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SWE Estimation Application of National Snow Model. Python script and estimations for the Upper Colorado River Basin Water Year 2022
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| Type: | Resource | |
| Storage: | The size of this resource is 169.9 MB | |
| Created: | Jul 26, 2023 at 6:55 p.m. | |
| Last updated: | Sep 05, 2023 at 6:24 p.m. | |
| DOI: | 10.4211/hs.56bd02c71ebe447a844758898e675bad | |
| Citation: | See how to cite this resource | |
| Content types: | Geographic Feature Content |
| Sharing Status: | Published |
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| Views: | 290 |
| Downloads: | 17 |
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| Comments: | 1 comment |
Abstract
Directory of files to perform historical and near-to-date regional snow water equivalent (SWE) estimates across the Western United States. Estimates are performed by the large-scale, machine-learning National Snow Model (https://github.com/AlabamaWaterInstitute/National-Snow-Model), which was trained on historical in-situ snow observations from 2013-2019.
This resource contains Python scripts to produce SWE estimates for a region defined by a user-input shapefile at 1-km resolution. The model is ready to run "out-of-the-box" upon download provided the directory structure is maintained; the user must only provide a shapefile of the region of interest. Estimates are saved in the submission_format_DATE.csv and may be joined with geographical location information in ..._Geo_df.csv. Detailed descriptions of function arguments can be found in the Region_SWE.py file. Note that pre-processing and estimation of large regions is computationally and memory intensive and high-performance computing is recommended for such areas. Smaller regions may easily be executed on a personal machine.
This resource also contains weekly SWE simulations of water year 2021-22 for the Upper Colorado River Basin. This is a preliminary estimate that has not been cross-validated. It is provided as an example of model application.
***This is a preliminary research product and its results should not be used for operational purposes. The National Snow Model is undergoing constant performance and functionality improvement and validation testing.***
Funding for this project was provided by the National Oceanic and Atmospheric Administration (NOAA), awarded to the Cooperative Institute for Research to Operations in Hydrology (CIROH) through the NOAA Cooperative Agreement with The University of Alabama, NA22NWS4320003.
Subject Keywords
Coverage
Spatial
Temporal
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Content
Readme.txt
Requires Pyton 3.9 or higher Required packages and libraries can be found in National_Snow_Model_Regional.py and Regional_SWE.py ------Description----- This resource contains Python scripts to produce SWE estimates for a region defined by a user-input shapefile at 1-km resolution. The model is ready to run "out-of-the-box" upon download provided the directory structure is maintained; the user must only provide a shapefile of the region of interest. Estimates are saved in the submission_format_DATE.csv and may be joined with geographical location information in ..._Geo_df.csv. Detailed descriptions of function arguments can be found in the Region_SWE.py file. Note that pre-processing and estimation of large regions is computationally and memory intensive and high-performance computing is recommended for such areas. Smaller regions may easily be executed on a personal machine.
Data Services
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| National Oceanic and Atmospheric Administration (NOAA) | NA22NWS4320003 |
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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