Forestry best management practices (BMPs) programs were developed by individual states in response to the Clean Water Act in order to protect water quality during and after timber harvests. Our research goals are to compare BMP implementation at stream crossings by region and road type in Virginia and to quantify effectiveness of BMPs by developing hypothetical upgrades and determining upgrade costs. Stream crossings (75 truck, 79 skidder) sampled for BMP implementation were on operational harvests conducted in 2016, from the Mountains, Piedmont, and Coastal Plain of Virginia. Erosion rates of stream crossing approaches were modeled using the Universal Soil Loss Equation modified for forest lands (USLE-Forest) and Water Erosion Prediction Project (WEPP) methodologies. Implementation ratings (BMP-, BMP-standard, BMP+) were developed to characterize crossings with respect to state implementation standards. Costs for upgrading crossings to a higher BMP category were estimated by adjusting cover percentages and approach lengths. Sixty-three percent of stream crossings were classified as BMP-standard, with an average erosion rate of 7.6 Mg/ha/yr; 25% of crossings were classified as BMP+, with an average erosion rate of 1.7 Mg/ha/yr; and 12% of crossings were classified as BMP-, with an average erosion rate of 26.2 Mg/ha/yr. Potential erosion rates decreased with increasing BMP implementation (p <0.0001). Average BMP implementation audit scores for stream crossings were 88% on skid trails and 82% on truck roads. To upgrade from a BMP- to BMP-standard, the cost-benefit ratio of dollars to tons of sediment prevented averaged $166.62/Mg for skid trails and $2274.22/Mg for truck roads. Enhancement to the BMP+ level is not economically efficient and BMP implementation at stream crossings reaches maximum efficiency at the BMP-standard level. / M. S. / Timber harvesting can accelerate erosion processes and result in the discharge of large quantities of sediment into nearby water resources if proper management is not used during and after harvests. Most of sediment entering streams is generated from forest roads and trails, particularly at stream crossings. This study includes 154 crossings (75 truck, 78 skidder) randomly selected from the Mountains, Piedmont, and Coastal Plain regions of Virginia. Evaluations were conducted on lands with silvicultural operations completed in 2016 that had truck road crossings and/or skidder crossings over any stream. Information was gathered at each crossing to model the erosion rates from both approaches to the stream by using the Universal Soil Loss Equation modified for forest lands and the Water Erosion Prediction Project. Implementation ratings (BMP-, BMP-standard, BMP+) were developed to characterize crossings with respect to state implementation standards, and evaluate other factors, including stream bank stability, ground cover, and evidence of sedimentation in the stream. The Virginia Department of Forestry BMP audit was used to score the crossings. Costs for upgrading the crossing to a higher BMP category were estimated by adjusting cover and approach lengths, and then using previous research data and existing road cost models. Potential erosion rates decreased with increasing BMP implementation (p <0.0001). Average BMP implementation audit scores for stream crossings were 88% on skid trails and 82% on truck roads. This research contributes to the evidence of BMP effectiveness and provides transparency to the citizens of Virginia regarding sustainable forestry practices.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/95892 |
| Date | 08 June 2018 |
| Creators | Dangle, Chandler Lipham |
| Contributors | Forest Resources and Environmental Conservation, Bolding, M. Chad, Aust, W. Michael, Barrett, Scott M. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
Page generated in 0.0029 seconds