quantifying spatial patterns of transpiration in xeric and mesic forests

17
Quantifying spatial patterns of transpiration in xeric and mesic forests Jonathan D. Adelman 1 Brent E. Ewers 1 Mike Loranty 2 D. Scott Mackay 2 June 1, 2005 1: University of Wyoming 2: SUNY Buffalo

Upload: zulema

Post on 29-Jan-2016

38 views

Category:

Documents


0 download

DESCRIPTION

Quantifying spatial patterns of transpiration in xeric and mesic forests. Jonathan D. Adelman 1 Brent E. Ewers 1 Mike Loranty 2 D. Scott Mackay 2 June 1, 2005 1: University of Wyoming 2: SUNY Buffalo. Cookie-cutter approach vs. spatially explicit upscaling. - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: Quantifying spatial patterns of transpiration in xeric and mesic forests

Quantifying spatial patternsof transpiration in xeric

and mesic forests

Jonathan D. Adelman 1

Brent E. Ewers 1

Mike Loranty 2

D. Scott Mackay 2

June 1, 2005

1: University of Wyoming2: SUNY Buffalo

Page 2: Quantifying spatial patterns of transpiration in xeric and mesic forests

Cookie-cutter approachvs. spatially explicit upscaling

Traditional method: pick one point in a stand, measure parameter(s) of interest, and assume the rest of the stands exhibits identical behavior.

-Traditional means of quantifying carbon and water fluxes have not been spatially explicit.

-Some ChEAS-based studies have successfully utilized this approach; however, changes in site gradient or management plan would have likely rendered traditional sampling ineffective.

EC=JS·(AS:AG)EC=KL·(ΨS-ΨL)EC=GS·LAI·(VPD)

EC = canopy transpirationJS = sap fluxAS:AG = sap wood to ground area ratioKL = hydraulic conductanceΨS = soil water potentialΨL = leaf water potentialGS = canopy stomatal conductanceLAI = leaf area indexVPD = vapor pressure deficit

Page 3: Quantifying spatial patterns of transpiration in xeric and mesic forests

Cookie-cutter approachvs. spatially explicit upscaling

Spatially explicit method: allow parameter to vary across the stand.

-Geostatistical analyses appearing more often in ecology literature.

-Rarely used with flux ecology, mostly with soils; no prominent studies quantify ecophysiological spatial patterns.

-Water is easy to measure spatially; is continuous; good eventual proxy for carbon fluxes.

EC=JS·(AS:AG)EC=KL·(ΨS-ΨL)EC=GS·LAI·(VPD)

EC = canopy transpirationJS = sap fluxAS:AG = sap wood to ground area ratioKL = hydraulic conductanceΨS = soil water potentialΨL = leaf water potentialGS = canopy stomatal conductanceLAI = leaf area indexVPD = vapor pressure deficit

Page 4: Quantifying spatial patterns of transpiration in xeric and mesic forests

Traditional geostatistical analyses

The semivariance of a measured parameter (in this case, soil moisture) is used to create a kriged surface. This methodology can also be used with flux measurements.

Page 5: Quantifying spatial patterns of transpiration in xeric and mesic forests

Objectives• Determine whether spatial patterns of transpiration exist

– If not spatial patterns exist, use cookie-cutter approach– If spatial patterns exist, models must be spatially explicit

• Determine whether spatial patterns change in time

• Determine whether spatial patterns change with scaling

• Determine whether spatial patterns change across ecosystems– If so, easily measured proxy is needed

• Test methodology in two differing ecosystems– Wisconsin: mesic site, lowland upland gradient– Wyoming: xeric site, low-lying creek hilltop gradient

Page 6: Quantifying spatial patterns of transpiration in xeric and mesic forests

Study sites

Page 7: Quantifying spatial patterns of transpiration in xeric and mesic forests

Study sites

Page 8: Quantifying spatial patterns of transpiration in xeric and mesic forests

Study sites

Page 9: Quantifying spatial patterns of transpiration in xeric and mesic forests

WetlandTransition

Upland

Study sites

Wisconsin

120m x 120m area 80m x 184m area Low slope

Mid slope

High slope

Wyoming

-moisture gradients

-VPD

-sap flux -soil moisture-VPD

Page 10: Quantifying spatial patterns of transpiration in xeric and mesic forests

The semi-variogram

= semi-variancedistance = distance between point pairsa = sill b = range c = nugget

c

Page 11: Quantifying spatial patterns of transpiration in xeric and mesic forests

Semi-variograms of JS across time

Page 12: Quantifying spatial patterns of transpiration in xeric and mesic forests

Semi-variograms of VPDand soil moisture

July 28, 2004 August 5, 2004

Page 13: Quantifying spatial patterns of transpiration in xeric and mesic forests

Semi-variograms of JS and EC

Page 14: Quantifying spatial patterns of transpiration in xeric and mesic forests

Semi-variograms of JS and EC

VS.

Page 15: Quantifying spatial patterns of transpiration in xeric and mesic forests

Kriging

Page 16: Quantifying spatial patterns of transpiration in xeric and mesic forests

Conclusions• Spatial patterns of sap flux and transpiration exist:

– models must be spatially explicit, OR– easily measurable proxies must be found

• Spatial patterns of sap flux and transpiration change:– across time– across ecosystems– with upscaling

• Implications for carbon flux measurements

• Proxies:– remotely sensed imagery– physiological parameters such as LAI

Page 17: Quantifying spatial patterns of transpiration in xeric and mesic forests

Acknowledgements

Wisconsin-based research has been funded by NSF Hydrologic Sciences (EAR-0405306 to D.S. Mackay, EAR-0405381 to B.E. Ewers, and EAR-0405318 to E.L. Kruger).

Wyoming-based research has been funding by Wyoming NASA Space Grant Consortium’s 2004 Graduate Research Fellowship (to J.D. Adelman).

Thanks to the Principal Investigators, as well as Mike Loranty, Erin Loranty, and Tim Wert for assistance at the ChEAS study site, and Mel Durrett and Ian Abernethy for assistance at the Snowy Range study site.

Special thanks to Sarah Kerker, whose fingerprints have left indelible marks at both sites.