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TerrainViewer

Data Descriptions
 
Data are analyzed at "scales" of Hydrologic Unit Code" (HUC) 4th (8 digit), 5th (10 digit) and 6th (12 digit) watersheds. The data summarized at these scales (see below) are derived from grided data (raster) and NetMap's stream segment scale data (50 - 200 m).
 
For US Forest Service Jurisdictional scales (National Forests, Districts), data are summarized at those scales; for HUC scale analysis within National Forests and Districts, only those HUCs circumscribed within jurisdictions are used.
 
Channel networks are based on TerrainWorks (NetMap) synthetic stream layers; learn more.
 
Definitions of Watershed Attributes occur in order as displayed in the TerrainViewer attribute drop down list.
 
(A) Fish Habitat Density
 
For "Fish Habitat Density" and "Habitat Quality" the spatial distribution of fish accessible streams is based on here and here.
 
(1) Anadromous Habitat Density (fish habitat, coho, steelhead, Chinook)
{[∑SL)]/L2
where SL is channel length for segments < 10% and L2 is the cumulative length of all channels.
 
(2) Resident Habitat Density (fish habitat, cutthroat, bull trout)
{[∑SL)]/L2
where SL is channel length for segments < 20% and L2 is the cumulative length of all channels.
 
This index provides information on the varying proportions of fish accessible channels at the scale of entire channel networks as summarized by HUC and USFS jurisdictional scales.
 
(B) Habitat Quality (two attributes)
The two habitat quality attributes (for anadomous fish) use the Habitat Intrinsic Potential model (Burnett et al. 2007).
 
(3) Coho Habitat Quality (Oncorhynchus kisutch)
{[∑(IP score * SL)]/∑L1}
(4) Coho Habitat Density
{∑[(IP score * SL)]/∑L2}
where SL is channel segment length associated with individual IP scores >0, L1 is the cumulative length of all channels with IP score > 0, and L2 is the cumulative length of all channels.
 
(5) Steelhead Habitat Quality (O. mykiss)
{[∑(IP score * SL)]/∑L1}
(6) Steelhead Habitat Density
{∑[(IP score * SL)]/∑L2}
where SL is channel segment length associated with individual IP scores >0, L1 is the cumulative length of all channels with IP score > 0, and L2 is the cumulative length of all channels.
 
(7) Chinook Habitat Quality (O. tshawytscha)
{[∑(IP score * SL)]/∑L1}
(8) Chinook Habitat Density
{∑[(IP score * SL)]/∑L2}
where SL is channel segment length associated with individual IP scores >0, L1 is the cumulative length of all channels with IP score > 0, and L2 is the cumulative length of all channels .
 
(9) Cutthroat trout Habitat Quality (O. clarki lewisi); see model
{[∑(IP score * SL)]/∑L1}
(10) Cutthroat trout Habitat Density
{∑[(IP score * SL)]/∑L2}
where SL is channel segment length associated with individual  IP scores, L1 is the cumulative length of all channels with IP score > 0, and L2 is the cumulative length of all channels.
 
The first attribute (species_average) is the average IP score (length weighted) for all channels with IP scores > 0. Higher values mean better fish habitat potential in the fish bearing part of the network.
 
The second attribute (species_network) is the average IP score for all channels in the network.
Thus, the first attribute  provides information on fish habitat quality confined to the fish bearing network while the second attribute provides information about the relative proportion and quality of habitat in a basin. Higher values mean better and a higher proportion of fish habitat in basins or jurisdictional areas, although the same values can be arrived at in different ways (such as a little proportion of high quality (IP) habitat or a higher proportion of lower quality habitat).
 
