Development and validation of a storm surge forecasting/hindcasting modelling system for the extensive Río de la Plata Estuary and its adjacent Continental Shelf

Abstract

The Rio de la Plata (RdP) Estuary is affected by significant surges several times per year. This phenomenon has historically caused catastrophic water-level enlargements of up to 4.44 m, threatening and claiming human lives and producing major economic and material damages. The negative surges are less frequent, but when they do occur, inhibit the access to the principal harbors and waterways and disable the drinking water intakes of the Metropolitan Area of Buenos Aires (the Capital City of Argentina) with a population of more and 16 million people. Recent works suggest that the number and strength of the surge events have been increasing with time. Nevertheless, a state-of-the-art system for the forecast of those events is not available yet. In this work, the implementation of a numerical modelling system for the forecast/hindcast of storm surges and the associated currents in the RdP and the adjacent continental shelf are presented and validated. This pre-operational system is based on an adaptation of the CROCO community ocean model to solve the dynamics associated with the surge. The model was implemented using a set of routines written in open-source programming language (Linux and Python) to be cheap and efficient and to ensure an easy future transfer to the services responsible for the alerts. For a better representation of the regional atmospheric dynamics, wind speed and sea-level pressure used to force the simulations were corrected using direct observations collected at an oceanographic buoy anchored at the estuary. The model system performance in hindcast mode was quantified by comparison with observations from tidal gauges and current meters at several locations of the estuary and the adjacent shelf. Percent errors for water level over the whole estuary and currents in the intermediate and exterior estuary drove to average results of 8 and 13%, respectively. The skill scores resulted, on average, of 0.90 and 0.80, respectively. The model performance in both hindcast and forecast modes was evaluated during historical extreme storm surges. Results support the good performance of the model to simulate even extreme events with average skill scores of 0.97 and 0.92, respectively. Results are encouraging, particularly taking into account the limitations in the atmospheric forcing for the region, where only a relatively small number of direct observations are assimilated by the reanalysis and forecast models.