@article {bnh-7974, title = {On the Impacts of Observation Location, Timing, and Frequency on Flood Extent Assimilation Performance}, journal = {Water Reseources Research}, volume = {57}, year = {2021}, month = {01/2021}, abstract = {

Flood inundation forecasts from hydrodynamic models can help with flood preparedness, but uncertainty in the inputs and parameters can lead to erroneous flood inundation estimates. However, Synthetic aperture radar (SAR)-based flood extent information can be used to constrain such model forecasts through data assimilation thus making them more accurate. Since high-resolution SAR satellites can only provide partial coverage for medium to large catchments, it is expedient to evaluate the combination of observation footprint, timing, and frequency which can lead to maximum forecast improvements. Consequently, multiple spatiotemporal SAR-based flood extent assimilation scenarios have been simulated here to identify the optimum observation design for improved flood inundation forecasts. A mutual information-based particle filter was implemented in a synthetic setup for the 2011 flood event in the Clarence Catchment, Australia, to combine SAR-based flood extents with the hydraulic model LISFLOOD-FP. The open loop ensemble was forced using uncertain inflows and the impact of assimilating flood extents in morphologically homogenous river reaches was evaluated for different first visit and revisit scenarios. Results revealed that the optimum temporal acquisition strategy strongly depends on reach morphology and flood wave arrival timing. Further, it was found that a single image at the right time could improve the 8-days forecast by \~{}95\% when assimilated at reaches with large flat floodplains but limited tidal influence, while in reaches with narrow valleys over 10 images were needed to achieve the same outcome. Experiments such as the one presented here can therefore inform targeted observation strategies to ensure cost effective flood monitoring and maximize the forecast accuracy resulting from flood extent assimilation.

}, keywords = {data assimilation, flood forecasting, hydraulic modeling, observation spatiotemporal characteristics, particle filter, synthetic aperture radar}, doi = {https://doi.org/10.1029/2020WR028238}, url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020WR028238}, author = {Antara Dasgupta and Renaud Hostache and RAAJ Ramsankaran and Guy Schumann and Stefania Grimaldi and Valentijn Pauwels and Jeffrey Walker} } @article {bnh-7975, title = {A Mutual Information-Based Likelihood Function for Particle Filter Flood Extent Assimilation}, journal = {Water Reseources Research}, volume = {57}, year = {2021}, month = {01/2021}, abstract = {

Accurate flood inundation forecasts have the potential to minimize socioeconomic losses, but uncertainties in inflows propagated from the precipitation forecasts result in large prediction errors. Recent studies suggest that by assimilating independent flood observations, inherent uncertainty in hydraulic flood inundation modeling can be mitigated. Satellite observations from Synthetic Aperture Radar (SAR) sensors, with demonstrated flood monitoring capability, can thus be used to reduce flood forecast uncertainties through assimilation. However, researchers have struggled to develop an appropriate cost function to determine the innovation to be applied at each assimilation time step. Thus, a novel likelihood function based on mutual information (MI) is proposed here, for use with a particle filter-based (PF) flood extent assimilation framework. Using identical twin experiments, synthetic SAR-based probabilistic flood extents were assimilated into the hydraulic model LISFLOOD-FP using the proposed PF-MI algorithm. The 2011 flood event in the Clarence Catchment, Australia was used for this study. The impact of assimilating flood extents was evaluated in terms of subsequent flood extent evolution, floodplain water depths, flow velocities and channel water levels (WLs). Water depth and flow velocity simulations improved by \~{}60\% over the open loop on an average and persisted for up to 7\ days, following the sequential assimilation of two post-peak flood extent observations. Flood extents and channel WLs also showed mean improvements of \~{}10\% and \~{}80\% in accuracy, respectively, indicating that the proposed MI likelihood function can improve flood extent assimilation.

