"R1-R4" and "BOISED" sediment prediction model tests using forest roads ingranitics

Citation
Gl. Ketcheson et al., "R1-R4" and "BOISED" sediment prediction model tests using forest roads ingranitics, J AM WAT RE, 35(1), 1999, pp. 83-98
Citations number
19
Language
INGLESE
art.tipo
Article
Categorie Soggetti
Environment/Ecology
Journal title
Journal of the american water resources association
ISSN journal
1093-474X → ACNP
Volume
35
Issue
1
Year of publication
1999
Pages
83 - 98
Database
ISI
SICI code
1093-474X(199902)35:1<83:"A"SPM>2.0.ZU;2-O
Abstract
Erosion and sedimentation data from research watersheds in the Silver Creek Study Area in central Idaho were used to test the prediction of logging ro ad erosion using the R1-R4 sediment yield model, and sediment delivery usin g the "BOISED" sediment yield prediction model. Three small watersheds were instrumented and monitored such that erosion from newly constructed roads and sediment delivery to the mouths of the watersheds could be measured for four years following road construction. The errors for annual surface eros ion predictions for the two standard road tests ranged from +31.2 t/ha/yr ( +15 percent) to -30.3 t/ha/yr (-63 percent) with an average of zero t/ha/yr and a standard deviation of the differences of 18.7 t/ha/yr. The annual pr ediction errors for the three watershed scale tests had a greater range fro m -40.8 t/ha/yr (-70 percent) to +65.3 t/ha/yr (+38 percent) with a mean of -1.9 t/ha/yr and a standard deviation of the differences of 25.2 t/ha/yr. Sediment yields predicted by BOISED (watershed scale tests) were consistent ly greater (average of 2.5 times) than measured sediment yields. Hillslope sediment delivery coefficients in BOISED appear to be overly conservative t o account for average site conditions and road locations, and thus over-pre dict sediment delivery. Mass erosion predictions from BOISED appear to pred ict volume well (465 tonnes actual versus 710 tonnes predicted, or a 35 per cent difference) over 15 to 20 years, however mass wasting is more episodic than the model predicts.