@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-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} }