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 proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.

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). The results indicate that the main pollution sources identified with PCA method were consistent with the potential sources revealed by DOM's EEM-PARAFAC components. Furthermore, absolute principal components score combined with multivariate linear regression (APCS-MLR) was conducted. The results illustrated that domestic wastewater contributes more than 70% of N pollution and river-bottom sediments contribute more than 50% of P pollution under dry conditions. On the contrary, discharged water from the stormwater outlets contributes more than 41% of P and 75% of N under wet conditions. Specifically, about 48% of them come from domestic wastewater, and about 38% come from urban surface runoff. This study highlights the effectiveness of DOM components derived from EEM-PARAFAC in identifying the sources of N and P pollution and the PCA-APCS-MLR in apportioning the contributions of each potential pollution source in lowland urban rivers.

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. After extracting characteristic parameters from hyetographs, the correlation between storm events characteristic and urban flood is analyzed according to the hydraulic model simulation results. Based on it, the storm characteristic parameters sensitive to catchment-specific drainage system response and its threshold to urban flood can be determined. And then, the storm events probability can be described with joint probability distribution of the sensitive parameters through using the Frank Copula. Therefore, flood risks for specific urban catchment can then be assessed by calculating the frequency of occurrence of all the storm events for which sensitive parameter exceed the threshold. This methods was successfully applied for the Chengzhong drainage system in Jiaxing, China. For the catchment-specific system it was found that the most important storm event characteristics are the mean rainfall intensity (I) and the peak 30-min intensity (30-Rp). Thus, the bivariate joint probability distribution of I and 30-Rp was estimated and based on that the risks that the catchment may be flooded every year can be assessed by calculating the probabilities of occurrence of flood-causing storm events per year. The proposed method is applicable for urban areas with different catchment conditions and drainage facilities, and it can provide alternative efficient means for urban flood risk assessment.