Modeling the Influence of Large-Scale Circulation Patterns on Precipitation in Mauritius

Abstract

Mauritius suffers from chronic water shortages that can severely impact its economy and the well-being of its population. Both surface and groundwater availability are determined by rainfall, which is in turn influenced by large-scale circulation patterns such as the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). Here we report on the influence of these two teleconnection patterns and present the result of a simple neural network for precipitation forecasting, based on the state of ENSO and IOD. Data from the Vacaos station, for the period 1961 to 2012 is used. We found statistically significant correlation between average winter rainfall and ENSO and IOD indices. The correlation for summer was negligible. The prediction of summer precipitation was less accurate than that of winter precipitation. The findings from this study can help in more efficient planning and management of water resources on the island.