The model uses a uniform grid to represent the topographic features of the model domain, consistent with the raster grid of the DEM. They cannot reliably predict the highly transient flooding process from intense rainfall, in which case a fully 2‐D hydrodynamic model is required. According to the characteristics in this basin, the forecast lead time selected for this study is 5 h. Table 1 lists the information of these flood events, including the beginning and ending time. This essentially means those areas that are not covered by the surveyed flood map cannot be interpreted as never been flooded during the event. Finally, the optimal result is obtained by the voting or averaging method. Table 4 presents the NSEs calculated for the different water levels at the three selected gauges (Great Corby, Linstock, and Sheepmount). In this section, the proposed flood forecasting system set up for the Eden Catchment is tested by reforecasting a severe flood event that occurred on 6 December 2015. Therefore, the NIMROD radar data are treated in this work as the reliable/accurate rainfall observations on the ground. The Eden Catchment is outlined by the red curves. For both arid/semiarid and humid catchments, remote sensing products and outputs from regional scale hydrological models may provide useful data sources and information for estimating infiltration parameters. Punjab List of Flood Forecasting Sites 2019. Box plots of the hourly rainfall rates of all cells within the Eden Catchment from 21:00 on 4 December to 9:00 on 6 December 2015: (a) radar, (b) forecasted, and (c) their difference. Uncertainties from the UKV model may propagate to HiPIMS and affect the flood forecasts. Reliable simulation of this type of highly transient flooding process requires the use of fully hydrodynamic models. The calculated RMSEs demonstrate similar trends. The CC and the RMSE of the hybrid model and empirical model in the calibration period are summarized and shown in Table 6. Regardless of the type of floods being considered, a complete flood forecasting system normally includes at least two components, that is, a model to predict the sources/drivers of flooding, such as precipitation, river flow, and storm surge, and a hydrological or hydraulic model to efficiently simulate the catchment response and flooding processes along the river networks and in the floodplains. Driven by radar rainfall data, both of the 5‐ and 10‐m simulations use the same model parameters and initial conditions as the aforementioned whole‐catchment simulation. The surveyed flood map provided by the EA covers the two most seriously flooded zones in the city center: (1) the area between Skew Bridge and Sheepmount and (2) the area in either sides of Botcherby Bridge and Melbourne Park (Environment Agency, 2016). To evaluate the model performance, water levels measured at a number of gauges are compared with the simulation results. The hybrid model combines the random forest model and a flood hydrograph generalization method. high‐resolution DEMs and spatial data to resolve complex topographic features and river geometry; high‐quality rainfall forecasts with sufficient lead time and tempo‐spatial resolution; estimation of the spatial distributions of soil and land cover types, and soil moisture conditions; and. Carlisle et al. At present, the use of hydrological models is the main technical approach for real-time flood forecasting. To fill the current research and practical gaps, this work develops a new forecasting system by coupling a graphics processing unit (GPU) accelerated hydrodynamic model with NWP products to provide high‐resolution, catchment‐scale forecasting of rainfall‐runoff and flooding processes induced by intense rainfall. 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The specific findings of this study are as follows: RF cannot make prediction beyond the range of training set data, which may lead to the poor prediction effect when we do the extreme value prediction. LIDAR Composite Digital Surface Model (DSM), which may be downloaded from the U.K. The advances in high‐resolution NWP provide a great opportunity to substantially improve the current practice in forecasting floods from intense rainfall, which is still a great challenge in research and practice (Bauer et al., 2015). Another type of the flood forecasting model is the data-driven model. This work is supported in part by the National Key Research and Development Program of China (2016YFC0402709, 2016YFC0402706), National Natural Science Foundation of China (41730750), and National Natural Science Foundation of China (41877147). Station have missing data in list based flood forecast data used in this work are provided Appendix! 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