Mapping land dryness at high resolution for fire prediction
Vinodkumar, Imtiaz Dharssi and Paul Fox-Hughes
Bureau of Meteorology
Accurate soil moisture information is essential for the monitoring and prediction of fire danger, soil moisture being an important factor in determining the moisture content and flammability of fuels. The soil moisture status in operational fire prediction is often provided in the form of moisture deficit, assessed using drought indices. There is evidence that these operational drought indices perform poorly in estimating soil moisture status. A research project was initiated in partnership with the Bushfire and Natural Hazards Cooperative Research Centre to develop an advanced, state-of-the-art soil moisture analysis for Australia. Consequently, a prototype, high-resolution, land surface modelling-based soil moisture analysis called JULES based Soil Moisture Information (JASMIN) has been developed. JASMIN can provide hourly moisture estimates for 4 soil layers, at a spatial resolution of 5 km.
The presentation will discuss the JASMIN project, focussing on the progress made so far on various components, including evaluation, calibration and downscaling of the JASMIN product. The evaluation is carried out against observations from ground-based networks. Among the results, the mean Pearson’s correlation for surface soil moisture across the three in-situ networks is found to be between 0.78 and 0.85. The calibration offers a simple, fast and cost-effective way to make significant upgrades to the existing operational soil moisture monitoring systems used by fire and other environmental agencies. The validation of the calibrated products using in-situ observations and MODIS fire radiative power data will be presented.
The downscaling research is motivated by the desirable impact a higher resolution soil moisture product can provide for fire prediction, considering the high spatial variability in soil moisture and fuel moisture. In this talk, we will focus on some of the research carried out to downscale the JASMIN product from 5 km to 1 km spatial resolution. We discuss the application of three downscaling algorithms: two regression-based methods and one with a theoretical basis. The three methods applied are based on the well-known surface temperature-vegetation index space. We will give an overview of each method applied, along with their comparison against each other and against ground-based soil moisture observations.
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