Change of flow: Anthropogenic influence on climate has changed temperatures, precipitation, atmospheric circulation, and many other related physical processes, but has it changed river flow as well? Gudmundsson et al. analyzed thousands of time series of river flows and hydrological extremes across the globe and compared them with model simulations of the terrestrial water cycle (see the Perspective by Hall and Perdigão). They found that the observed trends can only be explained if the effects of climate change are included. Their analysis shows that human influence on climate has affected the magnitude of low, mean, and high river flows on a global scale. Science , this issue p. 1159 ; see also p. 1096

Compound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs.

This is the first part of a two-paper series presenting the Global Streamflow Indices and Metadata archive (GSIM), a worldwide collection of metadata and indices derived from more than 35 000 daily streamflow time series. This paper focuses on the compilation of the daily streamflow time series based on 12 free-to-access streamflow databases (seven national databases and five international collections). It also describes the development of three metadata products (freely available at https://doi.pangaea.de/10.1594/PANGAEA.887477): (1) a GSIM catalogue collating basic metadata associated with each time series, (2) catchment boundaries for the contributing area of each gauge, and (3) catchment metadata extracted from 12 gridded global data products representing essential properties such as land cover type, soil type, and climate and topographic characteristics. The quality of the delineated catchment boundary is also made available and should be consulted in GSIM application. The second paper in the series then explores production and analysis of streamflow indices. Having collated an unprecedented number of stations and associated metadata, GSIM can be used to advance large-scale hydrological research and improve understanding of the global water cycle.

Large-sample hydrology (LSH) relies on data from large sets (tens to thousands) of catchments to go beyond individual case studies and derive robust conclusions on hydrological processes and models. Numerous LSH datasets have recently been released, covering a wide range of regions and relying on increasingly diverse data sources to characterize catchment behaviour. These datasets offer novel opportunities, yet they are also limited by their lack of comparability, uncertainty estimates and characterization of human impacts. This article (i) underscores the key role of LSH datasets in hydrological studies, (ii) provides a review of currently available LSH datasets, (iii) highlights current limitations of LSH datasets and (iv) proposes guidelines and coordinated actions to overcome these limitations. These guidelines and actions aim to standardize and automatize the creation of LSH datasets worldwide, and to enhance the reproducibility and comparability of hydrological studies.