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This work explores the relationship between catchment size, rainfall duration, and future streamflow increases on 133 North American catchments with sizes ranging from 66.5 to 9886 km2. It uses the outputs from a high spatial (0.11°) and temporal (1-h) resolution single model initial-condition large ensemble (SMILE) and a hydrological model to compute extreme rainfall and streamflow for durations ranging from 1 to 72 h and for return periods of between 2 and 300 years. Increases in extreme precipitation are observed across all durations and return periods. The projected increases are strongly related to duration, frequency, and catchment size, with the shortest durations, longest return periods, and smaller catchments witnessing the largest relative rainfall increases. These increases can be quite significant, with the 100-yr rainfall becoming up to 20 times more frequent over the smaller catchments. A similar duration–frequency–size pattern of increases is also observed for future extreme streamflow, but with even larger relative increases. These results imply that future increases in extreme rainfall will disproportionately impact smaller catchments, and particularly so for impervious urban catchments which are typically small, and whose stormwater drainage infrastructures are designed for long-return-period flows, both being conditions for which the amplification of future flow will be maximized. [ABSTRACT FROM AUTHOR]

• 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]

Currently, there are a large number of diverse climate datasets in existence, which differ, sometimes greatly, in terms of their data sources, quality control schemes, estimation procedures, and spatial and temporal resolutions. Choosing an appropriate dataset for a given application is therefore not a simple task. This study compares nine global/nearglobal precipitation datasets and three global temperature datasets over 3138 North American catchments. The chosen datasets all meet the minimum requirement of having at least 30 years of available data, so they could all potentially be used as reference datasets for climate change impact studies. The precipitation datasets include two gauged-only products (GPCC and CPC-Unified), two satellite products corrected using ground-based observations (CHIRPS V2.0 and PERSIANN-CDR V1R1), four reanalysis products (NCEP CFSR, JRA55, ERA-Interim, and ERA5), and one merged product (MSWEP V1.2). The temperature datasets include one gauge-based (CPC-Unified) and two reanalysis (ERAInterim and ERA5) products. High-resolution gauge-based gridded precipitation and temperature datasets were combined as the reference dataset for this intercomparison study. To assess dataset performance, all combinations were used as inputs to a lumped hydrological model. The results showed that all temperature datasets performed similarly, albeit with the CPC performance being systematically inferior to that of the other three. Significant differences in performance were, however, observed between the precipitation datasets. TheMSWEPdataset performed best, followed by the gauge-based, reanalysis, and satellite datasets categories. Results also showed that gauge-based datasets should be preferred in regions with good weather network density, but CHIRPS and ERA5 would be good alternatives in data-sparse regions. [ABSTRACT FROM AUTHOR]

Bias correction methods have now emerged as the most commonly used approach when applying climate model outputs to impact studies. However, comparatively much fewer studies have looked at the limitations of bias correction caused by the very nature of the climate system. Two main sources of errors can affect the efficiency of bias correction over a future period: climate sensitivity and internal variability of the climate system. The former is related to differences in the forcing response between a climate model and the real climate system, whereas the latter results from the chaotic nature of the climate system. Using a "pseudo-reality" approach, this study investigates the contribution of these two sources of error to remaining biases of climate model after bias correction for future periods. The pseudo-reality approach uses one climate model as a reference dataset to correct other climate models. Results indicate that bias correction is beneficial over the reference period and in near future periods. However, large biases remain in future periods. The difference in climate sensitivities is the main contributor to the remaining biases in corrected data. Internal variability affects the near and far future similarly and may dominate in the near future, especially for precipitation. The impact of differences in climate sensitivity between the reference dataset and climate model data cannot be eliminated, while the impact of internal variability can be lessened by using a reference period for as long as possible to filter out low-frequency modes of variability. [ABSTRACT FROM AUTHOR]

