Sourcing CHIRPS precipitation data for streamflow forecasting using intrinsic time-scale decomposition based machine learning models

Abstract

This study evaluated the effectiveness of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite rainfall data for the development of multi-step ahead streamflow forecasting models. Daily time scale precipitation data of nearly three decades (1986–2012) over the Varahi river basin in Western Ghats of Karnataka, India were used for the analysis. Machine learning (ML) models, namely, the Group Method of Data Handling (GMDH), Chi-square Automatic Interaction Detector (CHAID), and Random Forest (RF) were simulated for one, three and seven days ahead streamflow forecasting. Additionally, the developed forecasting models were improved through the integration with Intrinsic Time-scale decomposition (ITD) (by decomposing the input data into a series of proper rotation components (PRC) and a monotonic trend). The uniqueness of this study lies in coupling ITD with machine learning models to forecast daily streamflow time-series. Concurrently, the precipitation data derived from India Meteorological Department (IMD) gridded rainfall dataset were also employed for developing analogous multistep ahead streamflow forecasting models. The proposed methodology was aimed to have an accurate and a reliable forecasting model that can assist water resources management and operation. Comparative performance evaluation using various statistical indices portrayed the superiority of CHIRPS satellite rainfall data product in forecasting daily streamflows up to a week lead time. The results indicate that, the hybrid ITD-based ML models developed using CHIRPS precipitation data as inputs held a better performance at all lead times.