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• The impact of internal variability and climate change on streamflow is studied in 133 catchments of different sizes. • Internal climate variability is important for masking anthropogenic climate change, especially in the near future. • The size of the catchment used to have a significant impact on the timing of the emergence of the extreme flow. • Smaller catchments experience floods (droughts) at earlier (later) times. • Instead of KS-test and standard error of the mean (SEM), signal-to-noise (SNR) approaches are too conservative to detect the timing of emergence. Uncertainty about the future impacts of climate change represents a significant barrier to implementing adaptation measures. This work explores the impact of internal climate variability on streamflow projections for 133 catchments across the eastern and northeastern United States. Using data from a single model initial-condition large ensemble (SMILE) at high spatial and temporal resolution, this work assesses the magnitude of anthropogenic climate change and internal climate variability on projected future streamflow. The impact of catchment size is studied by grouping catchments into three different size classes (less than500 km2, between 500 and 1000 km2, and greater than 1000 km2). Results show that in a warmer climate, low to middle quantiles of future streamflow will systematically decrease, while the upper quantiles will increase. Increases are largest for more extreme streamflow indices. Using three different approaches, the role of internal variability is studied to estimate the time of emergence (TOE). In this case, results show that the climate change signal of extreme floods and droughts emerges later than that of median flow quantiles, even though the changes for floods as droughts is more significant. There is a clear relationship between catchment size and TOE, with small catchments seeing an earlier TOE for floods, and a later one for droughts. These results provide insight into adaptation times for small to large watersheds. [ABSTRACT FROM AUTHOR]

• Future change in global extreme meteorological droughts. • Two climate model large ensembles were used to assess the change of frequency. • Changes in meteorological droughts are mostly correlated to mean precipitation. • Projected increases in frequency are much more severe for short-term droughts. • Total area affected by a worsening of droughts gets larger with the return periods. Several studies have looked at the evolution of droughts in a changing climate in various locations and have typically found that droughts are expected to become more severe in the future. However, relatively little work has been done on extreme droughts. This study focuses on the evolution of extreme meteorological droughts over near (2036–2065) and far (2071–2100) future time periods. Monthly precipitation from two large multi-member climate model ensembles: the Canadian Earth System Model (CanESM2) 50-member ensemble and the Community Earth System Model (CESM1) 40-member ensemble, was used to calculate the Standard Precipitation Index (SPI) to quantify and explore the evolution of future meteorological droughts. The use of these large ensembles allows for the calculation of the 20- and 100-year droughts return period and offers a unique perspective into those rare events. Both climate models project increases in extreme meteorological drought frequency over many of the world's regions. The spatial patterns of regions with worsening droughts generally match those of projected change in mean annual precipitation, although the former is more extensive, indicating that changes in internal variability will increase drought frequency even in some regions projected to see increased mean annual precipitation. The projected increase in meteorological drought frequency is more significant for short-term JJA droughts, and for the larger return periods. Large increases in frequency are observed in many regions, all the way up to 20 times for the historical100-year JJA drought indicating a return period shift from 100 to 5 year. Considering the significant predicted increases in temperature, it follows that future changes for both agricultural (soil water deficit) and hydrological droughts (low stream levels), which result from the interaction between precipitation deficit and temperature (as well as other variables), will be even more severe. [ABSTRACT FROM AUTHOR]

Weighting climate models is controversial in climate change impact studies using an ensemble of climate simulations from different climate models. In climate science, there is a general consensus that all climate models should be considered as having equal performance or in other words that all projections are equiprobable. On the other hand, in the impacts and adaptation community, many believe that climate models should be weighted based on their ability to better represent various metrics over a reference period. The debate appears to be partly philosophical in nature as few studies have investigated the impact of using weights in projecting future climate changes. The present study focuses on the impact of assigning weights to climate models for hydrological climate change studies. Five methods are used to determine weights on an ensemble of 28 global climate models (GCMs) adapted from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. Using a hydrological model, streamflows are computed over a reference (1961–1990) and future (2061–2090) periods, with and without post-processing climate model outputs. The impacts of using different weighting schemes for GCM simulations are then analyzed in terms of ensemble mean and uncertainty. The results show that weighting GCMs has a limited impact on both projected future climate in term of precipitation and temperature changes and hydrology in terms of nine different streamflow criteria. These results apply to both raw and post-processed GCM model outputs, thus supporting the view that climate models should be considered equiprobable. [ABSTRACT FROM AUTHOR]

