Presentation on the study of an urban watershed analysis completed with GIS.

1. ANALYSIS OF TRANSPORTATION INFRASTRUCTURE INFLUENCE ON
WATERSHED BOUNDARY AND STREAM NETWORK:
A CASE STUDY OF THE ROUGE RIVER WATERSHED, MICHIGAN
BY: BRIAN GIROUX
FACULTY ADVISOR: DR. JACOB NAPIERALSKI
ENVIRONMENTAL SCIENCE 499 – LAB RESEARCH IN ENVIRONMENTAL SCIENCE

2. OUTLINE
1. Introduction
1. The Urban Landscape
2. Linear Disturbances
3. Impact of Roads
4. Separating Steams and Roads
5. Purpose
2. Methods
1. Site Identification
2. LiDAR Data
3. Watershed Delineation and Stream
Extraction
4. Data Analysis
5. Summary
3. Results
1. Watershed Boundary
2. Stream Network
3. Urban v. Rural Watershed
4. Discussion and Conclusions
1. Limitations and Assumptions
2. What we Have Learned
3. What Does this Mean?

3. 1. 1 INTRODUCTION
THE URBAN LANDSCAPE
 Urban landscapes reveal complex interactions between natural and human processes.
 Humans are particularly proficient at transforming landscapes, altering surface and subsurface
hydrologic processes, soil functionality and quality, and topography.
 Human impact is so large that geologists have recently proposed a modern Epoch (Anthropocene)
because Earth’s landscapes and subsurface properties are now influenced by human activities.

4. 1. 2 INTRODUCTION
LINEAR DISTURBANCES
 Roads are localized, artificial, linear disturbances that have an impact on:
 Human health (pollution that follows from road construction)
 Biological richness and integrity (removing stream network linkages)
 Runoff organization and efficiency (elimination of streams)
 In many urban areas, road patterns and management have higher priority than maintaining streams
 Streams are modified and channelized parallel to existing road networks, if not altogether removed from the
urban landscape
>

5. 1. 3 INTRODUCTION
IMPACT OF ROADS
 The environmental impact of roads has been evaluated based on damage assessment within contiguous
areas extending from the road (i.e., buffers), frequently referred to as the “road effect zone”.
 Within the effect zone, impacts include: physical emissions (e.g., carbon monoxide, particulates), sensory emissions
(e.g., noise, light), and localized climate variability (e.g., temperature, wind) on habitat, soil, or waterways.
 Studies have suggested the most significant impact is within 100 to 600 meters of a road, depending on
road size and traffic characteristics; however, less is known about the zone of impact between a road and
nearby streams
 Often, streams are modified to:
 Intersect roads at perpendicular angles
 Conform to the road pattern
 Be completely removed from the surface

6. 1. 4 INTRODUCTION
SEPARATING STREAMS AND ROADS
 Separating the road and stream networks in densely urbanized cities has been a substantial challenge,
but recent advances in geospatial technology offer better opportunities to study the interaction between
urban and natural processes.
 Light Detection and Ranging (LiDAR) can distinguish between artificial and natural channels, as well as
engineered structures, in urban areas.
 As the topographic gradient in urban areas is already relatively low, in addition to being “built up”,
LiDAR derived topographic data are effective in feature extraction and process assessment under these
conditions.
 As a result, urban watershed boundaries and stream networks derived from high-resolution LiDAR data
should reveal the subtle conformity between urban structures and natural features.

7. 1. 5 INTRODUCTION
PURSPOSE
The fundamental objectives are to geospatially quantify the impact urban
infrastructure (particularly roads) has on watershed boundary delineation and
the parallelity between stream and road networks.
1. What percentage of a low gradient, highly urbanized watershed boundary includes a road?
2. Can small road effect zones (buffers) sufficiently indicate where streams and rivers spatially
correlate with road networks?

8. 2.1 INTRODUCTION
SITE IDENTIFICATION
 The Rouge River watershed characteristics:
 Size: 1209 km2
 Relief ratio: 3.74 m/km (very low)
 Developed land cover: 84%
 Impervious surface coverage (ISC): 25%
 Population: ~1,263,000
 Road density: 8.8 km/km2
 Stream density:1.1 km/km2

9. 2.2 METHODS
LIDAR DATA
 A mosaic of the area was created using raster tiles derived from high-resolution light detection and
ranging (LiDAR) topographic data (think of SONAR, except it is from a plane)
 Tile size:1.6 km2
 Cell resolution: 0.6 m, with a vertical accuracy of ± 8.8239 cm
 There were 655 tiles in the mosaic, or, 4.6 billion cells
Cell
Tile
Mosaic

