Transcript 2005 NE GSA poster - University of Vermont

Using GIS to select drainage basins for sampling:
10Be
An example from a cosmogenic
study of
erosion rates within the Susquehanna River Basin
1
Reuter
Joanna M.
Paul R. Bierman
Milan J. Pavich
} University of Vermont, Burlington, VT
05401
U.S. Geological Survey, Reston, VA 20192
[email protected]
Abstract
We used GIS analysis to effectively select a series of sub-basins within the 71,250 km2 Susquehanna
River Basin for fluvial sediment sampling. Measuring cosmogenic 10Be from these samples allowed us to
develop a better understanding of erosion rates and patterns within the Susquehanna Basin and to
investigate relationships between erosion rates and basin-scale characteristics. We began the analysis by
obtaining regional datasets of topography, bedrock geology, glacial extent, precipitation, land cover, and
physiographic province, all of which are freely available. After delineating boundaries of thousands of subbasins at a range of scales, we summarized the landscape characteristics within the basin boundaries. We
plotted the summarized characteristics to gain insight regarding the types and quantities of basins available
for sampling. Guided by what we learned, we decided to base the sampling strategy on the following
factors: basin scale (3-10 km2), position south of the glacial margin, physiographic province, lithology, and
mean basin slope. Finally, we used a series of queries to display basins with the desired characteristics.
Because the pool of remaining candidate basins was small, we manually selected basins at this point,
examining the digital topographic maps to screen for excessive disturbance (such as strip mines) and
difficulty of access. Our results demonstrate the effectiveness of using GIS to aid in developing a sampling
strategy. Using the 10Be data generated from 60 sampled basins, we identified positive correlations
between slope and inferred erosion rates, though the erosion rates for the sampled lithologies (sandstone,
shale, and schist) are indistinguishable from each other after accounting for slope. Rates extrapolated from
the small basins, based on the identified relationships, are close to rates measured for larger basins. This
systematic approach to basin selection can be applied to any research that requires sampling of a small
subset of basins from a large group of possibilities.
1) Acquisition and preparation of digital spatial data
We obtained the following digital data sources and projected to UTM, NAD 83:
Elevation: National Elevation Dataset (NED)
http://ned.usgs.gov
Slope
derived from NED
Precipitation: PRISM
http://www.ocs.orst.edu/prism/
Land use: National Land Cover Data (NLCD)
http://landcover.usgs.gov/natllandcover.asp
Geology: Digital bedrock geology of Pennsylvania
http://www.dcnr.state.pa.us/topogeo/map1/bedmap.aspx
Glacial margin:
http://www.pasda.psu.edu/summary.cgi/dcnr/pags/pags_glacier1k.xml
Physiography
http://water.usgs.gov/GIS/metadata/usgswrd/XML/physio.xml
Digital Raster Graphics (DRGs)
ftp://www.pasda.psu.edu/pub/pasda/drg24k-cu/
2) Delineation of thousands of sub-basins
We delineated basins using the following steps:
• Filled sinks in digital elevation data.
• Computed a flow direction grid (FlowDirection).
• Computed a flow accumulation grid (FlowAccumulation).
• Iteratively performed the following steps:
• Queried flow accumulation grid for basin size range of interest (maximum size < 2x minimum size, to
avoid problems with nested basins and basin fragments; quarter log increments accomplish this).
• Assigned a unique ID to each point (StreamLink).
• Determined drainage basin boundaries (Watershed).
• Converted basins to a shapefile.
Examples of basins delineated using these steps for three different size ranges:
5.6 – 10 km2
56 – 100 km2
562 – 1,000 km2
3) Summary & analysis of sub-basin characteristics
Basins essentially serve as cookie cutters to summarize the data values within the basin boundaries.
Summarizing continuous value data
Examples: slope, elevation, precipitation
Summarizing discrete value data
Examples: lithology, land use
In ArcView 3.x: Analysis > Summarize Zones...
In ArcGIS: Spatial Analysis > Zonal Statistics...
In ArcView 3.x: Analysis > Tabulate Areas...
In ArcGIS: not available in menus of ArcGIS 8.x
Output for each drainage basin polygon:
pixel count, area, minimum, maximum, range,
mean, standard deviation, and sum
Output for each drainage basin polygon:
the area of the basin mapped according to each
unique value of the grid (such as forested, urban,
agricultural); can be converted to % of basin
Examples of plots of summarized data that provided useful insight for the development of a basin
selection strategy:
(Right) Each point represents a subbasin of the Susquehanna River
Basin. As basin area increases, the
range of mean basin slope decreases.
The vertical lines are artifacts of the
basin size ranges that were specified
when delineating basins.
(Left) Each point represents a nonglaciated sub-basin of the
Susquehanna River Basin. The
basins included in this plot are > 1
km2 in area and are mapped as a
single lithology.
4) Development of basin selection strategy
7)
Guided by what we learned from the GIS analysis, we developed a basin selection strategy in order to
assess relationships between drainage basin characteristics and erosion rates inferred from 10Be in fluvial
sediment. This table summarizes several important factors we considered:
Basin selection goals and
Insight from GIS
10Be considerations
Goal was to select basins with a range of
As basin size increases, the range of basin mean
Slope
Lithology
Basin scale
slopes, from gentle to steep, in order to
investigate relationships between topography
and erosion rates.
10Be concentrations from single lithology
basins can be robustly interpreted as erosion
rates, due to uniformity of quartz distribution.
slope decreases.
The number of available single-lithology basins
increases as basin scale decreases.
