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Abstract Maize, rice, wheat and soybean—the major staple food crops in China—have a crucial role in national food security and economic development. Predictions of changes in the requirements for irrigation water in food crop production under climate change may provide scientific support for the optimum allocation of water resources and measures to mitigate climate change. We conducted a spatial grid-based analysis using projections of future climate generated by a bias-correction and spatial disaggregation multi-model ensemble for three representative concentration pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) adopted by the fifth phase of the Coupled Model Intercomparison Project. We investigated the effects of climate change associated with increasing temperature, changed precipitation and increased concentrations of atmospheric carbon dioxide (CO 2) on the irrigation water requirements of maize, rice, wheat and soybean in China at the end of the 21st century (2081–2100). Our results indicate that the irrigation water requirements of maize and wheat are driven by temperature and especially by CO 2 concentrations in the northwest interior area as a result of the low rainfall and high rates of evaporation; the irrigation water requirement of soybean is influenced by a combined effect of temperature, precipitation and CO 2 concentration, whereas the irrigation water requirement for rice is dominated by precipitation alone in the southern coastal region, which has high rainfall. The irrigation water requirements of crops decrease mainly as a result of the beneficial effects of CO 2 on plant growth in China. The regions requiring vast amounts of irrigation water as a result of climate change are mainly concentrated in northwestern China. The effects of climate change affect the requirement for irrigation water, especially under high-emission scenarios, and should be studied further to design appropriate adaptation strategies for the management of agricultural water to maintain the sustainable development of agriculture. Graphical abstract Irrigation water requirements with temperature-only (T-only), precipitation-only (P-only) and carbon dioxide-only (CO 2 -only) for (a) maize, (b) rice, (c) soybean and (d) wheat under the representative concentration pathway scenario RCP8.5 in China in the 2010s (2006–2020), 2030s (2021–2040), 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100). Unlabelled Image Highlights • We predict the changes in irrigation water requirement for four crops in China. • The irrigation water requirements for crops are dominated by various climatic factors. • The irrigation water requirements under RCP8.5 reduce mainly by the CO 2 effect. • Northwestern China always requires vast amounts of irrigation water for crops. [ABSTRACT FROM AUTHOR]

Highlights • PDSI is highly correlated with the variations of P and ET 0 on 12-month timescale. • PDSI is more sensitive to P on national scales, except the northeastern China. • PDSI is preferred to recommend to estimate long-term drought in China. Abstract The self-calibrating Palmer Drought Severity Index (sc_PDSI) is developed within the frame of the PDSI model, but is considered to be more appropriate for global drought monitoring. The sc_PDSI can automatically calibrate itself at any location using dynamically computed values and can calculate evapotranspiration using the FAO-56 Penman–Monteith (P–M) equation. However, the correlation of the sc_PDSI(P–M) with some factors that drive drought, such as precipitation (P) and the reference evapotranspiration (ET 0), is still unclear in China. With the aim of solving this issue, we analyzed the correlation of the detrended sc_PDSI(P–M) with the detrended P and ET 0 on different timescales (one, three, six and 12 months) in China for the period 1951–2015. The results show that both the P and ET 0 are highly correlated with the sc_PDSI(P–M) on a 12-month timescale. On this timescale, the sc_PDSI(P–M) is more sensitive to P than to ET 0 on the national scale, except for northeastern China. Thus the sc_PDSI(P–M) may effectively fit the long-term variations in these drivers of drought, especially P. These results provide guidance on the use of the sc_PDSI(P–M) to detect the impacts of climate change on drought severity under the climatic conditions found in China. [ABSTRACT FROM AUTHOR]

Abstract A Standardized Precipitation Evapotranspiration Index (SPEI) model, which uses the FAO-56 Penman–Monteith equation to calculate potential evapotranspiration, has been developed and is considered appropriate for drought monitoring and assessment. However, the correlation of SPEI to total water storage (TWS) across multiple timescales, derived from future climate scenarios, is still unclear for China. In this study, the correlation of SPEI on different timescales (1-, 3-, 6-, 12-, 24- and 48-month) to TWS projected by a global climate model [the Community Climate System Model, version 4 (CCSM4)] under two representative concentration pathway scenarios (RCP2.6 and RCP8.5) for the period 2021–2100 in China is analyzed. The results show that, in general, SPEI is highly correlated with CCSM TWS in most of China (especially in eastern China) at the 12-month timescale, and is therefore regarded as an adequate indicator for representing situations of water resources and evaluating hydrological droughts. At the 12-month timescale, the correlations of SPEI to CCSM TWS vary across different river basins; and the variations in the pattern of correlations are greater due to faster warming and greater precipitation under RCP8.5. It is hoped that this article will provide guidance on the use of SPEI for detecting the impacts of future climate change on drought severity in China. Highlights • SPEI is highly correlated with CCSM TWS on 12-month timescale in general. • Good fitting effects mainly show in eastern river basins in China. • The variation of correlation patterns is greater under RCP8.5 than that under RCP2.6 • SPEI gives a good indication of hydrological variability in China's future. [ABSTRACT FROM AUTHOR]

Accurately assessing the carbon sink and spatial distribution pattern of China's terrestrial ecosystems is of great significance to the implementation of climate change and carbon neutrality strategy. However, the views of various studies are still very controversial due to the differences in carbon sink estimation methods and data sources. In this study, vegetation net primary productivity (NPP) and ecosystem heterotrophic respiration (Rh) estimation models were constructed based on machine learning methods by fusing multisource data, such as remote sensing and ground observation data. The magnitude and spatial pattern of carbon sink in China from 2000 to 2018 were then revealed, and the carbon sink capacity of various ecosystems was quantitatively assessed. The main conclusions include the following: (1) The use of scale-matched carbon input and output data can help reduce the system error in carbon sink estimation. (2) China's terrestrial ecosystem carbon sink since the twenty-first century is approximately 0.458 Pg C/yr, which is equivalent to 22.72% of China's anthropogenic carbon emissions. (3) Deciduous forest has a higher carbon sink capacity than evergreen forest, while coniferous forest has a more stable carbon sink capacity than broad-leaved forest. The magnitude and spatial distribution of carbon sink in China reported in this study provides a scientific reference for achieving carbon neutrality and sustainable development. • The use of scale-matched carbon input and output data can help reduce the system error in carbon sink estimation. • China's terrestrial ecosystem carbon sink since the 21st century is approximately 0.458 Pg C/yr. • China's terrestrial ecosystem carbon sink can absorb approximately 22.72% of China's anthropogenic carbon emissions. • Deciduous forest has a higher carbon sink capacity than evergreen forest. • Coniferous forest has a more stable carbon sink capacity than broad-leaved forest. [ABSTRACT FROM AUTHOR]

Predictions of changes in the distribution of areas suitable for the cultivation of rice and maize in China under future climate change scenarios may provide scientific support for the optimization of crop production and measures to mitigate climate change. We conducted a spatial grid-based analysis using projections of future climate generated by the National Center for Atmospheric Research Community Climate System Model version 4 for two representative concentration pathway scenarios (RCP2.6 and RCP8.5), adopted by the fifth phase of the Coupled Model Intercomparison Project to study the areas suitable for the cultivation of rice and maize in China. We investigated the migration of the centers of gravity of the cultivation areas based on climatic and hydrological factors from 2021 to 2100. The results indicated that, under RCP2.6, the areas suitable for the cultivation of rice were located throughout China, except for on the Qinghai–Tibetan Plateau, while the areas suitable for the cultivation of maize were located in northern, southwestern, central, eastern, parts of northeastern and some northern parts of western China. The distributions of both crops under RCP2.6 showed little change over time. In contrast, the areas suitable for the cultivation of rice and maize under RCP8.5 shifted northward and expanded from northwestern to northern China, as a result of greater warming in northern China and the faster warming trend under RCP8.5. This scenario would require much stronger climate mitigation policies to maintain the stable development of agriculture and to slow down the future migration of crop cultivation areas in China. The distribution of areas suitable for the cultivation of rice and maize should be studied further to design appropriate adaptation strategies for dealing with future climate change. [ABSTRACT FROM AUTHOR]

• · The production areas of crops under RCPs slightly shift northward in future China. • · The crop yield changes under RCPs remain stable or increase in future China. • · This is caused by the combined effect of changed climatic variables on crop yields. • · The crop yield changes are mainly driven by temperature and CO 2 factors. Maize, rice, and wheat are the major staple food crops in China and are crucial components of national food security and economic development. The cultivation and production of these crops are expected to be affected by climate change and elevated atmospheric carbon dioxide (CO 2) concentration, and have drawn considerable public attention. The objective of this experiment was to understand the impact of future climate change (including increased temperature and changed precipitation patterns) and elevated CO 2 concentration on variations of crop yields in their suitable planting areas. We conducted a spatial grid-based analysis of maize, rice, and wheat yields using projections of future climate generated by a multi-model ensemble of global climate models for three representative concentration pathway scenarios (RCP2.6, RCP4.5, and RCP8.5) in suitable planting areas in China for the 2030s (2021–2040) and the 2050s (2041–2060). Suitable areas for the planting of maize, rice, and wheat under the high-emission scenarios migrated slightly northward over time. Yield of all three crops would be expected to remain stable or to slightly increase across China in the future. A possible reason for this result may be because the positive effects of increased precipitation and CO 2 offset the negative effect of increased temperature on crop yields, resulting in a much more appropriate growth environment and increased biomass accumulation and crop yield. In addition, this study also indicated that changes in crop yields were mainly driven by temperature and CO 2 factors. The potential effects of climate change and elevated CO 2 concentration on migration of planting areas and yield fluctuations for crops should be given greater attention in the future. [ABSTRACT FROM AUTHOR]

Gross primary productivity is one of the most important indicators of ecosystem function, which is related to water conditions and shown high interannual variation. Due to the time-lag effect, not only the current water condition but also the previous water conditions (e.g., one year before) impact the gross primary productivity (GPP). Revealing the impacts of current and previous years' water status is currently a hot topic. In this study, we designed a series of water deficit scenarios based on the meteorological dataset of the Climatic Research Unit (CRU) and then analysed the responses of the remote sensing-based moderate resolution imaging spectroradiometer (MODIS) gross primary productivity (GPP) in China, from which the role of water deficit in time periods was evaluated. The results indicate that the impact of climate factors (i.e., water, temperature and radiation) on GPP has a high spatial heterogeneity and that water-limited regions that are primarily distributed in North and Northwestern China show a stronger water-GPP relationship than water-unlimited regions. The water deficit that occurred in different periods had a variable impact on GPP. Specifically, GPP was primarily controlled by the current year's water conditions in the water-limited regions, with the importance value of 52.8% (the percentage of Increased Mean Square Error, %IncMSE) and 3.8 (the mean decrease in node impurity, IncNodePurity), but at the same time, it was conditionally affected by the water status in the previous year, with the importance value of 20.4% (%IncMSE) and 0.6 (IncNodePurity). The role of water in previous years is multifarious, which depends on the water conditions of the current year. The results revealed by the scenarios indicate that the influence of water conditions in the previous year was not statistically significant when the water conditions of the current year were in a drought. In contrast, when the current year's water conditions were normal or wetter, the water conditions in the previous year (i.e., one-year time lag) were also important and the increase of GPP significantly depended on the water condition (p < 0.05). The diverse roles of water conditions in previous years on GPP and its non-ignorable time-lag effect revealed in this study imply that not only the current year's water condition but also its dynamic changes in previous years should be considered when predicting changes in GPP caused by climate change. [ABSTRACT FROM AUTHOR]

• The spatiotemporal characteristics of shrinking cities were identified from 1990 to 2010 in China. • The number of shrinking cities and the shrinking degree were continuously increasing. • The aging population was a key factor pushing city shrinkage. • A negative correlation between economic level and city shrinkage was observed. City shrinkage characterized by population loss is a difficult issue challenging urban planning and regional development policy. China has been experiencing rapid urbanization over the past several decades. Although numerous studies have identified the characteristics and factors of urban expansion in China, city shrinkage has been widely ignored. This paper measured prefecture-level city shrinkage from the regional dimension based on the fourth (1990), fifth (2000) and sixth (2010) national census data in China. The spatiotemporal characteristics of shrinking cities were analyzed in different economic regions, and the relevant factors contributing to city shrinkage were explored based on the Pearson correlation coefficient. The results showed that the number of shrinking cities and the shrinking degree continuously increased from 1990 to 2010 in China. City shrinkage showed a significant positive correlation in space and was highly related to the regional economic level and population structure. Specifically, the aging population was a key factor responsible for city shrinkage, and a negative correlation was observed between the economic level and city shrinkage. These findings contribute to our understanding of the relationship between city shrinkage and economic development and provide important information for governments and planners formulating scientifically-based city development policies. [ABSTRACT FROM AUTHOR]