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Monitoring Road-Watershed Performance

Monitoring Road-Watershed Performance. An Initiative for Efficient and Effective Road Performance Monitoring: Combine effort to complete DSRs and INFRA to achieve road performance monitoring. mj furniss, PNW. 2005. Roads are a focus of watershed monitoring.

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Monitoring Road-Watershed Performance

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  1. Monitoring Road-Watershed Performance An Initiative for Efficient and Effective Road Performance Monitoring: Combine effort to complete DSRs and INFRA to achieve road performance monitoring mj furniss, PNW. 2005

  2. Roads are a focus of watershed monitoring • But roads vary greatly in performance • Most do not fail • Failures tend to cluster in areas of inherent instability

  3. Why? • Failure sites create a useful dataset for defining road performance through time • Failures define the limits of practice in various landscape situations • When experienced road managers retire, mission-critical knowledge could be conserved

  4. Why? • Little added effort for substantial value returned • INFRA in place and working • DSRs completed • Related monitoring

  5. What you get • Ability to determine thresholds of performance • Ability to determine relative risk of failure • Quantitative description of risks

  6. Failure Rate vs Distance from Stream

  7. Failure Rate vs Slope Class

  8. Slope Position vs Failure Rate

  9. Geology and Failure Rate

  10. Olympic National Forest

  11. ONF Northwest District

  12. Calawah R. N. Fk. Headwaters Pistol Cr. Bonidu Cr.

  13. Use Topograpy to Define Landscape Types for Chi-square Analysis Slope:<=15%, 15-30%, 30-45%, >45%Slope Position:<=20%, 20-55%, 55-85%, 85-100%Distance to Stream:<34m, 34-74m, 74-135m, <=135m

  14. Example Landscape Units for 2

  15. Chi-Square Results: Landscape types with fewer failures than expected were generally in gentler slope areas; those at lower slope positions and further from streams. Types with more failures than expected were generally at higher slope positions, steeper slopes, and closer to streams.

  16. A Need for More Specific Risk Information Logistic Regression Modelling: • Combine 509 known failures with 1008 randomly selected locations. • Use slope, slope position, and stream proximity to estimate relative risk of road-related landslides.

  17. Logistic Regression Sample Units

  18. Logistic Regression Model:ln(odds) = -1.8802 + 0.0238Slope + 0.0192Slope Position – 0.016Distance + 0.0001SlopeDistance

  19. Slope 7% Slopos 4% Distance 27m Landslide Odds 19X Reference Segment 95% CL: 7, 51 Slope 23% Slopos 19% Distance 27m Landslide Odds 39X Reference Segment 95% CL: 15, 100 Reference Segment: Slope 3% Slpos 8% Distance 213m Relative Odds of Road-Related Landslides

  20. 127 167 17 72 73 65 50 53 Relative Odds Compared to 2% Slope, 2% Slope Position, 200m to Stream

  21. Average Relative Odds by Watershed

  22. Point swarms show problem areas clearly

  23. How you get it… • Add DSR points and attributes to INFRA • Attributes of failure type, cause, coarse magnitude

  24. How you get it • Modify description block in DSR to include:  Failure type  Cause  Volume (quantity classes) • Total • To stream • To riparian area (within 50 m)

  25. Cause Attributes…Questions • Perpetrator or innocent bystander? • Context • Impact Sometimes roads catch and preventsediment delivery

  26. Other road monitoring Use categories created in this effort for consistency and combined analysis

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