Summary: Statistical and dynamical downscaling techniques have been proposed to bridge the gaps between coarse-scale and generally biased climate model outputs and the point-scale requirements of impact model inputs. Amongst the various statistical approaches, empirical downscaling methods are the most commonly used due to their ease of implementation. Several empirical downscaling approaches have been proposed and need to be assessed as to which method contributes (or not) to the overall climate change uncertainty. Accordingly, this work aims at assessing the uncertainty of six empirical downscaling methods in quantifying the hydrological impact of climate change over two North American river basins under different climate conditions. The six empirical downscaling methods are grouped into change factor (two methods) and bias correction (four methods) approaches. The uncertainty related to the choice of an empirical downscaling method is compared to that associated with the choice of climate simulation, through the use of two Regional Climate Models (RCMs) driven by three different General Circulation Models (GCMs), totaling four RCM simulations, taken from the NARCCAP inter-comparison project. The future (2041–2065) hydrological regimes simulated with an empirical lumped hydrology model (HSAMI) are compared to the reference period (1971–1995) using a set of hydrology criteria which includes statistics of both mean and extreme values. The results show a large uncertainty envelope associated with the choice of a given empirical downscaling method, as well as for the choice of an RCM simulation. The uncertainty due to empirical downscaling and RCM simulation was more significant in projecting extreme streamflow than in projecting mean flows. Comparing the uncertainty envelope of empirical downscaling methods to the envelope resulting from four RCM simulations indicates that both are similar, even though the latter was slightly larger for some statistics. Finally, the uncertainty linked to the choice of an empirical downscaling approach (change factor vs. bias correction) was much larger than within each type. Overall, this work emphasizes the importance of using several climate projections and empirical downscaling approaches to delineate uncertainty when assessing the climate change impacts on hydrology. [Copyright &y& Elsevier]

Summary: This paper investigates the effects of model structure and parameter equifinality on the uncertainty related to hydrological modelling in climate change impact studies. The study is conducted on a snow-dominated watershed located in the southern part of the province of Quebec (Canada). Hydrological model structure uncertainty is examined through the use of two very different simulation tools, one lumped conceptual model and one spatially-distributed physically-based model. Parameter equifinality is examined by performing multiple automatic calibrations with both hydrological models. The analysis is first carried out under recent past climate and then under modified climate conditions following two contrasted projections that are analysed separately. The delta change approach is used to build the two climate projections. Overall, this study reveals that the impact of hydrological model structure uncertainty is more significant than the effect of parameter uncertainty, under recent past climate as well as future climate conditions. Ultimately, the use of hydrological models with different levels of complexity should be considered as part of the global uncertainty related to hydrological model structure. [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: Weather generators are computer models that produce time series of meteorological data that have similar statistical properties as that of observed data. The past decade has seen a sharp and renewed increase in interest in weather generators, linked to their potential use in climate change studies. One appealing property of weather generators is their ability to rapidly produce time series of unlimited length, thus permitting impact studies of rare occurrences of meteorological variables. However, one problem with daily weather generators is that they underestimate monthly and inter-annual variances because they do not take into account the low-frequency component of climate variability. This research aims to present an approach for correcting the low-frequency variability of weather variables for weather generator and to assess its ability to reproduce key statistical parameters at the daily, monthly and yearly scales. The approach is applied to precipitation which is usually the variable displaying the largest inter-annual variability. The daily stochastic precipitation model is a Richardson-based weather generator that uses a first-order two-state Markov chain for precipitation occurrence and a gamma distribution for precipitation amounts. Low-frequency variability was modeled based on observed power spectra of monthly and annual time series.Generation of synthetic monthly and yearly precipitation data was achieved by assigning random phases for each spectral component.This preserved the power spectra, variances and the autocorrelation functions at the monthly and annual scales. The link to daily parameters was established through linear functions.The quality of these corrections was assessed through direct and indirect validation tests, with the direct validation focusing on comparing the means, standard deviations and autocorrelations of different weather series. The results showed that standard deviations of both monthly and annual precipitations were produced almost exactly. The proposed method also preserved the autocorrelation of annual precipitation. The indirect validation involved modelling the discharge of a river basin using a hydrological model driven by different precipitation series. The results showed that the corrected weather series significantly improved the variability of simulated flow discharges at the monthly and annual scales compared to those simulated using the data generated by the standard weather generator. [Copyright &y& Elsevier]

