This extended review asks whether hydrological (river flow) droughts have become more severe over time in the UK, based on literature review and original analyses. The UK is a good international exemplar, given the richness of available data. We find that there is little compelling evidence towards a trend towards worsening river flow droughts, at odds with future climate change projections. We outline reasons for this discrepancy and make recommendations to guide researchers and policymakers.

Stream water temperature (SWT) is a key indicator of water quality that benefits public use and aquatic life health. Advances in computer modeling have helped improve our understanding of SWT dynamics, but challenges remain. Recently, scientists have begun to use machine learning (ML) in SWT modeling, but it is unclear how ML is increasing our understanding of SWT causes and effects. This work reviews the application of ML in SWT modeling and discusses where there is still room for improvement.

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.

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.