Our analysis of over 130,000 peer-reviewed articles on drought research reveals critical shifts towards interdisciplinary approaches. Research priorities are identified in methodological advancements of drought forecasting and in plant genetics. The systemic nature of drought impacts is demonstrated. Challenges identified are the integration of plant physiological response in forecasting, fostering machine learning and early warning systems, and more systemic drought resilience frameworks.

1. Effective early identification is the key to predicting flash floods. 2. Considering the impact of local sediment deposition will improve the early identification ability . 3. Based on bibliometric analysis, a comprehensive knowledge system has been provided. 4. Conduct practical research focusing on mechanisms, models, and uncertainties. 5. Application of Expandable Knowledge Graph in Early Identification of Mountain Floods in the Future.

We assess 60 gridded climate datasets [ground- (G), satellite- (S), reanalysis-based (R)]. Higher-density station data and less-hilly terrain improved climate data. In mountainous and humid regions, dataset types performed similarly; but R outperformed G when underlying data had low station density. G outperformed S or R datasets, though better streamflow modeling did not always follow. Hydrologic analyses need datasets that better represent climate variable dependencies and complex topography.

The concept of root zone is widely used, but still lacks a precise definition. Moreover, its importance in Earth system science is not well elaborated. Here, we clarified its definition with several similar terms, to bridge the multi-disciplinary gap. We underscore the key role of root zone in Earth system, which links biosphere, hydrosphere, lithosphere, atmosphere, and anthroposphere. To better represent root zone, we advocate a paradigm shift towards ecosystem-centered modelling.

Pedotransfer functions (PTFs) are used to predicts parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically rely on the nature of the datasets for training the PTFs as well the physical comprehensiveness of the models. This roadmap papers is addressed at PTF developers and users, and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTFs research.