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Data on influence of atmospheric rivers on vegetation productivity and fire patterns in the southwestern US

Metadata Updated: October 29, 2023

In the southwestern US, the meteorological phenomenon known as atmospheric rivers (ARs) has gained increasing attention due to its strong connections to floods, snowpacks and water supplies in the West Coast states. Relatively less is known about the ecological implications of ARs, particularly in the interior Southwest, where AR storms are less common. To address this gap, we compared a chronology of AR landfalls on the west coast between 1989-2011 and between 25-42.5ºN, to annual metrics of the Normalized Difference Vegetation Index (NDVI; an indicator of vegetation productivity) and daily-resolution precipitation data to assess influences of AR-fed winter precipitation on vegetation productivity across the southwestern US. We mapped correlations between winter AR precipitation during landfalling ARs and 1) annual maximum NDVI and 2) area burned by large wildfires summarized by ecoregion during the same year as the landfalls and during the following year. The data produced by this study include four sets of eight raster grids (total = 32 grids) representing Spearman Rank correlation coefficients for four types of comparisons across eight different latitudinal bands. Each dataset is named according to the comparison type and latitude of AR landfall. The four types of comparisons (with corresponding filenames indicated in parentheses) include: 1) annual winter atmospheric river precipitation vs. total annual winter precipitation (AR_WinterPrecip), 2) annual winter atmospheric river precipitation vs. annual maximum NDVI (AR_NDVI), 3) spatially-averaged annual winter atmospheric river precipitation vs. area burned by wildfire during the same year by Level IV ecoregion (AR_Fire_SameYear), and 4) spatially-averaged annual winter atmospheric river precipitation vs. area burned by wildfire with a 1-year lag by Level IV ecoregion (AR_Fire_OneYearLag). The eight landfall latitudes are indicated in filenames as follows: 25N, 27_5N, 30N, 32_5N, 35N, 37_5_N, 40N, 42_5N.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date June 1, 2023
Metadata Updated Date October 29, 2023

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date June 1, 2023
Metadata Updated Date October 29, 2023
Publisher Climate Adaptation Science Centers
Maintainer
@Id http://datainventory.doi.gov/id/dataset/2f2e0489c7cf528915f7165db56abbad
Identifier 94d98155-28bf-4150-948d-dfe058059c6b
Data Last Modified 2020-08-14
Category geospatial
Public Access Level public
Bureau Code 010:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://datainventory.doi.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id a829b7c1-c9ad-4e45-b5d6-364e55257a87
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -126.785592817,28.403475491,-106.267663646,45.753260345
Source Datajson Identifier True
Source Hash 4deb3888cc7be4bb65a1bf578894750db63dd50fb9aca4936b6d4cbf7623cf8d
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -126.785592817, 28.403475491, -126.785592817, 45.753260345, -106.267663646, 45.753260345, -106.267663646, 28.403475491, -126.785592817, 28.403475491}

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