Summary: The impact of climate change on the hydrology of the Chute-du-Diable watershed (Quebec, Canada) is studied by comparing statistics on current and projected future discharge resulting from a wide range of climate change scenarios. The use of 10 equally weighted climate projections from a combination of 5 general circulation models (GCMs) and 2 greenhouse gas emission scenarios (GHGES) allows for the definition of an uncertainty envelope of future hydrologic variables. GCM data is downscaled using the change factor approach for 30-year time slices centered around years 2020, 2050 and 2080. To estimate natural variability, synthetic time series are then computed for each horizon and for each climate change scenario, using a stochastic weather generator (30 series of 30 years), and are entered into a hydrology model. Future hydrological regimes are then compared to the control period (1961–1990) using the annual and seasonal mean discharge, peak discharge and timing of peak discharge criteria. Results indicate a 1–14°C increase in seasonal temperature and a −9 to +55% change in seasonal precipitation. The largest increases in both temperature and precipitation are observed in the winter and spring seasons. The main hydrologic impact observed is a spring flood appearing 1–5 weeks earlier than usual and a modification of its amplitude from −40 to +25%. Most scenarios suggest increases in the winter, spring and fall discharge, whereas summer is expected to see a decrease in discharge. While there is still a large scatter in projected values, the uncertainty analysis projects a better view of the most probable future hydrologic behaviour of the watershed. Of all sources of uncertainty considered in this study, the largest comes from the choice of a GCM. Accordingly, all impact studies based on results from only one GCM should be interpreted with caution. [Copyright &y& Elsevier]

• Future change in extreme droughts over 5797 North American watersheds was studied. • Two climate model large ensembles were used to assess the change of frequency. • Changes in meteorological droughts are mostly correlated to mean precipitation. • Large increases in the frequency of hydrological droughts are observed over NA. • Projected changes in future extreme droughts get worse for longer return periods. This paper describes projected frequency changes in extreme summer meteorological and hydrological droughts over North American catchments. It uses two large ensemble climate models (50-member CanESM2 and 40-member CESM1) under the RCP 8.5 scenario to robustly assess frequency changes up to the 100-year drought, relative to the 1980–2009 reference period. Meteorological droughts, linked solely to precipitation deficit, are represented by the 1-month Standardized Precipitation Index (SPI), whereas hydrological droughts are characterized by the 1-month Streamflow Drought Index (SDI), based on hydrological simulation using a lumped hydrological model. Results show widely different patterns for future changes in extreme meteorological versus hydrological droughts. Future meteorological droughts show patterns of increasing and decreasing frequency that roughly match future expected changes of mean summer precipitation, although some regions are nonetheless projected to see more frequent extreme meteorological droughts despite increases in mean summer precipitation. The frequency changes for the 100-year meteorological droughts can be quite severe, with up to a 20-fold increase observed over some watersheds. On the other hand, hydrological droughts, which combine the effect of precipitation and temperature changes, show a mostly uniform pattern of large to very large increases in drought frequency. This shows that the projected temperature increase is a main driver of future extreme hydrological droughts, enough to overcome the projected increase in mean summer precipitation for many North American catchments. Projected changes in both meteorological and hydrological droughts get consistently worse for the longer considered return periods. In other words, frequency changes for the 100-year droughts are more significant than those expected for the 2- and 20-year droughts. This gradual worsening toward larger extremes has potentially large societal and economic impacts. The large projected increases in the frequency of extreme hydrological drought frequency (up to 27 times) are likely to severely stress water management systems across North America. [ABSTRACT FROM AUTHOR]

Bias correction is usually implemented prior to using climate model outputs for impact studies. However, bias correction methods that are commonly used treat climate variables independently and often ignore inter-variable dependencies. The effects of ignoring such dependencies on impact studies need to be investigated. This study aims to assess the impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. To this end, a joint bias correction (JBC) method which corrects the joint distribution of two variables as a whole is compared with an independent bias correction (IBC) method; this is considered in terms of correcting simulations of precipitation and temperature from 26 climate models for hydrological modeling over 12 watersheds located in various climate regimes. The results show that the simulated precipitation and temperature are considerably biased not only in the individual distributions, but also in their correlations, which in turn result in biased hydrological simulations. In addition to reducing the biases of the individual characteristics of precipitation and temperature, the JBC method can also reduce the bias in precipitation-temperature (P-T) correlations. In terms of hydrological modeling, the JBC method performs significantly better than the IBC method for 11 out of the 12 watersheds over the calibration period. For the validation period, the advantages of the JBC method are greatly reduced as the performance becomes dependent on the watershed, GCM and hydrological metric considered. For arid/tropical and snowfall-rainfall-mixed watersheds, JBC performs better than IBC. For snowfall- or rainfall-dominated watersheds, however, the two methods behave similarly, with IBC performing somewhat better than JBC. Overall, the results emphasize the advantages of correcting the P-T correlation when using climate model-simulated precipitation and temperature to assess the impact of climate change on watershed hydrology. However, a thorough validation and a comparison with other methods are recommended before using the JBC method, since it may perform worse than the IBC method for some cases due to bias nonstationarity of climate model outputs. [ABSTRACT FROM AUTHOR]

