a framework to develop nationwide flooding extents … · neering center-river analysis system...

A FRAMEWORK TO DEVELOP NATIONWIDE FLOODING EXTENTS USING CLIMATE

MODELS AND ASSESS FORECAST POTENTIAL FOR FLOOD RESILIENCE1

Sivasankkar Selvanathan, Mathini Sreetharan, Seth Lawler,

Krista Rand, Janghwoan Choi, and Mathew Mampara2

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

(KEY TERMS: climate variability/change; NHDPlus; flood forecasting; quantitative precipitation forecasts;large-scale flooding extents mapping.)

Selvanathan, Sivasankkar, Mathini Sreetharan, Seth Lawler, Krista Rand, Janghwoan Choi, and Mathew Mam-para, 2018. A Framework to Develop Nationwide Flooding Extents Using Climate Models and Assess ForecastPotential for Flood Resilience. Journal of the American Water Resources Association (JAWRA) 54(1): 90-103.https://doi.org/10.1111/1752-1688.12613

INTRODUCTION

Flood events are a yearly occurrence in much ofthe United States (U.S.), resulting in widespread lossof life and damage to both private property and pub-lic infrastructure. They are usually driven byincreases in extreme precipitation, and are projectedto increase nationwide at many locations (USGCRP,2014). In locations historically vulnerable to flooding

which are currently experiencing land-use changesand population increases, the need to develop floodingextents for use in planning and flood event responsesis especially urgent. For short-term flood emergen-cies, there is growing acknowledgment of the need toutilize near–real-time hydrologic simulations toadvise those involved in emergency response, opera-tions, and recovery.

Development of flooding extent estimation productssuitable for community-level action is ongoing. The

1Paper No. JAWRA-16-0034-P of the Journal of the American Water Resources Association (JAWRA). Received January 27, 2016; acceptedNovember 7, 2017. © 2018 American Water Resources Association. Discussions are open until six months from issue publication.

2Engineer (Selvanathan, Sreetharan, Lawler, Rand, Choi, Mampara), Department of Water Resources, Dewberry, Fairfax, Virginia 22031(Selvanathan: [email protected]).

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JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

Vol. 54, No. 1 AMERICAN WATER RESOURCES ASSOCIATION February 2018

Iowa Flood Information System developed by theUniversity of Iowa Flood Center (Demir and Krajew-ski, 2013), National Weather Service’s (NWS) Quanti-tative Precipitation Forecasts (QPF) (Bushong, 1999),and the National Water Model (NWM) (Maidment,2016) released under the leadership of the NationalOceanic and Atmospheric Administration are some ofthe products used for mapping and mitigation efforts.Flooding extents corresponding to NWM dischargesare not available yet.

The hydraulic models developed for the FederalEmergency Management Agency (FEMA) to produceflood maps are perhaps the most widely availablemodeling data in the U.S. These hydraulic modelsproduce the 100-year floodplains. These models canalso be used to work with forecasted stream dis-charges. In order to prioritize limited resources, it isadvantageous to understand where high-risk “hotspot” locations are and develop more accurate flood-plain forecasts. “Hot spots” for risk due to floodingare defined in terms of both current and future risk,taking into consideration projected increases in thefloodplains, and are determined based on criticalinfrastructure as well as present and projectedincreases in population. Streamflows generated bythe River Forecast Centers (RFC) are readily avail-able for use in existing hydraulic models. The fore-casted flows can be applied to the hydraulic model ofthe streams (e.g., developed using United StatesArmy Corps of Engineers’ [USACE] Hydrologic Engi-neering Center-River Analysis System [HEC-RAS]model). On a basin scale, this coupling of hydrologicand hydraulic (H&H) models results in the computa-tion of not just stages likely to occur in the streamreach, but the approximate timing and floodplain inthe form of stage/flow hydrographs and correspondingfloodplain shapefiles.

The Advanced Hydrologic Prediction Service(AHPS) offers inundation mapping products that pro-vide high-quality flooding extent maps that have beenvalidated by emergency managers and are suitablefor local-level planning and response; however, theyare not available in all communities. For those com-munities without AHPS libraries, the ability to pro-duce near–real-time flood forecasts at a basin scalefor identified hot spots would support identification offlooding risks to assets and population, representinga significant advance in information available to sup-port emergency planning and response. Since theflood risk to assets and population is smaller in areasoutside of population centers, less rigorous H&H pro-cedures can be generated to develop flooding extents.The proposed approach to developing forecast-basedflooding extent estimates can be rapidly deployed toaddress gaps in the AHPS libraries. This approach issufficiently flexible for nationwide application and

will pave the way for generating floodplain forecastsat a national scale.

In order to develop approximate national-scaleflooding extent forecasts, the relationship betweenpeak flow discharge and flood depth must be estab-lished. AECOM (2013) examined over 11,000 floodprofiles published in Flood Insurance Studies anddeduced a power relationship between the peak flowdischarges and flood depth. AECOM incorporatedemission scenarios outlined in the IntergovernmentalPanel for Climate Change’s (IPCC) Fourth Assess-ment report. For a national-scale flood forecast studyas is described in this paper, the AECOM relation-ship provides a convenient method to translate peakdischarges to approximate flood depths.

