Flood Routing Model Research Articles

Flood routing in an ephemeral channel with compound cross-section

Natural phenomenon of surface and subsurface flow interaction is an intrinsic component of the hydrological processes in any watershed. It is a highly sensitive process, especially in arid and semi-arid regions, and should be considered while dealing with any water management activity in these regions. This paper describes a novel approach for flood routing in an ephemeral channel with compound cross-sections. The proposed mathematical model couples the numerical solution for complete Saint-Venant equations for surface flow with the numerical solution for one-dimensional Richards equation for sub-surface flow through an iterative procedure. Recently developed interactive divided channel (IDC) method is incorporated for simulating the main channel and flood plain flow interactions. In the one-dimensional surface and pseudo two-dimensional sub-surface (1DSP2DSS) model presented here, the effect of lateral variation in infiltration rate at a cross section arising due to (i) lateral variation in flow depth and (ii) lateral variation in soil characteristics is incorporated by considering infiltration into different soil columns for main channel and flood plains. The proposed model is verified by comparing the model results with those available in literature for benchmark problems. Simulations are presented to demonstrate the capability of the model for flood routing in ephemeral channels with flood plains and the effect of lateral variation in infiltration rate on transmission losses.

Learning for improvement

Learning for improvement

Nonlinear Muskingum Model for Flood Routing in Irrigation Canals Using Storage Moving Average

The Muskingum routing method involves the calculation of the storage volume in river reaches. The accurate calculation of the storage volume is difficult to achieve with the nonlinear Muskingum model (NMM). This paper couples the storage moving average (SMA) with NMM to produce a Muskingum storage moving average model (MUSSMAM), which has improved the accuracy of storage volume and output hydrograph calculation over the NMM. The parameters of the NMM and the weighting coefficients in the SMA are treated as dimensionless parameters in the combined MUSSMAM formulation. A hybrid method that merges the generalized reduced gradient (GRG) solver with an evolutionary (EV) solver is employed to obtain the best combination of parameter values of the MUSSMAM. The sum of the squared deviation (SSQ) between observed and routed inflows is the objective function optimized with the hybrid GRG-EV solver. The proposed MUSSMAM is tested with experimental, real, and multimodal hydrograph-routing problems. These results demonstrate that MUSSMAM reduces the SSQ by 2, 10, and 29 percent compared to the NMM in experimental, real, and multimodal problems, respectively.

Development of predictive model for flood routing using genetic expression programming

AbstractFlood disasters continue to occur in many countries around the world and cause tremendous casualties and property damage. Flood peak values are required in the design of bridges, culvert waterways, spillways for dams, and estimation of scour at a hydraulic structure. When a flood wave passes through a reservoir its peak is attenuated, and the time base is enlarged due to effect of storage. Modification in the hydrograph is studied through flood routing. Flood routing is the technique of determining the flood hydrograph at a section of a river by utilising the data of flood flow at one or more upstream sections. Advances in the field of artificial intelligence (AI) offer opportunities for utilising new algorithms and models. Experiments have shown that the gene‐expression programming (GEP) model and its algorithms are 100–60 000 times faster than older genetic algorithms. This study presents the GEP method, which is an extension to genetic programming, as an alternative approach to modelling of flood routing in natural channels. Thus, novel models for prediction of flood routing using the GEP technique are developed. The proposed GEP‐based approach is compared with three hydrograph examples and the results of other solution techniques. The GEP method makes use of few hydrologic parameters (inflow, outflow, and time). The performance of the models is evaluated by two goodness‐of‐fit measures, namely, the root‐mean‐square error (RMSE) and the coefficient of determination (R2). In the results, the GEP models show superior performance to the other solution techniques based on the Muskingum model.

Flood-routing modeling with neural network optimized by social-based algorithm

Forecasting and operational routing flood requires accurate forecasts on proper feed time, to be able to issue suitable warnings and take suitable emergency actions. Flood-routing problem is one of the most complicated matters in hydraulics of open channels and river engineering. Flood routing is the process of computing the progressive time and shape of a flood wave at successive points along a river. To get an approximate solution of the flood-routing problem, different techniques are used. This paper describes an approach to train artificial neural network (ANN) using social-based algorithm (SBA). The approach illustrates feed-forward neural network optimization for the flood-routing problem of Kheir Abad River called FF-SBA. To this end, the number and effective time lag of input data in ANN models are initially determined by means of linear correlation between input and output time series; subsequently, the weights of the feed-forward network is optimized by SBA. Optimization algorithms and statistical models like Genetic Algorithm and linear regression are compared to FF-SBA. Compared to the results of optimization algorithms and statistical models, the FF-SBA model for the Kheir Abad River in Iran shows more flexibility and accuracy.

System development for storm surge hazard assessment based on WebGIS for Tianjin Binhai New Area

It is imperative to develop a risk assessment system for quickly predicting storm surge disaster due to the vulnerability of Tianjin Binhai New Area. The flood routing model with user-defined breaches was firstly established based on the seed spread algorithm in order to achieve a rapid forecasting of storm surge flood information. Furthermore, fuzzy mathematics was utilized to identify the storm disaster grade, and the hazard mapping was conducted to visually obtain the hazard spatial and temporal distribution. Finally, the flood routing visualization method was proposed based on numerical simulation of storm surge to achieve the reappearance scene of dynamic evolution process. The developed system can play a vital role in the management and decision-making of sea dyke mitigation engineering in Tianjin Binhai New Area.

A new modified nonlinear Muskingum model and its parameter estimation using the adaptive genetic algorithm

First, a novel nonlinear Muskingum flood routing model with a variable exponent parameter and simultaneously considering the lateral flow along the river reach (named VEP-NLMM-L) was developed in this research. Then, an improved real-coded adaptive genetic algorithm (RAGA) with elite strategy was applied for precise parameter estimation of the proposed model. The problem was formulated as a mathematical optimization procedure to minimize the sum of the squared deviations (SSQ) between the observed and the estimated outflows. Finally, the VEP-NLMM-L was validated on three watersheds with different characteristics (Case 1 to 3). Comparisons of the optimal results for the three case studies by traditional Muskingum models and the VEP-NLMM-L show that the modified Muskingum model can produce the most accurate fit to outflow data. Application results in Case 3 also indicate that the VEP-NLMM-L may be suitable for solving river flood routing problems in both model calibration and prediction stages.

Application of the Elitist-Mutated PSO and an Improved GSA to Estimate Parameters of Linear and Nonlinear Muskingum Flood Routing Models.

Heuristic search algorithms, which are characterized by faster convergence rates and can obtain better solutions than the traditional mathematical methods, are extensively used in engineering optimizations. In this paper, a newly developed elitist-mutated particle swarm optimization (EMPSO) technique and an improved gravitational search algorithm (IGSA) are successively applied to parameter estimation problems of Muskingum flood routing models. First, the global optimization performance of the EMPSO and IGSA are validated by nine standard benchmark functions. Then, to further analyse the applicability of the EMPSO and IGSA for various forms of Muskingum models, three typical structures are considered: the basic two-parameter linear Muskingum model (LMM), a three-parameter nonlinear Muskingum model (NLMM) and a four-parameter nonlinear Muskingum model which incorporates the lateral flow (NLMM-L). The problems are formulated as optimization procedures to minimize the sum of the squared deviations (SSQ) or the sum of the absolute deviations (SAD) between the observed and the estimated outflows. Comparative results of the selected numerical cases (Case 1–3) show that the EMPSO and IGSA not only rapidly converge but also obtain the same best optimal parameter vector in every run. The EMPSO and IGSA exhibit superior robustness and provide two efficient alternative approaches that can be confidently employed to estimate the parameters of both linear and nonlinear Muskingum models in engineering applications.

