Thesis Presentation

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My MS GIS thesis defense presentation.

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Thesis Presentation

  1. 1. Richard Immell A Thesis Erik Schiefer, Ph.D., ChairRuihong Huang, Ph.D., Mark Manone M.A., Committee Members Department of Geography Planning and Recreation.
  2. 2.  Sediment yield  Landscape measurements disturbances (natural  Index of landscape and anthropogenic) denudation  Can increase  Assess environmental sediment yield from processes affecting forested watershed land surface systems  Degrading aquatic habitat  Impede water purification  Kerr, 1995  Schiefer et al., 2001 2
  3. 3. IntroductionThe impact on aquatic life can besevere for fishes such as salmonidswhich use substrate as incubationhabitats (Bjornn and Reiser, 1991;Curry and MacNeill, 2004)Increased course grained sedimentcan cause channel aggradation,resulting in reduced flow capacityleading to flooding and channelinstability (Nelson and Booth, 2002)Assessing the degree to which Seven examples of salmonids Photo is from National Parks Serviceland-use change impacts sediment http://www.nps.gov/olym/naturesc ience/potential-range-of-salmonids-yield is vital to understanding and in-the-elwha.htmmanaging this problem Road erosion in Tongass National Forest, which is filling a pool habitat in a stream channel. http://www.fs.fed.us/r10/tongass/districts/pow/projects_plans/fish/n_thorne_eis.shtml 3
  4. 4.  Approach for studying  210PB (lead 210) dating is watershed dynamics used to establish a Linkages exist between chronology of lake sediment landscape  210PB has half life of 22.26± characteristics, Terrestrial 0.22 years, ideal for ≤ 200 disturbance, and lacustrine years from present (in lake) sediments  Because of a predictable rate Lake sediment represent a of decay, 210PB analysis historical record of sediment establishes a chronology of yield. deposition  Schiefer et al., 2000  Foster et al., 1990 Lake sediments can be used to develop profiles of quantitative sediment yields Sedimentation due to land- use (or other disturbance) can be identified  Foster et al., 1990 4
  5. 5.  Watersheds are linked with hill slope processes Regional climate, geology, vegetation, and human land-use are contributing factors Input from drainage basins feed into main channels influencing downstream channel morphometry and hydrologic processes Conceptual model of sediment transfer for headland watershed  Ritter et al., 2005 in the Rocky Mountain Foothills and adjacent Alberta Plateau Modified from Robert and Church (1986) 5
  6. 6.  Roads  Roads constructed for  Wide range of effects timber harvest  Chronic, long-term increased sedimentation contributions rates in the interior of  Large scale mass failure British Columbia (BC) of road fill material  Primary mechanisms affecting geomorphic processes  Tree harvest in BC has a  Accelerated erosion from negligible impact on road surface sedimentation  Altering channel  Jordan, 2006 structure  Altering flow path and diverting channels  Interactions at road/stream crossings  Gucinski et al., 2001 6
  7. 7.  Timber harvest  Harvest practice is important  Clear cut practice lead to increased sediment  Partial cut practice did not show increase in sediment yield  Karwan et al., 2007 7
  8. 8.  Oil and gas extraction  Sediment increases  Rapidly growing  Alberta is industry in Canada experiencing  Involves the increases in oil and construction of gas extraction  Roads  well sites  Pipelines  Alberta has large  Once established timber industry these features may be a chronic source of sediment  Wachal et al., 2009 8
  9. 9.  Relate catchment  Watershed characteristics to description sediment yield  Digital Elevation  Accurate data is Models (DEMs) essential  Land-use change  Topographic data description  Air photos  Air photos and  Land-use maps satellite imagery  Jenson and  Awasthi et al. Dominque, 1988 2002; Franklin et al., 2005 9
  10. 10.  What are the effects of land use change on the accumulation rates of lacustrine sedimentation in lake-catchments in West Central Alberta Canada? Does the core analysis combined with GIS offer a good measure of land use impacts on sediment yield? How does the magnitude of highly disturbed watersheds compare to moderately disturbed water sheds? 10
