1. 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.

2. March 30, 2005 2 Agenda  Factors Contributing to Floodplain Boundary Accuracy  A. Terrain Data  B. Hydrologic Analysis  C. Hydraulic Analysis  D. Floodplain Mapping

3. March 30, 2005 3 A. Terrain Error Management 1.Blending of Different Data Sources 2.Use of TINs vs DEMs 3.Methods for creating hydrologically correct DEMs

4. March 30, 2005 4 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

5. March 30, 2005 5  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

6. March 30, 2005 6 10-15 cm LIDAR RMSE Error 15-20 cm 20-25 cm Consistent success over large areas … Errors in elevation measurement

7. March 30, 2005 7 Breakline Synthesis for Stream Channels

8. March 30, 2005 8 Stream channel is not correctly modeled in TIN from LIDAR points

9. March 30, 2005 9 Digitize Stream Edge and Centerline in 2D from Ortho Image

10. March 30, 2005 10 Elevate Stream Centerline to Elevation of LIDAR Points

11. March 30, 2005 11 Use centerline Z values to elevate stream edges

12. March 30, 2005 12 Create TIN from LIDAR points and synthetic breaklines

13. March 30, 2005 13 Lesson: Don’t try to use dense mass points to model breakline features

14. March 30, 2005 14 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

15. March 30, 2005 15 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

16. March 30, 2005 16 Drainage Area (mi. 2 ) 1% Annual Chance Discharge (cfs) B1. Standard Methods of Discharge Estimation result in Large Prediction Intervals

17. March 30, 2005 17 446.8’ = Regression Estimate Upper Prediction Limit Water Surface 434.4’ = Regression Estimate Lower Prediction Limit Water Surface 441.5’ = Regression Estimate Water Surface 5.3’ 7.1’ B2. Uncertainty in Discharge Estimates Translates to Uncertainty in Flood Elevation

18. March 30, 2005 18 B3. Uncertainties in Flood Elevations Translate to Uncertainties in Mapped Flood Boundary Regression Estimate Upper & Lower Prediction Limits Water Surface Regression Estimate Water Surface

19. March 30, 2005 19 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

20. March 30, 2005 20 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 Comparison Reach

21. March 30, 2005 21 C2. Hydraulics Sensitivity Higher n-values With structures) Lower n-values With structures 1.0 ft. Model A vs. Model B

22. March 30, 2005 22 C3. Hydraulics Sensitivity Higher n-values With structures Lower n-values Without structures 3.3 ft. Model A vs. Model C

23. March 30, 2005 23 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)

24. March 30, 2005 24 C5. Historical Calibration  Importance of Calibration  Need to collect and utilize High Water Marks  This data tends to validate the results

25. March 30, 2005 25 D. Mapping Error Management 1.Common Method for mapping flood boundaries 2.Delineation of Boundaries 3.Flat Areas Situations

26. March 30, 2005 26 D1. Floodplain Mapping

27. March 30, 2005 27 D1. Floodplain Mapping

28. March 30, 2005 28 D1. Floodplain Mapping

29. March 30, 2005 29 D2. Backwater & Gap Mapping  Areas of Backwater need to be mapped  Can be automated or manual method  If manual, areas need to be checked

30. March 30, 2005 30 D3. Mapping Around Structures Lettered FEMA Sections If you strictly interpolate between lettered cross sections – mapped boundaries are typically overestimated

31. March 30, 2005 31 Straight Branch Without Mapping Xsects Flooding is Over Predicted

32. March 30, 2005 32 D3. Mapping Around Structures Lettered FEMA Sections Adding Mapping Cross Sections will accurately represent the head loss and not over predict the flooding.

33. March 30, 2005 33 Straight Branch With Mapping Xsects Flooding is Accurately Predicted

34. March 30, 2005 34 D4. Floodplain Mapping with DEMs vs TINs  Difference of using TINs vs DEMs in floodplain boundary accuracy TIN Mapping GRID Mapping

35. March 30, 2005 35 D5. Comparison: 10m DEM vs. LiDAR Holding all other variables the same… Boundaries DEM LiDAR

36. March 30, 2005 36 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