Toward the probabilistic forecasting of cyclone-induced marine flooding by overtopping at Reunion Island aided by a time
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Toward the probabilistic forecasting of cyclone‑induced marine flooding by overtopping at Reunion Island aided by a time‑varying random‑forest classification approach S. Lecacheux1 · J. Rohmer1 · F. Paris1 · R. Pedreros1 · H. Quetelard2 · F. Bonnardot2 Received: 24 January 2019 / Accepted: 4 September 2020 © Springer Nature B.V. 2020
Abstract In 2017, Irma and Maria highlighted the vulnerability of small islands to cyclonic events and the necessity of advancing the forecast techniques for cyclone-induced marine flooding. In this context, this paper presents a generic approach to deriving time-varying inundation forecasts from ensemble track and intensity forecasts applied to the case of Reunion Island in the Indian Ocean. The challenge for volcanic islands is to account for the full complexity of wave overtopping processes while also ensuring a robustness and timeliness that are compatible with emergency requirements. The challenge is addressed by following a hybrid approach relying on the combination of process-based models with a statistical model (herein, a random-forest classifier) trained with a precalculated database. The latter enables one to translate any time series of coastal marine conditions into the time-varying probability of inundation for different sectors. The application detailed for the case of Cyclone Dumile at Sainte-Suzanne city shows that the proposed approach enables quick discrimination, in both space and time, thereby identifying safe and exposed areas and demonstrating that probabilistic forecasting of marine flooding by overtopping is feasible. The whole method can be easily adapted to other territories and scales provided that validated process-based models are available. Beyond early warning applications, the developed database and statistical models may also be used for informing risk prevention and adaptation strategies. Keywords Cyclones · Modeling · Marine flooding · Overtopping · Probabilistic forecast · Machine learning
* S. Lecacheux [email protected] 1
BRGM, 3 av. C. Guillemin, 45060 Orléans, France
2
Météo-France Océan Indien, 50 Blvd du Chaudron, 97491 Sainte‑Clotilde, La Réunion, France
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Vol.:(0123456789)
Natural Hazards
1 Introduction An increasing number of countries are taking measures at national and local levels to reduce risks associated with cyclonic hazards. The development of efficient early warning systems (Lumbroso et al. 2017) and crisis management strategies is complementary to prevention and preparedness measures (Lavell et al. 2012), as exemplified by recent Cyclone Irma and Cyclone Maria in 2017. In particular, an accurate real-time forecast of cycloneinduced marine flooding is critical for emergency managers to plan effective responses such as evacuations or deployment of protection measures. The technical requirements of such forecast systems include timeliness, practicality, and adaptation to particular local physical processes (the relative contribution of storm surge and waves depending on the configuration of the coast, marine floo
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