  11. 11. Watershed area Lake Latitude Longitude Lake area (km2) (km2)1) Bear 53.74 N -116.15 W 1.54 7.542) Dunn 53.65 N -117.69 W 0.12 0.863) Fairfax 52.97 N -116.58 W 0.31 1.624) Fickle 53.45 N -116.77 W 3.77 102.215) Goldeye 52.45 N -116.19 W 0.10 5.006) Iosegun 54.46 N -116.84 W 13.54 273.217) Jarvis 53.45 N -117.80 W 0.70 31.868) Mayan 53.90 N -117.39 W 0.06 0.739) McLeod 54.30 N -115.65 W 3.42 47.8810) Musreau 54.54 N -118.62 W 5.58 104.5711) Pierre Gray 53.91 N -118.59 W 0.38 0.5012) Rainbow 53.91 N -117.18 W 0.07 4.4413) Smoke 54.36 N -116.94 W 9.15 133.93 11
  12. 12.  Climate  Two weather stations were compared (Jasper and Edson)  Peak rainfall is in June/July  Peak snow in January  Edson averages:  June rain 106.7mm  January snow 35.8cm  July high 14.6°C  January low -11.8°C  Jasper averages:  July rain 60.1mm  January snow 30.5cm  July high 15°C  January low -9.8°C 12
  13. 13.  Vegetation  Geology and surficial  Dominant tree species: materials  aspen (P. tremuloides  Most of the study area Michx.) lies within the Cenozoic  lodgepole pine (P. Paskapoo Formation contorta Dougl. ex Loud.  Other formations are var. latifolia Engelm.) Brazeau and Scollard  white spruce (P. glauca Moench Voss)  mudstone,  balsam poplar (Populus  siltstone and balsamifera L.)  sandstone  (Natural Regions Committee 2006).  subordinate limestone, coal, pebble  Study area is mostly conglomerate and continuous forest bentonite  Area surficial material is dominated by glacial till 13
  14. 14. 14
  15. 15.  Sedimentation data  Watershed inventory  Cores were collected  Landscape and land- previously by Erik use indices Schiefer Ph.D.  Environmental  Dating was completed Research Institute by Jack Cornett of (ESRI) ArcGIS 9.3.1 Mycore Scientific  Data sources:  Detailed descriptions of the sediment core sampling and associated laboratory procedure are  Topographic data available in Schiefer (1999).  DEMs and shapefiles  Lakes were chosen  Vector data: Natural which: Resources Canada http://ftp2.cits.rncan.g  Were deep enough c.ca/pub/bndt/50k_sh  Had a range of historic p_en land-use  DEMs: Geo Base website http://www.geobase.c a/geobase/en/index.ht ml 15
  16. 16.  National Topographic  Aerial Photography and System: Satellite Imagery  Detailed ground relief,  Accessed from the drainage, forest cover, National Provincial Air administrative areas, Photo Reference Library in populated areas and Edmonton transportation  Digital images of air  Large dataset, only retained photos were obtained by useful shapefiles of land-use Erik Schiefer Ph.D. or watershed characteristics  Covering the 13 lakes  Natural Resources Canada, 2007  Repeat photography at Canadian Digital Elevation roughly decadal intervals Data:  Mostly pan chromatic  Evenly spaced grid of with a few infrared and elevations color photos  Multiple DEMs were  Scales ranged from small needed to cover each scale (1:60,000) to large watershed scale (1:15,000)  Elevations is in Meters  Google earth imagery was relative to mean sea level used for recent land-use  GeoBase, 2010 identification 16
  17. 17. Landscape Indices Description Units1)Watershed Area Total land area of the lake catchment km22)Proportion Study Lake Area Area of the inventoried lake per area watershed km2/km2 Total surface area of wetlands (swamps and marsh3)Proportion Water Features land) and other lakes except the study lake, within km2/km2 the lake catchment, per area Length of river and streams per area of the4)Drainage Density km/km2 catchment5)Elevation Statistics Maximum, and minimum elevation, and mean slope km Land Use Indices Description Units Percentage of total land area of the lake catchment 1)Percent Area Cut that has been logged (includes proportions within a km2/km2 given distance via buffers) Density of roads within the lake catchment 2)Road Density (includes road densities for each road type and/or km/km2 within a given distance via buffers) Density of wells within the lake catchment (includes #wells/k 3)Well Density well densities within a given distance via buffers) m2 Density of cut lines within the lake catchment 4)Cutline Density (includes cut line densities within a given distance km/km2 via buffers) 17
  18. 18.  Database generation and base map construction  A directory was Delineated Watershed Portion of Jarvis removed generated for each lake  Housed retained data and derived data  Multiple data features were condensed  Maps were generated for each lake and the .MXD file stored in the appropriate directory 18
