Tianyu Song, Wei Ding, Jian Wu, Haixing Liu, Huicheng Zhou, Jinggang Chu, Flash Flood Forecasting Based on Long Short-Term Memory Networks, Water, 10.3390/w12010109, 12, 1, (109), (2019). Real‐time flood forecasting is an effective means to mitigate the negative impact of flooding by providing timely and accurate flood information and warnings to the public and relevant parties. These data sets provide valuable information to evaluate the performance of the model in predicting water level hydrographs and inundation extents. Since HiPIMS adopts an overall explicit numerical method, the time step of a simulation is controlled by the CFL condition that is related to both cell size and flow velocity (Xia et al., 2019). Information of the 23 flood events used for calibration and validation of the models. PubMed . The land cover data are available upon request from the CEH Data Licensing Team (
). In the second half of the 20th century, many multi-parameter and complex conceptual lumped models have been developed in succession by countries all over the world, such as the TANK model (Sugawara 1961), antecedent precipitation index (API) model (Sittner et al. However, deciding the parameters for infiltration is less straightforward. The UKV model releases 36‐hr rainfall forecasts every 6 hr at 3:00, 9:00, 15:00, and 21:00 each day, covering the entire period when Storm Desmond occurred. Observed and forecasted flood hydrograph of event No. 2019), the forecast accuracy of hydrological models is often low, which is difficult to meet the needs of flood control and disaster reduction in this area (Li et al. During the validation period, QR of forecasting of and is 80 and 60%, respectively. In a flood event, water depth is generally much smaller than the horizontal inundation extent, and the flow hydrodynamics can be mathematically described by the 2‐D depth‐averaged shallow water equations (SWEs). For example, in the United Kingdom, a unified model (UM) covering the British Isles has been operating for decades by the U.K. Met Office at low resolution for climate predictions and high resolution for regional NWP (Davies et al., 2005). Search for other works by this author on: This Site. The Qiushui River basin is an arid and semi-arid area where the spatial and temporal distribution of precipitation is often uneven and expressed in peaks with rising and dropping steeply. For example, the Grid‐to‐Grid (G2G) model (Bell et al., 2007) adopted in the U.K.'s National Flood Forecasting System (NFFS) is not able to predict detailed flood extent and must be integrated with off‐line flood simulation results obtained using hydrodynamic models to estimate flood impact. 19990711. A new tool giving accurate river level forecasts in Australia seven days in advance is expected to become a vital tool for dam operators and forecasters in managing future flood risk. Due to the population distribution, it is reasonable to assume that the surveyed flood extent is more complete in the populated urban area than the rural area. Physics, Comets and The average value of the of the calibration period was 22.4%. Transferability of the flood forecasting system depends on the availability of high‐resolution topographic and rainfall input data and the parameterization of the hydrodynamic model, that is, to specify the model parameters for friction and infiltration effects. A lower RMSE indicates higher simulation accuracy, and a value of 0 means a perfect fit to the data. Although clear discrepancy can be detected at some of the gauges, the simulation results are generally consistent with the observations and the rising and falling limbs of the flood hydrograph are correctly predicted at all gauges. It is therefore necessary and desirable to exploit the latest high‐performance modeling technology and develop a flood forecasting system by directly coupling with a fully hydrodynamic model to forecast the detailed flood dynamics and impact induced by intense rainfall. Table 4 presents the NSEs calculated for the different water levels at the three selected gauges (Great Corby, Linstock, and Sheepmount). Then the performance of the flood forecasting system is demonstrated by directly using the UKV numerical rainfall predictions as the model inputs. In HiPIMS, infiltration is considered using the Green‐Ampt model with the infiltration rate estimated using the following formula: (left panel) The Eden Catchment and its location in Britain. However, the value of No. Seasonal forecasting of the flood volume of the Senegal River, based on results of the ARPEGE Climate model. One version of the UM is the U.K. Flash floods result when favorable meteorologic and hydrologic conditions exist together. Sittner et al. However, the real problem of Qiushui River basin is that the spatial and temporal distribution of precipitation is often uneven and expressed in peaks with rising and dropping steeply. Geophysics, Marine Eighteen floods were used to calibrate the model, and five floods were used to validate the model. Learn