@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-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-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-5146, title = {Identification of hydrologic models, optimized parameteres, and rainfall inputs consistent with in situ streamflow and rainfall and remotely sensed soil moisture}, journal = {Journal of Hydrometeorology}, volume = {19}, year = {2018}, month = {08/2018}, abstract = {

An increased understanding of the uncertainties present in rainfall time series can lead to improved confidence in both short- and long-term streamflow forecasts. This study presents an analysis that considers errors arising from model input data, model structure, model parameters, and model states with the objective of finding a self-consistent set that includes hydrological models, model parameters, streamflow, remotely sensed (RS) soil moisture (SM), and rainfall. This methodology can be used by hydrologists to aid model and satellite selection. Taking advantage of model input data reduction and model inversion techniques, this study uses a previously developed methodology to estimate areal rainfall time series for the study catchment of Warwick, Australia, for multiple rainfall{\textendash}runoff models. RS SM observations from the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) satellites were assimilated into three different rainfall{\textendash}runoff models using an ensemble Kalman filter (EnKF). Innovations resulting from the observed and predicted SM were analyzed for Gaussianity. The findings demonstrate that consistency between hydrological models, model parameters, streamflow, RS SM, and rainfall can be found. Joint estimation of rainfall time series and model parameters consistently improved streamflow simulations. For all models rainfall estimates are less than the observed rainfall, and rainfall estimates obtained using the Sacramento Soil Moisture Accounting (SAC-SMA) model are the most consistent with gauge-based observations. The SAC-SMA model simulates streamflow that is most consistent with observations. EnKF innovations obtained when SMOS RS SM observations were assimilated into the SAC-SMA model demonstrate consistency between SM products.

}, doi = {https://doi.org/10.1175/JHM-D-17-0240.1}, url = {https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-17-0240.1}, author = {Ashley Wright 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} } @mastersthesis {bnh-4629, title = {Estimating areal rainfall time series using input data reduction, model inversion, and data assimilation}, year = {2017}, month = {11/2017}, pages = {146}, school = {Monash University}, address = {Melbourne}, abstract = {

Floods are devastating natural hazards that can have severe socio-economic impacts and lead to a loss of life. Consequently, the key driver for this research is to provide techniques that will lead to an increase in flood forecast skill. As fluvial floods are a direct result of rainfall, detailed knowledge of uncertainties in rainfall observations provides a fundamental foundation for improving both rainfall and flood forecast skill. As the understanding of uncertainties present in rainfall time series is developed, so too will the confidence in rainfall forecasts, and short- and long-term streamflow forecasts. For a description of rainfall uncertainty to be complete it must take into account uncertainty when rainfall was not observed, thus allowing, model structural errors to be correctly identified, analyzed, and treated. Therefore the focus of this thesis is to develop a robust methodology to estimate rainfall time series and its uncertainty such that it is consistent with both streamflow and soil moisture observations. To effectively estimate rainfall time series, a method to reduce hydrological input data dimensionality was identified. The effective reduction of hydrological input data dimensionality allows modern parameter estimation algorithms to simultaneously estimate rainfall time series and model parameters. Due to their wide-spread use as model input data reduction techniques in other fields, the discrete cosine transform (DCT) and discrete wavelet transform (DWT) were used, for comparative purposes, to reduce the dimensionality of observed rainfall time series for the 438 catchments in the Model Parameter Estimation Experiment (MOPEX) data set. Once the time series were reduced to a small number of parameters, the rainfall time series were reconstructed for comparison with the observed hyetographs. The rainfall time signals are then reconstructed and compared to the observed hyetographs using standard simulation performance summary metrics and descriptive statistics. Analysis of the results demonstrate that, when compared to the DCT, the DWT is superior at preserving both short- and long-term rainfall patterns. Second, the DWT was used to reduce the dimensionality of the input rainfall time x series for the catchment of Warwick, Queensland, Australia. The DREAM(ZS) sampling algorithm, in conjunction with a likelihood function that considers both rainfall and streamflow, was then used to estimate the input rainfall time series. Model parameters and rainfall time series were simultaneously estimated. The inclusion of rainfall in the estimation process improved the root mean square error (RMSE) of streamflow simulations by a factor of up to 1.78. This was achieved while estimating an entire rainfall time series, inclusive of days when none was observed. Last, rainfall time series for the catchment of Warwick were estimated using three different rainfall-runoff models. Using the rainfall time series and model parameter estimates, remotely sensed soil moisture observations from the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer - Earth observing system (AMSR-E) satellites were assimilated into each of the models using an ensemble Kalman filter (EnKF). Through analysis of the innovations from the observed and simulated soil moisture it was found that the combination of model choice and remotely sensed soil moisture product had a significant impact on the quality of rainfall estimated. When compared to streamflow simulations obtained via the sole estimation of model parameters, all models that jointly estimated rainfall time series and model parameters produced superior streamflow estimates. Rainfall estimates obtained using the Sacramento Soil Moisture Accounting (SAC-SMA) model were the most realistic. When the SMOS remotely sensed soil moisture product was assimilated into the SAC-SMA, innovations that indicated errors are of a Gaussian nature were obtained. Further, streamflow simulations obtained from the SAC-SMA had the best RMSE. The research presented in this thesis developed a methodology that can be used to estimate and evaluate rainfall estimates obtained using model input data reduction, model inversion, and data assimilation techniques. These rainfall estimates can be used to condition rainfall forecasts and consequently improve flood forecast skill.

