trend analysis indicated that the entire Alpine Range, characterised by the rainiest and snowiest areas (Diodato et al 2021 andTerzago et al 2012), will also experience an increase in drought severity in the RCP 4.5 scenario.…”

Section: Drought Trends and Spatial Behaviourmentioning

confidence: 99%

Future droughts in northern Italy: high-resolution projections using EURO-CORDEX and MED-CORDEX ensembles

et al. 2022

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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.

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“…This study provided, for rst time, drought spatial distribution maps for the whole of northern Italy. The trend analysis indicated that the entire Alpine Range, characterised by the rainiest and snowiest areas (Diodato et al 2021 andTerzago et al 2012), will also experience an increase in drought severity in the RCP 4.5 scenario.…”

Section: Drought Trends Spatial Behaviourmentioning

confidence: 99%

Future droughts in northern Italy: high resolution projections using EURO-CORDEX and MED-CORDEX ensembles

Baronetti

Dubreuil²,

Provenzale

et al. 2022

Preprint

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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, were collected at spatial resolution of 0.11 degrees. Before the analysis, model outputs were validated on daily weather station time series, and scaling factors for possible use in bias correction were identified. Annual temperature and precipitation anomalies for near and far future conditions were investigated; drought events were identified by the Standardized Precipitation Evapotranspiration Index and Standardized Precipitation Index at the 12-, 24- and 36-month time scales. This study highlights the importance of using multiple drought indicators in the detection of drought events, since the comparison revealed that evapotranspiration anomaly is the main triggering factor. For both scenarios, the results indicated 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 was also observed.

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Reconstruction of erosivity density in northwest Italy since 1701

Cited by 10 publications

References 87 publications

Global assessment of storm disaster-prone areas

Global assessment of storm disaster-prone areas

Future droughts in northern Italy: high-resolution projections using EURO-CORDEX and MED-CORDEX ensembles

Future droughts in northern Italy: high resolution projections using EURO-CORDEX and MED-CORDEX ensembles

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