Title: Estimating forest ecosystem evapotranspiration at multiple temporal scales with a dimension analysis approach
Author: Zhou, Guoyi; Sun, Ge; Wang, Xu; Zhou, Chuanyan; McNulty, Steven G.; Vose, James M.; Amatya, Devendra M.
Source: Journal of the American Water Resource Association, Vol. 44(1): 208-221
Publication Series: Miscellaneous Publication
Description: It is critical that evapotranspiration (ET) be quantified accurately so that scientists can evaluate the effects of land management and global change on water availability, streamflow, nutrient and sediment loading, and ecosystem productivity in watersheds. The objective of this study was to derive a new semi-empirical ET modeled using a dimension analysis method that could be used to estimate forest ET effectively at multiple temporal scales. The model developed describes ET as a function of water availability for evaporation and transpiration, potential ET demand, air humidity, and land surface characteristics. The model was tested with longterm hydrometeorological data from five research sites with distinct forest hydrology in the United States and China. Averaged simulation error for daily ET was within 0.5 mm⁄ day. The annual ET at each of the five study sites were within 7% of measured values. Results suggest that the model can accurately capture the temporal dynamics of ET in forest ecosystems at daily, monthly, and annual scales. The model is climate-driven and is sensitive to topography and vegetation characteristics and thus has potential to be used to examine the compounding hydrologic responses to land cover and climate changes at multiple temporal scales.
Keywords: dimension analysis, evapotranspiration, empirical modeling, forest hydrology, water balance
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Zhou, Guoyi; Sun, Ge; Wang, Xu; Zhou, Chuanyan; McNulty, Steven G.; Vose, James M.; Amatya, Devendra M. 2008. Estimating forest ecosystem evapotranspiration at multiple temporal scales with a dimension analysis approach. Journal of the American Water Resource Association, Vol. 44(1): 208-221
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