Christoph Langhans

Downscaling regional climate data to calculate the radiative index of dryness in complex terrain

The radiative index of dryness (or aridity index) is a non-dimensional measure of the long-term balance between rainfall and net radiation. Quantifying aridity requires spatially distributed information on net radiation and rainfall. The variability in net radiation in complex terrain can be modelled at high spatial resolution by combining point data with equations that incorporate the effects of elevation, surface geometry and atmospheric attenuation of incoming radiation. At large spatial scales and over long time periods, however, the combination of seasonality, year to year variations and spatial variability in climate result in complex spatial-temporal patterns of incoming radiation, which are more effectively captured in satellite-based measurements. This study uses a high resolution model of shortwave radiation as a tool for downscaling satellite-derived data on incoming radiation. The aim was to incorporate topographic effects on net radiation in complex terrain while retaining information on regional and seasonal trends captured in satellite data. The method relies on satellite-based measures of incoming radiation from the Australian Bureau of Meteorology (BoM) to provide the spatial coverage and long-term data that represent the average incoming radiation across the state of Victoria in southeast Australia. These long-term data were coupled with a topographic downscaling algorithm to produce estimates of net radiation and aridity at the resolution of a 20 m digital elevation model. Results show that annual precipitation (and cloud fraction) gradients drive the variability in aridity at large scales (10–100 km) while topography (e.g. slope aspect and slope angle) are the main drivers at small scales (e.g. 1 km). The aridity index varied between 0.24 and 10.95 across the state of Victoria. The effect of aridity on vegetation was apparent at local scales through systematic variations in tree-height along rainfall gradients and across aspects with different levels of exposure to solar radiation.

Is aridity a high-order control on the hydro–geomorphic response of burned landscapes?

Fire can result in hydro–geomorphic changes that are spatially variable and difficult to predict. In this research note we compile 294 infiltration measurements and 10 other soil, catchment runoff and erosion datasets from the eastern Victorian uplands in south-eastern Australia and argue that higher aridity (a function of the long-term mean precipitation and net radiation) is associated with lower post-fire infiltration capacities, increasing the chance of surface runoff and strongly increasing the chance of debris flows. Post-fire debris flows were only observed in the more arid locations within the Victorian uplands, and resulted in erosion rates more than two orders of magnitude greater than non-debris flow processes. We therefore argue that aridity is a high-order control on the magnitude of post-wildfire hydro–geomorphic processes. Aridity is a landscape-scale parameter that is mappable at a high resolution and therefore is a useful predictor of the spatial variability of the magnitude of post-fire hydro–geomorphic responses.

Quantifying relations between surface runoff and aridity after wildfire: Relations between surface runoff and aridity after wildfire

Post-wildfire runoff and erosion are major concerns in fire-prone landscapes around the world, but these hydrogeomorphic responses have been found to be highly variable and difficult to predict. Some variations have been observed to be associated with landscape aridity, which in turn can influence soil hydraulic properties. However, to date there has been no attempt to systematically evaluate the apparent relations between aridity and post-wildfire runoff. In this study, five sites in a wildfire burnt area were instrumented with rainfall-runoff plots across an aridity index (AI) gradient. Surface runoff and effective rainfall were measured over 10months to allow investigation of short(peak runoff) and longer-term (runoff ratio) runoff characteristics over the recovery period. The results show a systematic and strong relation between aridity and post-wildfire runoff. The average runoff ratio at the driest AI site (33.6%) was two orders of magnitude higher than at the wettest AI site (0.3%). Peak runoff also increased with AI, with up to a thousand-fold difference observed during one event between the driest and wettest sites. The relation between AI, peak 15-min runoff (Q15) and peak 15-min rainfall intensity (I15) (both in mm h ) could be quantified by the equation:Q15 = 0.1086I15 ×AI 2.691 (0.65<AI<1.80, 0<I15<45) (adjusted r 2 = 0.84). The runoff ratios remained higher at drier AI sites (AI 1.24 and 1.80) throughout the monitoring period, suggesting higher AI also lengthens the window of disturbance after wildfire. The strong quantifiable link which this study has determined between AI and post-wildfire surface runoff could greatly improve our capacity to predict the magnitude and location of hydro-geomorphic processes such as flash floods and debris flows following wildfire, and may help explain aridity-related patterns of soil properties in complex upland landscapes. Copyright