Hydrological droughts affect ecosystems and socioeconomic activities worldwide. Despite the fact that they are commonly described with the Standardized Streamflow Index (SSI), there is limited understanding of what they truly reflect in terms of water cycle processes. Here, we used state-of-the-art hydrological models in Andean basins to examine drivers of SSI fluctuations. The results highlight the importance of careful selection of indices and timescales for accurate drought characterization and monitoring.

This paper presents a method to solve a certain type of equation controlling the storage of water in hydrological models. This equation is often solved with complex numerical methods that may lead to slow runtimes. The method, called the Quadratic Solution of the Approximate Reservoir Equation (QuaSoARe), is both fast and applicable to any equation of this kind regardless of its complexity. The method reduces runtime by a factor of 10 to 50 depending on the model.

Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme weather events, yet current long-term forecast products are often embedded with uncertainties. We develop a deep-learning-based modelling framework to improve 30 d rainfall and streamflow forecasts by combining advanced neural networks and physical models. With the flow forecast error reduced by up to 33 %, the framework has the potential to enhance water management and disaster prevention.