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CHAPTER 11 Application of GIS and Remote Sensing for Watershed Assessment Lloyd P. Queen, Wayne L. Wold, and Kenneth N. Brooks INTRODUCTION Located at the western terminus of Lake Superior, the Nemadji River Watershed covers 670 square kilometers in Minnesota (Figure 11.1). Approximately 40% of the area of the Nemadji Basin occurs in what is termed the Red Clay Area, a band of montmorillonite clays, up to 60 me-ters in depth. The clays, which are a result of offshore sediment deposits from glacial Lake Duluth, are highly erodible and prone to mass wasting. The high levels of sediment transported by the Ne-madji River system adversely affect the designated trout streams in the basin, and deposit an esti-mated 525,000 metric tons of sediment annually into the Duluth/Superior harbor. The erosion and sedimentation problems associated with the Nemadji Watershed led to the cre-ation of the multiagency research and demonstration “Red Clay Project.” This project concluded that erosion in the red clay area and subsequent transport and sedimentation is a natural process that has been intensified by forest harvesting, road building, and land-use conversions from forest to agriculture and open land (Andrews et al., 1980). Forest removal reduced evapotranspiration rates, resulting in a reduced resistance in soils to shear forces because of increased soil moisture content. In addition, the weaker root systems of replacement vegetation compared to the original white pine forests contribute to soil mass movement. The loss of large woody debris in stream channels also is thought to have reduced stream channel stability. In 1991, the conclusions of the Red Clay Project were reassessed, focusing attention on watershed factors that may be of greatest importance in correcting or mitigating the problem of soil mass wasting in the Nemadji basin. This chapter discusses the results of this reassessment. SETTING Soil mass wasting is the process of downslope movement of soil that consists of shear stress and displacement along surfaces that are either visible or that can reasonably be inferred (Huang,1983). Movement is thought to be the result of three contributing factors: (1) the high transportability rate potential of the soils; (2) the high soil moisture content; and (3) the absence of strong root systems to hold the soil in place. The Nemadji River Watershed exhibits excessive amounts of soil mass wasting (hereafter referred to as slumping). Most evident in the watershed are the chiefly rotational slumps that occur on hillslopes in the stream valleys and along the streams themselves. These slumps are suspected as a significant source of sediment to both the tributaries and the main stem of the Nemadji (Andrews et al.,1980). The purpose of this research was to relate the frequency of slumping to watershed characteris- 119 © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing 120 GIS FOR WATER RESOURCES AND WATERSHED MANAGEMENT Figure 11.1. Nemadji Basin location map. tics that potentially have the greatest effect on soil mass wasting. These watershed characteristics were considered independent predictor variables of the dependent variable, frequency of slump-ing. Secondarily, the research was to determine data requirements for efficient and cost-effective development of a Geographic Information System (GIS) that could be applied for future water-shed planning and management. Based on previous work, this research analyzed the following nine watershed variables thought to affect soil mass wasting: stream channel gradient, time of concentration, stream length, degree of slope, total length of roads in the subwatershed, watershed area, percent coniferous cover, per-cent deciduous cover, and total forested area. Each of these variables was examined with fre-quency of slump sites in each of nine subwatersheds (Figure 11.2). Fortunately, the relationships between forest cover and water yield have been the subject of considerable research. Results from research conducted in humid-temperate regions indicate that, in general, water yield is considerably greater under hardwoods than under conifers due to de-creased interception losses and the longer dormant period of deciduous species (Swank and Dou-glas, 1974). © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing APPLICATION OF GIS AND REMOTE SENSING FOR WATERSHED ASSESSMENT 121 Figure 11.2. Map showing subwatersheds within the Nemadji Basin. Increased streamflow provides additional energy for streambank degradation. Logging or thin-ning forests, converting from deep rooted to shallow rooted vegetation, or changing vegetative cover from one with a high interception capacity to one of lower capacity, all have been shown to increase water yield (Brooks et al., 1991). Studies in Minnesota by Verry (1986) indicate that annual water yield increases and that aver-age annual peak flows can double after cutting upland forests. As the amount of nonforested area in a basin increases, one would expect water yield to increase and with it, higher streamflows with higher velocities and energy. Such conditions could