Systematic literature reviews can provide an objective global overview of background research on hydrological questions. This study presents a methodology to ensure rigor, traceability, and replicability in evaluating state-of-the-art hydrological topics. The proposed methodology involved systematic, objective, and explicit steps. Strategies with defined selection criteria were used to search the relevant literature comprehensively and accurately on hydrological uncertainty as a case study. The most pertinent documents were filtered to build a critical state-of-the-art synthesis for evaluating their quality and relevance. This methodology allows systematic literature analysis to provide an objective summary of the evidence, with a structured procedure for state-of-the-art reviews, which promotes transparency in the search, unlike typical review papers that lack reproducible methodologies that may lead to obtaining a potentially subjective reference selection. Consequently, the proposed methodology improves reference reliability and study reproducibility while generalizing the methodology. This procedure has proven effective and practical for building state-of-the-art research in the study case (State-of-the-art Uncertainty in Hydrological forecasting from 2017 to 2023) and can be considered a relevant tool in hydrology and other STEM disciplines, providing a rigorous and transparent approach that enhances evidence quality and reliability.

Although Essential Climate Variables (ECVs) have been widely adopted as important metrics for guiding scientific and policy decisions, the Earth Observation (EO) and Land Surface and Hydrologic Model (LSM/HM) communities have yet to treat terrestrial ECVs in an integrated manner. To develop consistent terrestrial ECVs at regional and continental scales, greater collaboration between EO and LSM/HM communities is needed. An essential first step is assessing the LSM/HM simulation uncertainty. To that end, we introduce a new hydrological reference dataset that comprises a range of 19 existing LSM/HM simulations that represent the current state-of-the-art of our LSM/HMs. Simulations are provided on a daily time step, covering Europe, notably the Rhine and Po river basins, alongside the Tugela river basin in Africa, and are uniformly formatted to allow comparisons across simulations. Furthermore, simulations are comprehensively validated with discharge, evapotranspiration, soil moisture and total water storage anomaly observations. Our dataset provides valuable information to support policy development and serves as a benchmark for generating consistent terrestrial ECVs through the integration of EO products.

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.

Diferentes investigadores en hidroclimatología aplican técnicas basadas en autovectores para comprimir grandes volúmenes de información mientras conservan la estructura invariante de los datos originales. La presente investigación desarrolló una metodología que aplica una de estas técnicas, Análisis de Componentes Principales, a los elementos de variabilidad en series de tiempo hidroclimáticas y luego se identifican grupos o clústers mediante el método “k-means”. El resultado es un mapa regionalizado por variable. Finalmente se hace la intersección de estos mapas y obteniéndose áreas que presentan una estructura hidroclimática homogénea debido que las variables comparten su estructura de varianza. En el caso de estudio se evaluaron 8 variables para Colombia (9268 series de tiempo), obteniendo como resultado 26 regiones hidroclimáticas. Obtener regiones hidroclimáticamente homogéneas brinda la posibilidad de generar, entre otros, proyectos de adaptación al cambio climático de forma localizada con el fin de dar soluciones cuasi particulares que maximicen los resultados.