Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...
This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.
This resource contains content types with files that need to be updated to match with metadata changes. Show content type files that need updating.
Supporting Data for Balson et al., A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, USA
Authors: |
|
|
---|---|---|
Owners: |
|
This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource. |
Type: | Resource | |
Storage: | The size of this resource is 61.5 MB | |
Created: | Aug 31, 2021 at 2:14 p.m. | |
Last updated: | Aug 31, 2021 at 8:10 p.m. | |
Citation: | See how to cite this resource |
Sharing Status: | Public |
---|---|
Views: | 1171 |
Downloads: | 5 |
+1 Votes: | Be the first one to this. |
Comments: | No comments (yet) |
Abstract
This resource contains supporting data for the manuscript:
Balson & Ward
A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, USA
In review at Hydrological Processes
(full citation to be updated here upon manuscript publication)
The data presented are tabular outputs of discharge and stream nitrogen concentrations (nitrate-as-N) for all USGS sites within the Wabash River Basin, spanning the period of simulation 1948-2007. Data were generated using Agro-IBIS and THMB, matching exactly previously published modeling results.
Subject Keywords
Coverage
Spatial
Content
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
Comments
There are currently no comments
New Comment