The Interaction Between Trees and the Landscape Through Debris Flows

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1 The Interaction Between Trees and the Landscape Through Debris Flows Stephen T. Lancaster 1, Shannon K. Hayes 1, and Gordon E. Grant 2 1. Dept. of Geosciences, Oregon State University (slancast@fsl.orst.edu) 2. Forestry Sciences Laboratory, Pacific Northwest Research Station, U.S. Forest Service Abstract: We have developed a drainage basin-scale landslide initiation and debris flow runout model. We are now attempting to verify the model through a field study and, possibly, some upcoming physical experiments. A salient model feature is the inclusion of vegetation in both the landslide failure criterion and the debris flow momentum equation. It is widely recognized that tree roots contribute to the cohesive strength resisting landslide failure. We hypothesize that woody debris also adds resistance to debris flow motion and, therefore, affects the network-scale pattern of debris flow deposits observable in the field. Specifically, woodier debris flow deposits should be found further upstream in the network. No single variable describes deposit location because debris flow motion, even in our simplified model, is complex. Through our current field study we seek to constrain the model by comparing its results--deposit locations, depths, and woodiness --to field observations for a single, small basin in the Oregon Coast Range. Field observations characterizing each reach--bedrock or deposit, standing tree size and density, fallen wood loading--and each debris flow deposit--location, total and wood constituent volumes--will help constrain model parameters such as that describing wood-related resistance to motion. Physical flume experiments may also help constrain such parameters. True verification of such a complex model may be elusive, but field studies may still help answer the question: What must the model include to reasonably describe the real system and provide a useful tool for assessing the effects of land management practices?

2 Introduction: Retention of live trees along streams and on unstable slopes has emerged in the past 10 years as a primary strategy to both protect and restore forest watersheds and endangered aquatic ecosystems throughout the West. The rationale for extensive riparian reserves or no-cut areas is that leaving trees along streams maintains a wide range of ecological functions, primarily shade and woody debris input. In deeply dissected landscapes, such as the Oregon Coast range, most of the riparian reserves are located along small, steep, headwater streams, many of which are intermittent and ephemeral, and do not directly support fish runs. Here, the rationale for riparian reserves is that such streams are source areas for wood and sediment important for structuring aquatic habitat in lower parts of the stream network; this material is episodically delivered downstream primarily through mass movements and debris flows. We know very little, however, about the interactions among mass erosion processes, presence or absence of vegetation, and the response of the channel network. In particular, we cannot predict how both old and new forest practices may influence riparian and channel conditions and the overall geomorphic regime of forested watersheds over decadal to century time scales. As conservation strategies for Northwest forests are now being used as models nationwide and even internationally, it is critical that we test some of the fundamental assumptions embedded in these plans. We are specifically interested in understanding how upland and riparian zone management practices interact with the channel network. In many mountainous regions, such as the Oregon Coast Range, the dominant hillslope erosion process is landsliding. This process is also the main way that upland forest landscapes interact with channel networks, as hillslope landslides transform into debris flows that move through and deposit in channels creating complex structures. In forested environments, vegetation plays a key role in landslide initiation, debris flow runout, debris flow deposit form and longevity, and long term channel conditions. Both wildfire and forest harvest directly affect vegetation on hillslopes and riparian zones. Understanding how these natural and anthropogenic disturbances and the resulting vegetation patterns affect landsliding, debris flows, and depositional patterns in channel networks is the focus of our linked modeling and field studies. We have developed a model to simulate landslide and debris flow dynamics in a wood-rich environment on the channel network scale (Lancaster and Grant, 1999). It is a physics- and processbased evolution model designed especially for studying the forest s influence on landslides and debris flows and the interaction between all of the above and the channel network. The aim of our field work is to collect data against which to test various aspects of the model, from specific modeling of debris flow runout to general representation of depositional patterns in the channel network.

3 Modeling the forest s influence on landslides and debris flows: Root strength enhances slope stability: Critical precipitation for shallow slope failure (e.g., Dietrich, et al., 1995): K sat hbcosθsinθρ s P cr A eff ρ w tanφ θ C tan r + C = s i hρ s gcos 2 θ where K sat is saturated soil hydraulic conductivity; h is vertical soil thickness (from soil production and diffusion, as in Heimsath et al., 1997; in simulations, h set to 5 cm, then 6000 yrs. of evolution); b is flow width; θ is slope angle; ρ s is soil material density; ρ w is water density; A eff is area contributing to flow and is dependent on storm duration; φ i is internal friction angle; C r is cohesive root strength (from Sidle, 1992); C s is soil cohesion; and g is gravitational acceleration. Wood entrainment expends debris flow momentum: Debris flow must entrain fallen wood on the surface and standing trees in its path to continue. For debris flow scour, use excess shear stress law for soil and deposits (sediment and wood) and neglect bedrock erosion: h t e = K e ( ρ m C f v 2 ) where K e is erodibility, 0.1 m/s-pa; C f is a friction factor, 0.02; τ cr is critical shear stress, 2000 Pa; and the rate of scour is constrained to be positive (increasing debris flow depth) or zero. Values are rough estimates. Wood component of debris flow increases resistance and standing trees resist uprooting: For debris flow runout, use simplified downstream momentum conservation equation (R. Iverson, pers. comm., 1999) and add terms for woody debris resistance and uprooting force: v h t h + v = sgn v t p b ( ) hgcosθ hv 2 θ + C r s tanφ b + h d R d v dt + hgsinθ L ρ m where h is slope-normal debris flow depth; v is slope-parallel debris flow velocity; θ is slope angle; g is gravitational acceleration; p b is pore pressure at the bed (assumed hydrostatic); ρ m is debris flow mixture density; s is the slope-parallel direction; φ b is bed friction angle; h d is slope-normal depth of debris flow wood constituent; R d is wood-related deceleration, guessed to be m/s 2 ; C r is the root strength; and L is the debris flow length. Debris flow velocity must conform to changes in flow direction, and depth must conform to changes in flow width. Debris flow length is constant. τ cr ρ m

