Modelling interception in coastal and montane rainforests in northern Queensland, Australia
Introduction
The interception of water on rainforest canopies and its subsequent evaporation back into the atmosphere (i.e. the interception loss) has been shown to be an important factor influencing both local climate (e.g. Salati and Vose, 1984) and downstream river flows (Bruijnzeel, 1996). In northern Queensland, tropical rainforests cover large areas of the coastal fringe in the Wet Tropics region and play a major role in the generation of runoff across the coastal plains and into the Coral Sea. The amount of runoff from these tropical forests is highly influenced by the canopy interception loss, yet until recently this has not been well quantified. Measurements around three single rainforest trees in the sub-tropical rainforest in south east Queensland indicated that up to 40% of the total water input to the canopy (rain plus cloud interception) was lost as interception (Hutley et al., 1997). More recently McJannet et al., 2007a, McJannet et al., 2007b have reported comprehensive, long term stand level interception losses in six rainforest locations in the wet tropics that span the altitudinal gradient from the coastal plains to the second highest mountain. Interception losses ranged from 22% to 29% of total water input (rainfall and cloud interception) at all sites except the highest altitude site on Bellenden Ker, where interception was only 6% of the total water input.
The Australian rainforest interception losses span the complete range of values reported for other rainforests around the world. For example, low interception losses are associated with continental rainforests, such as in Amazonia, e.g. ∼9% (Lloyd et al., 1988). Higher interception losses have been reported for mountain rainforests in other humid tropical regions, e.g. 19–23% in Puerto Rico (Holwerda et al., 2006), 35% in Uganda (Hopkins, 1960) and 37% in Panama (Cavelier et al., 1997). The lower, continental rainforest interception losses can be explained using an appropriate value of the canopy storage (usually ∼1 mm) and wet canopy evaporation rates calculated using local weather data (e.g. see Roberts et al., 2005). At these sites the energy used in the interception process is less than the total available energy, the remainder being used to heat the atmosphere (Shuttleworth, 1989). The higher interception losses occur in rainforests in coastal or island locations, and are usually explained by the addition of advected energy from surrounding areas of dry vegetation or from nearby oceans. However, the amount of advected energy required is often several times the incident net radiation and the origin of this additional energy is still largely a matter of speculation (Schellekens et al., 1999, Murakami, 2006).
The current paper presents an analysis of interception at six rainforest sites in northern Queensland. We have used a physically based interception model to explore the different processes contributing to canopy evaporation losses to identify the key differences between coastal and mountain rainforests in Australia.
Section snippets
Site and measurements
A full description of the six rainforest sites in the Wet Tropics region of north Queensland, Australian is given by McJannet et al., 2007a, McJannet et al., 2007b, hence only a brief description is given here. The locations and altitudes of the sites, which are all World Heritage listed rainforests, are given in Fig. 1. Two of the sites, Hutchinson Creek (HC) and Oliver Creek (OC), are lowland coastal rainforests located in the Daintree National Park. They are at an altitude of 20 m and only 3
The Gash rainfall interception model
We have used the revised Gash interception model (Gash et al., 1995), with canopy cover values in the range 94–97%, estimated using a fish eye lens camera (see McJannet et al., 2007a). Table 2 (reproduced from Wallace and McJannet, 2006) summarises the five terms in the Gash model, which assumes that rainfall is intercepted in a series of discrete storms, with sufficient time between each storm for the canopy to dry. Each storm can have up to three sequential phases; (i) a wetting phase during
Canopy storage capacity
Two examples of the relationship between (Tf + Sf) and Pga (at ML1 and UB) are shown in Fig. 2. The linear regressions through all the points gave Smean as 3.6 (±0.25) mm at Mount Lewis and 2.7 (±0.78) mm at Upper Barron. In contrast, the minimum estimates of the canopy storage capacity, Smin, for these two sites were much lower at 0.13 (±0.46) mm and 0.56 (±0.71), respectively. As Wallace and McJannet (2006) have already pointed out, the value of Smin is highly dependent on the points chosen to
Conclusions
This study has demonstrated that the most reliable methods for estimating canopy storage (S) is the ‘mean’ method described by Klaassen et al. (1998). The more conventional ‘minimum’ method consistently gave canopy storage values that were so low it was not possible to reproduce measured interception values. The optimisation method for estimating gave more plausible results, but there is evidence that the values derived in this way may have been uncertain due to the RMS minima being very
Acknowledgements
The authors would like to thank Mark Disher, Andrew Ford, Peter Richardson, Trevor Parker, Adam McKeown, Trudi Prideaux, Jenny Holmes and Pepper Brown for their help with field installation and maintenance. We are also grateful to the internal CSIRO reviewers, Peter Hairsine and Helen Cleugh, and the two anonymous external reviewers for their helpful comments on the manuscript and the thought provoking ideas on wet canopy evaporation rates. Funding for this research was provided by the
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