National Research Council Mapping Science Committee Floodplain Mapping – Sensitivity and Errors

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.

2. Agenda • Factors Contributing to Floodplain Boundary Accuracy • A. Terrain Data • B. Hydrologic Analysis • C. Hydraulic Analysis • D. Floodplain Mapping

3. A. Terrain Error Management • Blending of Different Data Sources • Use of TINs vs DEMs • Methods for creating hydrologically correct DEMs

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. 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

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

7. Breakline Synthesis for Stream Channels

8. Stream channel is not correctly modeled in TIN from LIDAR points

9. Digitize Stream Edge and Centerline in 2D from Ortho Image

10. Elevate Stream Centerline to Elevation of LIDAR Points

11. Use centerline Z values to elevate stream edges

12. Create TIN from LIDAR points and synthetic breaklines

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

14. 50 ft 50 ft 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

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

17. 5.3’ 7.1’ 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

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. 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. Comparison Reach 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

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

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

23. Model A (High) 5.5 ft. Model D (Low) 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

24. C5. Historical Calibration • Importance of Calibration • Need to collect and utilize High Water Marks • This data tends to validate the results

25. D. Mapping Error Management • Common Method for mapping flood boundaries • Delineation of Boundaries • Flat Areas Situations

26. D1. Floodplain Mapping

27. D1. Floodplain Mapping

28. D1. Floodplain Mapping

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

31. Straight Branch Without Mapping Xsects Flooding is Over Predicted

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

33. Straight Branch With Mapping Xsects Flooding is Accurately Predicted

34. D4. Floodplain Mapping with DEMs vs TINs • Difference of using TINs vs DEMs in floodplain boundary accuracy TIN Mapping GRID Mapping

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

36. Boundaries DEM LiDAR D6. Comparison: 10m DEM vs. LiDAR XSect 1% annual chance Water Surface Elevation (NAVD88) 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