HR: 08:30h
AN: H21B-02    [PDF]
TI: Predictive Uncertainty and Scalability of Transpiration in Heterogeneous Watersheds
AU: * Mackay, D S
EM: dsmackay@buffalo.edu
AF: State University of New York at Buffalo, Department of Geography, 105 Wilkeson Quad, Buffalo, NY 14261 United States
AU: Ewers, B E
EM: beewers@uwyo.edu
AF: University of Wyoming, Department of Botany, 16th and Gibbon, Laramie, WY 82070 United States
AU: Samanta, S
EM: ssamanta@wisc.edu
AF: University of Wisconsin - Madison, Department of Forest Ecology and Management, 1630 Linden Dr., Madison, WI 53706 United States
AU: Burrows, S N
EM: sburrows@ascendanalytics.com
AF: Ascend Analytics, 221 Stonehaven Circle, Newfield, NY 14867 United States
AB: Spatially variable water fluxes from transpiration represent a significant and as yet largely unquantified source of uncertainty in the prediction of ungauged basins. Current models of transpiration can be traced to "center-of-stand" approaches that quantify fluxes in the field and distribute parameters derived from these observations to watershed scales using land cover classification. In this approach, fluxes are extrapolated to larger scales without regard for gradients in environmental drivers. There is evidence that transpiration fluxes change at stand edges, which would magnify the uncertainty in watershed scale fluxes as the spatial heterogeneity of land cover increases. An initial conceptual framework for spatial transpiration will be presented that builds on the fact that canopy stomatal conductance is regulated primarily by leaf water potential when water fluxes are high and of significant hydrologic import. Species plasticity in canopy stomatal conductance, which determines its spatial variability in response to environmental drivers, follows a linear relationship that is keyed off of an easily quantifiable reference conductance. Numerous recent studies have shown that vegetation regulates water fluxes according to internal set points in leaf water potential, despite large variations in environmental drivers such as soil moisture. These internal set points provide for simple, yet mechanistically sound models of transpiration. The transfer of predictive uncertainty in transpiration to basin scales is illustrated using multi-year data from the Chequamegon Ecosystem - Atmosphere Study (ChEAS), with supporting data from other sites. Suitable parameter sets for quantifying predictive uncertainty are derived using a flexible, fuzzy logic approach. A series of simulation experiments are conducted in which the fragmentation of forest cover is increased, thereby increasing edge effects that alter total water fluxes.
DE: 1655 Water cycles (1836)
DE: 1818 Evapotranspiration
DE: 1851 Plant ecology
SC: Hydrology [H]
MN: 2003 Fall Meeting