@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} } @mastersthesis {bnh-8092, title = {Optimising SAR-based flood extent assimilation for improved hydraulic flood inundation forecasts}, volume = {Doctor of Philosophy}, year = {2020}, month = {05/2020}, pages = {363}, school = {Monash University}, address = {Melbourne}, abstract = {

Accurate forecasts of flood inundation are vital to effective flood rescue, response, and resource allocation. However, uncertainty in inputs, boundaries, and parameters necessitate the use of independent observations to constrain flood predictions. Radar remote sensing allows the synoptic and systematic coverage of flooded areas and is thus a valuable resource for more accurate flood forecasts when combined with models. Accordingly, this thesis first improved the satellite-based probabilistic flood extent observation, and then designed a novel likelihood function to integrate such observations with flood model estimates yielding improved flood inundation forecasts.

}, keywords = {crowdsourcing, data assimilation, flood forecasting, flood inundation mapping, flood inundation modelling, hydraulic modelling, mutual information, sensitivity analysis, synthetic aperture radar, uncertainty reduction}, url = {https://bridges.monash.edu/articles/thesis/Optimizing_SAR-based_Flood_Extent_Assimilation_for_Improved_Hydraulic_Flood_Inundation_Forecasts/12375188}, author = {Antara Dasgupta} } @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} }