Evaluation of Annual Rainfall Erosivity Index Based on Daily, Monthly, and Annual Precipitation Data of Rainfall Station Network in Southern Taiwan

Abstract

The erosivity factor in the universal soil loss equation (USLE) provides an effective means of evaluating the erosivity power of rainfall. The present study proposes three regression models for estimating the erosivity factor based on daily, monthly, and annual precipitation data of rainfall station network, respectively. The validity of the proposed models is investigated using a dataset consisting of 16,560 storm events monitored by 55 rainfall stations in southern Taiwan. The results show that, for 49 of the 55 stations, a strong positive correlation false(r2>0.5false) exists between the annual rainfall amount and the annual rainfall erosivity factor. In other words, the estimation model based on the annual precipitation data provides a reliable means of predicting the long-term annual rainfall erosivity in southern Taiwan. Furthermore, the root mean square error (RMSE) and mean absolute percentage error (MAPE) analysis results show that the estimation models based on annual and monthly precipitation data have a more accurate prediction performance than that based on daily precipitation data.

The rainfall erosivity factor in the Czech Republic and its uncertainty

“…In addition to a point estimate of the R factor, a spatial information (map) is often required for practical purposes. A number of such maps have been released recently at national (Lu and Yu, 2002;Yin et al, 2007;Bonilla and Vidal, 2011;Oliveira et al, 2013;Borrelli et al, 2016;Panagos et al, 2016a;Meddi et al, 2016) or larger (Panagos et al, 2015) scales.…”

“…In general, the uncertainty in the estimated R factor increases as the length of the available record decreases, but can be reduced by combination of data from different sites (Catari et al, 2011) or by considering covariates that are better sampled and/or whose variation over space and time is smaller (Goovaerts, 1999). For the spatial interpolation of the R factor, variables like longitude, latitude, and elevation (Goovaerts, 1999;Angulo-Martínez et al, 2009) or long-term precipitation (Lee and Lin, 2014) are often considered.…”

Identification of inventory-based susceptibility models for assessing landslide probability: a case study of the Gaoping River Basin, Taiwan

“…South and southeast-facing slopes have the highest weighting (1.00), due to the correlation with the southwesterly flow pattern of typhoon activity (Yu and Cheng 2014), which exposes slopes to more intense precipitation over a significantly longer period of time during a storm. Furthermore, elevations between 400 and 1000 m (1.0) and 1000 and 1400 m (0.9) receive high weighting for the reason of the increased spatial distribution of rainfall within the zones (Lee and Lin 2015). Furthermore, the LC model concentrates a high percentage landslide values in the H & VH classes (22.11% and 65.42%) which demonstrates that this model uses a high proportion of the LSI class to organize landslide pixels within it.…”

Spatial and temporal variability of the rainfall erosivity factor in Northern Algeria

“…This approach is suitable for large areas with highly variable rainfall regimes and variable topography with a limited network of recording stations (Daly et al 2002;Meusburger et al 2012). Kreageae technique has been used by many authors to map the parameters pertaining to pluviometry such as the index R. This technique gave better results compared to interpolation techniques Bspline and inverse distance weighting^according to Khorsandi et al (2012) and Lee and Lin (2015).…”