Predicted riparian vegetation - Potential for Habitat Improvement in the Columbia River Basin

Basin-wide analysis of potential to improve tributary habitats in the Columbia River basin through restoration of habitat-forming processes.

Identification of geomorphological target conditions for river restoration is typically based on locally measured reference conditions, yet few reference sites remain in much of the 630,000 km2 Columbia River Basin, USA. Therefore, we predicted reference conditions throughout the basin based on key reach-scale variables, which we empirically derived from a limited number of reference sites. We developed a GIS data set that depicts pre-settlement riparian vegetation in the Columbia River Basin to guide stream restoration for endangered salmon. However, the modeled riparian species composition was quite inaccurate, so we are not distributing these model results.

Methods: We first created a data layer of historic riparian vegetation information from survey notes that were taken mid-19th to early 20th century during the Public Land Survey System (PLSS) conducted by General Land Office (GLO). Our reconstructed riparian vegetation data include randomly sampled basin-wide data (drainage area 200,000 km2), as well as intensively reconstructed watershed-level data (3,000 km2). Second, based on the reconstructed riparian vegetation points, which are arrayed along a 1-mile (1600 m) grid, we are developing statistical models to estimate potential historic riparian vegetation types (conifer, hardwood, willow-shrub, grass, sage) as well as the probability of occurrence of individual species at stream reach level (~ 200 m) in the basin. We examined environmental variables, such as mean annual precipitation, average minimum and maximum temperature, channel gradient, channel bankful width, floodplain width, and fine sediment supply potential, against five vegetation types and found that precipitation and temperature discriminate vegetation groups. We also developed vegetation response curves against each variable, using kernel density estimates to describe the probability of each vegetation type occurring across the range of each environmental variable.
GIS hydrography layer with riparian attributes.