Abstract Recently, all kinds of geological disasters happen frequently on the earth. In China, there are countless earthquakes every year, which greatly affect the country’s economic level and development as well as the people’s life and health. The analysis of seismic activity is becoming more and more significant. In this article, the spatial distribution of China’s seismic activities was analyzed by using the provincial seismic data from 1970 to 2013. On the basis of spatial autocorrelation analysis theory, Global Moran’s I, Local Moran’s I, and the Local Indicators of Spatial Association are used to measure the geospatial distribution characteristics of China’s seismic activities. The research results show that earthquakes in mainland China have significant global autocorrelation characteristics as a whole, and the global autocorrelation coefficients are all positive. And the Z-value test (P < 0.05) shows that earthquakes in mainland China present a spatial agglomeration pattern. Furthermore, we observed a reduction trend in disparities of seismic activity among regions in China.

https://www.degruyter.com/document/doi/10.1515/geo-2020-0332/pdf

Spatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China

Grain security is the foundation for national stability and prosperity. In China, grain production depends heavily on irrigation water, direct energy for machinery use, and indirect energy (such as fertilizers, pesticides, and agricultural film), resulting in growing pressure on water supply and demand and high carbon emissions. Therefore, it is urgent to study the regulation of planting structures to reduce the irrigation water input and carbon emissions on the premise of grain security. It is within the scope of the water–energy–food nexus. In China, water resources are mostly managed at the basin scale. Because of missing or inadequate basin-scale data, in this study, the Yellow River Basin (YRB) was chosen as an example to shed light on basin scale regulation analysis; YRB is an important grain production base in China, but it is facing serious water shortage and severe environmental problems. First, production and consumption of diverse grain species in the basin were estimated using remote sensing and statistical data simultaneously. Then, the blue water and carbon footprints were calculated to analyze the water use and carbon emission characteristics of grain production. Finally, the grain planting structure was regulated to reduce the irrigation water input and carbon emissions. Our findings suggest that, with effective planting structure regulation, 4.11×108 m3 irrigation water and 16.98×104 tons CO2 equivalents could be reduced and the grain production increased by about 31×104 tons in the YRB. This study can be viewed as a step towards the use of multisource data to facilitate the analysis of diverse grain species characteristics and sustainable grain development while considering the water–energy–food nexus. A combination of effective planting structure regulation in arid regions and policies will contribute to an increase in grain production as well as saving of resources. 

Regulation of Planting Structure Considering Irrigation Water, Carbon Emission, and Grain Security in the Yellow River Basin, China, by Using Multisource Data

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Xin Zhao

Papers

Crops planting structure and karst rocky desertification analysis by Sentinel-1 data