National Research CouncilMapping Science CommitteeFloodplain Mapping – Sensitivity and ErrorsScott K. Edelman, PE

Watershed Concepts

and Karen

Schuckman,

EarthData

March 30, 2005

Washington, D.C.

March 30, 2005 2

Agenda Factors Contributing to Floodplain Boundary

Accuracy

A. Terrain Data

B. Hydrologic Analysis

C. Hydraulic Analysis

D. Floodplain Mapping

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A. Terrain Error Management

1. Blending of Different Data Sources

2. Use of TINs vs DEMs

3. Methods for creating hydrologically correct DEMs

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Blending of Terrain Data Typically many terrain data sets are used in the

calculations of the flood boundaries

Floodplain boundaries require special attention at the intersection of different topographic data sets

Insert Graphic showing Shelving of Data

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LIDAR is a powerful tool in the professional mapper’s toolbox.

LIDAR can be used to produce a wide variety of products

Good project design ensures product suitability for end user application

LIDAR for measuring terrain

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10-15 cm

LIDAR RMSE Error

15-20 cm

20-25 cm

Consistent success over large areas …Errors in elevation measurement

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Breakline Synthesis for Stream Channels

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Stream channel is not correctly modeled in TIN from LIDAR points

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Digitize Stream Edge and Centerline in 2D from Ortho Image

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Elevate Stream Centerline to Elevation of LIDAR Points

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Use centerline Z values to elevate stream edges

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Create TIN from LIDAR points and synthetic breaklines

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Lesson: Don’t try to use dense mass points to model breakline features

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TINs vs DEMs DEMs are Derived from TINs and is a generalization of the data

within Defined Cell Size

In general, DEM data requires more “smoothing” routines than does TIN data

TINs can be used to reduce generalization of data

Insert Graphic showing TIN Data

Insert Graphic showing DEM Data

50

ft

50 ft

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B. Hydrology Error Management

Hydrology is the amount of water to expect during a flooding event.

Prediction of the 1% or 0.2% chance storm (100-year, 500-year) is based on relatively small periods of record

Hydrology may be the highest source of error in floodplain boundaries

March 30, 2005 16Drainage Area (mi.2)

1% A

nn

ua

l C

han

ce D

isc

har

ge

(cfs

)

North Carolina USGS Regression Equation Blue Ridge/Piedmont Hydrologic Area: Deep River

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

0 200 400 600 800 1000 1200

Regression Estimate

Average Error of PredictionLower Limit (-47.1%)

Average Error of PredictionUpper Limit (+47.1%)

B1. Standard Methods of Discharge Estimation result in Large Prediction Intervals

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446.8’ = Regression Estimate Upper Prediction Limit Water Surface

434.4’ = Regression Estimate Lower Prediction Limit Water Surface

441.5’ = Regression Estimate Water Surface5.3’

7.1’

B2. Uncertainty in Discharge Estimates Translates to Uncertainty in Flood Elevation

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B3. Uncertainties in Flood Elevations Translate to Uncertainties in Mapped Flood Boundary

Regression Estimate Upper & Lower

Prediction Limits Water Surface

Regression Estimate Water Surface

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C. Hydraulic Error Management Hydraulics Determines How Deep

is the Water

Sources of error due to: Manning’s n roughness values

Cross-section alignment & spacing

Method for modeling structures (approximate, limited detail, detail)

Accuracy of the terrain (LiDAR, DEM, contours, etc.)

Accuracy of the Survey Data

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C1. Hydraulics Sensitivity 1 mile stretch of stream w/ LiDAR data

Same discharges used (upper prediction limit of regression equation)

Hydraulic Model A: Upper limit of reasonable n-values

Channel: 0.055-0.065 Overbank: 0.13-0.16

Includes structures

Hydraulic Model B: Lower limit of reasonable n-values

Channel: 0.035-0.040 Overbank: 0.08-0.10

Includes structures

Hydraulic Model C: Lower limit of reasonable n-values

Channel: 0.035-0.040 Overbank: 0.08-0.10

Does not include structures

ComparisonReach

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C2. Hydraulics Sensitivity

Higher n-valuesWith structures)

Lower n-valuesWith structures

1.0 ft.

Model A vs. Model B

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C3. Hydraulics Sensitivity

Higher n-valuesWith structures

Lower n-valuesWithout structures

3.3 ft.

Model A vs. Model C

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C4. Worst-case Scenario

Hydraulic Model A: Upper prediction limit of the regression equation

estimate Upper limit of reasonable n-values Includes structures

Hydraulic Model D: Lower prediction limit of the regression equation

estimate Lower limit of reasonable n-values Does not include structures

Model A (High)

5.5 ft.

Model D (Low)

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C5. Historical Calibration

Importance of Calibration

Need to collect and utilize High Water Marks

This data tends to validate the results

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D. Mapping Error Management

1. Common Method for mapping flood boundaries

2. Delineation of Boundaries

3. Flat Areas Situations

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D1. Floodplain Mapping

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D1. Floodplain Mapping

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D1. Floodplain Mapping

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D2. Backwater & Gap Mapping

Areas of Backwater need to be mapped

Can be automated or manual method

If manual, areas need to be checked

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D3. Mapping Around Structures

Lettered FEMA Sections

If you strictly interpolate between lettered cross sections – mapped boundaries are typically overestimated

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Straight Branch Without Mapping Xsects

Flooding is Over Predicted

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D3. Mapping Around Structures

Lettered FEMA Sections

Adding Mapping Cross Sections will accurately represent the head loss and not over predict the flooding.

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Straight Branch With Mapping Xsects

Flooding is Accurately Predicted

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D4. Floodplain Mapping with DEMs vs TINs

Difference of using TINs vs DEMs in floodplain boundary accuracy

TIN Mapping

GRID Mapping

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D5. Comparison: 10m DEM vs. LiDAR

Holding all other variables the same…

Boundaries DEM LiDAR

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D6. Comparison: 10m DEM vs. LiDAR

-2.3214.4212.14297

-2.9217.2214.34813

-3.9221.2217.35242

1.9222.9224.85783

2.5224.3226.86036

7.4225.2232.66421

8.6226.0234.66766

10.6227.5238.17374

9.3230.1239.47637

7.1233.3240.48041

6.6235.5242.18514

6.6237.3244.98974

6.2240.2246.49467

249.4

DEM

1% annual chance

Water Surface Elevation (NAVD88)

7.0242.49934

DifferenceLiDARStation

XSect

Boundaries DEM LiDAR