Global climate models (GCMs) provide the fundamental information used to assess potential impacts of future climate change. However, the mismatch in spatial resolution between GCMs and the requirements of regional applications has impeded the use of GCM projections for impact studies at a regional scale. This study applied statistical post‐processing methods that preserve long‐term temporal trends, bias‐correction/spatial disaggregation with detrended quantile mapping (SDDQM) and BCSD with quantile delta mapping (SDQDM), to downscale 20 CMIP5 GCM climate projections for daily precipitation, minimum temperature, and maximum temperature over South Korea. Using the downscaled CMIP5 climate projections, we investigated absolute changes in extreme indices between the reference and three 30‐year future periods. In addition, the biases in change signals from GCM projections for different statistical downscaling methods were compared to evaluate how well long‐term trends in indices are preserved. The results showed that the statistical downscaling methods significantly improved the skill in reproducing extreme indices. For temperature‐related extreme indices, we found strong significant trends while trends for precipitation‐related indices varied depending on the index and climate projection horizon. Specifically, more frequent, longer duration, and more intense hot extremes may occur under the CMIP5 climate projections, while corresponding decreases may occur for extreme cold indices. Prominent upward trends are found in extreme precipitation events. Regarding analysis of the bias in change signals, SDQDM, which explicitly preserves changes in all quantiles of the underlying variables, better preserved long‐term trends in extreme indices simulated by GCMs.

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Abstract:Several different gridded climate data sets have recently been made available with the purpose of providing a consistent set of climatic data for many hydro-climatic studies. Recent advances in land-surface schemes and their implementation in fully distributed processes-based hydrologic models have demanded even higher-resolution gridded data. It remains, however, a challenge to identify the most reliable gridded climate data for hydrologic modelling, especially in mountainous headwater regions where there is significant spatial variability but few observing stations. Moreover, the accuracy of such climate forcing data applied to alpine headwaters directly affects the modelled hydrologic responses of the lower, downstream portions of river basins. This study evaluates the spatial and temporal differences in precipitation and temperature fields among three highresolution climate data sets available in Canada, namely, the North American Regional Reanalysis, the Canadian Precipitation Analysis and the thin-plate smoothing splines (ANUSPLIN). Inter-comparison of the quality of these data sets was undertaken for the Athabasca River basin in western Canada. The hydrologic responses of this watershed with respect to each of the three gridded climate data sets were also evaluated using the Variable Infiltration Capacity model. Results indicate that the data sets have systematic differences, which vary with regional characteristics -the largest differences being for mountainous regions. The hydrologic model simulations corresponding to those three forcing data sets also show significant differences and more with North American Regional Reanalysis than those between Canadian Precipitation Analysis and ANUSPLIN.

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This study presents a combined weighting scheme which contains five attributes that reflect accuracy of climate data, i.e. short-term (daily), mid-term (annual), and long-term (decadal) timescales, as well as spatial pattern, and extreme values, as simulated from Regional Climate Models (RCMs) with respect to observed and regional reanalysis products. Southern areas of Quebec and Ontario provinces in Canada are used for the study area. Three series of simulation from two different versions of the Canadian RCM (CRCM4.1.1, and CRCM4.2.3) are employed over 23 years from 1979 to 2001, driven by both NCEP and ERA40 global reanalysis products. One series of regional reanalysis dataset (i.e. NARR) over North America is also used as reference for comparison and validation purpose, as well as gridded historical observed daily data of precipitation and temperatures, both series have been beforehand interpolated on the CRCM 45-km grid resolution. Monthly weighting factors are calculated and then combined into four seasons to reflect seasonal variability of climate data accuracy. In addition, this study generates weight averaged references (WARs) with different weighting factors and ensemble size as new reference climate data set. The simulation results indicate that the NARR is in general superior to the CRCM simulated precipitation values, but the CRCM4.1.1 provides the highest weighting factors during the winter season. For minimum and maximum temperature, both the CRCM4.1.1 and the NARR products provide the highest weighting factors, respectively. The NARR provides more accurate short-and mid-term climate data, but the two versions of the CRCM provide more precise long-term data, spatial pattern and extreme events. Or study confirms also that the global reanalysis data (i.e. NCEP vs. ERA40) used as boundary conditions in the CRCM runs has non-negligible effects on the accuracy of CRCM simulated precipitation and temperature values. In addition, this study demonstrates that the proposed weighting factors reflect well all five attributes and the performances of weighted averaged references are better than that of the best single model. This study also found that the improvement of WARs' performance is due to the reliability (accuracy) of RCMs rather than the ensemble size.

This study begins with the premise that current reservoir management systems do not take into account the potential effects of climate change on optimal performance. This study suggests an approach in which multi-purpose reservoirs can adapt to climate change using optimal rule curves developed by an integrated water resources management system. The system has three modules: the Weather Generator model, the Hydrological Model, and the Differential Evolution Optimization Model. Two general circulation models (GCMs) are selected as examples of both dry and wet conditions to generate future climate scenarios. This study is using the Nakdong River basin in Korea as a case study, where water supply is provided from the reservoir system. Three different climate change conditions (historic, wet and dry) are investigated through the compilation of six 60 years long scenarios. The optimal rule curves for three multi-purpose reservoirs in the basin are developed for each scenario. The results indicate that although the rule curve for large-size reservoir is less sensitive to climate change, medium or small-size reservoirs are very sensitive to those changes. We further conclude that the large reservoir should be used to release more water, while small or medium-size reservoirs should store inflow to mitigate severe drought damages in the basin.

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