@article {bnh-7198, title = {Guidelines on the optimal use of remote sensing data to improve the accuracy of hydrologic and hydraulic models}, number = {600}, year = {2020}, month = {08/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {MELBOURNE}, abstract = {

Floods are among the most damaging natural disasters in Australia. In order to limit the personal and economic damage caused by floods, land and emergency managers need to rely on flood forecasting systems. These systems consist of a hydrologic model and a hydraulic model. The hydrologic model calculates the amount of water that enters the river network, while the hydraulic model computes how that water moves throughout the river and floodplain. The accuracy and reliability of flood forecasting systems has significantly improved in the last decades. However, errors and/or uncertainties in model structures and parameters, input data, and/or meteorological forcings often hamper the accuracy of predictions. This document confirms that remote sensing data can be used to improve the accuracy of hydrologic and hydraulic models and thus ultimately improve the flood forecast accuracy.

More specifically, remotely sensed soil moisture data are used to improve the hydrologic forecast skill of ungauged sub-catchment streamflow locations through multi-objective calibration. A pragmatic approach to select the optimal hydrologic model, optimized rainfall product, and remotely sensed soil product is outlined. Routines to assimilate and smooth streamflow and remotely sensed soil moisture observations over the length of a unit hydrograph are provided for improving forecast capability. Further, remotely sensed inundation extent and water level are used to improve the accuracy of the hydraulic model. This spatially distributed information is essential for understanding the floodplain inundation dynamics, adequately setting-up the hydraulic model and effectively constraining its parameters. The research underpinning these guidelines is consistent with the findings of ongoing research efforts worldwide and has contributed to the development of knowledge and a pragmatic framework for application in the Australian context.

The methodologies presented in these guidelines for optimal use of remotely sensed data to improve the predictive skill of flood forecasting models can be applied by operational agencies. Moreover, the techniques for the analysis of remotely sensed data support and complement the existing capabilities of Geoscience Australia, and the hydrologic model assimilation has been implemented by the Australian Bureau of Meteorology.\ 

}, keywords = {flood forecast, hydrologic, hydrological models, remote sensing data}, issn = {600}, author = {Valentijn Pauwels and Jeffrey Walker and Stefania Grimaldi and Ashley Wright and Yuan Li} } @article {bnh-5744, title = {Improving flood forecast using remote sensing data - annual report 2018-2019}, number = {497}, year = {2019}, month = {07/2019}, 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 Queenslad, 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 totaling $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 heavily rely on flood forecasting systems. Further improvements to current flood forecasting systems are likely to reduce the personal and economic damage caused by floods.

}, keywords = {Flood, flood forecast, improving forecast, remote sensing data}, author = {Valentijn Pauwels and Jeffrey Walker and Stefania Grimaldi and Ashley Wright and Yuan Li} } @article {bnh-6927, title = {JASMIN: A prototype high resolution soil moisture analysis system for Australia}, year = {2017}, month = {10/2017}, institution = {Bureau of Meterology}, address = {Canberra}, abstract = {

Accurate soil dryness information is essential for the calculation of accurate fire danger ratings, fire behavior prediction, flood forecasting and landslip warnings. Soil dryness also strongly influences temperatures and heatwave development by controlling the partitioning of net surface radiation into sensible, latent and ground heat fluxes. Rainfall forecasts are crucial for many applications and many studies suggest that soil dryness can significantly influence rainfall. Currently, soil dryness for fire danger prediction in Australia is estimated using very simple water balance models developed in the 1960s that ignore many important factors such as incident solar radiation, soil types, vegetation height and root depth. This work presents a prototype high resolution soil moisture analysis system based around the Joint UK Land Environment System (JULES) land surface model. This prototype system is called the JULES based Australian Soil Moisture INformation (JASMIN) system. The JASMIN system can include 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 short-wave radiation, skin temperature, leaf area index and tree heights. The JASMIN system estimates soil moisture on four soil layers over the top 3 meters of soil, the surface layer has a thickness of 10 cm. 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. For the surface soil layer, verification against ground based soil moisture observations from the OzNet, CosmOz and OzFlux networks shows that the JASMIN system is significantly more accurate than other soil moisture analysis system used at the Bureau of Meteorology. For the root-zone, the JASMIN system has similar skill to other commonly used soil moisture analysis systems. The Extended Triple Collocation (ETC) verification method also confirms the high skill of the JASMIN system.

}, keywords = {Fire behaviour, fire danger ratings, flood forecast, soil dryness}, issn = { Bureau Research Report {\textendash} BRR026}, url = {http://www.bom.gov.au/research/publications/researchreports/BRR-026.pdf}, author = {Imtiaz Dharssi and Vinod Kumar} }