Erosion Processes and Prediction with WEPP Technology in Forests in the Northwestern U.S.

Abstract

In the northwestern U.S., the greatest amounts of forest erosion usually follow infrequent wildfires. Sediment from these fires is gradually routed through the stream system. The forest road network is usually the second greatest source of sediment, generating sediment annually. Erosion rates associated with timber harvest, biomass removal, and prescribed fire are generally minimal with current management practices. Landslides and debris flows can contribute significant amounts of sediment during infrequent wet years or following wildfire. A relatively new source of sediment in forested watersheds is recreation, particularly all-terrain vehicle trails. Stream channels store and route sediment; in the absence of channel disturbance, they tend to reach an equilibrium condition in which sediment entering a given reach is balanced by sediment carried downstream. At times, sediment from roads, wildfire, or landslides may accumulate in channels until higher flow rates, often associated with rainfall on melting snow, flush it downstream. Prediction tools are needed to aid forest managers in estimating the impacts of soil erosion on upland productivity and the risks of sediment delivery to downstream habitats and water users. Tools have been developed to aid in estimating long-term, low-level erosion in undisturbed forests and delivery of sediment from roads, and tools for estimating short-term, event-driven sediment from disturbed forests have also been developed. Online and GIS interfaces were developed using the Water Erosion Prediction Project (WEPP) model, including soil, vegetation, and climate databases. The online interfaces were developed to allow users to more easily predict soil erosion and sediment delivery for a wide range of climatic and forest conditions, including roads, fires, and timber harvest. There have been ongoing efforts to improve the online watershed interface to better model channel processes, road networks, and spatial variability associated with wildfire and weather.