Techniques and Developments in Stochastic Streamflow Synthesis—A Comprehensive Review

Stochastic streamflow synthesis has long been the cornerstone of water resource planning, enabling the generation of extended hydrological sequences that reflect natural variability beyond the limitations of observed records. This paper presents a comprehensive review of the theoretical foundations, methodological advancements, and evolving trends in synthetic streamflow generation. Historical progression is explored through three distinct eras: the pre-modern formulation era (pre-1960), the era dominated by autoregressive models (1960–2000), and the recent period marked by the rise of data-driven AI/ML approaches. Various modelling paradigms, parametric versus non-parametric, traditional versus AI-based, and single- versus multi-scale approaches, are critically assessed and compared with a focus on their applicability across temporal resolutions and hydrological regimes. This study also categorizes evaluation criteria into four dimensions: preservation of stochastic characteristics, distributional consistency, error-based metrics, and operational performance. In addition, the use and impact of transformation techniques (e.g., log or Box-Cox) employed to normalize streamflow distributions for improved model fidelity are examined. A bibliometric analysis of over 200 studies highlights the global research footprint, showing that the United States leads with 70 studies, followed by Canada with 15, reflecting the growing international engagement in the field. The analysis also identifies the most active journals publishing streamflow synthesis research: Water Resources Research (50 publications, since 1967), Journal of Hydrology (25 publications, since 1963), and Journal of the American Water Resources Association (9 publications, since 1974). This review not only synthesizes past and current practices but also outlines key challenges and future research directions to advance stochastic hydrology in an era of climatic uncertainty and data complexity.