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


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Created: Apr 20, 2024 at 12:48 a.m.
Last updated: Apr 24, 2024 at 6:13 a.m.
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Content types: Geographic Feature Content  Geographic Raster Content 
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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:
End Date:

Content

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.

Additional Metadata

Name Value
Gage Type River Gage
Spatial data GCS_WGS_1984 Projection
NWM Forecasts Time series data
Analysis Periods start=2021-04-21T00:00:00; end=2023-04-21T00:00:00
USGS Observation Streamflow Time series data

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

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