A downscaling model system for early warning flooding forecast

Abstract

<p>A     recent report “The Future is Now: Science for Achieving Sustainable Development” Global Sustainable Development Report 2019 - SDG Summit’      as part of the activity of Agenda 2030 of UN, highlights the opportunity to develop Early warning system for drought, floods and other meteorological events, that by providing timely information can be used by vulnerable countries to build resilience, reduce risks and prepare more effective responses. Following the suggestion,      combining outputs from Global Circulation models, remote sensing, hydraulic models and machine learning tools,       a local scale flooding Early Warning System (EWS) is proposed for the St. Lucia island (     Caribbean). The objective of the EWS is to provide forecasts of potentially dangerous flooding phenomena at different time scale: a) 0-2 hours, nowcasting; b) 24-48 hours, short range; c) 3-10 days, middle to long range. Data used to build the model are: Geopotential Height (GPH) fields at 850 hPa and Integrated Vapor Transport (IVT) fields from European Centre for Medium-range Weather Forecasts (ECMWF) - Reanalysis v5 (ERA5); Tropical Cyclone tracks from NOAA-NHC; 18 weather stations homogeneously distributed in the island; rainfall map data from the weather radar in Saint Lucia. GPH and IVT fields were defined between 110°W - 10°W and 45°N - 10°S. The EWS is constituted by an ensemble of flooding risk forecast subsystems which is potentially applicable to Atlantic tropical and extra-tropical regions. Different approaches are used for each subsystem      to link large scale atmospheric features to local rainfall and flooding: a) Non-homogeneous Hidden Markov and Event Synchronization models to translate IVT and GPH at 850 hPa  fields (from ECMWF-Set II- Atmospheric Model Ensemble) in local      daily rainfall amount and probability of  exceedance of  a prefixed heavy rainfall threshold; b) a physical based cyclone/rainfall  model to convert      Tropical cyclone attributes – position and      maximum wind      velocity       (provided from National Hurricane Center)- in rainfall intensity spatial distribution on the island; c) a surrogate model for a  fast and accurate prediction of flooding events that is obtained from a multi-layer perceptron neural network (MLPNN), which is trained on a high-fidelity dataset relying on solution of the full two-dimensional shallow water equations with direct rainfall application.        Results show an excellent ability of the models to identify the climatic configurations that determine the occurrence of extreme events and the exceeding of threshold values ​​that generate floods. In particular, during the late hurricane season September-October-November, when is highest the probability of flood events, the EWS was able to forecast the occurrence of critical climatic configurations 86% of the times they occurred. The EWS was able to predict the exceeding of the rainfall threshold that generated floods 80% of times.</p>