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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
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
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
slide3
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
slide4
Why?
  • Little added effort for substantial value returned
    • INFRA in place and working
    • DSRs completed
    • Related monitoring
what you get
What you get
  • Ability to determine thresholds of performance
  • Ability to determine relative risk of failure
  • Quantitative description of risks
slide12

Calawah R.

N. Fk. Headwaters

Pistol Cr.

Bonidu Cr.

use topograpy to define landscape types for chi square analysis
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

chi square results
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.

a need for more specific risk information
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.
slide18

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

relative odds of road related landslides

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
how you get it
How you get it…
  • Add DSR points and attributes to INFRA
  • Attributes of failure type, cause, coarse magnitude
how you get it1
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)
cause attributes questions
Cause Attributes…Questions
  • Perpetrator or innocent bystander?
  • Context
  • Impact

Sometimes roads catch and preventsediment delivery

other road monitoring
Other road monitoring

Use categories created in this effort for consistency and combined analysis

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