Increasing concentrations of greenhouse gases in the atmosphere are expected to modify the global water cycle with significant consequences for terrestrial hydrology. We assess the impact of climate change on hydrological droughts in a multimodel experiment including seven global impact models (GIMs) driven by bias-corrected climate from five global climate models under four representative concentration pathways (RCPs). Drought severity is defined as the fraction of land under drought conditions. Results show a likely increase in the global severity of hydrological drought at the end of the 21st century, with systematically greater increases for RCPs describing stronger radiative forcings. Under RCP8.5, droughts exceeding 40% of analyzed land area are projected by nearly half of the simulations. This increase in drought severity has a strong signal-to-noise ratio at the global scale, and Southern Europe, the Middle East, the Southeast United States, Chile, and South West Australia are identified as possible hotspots for future water security issues. The uncertainty due to GIMs is greater than that from global climate models, particularly if including a GIM that accounts for the dynamic response of plants to CO₂ and climate, as this model simulates little or no increase in drought frequency. Our study demonstrates that different representations of terrestrial water-cycle processes in GIMs are responsible for a much larger uncertainty in the response of hydrological drought to climate change than previously thought. When assessing the impact of climate change on hydrology, it is therefore critical to consider a diverse range of GIMs to better capture the uncertainty. Significance Increasing concentrations of greenhouse gases in the atmosphere are widely expected to influence global climate over the coming century. The impact on drought is uncertain because of the complexity of the processes but can be estimated using outputs from an ensemble of global models (hydrological and climate models). Using an ensemble of 35 simulations, we show a likely increase in the global severity of drought by the end of 21st century, with regional hotspots including South America and Central and Western Europe in which the frequency of drought increases by more than 20%. The main source of uncertainty in the results comes from the hydrological models, with climate models contributing to a substantial but smaller amount of uncertainty.

We present the first large-sample catchment hydrology dataset for GreatBritain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sampleStudies). CAMELS-GB collates river flows, catchment attributes and catchmentboundaries from the UK National River Flow Archive together with a suite ofnew meteorological time series and catchment attributes. These data areprovided for 671 catchments that cover a wide range of climatic,hydrological, landscape, and human management characteristics across GreatBritain. Daily time series covering 1970–2015 (a period including severalhydrological extreme events) are provided for a range ofhydro-meteorological variables including rainfall, potentialevapotranspiration, temperature, radiation, humidity, and river flow. Acomprehensive set of catchment attributes is quantified includingtopography, climate, hydrology, land cover, soils, and hydrogeology.Importantly, we also derive human management attributes (includingattributes summarising abstractions, returns, and reservoir capacity in eachcatchment), as well as attributes describing the quality of the flow dataincluding the first set of discharge uncertainty estimates (provided atmultiple flow quantiles) for Great Britain. CAMELS-GB (Coxon et al., 2020;available at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9)is intended for the community as a publicly available, easily accessibledataset to use in a wide range of environmental and modelling analyses.

Observations of climate are often available on very different spatial scales from observations of the natural environments and resources that are affected by climate change. In order to help bridge the gap between these scales using modelling, a new dataset of daily meteorological variables was created at 1 km resolution over Great Britain for the years 1961–2012, by interpolating coarser resolution climate data and including the effects of local topography. These variables were used to calculate atmospheric evaporative demand (AED) at the same spatial and temporal resolution. Two functions that represent AED were chosen: one is a standard form of potential evapotranspiration (PET) and the other is a derived PET measure used by hydrologists that includes the effect of water intercepted by the canopy (PETI). Temporal trends in these functions were calculated, with PET found to be increasing in all regions, and at an overall rate of 0.021 ± 0.021 mm day⁻¹ decade⁻¹ in Great Britain. PETI was found to be increasing at a rate of 0.019 ± 0.020 mm day⁻¹ decade⁻¹ in Great Britain, but this was not statistically significant. However, there was a trend in PETI in England of 0.023 ± 0.023 mm day⁻¹ decade⁻¹. The trends were found to vary by season, with spring PET increasing by 0.043 ± 0.019 mm day⁻¹ decade⁻¹ (0.038 ± 0.018 mm day⁻¹ decade⁻¹ when the interception correction is included) in Great Britain, while there is no statistically significant trend in other seasons. The trends were attributed analytically to trends in the climate variables; the overall positive trend was predominantly driven by rising air temperature, although rising specific humidity had a negative effect on the trend. Recasting the analysis in terms of relative humidity revealed that the overall effect is that falling relative humidity causes the PET to rise. Increasing downward short- and longwave radiation made an overall positive contribution to the PET trend, while decreasing wind speed made a negative contribution to the trend in PET. The trend in spring PET was particularly strong due to a strong decrease in relative humidity and increase in downward shortwave radiation in the spring.

The Mock LISA Data Challenges are a program to demonstrate LISA data-analysis capabilities and to encourage their development. Each round of challenges consists of several data sets containing simulated instrument noise and gravitational-wave sources of undisclosed parameters. Participants are asked to analyze the data sets and report the maximum information about source parameters. The challenges are being released in rounds of increasing complexity and realism: in this proceeding we present the results of Challenge 2, issued in January 2007, which successfully demonstrated the recovery of signals from supermassive black-hole binaries, from ~20,000 overlapping Galactic white-dwarf binaries, and from the extreme-mass-ratio inspirals of compact objects into central galactic black holes.

A stochastic background of gravitational waves is expected to arise from a superposition of many incoherent sources of gravitational waves, of either cosmological or astrophysical origin. This background is a target for the current generation of ground-based detectors. In this article we present the first joint search for a stochastic background using data from the LIGO and Virgo interferometers. In a frequency band of 600–1000 Hz, we obtained a 95% upper limit on the amplitude of ${\Omega }_{\mathrm{GW}}(f)={\Omega }_3(f/900\mathrm{Hz}{)}^3$, of ${\Omega }_3<0.32$, assuming a value of the Hubble parameter of $h_{100}=0.71$. These new limits are a factor of seven better than the previous best in this frequency band.