Effect of GIS D atasets and M odeling approach on Flood Inundation Mapping

1. Effect of GIS Datasets and Modeling approach on Flood Inundation Mapping Venkatesh Merwade School of Civil Engineering, Purdue University Indiana Geological Survey Seminar, Nov 19, 2009.

2. Acknowledgement Aaron Cook, MSE 08 Now at CDM in Denver, CO

3. Overview • Flood inundation mapping process • National Flood Insurance Program • Objective, methods, and results from our study • Summary

4. Q t Flood Inundation Mapping (FIM) Qp Digital Elevation Model (DEM)

5. Need for FIM • FIM show extent of flooding expected over an area including roadways and buildings. • Assist state and local agencies during emergency operations • Flood Insurance Rate Map (FIRM) • The U.S. Congress established the National Flood Insurance Program in 1968 enabling property owners to purchase insurance against flood losses • Flood maps for 100-yrflow (1% flow)

6. More on NFIP • Early stage – subsidized flood insurance – 95,000 policies in 1972 • Mandatory Flood Insurance in 1973 – 1.2 million policies by 1977 • 1994 amendment – inventory assessment at least once every 5 years • 100,000 flood map panels in 19,200 communities (approx. 150,000 sq. mi. of floodplain areas)

7. Map Modernization Program • Initiated in 1997 • Level -1 upgrade for approximately 11,400 communities (upgrading maps, redelineation of floodplain boundaries, etc) • Level - 2 upgrade for 4,700 communities (Level - 1 + more studies) • New maps for 2,700 flood-prone communities. • Digital FIRM

8. FEMA Flood Study Types

10. Model Parameters! Flood Inundation Mapping Train • Hydrologic Modeling – Q • Hydraulic Modeling – WSE • Flood Inundation Mapping – Flood Map Hydrologic Modeling Hydraulic Modeling Flood Inundation Mapping Q WSE Soil Streamflow GIS Precipitation LU/LC Topography

11. Objectives • To investigate the effect of data, model parameters and GIS techniques on flood inundation maps • How error or uncertainty in data or model propagates through the flood inundation train

12. Study Areas Strouds Creek, Orange County, NC Brazos River, Fort Bend County, Texas 6.5 km long; w = 10m; v-shaped valley 62 km long; w = 150m; flat terrain

13. Data HEC-RAS Models and Topography 53 XS 55 XS (a) (b) (a) (b) (c) (d) (c) (d) Strouds Creek Brazos River (a) 30m DEM; (b) 10m DEM; (c) LIDAR; (d) Integrated Terrain

14. Digital Elevation Model Number of columns Cell Size Number of rows Cell Cell Value Public domain DEMs from USGS are created by interpolating old contour maps.

15. LIght Detection And Ranging LIDAR data are interpolated to create DEM

16. Interpolated Mesh Island 2 Island 1 Surveyed cross-sections Interpolated mesh Surveyed cross-sections Integrated Terrain + = LIDAR Interpolated Mesh Integrated Terrain

17. HEC-RAS Hydrologic Engineering Center – River Analysis System One-dimensional steady state simulation The energy equation is solved between cross sections to compute the water surface profile for steady gradually varied flow Models and Tools • FESWMS • Finite Element Surface Water Modeling System • Developed by the Federal Highway Administration • Solves two-dimensional depth-averaged surface water flow equation ArcGIS HEC-GeoRAS SMS

18. Q Estimation NFF equation Q100 = 745 DA0.625 Q100: 100-yr flow DA: drainage area in sq miles. (range 0.1 – 41) Standard error of prediction is in the range of -34% to 57% Q100 for Strouds Creek is 83.3 m3/s (error range: 53.3 m3/s and 130.7 m3/s)

19. Effect of Q100 bounds on WSE • NFF estimated flow of 83.3 m3/s • 64% of 83.3 m3/s (lower bound) • 156.5% of 83.3 m3/s(upper bound) Variations in WSE at one of the cross-sections in HEC-RAS resulting from variations in Q

20. Effect of topography • Effect of topography – 30m DEM, 10m DEM, LIDAR and Integrated Terrain • HEC-RAS • Usedifferent topographic datasetsto create HEC-RAS projects and delineate inundation • FESWMS • Use different topographic datasets to create finite element mesh and get inundation