 
Note, the fish distribution for anadromous habitats follows the "streamnet" mapping (for confirmed fish bearing streams) and it extends the distribution beyond that to all streams and rivers that are potential habitats based on gradient and flow thresholds in the intrinsic potential model, including those systems where fish are extinct or extirpated. In this approach, all channels that might have been accessible, but are no longer, are included in the TerrainViewer. For source data, see: http://www.streamnet.org/mapping_apps.cfm
 
In Alaska, ADF&Gs Anadromous catalogue is used (for confirmed fish bearing) although the mapped distribution in the TerrainViewer extends beyond that to include all potential habitats as defined in the channel gradient and flow thresholds in available intrinsic potential models.
 
For resident fish, a channel gradient threshold cutoff of 20% is used.
 
Figure 1. Streamnet distribution in the lower 48 states for coho and Chinook, combined (left panel) and steelhead (right panel).
 
(C) Stream Attributes
(11) Floodplain Density; Learn more about how floodplains are calculated.
{[∑(Floodplain width * SLF)]/A};
where SLF is the segment length with floodplains > 0 and with drainage area > 1 km2. A is drainage basin area.
 
This index provides information on the varying proportions of floodplains (and related habitats) at the scale of basins (HUC 4, 5 and 6) and USFS jurisdictional boundaries. It provides a relative index of ecological quality (or potential) given the importance of floodplain riparian and aquatic habitats.
 
(12) Habitat Sensitivity
{[∑(4 x Lg1) + ∑(3 * Lg2) + ∑(2 * Lg3) + [∑(1 * Lg4)]/L2]}
where Lg1 is the cumulative channel length < 2% gradient, Lg2 is the cumulative channel length between 2% - 3% gradient, Lg3 is the cumulative channel length between 3% - 5% gradient, and Lg4 is the cumulative channel length > 5% gradient; L2 is the cumulative length of all channels.
 
This attribute provides a simple means to evaluate the proportion of sensitive channels (those that are highly responsive to changes in sediment supply, flows and large wood).
 
(D) Slope Stability Hazards (see cautionary note on use of slope stability predictions)
(13) Shallow Landslide Potential
 
Attribute value = the average of the Generic Erosion Potential (GEP) using gridded data. See how this attribute is calculated.
 
This attribute provides information on the varying degree of predicted erosion potential and the relative proportions of those areas at varying spatial scales. Basins or jurisdictional areas with steeper slopes and more highly dissected areas (hollows, swales) will have higher values. Areas of higher values will be more erosion prone, although see Figure 2 below about how combinations of climate and vegetation across the western US will lead to different forms of erosion.
 
Figure 2. The shallow landslide attribute (GEP) in the TerrainViewer can reflect different forms of erosion according to variations in climate and vegetation across the western US.
 
(14) Gully Erosion Potential
Although similar to GEP, the gully erosion potential is more focused on erosion by flowing water. See how gully erosion is predicted.
 
Attribute value = the average of the gully erosion potential using gridded data (10 m).
 
(15) Debris Flow Potential (in streams, particularly in headwater channels). How is debris flow potential calculated?
{∑[(Debris flow potential * SLdf)]/A}
where SLdf are channels with Debris flow potential > 0. L1 and A is drainage basin area.
 
This index provides a measure of debris flow or mudflow potential in headwater streams and thus the degree of risk posed to sensitive aquatic habitats and engineered structures (homes and highways). In some landscapes (humid, coastal) debris flows are most likely triggered by shallow landslides near the heads of first-order channels. In other landscapes (semi arid), debris flows or mudflows are most likely triggered following fires and during periods of extreme snowmelt. Higher values are equal to higher risk.
 
(E) Wildfire Potential Hazards (see origin of data)
 
Figure 3. Wildfires can lead to a cascading sequence of impacts in watershed such as accelerated runoff and erosion, channel scour and sedimentation and resulting impacts to aquatic habitats and water quality.
 
 
(16) Fire Probability (gridded 30 m data).  The raw data are in a form of a series of fire probabilities associated with a series of six fire severity classes. The probability reported in the TerrainViewer is the maximum fire probability (e.g., the most probable).
 
Attribute = the average of predicted wildfire probability using gridded data (30 m).
Larger values equal more frequent fires (1/p = recurrence interval).
 
