Streamflow USGS Observation and National Water Model Forecast data for Some Stations

Authors:
Roja NajafiDaniel P. Ames
Owners: Roja Najafi
  • Contributions
    Joined

Type: Resource
Storage: The size of this resource is 174.8 MB
Created: Apr 20, 2024 at 12:48 a.m.
Last updated: Apr 24, 2024 at 6:13 a.m.
Citation: See how to cite this resource
Content types: Geographic Feature Content  Geographic Raster Content 
Sharing Status: Public
Views: 673
Downloads: 83
+1 Votes: Be the first one to 
 this.
Comments: No comments (yet)

Abstract

This study investigates streamflow dynamics across contrasting climatic regions, focusing on arid Arizona, near-average California, and wet Michigan. The analysis integrates streamflow data from both USGS observational records and National Water Model forecasts for select stations within these regions. By examining these datasets, the study aims to provide insights into the hydrological behavior of diverse environments, elucidating the impacts of varying climatic conditions on streamflow patterns. Understanding these dynamics is essential for informed water resource management strategies tailored to the specific needs and vulnerabilities of each region.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Clair River, Michigan
North Latitude
43.0053°
East Longitude
-82.4048°
South Latitude
42.9758°
West Longitude
-82.4765°

Temporal

Start Date: 04/21/2021
End Date: 04/21/2023
Leaflet Map data © OpenStreetMap contributors

Content

    No files to display.

Data Services

The following web services are available for data contained in this resource. Geospatial Feature and Raster data are made available via Open Geospatial Consortium Web Services. The provided links can be copied and pasted into GIS software to access these data. Multidimensional NetCDF data are made available via a THREDDS Data Server using remote data access protocols such as OPeNDAP. Other data services may be made available in the future to support additional data types.

Web Map Service

https://geoserver.hydroshare.org/geoserver/HS-8ca836e3038840ba9e519938e60024a7/wms?request=GetCapabilities

Web Feature Service

https://geoserver.hydroshare.org/geoserver/HS-8ca836e3038840ba9e519938e60024a7/wfs?request=GetCapabilities

Web Coverage Service

https://geoserver.hydroshare.org/geoserver/HS-8ca836e3038840ba9e519938e60024a7/wcs?request=GetCapabilities

Additional Metadata

Related Resources

This resource is described by Han, H., Morrison, R. R., (2022). Improved runoff forecasting performance through error predictions using a deep-learning approach. Journal of Hydrology (Elsevier) https://doi.org/10.1016/j.jhydrol.2022.127653

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
Cooperative Institute for Research to Operations in Hydrology (CIROH) Collaborative Research: Advancing Data Scienceand Analytics for Flood Predictions None

Contributors

People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Daniel. P. Ames Bigham Young University Utah, US (801) 422-3620 ResearchGateID , GoogleScholarID

How to Cite

Najafi, R., D. P. Ames (2024). Streamflow USGS Observation and National Water Model Forecast data for Some Stations, HydroShare, http://www.hydroshare.org/resource/8ca836e3038840ba9e519938e60024a7

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

Comments

There are currently no comments

New Comment

required