The Utility Of Climatic Water Balance For Ecological Inference Depends On Vegetation Physiology Assumptions

Background In explaining and predicting spatial vegetation patterns, ecologists have increasingly favoured the use of climatic water balance variables, including actual evapotranspiration (AET) and climatic water deficit (CWD), for representing the hydrologic and energetic environment experienced by plants. Much of the interest in these variables lies in their hypothesized potential to characterize biologically relevant environmental variation more directly than simple climate variables, such as precipitation and temperature. Practically, obtaining AET and CWD values across space requires hydrologic process models that involve assumptions, including assumptions about vegetation transpiration rates. However, transpiration parameter values are rarely known with precision and can vary several-fold within and among vegetation types.Approach We evaluate the extent to which assumptions about vegetation physiology in water balance models affect (a) relative spatial variation in modelled water balance values and (b) ecological inferences that are derived from analyses using water balance variables. We demonstrate an approach for identifying inferences that are robust to these assumptions.Results Assumptions about vegetation physiology can substantially affect relative spatial variation in modelled water balance values. More importantly, such assumptions can also substantially affect the inferences (e.g., expected vegetation distributions) drawn from ecological analyses that use water balance variables as predictors. Water balance variables are less sensitive to assumptions in environmental settings with abundant water supply, where AET variation is driven primarily by available energy (e.g., temperature and insolation), but they can be highly sensitive to assumptions in drier environments.Conclusion Because of their sensitivity to assumptions, water balance variables are not unambiguously superior to simpler climatic and topographic variables, such as precipitation and temperature. However, they retain some advantages, primarily related to their mechanistic incorporation of interactions between water and energy, which may support their use in applications where sensitivity to hydrologic modelling assumptions is low or of minor concern.