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Abstract This paper focuses on evaluating the uncertainty of three common regionalization methods for predicting continuous streamflow in ungauged basins. A set of 268 basins covering 1.6 million km 2 in the province of Quebec was used to test the regionalization strategies. The multiple linear regression, spatial proximity, and physical similarity approaches were evaluated on the catchments using a leave‐one‐out cross‐validation scheme. The lumped conceptual HSAMI hydrological model was used throughout the study. A bootstrapping method was chosen to further estimate uncertainty due to parameter set selection for each of the parameter set/regionalization method pairs. Results show that parameter set selection can play an important role in regionalization method performance depending on the regionalization methods (and their variants) used and that equifinality does not contribute significantly to the overall uncertainty witnessed throughout the regionalization methods applications. Regression methods fail to consistently assign behavioral parameter sets to the pseudoungauged basins (i.e., the ones left out). Spatial proximity and physical similarity score better, the latter being the best. It is also shown that combining either physical similarity or spatial proximity with the multiple linear regression method can lead to an even more successful prediction rate. However, even the best methods were shown to be unreliable to an extent, as successful prediction rates never surpass 75%. Finally, this paper shows that the selection of catchment descriptors is crucial to the regionalization strategies' performance and that for the HSAMI model, the optimal number of donor catchments for transferred parameter sets lies between four and seven. , Key Points Uncertainty can be limited in regionalization Physical similarity method is best, followed by spatial proximity Regression‐augmented methods can yield better performance

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.

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.

Over the last decades, different methods have been used by hydrologists to extend observed hydro-climatic time series, based on other data sources, such as tree rings or sedimentological datasets. For example, tree ring multi-proxies have been studied for the Caniapiscau Reservoir in northern Quebec (Canada), leading to the reconstruction of flow time series for the last 150 years. In this paper, we applied a new hydro-climatic reconstruction method on the Caniapiscau Reservoir and compare the obtained streamflow time series against time series derived from dendrohydrology by other authors on the same catchment and study the natural streamflow variability over the 1881–2011 period in that region. This new reconstruction is based not on natural proxies but on a historical reanalysis of global geopotential height fields, and aims firstly to produce daily climatic time series, which are then used as inputs to a rainfall–runoff model in order to obtain daily streamflow time series. The performances of the hydro-climatic reconstruction were quantified over the observed period, and showed good performances, in terms of both monthly regimes and interannual variability. The streamflow reconstructions were then compared to two different reconstructions performed on the same catchment by using tree ring data series, one being focused on mean annual flows and the other on spring floods. In terms of mean annual flows, the interannual variability in the reconstructed flows was similar (except for the 1930–1940 decade), with noteworthy changes seen in wetter and drier years. For spring floods, the reconstructed interannual variabilities were quite similar for the 1955–2011 period, but strongly different between 1880 and 1940. The results emphasize the need to apply different reconstruction methods on the same catchments. Indeed, comparisons such as those above highlight potential differences between available reconstructions and, finally, allow a retrospective analysis of the proposed reconstructions of past hydro-climatological variabilities.