The National Hydrography Dataset (NHD) streamnetwork database, published by the U.S. GeologicalSurvey (USGS) is the best source of geospatial datafor identifying the locations of the nation’s streams. Aversion of the NHD called the NHDPlus is integratedwith elevation and other landscape data to createdetailed drainage catchments and flow volume andvelocity estimates for streams and rivers of the U.S.at 1:100,000 scale. The data are available as ageospatial hydrologic network dataset due to the col-lective efforts of the U.S. Environmental ProtectionAgency (USEPA) Office of Water and USGS.NHDPlus Version 2 (McKay et al., 2012) provideshydrologic data (vector and raster data) broken downby custom processing units spanning a reasonablespatial extent to conserve computational efficiency.This format, along with the attributes, enables thedata to be used effectively for geospatial processingfor hydrologic applications. For example, the rasterelevation attributes can be used to calculate thestream slope necessary to model streamflows. Theflow accumulation grid values aid drainage area com-putations. These datasets can also be applied for fore-casting applications.

USEPA’s Integrated Climate and Land-Use Sce-narios (ICLUS) provides demographic projections forthe contiguous U.S. based on IPCC’s Fourth Assess-ment Report. USEPA’s ICLUS dataset is currentlythe only nationwide population projection dataset fac-toring for climate change, population fertility, mortal-ity, and migratory patterns deriving projections outto the year 2100. The resolution of ICLUS housingdensity data (0.01 km) cannot be used readily to esti-mate local impacts due to flash flooding events. How-ever, geospatial processing of that data followingareal interpolation techniques can alleviate the reso-lution issue to some extent. The data, incorporatedwith projected flooding extents and locations, can beused to identify flooding risk hot spots.

This research paper introduces a framework tohelp in producing future flooding extents based on

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION JAWRA91

A FRAMEWORK TO DEVELOP NATIONWIDE FLOODING EXTENTS USING CLIMATE MODELS AND ASSESS FORECAST POTENTIAL FOR FLOOD RESILIENCE

climate model outputs and to identify potential areasvulnerable to flooding based on the mapped floodingextents. Primarily, this paper demonstrates theframework’s ability to map future flooding extentsusing empirical methods and to model flood forecast-ing using existing hydraulic models. The outputs canbe used to inform emergency response operationsduring major local flooding events. To illustrate thecapability of this framework, a case study on theDuck Creek Basin in Dallas, Texas, which recentlyexperienced flash flooding, is presented.

RESEARCH METHODOLOGY

The proposed framework discussed here is com-prised of three main components — hydrologic,

hydraulic, and mapping components. The hydrauliccomponent in our framework can take dischargesbased on any hydrologic model. In order to demon-strate the hydraulic methodology, we are utilizingdischarges developed based on climate variables fromthe general circulation models (GCM) for Representa-tive Concentration Pathways (RCPs) 2.6 and 8.5. Thehydraulic component was derived from open spatialdata — NHDPlus and National Flood Hazards Layer(NFHL). The mapping component produces floodingextents for evaluating flood hazard risk.

Regression equations for estimating flows for futureclimate scenarios were developed for 18 HydrologicUnit Code 2 (HUC-2) regions within the contiguousU.S. (Selvanathan et al., 2016). Table 1 lists the basinand climate parameters that significantly influencedflow in each HUC-2 region based on these establishedequations. Table 2 provides an explanation for theparameters used. Q100c and Q10c represent the 100-year

TABLE 1. Basin and Climate Parameters that Significantly Influenced Discharges in Each HUC-2 Region.

HUC-2 Equation Exc. Freq. (%) Std Error (%) Std Error Prediction (%) R2 (%)

