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The time at which climate change signal can be clearly distinguished from noise is known as time of emergence (ToE) and is typically detected by a general circulation model (GCM) signal-to-noise ratio exceeding a certain threshold. ToE is commonly estimated at large scales from GCMs, although management decisions and adaptation strategies are implemented locally. This paper proposes a methodology to estimate ToE for both precipitation and temperature at local scales (i.e., river basin). The methodology considers local climatic conditions and unbiased GCM projections to estimate ToE by using the statistical power to find when the climate significantly differs from the historical one. The method suggests that ToE for temperature already occurred in three Chilean basins (Limarí, Maipo, and Maule). However, in terms of precipitation, an earlier ToE is clearly identified for the Maule basin, indicating that risk assessment and adaptation measures should be implemented first in this basin. [ABSTRACT FROM AUTHOR]

Because of the limitations of coarse-resolution general circulation models (GCMs), delta change (DC) methods are generally used to derive scenarios of future climate as inputs into impact models. In this paper, the impact of future climate change on irrigation was investigated over China using the Community Land Model, version 4 (CLM4), which was calibrated against observed irrigation water demand (IWD) at the provincial level. The results show large differences in projected changes of IWD variability, extremes, timing, and regional responses between the DC and bias-corrected (BC) methods. For example, 95th-percentile IWD increased by 62% in the BC method compared to only a 28% increase in the DC method. In addition, a shift of seasonal IWD peaks (averaged over the country) to one month later in the year was projected when using the BC method, whereas no evident changes were predicted when using the DC method. Furthermore, low-percentile runoff has larger impacts in the BC method compared with proportional changes in the DC method, indicating that hydrological droughts seem to be exacerbated by increased climate variability. The discrepancies between the two methods were potentially due to the inability of the DC method to capture the changes in precipitation variability. Therefore, the authors highlight the potential effects of climate variability and the sensitivity to the choice of particular strategy-adjusting climate projection in assessing climate change impacts on irrigation. Some caveats, however, should be placed around interpretation of simulated percentage changes for all of China since a large model bias was found in southern China. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) forced under different greenhouse gases emission and socioeconomic scenarios are currently the most extended tool throughout the scientific community that is used to infer the future climate on Earth. However, these models still have problems in capturing several aspects of regional climate variability in many parts of the globe. In this paper, the hydrological cycle of the La Plata Basin is simulated using the variable infiltration capacity (VIC) distributed hydrology model and forced with atmospheric data from different GCMs to determine to what extent errors in temperature and precipitation fields impact the hydrology of the basin. The skill assessment is performed in terms of simulated runoff at different closing points. Simulated hydrographs show that all of the GCMs present deficiencies in simulating the regional climatology of southern South America, and this leads to a very poor representation of the hydrological cycle of the main rivers across the basin. Two unbiasing schemes are then proposed as a means of correcting the GCM outputs before forcing the hydrology model, and comparisons between biased and unbiased simulations are also performed. Results indicate that both schemes, though methodologically different, reduce the water cycle simulation bias. Finally, VIC is forced with bias-corrected data from the GCMs for future decades (2030 and 2070) under different socioeconomic scenarios [e.g., the Intergovernmental Panel on Climate Change's (IPCC) Special Report on Emissions Scenarios (SRES) scenarios A1B, A2, and B1] to determine the potential changes in streamflow due to climate change for the rest of the present century. [ABSTRACT FROM AUTHOR]