21. Effect of topography on WSE • Six Topographic Datasets • 30m USGS DEM (T1), 10m USGS DEM (T2), LIDAR DEM (T3), and integrated version of each dataset (T4-T6)

22. Effect of topography on Flood Inundation Extent Strouds Creek Brazos River Inundation increases as DEM resolution decreases

23. Effect of Topography on Flood Inundation Extent

24. Effect of Geometry • HEC-RAS • For each topographic dataset, changecross-section configurations • FESWMS • For each topographic dataset, changemesh resolution

25. Geometry Definitions Strouds (a) (b) (c) (d) Brazos (a) (b) (c) (d) (a) Original XS; (b) XS * 2; (c) XS * 3; (d) XS * 0.5

26. Effect of Geometry - HECRAS Strouds Creek Brazos River For Integrated DEM For Integrated DEM Original XS XS * 3 Trend with increasing and decreasing XS Trend with decreasing XS only

27. Mesh Resolution Strouds Creek Brazos River 10 ft 125 ft main channel 20 ft 250 ft main channel

28. Effect of Geometry - FESWMS Strouds Creek Brazos River 10 ft 125 ft 20 ft 250 ft Higher mesh resolution predicts slightly larger inundation Lower resolution mesh predicts slightly larger inundation

29. HECRAS and FESWMS Comparison Strouds Creek Brazos River FESWMS 125 HECRAS HEC-RAS FESWMS 10

30. Effects of Manning’s n on WSE (1) a mix of Manning’s n for channel (0.035 – 0.065) and over bank (0.08 – 0.15) (2) 0.035 for channel and 0.08 for over banks (lower bound) (3) 0.065 for channel and 0.15 for over banks (upper bound).

31. 1.25m floodplain 1.75m floodplain DEM v/s TIN Floodplain for both 1.25m and 1.75m WSE A A A A 2.0m 1.75m 1.25m 1.0m Section A-A Section A-A

32. Train has reached its destination! • Currently a flood map derived by using any approach is considered as “The Map” for a particular area • A single line defines who is in the flooding zone and who is not • How much confidence do we have in our approach and flood maps?

33. Where do we go from here? Probabilistic Flood Inundation Maps

34. Random Sampling Uncertainty Analysis Input Parameters Latin Hypercube Sampling (LHS) Systems without Data Morris’s OAT Flood inundation map Geospatial Data Terrain n Samples FAST Systems with Data Temporal data Precip., flow, etc. System Behavior Model RSA Model parameters ( Mannings N) GLUE FLDWAV/ RAM2/ RMA10 Hydraulic structures Key Input Factors Data spatial/temporal Model parameter/structure Evaluate selected distribution functions Integrated Modeling and Uncertainty Framework + Uncertainty Analysis Framework Map-to-map application from UT, Austin

35. Summary • Uncertainty is associated with all inputs and processes involved in flood inundation mapping, which are not communicated in the current flood mapping practice • Some of the uncertain inputs and their effects on flood inundation maps are highlighted • Technology exists to produce probabilistic flood inundation maps, and its applicability should be explored

36. New Developments: Flood Mapping Using Soil Data

37. Some historical perspective • Eve: lets go for vacation • Adam: where do you want to go? • Eve: How about Orange County? • Adam: According to the soil map, orange county is in a flood zone. Lets go to Apple Town • Eve: Apple town sounds great!

38. How do we do it today? • Jane: lets go for vacation • Joe: where do you want to go? • Jane: How about Orange County • Joe: I just booked beach home by going to waterhomes.com. • Jane: Excellent! Did you buy flood insurance? • Joe: Oh, I forgot, let me go to FEMA.gov

39. Using Map Unit Name FEMA SSURGO There is more than 90% match between SSURGO and FEMA maps for Tippecanoe County.

40. Using Component Subgroup FEMA SSURGO

41. Using Component Geomorphic Description FEMA SSURGO

42. Using Muaggatt Flooding Frequency FEMA SSURGO

43. Montgomery, IN

44. Parker, TX

45. Fayette, TX

46. Thank you! Contacts: Venkatesh Merwade – vmerwade@purdue.edu http://web.ics.purdue.edu/~vmerwade