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The precipitation oxygen isotopic composition is a useful environmental tracer for climatic and hydrological studies. However, the observed precipitation oxygen is limited at both temporal and spatial scales. Isotope-equipped general circulation models (iGCMs) can compensate for the temporal and spatial discontinuity of observation networks, but they suffer from coarse spatial resolutions and systematic biases. The objective of this study is to build a high-resolution precipitation oxygen isoscape in China for a period of 148 years by integrating observed and iGCMs-simulated precipitation oxygen isotope composition (δ18Op) using data fusion and bias correction methods. The temporal and spatial resolutions are month and 50-60 km for the isoscape, respectively. Prior to building the oxygen isoscape, the performance of two bias correction methods (BCMs) and three data fusion methods (DFMs) is compared after post-processing of eight iGCM simulations. Results show that the outputs of the Convolutional Neural Networks (CNN) fusion method exhibit the strongest correlation with observations with correlation coefficient mostly larger than 0.8, and the smallest bias with root mean square error mostly smaller than 2 ‰. The other two DFMs also perform slightly better than the two BCMs, which show similar performance. Thus, precipitation oxygen isoscape is generated by using the CNN fusion method for the 1969-2007 period in which all iGCMs have output and by using the bias correction methods for the remaining years. Based on the precipitation oxygen isoscape, the spatiotemporal patterns of δ18Op across China are investigated. The generated isoscape shows similar spatial and temporal distribution characteristics to observations. In general, the distribution pattern of δ18Op is consistent with the temperature effect in northern China, and with the precipitation amount effect in southern China, and be more specific in the Qinghai-Tibet Plateau of China. The δ18Op time series mirrors a fluctuating upward trend of the temperature or precipitation in most regions of China. The temporal and spatial distribution characteristics of the generated isoscape are consistent with the characteristics of atmospheric circulation and climate change, indicating successful assimilation and extension of the observed precipitation oxygen isotopes in time and space. Overall, the built isoscape is reliable and useful for providing strong support for tracking atmospheric and hydrological processes. [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]

Weather data are usually required at several locations over a large watershed, especially when using distributed models for hydrological simulations. In many applications, spatially correlated weather data can be provided by a multisite stochastic weather generator, which considers the spatial correlation of weather variables. Prior to using a multisite weather generator for hydrological modeling, its ability to adequately represent the proper hydrological response needs to be assessed. This study assesses the effectiveness of a new multisite weather generator (MulGETS) for hydrological modeling over a Canadian watershed in the Province of Québec. Prior to hydrological modeling, MulGETS is first evaluated with respect to reproducing the spatial correlation and statistical characteristics of precipitation and temperature for the studied watershed. Hydrological simulations obtained from MulGETS-generated precipitation and temperature are then compared with those obtained from a single-site weather generator (WeaGETS) and a WeaGETS-based lumped approach (WeaGETS-lumped) that averages the climate series over all stations in a watershed before running the single-site weather generator. The hydrology is simulated using two hydrological models: the conceptually lumped model HSAMI and the physically based distributed model CEQUEAU. When using the conceptually lumped model, the weather time series is first averaged over all stations in the watershed. The results show that the monthly mean discharge is accurately represented by both MulGETS-generated and WeaGETS-lumped-generated precipitation and temperature, whereas it is considerably underestimated by WeaGETS data for the snowmelt period. The MulGETS and WeaGETS-lumped data also show significant advantages in representing the monthly streamflow variability, which is underestimated by the WeaGETS outputs. Additionally, MulGETS and WeaGETS-lumped consistently perform better than WeaGETS for simulating extreme flows (snowmelt high flows and summer-autumn high and low flows). However, no obvious difference in performance was found between MulGETS and WeaGETSlumped data for hydrological modeling. Moreover, the use of a physically based distributed model with MulGETS did not result in any significant performance gain compared with the much simpler combination of WeaGETS-lumped with a lumped hydrological model for the studied watershed. Overall, this study indicates that a single-site weather generator combined with a lumped hydrological model is sufficient for accurate hydrological simulations, even in the case of a large watershed. [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]

Lack of spatially distributed soil erosion data has greatly hampered the development and improvement of process-based soil erosion models. The 137Cs method has been widely used to estimate soil erosion and redistribution but has seldom been used to evaluate process-based erosion models. This study evaluated spatial erosion patterns predicted by the Water Erosion Prediction Project (WEPP) model using 137Cs-estimated spatially distributed erosion data and quantified the effects of slope length on erosion along rangeland hillslopes. Eight rangeland hillslopes in midwestern Oklahoma were selected. The WEPP rangeland model was parameterized with observed soil and vegetation data. One downslope transect was sampled for each hillslope. At each sampling position, seven samples along a contour line were composited to represent the 137Cs inventory for that slope position to reduce random spatial variation. Both methods predicted low soil erosion rates, with the maximum erosion rates being near 2 Mg ha-1 yr-1. The erosion rates predicted by the two methods were significantly correlated at P = 0.01, and the medians were not significantly different; however, a paired t-test showed that WEPP predicted significantly lower erosion rates than the 137Cs method. More importantly, the WEPP-predicted soil erosion patterns in response to slope length agreed very well with those predicted by the 137Cs method. The loci of the maximum and minimum erosion rates were similar between the two methods for seven transects. The WEPP rangeland model predictions for the spatial patterns of relative soil erosion intensity along a hillslope compared favorably with 137Cs method estimates. [ABSTRACT FROM AUTHOR]