Monthly Rainfall Prediction at Catchment Level with the Facebook Prophet Model Using Observed and CMIP5 Decadal Data

Abstract

Early prediction of rainfall is important for the planning of agriculture, water infrastructure, and other socio-economic developments. The near-term prediction (e.g., 10 years) of hydrologic data is a recent development in GCM (General Circulation Model) simulations, e.g., the CMIP5 (Coupled Modelled Intercomparison Project Phase 5) decadal experiments. The prediction of monthly rainfall on a decadal time scale is an important step for catchment management. Previous studies have considered stochastic models using observed time series data only for rainfall prediction, but no studies have used GCM decadal data together with observed data at the catchment level. This study used the Facebook Prophet (FBP) model and six machine learning (ML) regression algorithms for the prediction of monthly rainfall on a decadal time scale for the Brisbane River catchment in Queensland, Australia. Monthly hindcast decadal precipitation data of eight GCMs (EC-EARTH MIROC4h, MRI-CGCM3, MPI-ESM-LR, MPI-ESM-MR, MIROC5, CanCM4, and CMCC-CM) were downloaded from the CMIP5 data portal, and the observed data were collected from the Australian Bureau of Meteorology. At first, the FBP model was used for predictions based on: (i) the observed data only; and (ii) a combination of observed and CMIP5 decadal data. In the next step, predictions were performed through ML regressions where CMIP5 decadal data were used as features and corresponding observed data were used as target variables. The prediction skills were assessed through several skill tests, including Pearson Correlation Coefficient (PCC), Anomaly Correlation Coefficient (ACC), Index of Agreement (IA), and Mean Absolute Error (MAE). Upon comparing the skills, this study found that predictions based on a combination of observed and CMIP5 decadal data through the FBP model provided better skills than the predictions based on the observed data only. The optimal performance of the FBP model, especially for the dry periods, was mainly due to its multiplicative seasonality function.