Moisture Isotopes in the Biosphere and Atmosphere (MIBA) in Australia: A priori estimates and preliminary observations of stable water isotopes in soil, plant and vapour for the Tumbarumba Field Campaign

• Published in: Global and planetary change Vol. 51; no. 1; pp. 59 - 72
• Main Authors: Twining, J., Stone, D., Tadros, C., Henderson-Sellers, A., Williams, A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2006
• Subjects: Australia; IAEA; land model evaluation; measurement; MIBA; stable water isotopes; Australia; Australia; stable water isotopes; IAEA; land model evaluation; measurement; MIBA
• ISSN: 0921-8181; 1872-6364
• DOI: 10.1016/j.gloplacha.2005.12.005

An integral part of isotopes in the Program for Intercomparison of Land-surface Parameterisation Schemes (iPILPS) [Henderson-Sellers, A., in press. Improving land-surface parameterization schemes using stable water isotopes; introducing the iPILPS initiative. Global and Planetary Change, in press] is that the models' outputs be evaluated against measured values of δ 2H and δ 18O in the various simulated environmental compartments. This paper outlines the steps taken in Australia to initiate measurement of these stable water isotopes (SWIs) in the field, at a cool-temperate forest site in S.E. Australia near Tumbarumba, specifically to facilitate the model evaluation process. The selected sampling methodologies are detailed within the context of a conceptual model developed to describe the land–atmosphere exchange systems. This model has also been used to make a priori estimations of the isotopic values to be expected in each measured sub-system. As the data resulting from the Tumbarumba field campaign emerge, they will be compared with these working hypotheses to evaluate and, where necessary, amend the conceptual model. Initial comparisons based on preliminary data are presented here. The new observations derived in March 2005 should allow the land surface schemes used in weather forecast and climate change models to better reflect the environments for which they are attempting to make predictions.