10. 2.3 METHODS
WATERSHED DELINEATION AND STREAM NETWORK EXTRACTION
 Watershed boundary and stream network were delineated and extracted using ESRI’s ArcGIS 10.4
Hydrology toolset.
 Delineation involved surface fill of mosaic flow direction  flow accumulation  basin
 Network extraction involved setting a threshold of 5,000 upslope cells (1,800 m2) to select the cells that best
represent perennial streams, and which also matched National Hydrology Dataset (NHD) flowlines and channels
visible from satellite images.
 These are where “theoretical streams” should be

11. 2.4 METHODS
DATA ANALYSIS
 The effect of transportation infrastructure on watershed boundary and stream network was quantified using a
modified version of the Automated Proximity and Conformal Analysis (APCA).
1. Subdivide watershed boundary and stream network.
2. Generate multi-ring buffers around roads and railroads and single buffers around watershed boundary and
stream network.
-The watershed
boundary was split
where it intersected a
road
- The stream network was split
into segments by stream order
so that each stream of the same
order was a unique line segment

12. 2.4 METHODS
DATA ANALYSIS CONTINUED
3. The third step was to overlay the multiple infrastructure buffers with the single buffer of each
watershed feature line segment to produce a series of polygons associated with the buffer rings
with areas, {a1, a2, …, a8}.
1. Calculate the percentage area of each polygon
associated with an average distance
2. Calculate he mean (μ) and standard deviation
(σ) for each watershed feature line segment
Note: A smaller standard deviation
indicates a higher proximity between the
watershed feature and transportation
infrastructure while a higher standard
deviation means they are more
perpendicular.

13. 2.5 METHODS
SUMMARY
Input
 A line segment from either the divided watershed boundary or stream network
Processing
 Buffering, intersecting, calculations
Output
 A standard deviation for each line segment which is an indicator of conformity

14. 3.1 RESULTS
WATERSHED BOUNDARY
Main 1 Main 2 Upper Middle 1 Middle 2 Lower 1 Lower 2
Boundary Within
Infrastructure Buffer Zone
(%)
89.6 70.3 90.1 61.2 NA 59.3 89.8
High Conformity (%) 24.0 38.8 46.8 42.0 NA 53.1 40.2
Medium Conformity (%) 42.6 50.0 22.6 20.2 NA 10.7 40.9
Low Conformity (%) 33.4 11.2 30.6 37.8 NA 36.2 19.0
• The percent of roads and railroads within close proximity of the watershed
boundary is highest in Main 1, Lower 2, and Upper, which include communities
with a lengthy history of development and industrialization.
• Lower 1, which contains some of the last remaining agricultural and natural land
use areas, has a relatively low percent of road and railroads near or adjacent to the
watershed boundary

15. 3.1 RESULTS
WATERSHED BOUNDARY
• Illustrations of
angularity in the Central
study area
• High conformity in the
Eastern study area
compared to the large
amount of watershed
boundary that has no
conformity in the
Western studay area

16. 3.2 RESULTS
STREAM NETWORK

17. 3.1 RESULTS
URBAN V. RURAL WATERSHED
3.3 Urban v. Rural
Watershed

18. 4.1 DISCUSSION AND CONCLUSION
LIMITATIONS AND ASSUMPTIONS
 Uniform buffer effect zone regardless of road type
 Underestimate of large roads influence
 Stream network split at confluence points
 Underestimate of long stream segments conformity
 Subjectivity of thresholds, buffer widths
 Different results with different methods, but similar conclusions

19. 4.1 DISCUSSION AND CONCLUSION
WHAT WE HAVE LEARNED
 Urbanization plays a substantial role in the shape of low-gradient watersheds
 Are watershed boundaries independent from political boundaries?
 High resolution topographic data (LiDAR) reveals landscape subtleties that older, more coarse data could
not show
 The stream network demonstrates substantial conformity to the road network
 “Streams” produced from LiDAR that are not actually streams reinforce the concept of the urban stream desert
 Low-relied watersheds are more likely to have higher conformity since complex road networks replace
the watershed boundary and divert surface runoff
 High-relief watersheds are less likely to influences by small vertical features.

20. 4.1 DISCUSSION AND CONCLUSION
WHAT DOES THIS MEAN?
 We think that stream conformity to artificial features is a major contributor to stream degradation
 High conformity can potentially mean:
 Increase in flashiness
 Increase in streambank erosion
 Increase disconnecting the stream channel from the floodplain
 Reduction in biochemical processes
 Reduction in biodiversity
What can be done next:
 More research needs to be done to relate stream conformity to effects of the urban stream syndrome.
 Do zones of high conformity correlate to areas of stream burial?

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