Result: Screening for undersized culverts : an analysis of estimated characteristics

Title:
Screening for undersized culverts : an analysis of estimated characteristics
Authors:
Day, Molly Rose
Source:
Theses, Dissertations, and Projects
Publisher Information:
Smith ScholarWorks
Publication Year:
2019
Collection:
Smith College: Smith ScholarWorks
Subject Terms:
Hydrology, Hydraulics, Culverts, Modeling, GIS, Stormwater, TR-55, Stream Stats, Civil and Environmental Engineering
Document Type:
Academic journal text
File Description:
application/pdf
Language:
English
Relation:
https://scholarworks.smith.edu/theses/2127; https://scholarworks.smith.edu/context/theses/article/3192/viewcontent/Honors_Thesis_Final.pdf
Availability:
https://scholarworks.smith.edu/theses/2127
https://scholarworks.smith.edu/context/theses/article/3192/viewcontent/Honors_Thesis_Final.pdf
Rights:
©2019 Molly Rose Day. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.
Accession Number:
edsbas.850B7531
Database:
BASE

Further Information

When culverts are not adequately sized, many structural and ecological problems occur. This issue will increase in frequency and severity as infrastructure ages and climate change increases the severity of storms. Currently there are several methods to determine if a culvert is undersized. Yet, they require a substantial amount of physical data about the culvert and its surrounding area to complete. It is necessary to measure variables such as the slope of the culvert and the height of the road fill above the culvert opening for accurate analysis, but these factors often unmeasured when culverts are assessed by cities. In this project, I investigated whether cities without data stored about slope, road fill height, and inlet type can make assumptions about these data to make a preliminary evaluation of sizing. This information could be used in justifying further specific analysis in grant proposals and to the department as well as determine the order in which culverts are fully evaluated. I investigated two sets of assumptions: one that underestimates culvert capacity and one that tries to match the true culvert capacity. To do this, I used the Cornell Culvert Model. This model uses a mixture of ArcGIS and Python scripts to output the most extreme possible storm the culvert can pass without overtopping the road, given physical data about the culvert in question and the surrounding area. This measure is also called maximum return period. Using data from Livingston county and Genesee counties in New York and the two sets of assumptions, I used the model to find the maximum return period for 101 culverts. The underestimation and accuracy assumptions correctly identified which culverts were undersized 79.2% and 93.7% of the time, respectively. Then, I investigated the accessibility of calculating maximum return period with the StreamStats web tool and the Cornell Culvert Model. I used both methods to determine which culverts in Northampton were possibly undersized. I also created maps showing my results. These ...