Conceptual Framework for the National Flood Interoperability Experiment

The National Flood Interoperability Experiment is a research collaboration among academia, National Oceanic and Atmospheric Administration National Weather Service, and government and commercial partners to advance the application of the National Water Model for flood forecasting. In preparation for a Summer Institute at the National Water Center in June‐July 2015, a demonstration version of a near real‐time, high spatial resolution flood forecasting model was developed for the continental United States. The river and stream network was divided into 2.7 million reaches using the National Hydrography Dataset Plus geospatial dataset and it was demonstrated that the runoff into these stream reaches and the discharge within them could be computed in 10 min at the Texas Advanced Computing Center. This study presents a conceptual framework to connect information from high‐resolution flood forecasting with real‐time observations and flood inundation mapping and planning for local flood emergency response.     

APPLICATION OF GREY MODEL TOWARD RUNOFF FORECASTING1

ABSTRACT: A reliable forecasting model is essential in real‐time flood forecasting for reducing natural damage. Efforts to develop a real‐time forecasting model over the past two decades have been numerous. This work applies the Grey model to forecast rainfall and runoff owing to the model's relative ability to predict the future using a small amount of historical data. Such a model significantly differs from the stochastic and deterministic models developed previously. Ten historical storm events from two catchment areas in northern Taiwan are selected to calibrate and verify the model. Results in this study demonstrate that the proposed models can reasonably forecast runoff one to four hours ahead, if the Grey error prediction method is further used to update the output of the model.     

OPTIMIZATION OF TRANSPORTATION NETWORKS DURING URBAN FLOODING1

ABSTRACT: In this paper a numerical model for flood propagation in urban areas is proposed. It has been applied to evaluate flooding hydraulic characteristics in terms of potential flood elevations, depths, and inundated areas. Furthermore, the algorithm efficiency and the consequent reduced computation time allow the use of the hydraulic model as a part of a more complex system for civil protection actions, planning, and management. During flood events, the transportation network plays a main role both in rescuing people when they are more vulnerable and in moving people and materials from and toward affected areas. The reduced efficiency of this transportation network is evaluated based on a least‐flood‐risk path‐finding algorithm. The results of a case study concerning the northern part of the city of Rome, show that the numerical model for unsteady flow in open channel networks achieves the proposed aims. It has proven to be able to describe the flood hydraulic characteristics and to be suitable for real‐time flood emergency management in urban areas.     

MANAGEMENT OF FLOOD CONTROL SUMPS AND POLLUTANT TRANSPORT1

ABSTRACT: Levee sump systems are used by many riverine communities for temporary storage of urban wet weather flows. The hydrologic performance and transport of stormwater pollutants in sump systems, however, have not been systematically studied. The objective of this paper is to present a case study to demonstrate development and application of a procedure for assessing the hydraulic performance of flood control sumps in an urban watershed. Two sumps of highly variable physical and hydraulic characteristics were selected for analysis. A hydrologic modeling package was used to estimate the flow hydrograph for each outfall as part of the flow balance for the sump. To validate these results, a water balance was used to estimate the total runoff using sump operational data. The hydrologic model calculations provide a satisfactory estimate of the total runoff and its time‐distribution to the sump. The model was then used to estimate pollutant loads to the sump and to the river. Although flow of stormwater through a sump system is regulated solely by flood‐control requirements, these sumps may function as sedimentation basins that provide purification of stormwater. A sample calculation of removals of several conventional pollutants in the target sumps using a mass balance approach is presented.     