}, keywords = {data assimilation, flood forecasting, hydraulic modeling, particle filter, predictive uncertainty, synthetic aperture radar}, doi = {https://doi.org/10.1029/2020WR027859}, url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020WR027859}, author = {Antara Dasgupta and Renaud Hostache and RAAJ Ramsankaran and Guy Schumann and Stefania Grimaldi and Valentijn Pauwels and Jeffrey Walker} } @article {bnh-6266, title = {Flood mapping under vegetation using single SAR acquisitions}, journal = {Remote Sensing of Environment}, volume = {237}, year = {2020}, month = {02/2020}, abstract = {

Synthetic Aperture Radar (SAR) enables 24-hour, all-weather flood monitoring. However, accurate detection of inundated areas can be hindered by the extremely complicated electromagnetic interaction phenomena between microwave pulses, and horizontal and vertical targets. This manuscript focuses on the problem of inundation mapping in areas with emerging vegetation, where spatial and seasonal heterogeneity makes the systematic distinction between dry and flooded backscatter response even more difficult. In this context, image interpretation algorithms have mostly used detailed field data and reference image(s) to implement electromagnetic models or change detection techniques. However, field data are rare, and despite the increasing availability of SAR acquisitions, adequate reference image(s) might not be readily available, especially for fine resolution acquisitions. To by-pass this problem, this study presents an algorithm for automatic flood mapping in areas with emerging vegetation when only single SAR acquisitions and common ancillary data are available. First, probability binning is used for statistical analysis of the backscatter response of wet and dry vegetation for different land cover types. This analysis is then complemented with information on land use, morphology and context within a fuzzy logic approach. The algorithm was applied to three fine resolution images (one ALOS-PALSAR and two COSMO-SkyMed) acquired during the January 2011 flood in the Condamine-Balonne catchment (Australia). Flood extent layers derived from optical images were used as validation data, demonstrating that the proposed algorithm had an overall accuracy higher than 80\% for all case studies. Notwithstanding the difficulty to fully discriminate between dry and flooded vegetation backscatter heterogeneity using a single SAR image, this paper provides an automatic, data parsimonious algorithm for the detection of floods under vegetation.

}, keywords = {Flooded vegetation, Fuzzy logic, Inundation extent, SAR}, doi = {https://doi.org/10.1016/j.rse.2019.111582}, url = {https://www.sciencedirect.com/science/article/pii/S0034425719306029?dgcid=author}, author = {Stefania Grimaldi and Xu, J and Yuan Li and Jeffrey Walker} } @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-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-5646, title = {River reconstruction using a conformal mapping method}, journal = {Environmental Modelling \& Software}, volume = {119}, year = {2019}, month = {06/2019}, pages = {197-213}, abstract = {

Accurate river bathymetry is required for applications including hydrodynamic flow modelling and understanding morphological processes. Bathymetric measurements are typically a set of depths at discrete points that must be reconstructed into a continuous surface. A number of algorithms exist for this reconstruction, including spline-based techniques and kriging methods. A novel and efficient method is introduced to produce a co-ordinate system fitted to the river path suitable for bathymetric reconstructions. The method is based on numerical conformal mapping and can handle topological features such as islands and branches in the river. Bathymetric surfaces generated using interpolation over a conformal map are compared to spline-based and kriging methods on a section of the Balonne River, Australia. The results show that the conformal mapping algorithm produces reconstructions comparable in quality to existing methods, preserves flow-wise features and is relatively insensitive to the number of sample points, enabling faster data collection in the field.

}, keywords = {Conformal mapping, Depth reconstruction, flood management, River bathymetry, Spatial interpolation}, issn = {1364-8152}, doi = {https://doi.org/10.1016/j.envsoft.2019.06.006}, url = {https://www.sciencedirect.com/science/article/pii/S1364815219301082}, author = {James Hilton and Stefania Grimaldi and Cohen, R and Garg, N and Yuan Li and Marvanek, S and Valentijn Pauwels and Jeffrey Walker} } @article {bnh-4461, title = {Effective Representation of River Geometry in Hydraulic Flood Forecast Models}, journal = {Water Resources Research}, volume = {54}, year = {2018}, month = {01/2018}, abstract = {