}, keywords = {Emergency management, Flood, hydrology, modelling., rainfall}, url = {http://users.monash.edu.au/~jpwalker/theses/AshleyWright.pdf}, author = {Ashley Wright} } @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} } @conference {bnh-3903, title = {Improving flood forecast skill using remote sensing data}, booktitle = {AFAC17}, year = {2017}, month = {09/2017}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Sydney}, abstract = {

Floods are among the most important natural disasters in Australia.\  The average annual cost of floods in the last 40 years has been estimated to amount to $377 million, with the 2010-2011 Brisbane and South-East Queensland floods alone leading to $2.38 billion in economic damage, and 35 confirmed deaths. Flood forecasting systems are the most important tools to limit this damage, but are prone to a considerable degree of uncertainty.\ 

During the last decades, significant research focusing on the monitoring of the global water cycle through satellite remote sensing has been performed. The strength of remote sensing is the opportunity to provide information at large spatial scales, including areas that are difficult or impossible to monitor using on-ground techniques. For these reasons it is believed that the use of remote sensing data can improve the quality of operational flood forecasts.

Operational flood forecasting systems typically consist of a hydrologic model, which estimates the amount of water entering a river system, and a hydraulic model, which models the flow of water inside the river system. However, hydrological and hydraulic models are prone to a significant degree of uncertainty and error, caused by errors and uncertainties in the initial conditions, meteorological forcing data, topographic data, and model errors and/or oversimplification (Li et al., 2016; Grimaldi et al., 2016). In order to reduce this predictive uncertainty, we propose to constrain the models using remote sensing data. In particular, remotely sensed soil moisture data are being used to improve the hydrologic model results, while remotely sensed water levels and/or flood extent data can be used to support the hydraulic model implementation, calibration and real time constraint.

The project focusses on two test sites, the Clarence River in New South Wales and the Condamine-Balonne River in Queensland. Figure 1 shows an overview of these test sites.

Initial catchment soil moisture plays an important role in controlling runoff generation and infiltration processes, which consequently impact streamflow forecasting. Recent development in remote sensing techniques provide a new potential to monitor spatially distributed surface soil moisture. As a result, soil moisture assimilation for flood forecasting has been a hot research topic in the resent years. The ensemble Kalman filter (EnKF) has been widely used for soil moisture assimilation by the scientific and operational communities, due to its relatively satisfactory efficacy and efficiency. However, one of the challenge is that streamflow forecasts are calculated not only from current states, but also from antecedent states in many hydrologic models, while the EnKF updates the current states only, and so may not achieve an optimal performance. As an alternative, assimilation of surface soil moisture by the ensemble Kalman smoother (EnKS) has been demonstrated to give better soil moisture reanalysis (Dunne et al., 2006). Nevertheless, the impact of the EnKS-based soil moisture assimilation on flood forecasting remains a research question.\ 

}, author = {Yuan Li and Stefania Grimaldi and Ashley Wright and Jeffrey Walker and Valentijn Pauwels} } @article {bnh-4200, title = {Improving flood forecast skill using remote sensing data: annual report 2016-17}, number = {316}, year = {2017}, month = {09/2017}, 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 approximately $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 recent floods in 2016 resulted in three casualties with three more people missing. The Insurance Council of Australia stated on June 7 that about 14,500 claims totaling $56 million have already been lodged from across the country. In order to limit the personal and economic damage caused by floods, operational water and emergency managers heavily 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 converts this flow volume into river water levels/velocities and floodplain extents. Over recent times, the accuracy and reliability of these flood forecasting systems has significantly improved. However, it remains difficult to provide accurate flood warnings. This is because of errors and/or uncertainties in the model structure, the model parameters, and/or the meteorological forcings (mainly the rainfall). The hypothesis of this project is that remote sensing data can be used to improve modelled flood forecasts.