• Flood volume evolution under climate change is evaluated on 1403 catchments. • Multiple climate models, hydrological models and bias correction methods are used. • Uncertainty analysis showed climate and hydrological models as key contributors. • Approximately 80% of basins will see increased extreme flood volumes in the future. • Changes seem to follow a pattern that can help predict flood volume evolution. Climate change is expected to increase the frequency and intensity of extreme events, such as droughts and floods. Assessing the impacts of climate change on flood volumes is crucial to provide better management of flooding disasters given the devastating consequences they can cause. Using flood volume instead of flood peak is critical because the latter focuses on the highest possible discharge observed during a flood event, while flood volume also considers flow duration, which is an important factor in terms of the hazard caused to the surrounding environment. This study aims to evaluate the overall impact of climate change on floods caused by long-duration flows exceeding synthetic flooding thresholds. These flows are used to compute flood storage with various flow thresholds increasing from the 50th to 95th percentile of annual maximum observed discharge over a large sample of 1403 North American catchments. This study also aims to evaluate the contribution of each uncertainty source of the ensemble approach (climate models, bias-correction methods, and hydrological models) on future flood volumes. The results show that flood volumes are expected to decrease in western mountainous areas, the Great Lakes region, and the Maritimes, while increases are expected over most of eastern North America. The study finds that climate models contribute the most to the variance of flood volume uncertainty, followed by hydrological models. Overall, this study provides projected flood volume changes for North American catchments from a comprehensive ensemble that includes eleven climate models driven by two RCP scenarios and four hydrological models of varying complexity. This leads to a large-sample assessment of future flood volumes that could be useful to policymakers for making better-informed decisions in flood risk management. [ABSTRACT FROM AUTHOR]

Projected climate change effects on snow hydrology are investigated for the 2041–2060 horizon following the SRES A2 emissions scenario over three snowmelt-dominated catchments in Quebec, Canada. A 16-member ensemble of eight snow models (SM) simulations, based on the high-resolution Canadian Regional Climate Model (CRCM-15 km) simulations driven by two realizations of the Canadian Global Climate Model (CGCM3), is established per catchment. This study aims to compare a range of SMs in their ability at simulating snow processes under current climate, and to evaluate how they affect the assessment of the climate change-induced snow impacts at the catchment scale. The variability of snowpack response caused by the use of different models within two different SM approaches (degree-day (DD) versus mixed degree-day/energy balance (DD/EB)) is also evaluated, as well as the uncertainty of natural climate variability. The simulations cover 1961–1990 in the present period and 2041–2060 in the future period. There is a general convergence in the ensemble spread of the climate change signals on snow water equivalent at the catchment scale, with an earlier peak and a decreased magnitude in all basins. The results of four snow indicators show that most of the uncertainty arises from natural climate variability (inter-member variability of the CRCM) followed by the snow model. Both the DD and DD/EB models provide comparable assessments of the impacts of climate change on snow hydrology at the catchment scale. [ABSTRACT FROM AUTHOR]

Summary Projected climate change effects on streamflow are investigated for the 2041–2070 horizon following the SRES A2 emissions scenario over two snowmelt-dominated catchments in Canada. A 16-member ensemble of SWAT hydrological model (HM) simulations, based on a comprehensive ensemble of the Canadian Regional Climate Model (CRCM) simulations driven by two global climate models (GCMs), with five realizations of the Canadian CGCM3 and three realizations of the German ECHAM5 is established per catchment. This study aims to evaluate, once model bias has been removed by statistical post-processing (SP), how the RCM-simulated climate changes differ from those of the parent GCMs, and how they affect the assessment of climate change-induced hydrological impacts at the catchment scale. The variability of streamflow caused by the use of different SP methods (mean-based versus distribution-based) within each statistical post-processing pathway of climate model outputs (bias correction versus perturbation) is also evaluated, as well as the uncertainty of natural climate variability. The simulations cover 1971–2000 in the reference period and 2041–2070 in the future period. For a set of criteria, results based on raw and statistically post-processed model outputs for the reference climate are compared with observations. This process demonstrates that SP is important not only for GCMs outputs, but also for CRCM outputs. SP leads to a high level of agreement between the CRCM and the driving GCMs in reproducing patterns of observed climate. The ensemble spread of the climate change signal on streamflow is large and varies with catchments and hydrological periods (winter/summer flows). The results of various hydrological indicators show that most of the uncertainty arises from the natural climate variability followed by the statistical post-processing. The uncertainty linked to the choice of statistical pathway is much larger than that associated with the choice of the method in quantifying the hydrological impacts. We find that the incorporation of dynamical downscaling of global models through the CRCM as an intermediate step in the GCM–RCM–SP–HM model chain does not lead to considerable differences in the assessment of the climate change impacts on streamflow for the study catchments. [ABSTRACT FROM AUTHOR]