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| Type: | Resource | |
| Storage: | The size of this resource is 154.0 GB | |
| Created: | Sep 25, 2024 at 7:59 p.m. (UTC) | |
| Last updated: | Feb 12, 2025 at 5:16 p.m. (UTC) (Metadata update) | |
| Published date: | Jan 10, 2025 at 7:04 p.m. (UTC) | |
| DOI: | 10.4211/hs.de74b0a457c74deca09f9a41afa03c8f | |
| Citation: | See how to cite this resource | |
| Content types: | Multidimensional Content CSV Content |
| Sharing Status: | Published |
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| Views: | 4111 |
| Downloads: | 316 |
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Abstract
Vapor pressure deficit (VPD) is a critical variable in assessing drought conditions and evaluating plant water stress. Gridded products of global and regional VPD are not freely available from satellite remote sensing, model reanalysis, or ground observation datasets. We present two versions of the first gridded VPD product for the Continental US and parts of Northern Mexico and Southern Canada (CONUS+) at a 1 km spatial resolution and daily time step. We derived VPD from Daymet maximum daily temperature and average daily vapor pressure and scale the estimates based on (1) climate determined by the Köppen-Geiger classifications and (2) land cover determined by the International Geosphere-Biosphere Programme. Ground-based VPD data from 253 AmeriFlux sites representing different climate and land cover classifications were used to improve the Daymet-derived VPD estimates for every pixel in the CONUS+ grid to produce the final datasets. We evaluated the Daymet-derived VPD against independent observations and reanalysis data. The CONUS+ VPD datasets will aid in investigating disturbances including drought and wildfire, and informing land management strategies.
Subject Keywords
Coverage
Spatial
Temporal
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Data Services
Related Resources
| This resource is described by | Corak, N.K., Thornton, P.E. & Lowman, L.E.L. A high resolution, gridded product for vapor pressure deficit using Daymet. Sci Data 12, 256 (2025). https://doi.org/10.1038/s41597-025-04544-5 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| U.S. National Science Foundation | ORE-CZ: Integrating Vegetation Phenology to Understand the Sensitivity of Dynamic Water Storage to Drought Using Remote Sensing Data and Hydrology Modeling | 2228047 |
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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