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.

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

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

March 30, 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

March 30,  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

March 30, cm LIDAR RMSE Error cm cm Consistent success over large areas … Errors in elevation measurement

March 30, Breakline Synthesis for Stream Channels

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

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

March 30, Elevate Stream Centerline to Elevation of LIDAR Points

March 30, Use centerline Z values to elevate stream edges

March 30, Create TIN from LIDAR points and synthetic breaklines

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

March 30, 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

March 30, 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

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

March 30, ’ = 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

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

March 30, 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

March 30, 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:  Overbank:  Includes structures  Hydraulic Model B:  Lower limit of reasonable n-values  Channel:  Overbank:  Includes structures  Hydraulic Model C:  Lower limit of reasonable n-values  Channel:  Overbank:  Does not include structures Comparison Reach

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

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

March 30, 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)

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

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

March 30, D1. Floodplain Mapping

March 30, D1. Floodplain Mapping

March 30, D1. Floodplain Mapping

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

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

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

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

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

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

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

March 30, D6. Comparison: 10m DEM vs. LiDAR DEM 1% annual chance Water Surface Elevation (NAVD88) DifferenceLiDARStation XSect Boundaries DEM LiDAR