Articles | Volume 25, issue 7
https://doi.org/10.5194/hess-25-3875-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3875-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jul 2021
Research article |
| 07 Jul 2021
| 07 Jul 2021
Influence of ENSO and tropical Atlantic climate variability on flood characteristics in the Amazon basin
Department of Geography and Environmental Science, University of
Reading, Reading, UK
Andrea Ficchí
Department of Geography and Environmental Science, University of
Reading, Reading, UK
Hannah L. Cloke
Department of Geography and Environmental Science, University of
Reading, Reading, UK
Department of Meteorology, University of Reading, Reading, UK
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Centre of Natural Hazards and Disaster Science, CNDS, Uppsala, Sweden
Juan Bazo
Red Cross Red Crescent Climate Centre, the Hague, 2521 CV, the
Netherlands
Universidad Tecnológica del Perú (UTP), Lima, Peru
Erin Coughlan de Perez
Universidad Tecnológica del Perú (UTP), Lima, Peru
International Research Institute for Climate and Society, Columbia
University, Palisades, NY 10964, USA
Elisabeth M. Stephens
Department of Geography and Environmental Science, University of
Reading, Reading, UK
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Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
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Colin Keating, Donghoon Lee, Juan Bazo, and Paul Block
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Disaster planning has historically underallocated resources for flood preparedness, but evidence supports reduced vulnerability via early actions. We evaluate the ability of multiple season-ahead streamflow prediction models to appropriately trigger early actions for the flood-prone Marañón River and Piura River in Peru. Our findings suggest that locally tailored statistical models may offer improved performance compared to operational physically based global models in low-data environments.
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Hydrometeorological drivers are investigated to study three different flood types: long duration, rapid rise and high water level of the Brahmaputra river basin in Bangladesh. Our results reveal that long duration floods have been driven by basin-wide rainfall whereas rapid rate of rise due to more localized rainfall. We find that recent record high water levels are not coincident with extreme river flows. Understanding these drivers is key for flood forecasting and early warning.
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Short summary
We examine whether several climate indices alter the magnitude, timing and duration of floods in the Amazon. We find significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative SST years in the central Pacific Ocean. This response is not repeated when the negative anomaly is positioned further east. These results have important implications for both social and physical sectors working towards the improvement of flood early warning systems.
We examine whether several climate indices alter the magnitude, timing and duration of floods in...
