Abstract
The erosive capacity of precipitation depends on its intensity, volume, and duration. The rainfall erosivity factor (R) of the Universal Soil Loss Equation (USLE) requires high frequency (subhourly) data. When these are not available, R can be estimated from simplified indices such as the Modified Fournier Index (MFI), the Precipitation Concentration Index (PCI), and the Seasonality Index (SI), which are computed from monthly precipitation. We calculated these indices for 34 stations in the complex terrain Abruzzo region (central Italy) during 1980–2018, based on both gauge (point) and grid datasets. Using 30-min rainfall data of 14 stations, we verified that MFI and PCI are reliable predictors of R (R2 = 0.91, RMSE = 163.6 MJ mm ha−1 h−1 year−1). For MFI, grid data do not capture the peaks in high-altitude stations and the low values in some inland areas, detected by the point dataset. Grid data show significant MFI positive trends in 74% of the stations, while the point data display significant positive trends in only 26% of stations and significant negative trends in four stations in the inland areas. The grid data complex orography requires preliminary validation work.
Highlights
Soil erosion is a process of considerable complexity that depends on several factors such as climate, soil type, topography, vegetation cover, and cultivation systems [1]
Atmosphere 2021, 12, x FOR PEER REVIEW Figure 3 shows the scatter plots of the mean R vs. the mean value of the in7doicf e17s (MFI, Precipitation Concentration Index (PCI), and Seasonality Index (SI), respectively); the regression lines with the corresponding coefficient of determination are reported inset
The analysis reveals that, among the indices considered, Modified Fournier Index (MFI) is the one characterized by the greatest spatial variability in the region
Summary
Soil erosion is a process of considerable complexity that depends on several factors such as climate, soil type, topography (slope and length), vegetation cover, and cultivation systems [1]. We estimate the trends of rainfall erosivity detected in a complex terrain region in central Italy (Abruzzo) using monthly data at 34 locations, comparing the calculation using homogenized rain-gauge timeseries (referenced as “point” in the manuscript) and timeseries interpolated from a widely used European gridded climatic dataset (E-OBS, referenced as “grid”) at the raingauge locations. High-quality and high-frequency rainfall datasets are required for a correct quantification of the R factor, and few studies were able to carry out large scale erosivity assessment based on the R factor. In this regard, a worthy example is a thorough study conducted by the Joint Research Center of the European Commission and other European institutes and universities [3]
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