A Hydraulic MultiModel Ensemble Framework for Visualizing Flood Inundation Uncertainty

While deterministic forecasts provide a single realization of potential inundation, the inherent uncertainty associated with forecasts also needs to be conveyed for improved decision support. The objective of this study was to develop an ensemble framework for the quantification and visualization of uncertainty associated with flood inundation forecast maps. An 11‐member ensemble streamflow forecast at lead times from 0 to 48 hr was used to force two hydraulic models to produce a multimodel ensemble. The hydraulic models used are (1) the International River Interface Cooperative along with Flow and Sediment Transport with Morphological Evolution of Channels solver and (2) the two‐dimensional Hydrologic Engineering Center‐River Analysis System. Uncertainty was quantified and augmented onto flood inundation maps by calculating statistical spread among the ensemble members. For visualization, a series of probability flood maps conveying the uncertainty in forecasted water extent, water depth, and flow velocity was disseminated through a web‐based decision support tool. The results from this study offer a framework for quantifying and visualizing model uncertainty in forecasted flood inundation maps.     

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.     

Intercomparison of Satellite Remote Sensing‐Based Flood Inundation Mapping Techniques

The objective of this study was to determine the accuracy of five different digital image processing techniques to map flood inundation extent with Landsat 8–Operational Land Imager satellite imagery. The May 2016 flooding event in the Hempstead region of the Brazos River, Texas is used as a case study for this first comprehensive comparison of classification techniques of its kind. Five flood water classification techniques (i.e., supervised classification, unsupervised classification, delta‐cue change detection, Normalized Difference Water Index [NDWI], modified NDWI [MNDWI]) were implemented to characterize flooded regions. To identify flood water obscured by cloud cover, a digital elevation model (DEM)–based approach was employed. Classified floods were compared using an Advanced Fitness Index to a “reference flood map” created based on manual digitization, as well as other data sources, using the same satellite image. Supervised classification yielded the highest accuracy of 86.4%, while unsupervised, MNDWI, and NDWI closely followed at 79.6%, 77.3%, and 77.1%, respectively. Delta‐cue change detection yielded the lowest accuracy with 70.1%. Thus, supervised classification is recommended for flood water classification and inundation map generation under these settings. The DEM‐based approach used to identify cloud‐obscured flood water pixels was found reliable and easy to apply. It is therefore recommended for regions with relatively flat topography.     

Using Steady‐State Backwater Analysis to Predict Inundated Area from National Water Model Streamflow Simulations

National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts for 2.7 million reaches in the National Hydrography Dataset for continental United States (U.S.). NWM uses Muskingum–Cunge channel routing, which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain more accurate estimates of streamflow and stage in rivers, especially for applications such as flood‐inundation mapping. Here, we used a steady‐state backwater version of Simulation Program for River NeTworks (SPRNT) model. We evaluated SPRNT’s and NWM’s abilities to predict inundated area for the record flood of Hurricane Matthew in October 2016. The Neuse River experienced record‐breaking floods and was well‐documented by U.S. Geological Survey. Streamflow simulations from NWM retrospective analysis were used as input for the SPRNT simulation. Retrospective NWM discharge predictions were converted to stage. The stages (from both SPRNT and NWM) were utilized to produce flood‐inundation maps using the Height Above Nearest Drainage method which uses the local relative heights to find out the local draining potentials and provide spatial representation of inundated area. The inundated‐area accuracies for NWM and SPRNT (based on comparison to a remotely sensed dataset) were 65.1% and 67.6%, respectively. These results show using steady‐state SPRNT results in a modest improvement of inundation‐forecast accuracy compared to NWM.     

Error Assessment for Height Above the Nearest Drainage Inundation Mapping

Real‐time flood inundation mapping is vital for emergency response to help protect life and property. Inundation mapping transforms rainfall forecasts into meaningful spatial information that can be utilized before, during, and after disasters. While inundation mapping has traditionally been conducted on a local scale, automated algorithms using topography data can be utilized to efficiently produce flood maps across the continental scale. The Height Above the Nearest Drainage method can be used in conjunction with synthetic rating curves (SRCs) to produce inundation maps, but the performance of these inundation maps needs to be assessed. Here we assess the accuracy of the SRCs and calculate statistics for comparing the SRCs to rating curves obtained from hydrodynamic models calibrated against observed stage heights. We find SRCs are accurate enough for large‐scale approximate inundation mapping while not as accurate when assessing individual reaches or cross sections. We investigate the effect of terrain and channel characteristics and observe reach length and slope predict divergence between the two types of rating curves, and SRCs perform poorly for short reaches with extreme slope values. We propose an approach to recalculate the slope in Manning’s equation as the weighted average over a minimum distance and assess accuracy for a range of moving window lengths.     

