干旱区生态环境知识资源中心

Tree water use is a major component of the water balance in forested catchments of semi-arid areas, as more than 80% of the incoming rainfall may be used by overstory trees. Managers are unable to easily predict water use and thus water yield, for the majority of eucalypt-dominated catchments in south-east Australia, owing to the variety of dominant and co-dominant species, their distributions with respect to landform, and the lack of species- and landform-specific knowledge of the regulation of water use. Moreover, the costs incurred to quantify input variables for available complex, process-based models, generally encourage finding alternative approaches. This study tested the adequacy of using just two easily measured variables for estimating rates of tree water use, using a model derived from data-learning techniques. The inputs are (1) measured daily atmospheric demand for water and (2) potential incoming radiation derived from surface topography and solar declination. Artificial neural networks (ANNs) and genetic programming (GP) models were trained and validated using in situ observations of vapour pressure deficit (VPD) and estimates of potential solar radiation (Q(pot)), for a period of two years, at each of 10 forest stands across the high country of the states of New South Wales and Victoria. The models were tested using a random 50% of the collected data that was independent, i.e. not used in model development.

Atmospheric demand was selected because it strongly affects tree water use irrespective of site and species. Potential solar radiation was selected as a proxy for radiation, because it is relatively easy to estimate for any location for which elevation data are available in digital format, and since radiation strongly controls photosynthesis (through stomatal behaviour) and thermal balance.

Genetic programming resulted in models better able to predict rates of sap flux. A selected GP model was able to describe the relationship between tree sap flux, VPD, and potential radiation with good accuracy, and was used to map tree water use across the catchment. (C) 2014 Elsevier B.V. All rights reserved.