Stratification of variability in runoff and sediment yield based on vegetation characteristics

Runoff and sediment yield were collected from 100 plots during simulated ra infalls (100 mm/hr for 15 minutes) at antecedent soil moisture conditions. A clustering technique was used to stratify the variability of a single dat a set within a sagebrush-grass community into four groups based on vegetati on life form and amount of cover. The four cluster groups were grass, grass /shrub, shrub, and forb/grass and were found to be significantly different in plant height, surface roughness, soil bulk density, and soil organic mat ter. Stepwise multiple regression analyses were performed on the single dat a set and each cluster group. Results for individual groups resulted in mor e robust predictive equations for runoff (r(2) = 0.65-0.73) and sediment yi eld (r(2) = 0.37-0.91) than for equations developed from the single data se t (r(2) = 0.56 for runoff and r(2) = 0.27 for sediment yield). The standard errors of the cluster group regression equations were also improved in thr ee of the four group equations for both runoff and sediment yield compared to the single data set. Runoff was found to be significantly less (p < 0.01 ) in the forb/grass group compared with other vegetation cluster groups, bu t this was influenced by four plots that produced Little or no runoff. Sedi ment yield was not found to be significantly different among any cluster gr oups. Discriminant analysis was then used to identify important variables a nd develop a model to classify plots into one of the four cluster groups. T he discriminant model could be incorporated into rangeland hydrology and er osion models. The percentage cover of grasses, shrubs, litter, and bare gro und effectively stratified about 12 percent of the variation observed in ru noff and 26 percent of the variability for sediment yield as determined by r(2).