Post-processing of short to medium range NWP based reference evapotranspiration forecasts using Machine Learning Techniques across the Indian subcontinent

Abstract

The main objective of this study is to evaluate the efficacy of machine learning (ML) techniques in improving numerical weather prediction (NWP) based reference evapotranspiration (ETo) forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) at short to medium range time scale across different zones in the Indian region. The meteorological hindcasts from ECMWF are used to estimate ETo forecasts using the FAO Penman-Monteith equation. Thereafter, the raw forecasts are post-processed using two ML techniques: Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost). The ML techniques are applied to rawETo forecast in order to improve its reliability and accuracy. The raw and ML post-processed ETo forecasts are assessed using deterministic evaluation metrics. Results highlight that ML post-processed ETo forecasts have superior skill than raw ETo forecasts. The highest improvement is reported in the Himalayan regions, and the XGBoost model outperformed the SVR model across all zones. The outcomes of this study has implications towards agricultural water management and irrigation scheduling over the Indian subcontinent.