<abstract xmlns="http://www.w3.org/1999/xhtml">

Rainfall erosivity factor (R) of the USLE model is one of the most popular indicators of areas potentially susceptible to soil erosion. Its value is influenced by the number and intensity of extreme rainfall events. Since the regional climate models expect that the intensity of heavy rainfall events will increase in the future, the currently used R-factor values are expected to change as well. This study investigates possible changes in the values of R-factor due to climate change in the Myjava region in Slovakia that is severely affected by soil erosion. Two rain gauge stations with high-resolution 1-minute data were used to build a multiple linear regression model (<italic>r</italic>2 = 0.98) between monthly <italic>EI</italic>30 values and other monthly rainfall characteristics derived from low-resolution daily data. The model was used to estimate at-site <italic>R</italic>-values in 13 additional rain gauge stations homogeneously dispersed over the whole region for four periods (1981–2010, 2011–2040, 2041–2070, 2071–2100). The at-site estimates were used to create <italic>R</italic>-factor maps using a geostatistical approach. The results showed that the mean <italic>R</italic>-factor values in the region might change from 429 to as much as 520 MJ.mm.ha−1.h−1.yr−1 in the second half of the 21st century representing a 20.5% increase.
</abstract>

Rainfall erosivity factor (R) of the USLE model is one of the most popular indicators of areas potentially susceptible to soil erosion. Its value is influenced by the number and intensity of extreme rainfall events. Since the regional climate models expect that the intensity of heavy rainfall events will increase in the future, the currently used R-factor values are expected to change as well. This study investigates possible changes in the values of R-factor due to climate change in the Myjava region in Slovakia that is severely affected by soil erosion. Two rain gauge stations with high-resolution 1-minute data were used to build a multiple linear regression model (r2 = 0.98) between monthly EI30 values and other monthly rainfall characteristics derived from low-resolution daily data. The model was used to estimate at-site R-values in 13 additional rain gauge stations homogeneously dispersed over the whole region for four periods (1981–2010, 2011–2040, 2041–2070, 2071–2100). The at-site estimates were used to create R-factor maps using a geostatistical approach. The results showed that the mean R-factor values in the region might change from 429 to as much as 520 MJ.mm.ha−1.h−1.yr−1 in the second half of the 21st century representing a 20.5% increase.

Estimating Rainfall Erosivity Factor Using Future Climate Projection in the Myjava Region (Slovakia)

Slovak University of Technology in Bratislava

Acta Horticulturae et Regiotecturae

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Rainfall erosivity factor (R) of the USLE model is one of the most popular indicators of areas potentially susceptible to soil erosion. Its value is influenced...

Estimating Rainfall Erosivity Factor Using Future Climate Projection in the Myjava Region (Slovakia)

Published Online: May 21, 2021

Page range: 31 - 36

DOI: https://doi.org/10.2478/ahr-2021-0007

Keywords
R-factor, soil erosion, climate change, ordinary kriging

© 2021 Peter Valent et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Estimating Rainfall Erosivity Factor Using Future Climate Projection in the Myjava Region (Slovakia)

Published Online: May 21, 2021

Page range: 31 - 36

DOI: https://doi.org/10.2478/ahr-2021-0007

KeywordsR-factor, soil erosion, climate change, ordinary kriging

© 2021 Peter Valent et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
R-factor, soil erosion, climate change, ordinary kriging