Dynamic visual simulation of flood risk is crucial for scientific and intelligent emergency management of flood disasters, in which data quality, availability, visualization, and interoperability are important. Here, a seamless integration of a spatio-temporal Geographic Information System (GIS) with one-dimensional (1D) and two-dimensional (2D) hydrodynamic models is achieved for data flow, calculation processes, operation flow, and system functions. Oblique photography-based three-dimensional (3D) modeling technology is used to quickly build a 3D model of the study area (including the hydraulic engineering facilities). A multisource spatio-temporal data platform for dynamically simulating flood risk was built based on the digital earth platform. Using the spatio-temporal computation framework, a dynamic visual simulation and decision support system for flood risk management was developed for the Xiashan Reservoir. The integration method proposed here was verified using flood simulation calculations, dynamic visual simulations, and downstream river channel and dam-break flood simulations. The results show that the proposed methods greatly improve the efficiency of flood risk simulation and decision support. The methods and system put forward in this study can be applied to flood risk simulations and practical management.

The outbreak of Corona Virus Disease 2019 (COVID-19) has affected the lives of people all over the world. It is particularly urgent and important to analyze the epidemic spreading law and support the implementation of epidemic prevention measures. It is found that there is a moderate to high correlations between the number of newly diagnosed cases per day and temperature and relative humidity in countries with more than 10,000 confirmed cases worldwide. In this paper, the correlation between temperature/relative humidity and the number of newly diagnosed cases is obvious. Governments can adjust the epidemic prevention measures according to climate change, which will more effectively control the spread of COVID-19.

Grand sites are important witnesses of human civilization. The archeology of grand sites has the characteristics of a long period, interdisciplinary study, irreversibility and uncertainties. Because of the lack of effective methods and valid tools, large amounts of archeological data cannot be properly processed in time, which creates many difficulties for the conservation and use of grand sites. This study provides a method of integrating spatio-temporal big data of grand sites, including classification and coding, spatial scales and a spatio-temporal framework, through which the integration of archeological data of multiple sites or different archeological excavations is realized. A system architecture was further proposed for an archeological information cloud platform for grand sites. By providing services such as data, visualization, standardizations, spatial analysis, and application software, the archeological information cloud platform of grand sites can display sites, ruins, and relics in 2D and 3D according to their correlation. It can also display the transformation of space and time around archeological cultures, and restored ruins in a 3D virtual environment. The platform provides increased support to interdisciplinary study and the dissemination of research results. Taking the Origin of Chinese Civilization Project as a case study, it shows that the method for data aggregation and fusion proposed in this study can efficiently integrate multi-source heterogeneous archeological spatio-temporal data of different sites or different periods. The archeological information cloud platform has great significance to the study of the origin of Chinese civilization, dissemination of Chinese civilization, and the public participation in archeology, which would promote the sustainable development of the conservation and use of grand sites.

Land albedo is an essential variable in land surface energy balance and climate change. Within regional land, albedo has been altered in Greenland as ice melts and runoff increases in response to global warming against the period of the pre-industrial revolution. The assessment of spatiotemporal variation in albedo is a prerequisite for accurate prediction of ice sheet loss and future climate change, as well as crucial prior knowledge for improving current climate models. In our study, we employed the satellite data product from the global land surface satellite (GLASS) project to obtain the spatiotemporal variation of albedo from 1981 to 2017 using the non-parameter-based M-K (Mann-Kendall) method. It was found that the albedo generally showed a decreasing trend in the past 37 years (−0.013 ± 0.001 decade⁻¹, p < 0.01); in particular, the albedo showed a significant increasing trend in the middle part of the study area but a decreasing trend in the coastal area. The interannual and seasonal variations of albedo showed strong spatial-temporal heterogeneity. Additionally, based on natural and anthropogenic factors, in order to further reveal the potential effects of spatiotemporal variation of albedo on the regional climate, we coupled climate model data with observed data documented by satellite and adopted a conceptual experiment for detections and attributions analysis. Our results showed that both the greenhouse gas forcing and aerosol forcing induced by anthropogenic activities in the past 37 decades were likely to be the main contributors (46.1%) to the decrease of albedo in Greenland. Here, we indicated that overall, Greenland might exhibit a local warming effect based on our study. Albedo–ice melting feedback is strongly associated with local temperature changes in Greenland. Therefore, this study provides a potential pathway to understanding climate change on a regional scale based on the coupled dataset.