CUMULATIVE IMPACT ESTIMATION FOR LANDSCAPE SCALE FOREST PLANNING

Cumulative Impact Estimation for Landscape Scale Forest Planning

GIS/EM4 No. 59

Finn Krogstad
Peter Schiess

Abstract

The combination of grid data representation and grid based hydrologic functions allows a simple yet comprehensive language for discussing environmental impacts of forest management. Models of hydrologic process tend to focus on local topographic or downstream gradient and on flow volumes accumulated from upslope. The existing grid functions allow integration over upslope area and along the path downslope, provide the basic tools for relating point hydrologic processes (snowmelt, erosion, landsliding) to downstream consequences (channel incision, pool filling, sediment coarsening). This simplicity by which ideas can be converted into functions, and functions into predictions makes grid-based hydrology a plausible approach for introducing basic hydrologic concepts and analysis.

Keywords

Landscape Management, Cumulative Impacts, Harvest Planning

Introduction

Forest management in the Pacific Northwest can impact stream habitat in a number of ways (WFPA, 1997), as shown in Figure 1. Landsliding can deliver coarse sediments, which fill stream pools necessary for summer rearing habitat. Surface erosion can deliver fine sediments that fill gravels and suffocate incubating fish eggs. Changing runoff patterns can impact water delivery, which can flood and scour downstream reaches. Direct streamside management can impact stream shading and temperature. Streamside management can also remove trees so there are no large logs that can form pools and other structures necessary for stream habitat.

Hydrologic Modeling

The hydrologic nature of the production and delivery of these inputs to vulnerable stream reaches makes them particularly amenable to modeling in grid-based GIS. A simple grid-based approach to hydrologic modeling is founded on a grid of the direction of steepest flow from each cell to its eight nearest neighbors. With this flow direction grid, it is thus possible to move cell inputs (such as rainfall) downslope and thus identify all material that flows past each downstream cell. Reversing this process allows identification of all cells that deliver to (are in the watershed of) specific cells. The distance down the flow path from each cell to the stream can be accumulated, along with derived values such as time to transit each cell in the path. These values such as local slope, upslope contributing area, and distance to stream form the core of the basic equations of hydrologic process.

Figure 1. Impacts of forest management can each be modeled as the production, delivery, and accumulation of an environmental input at vulnerable downstream reaches.

Spatial Data

In focusing on growing and extracting timber, forest managers collect large amounts of soils, stands, topography, road alignment, data that can be useful in environmental modeling of management impacts. As forest managers shift to GIS, this data is rapidly being turned into readily accessible coverages. Stand inventories generally contain information about tree height (length of shadow cast over stream), canopy density (ability to insulate underlying streams and snowpack), tree diameter (stability when they fall into the stream), and stand age (root strength, etc.). Soil inventories provide information about soil strength (landsliding), composition (erosion), and depth (both). Turning these coverages into grids allow cell-by-cell access to these site properties, for calculation of the processes that produce water, sediment, and other environmental inputs.

Environmental Inputs

Much research has gone into calculation of production of environmental inputs (WFPA, 1997). These equations have generally been either detailed models of processes at a specific point (will this hillslope slide), or general models for broad areas (how much sediment will come out of a square kilometer of forest). With a grid-based approach it is possible to apply local equations to every grid cell on the landscape, acknowledging the site-specific properties of each. For example, combining soil depth, strength, and porosity from the soils layer, with stand weight and age (for root reinforcement) from the stand layer, with topographic slope and upslope area (for soil saturation) from topography, it is thus possible to estimate hillslope stability using several different methods (Hammond et al, 1992; Montgomery & Diethrich, 1994; Wu & Sidle, 1995). The key to coding these equations is the stacking of several grids, trough which the physical property of interest for each grid cell is stacked with all the other properties for that cell, so one equation can be written and applied to all cells.

Delivery to the Streams

Inputs such as sediment and runoff can't impact the stream if they don't get to the stream. The intervening ground on the path between the hillslope and the stream thus controls whether and how much of an impact upslope processes can have on a stream. In some cases (such as large flat areas below a landslide) the downslope cells can stop the delivery of the material to the stream. Such cells can be identified and the watershed above each can be delineated as hillslope from which output has no impact.

