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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]

Stationarity is an assumption that permeates training and practice in water-resource engineering. However, with global change, the validity of stationarity as well as uncertainty of nonstationarity in water-resource planning are being questioned; thus, it is critical to evaluate the stationarity of climate variables, especially precipitation. Based on the continuous observation data of precipitation from 1427 stations across China, 593 efficient grid cells (1° × 1°) are constructed, and the annual precipitation stationarities from 1959 to 2018 are analyzed. The evaluated autocorrelation stationarity indicates that 92.24%–96.12% of the grid cells for an autocorrelation coefficient of lag 1–8 years of precipitation are indistinguishable from 0 [90% confidence level (CL)]. The mean stationarity indicates that 97.47% of the grid cells have a stable mean for 30 years (90% CL); beyond the confidence limits, they are mainly located in the northwest of China, where annual precipitation is less, and the average exceeding range is ±3.78 mm. The long-term observation of annual precipitation in Beijing (1819–2018) and Shanghai (1879–2018) also yields autocorrelation and mean stationarities. There is no significant difference in the annual precipitations between the past 20 years (1999–2018) and the past 60 years (1959–2018) over China. Therefore, the annual precipitation in China exhibits a weak stationary behavior that is indistinguishable from the stationary stochastic process. The average variation in precipitation is ±9.55% between 30 successive years and 16.53% between 10 successive years. Therefore, it is valuable and feasible to utilize the historical data of annual precipitation as the basis of water-resources application. [ABSTRACT FROM AUTHOR]

The potential predictability of seasonal extreme precipitation accumulation (SEPA) across mainland China is evaluated, based on daily precipitation observations during 1960-2013 at 675 stations. The potential predictability value (PPV) of SEPA is calculated for each station by decomposing the observed SEPA variance into a part associated with stochastic daily rainfall variability and another part associated with longer-time-scale climate processes. A Markov chain model is constructed for each station and a Monte Carlo simulation is applied to estimate the stochastic part of the variance. The results suggest that there are more potentially predictable regions for summer than for the other seasons, especially over southern China, the Yangtze River valley, the north China plain, and northwestern China. There are also regions of large PPVs in southern China for autumn and winter and in northwestern China for spring. The SEPA series for the regions of large PPVs are deemed not entirely stochastic, either with long-term trends (e.g., increasing trends in inland northwestern China) or significant correlation with well-known large-scale climate processes (e.g., East Asian winter monsoon for southern China in winter and El Niño for the Yangtze River valley in summer). This fact not only verifies the claim that the regions have potential predictability but also facilitates predictive studies of the regional extreme precipitation associated with large-scale climate processes. [ABSTRACT FROM AUTHOR]