Study Watershed:

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Wilson: The Wilson River originates in the mountains of the Northern Oregon Coast Range and drains to Tillamook Bay. This coastal river is known for its runs of steelhead and Chinook salmon. The watershed is characterized by steep mountainous terrain, narrow valleys, dense Pacific Coast rainforest, and areas allocated to timber-production. Much of the watershed was burned in the 1933-1951 Tillamook Burn and was reforested primarily with Douglas fir.

MFJD: The headwaters of the Middle Fork John Day River are in the Blue Mountains of northeastern Oregon. The upper MFJD provides important habitat for Chinook salmon, steelhead, and bull trout. Dry ponderosa pine and mixed conifer forests dominate south-facing slopes while moist coniferous forests predominate on north-facing slopes. This watershed has a long history of logging, mining, and livestock grazing.

Methods Overview:

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Stream network classification and delineation:
The stream network and valley floors were delineated with Netstream and ArcGIS tools. Stream reaches were classified into potential channel morphologies based on channel gradient and reach drainage area derived from Netstream, following the Montgomery and Buffington (1997) classification. From this initial classification, a stratified random sample of ~30 stream reaches per study watershed was selected for field validation of reach morphologies and valley floor delineation. These data were used to refine channel morphology and valley floor thresholds which were then applied in a final stream network classification (Fig. 1) and riparian zones delineation (Fig. 2). The final classification correctly classified 68% of reach morphologies in the upper Middle Fork John Day River watershed and 86% of reaches in the Wilson River.

Initial conditions for riparian vegetation:
Spatial modeling techniques and high density lidar point data (all returns above bare earth) were used to classify and map current riparian vegetation composition and structure within each riparian polygon in the stream network. Vegetation data collected in the field were used to assess the accuracy of the riparian vegetation attributes mapped from lidar (Fig. 3). Prediction accuracy was better than 66% for all attributes except riparian shrubs. The lidar data proved poor at distinguishing between young trees and tall shrubs and short statured shrubs (height < 0.5 m) could not reliably be distinguished from the bare earth DEM. Overall, mapped vegetation provided a reasonable fit to current conditions and was used to set initial conditions for model simulations.

Figure 3. Lidar-derived canopy height map for a stream reach on the MFJD. Initial Netstream-derived riparian polygons are shown in bold straight lines, field delineations of the valley floor and vegetation patches are shown with curved lines.

Aquatic-riparian state and transition models:
We intersected the classified stream network map with a potential vegetation map of each watershed to identify all possible combinations of channel and vegetation types (strata). We built separate state and transition models for each strata using the Vegetation Development Dynamics Tool (VDDT). These aquatic-riparian VDDT models were based on prototype aquatic models developed for the upper Grande Ronde River (Fig. 4), OR and upland forest VDDT models developed for eastern and northwest Oregon. We modified these models to include riparian shrub and hardwood state classes, effects of herbivory (grazing and browsing), hydrogeomporhpic processes, and riparian restoration practices. We also expanded the pathways and transition probabilities to characterize the historic and current land-use activities present in each watershed. The completed models simulate all possible states of a stream reach, from highly disturbed states resulting from disturbances such as logging, grazing, landslides or wildfire to relatively pristine states where major disturbances have not occurred.

Figure 4. A highly simplified wet-meadow aquatic-riparian VDDT model illustrating some of the dominant transition pathways. Intermediate state classes and most transition pathways are not shown in order to keep the figure readable.

Channel conditions and aquatic habitat quality:
We qualitatively ranked channel morphologic conditions on a 4-factor scale for each state class in the models. Morphologic condition variables included: shade, erosion, undercut banks, large wood, pools, large pools, pool structure, off-channel habitat, width-depth ratio, and riparian shrub abundance. These variables were then used in an expert systems model to rank the habitat quality for key life-history stages of spring Chinook salmon and steelhead in the upper Middle Fork John Day River and coho salmon and steelhead in the Wilson River.

Model Application:

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The models are being applied to two intensively monitored watersheds – the Wilson River in the Oregon Coast Range and the upper Middle Fork John Day River in eastern Oregon’s Blue Mountains to examine: 1) current conditions relative to the historic range of variability, 2) likely trajectories of aquatic and riparian habitats given current and expected land-use practices, 3) the potential of passive restoration to meet recovery goals, and 4) the potential of active restoration to accelerate recovery.

The models can be run without anthropogenic disturbances to recreate historical conditions (pre-Euro-American settlement) as shown in the examples below (Fig. 5-8). Alternatively, the models can be initialized to current riparian and channel conditions in the watersheds and then run forward to project likely outcomes of alternative management scenarios. Realistic management scenarios were developed based on current policy and land management issues faced by managers within each watershed. The effects of policy decisions can be reflected in the models by either changing the rates of selected disturbances (e.g., decreasing grazing intensity), or by either adding or removing selected disturbance pathways from the models (e.g., removing all forest harvest treatments from riparian areas or adding fuels treatments). Consequently, decision makers could use these models to evaluate changes in habitat conditions under alternative management scenarios at sub-basin to regional scales.

Discussion:

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The simulated changes in both riparian vegetation and channel morphology result in large simulated changes in habitat quality within the upper MFJD stream network. It is important to note that, while some improvement in habitat quality is apparent relatively quickly, even after 50 years with complete protection from most anthropogenic disturbances, the condition of habitat within the watershed is far from fully recovered.

The model results suggest that both riparian vegetation and channel morphology can be highly dynamic for many attributes. A notable exception is the time needed to grow large trees as well as the recruitment of large wood from stream-adjacent riparian forests. In general, the rapid dynamics simulated by the models agree with observations made within the watersheds. For example, growth of riparian hardwoods has been very rapid in the Camp Creek (upper MFJD) livestock grazing and deer and elk browsing exclosure (Fig. 9). Similarly, a study of changes in channel conditions in grazing exclosures located throughout the western United States documented relatively rapid changes in channel conditions through time (Fig. 10).

Data and Downloads:

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Key References:

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