A new empirical model of sub-daily rainfall intensity and its application in a rangeland biophysical model

Abstract

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.

Rainfall intensity bursts and the erosion of soils: an analysis highlighting the need for high temporal resolution rainfall data for research under current and future climates

“…Barbero et al (2017), using data from a large set of US data, found increasing annual maximum daily precipitation compatible with the expected CC scaling (they found ∼ 6.9 % • C −1 ) but only a lesser scaling for hourly data. Fu et al (2016) established a significant trend of increasing hourly rainfall over South China, using 31 years (1982China, using 31 years ( -2012 of data from a large network of precipitation stations. They showed that an increasing frequency of rainfalls accounted for most of the observed effect, with only about 10 % attributable to changes in hourly intensity.…”

“…Rainfall bursts extracted from data tallied over longer periods, such as hourly (I 60 ), proved to have less explanatory power. Likewise,Fraser et al (2011) demonstrated the importance of I 15 in erosion within the rangelands of northern Australia, and Wagenbrenner and Robichaud (2014) highlighted the importance of I 10 in post-fire sediment movement in the western USA. At plot scale,Xia et al (2013) showed that, for cropland in China, I 10 , I 20 , I 30 , or I 60 were significantly correlated with event losses of N and P and offered some hypotheses about why one or another index of intensity should become significant at a particular field site Nunes et al (2011).…”

Sub-daily rainfall events in an arid environment with marked climate variability: Variation among wet and dry years at Fowlers Gap, New South Wales, Australia

“…In addition, short intensity peaks are ubiquitous in rainfall events, and generally, though not always, occur in the event quartile that has the largest average intensity, and from which the Huff classification is made. Intensities peaks over short time periods are important in the mechanisms of soil erosion (Fraser et al, 2011). Moreover, rainfall often exhibits intermittency at the event scale, and though periods when rain ceases influence the Huff class to which an event belongs, the classification provides no actual information on intermittency.…”

“…Though the link between temperature and rainfall has often been explored using daily data (Berg et al, 2009;Power et al, 1998), the use of daily temperature data has been used to predict the intensity of rain even at sub-hourly timescales. For instance, various measures of daily temperature were used as a basis for predicting I 15 (the maximum intensity through a 15 min storm interval) for a number of Australian locations, in a model that was able to account for 70% of the variability in I 15 (Fraser et al, 2011). In this work, though actual mechanisms were unclear, daily temperature data such as daily maximum and minimum temperatures, were envisaged as holding information relevant to the intensity of convection.…”

Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia

“…The rain gauge network lacks in continuous long-term records, spatial representativeness and climate homogeneity [11,12]. For example, Australia has only 184 pluviograph stations with coverage exceeding thirty years [13].…”