Towards Real‐Time Continental Scale Streamflow Simulation in Continuous and Discrete Space

The National Weather Service (NWS) forecasts floods at approximately 3,600 locations across the United States (U.S.). However, the river network, as defined by the 1:100,000 scale National Hydrography Dataset‐Plus (NHDPlus) dataset, consists of 2.7 million river segments. Through the National Flood Interoperability Experiment, a continental scale streamflow simulation and forecast system was implemented and continuously operated through the summer of 2015. This system leveraged the WRF‐Hydro framework, initialized on a 3‐km grid, the Routing Application for the Parallel Computation of Discharge river routing model, operating on the NHDPlus, and real‐time atmospheric forcing to continuously forecast streamflow. Although this system produced forecasts, this paper presents a study of the three‐month nowcast to demonstrate the capacity to seamlessly predict reach scale streamflow at the continental scale. In addition, this paper evaluates the impact of reservoirs, through a case study in Texas. Validation of the uncalibrated model using observed hourly streamflow at 5,701 U.S. Geological Survey gages shows 26% demonstrate PBias ≤ |25%|, 11% demonstrate Nash‐Sutcliffe Efficiency (NSE) ≥ 0.25, and 6% demonstrate both PBias ≤ |25%| and NSE ≥ 0.25. When evaluating the impact of reservoirs, the analysis shows when reservoirs are included, NSE ≥ 0.25 for 56% of the gages downstream while NSE ≥ 0.25 for 11% when they are not. The results presented here provide a benchmark for the evolving hydrology program within the NWS and supports their efforts to develop a reach scale flood forecasting system for the country.     

THE VALUE OF STREAMFLOW FORECASTING IN RESERVOIR OPERATION1

ABSTRACT: The value of streamflow forecasts in reservoir operation depends on a number of factors and may vary considerably. Assessment of forecast benefits is presented here for three specific systems. Statistical streamflow models of increasing forecasting ability are coupled with a recently developed stochastic control method in extensive simulation experiments. The performance of the system is statisticafly evaluated with regard to energy generation and flood and drought prevention. The results indicate that forecast benefits are system specific and may range from quite substantial to fairly minimal.     

A HYDROCLIMATOLOGICAL ANALYSIS OF THE RED RWER OF THE NORTH SNOWMELT FLOOD CATASTROPHE OF 19971

ABSTRACT: The flood hydroclimatology of the Grand Forks flood of April 1997, the most costly flood on a per capita basis for a major metropolitan area in United States history, is analyzed in terms of the natural processes that control spring snowmelt flooding in the region. The geomorphological characteristics of the basin are reviewed, and an integrated assessment of the hydroclimatological conditions during the winter of 1996 to 1997 is presented to gain a real‐world understanding of the physical basis of this catastrophic flood event. The Grand Forks flood resulted from the principal flood‐producing factors occurring at either historic or extreme levels, or at levels conducive to severe flooding. Above normal fall precipitation increased the fall soil moisture storage and reduced the spring soil moisture storage potential. A concrete frost layer developed that effectively reduced the soil infiltration capacity to zero. Record snowfall totals and snow cover depths occurred across the basin because of the unusual persistence of a blocking high circulation pattern throughout the winter. A severe, late spring blizzard delayed the snowmelt season and replenished the snow cover to record levels for early April. This blizzard was followed by a sudden transition to an extreme late season thaw due to the abrupt breakdown of the blocking circulation pattern. The presence of river ice contributed to backwater effects and affected the timing of tributary inflows to the main stem of the Red River. Only the absence of spring rains prevented an even more catastrophic flood disaster from taking place. This paper contributes to our understanding of the flood hydroclimatology of catastrophic flood events in an unusual flood hazard region that possesses relatively flat terrain, a north‐flowing river, and an annual peak discharge time series dominated by spring snowmelt floods.     

A RISK-BASED APPROACH FOR FLOOD CONTROL OPERATION OF A MULTIPURPOSE RESERVOIR1

ABSTRACT: Many approaches are available for operation of a multipurpose reservoir during flood season; one of them is allocation of storage space for flood control. A methodology to determine a reservoir operation policy based on explicit risk consideration is presented. The objective of the formulation is to maximize the reservoir storage at the end of a flood season while ensuring that the risk of an overflow is within acceptable limits. The Dynamic Programming technique has been used to solve the problem. This approach has been applied to develop operation policies for an existing reservoir. The performance of the policy was evaluated through simulation and was found to be satisfactory.     

