Published works

Published works

A high-resolution land dryness analysis system for Australia

TitleA high-resolution land dryness analysis system for Australia
Publication TypeConference Paper
Year of Publication2016
AuthorsDharssi, I, Kumar, V
Conference NameAFAC16
Date Published08/2016
PublisherBushfire and Natural Hazards CRC
Conference LocationBrisbane
Abstract

Good estimates of landscape dryness underpin fire danger rating, fire behaviour models, flood prediction, and landslip warning. Soil dryness also strongly influences heatwave development by driving the transfer of solar heating from the soil surface into air temperature rise. Currently landscape dryness, for fire danger prediction, is estimated using very crude models developed in the 1960s that do not take into account different soil types, slope, aspect and many other factors. This work presents a high-resolution soil dryness analysis system that includes data from many sources; such as surface observations of rainfall, temperature, dew-point temperature, wind speed, surface pressure, as well as satellite-derived measurements of rainfall, surface soil moisture, downward surface shortwave radiation, skin temperature, leaf area index and tree heights. The analysis system estimates soil dryness on four soil layers over the top three metres of soil, the surface layer has a thickness of 10cm. The system takes into account the effect of different vegetation types, root depth, stomatal resistance and spatially varying soil texture. The analysis system has a one hour time-step with daily updating. Data assimilation methods are used to extract the maximum amount of useful information from the observations and model. The only practical way to observe the land surface on a national scale is through satellite remote sensing. Unfortunately, such satellite data is prone to biases and corruption. Therefore, it is essential to apply quality control and bias correction. In addition, satellite measurements are infrequent with repeat times of about one day and contain gaps. Data assimilation can filter the random errors from the satellite measurements and fill in both the spatial and temporal gaps in the measurements. Verification against ground-based soil moisture observations from the OzNet, CosmOz and OzFlux networks shows that the new system is significantly more accurate than the traditional soil dryness indices.

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