Crop production on the Chinese Loess Plateau under 1.5 and 2.0 °C global warming scenarios.

Global warming is a crucial factor affecting crop production in ecologically vulnerable areas. Warming-induced changes in the yields of different crops could pose significant challenges to food security and sustainability assessment. In this study, the World Food Studies model and a remote sensing product assimilation algorithm were used to develop a spatially explicit crop assimilation model applicable to the Loess Plateau of China. The model was used to simulate potential changes in actual yields and yield gaps for winter wheat and maize under three typical climate scenarios (Representative Concentration Pathways (RCPs): RCP 2.6, RCP 4.5, and RCP 8.5) from 2016 to 2060. Average yields increased in both winter wheat (2.38 %-4.96 %) and maize (5.41 %-6.85 %), with maize (RCP 4.5 > RCP 8.5 > RCP 2.6) more adapted to climate warming than winter wheat (RCP 2.6 > RCP 8.5 > RCP 4.5) in terms of yield increase rate. The yield increase and yield gap for winter wheat decreased most significantly in RCP2.6 (-2.28 %). Maize yield did not exceed 80 % of the potential yield in any scenario. The average phenological periods for winter wheat and maize are predicted be 2-4 and 9-16 days earlier, respectively. Crop yields were negatively correlated with radiation and yield gaps were positively correlated with precipitation. Future climate change will likely cause dramatic interannual crop yield fluctuations. Winter wheat is predicted to experience yield stagnation after 2050, whereas maize production potential will increase briefly before experiencing a long-term decline in growth. The results of this multi-scenario simulation assessment of crop production provide scientific support for implementing climate-adapted crop management strategies and integrated dry-crop-irrigated agriculture to meet food security objectives in this ecologically fragile area. We recommend integrated management measures to ensure regional food security through crop variety improvement, irrigation regulation, and planting structure optimization.

Habitat quality assessment provides indicators for socio-ecological management: a case study of the Chinese Loess Plateau.

The impact of changing land use and land cover (LULC) on regional habitat quality have attracted extensive attention. The Loess Plateau is an ecologically fragile area; LULC changes in this region have complex impacts on habitat quality at multiple spatiotemporal scales. This study developed an integrated assessment method based on multi-source data to assess habitat quality changes in the Loess Plateau during recent years (2000-2015) and in the future (2015-2050) under four typical scenarios. A significant increase in urban land use was observed on the Loess Plateau from 2000 to 2050, which resulted in a continuous decrease in the cropland area. The area of forest and grassland landscapes was also reduced by both urban and cropland expansion, with the most significant loss in the grasslands. A future overall decreasing trend in overall habitat quality is predicted, but the SSP1-2.6 scenario is significantly better than the SSP5-8.5 scenario. Urban expansion contributes a rapidly increasing proportion of habitat quality decline on the Loess Plateau; urban land will become the most significant threat to regional habitat quality by 2030. Policies for socio-ecological protection with clear, high-level objectives can effectively promote habitat quality. It is recommended that national nature reserves be delineated and ecological functions in the study area be continuously monitored. This research provides a potential socio-ecological baseline and implementation strategy for the habitat conservation-oriented management of large and fragile ecological regions.

Measuring of the COVID-19 Based on Time-Geography.

At the end of 2019, the COVID-19 pandemic began to emerge on a global scale, including China, and left deep traces on all societies. The spread of this virus shows remarkable temporal and spatial characteristics. Therefore, analyzing and visualizing the characteristics of the COVID-19 pandemic are relevant to the current pressing need and have realistic significance. In this article, we constructed a new model based on time-geography to analyze the movement pattern of COVID-19 in Hebei Province. The results show that as time changed COVID-19 presented an obvious dynamic distribution in space. It gradually migrated from the southwest region of Hebei Province to the northeast region. The factors affecting the moving patterns may be the migration and flow of population between and within the province, the economic development level and the development of road traffic of each city. It can be divided into three stages in terms of time. The first stage is the gradual spread of the epidemic, the second is the full spread of the epidemic, and the third is the time and again of the epidemic. Finally, we can verify the accuracy of the model through the standard deviation ellipse and location entropy.

A spatial data model for urban spatial-temporal accessibility analysis.

Time geography represents the uncertainty of the space-time position of moving objects through two basic structures, the space-time path and space-time prism, which are subject to the speed allowed in the travel environment. Thus, any attempt at a quantitative time-geographic analysis must consider the actual velocity with respect to space. In a trip, individuals tend to pass through structurally varying spaces, such as linear traffic networks and planar walking surfaces, which are not suitable for use in a single GIS spatial data model (i.e., network, raster) that is only applicable to a single spatial structure (i.e., point, line, polygon). In this study, a velocity model is developed for a traffic network and walking surface-constrained travel environment through the divide-and-conquer principle. The construction of this model can be divided into three basic steps: the spatial layering of the dual-constrained travel environment; independent modelling of each layer using different spatial data models; and generation of layer-based time-geographic framework by merging models of each layer. We demonstrate the usefulness of the model for studying the space-time accessibility of a moving object over a study area with varying spatial structures. Finally, an example is given to analyse the effectiveness of the proposed model.