Adapted from a Presentation to NRC Committee on FEMA Flood Maps January 14, 2008
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Transcript of Adapted from a Presentation to NRC Committee on FEMA Flood Maps January 14, 2008
Assessment of Economic Benefits of the North Carolina Floodplain
Mapping Program
Hydrologic and Hydraulic Case Studies
Adapted from a Presentation to NRC Committee on FEMA Flood Maps
January 14, 2008
by Thomas Langan, P.E., CFMNorth Carolina Floodplain Mapping Program
Purpose
Assess impacts to 100-yr water surface elevations (WSELs) and special flood hazard areas boundaries by changing:Physiographic regionHydrologic modeling methodologyHydraulic study type (Primary Goal)Topographic data source
Site Selection Criteria
Physiographic Regions – Coastal, Piedmont, Mountains Should be in or near County w/ GIS building footprints
Hydrologic Methodology All locations should have stream gage One County with a rainfall runoff model Suburban/Rural land use
Hydraulic Study Type Existing detail study (DS) HEC-RAS model
20 years of peak and average daily discharge record Topography
NC LiDAR or Equivalent Accuracy (2 ½ -ft contour)
Length (mi) 7.1
XSs 80
Average XS Spacing 472
Structures 6
US Drainage Area (mi2) 60
DS Drainage Area (mi2) 136 (7545 cfs)
Length (mi) 5.7
XSs 66
Average XS Spacing 459
Structures 4
US Drainage Area (mi2) 8
DS Drainage Area (mi2) 32 (4394 cfs)
Length (mi) 4.8
XSs 59
Average XS Spacing 496
Structures 9
US Drainage Area (mi2) 108
DS Drainage Area (mi2) 133 (19710 cfs)
Analytical Approach
Hydrologic Methodology
Hydraulic Study Type
Topography
Statistics
Hydraulics
Hydrology
Floodplain Mapping
Physiographic Region
Variables
Analyses
Hydrologic Methodology
Flow estimate methods Rainfall-runoff (RR) – HEC-1 or HEC-HMS LPIII Weighted Adjusted Regional Regression
(ADJREG) Rural Regional Regression (REG)
Upper and Lower Limit of the Standard Error of Prediction of Regional Regression (REGUP & REGLOW) (+/- 42% Coast, Piedmont, Mountains - +/- 47%)
Parameters varied for only DS model runs and for 100-yr return period
USGS Regional Regression Equations
For a 100 square mile drainage area, the 100-year flood discharge estimate is • 13,250 cfs in the
Blue Ridge-Piedmont,
• 6340 cfs in the coastal plain,
• 3400 cfs in the Sand Hills.
Blue Ridge - Piedmont
Sand Hills
Coastal Plain
95% Confidence interval is +/- 42-47% of the estimated flow
Ahoskie Creek, Herford County - NCHydrologic Methodology
100-yr WSEL Profiles
15
20
25
30
35
40
45
29000 34000 39000 44000 49000 54000 59000 64000 69000
Station
Ele
va
tio
n
Adjusted Regression
STRUCTURE
USGS Gage
14-ft
WSEL
Downstream
Bottom of Bridge Deck
Ahoskie Creek, Herford County - NCHydrologic Methodology
100-yr WSEL Profiles
15
20
25
30
35
40
45
29000 34000 39000 44000 49000 54000 59000 64000 69000
Station
Ele
va
tio
n
Adjusted Regression
Rural Regression
Rural Regression Upper Limit
Rural Regression Lower Limit
STRUCTURE
USGS Gage
Long Creek, Mecklenburg County - NCHydrologic Methodology
100-yr WSEL Profiles
600
610
620
630
640
650
660
670
19000 24000 29000 34000 39000 44000 49000 54000
Station
Ele
va
tio
n
Rainfall Runoff
STRUCTURE
USGS Gage
15-ft
Long Creek, Mecklenburg County - NCHydrologic Methodology
100-yr WSEL Profiles
600
610
620
630
640
650
660
670
19000 24000 29000 34000 39000 44000 49000 54000
Station
Ele
va
tio
n
Rainfall Runoff
Adjusted Regression
Rural Regression
Rural Regression Upper Limit
Rural Regression Lower Limit
STRUCTURE
USGS Gage
Swannanoa River, Buncombe County - NCHydrologic Methodology
100-yr WSEL Profiles
1970
1980
1990
2000
2010
2020
2030
2040
2050
2060
0 5000 10000 15000 20000 25000
Station
Ele
va
tio
n
Rainfall Runoff
STRUCTURE
USGS Gage
19-ft
Swannanoa River, Buncombe County - NCHydrologic Methodology
100-yr WSEL Profiles
1970
1980
1990
2000
2010
2020
2030
2040
2050
2060
0 5000 10000 15000 20000 25000
Station
Ele
va
tio
n
Rainfall Runoff
Adjusted Regression
Rural Regression
Rural Regression Upper Limit
Rural Regression Lower Limit
STRUCTURE
USGS Gage
Conclusions
All hydrologic methods are calibrated to results from frequency analysis at USGS gages so gage analysis drives results
Main effect is produced by stretching USGS regression equation results out to their limits (not realistic actually)
Varying hydrologic methods changes elevation by 1-2 ft
Analytical ResultsWater Surface Elevation Profiles
Hydraulic Study Type
Ahoskie Creek, Herford County - NCStudy Type
100-yr WSEL Profiles
15
20
25
30
35
40
45
29000 34000 39000 44000 49000 54000 59000 64000 69000
Station
Ele
va
tio
n
DS
STRUCTURE
USGS Gage
Ahoskie Creek, Herford County - NCStudy Type
100-yr WSEL Profiles
15
20
25
30
35
40
45
29000 34000 39000 44000 49000 54000 59000 64000 69000
Station
Ele
va
tio
n
DS
LDSNC
LDSNAT
APPROX
APPROX-NED
STRUCTURE
USGS Gage
Changes SFHA
Long Creek, Mecklenburg County - NC Study Type
100-yr WSEL Profiles
600
610
620
630
640
650
660
670
680
690
19000 24000 29000 34000 39000 44000 49000 54000
Station
Ele
va
tio
n
DS
STRUCTURE
USGS Gage
Long Creek, Mecklenburg County - NC Study Type
100-yr WSEL Profiles
600
610
620
630
640
650
660
670
680
690
19000 24000 29000 34000 39000 44000 49000 54000
Station
Ele
va
tio
n
DS
LDSNC
LDSNAT
APPROX
APPROX-NED
STRUCTURE
USGS Gage
Swannanoa River, Buncombe County - NCStudy Type
100-yr WSEL Profiles
1970
1980
1990
2000
2010
2020
2030
2040
2050
2060
0 5000 10000 15000 20000 25000
Station
Ele
va
tio
n
DS
STRUCTURE
USGS Gage
Swannanoa River, Buncombe County - NCStudy Type
100-yr WSEL Profiles
1970
1980
1990
2000
2010
2020
2030
2040
2050
2060
0 5000 10000 15000 20000 25000
Station
Ele
va
tio
n
DS
LDSNC
LDSNAT
APPROX
APPROX-NED
STRUCTURE
USGS Gage
Conclusions Uncertainty induced by changing the
terrain data dominates other hydraulic model uncertainties
There is a huge effect of terrain data uncertainty in the in the Long Creek example in Charlotte, NC Floodplain derived from the LIDAR data is
underground when viewed on the National Elevation Dataset!