Work Description

Title: Data for "Using sensor data to dynamically map large-scale models to site-scale forecasts: A case study using the National Water Model" Open Access Deposited

http://creativecommons.org/licenses/by/4.0/
Attribute Value
Methodology
  • This is raw data scraped from the National Water Model and the Iowa Flood Information System using python and stored in pandas dataframes.
Description
  • This data is in support of the publication in review "Using sensor data to dynamically map large-scale models to site-scale forecasts: A case study using the National Water Model". It is all the raw data extracted from the NWM flow forecasts for Iowa and the IFIS stage readings. For the NWM data, each date has it's own tab-delimited file with columns being the time (hrs) and rows being the NHD site. For the IFIS gages, each tab delimited file is for a single site for the period of record.
Creator
Depositor
  • kjfries@umich.edu
Contact information
Discipline
Funding agency
  • National Science Foundation (NSF)
Other Funding agency
  • National Science Foundation (NSF)
Resource type
Last modified
  • 12/19/2017
DOI
License
To Cite this Work:
Fries, K. Data for "Using sensor data to dynamically map large-scale models to site-scale forecasts: A case study using the National Water Model" [Data set]. University of Michigan - Deep Blue.

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Files (Count: 851; Size: 6.98 GB)