Flood Operation Research Articles

Application of an Optimization/Simulation Model for the Real-Time Flood Operation of River-Reservoir Systems with One- and Two-Dimensional Unsteady Flow Modeling

An application is presented of a new methodology for the real-time operations of river-reservoir systems. The methodology is based upon an optimization/simulation modeling approach that interfaces optimization with a one and/or two-dimensional unsteady flow simulation model (U.S. Army Corps of Engineers HEC-RAS). The approach also includes a model for short-term rainfall forecasting, and the U.S. Army Corps of Engineers HEC-HMS model for rainfall-runoff modeling. Both short-term forecasted rainfall in addition to gaged streamflow data and/or NEXRAD (Next-Generation Radar) can be implemented in the modeling approach. The optimization solution methodology is based upon a genetic algorithm implemented through MATLAB. The application is based upon the May 2010 flood event on the Cumberland River system in the USA, during which releases from Old Hickory dam caused major flooding in the downstream area of Nashville, TN, USA, and allowed the dam to be placed in an emergency operational situation. One of the major features of the modeling effort and the application presented was to investigate the use of different unsteady flow modeling approaches available in the HEC-RAS, including one-dimensional (1D), two-dimensional (2D), and the combined (1D/2D) approach. One of the major results of the application was to investigate the use the different unsteady flow approaches in the modeling approach. The 2D unsteady flow modeling, based upon the diffusion wave approach, was found to be superior for the application to the Cumberland River system. The model application successfully determined real-time operations that would have maintained the flood water surface elevations at the downstream control point in Nashville below the 100-year return period river water surface and maintaining the gate openings at the Old Hickory Dam from reaching an emergency operational situation, which could have caused major losses at the dam.

Dez Dam Rehabilitation Project Role in Flood Damage Reduction

As a result of climate change, extreme precipitation events from mid-March to April 2019 in southwest Iran generated catastrophic floods taken 78 lives and left $4.1 billion financial damages. The extent of damages could have been even worse without the existence of upstream flood control dams including Dez dam. Dez Dam is the only large dam in its basin which play a significant role in flood control. A multi-purpose rehabilitation project including new power intakes, flushing tunnels and dam heightening with required investment of $350 million is defined in this dam to compensate reservoir live volume losses through 57 years of sedimentation. In this paper, the effect of dam heightening and the flushing tunnels will be evaluated by their roles in flood damage reduction. Assuming that the actual magnitude of a flood cannot be predicted beforehand, a flexible multi-stage routing method considering operator judgment during flood operations is proposed to produce an optimized flood routing policy. The related operation rule is derived for existing and rehabilitated dam and the expected annual damage (EAD) compared in both conditions. The results show that the dam heightening could be considered as an effective way to improve the dam role in flood damage reduction.

A Hydraulic Analysis of Shock Wave Generation Mechanism on Flat Spillway Chutes through Physical Modeling

Shock waves are generated downstream of spillways during flood operations, which have adverse effects on spillway operations. This paper presents the physical model study of shock waves at the Mohmand Dam Spillway project, Pakistan. In this study, hydraulic analysis of shock waves was carried out to investigate its generation mechanism. Different experiments were performed to analyze the rooster tail on a flat spillway chute and to examine the factors affecting the characteristics of the rooster tail. The study results show that shock wave height is influenced by spillway chute slope, pier shape, and flow depth. Moreover, the height of the shock wave can be minimized by installing a semi-elliptical pier on the tail part of the main pier. Further modifications in the geometry of the extended tail part of the pier are recommended for the elimination of the shock wave. Based on observed data collected from the model study, an empirical equation was developed to estimate the shock wave height generated on the flat slope spillway chutes (5° to 10°).

Motivation, Satisfaction, and Risks of Operational Forces and Helpers Regarding the 2021 and 2013 Flood Operations in Germany

Pluvial floods claimed more than 180 lives in Germany in July 2021, when a large and slow-moving storm system affected Germany and many neighbouring countries. The death tolls and damages were the highest since 1962 in Germany, and soon after, the crisis management was under public critique. This study has undertaken an online survey to understand crisis management better and identify lessons to learn. It has received a positive interest among operational relief forces and other helpers (n = 2264). The findings reveal an overall satisfaction with the operation in general as well as personal lessons learned. It also reveals shortcomings in many areas, ranging from information distribution, coordination, parallel ongoing COVID-19 pandemic, infrastructure resilience, and other factors. Just as well, areas for improvement of the crisis management system are suggested by the respondents. Cooperation and support by the affected population are perceived as positive. This helps to inform other areas of research that are necessary, such as studies on the perception by the affected people. The gaps in assessments of operational forces and some methodological constraints are discussed to advance future follow-up studies.

