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.

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks¹. Accurate and timely warnings are critical for mitigating flood risks², but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.

Quantifying the effects of future changes in the frequency of precipitation extremes is a key challenge in assessing the vulnerability of hydrological systems to climate change but is difficult as climate models do not always accurately simulate daily precipitation. This article compares the performance of four published techniques used to reduce the bias in a regional climate model precipitation output: (1) linear, (2) nonlinear, (3) γ‐based quantile mapping and (4) empirical quantile mapping. Overall performance and sensitivity to the choice of calibration period were tested by calculating the errors in the first four statistical moments of generated daily precipitation time series and using a cross‐validation technique. The study compared the 1961–2005 precipitation time series from the regional climate model HadRM3.0‐PPE‐UK (unperturbed version) with gridded daily precipitation time series derived from rain gauges for seven catchments spread throughout Great Britain. We found that while the first and second moments of the precipitation frequency distribution can be corrected robustly, correction of the third and fourth moments of the distribution is much more sensitive to the choice of bias correction procedure and to the selection of a particular calibration period. Overall, our results demonstrate that, if both precipitation data sets can be approximated by a γ‐distribution, the γ‐based quantile‐mapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation data sets cannot adequately be approximated using a γ‐distribution, the nonlinear method is more effective at reducing the bias, but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period. However, it should be borne in mind that bias correction introduces additional uncertainties, which are greater for higher order moments. Copyright © 2012 Royal Meteorological Society

This paper presents a novel framework for undertaking climate change impact studies, which can be used for testing the robustness of precautionary climate change allowances used in engineering design. It is illustrated with respect to fluvial flood risk in the UK. The methodology departs from conventional scenario-led impact studies because it is based on sensitivity analyses of catchment responses to a plausible range of climate changes (rather than the time-varying outcome of individual scenarios), making it scenario-neutral. The method involves separating the climate change projections (the hazard) from the catchment responsiveness (the vulnerability) expressed as changes in peak flows. By combining current understanding of likelihood of the climate change hazard with knowledge of the sensitivity of a given catchment, it is possible to evaluate the fraction of climate model projections that would not be accommodated by specified safety margins. This enables rapid appraisal of existing or new precautionary allowances for a set of climate change projections, but also for any new set of climate change projections for example arising from a new generation of climate models as soon as they are available, or when focusing on a different planning time horizon, without the need for undertaking a new climate change impact analysis with the new scenarios. The approach is demonstrated via an assessment of the UK Government’s 20% allowance for climate change applied in two contrasting catchments. In these exemplars, the allowance defends against the majority of sampled climate projections for the 2080s from the IPCC-AR4 GCM and UKCP09 RCM runs but it is still possible to identify a sub-set of regional scenarios that would exceed the 20% threshold