  19. 19.  DEMs were  Basic watershed decompressed and delineation converted to ESRI  Fill – remove sinks Grid format  Flow direction Merged into one  Delineate watershed continuous file and – watershed tool projected to match  Study lake converted to NTS data (NAD83 UTM Zone 11N) raster used as pour point DEM was used to  Convert watershed delineate the raster to polygon watershed boundary for each lake 19
  20. 20.  Landscape indices  Land-use indices  Watershed area calculated  Air photos organized by lake and year  Proportion study lake,  Images were geo- proportion water features, referenced and land-use and drainage density, confirmed or digitized  Date attributes assigned  Elevation and slope to land-use statistics were calculated  Length, area, or number by use of zonal statistics of features was calculated tool for land-use (summarize tool)  Cumulative totals and densities calculated  Buffers analysis completed at 10m, 50m, 150m and 250m 20
  21. 21. Bear lake: 21
  22. 22. Smoke Lake: 22
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  30. 30.  Sediment data received  Background from Mycore Labs sedimentation rates:  Sediment accumulation rates (SAR) (g/m2/yr)  Age at top of sediment core sub-sections  Background sedimentation yield:  Data was used to calculate background sedimentation rates,  Background specific percent above sediment yield: background, specific background sedimentation rates,  Percent above and specific sediment background: yield Note that for Bear, Jarvis, and Pierre Gray, there was a slight deviation in the calculation of background sedimentation rate, due to outlier data points 30
  31. 31.  Statistical analysis  Watershed analysis  Regressions were completed  Correlation tests: for all static landscape Spearman’s rank variables  Bivariate regression  Spearman’s rank test  Spatial analysis  Multivariate regression  Regressions were completed between average percent above background sediment and land-use density (buffer Comparisons were and watershed level) between landscape  Spearman’s rank tests were also used and land-use  Temporal analysis indices, and  Data broken into intervals sediment data  based on photo dates Date of top layer of sediment layer averaged over the interval to obtain rates  Regressions (bi- and multivariate) completed Note: For all statistic analyses trails and roads are combined 31
  32. 32. Background Average Percent Cutline Density Road and Trail Lake Percent Area Cut Well Density Sedimentation Above km/km2 Densities km/km2 Rate BackgroundBear 1.04 1.098 0.236 0 52.222 35.441Dunn 1.669 1.549 0.141 0 36.186 100.628Fairfax 0.619 0.275 0.278 0 112.582 42.629Fickle 3.437 0.408 0 0.166 75.003 34.133Goldeye 0.364 0.823 0.013 0 48.222 67.635Iosegun 1.847 0.936 0.073 0.479 154.834 190.31Jarvis 0.459 1.52 0.045 0.157 75.702 110.832Mayan 2.744 2.391 0.346 2.74 75.196 48.936McLeod 1.169 1.182 0.175 0.522 86.865 251.669Musreau 1.353 0.546 0.409 0.23 284.447 55.169Pierre Gray 0 0.53 0 0 70.284 -2.134Rainbow 2.51 1.139 0 0 79.15 43.236Smoke 1.558 0.925 0.262 0.777 112.582 48.579 32
  33. 33.  Lake area vs. watershed area:  Background sediment  Larger catchments have larger accumulation vs. percent water lakes features Background sedimentation and  As percent water features watershed area: increase, background  Larger catchments have higher sedimentation rates decrease sedimentation rates  Supports the exclusion of streams which flow into large Specific sediment yield vs. wetlands or lakes upstream watershed area:  Not a significant relation  Indicates no scaling relation, model conforms weakly to  No other relations were noted conventional sediment model indicating that there are no Maximum catchment elevation complex relations between vs. mean catchment slope watershed variables and sedimentation rates  Higher elevation watersheds have higher slopes  Land-use might be important factor 33