Evaluating Agile Information-Based Framework for Flood Management Utilizing Metadata Concept to Support Flood Operation Activities

Operational policies are established to handle natural hazards including floods to minimize the effect with differing degrees of effectiveness and increasing relaxation. Sometimes policies are time-consuming of rigid protocols that are inadequate in a dynamic and somewhat chaotic environment synonymous with the complexity of flood disaster. Hence, this research aimed at recommending the incorporation of agile concepts in flood control, which would offer stability and adaptability in the control of the complex flood situation. Extensive reviews on flood management and existing frameworks for disaster management were conducted to understand the problems and the potential solution to construct an agile framework. A grounded analysis was conducted to obtain insight into how the agility of standard operating procedures could be enhanced. The agile components have been defined by contrasting characteristics from other effective disciplines, including software development and health care, that share common complexity in management environments. Consequently, an Agile Information-Based for Flood Management Framework is proposed in previous publication. The validation component for agile key-values presented in the earlier article is, however, absent. This study therefore presents the validation component from earlier publication on the Agile Information Based Framework. A theoretical evaluation of the proposed key-values for the agile framework has been conducted using the metadata concept. The evaluation identified the similarity feature in the same area where the proposed framework was agreed to be implemented in tandem with electricity company emergency response plan to improve flood operations. The proposed key-values in the agile framework are required to be adopted and further strengthened by other significant variables.

Optimization of physico-chemical and membrane filtration processes to remove high molecular weight polymers from produced water in enhanced oil recovery operations

Polymer flooding is an enhanced oil recovery technique to extract the large portion of leftover subsurface oil following conventional extraction methods. In the flooding process, a long-chain polymer, such as partially hydrolyzed polyacrylamide (HPAM), is added to the displacing fluid to increase the mobility and extraction of the oil phase. Nevertheless, the challenge of managing produced water from polymer flooding operations is high because residual HPAM results in significantly high viscosity and organic content in the stream. Commonly used methods for produced water treatment, such as gravity settling and flotation, cannot be applied to obtain a purified stream efficiently, while innovative techniques are not yet feasible in practical operations. In this work, a simple method of polymer precipitation prompted by divalent ions is evaluated, optimized, and compared to membrane ultrafiltration. The physico-chemical properties of the HPAM are investigated and polymer precipitation tests are conducted by varying the main operational parameters, including pH, salinity, temperature, calcium and/or magnesium concentration, and polymer concentration. Response surface developed by central composite design method is used to optimize the process and identify the correct dosage of divalent cations coagulants and pH, the two main factors promoting HPAM separation. The removal of HPAM is well-described and maximized (>85%) by the model, which is also validated on three synthetic samples representing real wastewaters from polymer flooding applications. Optimized ultrafiltration, using ceramic membranes with surface pore size of 15 kDa, also shows the ability to remove HPAM effectively from water, but the precipitation method seems to be more versatile and easier to apply. The two processes, precipitation and ultrafiltration, may potentially be used in sequence as they complement each other in several ways.