홍수위험지도 작성을 위한 하천 제방 붕괴 유량공식 제안

To compile a flood hazard map it is essential to identify the potential risk areas. A scenario-based numerical modeling approach is commonly used to build a flood hazard map in the case of a levee-break. The model parameters that capture peak discharge, including breach formation and progress, are important in the modeling method. In this study an earth-levee-break model is constructed under the assumption that the failure mechanism and hydraulic processes are identical for all levee-break river activities. Estimation of the hydrograph at the outlet as a function of time is highlighted. The constructed hydrograph can then serve as an upper boundary condition in running the flood routing model downstream, although flood routing is not considered in this study.

An improved method to represent DEM uncertainty in glacial lake outburst flood propagation using stochastic simulations

Modelling glacial lake outburst floods (GLOFs) or ‘jokulhlaups’, necessarily involves the propagation of large and often stochastic uncertainties throughout the source to impact process chain. Since flood routing is primarily a function of underlying topography, communication of digital elevation model (DEM) uncertainty should accompany such modelling efforts. Here, a new stochastic first-pass assessment technique was evaluated against an existing GIS-based model and an existing 1D hydrodynamic model, using three DEMs with different spatial resolution. The analysis revealed the effect of DEM uncertainty and model choice on several flood parameters and on the prediction of socio-economic impacts. Our new model, which we call MC-LCP (Monte Carlo Least Cost Path) and which is distributed in the supplementary information, demonstrated enhanced ‘stability’ when compared to the two existing methods, and this ‘stability’ was independent of DEM choice. The MC-LCP model outputs an uncertainty continuum within its extent, from which relative socio-economic risk can be evaluated. In a comparison of all DEM and model combinations, the Shuttle Radar Topography Mission (SRTM) DEM exhibited fewer artefacts compared to those with the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), and were comparable to those with a finer resolution Advanced Land Observing Satellite Panchromatic Remote-sensing Instrument for Stereo Mapping (ALOS PRISM) derived DEM. Overall, we contend that the variability we find between flood routing model results suggests that consideration of DEM uncertainty and pre-processing methods is important when assessing flow routing and when evaluating potential socio-economic implications of a GLOF event. Incorporation of a stochastic variable provides an illustration of uncertainty that is important when modelling and communicating assessments of an inherently complex process.

Use of a flood-routing model to assess lateral flows in a karstic stream: implications to the hydrogeological functioning of the Grands Causses area (Tarn River, Southern France)

The aim of this study is to assess the spatio-temporal variability of lateral flows in the streams of a large karstic basin to construct a conceptual model of karst contributions to flood generation. The lateral flows of the Tarn River, which crosses the Grands Causses karst zone in Southern France, were investigated between several gauging stations along the river. First, through analysing the lateral flows on an event time scale of 30 floods, it was possible to identify the losing and gaining reaches, highlighting a highly variable attenuation/amplification role of karsts on flood generation. Second, the diffusive wave model was used to quantify the lateral flows on an hourly time step within a flood event. The simulations show a high variability of lateral outflows and inflows within a same reach according to the hydrometeorological conditions, with in some cases an inversion of the lateral flow direction during the flood. The results highlight complex surface/groundwater exchanges during a single flood event, with high river losses despite the concurrent flow of large springs feeding the stream. This spatio-temporal variability of the karst influence on flood generation was linked to the aquifer’s structure, which has improved the understanding of the hydro(geo)logical functioning of the Grands Causses massif. Finally, the new methodology proposed here opens challenging perspectives towards a framework for the analysis of surface–groundwater exchanges in karstic rivers.

A Re-Parameterized and Improved Nonlinear Muskingum Model for Flood Routing

The nonlinear form of the Muskingum model has been widely applied to river flood routing. There are four variants of the nonlinear Muskingum model based on alternative formulations of the nonlinear storage equation. This paper proposes a new Muskingum model with an improved, seven-parameter, nonlinear storage equation. The proposed model provides more degrees of freedom in fitting observed hydraulic data than other nonlinear Muskingum models. The proper estimation of the proposed Muskingum nonlinear model’s parameters is essential to achieve accurate flood-routing predictions. This paper introduces a hybrid method for the estimation of Muskingum parameters. The parameter-estimation method combines the shuffled frog leaping algorithm (SFLA) and the Nelder-Mead simplex (NMS). The proposed Muskingum model and parameter estimation method were applied to the routing of several hydrographs. Our results indicate improved performance of the methodology described in this work when compared with those of other Muskingum models.