01 Q100c = f (DA, MeanELb, FD) 1 64 65 81Q10c = f (DA, SLb, FD) 10 49 50 88

02 Q100c = f (DA, MeanELb, ST, RX5) 1 707 71 82Q10c = f (DA, MeanELb, ST, SnDpth) 10 688 68 84

03 Q100c = f (DA, MeanELb, ST, CDD, SDII) 1 59 60 83Q10c = f (DA, MeanELb, ST, CDD, SDII) 10 54 54 86

04 Q100c = f (DA, MeanELb, ST, R95p) 1 71 72 73Q10c = f (DA, MeanELb, ST, R95p) 10 65 65 78

05 Q100c = f (DA, MeanELb, RX5, R10) 1 50 51 83Q10c = f (DA, MeanElb, RX5, R10) 10 46 46 86

06 Q100c = f (DA, RX5) 1 50 51 82Q10c = f (DA, FD, SDII) 10 46 47 84

07 Q100c = f (DA, MeanELb, SLb, ST, SDII) 1 55 56 84Q10c = f (DA, MeanELb, Slb, ST, RX5) 10 49 49 87

08 Q100c = f (DA, MeanElb, Slb, Lch, ST, IA, CDD, R10) 1 53 56 74Q10c = f (DA, MeanElb, SLb, IA, CDD) 10 60 62 64

09 Q100c = f (DA, ST, IA, R95p) 1 58 61 65Q10c = f (DA, MeanELb, CDD) 10 59 62 62

10 Q100c = f (DA, MeanELb, IA, R95p) 1 91 91 62Q10c = f (DA, MeanELb, RX5, CDD) 10 85 85 67

11 Q100c = f (DA, MeanELb, CDD) 1 98 99 57Q10c = f (DA, MeanELb, IA, CDD) 10 90 91 66

12 Q100c = f (DA, SLb, CDD) 1 73 74 55Q10c = f (DA, SLb, CDD, SDII) 10 60 61 56

13 Q100c = f (DA, MeanELb, SDII) 1 94 97 77Q10c = f (DA, MeanELb, SLb, CDD) 10 86 89 76

14 Q100c = f (DA, SLb, RX5, CDD) 1 57 58 84Q10c = f (DA, ST, TN90p) 10 64 64 81

15 Q100c = f (Lch, ST, R95p, SDII) 1 81 85 70Q10c = f (DA, ST, R95p, SDII) 10 89 92 67

16 Q100c = f (DA, SDII) 1 88 90 68Q10c = f (DA, IA) 10 104 105 64

17 Q100c = f (DA, SLb, Lch, R95p) 1 83 84 79Q10c = f (DA, SLb, Lch, RX5) 10 103 104 73

18 Q100c = f (DA, SLb, SDII) 1 82 84 68Q10c = f (DA, Lch, R95p, SDII) 10 80 81 70

Notes: HUC-2, Hydrologic Unit Code 2; Exc. Freq., exceedance frequency.Equations reproduced from Selvanathan et al. (2016).


SELVANATHAN, SREETHARAN, LAWLER, RAND, CHOI, AND MAMPARA

and 10-year flood return period discharges, respec-tively. Peak flow estimates using seven climate modelresults for 18 HUC-2 regions within the contiguousU.S. were developed. These values were used in thehydraulic analyses to develop estimated upper andlower bounds for anticipated changes to floodingextents nationally.

The main goal of the hydraulic component was toestimate the change in topwidth for the 100-year flood-plains at a national scale for future climate scenarios.All the 100-year floodplains, designated as SpecialFlood Hazard Areas (SFHA), available in the FEMANFHL were compiled and combined with new prelimi-nary floodplain maps and other available digital flood-plain maps (paper maps that were georeferenced anddigitized) to produce a single best available source of100-year floodplains.

The 100-year flooding extents are typically con-structed as large polygons covering multiple streamsin a given geography. The NHDPlus flowlines, repre-senting the streamlines and their catchment polygons,were downloaded from the NHDPlus geospatial datasuite and used in this research. The flooding extentswere spatially associated with the streamlines byintersecting with the associated NHDPlus catchment(drainage area boundary) polygons. The portion of thefloodplain inside each catchment was associated withthe streamline in the catchment. Thus, a unique spa-tial association was developed between flooding extentpolygons and the streamlines.

Cross sections were generated along the streamlinesat approximately 200-m intervals. The cross sectionsspanned the extent of the existing floodplain at thatlocation. A sample location with NHD catchments,

TABLE 2. Basin and Climate Parameters that Significantly Influenced Discharges in Each HUC-2 Region.

Parameter Category Description

DA Basin Drainage area (km2)MeanElb Basin Mean watershed elevation (m)ST Basin Percentage of storage areas (%)IA Basin Percentage of impervious area (%)Lch Basin Longest flowpath (m)SLb Basin Basin slope (ratio)FD Climate Number of frost days per year when daily minimum temperature is <0°CRX5 Climate Monthly maximum consecutive five-day precipitation (mm)CDD Climate Consecutive dry days — maximum length of dry spell with daily precipitation <1.0 mm (days)SnDpth Climate Snow depth (mm)SDII Climate Simple precipitation intensity index — precipitation total averaged over total days with precipitation (mm/day)R95p Climate Annual total contribution of precipitation generated by 95th percentile rainfall events (mm)R10 Climate Annual count of days when daily precipitation is larger than 10 mm (days)Tn90p Climate Percentage of days when daily minimum temperature is larger than 90th percentile daily minimum temperature

FIGURE 1. Map Showing Cross Sections Cut Along NHD Streamlines that Span the Extent of the Existing Floodplain.The NHDPlus catchments are associated 1:1 with every streamline feature. NHD, National Hydrography Dataset;

SFHA, Special Flood Hazard Areas.



streamlines, and their cross sections is shown in Fig-ure 1. Using the flow accumulation grids downloadedfrom NHDPlus, the drainage area at the point of inter-section of the streamline and each cross section wascomputed. The drainage areas were used in the regres-sion equations (current and future) to estimate flows ateach flood frequency (10- and 100-year). Using the esti-mated flows and established depth-flow relationships(AECOM, 2013), the changes to topwidth at each crosssection were computed.

The depth-flow relationship used in AECOM’sresearch (AECOM, 2013) is shown in Equation (1).