Tracing design flood hydrograph uncertainty in reservoir flood control system

Increasing studies have highlighted that the remarkable inherent uncertainty in design flood hydrograph (DFH) can potentially undermine flood management decisions. In order to quantitatively trace the propagation of DFH uncertainty in reservoir flood control system, we propose a novel methodological framework including three corn parts. First, a copula-based DFH estimation model integrating Bayes’ theorem is presented to estimate DFH under model parameter uncertainty. Second, we perform an optimal reservoir operation model for flood control (OROMFC) with uncertain DFH as input variable to derive reservoir flood control operations (i.e. output variable). Third, an information theory-based model is designed to trace the DFH uncertainty propagation in reservoir flood control system. A reservoir flood control system in the Han River basin in China is selected as case study. Related results indicate that uncertainty in reservoir flood control operations reduces in comparison with the remarkable uncertainty in DFH due to the performance of the OROMFC. Specifically, uncertainty in reservoir flood control operations in periods close to peak flow is much smaller compared with that in other periods. The phenomenon further highlights the importance of reservoir flood control operations during periods prior to peak flow. Additionally, we find that uncertainty in flood peak of DFH is the dominant factor affecting reservoir flood control operations compared with that in flood volume of DFH. Moreover, we explore the impact of reservoir flood control capacity on reservoir flood operations in the context of DFH uncertainty. An interesting linear expression is found and fitted for identifying design flood events inducing reservoir overtopping under specific reservoir flood control capacity.

Artificial Neural Network to Forecast Enhanced Oil Recovery Using Hydrolyzed Polyacrylamide in Sandstone and Carbonate Reservoirs.

Polymer flooding is an important enhanced oil recovery (EOR) method with high performance which is acceptable and applicable on a field scale but should first be evaluated through lab-scale experiments or simulation tools. Artificial intelligence techniques are strong simulation tools which can be used to evaluate the performance of polymer flooding operation. In this study, the main parameters of polymer flooding were selected as input parameters of models and collected from the literature, including: polymer concentration, salt concentration, rock type, initial oil saturation, porosity, permeability, pore volume flooding, temperature, API gravity, molecular weight of the polymer, and salinity. After that, multilayer perceptron (MLP), radial basis function, and fuzzy neural networks such as the adaptive neuro-fuzzy inference system were adopted to estimate the output EOR performance. The MLP neural network had a very high ability for prediction, with statistical parameters of R2 = 0.9990 and RMSE = 0.0002. Therefore, the proposed model can significantly help engineers to select the proper EOR methods and API gravity, salinity, permeability, porosity, and salt concentration have the greatest impact on the polymer flooding performance.

Testing of an Optimization-Simulation Model for Real-Time Flood Operation of River-Reservoir Systems

A new methodology was developed for the real-time determination gate control operations of a river-reservoir system to minimize flooding conditions. The methodology is based upon an optimization-simulation model approach interfacing the genetic algorithm within MATLAB with simulation software for short-term rainfall forecasting, rainfall–runoff modeling (HEC-HMS), and a one-dimensional (1D), two-dimensional (2D), and combined 1D and 2D combined unsteady flow models (HEC-RAS). Both real-time rainfall data from next-generation radar (NEXRAD) and gaging stations, and forecasted rainfall are needed to make gate control decisions (reservoir releases) in real-time so that at time t, rainfall is known and rainfall over the future time-period (Δt) to time t + Δt can be forecasted. This new model can be used to manage reservoir release schedules (optimal gate operations) before, during, and after a rainfall event. Through real-time observations and optimal gate controls, downstream water surface elevations are controlled to avoid exceedance of threshold flood levels at target locations throughout a river-reservoir system to minimize the damage. In an example application, an actual river reach with a hypothetical upstream flood control reservoir is modeled in real-time to test the optimization-simulation portion of the overall model.

Experimental investigation of the displacement flow mechanism and oil recovery in primary polymer flood operations

Polymer flooding is a mature chemical enhanced oil recovery method employed in oilfields at pilot testing and field scales. Although results from these applications empirically demonstrate the higher displacement efficiency of polymer flooding over waterflooding operations, the fact remains that not all the oil will be recovered. Thus, continued research attention is needed to further understand the displacement flow mechanism of the immiscible process and the rock–fluid interaction propagated by the multiphase flow during polymer flooding operations. In this study, displacement sequence experiments were conducted to investigate the viscosifying effect of polymer solutions on oil recovery in sandpack systems. The history matching technique was employed to estimate relative permeability, fractional flow and saturation profile through the implementation of a Corey-type function. Experimental results showed that in the case of the motor oil being the displaced fluid, the XG 2500 ppm polymer achieved a 47.0% increase in oil recovery compared with the waterflood case, while the XG 1000 ppm polymer achieved a 38.6% increase in oil recovery compared with the waterflood case. Testing with the motor oil being the displaced fluid, the viscosity ratio was 136 for the waterflood case, 18 for the polymer flood case with XG 1000 ppm polymer and 9 for the polymer flood case with XG 2500 ppm polymer. Findings also revealed that for the waterflood cases, the porous media exhibited oil-wet characteristics, while the polymer flood cases demonstrated water-wet characteristics. This paper provides theoretical support for the application of polymer to improve oil recovery by providing insights into the mechanism behind oil displacement.Graphic abstractHighlightsThe difference in shape of relative permeability curves are indicative of the effect of mobility control of each polymer concentration.The water-oil systems exhibited oil-wet characteristics, while the polymer-oil systems demonstrated water-wet characteristics.A large contrast in displacing and displaced fluid viscosities led to viscous fingering and early water breakthrough.

