Beyond Deterministic Forecasts: A Scoping Review of Probabilistic Uncertainty Quantification in Short-to-Seasonal Hydrological Prediction

This Scoping Review methodically synthesizes methodological trends in predictive uncertainty (PU) quantification for short-to-seasonal hydrological modeling-based forecasting. The analysis encompasses 572 studies from 2017 to 2024, with the objective of addressing the central question: What are the emerging trends, best practices, and gaps in this field? In accordance with the six-stage protocol that is aligned with PRISMA-ScR standards, 92 studies were selected for in-depth evaluation. The results of the study indicate the presence of three predominant patterns: (1) exponential growth in the applications of machine learning and artificial intelligence; (2) geographic concentration in Chinese, North American, and European watersheds; and (3) persistent operational barriers, particularly in data-scarce tropical regions with limited flood and streamflow forecasting validation. Hybrid statistical-AI modeling frameworks have been shown to enhance forecast accuracy and PU quantification; however, these frameworks are encumbered by constraints in computational demands and interpretability, with inadequate validation for extreme events highlighting critical gaps. The review emphasizes standardized metrics, broader validation, and adaptive postprocessing to enhance applicability, advocating robust frameworks integrating meteorological input to hydrological output postprocessing for minimizing uncertainty chains and supporting water management. This study provides an updated field mapping, identifies knowledge gaps, and prioritizes research for the operational integration of advanced PU quantification.