  34. 34.  Spearman’s rank Regression of Average Percent Above background by Roads and sediment50m Road/trail Density at a 10m a were not Regression of Average Percent Above Background by Road and Trail Density at Buffer  Buffer (R²=0.496) (R²=0.402)  Significant relation exists significantly related at 50m until 300 350 between road and trail density the outlier Pierre Gray was and average percent above removed 300 250 background  Pierre Gray’s roads and trails are paved 250 200  Impervious surfaces contribute little to sediment outputAverage Percent Above Background Linear regression corroborated  Reid and Dunne, 1984 this result Percent above Background 200 150  Road direction and orientation  Indicated significant relation are important to road impacts between road and trail density 150 on sedimentation 100 and average percent above  Gucinski et al., 2001 100 background at short distances  Larger buffer distances were not 50 (10m and 50m) significant 50  Vegetation acts as buffer or 0 sink 0  Muñoz-Carpena et al., 0.8 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1999 1.8 2 -50 -50  Larger distances increase chance of sedimentation loss to buffer and sinks -100 -100 Road and Trail Density at 50m Buffer Road and Trail Density 10m Buffer Km/km22 km/km Active Active Model Model Conf. interval (Mean 95%) Conf. interval (Mean 95%) Conf. interval (Obs. 95%) Conf. interval (Obs. 95%) 34
  35. 35.  Strong relationAbove Background by Well  Well density is related to Regression of Percent noted in Count Density at a 50m Buffer (R²=0.516) 350 the regression of well road density because 300 density vs. average roads are built to access percent above background wells  Wachal et al. 2009 250  10m buffer results might  Multivariate regression be spurious because of with well density, roadPercent Above Background 200 low well counts and trail density, and 150  50m buffer showed average percent above strongest relation background yielded 100  Significance drops with weaker relations than larger buffer distances either land-use variable alone 50 0  Spearman’s rank analysis 0 0.1 0.2 0.3 indicated relation between 0.4 0.5 0.6 0.7 -50 road and trail density and average percent above -100 background p-value 0.046 Well Count Density at 50m Buffer #wells/km2 Active Model Conf. interval (Mean 95%) Conf. interval (Obs. 95%) 35
  36. 36.  Regressions both bi- andAbove Background Cumulative inspection of the Regression of Average Percent by Closer Road and Trail Density (R²=0.379) multivariate were run on the regression plot indicated a 300 temporal dataset. significant amount of heteroscedasticity  250 Only one significant regression  To further investigate an f-test was identified and Welch two sample t-test 200 were runAverage Percent Above Background  Dataset was subset into high  Cumulative road and trail and low densities 150 density vs. average percent above background at 10m  t-test indicated difference 100 buffer R2 = 0.379 between mean of subset data  cumulative lengths of roads  Roads continue to contribute greater than 0.4km/km2, higher 50 to sediment increases in years average sedimentation rates will following construction likely exist 0  Karwan et al. 2007  F-test indicated that there is 0 0.1 0.2 0.3 0.4 higher variability in 0.5 0.6 0.7 0.8 sedimentation rates for -50 cumulative lengths of road and trail densities above -100 0.4km/km2 Cumulative Road and Trail Density Km/km2 Active Model Conf. interval (Mean 95%) Conf. interval (Obs. 95%) 36
  37. 37.  Although this research indicates land-use influences sedimentation with close proximity to water sources, further research is needed to determine if other factors influence sedimentation rates Timber harvest and oil and gas extraction practices have changed over time, and therefore effect sedimentation differently Road size and use intensity were not considered in this work Changes in weather patterns could have a strong impact on sedimentation. These factors were not included in this work Land-use outside the watershed could impact sedimentation rates via wind transport. Buffer analysis outside the watershed could address this issue 37
  38. 38.  I would like to thank my Committee for their support and dedication to getting this project completed. Special thanks goes to my Chair, Erik Schiefer Ph.D. who without his hard work and quick responses to my many questions, this thesis would not have been completed in a timely manner. I would like to thank my friends and fellow grad students, Kristen Honig and Donovan Sherratt, who have provided emotional support, study assistance, and the occasional pep talk. I would also like to thank my family for their support and understanding over the last 3 years. Finally I would like to thank my wife Marcie, Daughter Arwyn, and my son Corrin. They had to put up with many evenings, meals, and events without me, as I worked on completing this thesis. Without their support, understanding and love I would not have attempted graduate school. 38

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