A Rapid Response Survey to Characterize the Impacts of the 2017 High Water Event on Lake Ontario

In the spring and summer of 2017, communities along the Lake Ontario shoreline suffered from the worst flood event on record. In late May, daily water levels reached their highest point in over 100 years, and flooding continued throughout much of the summer as lake levels slowly declined, with inundation and erosion significantly impacting shoreline homes and businesses. In this work, we present results from a rapid response online survey of property owners along the New York Lake Ontario shoreline to quantify the perceived flood impacts of the 2017 extended high water event. The survey focused on the degree and spatial distribution of inundation and erosion; the duration and drivers of inundation; the associated damages to different property features, with an emphasis on shoreline protection; and the degree of disruption to business and other activities and services. Photographic documentation of inundation extent and property damage also was provided by survey respondents. We demonstrate the potential utility of this dataset by characterizing key features of inundation and erosion impacts across the shoreline, and by using classification and regression trees to explore the predictability of inundation and erosion based on property characteristics. This work is part of a larger effort to develop models of inundation and erosion that can support flood impact assessments across the shoreline and help communities better prepare for future extended high water events.     

A Faster and Economical Approach to Floodplain Mapping Using Soil Information

Flood inundation maps play a key role in assessment and mitigation of potential flood hazards. However, owing to high costs associated with the conventional flood mapping methods, many communities in the United States lack flood inundation maps. The objective of this study is to develop and examine an economical alternative approach to floodplain mapping using widely available soil survey geographic (SSURGO) database. In this study, floodplain maps are developed for the entire state of Indiana, and some counties in Minnesota, Wisconsin, and Washington states by identifying flood‐prone soil map units based on their attributes. For validation, the flood extents obtained from SSURGO database are compared with the extents from other floodplain maps such as the Federal Emergency Management Agency issued flood insurance rate maps (FIRMs), flood extents observed during past floods, and flood maps derived using digital elevation models. In general, SSURGO‐based floodplain maps (SFMs) are largely in agreement with other flood inundation maps. Specifically, the floodplain extents from SFMs cover 78‐95% area compared to FIRMs and observed flood extents. Thus, albeit with a slight loss in accuracy, the SSURGO approach offers an economical and fast alternative for floodplain mapping. In particular, it has potentially high utility in areas where no detailed flood studies have been conducted.     

Coastal Flood Inundation Monitoring with Satellite C‐band and L‐band Synthetic Aperture Radar Data

Satellite Synthetic Aperture Radar (SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm‐ and tidal‐related flooding of spatially extensive coastal marshes within the north‐central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite (ALOS) Phased Array type L‐Band SAR (PALSAR) (L‐band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite (ENVISAT) Advanced SAR (ASAR) (C‐band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006‐2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR‐ and ASAR‐based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference‐scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR‐based inundation accuracies averaged 84% (n = 160), while ASAR‐based mapping accuracies averaged 62% (n = 245).     

River Channel Geometry and Rating Curve Estimation Using Height above the Nearest Drainage

River channel geometry is an important input to hydraulic and hydrologic models. Traditional approaches to quantify river geometry have involved surveyed river cross sections, which cannot be extended to ungaged basins. In this paper, we describe a method for developing a synthetic rating curve to relate flow to water level in a stream reach based on reach‐averaged channel geometry properties developed using the Height above Nearest Drainage (HAND) method. HAND uses a digital elevation model (DEM) of the terrain and computes the elevation difference between each land surface cell and the stream bed cell to which it drains. Taking increments in water level in the stream, HAND defines the inundation zone and a water depth grid within this zone, and the channel characteristics are defined from this water depth grid. We apply our method to the Blanco River (Texas) and the Tar River (North Carolina) using 10‐m terrain data from the United States Geological Survey (USGS) 3D Elevation Program (3DEP) dataset. We evaluate the method's performance by comparing the reach‐average stage‐river geometry relationships and rating curves to those from calibrated Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models and USGS gage observations. The results demonstrate that after some adjustment, the river geometry information and rating curves derived from HAND using national‐coverage datasets are comparable to those obtained from hydraulic models or gage measurements. We evaluate the inundation extent and show our approach is able to capture the majority of the Federal Emergency Management Agency (FEMA) 100‐year floodplain.     

