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STRATIFICATION OF VARIABILITY IN RUNOFF AND SEDIMENT YIELD BASED ON VEGETATION CHARACTERISTICS 1
Author(s) -
Mergen Daryl E.,
Trlica M. J.,
Smith James L.,
Blackburn Wilbert H.
Publication year - 2001
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2001.tb05498.x
Subject(s) - surface runoff , hydrology (agriculture) , forb , vegetation (pathology) , environmental science , sediment , regression analysis , stepwise regression , soil science , mathematics , statistics , grassland , agronomy , geology , ecology , geotechnical engineering , pathology , biology , medicine , paleontology
Runoff and sediment yield were collected from 100 plots during simulated rainfalls (100 mm/hr for 15 minutes) at antecedent soil moisture conditions. A clustering technique was used to stratify the variability of a single data set within a sagebrush‐grass community into four groups based on vegetation 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 matter. Stepwise multiple regression analyses were performed on the single data set and each cluster group. Results for individual groups resulted in more robust predictive equations for runoff (r 2 = 0.65–0.73) and sediment yield (r 2 = 0.37–0.91) than for equations developed from the single data set (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 three 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, but this was influenced by four plots that produced little or no runoff. Sediment yield was not found to be significantly different among any cluster groups. Discriminant analysis was then used to identify important variables and develop a model to classify plots into one of the four cluster groups. The discriminant model could be incorporated into rangeland hydrology and erosion models. The percentage cover of grasses, shrubs, litter, and bare ground effectively stratified about 12 percent of the variation observed in runoff and 26 percent of the variability for sediment yield as determined by r 2 .