More specifically, in this project we are constraining the hydrologic model using remotely sensed soil moisture values, as this variable determines the partitioning of rainfall into surface runoff and infiltration. Further, we are constraining the hydraulic model using remotely sensed water levels and/or flood extents. Thus every time a remote sensing image becomes available, we correct the model predictions, which should lead to improved model forecasts of flow depth, extent and velocity for a number of days in the future.

}, issn = {316}, author = {Valentijn Pauwels and Jeffrey Walker and Yuan Li and Stefania Grimaldi and Ashley Wright} } @article {bnh-3101, title = {Improving flood forecast skill using remote sensing: Annual project report 2015-2016}, number = {228}, year = {2016}, month = {09/2016}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, issn = {228}, author = {Valentijn Pauwels and Jeffrey Walker and Yuan Li and Stefania Grimaldi and Ashley Wright} } @conference {bnh-2946, title = {Improving flood forecast skill using remote sensing data}, booktitle = {AFAC16}, year = {2016}, month = {08/2016}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Brisbane}, abstract = {

Floods are among the most important natural disasters in Australia. The average annual cost of floods in the last 40 years has been estimated to amount to $377 million, with the 2010{\textendash}2011 Brisbane and south-east Queensland floods alone leading to $2.38 billion in economic damage and 35 confirmed deaths. Flood forecasting systems are the most important tools to limit this damage but are prone to a considerable degree of uncertainty.\ During the last decades, significant research focusing on the monitoring of the global water cycle through satellite remote sensing has been performed. The strength of remote sensing is the opportunity to provide information at large spatial scales including areas that are difficult or impossible to monitor using on-ground techniques. For these reasons it is believed that the use of remote sensing data can improve the quality of operational flood forecasts.\ Operational flood forecasting systems typically consist of a hydrologic model, which estimates the amount of water entering a river system, and a hydraulic model, which models the flow of water inside the river system. Remotely sensed soil moisture data is being used to improve the hydrologic model results (i.e. the modeled hydrograph into the river network), while remotely sensed water levels and/or flood extent data are being used to improve the hydraulic model results (i.e. the modeled water velocities, depths, and floodplain extents).\ The project focusses on two test sites, the Clarence River in New South Wales and the Condamine-Balonne Rriver in Queensland. Figure 1 shows an overview of these test sites.

}, author = {Yuan Li and Stefania Grimaldi and Ashley Wright and Jeffrey Walker and Valentijn Pauwels} } @conference {bnh-2101, title = {Combining hydrologic and hydraulic models for real time flood forecasting - non peer reviewed extended abstract}, booktitle = {Adelaide Conference 2015}, year = {2015}, month = {08/2015}, address = {Adelaide, Australia}, abstract = {

Research proceedings from the Bushfire and Natural Hazards CRC \& AFAC Conference in Adelaide, 1-3 September 2015.\ 

}, author = {Yuan Li and Stefania Grimaldi and Valentijn Pauwels and Jeffrey Walker and Ashley Wright} } @article {bnh-2334, title = {Improving flood forecast skill using remote sensing data: Annual project report 2014-2015}, number = {123}, year = {2015}, month = {11/2015}, 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 approximately $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. In order to limit the personal and economic damage caused by floods, operational water and emergency managers heavily 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 converts this flow volume into river water levels/velocities and floodplain extents.\  Over recent times, the accuracy and reliability of these flood forecasting systems has significantly improved. However, it remains difficult to provide accurate flood warnings. This is because of errors and/or uncertainties in the model structure, the model parameters, and/or the meteorological forcings (mainly the rainfall). The hypothesis of this project is that remote sensing data can be used to improve modelled flood forecasts. More specifically, in this project we are constraining the hydrologic model using remotely sensed soil moisture values, as this variable determines the partitioning of rainfall into surface runoff and infiltration.\  Further, we are constraining the hydraulic model using remotely sensed water levels and/or flood extents. Thus every time a remote sensing image becomes available, we correct the model predictions, which should lead to improved model forecasts of flow depth, extent and velocity for a number of days in the future.

}, issn = {123}, author = {Valentijn Pauwels and Jeffrey Walker and Yuan Li and Stefania Grimaldi and Ashley Wright} }