This paper describes an innovative method, commonly referred to as ‘spider mapping’, that allows pasture biomass and related data to be collected over large areas in a timely and efficient manner. Spider mapping was developed initially to collect data to allow calibration and validation of a spatial and temporal pasture growth model operating across Queensland on a 5 km grid basis. Two field officers made over 220 000 estimates and collected about 1300 samples of pasture biomass between January 1994 and August 1995. A number of selected biomass samples were analysed for nitrogen, phosphorus and carbon content. In addition, data were also collected on foliage projective cover and tree basal area for a range of woodland communities and both variables compared with mean long-term Normalised Difference Vegetation Index values derived from a time series of National Oceanographic and Atmospheric Administration satellite imagery. Both variables were strongly related to the satellite data with overstorey foliage projective cover having the strongest non-linear correlation (r2 = 0.91). The method described here is currently being used in related work in the rangelands of New South Wales, South Australia, Western Australia and the Northern Territory.
Sub-daily rainfall intensity has a significant impact on runoff and erosion rates in northern Australian rangelands. However, it has been difficult to include sub-daily rainfall intensity in rangeland biophysical models using historical climate data due to the limited number of pluviograph stations with long-term records. In this paper a new empirical model (‘Temperature I15’ model) was developed to predict the daily maximum 15-min rainfall intensity (I15) using daily minimum and maximum temperature and daily rainfall totals from 12 selected pluviograph stations across Australia. The ‘Temperature I15’ model accounted for 46% (P < 0.01) of the variation in observed daily I15 for an independent validation dataset derived from 67 Australia-wide pluviograph stations and represented both geographical and seasonal variability in I15. The model also accounted for 70% (P < 0.01) of the variation in the observed historical trend in I15 for the full record period (average record period was 37 years) of 73 Australia-wide pluviograph stations. The ‘Temperature I15’ model was found to be an improvement on a past empirical model of I15 and can be easily implemented in biophysical models by using readily available daily climate data. However, as the ‘Temperature I15’ model only represented 46% of the variation in daily observed I15, the model is best used in simulation studies on ‘timeframes’ in excess of 5 years. The new ‘Temperature I15’ model was implemented in the runoff equation of the Australia-wide spatial pasture growth model AussieGRASS, which predicts daily water balance and pasture growth for 185 different pasture communities. This resulted in an improved simulation of green cover for 71% of pasture communities but was worse for 25% of communities, with no change for 4% of communities.
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