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The Qilian Mountains, located in northwest China and serving as a crucial water recharge area, have exhibited significant regional differences in precipitation patterns in recent decades. However, the limited temporal coverage of instrumental data has hindered a deep understanding of hydroclimate variations and regional differences. Further investigation into their long-term spatial and temporal precipitation characteristics is urgently needed. In this study, a new tree-ring-width chronology spanning 1743 years was established in the central Qilian Mountains using Qilian juniper (Juniperus przewalskii Kom.) samples. Significant correlations were found between the tree-ring indices and precipitation during both the growing and pre-growing seasons. Based on these correlations, annual precipitation from August of the previous year to July of the current year was reconstructed. The reconstruction model successfully explains 34.5% of the variation in precipitation during the calibration period. The analysis of the reconstructed series reveals notable interannual to multi-decadal dry–wet variability during the period from 614 AD to 2016 AD. The mid- to late-15th century emerges as the longest-lasting dry period, while the last decade stands out as the wettest. Comparative analysis with other precipitation reconstructions in the eastern and western Qilian Mountains reveals that regional drought events tend to be more pronounced and enduring. Low-frequency fluctuations on decadal to century scales show distinct wet and dry periods in the 12th–18th centuries in both the eastern and western parts of the Qilian Mountains, with weaker fluctuations in subsequent centuries. However, the central part of the Qilian Mountains exhibits opposite trends, possibly due to the complex interactions of multiple circulation systems.

Purpose – The intended nationally determined contributions (INDCs) is a major outcome of the Paris Agreement on international cooperation to reduce emissions, and is likely to be the future scenario for carbon emissions. This paper aims to obtain the fine spatial pattern of carbon emissions in 2030, identify hot spots and analyze changes of carbon emissions with a spatial grid method. Design/methodology/approach – Based on the integrated quantified INDCs of each economy in 2030, the authors predict the population density pattern in 2030 by using the statistics of current population density, natural growth rates and differences in population growth resulting from urbanization within countries. Then the authors regard population density as a comprehensive socioeconomic indicator for the top-bottom allocation of the INDC data to a 0.1° × 0.1° grid. Then, the grid spatial pattern of carbon emissions in 2030 is compared with that in 2016. Findings – Under the unconditional and conditional scenarios, the global carbon emission grid values in 2030 will be within [0, 59,200.911] ktCO2 and [0, 51,800.942] ktCO2, respectively; eastern China, northern India, Western Europe and North America will continue to be the major emitters; grid carbon emissions will increase in most parts of the world compared to 2016, especially in densely populated areas. Originality/value – While many studies have explored the overall global carbon emissions or warming under the INDC scenario, attention to spatial details is also required to help us make better emissions attributions and policy decisions from the perspective of the grid unit rather than the administrative unit.

A key focus in research on changes in historical land cover has been to improve existing gridded cropland allocation methods based on land suitability for cultivation to generate credible historical cropland cover data. This study developed a settlement-density-based method for gridded cropland allocation using the locations of settlements to identify the cropland grid and the settlement density as the weight for allocating the cropland area to the grid. This method was applied to allocate the provincial cropland areas in Jilin Province, China, to a 5′ × 5′ cropland cover at six time points during the last 300 years. The credibility of the reconstruction was assessed using three methods. The following conclusions emerged. First, the settlement density method is funded on the fact of coexistence between rural settlements and cropland. Cropland is only distributed in the grid where the settlements exist, and the cropland area of a grid equals to the cropland area per settlement multiplying by the number of settlements within the grid, without considering differences of settlement size. Second, all three quantitative or qualitative assessments of Jilin Province confirmed the credibility and feasibility of the settlement density method. Therefore, the use of this method to reproduce the temporal and spatial changes in cropland cover in new reclamation regions, such as Jilin Province, is valid. This study provides valuable inputs for enhancing the credibility of historical global land cover data by incorporating human factors into the cropland allocation method.

Reconstruction of historical deforestation helps to understand the dynamics of forest cover change and provides a basis for the further study of human-nature interactions over the long term. Significant agriculture-driven deforestation occurred in the 18th century in China due to its socio-cultural transformation. To understand this deforestation during the 18th century, we took typical counties in western Hunan as a case study area and reconstructed the settlements’ expansion and densification as indicators of socio-cultural factors. We then reconstructed the agricultural land expansion and agriculture-driven deforestation based on these settlements. The results showed that the agricultural land area increased by 40.4% from the early to the late 18th century, while the proportion of forest area covering the region decreased from 78.0% to 69.1%. Meanwhile, agriculture-driven deforestation mainly unfolded in the eastern and western parts of the region at relatively low elevation in the early 18th century, and this mainly happened in the middle of the region lying at relatively high elevation in the late 18th century. This study’s results provide an improved spatial resolution for the reconstruction of historical land use/cover change, thus enabling insights to be gained from a more detailed spatiotemporal pattern of historical deforestation trends. This study helps to understand the anthropogenic land cover change on a larger spatiotemporal scale through a regional case study.