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Estimation of the annual rainfall erosivity index based on hourly rainfall data in a tropical region

Author

Listed:
  • Ming-Hsi Lee
  • I-Ping Hsu

    (Department of Soil and Water Conservation, National Pingtung University of Science and Technology, Pingtung, Taiwan)

Abstract

The annual mean rainfall erosivity (R) indicates the potential soil loss caused by the precipitation and runoff and is used to predict the soil loss from agricultural hillslopes. R is calculated from rainfall stations with continuously recording rainfall databases. However, many short-term real-time rainfall databases that also relate to the rainfall intensity are not readily available around Taiwan, with the hourly rainfall data being predominantly available. The annual mean rainfall erosivity calculated by the 10-min rainfall data accumulation converted to the 30-min rainfall data (R10_30) can be estimated using the annual mean rainfall erosivity calculated by the 10-min rainfall data accumulation convert to the hourly rainfall data (R10_60) that are calculated from the kinetic energy calculated by the 10-min rainfall data accumulation converted to the hourly rainfall data (E60j). The maximum 60-min rainfall intensity calculated by the 10-min rainfall data accumulation converted to the hourly rainfall data (I60j) has been established in rainfall stations throughout southern Taiwan. The 10-min rainfall data set consists of 15 221 storm events from 2002 to 2017 monitored by 51 rainfall stations located in the tropical regions in Taiwan. According to the results of this study, the average conversion factors of the kinetic energy (1.04), rainfall erosivity (1.47), and annual mean rainfall erosivity (1.30) could be estimated based on the 10-min rainfall data.

Suggested Citation

  • Ming-Hsi Lee & I-Ping Hsu, 2021. "Estimation of the annual rainfall erosivity index based on hourly rainfall data in a tropical region," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 16(2), pages 74-84.
    • Handle: RePEc:caa:jnlswr:v:16:y:2021:i:2:id:25-2020-swr
    DOI: 10.17221/25/2020-SWR
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