Detecting soil disturbance/restoration effects on stream sediment loading in the Lake Tahoe Basin,USA—modelling predictions |
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| Authors: | M E Grismer |
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| Affiliation: | Department of LAWR‐Hydrology, UC Davis, Davis, CA, USA |
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| Abstract: | Establishment and ‘crediting’ for total maximum daily loads (TMDL) of sediment require development of stream monitoring programs capable of detecting changes in land use and erosion ‘connectivity’ conditions across the watershed. As a ‘proof of concept’ directed at developing such an effective stream monitoring program considering only the effects of soil disturbances or restoration in the Lake Tahoe Basin, variability in daily stream sediment load predictions from a local‐scale, field data–based distributed runoff and erosion model developed previously is analysed for the west‐shore watersheds of Homewood (HMR) and Madden Creeks. The areal extent effects of forest fuel reductions (slight soil disturbances in Madden) and soil restoration efforts (e.g. dirt road removal and ski‐run rehabilitation in HMR) on watershed daily sediment loads for the 1994–2005 period are considered. Based on model predictions, forest fuel management in the Madden Creek watershed must occur across more than 30% of the basin area to result in a detectable increase in daily sediment loads at the >95% confidence level. Similarly, a daily load reduction that could be assessed with >95% confidence within the HMR basin required substantial dirt road removal (50% by roaded area) and restoration of 20% of the ski‐run area (combined for ~5% of the basin area) for the 11‐year record but was also possible within 2–3 years following restoration. These modelling results suggest that despite considerable flow–load variability, it may be possible to detect cumulative changing land‐use conditions within several years of project completion such that quantitative TMDL ‘crediting’ may be developed. Copyright © 2012 John Wiley & Sons, Ltd. |
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| Keywords: | erosion forest soils modelling connectivity soil restoration sediment yields |
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