A more common situation is downslope diffusion of water (and what it carries) on its way to the stream. For example a ditch may collect water from upslope and route it through a culvert to the slope below, but the further it has to flow across the forest floor, the less likely that this sediment will be delivered to the stream (Ketcheson & Megahan, 1996). This flowpath distance to the stream is calculated by backing up the flow direction grid.

Accumulation

Once the production of water, sediment, and other inputs is calculated for each cell, along with the fraction of each that is delivered to the stream, an accumulation function can be run on the flow direction grid to integrate over all upstream sources. This accumulation function is the key to estimating cumulative impacts, since in one step, it identifies all the material that the stream network will deliver to it. Since this accumulation function accumulates all inputs for all cells in the landscape at once, it is possible to calculate impacts to all stream reaches everywhere across the landscape.

Timing

The timing of delivery of a specific input to a specific stream reach can control its impact. A flood wave (or sediment wave) moving downstream might combine with one from another tributary stream to produce a downstream impact that can be much larger than the two arriving separately. This timing of delivery is a function of when the input (landslide, snowmelt, etc) occurred, and of how long it took the material to move from one cell to the next on its way to the channel cell of interest. Depending on the material, the rate of downslope/downstream movement will be a function of local slope. The time needed to transit across each cell can be accumulated using a flow length function to identify the transit time from each cell to the stream.

Vulnerability

The impact that this delivered water and sediment will have when accumulated at a given reach depends on the nature of that reach. For example, reducing sediment supply or increasing erosive flow will have little impact on a channel flowing across bedrock. The conditions of the channel before the arrival of these inputs will be a function of the channel gradient, contributing area, and channel confinement (WFPA, 1997) along with usage by fish.

Synergy

Having accumulated various delivered inputs to each stream reach, it remains to identify how these inputs interact to alter stream habitat. These synergistic effects of multiple inputs are difficult to study, but are needed to identify the cumulative impact of an entire management plan. A 50% increase in coarse sediment can cause channel filling, and a 50% increase in peak stream flow can cause channel incision, but it is necessary to identify result of the combination of the two. The combination may result in a cancellation of the two effects with no net change, or whether some other process will come to dominate the cumulative impact. Lacking synergistic models to predict cumulative impact, we may need to use the accumulated delivered inputs in regression modeling of observed impacts, such as observed fish populations.

Discussion

Grid based hydrologic modeling is not so much an analysis tool, as it is a tool for discussing and thinking about hydrologic processes. In its simplicity and general utility, grid-based hydrologic modeling is much like arithmetic. The set of basic hydrologic function (flow accumulation, flow length, flow gradient, and watershed functions) are the addition, subtraction, multiplication, and division of spatial hydrology. The ability to write algebraic functions that apply existing equations to existing spatial data at every grid cell in a landscape is a more basic tool of communication than an advanced approach at hydrologic simulation. Grid-based hydrology appears well suited as the introductory tool for explaining hydrologic concepts to undergraduate students.

Acknowledgements

Funding was provided by the Olympic Natural Resources Center.

References used

Hammond, C., D. Hall, S. Miller, and P. Swetik, Level I stability analysis (LISA) documentation for version 2.0, Gen. Tech. Rep. INT-285, For Serv. U.S. Dep. of Agric., Ogden, Utah, 1992.

Ketcheson, G. L. and W.F. Megahan, 1996. Sediment production and downslope sediment transport from forest roads in granitic watersheds. USDA Forest Service, Intermountain Research Station, Research Paper, INT-RP-486. 11 pp.

Montgomery, D.R. and W.E. Dietrich, 1994. A physically based model for the topographic control on shallow landsliding. Water Resources Research, 30(4):1153-1171.

Washington (State) Forest Practices Board, 1997. Board manual: standard methodology for conducting watershed analysis under chapter 222-22 WAC, version 4.0, Washington Forest Practices Board, Olympia, Washington.

Wu, W. and R.C. Sidle, 1995. A distributed slope stability model for steep forested basins, Water Res. R. 31(8):2097-2110.

Authors

Finn Krogstad, Ph.D. Candidate, College of Forest Resources
University of Washington, Seattle, Washington, 98195.
Email:fkrogsta@u.washington.edu, Tel: +1-206-685-2198, Fax: +1-206-685-3091.

Peter Schiess, McMc Resources Professor, College of Forest Resources
University of Washington, Seattle, Washington, 98195.
Email:schiess@u.washington.edu, Tel: +1-206-543-1583, Fax: +1-206-685-3091.