[1] This work compares the performance of six bias correction methods for hydrological modeling over 10 North American river basins. Four regional climate model (RCM) simulations driven by reanalysis data taken from the North American Regional Climate Change Assessment Program intercomparison project are used to evaluate the sensitivity of bias correction methods to climate models. The hydrological impacts of bias correction methods are assessed through the comparison of streamflows simulated by a lumped empirical hydrology model (HSAMI) using raw RCM-simulated and bias-corrected precipitation time series. The results show that RCMs are biased in the simulation of precipitation, which results in biased simulated streamflows. All six bias correction methods are capable of improving the RCM-simulated precipitation in the representation of watershed streamflows to a certain degree. However, the performance of hydrological modeling depends on the choice of a bias correction method and the location of a watershed. Moreover, distribution-based methods are consistently better than mean-based methods. A low coherence between the temporal sequences of observed and RCMsimulated (driven by reanalysis data) precipitation was observed over 5 of the 10 watersheds studied. All bias corrections methods fail over these basins due to their inability to specifically correct the temporal structure of daily precipitation occurrence, which is critical for hydrology modeling. In this study, this failure occurred on basins that were distant from the RCM model boundaries and where topography exerted little control over precipitation. These results indicate that bias correction performance is location dependent and that a careful validation should always be performed, especially on studies over new regions.Citation: Chen, J., F. P. Brissette, D. Chaumont, and M. Braun (2013), Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America, Water Resour. Res., 49,[4187][4188][4189][4190][4191][4192][4193][4194][4195][4196][4197][4198][4199][4200][4201][4202][4203][4204][4205]

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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. Little quantitative knowledge is as yet available about the role of hydrological model complexity for climate change impact assessment. This study investigates and compares the varieties of different model response of three hydrological models (PROMET, Hydrotel, HSAMI), each representing a different model complexity in terms of process description, parameter space and spatial and temporal scale. The study is performed in the Ammer watershed, a 709 km 2 catchment in the Bavarian alpine forelands, Germany. All models are driven and validated by a 30-year time-series of observation data. It is expressed by objective functions, that all models, HSAMI and Hydrotel due to calibration, perform almost equally well for runoff simulation over the validation period. Some systematic deviances in the hydrographs and the spatial patterns of hydrologic variables are however quite distinct and thus further discussed.Virtual future climate (2071-2100) is generated by the Canadian Regional Climate Model (vers 3.7.1), driven by the Coupled Global Climate Model (vers. 2) based on an A2 emission scenario (IPCC 2007). The hydrological model performance is evaluated by flow indicators, such as flood frequency, annual 7-day and 30-day low flow and maximum seasonal flows. The modified climatic boundary conditions cause dramatic deviances in hydrologic model response. HSAMI shows tremendous overestimation of evapotranspiration, while Hydrotel and PROMET behave in comparable range. Still, their significant differences, like spatially explicit patterns of summerly water shortage or spring flood intensity, highlight the necessity to extend and quantify Correspondence to: R. Ludwig (r.ludwig@lmu.de) the uncertainty discussion in climate change impact analysis towards the remarkable effect of hydrological model complexity. It is obvious that for specific application purposes, water resources managers need to be made aware of this effect and have to take its implications into account for decision making. The paper concludes with an outlook and a proposal for future research necessities.

Discharge projections into the Hudson Bay Complex to 2070 are investigated for global mean temperature warming levels of 1.5 and 2.0 °C. Median precipitation increases from 1986–2005, ranging from 2% during summer to 19% during winter, are projected to increase discharge in all seasons except summer. The rise in discharge is greatest furthest north, into Foxe Basin, Ungava Bay, and Hudson Strait, exceeding 10% above historical annual means. A 2.0 °C warming results in higher discharge than 1.5 °C warming owing to greater precipitation (e.g., 6.5% greater spring discharge increase); however, summer discharge for 2.0 °C warming is lower due to enhanced evaporation and lower precipitation increase from historical (4.0% lower summer discharge increase). Extreme daily high flows are projected to be greater than historical, more so for 2.0 °C warming than 1.5 °C warming, and this is greatest in the eastern and northern regions. These projections suggest continued increasing river discharge into pan‐Arctic coastal oceans.