A high resolution, gridded product for vapor pressure deficit using Daymet

Vapor pressure deficit (VPD) is a critical variable for assessing drought conditions and evaluating plant water stress. VPD represents the difference between the amount of water vapor in the atmosphere and the amount that can be held under saturated conditions. It provides a measure of the atmospheric demand for water, and high VPD is strongly linked to dry soil conditions. This information is relevant for understanding the impact of drought on plant growth and productivity. Gridded products of global and regional VPD are not freely available from satellite remote sensing and reanalysis datasets, although they may be derived. Further we lack gridded VPD products that are derived from ground observations. Here, we present a high resolution (1 km, 1-day) gridded VPD product for the Continental US (CONUS) derived from Daymet daily temperature and vapor pressure data. In order to develop reliable estimates of daily average VPD, several different methods for representing daily average temperature from the Daymet dataset were investigated. Estimates of VPD derived from Daymet were compared against ground-based measurements from AmeriFlux eddy covariance towers and reanalysis datasets for the period 2015 to 2020. Initial results show that the Daymet-derived VPD dataset captures seasonal and subseasonal variability observed in ground observations and reanalysis. This suggests that the new dataset could serve as a valuable tool for future drought studies.