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| Type: | Resource | |
| Storage: | The size of this resource is 145.7 KB | |
| Created: | Jan 02, 2018 at 9:47 p.m. (UTC) | |
| Last updated: | Mar 01, 2018 at 10:13 p.m. (UTC) (Metadata update) | |
| Published date: | Mar 01, 2018 at 10:13 p.m. (UTC) | |
| DOI: | 10.4211/hs.41c8d8f8788c4ba0b0bfbb924fe1d403 | |
| Citation: | See how to cite this resource | |
| Content types: | Single File Content |
| Sharing Status: | Published |
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| Views: | 3501 |
| Downloads: | 199 |
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Abstract
Script and accompanying ipython notebook written in Python 2.7 for aggregating sub-daily environmental data (rainfall, tide, wind, groundwater) to a daily timescale. The input data are from Norfolk, Virginia. Several different methods of aggregation are used including averages and maximums. The processed/aggregated data are combined with street flood report data to be used in data-driven, predictive modeling. The script in this resource was used in the analysis described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.
Subject Keywords
Coverage
Spatial
Temporal
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Content
Related Resources
| The content of this resource references | https://doi.org/10.1016/j.jhydrol.2018.01.044 |
| Title | Owners | Sharing Status | My Permission |
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| Data-driven street flood severity modeling in Norfolk, Virginia USA 2010-2016 | Jeff Sadler · Jonathan Goodall | Public & Shareable | Open Access |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| Mid-Atlantic Transportation Sustainability Center |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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