Multi‐member ensembles of climate models are used to study the role of internal climate variability and its potential impact on climate change impact studies. The reliability of such ensembles with respect to representing the internal climate variability is generally implicitly accepted. Using the latest version of the Climate Research Unit (CRU) data set as a baseline, this study verifies the reliability of multi‐member ensembles in estimating the internal precipitation and temperature variability at the multi‐decadal scale. To achieve this, multi‐decadal variability calculated using climate model multi‐member ensembles is first compared to that calculated using CRU data. The inter‐member variability of a single climate model is then compared to the multi‐decadal variability of CRU precipitation and temperature. Three climate models, with the number of members ranging between 5 and 40, are used to investigate whether the reliability is dependent upon the number of members of a climate model. The results show that multi‐member ensembles are capable of capturing the observed spatial pattern of multi‐decadal variability for both precipitation and temperature at the global scale. However, multi‐member ensembles perform better over regions with smaller variability. For the variables and timescale investigated, all three multi‐member ensembles show similar performances, suggesting that a five‐member ensemble may be sufficient to estimate the internal multi‐decadal climate variability for the chosen variables. Overall, this study indicates that the multi‐member ensemble of a climate model can be used to estimate the internal climate variability of annual and seasonal precipitation and temperature at the multi‐decadal and regional scales, if long historical records are not available. Climate model multi‐member ensembles are capable of capturing the observed spatial pattern of MDV for both precipitation and temperature. Climate model multi‐member ensembles perform better over regions with smaller variability. Performance of multi‐member ensembles did not depend on the number of members within each ensemble for annual and seasonal precipitation at the multi‐decadal scale. In the figure, multi‐decadal variability of mean annual precipitation estimated using CRU data and different climate model ensembles. Different colors represent the standard deviation of precipitation in millimeters. [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]

The source of the Richelieu River is Lake Champlain, located between the states of New York, Vermont, and Québec. In 2011, the lake and the Richelieu River reached historical flood levels, raising questions about the influence of climate change on the watershed. The objectives of this work are to model the hydrology of the watershed, construct a reservoir model for the lake and to analyze flooding trends using climate simulations. The basin was modeled using the HSAMI lumped conceptual model from Hydro-Québec with a semi-distributed approach in order to estimate the inflows into Lake Champlain. The discharge at the Richelieu River was computed by using a mass balance equation between the inputs and outputs of Lake Champlain. Future trends were estimated over the 2041-2070 and 2071-2100 periods using a large number of outputs from general circulation models and regional climate models downscaled with constant scaling and daily translation methods. While there is a certain amount of uncertainty as to future trends, there is a decreasing tendency in the magnitude of the mean spring flood. A flood frequency analysis showed most climate projections indicate the severity of most extreme spring floods may be reduced over the two future periods although results are subject to a much larger uncertainty than for the mean spring flood. On the other hand, results indicate summer-fall extreme events such as caused by hurricane Irene in August 2011 may become more frequent in the future. [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]

The impacts of climate change on medium-term reservoir operations for the Peribonka water resource system (Quebec, Canada) were evaluated with annual and seasonal hydropower production indicators and flood control criteria. According to simulations under the current operating rules in a climate change context, the tendency is for a reduction in mean annual hydropower production and an increase in spills, despite an increase in the annual average inflow to the reservoirs. The main results indicate that annual mean hydropower would change by -12 to +2%, and spills by -49 to +152%. A broad range of climate projections—a combination of five general circulation models with two greenhouse gas scenarios each—were used in order to evaluate the uncertainty of these future potential climates on floods and hydroelectric production. Climate projections were downscaled with the change factor method (also called the Delta method) at a horizon centered in 2050. To represent natural variability, a stochastic weather generator was used to produce 30 synthetic climate series of 30 years each, representative of each climate change projection as well as of the climate of the control period. The hydrological impacts of climate change were evaluated with a lumped hydrological model and the hydrological regimes were analyzed according to spring flood characteristics and the average inflows. In general, the projections indicate an increase in annual inflow, earlier peaks and greater volumes during the spring flood. The analyses show that a power plant managed with a reservoir is sensitive to the operating rules and that these rules should be re-examined in order to take account of new seasonal hydrological contexts. [ABSTRACT FROM AUTHOR]

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]