promote the undercutting of slopes and conse-quently, streambank slumpage. Streambank undercutting, pervasive in the highly erosive red clays, leaves the banks susceptible to slides and slumping. Such effects could be greater following the removal of conifers than hardwoods. © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing 122 GIS FOR WATER RESOURCES AND WATERSHED MANAGEMENT In addition to the role of forest cover type on stream flow, the root strength of different species also can influence soil mass wasting (Sidle, 1985; Abe and Ziemer, 1991). The loss of tree roots diminishes resistance to slumping. On steep slopes bordering streams this combination induces soil creep and slumping. According to the Red Clay Project, high (prefrost) soil moisture levels can be positively correlated with the rate of spring soil mass soil wasting along stream banks (An-drews et al.,1980). Conifers, which transpire later into the fall and retain their interception capac-ity year-round, should reduce soil moisture during this critical period. The dependent variable, slump frequency, was determined by tabulating areas where the min-eral soil was exposed due to slumping; these sites were identified and mapped from high-resolu-tion low-altitude aerial photographs. Frequency of slumps served as the primary indicator of the relative rate of erosion in each subbasin. In this application, slump frequency was evaluated for both entire subwatersheds and for a series of five discrete buffer zones surrounding each of the streams in those subwatersheds. APPROACH Pronounced differences in stream sediment concentrations and related turbidity exist among streams in the Nemadji Basin. To encompass the range of sediment-turbidity conditions, three sub-watersheds were selected from each of three turbidity classes, low, moderate, and highly turbid. Classifications based on turbidity were verified using suspended sediment samples and discharge data collected during April and October 1993 and April 1994 (Table 11.1). Suspended sediment, turbidity, and streamflow discharge were measured for both upstream and downstream locations from each of the nine streams (USGS, 1977) at the locations shown in Figure 11.3 (see color section). Each of the nine predictor variables and maps of slump sites were compiled for each of the nine subbasins in a vector-based GIS. A vector model was chosen for data development and analysis because these systems provide an excellent platform for development and mapping of point (e.g., slump sites), line (e.g., streams), and polygon (e.g., forest stands) data as well as for spatial data analysis. Additionally, local and regional management organizations had previously adopted vec-tor GIS for in-house use. Design criteria for the Nemadji GIS data are map themes at a scale of 1:24,000, with Universal Transverse Mercator (UTM) Zone 15 coordinates applied to all spatial entities, built using conic projections. Base maps for the GIS were derived from existing USGS 7.5 minute quadrangle maps. Quad maps also served as source data for topographic (slope) data, stream locations, and stream gradient estimates. The generation of data themes, the crux of most GIS efforts, is described in the next two sections. The three primary hydrologic themes are stream gradient, time of concentration, and watershed area. All blue-line (perennial and intermittent) streams were digitized from the USGS 1:24,000 quadrangle maps (Figure 11.4, see color section). The change in elevation from the headwaters to the mouth was taken directly from the quadrangle maps and divided by the length of the stream to determine gradient. Time of concentration was estimated using the Kirpich formula as reported by Gray (1970). The primary terrestrial variables are slope, stream length, road length, and land cover; and the dependent variable, slump sites. Slope maps were interpolated from a series of contour lines digi-tized as points from the USGS quads, and a triangulated irregular network (TIN) algorithm was used to create an interpolated slope coverage for all basins. Mean slope was then calculated for each basin (Figure 11.4). The roads theme was acquired in digital vector format from the Min-nesota Department of Transportation. Stereo pairs of conventional color photographs, acquired in May 1992, were used to identify © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing Table 11.1. Summary of Basin Characteristics High Turbidity Moderate Turbidity Low Turbidity Variable Mud Rock Deer Skunk Noname Clear Silver Little Net Stateline Gradient (m/km) 5.3 7.0 7.9 5.7 13.2 5.9 9.1 7.8 12.3 Discharge (L/s)a 17–382 6–49 54–106 26–371 4–86 19–47 98–193 66–357 36–95 S. Sediment (ppm)a 6–35 33–127 43–105 6–30 10–73 5–24 1–17 1–11 4–12 TOC Stream Length No. (hr) (km) Slumps 5.37 2.22 45 3.57 13.8 39 3.25 11.1 45 3.95 14.3 27 1.53 5.5 25 2.98 10.3 9 2.13 6.9 2 4.71 19.5 13 3.38 14.3 25 Mean Slope (%) 52 57 50 66 41 18 36 52 49 Road Length (km) 49.2 22.0 13.5 33.3 14.2 11.6 11.7 18.7 11.3 Total Area (ha) 3889 1830 2068 2680 750 1031 1234 3224 1631 % Conifer 21 ... - tailieumienphi.vn
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