My MS GIS thesis defense presentation.

1. Richard Immell
A Thesis
Erik Schiefer, Ph.D., Chair
Ruihong Huang, Ph.D., Mark Manone M.A., Committee
Members
Department of Geography Planning and Recreation.

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. Introduction
The impact on aquatic life can be
severe for fishes such as salmonids
which use substrate as incubation
habitats (Bjornn and Reiser, 1991;
Curry and MacNeill, 2004)
Increased course grained sediment
can cause channel aggradation,
resulting in reduced flow capacity
leading to flooding and channel
instability (Nelson and Booth, 2002)
Assessing the degree to which Seven examples of salmonids
Photo is from National Parks Service
land-use change impacts sediment http://www.nps.gov/olym/naturesc
ience/potential-range-of-salmonids-
yield is vital to understanding and in-the-elwha.htm
managing 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
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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.  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)
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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
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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
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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
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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.  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?
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11. Watershed area
Lake Latitude Longitude Lake area (km2)
(km2)
1) Bear 53.74 N -116.15 W 1.54 7.54
2) Dunn 53.65 N -117.69 W 0.12 0.86
3) Fairfax 52.97 N -116.58 W 0.31 1.62
4) Fickle 53.45 N -116.77 W 3.77 102.21
5) Goldeye 52.45 N -116.19 W 0.10 5.00
6) Iosegun 54.46 N -116.84 W 13.54 273.21
7) Jarvis 53.45 N -117.80 W 0.70 31.86
8) Mayan 53.90 N -117.39 W 0.06 0.73
9) McLeod 54.30 N -115.65 W 3.42 47.88
10) Musreau 54.54 N -118.62 W 5.58 104.57
11) Pierre Gray 53.91 N -118.59 W 0.38 0.50
12) Rainbow 53.91 N -117.18 W 0.07 4.44
13) Smoke 54.36 N -116.94 W 9.15 133.93
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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
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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
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14. 14

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.  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
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17. Landscape Indices Description Units
1)Watershed Area Total land area of the lake catchment km2
2)Proportion Study Lake Area Area of the inventoried lake per area watershed km2/km2
Total surface area of wetlands (swamps and marsh
3)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 the
4)Drainage Density km/km2
catchment
5)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)
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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
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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
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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
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21. Bear lake:
21

22. Smoke Lake:
22

23. 23

24. 24

25. 25

26. 26

27. 27

28. 28

29. 29

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
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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. Background Average Percent
Cutline Density Road and Trail
Lake Percent Area Cut Well Density Sedimentation Above
km/km2 Densities km/km2
Rate Background
Bear 1.04 1.098 0.236 0 52.222 35.441
Dunn 1.669 1.549 0.141 0 36.186 100.628
Fairfax 0.619 0.275 0.278 0 112.582 42.629
Fickle 3.437 0.408 0 0.166 75.003 34.133
Goldeye 0.364 0.823 0.013 0 48.222 67.635
Iosegun 1.847 0.936 0.073 0.479 154.834 190.31
Jarvis 0.459 1.52 0.045 0.157 75.702 110.832
Mayan 2.744 2.391 0.346 2.74 75.196 48.936
McLeod 1.169 1.182 0.175 0.522 86.865 251.669
Musreau 1.353 0.546 0.409 0.23 284.447 55.169
Pierre Gray 0 0.53 0 0 70.284 -2.134
Rainbow 2.51 1.139 0 0 79.15 43.236
Smoke 1.558 0.925 0.262 0.777 112.582 48.579
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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
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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 output
Average 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%)
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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, road
Percent 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.  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 run
Average 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.  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.  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.
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