Assessing the sources of uncertainty associated with the calculation of rainfall kinetic energy and the erosivity R factor. Application to the upper Llobregat Basin, NE Spain

Abstract

The sources of uncertainty associated with the calculation of rainfall kinetic energy and rainfall erosivity were investigated when the USLE R factor was operationally calculated for a mountainous river basin (504 km2) in the Southeastern Pyrenees. Rainfall kinetic energy was first obtained at the scale of the rainfall event by means of sub-hourly precipitation tipping-bucket rain gauge records and updates of the Kinnell (1981) equation. Annual erosivity values for the nearby pluviometric stations were then derived from the linear regressions between daily rainfall erosivity and daily precipitation, obtained for two different seasons. Finally, maps for rainfall erosivity estimates were obtained from the station values with Thiessen polygons. The sources of uncertainty analysed were i) the tipping-bucket instrumental errors, ii) the efficiency of the Kinnell (1981) equation, iii) the efficiency of the regressions between daily precipitation and kinetic energy, iv) the temporal variability of annual rainfall erosivity values, and the spatial variability of v) annual rainfall erosivity values and vi) long-term R factor values. The results showed that the uncertainty associated with the calculation of rainfall kinetic energy from rainfall intensity at the event and station scales is highly relevant and must be taken into account for experimental or modelling purposes; for longer temporal scales, the relevance of this source of uncertainty remains high if there is a low variability of the types of rain. Temporal variability of precipitation at wider spatial scales is the main source of uncertainty when rainfall erosivity is to be calculated on an annual basis, whereas the uncertainty associated with the long-term R factor is rather low and less important than the uncertainty associated with the other RUSLE factors when operationally used for long-term soil erosion modelling. © Author(s) 2010.