Weather generators are usually used to produce an ensemble of climate time series for vulnerability assessments and impact studies in hydrological and agricultural communities. Multisite and multivariate weather generators (MMWGs) have many advantages over single-site counterparts in terms of coupling with distributed models for the assessment of spatial variability in various impact sectors. However, the existing MMWGs usually suffer from limitations in preserving multisite and multivariate dependencies at multiple time scales, as well as preserving the low-frequency variability of climate variables. This study proposes a new MMWG which preserves low-frequency climate variability by coupling annual, monthly and daily weather generators into a single model. Specifically, the daily precipitation and temperature time series generated by a widely used multisite daily weather generator is adjusted using monthly and annual climate time series generated by a first-order linear autoregressive model. This combination preserves the multisite and multivariate attributes, as well as low-frequency variability at monthly and annual scales. The performance of the proposed MMWG was evaluated by comparing the baseline model against that of its variants with monthly or annual adjustments for climate generation. The performance was also assessed using a hydrological model over two watersheds with different hydroclimatic characteristics. The results show that the proposed weather generator performs well with respect to reproducing the marginal distributional attributes, multisite and multivariate dependencies, and climate variability at the daily, monthly and annual scales. Weather generators with either monthly or annual adjustments (and not both) only improve the simulations in multisite and multivariate dependencies and low-frequency variability at the corresponding time scales. In terms of hydrological modeling, the proposed model consistently performs better than the baseline model and its variants with only monthly or annual adjustments in representing the mean and variance of monthly and annual streamflows. It also performs better in representing the frequency distribution of mean and extreme streamflow events. Overall, the proposed MMWG can effectively produce multisite and multivariate climate time series with low-frequency variability and has a strong potential for use in climate change impact studies. [ABSTRACT FROM AUTHOR]

Climate change will impact both mean and extreme precipitation, having potentially significant consequences on water resources. The implementation of efficient adaptation measures must rely on the development of reliable projections of future precipitation and on the assessment of their related uncertainty. Natural climate variability is a key uncertainty component, which can result in apparent decadal trends that may be greater or lower than the long-term underlying anthropogenic climate change trend. The goal of the present study is to assess how natural climate variability affects the ability to detect the climate change signal for mean and extreme precipitation. Annual and seasonal total precipitation are used as indicators of the mean, whereas annual and seasonal maximum daily precipitation are used as indicators of extremes. This is done using the CanESM2 50-member and CESM1 40-member large ensembles of simulations over the 1950-2100 period. At the local scale, results indicate that natural climate variability will dominate the uncertainty for annual and seasonal extreme precipitation going up to the end of the century in many parts of the world. The climate change signal can, however, be reliably detected much earlier at the regional scale for extreme precipitation. In the case of annual and seasonal total precipitation, the climate change signal can be reliably detected at the local scale without resorting to a regional analysis. Nonetheless, natural climate variability can impede the detection of the anthropogenic climate change signal until the middle to late century in many parts of the world for mean and extreme precipitation. [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]

Postprocessing of climate model outputs is usually performed to remove biases prior to performing climate change impact studies. The evaluation of the performance of bias correction methods is routinely done by comparing postprocessed outputs to observed data. However, such an approach does not take into account the inherent uncertainty linked to natural climate variability and may end up recommending unnecessary complex postprocessing methods. This study evaluates the performance of bias correction methods using natural variability as a baseline. This baseline implies that any bias between model simulations and observations is only significant if it is larger than the natural climate variability. Four bias correction methods are evaluated with respect to reproducing a set of climatic and hydrological statistics. When using natural variability as a baseline, complex bias correction methods still outperform the simplest ones for precipitation and temperature time series, although the differences are much smaller than in all previous studies. However, after driving a hydrological model using the bias-corrected precipitation and temperature, all bias correction methods perform similarly with respect to reproducing 46 hydrological metrics over two watersheds in different climatic zones. The sophisticated distribution mapping correction methods show little advantage over the simplest scaling method. The main conclusion is that simple bias correction methods appear to be just as good as other more complex methods for hydrological climate change impact studies. While sophisticated methods may appear more theoretically sound, this additional complexity appears to be unjustified in hydrological impact studies when taking into account the uncertainty linked to natural climate variability. [ABSTRACT FROM AUTHOR]

This research project investigates the hydrologic regime of the Manicouagan River watershed in the future climate from 2010 to 2099. The Manicouagan River is used mainly to produce hydroelectricity. Thirteen general circulation models and three greenhouse gas emissions scenarios (A1B, A2, and B1) were used to project the hydrologic regime from the SWAT (soil water assessment tool) hydrologic model. The earlier and earlier start of the spring freshet and its easing off as well as the increase in the annual flow volume are among the anticipated impacts of climate change on the hydrologic regime. The spring freshet will be a few days earlier and should ease off more and more. Winter mild spells could become more frequent and lead to a decrease in the volume of water stored in the snow cover. The results show that the uncertainty linked to evaluating the impacts of climate change on the hydrologic regime increases with time. [Journal translation] [ABSTRACT FROM AUTHOR]

