Floods and water resource management are major challenges for human in present and the near future, and snowmelt floods which usually break out in arid or semi-arid regions often cause tremendous social and economic losses, and integrated information system (IIS) is valuable to scientific and public decision-making. This paper presents an integrated approach to snowmelt floods early-warning based on geoinformatics (i.e. remote sensing (RS), geographical information systems (GIS) and global positioning systems (GPS)), Internet of Things (IoT) and cloud services. It consists of main components such as infrastructure and devices in IoT, cloud information warehouse, management tools, applications and services, the results from a case study shows that the effectiveness of flood prediction and decision-making can be improved by using the IIS. The prototype system implemented in this paper is valuable to the acquisition, management and sharing of multi-source information in snowmelt flood early-warning even in other tasks of water resource management. The contribution of this work includes developing a prototype IIS for snowmelt flood early-warning in water resource management with the combination of IoT, Geoinformatics and Cloud Service, with the IIS, everyone could be a sensor of IoT and a contributor of the information warehouse, professional users and public are both servers and clients for information management and services. Furthermore, the IIS provides a preliminary framework of e-Science in resources management and environment science. This study highlights the crucial significance of a systematic approach toward IISs for effective resource and environment management.

The Turpan Oasis is a typical fragile environment that lies in an arid region of eastern Xinjiang and is affected by natural conditions and human activities. The severity of the land degradation and desertification in this area is increasing; therefore, ecological vulnerability evaluations are important for environmental management of the region. In this study, theories and methods of evaluating ecological vulnerability and the typical characteristics of ecological vulnerability were summarized. By combining the environmental characteristics of the research area and the driving factors of ecological vulnerability, a multilayer vulnerability evaluation index system was built, and a pressure-state-response model was established to evaluate the ecological vulnerability. GIS and remote sensing technologies were applied to extract each index and create a spatial distribution map of vulnerability. The results showed that the vulnerability index values ranged from 3.08 to 6.59, with an average value of 5.19. Regions with moderate vulnerability accounted for 81.85 % of the total area, whereas regions with light and serious vulnerability accounted for 4.19 and 13.95 % of the total area, respectively. Thus, more than 80 % of the area had moderate vulnerability, and nearly 14 % of the area had serious vulnerability. The degree of vulnerability increased from east to west, and the ecological vulnerability in the inner oasis was significantly lower than that in the outer oasis. The key factors for ecological restoration and reconstruction are to control desertification and to ensure ecological water use to the greatest extent.

With the development of cities, the relationship between cities is becoming closer, and the study of urban livability based on a single city can no longer meet the guidelines and suggestions for urban agglomerations. A scientific evaluation of livability in urban agglomerations can better help cities to recognize the advantages and disadvantages. However, most studies on urban livability focus on its connotation and history and neglect simulations and analyses of the future. Based on the Yangtze River Delta agglomeration, this paper establishes an index system using data from 2011 to 2019 to simulate urban livability from 2020 to 2025 through the ARIMA model and analyzes the historical and future data by using GIS methods. The results show the following: (1) The ARIMA model has good simulation accuracy when applied to urban livability analysis and can provide a reference for future urban livability development. (2) The urban livability of the Yangtze River Delta agglomeration has obviously changed both on the whole and in subsystems. Cities in the upper ranking of livability have developed rapidly, and the difference in urban livability has increased. (3) The spatial autocorrelation of urban livability in the Yangtze River Delta agglomeration is obvious both on the whole and in subsystems. (4) The influencing factors of urban livability development are diverse. The general public budget expenditure for social security and employment, fixed assets investment in municipal public facilities, total retail sales of consumer goods, and education and medical expenditures have positive effects on the development of urban livability, while industrial ${\mathrm{SO}}_2$ emissions have a negative effect. The results show that cities should strengthen inter-city relationships, promote the coordinated development of inter-regional cities, and formulate relevant policies to improve the level of urban environmental governance in the region.