Climate Change and the Pattern of the Hot Spots of War in Ancient China

Zhang, Sheng-Da; Zhang, David Dian; Li, Jinbao

doi:10.3390/atmos11040378

Cited by 7 publications

(9 citation statements)

References 31 publications

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“…The cold and hot spots analysis is one of the commonly-used methods to explore the local spatial clustering distribution characteristics. This method could collect the Getis-Orid G * i index of each element by calculating the elements, so as to determine whether there is high-value clustering or low-value clustering as well as to identify its location [50,54]. Furthermore, Getis-Orid G * i is a commonly-used index to describe the regional cold and hot spots, thus reflecting the correlation degree of SFQ grade between different research areas.…”

Section: Spatial Cold and Hot Spots Analysis Methodsmentioning

confidence: 99%

Spatial Structure Characteristics of Slope Farmland Quality in Plateau Mountain Area: A Case Study of Yunnan Province, China

Chen

Shi

2020

Sustainability

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As an important part of farmland, the slope farmland is widely distributed in the central and western plateau mountain region in China. It is necessary to scientifically evaluate the slope farmland quality (SFQ) and analyze the spatial structure characteristics of SFQ to ensure reasonable utilization and partition protection of slope farmland resources. This paper takes the typical plateau mountain region—Yunnan Province in China—as an example and systematically identifies the leading factors of SFQ. The sloping integrated fertility index (SIFI) is adopted to reflect the SFQ. The evaluation system is built to quantitatively evaluate the SFQ and the spatial structure characteristics of SFQ were analyzed by a geostatistical model, autocorrelation analysis and spatial cold–hot spot analysis. The results show that the SFQ indexes in Yunnan Province are between 0.36 and 0.81, with a mean of 0.59. The SFQ grade is based on sixth-class, fifth-class, seventh-class and fourth-class land. The SFQ indexes present a normal spatial distribution, and the Gaussian model fits well with the semi-variance function of the spatial distribution of SFQ indexes. Furthermore, the spatial distribution of SFQ indexes is moderately autocorrelated. The structural factors play a major role in the spatial heterogeneity of SFQ indexes, but the influence of random factors should not be ignored. The spatial distribution of SFQ grades has a significant spatial aggregation characteristic, and the types of local indicators of spatial association (LISA) are based on high–high (HH) aggregation and low–low (LL) aggregation. The cold spot and hot spot distributions of SFQ grades display the significant spatial difference. The hot spot area is mainly distributed in Central Yunnan and the Southern Fringe, while the cold spot area mainly distributes in the Northeastern Yunnan, Northwestern Yunnan and Southwestern Yunnan. This study could provide a scientific basis for SFQ management and ecological environment protection in the plateau mountain region.

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Section: Spatial Cold and Hot Spots Analysis Methodsmentioning

confidence: 99%

Spatial Structure Characteristics of Slope Farmland Quality in Plateau Mountain Area: A Case Study of Yunnan Province, China

Chen

Shi

2020

Sustainability

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“…However, this nationwide precipitation reconstruction only illustrates three major wet/dry cycles (multicentennial scale) in history as precipitation varies asynchronously in China. Therefore, according to and Zhang et al (2020b), a 300-year Butterworth low-pass filter was used to retrieve the low-frequency signal, and the average of this sequence was calculated to facilitate comparisons, based on which three wet (WP, to avoid confusion with the abbreviation of "warm")-dry (D) cycles were divided (also, please see Table S2b in Online Resource for details). The imperial era from 5 to 1911 CE was thus considered as the study period for consistency.…”

Section: Datamentioning

confidence: 99%

“…Such "outliers" of war centers during cycle 5 (1206-1490 CE) may not be attributed to temperature change. In fact, as the national precipitation series reconstructed by and the related wet-dry cycles divided by Zhang et al (2020b) show, the stage D2 (1042-1262 CE) is basically dry; besides, the period around 1150-1250 CE is the driest century during the last 2000 years . Since W5 partly overlaps with this dry interval (1206-1262 CE), and Fang (1989) and Pei and Zhang (2014) have indicated that precipitation rather than temperature is more influential in determining southward nomadic migration and the subsequent conflicts between pastoralists and agriculturalists, the anomalous southward shift of war center in W5 could be explained to some extent.…”

Section: Other Factors Beyond Temperaturementioning

confidence: 99%

Spatiotemporal shifts of population and war under climate change in imperial China

Zhang

Pei

2021

Climatic Change

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Studies on the spatiotemporal relationship between historical climate change and the patterns of population and war are rare. In this research, statistical methods (such as correlation test and Granger causality analysis) and visualization technique are applied to demonstrate how temperature, in terms of long-term trend and cyclic mode, fundamentally affects the temporal-spatial variations of population center and war center during imperial China (5–1911 CE). Results show that (1) the consistent southward migration of population center and war center overall accords with the macro-trend of temperature cooling over the last two millennia. (2) The extent of the outward expansion of the Chinese Empire is measured by the population center–war center distance that lengthens during warm periods but shortens in cold phases, which correspond to the north/west/northwestward advancement and south/east/eastward retreatment of war center, respectively, while population center moves within a small range. (3) The shift of population latitude precedes that of war latitude, indicating the change from ecological-demographic to social-political sphere in space. We suggest that similar to population center, the temperature-influenced ancient Hu Line, which symbolizes the disparity of population density in different regions of China, may shift by several hundred kilometers; latitudinal rather than longitudinal variations of population center and war center are more robust in history. We also find that precipitation controls war center and population center on the multicentennial scale, but not the scale focused upon in this study. These findings provide new insights and theoretical implications into the in-depth understanding of the nature–human nexus.

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“…The common theme of the associated papers is to reveal the non-linear and complex relationship between climate change and the positive checks in historical China and pre-industrial Europe. Zhang et al [3] employ Emerging Hot Spot Analysis to examine war hot spots in China from 1-1911. They show that war hot spots were generally located in the Loess Plateau and the North China Plain during warm and wet periods, but in the Central Plain, the Jianghuai area, and the lower reaches of the Yangtze River during cold and dry periods.…”

Section: Historical Periodsmentioning

confidence: 99%