Transcript National Research Council Mapping Science Committee Floodplain Mapping – Sensitivity and Errors Scott K.

National Research Council
Mapping Science Committee
Floodplain Mapping – Sensitivity
and Errors
Scott K. Edelman, PE
Watershed Concepts
and Karen
Schuckman,
EarthData
March 30, 2005
Washington, D.C.
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 for measuring terrain
 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
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Errors in elevation measurement
Consistent success over large areas …
LIDAR RMSE Error
10-15 cm
15-20 cm
20-25 cm
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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
50 ft
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, 500year) 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
90,000
North Carolina USGS Regression Equation
Blue Ridge/Piedmont Hydrologic Area: Deep River
1% Annual Chance Discharge (cfs)
80,000
Average Error of Prediction
Upper Limit (+47.1%)
70,000
60,000
50,000
Regression
Estimate
40,000
30,000
20,000
Average Error of Prediction
Lower Limit (-47.1%)
10,000
0
0
200
400
600
Drainage Area (mi.2)
800
1000
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1200
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B2. Uncertainty in Discharge Estimates Translates to
Uncertainty in Flood Elevation
5.3’
446.8’ = Regression Estimate Upper Prediction Limit Water
Surface
441.5’ = Regression Estimate Water Surface
7.1’
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:
 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:
Comparison
Reach
 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
Model A vs. Model B
Higher n-values
With structures)
1.0 ft.
Lower n-values
With structures
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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
Model A (High)
5.5 ft.
Model D (Low)
 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
If you strictly interpolate
between lettered cross
sections – mapped
boundaries are typically
overestimated
Lettered FEMA Sections
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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.
Lettered FEMA Sections
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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
1% annual chance
XSect
Water Surface Elevation (NAVD88)
Boundaries
DEM
LiDAR
Station
DEM
LiDAR
Difference
9934
249.4
242.4
7.0
9467
246.4
240.2
6.2
8974
244.9
237.3
6.6
8514
242.1
235.5
6.6
8041
240.4
233.3
7.1
7637
239.4
230.1
9.3
7374
238.1
227.5
10.6
6766
234.6
226.0
8.6
6421
232.6
225.2
7.4
6036
226.8
224.3
2.5
5783
224.8
222.9
1.9
5242
217.3
221.2
-3.9
4813
214.3
217.2
-2.9
4297
212.1
214.4
-2.3
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