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11 results for “vapor pressure deficit”
Derived SPEI and vapor pressure deficit for 15 NPP study sites on the Jornada Basin, 2013-ongoing
Standardized Precipitation Evapotranspiration Index (SPEI) and minimum, maximum, and average vapor pressure deficit (VPD) were calculated from meteorological data (temperature, precipitation, and relative humidity) from the 15 net primary production (NPP) study locations on the Jornada Experimental Range (JER) and the Chihuahuan Desert Rangeland Research Center (CDRRC) lands in southern New Mexico, U.S.A.
Riparian Evapotranspiration (ET) Study (SEON) from the Middle Rio Grande River Bosque, New Mexico (1999-2011 ): Vapor Pressure Deficit (VPD) Data
We hypothesized that flooding and invasions of non-native species would strongly impact ecosystem water use. Our objectives were to measure and compare water use of native (Rio Grande cottonwood, Populus deltoides ssp. wizleni) and non-native (saltcedar, Tamarix chinensis, Russian olive, Eleagnus angustifolia) vegetation and to evaluate how water use is affected by climatic variability resulting in high river flows and flooding as well as drought conditions and deep water tables. Eddy covariance flux towers to measure ET and shallow wells to monitor water tables were instrumented in 1999. Active sites in their second decade of monitoring include a xeroriparian, non-flooding salt cedar woodland within Sevilleta National Wildlife Refuge and a dense, monotypic salt cedar stand at Bosque del Apache NWR, which is subject to flood pulses associated with high river flows.
CMIP6 derived ensemble of global vapor pressure deficit, potential evapotranspiration, and reference evapotranspiration
<p>Climate change induced trends in long-term aridity—via changes to atmospheric water demand for have the potential to impact surface water availability across the globe by altering efficiency by which precipitation is converted to runoff. Quantification of aridity requires estimates of evaporative demand, often using vapor pressure deficit, potential evapotranspiration, and/or reference evapotranspiration, but no comprehensive estimate of these climate variables exists to date from the Coupled Model Intercomparison Project 6 (CMIP6). Here we present global monthly estimates of the Penman-Monteith short grass reference evapotranspiration, its advective and radiation components, Priestley-Taylor potential evapotranspiration, and vapor pressure deficit from 16 CMIP6 general circulation models (GCM) for the historical period and four future emission scenarios ranging from low to high projected emissions. The purpose of this dataset is to offer structured and well-documented estimates of historical and future projected evaporative demand derived from the state-of-the-science CMIP6 climate models for use in hydrologic and ecological analyses. We produce a single file for all monthly values of each variable for individual GCM/emission scenario combination gridded at the given GCMs native resolution. Produced alongside all of the files are descriptions of each of the variables and generated python scripts that contain the functions used to estimate vapor pressure deficit, potential evapotranspiration, and reference evapotranspiration.</p>
The lag and cumulative response of vegetation WUE to water vapor pressure deficit on the Shiyang River Basin, northwest of China_data description
<p>Data description</p> <p>This document provides details about the data description in the manuscript. The variables are described below along with their collection methods and calculation processes:</p> <p>the lag effect: The zip file of“The lag effect” contains three files: "rET_lag_result_z", "rGPP_lag_result"_, and" rWUE_lag_result_z".</p> <p>rET_lag_result_z: "rET_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Evapotranspiration (ET)</p> <p>rGPP_lag_result_z: "rGPP_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Gross Primary Productivity (GPP) </p> <p>rWUE_lag_result_z: "rWUE_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Water Use Efficiency (WUE)</p> <p> </p> <p><br>the Cumulative effect: The zip file of“The Cumulative effect” contains three files: "rET_acc_result_z", "rGPP_acc_result"_, and" rWUE_acc_result_z".</p> <p>rET_acc_result_z: "rET_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Evapotranspiration (ET)</p> <p>rGPP_acc_result_z: "rGPP_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Gross Primary Productivity (GPP) </p> <p>rWUE_acc_result_z: "rWUE_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Water Use Efficiency (WUE)</p> <p> </p> <p><br>the Slop_Partition: The zip file of“The Slop_Partition”contains three files: "Slop_ET_Partition", "Slop_GPP_Partition"_, and" Slop_WUE_Partition".</p> <p>Slop_ET_Partition: "Slop_ET_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Evapotranspiration (ET)</p> <p>Slop_GPP_Partition: "Slop_GPP_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Gross Primary Productivity (GPP)</p> <p>Slop_WUE_Partition: " Slop_WUE_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Water Use Efficiency (WUE)</p> <p> </p> <p> </p>
Genetic variation in leaf traits and gas exchange response to vapor pressure deficit in two contrasting conifer species
<p>1. Mechanistically predicting the evolutionary response of tree species to climate change requires an understanding of genetic variation in relevant traits. Here we compared the phenotypic and genetic variation in the Leaf Economics Spectrum (LES) traits and the response of gas exchange to vapor pressure deficit (VPD) in lodgepole pine (Pico) and white spruce (Pigl), an early and a late successional species dominating the boreal forests of western Canada. 2. We measured gas exchange, foliar nitrogen, and lamina mass to area ratio in 697 app. 30-year-old trees in two field progeny trials. We analyzed the response of gas exchange rates to VPD using a novel quantitative genetic model, the function-valued trait approach. 3. Pico showed greater phenotypic variation in the LES traits and greater genetic variation in photosynthetic rate than Pigl, but the species showed no significant difference in their phenotypic correlations between the LES traits. Pico showed a less sensitive stomatal response to VPD than Pigl and no significant genetic variation in stomatal sensitivity. In contrast, Pigl showed a positive correlation between the genetic values of stomatal sensitivity to VPD and stomatal conductance under low VPD. 4. Our study region is projected to see an increase in VPD with climate change; the less sensitive and genetically diverse stomatal response to VPD in Pico could make this species more vulnerable to climate change-induced droughts. </p>
Response of ecosystem productivity to high vapor pressure deficit and low soil moisture: lessons learned from the global eddy-covariance observations
<p>The generated datasets for conducting the analysis are available from the dataset of the FLUXNET2015 Tier one (https://fluxnet.org/data/download-data/), the AmeriFlux ONEFlux (https://ameriflux.lbl.gov/data/download-data/), and the ICOS Drought-2018 (https://www.icos-cp.eu/data-products/YVR0-4898). The FLUXNET2015 Tier one, the AmeriFlux, and the ICOS are all licensed under the Creative Commons Attribution 4.0 International license (CC-BY-4.0) (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p>
Genetic variation in leaf traits and gas exchange response to vapor pressure deficit in two contrasting conifer species
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Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity
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Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Vapor Pressure Deficit from SNPP CrIMSS and Aqua AIRS, V2 (SNDR13IML3SSDFCVPD)
The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Vapor Pressure Deficit from Aqua AIRS, V2 (SNDRAQIL3SSDFCVPD)
This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS).The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
Genes Differentially Expressed in Slow Wilting Phenotypes under a High Vapor Pressure Deficit
GEO Series GSE49097. Glycine max. 12 samples. Type: Expression profiling by high throughput sequencing.
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