A CyberGIS Integration and Computation Framework for High‐Resolution Continental‐Scale Flood Inundation Mapping

We present a Digital Elevation Model‐based hydrologic analysis methodology for continental flood inundation mapping (CFIM), implemented as a cyberGIS scientific workflow in which a 1/3rd arc‐second (10 m) height above nearest drainage (HAND) raster data for the conterminous United States (CONUS) was computed and employed for subsequent inundation mapping. A cyberGIS framework was developed to enable spatiotemporal integration and scalable computing of the entire inundation mapping process on a hybrid supercomputing architecture. The first 1/3rd arc‐second CONUS HAND raster dataset was computed in 1.5 days on the cyberGIS Resourcing Open Geospatial Education and Research supercomputer. The inundation mapping process developed in our exploratory study couples HAND with National Water Model forecast data to enable near real‐time inundation forecasts for CONUS. The computational performance of HAND and the inundation mapping process were profiled to gain insights into the computational characteristics in high‐performance parallel computing scenarios. The establishment of the CFIM computational framework has broad and significant research implications that may lead to further development and improvement of flood inundation mapping methodologies.     

Comparative Analysis of Inundation Mapping Approaches for the 2016 Flood in the Brazos River,Texas

Accurate and timely flood inundation maps serve as crucial information for hydrologists, first‐responders, and decision makers of natural disaster management agencies. In this study, two modeling approaches are applied to estimate the inundation area for a large flooding event that occurred in May 2016 in the Brazos River: (1) Height Above the Nearest Drainage combined with National Hydrograph Dataset Plus (NHDPlus‐HAND) and (2) International River Interface Cooperative — Flow and Sediment Transport with Morphological Evolution of Channels (iRIC‐FaSTMECH). The inundation extents simulated from these two modeling approaches are then compared against the observed inundation extents derived from a Landsat 8 satellite image. The simulated results from NHDPlus‐HAND and iRIC‐FaSTMECH show 56% and 70% of overlaps with the observed flood extents, respectively. A modified version of the NHDPlus‐HAND model, considering networked catchment behaviors, is also tested with an improved fitness of 67%. This study suggests that NHDPlus‐HAND has the potential for real‐time continental inundation forecast due to its low computational cost and ease to couple with the National Water Model. Better performance of NHDPlus‐HAND can be achieved by considering the inter‐catchment flows during extreme riverine flood events. Overall, this study presents a comprehensive examination made of remote sensing compared with HAND‐based inundation mapping in a region of complex topography.     

DEVELOPMENT OF A GIS‐BASED FLOOD INFORMATION SYSTEM FOR FLOODPLAIN MODELING AND DAMAGE CALCULATION1

ABSTRACT: This work presents a flexible system called GIS‐based Flood Information System (GFIS) for floodplain modeling, flood damages calculation, and flood information support. It includes two major components, namely floodplain modeling and custom designed modules. Model parameters and input data are gathered, reviewed, and compiled using custom designed modules. Through these modules, it is possible for GFIS to control the process of flood‐plain modeling, presentation of simulation results, and calculation of flood damages. Empirical stage‐damage curves are used to calculate the flood damages. These curves were generated from stage‐damage surveys of anthropogenic structures, crops, etc., in the coastal region of a frequently flooded area in Chia‐I County, Taiwan. The average annual flood damages are calculated with exceedance probability and flood damages for the designed rainfalls of 2, 5, 10, 25, 50, 100, and 200 year recurrence intervals with a duration of 24 hours. The average annual flood depth in this study area can also be calculated using the same method. The primary advantages of GFIS are its ability to accurately predict the locations of flood area, depth, and duration; calculate flood damages in the floodplain; and compare the reduction of flood damages for flood mitigation plans.     

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.     

Interpolating SRTM Elevation Data to Higher Resolution to Improve Hydrologic Analysis

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) has been a valuable resource for hydrological analysis, providing elevation data at a consistent resolution on a near‐global scale. However, its resolution (three arc‐second or 90 m) is sometimes too low to obtain the desired level of accuracy and precision for hydrologic analysis. We evaluated the performance of several methods for interpolating SRTM three arc‐second data to a 30‐m resolution grid to better represent topography and derive terrain characteristics of the landscape. STRM data were interpolated to 30‐m DEMs on a common grid using spline, inverse distance weighting (IDW), kriging (KR), natural neighbor methods, and cubic convolution (CC) resampling. Accuracy of the methods was assessed by comparing interpolated and resampled 30‐m grids with the reference data. Slope, aspect, sinks, and stream networks were derived for the 30‐m grids and compared on a cell‐by‐cell basis to evaluate their performance in reproducing the derivatives. The comparisons identify spline and KR as the most accurate interpolation methods, of which spline is preferred because of its relative simplicity. IDW provided the greatest bias in all methods with artifacts evident in slope and aspect maps. The performance of CC projection directly to a 30‐m resolution was comparable to spline interpolation, thus is recommended as the most convenient method for interpolating SRTM to a higher resolution.     