4 Effect of stand age on landslide initiation: Landscape nodes colors represent steady state rainfall intensity required to initiate landslides in the trial watershed. Channel network (blue lines) is defined by a 1 ha. contributing area threshold and corresponds well with the observed network. Channel-adjacent and low-slope nodes are defined as valley nodes (green lines). Landslide susceptibility is not significantly different for stands older than 40 yrs. x year-old stand meters north meters east x year-old stand

m 50-100 m 100-200 m 200-400 m 400-800 m

5 Field site for model trial: Hoffman Creek, Oregon Coast Range Elevation 0-50 m m m m m m Siuslaw River 0 40 Kilometers 500m

Methods Estimates of wood loading facilitate case and full-basin

Standing tree density and size range are measured for

Number density and size range are measured.

failure site dimensions; length, widths, slopes, and surface

6 Methods Estimates of wood loading facilitate case and full-basin modeling: Wood depth is measured for some cases representative of several loading classes. Class is estimated visually at other locations. Standing tree density and size range are measured for representative areas. Riparian alder stand. Number density and size range are measured. Measuring log diameters at channel cross-section to find equivalent wood depth. Field data for individual debris flows facilitates case modeling: failure site dimensions; length, widths, slopes, and surface characteristics of runout path; deposit dimensions, including wood constituent. Middle-aged landslide scar. Young debris flow runout track. Left: same young debris flow s deposit including large woody snout. Right: front of same deposit.

7 Network scale data will provide an initial condition and base for comparison to full-basin simulations: The entire channel network is classified as bedrock, mixed/transitional, or depositional, and deposit depths are found where possible. Much of the network is covered by deposits formed by coalescing debris flow and alluvial fan and fill complexes. Topography, stratigraphy, ages, and extent of these coalescing fan/ fill complexes are mapped. Results Wood loading: Bimodality of wood depth distribution reflects lack of wood in recent debris flow tracks vs. wood accumulation and/or deposition in other areas. Spread in accumulated volumes reflects not only different debris flow histories but also past harvest practices that left large quantities of wood in hollows and small channels. Wood depth m m m m m >0.39 m wood step/dam 1200 channel length (m) wood depth (m) > meters Wood loading distribution where known and class ranges.

8 Debris flow runout: Debris flows in this basin have stopped at high valley slopes, median ~18 o. Debris flows with higher wood content tend to stop at steeper slopes. Debris flows tend to stop on older deposits because these deposits fill in valley bottoms such that subsequent flows encounter wider, more gradually sloped surfaces. Runout lengths are somewhat bimodal because debris flows are more easily stopped before they have traveled far. Network structure dictates that most flows will encounter an obstacle such as a high-angle junction before they go far, but it also provides a few flows with longer paths before encountering such obstacles. Such flows have greater momentum when they encounter these obstacles and are, thus, more likely to traverse them. P[slope < X] Runout paths of mapped debris flows classified by age as inferred from field investigation and aerial photograph (1972 and 1996) inspection young (ca. 1996) middle-aged (ca. 1972) pre Benda & Cundy (1990), Knowles Creek 0.1 Lancaster et al., Hoffman Creek ODF, Mapleton site slope at debris flow terminus, degrees P[length < X] Benda & Cundy (1990), Knowles Creek 0.1 Lancaster et al., Hoffman Creek ODF, Mapleton site debris flow runout length, meters Cumulative distributions of valley slopes at debris flow deposits in straight reaches (top) and runout lengths of all deposits (bottom), Hoffman Creek data compared to Benda and Cundy [1990] Knowles Creek data and Oregon Department of Forestry Mapleton data. The latter two are both near Hoffman Creek in similar geologic and climatic settings. 250 meters

9 Depositional patterns: Most bedrock or transitional reaches are recent debris flow runout paths. Deposits fill the larger valleys in this basin and form coalescing fan/fill complexes. Woody debris dams form steps between lower gradient depositional areas. bedrock sediment transitional wood wood step/dam Depositional pattern in channel network. 250 meters

