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Rainfall nowcasting for landslides early warning systems: an integrated modeling approach.
  • Davide Luciano De Luca,
  • Giovanna Capparelli
Davide Luciano De Luca
Università della Calabria
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Giovanna Capparelli
Università della Calabria
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Peer review status:IN REVISION

17 Apr 2020Submitted to Hydrological Processes
20 Apr 2020Assigned to Editor
20 Apr 2020Submission Checks Completed
20 Apr 2020Reviewer(s) Assigned
23 May 2020Review(s) Completed, Editorial Evaluation Pending
30 May 2020Editorial Decision: Revise Major

Abstract

Effectiveness of floods and landslide early warning systems can be clearly improved by reliable quantitative predictions of rainfall, which represents the main precursor. With this aim, a methodology for probabilistic rainfall nowcasting, based on a coupling between a stochastic model and outputs provided by a Numerical Weather Prediction (NWP) model is proposed in this paper. The coupling among different types of models usually allows for improving the prediction, as the positive aspects of all the model components are merged. In this paper, the hybrid model, named PRAISE-ME (Prediction of Rainfall Amount Inside Storm Events with MEteo), is proposed. This model allows improving the rainfall prediction at hydrological scales, where only NWP models are not so suitable and the simple use of stochastic models provides the same forecast, regardless of weather forecasts as they depend only on previous rainfall. PRAISE-ME provides probabilistic quantitative predictions and it can be easily set up as input in other models for Rainfall-Runoff or Landslide prediction, as in the application case here illustrated. In this work, PRAISE-ME was used with the empirical FLaIR model (Forecasting of Landslides Induced by Rain-fall, Capparelli and Versace 2011) in order to obtain in real-time indications about exceedance probabilities associated to specific thresholds. The procedure was applied for a landslide case study, occurred in Montenero di Bisaccia (Central Italy) in March 2006. The obtained results encourage the use of this methodology as a component of early warning systems.