Work Description

Title: Clinton Smart Stormwater Management Project Open Access Deposited

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Attribute Value
Methodology
  • Various methods were used in the creation of this dataset including E. coli storm and baseline sampling for time series data, flow accumulation and direction analysis, and the analysis of above and below ground infrastructure on the shape of watersheds.
Description
  • We created various files, including GIS files and data files for both the UM Hydrologic Modeling Team and for our own Escherichia coli sampling project. The UM Hydrologic Team used the files we created to make their models more accurate. For example, we edited Clinton River subwatershed files to better reflect below and above-ground infrastructure, and provided them to the modeling team. For our own E. coli subproject we created time series, GIS files, and R code to better understand the influence of precipitation and streamflow on E. coli dynamics. Our time-series data is based on baseline and storm sampling we conducted in the summer of 2021. We used GIS files to explore the subwatersheds of our E. coli sampling locations. Finally, we created R code to help us visualize and analyze the data.
Creator
Depositor
  • tjmarchm@umich.edu
Contact information
Discipline
Resource type
Last modified
  • 11/17/2022
Published
  • 05/20/2022
DOI
  • https://doi.org/10.7302/tnt9-8p55
License
To Cite this Work:
Marchman, T., Dominique, D., Zhang, H., Daneshvar, F., Murphy Kevin. (2022). Clinton Smart Stormwater Management Project [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/tnt9-8p55

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Files (Count: 2; Size: 2.12 MB)

Title: Clinton Smart Stormwater Management Project

Introduction: We created various files, including GIS files and data files for both the UM Hydrologic Modeling Team and for our own Escherichia coli sampling project. The UM Hydrologic Team used the files we created to make their models more accurate. For example, we edited Clinton River subwatershed files to better reflect below and above-ground infrastructure, and provided them to the modeling team. For our own E. coli subproject we created time series, GIS files, and R code to better understand the influence of precipitation and streamflow on E. coli dynamics. Our time-series data is based on baseline and storm sampling we conducted in the summer of 2021. We used GIS files to explore the subwatersheds of our E. coli sampling locations. Finally, we created R code to help us visualize and analyze the data.

File Inventory: This dataset contains five main data subfolders. The first is “303(d) Impairments”, which contains a file with the 303(d) Impairments of the Clinton River assigned to every stream reach. The second subfolder is “E. coli, Precip & Flow”. This subfolder contains CSVs containing raw monitoring data for all four branches of the Clinton River, as well as R code designed to visualize the data. It also contains data translated to be run through logistic regression analysis for each branch, as well as R codes to perform the analysis. The third subfolder is “GWK Time Series”, which contains a daily time series of water releases from the George W. Kuhn Retention Treatment Basin (GWK). The fourth subfolder is “Map Components”. This folder contains miscellaneous files used to create various maps used throughout the project. These files include bedrock and quaternary geology maps of the watershed, a Clinton River stream map, site locations for all four E. coli sampling sites, and a map of all University of Michigan stream nodes within the watershed. The fifth subfolder is “Watersheds”. This folder contains all boundary files used within the project. These boundary files include the entire Clinton River Watershed, all Clinton River Watershed subwatersheds that have been edited to reflect below-ground infrastructure, and the GWK, watersheds for all four of our E. coli testing sites, and the catchment of the GWK.

Creators: Daniel Dominique, Timothy Marchman, and Huayile Zhang; With assistance from Fariborz Daneshvar and Kevin Murphy

Contact Information: djdom@umich.edu, tjmarchm@umich.edu, huayilez@umich.edu; fdanesh@umich.edu, kvmurph@umich.edu

Use and Access: The dataset can be used, free of charge, for research and educational purposes. Copy, redistribution, and any unauthorized commercial use are prohibited.

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