Articles | Volume 18, issue 3
https://doi.org/10.5194/hess-18-1225-2014
© Author(s) 2014. 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-18-1225-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Challenges in modelling river flow and ice regime on the Ningxia–Inner Mongolia reach of the Yellow River, China
C. Fu
NARI Group Corporation, water conservancy and hydropower technology branch company, Nanjing 211000, China
I. Popescu
UNESCO-IHE, Institute for Water Education, 2601DA, Delft, the Netherlands
C. Wang
UNESCO-IHE, Institute for Water Education, 2601DA, Delft, the Netherlands
Hydrology Bureau, Yellow River Conservancy Commission, Zhengzhou 450004, China
A. E. Mynett
UNESCO-IHE, Institute for Water Education, 2601DA, Delft, the Netherlands
Delft University of Technology, Faculty of CiTG, 2600GA, Delft, the Netherlands
F. Zhang
Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China
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Mostafa Saberian, Vidya Samadi, and Ioana Popescu
Hydrol. Earth Syst. Sci., 30, 371–399, https://doi.org/10.5194/hess-30-371-2026, https://doi.org/10.5194/hess-30-371-2026, 2026
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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.
Faisal Sardar, Muhammad Haris Ali, Ioana Popescu, Andreja Jonoski, Schalk Jan van Andel, and Claudia Bertini
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-276, https://doi.org/10.5194/hess-2023-276, 2023
Manuscript not accepted for further review
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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.
Betina I. Guido, Ioana Popescu, Vidya Samadi, and Biswa Bhattacharya
Nat. Hazards Earth Syst. Sci., 23, 2663–2681, https://doi.org/10.5194/nhess-23-2663-2023, https://doi.org/10.5194/nhess-23-2663-2023, 2023
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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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