Mapping rainfall erosivity for Africa with TRMM time series

Abstract

Remote sensing data offer important information about biophysicalparameters at various spatial and temporal scales. Soil erosion by wateris a function of many of these parameters. Regrettably, practical use ofremote sensing in soil erosion research is mostly confined tomono-temporal vegetation mapping. We argue that information fromspace-borne missions can be more effectively used in erosion mapping andmonitoring worldwide. For that appropriate spatial and temporal scalesshould be selected which reflect the dynamics triggering erosion. Coarsespatial scale data can provide important information at a fine temporalresolution, especially because erosion is an erratic phenomenon oftenlinked to high rainfall-intensity events. Both rainfall and vegetationcover are highly dynamic factors affecting erosion. To illustrate howthe erosion community could better make use of currently available timeseries of space-borne missions, we create monthly and yearly rainfallerosivity maps for the African continent from TRMM (Tropical RainfallMeasurement Mission) rainfall estimates. Currently most large-scaleerosivity maps have been based on extrapolated gauge data and monthly oryearly rainfall, often using generalized empirical formulas. TRMM 3B42data are constructed using active and passive microwave measurementsmade by instruments onboard TRMM combined with geosynchronous infraredrainfall estimates. The result is a 3-hourly product at 0.25-degreeresolution. TRMM data unarguably underestimate maximum intensity due tospatial and temporal attenuation (large cells and long 3-hour periods).Nevertheless, we argue that TRMM data can provide a great improvementfor large-scale erosivity mapping. Based on simple formulas linking TRMMintensity data to erosivity, we present average monthly and yearlyrainfall erosivity maps for Africa. We discuss the possible steps totake for validating our results, and indicate how results may beincorporated for effective large-scale erosion mapping and monitoring.