D ¼ 0:0504Q0:408; ð1Þ

where D is depth (m) and Q is discharge (m3/s).The slope of the channel, computed using estimated

depths, was used to estimate the change in topwidth ofthe floodplain as shown in Figure 2. The first step is todetermine the slope of the channel between the current100-year and 10-year flows. The slope (tanh) is deter-mined by the following set of equations.

y ¼ D100c �D10c ð2Þ

x ¼ W100c �W10c ð3Þ

tanh ¼ y

x¼ D100c �D10c

W100c �W10c; ð4Þ

where D100c is the depth for the current 100-year flow(m), D10c is the depth for the current 10-year flow(m), W100c is the station along the cross section onthe left-hand side of the channel for the current 100-year flow (m), and W10c is the station along the cross

section on the left-hand side of the channel for thecurrent 10-year flow (m).

The depths for the 10- and 100-year flows were deter-mined by the depth-flow relationship in Equation (1).The only remaining unknown variables in Equation (4)are the stations along the cross section where the cur-rent 10- and 100-year floodplains intersect the ground.The station for the current 100-year floodplains wasobtained by first determining the points of intersection(usually two) of the cross sections with the existing 100-year floodplain polygons. The two intersecting points arespatially located on each side of the streamline indicatingthe floodplain extent. The distance between the point ofintersection of the streamline with the cross section andthe floodplain extent point yields the desired stationing.

The stationing for the 10-year floodplain cannot bedetermined using standard geographic information sys-tem (GIS) methods because those floodplains are notmapped by FEMA. For the purposes of this research, itwas assumed that the stations for the 10-year floodplainwere coincident with the channel banks. The relation-ship used in this study was based on State/Regionalbankfull equations that provide the relationshipbetween bankfull topwidth and drainage area (Equa-tion 5). This equation was developed in 2013 (Bent andWaite, 2013) and is applicable to drainage areas rang-ing between 0.26 and 1,025 km2. It has a coefficient ofdetermination of 0.86. The relationship plotted in a log-arithmic chart is shown in Figure 3. This equation pro-duced widths that approximated the average of widthscomputed using all the relationships evaluated.

BW ¼ 22:6454DA0:419 ð5Þ

where BW is bankfull width (m) and DA is drainagearea (km2).

FIGURE 2. Schematic Showing the Channel and Floodplain Characteristics Used toCompute Difference in the Topwidth at Each Cross Section.



The bankfull width computed using the aboveequation was assumed to be the topwidth associatedwith the 10-year flood flows.

With all the unknown variables estimated in Equa-tion (4), the slope can be computed. Applying thesame slope for the future,

tanh ¼ D100f �D100c

DTWð6Þ

where DTW is the change in topwidth (m) and D100f

is the depth of water in the future (m).Equation (6) can be rewritten to produce the final

equation for topwidth change.

DTW ¼ D100f �D100c

tanhð7Þ

This change in topwidth was computed at eachcross section along the left-hand side and right-handside of the streamline. From this result, the meanchange in topwidth for each catchment (and hence forevery streamline) was computed. The mean topwidthchange was used as the width to buffer the existingfloodplain associated with that streamline to producefuture flooding extents. It should be noted that forstreamlines where the future flows were less thanthe existing flows, the flooding extents will reduce insize. The future flooding extents are not floodplains,but they represent an increased or decreased floodingarea based on future flow estimates.

Flooding extents developed using this techniquecan also be used to evaluate exposure to flood hazards

in a community by estimating the number of peopleand housing units in it. With the future floodingextents mapped, there is also a need to estimate pop-ulation in the future period of interest. In this paper,population and housing unit projections for the futurewere estimated based on USEPA’s ICLUS data onCensus Block entities. Using area-weighted tech-niques, population in the new flooding extents wasestimated. Based on factors such as the area of thenew flooding extents, population growth in the flood-ing extents, future zoning in community comprehen-sive plans, and short-term and long-term forecastinformation, “hot spots” of flood risk can be identifiedwhere further refinements can be performed toimprove the quality of the flooding extents beingmapped. Different criteria can be set based on localcommunity preferences to identify hot spots whereresiliency measures need to be undertaken.

To illustrate how real-time forecasting can beestablished for an identified hot spot, a case study onthe Duck Creek Basin in Dallas, Texas, is presented;this area recently experienced flash flooding(Figure 4). This approach leverages existing H&Hmodels to function as forecast models. The use ofwidely known techniques and software along withreadily available models renders this approach acces-sible to communities faced with current or projectedincreases to flood risk. The net result is a flood fore-cast methodology leading to increased emergency pre-paredness and improved lead time to coordinateresponse efforts.

This framework examines a recent flash floodevent occurring in late May of 2015 that impacted

FIGURE 3. Selected Bankfull Width Relationship with Drainage Area that Was Used in This Research.It was assumed that the bankfull width is representative of the existing 10-year floodplain width.



the Duck Creek Basin. Precipitation totals reachedup to 17.8 cm over a six-hour period, causing majorflooding. Emergency response is hindered duringflood events of this type, which have little lead time,particularly when floodplain forecasts are unavail-able. In this community, as in many cases, federalagencies such as FEMA and the NWS as well as stateand local stormwater management and environmen-tal agencies had developed H&H models for flood anddrainage studies, environmental impact analyses,and stream restoration/planning purposes. Modifyingthese existing models is a cost-effective way to beginthe development of a flood forecasting system (FFS).

