The Team

Science Team:

Jack Stanford (PI, UMT); John Kimball (UMT); Ric Hauer (UMT); Mark Lorang (UMT); Bonnie Ellis (UMT); Niels Maumenee (UMT); Tom Bansak (UMT); Samantha Chilcote (UMT); John Lucotch (UMT); Diane Whited (UMT); Andrew Neuschwander (UMT); Don Schenck (UMT); David Kuhn (GTG); Eric Sack (GTG)

 

Partners:

Kyle McDonald (JPL - California Institute of Technology); Dan Goodman (Environmental Statistics Lab, MSU)

 

Project Summary

Most wild Pacific salmon begin their lives in freshwater rivers – in the same rivers to which they eventually return to spawn and die.  The quantity, quality and complexity of freshwater habitat directly influence the production potential and survival rate of wild salmon (Stanford et al. 2005). The Riverscape Analysis Project (RAP) based at The University of Montana’s Flathead Lake Biological Station (FLBS) determines and ranks freshwater habitat abundance and production potential of Pacific salmon rivers. The project offers an overview of North Pacific salmon rivers by utilizing globally available satellite remote sensing imagery and other geospatial information to assess the physical structure of over 1500 rivers and watersheds around the Pacific Rim.

More than 30 physical characteristics, such as river gradient, floodplain area, and channel complexity, are used to rank rivers for their potential to produce wild salmon. Human influences, including dams, roads, and land use, can also be incorporated into the ranking, so it is possible to represent not only habitat characteristics but also habitat stresses. Physical and human impact data and rankings for all major rivers around the Northern Pacific Rim are publicly available through a web-based decision support system accessible at http://rap.ntsg.umt.edu.

 

Riverscape Analysis Project Details

How RAP Works

By leveraging certain characteristics of two orthorectified datasets, Digital Elevation Model (DEM) and Landsat, a new and innovative method for delineating water feature information is possible. The technique builds off of the existing DEM based flow models for developing potential stream networks and a new method for delineating and classifying water body features visible in digital multi-spectral satellite remote sensing data.

Figure 1. Example of a Digital Elevation Model in central British Columbia and the compliment infrared Landsat image.

 

Floodplain Delineation

An automated flood plain delineation algorithm has been developed using existing DEM information and derived stream networks generated from satellite remote sensing imagery. In mountainous regions (e.g Kitlope,Skeena basins), the floodplain algorithm works very well (Fig. 2a).  However in relatively flat tundra regions (e.g. Kuskokwim , Kol basins), the lack of significant elevation change across the landscape overestimates the extent of the flood plains.  To improve the floodplain delineation in these regions, a riparian vegetation classification is used to refine floodplain extent. (Fig. 2b). 

 

 

Basin Habitat Complexity Metrics

By combining information from DEM and remote sensing based water classification results, an accurate and reproducible method for characterizing basins across the north Pacific is now possible. We have implemented a preliminary set of metrics for several SaRON basins (Figure 3) in order to evaluate the biophysical significance of each variable and refine the top-down metrics relative to detailed biological surveys of salmonid habitat and population dynamics along regional productivity gradients. Our initial selection of variables for defining habitat complexity is based on a well developed body of literature (e.g. Leopold et al. 1964, Brown 2002, Whited et al. 2006), and the constraints of the regional datasets.   The results in Figure 3 identify marked differences in geomorphological patterns and associated habitat characteristics among the various basins that coincide with the regional biological productivity gradient defined from intensive field surveys. As expected, areas with a large amount of accessible floodplain area relative to basin size show the greatest productivity. For a given floodplain geomorphic domain, a moderate level of channel separations and returns (e.g. Taku ) is indicative of a dynamic shifting habitat mosaic, relatively complex riparian vegetation and parafluvial and orthofluvial habitats, and greater biological productivity than similar sized floodplains with less complex structure. Greater levels of complexity (e.g. Skeena ) are indicative of more frequent scouring and a greater rate of change in habitat dynamics, less developed riparian vegetation communities, reduced habitat gradients and corresponding lower productivity levels. The relative size and distribution of on-channel lakes is also a particularly relevant metric for some salmonid species (e.g. Sockeye), and less important for others. Continued development of these metrics and associated complexity analysis will include an assessment of the biological significance of habitat location and distribution within each basin, and an analysis of estuary structure.

Kuskokwim Skeena Taku Samarga Kitlope Basin Area km2 109,571 53,742 17,620 7,617 3,186 Number of floodplains 502 222 121 94 43 Floodplain Area km2 7,515 892 415 378 94 Ratio of Floodplain/Basin 6.85% 1.66% 2.36% 4.96% 2.95% Main Channel Length km 1050 648 318 223 102 Gradient of the Watershed m/km 0.49 0.43 0.40 0.43 5.89 Active Glaciation Yes Yes Yes No Yes Number of on Channel Lakes 409 52 30 0 2 Sample Floodplain Complexity (separations and returns/km) 0.54 8.11 4.76 0.96 2.64 Productivity Based on SaRON High Moderate High High Low

Figure 3. Example of metrics derived for select basins in across the north Pacific

 

Outcomes

1) Rankings of Production Potential

The RAP river basin rankings were compared with salmon data from a variety of sources, including FLBS’s Salmonid Rivers Observatory Network, literature reviews, and agency databases, to relate the rankings with known wild salmon populations.  Rivers with similar habitat characteristics are assumed to have similar production potentials for wild salmon, so production potential can be extrapolated to unstudied rivers based on RAP river habitat information.
The result of this work is a framework relating different river habitats, quality, and abundance to potential salmon production. This habitat-based approach can be used to help determine how many juvenile salmon a certain river could produce in the absence of fish removal (harvest) or supplementation (hatcheries) practices. This provides a reference condition for a river’s natural potential for salmon production and sustainability and can help guide management objectives.

2) Range Change Predictions

The spatially explicit RAP database allows each river to be tied to predictions of changes to flow and temperature under different climate scenarios. Modelled changes in climate conditions are also tied to known biotic thresholds for different life history stages of each salmon species. This allows changes in the range of salmon under future climate conditions to be modelled. These changes in the freshwater distribution of salmon species are then further refined by combining the freshwater models to climate change models of marine conditions in order to best represent expected future Pacific salmon distributions.

3) Rank by Climate Impacts

Rivers will be ranked based on their vulnerability to changes in climate conditions. It is ecpected that those rivers with more complex habitat and salmon populations will be more resilient to changing future environmental conditions. This is because that are likely to have more coldwater refugia, containa  greater proportion of unconstrained floodplain areas to absorb changes in flow, and have more diverse stock complexes. This info0rmation will allow researchers to produce an index of vulnerability of rivers to climate change by weighting the extent of climate change in a particular basin by its habitat complexity.

 

Current RAP-Typology White Papers: