Uncertainties of urban flood modeling: Influence of parameters for different underlying surfaces.

Rapid urbanization has dramatically increased the impermeable surface in urban area, which led to urban severe flooding and waterlogging in the world, especially in China and southeast Asia. There are more than 100 cities that suffered from urban flood every year since 2006, and more than 100 million citizens are involved in China. Urban flood mitigation is one of the most critical issues for both water administration and city management agency, in which urban flood modeling is vital and necessary. Whereas, there are relatively few data of waterlogging and runoff in urban area during flooding episodes to calibrate and validate the models, for there are usually few observation facilities installed in the cities. This paper used a combination of experiment and modelling to overcome the lack of reliable discharge data and be able to characterize the urban flooding problems in Xiamen Island, China. This paper simulated the urban flooding in Xiamen Island based on a hydrodynamic model coupled with hydrological model. The datasets of underlying surfaces were input to the model, including the terrain data, building plan, land use, etc. The uncertainty of the urban flood model was analyzed based on the generalized likelihood uncertainty estimation (GLUE) method with shuffled complex evolution Metropolis (SCEM-UA) sampling algorithm. The key parameters were evaluated by on-site experiment to reduce the uncertainties of the model, which could improve the accuracy of the model. If using the recommended parameter value range, the average relative error of flood depth was less than 27.2% at 90% confidence level. A typical rain pattern of 50 years return event was used for flood simulation. The results show that the main inundated areas (flooded depth more than 40 cm) are located in three groups: southeast to the Yundang Lake, around the Hubian Reservoir, along the Exhibition Road. The other inundated areas that less than 40 cm deep are scattered in some low-lying land of Xiamen Island. The main inundated areas simulated are consistent with the point survey of urban flooding, which verifies that the suggested model and the on-site experiment is effective and reliable for urban flood prediction.

Liu J ，Shao W ，Xiang C ，Mei C ，Li Z ... -  《-》

Urban flood risk warning under rapid urbanization.

In the past decades, China has observed rapid urbanization, the nation's urban population reached 50% in 2000, and is still in steady increase. Rapid urbanization in China has an adverse impact on urban hydrological processes, particularly in increasing the urban flood risks and causing serious urban flooding losses. Urban flooding also increases health risks such as causing epidemic disease break out, polluting drinking water and damaging the living environment. In the highly urbanized area, non-engineering measurement is the main way for managing urban flood risk, such as flood risk warning. There is no mature method and pilot study for urban flood risk warning, the purpose of this study is to propose the urban flood risk warning method for the rapidly urbanized Chinese cities. This paper first presented an urban flood forecasting model, which produces urban flood inundation index for urban flood risk warning. The model has 5 modules. The drainage system and grid dividing module divides the whole city terrain into drainage systems according to its first-order river system, and delineates the drainage system into grids based on the spatial structure with irregular gridding technique; the precipitation assimilation module assimilates precipitation for every grids which is used as the model input, which could either be the radar based precipitation estimation or interpolated one from rain gauges; runoff production module classifies the surface into pervious and impervious surface, and employs different methods to calculate the runoff respectively; surface runoff routing module routes the surface runoff and determines the inundation index. The routing on surface grid is calculated according to the two dimensional shallow water unsteady flow algorithm, the routing on land channel and special channel is calculated according to the one dimensional unsteady flow algorithm. This paper then proposed the urban flood risk warning method that is called DPSIR model based multiple index fuzzy evaluation warning method, and referred to as DMFEW method. DMFEW first selects 5 evaluation indexes based on the DPSIR model for flood risk warning evaluation, including driving force index, pressure index, state index, impact index and response index. Based on the values of all evaluation indexes, one evaluation index for the whole system evaluation result is determined by using the fuzzy comprehensive evaluation method. The flood risk level is divided into 4 levels, having Level 1 the most serious. Every evaluation index is also categorized as 4 levels, and a linear fuzzy subjection function is proposed to do the fuzzy comprehensive evaluation. Dongguan City is used as the study case to validate the proposed method. The urban flood forecasting model is set up with the topographic data, the city map, the underground pipelines and land cover types, and two flood events are simulated with observed precipitation, one is interpolated from the rain gauges data, and another is estimated by digital weather radar. The simulated results are compared with the investigated water depth, and the results show the model has very good performances. The results are further used for the flood risk warning simulation, and are very reasonable.

Chen Y ，Zhou H ，Zhang H ，Du G ，Zhou J ... -  《-》

Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China.

Xu H ，Wang Y ，Fu X ，Wang D ，Luan Q ... -  《-》

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region.

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

Liu C ，Li W ，Zhao C ，Xie T ，Jian S ，Wu Q ，Xu Y ，Hu C ... -  《-》

The Compound Effect of Spatial and Temporal Resolutions on the Accuracy of Urban Flood Simulation.

Flood disaster is one of the critical threats to cities. With the intellectualization tendency of Industry 4.0, refined urban flood models can effectively reproduce flood inundation scenarios and support the decision-making on the response to the flood. However, the spatiotemporal variability of rainfall and the spatial heterogeneity of the surface greatly increase the uncertainties in urban flood simulations. Therefore, it is crucial to account for spatiotemporal variability of rainfall events and grids of the model as accurately as possible to avoid misleading simulation results. This study aims to investigate the effect of temporal resolutions of rainfall and spatial resolutions of the model on urban flood modeling in small urban catchments and to explore a proper combination of spatiotemporal schemes. The IFMS Urban (integrated flood modeling system, urban) is used to construct a one-dimension and two-dimension coupled urban flood model in the typical inundated area in Dongguan, China. Based on five temporal resolutions of rainfall input and four spatial resolutions, the compound effect of spatiotemporal resolutions on the accuracy of urban flood simulations is systematically analyzed, and the variation characteristics are investigated. The results show that the finer the temporal resolution is, the higher the simulation accuracy of the maximum inundated water depth. Considering the spatial resolution, as the spatial grid becomes smaller, the relative error of the maximum inundated water depth decreases, but it also shows some nonlinear characteristics. Therefore, the smaller grid does not always mean a better simulation. The spatial resolution has a greater impact on the flood simulation accuracy than the temporal resolution. The simulation performance reaches the best when the grid interval is 100 m and the rainfall input interval is 5 min, 10 min, or 15 min. Affected by other factors such as terrain slope, the simulation accuracies under different spatiotemporal resolutions present complex nonlinear characteristics. The mechanisms of the compound effect of the spatiotemporal resolutions on the model simulation and the effect of underlying surface and topography on model simulation will be the focus of in-depth exploration for the future urban flood model.

Li X ，Wang L ，Zhou H ，Wang Y ，Niu K ，Li L ... -  《-》