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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/nhess-2019-384
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-384
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Feb 2020

03 Feb 2020

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A revised version of this preprint is currently under review for the journal NHESS.

Forecasting flood hazards in real-time: A surrogate model for hydrometeorological events in an Andean watershed

María Teresa Contreras1,2,3, Jorge Gironás1,2, and Cristián Escauriaza1,2 María Teresa Contreras et al.
  • 1Departamento de Ingeniería Hidráulica y Ambiental, Pontificia Universidad Católica de Chile. Av. Vicuña Mackenna 4860, 7820436, Santiago, Chile
  • 2Centro de Investigación para la Gestión Integrada de Desastres Naturales (CIGIDEN), Chile
  • 3Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, USA

Abstract. Growing urban development, combined with the influence of El Niño and climate change, have increased the threat of large unprecedented floods induced by extreme precipitation in populated areas near mountain regions of South America. High-fidelity numerical models with physically-based formulations can now predict inundations with a substantial level of detail for these regions, incorporating the complex morphology, and copying with insufficient data and the uncertainty posed by the variability of sediment concentrations. These simulations, however, might have large computational costs, especially if many scenarios need to be evaluated to develop early-warning systems and trigger preemptive evacuations. In this investigation we develop a surrogate model or meta-model to provide a rapid response flood prediction to extreme hydrometeorological events. We characterize the storms with a small set of parameters and use a high-fidelity model to create a database of flood propagation under different conditions. We perform an interpolation and regression procedure by using kriging on the space of parameters that characterize the events, approximating efficiently the flow depths in the urban area. This is the first application of its kind in the Andes region, which can be used to improve the prediction of flood hazards in real conditions, employing low computational resources. It also constitutes a new framework to develop early warning systems to help decision makers, managers, and city planners in mountain regions.

María Teresa Contreras et al.

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María Teresa Contreras et al.

María Teresa Contreras et al.

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Latest update: 18 Sep 2020
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Short summary
The prediction of multiple scenarios of flood hazard in mountain regions is typically based on expensive high-resolution models that simulate the flood propagation using significant computational resources. In this investigation we develop a surrogate model that provides a rapid evaluation of the flood hazard using a statistical approach and precomputed scenarios. This surrogate model is an advanced tool that can be used for early-warning systems, and to help decision makers and city planners.
The prediction of multiple scenarios of flood hazard in mountain regions is typically based on...
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