An existing model developed for the Duck CreekWatershed (DCW) was modified for evaluation as apotential FFS. Furthermore, the existing semi-distributed hydrologic model for the DCW was cre-ated using HEC-Hydrologic Modeling System (HMS)(USACE, 2000, 2013). Modification of the HEC-HMSmodel (Figure 5) for compatibility with NWS products(e.g., QPFs and Multisensor Precipitation Estimates[MPEs]) requires alignment and coordinate assign-ment for each subbasin in the model with the

Hydrologic Rainfall Analysis Project (HRAP) gridsystem used by the West Gulf Coast RFC (WGCRFC).With the creation of a .mod file, gridded precipita-tion can be input into the HMS model and forecastrunoff computed. In the case of DCW and othersmall watersheds, model runtime may be only a fewseconds.

The existing hydraulic model for the DCW was cre-ated using a one-dimensional (1D) version of HEC-RAS 5.0 (USACE, 2016a, b). Several major changeshave improved both the model capability and suitabil-ity for using HEC-RAS 5.0 in FFS. For this study,the detailed cross-sectional geometry developed usingsurveyed river cross sections was extracted from the1D model and burned into a USGS 3 m Digital Eleva-tion Model (DEM) to produce a bathymetrically cor-rected digital terrain model (DTM). The DTM wasthen used to create a series of 2D flow areas at pointsalong the reach suitable for connection to the hydro-logic model and at locations appropriate for “hand-offs” for upstream and downstream connectivity. Fig-ure 6 shows a 2D flow area developed for one reachin the study area.

FIGURE 4. Location of the Duck Creek Basin Showing Basin Boundaries and Existing Floodplains. USGS, U.S. Geological Survey.



Coupling of the H&H models is accomplished usingthe shared binary data storage system native to bothmodels, HEC-DSS. QPFs produced by RFC come in avariety of binary formats (e.g., netcdf, grib, and xmrg)which can be converted and input directly into theHEC-DSS file read by HMS (Figure 7). Two potentialissues are the size of the files and the average precipi-tation, provided in six-hour intervals unsuitable forhydrologic modeling. For this study, the burden of pro-cessing and storing QPF data was overcome throughdevelopment of python and bash scripts. Operating ina Linux environment, cron jobs were set up to call thescripts to perform the following functions:

1. Retrieve the data from the WGCRFC URL2. Extract the data into local subdirectories3. Extract only the grid cells that fall within the

area of interest4. Generate grids spreading averaged precipitation

over equal intervals suitable for hydrologicmodeling

5. Store the processed output directly in the DSSread by HMS and RAS

Due to the relative speed of HMS and RAS modelexecution, multiple forecast alternatives may be sim-ulated, enabling small-scale ensembles to provide

FIGURE 5. (A) Existing HMS Subbasins, (B) HRAP Grid, and (C) Modified HMS Subbasin forModeling Gridded Precipitation. HMS, Hydrologic Modeling System; HRAP, Hydrologic Rainfall Analysis Project.

FIGURE 6. Sample Reach in the DCW in the Original One-Dimensional (1D) Configuration and the ModifiedTwo-Dimensional (2D) Configuration. The 100-year floodplain developed using the 1D model is contrasted

with floodplains created using the hindcasted event in 2015. DCW, Duck Creek Watershed;HEC-RAS, Hydrologic Engineering Center-River Analysis System.



upper and lower bounds for emergency managementpurposes. HEC-RAS output thus includes vital infor-mation including not just stage information, but tim-ing and extent in the form of flood hydrographs andfloodplain shapefiles. Extensive modeling was notcompleted for this study. Preliminary results indicatethat timing and storm track considerations may dras-tically impact the extent and area of inundationresulting from the unique characteristics of naturallyoccurring events. Further automation of the FFS ispossible; however, for a small watershed such as theDCW, this may not be necessary.

RESULTS AND DISCUSSION

Regression equations developed for 18 HUC-2regions within the contiguous U.S. were used to com-pute estimated 100-year discharges for a futureepoch. Climate model estimates are published up tothe year 2100, so the year 2060 was chosen as a half-way point between now and 2100.

The results of this study show that the projected100-year peak flow discharge estimates were stronglydominated by increases across a majority of the U.S.

The future flooding extents for the year 2060 weremapped for the contiguous U.S. The percent popula-tion in the flooding extents increased from the cur-rent 7.3 to 11.2% and 14.6% for the RCP 2.6 and 8.5climate scenarios, respectively (Figure 8). The areacovered by the flooding extents showed a slightincrease from 9.2 to 9.8% nationally for the RCP 2.6scenario. RCP 8.5 produced a 2% increase in the areacovered by the flooding extents. The population isderived from modeled ICLUS data which takes intoaccount the socioeconomic factors and migratory pat-terns of the population. While the buffering techniqueused does not provide exact measurements and tendsto overestimate increases, the method captures theapproximate direction and magnitude of changes.