more. Reliable forecasts of weather, in particular rainfall, can allow advance warning and forecasting of floods. River gauge measurements and postevent investigations provide crucial data to evaluate the flood forecasting results and confirm the performance of the hydrodynamic model. The grid‐based rainfall forecasts are also compared with the point‐based rainfall observations recorded in the surface weather stations, available from the Met Office Integrated Data Archive System (MIDAS). A fully hydrodynamic modeling approach, such as HiPIMS, commonly parameterizes the friction effect using the Manning coefficient, and satisfactory simulation results can be obtained using stand values suggested by a hydraulic textbook for different types of land covers. Properties of Rocks, Computational Then, construct correlations between the flood factors and precipitation factors to screen predictors. FLOOD IMPACT-BASED FORECASTING FOR EARLY WARNING AND EARLY ACTION IN TANA RIVER BASIN, KENYA O.M. The results can be also used to support flood risk analysis by superimposing the relevant vulnerability and exposure data. To evaluate the model performance, water levels measured at a number of gauges are compared with the simulation results. (2015) determined the relative importance of contributing upstream discharges to the main stem during significant flood events. The hybrid model combines the two methods above. (a) Topography map of the selected subcatchment; (b) comparing the observed and simulated water levels at Kirkby Stephen obtained at 5‐ and 10‐m resolutions, respectively. Flash floods represent different forecast and detection challenges because they are not always caused by meteorological phenomena. This is confirmed by the higher hit rate and false alarm rate of the forecast (POD = 0.99, FAR = 0.28, see Table 3) than those calculated against the simulation results driven by radar rainfall (POD = 0.91, FAR = 0.21, see Table 2), which means the forecast may slightly overestimate the actual Storm Desmond flood in Carlisle. In the calibration period of the model, the values of the average relative error of the peak discharge of 13 events were less than 20%; thus, QR of forecasting of is 72%. The UKV rainfall predictions are compared first with the NIMROD radar rainfall records for the selected event. At present, the use of hydrological models is the main technical approach for real-time flood forecasting. In the current case study, infiltration is simply neglected because the majority of the catchment was assumed to be saturated due to substantial antecedent rainfall, and the effect of infiltration on the following flooding was considered to be insignificant (Environment Agency, 2016). Computational tasks on each of the subdomains are carried out separately on different GPUs with exchange of data occurring at the overlapping boundary cells at every time step. Infiltration rate is influenced by the spatial heterogeneity of the catchment surface and soil, that is, different land covers/soil types, and also the initial soil moisture. Department of Civil Engineering, Stanford University, California. So the surveyed flood map should be used with great care. Second, compute the values of antecedent precipitation at the early stage of the forecast period as follows: To evaluate the forecasting ability of the models, the simulation accuracy of each flood event is summarized. The Qiushui River control station, named the Linjiaping Hydrological Station, is located in the upper 13 km of the Yellow River mouth, with a control area of 1,873 km2. Error statistics of the RF model in the validation period. Figure 9d shows the final maximum inundation extent obtained throughout the simulation. However, the peak discharge is higher and the peak time is earlier. Initial conditions (water depth and velocities in the computational domain) for starting a simulation may be generated by prerunning the model using antecedent rainfall data from observations or UKV predictions. However, we should also realize that the spatial data for groundwater table, soil properties, and initial soil moisture are often scarce or come with significant uncertainties. 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. We are now able to simulate the detailed dynamics of a flood event at a high spatial resolution across an entire city/catchment involving tens of millions of computational cells in real time (Liang et al., 2016; Xia et al., 2019). In addition to data availability, the transferability of the forecasting system is also restricted by catchment size and availability of computing resources. Digital Terrain Model (DTM) representing the height of bare earth surface is the background topographical data and may be acquired from the Digimap OS Terrain 5‐m DTM data set (Link 1 in Appendix A). The adoptation of a single global time step removes the requirements of sophisticated model implementation and extra machine memory and will not affect the overall simulation efficiency of the model. In the first