@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} } @article {bnh-5720, title = {Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic-Hydraulic Modeling of Floods}, journal = {Water Resources Research}, volume = {55}, year = {2019}, month = {06/2019}, abstract = {

Flood modeling at the regional to global scale is a key requirement for equitable emergency and land management. Coupled hydrological-hydraulic models are at the core of flood forecasting and risk assessment models. Nevertheless, each model is subject to uncertainties from different sources (e.g., model structure, parameters, and inputs). Understanding how uncertainties propagate through the modeling cascade is essential to invest in data collection, increase flood modeling accuracy, and comprehensively communicate modeling results to end users. This study used a numerical experiment to quantify the propagation of errors when coupling hydrological and hydraulic models for multiyear flood event modeling in a large basin, with large morphological and hydrological variability. A coupled modeling chain consisting of the hydrological model Hydrologiska Byr{\r a}ns Vattenbalansavdelning and the hydraulic model LISFLOOD-FP was used for the prediction of floodplain inundation in the Murray Darling Basin (Australia), from 2006 to 2012. The impacts of discrepancies between simulated and measured flow hydrographs on the predicted inundation patterns were analyzed by moving from small upstream catchments to large lowland catchments. The numerical experiment was able to identify areas requiring tailored modeling solutions or data collection. Moreover, this study highlighted the high sensitivity of inundation volume and extent prediction to uncertainties in flood peak values and explored challenges in time-continuous modeling. Accurate flood peak predictions, knowledge of critical morphological features, and an event-based modeling approach were outlined as pragmatic solutions for more accurate prediction of large-scale spatiotemporal patterns of flood dynamics, particularly in the presence of low-accuracy elevation data.

}, keywords = {Emergency management, Flood, land management, modelling}, doi = {https://doi.org/10.1029/2018WR024289}, url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR024289}, author = {Stefania Grimaldi and Guy Schumann and Ashkan Shokri and Jeffrey Walker and Valentijn Pauwels} } @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} } @conference {bnh-6399, title = {Improving flood forecasting skill using remotely sensed data}, booktitle = {Bushfire and Natural Hazards CRC Research Day AFAC19}, year = {2019}, month = {12/2019}, address = {Melbourne}, abstract = {

This paper presents research undertaken to develop and implement a coupled hydrologic-hydraulic model which utilises remotely sensed data to improve flood forecasting skill in rural catchments subject to fluvial flooding. The discussion of literature reviews and subsequent knowledge gaps aids in the identification of key obstacles towards implementation. Collected data and modelling algorithms developed as part of the project are described before an overview of the progress towards implementation is given. To maximise the potential utilisation of this research end-users have been involved in providing data, setting up case studies, and defining research priorities and methods.

}, keywords = {catchments, Flood, Forecasting, hydraulic, hydrological, modelling}, url = {https://knowledge.aidr.org.au/resources/australian-journal-of-emergency-management-monograph-series/}, author = {Ashley Wright and Stefania Grimaldi and Yuan Li and Jeffrey Walker and Valentijn Pauwels} } @article {bnh-4627, title = {A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction}, journal = {Hydrology and Earth System Sciences}, year = {2017}, month = {02/2017}, abstract = {

The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The discrete cosine transform (DCT) and discrete wavelet transform (DWT) are used to reduce the dimensionality of observed rainfall time series for the 438\ catchments in the Model Parameter Estimation Experiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the observed hyetographs using standard simulation performance summary metrics and descriptive statistics. The results convincingly demonstrate that the DWT is superior to the DCT in preserving and characterizing the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.

}, keywords = {communities, emergency management., Flood, hydrological models, wavelet}, doi = {10.5194/hess-21-3827-2017}, url = {https://www.hydrol-earth-syst-sci.net/21/3827/2017/hess-21-3827-2017.html}, author = {Ashley Wright and Jeffrey Walker and Valentijn Pauwels} } @article {bnh-4626, title = {Estimating rainfall time series and model parameter distributions using model data reduction and inversion techniques}, journal = {AGU}, year = {2017}, month = {07/2017}, abstract = {

Floods are devastating natural hazards. To provide accurate, precise, and timely flood forecasts, there is a need to understand the uncertainties associated within an entire rainfall time series, even when rainfall was not observed. The estimation of an entire rainfall time series and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of entire rainfall input time series to be considered when estimating model parameters, and provides the ability to improve rainfall estimates from poorly gauged catchments. Current methods to estimate entire rainfall time series from streamflow records are unable to adequately invert complex nonlinear hydrologic systems. This study aims to explore the use of wavelets in the estimation of rainfall time series from streamflow records. Using the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia, it is shown that model parameter distributions and an entire rainfall time series can be estimated. Including rainfall in the estimation process improves streamflow simulations by a factor of up to 1.78. This is achieved while estimating an entire rainfall time series, inclusive of days when none was observed. It is shown that the choice of wavelet can have a considerable impact on the robustness of the inversion. Combining the use of a likelihood function that considers rainfall and streamflow errors with the use of the DWT as a model data reduction technique allows the joint inference of hydrologic model parameters along with rainfall.

}, keywords = {catchments, communities., Emergency management, Flood, rainfall, streamflow}, url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017WR020442}, author = {Ashley Wright and Jeffrey Walker and Valentijn Pauwels} }