An overview of approaches for reducing uncertainties in hydrological forecasting: progress, and challenges

Abstract

Abstract In the present study, we review the methods and approaches used for uncertainty handling in hydrological forecasting of streamflow, floods, and snow. This review has six thematic sections: (1) general trends in accounting uncertainties in hydrological forecasting, (2) sources of uncertainties in hydrological forecasting, (3) methods used in the studies to address uncertainty, (4) multi-criteria approach for reducing uncertainty in hydrological forecasting and its applications (5) role of remote sensing data sources for hydrological forecasting and uncertainty handling, (6) selection of hydrological models for hydrological forecasting. Especially, a synthesis of the literature showed that approaches such as multi-data usage, multi-model development, multi-objective functions, and pre-/post-processing are widely used in recent studies to improve forecasting capabilities. This study reviews the current state-of-the-art and explores the constraints and advantages of using these approaches to reduce uncertainty. The comparative summary provided in this study offers insights into various methods of uncertainty reduction, highlighting the associated advantages and challenges for readers, scientists, hydrological modelers, and practitioners in improving the forecast task. A set of freely accessible remotely sensed data and tools useful for uncertainty handling and hydrological forecasting are reviewed and pointed out.