Articles | Volume 16, issue 12
https://doi.org/10.5194/hess-16-4637-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-16-4637-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
A study of the climate change impacts on fluvial flood propagation in the Vietnamese Mekong Delta
P. D. T. Van
College of Environment and Natural Resources, Can Tho University, Vietnam
UNESCO-IHE Institute for Water Education, Delft, The Netherlands
I. Popescu
UNESCO-IHE Institute for Water Education, Delft, The Netherlands
A. van Griensven
UNESCO-IHE Institute for Water Education, Delft, The Netherlands
Department of Hydrology and Hydraulic Engineering, Vrije University Brussels, Belgium
D. P. Solomatine
UNESCO-IHE Institute for Water Education, Delft, The Netherlands
Water Resources Section, Delft University of Technology, The Netherlands
N. H. Trung
College of Environment and Natural Resources, Can Tho University, Vietnam
A. Green
Mekong River Comission, Phnom Penn, Cambodia
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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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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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