PARAMETER UNCERTAINTY IN RAINFALL-RUNOFF MODELING FOR POLDER SYSTEM DESIGN1

ABSTRACT: An approach is developed for incorporating the uncertainty of parameters for estimating runoff in the design of polder systems in ungaged watersheds. Monte Carlo Simulation is used to derive a set of realizations of streamflow hydrographs for a given design rainstorm using the U. S. Soil Conservation Service (SCS) unit hydrograph model. The inverse of the SCS curve number, which is a function of the antecedent runoff condition in the SCS model, is the random input in the Monte Carlo Simulation. Monte Carlo realizations of streamfiow hydrographs are used to simulate the performance of a polder flood protection system. From this simulation the probability of occurrence of flood levels for a particular hydraulic design may be used to evaluate its effectiveness. This approach is demonstrated for the Pluit Polder flood protection system for the City of Jakarta, Indonesia. While the results of the application indicate that uncertainty in the antecedent runoff condition is important, the effects of uncertainty in rainfall data, in additional runoff parameters, such as time to peak, in the hydraulic design, and in the rainfall-runoff model selected should also be considered. Although, the SCS model is limited to agricultural conditions, the approach presented herein may be applied to other flood control systems if appropriate storm runoff models are selected.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

MULTICRITERION DECISION MAKING FOR FLOOD CONTROL OPERATIONS: THEORY AND APPLICATIONS1

ABSTRACT: Flood control operations are generally complex in nature, as they are often associated with a large quantity of uncertain factors in combination of noncommensurable criteria. It has been found that the use of fuzzy set theory coupled with multicriterion decision making (MCDM) methods is very promising. In this paper, a multicriterion fuzzy recognition model is established for flood control operations. A subjective preference and iterative weights method is proposed for weight assessment. First, an initial solution of criteria weights, which is directly analyzed from the selected alternatives, is obtained by using the proposed fuzzy recognition model. Then, according to their knowledge related to real time flood operations, operators may modify the initial weights if necessary. When the relative membership grades of alternatives belonging to all rankings are fixed, a defuzzification equation is used to calculate the rank feature value vector. Based on the rank feature value vector, one can evaluate the alternative set. Two flood operation cases are provided to illustrate the application of the proposed model. With the incorporation of the operator's knowledge related to flood operations, the proposed model is flexible in giving weights and realizes more practical flood control operations.     

Reservoir Operation with Feedback in a Coupled Land Surface and Hydrologic Model: A Case Study of the Huai River Basin,China

Regarding emerging large‐scale reservoir operation models, reports of reservoir operation feedback for hydrologic modeling are rare, and little attention has been paid to flood control. An operation scheme considering multilevel flood control (MLFC) was first proposed in this study, but more reservoir information was needed. Thus, an alternative scheme was proposed that consisted of a modified version of the reservoir operation scheme in the Soil and Water Assessment Tool Model (MSWAT scheme). These schemes were coupled to a land surface and hydrologic model system with feedback, i.e., a system in which reservoir operation can affect the subsequent simulation, and were investigated in the Huai River Basin. The results show reservoir storage and peak flow were generally overestimated by the original SWAT reservoir scheme (SWAT scheme). Compared with the SWAT scheme, the MSWAT scheme successfully reduced the simulated storage and peak flow at the reservoir stations. For the downstream stations, the streamflow simulations were improved at a significance level of 5%. The performances of the MSWAT and MLFC schemes at the reservoir stations were nearly equivalent. Importantly, reservoir operation feedback to hydrologic modeling was necessary because the reservoir operation effects could not be transferred downstream without it. The streamflow simulation of a reservoir station located on a flat plain was less sensitive to feedback than that of a mountain reservoir station.     