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.     

WATER QUALITY MODEL OUTPUT UNCERTAINTY AS AFFECTED BY SPATIAL RESOLUTION OF INPUT DATA1

ABSTRACT: Resolution of the input GIS data used to parameterize distributed‐parameter hydrologic/water quality models may affect uncertainty in model outputs and impact the subsequent application of model results in watershed management. In this study we evaluated the impact of varying spatial resolutions of DEM, land use, and soil data (30 × 30 m, 100 × 100 m, 150 × 150 m, 200 × 200 m, 300 × 300 m, 500 × 500 m, and 1,000 × 1,000 m) on the uncertainty of SWAT predicted flow, sediment, NO₃‐N, and TP transport. Inputs included measured hydrologic, meteorological, and watershed characteristics as well as water quality data from the Moores Creek watershed in Washington County, Arkansas. The SWAT model output was most affected by input DEM data resolution. A coarser DEM data resolution resulted in decreased representation of watershed area and slope and increased slope length. Distribution of pasture, forest, and urban areas within the watershed was significantly affected at coarser resolution of land use and resulted in significant uncertainty in predicted sediment, NO₃‐N, and TP output. Soils data resolution had no significant effect on flow and NO₃‐N predictions; however, sediment was overpredicted by 26 percent, and TP was underpredicted by 26 percent at 1,000 m resolution. This may be due to change in relative distribution of various hydrologic soils groups (HSGs) in the watershed. Minimum resolution for input GIS data to achieve less than 10 percent model output error depended upon the output variable of interest. For flow, sediment, NO₃‐N, and TP predictions, minimum DEM data resolution should range from 30 to 300 m, whereas minimum land use and soils data resolution should range from 300 to 500 m.     

The use of Landsat multispectral scanner data for the analysis and management of flooding on the river Severn,England

Remote sensing has emerged as one of the major techniques for the analysis and delineation of large floods. This analysis can provide data invaluable for the hydrological management of large river systems. A need for information on the extent of floodplain inundation for the lower reaches of the largest river in the UK was met by a search through Landsat images of floods and the analysis of the best example recorded. Automated classification of the Landsat imagery of this flood on the river Severn in 1977 was used to provide estimates of the extent and spatial distribution of inundation. Flood images were generated using the Plessey IDP 3000 image processor, and the maps derived accorded well with aerial photography and qualitative flood information. Three distinct floodplain environments were delineated and flood images produced by different spectral bands compared. Specific questions prompted by flood hazard management and concerning the processes and extent of flooding were answered by the Landsat data analysis. Management of the flood risk of large rivers is expensive and remote sensing data is a relatively cheap and effective way of monitoring control works and providing data for the prediction of the effects of future hydrological works. Remote sensing is a practical way in which spatial information concerning the behavior of large dynamic systems can be obtained both quickly and relatively cheaply.     

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.     

From Global to Local: Providing Actionable Flood Forecast Information in a Cloud‐Based Computing Environment

Global and continental scale flood forecast provide coarse resolution flood forecast, but from the perspective of emergency management, flood warnings should be detailed and specific to local conditions. The desired refinement can be provided by the use of downscaling global scale models and through the use of distributed hydrologic models to produce a high‐resolution flood forecast. Three major challenges associated with transforming global flood forecasting to a local scale are addressed in this work. The first is using open‐source software tools to provide access to multiple data sources and lowering the barriers for users in management agencies at local level. This can be done through the Tethys Platform that enables web water resources modeling applications. The second is finding a practical solution for the computational requirements associated with running complex models and performing multiple simulations. This is done using Tethys Cluster that manages distributed and cloud computing resources as a companion to the Tethys Platform for web app development. The third challenge is discovering ways to downscale the forecasts from the global extent to the local context. Three modeling strategies have been tested to address this, including downscaling of coarse resolution global runoff models to high‐resolution stream networks and routing with Routing Application for Parallel computatIon of Discharge (RAPID), the use of hierarchical Gridded Surface and Subsurface Hydrologic Analysis (GSSHA) distributed models, and pre‐computed distributed GSSHA models.     

Efficient Delineation of Nested Depression Hierarchy in Digital Elevation Models for Hydrological Analysis Using Level‐Set Method

In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill‐merge‐spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level‐set method based on graph theory. The proposed level‐set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level‐set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine‐resolution Light Detection and Ranging‐derived DEMs show that the raster‐based level‐set algorithm is much more efficient (~150 times faster) than the vector‐based contour tree method. The proposed level‐set algorithm has great potential for being applied to large‐scale ecohydrological analysis and watershed modeling.