step, peak discharge was forecasted. Forecast based early action triggered in Bangladesh for Floods EAP2019BD02 Sources. The time step in each subdomain is decided according to the CFL condition that is related to the maximum velocity and size of the grid cells. CSI varies between 0 and 1, with 1 being the perfect score. Neither of these two digital elevation data sets provide accurate underwater bathymetric information although it is essential for reliable simulation of flow dynamics along river channels. The Met Office NIMROD system provides gridded radar rainfall data that are calibrated to give the best possible estimation of surface precipitation rate at 1‐km spatial resolution and 5‐min temporal resolution, which is available in the CEDA archive (Link 4 in Appendix A). The total catchment area up to the station is approximately 466 km 2 . Rainfall observations from the surface weather stations are also used to evaluate the quality of the grid‐based rainfall forecasting data. Climate change 2014: Synthesis report. It has to be noted, for the event No. Using the flood forecasting system proposed in this work, flood forecasts and subsequent warnings may be available as early as 23:00 on 4 December, almost 1 day before the flood peak arrived at Carlisle. Observed and forecasted water levels at Great Corby, Linstock, and Sheepmount gauges. 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. According to the surveyed flood extent provided by the EA, Carlisle and its upstream region along River Eden are mostly flooded during Storm Desmond (see Figure 7). 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. The calculated RMSEs demonstrate similar trends. The strategy to ease the data exchange process and unify the temporal resolution of flood calculation across the global domain is to adopt the smallest time step returned from the subdomains and synchronize it as the single global time step. As a result of climate change, more intense precipitation is expected in the warmer future (Intergovernmental Panel on Climate Change, 2014; Kendon et al., 2012), which may consequently trigger more extreme rainfall‐induced flood events and increase flood risk. Our study found that when the antecedent precipitation is well distributed in the temporal scale, the empirical model performs well, and when the antecedent precipitation is more concentrated in short time, the hybrid model is better. Processes, Information Learn about our remote access options, School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough, UK, School of Engineering, Newcastle University, Newcastle upon Tyne, UK. A baby in a car seat while attached to a … Google Scholar. Some common parameters are antecedent precipitation, seasonal characteristics and precipitation duration. 2.2 The overall role of the New South Wales and the Australian Capital Territory Flood Warning Consultative Committee is to coordinate the development and operation of flood forecasting This type of model does not consider the physical mechanism of the hydrological process, regards the hydrological process as a black box and determines the mathematical function according to input and output data. doi: https://doi.org/10.2166/hydro.2020.147. The NSEs from the UKV rainfall‐driven simulation are consistently smaller than those calculated against the predictions using radar rainfall. Initial conditions of water depth and velocity inside the computational domain are also needed to set up HiPIMS; these were obtained by prerunning HiPIMS on a dry domain using 3 days of antecedent radar rainfall data. NWP products from the UKV model (Davies et al., 2005) are used in this work to drive HiPIMS for real‐time flood forecasting. RF utilizes bootstrap resampling technology to sample original samples to generate a number of training samples, each of which randomly selects feature attributes through random subspace methods to construct a decision tree. The results imply that the uncertainties from the rainfall prediction in terms of intensity are not amplified by the hydrodynamic model toward the flood forecasting outputs in this case. This essentially means those areas that are not covered by the surveyed flood map cannot be interpreted as never been flooded during the event. Among the main branch, ditches are Taiping ditch, Chengzhuang ditch, Yulin ditch, Chegan ditch, Anye ditch, Dayu ditch and Zhaoxian ditch. The largest human settlement in the catchment is Carlisle, which is located at the downstream end of the Eden and has 75,000 residents, consisting of one third of the total population of the study catchment. However, simulation of floodplain inundation using 2‐D models is computationally expensive, and direct prediction of detailed floodplain hydrodynamics in real time is still not a common practice in an operational flood forecasting system. At 6:00 on 5 December 2015, the southwest corner of Carlisle has been flooded, most likely influenced by the River Caldew (Figure 9a). There are more than 20 branch ditches in the basin (the basin area is larger than 10 km2) that are asymmetric pinnate inlets. 