The erosivity of soils under a given rainfall energy appears to vary greatly among soil orders, probably reflecting differences in clay composition and organic matter content. This study was conducted to quantify microrelief, infiltration, and sediment yield changes during three consecutive simulated rain events on a Udic Haploboroll and a Typic Hapludalf from Minnesota, and a Mollic Kandiudalf, and Typic Palehumult from Uganda. Air dry aggregates (< 5 mm) were packed in 191 containers tilted to a 5% slope and were subjected to three consecutive high energy rain storms (63 mm h-1) for a duration of 1 h. Runoff and sediment were continuously monitored during a storm. Infiltration was measured by continued weighing of the soil and containers. An automated non-contact laser relief meter was used to measure changes in soil roughness initially and after each storm. Soil surface roughness decreased during the rain events indicating that aggregate breakdown was the dominant process in seal formation. For example, random roughness decreased form 5.9 to 4.0 mm on Barnes loam and from 9.7 to 6.9 mm on Renova silt loam with cumulative rainfall of 0 and 126 min. These infiltration rates indicated that the Barnes Loam (Haploboroll) and Kabanyolo clay (Kandiudalf) were unstable soils while Kachwekano clay (Palehumult) and Renova silt loam (Hapludalf) were quite stable. Final infiltration rates after 3 consecutive rainfalls on Kachwekano clay (15 mm h-1) and Renova silt loam (13 mm h-1) [the stable aggregate soils] were significantly higher than those of Barnes loam (4 mm h-1) and Kabanyolo clay (3 mm h- 1). For the two stable soils a high infiltration rate on a rough surface was maintained until aggregate breakdown occurred and runoff began. Sediment yield from Barnes loam (29 kg m-2) and Kabanyolo clay (28 kg m-2) was significantly greater than soil loss from Kachwekano clay (0 kg m-2) and Renova silt loam (6 kg m-2). The microrelief method to quantify aggregate stability is an improvement over wet sieving and other related measurements because of its rapidity and because the statistical quantification can be linked to physical processes.
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