5) Basin selection
We queried the delineated basins
for desired characteristics. For
example, the map (left) shows the
results of a query based on these
characteristics:
Lithology = “sandstone”
Area > 3 km2
Area < 10 km2
Barren < 3% (proxy for strip
mines and other disturbance)
Dams in basin = false
The queries yielded a manageable
number of basins for manual
screening with DRGs (digital
topographic maps). Access
(public or private land) was a
major concern.
We prepared a list of basins to
visit, allowing for some attrition
(due to access, for example).
Here is an excerpt from the list:
1
2
Lithology
Slope
Range
Appalachian
Plateaus
sandstone
0-5
Appalachian
Plateaus
sandstone
Appalachian
Plateaus
sandstone
Appalachian
Plateaus
3
Appalachian
Plateaus
0-5
sandstone
sandstone
5-10
ID
We used in-situ produced 10Be measured from fluvial sediment to infer basin-scale erosion rates on a 104105 year time scale (Brown et al., 1995; Bierman and Steig, 1996; Granger et al., 1996).
Samples come from two groups of basins:
GIS-selected basins: small basins selected with a GIS-based sampling strategy, grouped by
USGS basins: samples from USGS stream gaging stations, selected because they have sediment
The basin selection strategy is also based on physiographic province, because these provinces subdivide
the region by topographic and geologic characteristics. We selected only non-glaciated basins, because
glaciation violates assumptions for inferring erosion rates from 10Be. For each group of data, we wanted
enough basins to have statistically meaningful results, so we limited the number of physiographic and
lithologic combinations.
Physiography
results demonstrate effectiveness of
GIS-based approach to basin selection
physiography, lithology, and slope
Basins should have well developed streams that Sampling small basins helps to achieve goals of
serve to mix sediment from the basin.
single lithology basins across a wide range of
mean basin slopes. We decided upon a target
basin area of 3 to 10 km2.
EXPLANATION
10Be
Easting
17NAD27
Description
Name
4996
state forest land, short walk from long dirt road
trib to Little
Birch Island Run
4855
some new trails/dirt roads in vicinity (does that imply
logging??)
Moccasin Run
7317
road (state 120) parallels stream, access from road
Big Run
714623
2793
state game land?, possible 2.5 km hike in from south
(easy terrain) or 3km along stream from north
Pebble Run
2891
state forest land, short walk from long dirt road;
sample with trib to Little Birch Island Run
Little Birch
Island Run
748681
Northing
17NAD27
4565500
quad
Area
km2
Slope
mean
%
forest
yield data (15 to 8,700 km2 in area)
(Right) 10Be-inferred erosion
rates for the GIS-selected basins
in the Susquehanna River Basin
range from 4 to 54 m/My. This
is a notably wider range of rates
than for the Great Smoky
Mountains of the southern
Appalachians, where the
sampling strategy was
developed for other reasons,
without utilizing GIS to seek out
diverse basin characteristics.
(Right) As basin area
increases, the range
of 10Be erosion rates
decreases. The
greatest range of
erosion rates are
observed among the
GIS-selected basins
(16 ± 10 m/My,
mean and standard
deviation). The
USGS basins, at
larger basin scales,
yield a narrower
range of erosion
rates (14 ± 4 m/My).
(Below) Relationships between slope and 10Be-inferred erosion rate for the GIS-selected basins in each
physiographic province and (bottom right) for the USGS basins collectively. Correlations exist among
the groups of basins which show the widest range in mean basin slope. After accounting for slope, no
discernible relationship exists between lithology and erosion rate in the Valley and Ridge.
% ag
Pottersdale
3.37
4.8
99.87
0.04
Keating
5.53
4.2
99.87
0.04
4592664
Rathbun
3.16
4.29
97.29
2.71
728569
4568627
The Knobs
7.06
2.36
99.73
0.1
748657
4565431
Pottersdale
7.14
5.67
99.95
0.02
Appalachian Plateaus
GIS-selected basins
Valley and Ridge GIS-selected basins
Piedmont GIS-selected basins
USGS basins
6) Sampled the GIS-selected basins
The outlines of the basins that we sampled are shown in the context of the
sampling strategy, rather than in their geographic context:
References cited
Bierman, P.R., and Steig, E., 1996, Estimating rates of denudation and sediment transport using cosmogenic isotope abundances in
sediment: Earth Surface Processes and Landforms, v. 21, p. 125-139.
Brown, E.T., Stallard, R.F., Larsen, M.C., Raisbeck, G.M., and Yiou, F., 1995, Denudation rates determined from the accumulation of in
situ-produced 10Be in the Luquillo Experimental Forest, Puerto Rico: Earth and Planetary Science Letters, v. 129, p. 193-202.
Granger, D.E., Kirchner, J.W., and Finkel, R., 1996, Spatially averaged long-term erosion rates measured from in situ-produced
cosmogenic nuclides in alluvial sediments: Journal of Geology, v. 104, p. 249-257.
Matmon, A., Bierman, P.R., Larsen, J., Southworth, S., Pavich, M., Finkel, R., and Caffee, M., 2003, Erosion of an ancient mountain
range, the Great Smoky Mountains, North Carolina and Tennessee: American Journal of Science, v. 303, p. 817-855.
Acknowledgments
We thank E. Butler, J. Larsen, R. Finkel, and M. McGee for assistance with sample collection and
processing. Research was funded by the USGS and NSF EAR-0034447 and EAR-0310208. J. Reuter was
supported by an NSF Graduate Fellowship.