Mathematical model for flood routing based on cellular automaton

Increasing frequency and severity of flooding have caused tremendous damage in China, requiring more essential countermeasures to alleviate the damage. In this study, the dynamic simulation property of a cellular automaton was used to make further progress in flood routing. In consideration of terrain's influence on flood routing, we regarded the terrain elevation as an auxiliary attribute of a two-dimensional cellular automaton in path selection for flood routing and developed a mathematical model based on a cellular automaton. A numerical case of propagation of an outburst flood in an area of the lower Yangtze River was analyzed with both the fixed-step and variable-step models. The results show that the flood does not spread simultaneously in all directions, but flows into the lower place first, and that the submerged area grows quickly at the beginning, but slowly later on. The final submerged areas obtained from the two different models are consistent, and the flood volume balance test shows that the flood volume meets the requirement of the total volume balance. The analysis of the case shows that the proposed model can be a valuable tool for flood routing.

Mathematical model for flood routing in Jingjiang River and Dongting Lake network

The main stream of the Yangtze River, Dongting Lake, and the river network in the Jingjiang reach of the Yangtze River constitute a complex water system. This paper develops a one-dimensional (1-D) mathematical model for flood routing in the river network of the Jingjiang River and Dongting Lake using the explicit finite volume method. Based on observed data during the flood periods in 1996 and 1998, the model was calibrated and validated, and the results show that the model is effective and has high accuracy. In addition, the one-dimensional mathematical model for the river network and the horizontal two-dimensional (2-D) mathematical model for the Jingjiang flood diversion area were coupled to simulate the flood process in the Jingjiang River, Dongting Lake, and the Jingjiang flood diversion area. The calculated results of the coupled model are consistent with the practical processes. Meanwhile, the results show that the flood diversion has significant effects on the decrease of the peak water level at the Shashi and Chenjiawan hydrological stations near the flood diversion gates, and the effect is more obvious in the downstream than in the upstream.

Hydraulic model with roughness coefficient updating method based on Kalman filter for channel flood forecast

A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness updating technique was developed using the Kalman filter method to update Manning's roughness coefficient at each time step of the calculation processes. Channel shapes were simplified as rectangles, triangles, and parabolas, and the relationships between hydraulic radius and water depth were developed for plain rivers. Based on the relationship between the Froude number and the inertia terms of the momentum equation in the Saint-Venant equations, the relationship between Manning's roughness coefficient and water depth was obtained. Using the channel of the Huaihe River from Wangjiaba to Lutaizi stations as a case, to test the performance and rationality of the present flood routing model, the original hydraulic model was compared with the developed model. Results show that the stage hydrographs calculated by the developed flood routing model with the updated Manning's roughness coefficient have a good agreement with the observed stage hydrographs. This model performs better than the original hydraulic model.

Discharge Estimation in a Backwater Affected River Junction Using HPG

This study presents a method for estimating discharge at a stage-gauging station located in a backwater affected river junction. A hydraulic performance graph (HPG) summarizing the backwater profiles on all possible flow conditions was applied to the river system at the juncture of the main stream and the tributary. The HEC-RAS model for dendritic river was created, and the unsteady simulation was implemented for calibration and verification. The relationship between two water levels from existing stage stations at the main stream and the tributary and the discharge at the backwater affected section was estimated, and kriging interpolation was carried out. During a flood, the discharge was calculated using kriging-interpolated HPG and the water level measurements at two stage stations. It was then validated by comparing the automatic discharge measurements from ADVM. The result provides an economical and practical method for estimating discharge in a junction with a high hysteresis of looped stage- discharge relationships. River discharge observation data has been used in several fields including water balance, flood prediction, dam and reservoir operation, the design of hydraulic structures, water supply, and aquatic ecosystem conservation. It is essential data for the examination and calibration of rainfall-runoff models and flood routing models. Generally, discharge is observed directly through a field survey, but in the consideration of time and cost, it is difficult to observe discharge continuously for a long period of time. Therefore, in order to observe discharge, we use an indirect method to prepare a stage-discharge relation curve from empirical relationships between the observed discharge and water level of river for a limited period and convert the measured water level into discharge by applying the measured water level to this curve.

