Application of Multivariate Techniques for Spatial Drought Modelling using Satellite Rainfall Estimate

Abstract Monitoring hydrological extremes are essential for developing risk-mitigation strategies. One of the limiting factors for this is the absence of reliable on the ground monitoring networks that capture data on climate variables, which is highly evident in developing states such as Fiji. Fortunately, increasing global coverage of satellite-derived datasets facilitates the utilisation of this information to monitor dry and wet periods in sparse data regions. This study evaluated three global satellite rainfall datasets (CHIRPS, PERSIANN-CDR and CPC) for Fiji. All satellite products had reasonable correlations with station data, and CPC had the highest correlation with minimum error values. The Effective Drought Index (EDI), a useful index for understanding hydrological extremes, was then calculated. Thereafter, a canonical correlation analysis (CCA) was employed to forecast the EDI using sea surface temperature anomaly (SST) data. A high canonical correlation of 0.98 was achieved between the mean SST and mean EDI PCs, showing the influence of ocean-atmospheric interactions on precipitation regimes in Fiji. CCA was used to perform a hindcast and a short-term forecast. The training stage produced a coefficient of determinant (R2) value of 0.83 and mean square error (MSE) of 0.11. The results in the testing stage for the forecast were more modest, with an R2 of 0.45 and MSE of 0.26. This easy-to-implement system can be a helpful tool used by disaster management bodies to enact water restrictions, provide aid, and make informed agronomic decisions such as planting dates or extents.