Climate Change and Floodplain Delineation in Two Southern Quebec River Basins1

Laforce, Serge, Marie‐Claude Simard, Robert Leconte, and François Brissette, 2011. Climate Change and Floodplain Delineation in Two Southern Quebec River Basins. Journal of the American Water Resources Association (JAWRA) 47(4):785‐799. DOI: 10.1111/j.1752‐1688.2011.00560.x Abstract: A methodology is presented for mapping the flooded extent of rivers under projected climate change. The methodology follows a top‐down modeling approach, where future climate projections generated by global climate models (GCMs) are downscaled to the watershed scale and used as input to hydrological and hydrodynamic models for predicting future river flows and associated open water levels. A range of possible future climate responses are taken into account, allowing quantification of flood‐mapping uncertainties resulting from GCM structure and greenhouse gas emission scenarios (GHGES). Probabilistic projections of future flood zones are developed by assuming that all GCMs and GHGES be equally weighted. The proposed methodology was applied to two river basins located in southern Quebec, Canada, for the time horizons 2020 and 2080. Twenty‐ and hundred‐year floods were computed and corresponding flood maps have been produced. Results indicate that there is a general trend toward an increased spring peak discharge for the Châteauguay River Basin and a decrease for the du Nord River Basin at the 2020 horizon. A less obvious trend was observed for the 2080 horizon, some GCM‐GHGES producing an increase in spring peak flows, whereas others would result in a less severe spring flood. These uncertainties in flood flows have cascaded into uncertainties in the corresponding flooded extent and represented as probabilistic flood maps.     

FLOOD PLAIN MODELING1

Computer simulation of river basin hydrology has rapidly progressed from an interesting academic exercise to a practical engineering procedure of increasing utility. Mathematical models of the many interrelated processes which occur in a basin have been developed along with efficient numerical procedures for their solution. The present paper is concerned with a particular model which has been used to describe the transformation of a temporally and spatially varying rainfall into a time history of stage and discharge on a flood plain. Although developed principally as a model of the physical processes involved, it is envisioned that the model can serve as one component of an information system for flood plain planning and management. The model consists of the following elements: (i) a rainfall simulation which generates stochastic inputs to the model according to specified rainfall statistics, (ii) a catchment-runoff model which converts the rainfall to surface runoff, (iii) a flood stage model which converts the surface runoff to time histories of flood stage and discharge. The model has been used to investigate the effect of various structural flood control measures in a basin and, in particular, to establish frequency-stage information for each of these. Of particular interest in development of the model have been recurring and partially unanswered questions relative to the proper balance among availability of data for use in the model, data requirements of the model and the objectives of the outputs produced by the model.     

Mathematical Model on Flow Regime and Water Harvesting in Inundation Plains1

Abstract: A mathematical model on flow regime and water harvesting in inundation plains is presented. The flow profile is a free over‐fall at the end of the desired inundation. The flow front in the plain is on‐line for the entire coverage, in a sense that there is initiation of flow mass after each small reach of the flow traverse, and it is continuing to the extreme point of coverage. The water‐harvesting phenomenon depends upon the occurrences of the hydrologic events, the nature of surface flows in the valley, the expected favorable time of flood incidence, and the soil characteristics of the plains. The model has been tested for three micro‐watersheds of different soil characteristics. It is best suited to platykurtic nature of flood phenomenon in the study area, with the correlation co‐efficient in‐between computed and observed amount of water harvesting above 0.90.     

