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Studying the causes of drought is important because the formation of large regional droughts is linked to global atmospheric system abnormalities in China. Information about the effects of drought on crop production is needed in order to improve understanding of a region's drought vulnerability and to improve food production safety in the North China Plain. This paper uses agricultural statistics and monthly Southern Oscillation Index data between 1961 and 2007 to quantitatively evaluate regional agricultural meteorological disasters and to assess the regressive models used to predict the grain yield and climatic yield losses caused by drought disasters. The data showed that the drought-covered area ratios declined, but the drought-affected area ratios rose. During the La Niña stage, the probability of a drought disaster was higher than during the El Niño stage, especially in Hebei. In the El Niño years, there were negative trends for the drought-covered, -affected and -destroyed area ratios in winter and positive trends in the other three seasons. In the La Niña years, the drought disaster area ratios were positive for all seasons. In Neutral years, there was more likely to be a drought event in autumn. The regression analyses for the three provinces showed that the grain climatic yield loss was significantly associated with the grain yield loss caused by drought disasters. This showed that regional grain yield simulations and evaluations of agro-meteorological disasters reliably predicted the field measured statistical data for drought disasters. Therefore, our results showed that there is a mixed seasonal trend in the drought disaster areas in the El Niño years, but a consistent upward trend for all seasons in the La Niña years. We also showed that the damage caused by drought disasters can be quantified by estimating the yield loss. [ABSTRACT FROM AUTHOR]

Climate change impacts crop productivity as atmospheric conditions and water supply change, particularly in intensive cropping areas. This study used the validated AquaCrop Model, which was run with downscaled daily climate data produced by SDSM and CanESM2. The changes in the potential grain yield of winter wheat and summer maize and water budget during the cropping seasons were estimated for the Huang-Huai-Hai Plain (3H Plain) under RCP4.5 and RCP8.5 scenarios. The results show that the potential yield of winter wheat is increasing with similar spatial patterns in the 2030s, 2050s, and 2080s, with much of the increase is distributed in Shandong and northeastern parts of Henan. During the winter wheat growth period, the water budget deficit will likely improve from −210 mm in the 2030s to −202 mm in 2080s under RCP4.5 and from −206 mm in the 2030s to −191 mm in 2080s under RCP8.5 across the 3H Plain. The water budget during the winter wheat period will continue to be in deficit in the north 3H Plain and improvements are estimated mostly in the lower southern areas of the Plain. The summer maize potential yield is estimated to increase from the baseline period, but yields will decrease by 0.81%, 1.19%, and 2.10% in the 2030s, 2050s, and 2080s, respectively, under RCP8.5 compared to RCP4.5. During the summer maize growth period, the water budget is also estimated to improve from 109 mm in 2030s to 126 mm in 2080s under RCP4.5 and 107 mm in the 2030s to 163 mm in 2080s under RCP8.5. This increase is mainly estimated in the central and south of the 3H Plain. The estimated ET c of winter wheat shows no significant decrease, while the reduction of 6 mm and 13 mm for summer maize is observed under RCP4.5 and RCP8.5, respectively. The study provides scientific evidence to devise adaptation and mitigation climate change strategies for agricultural productivity and water resource management. • The potential winter wheat yield will increase under RCP4.5 and RCP8.5 scenarios. • The increase in wheat yield follows a similar spatial pattern under both RCP scenarios. • The potential summer maize yield will decrease under RCP8.5 as compared to RCP4.5. • Water deficit for wheat will remain similar to the present with increased temperature. • Water budget during summer will significantly improve in the latter half of this century. [ABSTRACT FROM AUTHOR]

Crop simulation models play an essential role in evaluating irrigation management strategies for improving agricultural water use in crop production. In this study, the AquaCrop model was used to calibrate and validate the grain yield and biomass of 11 cultivars (9 winter wheat and 2 summer maize) under full and water-saving irrigation practices. The model results were verified with the available data from the published literature. Under the full irrigation practice, widely used by farmers in the region for winter wheat, the Normalized Root Mean Square Error (NRMSE) was found to be between 2.00–9.90% for grain yield, 2.40–10.85% for biomass, and 0.42–19.61% for water productivity. Under water-saving irrigation for winter wheat, the NRMSE range was found to be between 5.80–16.00% for grain yield, 3.70–17.30% for biomass, and 3.80–13.79% for water productivity. For summer maize, the NRMSE was 5.95%, 6.08%, and 16.41% under full irrigation, and 9.51%, 8.41%, and 3.60% under water-saving irrigation for grain yield, biomass, and water productivity, respectively. For winter wheat and summer maize, the simulation accuracy of grain yield was high under full irrigation, with percent deviations under ± 11.00%. In this study, the simulation accuracy for winter wheat was low under water-saving irrigation, while the model simulated the yield and biomass for summer maize to acceptable accuracy. The model can be reliably used as a tool to simulate grain yield and biomass across the NCP region for winter wheat and summer maize. However, the limitations of the model must be considered when simulating winter wheat under water-stressed conditions. • AquaCrop is a crop growth model to simulate yield response to water. • The model simulation accuracy was high under full irrigation for wheat and maize. • For water-saving irrigation, model accuracy was high for maize compared to wheat. • The model can be reliably used as a tool to simulate yield and biomass across NCP. [ABSTRACT FROM AUTHOR]