A new frequency domain analytical solution of a cascade of diffusive channels for flood routing

AbstractSimplified flood propagation models are often employed in practical applications for hydraulic and hydrologic analyses. In this paper, we present a new numerical method for the solution of the Linear Parabolic Approximation (LPA) of the De Saint Venant equations (DSVEs), accounting for the space variation of model parameters and the imposition of appropriate downstream boundary conditions. The new model is based on the analytical solution of a cascade of linear diffusive channels in the Laplace Transform domain. The time domain solutions are obtained using a Fourier series approximation of the Laplace Inversion formula. The new Inverse Laplace Transform Diffusive Flood Routing model (ILTDFR) can be used as a building block for the construction of real‐time flood forecasting models or in optimization models, because it is unconditionally stable and allows fast and fairly precise computation.

Flood routing model incorporating intensive streambed infiltration

Flood routing models are critical to flood forecasting and confluence calculations. In the streams that dry up and disconnect from groundwater, the streambed infiltration is intensive and has a significant effect on flood wave movement. Streambed infiltration should be considered in flood routing. A flood routing model incorporating intensive streambed infiltration is proposed. In the model a streambed infiltration simulation method based on soil infiltration theory is developed. In this method the Horton equation is used to calculate infiltration capacity. A trial-and-error method is developed to calculate infiltration rate and determine whether the flood wave can travel downstream. A formula is derived to calculate infiltration flow per unit length. The Muskingum-Cunge method with streambed infiltration flow as lateral outflow is used for flood routing. The proposed model is applied to the stream from the downstream of the Yuecheng Reservoir to the Caixiaozhuang Hydrometric Station in the Zhangwei River of the Haihe River Basin. Simulation results show that the accuracy of the model is high, and the infiltration simulation method can represent infiltration processes well. The proposed model is simple and practical for flood simulation and forecasting, and can be used in river confluence calculations in a rainfall-runoff model for arid and semiarid regions.

Comparison of Different Grid Cell Ordering Approaches in a Simplified Inundation Model

This study proposes a simplified model for non-riverine flood routing using a digital elevation model. The model has the advantage of running with only a few types of input, such as topographic data and cumulative rainfall. Given its ease of use, the model is stable and reliable for developing a real-time inundation forecasting system. This model uses two approaches to determine the collection of cells from which flooding is assumed to originate: (1) A traditional “lowest-elevation approach” that assumes flooding originates from the lowest elevations and that is only based on topographic data; and (2) a novel “D-infinity contributing area approach” that assumes flooding originates at the cells toward which the flow moves and that considers in situ topography and upslope information. The flood water is transferred based on the flat-water assumption that the water levels of adjacent cells are equalized. The performance was evaluated by comparing the simulated results with those from a complex inundation model. The simplified model with the lowest elevation assumption has limited applicability in flat areas and did not provide reasonable locations of the source of the flood. The D-infinity approach can improve the simplified inundation model and extend its application in various topographical areas.

A river flash flood forecasting model coupled with ensemble Kalman filter

AbstractA flash flood forecasting model including a state‐of‐the‐art data assimilation method was developed to provide a precise water stage forecast for flood emergency response. The model integrates a flash flood routing model (FFRM) coupled with an ensemble Kalman filter (EnKF) and an artificial neural network (ANN) submodel. In the model, the ANN forecasts river water stages at gauge stations first. Then, these are used as the initial and boundary conditions of the FFRM. The water stages, simulated from the FFRM, are then corrected by the EnKF for lead time. The model was applied to the Tanshui River watershed in northern Taiwan during past typhoons. The model forecasts almost covered the data observed during a typhoon period to within 95% confidence intervals. Compared with the use of FFRM without EnKF, the forecast water stages from the EnKF improved the accuracy at the conjunctions between upstream and downstream channels and the steep slope location.