AutoRAPID: A Model for Prompt Streamflow Estimation and Flood Inundation Mapping over Regional to Continental Extents

This article couples two existing models to quickly generate flow and flood‐inundation estimates at high resolutions over large spatial extents for use in emergency response situations. Input data are gridded runoff values from a climate model, which are used by the Routing Application for Parallel computatIon of Discharge (RAPID) model to simulate flow rates within a vector river network. Peak flows in each river reach are then supplied to the AutoRoute model, which produces raster flood inundation maps. The coupled tool (AutoRAPID) is tested for the June 2008 floods in the Midwest and the April‐June 2011 floods in the Mississippi Delta. RAPID was implemented from 2005 to 2014 for the entire Mississippi River Basin (1.2 million river reaches) in approximately 45 min. Discretizing a 230,000‐km² area in the Midwest and a 109,500‐km² area in the Mississippi Delta into thirty‐nine 1° by 1° tiles, AutoRoute simulated a high‐resolution (~10 m) flood inundation map in 20 min for each tile. The hydrographs simulated by RAPID are found to perform better in reaches without influences from unrepresented dams and without backwater effects. Flood inundation maps using the RAPID peak flows vary in accuracy with F‐statistic values between 38.1 and 90.9%. Better performance is observed in regions with more accurate peak flows from RAPID and moderate to high topographic relief.     

A Framework to Develop Nationwide Flooding Extents Using Climate Models and Assess Forecast Potential for Flood Resilience

The methods used to simulate flood inundation extents can be significantly improved by high‐resolution spatial data captured over a large area. This paper presents a hydraulic analysis methodology and framework to estimate national‐level floodplain changes likely to be generated by climate change. The hydraulic analysis was performed using existing published Federal Emergency Management Agency 100‐year floodplains and estimated 100‐ and 10‐year return period peak flow discharges. The discharges were estimated using climate variables from global climate models for two future growth scenarios: Representative Concentration Pathways 2.6 and 8.5. River channel dimensions were developed based on existing regional United States Geological Survey publications relating bankfull discharges with channel characteristics. Mathematic relationships for channel bankfull topwidth, depth, and side slope to contributing drainage area measured at model cross sections were developed. The proposed framework can be utilized at a national level to identify critical areas for flood risk assessment. Existing hydraulic models at these “hot spots” could be repurposed for near–real‐time flood forecasting operations. Revitalizing these models for use in simulating flood scenarios in near–real time through the use of meteorological forecasts could provide useful information for first responders of flood emergencies.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

Modeling Flow and Sediment Transport in a River System Using an Artificial Neural Network

A river system is a network of intertwining channels and tributaries, where interacting flow and sediment transport processes are complex and floods may frequently occur. In water resources management of a complex system of rivers, it is important that instream discharges and sediments being carried by streamflow are correctly predicted. In this study, a model for predicting flow and sediment transport in a river system is developed by incorporating flow and sediment mass conservation equations into an artificial neural network (ANN), using actual river network to design the ANN architecture, and expanding hydrological applications of the ANN modeling technique to sediment yield predictions. The ANN river system model is applied to modeling daily discharges and annual sediment discharges in the Jingjiang reach of the Yangtze River and Dongting Lake, China. By the comparison of calculated and observed data, it is demonstrated that the ANN technique is a powerful tool for real-time prediction of flow and sediment transport in a complex network of rivers. A significant advantage of applying the ANN technique to model flow and sediment phenomena is the minimum data requirements for topographical and morphometric information without significant loss of model accuracy. The methodology and results presented show that it is possible to integrate fundamental physical principles into a data-driven modeling technique and to use a natural system for ANN construction. This approach may increase model performance and interpretability while at the same time making the model more understandable to the engineering community.     

FLOOD CONTROL STORAGE ALLOCATIONS BY LINEAR PROGRAMMING1

ABSTRACT. The setting of rule curves for reservoirs or lakes operation requires balancing the flood control storages reserved against the storage requirements for various conservation uses. In this study, a linear programming model is developed to perform single purpose analysis that minimizes flood damages of a multi-lake river system under various initial and input conditions. A flood control utility measure function is arrived from the resulting analysis, and the inclusion of the function in conservation analysis could provide the total functional analysis. The river-system transition function involving time-lags of short duration is described. The function constitutes the basis for the optimization model, and also provides the transformation to reduce significantly the size of the problem. The application to a critical subsystem in the Oswego River System is reported.