Parametric Review of Surfactant Flooding at the Tertiary Stage to Achieve the Accuracy for Proposing the Screening Criteria

: A critical review of previous studies is presented based on the contextual research background of surfactant flooding in this study. The parameters focused on achieving the analysis include permeability, salinity, temperature, and viscosity from different surfactant flooding operations. The principal theme of this review was to provide the regression analysis technique that may adopt to analyze the collected data and conduct contextual research. The set of analytical discussion is accomplished by extracting and plotting the basic parameters against recovery at Original Oil in Place (OOIP) and tertiary stages. Furthermore, the success rate of such studies is compared to the grounds of oil recovery efficiency at different stages. Moreover, the failure of the surfactant flooding project can also be ensured by the outcomes of this study. : It is revealed from this study that the recovery efficiency of surfactant flooding can be obtained maximum at lower permeability ranges, however, other parameters such as salinity and temperature may possess some influence on recovery. In fact, the fluid viscosity of reservoir fluid is inversely rated to recovery. The salinity, temperature and viscosity ranges for efficient surfactant flooding ranges may drop within the range from 1400 to 132606 ppm, 25 to 126 °C, and 1.9 to 150 cP, respectively.

Modeling and Solving of Joint Flood Control Operation of Large-Scale Reservoirs: A Case Study in the Middle and Upper Yangtze River in China

Flood disasters are the most frequent and most severe natural disasters in most countries around the world. Reservoir flood operation is an important method to reduce flood losses. When there are multiple reservoirs and flood control points in the basin, it is difficult to use reservoirs separately to fully realize their flood control potential. However, the multi-reservoir joint flood control operation is a multi-objective, multi-constrained, multi-dimensional, nonlinear, and strong-transition feature decision-making problem, and these characteristics make modeling and solving very difficult. Therefore, a large-scale reservoirs flood control operation modeling method is innovatively proposed, and Dynamic Programming (DP) combined with the Progressive Optimality Algorithm (POA) and Particle Swarm Optimization (PSO) methods, DP-POA-PSO, are designed to efficiently solve the optimal operation model. The middle and upper Yangtze River was chosen as a case study. Six key reservoirs in the basin were considered, including Xiluodu (XLD), Xiangjiaba (XJB), Pubugou (PBG), Tingzikou (TZK), Goupitan (GPT), and Three Gorges (TG). Studies have shown that DP-POA-PSO can effectively solve the optimal operation model. Compared with the current operation method, the joint flood control optimal operation makes the flood control point reach the flood control standard, moreover, in the event of the flood with a return period of 1000 years, Jingjiang, the most critical flood control point of the Yangtze River, does not require flood diversion, and the volume of flood diversion in Chenglingji is also greatly reduced.

Probabilistic Model for Real-Time Flood Operation of a Dam Based on a Deterministic Optimization Model

This paper presents a real-time flood control model for dams with gate-controlled spillways that brings together the advantages of an optimization model based on mixed integer linear programming (MILP) and a case-based learning scheme using Bayesian Networks (BNets). A BNet model was designed to reproduce the causal relationship between inflows, outflows and reservoir storage. The model was trained with synthetic events generated with the use of the MILP model. The BNet model produces a probabilistic description of recommended dam outflows over a time horizon of 1 to 5 h for the Talave reservoir in Spain. The results of implementing the BNet recommendation were compared against the results obtained while applying two conventional models: the MILP model, which assumes full knowledge of the inflow hydrograph, and the Volumetric Evaluation Method (VEM), a method widely used in Spain that works in real-time, but without any knowledge of future inflows. In order to compare the results of the three methods, the global risk index (Ir) was computed for each method, based on the simulated behavior for an ensemble of hydrograph inflows. The Ir values associated to the 2 h-forecast BNet model are lower than those obtained for VEM, which suggests improvement over standard practice. In conclusion, the BNet arises as a suitable and efficient model to support dam operators for the decision making process during flood events.