Table 3 shows the percentage of population in theflooding extents under the current and future climatescenario conditions. The New England HUC-2 regionsaw the highest increase in the population within theflooding extents. This is directly attributed to the flowchanges which estimated a very high change in

FIGURE 7. Quantitative Precipitation Forecast (QPF) Data for Texas, and Clipped Data for DCW Modeling.



topwidth and, to a lesser extent, the floodplain poly-gons geometry. The latter issue stems from unusualfloodplain geometry, where a number of floodplainpolygons in the New England region are disconnectedfrom the main floodplain polygons. These discon-nected polygons represented low-lying areas that tendto flood, but exhibited very limited stream definition,preventing placement of cross sections. Employing

the buffer-based estimation technique developed forthis study at locations with floodplains matchingthese characteristics may overestimate local flooding.

Figure 9 shows an example from WorcesterCounty, Massachusetts where a discontinuous flood-plain polygon gets a high buffer value. The buffervalue for these polygons is governed by the topwidthchanges in the streams that are nearby (and barelytouching) in the same catchment. The regression

FIGURE 8. Percent Increase in Population and Area Covered by the Flooding Extents in the Future.RCP, Representative Concentration Pathway.

TABLE 3. Statistics Showing the Percentage of Population in theFlooding Extents for Current and Future Climate Scenarios.

HUCNo. HUC Name

Current(2013) (%)

RCP2.6 (%)

RCP8.5 (%)

01 New England 6.7 20.8 34.402 Mid-Atlantic 6.6 9.5 12.103 South Atlantic-Gulf 16.0 20.6 22.304 Great Lakes 3.4 10.0 20.005 Ohio 4.9 7.5 13.706 Tennessee 4.7 6.3 9.207 Upper Mississippi 3.8 6.1 9.508 Lower Mississippi 21.0 32.1 37.009 Souris-Red-Rainy 6.3 11.7 18.910 Missouri 3.9 4.4 5.411 Arkansas-Red-White 6.0 5.1 5.912 Texas-Gulf 7.5 9.1 9.913 Rio Grande 8.4 9.3 9.514 Upper Colorado 4.3 5.0 5.215 Lower Colorado 3.4 5.8 7.716 Great Basin 2.8 5.8 7.117 Pacific Northwest 4.0 10.1 11.918 California 4.7 8.2 10.9

FIGURE 9. Unusually Large Buffers Drawn for FloodplainPolygons that Are Barely Touched by the NHDPlus Streams.

These are low-lying areas that get flooded but stayseparate from the main floodplain.



equations estimated high flows in the future resultingin a big buffer value. This effect is predominant espe-cially in areas where the islands are near very widefloodplains. In general, such a pattern existed nearcoastal or lake areas such as the Great Lakes HUC-2region.

The data model for this study is flexible enough tosupport reporting of population and flooding extentstatistics using various political and ecological bound-aries such as counties, states, FEMA regions, USEPAeco-regions, and National Climatic Data Center(NCDC) climatic regions. Figure 10 shows the changein population between the current and RCP 8.5 sce-nario for the NCDC climatic regions. Most of the lar-gest changes are noticed in climatic regions found inFlorida, Texas, California, the Pacific Northwest, andthe Mid-Atlantic shoreline. These areas have densepopulation pockets with an increasing coverage offloodplains. However, many areas in the Midwest andstates like Idaho, North and South Dakota, Iowa, andOregon demonstrate the population models in flood-ing extents decreasing between now and the year2060.

Summaries differentiating flooding extentsbetween the riverine and coastal areas can also be

derived. For example, the state of Oregon is expectedto see a threefold increase in people living in floodingextents from about 189,000 to 620,000. However, thepopulation in flooding extents along the coastal areasof the state is projected to decrease by 42% (17,600 to10,000 people). This spatial context becomes veryimportant from a city planning or emergencyresponse perspective. The flooding extent mappingtechniques used in this research are not a sure repre-sentation of the risk as it may unfold in the future;however, these techniques do support developing spa-tial correlations between the mapped areas and thelocations of people and property. In other words, thisgeospatial data framework can help in identifyingcritical areas (hot spots) for further refinement andproduce better quality maps based on forecasts.

The criteria for identification of hot spots are sub-jective. But, that subjectivity is one of the majorstrengths of this method, as the rules that definewhat comprises a hot spot should be varied based onthe local sensibilities of the region. An infusion oflocal knowledge into flood risk assessment, coupledwith data on current and future demographics willlead to the creation of robust development models incommunities. During flash flooding, existing

FIGURE 10. Map Showing the Population Difference between Current and Future (RCP 8.5) Scenariosin National Climatic Data Center (NCDC) Climatic Regions.



forecasting models and their outputs may not providesufficient lead time to plan a response. In such situa-tions, established hot spots can serve in identifyingareas requiring attention before others. This willassist in targeted response operations toward savinglives and assets.