The resolution of general circulation models ( GCMs) is too coarse to assess the site-specific impacts of climate change. Downscaling approaches have been developed to meet this requirement. As the resolution of climate model increases, it is imperative to know whether the finer resolution of regional climate models ( RCMs) would result in any improvement of statistical downscaling quality at the station scale for particular downscaling methods. The objective of this study is to assess the effects of climate model resolutions on statistical downscaling quality of precipitation using the generator for point climate change ( GPCC) model. The downscaling is conducted across three scales, from GCM, and mid- and high-resolution RCMs to a station scale for two Canadian stations in the Quebec province. Observed precipitation gridded to the corresponding scales is also studied in parallel, totalling six downscaling experiments. The results show that the statistics of downscaled precipitation are somewhat overestimated for the Sept-Iles station for all six downscaling experiments with the relative error of mean daily, monthly, and annual precipitation ranging between 1.1 and 5.0%, between 2.7 and 5.9% and between 0.5 and 6.2%, respectively, but underestimated for the Bonnard station with the relative error ranging between −4.1 and −10.2%, between −3.2 and −13.3%, and between −2.6 and −12.0%, respectively. The number of wet days per year is well preserved with the difference between observed and downscaled data ranging between −2.9 and 6 d across all downscaling experiments and two stations. The quality of downscaled precipitation is similar between using gridded observations and model-simulated data for both stations. Furthermore, there is no noteworthy scale effect on downscaling quality when downscaling climate model output with GPCC, indicating that for regions without RCM projections, high-quality daily series at stations can be derived directly from GCM projections with this particular downscaling model. © 2013 Royal Meteorological Society [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]

This study aims at evaluating the hydrologic impacts of climate change on the Châteauguay River basin in the province of Quebec, Canada. Three global climate models (GCMs) covering a range of climate sensitivities were selected, and their output was employed to adjust the parameters of a stochastic weather generator using simple transformation rules for precipitation and temperature. Values of monthly precipitation and temperature were extracted from the GCMs for the current (1960–1990) and future (2040–2060) climate. The International Panel on Climate Change emission scenario known as B2 was selected. It represents an average scenario and corresponds approximately to a doubling of the atmospheric CO2 concentration. Resorting to stochastically generated climate scenarios allowed assessing whether the modelled effects of climate change on flows were statistically significant. Results indicate that spring and summer–fall peak flows were reduced on average by 30% and 12%, respectively, using the Echam4 model derived scenarios. The Hadcm3 model produced a weaker signal that was not statistically significant. The CGCM2 model produced a statistically significant reduction in spring peak flows of 8% on average, whereas the simulated reduction in summer flows was not statistically significant for many of the return periods considered. Many sources of uncertainties were partially considered in this study. One is the downscaling of the GCM climatology at the watershed scale. The approach employed to generate the future climate scenarios changed the precipitation variability through an adjustment of the parameters of the Gamma distribution function used to model precipitation amounts. Whether this approach is truly typical of climate change effect remains to be ascertained. Using more physically based hydrological models would help reduce uncertainties in climate change impacts studies. [ABSTRACT FROM AUTHOR]

This study investigates the uncertainty in hydroclimatic modelling chain components on future changes in potential evapotranspiration, specially on low flows, at the watershed scale. A large domain covering 2080 watersheds located in Canada, the United States and Mexico is studied. The outputs from eight global climate models are post-processed using two methods to produce climate scenarios that drive 10 temperature- and radiation-based potential evapotranspiration (PET) formulas and three lumped conceptual hydrological models, under representative concentration pathways (RCP) 4.5 and 8.5 and for the future periods 2041–2070 and 2071–2100. All simulated results show an increase in PET and a decrease in low flows, computed through three statistical metrics. Based on the results, we advise the use of multiple hydrological model–PET formula combinations when investigating the impact of climate change on low flows. This study provides an assessment of the contribution of potential evapotranspiration formulas to uncertainty throughout the modelling chain. [ABSTRACT FROM AUTHOR]

