@article {bnh-2337, title = {Mitigating the effects of severe fires, floods and heatwaves through improvements to land dryness measures and forecasts: Annual project report 2014-2015}, number = {131}, year = {2015}, month = {02/11/2015}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

The Australian people, businesses and environment are all vulnerable to wildfires, floods and other natural hazards. Deloitte Access Economics estimate the 2012 total economic cost of natural disasters in Australia exceeded $6 billion. Some examples of recent extreme events are the Millennium drought spanning from 1998 to 2009, the 2009 Black Saturday bushfires, the 2011 cyclone Yasi and the summer 2010/2011 floods in eastern Australia.

Knowledge of landscape dryness is critical for the management and warning of fires, floods, heatwaves and landslips. This project will address fundamental limitations in our ability to prepare for these events. Currently landscape dryness is estimated using simplified soil moisture accounting systems developed in the 1960{\textquoteright}s. Similarly, flood prediction, runoff potential and water catchment/dam management also are not using the best available science and technology.

The McArthur Forest Fire Danger Index used in Australia for operational fire warnings has a component representing fuel availability called the Drought Factor (DF). The DF is partly based on soil moisture deficit, calculated as either the Keetch-Byram Drought Index (KBDI) or Mount{\textquoteright}s Soil Dryness Index (MSDI). The KBDI and MSDI are simplified water balance models driven by observation based daily rainfall and temperature. The KBDI and MSDI models oversimplify the parameterisations of evapotranspiration and runoff leading to significant errors.

A verification study has performed an inter-comparison of the traditional KBDI and MSDI with weather prediction models, satellite measurements and ground based measurements. The verification shows that soil moisture analyses from weather models have greater skill and smaller biases than the KBDI and MSDI. This is despite the weather prediction models having a coarse horizontal resolution and not using observed rainfall. Verification also shows that the remotely sensed Advanced Scatterometer soil wetness product is of good quality. This study suggests that analyses of soil moisture can be greatly improved by using physically based land surface models, remote sensing measurements and data assimilation.

The outputs of this project will improve Australia{\textquoteright}s ability to manage multiple hazard types and create a more resilient community, by developing a state of the art, world{\textquoteright}s best practice in soil moisture analysis that underpins flood, fire and heatwave forecasting.\ 

}, issn = {131}, author = {Imtiaz Dharssi and Vinod Kumar} }