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Clarifying the spatiotemporal variation of crop irrigation water requirement (IWR) under the background of climate change is an essential basis for water resource management, determining the irrigation quota and adjusting the planting structure. Using 61 years of climate data from 205 stations in Northwest China, this study investigated the spatiotemporal variations of climatic factors and IWR during the growth period of five main grain crops (spring wheat, winter wheat, spring maize, summer maize, and rice) and explored the dominant climatic driving factors of IWR variation. Results showed that (1) the IWR of grain crops showed distinct differences. Rice was the highest water consumption crop (mean of 753.78 mm), and summer maize was the lowest (mean of 452.90 mm). (2) The variation trends and average values of IWR of different grain crops have spatial heterogeneity across Northwest China. For most crops, high values and increasing trends of IWR were mainly located in eastern Xinjiang, western Gansu, and western Inner Mongolia. (3) T max (maximum temperature), T min (minimum temperature), and P eff (effective precipitation) showed an increasing trend during the growth period of each grain crop, while U 10 (wind speed at 10 m height), SD (solar radiation), and RH (relative humidity) presented decreasing trends. (4) SD, T max , and U 10 promoted the increase of grain crops' IWR, while P eff and RH inhibited it. The impacts of climatic factors on the grain crop IWR differed among different regions. P eff was the most influential factor to the IWR of all grain crops in most areas. Therefore, under the premise of a significant increase in T and uncertain precipitation mode in the future, it is urgent to take effective water-saving measures according to the irrigation needs of the region. To cope with the adverse impact of climate change on the sustainable development of agriculture in the northwest dry area, to ensure regional and national food security., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Northwest China (WTL) is an essential ecological barrier zone of China, an important node of the "Silk Road Economic Belt," and a crucial bearing area for China's execution of the "One Road and One Belt" and "Going Global" strategies. However, its ecology is exceedingly fragile and particularly vulnerable to climate change and human interference. This study explored the spatiotemporal evolution characteristics of vegetation in WTL using NDVI data and investigated its drive mechanisms by geodetector, partial correlation analysis, and residual trend analysis methods. As well as forecasting the trend for vegetation changes. The findings demonstrated that (1) the change in NDVI manifested an overall improvement trend and the distribution in space of NDVI rose from the center to the periphery. 57.07% of the area had a sparse cover of vegetation (NDVI between 0 and 0.2). In addition, about 49% of regions had deterioration tendencies, which were mainly aggregated in HX, QCXDB, QCXDN, and the eastern of QCXQN and QCXXB. (2) The NDVI's shifting trend was unsustainability, and the region of uncertain future accounted for 57.45% of the total, with apparent unsustainability features. (3) The key parameters influencing NDVI spatial distribution were Pre (precipitation), vegetation type, land use type, and soil type. The interaction between two factors enhanced the influence of any single element, which appeared as bivariate and nonlinear enhancements. (4) Both climate variations and human activities have been recognized as key variables affecting NDVI growth. NDVI variance in 73.02% of areas was influenced by the combined effects of climate variations and human activities. However, human activities were the most influential element in NDVI growth, with the relative contributions of 80.28% (19.72% of which was caused by climate variations). These results can be conducive to deepening insights into the local vegetation status, identifying the mechanisms driving vegetation change, and providing scientific recommendations for WTL's ecosystem restoration measures based on actual situations. [ABSTRACT FROM AUTHOR]

Climate change can impact the yield and water footprint of crops. Therefore, assessing such impacts carries great significance for regional water and food security. This study validated and verified the variety parameters of winter wheat for the Decision Support System for Agrotechnology Transfer (DSSAT) model, using the long-term (1993–2013) growth and yield data observed from six agricultural experiment stations in the Haihe River Basin (HRB), China. The growth process was simulated under three representative concentration pathways (RCPs), named RCP2.6, RCP4.5, and RCP8.5—climate scenarios driven by the HadGEM2-ES model. The variety parameters of winter wheat showed high accuracy in the simulation of the anthesis and maturity dates, and could be used for long-term prediction of the growth process. The trends of climate change had positive impacts on the water footprint of winter wheat but adverse impacts on the yield. The growing period was shortened by 3.6 days, 4.7 days, and 5.0 days per decade in the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, due to the rapid accumulation of heat. The yield would be increased in lower emissions scenarios (17% in RCP2.6), but decreased in high-emissions scenarios due to high temperatures, which may restrict the growth of wheat. The water footprint was decreased by 10%, 11%, and 13% in the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, indicating that the water-use efficiency could be improved in the future. The results showed broad application prospects of the DSSAT model in simulating the response of crop growth to climate change. [ABSTRACT FROM AUTHOR]

Aridity index, as the ration of potential evapotranspiration and precipitation, is an important indicator of regional climate. GIS technology, Morlet wavelet, Mann-Kendall test, and principal component analysis are utilized to investigate the spatial-temporal variation of aridity index and its impacting factors in China on basis of climate data from 599 stations during 1960–2013. Results show the following. (1) Boundaries between humid and semihumid region, and semihumid and semiarid region coincide with 400 mm and 800 mm precipitation contour lines. (2) Average annual aridity index is between 3.4 and 7.5 and shows decrease trend with a tendency of –0.236 per decade at 99% confidence level. (3) The driest and wettest month appear in December and July, respectively, in one year. (4) Periods of longitudinal and latitudinal shift of aridity index 1, 1.5, and 4 contours coordinate are 10 and 25 years, 6 and 26 years, and 5 and 25 years, respectively. (5) Four principal components which affect aridity index are thermodynamic factors, water and radiation factors, geographical and air dynamic factors, and evaluation factor, respectively. [ABSTRACT FROM AUTHOR]