In spring 2011, an unprecedented flood hit the complex eastern United States (U.S.)–Canada transboundary Lake Champlain–Richelieu River (LCRR) Basin, destructing properties and inducing negative impacts on agriculture and fish habitats. The damages, covered by the Governments of Canada and the U.S., were estimated to C$90M. This natural disaster motivated the study of mitigation measures to prevent such disasters from reoccurring. When evaluating flood risks, long‐term evolving climate change should be taken into account to adopt mitigation measures that will remain relevant in the future. To assess the impacts of climate change on flood risks of the LCRR basin, three bias‐corrected multi‐resolution ensembles of climate projections for two greenhouse gas concentration scenarios were used to force a state‐of‐the‐art, high‐resolution, distributed hydrological model. The analysis of the hydrological simulations indicates that the 20‐year return period flood (corresponding to a medium flood) should decrease between 8% and 35% for the end of the 21st Century (2070–2099) time horizon and for the high‐emission scenario representative concentration pathway (RCP) 8.5. The reduction in flood risks is explained by a decrease in snow accumulation and an increase in evapotranspiration expected with the future warming of the region. Nevertheless, due to the large climate inter‐annual variability, short‐term flood probabilities should remain similar to those experienced in the recent past. [ABSTRACT FROM AUTHOR]

To better understand the role of internal climate variability (ICV) in climate change impact studies, this study quantifies the importance of ICV [defined as the intermember variability of a single model initial-condition large ensemble (SMILE)] in relation to the anthropogenic climate change (ACC; defined as multimodel ensemble mean) in global and regional climate change using a criterion of time of emergence (ToE). The uncertainty of the estimated ToE is specifically investigated by using three SMILEs to estimate the ICV. The results show that using 1921--40 as a baseline period, the annual mean precipitation ACCis expected to emerge within this century over extratropical regions as well as along the equatorial band. However, ToEs are unlikely to occur, even by the end of this century, over intratropical regions outside of the equatorial band. In contrast, annual mean temperature ACC has already emerged from the temperature ICV for most of the globe. Similar spatial patterns are observed at the seasonal scale,while aweakerACCfor boreal summer (June--August) precipitation and additional ICV for boreal winter (December--February) temperature translate to later ToEs for some regions. In addition, the uncertainty of ToE related to the choice of a SMILEismostly less than 20 years for annualmean precipitation and temperature.However, it can be as large as 90 years for annualmean precipitation over someregions.Overall, results indicate that the choice of a SMILEis a significant source of uncertainty in the estimation of ToE and results based on only one SMILE should be interpreted with caution. [ABSTRACT FROM AUTHOR]

The potential impacts of floods are of significant concern to our modern society raising the need to identify and quantify all the uncertainties that can impact their simulations. Climate simulations at finer spatial resolutions are expected to bring more confidence in these hydrological simulations. However, the impact of the increasing spatial resolutions of climate simulations on floods simulations has to be evaluated. To address this issue, this paper assesses the sensitivity of summer–fall flood simulations to the Canadian Regional Climate Model (CRCM) grid resolution. Three climate simulations issued from the fifth version of the CRCM (CRCM5) driven by the ERA-Interim reanalysis at 0.44°, 0.22° and 0.11° resolutions are analysed at a daily time step for the 1981–2010 period. Raw CRCM5 precipitation and temperature outputs are used as inputs in the simple lumped conceptual hydrological model MOHYSE to simulate streamflows over 50 Quebec (Canada) basins. Summer–fall flooding is analysed by estimating four flood indicators: the 2-year, 5-year, 10-year and 20-year return periods from the CRCM5-driven streamflows. The results show systematic impacts of spatial resolution on CRCM5 outputs and seasonal flood simulations. Floods simulated with coarser climate datasets present smaller peak discharges than those simulated with the finer climate outputs. Smaller catchments show larger sensitivity to spatial resolution as more detail can be obtained from the finer grids. Overall, this work contributes to understanding the sensitivity of streamflow modelling to the climate model's resolution, highlighting yet another uncertainty source to consider in hydrological climate change impact studies. [ABSTRACT FROM AUTHOR]

The Canadian Regional Climate Model (CRCM5) Large Ensemble (CRCM5-LE) consists of a dynamically downscaled version of the CanESM2 50-member initial-conditions ensemble (CanESM2-LE). The downscaling was performed at 12-km resolution over two domains, Europe (EU) and northeastern North America (NNA), and the simulations extend from 1950 to 2099, following the RCP8.5 scenario. In terms of validation, warm biases are found over the EU and NNA domains during summer, whereas during winter cold and warm biases appear over EU and NNA, respectively. For precipitation, simulations are generally wetter than the observations but slight dry biases also occur in summer. Climate change projections for 2080–99 (relative to 2000–19) show temperature changes reaching 8°C in summer over some parts of Europe, and exceeding 12°C in northern Québec during winter. For precipitation, central Europe will become much dryer during summer (−2 mm day−1) and wetter during winter (>1.2 mm day−1). Similar changes are observed over NNA, although summer drying is not as prominent. Projected changes in temperature interannual variability were also investigated, generally showing increasing and decreasing variability during summer and winter, respectively. Temperature variability is found to increase by more than 70% in some parts of central Europe during summer and to increase by 80% in the northernmost part of Québec during the month of May as the snow cover becomes subject to high year-to-year variability in the future. Finally, CanESM2-LE and CRCM5-LE are compared with respect to extreme precipitation, showing evidence that the higher resolution of CRCM5-LE allows a more realistic representation of local extremes, especially over coastal and mountainous regions. [ABSTRACT FROM AUTHOR]

