National Research Council Mapping Science Committee Floodplain Mapping

1
National Research CouncilMapping Science
CommitteeFloodplain Mapping Sensitivity and
Errors

• Scott K. Edelman, PE
• Watershed Concepts
• and Karen
• Schuckman,
• EarthData
• March 30, 2005
• Washington, D.C.

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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

• Blending of Different Data Sources
• Use of TINs vs DEMs
• 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 for measuring terrain

• LIDAR is a powerful tool in the professional
  mappers toolbox.
• LIDAR can be used to produce a wide variety of
  products
• Good project design ensures product suitability
  for end user application

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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 Dont 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
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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

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B1. Standard Methods of Discharge Estimation
result in Large Prediction Intervals
1 Annual Chance Discharge (cfs)
Drainage Area (mi.2)
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B2. Uncertainty in Discharge Estimates Translates
to Uncertainty in Flood Elevation
446.8 Regression Estimate Upper Prediction
Limit Water Surface
441.5 Regression Estimate Water Surface
434.4 Regression Estimate Lower Prediction
Limit Water Surface
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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
• Mannings 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

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C2. Hydraulics Sensitivity
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C3. Hydraulics Sensitivity
Model A vs. Model C
Higher n-values With structures
3.3 ft.
Lower n-values Without structures
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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

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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
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Straight Branch Without Mapping Xsects
Flooding is Over Predicted
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D3. Mapping Around Structures
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
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D6. Comparison 10m DEM vs. LiDAR
1 annual chance Water Surface Elevation (NAVD88)
XSect
DEM
Difference
LiDAR
Station
249.4
7.0
242.4
9934
6.2
240.2
246.4
9467
6.6
237.3
244.9
8974
6.6
235.5
242.1
8514
7.1
233.3
240.4
8041
9.3
230.1
239.4
7637
10.6
227.5
238.1
7374
8.6
226.0
234.6
6766
7.4
225.2
232.6
6421
2.5
224.3
226.8
6036
1.9
222.9
224.8
5783
-3.9
221.2
217.3
5242
-2.9
217.2
214.3
4813
-2.3
214.4
212.1
4297