@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-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} }