The estimation of rainfall erosivity in the Revised Universal Soil Loss Equation (RUSLE) requires long series of sub-hourly rainfall observations. Due to the endemic unavailability of this information, at least with the required degree of detail and coverage, empirical models are frequently used for the estimation of rainfall erosivity basing on easily retrievable rainfall values at the monthly or annual scale. In this paper, an empirical model is calibrated for Italy to bridge the annual to monthly scale gap by means of 10-year series of monthly rainfall observations at 171 Italian rain gauges, and then applied using the ERA5-Land gridded rainfall dataset to obtain maps of monthly and seasonal rainfall erosivity covering the reference period 1981–2010. Successively, 29 EURO-CORDEX bias-corrected rainfall projections for two future horizons (2021–2050 and 2051–2080 under RCP 2.6, 4.5 and 8.5) are used to obtain expected anomalies of seasonal rainfall erosivity over Italy with respect to the reference period. Statistical and visual analysis of results shows that positive percentage anomalies are expected in large portions of the country, especially under RCP 2.6 but with significant increases expected also under RCPs 4.5 and 8.5, especially in autumn and, secondarily, in summer. This intra-annual rainfall erosivity anomaly pattern entails the existence of increasing trends in the erosion hazard that cannot be captured by empirical models only relying on annual rainfall. The position of those results in the related literature is deeply discussed, providing significant insights on uncertainty sources and possible future research developments.

Monthly to seasonal rainfall erosivity over Italy: Current assessment by empirical model and future projections by EURO-CORDEX ensemble / Padulano, R.; Santini, M.; Mancini, M.; Stojiljkovic, M.; Rianna, G.. - In: CATENA. - ISSN 0341-8162. - 223:106943(2023), pp. 1-15. [10.1016/j.catena.2023.106943]

Monthly to seasonal rainfall erosivity over Italy: Current assessment by empirical model and future projections by EURO-CORDEX ensemble

Padulano R.
;
Rianna G.
2023

Abstract

The estimation of rainfall erosivity in the Revised Universal Soil Loss Equation (RUSLE) requires long series of sub-hourly rainfall observations. Due to the endemic unavailability of this information, at least with the required degree of detail and coverage, empirical models are frequently used for the estimation of rainfall erosivity basing on easily retrievable rainfall values at the monthly or annual scale. In this paper, an empirical model is calibrated for Italy to bridge the annual to monthly scale gap by means of 10-year series of monthly rainfall observations at 171 Italian rain gauges, and then applied using the ERA5-Land gridded rainfall dataset to obtain maps of monthly and seasonal rainfall erosivity covering the reference period 1981–2010. Successively, 29 EURO-CORDEX bias-corrected rainfall projections for two future horizons (2021–2050 and 2051–2080 under RCP 2.6, 4.5 and 8.5) are used to obtain expected anomalies of seasonal rainfall erosivity over Italy with respect to the reference period. Statistical and visual analysis of results shows that positive percentage anomalies are expected in large portions of the country, especially under RCP 2.6 but with significant increases expected also under RCPs 4.5 and 8.5, especially in autumn and, secondarily, in summer. This intra-annual rainfall erosivity anomaly pattern entails the existence of increasing trends in the erosion hazard that cannot be captured by empirical models only relying on annual rainfall. The position of those results in the related literature is deeply discussed, providing significant insights on uncertainty sources and possible future research developments.
2023
Monthly to seasonal rainfall erosivity over Italy: Current assessment by empirical model and future projections by EURO-CORDEX ensemble / Padulano, R.; Santini, M.; Mancini, M.; Stojiljkovic, M.; Rianna, G.. - In: CATENA. - ISSN 0341-8162. - 223:106943(2023), pp. 1-15. [10.1016/j.catena.2023.106943]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/949678
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