@article {bnh-7343, title = {Effect of weather forecast errors on fire growth model projections}, journal = {International Journal of Wildland Fire}, year = {2020}, month = {08/2020}, abstract = {

Fire management agencies use fire behaviour simulation tools to predict the potential spread of a fire in both risk planning and operationally during wildfires. These models are generally based on underlying empirical or quasi-empirical relations and rarely are uncertainties considered. Little attention has been given to the quality of the input data used during operational fire predictions. We examined the extent to which error in weather forecasts can affect fire simulation results. The study was conducted using data representing the State of Victoria in south-eastern Australia, including grassland and forest conditions. Two fire simulator software packages were used to compare fire growth under observed and forecast weather. We found that error in the weather forecast data significantly altered the predicted size and location of fires. Large errors in wind speed and temperature resulted in an overprediction of fire size, whereas large errors in wind direction resulted in an increased spatial error in the fire{\textquoteright}s location. As the fire weather intensified, fire predictions using forecast weather under predicted fire size, potentially resulting in greater risks to the community. These results highlight the importance of on-ground intelligence during wildfires and the use of ensembles to improve operational fire predictions.

}, keywords = {Bayesian Network, fire prediction, meteorological forecast, sensitivity, simulation}, doi = {https://doi.org/10.1071/WF19199}, url = {https://www.publish.csiro.au/wf/wf19199}, author = {Trent Penman and Dan Ababei and Jane Cawson and Brett Cirulis and Thomas Duff and Swedosh, W and James Hilton} } @conference {bnh-6532, title = {JASMIN: a high-resolution soil moisture analysis system for fire prediction}, booktitle = {AFAC19 powered by INTERSCHUTZ - Bushfire and Natural Hazards CRC Research Forum}, year = {2019}, month = {12/2019}, publisher = {Australian Institute for Disaster Resilience}, organization = {Australian Institute for Disaster Resilience}, address = {Melbourne}, abstract = {

Soil moisture is found to be a key factor that influences fuel moisture content. Consequently, operational forest fire prediction systems typically include soil moisture as one of the inputs for fire behaviour calculations. The soil moisture input to these fire prediction models is usually provided in the form of moisture deficit. There is evidence that the current operational methods used in Australia for fire prediction 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 four soil layers, at a spatial resolution of 5 km.

The present paper will discuss the evaluation of JASMIN carried out against observations from ground-based networks. Among the results, the mean Pearson{\textquoteright}s correlation for surface soil moisture across three in-situ networks is found to be between 0.78 and 0.85. We also focus on the research carried out to downscale the JASMIN product from 5 km to 1 km spatial resolution. 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. We discuss the application of three downscaling algorithms: two regressionbased methods and one with a theoretical basis. The three methods applied in the present study are based on the information derived from characterizing a two-dimensional surface temperature/vegetation index scatterplot domain obtained from thermal and optical remote sensing observations. We present an overview of the application of each method, along with an evaluation against ground-based soil moisture observations. Evaluation results indicate that the regression methods, in general, fail to capture the observed temporal variability. The theoretically based method, on the other hand, provides a temporal correlation of 0.81 and captures the skill of the parent JASMIN product.

Download the full non-peer reviewed research proceedings\ from the Bushfire and Natural Hazards CRC Research Forumhere.

}, keywords = {analysis system, fire prediction, Preparedness, risk management, Soil moisture}, url = {https://knowledge.aidr.org.au/resources/australian-journal-of-emergency-management-monograph-series/}, author = {Vinod Kumar and Imtiaz Dharssi and Paul Fox-Hughes} }