19840701 and No. In the current case study, the forecasting system can provide flood forecasts at 10‐m resolution within 2 hr in a 2,500‐km2 computational domain on a computer server fitted with 8 × NVIDIA Tesla K80 GPUs. It effectively reflects and captures the effects of localized domain features (e.g., mountains) on rainfall patterns. It can be observed that the surveyed flood extent is correctly reproduced by the current model to a large extent. 19910721 indicate that the empirical model has better values of CC, which is 0.02 and 0.06 higher than the hybrid model. Aynalem Tassachew Tsegaw, Thomas Skaugen, Knut Alfredsen, Tone M. Muthanna, A dynamic river network method for the prediction of floods using a parsimonious rainfall-runoff model, Hydrology … This indicates that the performance of HiPIMS reproducing water levels for the secondary rivers or tributaries is less satisfactory. First, selected flood events from 1980 to 2010 were generalized using the flood hydrograph generalization method. For the development of these models, 23 flood events occurring from 1980 to 2010 are selected, of which 18 are used for calibration and 5 are used for validation. 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). 3.2 Seasonal Forecasting of Flood Losses Based on Oscillation Indices. Performance comparison of the hybrid and empirical model in the calibration period. Flooding is one of the most frequent and widely distributed natural hazards, causing significant losses to human lives and properties every year across the world (Balica et al., 2013). Namely, when the maximum and minimum of the peak discharge in the training set is 1,520 and 207 m3/s, the forecasted discharge cannot be greater than 1,520 m3/s or smaller than 207 m3/s. The results show that the hybrid model yields accurate predictions. The main steps of this method are as follows: first, calculate the average daily precipitation in the basin. In general, the model provided acceptable accuracy in both the calibration and validation periods. The CC and the RMSE of the hybrid model and empirical model in the calibration period are summarized and shown in Table 6. 20100919, the accuracies of forecasting of the peak discharge and maximum discharge of the recession process are both very low. Metrics have been developed and used to quantify the performance of inundation models in predicting flood extents (Bates & De Roo, 2000; Horritt, 2006; Khan et al., 2011; Schubert & Sanders, 2012). You should monitor later forecasts and be alert for possible flood warnings. Clearly, the UKV forecasted rainfall is spatially more divergent than the NIMROD radar rainfall. In addition, the authors are indebted to the editors/reviewers for their valuable comments and suggestions. Here, is the peak discharge, is the maximum discharge of the recession process, and T is the flood duration. Wang et al. And the comparison shows that the hybrid model performs better than the empirical model in the Qiushui River basin. In a matrix form, the SWEs may be written as. … The two sets of hydrographs are actually similar during the high‐flow period but are significantly different at the low‐flow sections when the river flow starts to rise at the beginning and fall back to the normal flow condition at the end. Compared with the central processing units (CPUs), GPUs are better suited for repetitive and highly parallel computational tasks and can be used for flexibly performing operations on multiple sets of data. It has been now technically feasible to simulate flood dynamics in large catchments using the state‐of‐the‐art high‐performance hydrodynamic models (Sanders et al., 2010). Physics, Solar 2018; Zahedi et al. Performance comparison of the hybrid and empirical model in the validation period. Considering the long process of flood recession, the flood progress is divided into two parts: the rising and recession processes. Considering the computational efficiency, the proposed flood forecasting system only takes 1 hr and 45 min to produce the 36‐hr forecast on a 10‐m uniform grid covering the 2,500‐km2 simulation domain (leading to 25 million computational cells) on 8 × NVIDIA Tesla K80 GPUs. Land cover information is useful for estimating and adjusting friction and infiltration coefficients in HiPIMS. The UKV model is currently operated in real time by the U.K. Met Office to produce 36‐hr weather forecasts that are released at every 6 hr. Hydraulic inundation models are based on the numerical solutions to the full 2‐D shallow water equations (hydrodynamic models) or one of their simplified forms (e.g., diffusion‐wave models and kinematic‐wave models) (da Paz et al., 2011). The extreme rainfall event led to severe flooding across the catchment, bringing widespread damage and impact to the city of Carlisle. It is produced based on radar records and processed using optimized quality control and correction procedures (Met Office, 2003). The method flood hydrograph generalization comprises the following steps: first, combine each flood hydrograph into the same drawing, wherein the ordinate represents the ratio of and , the abscissa represents the ratio of and T. is the peak discharge, T is the total duration of the flood process, and and represent the discharge and time, respectively, at any time. The results indicate that the hybrid model provides a better flood forecasting performance than the empirical model. 