Bathymetric data are a critical input to hydraulic models. However, river depth and shape cannot be systematically observed remotely, and field data are both scarce and expensive to collect. In flood modeling, river roughness and geometry compensate for each other, with different parameter sets often being able to map model predictions equally well to the observed data, commonly known as equifinality. This study presents a numerical experiment to investigate an effective yet parsimonious representation of channel geometry that can be used for operational flood forecasting. The LISFLOOD-FP hydraulic model was used to simulate a hypothetical flood event in the Clarence catchment (Australia). A high-resolution model simulation based on accurate bathymetric field data was used to benchmark coarser model simulations based on simplified river geometries. These simplified river geometries were derived from a combination of globally available empirical formulations, remote sensing data, and a limited number of measurements. Model predictive discrepancy between simulations with field data and simplified geometries allowed an assessment of the geometry impact on inundation dynamics. In this study site, the channel geometrical representation for a reliable inundation forecast could be achieved using remote sensing-derived river width values combined with a few measurements of river depth sampled at strategic locations. Furthermore, this study showed that spatially distributed remote sensing-derived inundation levels at the very early stages of a flood event have the potential to support the effective diagnosis of errors in model implementations.

}, keywords = {artificial neural networks (ANNs), calibration and evaluation, data allocation, data splitting, Emergency management, floods., hydrological models, model evaluation bias}, doi = {https://doi.org/10.1002/2017WR021765}, url = {http://onlinelibrary.wiley.com/doi/10.1002/2017WR021765/full}, author = {Stefania Grimaldi and Yuan Li and Jeffrey Walker and Valentijn Pauwels} } @conference {bnh-5396, title = {Evaluation of TanDEM-X and DEM-H digital elevation models over the Condamine-Balonne catchment}, booktitle = {Hydrology and Water Resources Symposium (HWRS 2018): Water and Communities }, year = {2018}, month = {2018}, publisher = {Monash University }, organization = {Monash University }, abstract = {

A digital elevation model (DEM) is a three-dimensional representation of the Earth surface showing mountain peaks, valley floors, rivers and lakes. DEMs are an essential input for the application of environmental and flood models and, for large catchments,these datasets are produced using data collected by instruments on-board satellite missions. However, data collected from such techniques are inevitably affected by inaccuracies which have to be accurately understood and evaluated in order to allow the implementation of any numerical model. This study evaluated the accuracy of the recently released 12 m resolution TanDEM-X (German Aerospace Center) and the widely used 30 m resolution DEM-H (Geoscience Australia). One of the most important sources of inaccuracy in satellite-derived DEMs is a dense vegetation canopy. This problem is particularly important in dryland catchments, where vegetation is not uniformly distributed across the landscape but often concentrates along the river corridors. The Condamine-Balonne catchment, a low-gradient dryland area in Queensland, offers a representative case study. Satellite-derived DEMs were compared with more than 600 ground control points, three LiDAR datasets, and over 60 ground measured river cross sections. This analysis allowed the evaluation of the capability of the satellite-derived DEMs to reproduce (i) surface elevation at the point scale, (ii) river channels and floodplain morphology, and (iii) catchment slope along the river network. TanDEM-X performed better at the point scale and in transects with sparse vegetation resulting in Root Mean Square Deviation (RMSD) values respectively 0.30 m and 0.12 m lower than the DEM-H. However, TanDEM-X was affected by large errors in densely vegetated areas. Moreover, the enforcement of hydrological connectivity is required. This analysis offers useful insight for the application of TanDEM-X and DEM-H data for environmental and flood modelling in the Condamine-Balonne catchment and in similar dryland areas.

}, keywords = {condamine-balonne, hydrology, resource, water}, isbn = {9781925627183}, url = {https://search.informit.com.au/documentSummary;dn=134740719984631;res=IELENG}, author = {Ankun Wang and Stefania Grimaldi and Saif Shaadman and Yuan Li and Valentijn Pauwels and Jeffrey Walker} } @article {bnh-4462, title = {Hydrologic model calibration using remotely sensed soil moisture and discharge measurements: The impact on predictions at gauged and ungauged locations}, journal = {Journal of Hydrology}, volume = {557}, year = {2018}, month = {01/2018}, chapter = {897}, abstract = {

