ORCID Profile
0000-0002-7025-7624
Current Organisation
Monash University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: American Geophysical Union (AGU)
Date: 02-2021
DOI: 10.1029/2020WR028238
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.
Publisher: Springer Science and Business Media LLC
Date: 02-08-2016
Publisher: American Geophysical Union (AGU)
Date: 07-2019
DOI: 10.1029/2018WR024289
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-4342
Abstract: & & Floods can have extremely expensive and often fatal consequences, thereby necessitating accurate flood inundation forecasts for increased preparedness and resilience. In an operational forecasting scenario, inflow uncertainties propagated from precipitation forecasts dominate and lead to inherently erroneous predictions of flood inundation, frequently impeding their application to rescue and response operations. Thus, it is expected that assimilating independent inundation observations, from Synthetic Aperture Radar (SAR) sensors for ex le, may reduce the inherent uncertainty in hydraulic modelling. The increasing number of SAR satellites, with their all-weather/all-day imaging capabilities, have increased the probability of monitoring flood dynamics from space. SAR-based flood extents were previously used to indirectly retrieve floodplain water levels in conjunction with digital elevation models. However, studies highlighted this process as an additional source of uncertainty, leading to the development of algorithms for the direct assimilation of flood extent into hydraulic flood inundation forecasting chains. The efficiency of flood extent assimilation is keenly sensitive to the spatiotemporal observation characteristics, and so the expected improvement in the forecast strongly depends on the acquisition timing with respect to the position of the flood wave. In this study, numerical experiments were used to simulate multiple spatiotemporal SAR acquisition scenarios, to identify the optimum measurement design for targeted satellite acquisition, to best facilitate flood extent assimilation. A particle filter based flood extent assimilation framework was developed using the hydraulic model LISFLOOD-FP, and implemented for the 2011 flood event in the Clarence Catchment, Australia. An operational forecasting scenario was emulated for the open loop model ensemble, with the consideration of temporally correlated, variance changing uncertainties in inflows, simulating hydrological model forecasts. The impact of assimilating flood extent at reaches exhibiting uniform flow behaviour, with different combinations of first visit and revisit intervals were investigated. Results indicate that the optimum timing and frequency of targeted SAR acquisitions differs with respect to reach hydraulic characteristics and that images acquired after the peak is observed in the channel are most informative for the forecast. Note that the maximum inundation extent in the floodplain always follows the channel peak, and therefore, post-peak images with respect to the within reach flood wave could improve predictions during the peak in the floodplain. Moreover, a single image assimilated at a reaches exhibiting more diffusive flow behaviour just after the peak, could result in improvements comparable to the assimilation of multiple images elsewhere. Findings from the study will allow the optimal utilization of SAR imagery to overcome localized model uncertainties, and help to maximize the accuracy of inundation forecasts.& & & & & & & & ul& & li& & strong& Keywords: & /strong& Flood inundation modelling, flood extent assimilation, SAR, data assimilation, hydraulic modelling, forecast uncertainty& /li& & /ul& & & & & &
Publisher: American Geophysical Union (AGU)
Date: 02-2018
DOI: 10.1002/2017WR021765
Publisher: American Geophysical Union (AGU)
Date: 02-2021
DOI: 10.1029/2020WR027859
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.
No related grants have been discovered for Stefania Grimaldi.