The Duck Creek Basin was chosen to develop asimple FFS partially because it had experienced aflooding event in May 2015. The population andflooding extent area statistics from the frameworkreinforced the fact that it is a hot spot to be consid-ered for further evaluation and refinement. The DuckCreek Basin falls within the area overseen by theWGCRFC. Seventy-two-hour QPF products on six-hour intervals are available for near–real-time model-ing of this area. Reconfigured and coupled existingH&H models, QPFs, and MPEs were used as inputsfor hindcasting the May 2015 event. In this study,the FFS was only used to forecast event(s) andrequired the presence of a modeler to initiate simula-tions. Such a system can be set up and operated atminimal cost. As opposed to a national forecast oper-ation, these systems can be developed and operatedby individual communities to assist flood emergencyoperations. Additional work is necessary to integrateobserved flows and rainfalls to replace forecastedvalues within a reasonable period to enhance theforecast and also to refine calibration.

Figure 11 shows the current and future floodingextents in the Duck Creek Basin. In the majority ofareas, the future flooding extents were smaller inwidth than the current flooding extents. However,while the area covered by the flooding extentsdecreased between the current conditions and the

future, the population in the flooding extentsincreased (Table 4). This indicates that the popula-tion density will continue to grow in the future. Theincreased population in the flooding extents makes ita hot spot for further analysis.

The decrease in the area covered by the floodingextents is attributed to the reduced flows (estimated bythe regression equations) between now (624.4 m3/s) and2060 (555 m3/s). Nevertheless, the area currently exhi-bits high vulnerability to flood impacts. A storm eventin May 2015 producing 2-7 in. of rain in the Duck CreekBasin was selected for evaluation of the potential bene-fits of an FFS in this region. An HMS simulation of thestorm was hindcast using a MPE derived from raingages in the watershed. Results indicate flows in thelower basin of approximately 566.3 m3/s are slighterhigher than future events but not quite reaching thecurrent 100-year flow rate. Hydrographs from the HMShydrologic model were then input into the RAS hydrau-lic model for floodplain development.

Many areas within the watershed indicated areduction in the area covered by the flooding extents.However, increasing population in this area indicatesa potential hot spot where risk to life and propertydue to flooding are expected to increase. Because ofunique storm characteristics and the varyingresponse of flooding sources, identified hot spots maybenefit greatly from local flood forecasting capabili-ties. Figure 11 contrasts two different locationswithin close proximity to each other along the samestream. In the upstream portion, the flooding extentfrom the May 2015 event did not exceed the flood-plains, while flooding just downstream exceeded thefloodplains along the main channel and a tributary.

FIGURE 11. Map Showing Flooding Extents in Future (right) and the May 2015(left) Event Compared to the Current Floodplains in DCW. FFS, flood forecasting system.



This type of localized flooding results from anymajor event, and highlights one of the many benefitsof using an FFS: the ability to identify potential areasalong a flooding source that may experience greaterflooding due to the distinct characteristics of a singleevent. The lead time afforded by FFS results mayenable emergency responders to prioritize criticalareas on a per-storm basis and coordinate effortsbefore the most severe flooding occurs.

Preliminary results indicate that a simple FFS,such as the system described here, may be developedand utilized at a minimum cost in terms of setup andoperation. The need for automated data downloads atregular intervals, which is a key element of moresophisticated or elaborate systems, is not requiredhere as the FFS will only be used in the case of fore-casted events and would require the presence of amodeler to initiate simulations. Finally, the improvedmapping capabilities in HEC-RAS 5.0 are an impor-tant tool for this FFS. Post-processing of floodplainsis computed internally and may be converted toshapefiles for dissemination immediately after com-pletion of the modeling run.

CONCLUSIONS AND FUTURE RESEARCH

In this paper, we have introduced a methodologyto develop flooding extents, at a national scale,using simplified hydraulic methods and NHDPlusdata. We applied this methodology to dischargesderived based on GCMs and regression equations.However, this method can be applied to read anyother discharge estimates, including those developedfor short-term flood emergencies. This methodologyworks best for stream reaches with existing hydrau-lic models.

The ability to integrate NWS meteorological fore-cast drivers with existing H&H models to forecastmodels is the key to develop forecast flooding extentsfor the nation. Currently, this knowledge is integratedinto forecast platforms. Publicly available platforms,such as USACE’s Real Time Simulation (HEC-RTS)and Deltares’ Delft Flood Early Warning System(Delft-FEWS), have a steep learning curve, which

translates into cost. Propagating the knowledgenecessary to integrate various models outside theseplatforms will considerably reduce the cost. Also,automating portions of this task, as available in theplatforms, will make the operation cost-effective.Extending that paradigm, this work is unique inbeing able to produce flooding extents and applying aconsistent and flexible spatial methodology across theentire U.S. This flooding extents development alsotakes into account the IPCC’s latest climate modelsimulations. The flooding extents produced serve as agood indicator of flood risk in a community and setup a good baseline for identifying areas of focus forhazard mitigation and flood forecasting projects.

The empirical methods discussed in this paper canbe tested using pilot studies applied to selectedwatersheds. Calibrating the results against observedwater elevations and floodplain extents will helpimprove the quality of forecast maps. Since empiricalmethods are cheaper to develop and operate, evaluat-ing the forecasts conducted using these methods withdetailed hydraulic and hydrologic model-based predic-tions will be useful in improving the performance andquality of the output products.