The skill of hydrologic models, such as those used in operational flood prediction, is currently restricted by the availability of flow gauges and by the quality of the streamflow data used for calibration. The increased availability of remote sensing products provides the opportunity to further improve the model forecasting skill. A joint calibration scheme using streamflow measurements and remote sensing derived soil moisture values was examined and compared with a streamflow only calibration scheme. The efficacy of the two calibration schemes was tested in three modelling setups: 1) a lumped model; 2) a semi-distributed model with only the outlet gauge available for calibration; and 3) a semi-distributed model with multiple gauges available for calibration. The joint calibration scheme was found to slightly degrade the streamflow prediction at gauged sites during the calibration period compared with streamflow only calibration, but improvement was found at the same gauged sites during the independent validation period. A more consistent and statistically significant improvement was achieved at gauged sites not used in the calibration, due to the spatial information introduced by the remotely sensed soil moisture data. It was also found that the impact of using soil moisture for calibration tended to be stronger at the upstream and tributary sub-catchments than at the downstream sub-catchments.

}, keywords = {Calibration, Remote sensing., Soil moisture, Streamflow forecasting}, url = {https://www.sciencedirect.com/science/article/pii/S0022169418300131}, author = {Yuan Li and Stefania Grimaldi and R N Pauwels and Jeffrey Walker} } @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-5150, title = {Impact of rain gauge quality control and interpolation on streamflow simulation: an application to the Warwick Catchment, Australia}, journal = {Frontiers in Earth Science}, year = {2018}, month = {01/2018}, abstract = {

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash{\textendash}Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In terms of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2008{\textendash}31/12/2011) and four gauges during the validation period (01/01/2012{\textendash}30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness, and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored under different catchment hydrological systems in future studies.

}, doi = {https://doi.org/10.3389/feart.2017.00114}, url = {https://www.frontiersin.org/articles/10.3389/feart.2017.00114/full}, author = {Shulun Liu and Yuan Li and Valentijn Pauwels and Jeffrey Walker} } @article {bnh-7976, title = {Towards operational SAR-based flood mapping using neuro-fuzzy texture-based approaches}, journal = {Remote Sensing of Environment}, volume = {215}, year = {2018}, month = {09/2018}, pages = {313-329}, abstract = {

Synthetic Aperture Radar (SAR) data are currently the most reliable resource for flood monitoring, though still subject to various uncertainties, which can be objectively represented with probabilistic flood maps. Moreover, the growing number of SAR satellites has increased the likelihood of observing a flood event from space through at least a single SAR image, but generalized methods for flood classification independent of sensor characteristics need to be developed, to fully utilize these images for disaster management. Consequently, a neuro-fuzzy flood mapping technique is proposed for texture-enhanced single SAR images. Accordingly, any SAR image is first processed to generate second-order statistical textures, which are subsequently optimized using a dimensionality reduction technique. The flood and non-flood classes are then modelled within a fuzzy inference system using Gaussian curves. Parameterization is achieved by training a neural network on the image through user-defined polygons. The results of the optimized texture-based neuro-fuzzy classification were compared against the performance of the SAR image alone and that of SAR enhanced with randomly selected texture features. This approach was tested for a COSMO-SkyMed SAR image at two validation sites, for which high resolution aerial photographs were available. An overall accuracy assessment using reliability diagrams demonstrated a reduction of 54.2\% in the Weighted Root Mean Squared Error (WRMSE) values compared to the stand-alone use of SAR. WRMSE values estimated for the proposed method varied from 0.027 to 0.196. A fuzzy validation exercise was also proposed to account for the uncertainty in manual flood identification from aerial photography, resulting in fuzzy spatial similarity values ranging from 0.67 to 0.92, with higher values representing better performance. Results suggest that the proposed approach has demonstrated potential to improve operational SAR-based flood mapping.