Projected climate change effects on hydrology are investigated for the 2041-60 horizon under the A2 emission scenarios using a multimodel approach over two snowmelt-dominated catchments in Canada. An ensemble of 105 members was obtained by combining seven snow models (SMs), five potential evapotranspiration (PET) methods, and three hydrological model (HM) structures. The study was performed using highresolution simulations from the Canadian Regional Climate Model (CRCM-15 km) driven by two members of the third-generation Canadian Coupled Global Climate Model (CGCM3). This study aims to compare various combinations of SM-PET-HM in terms of their ability to simulate streamflows under the current climate and to evaluate how they affect the assessment of the climate change-induced hydrological impacts at the catchment scale. The variability of streamflow response caused by the use of different SMs (degree-day versus degree-day/energy balance), PET methods (temperature-based versus radiation-based methods), and HM structures is evaluated, as well as the uncertainty due to the natural climate variability (CRCM intermember variability). The hydroclimatic simulations cover 1961-90 in the present period and 2041-60 in the future period. The ensemble spread of the climate change signal on streamflow is large and varies with catchments. Using the variance decomposition on three hydrologic indicators, theHMstructure was found to make the most substantial contribution to uncertainty, followed by the choice of the PET methods or natural climate variability, depending on the hydrologic indicator and the catchment. Snow models played a minor, almost negligible role in the assessment of the climate change impacts on streamflow for the study catchments. [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]

Laforce, Serge, Marie-Claude Simard, Robert Leconte, and François Brissette, 2011. Climate Change and Floodplain Delineation in Two Southern Quebec River Basins. Journal of the American Water Resources Association (JAWRA) 47(4):785-799. DOI: 10.1111/j.1752-1688.2011.00560.x Abstract: A methodology is presented for mapping the flooded extent of rivers under projected climate change. The methodology follows a top-down modeling approach, where future climate projections generated by global climate models (GCMs) are downscaled to the watershed scale and used as input to hydrological and hydrodynamic models for predicting future river flows and associated open water levels. A range of possible future climate responses are taken into account, allowing quantification of flood-mapping uncertainties resulting from GCM structure and greenhouse gas emission scenarios (GHGES). Probabilistic projections of future flood zones are developed by assuming that all GCMs and GHGES be equally weighted. The proposed methodology was applied to two river basins located in southern Quebec, Canada, for the time horizons 2020 and 2080. Twenty- and hundred-year floods were computed and corresponding flood maps have been produced. Results indicate that there is a general trend toward an increased spring peak discharge for the Châteauguay River Basin and a decrease for the du Nord River Basin at the 2020 horizon. A less obvious trend was observed for the 2080 horizon, some GCM-GHGES producing an increase in spring peak flows, whereas others would result in a less severe spring flood. These uncertainties in flood flows have cascaded into uncertainties in the corresponding flooded extent and represented as probabilistic flood maps. [ABSTRACT FROM AUTHOR]

This paper describes a method for water resources optimization in the context of climate change. The method takes into account the midterm variability or seasonality of inflows as well as the uncertainty in the climate change and resulting flows. The objective of the optimization algorithm is to find a compromise between the long-term planning of water resources systems and the midterm operations for optimum hydropower production. The proposed algorithm consists of the midterm dynamic programming formulation coupled with the use of the expected value of the cost-to-go function between two consecutive long-term periods. Future climate projections and transition probabilities between projections represent the stochastic nature of inflows and the nonstationarity of climate. The performance of the method was evaluated through the simulation of inflow projections for the Manicouagan River basin in Quebec, Canada. The results showed that the algorithm was able to adapt the operating policy to the climate seasonality and climate change uncertainties in the optimization problem. [ABSTRACT FROM AUTHOR]