Higher fire probability values mean higher potential for stand replacing fires and associated impacts to soils and an increasing likelihood of the cascading sequence of fire's negative effects (Figure 3). But also note, wildfires can have positive ecological effects when considered over longer time scales (Benda et al. 2003).
 
(17) Fire Severity (gridded 30 m data). Tthe raw data are in a form of a series of fire severities associated with a series of six fire probabilities. The fire severity reported in the TerrainViewer is the one associated with the highest probability fire (e.g., the most probable, attribute # 16).
 
Attribute = the average of predicted wildfire severity (in flame length, feet) using gridded data (30 m).
Larger values equal more severe fires
 
Higher fire severity values mean higher potential for stand replacing fires and associated impacts to soils and an increasing likelihood of the cascading sequence of fire's negative effects (Figure 2). But also note, wildfires can have positive ecological effects when considered over longer time scales (Benda et al. 2003).
 
(18) Post Fire Erosion (surface); in units of tons/year (gridded 30 m data).
Predictions are from the WEPP-disturbed model  based on results from Miller et al. (2011).
Miller, M.E., L.H. MacDonald, P.R. Robichaud and W.J. Elliot. 2011. Predicting post fire hillslope erosion in forest lands of the western United States. International Journal of Wildland Fire 20, 982-999.
 
Note the predicted post fire erosion is multipled by predicted fire probability (attribute #16) to produce an index of erosion that is scaled by the frequency of fire occurrence. Hence, the post fire erosion index provides information on the relative magnitude of erosion linked to fires and its variation across landscapes.
 
 
(F) Climate Change Vulnerability (adapted from UW Climate Impacts Group)
Climate Change attributes include temperature, precipitation, snowmelt, snow-water-equivalent, and summer and winter runoff (stream flows). Predictions are reported in percent (%) change from historical (1916-2006) to forecasts in 2040 (positive and negative values); however, the temperature predictions are in absolute change in degrees C.
 
The TerrainViewer can be used to quickly search for landscape vulnerability to climate change by examining intersections among climate change, wildfire risk, and in-stream habitat and channel sensitivity indicators (using any percentile of the distribution). See TerrainViewer's online Technical Help.
 
The climate change scenarios represent a composite average of ten global climate models (GCM) for the western US using four bracketing scenarios based on four GCMs (ECHAM5, MIROC_3.2, HADGEM1, and PCM1). Predictions are for one greenhouse gas scenario (A1B, a middle of the road scenario for future emissions). Results are in percent change from historical (1916-2006) to forecasts in 2040. Forecasts were obtained from University of Washington Climate Impacts Group. For additional background information on how forecasts were made, see here and here.
 
(19) Temperature -absolute change in oC (June, July, August average).  In degrees Centigrade.
The mean of gridded data (grids are approximately 7 km).
 
(20) Precipitation (all, rain and snow), percent change from historical (1916-2006).
The mean of gridded data (grids are approximately 7 km).
 
(21) Snow accumulation days, percent change from historical (1916-2006).
The mean of gridded data (grids are approximately 7 km).
 
(22) Summer Runoff, percent change from historical (1916-2006). Based on the VIC model.
The mean of gridded data (grids are approximately 7 km).
 
(23) Winter Runoff, percent change from historical (1916-2006). Based on the VIC model.
The mean of gridded data (grids are approximately 7 km).
 
(24) Snow-water equivalent, percent change from historical (1916-2006). What is this?
The mean of gridded data (grids are approximately 7 km).
 
(25) Snow-water equivalent ratio, percent change from historical (1916-2006). What is this?
The mean of gridded data (grids are approximately 7 km).
 
The climate change attributes are useful for examing large scale patterns of predicted change but also to consider how climate change intersects (and combines) with other stressors (such as fires and erosion) and also how they overlap with sensitive aquatic species. See TerrainViewer Technical Help.
 
 
 
 
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