Can we predict groundwater discharge from terrestrial ecosystems using existing eco-hydrological concepts?

There is increasing recognition of the role that groundwater plays in the maintenance of ecosystem structure and function. As a result, water resources planners need to develop an understanding of the water requirements for these ecosystems. In this study we reviewed estimates of groundwater discharge from terrestrial vegetation communities around Australia and explored this data set for empirical relationships that could be used to predict groundwater discharge in data poor areas. In particular we explored how leaf area index and the water balance of groundwater systems conformed to two existing ecohydrological frameworks; the Budyko framework, which describes the partitioning of rainfall into evapotranspiration and runoff within a simple supply and demand framework, and Eagleson's theory of ecological optimality. We demonstrate strong convergence with the predictions of both frameworks. Terrestrial groundwater systems discharging groundwater lie above the water limit line as defined in the Budyko framework. However, when climate wetness was recalculated to include groundwater discharge there was remarkable convergence of these sites along this water limit line. Thus, we found that there was a strong correlation between estimates of evapotranspiration derived from the Budyko's relationship with observed estimates of evapotranspiration. Similarly, the LAI of ecosystems with access to groundwater have higher LAI than those without access to groundwater, for a given climatic regime. However, again when discharge was included in the calculation of climate wetness index there was again strong convergence between the two systems, providing support for ecological optimality frameworks that maximize LAI under given water availability regimes. The simplicity and utility of these simple ecohydrological insights potentially provide a valuable tool for predicting groundwater discharge from terrestrial ecosystems, especially in data poor areas.