The 23 June 2016 West Virginia Flash Flood Event as Observed through Two Hydrometeorology Testbed Experiments

AbstractThe flash flood event of 23 June 2016 devastated portions of West Virginia and west-central Virginia, resulting in 23 fatalities and 5 new record river crests. The flash flooding was part of a multiday event that was classified as a billion-dollar disaster. The 23 June 2016 event occurred during real-time operations by two Hydrometeorology Testbed (HMT) experiments. The Flash Flood and Intense Rainfall (FFaIR) experiment focused on the 6–24-h forecast through the utilization of experimental high-resolution deterministic and ensemble numerical weather prediction and hydrologic model guidance. The HMT Multi-Radar Multi-Sensor Hydro (HMT-Hydro) experiment concentrated on the 0–6-h time frame for the prediction and warning of flash floods primarily through the experimental Flooded Locations and Simulated Hydrographs product suite. This study describes the various model guidance, applications, and evaluations from both testbed experiments during the 23 June 2016 flash flood event. Various model outputs provided a significant precipitation signal that increased the confidence of FFaIR experiment participants to issue a high risk for flash flooding for the region between 1800 UTC 23 June and 0000 UTC 24 June. Experimental flash flood warnings issued during the HMT-Hydro experiment for this event improved the probability of detection and resulted in a 63.8% increase in lead time to 84.2 min. Isolated flash floods in Kentucky demonstrated the potential to reduce the warned area. Participants characterized how different model guidance and analysis products influenced the decision-making process and how the experimental products can help shape future national and local flash flood operations.

Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction

Accurate and reliable prediction of streamflow is vital to the optimization of water resources management, reservoir flood operations, catchment, and urban water management. In this research, support vector regression (SVR) was optimized by six meta-heuristic algorithms, namely, Ant Lion Optimization (SVR-ALO), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Harris Hawks Optimization (SVR-HHO), Particle Swarm Optimization (SVR-PSO), and Bayesian Optimization (SVR-BO) to predict daily streamflow in Naula watershed, State of Uttarakhand, India. The significant inputs and parameter combinations for hybrid SVR models were extracted through Gamma Test before processing. The results obtained by hybrid SVR models during calibration (training) and validation (testing) periods, which were compared against observed streamflow using performance indicators of root mean square error (RMSE), scatter index (SI), coefficient of correlation (COC), Willmott index (WI), and by visual inspection (time-series plot, scatter plot and Taylor diagram). The results of comparison demonstrated that SVR-HHO during calibration/validation periods (RMSE = 92.038/181.306 m3/s, SI = 0.401/0.715, COC = 0.881/0.717, and WI = 0.928/0.777) had superior performance to the SVR-ALO, SVR-MVO, SVR-SHO, SVR-PSO, and SVR-BO models in predicting daily streamflow in the study basin. In addition, the new HHO algorithm outperformed the other meta-heuristic algorithms in terms of prediction accuracy.

Risk analysis for reservoir flood control operation considering two-dimensional uncertainties based on Bayesian network

The uncertainty of initial water level being above the designed flood limited water level and the uncertainty of inflow caused by flood forecast error can cause flood risk in real-time reservoir flood operation. A risk analysis model for reservoir flood regulation under two-dimensional uncertainties based on Bayesian network is proposed. The nodes of Bayesian network are determined and the network structure is established with expert knowledge; the parameter learning is conducted with the training samples obtained from Monte Carlo simulation. Thereafter, through the prior probability inference without posterior information and the posterior probability inference with given posterior information, the variation of flood risk is analyzed under singular-factor uncertainty and two-factors uncertainties. The case study of Xianghongdian Reservoir using a flood of 100 years return period indicates: the risk resulted from inflow uncertainty is greater than that of the uncertainty of initial water level; there is a certain complementarity between the uncertainties of inflow and initial water level, and the combination risk is between the results of two single-factor risk levels. Moreover, Bayesian Network is able to conduct bi-directional inferences and infer the probability distribution of any other node, which has practical value for risk assessment and control of reservoir flood control operation.

