Operational Daily Streamflow Forecasts by coupling Variational Ensemble Forecasting and Machine Learning (VEF-ML) approaches

The operational implementation of a Hydrologic Forecasting System (HFS) is limited in many catchments of the world by the lack of historical in-situ hydrologic data, i.e., long temporal records of rainfall or streamflow. By combining high-resolution Satellite Precipitation Products (SPPs), or Regional Climatological Models (RCMs), with Hydrologic Models, baselines can be established for the quantification and reduction of total hydrologic uncertainty in ungauged basins. We have studied how Variational Ensemble Forecasting (VEF) can be combined with Machine Learning (ML) techniques to improve a hydrologic system representation – i.e., raw data processing, model training, model evaluation, model selection, forecasts post-processing, etc. The VEF-ML method is applied and assessed with three general Hydrologic Processing Hypotheses (HPH): (1) Hydrologic Pre-processing (HPR), (2) Hydrologic Processing (HP), and (3) Hydrologic Post-processing (HPP). The operational implementation of VEF-ML was evaluated in the Upper Zambezi River Basin (UZRB) and its sub-basins, by using multiple precipitation products, multiple hydrologic models, and multiple optimal parameter sets. This extended VEF configuration and its coupling with ML techniques (VEF-ML) allows increasing the number of hydrologic ensembles available for the generation of operational streamflow forecasts products. The performance of VEF-ML is evaluated by comparing two hydrologic learning strategies (HLS) i.e. inference- and pattern-based approaches, which are used to improve hydrologic post-processing hypotheses (i.e. reduce total hydrologic uncertainty) in the poorly gauged UZRB.