The forecast modeling framework also facilitateseasy dissemination of the output data to decision-making professionals. HEC-RAS 5.0, used in thisFFS, has improved mapping capabilities that enableeasy conversion to shapefiles that can be translatedto printable maps with a quick turnaround time.Forecast models such as this that can estimate maxi-mum flood elevation and floodplain extents in aneasy-to-read format of the output with sufficient leadtime for engineers and emergency response officialsare imperative. Any level of automation on thesetasks will go a long way in improving resilience tofloods. It is recommended to include ensemble fore-casts to enhance the model estimates.

This study included a riverine flooding scenario. Itwould be compelling to produce a similar modelingframework for coastal areas. Forecasting flood levelsin a tidal stream reach depends on the tide level aswell as the streamflow. While wind effects dominatein determining the flood elevations for downstreamreaches of tidal stream reaches, streamflow con-tributes increasingly in determining flood levels.More research is necessary to develop necessary toolsto forecast flood levels for tidal stream reaches.

There are a few more improvements that should beconsidered for future work on producing similarframeworks for flood resilience. These are listedbelow.

1. Selection of GCMs for this study was based onpublished guidelines on the performance ofGCMs against observation. However, the GCMs

TABLE 4. Flood Extent and DemographicsStatistics for Duck Creek Basin.

Epoch\Statistic

Populationin the Flooding

Extents

Area Coveredby the FloodingExtents (km2)

Current 9,402 22.9Future (RCP 8.5) 11,046 20.0



(and applicable RCPs) can be selected based ontheir performance against observed climateparameters of precipitation and temperatureswithin area of interest.

2. Incorporation of downscaled climate data would behelpful in further refining local hydrology esti-mates. In this study, downscaled precipitation wasnot used because of the much larger scale focus,and because downscaled precipitation for CoupledModel Intercomparison Project Phase 5 (CMIP5)was not available at the time of this analysis.

3. Better methods for hydraulics and flood elevationestimates that support medium- to large-scalefloodplain development need to be researchedand incorporated into the framework.

4. The USEPA ICLUS dataset is based on the pre-vious iteration of IPCC’s assessment report(AR4). If an updated version of the datasetequipped with the latest GCMs and better demo-graphic modeling becomes available, the popula-tion estimates can be rerun to assess impacts.

LITERATURE CITED

AECOM, 2013. The Impact of Climate Change and PopulationGrowth on the National Flood Insurance Program Through2100. FEMA. http://www.aecom.com/content/wp-content/uploads/2016/06/Climate_Change_Report_AECOM_2013-06-11.pdf.

Bent, G.C. and A.M. Waite, 2013. Equations for Estimating Bank-full Channel Geometry and Discharge for Streams. U.S. Geologi-cal Survey Scientific Investigations Report 2013-5155. http://pubs.usgs.gov/sir/2013/5155/pdf/sir2013-5155.pdf.

Bushong, J.S., 1999. Quantitative Precipitation Forecast — ItsGeneration and Verification at the Southeast River ForecastCenter. Proceedings of the Georgia Water Resources Conference,The University of Georgia, Athens, Georgia, March 30-31.

Demir, I. and W.F. Krajewski, 2013. Towards an Adaptive FloodInformation System: Integrated Data Access, Analysis and Visu-alization. Environmental Modeling & Software 50:77-84.

Maidment, D.R., 2016. Open Water Data in Space and Time. Jour-nal of the American Water Resources Association 52(4):816-824.https://doi.org/10.1111/1752-1688.12436.

McKay, L., T. Bondelid, T. Dewald, J. Johnston, R. Moore, and A.Rea, 2012. NHDPlus Version 2: User Guide. Office of Water,U.S. Environmental Protection Agency, Washington, D.C.

Selvanathan, S., M. Sreetharan, K. Rand, D. Smirnov, J. Choi, andM. Mampara, 2016. Developing Peak Discharges for FutureFlood Risk Studies Using IPCC’s CMIP5 Climate Model Resultsand USGS WREG Program. Journal of the American WaterResources Association 52(4):979-992. https://doi.org/10.1111/1752-1688.12407.

USACE, 2000. Hydrologic Engineering Center, Hydrologic Model-ing System HEC HMS, Technical Reference Manual. http://www.hec.usace.army.mil/software/hec-hms/documentation/HEC-HMS_Technical%20Reference%20Manual_(CPD-74B).pdf.

USACE, 2013. Hydrologic Engineering Center, Hydrologic Model-ing System, HEC-HMS, Version 4.0, User’s Manual. http://www.hec.usace.army.mil/software/hec-hms/documentation/HEC-HMS_Users_Manual_4.0.pdf.

USACE, 2016a. Hydrologic Engineering Center, River AnalysisSystem HEC RAS, Technical Reference Manual. http://www.hec.usace.army.mil/software/hec-ras/documentation/HEC-RAS%205.0%20Reference%20Manual.pdf.

USACE, 2016b. Hydrologic Engineering Center, River AnalysisSystem, HEC RAS, Version 5.0, User’s Manual. http://www.hec.usace.army.mil/software/hec-ras/documentation/HEC-RAS%205.0%20Users%20Manual.pdf.

USGCRP, 2014. Climate Change Impacts in the United States.Subcommittee on Global Change Research. http://nca2014.globalchange.gov.



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