Articles | Volume 19, issue 2
https://doi.org/10.5194/nhess-19-353-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-19-353-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Flood risk assessment due to cyclone-induced dike breaching in coastal areas of Bangladesh
Md Feroz Islam
CORRESPONDING AUTHOR
Copernicus Institute, Department of Environmental Sciences, Utrecht
University, 3584 CB Utrecht, the Netherlands
Biswa Bhattacharya
IHE Delft Institute for Water Education, 2611 AX Delft, the
Netherlands
Ioana Popescu
IHE Delft Institute for Water Education, 2611 AX Delft, the
Netherlands
Faculty of Civil Engineering, Politehnica University of Timisoara, 300223 Timisoara,
Romania
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Recent progress in NN (neural network) accelerated improvements in the performance of catchment modeling. Yet flood modeling remains a very difficult task. Focusing on two headwater streams, we developed N-HiTS (Neural Hierarchical Interpolation for Time Series Forecasting) and N-BEATS (Network-Based Expansion Analysis for Interpretable Time Series Forecasting) models and benchmarked them with LSTM (long short-term memory) to predict flooding. N-HiTS and N-BEATS outperformed LSTM for flood predictions. We demonstrated how the proposed models can be augmented with an uncertainty approach to predict flooding that is interpretable without considerable loss in accuracy.
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This article analyzes surface and groundwater interactions in a small transboundary lowland catchment. The study also investigates the influence of rainfall representation in model on surface subsurface hydrological simulations. Emphasizing the significance of these interactions, the research highlighted the role of subsurface baseflow in contributing to river discharge. Despite minimal impact on streamflow, spatial variability in rainfall can cause localized fluctuations in groundwater levels.
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We used an integrated model to evaluate the impacts of nature-based solutions (NBSs) on flood mitigation across the Little Pee Dee and Lumber River watershed, the Carolinas, US. This area is strongly affected by climatic disasters, which are expected to increase due to climate change and urbanization, so exploring an NBS approach is crucial for adapting to future alterations. Our research found that NBSs can have visible effects on the reduction in hurricane-driven flooding.
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Short summary
Bangladesh, one of the most disaster-prone countries in the world, has a dynamic delta with 123 polders. Cyclone-induced storm surges cause severe damage to these polders. This paper presents an investigation of the inundation pattern in a polder due to dike failure caused by storm surges and identifies possible critical locations of dike breaches. Moreover, the risk of flooding was assessed and probabilistic flood maps were generated for the breaching of dikes.
Bangladesh, one of the most disaster-prone countries in the world, has a dynamic delta with 123...
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