Use of Disdrometer Dataset to Detect Kinetic Energy Expenditure and Rainfall Intensity Relationships

Soil is a vital component for supporting life on Earth and is substantially responsible for the operation of any ecological system. Soil erosion is a global problem because it has resulted in the destruction of agricultural land. Raindrops contacting the soil surface may cause soil separation. This process may be identified by assessing the rainfall kinetic energy (KE). Since direct measurements of KE are challenging, statistical formulas are often employed as an alternative option to estimate the KE using rainfall intensity (RI), which significantly influences soil erosion and is very simple to detect. In this study, we collected a rainfall dataset using a laser-based device (OTT Parsivel2 optical disdrometer) between June 2020 and December 2021 in Sangju City (Korea) to analyze the property of KE-RI relationship. A total of 37 rainfall events were selected and used for the formation of KE-RI equations. We derived three KE–RI equations in each group based on the three different rainfall intensity groups. Power-law, linear, and polynomial models were used to link KE to RI, and the best correlation between KE and RI was determined using a power-law form.