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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.
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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.
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Collaborative Research: Network Cluster: Bedrock controls on the deep critical zone, landscapes, and ecosystems
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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.
A couple of notes about the published in-tree temperature data here:
The spreadsheet here has temperature data labeled for 2, 10, and 20 cm depth. The 20 cm depth label is a typo and should be 22 cm depth. These data are for the probe inserted on the north side of the tree, starting in January of 2018, as outlined in the linked paper, above.
It appears we didn't publish the data from the south side of the tree, which has 26.5, 34, 38, 42, and 46 cm measurements. The south probe did not go in until June of 2018. We apologize for this error, and will share an updated .csv with anyone who would like the file since we cannot change this repository or add an extra file here. Apologies for this oversight.
Lastly, the negative temperatures were collected during the winter, when the sensor didn't collect meaningful data.
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Kamini Singha 10 months, 2 weeks ago
A couple of notes about the published in-tree temperature data here:
ReplyThe spreadsheet here has temperature data labeled for 2, 10, and 20 cm depth. The 20 cm depth label is a typo and should be 22 cm depth. These data are for the probe inserted on the north side of the tree, starting in January of 2018, as outlined in the linked paper, above.
It appears we didn't publish the data from the south side of the tree, which has 26.5, 34, 38, 42, and 46 cm measurements. The south probe did not go in until June of 2018. We apologize for this error, and will share an updated .csv with anyone who would like the file since we cannot change this repository or add an extra file here. Apologies for this oversight.
Lastly, the negative temperatures were collected during the winter, when the sensor didn't collect meaningful data.
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