Identifying Communities At Risk From Flooding in the...
Transcript of Identifying Communities At Risk From Flooding in the...
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Identifying Communities At Risk From Flooding in the Grayson Creek Watershed
Ari Frink
LA 221
May 6th, 2016
Introduction
Flooding is a regular occurrence along Grayson Creek in Contra Costa County, CA.
When Grayson Creek floods, it can endanger the people and property along its banks. Contra
Costa County would like to have a method for prioritizing where the creek channel can be
widened. One way to help prioritize sites is by determining the location of atrisk populations
(elderly, children) along the creek, and then comparing those locations with the creek
infrastructure most likely to fail and most likely to impact these residents. This will determine
where the communities that should be moved away from the creek are located, which would
limit the creek’s flooding impact.
This suitability analysis will help determine locations along the creek that fit these
criteria:
1) Vulnerable populations (Children, elderly, sick)
2) Creek areas most likely to flood
3) Infrastructure with the most impact on safety service areas
Conceptual Model
The following figures shows my process this analysis process and the eventual data
processing and map layers that will be developed.
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Figure #1: Conceptual Model for determining atrisk communities
Data Discovery
Much of the Data is readily available from Contra Costa County and previous work with the
Environmental Planning Studio. Parcel value will need to be determined as the data seems old
that we currently have. I will also use information obtained from the Flood Control District to
determine the previously flooded sites as well as where past repairs have happened.
Projection
All layers will be projected in NAD 1983, State Plane, California III.
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Processing and Modeling
Though most of my analysis was simply a matter of mapping locations and layering them over
each other, my model for determining the areas most atrisk of being disconnected from safety
services during a major storm required more indepth modeling.
Bridge Failure Modeling
After acquiring data for all of the bridges, I then created 581 network datasets, each one missing
one more bridge until all of the bridges were out in my model.
Figure #2: Conceptual Model for Bridge Failure Mapping
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Figure #3: Model for creating feature classes without bridges (Credit: TzuLing Chen)
This model takes the original dataset, iterates through selecting each bridge, deletes that bridge,
then creates a copy of that dataset by merging the remaining bridges with the rest of the streets to
form a new dataset. This data can then be put into another model to create service areas, give
weight to them and combine them for a suitability analysis.
Figure #4: Model for determining Service Area and uniting those service areas into one
analysis layer
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This analysis gave me a map that layered all of the areas that could not be reached by fire
stations during emergencies in the first 6 minutes, and showed the areas most atrisk.
Figure #5: Areas at risk for safety service disconnection during major storm
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Vulnerable Populations
Determining vulnerable populations was done by looking at census data merged with
parcel data for parcels along Grayson Creek. The vulnerable populations identified were elderly,
children (schools and daycare), hospitals, and higher density type housing (e.g. apartments, 2 or
more story buildings, assisted living).
Figure #6: Vulnerable Populations along Creek Channel
AtRisk Areas of Channel
Atrisk areas of the channel were determined by finding particular spatial characteristics of the
channel. This includes bends in the channel where it could flood more easily and steeper slopes
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as proxies for where channel could fail. This was combined with FEMA flood maps
georeferenced to the site.
Figure #7: AtRisk Areas of Channel
All of these layers were weighted (each layer was weighted equally) and then combined for a
final suitability analysis map.
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Figure #8: Combined Suitability Analysis
With the final maps from the three categories weighted and combined, we can begin to
see the areas of the watershed most atrisk during a flooding event. Zooming in to the area where
all of the highest weighted areas are, we see that the most impacted properties are Sequoia
Elementary School and an Apartment complex at 124 Moiso Ln, Pleasant Hill, CA.
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Figure #9: Areas which are most at risk during a major flooding event
Conclusions and Recommendations
Though limited in many ways, this model gives a good approximation of the areas where
communities are most at risk from flooding, based on both their community type and the nature
of the creek and its infrastructure in that vicinity.
There are many uncertainties in this study: how accurate is the road network? Are bends
in creeks more likely to flood and during what kinds of storms? Is the FEMA flood mapping
accurate? However, by aggregating several different data sources and combining them, this
model does provide more insight into which areas of the watershed are most at risk for flooding.
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Contra Costa County and its associated cities and municipalities should take this
information into consideration when planning land use and particularly when determining where
to site new safety resources in the community.