Data from Harmon et al. (2021), Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis


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Created: Mar 15, 2021 at 3:21 a.m.
Last updated: Aug 09, 2021 at 3:34 a.m.
DOI: 10.4211/hs.6e102de63a7943e1900aa8c6a8d412ac
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Abstract

These data are published in Harmon, R., Barnard, H., Day-Lewis, F.D., Mao, D., and Singha, K. (2021). Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis. Frontiers in Water, doi: 10.3389/frwa.2021.682285.

Internal water storage within trees can be a critical reservoir that helps trees overcome both short- and long-duration environmental stresses. We monitored changes in internal tree water storage in a ponderosa pine using moisture probes, a dendrometer, and time-lapse electrical resistivity imaging (ERI) to investigate how patterns of in-tree water storage are affected by changes in sapflow rates, soil moisture, and meteorologic factors such as vapor pressure deficit. ERI measurements are influenced by changes in moisture, temperature, solute concentration, and material properties; thus, to evaluate changes in moisture based on ERI, the first three factors must be considered. Measurements of xylem fluid electrical conductivity were constant in the early growing season, while inverted sapwood electrical conductivity steadily increased, suggesting that increases in electrical conductivity of the sapwood did not result from an increase xylem fluid electrical conductivity. Seasonal increases in stem electrical conductivity corresponded with seasonal increases in trunk diameter, suggesting that increased electrical conductivity may result from new growth. Changes in diel amplitudes of inverted sapwood electrical conductivity, which correspond to diel changes in sapwood moisture, indicated that tree water storage use was greatest ~4-5 days after storm events, when sapwood inverted electrical conductivity measurements suggest internal stores were high. A decrease in diel amplitudes of inverted sapwood electrical conductivity during dry periods, suggest that the ponderosa pine relied on internal water storage to supplement transpiration demands, but as drought conditions progressed, tree water storage contributions to transpiration decreased. Wavelet analyses indicated that lag times between inverted sapwood electrical conductivity and sapflow increased after storm events, suggesting that as soils dried reliance on internal water storage increased and the time required to refill daily deficits in internal water storage increased. Lag times peaked when soil moisture returned to pre-storm event levels and then decreased as drought progressed. Short time lags between sapflow and inverted sapwood electrical conductivity corresponded with dry conditions, when ponderosa pine are known to reduce stomatal conductance to avoid xylem cavitation. Time-lapse ERI- and wavelet-analysis results highlighted the important role internal tree water storage plays in supporting transpiration throughout the course of a day, and during periods of declining subsurface moisture.

Subject Keywords

Resource Level Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Gordon Gulch - Boulder Creek Critical Zone Observatory
Longitude
-105.4616°
Latitude
40.0126°

Temporal

Start Date:
End Date:

Content

Readme.txt

Overview of information on this Hydroshare page:

Sapflow, soil moisture, sapflow, and metrological data were collected between May and November 2018, as described in the Frontiers in Water manuscript "Exploring Environmental Factors
That Drive Diel Variations in Tree Water Storage Using Wavelet Analysis".


In the Content section here, you will find:

1) The Subsurface Data folder includes: 
 Uncalibrated soil moisture data collected with METER ECH2O EC-5 sensors located one-meter away from the study tree and at 10, 20, and 40-cm depth. 

2) The Surface Data folder includes: 
a. Climate data measured every 10 minutes at the south-facing Gordon Gulch weather station. Data includes precipitation, wind speed, solar radiation, air temperature, and relative humidity (see https://www.hydroshare.org/resource/d66f1f3239a94c7682c71217b1a94e0b/).

2) Tree Measurements folder includes: 
a. Dendrometer measurements using a METER digital dendrometer (https://www.metergroup.com/environment/#d6), which was attached 1.50-m above the ground surface.
b. Sapflow data [cm^3/hr] collected at 1.5m above ground surface on the east and west sides of the study tree (recorded every 30min).
c. Cross-sectional temperature profile of the tree trunk. Measurements were collected at 2, 10, and 20 cm depth on the south- and north-facing sides of the tree. Measurements were collected every 30 minutes.
d. Sapwood volumetric water content data measured with METER TEROS 12 frequency-domain reflectometry sensor (https://www.metergroup.com/environment/products/teros-12/), which was inserted into the xylem 2m above the ground surface, which provided an indirect measure of VWC and measured temperature every 30min.
e. Sapwood Electrical Conductivity:
	- electrical resistivity imaging data. 
	- bulk electrical conductivity of xylem water measured by coring into the xylem of nearby ponderosa pines.
	- electrical conductivity data measured with a METER TEROS 12 frequency-domain reflectometry sensor (https://www.metergroup.com/environment/products/teros-12/), which was inserted into the xylem 2m above the ground surface.





References

Related Resources

The content of this resource serves as the data for: Harmon, R., Barnard, H., Day-Lewis, F.D., Mao, D., and Singha, K. (2021). Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis. Frontiers in Water, doi: 10.3389/frwa.2021.682285.

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation From Roots to Rock - Linking Evapotranspiration and Groundwater Fluxes in the Critical Zone EAR1446161 and EAR1446231
National Science Foundation Collaborative Research: Network Cluster: Bedrock controls on the deep critical zone, landscapes, and ecosystems EAR-2012408

Contributors

People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Fred Day-Lewis Pacific Northwest National Lab
Deqiang Mao Shandong University
Aaron Engers Colorado School of Mines
Jackie Randell Colorado School of Mines

How to Cite

Harmon, R. E., K. Singha, H. R. Barnard (2021). Data from Harmon et al. (2021), Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis, HydroShare, https://doi.org/10.4211/hs.6e102de63a7943e1900aa8c6a8d412ac

This resource is shared under the Creative Commons Attribution-NoCommercial CC BY-NC.

 http://creativecommons.org/licenses/by-nc/4.0/
CC-BY-NC

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