With rapid urbanization, floods that occur are more frequently associated with non-riverine, urban flooding. Reliable and efficient simulation of rainstorm inundation in an urban environment is profound for risk analysis and sustainable development. Although sophisticated hydrodynamic models are now available to simulate the urban flooding processes with a high accuracy, the complexity and heavy computation requirement render these models difficult to apply. Moreover, a large number of input data describing the complex urban underlying surfaces is required to setup the models, which are typically unavailable in reality. In this paper, a simple and efficient urban rainstorm inundation simulation method, named URIS, was developed based on a geographic information system (GIS) with limited input data. The URIS method is a simplified distributed hydrological model, integrating three components of the soil conservation service (SCS) module, surface flow module, and drainage flow module. Cumulative rainfall-runoff, output from the SCS model, feeds the surface flow model, while the drainage flow module is an important waterlogging mitigation measure. The central urban area of Shanghai in China was selected as a study case to calibrate and verify the method. It was demonstrated that the URIS is capable of characterizing the spatiotemporal dynamic processes of urban inundation and drainage under a range of scenarios, such as different rainstorm patterns with varying return periods and different alterations of drainage diameters. URIS is therefore characterized with high efficiency, reasonable data input, and low hardware requirements and should be an alternative to hydrodynamic models. It is useful for urgent urban flood inundation estimation and is applicable for other cities in supporting emergency rescue and sustainable urban planning.

Green infrastructure (GI) plays a fundamental role in achieving urban pluvial flood management, mitigating urban heat island effect, and improving living suitability. Residents’ participation is the main driving force of GI implementation. Based on semi-structured interviews, GIS spatial analysis, and multiple regression, we investigated residents’ willingness to participate in the implementation of GI in public and private space and identified the influence factors in Shanghai, China. The results show that, compared with private space, residents prefer to implement GI in public space, where they have different preferences of GI measures. On urban scale, residents’ willingness to participate in the implementation of GI in private space is characterized as “high in the inner city, low in the suburban areas”, while the spatial difference is insignificant for public space. In addition, the factors affecting residents’ willingness to participate in the implementation of GI are different in private and public space. The deterministic factors of GI participation are gender, education level, and floor for private space, while only include building age for public space, in addition to the common factors of free time, cognition of GI, perception of pluvial flood risk, supportive factors, and environment-improving factors that can influence both private and public space GI participation. Our analysis therefore provides valuable information for policymakers concerning nature-based solutions to climate change adaptation and urban sustainability.

Landslide prediction is one of the complicated topics recognized by the global scientific community. The research on landslide susceptibility prediction is vitally important to mitigate and prevent landslide disasters. The instability and complexity of the landslide system can cause uncertainty in the prediction process and results. Although there are many types of models for landslide susceptibility prediction, they still do not have a unified theoretical basis or accuracy test standard. In the past, models were mainly subjectively selected and determined by researchers, but the selection of models based on subjective experience often led to more significant uncertainty in the prediction process and results. To improve the universality of the model and the reliability of the prediction accuracy, it is urgent to systematically summarize and analyze the performance of different models to reduce the impact of uncertain factors on the prediction results. For this purpose, this paper made extensive use of document analysis and data mining tools for the bibliometric and knowledge mapping analysis of 600 documents collected by two data platforms, Web of Science and Scopus, in the past 40 years. This study focused on the uncertainty analysis of four key research subfields (namely disaster-causing factors, prediction units, model space data sets, and prediction models), systematically summarized the difficulties and hotspots in the development of various landslide prediction models, discussed the main problems encountered in these four subfields, and put forward some suggestions to provide references for further improving the prediction accuracy of landslide disaster susceptibility.