Rainfall erosivity for the Njala Area in Southern Sierra Leone dataset
Rainfall erosivity is an important characteristic of rainfall that gives an indication of its aggressiveness and potential to cause soil erosion. There are models (such the USLE/RUSLE EI30) for estimating rainfall erosivity but they require pluviographic records which are limited or not available in developing countries like Sierra Leone. To address this challenge, methods have been developed to estimate rainfall erosivity from the relationship between rainfall erosivity (derived from pluviographic records) and the more readily available daily and monthly rainfall records. Furthermore despite the global application of the EI30 index, there are questions about its suitability at the local level. These datasets show how rainfall erosivity indices, EI30, EIavg and ETI were calculated from available 30-minute rainfall records in the Njala Area of Southern Sierra Leone. These indices were then used to derive relationships between rainfall erosivity and daily/monthly rainfall records which are readily available in the study area. These datasets have been generated to determining a suitable erosivity model for estimating rainfall erosivity from daily and monthly rainfall records for the Njala area.
Steps to reproduce
The available pluviographic rainfall records were obtained from an automatic recording station on Njala University Campus in Southern Sierra Leone. The station only has three years of complete records (2016, 2017 and 2018) which were used to calculate the rainfall erosivity indices. EI30: the exponential equation recommended by Brown and Foster (1987) and which was used in the RUSLE (K. Renard et al., 1997) was used to calculate the unit energy (er) of each 30-minutes rainfall record or subevent of a rainfall event as follows: er = 0.29*[1-0.72*exp(-0.05*Ir)] where, er = unit energy (MJ ha−1mm−1) ir = rainfall intensity during the 30minute time interval (mm h−1). The overall energy (Er) of each sub-event was calculated by multiplying the unit energy er by the 30-minute rainfall amount (vr), i.e. er.vr. Then, the total energy (KE) of each rainfall event was calculated by summing the energies (Er) of the sub-events: Total energy of rainfall event, KE = ∑(r=1)(er·vr). Max I30 was determined from the maximum 30-minute intensity among the sub-events of an event rainfall. Iavg (average intensity) was calculated by finding the average intensity of all rainfall sub-events i.e. the average intensity of a rainfall event. TI (total intensity) was calculated by summing all intensities of sub-rainfall events i.e. the total intensity of a rainfall event. The erosivity for each index was calculated by multiplying the total energy (KE) of each rainfall event with I30, Iavg or TI i.e. EI30, EIavg or ETI. Dataset “EventEr2.0”, was obtained by selecting all rainfall events with rainfall ≥2.0mm while Datasets “DailyEr2.0”, and “MonthlyEr2.0”, were obtained by selecting and summing all rainfall events with rainfall ≥2.0mm. Similarly, Dataset “EventEr2.0”, was obtained by selecting all rainfall events with rainfall ≥12.5mm while Datasets “DailyEr12.5”, and “MonthlyEr12.5”, were obtained by selecting and summing all rainfall events with rainfall ≥12.5mm. Thus, datasets “EventEr2.0”, “DailyEr2.0”, and “MonthlyEr2.0” refer to both erosive and non-erosive rainfall while Datasets “EventEr12.5”, “DailyEr12.5”, and “MonthlyEr12.5” refer to erosive rainfall only. Dataset “R Code for Daily Erosivity Modelling” refer to the R code used for establishing the relationship between rainfall erosivity (EI30, EIavg and ETI) and rainfall (event, daily, and monthly). The csv file of each dataset was an input file in R for modelling rainfall erosivity. logRain, logEI30, logEIavg and logETI in each file were calculated by finding the log10 of the respective value.