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The GRAIP model is a product of work we have been doing on roads for a number
of years in Oregon and Idaho. We have had help with the GIS tools from Dave and
Ajay at Utah State. The Boise NF contributed to this effort by collecting the dataset
for SFP. The EPA supported the data collection in SF Payette.
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Roads have a variety of impacts both acute and chronic on the land including
sediment production and delivery, mass wasting, stream crossing failure, habitat
fragmentation and cumulative effects.
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With 383,000 miles of roads on FS there are many decisions to be made.
Closing and decomissioning, Maintenance Upgrade
Repair and Urgency and Priority Where and when to spend limited resources
How do we make decisions – what is the information needed to make good
decisions
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Information Needed to make informed decisions about where to focus efforts
Whether assesing watershed condition, Cumulative Effects, Treatment/BMP
effectiveness
---How do we get that information?
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The GRAIP package includes the entire process from data collection to analysis and
mapping
2 person crew
Sub-meter GPS unit
Vehicle
Laptop and software
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A road inventory is conducted using GPS to locate drain points and road segments
A data dictionary queries the crew on road attributes and
drain point conditions
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This image outline the hydrology of the road as it interacts with the channel network
Each road segment has two potential flow paths, and drain numbers
Each drain point such as this culvert, has a drain number that is used to route water
and sediment from the road to the drain
We use two person crew, one to drive and one to run up and down the slope
collecting data. The progress of the inventory varies from 2-5 miles per day
depending on the drivability and condition of the road and the hydrologic connection
with the stream. Recent large watershed efforts using trained crews to collect data
on all sorts of open and closed roads have averaged 2.5 miles per day. The cost for
this type of study has averaged about $200/road mile. With most of the cost being
salary, travel and vehicle expenses.
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We have applied GRAIP widely across the Northwest. Since 2008 we have been
using the tool tomonitore a large number of road treatment projects (46) throughout
Regions 1, 4, 5, and 6.
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GRAIP begins with a preprocessing tool that checks the data for errors and
populates a database.
Once the data are imported to GIS GRAIP works as a set of drop downs in Arc Map.
First sediment production is calculated
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Sediment production is calculated using a base rate modified by a series of local
factors from the inventory
Zena Creek data from Megahan was used for a base rate for this work
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The following examples were taken from a survey of the SF Payette in Idaho, NE of
Boise. 450 miles of road were inventoried. MG=tons
This figure shows areas of high sed production(=road surface erosion)
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The sediment generated on road segments is routed to drain points shown as
brown circles
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The delivery is recorded at each drain point in the field.
Contributing sediment is routed downslope and accumulated in the receiving
channel segment shown in color
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2.1 thousand tons of road sediment delivered per year in SF Payette
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This map illustrates areas of high local sediment delivery to channels and should
highlight areas of impact.
Undisturbed basins in this area transport 10 tons/km2/year over several years as
measured by Megahan in the South Fork of the Salmon River.
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Drain point condition is evaluated to asses function and maintenance needs. Of the
7165 drain points asses in the SF Payette 13% needed help and 2% needed
replacement.
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The next module is mass wasting and associated analyses
SI is the stability index of Pack and Tarbotton, similar to SHALSTAB based on
infinite slope model
ESI is the erosion stability index
Stream crossing failure index
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First the hillslope SI is calculated without roads to show if roads were build on
places that would fail
Then the contributing area of the roads is added at the drain point to calculate the
additional risk
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The ESI predicts the risk of gullying below a drain point.
Plot of all the drain points in a subbasin in green. The red points have gullies. The
ESI lines were picked to divide the drain points into quarters. Points with an ESI >8
have a 25% probability of having a gully.
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These maps show 3 levels of road detail
Danny Lee observed the relationship between road density and aquatic habitat
degradation on a broad scale in the Columbia River Basin.
Many of our existing tools – E.G. R1/R4 or ECA/ERA, SEDMODL models rely on
line coverages – where are roads and how many, requiring assumptions about
delivery.
Substantial recent science says that how roads affect the environment depends
largely on where they discharge water and sediment at drain points
Until now, no tools took advantage of such data if they were available, GRAIP is
design to do that
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Other available tools such as BOISED, R1R4 and SedModL use existing road data
only, must make assumptions about sediment delivery.
Methods such as Roads Analysis assuming relationships between road location and
impacts require local validation.
GRAIP gives a snapshot of the current condition of the roads, shows where the
problems actually are, allows for prioritizatopn based on various risk factors,
monitoring over time
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BOISED is a tool commonly used in the Rocky Mtn region. Sediment production is
from base rates, slope classes, geologic types and delivery based on land types
BOISED predicts 48% of GRAIP production due primarily to slope class based
calculations
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Variation in production between HUCS due to road density
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Delivery based on land types No relationship i.e. landtypes are a poor predictor in
this environment
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GRAIP predicts 5 times more delivery. More variation between models at
watershed scale due to generalized delivery estimates in BOISED
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There is not similarity in the SDRs and they vary widely between basins
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Another common method of estimating road impacts is using density as a proxy for
road impacts. There is a great deal of scatter in the road density to sediment
delivery relationship that can be explained by finer scale inspection.
BOISED, SedModL, road density may get the trend right because more road miles,
stream crossings etc will yield more sediment but until you know something about
the connectivity and the length and slope of flow paths on roads it is hard to get the
delivery right.
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If the goal is monitoring watershed condition or prioritization of projects, it is
important to know the road hydrology
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How did the existing metrics work to predict gullies? Distance from stream did not
predict effectively as there is not a good relationship with distance.
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If contributing area were the main driver for gullies, low slope position would contain
the most gullies. Slope position discriminated better than distance from channel,
although gullies were not common in the lower slope position as was noted in
Bisson 1999
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Each slope bin contains approximately equal numbers of drain points. Local slope
has the expected relationship with gullies at the low and high end, but does not
discriminate well in the 15-55% range where we need to differentiate most. This
suggests that it is important to know how much water is contributing to these points
in addition to slope.
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ESI is the tool we have selected to asses risk of gullying. ESIt provides a
reasonable threshold value at which gullying becomes a high risk.
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GRAIP is a useful for assessing BMP effectiveness. We can screen by total
sediment delivery of each type of road drainage structure or the fractional delivery.
In this case BBDs were the superior drain type with only 2% delivering sediment to
the channel.
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The hydrologic impacts of roads are varied and diverse and analyzing them can be
challenging without the proper tools
Knowing where the water is moving on the road and assessing the delivery allow for
better predictions of road impacts.
The data collection can take time at 2-5 miles per day, but it is warranted in many
areas where good delivery estimates are required and where high value aquatic
resources and endangered species are at risk.
GRAIP provides fine sediment production and delivery predictions,
Mass wasting risk analysis
Surface erosion risk analysis
Habitat fragmentation maps
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