Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring.

With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring.

The source apportionment of N and P pollution in the surface waters of lowland urban area based on EEM-PARAFAC and PCA-APCS-MLR.

Multiple sources contribute to nitrogen(N) and phosphorus (P) pollution in lowland urban rivers, and apportioning the sources of N and P pollution is essential for improving the ecological health of urban environments. Three urban polders in Jiaxing were selected to investigate the temporal variations of N and P pollutants in lowland urban river waters under dry and wet conditions. Moreover, the main potential sources of N and P pollution were identified through the correlations of pollutants and components of dissolved organic matter (DOM) derived from excitation-emission matrix (EEM) and parallel factor analysis (PARAFAC).

Urban flood risk assessment using storm characteristic parameters sensitive to catchment-specific drainage system.

A recent increase in urban floods has necessitated more effective assessment of urban flood risks to quantify the failure probability of urban drainage systems. However, the random hyetographs of storm events influences the results of flood risk assessment using existing methods. In this study, an alternative and more effective method is developed.