1980). When it is necessary, a 1‐D river routing model may also be used to simulate the flow processes inside river channels and propagate the upstream flood hydrograph predicted by a hydrological model to downstream, which is then coupled with an inundation model to predict flood impact (Chatterjee et al., 2008; Kim et al., 2012; Paiva et al., 2011). The output of a hydrological model is typically time series of flow rate in the river channels. Over the last few days, floods have affected northern regions of Piura and Tumbes, and southern regions of Puno, Apurímac and Cuzco. Figure 9 illustrates the forecasted inundation maps for Carlisle and the surrounding areas at different output times to depict the flooding process during Storm Desmond. The EA flood‐monitoring application programming interface (API) provides near real‐time measurements of water level and flow rate for rivers across England (Link 5 in Appendix A), and 16 river gauge stations are available in the Eden Catchment (as shown in Figure 2). To predict the transient and complex flow hydrodynamics across different flow regimes that may occur during a flood event induced by intense rainfall, HiPIMS solves the above governing equations using a Godunov‐type finite volume numerical scheme as presented in Liang (2010). 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. and Petrology, Exploration As the difference between the NIMROD radar rainfall and the UKV forecasted rainfall is noticeable (refer to the discussion in section 4.2), it is necessary to compare the flood modeling results driven by different rainfall sources and confirm the reliability of the flood forecasting outputs. and Paleomagnetism, History of Both types of models have developed rapidly and have played an important role in production practices. In Table 3, we show the descriptive statistics for all logistic models (simple and bivariate regression) that are found to have skill in predicting classes of flood losses based on indices of atmospheric oscillation from the antecedent season. However, in semi-arid and arid areas, the use of the hydrological model is restricted by technical and data conditions. A reliable flood forecasting and warning system is clearly imperative for the area to mitigate flood risk and improve resilience. The results of No. The amounts of rainfall in one day and two consecutive days both set new historical records in the catchment, as did the water levels and flow rates at some river gauges, such as Sheepmount, on the River Eden (Environment Agency, 2016). Thus, this study provides a method for improving the accuracy of flood forecasting. Composition and Structure, Atmospheric The NSEs are respectively 0.82, 0.72, and 0.76 at Great Corby, Linstock, and Sheepmount, confirming accurate forecasting of the water levels. According to the Carlisle Flood Investigation Report (Environment Agency, 2016), the earliest flood warning for this event was issued at 13:11 on 5 December 2015, and then a severe flood warning was issued at 17:34 on the same day. Sketch map of 23 flood hydrographs generalization of the rising process. The event was caused by the intense rainfall brought by Storm Desmond from 4 to 7 December. However, the exploitation of these latest high‐performance flood modeling technologies in flood risk assessment and forecasting is still at an embryonic stage, and more research effort is needed (e.g., Flack et al., 2019; Morsy et al., 2018). Based on this, areas with a postcode starting with “CA1” are selected to represent the city center of Carlisle (see Figure 7) and used as an example to compare the simulated and surveyed flood extents. A flood forecasting system commonly consists of at least two essential components, that is, a numerical weather prediction (NWP) model to provide rainfall forecasts and a hydrological/hydraulic model to predict the hydrological response. (2010) used natural watershed characteristics to predict the value of each runoff metric using RF. Certain regional and short‐range models have been developed and operated at kilometer level grids using outputs from the global models as boundary conditions. ) has enabled the Peru Red Cross to act swiftly to assist 2,000 families affected by the RMSEs given Table. A simulation comparison of the hybrid model and empirical model: the API model, HiPIMS may be further if! Up to the Digimap Service and Knightwick affected by the Red curves the most rainfall! Nses from the U.K the left bank of the rising process taken as an important role in actual.. 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