Connecting the subsurface to the atmosphere: Exploring the sensitivity of parameterizations and interactions of evapotranspiration in the PF-CLM integrated hydrologic model
Wednesday, 17 December 2014
Integrated hydrologic models coupled to land surface models link water and energy movement between the subsurface, land surface and atmosphere. These connections are especially important when estimating a complex, nonlinear process like evapotranspiration (ET). In this work, a comprehensive sensitivity study of the method used to estimate ET in the ParFlow-Common Land Model (PF-CLM) is presented. Particular attention is paid to the forms of parameterizations used as well as to interactions between the groundwater and land surface models. Using this gained insight, estimates of ET from reduced forms of the parameterizations are directly compared to fully-coupled PF-CLM results. With this approach, several forms of the parameterizations are developed that vary in complexity, nonlinearity and coupling strength. The same forcing data is used to compare estimates of ET across all formulations, thus exploring the process and coupling sensitivity in a novel way. The goals of this research are to understand the sensitivity of ET estimates to parameterization complexity and establish relationships that can be used to scale ET from the tree (meter) to sub-watershed (kilometer) scale. Here we use a single column model equilibrated with atmospheric forcing from a snow-dominated alpine region (Breckenridge, Colorado, USA). Results show how subsurface pressure, ground temperature, atmospheric forcing, atmospheric stability and the fraction of wetted foliage uniquely compound to influence estimates of ET. Differences in ET magnitude are observed as the parameterization complexity changes; however, trends remain consistent throughout the year. The relationship between ET and subsurface pressure varies nonlinearly as a result of introduced resistance factors and the slope depends on ground surface temperature and parameterization complexity. Identification of sensitive interactions and driving factors are necessary to further understand hydrologic processes like ET.