}, keywords = {ANFIS, COSMO-SkyMed, Flood extent, Flood mapping, GLCM, Neuro-fuzzy classification, SAR, Texture optimization}, doi = {https://doi.org/10.1016/j.rse.2018.06.019}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0034425718302979?via\%3Dihub}, author = {Antara Dasgupta and Stefania Grimaldi and RAAJ Ramsankaran and Valentijn Pauwels and Jeffrey Walker} } @article {bnh-3599, title = {Comparison of soil wetness from multiple models over Australia with observations}, journal = {Water Resources Research}, volume = {53}, year = {2017}, month = {01/2017}, pages = {633-646}, chapter = {633}, abstract = {

The McArthur Forest Fire Danger Index used in Australia for operational fire warnings has a component representing fuel availability called the Drought Factor (DF). The DF is partly based on soil moisture deficit, calculated as either the Keetch-Byram Drought Index (KBDI) or Mount{\textquoteright}s Soil Dryness Index (MSDI). The KBDI and MSDI are simplified water balance models driven by observation based daily rainfall and temperature. In this work, gridded KBDI and MSDI analyses are computed at a horizontal resolution of 5 km and are verified against in-situ soil moisture observations. Also verified is another simple model called the Antecedent Precipitation Index (API). Soil moisture analyses from the Australian Community Climate and Earth System Simulator (ACCESS) global Numerical Weather Prediction (NWP) system as well as remotely sensed soil wetness retrievals from the Advanced Scatterometer (ASCAT) are also verified. The verification shows that the NWP soil wetness analyses have greater skill and smaller biases than the KBDI, MSDI and API analyses. This is despite the NWP system having a coarse horizontal resolution and not using observed precipitation. The average temporal correlations (root mean square difference) between cosmic ray soil moisture monitoring facility observations and modeled or remotely sensed soil wetness are 0.82 (0.15 {\textpm}0.02), 0.66 (0.33 {\textpm}0.07), 0.77 (0.20 {\textpm}0.03), 0.74 (0.22 {\textpm}0.03) and 0.83 (0.18 {\textpm}0.04) for NWP, KBDI, MSDI, API and ASCAT. The results from this study suggests that analyses of soil moisture can be greatly improved by using physically based land surface models, remote sensing measurements and data assimilation.

}, doi = {10.1002/2015WR017738}, url = {http://onlinelibrary.wiley.com/doi/10.1002/2015WR017738/abstract}, author = {Vinod Kumar and Imtiaz Dharssi and John Bally and Steinle, P. and D. McJannet and Jeffrey Walker} } @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} } @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-3420, title = {Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review}, journal = {Remote Sensing}, volume = {8}, year = {2016}, month = {05/2016}, chapter = {456}, abstract = {

Fluvial flooding is one of the most catastrophic natural disasters threatening people{\textquoteright}s lives and possessions. Flood forecasting systems, which simulate runoff generation and propagation processes, provide information to support flood warning delivery and emergency response. The forecasting models need to be driven by input data and further constrained by historical and real-time observations using batch calibration and/or data assimilation techniques so as to produce relatively accurate and reliable flow forecasts. Traditionally, flood forecasting models are forced, calibrated and updated using\ in-situ\ measurements, e.g., gauged precipitation and discharge. The rapid development of hydrologic remote sensing offers a potential to provide additional/alternative forcing and constraint to facilitate timely and reliable forecasts. This has brought increasing interest to exploring the use of remote sensing data for flood forecasting. This paper reviews the recent advances on integration of remotely sensed precipitation and soil moisture with rainfall-runoff models for rainfall-driven flood forecasting. Scientific and operational challenges on the effective and optimal integration of remote sensing data into forecasting models are discussed.

}, doi = {10.3390/rs8060456}, url = {http://www.mdpi.com/2072-4292/8/6/456}, author = {Yuan Li and Stefania Grimaldi and Jeffrey Walker and Valentijn Pauwels} } @article {bnh-3227, title = {Comparison of remotely sensed and modelled soil moisture data sets across Australia}, journal = {Remote Sensing of Environment}, volume = {186}, year = {2016}, month = {12/2016}, abstract = {

