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Summary: Climate change impact studies are often based on coarse scale projections of general circulation models (GCMs) under greenhouse gases emissions scenarios. Outputs from GCMs have then to be downscaled to obtain information relevant to hydrologic studies. This paper investigates the uncertainty in catchment-scale precipitation scenarios due to (1) the emissions scenario, (2) the configuration of the GCM, and (3) the downscaling method. Two emissions scenarios, six global climate models and four downscaling methods are used to build climate change scenarios for three catchment case studies in the UK. Missing combinations are reconstructed by using a variance decomposition algorithm. The highly increasing variance towards the end of the century is shown to be largely due to the spread of results from different GCMs. Multimodel averaged results at the catchment scale generally show an increase in the amplitude of the annual cycle of precipitation, with wetter winters and drier summers. Hydrologists and water resources planners make use of downscaled climate scenarios, often with little regard for the performance of scenario construction methods, for informing decisions on water resources and flood risk management policies and projects. This paper shows that both the downscaling method and the multimodel building scheme applied have a significant impact on the seasonal precipitation regime that may in turn lead to quite different conclusions in impacts assessments. [Copyright &y& Elsevier]

Summary: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature. It is generally assumed that the major sources of uncertainty are linked to General Circulation Models (GCMs) and Greenhouse Gases Emissions Scenarios (GGES). However, other sources of uncertainty such as the choice of a downscaling method have been given less attention. This paper focuses on this issue by comparing six downscaling methods to investigate the uncertainties in quantifying the impacts of climate change on the hydrology of a Canadian (Quebec province) river basin. The downscaling methods regroup dynamical and statistical approaches, including the change factor method and a weather generator-based approach. Future (2070–2099, 2085 horizon) hydrological regimes simulated with a hydrological model are compared to the reference period (1970–1999) using the average hydrograph, annual mean discharge, peak discharge and time to peak discharge as criteria. The results show that all downscaling methods suggest temperature increases over the basin for the 2085 horizon. The regression-based statistical methods predict a larger increase in autumn and winter temperatures. Predicted changes in precipitation are not as unequivocal as those of temperatures, they vary depending on the downscaling methods and seasons. There is a general increase in winter discharge (November–April) while decreases in summer discharge are predicted by most methods. Consistently with the large predicted increases in autumn and winter temperature, regression-based statistical methods show severe increases in winter flows and considerable reductions in peak discharge. Across all variables, a large uncertainty envelope was found to be associated with the choice of a downscaling method. This envelope was compared to the envelope originating from the choice of 28 climate change projections from a combination of seven GCMs and three GGES. Both uncertainty envelopes were similar, although the latter was slightly larger. The regression-based statistical downscaling methods contributed significantly to the uncertainty envelope. Overall, results indicate that climate change impact studies based on only one downscaling method should be interpreted with caution. [Copyright &y& Elsevier]

Summary: This paper develops tools to assess regional drought events based on limited periods of record and applies them to regional climate model output to examine potential future changes in drought due to increased greenhouse gases. The drought metric was defined as a monthly time series of standardized 12-month precipitation accumulations. Regional drought events were characterised by determining the severity, area, duration and frequency of dry periods. In order to improve statistical inference of drought events, a drought generator was adopted to synthesis a large number of events trained on the available data. In nearly all cases, the drought generator produces synthetic events with drought characteristics statistically indistinguishable from the training data. Severity–Area–Frequency (SAF) curves and Area–Probability (Area–P) curves could then be constructed from the synthetic data to determine the return period of a drought event of a given severity covering a given area. These techniques were applied to observations and to output from the Hadley Centre regional climate model (HadRM3). HadRM3, when driven by climate reanalysis data, has good correlation with the observed drought metric. HadRM3 also captures the timing of observed dry periods but not the relative seriousness of any particular period. Natural variability is large with regional drought characteristics highly dependent on the time period covered by the training data. Although the synthetic drought characteristics of HadRM3 generally overlap with the observed natural variability; the model slightly overestimates the area of observed drought; underestimate its frequency; and underestimate the severity at any given area. HadRM3 projections of changes in drought characteristics over the second half of the 21st century under increased atmospheric greenhouse gases hint at an increase in the severity of drought. However, any changes are not readily distinguishable from natural variability or projection uncertainty. Therefore it is not yet possible to robustly predict changes in UK meteorological droughts arising from increased greenhouse gasses. [Copyright &y& Elsevier]