10 Debris flow runout model illustration, calibration, and verification with small simulations: In preliminary runout simulations with boundary conditions mimicking the debris flow flume in Oregon, greater deposit wood content results in shorter runout such that enough wood stops the flow before it reaches the bottom of the ramp. Future simple runout simulations will mimic wood and erodible bed experiments and specific field cases in order to assess and calibrate wood-related terms in the momentum equation. Wood and erodible bed experiments at the USGS debris flow flume, Sept., 1999: Wood on the bed is picked up by the debris flow and pushed along in front as a woody snout. Upon deposition, the wood is pushed to the periphery of the deposit. Wood volume is small compared to the total volume. In the experiment shown above, the initial failure volume is 10 m 3 ; sediment/water mixture on the bed is 0.1 m thick; total wood volume is 0.2 m 3, or an equivalent depth of m on the bed Sensitivity of debris flow runout to wood deposit depth initial deposit composition: sediment depth = 0.07 m water depth = 0.03 m wood depth = 0 m initial flow composition: sediment depth = 0.7 m water depth = 0.3 m wood depth = 0 m elevation, meters initial deposit composition: sediment depth = 0.07 m water depth = 0.03 m wood depth = 0.1 m initial deposit composition: sediment depth = 0.07 m water depth = 0.03 m wood depth = 0.2 m sediment initial flow composition: sediment depth = 0.7 m water depth = 0.3 m wood depth = 0 m wood initial flow composition: sediment depth = 0.7 m water depth = 0.3 m wood depth = 0 m initial deposit composition: sediment depth = 0.07 m water depth = 0.03 m wood depth = 0.3 m distance, meters initial flow composition: sediment depth = 0.7 m water depth = 0.3 m wood depth = 0 m

11 Analysis and modeling of basin-scale process linkages with fullbasin modeling: Preliminary simulations with varying forest age produce varying depositional patterns, from more downstream, sediment-rich deposits in young forests to more upstream, woodrich deposits in older forests. Preliminary simulation of the study area with a 200 year-old forest (high wood loading) produces deposits in the study area, which is all quite steep, including wood-rich deposits in the steepest valleys and near tributary junctions. deposit composition 100% sediment 100% wood Full-basin simulation of Hoffman Creek tributary with a 200 year-old initial stand and run for 10 yrs. with stochastic storm input. Line segments in valley network represent deposits, where line thickness is proportional to deposit thickness and color represents composition.

12 Discussion: Wood appears to play a major role in the formation and longevity of deposits. Debris flows with greater wood volumes tend to stop higher in the network, and larger woody debris dams at the front of these deposits impound sediment for longer times. Forest harvest practices can have a significant and long-lasting geomorphic effect because they determine the age of trees on the hillslopes and affect the quantity of wood in the channels. Forestry practices in the 1960 s cleared the whole forest but left many large logs in hollows and headwater streams. These practices, therefore, increased landslide susceptibilities and, thus, frequency but shortened debris flow runout lengths and, thus, created more deposits further upstream. These deposits are, then, barriers to subsequent debris flows. We will use data from this field site to test and adjust the model. We can, then, use the model to investigate the implications of observed and alternative scenarios. For example, if debris flow return period decreases while debris dam longevity increases (e.g., due to decreased decay rate of wood buried by subsequent deposits), is there a threshold beyond which the geomorphic effect increases dramatically in both duration and magnitude? Are extensive headwater riparian reserves in clearcuts good or bad for downstream fish habitat? References: Benda, L.E., and T.W. Cundy, Predicting deposition of debris flows in mountain channels, Can. Geotech. J., 27, Benda, L., and T. Dunne, Stochastic forcing of sediment supply to channel networks from landsliding and debris flow, Water Resources Research, 33(12), Dietrich, W.E., R. Reiss, M.-L. Hsu, and D.R. Montgomery, A process-based model for colluvial soil depth and shallow landsliding using digital elevation data, Hydrological Processes, 9, Duan, J., A coupled hydrologic-geomorphic model for evaluating effects of vegetation change on watersheds, Ph.D. thesis, Dept. of Forest Engineering, Oregon State University. Eagleson, P.S., Climate, soil, and vegetation 2. The distribution of annual precipitation derived from observed storm sequences, Water Resources Research, 14(5), Heimsath, A.M., W.E. Dietrich, K. Nishiizumi, and R.C. Finkel, The soil production function and landscape equilibrium, Nature, 388, Iverson, R.M., The physics of debris flows, Reviews of Geophysics, 35(3), Robison, E.G., K.A. Mills, J. Paul, L. Dent, and A. Skaugset, Oregon Department of Forestry storm impacts and landslides of 1996: Final report, Forest Practices Technical Report Number 4, Oregon Department of Forestry Forest Practices Monitoring Program, 145 pp. Sidle, R.C., A theoretical model of the effects of timber harvesting on slope stability, Water Resources Research, 28(7),

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