@article {bnh-7761, title = {The influence of soil moisture on surface and sub-surface litter fuel moisture simulation at five Australian sites}, journal = {Agricultural and Forest Meteorology}, volume = {298-299}, year = {2021}, month = {03/2021}, abstract = {

Fuel moisture content (FMC) of the litter is a critical factor determining fire ignition and spread and is an important input for most fire behaviour prediction models. Several models, ranging from empirical regression functions to physics-based models, have been developed to forecast litter FMC. Soil moisture below the litter layer has been shown to influence litter FMC, but few models explicitly consider its effect. This study aimed to evaluate how soil moisture content may affect litter FMC by coupling soil moisture as a boundary condition to the physics-based {\textquoteleft}Koba{\textquoteright} model, which simulates radiation, energy and moisture fluxes in the surface and subsurface litter layer. The coupled model was tested at five sites in Victoria, Australia, where litter FMC values were recorded continuously during 2014-2015 using calibrated fuel stick sensors. Two versions of the model were compared against the observations: an uncoupled model and a model version accounting for the vapour flux from soil to litter. The simulation results show that the influence of soil moisture depends on environmental conditions. Soil moisture appeared to have a minor influence on FMC when both soil and litter are dry, but a stronger influence when the soil is relatively wet. Correspondingly, the coupled model explained observed FMC better than the uncoupled model for the two wetter study sites. As expected, the subsurface litter in contact with the soil appeared more sensitive to soil moisture conditions than the surface litter. The influence of soil vapour flux on litter FMC can be considerable during the transition from wet to dry litter and soil conditions. This has implications for hazard reduction burning, which is typically planned to take advantage of transitional fuel moisture conditions. Further research is needed to understand the influence of the structure and thickness of litter on the importance of soil vapour flux.

}, keywords = {Coupled model, Dead fuel, forecast, fuel moisture content, Soil moisture}, doi = {https://doi.org/10.1016/j.agrformet.2020.108282}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0168192320303841?dgcid=coauthor}, author = {Li Zhao and Marta Yebra and Albert van Dijk and Geoffrey J. Cary and Stuart Matthews and Gary J. Sheridan} } @article {bnh-7694, title = {Improving flood forecast skill using remote sensing data {\textendash} final project report}, number = {633}, year = {2020}, month = {12/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Floods are among the most damaging natural disasters in Australia. Over the last 40 years, the average annual cost of floods was estimated to be $377 million per year. The 2010-2011 floods in Brisbane and South-East Queensland alone resulted in 35 confirmed deaths and $2.38 billion damage. The floods in June 2016 in Queensland, New South Wales, and Tasmania, resulted in five confirmed casualties. The Insurance Council of Australia stated on June 7, 2016 that about 14,500 claims totalling $56 million have been lodged from across the country. The floods in March-April 2017 in Queensland and New South Wales caused five confirmed casualties. Furthermore, according to the Insurance Council of Australia, 823,560 Queensland homes are still unprepared for flooding (March 11, 2018).\  The floods in North Queensland in January-February 2019 resulted in four confirmed fatalities and an estimated total direct cost of 1.3 billion dollars. In order to limit the personal and economic damage caused by floods, operational water and emergency managers rely on flood forecasting systems.

These systems consist of a hydrologic and a hydraulic model to predict the extent and level of floods. Using observed and predicted rainfall, the hydrologic model calculates the amount of water that is flowing through the river network, while the hydraulic model computes water depth and velocity in the river and in the floodplain. In recent decades, the accuracy and reliability of these flood forecasting systems has significantly improved. However, it remains difficult to provide accurate and precise flood warnings. This is a result of errors and/or uncertainties in model structures, model parameters, input data, and/or meteorological forcing (mainly rainfall). The hypothesis of this project is that remote sensing data can be used to improve modelled flood forecast skill and value.

More specifically, this project developed optimal ways to constrain and update the hydrologic model using remotely sensed soil moisture data. The significance of soil moisture is its direct impact on the partitioning of rainfall into surface runoff and infiltration. Second, this project proposed an algorithm for the monitoring of floods under vegetation. Finally, we investigated optimal ways to use remote sensing-derived inundation extent and level to implement and calibrate the hydraulic model. \ The results of this project enable improved predictions of flow depth, extent and velocity in the floodplain.

}, keywords = {Flood, forecast, remote sensing data}, issn = {633}, author = {Valentijn Pauwels and Jeffrey Walker and Stefania Grimaldi and Ashley Wright and Yuan Li} }