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CHAPTER 4
National Estimates of Canopy Interception using the Variable Storage Gash
Analytical Interception Model in South Africa
National estimates of canopy interception using the Variable Storage Gash analytical interception model in South Africa
H.H. Bulcock
*, G.P.W. Jewitt, R.P. Kunz
School of Bioresources Engineering and Environmental Hydrology, University of KwaZulu-Natal, Private Bag X01, Scottsville, 3209, South Africa.
*Corresponding author. E-mail address: [email protected]
__________________________________________________________________________________________
ABSTRACT
The problem of modelling canopy interception at a catchment scale has received remarkably little attention, with even fewer studies at the national scale. Canopy interception for the three most common commercial forestry genera in South Africa (including Lesotho and Swaziland) viz., Eucalyptus, Acacia and Pinus was modelled using the “variable storage Gash model” together with data from a national database of climatic parameters available at the quinary catchment scale for all catchments with a mean annual precipitation exceeding 600 mm. The results of the study show that spatially, canopy interception is highly variable depending on the genus, rainfall intensity and rainfall seasonality. Canopy interception in South Africa was shown to range between less than 10% and up to 40% of gross precipitation, or between 100 and 300 mm.year-1.
Keywords: Canopy interception, Gash model, quinary catchment, South Africa
1. INTRODUCTION
Canopy rainfall interception plays an important role in the water balance of a forested catchment (Anzhi et al., 2005). There is evidence in the available literature that interception can be as high as 10 to 55% of the gross precipitation (McNaughton and Jarvis, 1983; Calder, 1990) and therefore an important consideration in water resources planning. The processes of canopy interception are complex and difficult to quantify and consequently much of our understanding originates from very intensive research studies undertaken at a single site. Modelling canopy interception at a catchment scale has received remarkably little attention (Dye and Versfeld, 1992) and even less at a national scale. However, given the very high estimates of canopy interception by some authors, and the significance attached to the water use of commercial forestry plantations in South Africa (Jewitt, 2002;
Dye and Versfeld, 2000), it is important to assess the potential impact of canopy interception on both catchment and national scale water resources. In order to achieve this requires a model that is not parameter intensive, that makes use of parameters that are easily attainable, but retains the conceptual clarity and scientific rigour necessary to ensure confidence in the model output (Stirzaker et al., 2010).
There have been many models developed to predict canopy interception according to the characteristics of the rainfall and canopy. These models can be grouped into three categories:
1. Empirical or Mathematical models (e.g. Horton, 1919; Merriam, 1960; Aston, 1979;
Massman, 1980; von Hoyningen-Huene, 1981).
2. Stochastic Models (e.g. Calder, 1986; Hall, 2003).
3. Physical and related models (e.g. Rutter et al., 1971, 1975; Rutter and Morton, 1977;
Gash, 1979; Gash et al., 1995).1
The Gash (1979) and Gash et al. (1995) models are probably the best known and most commonly applied canopy interception models. Essentially, these account for forest canopy, canopy structure, tree density and different climatic conditions and are modifications to the Rutter models introduced by Gash (1979) and Gash et al. (1995). These models do require considerable input of climatic data and vegetation-structure parameters (Aboal et al., 1999) limiting, until now, their applicability at a national scale. In this paper, we aim to assess the significance of canopy interception from commercial afforestation across South Africa. This is achieved by applying the “variable storage Gash model”1 (cf.
Chapter 3) to estimate potential canopy interception for all quinary catchments2 (QnC) in South Africa with a mean annual precipitation exceeding 600 mm as these are assumed to cover all catchments where commercial forestry plantations could exist across South Africa. The climatic parameters required include gross precipitation, evaporation, and rainfall rate. As described in Chapter 3, the canopy structure parameters are described using LAI and elemental volume as inputs to parameterise the model.
1 The reader is referred to Chapter 3 for a more detailed description of the “variable storage Gash model”.
2 Quinary catchments are 5th level sub-basins derived by Schulze and Horan (2009) and are commonly used in water resources studies in South Africa.