An experiment on reservoir representation schemes to improve hydrologic prediction: coupling the national water model with the HEC-ResSim

ABSTRACT This study experiments with reservoir representation schemes to improve the ability to model active water management in the National Water Model (NWM). For this purpose, we developed an integrated water management model, NWM-ResSim, by coupling the NWM with HEC-ResSim, and two reservoir representation schemes are tested: simulation of reservoir operations and retrieval of scheduled operations. The experiments focus on a pilot reservoir domain in the Russian River basin – Lake Mendocino, California – and its contributing watershed. The evaluation results suggest that the NWM-ResSim improves the simulation performance of reservoir outflow from this managed reservoir over the NWM default level pool routing scheme. The degree of this improvement depends on the suitability of the operation guidance; the reservoir operations simulation scheme could have acceptable errors for the purposes of water resources management, but not for flood operations. Results of the retrieval scheme of scheduled operations demonstrated better performance for sub-daily flood operations.

Influence of short-term rainfall forecast error on flood forecast operation: A risk assessment based on Bayesian theory

To study rainfall forecast error’s influence on flood operation, short-term (24 hours ahead) rainfall forecast accuracy in Qian river basin were analyzed, probabilities of event on flood operation risk were estimated based on Bayesian theory, and the dynamic control scheme was discussed. Results show that, accuracy rate and missing report rate decreased while vacancy rate increased with the increase of rainfall forecast magnitude. For flood operation based on current rainfall forecast information of different magnitudes, level I (“no rain”) error nearly has no impact on it, level II (“light rain”) error has a little impact on small flood operation, and level III (“no less than moderate rain”) error has a great impact on large flood. For the dynamic control scheme in flood season, it is not necessary to discharge flood ahead of schedule so that limited water level can be kept at the upper boundary to provide higher potential energy for power generation when forecasting rainfall shows “no rain” or “light rain”, while it should be discharged ahead of schedule so that limited water level can be kept at a lower value to ensure an adequate flood storage capacity when forecasting rainfall shows “no less than moderate rain”.

Ensemble flood simulation for a small dam catchment in Japan using nonhydrostatic model rainfalls – Part 2: Flood forecasting using 1600-member 4D-EnVar-predicted rainfalls

Abstract. This paper is a continuation of the authors' previous paper (Part 1) on the feasibility of ensemble flood forecasting for a small dam catchment (Kasahori dam; approx. 70 km2) in Niigata, Japan, using a distributed rainfall–runoff model and rainfall ensemble forecasts. The ensemble forecasts were given by an advanced four-dimensional, variational-ensemble assimilation system using the Japan Meteorological Agency nonhydrostatic model (4D-EnVar-NHM). A noteworthy feature of this system was the use of a very large number of ensemble members (1600), which yielded a significant improvement in the rainfall forecast compared to Part 1. The ensemble flood forecasting using the 1600 rainfalls succeeded in indicating the necessity of emergency flood operation with the occurrence probability and enough lead time (e.g., 12 h) with regard to an extreme event. A new method for dynamical selection of the best ensemble member based on the Bayesian reasoning with different evaluation periods is proposed. As the result, it is recognized that the selection based on Nash–Sutcliffe efficiency (NSE) does not provide an exact discharge forecast with several hours lead time, but it can provide some trend in the near future.

Research and application of the flood simulation and operation model in Beijing

This study built a coupling model of hydrology and hydrodynamics for flood forecasting and reservoir-dam joint scheduling. And using HTML5, java, and WebGIS technology, developed a flood control operation system based on B/S framework for Beijing, integrated real-time information and fundamental information of metrology, rainfall, water flow, engineering situation, and waterlogging of Beijing. Through verification, the system has been successfully adopted to analyze the 2018 Ampil typhoon and provide a scientific basis for reservoirs operation including Yaoqiaoyu, Shachang, Haizi, ensuring safety of the reservoirs and reducing the economic loss of downstream area of reservoir.