This study compared surface soil moisture from 11 separate remote sensing and modelled products across Australia in a common framework. The comparison was based on a correlation analysis between soil moisture products and\ in situ\ data collated from three separate ground-based networks: OzFlux, OzNet and CosmOz. The correlation analysis was performed using both original data sets and temporal anomalies, and was supported by examination of the time series plots. The interrelationships between the products were also explored using cluster analyses. The products considered in this study include: Soil Moisture Ocean Salinity (SMOS; both Land Parameter Retrieval Model (LPRM) and L-band Microwave Emission of the Biosphere (LMEB) algorithms), Advanced Microwave Scanning Radiometer 2 (AMSR2; both LPRM and Japan Aerospace Exploration Agency (JAXA) algorithms) and Advanced Scatterometer (ASCAT) satellite-based products, and WaterDyn, Australian Water Resource Assessment Landscape (AWRA-L), Antecedent Precipitation Index (API), Keetch-Byram Drought Index (KBDI), Mount{\textquoteright}s Soil Dryness Index (MSDI) and CABLE/BIOS2 model-based products. The comparison of the satellite and model data sets showed variation in their ability to reflect\ in\ situ\ soil moisture conditions across Australia owing to individual product characteristics. The comparison showed the satellite products yielded similar ranges of correlation coefficients, with the possible exception of AMSR2_JAXA. SMOS (both algorithms) achieved slightly better agreement with\ in situ\ measurements than the alternative satellite products overall. Among the models, WaterDyn yielded the highest correlation most consistently across the different locations and climate zones considered. All products displayed a weaker performance in estimating soil moisture anomalies than the original data sets (i.e.\ the absolute values), showing all products to be more effective in detecting interannual and seasonal soil moisture dynamics rather than individual events. Using cluster analysis we found satellite products generally grouped together, whereas models were more similar to other models. SMOS (based on LMEB algorithm and ascending overpass) and ASCAT (descending overpass) were found to be very similar to each other in terms of their temporal soil moisture dynamics, whereas AMSR2 (based on LPRM algorithm and descending overpass) and AMSR2 (based on JAXA algorithm and ascending overpass) were dissimilar. Of the model products, WaterDyn and CABLE were similar to each other, as were the API/AWRA-L and KBDI/MSDI pairs. The clustering suggests systematic commonalities in error structure and duplication of information may exist between products. This evaluation has highlighted relative strengths, weaknesses, and complementarities between products, so the drawbacks of each may be minimised through a more informed assessment of fitness for purpose by end users.

}, doi = {http://dx.doi.org/10.1016/j.rse.2016.09.015}, url = {http://www.sciencedirect.com/science/article/pii/S0034425716303583}, author = {C.M Holgate and R.A.M. De Jeu and Albert van Dijk and Y.Y Liu and Luigi J. Renzullo and Vinod Kumar and Imtiaz Dharssi and R.M. Parinussa and R. Van Der Schalie and A. Gevaert and Jeffrey Walker and D. McJannet and J. Cleverly and V. Haverd and Trudinger, C and Briggs, P} } @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} } @article {bnh-3546, title = {Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges}, journal = {Surveys in Geophysics}, volume = {37}, year = {2016}, month = {09/2016}, chapter = {977}, abstract = {

Accurate, precise and timely forecasts of flood wave arrival time, depth and velocity at each point of the floodplain are essential to reduce damage and save lives. Current computational capabilities support hydraulic models of increasing complexity over extended catchments. Yet a number of sources of uncertainty (e.g., input and boundary conditions, implementation data) may hinder the delivery of accurate predictions. Field gauging data of water levels and discharge have traditionally been used for hydraulic model calibration, validation and real-time constraint. However, the discrete spatial distribution of field data impedes the testing of the model skill at the two-dimensional scale. The increasing availability of spatially distributed remote sensing (RS) observations of flood extent and water level offers the opportunity for a comprehensive analysis of the predictive capability of hydraulic models. The adequate use of the large amount of information offered by RS observations triggers a series of challenging questions on the resolution, accuracy and frequency of acquisition of RS observations; on RS data processing algorithms; and on calibration, validation and data assimilation protocols. This paper presents a review of the availability of RS observations of flood extent and levels, and their use for calibration, validation and real-time constraint of hydraulic flood forecasting models. A number of conclusions and recommendations for future research are drawn with the aim of harmonising the pace of technological developments and their applications.

}, doi = {doi:10.1007/s10712-016-9378-y}, url = {https://link.springer.com/article/10.1007/s10712-016-9378-y}, author = {Stefania Grimaldi and Yuan Li and Valentijn Pauwels and Jeffrey Walker} } @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} }