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A DECISION MODEL TO PREDICT SEDIMENT YIELD FROM FOREST PRACTICES 1
Author(s) -
Burns R. G.,
Hewlett J. D.
Publication year - 1983
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.1752-1688.1983.tb04550.x
Subject(s) - hydrology (agriculture) , environmental science , sediment , watershed , streams , erosion , universal soil loss equation , storm , sampling (signal processing) , surface runoff , hazard , soil loss , geology , ecology , geography , meteorology , computer science , geotechnical engineering , paleontology , computer network , filter (signal processing) , machine learning , computer vision , biology
In order to choose among “best management practices,” forest managers need to predict sediment yield to perennial streams following various forest land operations. The “universal soil loss equation” (USLE) is not directly applicable to forest operations because of the heterogenous soil surface conditions left by harvesting, site preparation, and planting. A sediment hazard index is proposed, to be based on the amount of exposed mineral soil and its proximity to streams. The model offered includes rainfall erosivity, soil erodibility and average land slope, together with the index W. A paired watershed experiment in the central Georgia Piedmont was used to estimate parameters in the model. The experimental basin (80 acres) was clearcut, drum roller chopped twice, and planted by machine. The standard error of estimate of sediment yield was computed to be about 50/lbs/ac per sampling period (four months). Use of William's erogivity index (storm flow times peak flow) reduced the standard error to 33/lbs/ac.